Public Disclosure Authorized Public Disclosure Authorized AIR QUALITY MANAGEMENT PLANNING FOR LAGOS STATE Joseph Akpokodje; Christopher Weaver; Mofoluso Fagbeja; Francesco Forastiere; Joseph V. Spadaro; Todd M. Johnson; Obi Ugochuku; Oluwakemi Osunderu and Sarath Guttikunda. AUGUST 2022 TASK TEAM LEADER: JOSEPH ARPOKODJE * * * # AIR QUALITY MANAGEMENT PLANNING FOR LAGOS STATE Joseph Akpokodje; Christopher Weaver; Mofoluso Fagbeja; Francesco Forastiere; Joseph V. Spadaro; Todd M. Johnson; Obi Ugochuku; Oluwakemi Osunderu and Sarath Guttikunda * * * © 2022 World Bank Group 1818 H Street NW Washington DC 20433 Telephone: 202-473-1000 Internet: [www.worldbank.org](http://www.worldbank.org/) Email: [feedback@worldbank.org](mailto:feedback@worldbank.org) All rights reserved. This volume is a product of the staff of the World Bank Group. 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For permission to photocopy or reprint any part of this work, please send a request with com- plete information to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, USA, telephone: 978-750-8400, fax: 978-750-4470, [http:///www.copyright.com/](http://www.copyright.com/). Any queries on rights and licenses, including subsidiary rights, should be addressed to the Officer of the Publisher, World Bank Group, 1818 H Street NW, Washington, DC 20433, USA; fax: 202- 522-2625; e-mail: [pubrights@worldbank.org](mailto:pubrights@worldbank.org). Cover photos credit: Elohor Egbane / SmartEdge. ii Air Quality Management Planning for Lagos State * * * CONTENTS ACKNOWLEDGMENTS.....ix LIST OF ABBREVIATIONS.....xi EXECUTIVE SUMMARY.....xv CHAPTER 1: INTRODUCTION.....1 1.1. Lagos: population, economy, and environment.....2 1.2. Need for an integrated air quality strategy.....2 CHAPTER 2: AIR QUALITY CONDITIONS IN LAGOS.....5 2.1. Particulate matter.....6 2.2. Lead aerosol.....14 2.3. Gaseous pollutants.....14 2.4. Organic compounds and toxic air contaminants.....19 2.5. Greenhouse gases.....21 2.6. Pollutant emission inventory.....23 2.7. Pollutant dispersion modeling.....30 CHAPTER 3: HEALTH AND ECONOMIC IMPACTS OF AIR POLLUTION.....39 3.1. Methodology and exposure-response functions (ERFs) for air pollutants of concern.....39 3.2. Quantification of health impacts.....43 3.3. Val * * * # CHAPTER 6: RECOMMENDED AIR QUALITY MANAGEMENT STRATEGY FOR LAGOS STATE ..... 93 6.1. Institutional development ..... 93 6.2. Public involvement – AQI ..... 95 6.3. Recommended AQM actions ..... 98 ## ANNEX 1: ESTIMATING THE HEALTH AND MORTALITY EFFECTS OF AIR POLLUTION IN LAGOS ..... 101 A1.1. Introduction ..... 102 A1.2. Definition and applications of HIA of air pollution ..... 102 A1.3. Available ERF models ..... 103 A1.4. Methods and input data for the HIA in Lagos ..... 105 A1.5. Results ..... 120 A1.6. Discussion and conclusion ..... 131 ## ANNEX 2: SUPPLEMENTARY MATERIAL ..... 139 A2.1. Mortality and morb * * * # FIGURES **Figure 1.1.** PMEH institutional arrangements. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 **Figure 2.1.** Satellite view of Lagos showing the six monitoring sites, main roads, and the US Consulate (Source of satellite data – Google Earth). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 **Figure 2.2.** PM2. 5 measurements at each monitoring site. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 **Figure 2.3.** PM10 measurements at each monitoring site. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 **Figure 2.4.** Correlation between PM filter data and 24-hour average optical sensor PM estimates. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 **Figure 2.5.** Corrected optical sensor PM2. 5 readings versus 24-hour filter measurements – Jankara site. . . . . . . . . . . . . . . . . . . . . . . . 10 **Figure 2.6.** Corrected optical sensor PM10 readings versus 24-hour filter measurements – Jankara site. . . . . . . . . . . . . . . . . . . . . . . . 10 **Figure 2.7.** Summary of the chemical composition of PM2. 5 collected at the six monitoring sites. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .11 **Figure 2.8.** PM2. 5 source apportionment by PMF. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 **Figure 2.9.** PM2. 5 source apportionment by CMB. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 **Figure 2.10.** PM10 source apportionment of PM10 by PMF. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 **Figure 2.11.** PM concentration versus wind direction for Ikorodu. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 **Figure 2.12.** Quarterly average lead aerosol concentrations measured at each site. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 **Figure 2.13.** Eight-hour average CO concentrations at each monitoring site (green lines, Nigerian/WHO standard; red lines US NAAQS). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 **Figure 2.14.** Twenty-four-hour average NO2 concentrations at each monitoring site (red lines Nigerian standard, green lines WHO guideline). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 **Figure 2.15.** Eight-hour average O3 concentrations at each monitoring site (green lines, Nigerian/WHO standard; red lines US NAAQS). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 **Figure 2.16.** SO2 concentrations at LASEPA and UNILAG sites (green lines, Nigerian/WHO standard, red lines US NAAQS). . . . . . . . 24 **Figure 2.17.** Average GHG concentrations measured at each monitoring site. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 **Figure 2.18.** Average concentrations of CFCs C s, nd HFC s d t ch monitorin sit ... 25 **Figure 2.19.** Breakdown of estimated criteria pollutant emissions by type of source. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 **Figure 2.20.** CO2 equivalent emissions by source type. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 **Figure 2.21.** Episodes selected for air quality modeling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 **Figure 2.22.** Comparison of FARM model output with in situ measurement for O3, NO2, PM2. 5, and PM10 at UNILAG station for episode period of September 10–20, 2020. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 **Figure 2.23.** Comparison of FARM model output with in situ measurement for O3, NO2, PM2. 5, and PM10 at UNILAG station for episode period of December 10–20, 2020. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 **Figure 2.24.** Comparison of FARM model output with in situ measurement for O3, NO2, PM2. 5, and PM10 at UNILAG station for episode period of March 5–16, 2021. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 **Figure 2.25.** Comparison of FARM model output with in situ measurement for O3, NO2, PM2. 5, and PM10 at UNILAG station for episode period of April 25–May 5, 2021. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 **Figure 2.26.** Comparison of FARM model output with in situ measurement for O3, NO2, PM2. 5, and PM10 at UNILAG station for episode period of June 27–July 7, 2021. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 **Figure 3.1.** Schematic presentation of the main steps in the air pollution HIA. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 **Figure 3.2.** Size of the Lagos population by LGA according to the base and sensitive case populations. . . . . . . . . . . . . . . . . . . . . . . . . 41 **Figure 3.3.** Lagos State and LGA ambient air quality data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 **Figure 3.4.** PM2. 5 attributable morbidity and mortality in Lagos State for PWE data and GHE (WHO 2021) baseline mortality rates. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 **Figure 3.5.** Health benefits for a reduction in ambient air pollution across Lagos State. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 **Figure 4.1.** Lagos Light Rail: Blue and Red Lines. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 **Figure 4.2.** LASG sector budget compared to air pollution. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 Air Quality Management Planning for Lagos State v * * * Figure 4.3. Global green bond market .....74 Figure 5.1. Organization chart for Lagos State EPA .....86 Figure 6.1. Comparisons of the variations in breakpoints and index nomenclature across specific countries .....95 Figure 6.2. Seasonal cycle of PM $ \_{2.5} $ monitored from six stations in Lagos, August 2020 to July 2021.....96 Figure 6.3. AQI calculator page .....97 Figure 6.4. Recommended AQM actions for Lagos .....98 Figure A1.1. ERFs of the GBD 2000 study .....103 Figure A1.2. Schematic presentation of the main steps of the HIA .....106 Figure A1.3. Map of Lagos State Showing LGAs .....109 Figure A1.4. Lagos State Population in 2006 and 2018 .....109 Figure A1.5. Nigeria population long-term growth rate by age group, 2 \\mathsf{P{M}}\_{2.5} \\mathsf{P{M}}\_{2.5} \\mathsf{P M}\_{2.5} \\mathsf{P M}\_{2.5} \\mathsf{P M}\_{2.5} * * * # TABLES **Table 1.1.** Recommended air quality strategy for Lagos. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xviii **Table 2.1.** Annual average PM concentrations compared to WHO guidelines. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 **Table 2.2.** Ambient air quality standards and WHO guidelines for gaseous pollutants. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 **Table 2.3.** Estimated inventory of criteria pollutants and precursors for Lagos State. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 **Table 2.4.** Estimated inventory of global-warming pollutants for Lagos State—calculated with 20-year GWPs. . . . . . . . . . . . . . . . 28 **Table 2.5.** Estimated inventory of global-warming pollutants for Lagos State—calculated with 100-year GWPs. . . . . . . . . . . . . . . 29 **Table 3.1.** Value of morbidity (sensitivity case population). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 **Table 3.2.** Valuation of mortality due to air pollution in Lagos. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 **Table 3.3.** Value of lowering air pollution to WHO interim targets. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 **Table 3.4.** Comparison of current estimates of PM2. 5 mortality rates in Lagos State and previous work by Croitoru, Chang and Kelly (2020). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 **Table 4.1.** European diesel and gasoline standards and emissions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 **Table 4.2.** BRT corridors in Lagos. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 **Table 4.3.** Abatement measures in the National Action Plan (NAP) to Reduce Short-Lived Climate Pollutants. . . . . . . . . . . . . . . . . 69 **Table 4.4.** Clean air policies for Lagos. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 **Table 4.5.** Indicative costs and benefits of reducing air pollution. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 **Table 4.6.** Possible funding sources for AQM in Lagos. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 **Table 4.7.** “Air quality” projects in the LASG 2021 budget. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 **Table 4.8.** Five-year AQM financing scenarios ... 76 **Table 4.9.** Summary of possible funding instruments to support air quality. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 **Table 5.1.** AQM laws, regulations, policies, and institutions at Lagos State and federal levels. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 **Table 6.1.** Comparison of AQI results derived for Lagos. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 **Table A1.1.** Estimates of Lagos State population by age group, 2018. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 **Table A1.2.** Estimates of Lagos State Population by LGA. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 **Table A1.3.** Lagos State mortality (both sexes) by cause of death and age, base case 2018. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .111 **Table A1.4.** Lagos State mortality (both sexes) by cause of death and age, sensitivity case 2018. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112 **Table A1.5.** Lagos State mortality (both sexes) by cause of death and age, base case 2018. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114 **Table A1.6.** Lagos State mortality (both sexes) by cause of death and age, sensitivity case 2018. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 **Table A1.7.** Nigeria mortality rates (per 100,000, both sexes) by cause of death and age. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 **Table A1.8.** Lagos State mortality (GHDx hazard rates, both sexes) by LGA, 2018. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 **Table A1.9.** Lagos State mortality (GHE hazard rates, both sexes) by LGA, 2018. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118 **Table A1.10.** Annual PM PWE by LGA. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 **Table A1.11.** PM2. 5 attributable health burdens for the base case population. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 **Table A1.12.** PM2. 5 attributable health burdens for the sensitivity case population. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126 **Table A1.13.** PM10 attributable short-term mortality due to the Harmattan season. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 **Table A1.14.** Impact assessment of air lead contamination in Ikorodu. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 **Table A1.15.** Comparison of current estimates of PM2. 5 mortality rates in Lagos State to estimates from previous work by Croitoru, Chang and Kelly (2020). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 **Table A2.1.** Inpatient hospital admissions, 2017. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 **Table A2.2.** Share of total inpatient hospital admissions by disease. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140 **Table A2.3.** Outpatient hospital admissions, 2017. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140 **Table A2.4.** Share of outpatient hospital admissions by disease. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140 **Table A2.5.** Annual PM PWE by LGA for the sensitivity analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141 Air Quality Management Planning for Lagos State vii * * * \\mathsf{P M}\_{2.5} Table A2.6. PM2.5attributable health burdens for PWE sensitivity analysis, base case population. . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 Table A2.7. PM2.5attributable health burdens for PWE sensitivity analysis, sensitivity population. . . . . . . . . . . . . . . . . . . . . . . . . . . . 144 Table A3.1. Alternative vehicle technologies for large buses. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150 Table A3.2. World Bank Africa Climate Business Plan (ACBP) Funding Windows. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154 Table A3.3. Action areas in the ACBP. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155 Table A3.4. Apportionment of PM2. 5 emissions and ambient concentrations by sector. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156 Table A3.5. Cost-effectiveness of selected air quality measures. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158 Table A3.6. Climate Co-benefits of selected air quality measures. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159 \\mathsf{P M}\_{2.5} \\mathsf{P{M}}\_{2.5} BOXES Box 4.1. Measures to ensure fuel quality ..... 58 Box 4.2. From combis to minibuses in Mexico City ..... 62 Box 4.3. Lagos Climate Action Plan, 2020-2025 ..... 75 Box 4.4. Nigeria NDC targets and air quality ..... 76 Box A3.1. Air pollution versus climate change costs ..... 160 * * * # ACKNOWLEDGMENTS (REVISED OCTOBER 2022) This report was prepared by a team led by Joseph Akpokodje and comprising Christopher Weaver (Air Resources Engineer, California Air Resources Board), Mofoluso A. Fagbeja (Space Applications and Environmental Scientist, Centre for Space Science and Technology Education, Ile-Ife, Nigeria), Francesco Forastiere (Environmental Epidemiologist, National Research Council, Italy), Joseph V. Spadaro (Senior Environmental Research Scientist, Spadaro Environmental Research Consultants, USA and WHO Consultant, Bonn, Germany), Todd M. Johnson (Environmental Economist, former World Bank Group staff), Obi Ugochuku (Climate Finance Expert, former World Bank Group staff), Oluwakemi Osunderu (Principal Research Fellow, Forestry Research Institute of Nigeria), and Sarath Guttikunda (Director, Urban Emissions, India).The opinion expressed in this report are those of the authors and should not be attributed to their respective employer or affiliated organizations. The team would like to acknowledge, with thanks, the valuable support and advice from Jostein Nygard, Yewande Awe, Steve Baillie, Max Klotz, Urvashi Narain, Özgül Calicioglu, Oznur Oguz Kuntasal, Iguniwari Thomas Ekeu-Wei, Omezikam Onuoha, Jayne Kwengwere, Rohan Selvaratnam, and Abiodun Elufioye. This report benefited from contributions and inputs provided by the following colleagues: Silvia Calderon, Felix Ukeh, and Andrew Kelly. The team would like to acknowledge comments provided by the peer reviewers: Sameer Akbar – Senior Environmental Specialist (SCAEN); Craig Meisner – Senior Economist (SCCDR); Roger Gorham-Senior Transport Economist (ILCT1); Jian Xie – Senior Environmental Specialist (SAEE2); and Gary Kleiman – Senior Environ- mental Specialist Consultant (SCAEN). Editorial support was provided by Akashee Mehdi. The team would like to acknowledge the valuable support of His Excellency, Mr. Babajide Sanwo-Olu, Executive Governor of Lagos State and the following Lagos State Government officials: Mr. Tunji Bello, Honorable Commissioner for Environment and Water Resources; Prof Akin Abayomi, Honorable Commissioner for Health, Lagos State Ministry of Health; Mr. Sam Egube, Honorable Commissioner, Lagos State Ministry of Economic Planning and Budget.; Dr. Frederic Abimbola Oladeinde, Honorable Commissioner, Lagos State Ministry of Transportation; Engr. Olalere Odusote, Honorable Commissioner for Energy and Mineral Resources; Mrs. Abisola Olusanya, Honorable Commissioner for Agriculture; Dr. Dolapo Fasawe, General Manager, Lagos State Environmental Protection Agency (LASEPA); Mr. Air Quality Management Planning for Lagos State ix * * * Olajide Oduyoye, General Manager, Lagos State Metropolitan Transport Agency (LAMATA); Mr. Ibrahim Adejuwon Odumboni, General Manager, Lagos Waste Management Agency (LAWMA); Mr. Tayo Oseni-Ope (Director), Mr. Peter Kehinde Olowu (Deputy Director), and Mrs. Bolanle Pemede (Assistant Director) at the Lagos State Ministry of Economic Planning and Budget/ Lagos Bureau of Statistics; Dr. Idowu Abiola (Director, Lagos Health Management Information System) and Dr. Kuburat Enitan Layeni-Adeyemo (Director, Occupational Health Services) at Lagos State Ministry of Health; Mr. Ayodipupo Quadri (Environment and Safety Specialist) at Lagos Metropolitan Area Transport Authority; Mr. Lewis Gregory Adeyemi (Chief Scientific Officer) at the Lagos State Ministry of Environment/Lagos State Environmental Protection Agency; Mr. Adedotun Atobasire (Deputy Director, Census) at the National Population Commission; Mr. Charles Ikeah, Director Pollution Control and Environmental Management; and Mr.  Emmanuel Ojo (Former Focal Point and Deputy Director, Pollution Control and Environmental Health Department) at the Federal Ministry of Environment. Prof. Oluwatoyin Ogundipe, Vice Chancellor of the University of Lagos. Dr Rose Alani, Lead, Air Quality Monitoring Research Group of the Department of Chemistry, University of Lagos. Professor Wellington Oyibo, Director of Research and Innovation Unit, University of Lagos The report is a product of the Environment, Natural Resources and Blue Economy Global Practice of the World Bank. This work was conducted under the supervision of Ernesto Sanchez-Triana (PMEH Program Manager), Sanjay Srivastava (Practice Manager, SAWE4), and Christian Albert Peter (Practice Manager, SENGL). The financial support of the World Bank’s Pollution Management and Environmental Health (PMEH) Multi-Donor Trust Fund in the preparation of this report is gratefully acknowledged. PMEH is supported by the  governments of Germany, Norway, and the United  Kingdom. * * * LIST OF ABBREVIATIONS | ACBP | Africa Climate Business Plan | | --- | --- | | ACRIF | African Climate Resilience Infrastructure | | AFC | Africa Finance Corporation | | AfDB | African Development Bank | | AFOLU | Agriculture, Forestry and Other Land Use | | ALRI | Acute Lower Respiratory Infections | | AQI | Air Quality Index | | AQM | Air Quality Management | | BEV | Battery Electric Vehicle | | Bpd | Barrels per Day | | BRT | Bus Rapid Transport | | CBA | Cost-Benefit Analysis | | CFC | Chlorofluorocarbon | | CH4 | Methane | | CHA | Cardiovascular Hospital Admission | | CI | Confidence Interval | | Cl | Chloride Ion | | CMB | Chemical Mass Balance | | CNG | Compressed Natural Gas | | CO | Carbon Monoxide | | CO2 | Carbon Dioxide | | COPD | Chronic Obstructive Pulmonary Disease | | CSO | Civil Society Organization | | DPR | Department of Petroleum Resources | | ECOWAS | Economic Community of West African States | | EGASPIN | Environmental Guidelines and Standards for the Petroleum Industry in Nigeria | | EIB | European Investment Bank | | EPA | Environmental Protection Agency | | ERF | Exposure-Response Function | | EU | European Union | | FFMM | Fact-Finding Air Quality Monitoring Mission | | FMEnv | Federal Ministry of Environment | | FMPR | Federal Ministry of Petroleum Resources | | GBD | Global Burden of Disease | | GCF | Green Climate Fund | | GDP | Gross Domestic Product | | GEF | Global Environment Facility | | GEMM | Global Exposure Mortality Model | | GHDx | Global Health Data Exchange | \\mathbf{c H\_{4}} \ \\mathbf c O\_{2} * * * | GHE | Global Health Estimates | | --- | --- | | GHG | Greenhouse Gas | | GWP | Global-Warming Potential | | HCA | Human Capital Approach | | HCFC | Hydrochlorofluorocarbon | | HFC | Hydrofluorocarbon | | HFO | Heavy Fuel Oil | | HIA | Health Impact Assessment | | HRAPIE | Health Risk of Air Pollution in Europe | | IEA | International Energy Agency | | IER | Integrated Exposure-Response | | IFC | International Finance Corporation | | IHD | Ischemic Heart Disease | | IHME | Institute for Health Metrics and Evaluation | | I&M | Inspection and Maintenance | | IQ | Intelligence Quotient | | IPCC | Intergovernmental Panel on Climate Change | | ISDB | Islamic Development Bank | | IT | Interim Target | | kW | Kilowatt(s) | | LACVIS | Lagos Computerized Vehicle Inspection Service | | LAGFERRY | Lagos State Ferry | | LAMATA | Lagos State Metropolitan Transport Agency | | LASEPA | Lagos State Environmental Protection Agency | | LASG | Lagos State Government | | LASWMO | Lagos State Wastewater Management Office | | LAWMA | Lagos Waste Management Agency | | LBS | Lagos Bureau of Statistics | | LGA | Local Government Area | | LMoE | Lagos State Ministry of Environment and Water Resources | | LMoEMR | Lagos State Ministry of Energy and Mineral Resources | | LMEPB | Lagos State Ministry of Economic Planning and Budget | | LMoH | Lagos State Ministry of Health | | LMICs | Low- and Middle-Income Countries | | LMoT | Lagos State Ministry of Transport | | LPG | Liquefied Petroleum Gas | | LUTP | Lagos Urban Transport Project | | MDAs | Ministries, Departments, and Agencies | | MDB | Multilateral Development Bank | | MSW | Municipal Solid Waste | | NAAQS | National Ambient Air Quality Standards | | NAP | National Action Plan | | NAPEP | National Poverty Eradication Programme | | NCD | Noncommunicable Disease | | NCF | Nigerian Conservation Foundation | HFO Heavy Fuel Oil HIA Health Impact Assessment HRAPIE Health Risk of Air Pollution in Europe IER Integrated Exposure-Response IFC International Finance Corporation IEA International Energy Agency IHD Ischemic Heart Disease IFC International Finance Corporation IHD Ischemic Heart Disease IHME Institute for Health Metrics and Evaluation I&M Inspection and Maintenance I&M Inspection and Maintenance IQ Intelligence Quotient IQ Intelligence Quotient IPCC Intergovernmental Panel on Climate Change IPCC Intergovernmental Panel on Climate Change ISDB Islamic Development Bank ISDB Islamic Development Bank IT Interim Target IT Interim Target kW Kilowatt(s) kW Kilowatt(s) LACVIS Lagos Computerized Vehicle Inspection Service LACVIS Lagos Computerized Vehicle Inspection Service LAGFERRY Lagos State Ferry LAGFERRY Lagos State Ferry LAMATA Lagos State Metropolitan Transport Agency LASEPA Lagos State Environmental Protection Agency LASG Lagos State Government LASG Lagos State Government LASWMO Lagos State Wastewater Management Office LAWMA Lagos Waste Management Agency LBS Lagos Bureau of Statistics LBS Lagos Bureau of Statistics LGA Local Government Area LGA Local Government Area LMoE Lagos State Ministry of Environment and Water Resources LMEPB Lagos State Ministry of Economic Planning and Budget LMoH Lagos State Ministry of Health LMoH Lagos State Ministry of Health LMICs Low- and Middle-Income Countries LMICs Low- and Middle-Income Countries LMoT Lagos State Ministry of Transport LMoT Lagos State Ministry of Transport LPG Liquefied Petroleum Gas MDAs Ministries, Departments, and Agencies MDB Multilateral Development Bank NAAQS National Ambient Air Quality Standards NAP National Action Plan NAPEP National Poverty Eradication Programme NCD Noncommunicable Disease MDB Multilateral Development Bank MSW Municipal Solid Waste LPG Liquefied Petroleum Gas LUTP Lagos Urban Transport Project NCD Noncommunicable Disease NCF Nigerian Conservation Foundation NAP National Action Plan NCF Nigerian Conservation Foundation * * * | NDC | Nationally Determined Contribution | | --- | --- | | NEP | National Environmental Policy | | NESREA | National Environmental Standards and Regulations Enforcement Agency | | NH3 | Ammonia | | NILU | Norwegian Institute for Air Research | | NIMET | Nigerian Meteorological Agency | | NIS | Nigerian Industrial Standards | | NNPC | Nigerian National Petroleum Corporation | | N2O | Nitrous Oxide | | NO | Nitric Oxides | | NO2 | Nitrogen Dioxide | | NOx | Nitrogen Oxides | | NOSDRA | National Oil Spill Detection and Regulatory Agency | | NSE | Nigerian Stock Exchange | | NUPRC | Nigerian Upstream Petroleum Regulatory Commission | | O3 | Ozone | | OECD | Organisation of Economic Co-operation and Development | | PAF | Population Attributable Fraction | | PforR | Program-for-Results | | PCEH | Pollution Control and Environmental Health | | PM | Particulate Matter | | PM1 | Particulate Matter with Diameter Less than 1μm | | PM2.5 | Particulate Matter with Diameter Less than 2.5μm | | PM10 | Particulate Matter with Diameter Less than 10μm | | PMEH | Pollution Management and Environmental Health | | PMEH-MDTF | Pollution Management and Environmental Health Multi-Donor Trust Fund | | PMF | Positive Matrix Factorization | | ppb | Parts per Billion | | ppm | Parts per Million | | PPMC | Pipelines and Product Marketing Company | | ppv | Parts per Volume | | PWE | Population-Weighted Exposure | | REVIHAAP | Review of Evidence on Health Aspects of Air Pollution | | RHA | Respiratory Hospital Admission | | RR | Relative Risk | | SAWE4 | Environment, Natural Resources and Blue Economy West and Central Africa | | SCC | Social Cost of Carbon | | SENGL | Environment, Natural Resources and Blue Economy, Global team | | SIP | State Implementation Plan | | SO2 | Sulfur Dioxide | | SOx | Sulfur Oxides | | SON | Standards Organization of Nigeria | | SPO | Second-Party Opinion | | TSC | Technical Service Contractor | | TSP | Total Suspended Particulate Matter | \\mathbf{N H\_{3}} {\\bf N}\_{2}{\\bf O} \\mathbf{N O\_{x}} {\\bf N O}\_{2} {\\bf O}\_{3} \\mathbf{P M\_{1}} \\mathbf{P M}\_{2.5} 2.5,\\up\\mathrm{m} \\mathbf{P M\_{10}} 10;\\upmu\\mathrm{m} \ {tt s s}\_{2} \\mathtt{S O}\_{\\mathbf{x}} * * * | UK | United Kingdom | | --- | --- | | UNFCCC | United Nations Framework Convention on Climate Change | | UNILAG | University of Lagos | | US | United States | | US EPA | United States Environmental Protection Agency | | VOC | Volatile Organic Compound | | VSL | Value of Statistical Life | | WBG | The World Bank Group | | WHO | The World Health Organization | | μg/m3 | micrograms per cubic meter | * * * # EXECUTIVE SUMMARY ## INTRODUCTION Ambient air pollution is a major contributor to illness and premature deaths in much of the developing world, including Lagos. The World Bank is committed to supporting countries severely affected by pollution through its advisory service, tech- nical assistance, and lending. With funding from the Pollution Management and Environmental Health Multi-Donor Trust Fund (PMEH-MDTF), the World Bank, in collaboration with the Lagos State Government (LASG), and specifically the Lagos State Environmental Protection Agency (LASEPA), contracted consultants to establish the scientific basis for air quality management (AQM) and to develop an AQM plan for Lagos State. The effort included the following: » Establishment of a network of six air-quality-monitoring stations to collect 12 months of air quality data on PM2.5, PM10, other criteria pollutants (sulfur dioxide \[SO2\], nitrogen dioxide \[NO2\], carbon monoxide \[CO\], ozone \[O3\]), greenhouse gases – GHGs (carbon dioxide \[CO2\], methane \[CH4\], nitrous oxide \[N2O\], black carbon \[BC\], chlorofluorocarbons \[CFCs\], hydrofluorocarbons \[HFCs\]), and meteorological data » Chemical analysis and source apportionment analysis of collected aerosol par- ticulate matter (PM) to determine the composition and likely sources of PM emissions » Development of an inventory of air pollutant emissions » Photochemical dispersion modeling to reconcile the inventory with observed pollutant concentrations and to estimate the exposure in each local government area (LGA) » Assessment of the health impacts of air pollution in Lagos by estimating the effects of air pollution on the incidence of premature mortality and illness in each LGA » Economic and financial analysis of the costs of premature mortality and illness due to air pollution, and the costs and benefits of measures to control pollutant emissions » Assessment of existing institutional and governance structures for successful AQM in Lagos and Nigeria » Recommendation of an integrated AQM plan and establishment of an air quality index (AQI) for the State of Lagos. Air Quality Management Planning for Lagos State xv * * * AIR QUALITY CONDITIONS IN LAGOS The World Health Organization (WHO) guideline for annual average PM2.5 concentrations is 5 µg/m3 (micrograms—or one-millionth of a gram—per cubic meter) of air. This project found annual average PM2.5 concentrations at the six monitoring sites ranging from 30 to 97 µg/m3, with a population-weighted average of 47 µg/m3. The highest average PM2.5 was found in the industrial/ residential area of Ikorodu, an industrialized LGA in Lagos. Concentrations of lead aerosol in Ikorodu were also dangerously high—more than 10 times the US EPA standard of 0.15 µg/m3 for lead aerosol. Measurements of gaseous pollutants also showed concentrations of CO and NO2 in excess both of Nigerian air quality standards and of WHO guidelines. \\mathrm{P M}\_{2.5} 5,\\up{\\mathrm{{g}}}/\ \\mathrm{{{m}}^{3}} \\mathrm{M}}{\_}{{2.}}} \\mathrm{\\mu gg/m^{3}} 47,\\upmu\\mathrm{g}/\\mathrm{m}^{3} \\mathrm{M}{}\_{2.5} 0.15;\\upmu\\mathrm{g}/\\mathrm{m}^{3} \\mathrm{N O\_{2}} The PM source apportionment conducted for this project found that open burning of biomass and solid waste accounts for about 30 percent of the annual ambient PM2.5; gasoline and diesel engines combined account for about 16 percent; and industrial emissions account for about 18 percent on average (ranging from 48 percent at Ikorodu to less than 9 percent at other sites). Ammonium nitrate and ammonium sulfate—produced by chemical reactions between gaseous sulfur oxides (SOx), nitrogen oxides (NOx), and ammonia (NH3)—make up 10 percent of the PM2.5. Dust, including dust from roads, construction sites, and agricultural fields as well as the seasonal Harmattan, makes up about 26 percent of ambient PM2.5 and 50 percent of PM10. \\mathrm{P M}\_{2.5}, \\mathrm{(N O\_{x}),} \\mathrm{M}\_{2.5} \\mathrm{{({S\ O\_{x}}),}} \ mathrm P P M\_{2.5} HEALTH AND ECONOMIC IMPACTS OF AIR POLLUTION \\mathrm{P M}\_{10} \\mathrm{P M}\_{2.5} \\mathrm{(N H\_{3})} estimated to cause 180,000 to 350,000 acute lower respiratory infections (ALRI) per year, primarily cases of pneumonia in children under 5. Another 250 to 500 deaths are estimated to be due to PM10 exposure during the Harmattan season. Exposure to lead aerosol in Ikorodu is estimated to have cost the LGA’s children an average of 6.2 intelligence quotient (IQ) points and to be causing another 300 to 400 deaths from cardiovascular disease per year. Mortality and morbidity due to gaseous pollutants were not estimated but would likely increase these numbers by about 10 percent. \\mathrm{P M}\_{10} Using the human capital method, which essentially values a life at the time of death equal to the amount that a person could earn over his or her remaining life, the economic costs of PM2.5 air pollution in Lagos State are estimated at US$1.2–2.3 billion per year—1.6 to 3.2 percent of Lagos’ gross domestic product (GDP). Using the value of a statistical life (VSL) approach, which considers how much society is willing to pay to reduce a small risk of death, the costs are estimated at US$3.1–5.8 billion (4.2 to 8.1 percent of Lagos’ GDP). The economic costs of exposure to lead aerosol in Ikorodu are estimated at an additional US$300–600 million per year, or US$400– 600 for every resident of that LGA. \\mathrm{P M}\_{2.5} POTENTIAL EMISSION CONTROL MEASURES Given the range of human-made air pollution sources that have been identified in Lagos, a multi-sectoral approach is needed to improve air quality. The following are key air-quality policies recommended for near-term implementation in Lagos, based on measured pollution levels, assessed health impacts, readiness for implementation, and consistency with the National Action Plan (NAP) to Reduce Short-Lived Climate Pollutants. * * * waste should be banned, along with a public information program to explain the health impacts of open burning. Lagos should also strive to collect as much MSW as possible and ensure that open burning is not taking place at landfills or transfer stations. Recycling and composting can reduce the amount of MSW destined for landfills by as much as two-thirds while also generating revenue from the sale of products such as fertilizer and cardboard to offset the costs. Power. Small, engine-driven generating sets (gensets) are estimated to account for nearly half of the total electricity produced in Lagos and are responsible for a much larger share of air pollution from power generation. Gensets are one of the least regulated sources of air pollution in Lagos. There is an urgent need to reduce emissions from gensets by substituting electricity from the grid or from distributed power generation (such as from solar photovoltaic) and by setting and enforcing genset emission standards. Fuel quality. One of the key constraints on reducing emissions from transport vehicles (and stationary engines for industry or power generation) has been the lack of clean gasoline and diesel fuel. To reduce air pollution, many megacities around the world, including in Mexico City, Delhi, Santiago, and Rio de Janeiro, have established Transport. Over the long term, public transport (buses, light rail, ferries) can reduce air pollution from the transport sector in Lagos by lessening road congestion and the number of private passenger vehicles. At the same time, it is essential to control emissions from vehicles through a systematic process of improving vehicle emission standards and the quality of transport fuels. Achieving Euro 3 and Euro 4 vehicle standards could reduce PM emissions from transport in Lagos by an estimated 65–83 percent, compared to Euro 1 vehicles. Economic Commission of West African States (ECOWAS) directive C/Dir.2/09/20 requires imported light-duty vehicles to meet Euro 4 standards from January 2021, and requires vehicles in circulation to meet them from January 2025. Heavy-duty trucks and buses are required to meet Euro 6 standards. Given that Euro 4 vehicles have been manufactured globally since 2006, it is well within the capacity of Lagos State to achieve a high share of such vehicles in its total vehicle population through a program of emissions testing and vehicle retrofits. stricter fuel quality standards than their respective countries. In the face of numerous incentives to adulterate fuel, it is necessary for fuel quality to be regulated and enforced at retail outlets. Industry. Industrial emissions account for a sizable share of PM2.5 emissions in Lagos, principally in Ikorodu, but also throughout the state. Continuous-emissions-monitoring equipment should be employed to regulate emissions from large industrial sources, with fines imposed for noncompliance. LASEPA staff have legal authority to carry out emissions source tests to enforce emission standards; they should be trained and equipped to do so. Financing for air quality. An AQM program in Lagos could build on existing public support for the transport and solid waste sectors, and for industrial relocation through environmental financing. Green bonds, supplemental finance from multilateral organizations, and climate finance can be used to support the intersection between air quality and climate change, such as for solid waste management, electric power reform, public transport, and alternative fuels. \\mathrm{M}\_{2.5} LAWS, REGULATIONS, AND INSTITUTIONAL CAPACITY Under the Lagos Environmental Management Protection Law of 2017, LASEPA has the legal authority to enforce emission standards on industrial, agricultural, and government sources, as well as generating plants in residential and commercial areas; to set and enforce vehicle emission standards; and to set up an air quality monitoring network. However, it mostly lacks the technical capacity and staff to do so effectively. Training and capacity building, together with additional staff and equipment investments, are needed for LASEPA to effectively fulfill its statutory role in AQM. This will require an increase in budget. As a parastatal, the agency has the capacity to be self-funding and already derives a large fraction of its budget from fees, fines, and the Environmental Development Charge. * * * Table 1.1. RECOMMENDED AIR QUALITY STRATEGY FOR LAGOS | S/No | Short-term recommendation-1 year | Medium-term recommendation-3 years | Responsible authority | | --- | --- | --- | --- | | Air quality monitoring | | | | | 1 | Resume air quality monitoring at the six sites for which a monitoring record already exists, and begin planning an expanded network. | Establish 8-12 additional air quality monitoring sites, including upwind and downwind locations as well as sites influenced by the ports, traffic, and industrial areas, to better monitor population-based exposure and to strengthen the basis for air quality modeling. | LASEPA,Lagos State Ministry of Economic Planning and Budget(LMEPB) | | 2 | Train and equip LASEPA staff to carry out emission measurements on industrial sources and begin such testing with the largest and worst emitters. | Strengthen the scientific basis for AQM by continuing to develop the emissions inventory, strengthening oversight of the emissions auditing process,and strengthening the reporting of health and economic statistics. | LASEPA,Lagos State Ministry of Environment and Water Resources(LMOE),Lagos Bureau of Statistics(LBS) | | Health | | | | | 3 | Provide education,training,and lifelong learning to health personnel on the health effects of air pollution. | Strengthen the scientific basis for health impact assessment,expand the system of health information collection,and initiate epidemiological research on air pollution. | LASEPA,Lagos State Ministry of Health(LMoH) | | 4 | | Engage public opinion by adopting an AQI and routinely providing air quality data and forecasts to the media and on LASEPA's website. | LASEPA,LmoH | | Regulation and enforcement | | | | | Solid waste management | | | | | 5 | Redouble efforts to collect and dispose of solid waste by landfill, recycling,composting,and/or incineration with emission controls,and enforce prohibitions on the open burning of waste and biomass. | | LAWMA | | Industries | | | | | 6 | Locate and shut down any lead-smelting or battery-recycling operations in Ikorodu,measure lead levels in soil and in the blood of the potentially affected population,and take remedial action as necessary. | | LASEPA | | Transport | | | | | 7 | Implement ECOWAS Directive C/Dir.1/09/20,limiting sulfur in gasoline and diesel fuel to 50 ppm by weight;enforce this by collecting and analyzing fuel samples at the port and at retail stations,with fines and/or the loss of retail licenses for noncompliance. | | Nigerian Upstream Petroleum Regulatory Commission(NUPRC),Standards Organization of Nigeria(SON) | | 8 | Begin execution of ECOWAS Directive C/Dir2/09/20 by notifying vehicle importers and implementing inspections and testing to confirm that newly imported, light-duty vehicles (whether new or used) meet Euro 4 emission standards and that heavy-duty vehicles meet Euro 6 standards. | Strengthen the existing vehicle inspection and maintenance system to enforce the requirement of ECOWAS Directive C/Dir2/09/20 that vehicles in circulation meet Euro 4 emission standards from January 2025. | National Environmental Standards and Regulations Enforcement Agency(NESREA), Lagos State Metropolitan Transport Agency(LAMATA), Lagos State Ministry of Transport(LMoT) | | 9 | | Replace the existing danfo(microbus) fleet with larger minibuses, preferably plug-in hybrid electric vehicles with advanced emission controls,and restructure the routes to coordinate with the bus rapid transit(BRT) system. By charging from the power grid when it is available and from their onboard engine when not, plug-in hybrids could provide reliable service in the near term while retaining the ability to switch to all-electric operation in the future. | LAMATA | | 10 | | Consider measures to phase out engine-driven taxicabs,ohada motorcycle taxis,and keke NAPEP tricycle taxis in favor of battery electric vehicles(BEVs). | LAMATA | | Energy | | | | | 11 | Set and enforce emission standards for backup generators. | Increase the capacity and reliability of the electric-generating system to reduce the need for backup generators and consider retrofitting the Egbin power plant for combined-cycle operation with low-NOx gas turbines. | LASEPA,Federal Ministry of Power(FMP) | | 12 | | Consider grouping small power users into“mini grids”of a few hundredkW incorporating solar photovoltaic panels and diesel-generating sets with advanced emission controls. | Federal Ministry of Power,Lagos State Ministry of Energy and Mineral Resources(LMoEMR),LASEPA | | Air quality financing | | | | | 13 | Consider a percentage of existing or new emission fees and other charges as line charge to sustainably support increased staffing and equipment for LASEPA. | | LMEPB,LASEPA | | 14 | Consider multilateral financing and/or an air quality green bond to support needed investments in emission controls,air quality monitoring infrastructure, emissions measurement capabilities,and capacity building for air quality enforcement and management. | | LMEPB,LASEPA | * * * * * * ## CHAPTER 1 # INTRODUCTION Air pollution is a major contributor to illness and premature death in much of the developing world, including Lagos. The World Bank is committed to supporting coun- tries severely affected by pollution through its advisory service, technical assistance, and lending. The World Bank’s Environment, Natural Resources and Blue Economy Global Practice has set pollution management and environmental health (PMEH) as one of its five core business lines to increase support in this area. Consequently, the Pollution Management and Environmental Health Multi-Donor Trust Fund (PMEH- MDTF) was established in 2015 to drive actions to address air and land pollution issues in low- and middle-income countries (LMICs). With funding from the PMEH, the World Bank contracted with Technical Service Contractors (TSCs) to carry out the preliminary work to establish a scientific basis for air quality management (AQM) and to develop an AQM plan for the State of Lagos. This document is the final report of that effort. The effort included the following: » Establishment of a network of six air quality monitoring stations, which represent six of the land use classes, to monitor and collect 12 months of air quality data on PM2.5, PM10, other criteria pollutants (sulfur dioxide \[SO2\], nitrogen dioxide \[NO2\], carbon monoxide \[CO\], ozone \[O3\]), greenhouse gases – GHGs (carbon dioxide \[CO2\], black carbon \[BC\], methane (\[CH4\], nitrous oxide \[N2O\], chlorofluorocarbons \[CFC\] and hydrofluorocarbons \[HFC\]), as well as meteorological parameters » Chemical analysis and source apportionment analysis of collected aerosol par- ticulate matter (PM) to determine the composition and likely sources of PM emissions » Development of an inventory of air pollutant emissions, together with potential measures to reduce those emissions » Photochemical dispersion modeling to reconcile the emission inventory with observed pollutant concentrations and to estimate the severity of exposure in each local government area (LGA) Air Quality Management Planning for Lagos State * * * » Economic and financial analysis of the costs of premature mortality and illness due to air pollution and of the costs and benefits of measures to control pollutant emissions » Assessment of the existing institutional and governance structure for successful AQM in Lagos and, more broadly, in Nigeria » Recommendation of an integrated AQM plan » Recommendation of an integrated AQM plan and establishment of an air quality index (AQI) for the State of Lagos. 1.1. LAGOS: POPULATION, ECONOMY, AND ENVIRONMENT Lagos is the largest city in Sub-Saharan Africa and one of the world’s fastest-growing megacities. Although Lagos is the smallest state in the Federal Republic of Nigeria by area, it is among the highest in population. From 7.5 million in 2006 (the most recent census), the population in 2019 was estimated at about 13.5 million by the National Bureau of Statistics, and at 23 million by the Lagos State Bureau of Statistics (LBS). This rapid growth has produced urban sprawl and severely strained the infrastructure and the provision of basic services. More than half the population live in informal settlements. only about 40 percent of the waste generated is collected and transported to dumpsites. The remaining 60 percent is mostly burned. The dumpsites themselves are in poor condition. Compounding that, Olusosun dumpsite—the largest of three major dumpsites and second largest in Africa—is located within the city. These dumpsites are sources of biomass burning, fugitive dust, and various gaseous emissions such as methane (CH4). Lagos State has the highest gross domestic product (GDP) of any Nigerian state, accounting for about 25 percent of national GDP. It is a primary center for the transport and manufacturing industries. Lagos bears the additional burden of a coastal city with two busy seaports. Most of Nigeria’s maritime trade passes through Lagos’ ports of Apapa and Tin Can Island, the largest and busiest in West Africa. The ports are constantly overwhelmed with hundreds of old, diesel-engined tractor-trailers conveying containers from the seaports to other parts of the country and contributing to the traffic congestion and vehicular emissions. Ship traffic is equally congested, with ships often having to wait offshore for weeks to unload. Downwind of the seaports is the Okobaba sawmill, with emissions from the constant burning of sawdust. The Ikorodu industrial zone—one of several in the city—is a major source of the unregulated discharge of industrial emissions. 1.2. NEED FOR AN INTEGRATED AIR Physically, most of Lagos is built on a low-lying, wooded coastal plain and adjacent barrier islands surrounding a large lagoon. The climate is warm and humid, with a pronounced wet season from May to September and a dry season the remaining months. During the dry season, occasional strong northeasterly Harmattan winds carry dust from the Sahara Desert, resulting in low humidity and extremely high concentrations of airborne PM. INTEGRATED AIR QUALITY STRATEGY Lagos currently lacks a standardized AQM system despite the growing evidence of unhealthily high levels of PM and other pollutants and high emissions of GHGs. * * * A fact-finding air quality monitoring mission (FFMM) was conducted by the World Bank and uMoya-NILU 1 (Norwegian Institute for Air Research) in Lagos between December 11 and 18, 2015 to inform the Lagos AQM plan proposal. Air sampling data collected at eight locations with diverse source characteristics (industrial, commercial, residential, dumpsite, heavy traffic, high population density, conservation, and mixed land use) indicated several cases of extremely high mean PM 2 concentrations. Across locations, the results indicated mean PM2.5 levels ranging from 116 µg/m3 to 483 µg/ m3—up to 19 times higher than WHO’s 24-hour mean guideline3 of 25 µg/m3—and PM10 levels ranging from 55 µg/m3 to 442 µg/m3, up to nine times higher than the 24-hour mean WHO guideline of 50 µg/m3. The results of the FFMM showed remarkably high ambient \\mathrm{P M}\_{2.5} 116~\\upmu\\mathrm{g}/\\mathrm{m}^{3} 483~\\up\\mathrm{g//} \\mathrm{m^{3}} 25~\\up{upmu}mathrm{g{}/m^{3}} \\mathrm{P M}\_{10} 55~\\up{\ mu mu\ }mathrm{g/m}^{3} 442~\\up\\mu{\\mathrm{g}}/\ \ 50~\\up{\ up\\mu\\mathrm{g}/\\mathrm{m}^{3}} PM levels at industrial, commercial, traffic, dumpsites, and mixed-residential areas, signifying that substantial PM emissions are being generated from diverse sources such as motor vehicles, domestic power plants, and improper management of wastes. Both the Nigerian Federal Government and the State of Lagos have developed plans to address emissions that contribute to climate change but, compared to other megacities in the developing world, much less attention has been given to ambient air pollution in the major cities. The major exception was the Federal Government’s 2018 plan for managing short-lived GHGs (Government of Nigeria 2018), which explicitly considered the benefits of reducing PM2.5 pollution in concert with reductions in black carbon and CH4 emissions. \\mathrm{M}\_{2.5} \\mathrm{H\_{4}} FIGURE 1.1. PMEH INSTITUTIONAL ARRANGEMENTS * * * An integrated approach to pollution management, targeting global warming, air pollution, solid waste, and wastewater management is increasingly important in order to manage the interrelated and complex pollution issues and maximize the benefits of environmental regulation. Some of the impediments to an integrated approach in Lagos are: » The lack of an air quality monitoring network, resulting in the unavailability of good-quality data that would help identify the sources, extent, and impacts of pollution » Limited institutional and human resource capac- » Limited institutional and human resource capacity in pollution monitoring and management » Poor interinstitutional coordination between state » The low level of public awareness of sources and impacts of pollution. » Poor interinstitutional coordination between state and federal government agencies » The lack of detail and enforcement provisions in » The lack of detail and enforcement provisions in air quality standards and regulations » Limited capacity to monitor and enforce compli- » Limited capacity to monitor and enforce compliance with standards For Lagos State, the major obstacle to an integrated approach to addressing air pollution and GHG emissions in a cost-effective, cohesive manner is the in interinstitutional coordination between different sectors to ensure synergy among the different institutions ( indicated in figure 1.1). For Lagos State, the major obstacle to an integrated approach to addressing air pollution and GHG emissions in a cost-effective, cohesive manner is the complexity in interinstitutional coordination between different sectors to ensure synergy among the different institutions ( indicated in figure 1.1). REFERENCE Government of Nigeria. 2018. “Nigeria’s National Action Plan to Reduce Short-lived Climate Pollutants.” [https://climatechange.gov.ng/wp-content](https://climatechange.gov.ng/wp-content) /uploads/2020/09/nigeria-s-national-action-plan -nap-to-reduce-short-lived-climate-pollutants-slcps -.pdf. * * * ## CHAPTER 2 # AIR QUALITY CONDITIONS IN LAGOS AQM planning must start with knowledge of the existing conditions. Until August 2020, only limited and discontinuous measurements of air quality had been carried out in Lagos. The PMEH therefore funded a TSC to carry out 12 continuous months of air quality monitoring at six monitoring sites. Monitoring began in August 2020 and concluded at the end of July 2021. Details of the monitoring are given in the TSC’s report (EnvironQuest 2021a). The main air quality measurements at each monitoring site were PM2.5and PM10. These were collected on filters for 24-hour periods every three days. The filter collection followed US EPA reference methods, and the samplers used were con- sidered “near-reference” quality. Each monitoring site was also equipped with a weather station, equipment to collect ambient air samples in a vacuum canister over a 24-hour period, and a low-cost, continuous air quality monitoring system. The latter system was included for evaluation. It used an optical sensor to estimate concentrations of PM10, PM2.5, and PM1 and electrochemical sensors to measure gaseous pollutants. As further discussed in section 2.2, the results from this system were of limited value. From January 1, 2021, the US Consulate in Lagos also began reporting hourly PM2.5 concentrations measured by a US EPA reference-grade instrument. Those data are also summarized in this report. Figure 2.1 shows a satellite view of the Lagos metro- politan area, with markers showing the locations of the US Consulate and the six air quality monitoring sites. Air Quality Management Planning for Lagos State * * * Source: Satellite data—Google Earth. 2.1. PARTICULATE MATTER Table 2.1 shows the average of the PM2.5 and PM10 filter measurements taken every third day at each monitoring site as well as the year-to-date measurements by the US Consulate. The Abesan, Jankara, Lagos State Environmental Protection Agency (LASEPA), and University of Lagos (UNILAG) sites are all typical urban locations, with average PM2.5 concentrations ranging from 40.3 to 3 46.5 µg/m. This range is probably representative of most of the urban areas. The Nigerian Conservation Foundation (NCF) site is in a protected and undisturbed natural ecosystem near the coast, which is considered to represent regional background concentrations. The Ikorodu site shows extremely high PM concentrations. It is located at a school in a residential area near a concentration of heavy industry. Finally, the US Consulate site is located across the ship channel from Apapa and Tin Can Island ports, so it may be affected by the emissions there. \\mathrm{M}}{\_}{22.5} \\mathrm{P M}\_{10} \ mathrm P M M\_{2.} 46.5,\\upmu\\mathrm{g}/\\mathrm{m}^{3} Figure 2.2 is a chart showing the individual measurements taken at each site over the year of monitoring. These are 24-hour averages collected every three days, except for the US Consulate data, which are hourly. Figure 2.3 is a similar chart of PM10 measurements over the year. As these figures show, the WHO guidelines for 24-hour average PM2.5 and PM10 concentrations are exceeded nearly every day of the year. As Figure 2.2 and Figure 2.3 show, the PM concentrations at Ikorodu are systematically much higher than at the remaining sites, which tend to bunch closely together. The hourly PM2.5 data from the US Consulate also agree well with the PM filter measurements. Both PM2.5 and PM10 concentrations are noticeably higher during the dry season. Finally, the effects of the Harmattan winds in January 21–26 and February 19–24 can be seen in the extremely high concentrations of both PM2.5 and PM10 across all of the monitoring sites during those periods. \ \\mathrm\ p\\mathrm{M}\_{2.5} \\mathrm{P M}\_{2.5} \\mathrm{P M}\_{10} \\mathrm{P M}\_{10} * * * TABLE 2.1. ANNUAL AVERAGE PM CONCENTRATIONS COMPARED TO WHO GUIDELINES | | Concentration(μg/m3) | | | --- | --- | --- | | Location | PM2.5 | PM10 | | Abesan | 46.5 | 119.4 | | Jankara | 41.5 | 104.9 | | LASEPA | 40.3 | 103.3 | | UNILAG | 41.7 | 96.2 | | Ikorodu | 96.8 | 170.5 | | NCF | 29.5 | 73.6 | | US Consulate | 49.2a | | | WHO guidelines(μg/m3) | | | | Annual average | 5 | 15 | | 24-hour average | 15 | 45 | (\\mathbf{\ \ p g/m^{3}}) \\mathbf{P M}\_{2.5} \\mathbf{P M\_{10}} In addition to the PM filter measurements, the low-cost Earthsense Zephyr monitoring systems estimated PM2.5 and PM10 concentrations using an optical sensor. Unfortunately, these estimates were not very accurate. Figure 2.4 compares the PM concentrations determined by the filter samplers with the average of the optical sensor concentration estimates over the same sampling period. The correlation coefficient is only moderate, with R2 about 0.62. The slope of the best-fit line for PM2.5 is 2.94 and that for PM10 is 4.3. Thus, the optical sensor greatly underestimated the actual PM concentrations as measured by the filters. Note: a. January 1 to October 15, 2021 \ {bf R}^{2} \\mathrm{P M}\_{2.5} \\mathrm{P M}\_{10} While far from perfect, the inexpensive optical sensor in the Earthsense Zephyr correlates well enough to give a reasonable indicator of PM levels in real time. Figure 2.5 plots the optical sensor reading for PM2.5 for one of the sites— corrected using the best-fit equation shown in Figure 2.4— with the corresponding results from the 24-hour PM filter measurements. Figure 2.6 shows a similar plot for PM10. The two sets of data agree well, except for the month of October. The same is true for the remaining sites, except for Ikorodu. The anomalously high readings from the optical sensor during October occurred in the early- to mid-morning hours and may have been due to fog. {\\mathrm{P M}}\_{10}. \\mathrm{P M}\_{2.5} FIGURE 2.2. PM2.5 MEASUREMENTS AT EACH MONITORING SITE \\mathsf{P}\\mathsf{M}\_{2.5} \\mathbf{P M}\_{2.5} * * * FIGURE 2.3. PM10 MEASUREMENTS AT EACH MONITORING SITE \\mathsf{P}\|\\mathbb{M}\_{10} \\mathbf{P M}\_{1mathbf00} 2.1.1. PM COMPOSITION In addition to measuring the amount of PM mass col- In addition to measuring the amount of PM mass col- lected, the TSC carried out chemical analysis of a sub- lected, the TSC carried out chemical analysis of a subset of the PM filters. Generally, the filters analyzed were those from the three monitoring days at each site that showed the highest PM of filters, the measurements included concentrations of 41 chemical elements (from sodium to uranium), as well as elemental carbon, organic carbon, and six watersoluble ionic species. The concentrations of 225 organic marker species were also measured. These data were analyzed to apportion the PM present among different source categories. Details of the chemical analysis can be found in the TSC’s final report (EnvironQuest 2021a). lected, the TSC carried out chemical analysis of a subset of the PM filters. Generally, the filters analyzed were those from the three monitoring days at each site that showed the highest PM2.5 concentrations. For each set of filters, the measurements included concentrations of 41 chemical elements (from sodium to uranium), as well as elemental carbon, organic carbon, and six watersoluble ionic species. The concentrations of 225 organic marker species were also measured. These data were analyzed to apportion the PM present among different source categories. Details of the chemical analysis can be found in the TSC’s final report (EnvironQuest 2021a). \\mathrm{M}\_{2.5} Ammonium nitrate, ammonium bisulfate, and other sulfates are typically secondary pollutants, meaning that they are not emitted directly but are formed in the atmosphere through chemical reactions among gaseous pollutants: NH3, SO2, and NO2. Some of the particulate organic matter is also formed by secondary reactions involving gaseous hydrocarbons in the atmosphere. The remaining pollutants and the bulk of the organic matter are primary pollutants, meaning that they are directly emitted into the atmosphere by various sources. \\mathrm{N H\_{3},\ O\_{2}}, \\mathrm{{N O}{}\_{2}} * * * FIGURE 2.4. CORRELATION BETWEEN PM FILTER DATA AND 24-HOUR AVERAGE OPTICAL SENSOR PM ESTIMATES \\mathbf{P M}\_{2.5} PM10 - Filter vs Zephyr Continuous - All stations \\mathbf{P M}\_{10} * * * FIGURE 2.5. CORRECTED OPTICAL SENSOR PM2.5 READINGS VERSUS 24-HOUR FILTER MEASUREMENTS—JANKARA SITE \\mathsf{P}\\mathsf{M}\_{2.5} JANKARA - 24-hour PM2.5 filter vs. continuous Zephyr PM2.5 \\mathbf{P M}\_{2.5} \\mathbf{P M}\_{2.} FIGURE 2.6. CORRECTED OPTICAL SENSOR PM10 READINGS VERSUS 24-HOUR FILTER MEASUREMENTS – JANKARA SITE \\mathsf{P}\|\\mathbb{M}\_{10} JANKARA - 24-hour PM10 filter vs. continuous Zephyr PM10 \\mathbf{P M}\_{10} * * * FIGURE 2.7. SUMMARY OF THE CHEMICAL COMPOSITION OF PM2.5 COLLECTED AT THE SIX MONITORING SITES \\mathsf{P}\\mathsf{M}\_{2.5} –3 Abesan PM mass: 64.8 µg m –3 Jankara PM mass: 58.7 µg m –3 Lasepa PM mass: 57.6 µg m 6,up\ {mathfrak p p},{\\mathfrak m}^{-3} –3 NCF PM mass: 43.1 µg m To better determine the sources of ambient PM concentrations in Lagos, the TSC carried out a source apportionment analysis using the PM compositions. Two different source apportionment techniques were applied: positive matrix factorization (PMF) and chemical mass balance (CMB). Both analyses used techniques and software developed for this purpose by the US EPA. Further details are given in the TSC’s report (EnvironQuest –3 Unilag PM mass: 62.0 µg m Ammon nitrate Ammon sulphate Fine soil OM EC Sea salt Trace elements Unidentified 2.1.2. PM SOURCE APPORTIONMENT \\mathrm{P M}\_{2.5} 2021b). The results of the source apportionment of PM2.5 by PMF are summarized in Figure 2.8, while the results of the CMB analysis are summarized in Figure 2.9. The two techniques gave similar results for PM source apportionment in Lagos during the monitoring period. The PMF analysis showed nine source types: dust, * * * FIGURE 2.8. PM2.5 SOURCE APPORTIONMENT BY PMF \\mathsf{P}\\mathsf{M}\_{2.5} NCF Lasepa Abesan \\mathrm{N H\_{4}N O\_{3}} * * * FIGURE 2.9. PM2.5 SOURCE APPORTIONMENT BY CMB \\mathsf{P}\\mathsf\\mathrm{M}\_{2.5} \\mathrm{N H\_{4}N O\_{9}} in chloride ion (Cl-). The CMB analysis identified only six source types: dust, a combination of biomass with solid waste burning and cooking, motor (gasoline and diesel engines combined), industrial emissions, ammonium nitrate, and ammonium sulfate. Dust makes up 28 percent of the average source composition in PMF and 24 percent in CMB. Open burning of biomass and solid waste makes up 28 percent in PMF; that plus cooking make up 32 percent in CMB. Gasoline and diesel engines combined make up 14 percent in PMF and 17 percent in CMB. Secondary ammonium nitrate and ammonium sulfate make up 10 percent in PMF and 9 percent in CMB. Both techniques show industrial emissions at 18 percent of the average over the six sites. Industrial emissions at Ikorodu are 50 percent in PMF and 46 percent in CMB, less than 1–2 percent at Abesan, and 5–9 percent at the other sites monitored. The results of PMF source apportionment of the PM10 composition data are summarized in Figure 2.10 (no CMB analysis was done for PM10 that the percentage of the PM10 as high as for PM2.5, while the percentage due to the chloride-rich fraction is five times as high. This suggests that the chloride-rich fraction may be due to sea salt particles since these occur primarily in the coarse mode. The contributions of the other sources are reduced more or less proportionally. The results of PMF source apportionment of the PM10 composition data are summarized in Figure 2.10 (no CMB analysis was done for PM10). These results show that the percentage of the PM10 due to dust is about twice , while the percentage due to the chloride-rich fraction is five times as high. This suggests that the chloride-rich fraction may be due to sea salt particles since these occur primarily in the coarse mode. The contributions of the other sources are reduced more or less \\mathrm{P M}\_{10} \\mathrm{p M}\_{10}) \\mathrm{P M}\_{10} \\mathrm{M}\_{2.5} \\mathrm{P M}\_{10} \\mathrm{P M}\_{2.5} Average PM concentrations at the Ikorodu site were consistently higher than for any of the other monitoring sites. Although the site itself is at a secondary school, it is close to a number of factories that were hypothesized to be the source of the excess PM. To test this hypothesis, the TSC plotted the average PM as functions of wind direction and speed, as shown in Figure 2.11. Winds from the directions corresponding to the nearby industrial establishments consistently showed much higher PM concentrations than from any other direction. Average PM concentrations at the Ikorodu site were consistently higher than for any of the other monitoring sites. Although the site itself is at a secondary school, it is close to a number of factories that were hypothesized to be the source of the excess PM. To test this hypothesis, the TSC plotted the average PM2.5 and PM10 concentrations as functions of wind direction and speed, as shown in Figure 2.11. Winds from the directions corresponding to the nearby industrial establishments consistently showed much higher PM concentrations than from any other direction. 2.1.3. SOURCE OF HIGH PM CONCENTRATIONS AT IKORODU 2.2. LEAD AEROSOL Lead aerosol is a highly toxic component of airborne PM. Because of its former widespread use in gasoline and paints, the US EPA has established a separate ambient air standard of 0.15 µg/m3 for lead, measured as a quarterly average of total suspended particulate matter (TSP). With the worldwide elimination of leaded gasoline and paints, this limit has largely become irrelevant except in the vicinity of poorly controlled lead-smelting activities such as battery recycling. However, Figure 2.12 shows that the airborne lead concentrations measured at the Ikorodu site are more than 10 times the level of the EPA standard, indicating a grave threat to public health. Four of the other five monitoring sites marginally exceeded the standard during at least one quarter. Only the Abesan site did not. Abesan is also the site furthest from Ikorodu. This could indicate that a major source of lead emissions near the Ikorodu site may be causing exceedance of the lead standard throughout much of the city. 0.15~\\upmu\\mathrm{g}/\\mathrm{m}^{3} {\\bf O}\_{3}. \\mathrm{N O\_{2}} 2.3. GASEOUS POLLUTANTS \\mathrm{\ \ a,O\_{3},N O\_{2},C O}} Details of the gaseous pollutant measurements and quality assurance are given in the TSC’s report (Environ- Quest 2021a). In this monitoring campaign, the gaseous pollutants were measured using low-cost electrochemical sensors. Because of this, the data are not completely reliable. In particular, the measurements of O3 SO2 tend to show spuriously high “spikes” for an hour or two after the monitoring system starts up, while the NO2 measurements tend to show spuriously low values. During some periods, battery problems with the solar power systems at the LASEPA and Ikorodu sites caused the Details of the gaseous pollutant measurements and quality assurance are given in the TSC’s report (Environ- Quest 2021a). In this monitoring campaign, the gaseous pollutants were measured using low-cost electrochemical sensors. Because of this, the data are not completely reliable. In particular, the measurements of O3, CO, and tend to show spuriously high “spikes” for an hour or two after the monitoring system starts up, while the NO2 measurements tend to show spuriously low values. During some periods, battery problems with the solar power systems at the LASEPA and Ikorodu sites caused the * * * FIGURE 2.10. PM10 SOURCE APPORTIONMENT OF PM10 BY PMF \\mathsf{P}\|\ \\mathsf{M}\_{10} \\mathsf{P}\|!!!!\_{1}!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\ \\mathrm{N H\_{4}N O\_{3}} * * * FIGURE 2.11. PM CONCENTRATION VERSUS WIND DIRECTION FOR IKORODU a. b. FIGURE 2.12. QUARTERLY AVERAGE LEAD AEROSOL CONCENTRATIONS MEASURED AT EACH SITE * * * TABLE 2.2. AMBIENT AIR QUALITY STANDARDS AND WHO GUIDELINES FOR GASEOUS POLLUTANTS | Pollutant(units) | Averaging period | US EPA NAAQS | Nigeria AAQS | WHO Air Quality Standard | | --- | --- | --- | --- | --- | | O3(μg/m3) | 6monthsa | - | - | 60 | | 8 hours | 137(0.07ppm) | 100 | 100 | | | 1 hour | - | 180 | - | | | NO2(μg/m3) | Annual | 100(53 ppb) | - | 10 | | 24 hours | - | 120 | 25 | | | 1 hour | 188(100 ppb) | 200 | 200 | | | SO2(μg/m3) | 24 hours | - | 120 | 40 | | 1 hour | 196.5 | 350 | - | | | CO(mg/m3) | 24 hours | - | - | 4 | | 8 hours | 10(9ppm) | 5 | 5 | | | 1 hour | 40(35ppm) | 10 | 10 | | \\mathrm{O\_{3}(g/m^{3})} \\mathrm{N O\_{2}(g/m^{3})} \\mathrm{C O},(\\mathrm{m g}/\\mathrm{m}^{3}) o Note: US EPA National Ambient Air Quality Standards (NAAQS) values in parenthesis are µg/m3 equivalent at 1 atmosphere and 25 C. a. Average of daily 8-hour peaks. 10,\\upmu\\mathrm{g}/\\mathrm{m}^{3} 10\ \\upmu\\mathrm{g/m^{3}} After removing the spurious values, the results of the gaseous pollutant monitoring show that concentrations of CO exceeded the Nigerian air quality standards and WHO health guidelines at all six sites. Figure 2.13 shows the 8-hour average CO concentrations measured at each site. All six sites exceeded the Nigerian CO standard of 5 µg/m3. The Abesan, Jankara, and UNILAG sites show the highest and most frequent exceedances, sometimes exceeding the less stringent US standard of 10 µg/m3. One-hour average concentrations (not shown) often exceeded the Nigerian standard of 10 µg/m3 but not the US 40 µg/m3 standard. 5,\\up{\\mathrm{{mu g}m^{3}}} 25^{\\mathrm{o}}\\mathbf{C}. 40,\\upmu\\mathrm{g}/\\mathrm{m}^{3} \\mathrm{\ p g/m^{3}} 120,\\upmu\\mathrm{g}m^{\\mathrm{i}} 180\ \\upmu\\mathrm{g}/\\mathfrak{m}^{3}\ \\mathrm{I} Figure 2.15 shows the 8-hour average O3 concentrations measured at each of the six sites. The concentrations at these sites exceeded the Nigerian air quality standard of 100 µg/m3 twice, while the 1-hour average concentrations exceeded the 180 µg/m3 Nigerian standard once. The 6-month average peak concentration at Abesan likely exceeded the WHO guideline of 100 µg/m3 as well. However, all of these sites except NCF were situated close to significant sources 10,\\upmu\\mathrm{g}/\\mathrm{m}^{3} * * * FIGURE 2.13. EIGHT-HOUR AVERAGE CO CONCENTRATIONS AT EACH MONITORING SITE Green lines, Nigerian/WHO standard, red lines US NAAQS * * * FIGURE 2.13. (Continued) of nitrogen oxides (NOx). The air quality modeling confirmed that much higher O3 concentrations are to be expected in the region downwind of the city. This is because nitric oxide (NO), the major constituent of NOx, reacts with O3 to form NO2 and O2, thus suppressing O3 levels close to the emission source. In the presence of sunlight, NO2 reacts over time with volatile organic compounds (VOCs) in a complex process that produces O3 and other photochemical oxidants. Thus, as distance from the source of NOx emissions increases, so usually does the O3 concentration. \\mathrm{S O\_{2}} \\mathrm{O{.}} {\\mathrm O}\_{2}, {\\bf O}\_{3} {\\bf O}\_{3} 200~\\upmu\\mathrm{g}/\\mathrm{m}^{3}. 2.4. ORGANIC COMPOUNDS AND TOXIC AIR CONTAMINANTS At each of the six sites, the air monitoring TSC collected a total of 22 air samples in evacuated canisters for chemical analysis. Each sample was collected at a uniform rate over 24 hours. The resulting canister samples were * * * FIGURE 2.14. TWENTY-FOUR-HOUR AVERAGE NO2 CONCENTRATIONS AT EACH MONITORING SITE \\mathrm{N O\_{2}} Red lines Nigerian standard, green lines WHO guideline * * * FIGURE 2.14. (Continued) The results of the organic analysis showed that most compounds are present in less than parts per billion (ppb) concentrations. However, some are at concentrations of ppb or tens of ppb, among them ethane (a minor constituent of natural gas), propane and butanes (from liquefied petroleum gas, or LPG), and products of partial gasoline combustion such as ethylene and propylene. Also present in significant amounts were common solvents such as toluene, naphthalene, acetone, chloromethane, and dichloromethane—all of which are considered hazardous air pollutants. From February, high concentrations of dichloromethane were seen at the Ikorodu and LASEPA sites (67 and 74 parts ppb initially, tapering to about 33 ppb over months). This likely indicates the startup of a \\mathrm{L P G}) \\mathrm{H\_{4}} CO2 concentrations at all sites were above the global background level, reflecting the substantial CO2 emissions in the metropolitan area. The same is true of CH4 2.5. GREENHOUSE GASES \\mathrm{C O\_{2}} and nitrous oxide (N2O) concentrations. The significance of the variation in average concentrations among the six sites is not clear. Among the halocarbons measured, concentrations of CFC 11 and CFC 12 were highest at Ikorodu, followed by Jankara. CFC 113 was three times as high at Abesan as at any other site, while CFC 114 was similarly high at UNILAG. This suggests possible emission sources of these CFCs in the surrounding area. HCFCs 141b and 142b at all sites were significantly higher than the global background concentrations, suggesting that emission sources for these chemicals may be widespread throughout the metropolitan area. FIGURE 2.15. EIGHT-HOUR AVERAGE O3 CONCENTRATIONS AT EACH MONITORING SITE Green lines, Nigerian/WHO standard, red lines US NAAQS {mathcal O}\_{3} * * * FIGURE 2.15. (Continued) NCF An emissions inventory is an estimate of the emissions of each type of pollutant in a given area, broken down by the type of source. Using PMEH funds, the World Bank contracted with a TSC to develop a preliminary 2.6.1. CRITERIA POLLUTANTS Table 2.3 shows the inventory estimates of criteria pollutant4 emissions and their precursors. For discussion * * * FIGURE 2.16. SO2 CONCENTRATIONS AT LASEPA AND UNILAG SITES \\mathrm{S O\_{2}} Green lines, Nigerian/WHO standard, red lines US NAAQS LASEPA UNILAG * * * GHG Concentrations by Site FIGURE 2.18. AVERAGE CONCENTRATIONS OF CFCs C s, AND HFC s MEAs T EACH MONITORING SITE Halocarbon Concentrations by Site * * * TABLE 2.3. ESTIMATED INVENTORY OF CRITERIA POLLUTANTS AND PRECURSORS FOR LAGOS STATE | | Pollutant emissions(tons/year) | | | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | | Source type | $\\mathrm{PM}\_{10}$ | $\\mathrm{PM}\_{2.5}$ | $\\mathrm{NOx}$ | VOC | SOx | CO | NH3 | | Trash burning | 11,345 | 9,351 | 3,557 | 7,162 | 461 | 36,272 | 1,058 | | Biomass burning | 274 | 159 | 103 | 406 | 10 | 1,677 | 21 | | Generators | 1,021 | 1,021 | 21,528 | 27,054 | 3,542 | 10,77,489 | | | Road traffic | 1,820 | 1,470 | 38,388 | 38,275 | 6,529 | 2,36,771 | 723 | | Industry | 3,072 | 2,837 | 2,915 | 19,955 | 1,662 | 16,612 | 1,013 | | Power plants | 49 | 49 | 5,639 | 143 | 15 | 2,142 | 0 | | Seaport | 243 | 243 | 4,280 | 197 | 3,283 | 607 | 0 | | Airport | 2 | 0 | 726 | 71 | 41 | 587 | 0 | | Cooking | 185 | 180 | 694 | 168 | 168 | 1,536 | | | Waste disposala | 0 | 0 | | 2,585 | | | 58,100 | | Agriculture | 166 | 7 | 722 | 104 | 0 | 0 | 681 | | TOTAL | 18,177 | 15,317 | 78,552 | 96,120 | 15,711 | 13,73,693 | 61,596 | \\mathbf{P M\_{10}} \\mathbf{P M}\_{2.5} \\mathbf{N O x} \ \\mathrm{P{}bf M}\_{10}, Note: a. Other than open burning. Includes emissions from dumpsites and wastewater. \\mathrm{P M}\_{2.5} \\mathrm{M}\_{2.5} The PM emission estimates shown in Table 2.3 include only primary emissions of PM and not secondary particles (sulfates, nitrates, and some organic compounds) formed by the chemical reactions of other pollutants in the atmosphere. They also exclude resuspended dust as the emission numbers are not directly comparable to those for the other categories. Dust particles—even in the PM2.5 size range—are larger and settle out of the atmosphere much faster than particles from other sources, which are usually less than 1 µm. Based on the source apportionment analysis, dust averages about 25 percent of atmospheric PM2.5 and 50 percent of PM10, while secondary material averages about 15 percent of PM2.5. \\mathrm{M}\_{2.5} \\mathrm{{S O\_{x}}} \ \\mathrm{P M}\_{2.5} Of the roughly 60 percent of ambient PM2.5 attributable to primary emissions, the majority is estimated to be due to open burning of solid waste and other biomass. Most of the rest is attributed to industry, road traffic (mostly diesel vehicles and two-stroke motorcycles), and backup generators. Diesel and gasoline engines used in road vehicles and generators account for 76 percent of the NOx, 68 percent of the VOC, 96 percent of the CO, and 64 percent of the sulfur oxides (SOx) emissions (the latter due to average fuel sulfur concentrations of 0.24 percent in diesel fuel and 0.14 percent in gasoline). Nearly 80 percent of the CO emissions are attributed to generators—mostly small, inefficient, portable generators burning gasoline. The ports account for another 6 percent of NOx and 21 percent of SOx emissions—the latter due mostly to ships burning heavy fuel oil (HFO) containing up to 2 percent sulfur. \\mathrm{P M}\_{2. * * * FIGURE 2.19. BREAKDOWN OF ESTIMATED CRITERIA POLLUTANT EMISSIONS BY TYPE OF SOURCE Estimated GHG emissions are summarized in Table 2.4 and Table 2.5. Estimated CO2 emissions in Lagos total 16.3 million tons per year, but in the near term their warming effect is outweighed by the effects of shortlived greenhouse pollutants such as CH4, black carbon, 2.6.2. GREENHOUSE EMISSIONS \\mathrm{H\_{3}} VOCs, and CO. Table 2.4 shows the inventory with CO2-equivalent values calculated using the estimated 20-year GWP of each pollutant, while Table 2.5 is calculated using the 100-year GWP estimates. In both cases, the N2O and CH4 GWPs are those determined by the Intergovernmental Panel on Climate Change (IPCC) AR6 Working Group 1 (IPCC 2021), while those for black carbon, VOC, and CO were selected from among the lower values listed in appendix 8 of the IPCC AR5 Working Group 1 report (Myhre et al. 2013). VOCs, and CO. Table 2.4 shows the inventory with -equivalent values calculated using the estimated 20-year GWP of each pollutant, while Table 2.5 is calculated using the 100-year GWP estimates. In both GWPs are those determined by the Intergovernmental Panel on Climate Change (IPCC) AR6 Working Group 1 (IPCC 2021), while those for black carbon, VOC, and CO were selected from among the lower values listed in appendix 8 of the IPCC AR5 Working Group 1 report (Myhre et al. 2013). \\mathrm{C H\_{4}} * * * TABLE 2.4. ESTIMATED INVENTORY OF GLOBAL-WARMING POLLUTANTS FOR LAGOS STATE—CALCULATED WITH 20-YEAR GWPS | | GHG Emissions(tonnes/yr) | | | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | | Source Type | CO2 | Black Carbon | CH4 | N2O | VOC | CO | Total CO2Equivalent | | Global Warming Potential(20 yr) | 1 | 2,900 | 81.2 | 273 | 14 | 7.8 | | | Waste burning | 13,80,000 | 607 | 3,521 | 0 | 7,162 | 36,272 | 38,09,395 | | Biomass burning | 36,700 | 10 | 82 | 0 | 406 | 1,677 | 91,123 | | Generators | 36,52,686 | 464 | 0 | 0 | 27,054 | 10,77,489 | 1,37,81,456 | | Road traffic | 59,52,524 | 504 | 1,971 | 209 | 38,275 | 2,36,771 | 1,00,13,890 | | Industry | 7,30,000 | 724 | 0 | 0 | 19,955 | 16,612 | 32,38,544 | | Power plants | 29,50,926 | 1 | 53 | 5 | 143 | 2,142 | 29,78,784 | | Seaport | 2,62,349 | 43 | 4 | 12 | 197 | 607 | 3,98,142 | | Airport | 1,57,965 | 2 | 11 | 4 | 71 | 587 | 1,71,323 | | Waste disposala | 0 | 0 | 66,593 | 0 | 2,585 | 0 | 54,43,542 | | Cooking | 11,00,000 | 18 | 420 | 37 | 168 | 1,536 | 12,10,738 | | Agriculture | 0 | 0 | 93 | 2,904 | 104 | 0 | 8,01,800 | | TOTAL | 1,62,23,150 | 2,373 | 72,748 | 3,171 | 96,120 | 13,73,693 | 4,19,38,736 | | TOTAL CO2-eq. | 1,62,23,150 | 68,82,280 | 59,07,138 | 8,65,683 | 13,45,680 | 1,07,14,805 | 4,19,38,736 | \\mathbf{c H\_{4}} \\mathbf{c O}\_{2} \ \\mathbf N{{O O O}} \\mathbf{c O\_{2}} Note: a. Other than open burning. Includes emissions from dumpsites and wastewater. the 100-year rather than the 20-year GWPs, which are higher. We emphasize the 20-year GWPs here because of the urgency of reducing near-term warming to stay o within the 1.5 C target and because the air quality measures considered in this report would all take effect in the relatively near term (that is, the next 5 to 10 years). \\mathrm{C O\_{2}} 2.6.3. LIMITATIONS OF THE INVENTORY An emission inventory can be only as accurate as the data used to calculate it. Emissions from each type of source are calculated by multiplying an estimate of the activity attributable to that type of source by an estimate of the corresponding emission factor. Activity is typically * * * TABLE 2.5. ESTIMATED INVENTORY OF GLOBAL-WARMING POLLUTANTS FOR LAGOS STATE—CALCULATED WITH 100-YEAR GWPS | | GHG emissions (tons /year) | | | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | | | CO2 | Black carbon | CH4 | N2O | VOC | CO | Total CO2 equivalent | | Global warming potential(100 year) | 1 | 830 | 27.9 | 273 | 5 | 2.2 | | | Waste burning | 13,80,000 | 607 | 3,521 | 0 | 7,162 | 36,272 | 20,94,073 | | Biomass burning | 36,700 | 10 | 82 | 0 | 406 | 1,677 | 52,804 | | Generators | 36,52,686 | 464 | 0 | 0 | 27,054 | 10,77,489 | 65,30,025 | | Road traffic | 59,52,524 | 504 | 1,971 | 209 | 38,275 | 2,36,771 | 71,76,026 | | Industry | 7,30,000 | 724 | 0 | 0 | 19,955 | 16,612 | 14,57,264 | | Power plants | 29,50,926 | 1 | 53 | 5 | 143 | 2,142 | 29,60,122 | | Seaport | 2,62,349 | 43 | 4 | 12 | 197 | 607 | 3,03,649 | | Airport | 1,57,965 | 2 | 11 | 4 | 71 | 587 | 1,62,635 | | Waste disposala | 0 | 0 | 66,593 | 0 | 2,585 | 0 | 18,69,577 | | Cooking | 11,00,000 | 18 | 420 | 37 | 168 | 1,536 | 11,40,894 | | Agriculture | 0 | 0 | 93 | 2,904 | 104 | 0 | 7,95,855 | | TOTAL | 1,62,23,150 | 2,373 | 72,748 | 3,171 | 96,120 | 13,73,693 | 2,45,42,923 | | TOTAL CO2-eq | 1,62,23,150 | 19,69,756 | 20,29,669 | 8,65,683 | 4,32,540 | 30,22,125 | 2,45,42,923 | \\mathbf{c O}\_{2} \\mathbf{c H\_{4}} \\mathbf{c O}\_{2} \\mathbf{c O}\_{2}\\mathbf\\mathbf{e q} Note: a. Other than open burning. Includes emissions from dumpsites and wastewater. expressed in terms of outputs such as vehicle-kilometers traveled or inputs such as tons of fuel consumed or tons of trash burned. For many of the source types considered in this inventory, reliable data for estimating the output values were not available for Lagos State, so crude estimates or national-level statistics had to be applied. The missing data included information on amounts of trash and biomass burned, industrial production and energy consumption, and the numbers and utilization of small generators for electricity. Details of these estimates are expressed in terms of outputs such as vehicle-kilometers traveled or inputs such as tons of fuel consumed or tons of trash burned. For many of the source types considered in this inventory, reliable data for estimating the output values were not available for Lagos State, so crude estimates or national-level statistics had to be applied. The missing data included information on amounts of trash and biomass burned, industrial production and energy consumption, and the numbers and utilization of small generators for electricity. Details of these estimates are are needed, especially for critical activities such as trash burning and backup generators. The applicability of the emission factors used is also subject to question. Few emission measurements have been conducted in Nigeria, so the emission factors had to be based on measurements in other countries, typically in the Organisation for Economic Co-operation and Development (OECD). The degree to which emissions from, for example, small generators measured by the US EPA are representative of small generators used in Lagos is unknown. The same is true of vehicle emission factors, which were estimated by a model based on European emission standards. These factors were adjusted to try to account for the widespread Nigerian practice of removing catalytic converters from imported vehicles, but the * * * FIGURE 2.20. CO2 EQUIVALENT EMISSIONS BY SOURCE TYPE \\mathrm{C{}}\\mathrm}{{O}\_{2} 20-year GWP 100-year GWP The TSC responsible for the emissions inventory also conducted air quality modeling using the emissions inventory and the weather conditions recorded during the year of air quality monitoring to simulate pollutant concentrations. One goal of this modeling activity was to 2.7. POLLUTANT DISPERSION MODELING adequacy of this adjustment is unknown. There is also reason to think that the vehicle emission factor model may underestimate PM2.5, black carbon, and VOC from diesel vehicles in Lagos because the model assumes European vehicle maintenance practices and lifetimes. {\\mathrm{P M}}\_{2.5}; validate the emissions inventory by comparing the model results to measured pollutant concentrations. Another goal was to extend the geographic range of the air quality data from the six monitoring sites to all 19 of the LGAs defined in the State of Lagos. 2.7.1. MODELS AND METHODS The modeling approach is described in the TSC’s report (ARIA 2021). Modeling was performed on several different scales. The largest scale encompassed much of Africa and used a global emissions database. This was done using CHIMERE software to establish the boundary conditions for the more detailed modeling. The detailed model covered a rectangle, a little bigger than the State of Lagos, and used FARM software. Both the FARM and CHIMERE models are three-dimensional Eulerian photochemical models capable of modeling the dispersion, chemical transformation, and deposition of pollutants from both anthropogenic and biogenic sources over a given area. * * * # 2.7.2. EPISODES MODELED 2.7.3. MODEL RESULTS Modeling was conducted for the five selected episodes Initial simulations were conducted on the earliest two indicated by the brown horizontal bars in Figure 2.21. episodes in Figure 2.21. Those simulations showed NO2 These are September 10–20, December 10–20, March concentrations much higher than observed during the air 5–16, April 25–May 5, and June 27–July 7, 2021. quality modeling, and O3 concentrations much lower. These five periods were selected after consultation This suggested that the estimated NOx inventory was between the World Bank team and the TSC project probably too high. A review of the emission inventory team. The first three correspond to observed positive found that NOx emissions from generators had been sig- peaks or “spikes” in several air quality variables, nificantly overestimated. Several other errors in the notably PM2.5 and PM10. The final two periods are inventory were also found and corrected. Figure 2.22 to considered more representative of “background” Figure 2.26 show the correspondence between the moni- conditions, during which the air quality variables toring data at the UNILAG station and the model results O3, NO2, PM2.5, and PM10 varied only subtly above using the revised emissions inventory. These show rea- baseline concentrations. sonable agreement with the monitoring results. **FIGURE 2.21. EPISODES SELECTED FOR AIR QUALITY MODELING** **PM** **2.5** **Measurements-Every 3 Days** 350 250 3 200 ug/m 5 2. 150 PM 100 50 0 0 0 0 1 1 g-20 g-20 v-20 n-21 b-21 -2 pr-2 n-21 Ju l-21 Au Au Ja Fe Ju 1- 31-30-Sep-2 30-Oct-2 29-No 29-Dec-2 28-27-29-Mar 28-A 28-May-21 27- 27- Abesan Ikorodu Jankara LASEPA NCF UNILAG Episodes to be Modeled Air Quality Management Planning for Lagos State * * * FIGURE 2.22. COMPARISON OF FARM MODEL OUTPUT WITH IN SITU MEASUREMENT FOR O3, NO2, PM2.5, AND PM10 AT UNILAG STATION FOR EPISODE PERIOD OF SEPTEMBER 10–20, 2020 \\bigcirc\_{3},N O\_{2},P M\_{2.5} \\mathsf{P}\|\ \\mathsf{M}\_{10} Farm Station data * * * FIGURE 2.23. COMPARISON OF FARM MODEL OUTPUT WITH IN SITU MEASUREMENT FOR O3, NO2, PM2.5, AND PM10 AT UNILAG STATION FOR EPISODE PERIOD OF DECEMBER 10–20, 2020 O\_{3},N O\_{2},P O\_{2.5 \\mathsf{P}\|\ \\mathsf{M}\_{10} Unilag - air quality Farm Station data * * * FIGURE 2.24. COMPARISON OF FARM MODEL OUTPUT WITH IN SITU MEASUREMENT FOR O3, NO2, PM2.5, AND PM10 AT UNILAG STATION FOR EPISODE PERIOD OF MARCH 5–16, 2021 {\\sf O} _{3},{\\sf N O}_{2},{\\sf P M} _{2.5},{\\sf A N D}\ {\\sf P M}_{10}\ {\\sf A T} Unilag - air quality Farm Station data * * * FIGURE 2.25. COMPARISON OF FARM MODEL OUTPUT WITH IN SITU MEASUREMENT FOR O3, NO2, PM2.5, AND PM10 AT UNILAG STATION FOR EPISODE PERIOD OF APRIL 25–MAY 5, 2021 O\_{3},N O\_{2},P O\_{2.5 \\mathsf{P}\|\ \\mathsf{M}\_{10} Farm Station data * * * FIGURE 2.26. COMPARISON OF FARM MODEL OUTPUT WITH IN SITU MEASUREMENT FOR O3, NO2, PM2.5, AND PM10 AT UNILAG STATION FOR EPISODE PERIOD OF JUNE 27–JULY 7, 2021 \\bigcirc\_{3},N O\_{2},P M\_{2.5} \\mathsf{P}\|\ \\mathsf{M}\_{10} * * * REFERENCES ARIA. 2021. Air Pollutant Emission Inventory Development, Modeling and Potential Emission Control Measures for Lagos. Final report under World Bank Technical Services Contract.7199005. EnvironQuest Limited. 2021a. Lagos Air Quality and PM Source Apportionment Study Final 12 Months Summary Report. Final report under World Bank Technical Services Contract 7195720. EnvironQuest Limited. 2021b. Lagos Air Quality and PM Source Apportionment Study Final Source Apportionment Report. Report under World Bank Technical Services Contract 7195720. IPCC. 2021. “Summary for Policymakers.” In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by V. Masson- Delmotte, P. Zhai, A. Pirani, S. L. Connors, C. Péan, S. Berger, N. Caud, et al. Cambridge University Press. Myhre, G., D. Shindell, F.-M. Bréon, W. Collins, J. Fuglestvedt, J. Huang, D. Koch, et al. 2013: “Anthropogenic and Natural Radiative Forcing.” In Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by T. F. Stocker, D. Qin, G.-K. Plattner, M. Tignor, S. K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley. Cambridge, United Kingdom: Cambridge University Press and New York: IPCC. * * * * * * ### CHAPTER 3 # HEALTH AND ECONOMIC IMPACTS OF AIR POLLUTION PM air pollution (PM2.5 and PM10) is the leading environmental risk factor for poor health. Globally, ambient and household air pollution together currently rank 4th for attributable disease and mortality among 20 major risk factors evaluated in the Global Burden of Disease study (GBD), following hypertension, smoking, and dietary fac- tors (GBD 2020). The estimates indicate that around 7 million deaths, 5 mainly from noncommunicable diseases (NCDs), are attributable annually to the joint effects of ambient and household air pollution, with the greatest attributable disease burden seen in low- and middle-income countries (LMICs)—89 percent of the global total, with low- and lower-middle-income countries alone contributing around 40 percent of the total burden. Higher estimates have been published (Burnett et al. 2018). A recent report indicated 10.2 million premature deaths annually from fossil fuel use (Vohra et al. 2021). Regions with large anthropogenic contributions had the highest attribut- able deaths, suggesting substantial health benefits from replacing traditional fossil fuel- based energy sources (McDuffie et al. 2021). ## 3.1. METHODOLOGY AND EXPOSURE- RESPONSE FUNCTIONS (ERFS) FOR AIR ## POLLUTANTS OF CONCERN Traditionally, PM2.5 mass has been used as the index pollutant for quantifying the impact of outdoor air pollution. First, previous studies have demonstrated that mortality from long-term exposure to PM2.5 dominates the overall health impact of air pollution. Air Quality Management Planning for Lagos State * * * Second, there is a vast set of published in epidemiological studies from around the world linking PM2.5 to mortality (Chen and Hoek 2020). Third, the PM2.5 effects observed in epidemiological studies are supported by toxicological and human clinical studies (US EPA 2019). Fourth, PM2.5 concentrations can be obtained from monitors and/or satellite data, while chemical transport models can generate modeled data that can be used to assess the impact of emission-reduction strategies on health. Finally, PM2.5 is ubiquitous and is generated by many fuel combustion sources in Lagos, including mobile sources (cars, buses, trucks and motorcycles), stationary sources (power plants, port emissions, diesel or gasoline electrical generators, industrial boilers), biomass use, open waste burning, and suspended dust. This set of factors sets PM2.5 apart from all other air pollutants. \\mathrm{P M}\_{2.5} \\mathrm{P M}\_{2.5} \\mathrm{P M}\_{2.5} \\mathrm{M}}{\_}{22.5} \\mathrm{M}{}\_{2.5} The health impact assessment (HIA) methodology for air pollution is well documented. A WHO publication (WHO Regional Office for Europe 2016) provides the basic concepts and general principles. Estimations of the burden of diseases linked either to air pollution or to evaluation of policy scenarios and cost-benefit analyses (CBAs) are both possible. Annex 1 details the methods and input data for the HIA applied in Lagos for 2020–2021. 3.1.1. METHODS: INPUT DATA FOR THE HIA IN LAGOS Figure 3.1 illustrates the key steps in the burden calculation of mortality and morbidity in Lagos due to air pollution. 3.1.1.1. AIR POLLUTION DATA We have used the PM2.5 and PM10 concentrations measured during the period of the project. The continuous and filter-based monitored data were limited to six sites in the city of Lagos for the 1-year period from August 2020 to July 2021. The measurements from the six monitors were used to assign an annual exposure for the population of each LGA (local government area) in Lagos State, where the monitors were located. For the LGAs without monitors, we have used the results of the dispersion modeling described in section 2.7 to derive adjustment factors between the LGAs with and without monitors. These models covered five distinct episodes distributed throughout the monitoring period. For this exercise, data from the recent emission inventory were used as inputs to the \\mathrm{P M}\_{10} a If modelled data are used, the approach can be used to assess the impact of emission reduction strategies on different health outcomes. * * * dispersion analysis. We first estimated a provisional population-weighted exposure (PWE) for each LGA using the results of the dispersion analysis, coupled with a high-resolution map of the population density distribution within each LGA, to calculate the gridlevel representation of the LGA-specific PWE. We then derived the PWE for the entire Lagos State by weighting each LGA by its population size. Average annual exposures for Lagos State and each LGA were used in the impact assessment (Figure 3.2). 3.1.1.2. POPULATION DATA Two alternative population compositions (base and sensitivity case population) by quinquennial age group for Lagos State in 2018 have been used with further details specified by LGA. The base case reflects the population as estimated by the National Bureau of Statistics, and the sensitivity case reflects that estimated by the Lagos Bureau of Statistics (LBS). Estimates at the LGA level are calculated assuming the same age composition in each LGA. Figure 3.2 illustrates the population by LGA according to the base and sensitivity case populations. 3.1.1.3. MORTALITY AND MORBIDITY DATA For the estimation of the mortality data, two international sources were consulted to derive the required information for the base case and sensitivity case populations: the Global Health Data Exchange (GHDx) database of the GBD (IHME 2021), and the Global Health Estimates (GHE) database (WHO 2021). The number of deaths for each age group is calculated as the product of the national hazard rate (number of deaths of a particular outcome per 100,000 population from either the GHDx or GHE database) and the age-specific population size in Lagos. For PM2.5 (long-term exposure), the following health endpoints were considered: \\mathrm{M}\_{2.5} FIGURE 3.2. SIZE OF THE LAGOS POPULATION BY LGA ACCORDING TO THE BASE AND SENSITIVE CASE POPULATIONS * * * » Mortality due to NCDs and specific GBD categories, including acute lower respiratory infections (ALRI), ischemic heart disease (IHD), stroke, chronic obstructive pulmonary disease (COPD), lung cancer, and type 2 diabetes » Infant (<1 year) mortality. According to the GHDx » Infant (<1 year) mortality. According to the GHDx and GHE databases, the infant mortality rate stands at 6.7 percent and 7.5 percent, respectively » Lower respiratory tract infections in children un- » Lower respiratory tract infections in children under age 5 (mainly pneumonia). The number of incidences per 1,000 children is 302 (95 percent confidence interval \[CI\]: 160–538) and was obtained from the study by McAllister et al. (2019) » Chronic bronchitis incidences in adults over » Restricted activity days (19 days per year per person of all ages). These data are from HRAPIE (WHO 2013). Hospital admissions were subtracted to calculate net PM-related cases » Respiratory hospital admissions (RHAs) and » Respiratory hospital admissions (RHAs) and emergency room visits, which include pneumonia, bronchitis, and asthma. The baseline statistics for the entire Lagos State were estimated based on public hospital data, assuming that private hospitals had a similar caseload of patients (2017 data) » Cardiovascular hospital admissions (CHAs) and emergency room visits, which consist of IHD ( including heart attacks), heart failure, and stroke. The baseline statistics for the entire Lagos State were estimated based on public hospital data, assuming that the private hospitals had a similar case load of patients (2017 data). We also estimated the impact of short-term exposure to PM10 on daily mortality during the Harmattan season. In the specific situation of Lagos, daily population exposure to PM10 has importance and, in some instances, it does not correlate well with that of PM2.5. This happens on days when the Harmattan wind is prevalent (between the end of November and mid-March). It is a dry, dusty wind from the North-East originating from the Sahara \\mathrm{M}\_{2.5} \\mathrm{P M}\_{10} Desert, and it involves a large increase of particles in the air, especially the coarse fraction of PM (between 2.5 and 10 µm in diameter). \\mathrm{P M}\_{10} \\mathrm{M}{}mathrm{}}\_{2.5} 3.1.1.4. EXPOSURE-RESPONSE FUNCTIONS The ERFs from the epidemiological literature, which quantitatively relate the health risk to a PM2.5 exposure level, have been reviewed in annex 1. The epidemiological studies provide an estimate of the percent change in risk that might be expected per unit change in air pollution. The best approach has been to use the integrated exposure-response (IER) functions developed by GBD (2020) for cause-specific mortality, and the exposure response function (ERF) of the Global Exposure Mortality Model (GEMM) (Burnett et al. 2018) for the noncommunicable plus ALRI diseases. For infant mortality, we used the ERF for Africa derived by Heft-Neal et al. (2018). For the assessment of the short-term burden on mortality due to the Harmattan season, we applied the short-term ERF for PM10 from Orellano et al. (2020). Graphical representations of the ERFs used in this work are presented in annex 1. \\mathrm{P M}\_{10} \\mathrm{P M}\_{10} \\mathrm{M}{}\_{2.5} Finally, the concentration of lead in PM2.5 and PM10 observed at Ikorodu LGA (1.35 μg/m3) has been found to be particularly elevated when compared to the US EPA standard (0.15 μg/m3). Following the methodology in the US EPA report (1999), the air lead concentration has been converted into blood lead levels, using a conversion factor of 4 for children (0–6 years) and 2 for adults (over 40 years). Based on the estimated blood lead levels, the impact of lead exposure on children’s IQ (intelligence quotient) has been estimated (change in IQ equal to 1.15 points per 1 µg/dl (microgram per deciliter) blood lead change; Pew Charitable Trusts, 2017, 104) as well as the impact of lead on adult cardiovascular mortality (Brown et al. 2020). Lead exposure is also implicated in adverse behavioral outcomes (for example, learning disabilities and disorderly conduct), but lack of local data in Lagos prevented a quantitative estimation of these health burdens. 00.15~\\up{up\ {\\mathrmmu}\\mathrm g m m/}}{ * * * 3.1.2. COUNTERFACTUAL CONCENTRATIONS (AND AIR QUALITY TARGETS) In the HIA, a PM2.5 counterfactual concentration (the lowest level of PM below which no health effects are calculated) has been used to estimate the burden of disease. For the IER assessment, the counterfactual is a uniform distribution over the interval 2.4–5.9 μg/m3 PM2.5 used in the GBD (2020) study. A single value (2.4 µg/m3 PM2.5.) is applied in the case of GEMM and for infant mortality. Multiple annual air quality targets have been examined, such as the new WHO air quality guideline of 5 µg/m3 and the WHO four interim targets (35, 25, 15, 10 µg/m3 PM2.5) to quantify the health benefits achieved from exposure reductions. \\mathrm{M}\_{2.5} 2.4{-}5.9\ {mu y}m^{3},{mathrm P M}\_{2.5} \\mathrm{P M}\_{2.5}. (2.4,\\up\ \ {upmu}\\mathrm{g}/\\mathrm{m}^{3} 5,\\up\\mathrm{g/m/^^{3}} 10,\\upmu\\mathrm{g}/\\mathrm{m}^{3},\\mathrm{P M}\_{2.5}) 3.2. QUANTIFICATION OF HEALTH IMPACTS 3.2.1. PM2.5 RELATED PREMATURE MORTALITY AND MORBIDITY Figure 3.3 shows the estimates of PM2.5 PWE by LGA. The overall concentration for Lagos State is 47 µg/m3 and 114 µg/m3 for PM2.5 and PM10, respectively, for the base case. Only a small difference has been estimated when using the sensitivity case population (46 µg/m3 and 116 µg/m3 for PM2.5 and PM10, respectively). The population living in Ikorodu, Shomolu, Mushin, and Oshodi are exposed to particularly high values of PM2.5 ambient pollution (97, 85, 71, and 60 µg/m3, respectively). \\mathsf{P}\\mathsf{M}\_{2.5} \\mathrm{P M}\_{2.5} \\mathrm{P M}\_{1/} 114,\\upmu\\mathrm{g/m}^{3} \\mathrm{M}\_{2.5} (46,\\upmu\\mathrm{g/\\mathrm{m}^{3}} \ \\mathrm{P{}bf{M}}\_{10}, 60,\\upmu\\mathrm{g}/\\mathrm{m}^{3} \\mathrm{P M}\_{2.5} \ mathrm P M\_{2.5} to 5 years were estimated, together with 14,700 new cases of chronic bronchitis in adults, 46 million restricted activity days, and 1,490 hospital admissions for cardiovascular and respiratory diseases. Alimosho, Ikorodu, and Oshodi are the LGAs with the greatest impact. The estimates are double when considering the sensitivity case population: Annual mortality is 30,350 deaths (14,890 infant deaths), 349,000 annual cases of lower respiratory infections in children up to 5 years, 28,300 new cases of chronic bronchitis in adults, 88 million restricted days, and 2,840 hospital admissions. In this sensitivity calculation, the LGAs with the greatest impact were Alimosho, Mushin, Shomolo, and Oshodi. Additional results are reported in annex 1, including attributable cases of premature mortality by cause of death, applying the GBD 2020 IER functions. The agespecific mortality results using baseline mortality data from GHDx and GHE for the base case population and sensitivity case population are also reported. 3.2.2. HARMATTAN HEALTH BURDEN Attributable mortality due to short-term exposure to PM10 during January and February has been estimated. We have assumed an excess PM10 exposure equal to the difference of the average concentration for the months January and February and the average of the shoulder months December and March. In January and February, the excess PM10 concentration was 88 µg/m3 PM10 for the base case population and 90 µg/m3 for the sensitivity case population, contributing a total of 250 and 500 premature deaths, respectively. These deaths are in addition to the long-term PM2.5 -related mortality. \\mathrm{P M}\_{10} 3.2.3. HEALTH BENEFIT ANALYSIS FROM IMPROVEMENTS IN AIR QUALITY 88,\\upmu\\mathrm{g}/\\mathrm{m}^{3},\\mathrm{P M}\_{10} Figure 3.5 indicates the health benefits that could be achieved if PM2.5 concentrations across Lagos State 30,\\upmu\\mathrm{g}/\\mathrm{m}^{3} ;\\mathrm{P M M}\_{1} \ mathrm P M\_{2.5} * * * FIGURE 3.3. LAGOS STATE AND LGA AMBIENT AIR QUALITY DATA Note: The names of the six LGAs where daily ambient concentrations were monitored during the 1-year campaign between August 2020 and July 2021 are highlighted by the gray boxes along the y-axis on the left. \\mathrm{M\_{2.5}\ I T\ }(35\ \\mathrm{\\text g}}/{m^{3}}) (5,\\upmu\\mathrm{g}/\\mathrm{m}^{3}) \\mathrm{I T ~~2}~~(25 ~~\\mathrm{g/m^{3}}),,{\\mathrm{I T~~}} ~~3~~(15 ~~\\mathrm{\ g/m^{3}}),,{\\mathrm{I T~~}}4 ~~(10~~\\mathrm{g/m^{3}}) The results of the impact assessment of lead contamination in Ikorodu based on measured air contamination (1.35 µg/m3 air lead) indicate that every child in Ikorodu (125,500 according to the base case and 163,800 according to the 3.2.4. IMPACT OF LEAD EXPOSURE ON CHILDREN’S IQ AND CARDIOVASCULAR MORTALITY \\mathrm{\\mu g/m^{3}} * * * FIGURE 3.4. PM2.5 ATTRIBUTABLE MORBIDITY AND MORTALITY IN LAGOS STATE FOR PWE DATA AND GHE (WHO 2021) BASELINE MORTALITY RATES \\mathsf{P}\\mathsf{M}\_{2.5} Base Case 2018 Population Lagos State Base Case 2018 Population * * * Lagos State Sensitivity Case 2018 Population * * * FIGURE 3.5. HEALTH BENEFITS FOR A REDUCTION IN AMBIENT AIR POLLUTION ACROSS LAGOS STATE Rollback (Reduction) in ambient air PM concentration, µg/m3 2.5 \\mathrm{P M}\_{2.5} \\mathrm{P M}\_{2.5} Note: The top figure shows the relative mortality and morbidity reduction compared to the current state, while the figure below shows the averted deaths for the base case population and applying the WHO GHE baseline mortality rates. * * * TABLE 3.1. VALUE OF MORBIDITY (SENSITIVITY CASE POPULATION) | | Childhood pneumonia(Under5) | Onset chronic bronchitis(adult27+) | Restricted activity days(all ages) | Hospital admissions(all ages) | Total | | --- | --- | --- | --- | --- | --- | | Number of incidences | 348900 | 28280 | 87.61 million | 2840 | | | Value(US$, millions) | 87.2 | 109.0 | 674.6 | 0.4 | 871 | | Share of GDP(2018)-% | 0.121 | 0.151 | 0.94 | 0.001 | 1.2 | Note: Childhood pneumonia is valued at five times the daily wage assuming the illness lasts for one week, during which time a parent or guardian remains home. Onset chronic bronchitis assumes EUR 68,000 per case transferred to Lagos using the benefit-transfer method of US$3,855 per incident. Restricted activity days are valued at an average cost of $7.7 per day. Hospital admissions are valued at between N30,000 and N100,000, or an average of US$158 per hospital admission. 3.3. VALUATION OF HEALTH IMPACTS The financial value of the air pollution health impacts has been estimated to be between 1.0 and 6.9 percent of the GDP of Lagos State (Table 3.2). The wide range in value is due to (a) the difference in the total population exposed to air pollution, reflected in the base case and sensitivity case population estimates for Lagos State, and (b) different methodologies for valuing loss of human life. cost. In this study, mortality values accounted for more than 85 percent of the total health impacts using the VSL (value of a statistical life) method for valuing the loss of human life. 3.3.1. LEAD IMPACTS Lead exposure in children has been found to be associated with additional medical costs as well as impacts on cognitive development, which in turn can affect earnings later in life. Recent studies in the US have estimated that the value of a reduction in IQ on earnings can amount to US$12,000–17,500 per IQ point lost (Zhou and Grosse 2019). Based on the measurements of lead exposure in Ikorodu, the impacts amount to US$318 million–464 million in the base case (780,000 IQ points lost) and US$416 million–606 million in the sensitivity case (1,017,000 IQ points lost). Additionally, adult deaths associated with lead exposure are valued at US$47 million–61 million using the VSL method. 3.3.2. VALUE OF STATISTICAL LIFE (VSL) * * * The VSL is society’s willingness to pay to reduce the marginal risk of mortality. This can be observed in the market, for example, through wage-risk studies or contingent valuation studies. For this study, the value of a statistical life has been estimated as US$164,000 (2018) per prema- 6 ture mortality. 3.3.3. HUMAN CAPITAL APPROACH (HCA) 45~\\up{\ up mu\ }mathrm{g/m^^{3}} The HCA values life as the productivity or lifetime income that is lost due to premature mortality. Because lost productivity is age-specific, the HCA value will be different depending on the age when the life was lost. The average wage used for the HCA calculations was US$10,000 per year (2018), multiplied by the average years lost, with the total discounted over time at 3 percent. of Lagos’ 2018 GDP. The value of reducing air pollution from current levels of average of 45 μg/m3 to 10 μg/m3 is estimated to be between US$0.55 billion and US$3.8 billion, or between 0.76 and 5.3 percent of GDP (Table 3.3). 10~\\up{\ mu mu\ m^{3}} 3.4. DISCUSSION AND CONCLUSIONS These health impact estimates show that air pollution from PM2.5 poses a serious public health hazard, especially among children younger than 5. The PWE is high, reaching 47 µg/m3, a value nearly 10 times higher than the new recommended WHO air quality guideline of 5 µg/m3 (WHO 2021). Urgent action to reach at least the WHO IT 1 (35 µg/m3) is therefore recommended. The overall impact of the present PM2.5 levels on mortality across all the age groups is responsible for between 15,850 and 30,350 premature deaths per year, with the largest contribution related to infant mortality (between 7,790 and 14,890 infant deaths). For adult mortality, the impact is largest for cardiovascular diseases. The impact on morbidity, especially pneumonia and other acute respiratory \\mathrm{P M}\_{2.5} 47~\\upmu\\mathrm{g/m^{3}}},end{{,} \\mathrmmu\\mathrm{g/m^{3}} (35,\\upmu\\mathrm{g}/\\mathrm{m}^{3}) TABLE 3.2. VALUATION OF MORTALITY DUE TO AIR POLLUTION IN LAGOS | Lagos Population Scenarios | Air Pollution Mortality | Value of Statistical Life | | Human Capital | | | --- | --- | --- | --- | --- | --- | | Total Attributable Mortality(all ages) | Value of Mortality(VSL=USD164,000)(b USD) | Share of GDP(%) | Human Capital Approach(annual wage=USD10,000)(b USD) | Share of GDP(%) | | | Base CasePopulation(13.3m) | 15850 | $2.6b | 3.6% | $0.7b | 1.0% | | SensitivityCase(25.6m) | 30350 | $5.0b | 6.9% | $1.4b | 1.9% | * * * TABLE 3.3. VALUE OF LOWERING AIR POLLUTION TO WHO INTERIM TARGETS | | Air Pollution Reduction Scenarios | | | | | --- | --- | --- | --- | --- | | Air Quality Target\*0 | 35ug/m3 | 25ug/m3 | 15ug/m3 | 10ug/m3 | | Reduction inPM2.5 | -10.0 | -20.0 | -30.0 | -35.0 | | Reduction in Total Mortality | 4,533-8,316 | 7,307-13,747 | 10,477-19,969 | 12,239-23,370 | | Benefit of Reduced Mortality(VSL)mUS$ | $734-$1,364 | $1,198-$2,255 | $1,718-$3,275 | $2,007-$3,833 | | Share of GDP(2018) | 1.03-1.89% | 1.66-3.13% | 2.39-4.55% | 2.79-5.32% | | Benefit of Reduced Mortality-Human Capital Approach(HCA)mUS$ | $202-$371 | $326-$613 | $467-$890 | $546-$1,042 | | Share of GDP(2018) | 0.28-0.51% | 0.45-0.85% | 0.65-1.24% | 0.76-1.45% | \\mathrm{p M}\_{2.5} conditions in children 0–5 years (between 182,400 and 349,000 incidences), is particularly worrisome. Other outcomes were also estimated and they contribute to increasing the overall burden. Two additional critical contributions should be added to the estimated loss of life from long-term exposure to PM2.5: (a) the impact of the daily high levels of PM10 during the Harmattan period and (b) industrial air pollution in Ikorodu with the relevant lead contamination, which accounts for a sizable loss of intellectual capabilities in children (a total of 780,000 to 1 million IQ points lost at the population level), and a high attributable cardiovascular mortality in that particular LGA (285 to 373 premature cardiovascular deaths). The quantified health burdens should be interpreted as conservative estimates because the additional impact from direct exposure to other critical pollutants (for example, gaseous air pollutants such as NO2 and SO2) has not been quantified in this work. A preliminary estimate of the potential burden on mortality from direct exposure to NO2, for example, could add another 10 percent to the PM2.5 total mortality. However, the adverse health effects from exposure to secondary inorganic aerosols (a component of PM) created through chemical transformation of NO2 and SO2 precursor emissions are already incorporated in the main PM2.5 impact assessment. \\mathrm{S O\_{2}} \\mathbf{P M}\_{10} \\mathrm{P M}\_{2.5}\ \\mathrm{(a)} {mathrm\\mathrm O\_{2}}, \\mathrm{N O\_{2}} The exposure assessment is one of the most important aspects of the study; it is based on an extensive monitoring program of ambient air pollution that has been set up in several locations with standardized procedures and quality controls. The results of the monitoring program have been coupled with the results of a dispersion model and with population data to estimate population-weighted exposures (PWEs) at the LGA level. In this way, the concentration values are referred to the population, which is the target of the HIA. We have considered a variety of possible outcomes, encompassing both mortality (natural mortality, which excludes accidental deaths, and causespecific mortality) and several morbidity outcomes. We have addressed not only PM2.5 but also the complementary contributions of daily levels of PM10, mainly attributable to episodes of Harmattan desert dust, and the lead contamination in Ikorodu. Children are the segment of the population most affected by air pollution: they suffer from extraordinarily high infant mortality, experience frequent episodes of pneumonia and other respiratory disorders, and have to cope with a large limitation of their intellectual capability. It is irreversible damage to the next generation. Finally, we have considered several methodological aspects in our assessment (exposure estimation, choice of the ERFs, alternative demographic assumptions) to overcome the main limitations described below. \ mathrm P P M\_{10}, \\mathrm{P M}\_{2.5} * * * The HIA for Lagos refers to the most recent period of ambient air pollution monitoring—August 2020 to July 2021\. This is the period with the most accurate measurement of air pollution. The other data for the HIA refer to a preceding period (that is, 2018 population data and available health statistics for 2017 and 2018). We believe that the error induced by this choice is minimal because the recent mortality rate has trended lower over the past decade, although population growth has been observed at the same time. The net effect is that our estimates are on the conservative side. In addition, it should be noted that the measurement period occurred during the COVID-19 pandemic. The pandemic has affected Africa including Nigeria, with a decrease in economic activity as reflected by the change in the internal gross product, a 3.5 percent drop in 2020 at the national level compared to the previous year when there was no COVID-19. This aspect makes our assessment for 2020–2021 somehow conservative in comparison to the air pollution data probably experienced in past years. \\mathrm{M}\_{2.5} The most relevant uncertainty regarding our work stems from the difficulty of estimating the population at risk. Two different sources have been considered in this work because they provide potential extremes of the population size estimate. Assumptions about age distribution across different LGAs, often driven by operational choices, are another source of uncertainty. The difficulties in such estimations stem from the large size of slum settlements that are a prominent feature of the urban landscape of Sub-Saharan Africa, and from the dynamic nature of this population (Amegah 2021; Thomson et al. 2021). We are confident that our sensitivity choices, though imperfect and leading to a wide spread in the estimates, are the best approach to characterizing the size of the Lagos population. tions (the public sector) register mortality and morbidity statistics, and a large fraction of health care providers do not release regular information. This difficulty is coupled with the traditional lack of medical certification for persons dying at home. We have used two sources of mortality information related to Nigeria (GBD and WHO) and have scaled down to Lagos, accounting for the differences between national and local age distribution. For hospitalizations, we have used the registrations of the events in the public sector with the strong assumption that the private sector has a proportionally similar load of patients. Finally, it is clear that a source-specific HIA was not performed because a clear partition of PM2.5 exposure data was not available. Heft-Neal et al. (2018) for infant mortality and GEMM (Burnett et al. 2018) for deaths in the broader category of NCDs. The baseline mortality was estimated using the WHO GHE hazard rates. As can be seen, our mortality rates are consistent with the results of Croitoru, Chang, and Akpokodje, when assuming the same PM (68 μg/m3) and using the same impact risk model (GBD IER), but our mortality estimates increase by a factor of 2.5 when switching from the IER model to the GEMM Heft-Neal et al. (2018) for infant mortality and GEMM (Burnett et al. 2018) for deaths in the broader category of NCDs. The baseline mortality was estimated using the WHO GHE hazard rates. As can be seen, our mortality rates are consistent with the results of Croitoru, Chang, and Akpokodje, when assuming the same PM2.5 exposure ) and using the same impact risk model (GBD IER), but our mortality estimates increase by a factor of 2.5 when switching from the IER model to the GEMM 2.5 when switching from the IER model to the GEMM and Heft-Neal et al. relationships. The higher premature mortality estimate can be explained in part due to the size (68,\\upmu\\mathrm{g}/\\mathrm{m}^{3}) \\mathrm{M}\_{2.5} * * * TABLE 3.4. COMPARISON OF CURRENT ESTIMATES OF PM2.5 MORTALITY RATES IN LAGOS STATE AND PREVIOUS WORK BY CROITORU, CHANG AND KELLY (2020) \\mathsf{P{M}}\_{2.5} | Risk model | Croitoru, Chang,and Akpokodje(2020) | This study | | --- | --- | --- | | IER functions for deaths due to cardiovascular and respiratory plus lung cancer and diabetes | | PM$\_{2.5}$ concentration:47μg/m$^{3}$ based on 1-year,2020-21,measuring campaign2019 IER functions(GBD2020) | | Base case population:13.3 million Mortality rate(per10$^{5}$):38.5 | | | | Sensitivity population:25.6 million Mortality rate(per10$^{5}$):37.0 | | | | IER functions for deaths due to cardiovascular and respiratory plus lung cancer and diabetes | PM$\_{2.5}$ concentration:68μg/m$^{3}$ Population size:24.4 million | | | 2017 IER functions(GBD2018) Mortality rate(per10$^{5}$):45.9 | PM$\_{2.5}$ concentration:68μg/m$^{3}$,same as Croitoru,Chang,and Akpokodje(2020)2019 IER functions(GBD2020) | | | Base case population:13.3 million Mortality rate(per10$^{5}$):47.0 Sensitivity population:25.6 million Mortality rate(per10$^{5}$):45.5 | | | | GEMM for NCD and lower respiratory illnesses plus Heft-Neal et al.(2018)for infant mortality | | PM$\_{2.5}$ concentration:47μg/m$^{3}$ based on 1-year,2020-21,measuring campaign | | Base case population:13.3 million Mortality rate(per10$^{5}$):119.2 | | | | Sensitivity population:25.6 million Mortality rate(per10$^{5}$):118.6 | | | \\mathrm{M\_{2.5}} 10^{\\bar{5}}\ \\mathrm{M\_{2.5}} 68,\\upmu\\mathrm{g}/\\mathrm{m}^{3} 10^{\\dot{5}} \ {up mu}\ {\ }}{\\mathrm{g}m/^33,, \\mathrm{M}\_{2.5} In conclusion, the work illustrates a dramatic situation in Lagos that highlights the large burden of PM air pollution on public health. 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Particulate matter (PM2.5 and PM10), Ozone, Nitrogen Dioxide, Sulfur Dioxide and Carbon Monoxide.” [https://apps.who.int](https://apps.who.int/) /iris/handle/10665/345329. * * * ### CHAPTER 4 # POTENTIAL EMISSION CONTROL MEASURES In considering potential air pollution control measures, policy makers should consider both the cost to the Government and the overall economic costs and benefits, as well as the implications for other social goals. At COP26, Nigeria committed to achieving net-zero GHG emissions by 2060, and the Climate Action Plan of the Lagos State Government (LASG) calls for net zero by 2050. Thus, any pollution control measures adopted should be consistent with a transition away from fossil fuels. Other social goals to consider include reducing road transport and traffic congestion, with its wastage of time and resources, and reducing pollution of land and water as well as the air. ## 4.1. POLLUTION CONTROL STRATEGIES BY SECTOR As explained in chapter 2, the PMEH source apportionment study showed that about 28 to 32 percent of ambient PM2.5 is due to open burning, mostly burning of solid waste. Industrial emissions account for about 18 percent on average, though this var- ies greatly from one location to another. Diesel and gasoline engines used in transport and backup generators account for another 14 to 17 percent. Together, these readily controllable sources of primary PM emissions account for 60 to 67 percent of the PM2.5 in the air. Another 28 to 32 percent of PM2.5 and 48 to 50 percent of PM10 is suspended dust, some of which has natural causes such as the Harmattan but much of which is human-generated and therefore controllable. The remaining 15 percent or so comprises sulfates, nitrates, and organic aerosol formed from SOx, NOx, and VOC, respectively, through chemical reactions in the atmosphere. This secondary PM2.5 can be controlled by reducing emissions of the reactant species. Air Quality Management Planning for Lagos State * * * 4.1.1. SOLID WASTE Open burning of municipal solid waste (MSW) is estimated to account for as much as 30 percent of the PM2.5 present in the air. Present solid waste production in Lagos is estimated at 4.2 million tons per year, of which only about 1.8 million is collected and disposed at one of four active dumpsites. Waste pickers scavenge for usable items, mainly metal and plastics, and the remainder of the waste, much of it organic, is left to decompose. There is a well-developed market for recycled materials (Salau et al. 2017). Most of the waste that is not collected is believed to be burned, and some burning also goes on at dumpsites. \\mathrm{P M}\_{2.5} To eliminate the open burning of solid waste would require a substantial improvement in collection efficiency, together with improved enforcement of regulations on waste disposal and open burning. The LASG is already taking steps in this direction, with the planned acquisition of another 100 collection trucks in 2021 and the planned establishment of 20 transfer-loading stations and several material recovery facilities by 2030. Plans are also being developed to replace the existing dumpsites with modern landfills. Modern landfills are built to capture CH4 produced from decomposing organic matter. CH4 captured can be used to generate electricity or supplied to other energy users as fuel. Because CH4 is 7 a powerful GHG, landfills can earn carbon credits by mitigating the release of CH4. \\mathrm{H}\_{4} \\mathrm{H\_{4}} Another potential destination for solid waste is incineration and the production of energy. While generally the costliest method of disposing solid waste, incinerators can reduce the overall volume of solid waste and produce electricity, which can be sold or used by waste management facilities. A project currently under discussion in Lagos is the establishment of a waste-to-energy plant that would convert MSW to electricity (Omorogbe 8 2021). Aside from investment costs, the main drawback of incineration is that most of the combustible solid waste in Lagos is potentially recyclable—such as paper and plastics—or is organic matter that is not returned to the soil. \\mathrm{H}\_{4} \\mathrm{C H\_{4}} Government policies can help minimize the amount of waste to be landfilled by encouraging the separation of solid waste to facilitate recycling and composting. Organic waste, for instance, needs to be separated so that it does not contaminate recyclables such as paper and cardboard. Neighborhood collection sites that allow plastics, metals, and glass to be separated could enhance the feasibility of recycling by reducing the cost of collection and ensuring a higher-quality recycled product. Collection fees for solid waste, if built into the fee structure of essential urban services such as water, could help cover the costs of collecting and disposing of solid waste. \\mathrm{C H\_{4}} Organic matter, which is estimated to be about half of the total MSW in Lagos (LAWMA 2014), has the potential to be composted and turned into fertilizer or a soil additive. Because the separation of organic wastes can be costly, some municipalities have begun their composting programs by targeting large sources of organic wastes, such as produce markets, grocery stores, and restaurants. Compost can also be an important product for urban gardens and municipal landscaping. Cities around the world are increasingly providing collection services for organic waste, such as bins for households, buildings, or neighborhoods, as well as trucks and infrastructure for transport and processing in large-scale compost facilities. Because organic waste is the raw material for CH4 production in a landfill, reducing the amount of organic matter through composting can limit the amount of CH4 released into the atmosphere. Composting programs can thus earn carbon credits by diverting organic matter that would otherwise have resulted in the production of CH4. The EarthCare program in Lagos had an estimated CO2 reduction of 253,000 tons per year over a 10-year period, for which it could earn carbon credits (World Bank 2010). Fuel “quality” is best understood as compliance with the specifications for that type of fuel. From the standpoint of air quality, the most important specification for both diesel and gasoline is the allowable sulfur content. 4.1.2. FUEL QUALITY \\mathrm{C H}\_{4}. * * * Most of the sulfur present in motor fuel burns to form SO2 in the exhaust; this is the main source of SO2 emission in Lagos. A few percent of the sulfur is emitted in the form of sulfate particles, and these can increase diesel PM emissions significantly. Some of the SO2 emitted to the atmosphere reacts to form sulfate particles as well. \\mathrm{S O\_{2}} \\mathrm{S O\_{2}} SO2 in the exhaust also binds to the catalyst materials used in catalytic converters and diesel particulate filters, reducing their efficiency. Diesel oxidation catalysts can also oxidize SO2 to SO3, which combines with water vapor in the exhaust to form sulfuric acid. Operating an engine on high-sulfur fuel can clog diesel particulate filters; for this reason, fuel sulfur limits are needed for adequate emissions control for diesel engines. The US, European Union (EU), and many other countries have set ultra-low fuel sulfur limits of 10 to 15 ppm (by weight) for distillate fuels. \\mathrm{S O\_{2}} \\mathrm{S O\_{2}} \\mathrm{S O\_{3}} vehicles imported from developed countries to retain the particle filters with which they come equipped, as well as allow the operation of new vehicles meeting Euro 4 9 or better emission standards (Table 4.1). This would also permit the use of particle filters and other advanced emission controls on backup generators. Diesel fuel samples collected at Lagos retail stations in 2020 averaged 2,389 ppm sulfur: for gasoline, 1,424 ppm. New fuel standards allowing 150 ppm sulfur for gasoline and 50 ppm for diesel were set to be introduced in Nigeria in 2017 but have not yet been implemented. Nigeria is also signatory to the agreement of the Economic Commission of West African States (ECOWAS) to limit sulfur to 50 ppm in both diesel and gasoline. By implementing and enforcing these standards, the Nigerian and/or Lagos State Government would make it feasible for used Nigeria presently has no operational petroleum refineries, except for some small illegal operations in the Niger delta. The Nigerian National Petroleum Corporation (NNPC) is the only legal importer of refined products, but smuggling is thought to be widespread. The new Dangote refinery10 now under construction will have more than enough capacity to supply Nigeria’s needs and has been designed to produce ultra-low sulfur diesel and gasoline (Euro 5–6 specifications). Until that refinery comes online, all legal refined petroleum products used in Lagos will continue to be purchased on the world market and imported by ship. Gasoline and diesel fuel meeting low or ultra-low sulfur specifications are available on the world market. Thus, to begin enforcing the 2017 or ECOWAS fuel sulfur limits would be relatively simple— NNPC would need to change its purchase specifications and contract to buy fuel meeting the sulfur standards. 5-6 The new fuel standards would need to be enforced through collection and analysis of fuel samples both at dockside and in the retail stations. Sulfur removal adds to the fuel cost, so shippers would be tempted to substitute TABLE 4.1. EUROPEAN DIESEL AND GASOLINE STANDARDS AND EMISSIONS | European diesel(AGO)和gasoline(PMS)standards and emissions | | | | | | | --- | --- | --- | --- | --- | --- | | Standard | Implementation date(Europe) | Sulfur limit AGO(ppm) | Emissions(g/km) | Sulfur limit PMS(ppm) | Emissions(g/km) | | Euro1 | 1994 | 2000 | 0.14 | 2000 | 0.09 | | Euro2 | 1996 | 500 | 0.08 | 500 | 0.01 | | Euro3 | 2000 | 350 | 0.05 | 150 | 0.01 | | Euro4 | 2005 | 50 | 0.025 | 50 | 0.01 | | Euro5 | 2009 | 10 | 0.005 | 10 | 0.005 | \\mathbf{(A G O)} high-sulfur for low-sulfur products if there were no danger of being detected. Similarly, the higher cost of lowsulfur fuel would increase the incentives for smuggling and adulteration at the retail level. Improved fuel quality would help lower PM2.5 emissions, primarily through the introduction of more sophisticated emissions control systems on vehicles, especially advanced catalytic converters and diesel particulate filters. The current fuel quality in Nigeria does not allow the effective operation of vehicle catalysts beyond Euro 1, standards that were implemented in Europe in the early 1990s. Properly functioning Euro 5 vehicles can lower emissions of PM2.5 by 28 times compared to Euro 1. To guard against fuel adulteration and protect the emissions control equipment on vehicles, it is necessary to ensure that fuel quality at fueling stations is maintained (box 4.1). \\mathrm{P M}\_{2.5} \\mathrm{P M}\_{2.} fuels but not for industrial fuels, there could be leakage or cases of misfueling where vehicle owners end up putting cheaper and dirtier fuels in their vehicles. Requiring that all petroleum products in Lagos meet higher quality standards will reduce misfueling and thus contribute to lowering emissions from the transport sector, industry, and backup electricity generators used throughout Lagos State and Nigeria. fuels but not for industrial fuels, there could be leakage or cases of misfueling where vehicle owners end up putting cheaper and dirtier fuels in their vehicles. Requiring that all petroleum products in Lagos meet higher quality standards will reduce misfueling and thus contribute to lowering emissions from the transport sector, industry, and backup electricity generators used throughout Lagos To the extent that the same quality of petroleum products is available to all consumers in Lagos, requiring that they all meet higher fuel specifications will reduce emissions. Conversely, if fuel standards are tightened for transport 4.1.3. ROAD TRANSPORT The road transport sector accounts for about 10 percent of primary PM2.5 emissions in Lagos and is also a main source of NOx and SO2, which react to form secondary PM2.5. It is also the second-largest source of GHG emissions. Given its importance to the economy— moving people and goods—and the fact that it will certainly grow, effective air pollution control measures for this sector will be essential to sustainable growth. Equally, appropriate technology choices will be needed for the government to meet its target of net-zero GHG emissions by 2050. \\mathrm{P M}\_{2.5} \\mathrm{S O\_{2},,} \\mathrm{P M}\_{2.5} BOX 4.1. MEASURES TO ENSURE FUEL QUALITY Tamper-proof locks. Products are supplied to retail outlets in modified tank lorries fitted with tamper-proof locks. Comprehensive sealing. Dispensing units are sealed in a comprehensive manner, which makes meter tampering impossible. Periodic and surprise checks by staff. Stringent, periodic, surprise checks are carried out to ascertain correct delivery and the sealing of the pumps. Testing of product samples. Regular comprehensive testing of samples is done without prior warning. Certification of retail outlets. Periodic audits and recertification of retail outlets are carried out by a reputable certification agency. Source: Rogers 2002 as quoted in Gwilliam, Kojima, and Johnson 2004. Note: Based on practices of Bharat Petroleum, India. * * * 4.1.3.1. GLOBAL WARMING The Nigerian Government has committed to achieving net-zero GHG emissions by 2060. Achieving net-zero emissions in the road transport sector will require a massive shift away from fossil fuels and toward more sustainable technologies. Planning to accommodate this shift is needed now, as infrastructure commissioned today is quite likely to be in use in 2060. Thus, shifting to another fossil fuel, even a “clean” fuel such as natural gas, would be contraindicated. Througout most of the world, battery electric vehicles (BEVs) are now the clear favorites to replace internal combustion engines that burn fossil fuels. BEVs make up a significant and increasing fraction of the new vehicle fleet in Europe, China, and North America. BEVs are most effective where vehicle range requirements are fairly short, such as private automobiles, city buses, drayage and delivery trucks, and taxicabs. Where long range is important, as in long-haul trucking and air travel, liquid fuels such as biodiesel and renewable (synthetic) diesel and jet fuel may continue to have a role. To be practical in Nigeria, BEVs will require a reliable, low-cost source of electricity for charging, which currently excludes Nigeria’s electric grid. Substantial investment will be required in electric generation and charging infrastructure. This is discussed in the section on electricity generation, below. In the meantime, hybrid vehicles— especially plug-in hybrids—have much to recommend them. This is especially true in stop-and-go traffic, where the use of regenerative braking both saves energy and reduces brake wear. By charging from the power grid when it is available and from their onboard engine when not, plug-in hybrids could provide reliable service in the near term while retaining the ability to switch to allelectric operation in the future. substantial benefits for the sake of both air quality and climate change priorities. 4.1.3.2. VEHICLE EMISSION STANDARDS The most effective and least costly way to reduce vehicle emissions is to require new vehicles to comply with emissions standards. Incorporating emission controls into the vehicle design from the beginning is much less costly than to retrofit them. The main drawback is that the vehicle fleet turns over slowly, so the full benefit of new emission standards is not seen for more than a decade. In specific cases such as drayage trucks and public transport vehicles, it may be cost-effective to accelerate fleet turnover by mandating specific emission standards. Most vehicles in Nigeria were originally sold in Europe or North America and are imported into Nigeria as used upon reaching the end of their economic life in their country of origin. This has the advantage that the vehicles are likely equipped with advanced emission control systems, but the disadvantage that those systems may be in poor condition. At present, the importers typically remove the catalytic converters and/or particulate filters before reselling the vehicles. Nigeria has joined in the decision of the ECOWAS11 to require all newly registered vehicles, both new and used, to meet Euro 4 emission standards and for the sulfur content of vehicle fuels to be limited to 50 ppm (UNEP 2020). Imported vehicles are also subject to age limits of 5 years for light-duty vehicles and 10 years for heavyduty vehicles. Meeting the Euro 4 standards requires that diesel engines be equipped with a particulate filter, and gasoline engines with a three-way catalytic converter and near term while retaining the ability to switch to all-electronic engine controls. These standards would not be feasible without the fuel sulfur limit because sulfur poisons the catalysts used, reducing their effectiveness. So far, the 50 ppm sulfur limit has not been implemented in Nigeria, and it does not appear that the Euro 4 requirement is being enforced. * * * 4.1.3.3. VEHICLE INSPECTION AND MAINTENANCE (I&M) Even in the absence of emission control systems, a poorly adjusted or worn-out engine will produce much higher emissions than if it were well maintained. Where vehicles are equipped with emission controls, this difference is much greater, and it has been commonly observed that most of the emissions from the vehicle fleet are concentrated in a small percentage of “gross emitters” (Krzyz- 12 anowski et al. 2014). These gross emitters include vehicles where the emission control system is malfunctioning and those where it has been removed or tampered with. Inspection and maintenance (I&M) programs are designed to identify those gross emitters and require their repair. They can also perform related tasks such as checking that all required emission control systems are present, functional and interrogating engine electronic control systems for malfunction codes. An effective I&M program is a prerequisite for implementing almost any vehicle improvement program. being able to monitor and enforce vehicle emission regulations. Currently, the program requires private vehicles to be inspected for emissions and roadworthiness once a year, and commercial vehicles every 6 months. Vehicles are required to display their roadworthiness certificates on the vehicle or face a fine. As vehicle emission controls are phased in, LACVIS will need to be strengthened to maintain its effectiveness. This should include upgraded testing equipment to measure particulate emissions and to allow vehicles to be tested under load using a chassis dynamometer. This is especially important for diesel vehicles. ECOWAS directive C/Dir.2/09/20 requires that all vehicles in circulation meet Euro 4 emission standards by January 1, 2025. Confirming compliance with the Euro 4 standard would require testing under load in a transient driving cycle. 4.1.3.4. UPGRADING VEHICLE FLEETS Focusing on fleet vehicles such as taxis, buses, or delivery trucks has been a proven approach to introducing alternative fuel vehicles since this requires the establishment and maintenance of fewer refueling facilities, and conversion and maintenance can be handled by dedicated service personnel (Faiz Walsh, and Weaver 1996). It also allows the refueling facilities to maintain their own fuel quality, such as ultra-low sulfur diesel that is required for advanced catalysts and particulate filters. Given the global trend and falling costs of hybrid and electric vehicles, an evaluation of the costs of such vehicles should be undertaken, particularly for fleet vehicles such as buses, 13 taxis, and delivery trucks (Mufson and Kaplan 2021). * * * 4.1.3.4.1 Minibuses/danfos According to Lagos State Metropolitan Transport Agency (LAMATA), as recently as 2015, the small passenger vans known as danfos accounted for about 45 percent of all motorized passenger trips in Lagos. Given the high mileage of danfos—reportedly as much as 80,000 km per year—and the relatively old average age of the fleet (two-thirds may be over 17 years old), they would be logical targets for replacement. The government’s 2018 plan for reducing short-lived GHGs (Government of Nigeria 2018) calls for phasing out the danfos in favor of 5,000 new full-size buses. However, this is likely to face resistance from the danfo owners and operators. The danfos are mostly equipped with gasoline engines, which emit little PM2.5 unless the engines are worn out and leaking oil into the exhaust. However, these engines are inefficient in urban traffic and emit large quantities of CO and VOC as well as NOx and CO2. The danfos’ small passenger capacity of 14 to 18 (crowded) people requires a large number of vehicles to meet passenger demand, so that they are major contributors to traffic congestion. Replacing the danfos with a smaller number of 35-passenger minibuses, as in Mexico City, would help reduce congestion, improve passenger comfort and safety, and reduce emissions and fuel consumption. The replacements should preferably be plug-in hybrids to allow for the possibility of electrification later, but even conventional engines meeting Euro 4 emission standards would greatly reduce pollutant and GHG emissions. Such a replacement might logically be combined with improved safety standards and a reorientation of the danfo routes to better coordinate with the bus rapid transit (BRT) system. \\mathrm{P M}\_{2.5} \ mathrm C O O\_{2}. \\mathrm{P M}\_{2.5} 4.1.3.4.2. Buses especially suitable targets for emission control efforts. Many cities around the world have switched their bus fleets to CNG, and an increasing number of BEV buses are also being produced. Many cities have also purchased “clean” diesel buses that are equipped with particulate filters and burn ultra-low sulfur fuel. Some, such as Mexico City, have successfully retrofitted older-model buses with diesel oxidation catalysts and particulate filters (Schipper et al. 2006) (See Box 4.2). Nigeria has abundant natural gas, so a transition to CNG fuel would be a feasible strategy for buses in Lagos. The government’s plan (Government of Nigeria 2018) for reducing short-lived GHGs includes a shift to CNG in buses nationally. This may not be the best course, however, as it would require substantial investment in new fueling infrastructure, which would have limited useful life as Nigeria transitions away from fossil fuels. CH4 emissions from CNG vehicles and infrastructure are also a concern. Biogas from digesters and sewage treatment plants could be upgraded to “renewable natural gas” for vehicular use but at considerable cost. \\mathrm{C H\_{4}} Clean diesel technology using particulate filters and ultra-low sulfur diesel can achieve emission levels similar to CNG and would pose less risk of stranded investment. Hybrid buses using clean diesel technology offer the potential for even lower emissions as well as fuel savings and savings on brake maintenance. Pure BEV buses would be the most practical either on short feeder routes or in BRT service, where wireless charging systems could be installed in the stations. In Nigeria, motorcycle taxis, both two- and threewheelers, play an important role in urban transport by providing short-distance mobility and a link for the “first and last mile” of daily commutes. In Lagos, the two-wheel okada and three-wheel keke NAPEP taxis14 are also an important source of employment. The 2,000 small buses approved for “first and last mile” trips in 4.1.3.4.3. Motorcycle and tricycle taxis (okada and keke NAPEP) * * * BOX 4.2. FROM COMBIS TO MINIBUSES IN MEXICO CITY In the early 1990s, public transit in Mexico City was dominated by large number of “combis”—11-seat Volkswagen microbuses with air-cooled gasoline engines that were extremely polluting. These vehicles were privately owned and operated and organized themselves into cooperatives to provide service on defined routes. Their large numbers and lack of regulation led to traffic congestion, competition for passengers, haphazard stops outside of bus zones and in the middle of traffic, and other safety hazards. As part of its air pollution control program, the Mexico City Government, in 1993, established a maximum age limit of 8 years for combis and required that the replacement vehicles be minibuses. The minibuses are specialized vehicles built on extended van chassis, with seats for 23 passengers and a maximum capacity of about 32. They were equipped with catalytic converters and used unleaded gasoline or in some cases LPG or compressed natural gas (CNG) fuel. Similar minibuses are still in widespread use today. Combi owners were provided with financing assistance to purchase the new minibuses through credit lines funded in part by the World Bank Transport Air Quality Project. Many combi owners also chose to lease their vehicles through specialized financing companies. 2021 demonstrate the importance of this segment of the commute for getting passengers from their homes to their jobs. Lagos restricted the import of two-stroke motorcycles in 2014, but it is not clear if that has limited their circulation. Two-stroke motorcycles have been a major contributor to transport-related air pollution in cities all over the world where motorcycles are numerous, such as in South and Southeast Asia. Measures to phase out two-stroke engines in favor of four-stroke have been one of the air quality successes in cities such 15 as Bangkok, Dhaka, and Jakarta (Shah 2003). Like other public transport vehicles, motorcycle taxis should be required to meet emission standards as a condition for their operation. would need to be supplied but could be provided by solar photovoltaic panels. 4.1.3.5. PUBLIC TRANSPORT The upgrading and expansion of public transport in Lagos could have a large positive impact on air quality. Public transport investments are large and multi-year and must be justified largely on their transportation benefits rather than on their contribution to improving air quality. Although the air quality benefits of public transport can be large, public transport investments need to be evaluated on their long-term contribution to air quality rather than on their capacity to make an immediate positive impact on air quality. * * * 4.1.3.5.1. Bus rapid transit (BRT) 4.1.3.5.1. Bus rapid transit (BRT) In 2008, Lagos opened the first BRT corridor in Africa. Before the BRT line, passengers in Lagos mostly used “small commuter buses, known as danfos (85 percent), large commercial buses (8 percent), and cars (4 percent), and the remaining 3 percent taxis, motorcycle taxis (okada) and shared taxis (kabu kabu)” (World Bank 2013). Among its benefits, the BRT system in Lagos has been successful in reducing travel times, public transport expenditures by low-income households, and road accidents. The BRT system has also proven to be profitable, with the operator of the first BRT corridor recouping the entire capital investment of the bus fleet within 18 months and without attempting to bar competitor services (Gorham et al. 2017). By using larger buses traveling in dedicated bus lanes, the By using larger buses traveling in dedicated bus lanes, the efficiency of transportation can improve and PM2.5 emissions can be reduced. BRT systems can lower air pollution through several means: (a) BRT buses can displace smaller buses (such as danfos) and automobiles, which in turn would result in less pollution per passenger-kilometer; (b) dedicated BRT lanes allow buses to run unhindered at higher speeds than traffic on congested roadways—vehicles produce the least amount of air pollution when they are cruising at an even speed rather than stopping and starting; and (c) BRT buses can employ newer bus technologies—clean diesel, CNG, electric—that are able to reduce air pollution emissions better than current vehicle technologies. \\mathrm{P M}\_{2.5} The BRT program that has been under development in Lagos since 2008 has resulted in faster commutes, lower fares, and reduced fuel consumption per passenger-kilometer. Under the first phase of the Lagos Urban Transport Project (LUTP), a 22 km BRT corridor was constructed, ultimately transporting around 200,000 passengers per day or 37 percent of all public transport trips in the corridor, while accounting for only 4 percent of vehicles in 2008 (Mobereola, 2009). Several additional BRT corridors have been constructed over the past decade (see Table 4.2). Lower fuel consumption results in reductions in PM2.5 as well as CO2. Based on assessments from phase 1, the original BRT line resulted in 13 percent lower overall fuel consumption as well as lower CO2 emissions in the corridor. TABLE 4.2. BRT CORRIDORS IN LAGOS \\mathrm{C O\_{2}} \\mathrm{P M}\_{2.5} | | Distance(km) | Cost/km(US$, millions) | Total cost(US$, millions) | | --- | --- | --- | --- | | Phase 1: Pilot Corridor(2008) | 22.0 | 1.7 | 37.4 | | Phase 2: Ikorodu Extension | 13.5 | 7.4 | 99.9 | | Phase 3: Oshodi to Abule-Egba | 27.0 | 4.4 | 118.8 | 4.1.3.5.2. Light rail The construction of light rail in Lagos is meant to augment the public transit system, resulting in less private vehicle traffic and lower emissions per passenger-kilometer. The fact that Lagos has a relatively low amount of rail-based public transit compared to other global megacities implies that there is significant potential for expanding light rail in Lagos. For instance, Lagos has about 2 km of light rail per million residents compared to Beijing which has 29 km and London which has 49 km (Croitoru, Chang, and Kelly 2020). Two light-rail corridors (Figure 4.1) are to be the first lines in a passenger rail system in Lagos planned to ultimately include seven lines: blue, red, 16 green, yellow, purple, brown, and orange. Given the proximity of Lagos to large inland waterways, ferries could provide an additional source of transport for commuters. Ferry service currently operates on Lagos Lagoon, connecting multiple locations with the commercial center on Lagos Island. Like the investments made for BRT and light rail, it is assumed that ferry service in Lagos could be expanded to help relieve road traffic. 4.1.3.5.3. Public ferries * * * FIGURE 4.1. LAGOS LIGHT RAIL: BLUE AND RED LINES17 Source: [https://www.railway-technology.com/projects/lagosrailmasstransit/](https://www.railway-technology.com/projects/lagosrailmasstransit/). * * * Private ferries carry considerably more passengers, with 67 private ferries carrying 6.5 million passengers in 2012, and 165 private ferry operators with a combined public and private tally of 18.8 million passengers (51,507 passengers per day) in 2016 (Lagos State Waterways Authority 2017). 4.1.3.6. FREIGHT TRANSPORT Much of the container freight traffic for all of Nigeria flows through Apapa and Tin Can Island ports. The resulting diesel truck traffic is probably a major source of PM2.5 and other emissions. The new Lagos-Ibadan railway began service in June 2021 and reportedly offers intermodal service from dockside in Apapa to the Inland Container Depot in Ibadan. The line will eventually extend to Kano in northern Nigeria. By diverting large numbers of heavy trucks from Lagos, this intermodal service could save greatly on fuel consumption and travel time while reducing GHG emissions and air pollution. \\mathrm{P M}\_{2.5} Further developments could be made by improving the management of local truck traffic to the ports. As Lagos is the economic and manufacturing hub of the country, a significant part of the container flow must be to and from locations in Lagos itself. This will continue to go by truck. In most ports, short-haul container delivery is the last resort for truck-tractors that are too old and unreliable for long-haul service. These trucks are often in poor condition, with high emissions. However, in California, the ports of Los Angeles and Long Beach have had success in limiting access to trucks that meet emission standards. Owner-operators of trucks that do not meet the standards have been provided with financial assistance to replace their old tractors. Apapa and Tin Can Island ports may wish to consider similar actions. Among other advantages, limiting access to only those trucks and drivers that meet standards could well improve safety and efficiency and reduce the need for congestion-causing checkpoints. Measures to reduce emissions from other freight trucks must also be part of the solution to air pollution in Lagos. In parallel with measures to upgrade light-duty vehicles and buses, there needs to be an expanded program combining vehicle inspections, emissions certificates, fines and removal from service for noncompliance, and a guaranteed supply of clean diesel for heavy-duty trucks. This will require investments in newer trucks and in some cases the retrofitting of existing trucks with pollution controls such as catalysts and diesel particulate filters. California has had considerable success in mandating the retrofitting or replacement of older vehicles in diesel truck fleets. While costly for truck owners, the gains are also likely to be large given the high share of PM2.5 emissions from diesel combustion. Although diesel fuel accounted for about 30 percent of petroleum product consumption in Lagos (IEA 2021) in 2020, compared to 65 percent for gasoline, PM2.5 emissions from diesel fuel have been found to be 3–4 times higher than from gasoline (see Figure 2.8). 4.1.4. ELECTRICITY GENERATION \\mathrm{P M}\_{2.5} The electric power sector in Nigeria is expected to grow rapidly over the next 15 years to meet power demand, which has far outgrown the country’s existing capacity. Current electricity consumption per capita in the country is extremely low by international standards. Currently, per capita electricity consumption in Nigeria is only 147 kWh, compared to the average for LMICs of 736 kWh and a global average of 3,298 (World Bank 2020). Nigeria’s power sector is characterized by high technical and financial losses, and the current system cannot provide adequate electricity to the economy. As such, Nigeria has among the highest share of electricity provided by backup generators (“gensets”) in the world. These generators are expensive to operate, noisy, and highly polluting. Long-term investment in power grid expansion and reliability will eventually reduce genset usage. Improvements to Nigeria’s power sector are critical for the sustainability of the system and the economy. * * * By increasing the supply of electricity, economic losses would be reduced, while tariff revenues would increase, generating considerable income to pay for the reforms. With the growth of the power sector, baseline emissions of both CO2 and NOx might be expected to rise (World Bank 2014). (Since the power plants almost exclusively use natural gas fuel, their PM2.5 emission are negligible). However, meeting the commitment to achieving “net zero” emissions by 2050 (Lagos) and by 2060 (Nigeria) will require that most of the new power generation capacity be renewable—for example, wind and solar. Fortunately, the costs of wind and solar power generation have decreased considerably, to the point that they can be cost-competitive with thermal power plants in many locations. In the 2014 low-carbon study for Nigeria, the power sector was estimated to have the largest potential (48 percent of total reduction) among four large sectors (agriculture, forestry and other land use \[AFOLU\], Oil and Gas, and Transport) for the reduction of GHG emis- 18 sions over the next 15 years (Cervigni et al. 2013). \\mathrm{C O\_{2}} \\mathrm{P M}\_{2.5} One step that could both reduce emissions and increase power output would be to retrofit the Egbin power plant for combined cycle operation. Presently, the plant uses natural gas-fired boilers to generate steam. Adding a gas turbine topping cycle could increase the overall plant efficiency, producing more power from nearly the same fuel input. By fitting these gas turbines with low NOx burners, the NOx output from the plant could also be reduced by 75 percent or more. Backup generators (gensets) may account for as much as 40 percent or 1,940 GWh of electricity generation in Lagos and a much greater share of pollutant emissions 19 from power generation. While total PM emissions from gensets are difficult to calculate, gensets represent one of least regulated sources of air pollution in Lagos. As noted earlier, improving the quality of diesel and gasoline suppled to Lagos would help reduce emissions from gensets. 4.1.4.1. CONTROLLING EMISSIONS FROM BACKUP GENERATORS Currently there are no emissions standards for electricity gensets in Nigeria. As in other countries, the emissions from such generators should be regulated, requiring (a) improvements in fuel and (b) the installation of pollution-control equipment. While improving the generating capacity and reliability of the electric grid would reduce the need for backup generators, this is at best a long-term goal. One approach in the short run could be to encourage the formation of “mini grids” based on standardized generating sets of a few hundred kW, each equipped with advanced emission controls (for example, US Tier 4 final or EU Stage V). Thus, instead of each family or business having its own 2 kW generator, a 100 such might be connected to a single 200 kW generator. This mini-grid would connect to the main grid through a single transfer switch. A single medium-size generator would be far more efficient than many small ones and would have much lower emissions. Such mini-grids could then be augmented by renewable sources such as solar photovoltaics, with the genset retained as backup. \\mathrm{P M}\_{2.} 4.1.5. INDUSTRY Industry is one of the major contributors to air pollution in Lagos. While a large share of industry is located near Ikorodu, surveys of industry activity confirm that industries are located throughout the metropolitan area, including in the industrial zones of Apapa, Idumota, Ikeja, and Odogunyan. At the Odogunyan site, iron smelting is responsible for extremely high PM2.5 concentrations as well as lead emissions (Kemper and Chaudhuri 2020). Industries located in densely populated parts of Lagos need to either control their emissions or relocate. Several such industries have moved in recent years, and the government has assisted in the relocation. The government’s main role, however, is the monitoring and enforcement of air pollution standards, which may require automatic pollution-monitoring equipment at major industrial plants. Augmenting LASEPA’s ability to perform such monitoring of industrial emissions is critical for their effective control. In general, if an industry is exceeding the emissions standard, it is up to LASEPA to enforce the standard and for the industry to remedy the situation through investments in cleaner fuel and/or pollution control equipment. Improving fuel quality is one of the most effective ways of reducing air pollution from industrial facilities. Especially for small industries, for which baghouses or other expensive emissions control equipment is not feasible, upgrading fuel quality is the most cost-effective way to reduce air pollution. To the extent that firms can convert from polluting fuels such as fuel oil, diesel, and biomass to cleaner fuels such as electricity and natural gas, it may be possible for industries to remain in urban areas and not contribute significantly to air pollution. The conversion to cleaner fuels can greatly reduce industrial air pollution emissions and often allow industries to meet minimum pollution standards. Industry-wide investments in cleaner energy sources, such as solar photovoltaic on factory rooftops, could be an option for some types of industries that have modest energy requirements and are currently relying on dirty fuels. The monitoring and enforcement of industrial emission standards, particularly for large and polluting industries such as metallurgy, chemicals, and cement, is important for ensuring that industries control their air pollution. At the same time, helping industries convert to cleaner technologies or fuels, through training and technical assistance, can be an important way for them to remain competitive and improve their productivity and profitability. Cleaner production is critical for the survival of many industries such as food processing or the information technology sector, which is why so many countries have established cleaner production programs for industry. 4.1.6. OTHER SOURCES \\mathrm{(P M\_{2.5})} systematic program of cleaner fuels, which for petroleum products such as diesel and fuel oil could correspondingly lower genset and industrial emissions, and pollution from ships. 4.1.6.1. CROP RESIDUE BURNING \\mathrm{P M}\_{2.} Recent studies using satellite imagery indicate severe air pollution (PM2.5) associated with the burning of agricultural crop residues in Sub-Saharan Africa, including 20 Nigeria (Hickman et al. 2021). Given the high population density in Nigeria and Lagos State, air pollution from agricultural fires is a risk for human health. Field burning is most severe in Nigeria during the dry season (November–February), and preliminary air-quality monitoring data confirm significantly higher PM2.5 levels during this period in Lagos. This is an area where pollution sources outside of Lagos State could be having a significant impact on air pollution, and thus measures to reduce field burning in surrounding areas, especially during the dry season, could be an important air quality measure. \ mathrm\\left({P M\_{2.5}\\right)} 4.1.6.2. COOKING FUELS 4.1.6.3. PORT EMISSIONS Another source of biomass burning in Lagos State is the combustion of charcoal and fuelwood for cooking (residential and commercial). Among low- to mediumincome residential areas in Lagos, the use of LPG for cooking is not widespread, unlike kerosene and charcoal 21 (Ozoh 2018). LPG is preferred by most consumers in Lagos but is more costly than other fuels, including for the upfront purchase of the gas cylinder and the fuel (Emagbetere, Odia, and Oreko 2016). A consistent supply of LPG, along with subsidies for low-income consumers, could be effective in reducing PM emissions from solid fuels used for residential and commercial cooking. Lagos is home to some of the busiest ports in Africa, Apapa and Tin Can Island being the two largest. * * * Anecdotal evidence suggests that pollution from ships in Lagos is severe. Measures to reduce the fuel consumption and air pollution emissions associated with ships at port, such as through shore-based electrification, fuel standards, or alternative fuels, may be an effective way to reduce air pollution in Lagos (Sofiev et al. 2018; Winkel et al. 2016). The port authorities should also consider the feasibility of enforcing the limit of 0.5 percent sulfur in marine bunker fuel. Since Nigeria has no refineries, sales of marine HFO are probably limited but the port could take and analyze samples of the fuel on board. A second source of emissions at the ports relates to the diesel trucks that pick up or drop off loads. Reportedly, as many as 5,000 high-polluting diesel trucks seek access to the ports every day, resulting in congestion and air pollution (Kemper and Chaudhuri 2020). Measures to deal with emissions from the thousands of diesel trucks could include investments in traffic management or systems for better loading and unloading. 4.1.6.4. ABATTOIRS There are a reported 16 abattoirs (slaughterhouses) in Lagos that generate air and other pollutants associated with the processing of meat and hides and the disposal of animal wastes. Improving the management of abattoirs, as has been done at several state-run facilities, can reduce air pollution emissions. Investment in biogas production from animal wastes is one method that has been used to both reduce air pollution and generate energy for sale or self-use. 4.1.6.5. DUST One of the major sources of air pollution identified through air quality monitoring—as much as 28 percent of total PM2.5—is “dust.” The sources of dust include resuspended particulates from unpaved roads, industrial pollution, and windblown soil and sand. Much of this \\mathrm{P M}\_{2.5} dust may be due to traffic traveling on unpaved roads. While such dust could be assigned to the transport sector, the remedy involves paving roads, watering roads, or simply reducing the amount of traffic or enforcing speed limits on unpaved roads. Sustainable agricultural practices such as those that reduce deforestation or do not leave fields barren between crop seasons can help reduce the amount of windblown soil from agricultural land, while industrial practices that reduce overall pollution will also limit the amount of dust from industry. 4.2. NATIONAL ACTION PLAN TO REDUCE SHORT-LIVED CLIMATE POLLUTANTS Many of the pollutants that contribute to urban air pollution are also short-lived GHGs. The progress in the implementation of the Federal Government’s plan to reduce short-lived climate pollutants (Government of Nigeria 2018) forms part of Nigeria’s NDC. Table 4.3 lists the 22 abatement measures included in that plan. While the NAP applies to the rest of Nigeria as well as Lagos, there is considerable overlap between the planned abatement measures and those discussed in section 4.1. In the transport section, common measures include the renewal of the urban bus fleet in Lagos, introduction of low-sulfur diesel and petrol, elimination of high-emitting vehicles by means of I&M, and reduction of car-based vehicle trips through public transport. The measures proposed for the residential sector in the NAP are of little relevance to Lagos, as they have already been surpassed. Likewise, there is little oil and gas activity and relatively little agriculture in Lagos State so the NAP measures for those sectors have little relevance. Waste management and electric generation, however, are key sectors both for Lagos and the country at large, and the measures included in the NAP are consistent with those recommended here. * * * TABLE 4.3. ABATEMENT MEASURES IN THE NATIONAL ACTION PLAN (NAP) TO REDUCE SHORT-LIVED CLIMATE POLLUTANTS | Source Sector | SLCP Abatement Measures | Target | | --- | --- | --- | | Transport | 1\. Renewal of urban bus fleet in Lagos | 5000 new buses in Lagos complete and Danfo buses fully replaced by 2021 | | 2\. Adoption of CNG Buses in Nigeria | 25% all Buses converted to CNG by 2030 | | | 3\. Introduction of low sulphur Diesel and Petrol | 50 ppm diesel fuel introduced in 2019; 150 ppm petrol introduced in 2021 | | | 4\. Elimination of high emitting vehicles that do not meet vehicle emission standards | Euro IV limits met by all vehicles by 2030 | | | 5\. Reduction of vehicle journey's by car through transport modal shifts | 500,000 daily journeys shifted from road to rail & waterways | | | Residential | 6\. Increase in population using modern fuels for cooking (LPG, electricity, kerosene, biogas, solar cookers) | 80% of H/H using modern fuels for cooking in 2030 | | 7\. Replacement of traditional biomass cookstoves with more efficient improved biomass stoves | 20%>H/H using improved biomass stoves for cooking in 2030 | | | 8\. Elimination of kerosene lamps | All kerosene lighting replaced by solar lamps by 2022 | | | | | | | Oil & Gas | 9\. Elimination of gas faring | 100%) of gas faring eliminated by 2020 | | 10\. Fugitive emissions/leckikages Control | 50% Methane Reduction by 2030 | | | 11\. Methane Leakage Reduction | 50% Methane Reduction by 2030 | | | Industry | 12\. Improved Energy Efficiency in industrial Sector | 50% improvement in energy efficiency by 2050 | | Waste Management | 13\. Reduction of methane emissions and open burning of waste at open dumpsites through adoption of digesters at dump sites | 50% methane recovered from landfills by 2030; 50% reduction in open burning of waste by 2030 | | 14\. Septic sludge collection | Promote Septic sludge collection, treatment and recycling in 37 municipalities | | | 15\. Sewerage Systems and Municipal wastewater treatment plants | Establish, expand Sewerage Systems and municipal wastewater treatment plants in Lagos, Kami and Port Harcourt | | | | | | | Agriculture | 16\. Increased adoption of intermittent aeration of rice paddy fields (AWD) | 50% cultivated land adopt ABD management system by 2030 | | 17\. Reduce open-field burning of crop residues. | 50% reduction in the fraction of crop residue burned infields by 2030 | | | 18\. Anaerobic Digestion(AD) | 50% reduction by 2030 | | | 19\. Reduce methane emissions from enteric fermentation | 30% reduction in emission intensity bv 2030 | | | Power \[Energy\] | 20\. Expansion of National Electricity Coverage | 90% of the Population have access to electricity grid by 2030 | | 21\. Increase share of electricity generated in Nigeria from renewables | 30% electricity generated using renewable energy in 2030 | | | HFCs | 22\. Elimination of HFC Consumption. | 10% of HFCs phased out by 2030, 50% by 2040 and 80% by 2045 | Source: Government of Nigeria 2018 readiness for implementation, and consistency with # 4.3. POLICIES AND the NAP to Reduce Short-Lived Climate Pollutants. # INVESTMENTS TO IMPROVE AIR QUALITY ## 4.3.1. COSTS OF AIR POLLUTION CONTROL From the sectoral air quality interventions outlined earlier, it is possible to sketch out potential AQM To assess the cost of potential air pollution control meas- scenarios for Lagos to progressively reduce ambient ures in Lagos, the financial and economic costs of selected air pollution. Different combinations of policies and interventions have been estimated, along with their actions can be used to reduce PM2.5 emissions. Table potential to reduce air pollution (measured as avoided 4.4 contains a list of key air quality policies recom-tons of PM2.5 per year). While it has not been possible to mended for near-term implementation in Lagos based undertake field visits and conduct detailed CBAs for all on measured pollution levels, assessed health impacts, potential air quality projects in Lagos, a preliminary desk **TABLE 4.4. CLEAN AIR POLICIES FOR LAGOS** **Sector Policies and Actions** Solid Waste • Solid waste collection strategy to raise the share collected - Recycling, composting, and WTE to reduce MSW landfilled - Ban on open burning of solid waste Industry • Monitoring and enforcement of industrial emissions - Clean fuel and “cleaner production” incentives Transport • Vehicle emissions testing and display of inspection certificates - Fines and cancellation of certificates of violators - Transition to Euro 3 and 4 vehicle standards Fuel quality • Clean fuel import strategy for both diesel and gasoline (Euro 4) - Guaranteed fuel quality among fuel distributors and retailers Power • Power sector reform - Genset emissions standards Other • LPG for residential and commercial cooking - Ban on field burning during the dry season - Paving, watering, and speed limits on unpaved roads Administrative • Installation of air quality monitoring stations - Creation of an air quality monitoring index and information system to alert vulnerable groups to hazardous air days - Installation of automatic pollution-monitoring equipment at major pollution sources (e.g., large industries). _Source: Author’s own elaboration_ Air Quality Management Planning for Lagos State exercise was undertaken to estimate indicative costs for several high-priority interventions, using data from existing projects in Lagos and elsewhere. Two sets of costs for reducing air pollution have been evaluated. Public costs include the building of public infrastructure such as landfills and roads, as well as public administrative costs such as pollution monitoring, regulation, and licensing. The costs of reducing emissions from vehicles, factories, or electric generators to comply with emission standards lie with the owners and are referred to as private compliance costs. Where possible, the net cost (investment minus revenue) of public investments, regulatory costs, and compliance costs have been estimated for selected air quality measures. Two sets of costs for reducing air pollution have been evaluated. Public costs include the building of public infrastructure such as landfills and roads, as well as public administrative costs such as pollution monitoring, regulation, and licensing. The costs of reducing emissions from vehicles, factories, or electric generators to comply with emission standards lie with the owners and are referred to as private compliance costs. Where possible, the net cost (investment minus revenue) of public investments, regulatory costs, and compliance costs have been estimated for selected air quality measures. \\mathrm{P M}\_{2.5} » Solid waste burning. Control costs include the increased cost of collecting a growing share of MSW, based on private concessionaire costs for Lagos (Aliu et al. 2014). To accommodate the increased amount of MSW, the costs of additional landfills and recycling and composting are included. » Road transport. Control costs include the costs \\mathrm{P M}\_{2.5} » Road transport. Control costs include the costs to upgrade vehicle fleets to Euro 3 and Euro 4 standards, plus the additional costs of cleaner gasoline and diesel. » Industry. Control costs include the installa- » Industry. Control costs include the installation of emission-monitoring equipment on large industrial enterprises and the enforcement of emissions standards. The compliance costs for industrial enterprises have not been estimated. » Electricity generation. Control costs include (a) » Electricity generation. Control costs include (a) the estimated costs needed to increase the supply and reliability of power from the grid to offset power supplied by backup generators or (b) the costs of emissions control for backup generators in Lagos. Using the cost estimates and the health benefits outlined in chapter 3 (Table 3.3), it is possible to create scenarios for lowering air pollution in Lagos to meet WHO interim targets. The results are presented in Table 4.5. TABLE 4.5. INDICATIVE COSTS AND BENEFITS OF REDUCING AIR POLLUTION | | Air pollution reduction scenarios | | | | | --- | --- | --- | --- | --- | | Air quality target | 35ug/m3 | 25ug/m3 | 15ug/m3 | 10ug/m3 | | Reduction inPM2.5 | -10.0 | -20.0 | -30.0 | -35.0 | | Cost(public and private)-US$, millions | 200-300 | 350-500 | 450-600 | 500-700 | | Reduction in total mortality | 3,598-6,840 | 7,196-13,680 | 10,793-20,521 | 12,592-23,941 | | Reduction in infant mortality | 1,829-3,468 | 3,657-6,936 | 5,486-10,404 | 6,400-12,138 | | Benefit of reduced mortality(VSL)-US$, millions | 890-1,691 | 1,780-3,381 | 2,670-5,072 | 3,115-5,917 | | Benefit(VSL)/Cost ratio | 3.0-8.4 | 3.6-9.7 | 4.5-11.3 | 4.5-11.8 | | Benefit of reduced mortality-HCA(US$, millions) | 235-446 | 469-891 | 704-1,337 | 821-1,559 | | Benefit(HCA)/Cost ratio | 0.8-2.2 | 0.9-2.5 | 1.2-3.0 | 1.2-2.3 | \\mathrm{P M}\_{2.5} 10,\\mathbf{u g/m^{3}} * * * 4.4. FINANCING AIR QUALITY MANAGEMENT An essential element for improving air quality in Lagos is An essential element for improving air quality in Lagos is to identify viable financing resources to support air pollution interventions. Based on discussions with both public tion interventions. Based on discussions with both public and private officials in Lagos, an explicit AQM program in Lagos could be funded through a variety of financing sources, including the state capital budget, private tion interventions. Based on discussions with both public and private officials in Lagos, an explicit AQM program in Lagos could be funded through a variety of financing sources, including the state capital budget, private sector sources, including the state capital budget, private sector interventions, multilateral support, and climate funds interventions, multilateral support, and climate funds (Table 4.6). interventions, multilateral support, and climate funds \\mathrm{M}\_{2.5} 4.4.1. LASG BUDGET where, in addition to the financial analysis, investors will require a technical plan for meeting the environmental objectives of the project, in this case the lowering of air pollution. The current LASG budget is not well aligned with the priority areas for reducing PM air pollution (Figure 4.2), which is understandable given that Lagos has many important economic and social issues to address. Based on “air quality” projects and activities in the current LASG budget (Table 4.7), a strategic program of policy and regulatory initiatives focused on a broader set of air pollution sources could be developed. where, in addition to the financial analysis, investors will require a technical plan for meeting the environmental objectives of the project, in this case the lowering of air pollution. The current LASG budget is not well aligned with the priority areas for reducing PM2.5 air pollution (Figure 4.2), which is understandable given that Lagos has many important economic and social issues to address. Based on “air quality” projects and activities in the current LASG budget (Table 4.7), a strategic program of policy and regulatory initiatives focused on a broader set of air pollution sources could To better align the budget with air quality concerns, the LASG budget should focus on those policies most important to reduce air pollution, expanding beyond public transport to other priority sectors, such as solid waste and power. Priority areas for AQM have been identified in THEMES, an acronym for Lagos’ current administration’s development agenda that includes projects in transport, health, and the environment. 4.4.2. PRIVATE FUNDING The global market for issuance of green bonds continues to expand (Figure 4.3) and country commitments from COP26 will likely expand those related to climate and air quality. As of 2020, domestic financial institutions had a total of N16 trillion (US$39 billion) in short-term instruments. The local pension funds had over N12 trillion TABLE 4.6. POSSIBLE FUNDING SOURCES FOR AQM IN LAGOS | Lagos State Budget | The LASG budget is currently financing projects that address air pollution and could be strategically realigned to make a larger contribution to both climate and air quality goals. | | --- | --- | | Green Bonds | Worldwide, the issuance of environmental finance products continues to grow annually. Lagos’ recent announcement of plans to issue a green bond is evidence of the trend.See Figure 4. | | Multilateral Lines | Bilateral and Multilateral Development Banks (MDBs) have supported projects that address air pollutionincluding in areas such as solid waste management, power,and transport. | | Climate Funds | Climate funds are available that can support air quality interventions.For example,the World Bank Group’s Climate Business Plan has targets and funding commitments within the sectors identified as principal contributors to air pollution in Lagos. | * * * FIGURE 4.2. LASG SECTOR BUDGET COMPARED TO AIR POLLUTION Source: Own elaboration. (US$29 billion), with a significant share of those resources invested in green bonds. By being able to develop and design green projects and interventions, the LASG can put itself in a position to access some of these resources through dialogue with the private sector. 4.4.3. MULTILATERAL SUPPORT Multilateral development banks (MDBs) have provided financing for interventions to address air pollution from all the key contributing sectors, including solid waste, transport, and power. Although air quality has not been the primary motivation for such projects in Africa, this study demonstrates the seriousness of the problem and could be TABLE 4.7. “AIR QUALITY” PROJECTS IN THE LASG 2021 BUDGET | WASTE | | | --- | --- | | Lagos Waste Management Authority(LAWMA) | Reconstruction and upgrading of three solid waste transfer stations. | | Construction of new landfill. | | | INDUSTRY | | | Ministry of Agriculture | Relocation of sawmill from Oko baba to Agbowa timber village. | | TRANSPORT | | | Lagos Metropolitan Area Transit Authority(LAMATA) | Bus Rapid Transit. High-speed buses operating in segregated lanes. | | 2,000 buses to transport passengers from Oshodi to Abuie Egba. | | | Completion of 27km blue line from Okoko to Marina. | | | Phase 1 construction of 27km red line from Agbado to Marina. | | | Bike share program. | | | LAGFERRY | Passenger transport by ferries displacing private road vehicles. | | POWER | | | Ministry of Energy and Mineral Resources | Light-Up Lagos | | Expansion of LPG in two government estates. | | | High-tension power lines, including pilot solar project for hospitals. | | | Installation of electricity meters. | | | OTHER | | | LASEPA | Installation of 8 air quality monitoring stations across Lagos State. | Source: Lagos State Ministry of Economic Planning and Budget * * * FIGURE 4.3. GLOBAL GREEN BOND MARKET Source: Bloomberg. the basis of developing an air quality program. Projects specifically related to air quality improvement have been developed by MDBs, and have included sector financing as well as pollution-monitoring equipment and technical assistance for effective AQM (Croitoru, Chang and Kelly 2020). The World Bank has financed numerous air quality improvement projects, including a recent project in Cairo. The financing of environmental improvement has sometimes been through performance-based programs, where financing is provided as pollution-reduction targets are met (World Bank 2016a). The World Bank’s “Program-for-Results (PforR)” instrument has been used for social, health, and environment projects. In China, a A future AQM plan for Lagos should link with its Climate Action Plan (Lagos State Government 2021) and Nigeria’s NDC to mobilize resources. COP26 has created a renewed interest in mobilizing private sector resources toward the US$100 billion per year 4.4.4. CLIMATE FUNDS commitment by developed countries to address climate-related issues. Leveraging the Lagos Climate Action Plan. The Lagos Climate Action Plan targets interventions in sectors that are major contributors to air pollution (Box 4.3). Additional data on baseline numbers and reductions in PM2.5 emissions would be needed to list the actions in the financing plan. \\mathrm{P M}\_{2.5} Leveraging Nigeria’s NDCs. Under the United Nations Framework Convention on Climate Change (UNFCCC), Nigeria has set targets that align with the sectors responsible for air pollution in Lagos. Most of the priority actions for air quality outlined in this chapter are included in Nigeria’s NDC (Box 4.4). 4.4.5. SUMMARY OF FUNDING FOR AQM The amount of funding that is available to address air quality appears well within the reach of Lagos. Table 4.8 provides a 5-year perspective on such a plan that includes green bonds, plain vanilla bonds, multilateral credit lines, and access to grants through climate funds. Table 4.9 provides a summary of the potential size of such a 5-year program. It could mobilize resources of up to US$1.29 billion (N537 billion) for the state with an increase in the green component of its financial mix. Being able to achieve this mix could likely incentivize access to multilateral lines of US$299 million as well as climate funds of US$190 million. The five-year plan aims to put Lagos on a pathway to zero carbon by 2050, enhance the climate resilience of the city and its population and to maximize the co-benefits of climate action, such as greener and healthier lifestyles. It was developed through a stakeholder engagement process, that allowed the plan to gain broad buy-in from business, civil society and the wider public. The plan envisages a range of actions to reduce GHG emissions in each section, including: BOX 4.3. LAGOS CLIMATE ACTION PLAN, 2020–2025 » Expansion of the BRT network in Lagos. » Spatial planning to promote transit-oriented development. » Spatial planning to promote transit-oriented development. » Encourage the uptake of low-emission vehicles. » Encourage the uptake of low-emission vehicles. » Encourage the shift of freight from road to rail. » Divert organic waste from landfill by encouraging separation at source and introducing composting technologies. » Implement composting, waste-to-energy and other waste recovery initiatives in underserved » Installing solar PV systems on all schools, hospitals and municipal buildings. » Reduce emissions in the residential sector by promoting the development of energy storage tech- » Implement composting, waste-to-energy and other waste recovery initiatives in underserved communities. » Encourage the shift of freight from road to rail. * * * BOX 4.4. NIGERIA NDC TARGETS AND AIR QUALITY | Sector | Measure | | --- | --- | | Residential | 48% of population(26.8 million households) using LPG and 13%(7.3 million households)using improved cookstoves by 2030 | | Elimination of kerosene lighting by 2030 | | | Energy efficiency | 2.5% per year reduction in energy intensity across all sectors | | Transport | 100,000 extra buses by 2030 | | Bus Rapid Transport(BRT)will account for22.1%of passenger-km by 2035 | | | 25%of trucks and buses using CNGby 2030 | | | All vehicles meetEURO lllemission limits by 2023and EURO IVby 2030 | | | Electricity generation | 30% of on-grid electricity from renewables(12GW additional large hydro,3.5GW small hydro,6.5GW Solar PV,3.2GW wind) | | 13GW off grid renewable energy(i.e.,mini-grids5.3GW,Solar Home Systems and street lights2.7GW,self-generation5GW) | | | Reduce grid transmission and distribution losses to8%of final consumption of electricityin 2030,downfrom15%in2018. | | | 100%of diesel and single cycle steam turbines replaced with combined cycle | | | Elimination of diesel and gasoline generators for electricity generation by2030 | | | Oil and gas | Zero gas flaringby2030 | | 60% reduction in fugitive methane emissionsby2031 | | TABLE 4.8. FIVE-YEAR AQM FINANCING SCENARIOS | | Program Funding Components | | | | | | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | 2021 | | 2022 | | 2023 | | 2024 | | 2025 | | | | Allocation(%) | N'B | Allocation(%) | N'B | Allocation(%) | N'B | Allocation(%) | N'B | Allocation(%) | N'B | | | Borrowing Plan | | 100.0 | | 125.0 | | 125.0 | | 130.0 | | 140.0 | | Multilateral Lines | 0.0% | - | 25.0% | 31.3 | 30.0% | 37.5 | 30.0% | 37.5 | 30.0% | 37.5 | | Green BondIssuance | 25.0% | 25.0 | 30.0% | 30.0 | 30.0% | 30.0 | 30.0% | 30.0 | 30.0% | 30.0 | | Grant Funding(climate funds) | 0.0% | - | 0.0% | 18.8 | 0.0% | 17.5 | 0.0% | 22.5 | 0.0% | 32.5 | | LASG VanillaBonds | 75.0% | 75.0 | 45.0% | 45.0 | 40.0% | 40.0 | 40.0% | 40.0 | 40.0% | 40.0 | * * * TABLE 4.9. 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Urban Development Series Knowledge Papers. [https://documents1.worldbank](https://documents1.worldbank/) .org/curated/en/529431489572977398/pdf/11348 [https://documents1.worldbank.org/curated](https://documents1.worldbank.org/curated) 7-WP-compostingnoweb-24-PUBLIC.pdf. World Bank. 2020. Nigeria – Power Sector Recovery Operation (English). Washington, DC: World Bank [http://documents.worldbank.org/curated/en](http://documents.worldbank.org/curated/en) /991581593223433078/Nigeria-Power-Sector -Recovery-Operation. * * * ### CHAPTER 5 # LAWS, REGULATIONS, AND INSTITUTIONAL CAPACITY A study has been conducted as part of the Lagos PMEH to describe and analyze the regulatory and institutional arrangements for air quality and pollution control objec- tives in Lagos State in relation to Nigeria’s national framework. The outcome of this study, as presented in this chapter, reveals the deficiencies in the legal, regulatory, and institutional frameworks for air quality that currently exist at both the federal and Lagos State levels. Having adopted the national regulations operated by National Environ- mental Standards and Regulations Enforcement Agency (NESREA), the Lagos State framework operated by LASEPA inherits the federal-level regulatory deficiencies. Key challenges to air quality governance in Nigeria and Lagos State include the lack of a unified regulatory framework, inadequate and ineffective regulations, deficiencies in the technical and enforcement capacity of regulatory bodies, and budgetary con- straints. We therefore make recommendations to strengthen existing legislation and regulations, establish a scientific basis for deriving air quality and emission standards, strengthen the technical capacity of relevant institutions to undertake air quality mon- itoring and health impact assessments, strengthen the enforcement capacity of regula- tory institutions, and ensure adequate funding for the relevant institutions to establish monitoring stations and build other necessary capacity. ## 5.1. NIGERIAN LEGAL AND REGULATORY FRAMEWORK The Federal Constitution of Nigeria allows the federal, state, and local governments to legislate with respect to pollution (Suleiman et al. 2017). At the federal level, the key leg- islation is the 2007 NESREA Act, which established NESREA as the agency under the FMEnv responsible for setting and enforcing environmental regulations, except for the Air Quality Management Planning for Lagos State petroleum industry. Another important law is the 1996 2 Petroleum Act, which assigned responsibility to the Federal Ministry of Petroleum Resources (FMPR) for setting and enforcing the Environmental Guidelines and Standards for the Petroleum Industry in Nigeria (EGASPIN). Also significant is the Environmental Impact Assessment Act of 1992, the scope of which covers any project undertaken by the government (at any level), or which requires a government license or permit, and which could have a significant impact on the environment. Of potential significance for air quality are directives C/Dir.1/09/20 and C/Dir.2/09/20, of the Commission of the ECOWAS, to set a common fuel sulfur standard for ECOWAS of 50 ppm by weight for both diesel and gasoline fuels, and to establish Euro 4 and Euro 6 emission standards for light-duty vehicles and Euro 6 standards for heavy-duty vehicles, respectively. The directives lay down more stringent standards for motorcycles and tricycles and call for the emission standards to apply to newly imported vehicles—whether new or used—from January 1, 2021. Euro 4 emission standards would apply to both light- and heavy-duty vehicles that are in the existing vehicle fleet from January 1, 2025. To date, it does not appear that either directive has been implemented in Nigeria. 5.1.1. EXISTING NATIONAL REGULATIONS ON AIR QUALITY \\mathrm{M}\_{2.5} 5.1.1.1. NON-OIL AND GAS REGULATIONS—NESREA ACT The 2014 Air Quality Control Regulations (Air Regulations) set provisions for the maximum permissible limit values for six criteria pollutants, excluding PM2.5. Standards for some pollutants exceed the WHO guidelines threefold. Moreover, the standards do not set limits for population exposure, and it is unclear if their definition was based on scientific country-level studies. Under the Air Regulations, stationary sources and facilities must submit annual emissions reports. However, the emission limits for different source categories—combustion of fossil fuel, industrial operations such as paint manufacturing, textile, quarries—lack clarity, and there is no guidance on the methodologies and protocols to calculate emissions from various sources, such as emissions factors for different emission sources (Ukeh 2021). The 2011 National Environment Control of Vehicular Emissions from Petrol and Diesel Engines Regulations (Vehicle Emissions Regulations) are aimed at reducing and preventing air pollution from automobiles. The regulations also make provision for citizens’ right to clean air and for the improvement of the health of Nigerians, especially in urban settings with high incidences of air pollution caused by the increased number of automobiles. The regulations set standards for specific pollutants for different on-road vehicles manufactured after certain years. However, the implementation of this regulation has run into multiple obstacles, including enforcement of the ban placed on importing two-stroke engines, prohibition of vehicles that do not comply with emissions-reduction technologies, and conducting of annual testing of vehicles for gas emissions (Center for Science and Environment 2013; Ukeh 2021). The 2009 Permitting and Licensing System Regulations set provisions for the issuance of environmental permits to operators of stationary sources. However, the regulation is not specific on the nature or type of permit, lacks clarity about which phase of the construction or operation of a facility a permit is required, and offers no guidance to prepare and submit an application. Additionally, the few permit requirements in the regulations—such as how to quantify a facility’s emissions footprint to determine the type and nature of air permit required, protocols to adopt in determining a facility’s emissions footprint, and emissions stack requirements—are not sciencebased. Moreover, the terms of the permit are vague and unrealistic. Most regulated entities are therefore not motivated or incentivized to prepare and submit air pollution permits. NESREA does not currently implement auditing, monitoring, and evaluation of performance to ensure compliance with permits. Unfortunately, the regulators lack the financial, technical, and human resource capacities to effectively enforce compliance with the permit conditions (Ukeh 2021). * * * Other regulations under the NESREA Act that have implications for air quality and emissions produced by other mobile and stationary sources are the Pollution Abatement in Industries and Facilities producing Waste Regulations, the Ozone Layer Protection Regulations, and Regulations for Sanitation and Waste Control, and for Energy and Industry. The 2011 Control of Bush or Forest Fire and Open Burning Regulations were enacted to minimize and prevent the destruction of the natural ecosystem owing to fire outbreaks and uncontrolled burning of materials that may affect human health and the environment because of emissions of hazardous and criteria air pollutants. Despite the existing legal framework, there are increasing environmental problems and air pollution in Nigeria, which are largely due to the lack of compliance with environmental laws. NESREA currently does not have the capacity to monitor pollution or engage in technical discussions with regulated entities to gather the information needed to establish adequate standards, which compels the agency to adopt regulations without knowing emissions levels or existing technologies (Suleiman et al. 2017). The compliance requirements set by the legal and regulatory framework are emphatic about enforcement for noncompliance but not clear about how regulated entities should realistically go about demonstrating compliance, nor about the pathway or timeline to demonstrate compliance. In addition, penalties are rarely calculated and imposed on violators because the regulators lack the resources. In general, enforcement actions are often implemented without proof of violation (Ukeh 2021). 5.1.1.2. OIL AND GAS REGULATIONS—PETROLEUM ACT decommissioning phases (Olawuyi and Tubodenyefa 2018). In relation to air emissions, the EGASPIN sets requirements regarding gaseous point-source emissions, which include their estimation, registration, inventories, the installation of equipment to reduce or prevent them, the implementation of air quality and emissions-monitoring programs, and the installation of appropriate sampling points. Fuel standards are established through the Nigerian Industrial Standards (NIS) issued by the Standards Organization of Nigeria (SON). In 2003, Nigeria phased out leaded gasoline. In 2017, the Nigerian Industrial Standard for Petroleum Products established a low-sulfur policy through NIS No. 116 and 949. The maximum permissible sulfur content in diesel was set at 50 ppm, 150 ppm for gasoline, and 150 ppm for kerosene. Currently, these standards lack government approval and implementation (Croitoru, Chang and Kelly 2020). In 2020 as part of a high-level meeting of the ECOWAS, Nigeria agreed to set regulations for cleaner fuels and vehicles to permit a maximum of 50 ppm sulfur content for gasoline and diesel by 2021, a minimum of Euro 4 vehicle emissions standard for all vehicles imported, and a plan to improve vehicle efficiency for all vehicles imported (UNEP 2020). The government had committed to adopting such standards by 2020, but the deadline, which was extended to 2021, was not met (SDN 2022). Currently, no specific date has been set. In summary, despite the existence of official standards and formal commitments, fuel quality in Nigeria continues to be poor compared to other African countries, even as the importation of dirty fuels continues (Croitoru, Chang and Kelly 2020). There are additional acts that also aim at controlling atmospheric and other types of pollution in Nigeria. The Harmful Waste Act prohibits, without lawful authority, the carrying, dumping, or depositing of harmful waste in the air. The Environment Impact Assessment Act details the procedures and sectors required to perform environment impact assessments of potential negative impacts to the environment, including air resources. The Environmental Impact Assessment Act is relevant to assessing the environmental impacts of the oil and gas sector and controls its air emissions. 5.1.1.4. INTERNATIONAL AGREEMENTS Nigeria is a signatory of multilateral agreements for environmental protection and pollution control. Nigeria 4 ratified the Vienna Convention (1987) and the Montreal 5 Protocol (1988) to protect the O3 layer, and the Stock- 6 holm Convention (2003) that regulates persistent organic pollutants (POPs). Regarding GHGs, Nigeria ratified the Kyoto Protocol (2000) and the Paris Agreement (2016). In 2015, Nigeria submitted an NDC in the form of an unconditional contribution of 20 percent below businessas-usual levels, and a 45 percent contribution conditional on international support by 2030. The government submitted an updated NDC in June 2021 with unconditional contribution still at 20 percent below business as usual, but a slightly more ambitious conditional contribution of 47 percent, with the addition of two new sectors (waste and water) to the existing five: AFOLU, Energy, Oil and Gas, Industry, and Transport. The enhanced NDC will cover short-lived pollutants, including black carbon, an air pollutant with a high incidence of morbidity and premature mortality (Federal Government of Nigeria 2021). {\\bf O}\_{3} (1988)^{5} funding sources. No information was found on the level of implementation of NEP’s policy statement. 5.1.2. NATIONAL STRATEGIC VISION FOR AQM Nigeria does not have a stand-alone policy or strategy on pollution control and AQM. However, it has the National Environmental Policy (NEP) of 2016, which sets out the Federal Government’s vision for AQM. The NEP contains several policy statements that express the intention of the Federal Government to improve air and atmospheric resources institutional arrangements, strengthen guidelines and standards, enhance enforcement capacity, improve monitoring of emissions, and promote efficient transport systems (Federal Ministry of Environment 2016). However, NEP policy statements lack specific targets, sector abatement measures, responsible parties, timelines, and Nigeria has a comprehensive NAP to Reduce Short-Lived Climate Pollutants covering important air criteria pollutants. In 2019, after a 2-year consultation process, the country’s National Council of Ministers approved a crosssector action plan to reduce short-lived climate pollutants (NAP-SLP). The plan identifies emissions levels and sources for PM2.5, SO2, NOx, and CO and other air and climate pollutants, and prioritizes 22 abatement measures based on economic and engineering modeling. The plan introduces specific emissions reductions and sector policy targets and is associated with achieving emissions reductions of between 58 and 78 percent for PM2.5, SO2, NOx, and CO by 2030, if the plan were implemented (Federal Government of Nigeria 2018). The adoption of this action plan elevated the importance of tackling air pollution at the national level. The implementation of the NAP-SLP is coordinated by FMEnv’s Climate Change Department. However, because the action plan includes targets and sectoral actions for the reduction of atmospheric pollutants, it is unclear if such policy actions should be headed by the Climate Change unit or the Pollution Control and 7 Environmental Health (PCEH) unit. \\mathrm{P M\_{2.5},S O\_{2},N O\_{x}} \\mathrm{P M\_{2.5},,S O\_{2},,O O\_{x}}, 5.1.3. FEDERAL INSTITUTIONS 5.1.3.1. FEDERAL MINISTRY OF ENVIRONMENT The FMEnv leads the governance architecture for the protection of the environment in Nigeria. The ministry administers environmental law and policy and shoulders a key responsibility—to ensure that environmental matters are adequately mainstreamed into all developmental activities in the country. The ministry’s mandate was further strengthened by an NAP for the Promotion of Human Rights. NAP recognizes Nigerians’ collective rights to a safe, healthy, and ecologically sustainable environment for the present and future generations (Ukeh 2021). However, the ministry does not have a strategic approach to the regulation-making process, or the technical and financial capacity to adopt other policy instruments to guide the country’s efforts to improve air quality. In 2021, the FMEnv had allocations corresponding to 0.34 percent of the total budget appropriations 8 act. Air quality does not seem to be a priority within the FMEnv’s budget. For example, in 2021 only N33 million (approximately US$8,000) was allocated to air quality 9 monitoring equipment and studies on air pollution. The FMEnv and other ministries, departments and agencies (MDAs) rely heavily on intervention funds from multilateral and bilateral development institutions to battle both short-lived and long-lived air pollutants (Ukeh 2021). The FMEnv leads a comprehensive set of departments, institutions, and regional offices. The ministry is made up of six technical departments, which include PCEH and Climate Change departments, and seven regulatory agencies, which include NESREA and the National Oil Spill Detection and Response Agency (NOSDRA). The ministry is structured into six zonal operational offices and 36 state-level field offices. The state zonal offices work in partnership with and provide operational guidance to their respective state ministries of environment (Ukeh 2021). NESREA has a series of policy instruments to implement environmental policy and air pollution control, composed mainly of enforcement instruments. The NESREA Act empowers the agency with multiple instruments to enforce environmental law. The agency has the power to perform inspections and searches, forbid polluting equipment, issue enforcement notices, establish mobile courts, conduct public investigations, and prosecute and take legal action against citizens or companies violating the law. The NESREA Act has been discussed in Section 5.1.1.1. 5.1.3.2. NESREA 10 to AQM in the oil and gas sector. In 2018, an amendment to the NOSDRA Act was passed by the Nigerian Senate Committee on Environment. Based on a 2018 report of the Senate Committee on Environment, A Bill for an Act to Amend the National Oil Spill Detection and Response Agency, Act 2006 and for Other Matters Connected 11 Therewith (SB557), changes proposed to the functions of NOSDRA which could potentially give the agency jurisdiction for AQM were excluded from the final amended bill. The exclusion suggests that the functions of NOSDRA are intended to be limited to pollution from oil spillage, but not gaseous emissions. Based on this, the roles NOSDRA will play in regulating air quality matters in the oil and gas industry remain unclear when the 2018 NOSDRA Amendment Bill is eventually signed into law. 5.1.3.4. NIMET The Nigeria Meteorological Agency (NIMET) is involved in air pollution monitoring and currently pursues objectives related to air quality analysis and policy advising. NIMET is an agency under the Federal Ministry of Aviation. Its statutory mandate is continuous observation of national weather and climate and generation of timely meteorological, hydrological, and oceanographic data to support national needs and in fulfilment of relevant international obligations. NIMET maintains 60 weather observation and air quality monitoring stations across the country. In relation to air quality, the agency has a Dobson O3 spectrophotometer at its Regional Meteorological Training Center in Lagos. It has installed environmental safety monitoring instrument gas analyzers at its observation centers in Abuja, Lagos, Enugu, Kano, and Maiduguri Airports to monitor CO, CO2, NOx, PM2.5, PM10, and O3. These gas analyzers are currently not collecting data. BAM gas analyzer was deployed at the National Hospital, Abuja in January 2019 to measure the listed air pollutants. The agency also had a portable Technologies Ozone Monitor Model 202 installed at its headquarters Abuja in 2018 to monitor for tropospheric O3. The agency has also adopted a comprehensive list of air quality and GHG emissions objectives to be implemented across the country (Ukeh 2021). * * * 5.1.3.5. OTHER INSTITUTIONS Other institutions with mandates related to air pollution are the FMPR, SON, National Automotive Design and Development Council (NADDC) under the Federal Ministry of Industry, Trade and Development, and the Federal Ministry of Health. 5.2. LAGOS STATE’S LEGAL AND REGULATORY FRAMEWORK Lagos’ institutional air quality framework faces multiple development challenges. The existing legal and regulatory framework lacks certain key elements that are required for adequate AQM. Most regulations in Lagos depend on the national framework, which itself is inadequate. AQM plans have not been developed, nor have jurisdictional monitoring and reporting requirements been implemented. Although the LASEPA under the Lagos State Ministry of Environment has the statutory role of regulating air quality in the state, multiple institutions have duplicative or overlapping functions related to pollution control—which blurs the lines of accountability—while coordination, enforcement, and implementation capacities remain weak. and Water of the MoE; and the General Manager of LASEPA. Lagos has adopted environmental legislation and established a Lagos State Ministry of Environment and Water Resources (LMoE) as well as several parastatal agencies with environmental responsibilities. The key environmental legislation in Lagos State is the Environmental 12 Management Protection Law of 2017, which consolidated and expanded the previous environmental laws. Part VI of that law establishes LASEPA as a parastatal agency within LMoE, with a board comprising a chairman, three public members, and eight ex officio members: the permanent secretaries of the Ministries of Health, Agriculture, Works and Infrastructure, Transportation, Finance, and Local Government and Community Affairs; the Director of Environmental Services, Sewage The 2017 law gives LASEPA broad powers to, among others, “monitor and control all forms of environmental degradation from agricultural, industrial and government operations; set, monitor and enforce standards and guidelines on vehicular emissions; survey and monitor surface, underground and potable water, air, land and soil environments in the state to determine pollution levels in them and collect baseline data; and prepare a periodic master plan to enhance capacity building for the Agency and for the environment and natural resources management.” The law also establishes that “the funds of the Agency shall consist of: (a) such monies as may be appropriated to the Agency by the state; and (b) all subscriptions from the charge, fees and charges for services rendered by the Agency.” Thus, the law specifically provides for the agency to supplement its appropriated funding with (for example) permit fees, emission fees, and other charges paid by the organizations it regulates. Another issue is the challenges that LASEPA encounters in regulating the operations of federal establishments that operate within Lagos State. For instance, LASEPA is unable to regulate fuel quality within Lagos, which has impacts on vehicular emissions. This is due to the inability of Lagos State to determine the quality of refined petroleum product imports and the distribution of the imported products within the state. * * * Second, Lagos State’s existing legislation does not require LASEPA to adopt specific plans to achieve air quality standards and control pollution, a shortcoming of the federal regulations adopted by LASEPA (Center for Science and Environment 2013). This further renders actions by LASEPA in air pollution control ineffective and results in the inefficient allocation of resources. An AQM plan is just being developed for Lagos State through the PMEH intervention. Third, the adopted legal and regulatory framework does not have air quality and emissions monitoring requirements for specific jurisdictions, or requirements to report compliance with national air quality standards. Fourth, airshed delineation or transboundary air management is not mandated or incentivized by existing regulations. As a result, LASEPA does not have an understanding of the airshed responsible for air pollution within Lagos. This effectively limits any collaborative efforts among EPAs across geographical boundaries to monitor and manage transboundary air pollution. Finally, current legislation is heavy on enforcement mechanisms but is less developed on other policy tools to incentivize compliance, tools such as market-based instruments like voluntary certification programs, pollution taxes and emissions trading systems, which when combined with enforcement tools would likely yield better policy results. In Lagos State, LMoE is charged with securing a cleaner, healthier, and more sustainable environment conducive to tourism, economic growth, and the wellbeing of all citizens. It serves as the coordinating ministry for all the offices and parastatals under Environment. The key objective of the ministry is to ensure that environmental matters are adequately mainstreamed into all developmental activities in the state. LMoE is made up of two offices and seven parastatal agencies. LASEPA is the parastatal 5.3. ORGANIZATIONS INVOLVED IN LAGOS STATE with statutory responsibility for AQM in the state. The Lagos State Waste Management Agency (LAWMA) is responsible for solid waste management, and the Lagos State Waste Water Management Office (LASWMO) is responsible for sewage collection and treatment. LASEPA was established in 1996 to enforce measures to combat environmental degradation on manufacturing premises. Figure 5.1 shows the present organization chart, which comprises seven scientific offices, eight zonal offices, four scientific units, and seven non-scientific units. In 2021, it was slated to receive only the equivalent of US$1.25 million from the state budget. That means that most of LASEPA’s funding has to come from fees and an annual Environmental Development Charge on industry. Under the Lagos Environmental Management Protection Law of 2017, LASEPA has the legal authority to enforce emission standards on industrial, agricultural, and government sources and on generating plants in residential and commercial areas, to set and enforce vehicle emission standards, and to set up an air quality monitoring network. However, it mostly lacks the technical capacity and staff to do so effectively. Training and capacity building, as well as additional staff and equipment investments, are needed for LASEPA to effectively fulfill its statutory role in AQM. This will require an increase in budget. As a parastatal, the agency has the capacity to be self-funding and already derives a large fraction of its budget from fees, fines, and the Environmental Development Charge. In Lagos, the discharge of injurious gases that cause air pollution is an offence, and individuals and corporate bodies can be fined for causing pollution. Over time, LASEPA has demonstrated the capacity to enforce regulations on noise pollution in Lagos. However, the agency lacks the instruments and training to effectively control emissions of air pollutants and GHGs. A key objective of the PMEH program is to enhance the capacity of LASEPA to effectively monitor and regulate air pollution. To realize this objective, the PMEH has engaged LASEPA personnel in on-field and classroom capacity-building sessions on the various components of AQM. * * * FIGURE 5.1. ORGANIZATION CHART FOR LAGOS STATE EPA * * * Along with NESREA, several other federal MDAs are stakeholders with interest and influence in AQM in Lagos. The Nigerian Ports Authority, which controls the two ports of Apapa and Tin Can Island, the Airports Authority, and the Nigerian Railway Corporation, reports to the Federal Ministry of Transportation. Under the FMPRs, the NNPC has exclusive authority to import refined petroleum products, which are distributed across the country by its subsidiary, the Pipelines and Product Marketing Company (PPMC) (Ehinomen and Adeleke 2012). Tertiarylevel hospitals report to the Federal Ministry of Health. In the past, LASEPA has been limited in its ability to enforce regulations at federal institutions located in Lagos due to jurisdictional conflicts with NESREA. 5.4. EXISTING REGULATIONS IN LAGOS STATE LASEPA has largely adopted the NESREA standards and regulations rather than establish its own. Table 5.1 looks at the Lagos State and national AQM policies, regulations, and standards from the perspective of recommended international AQM systems aimed at realizing 13 the five strategic AQM goals. 5.5. STRENGTHENING THE SCIENTIFIC BASE FOR AQM To effectively manage air quality requires systematic, ongoing measurements of ambient air pollution levels (air quality monitoring), a detailed understanding of the sources of air pollution (emissions inventory), and the ability to predict the effects of changes in the emission inventory on ambient levels of pollution (air quality modeling). Until now, none of these three capabilities have been in operation in Lagos and Nigeria. A previous effort at developing an emissions inventory in Nigeria highlighted critical missing links in the development of an emissions inventory infrastructure in a typical Nigerian environment (Fagbeja et al. 2017). As documented in chapter 2, the World Bank PMEH program has taken the first steps to fill these gaps in Lagos by (a) sponsoring one year of continuous data collection at six selected locations in Lagos State, (b) funding the development of an emissions inventory for the state, and (c) funding initial efforts to model specific episodes during the year of monitoring to compare those results with the measured data. The LASG should build on these initial steps. Ground-based air quality information in Nigeria is sparse. The FMEnv and other MDAs own air quality monitoring stations, but there is little information on their location, functionality, and datasets and whether the generated data are actually informing public decisions. Until this project, analyses of air quality had been based on irregular, short-term, sampling efforts. This precluded the country and cities like Lagos from developing a longer-term understanding of the dynamics of air pollution. \\mathrm{P M}\_{2.5} Due to the lack of consolidated information on air quality, most national and international studies use satellite observations, aircraft observations, and simulation models to understand pollution sources and the concentrations. Robust studies of other pollutants, such as particulate matter, require near-source measurements, which are limited in the country. LASEPA still needs to work on the following priority areas to enhance its AQM information system: conduct long-term monitoring of pollutants, including PM2.5 in several representative locations, collaborate with the LSMoH to centralize city health data, extend and improve the present emissions inventory, conduct refined source apportionment studies, and establish a platform for public dissemination of air quality information. This will require funding for the procurement of new equipment and the maintenance of existing infrastructure and datasets, integration of new technologies such as satellite data and machine learning to augment ground-based monitoring, establishment of standards for measuring pollutant emissions from sources and for monitoring air quality, and improvement of the community’s acceptance of public air monitoring infrastructure to decrease vandalism of monitoring equipment. * * * TABLE 5.1. AQM LAWS, REGULATIONS, POLICIES, AND INSTITUTIONS AT LAGOS STATE AND FEDERAL LEVELS | Policy, regulation, standard | Lagos State level | National level | | --- | --- | --- | | 1\. Ambient Air Quality Standards(AAQS) | a. Part VI of Lagos State Environmental Regulations,2017(LMoE-LASEPA) references and recognizes the NAAQS consistent with the federal regulations. | | | b. Lagos environmental regulations adopts the languages of the federal(NESREA)air quality regulations. | Part VI of National Air Quality Control Regulations,2014(FMEnv-NESREA), established the NAAQS. | | | 2\. Ambient Air Quality Monitoring and Modeling Program | a. Part VI of Lagos State Environmental Regulations,2017(LMoE-LASEPA) referenced air quality monitoring requirements. | | | b. Lagos State adopts most of the federal(NESREA)air quality regulations. | a. National Air Quality Control Regulations,2014(FMEnv-NESREA). | | | b. EGASPIN,1991,Revised 2002,2016,2018(FMPR-DPR). | | | | 3\. Standards for Stationary Sources | a. Part VI of Lagos State Environmental Regulations,2017(LMoE-LASEPA). | | | b. Lagos State adopts most of the federal(NESREA)air quality regulations. | | | | c. Industrial Guidelines-LASEPA. | a. Part II of National Air Quality Control Regulations,2014(FMEnv-NESREA). | | | b. National Environmental(Control of Bush or Forest Fire and Open Burning)Regulations,2011(FMEnv-NESREA). | | | | 4\. Standards for Mobile Sources | a. Part VI of Lagos State Environmental Regulations,2017(LMoE-LASEPA). | | | b. Lagos State adopts most of the federal(NESREA)air quality regulations. | a. Part III of National Air Quality Control Regulations,2014(FMEnv-NESREA). | | | b. Control of Vehicular Emissions from Petrol and Diesel Engines(2011). | | | | 5\. Compliance Requirements and Penalties | a. Part VI of Lagos State Environmental Regulations,2017(LMoE-LASEPA). | | | b. Lagos State adopts most of the federal(NESREA)air quality regulations. | Part VII and X of National Air Quality Control Regulations,2014(FMEnv-NESREA). | | | 6\. Operating Permit Program | a. Part VI of Lagos State Environmental Regulations,2017(LMoE-LASEPA). | | | b. Lagos State adopts most of the federal(NESREA)air quality regulations. | Part IX of National Air Quality Control Regulations,2014(FMEnv-NESREA) | | | 7\. Continuous Emissions Monitoring | No emissions monitoring regulations exist. | No emissions monitoring regulations exist. | | 8\. Area Designation for Air Quality Planning | No related policies or legislation found. | No related policies or legislation found. | | 9\. Climate Change Program | Lagos State adopts the National Policy on Climate Change. | National Policy on Climate Change-2012. | | 10\. Energy and Alternative Energy | No related policies or legislation found. | Part IV of National Environmental(Energy Sector)Regulations(2014). | | 11\. Emissions Trading Policy | No emission trading policy found. | No emission trading policy found. | * * * 5.6. NEW REGULATORY AND ENFORCEMENT STRUCTURES The current air quality regulatory framework in Lagos State and Nigeria is insufficient to tackle increasing pollution challenges. This is due to the identified institutional and legislative deficiencies. Therefore, the Lagos State and Federal Governments need to modify the existing legal and regulatory framework to promote adequate governance structures and more effective enforcement of pollution control. The following four recommendations are aimed at establishing new regulatory and enforcement structures. The current air quality regulatory framework in Lagos State and Nigeria is insufficient to tackle increasing pollution challenges. This is due to the identified institutional and legislative deficiencies. Therefore, the Lagos State and Federal Governments need to modify the existing legal and regulatory framework to promote adequate governance structures and more effective enforcement of pollution control. The following four recommendations are aimed at establishing new regulatory and enforce- Strengthen current ambient air quality standards established in the 2014 Federal Air Quality Control Regulations. A standard for PM2.5 needs to be established based on scientific studies, and other standards need to be revised based on scientific knowledge. LASEPA should create a schedule for the adoption of lower concentration limits to comply with the WHO’s recommendations, and a national exposure reduction target for key pollutants such as PM2.5. This will represent the realities in the state and provide a basis for improving the existing regulations. \\mathrm{P M}\_{2.5} Amend the NESREA Act to establish clear and differentiated institutional roles and responsibilities. Federal institutions and state institutions need to have differentiated and complementary roles and responsibilities to enforce Amend the NESREA Act to establish clear and differentiated institutional roles and responsibilities. Federal institutions and state institutions need to have differentiated and complementary roles and responsibilities to enforce and complementary roles and responsibilities to enforce air quality control measures with regulated entities and to implement air quality policy in a way that avoids the to implement air quality policy in a way that avoids the duplication of effort. Lagos State Management Protection Law also needs to be revised accordingly and clarify roles and responsibilities. to implement air quality policy in a way that avoids the duplication of effort. Lagos State Management Protection Law also needs to be revised accordingly and clarify The amendment should Federal Government to lead a multi-stakeholder discussion and adoption of a National Air Quality Strategy with clear emissions reductions targets, sectoral actions, and budget allocations. The NESREA Act amendment should also mandate federal and state governments to work together to delineate regional airsheds. State governments should be required to establish air zones for air quality monitoring and management purposes based on airshed dynamics. The NESREA Act amendment should exhort the Federal Government to develop an AQI methodology to be adopted by state governments. Finally, the proposed amendment should include demanding state governments to comply with air monitoring and reporting requirements, develop air pollutant emission inventories, and disclose information to the public on the state of air quality. Lagos State must therefore amend its regulatory framework to also foster cooperation within the state. LASEPA, through its regional offices, can then coordinate with the local government on the adoption of state implementation plans (SIPs) and air zone monitoring and management plans incentivizing transboundary cooperation. amendment should have a requirement for the Federal Government to lead a multi-stakeholder discussion and adoption of a National Air Quality Strategy with clear emissions reductions targets, sectoral actions, and budget allocations. The NESREA Act amendment should also mandate federal and state governments to work together to delineate regional airsheds. State governments should be required to establish air zones for air quality monitoring and management purposes based on airshed dynamics. The NESREA Act amendment should exhort the Federal Government to develop an AQI methodology to be adopted by state governments. Finally, the proposed amendment should include demanding state governments to comply with air monitoring and reporting requirements, develop air pollutant emission inventories, and disclose information to the public on the state of air quality. Lagos State must therefore amend its regulatory framework to also foster cooperation within the state. LASEPA, through its regional offices, can then coordinate with the local government on the adoption of state implementation plans (SIPs) and air zone monitoring and management plans incentivizing transboundary cooperation. \\mathrm{P M}\_{2.5} Strengthen NESREA, LASEPA, and other state EPAs enforcement capacities. tional institutions, NESREA should work with LASEPA and other state EPAs to devise measures to enhance national and state institutional capacities to (a) develop sound air quality regulations; (b) determine realistic emissions standards across different emissions source categories; and (c) develop practical enforcement mechanisms, such as through the use of incentives and market-based instruments, with adequate science-based infrastructure, including research and development and meteorological observation technologies. Regulated institutions, including LASEPA, should implement a robust stafftraining program to promote professionalism, integrity, consistency, and transparency for AQM. LASEPA should explore collaboration with international donor agencies to fund a technical service consultancy to equip and train LASEPA personnel on air quality enforcement strategies. Strengthen NESREA, LASEPA, and other state EPAs enforcement capacities. With the support of international institutions, NESREA should work with LASEPA and other state EPAs to devise measures to enhance national and state institutional capacities to (a) develop sound air quality regulations; (b) determine realistic emissions standards across different emissions source categories; and (c) develop practical enforcement mechanisms, such as through the use of incentives and market-based instruments, with adequate science-based infrastructure, including research and development and meteorological observation technologies. Regulated institutions, including LASEPA, should implement a robust stafftraining program to promote professionalism, integrity, consistency, and transparency for AQM. LASEPA should explore collaboration with international donor agencies to fund a technical service consultancy to equip and train LASEPA personnel on air quality enforcement strategies. * * * 5.7. THE HEALTH SYSTEM SHOULD BE AN ACTIVE ACTOR The health sector in Lagos should proactively tackle the health impact of air pollution and act with a strong advocacy voice to promote urgent intervention actions to reduce air pollution emissions and population exposure, perform continuous health impact evaluation, improve and expand the system of health information collection, and initiate epidemiologic research on air pollution. There is a major need for the health sector in Lagos to be better informed about the health hazards of air pollution. Air pollution is a powerful causative factor of mortality and morbidity. This association is not widely acknowledged within the Lagos health care community and appropriate education on the scientific evidence for it should be pursued. There is a major need to educate personnel on how pollution exposure is driving the rise of NCD because air pollution impedes the formation of new human capital and undermines the prospects of future development by causing damage across the entire population. This is particularly true in children. It has been shown that the two periods when pollution exposure is the most critical to the health status of an individual at later stages in life are during gestation and during the first few years after birth. As the WHO recommends, member countries should “enable health systems, including health protection authorities, to take a leading role in raising awareness in the public and among all stakeholders of the impacts of air pollution on health and of opportunities to reduce or avoid exposure” (WHO, 2015). Health systems have a key role in monitoring and responding to air pollution health risks and should raise their voice. The Lagos State Ministry of Health (LMoH), together with other relevant health institutions, should recognize the growing danger of ambient air pollution and engage health care personnel (doctors and nurses), the civil society, and the public to take bold, evidencebased actions to stop pollution at source as a key public health measure for health prevention. It is essential to build multisectoral partnerships. Pollution prevention strategies that hold great promise include a transition to less-polluting renewable energy sources, a reduction in the reliance on fossil fuels, promotion of less-polluting public transport, proper management of wastes, and incorporation of pollution prevention into all forward planning. HIA is a critical element of air quality assessment, management, and planning. The institutional and policy framework for HIA and health monitoring of air pollution needs to be improved. HIA provides a basis to draw policy recommendations that should be considered in the AQM plan for Lagos. The Lagos health sector should be informed about the results of air pollution monitoring and should be able to perform the necessary HIA to quantify the health benefits of changes in air pollution levels. Appropriate education of technical personnel should be undertaken to equip them with the necessary professional skills, and the health information system should be upgraded. Promote research and build research capacity on air pollution, human health, and the economy in Lagos/ Nigeria research institutions. Support for research will build long-term, local scientific and technical capacity and strengthen the national economy. The creation of a research infrastructure is an investment in the future, and it will be of particular value to start epidemiologic studies and derive ERFs for the Lagos/Nigeria population. * * * Federal Ministry of Environment. 2016. “National Policy # REFERENCES on the Environment.” Olawuyi, D. S., and Z. Tubodenyefa. 2018. “Review of Center for Science and Environment. 2013. “Stake- the Environmental Guidelines and Standards for holder Workshop on Air Quality and Transporta- the Petroleum Industry in Nigeria (EGASPIN).” tion Challenges in Nigeria and Agenda for Clean [https://www.iucn.org/sites/dev/files/content](https://www.iucn.org/sites/dev/files/content) Air Action Plan.” [https://www.cseindia.org/stake](https://www.cseindia.org/stake) /documents/2019/review\_of\_the\_environmental holder-workshop-on-air-quality-and-transportation \_guidelines\_and\_standards\_for\_the\_petroleum -challenges-in-nigeria-and-agenda-for-clean-air \_industry\_in\_nigeria.pdf. -action-plan-6130. Stakeholder Democracy Network (SDN). 2022. “Dirty Croitoru, L., J. C. Chang, and A. Kelly. 2020. “The Cost fuel imports continue to pose a serious health risk of Air Pollution in Lagos.” Washington, DC: World in Nigeria.” [https://www.stakeholderdemocracy.org](https://www.stakeholderdemocracy.org/) Bank. [https://openknowledge.worldbank.org/handle](https://openknowledge.worldbank.org/handle) /dirty-fuel. /10986/33038. Suleiman, R. M., M. O. Raimi, and H. O. Sawyerr. Ehinowen, C., and A. Adeleke. 2012. “An Assessment of 2017. “A Deep Dive into the Review of National the Distribution of Petroleum Products in Nigeria.” E3 Environmental Standards and Regulations _Journal of Business Management Economics 3(6): 234–241._ Enforcement Agency (NESREA Act).” Fagbeja, M. A., J. L. Hill, T. L. Chatterton, J. W. S. Long- Ukeh, Felix. 2021. “Stakeholders and Institutional hurst, J. E. Akpokodje, G. I. Agbaje, and S. A. Halilu, Assessment Study, Air Quality Management Program 2018. Challenges and opportunities in the design in Lagos, Nigeria.” and construction of a GIS-based emissions inventory UNEP (UN Environment Programme). “West African infrastructure for the Niger Delta region of Nigeria. Ministers Adopt Cleaner Fuels and Vehicle Environmental Science and Pollution Research Standards.” [https://www.unep.org/news-and](https://www.unep.org/news-and) 24(8): 7788–7808. -stories/story/west-african-ministers-adopt-cleaner Federal Government of Nigeria. 2018. “Nigeria’s -fuels-and-vehicles-standards. National Action Plan (NAP) to reduce Short-Lived World Health Assembly, 68. (2015). Health and the Climate Pollutants.” environment: addressing the health impact of air Federal Government of Nigeria. 2021. “Nigeria’s Nationally pollution. World Health Organization. [https://apps](https://apps/) Determined Contribution.” [https://www4.unfccc.int](https://www4.unfccc.int/) .who.int/iris/handle/10665/253237. /sites/ndcstaging/PublishedDocuments/Nigeria%20 First/NDC%20INTERIM%20REPORT%20SUB MISSION%20-%20NIGERIA.pdf. Air Quality Management Planning for Lagos State * * * * * * ### CHAPTER 6 # RECOMMENDED AIR QUALITY MANAGEMENT STRATEGY FOR LAGOS STATE ## 6.1. INSTITUTIONAL DEVELOPMENT The State of Lagos, and indeed Nigeria, needs a new policy vision for AQM that is supported by regulatory changes. The regulatory changes discussed in section 5.5 will provide a more stable basis for the implementation of Lagos State’s and Nige- ria’s new policy on air quality. The following seven recommendations, however, can be worked in parallel to the proposed regulatory modifications. LASEPA to undertake a holistic assessment of Lagos State’s AQM challenges and opportunities as identified by the PMEH program and develop a policy strategy to engage stakeholders drawn from the relevant MDAs, private sector, academia, and the civil society. The main purpose of this engagement is to chart the State Air Quality Strategy with air pollution reduction goals, specifically for the pollutants of concern. The strategy should also have an implementation plan with specific cross- sectoral actions, responsible actors, budget allocations, and a monitoring plan. The strategy will develop a principal framework for the state and local government efforts to protect air quality across the state, giving considerations to the existing national laws and regulations guiding air quality. It will focus on enhancing the capacity to respond to criteria and climate change air pollutants with adequate science-based infrastructure, including research and development initiatives and meteorological observation technologies. The strategy will also focus on strengthening the collection and reporting of data by the relevant public and private institutions, which will be useful for estimating and inventorying emissions of air pollutants and GHGs. The policy should have a broad communication strategy and its level of implementation be periodically reported to the State Executive Council. Ensure that the Lagos State air quality institutions work collaboratively with federal air quality institutions. The State Air Quality Strategy should devise mechanisms to Air Quality Management Planning for Lagos State promote collaboration between the state and federal institutions to achieve their respective mandates with limited overlap and duplication of efforts. LASEPA should establish clear, measurable objectives with longterm roadmaps, including mechanisms for information dissemination, data sharing between agencies, and periodic assessment of health and economic impacts. The process should consolidate and streamline air quality regulatory, monitoring, and enforcement functions of various state agencies to minimize duplication and overlap and ensure better use of public resources, minimize burden on regulated entities, and maximize effectiveness. The State Air Quality Plan should also leverage Nigeria’s signatory status to multilateral agreements for environmental protection and pollution control—the 35 Vienna Convention (1987), the Montreal Protocol 36 37 (1988), and the Stockholm Convention (2003) —to explore the co-benefits of air quality and GHG emissions monitoring to support Nigeria’s reporting on the NDC. Nigeria’s updated NDC should cover short-lived pollutants including black carbon, an air pollutant with high morbidity and premature mortality incidence (Federal Government of Nigeria 2021). Work toward establishing internationally standardized air quality research facilities in LASEPA and other state-owned educational institutions to develop an air pollutant database across sectors. The institutions should partner with NESREA to delineate the air quality regions (airsheds) into which the state falls within Nigeria for enhancing cross-boundary collaboration with relevant states to improve air quality planning within Lagos. The air pollutant database developed by the institutions should support setting realistic targets for the state’s air pollution reduction strategies and provide support for setting national air pollution reduction targets. An SIP designed by LASEPA should encourage the establishment of policies, regulations, standards, research, technologies, and so on. capacities, identifying the state, location, and integrity of air monitoring equipment, laboratories, and data. Based on results, design and fund a plan to establish an air quality information system based on a consolidated air monitoring network, capable of reporting real-time data and responsive to state and federal assessment and monitoring criteria. The Governments of Lagos State and Nigeria should join efforts to build partnerships with national and international research institutions to plant the seed for the future development of air quality forecast systems. Lagos State should work on the following priority areas to enhance its AQM information system: conduct long-term monitoring of pollutants, including PM2.5, in several representative locations, centralize city health data, implement an emissions inventory of air pollutants, and conduct refined source apportionment studies. \\mathrm{M}\_{2.5} Work with civil society organizations (CSOs) and the media. By implementing training workshops for journalists and public sensitization campaigns, the Lagos State Ministry of Environment can collaborate to increase the general public’s knowledge about air pollution and its health effects. Working with CSOs to increase the citizenry’s awareness of its rights to clean air, and the existing mechanisms to sanction violations, will improve citizens’ accountability and engagement. Strengthen the financial capabilities of LASEPA by enhancing funding through line charges, taxes, and levies, in addition to statutory budgetary allocations. This will position LASEPA to acquire the necessary equipment and build human and infrastructure capacity to implement the State Air Quality Strategy without recourse to support from polluters. This will also enhance LASEPA’s capacity to effectively enforce established regulations. * * * 6.2. PUBLIC INVOLVEMENT— AQI The development of an Air Quality Index provides a platform for public awareness and information dissemination that essentially ensures that the public understands the level of air quality within their vicinity and participates in protecting public health. An AQI unifies the complicated science of pollution composition, exposure rates-based health severity, ambient standards, measurement and standard protocols and breaks it down into simple, color-coded bins that give people an instant visual grasp of pollution levels in their surroundings, enabling them to develop the necessary alertness. While the methods to monitor air pollution and estimate its health impacts are becoming standardized across the globe, this is not the case for methods used for calculating an AQI and the AQI nomenclature. These methods, and the degree of alertness disseminated by health alert systems, vary depending on different countries’ interpretation of thresholds for regulatory purposes and background conditions. FIGURE 6.1. COMPARISONS OF THE VARIATIONS IN BREAKPOINTS AND INDEX NOMENCLATURE ACROSS SPECIFIC COUNTRIES Break points and index nomenclatures vary with country. * * * FIGURE 6.2. SEASONAL CYCLE OF PM2.5 MONITORED FROM SIX STATIONS IN LAGOS, AUGUST 2020 TO JULY 2021 \\mathsf{P}\\mathsf{M}\_{2.5} Based on a review of methodologies from seven countries—the US, the EU, the UK, India, China, Republic of Korea, and Singapore— to develop an AQI centered on air quality breakpoints and timescale variations, comparative AQIs for Lagos State have been developed. The AQI for Lagos used the 12-month air quality monitoring data from the six monitoring stations in the state. The process relies on the average seasonal and diurnal cycles of the concentrations of PM2.5, PM10, and the other pollutants monitored from the sites (Figure 2). The data were available at 5-minute intervals for 1 year, spanning August 2020 to July 2021. A summary of monthly PM2.5 concentrations is presented in Figure 2. Wintertime highs and rainy season lows are immediately evident in the data, with highs around 120 µg/m3 and lows under 20 mg/m3. The presence of higher commercial and industrial activity in the Abesan area is represented in its higher averages, compared to the other five stations. \\mathrm{P M} _{2.5},\\mathrm{P M}_{10}, Figure 6.3 shows a Microsoft Excel-based AQI calculator developed to explore the methodologies and their interpretations for an application using the ambient-monitoring data from the Lagos network. \\mathrm{M}\_{2.5} 20,\\mathrm{m g}/\\mathrm{m}^{3}. 120,\\upmu\\mathrm{g/}m^{3} respective country specifications. The general understanding is that GREEN refers to good air quality and BROWN and PURPLE indicate severe air quality. The most used/adapted methodology in the world is from the US. According to this methodology, taking PM2.5 as the limiting pollutant, between August 2020 and July 2021, the City of Lagos experienced only 2 percent of days in category GOOD, 29 percent of days in category MODERATE, 45 percent of days in category UNHEALTHY FOR SENSITIVE PEOPLE, 23  percent of days in category UNHEALTHY, and 2 percent of days in category VERY UNHEALTHY. There were no SEVERE alert days. \ mathrm\\\ {M}\_{2.5} There is no evidence that the current national air quality standards operational in Lagos State, and in Nigeria, have been developed based on extensive monitoring, emissions inventory development, and modeling. Consequently, in developing the methodology for an AQI in Lagos, there has to be a scientific basis to establish new standards. This will establish clearly defined breakpoints and an AQI range for each pollutant, centered on evidence-based health and economic impacts of local air quality. The recommendation is for LASEPA to build on the outcomes of the PMEH study and ensure expanded, continuous air-quality monitoring across Lagos State. * * * FIGURE 6.3. AQI CALCULATOR PAGE | PM2.5 | | | | | | | | --- | --- | --- | --- | --- | --- | --- | | % points in each bin | | | | | | | | USA | EU | UK | India | China | S.Korea | Singapore | | 2% | 1% | 1% | 21% | 30% | 0% | 2% | | 29% | 6% | 9% | 63% | 63% | 0% | 73% | | 45% | 5% | 20% | 11% | 4% | 0% | 23% | | 23% | 54% | 15% | 3% | 1% | 0% | 2% | | 2% | 27% | 14% | 2% | 2% | 0% | 0% | | 0% | 7% | 13% | 0% | 0% | 0% | 0% | | | | 10% | | | | | | | | 7% | | | | | | | | 4% | | | | | | | | 8% | | | | | \\mathbf{P M}\_{2.5} * * * TABLE 6.1. (Continued) | PM$\_{10}$ | | | | | | | | --- | --- | --- | --- | --- | --- | --- | | % points in each bin | | | | | | | | USA | EU | UK | India | China | S.Korea | Singapore | | 10% | 1% | 0% | 9% | 9% | 2% | 9% | | 80% | 4% | 2% | 40% | 80% | 29% | 80% | | 7% | 4% | 6% | 49% | 10% | 39% | 10% | | 1% | 40% | 4% | 1% | 1% | 25% | 1% | | 1% | 39% | 5% | 1% | 1% | 3% | 1% | | 1% | 12% | 7% | 1% | 0% | 2% | 0% | | | | 7% | | | | | | | 9% | | | | | | | | 7% | | | | | | | | 51% | | | | | | FIGURE 6.4. RECOMMENDED AQM ACTIONS FOR LAGOS 10,\\upmu\\mathrm{g}/\\mathrm{m}^{3} 6.3. RECOMMENDED AQM ACTIONS Given the large health impacts associated with air pollution that have been estimated for Lagos, immediate action is needed. An initial goal would be to lower ambient PM2.5 air pollution by 10 µg/m3, which could reduce \\mathrm{P M}\_{2.5} annual premature mortality by 3,598–6,840 deaths, over half of which are infant deaths. Such premature mortality is valued at US$235–1,691 million or between 0.33–2.35 percent of Lagos’ GDP. * * * Within 1 year Air quality monitoring 1. Resume air quality monitoring at the six sites for which a monitoring record already exists, and begin planning an expanded network. 2. Train and equip LASEPA staff to carry out emission 3. Train and equip LASEPA staff to carry out emission measurements on industrial sources, and begin such testing with the largest and worst emitters. Health 3. Start education, training, and lifelong learning of health personnel on the health effects of air pollution. Regulation and enforcement Solid waste management Solid waste management 4. Redouble efforts to collect and dispose of solid waste by landfill, recycling, composting, and/or incineration with emission controls, and enforce prohibitions on open burning of waste and biomass. 5. Locate and shut down any lead smelting or battery recycling operations in Ikorodu, measure lead levels in soil and in the blood of the affected population, and take remedial action as necessary. 6. Implement ECOWAS Directive C/Dir.1/09/20, limiting sulfur in gasoline and diesel fuel to 50 ppm by weight; enforce this by collecting and analyzing fuel samples at the ports and at retail stations, with fines or the loss of retail licenses for noncompliance. Implement ECOWAS Directive C/Dir.1/09/20, limiting sulfur in gasoline and diesel fuel to 50 ppm by weight; enforce this by collecting and analyzing fuel samples at the ports and at retail stations, with fines or the loss of retail licenses for Energy Transport 7. Begin implementation of ECOWAS Directive C/ Dir.2/09/20 by notifying vehicle importers and implementing inspections and testing to confirm that newly imported light-duty vehicle (whether new or used) meet Euro 4 emission standards and Euro 6 standards for heavy-duty vehicles. Begin implementation of ECOWAS Directive C/ Dir.2/09/20 by notifying vehicle importers and implementing inspections and testing to confirm that newly imported light-duty vehicle (whether new or used) meet Euro 4 emission standards and Euro 6 standards for heavy-duty vehicles. 8. Set and enforce emission standards for backup generators. Air quality financing 9. Consider allocating a percentage of existing or new emission fees or other charges as line charges for LASEPA to sustainably support increased staffing and equipment. 10. Consider multilateral financing and/or an air quality 11. Consider multilateral financing and/or an air quality green bond to support needed investments in emission controls, air quality monitoring infrastructure, emissions measurement capabilities, and capacity building for air quality enforcement and management. Within 3 years Air quality monitoring 11. Establish 8 to 12 additional air quality monitoring sites, including upwind and downwind locations as well as sites influenced by the ports, traffic, and industrial areas, to better monitor population-based exposure and to strengthen the basis for air quality modeling. 12. Strengthen the scientific basis for AQM by continuing 13. Strengthen the scientific basis for AQM by continuing to develop the emissions inventory, strengthening oversight of the emissions auditing process, and strengthening the reporting of health and economic statistics. Regulation and enforcement Transport 13. Strengthen the scientific basis for health impact assessment, expand the system of health information collection, and initiate epidemiological research on air pollution. 14. Engage public opinion by adopting an AQI and rou- 15. Strengthen the existing vehicle inspection and maintenance system to enforce the requirement of Health Transport * * * ECOWAS Directive C/Dir.2/09/20 that vehicles in circulation meet Euro 4 emission standards from January 2025. 16\. Replace the existing danfo (microbus) fleet with larger 16. Replace the existing danfo (microbus) fleet with larger minibuses, preferably plug-in hybrid-electric vehicles with advanced emission control, and restructure the routes to coordinate with the BRT. By charging from the power grid when it is available and from their onboard engine when not, plug-in hybrids could provide reliable service in the near term while retaining the ability to switch to all-electric operation in the future. 17. Consider measures to phase out engine-driven taxi- Energy 17. Consider measures to phase out engine-driven taxicabs, okada motorcycle taxis, and keke NAPEP tricycle taxis in favor of BEVs. 18. Increase the capacity and reliability of the electricgenerating system to reduce the need for backup generators, and consider retrofitting the Egbin power plant for combined cycle operation with low-NOx gas turbines. 19. Consider grouping small power users into “mini 20. Consider grouping small power users into “mini grids” of a few hundred kilowatts incorporating solar photovoltaic panels and diesel-generating sets with advanced emission controls. * * * ### ANNEX 1 # ESTIMATING THE HEALTH AND MORTALITY EFFECTS OF AIR # POLLUTION IN LAGOS ## KEY MESSAGES » Current levels of PM2.5 ambient air concentration (47 μg/m3 weighted by pop- ulation) pose a serious, but preventable, public health hazard, especially in chil- dren under 5 years. » The morbidity burden is especially high in children, including 180,000 to 350,000 ALRI (primarily cases of pneumonia) and infant mortality (8,000 to 15,000 deaths, or one-half of the total mortality burden). » Lead exposure contributes to a high loss of IQ in young children (especially those younger than 6 years), with a mean loss of 6.2 IQ points per child, and up to 1 million IQ points lost at the population level. » Current PM2.5 pollution is responsible for 16,000 to 30,000 premature deaths annually, or 18 percent of all natural deaths, in Lagos State. An additional 250 to 500 deaths are attributable to PM10 exposure during the Harmattan season and 300 to 400 excess cardiovascular deaths in adults from exposure to lead. » Reducing the PM2.5 concentration to the WHO-recommended IT 1 (35 μg/m3) would reduce premature mortality by 28 percent (4,300 deaths among infants and adults), and additionally prevent 64,000 lower respiratory infections in children under 5 years. » Additional efforts to collect baseline health data, including mortality statistics and data on hospital admissions, are necessary to improve the HIA. Air Quality Management Planning for Lagos State * * * A1.1. INTRODUCTION Air pollution, in particular particulate matter (PM2.5 and PM10), is the leading environmental risk factor worldwide. Globally, among 20 major risk factors evaluated in the GBD study, ambient and household air pollution together currently rank 4th for attributable disease and mortality—after hypertension, smoking, and dietary factors (GBD 2020). The estimates indicate 1 that around 7 million deaths, mainly from NCDs, are attributable to the joint effects of ambient and household air pollution, with the greatest attributable disease burden seen in LMICs (89 percent of the global total, with low-income and lower-middle-income countries alone contributing around 40 percent of the total impact). Higher estimates than these have been published (Burnett et al. 2018). A recent report indicated 10.2 million premature deaths from fossil fuels use (Vohra et al. 2021). Regions with large anthropogenic contributions had the highest attributable deaths, suggesting substantial health benefits from replacing traditional, fossil fuel-based energy sources as well as taking actions on the other different anthropogenic sources such as industry, transport, and agriculture practices (McDuffie et al. 2021). \ mathrm(\\mathrm{P M}\_{2.5} \\mathrm{P M}\_{10}) \\mathrm{P M}\_{2.5} PM2.5 mass has been generally used as the index pollutant for quantifying the impact of outdoor air pollution. First, previous studies have demonstrated that mortality from long-term exposure to PM2.5 dominates the overall \\mathbf{P M}\_{2.5} \\mathrm{M}\_{2.5} \\mathrm{P M\_{0\ 5}} \\mathrm{P M}\_{2.5} impact of air pollution. Second, there is a vast set of published studies from around the world linking PM2.5 to mortality in humans (Chen and Hoek 2020). Third, the PM2.5 effects observed in epidemiologic studies are supported by toxicological and human clinical studies (US EPA 2019). Fourth, concentrations of PM2.5 can be obtained from monitors, chemical transport models, and/or satellite data. Finally, PM2.5 is ubiquitous and is generated from many different sources in Lagos, including fuel combustion from mobile sources (cars, buses, trucks, and motorcycles) and stationary sources (for example, power plants, port emissions, diesel- or gasoline-powered electrical generators, industrial boilers, and factories), biomass burning, cooking, waste combustion, and road dust. This set of factors sets PM2.5 apart from all other air pollutants. \ mathrm\ M\_{2.5} \\mathrm{P M}\_{2.5} \\mathrm{P M}\_{2.5} The approaches and the methods of HIA (health impact assessment) of air pollution are well documented. A publication from WHO (WHO Regional Office for Europe 2016) provides the basic concepts and general principles of air pollution health risk assessment for various scenarios and purposes. In fact, both estimation of the burden of diseases attributable to air pollution, and evaluation of policy scenarios and CBAs, are possible. The present report illustrates the methods and input data for the HIA of particulate-matter air pollution in Lagos, Nigeria, as of 2020–2021. A1.2. DEFINITION AND APPLICATIONS OF HIA OF AIR POLLUTION 1. Estimate the exposure of the population under consideration to specific air pollutants. Ground-level monitoring data, together with air quality modeling and satellite data, are currently used to evaluate current (or past) exposure or to predict levels in future scenarios, provided that future emission inventories are available. 2. Select the counterfactual (or cut-off value) of the spe- 3. Select the counterfactual (or cut-off value) of the specific pollutant above which the estimate of the health impact is actually performed. 4. Assess the health impact associated with the estimated 5. Assess the health impact associated with the estimated exposure to air pollution in the specific population. Both the appropriate exposure-response functions (ERFs) from epidemiological studies and the baseline local health statistics are required. The results are reported as numbers of premature deaths, cases of disease, years of life lost, disability-adjusted life years, or change in life expectancy attributable to exposure, or a change in exposure to air pollution. 6. Finally, a critical evaluation of the uncertainties and 7. Finally, a critical evaluation of the uncertainties and potential errors involved in the calculation is an essential step of the assessment, which is also carried out through sensitivity analyses. There are historical landmarks in risk assessment of air pollution. In 1998, Ostro and Chestnut were the first to propose a methodology to quantify the health benefits of potential nationwide reductions in ambient PM10 in the US (Ostro and Chestnut 1998). Kunzli et al. (2000) evaluated the impact of outdoor and traffic-related air pollution on public health in Austria, France, and Switzerland. In the same period, two WHO documents (WHO 2000, 2001) provided guidance on several aspects related to air pollution HIAs. \ mathrm P M M\_{10} \ mathbf P M M\_{2.} 7.5,\\up{\ mathrm mu g}/\\mathrm{m}^{3} of PM2.5 and PM10 were estimated, and the two health outcomes for adults were mortality from cardiopulmonary disease and mortality from lung cancer, using risk coefficients from the large American Cancer Society cohort study of adults in the US (Pope et al. 2002). Cohen and colleagues assumed that the risk of death increased linearly over a range of annual average concentrations of PM2.5, between a counterfactual concentration of 7.5 μg/m3 and a maximum of 30 μg/m3, the highest observed concentration at the time of any cohort study of PM2.5, with no additional increase in the health risk assumed for concentrations beyond 30 μg/m3. Sensitivity analyses were 30~\\up{upmu}\\mathrm{g m}^{3} \\mathrm{M}}{\_{2.5}\ \\mathrm{P M}\_{10} FIGURE A1.1. ERFS OF THE GBD 2000 STUDY Source: Cohen et al. 2004. conducted assuming a linear association from the same counterfactual to 50 μg/m3, with no additional risk change above this value. A risk model based on the logarithm of concentration was also considered. These risk associations are depicted in Figure A1.1. A1.3. AVAILABLE ERF MODELS 50,\\upmu\\mathrm{g}/\\mathrm{m}^{3} A1.3.1. LINEAR (LOG-LINEAR) ERFS \\mathrm{C I}=1.040,,1.083\\rangle In 2013, the WHO Regional Office for Europe coordinated two projects (REVIHAAP – Review of evidence on health aspects of air pollution, and HRAPIE – Health risks of air pollution in Europe) to provide the European Commission and its stakeholders with evidence-based advice on the adverse effects of ambient air pollution. In particular, the documents provide the health outcomes and ERFs that could be used for risk assessment of short- and long-term exposure on morbidity and mortality in the European context (WHO 2013a, 2013b). * * * per increment of 10 µg/m3, was recommended. The recommended risk coefficient was based on a metaanalysis of all cohort studies published before January 2013 by Hoek et al. (2013) and included 11 different studies conducted in adult populations of North America and Europe. The review conducted by Hoek et al. (2013) also provided meta-analyses for cardiovascular mortality with a stronger and statistically significant effect (RR of 1.11, 95 percent CI = 1.05, 1.16 per 10 µg/m3, based on 11 studies). The effect of PM2.5 on respiratory mortality (excluding mortality from lung cancer) was weaker and with a large uncertainty (RR of 1.029, 95 percent CI = 0.94, 1.126 per 10 µg/m3, based on six studies). 10~\\upmu\\mathrm{g/m^{3}}}\\end{array, 10~\\up{\ up mu\ }mathrm{g/m/^33}} \\mathrm{M}\_{2.5} 10,\\upmu\\mathrm{g}/\\mathrm{m}^{3} Following the review by Hoek et al. (2013), several additional cohort studies have been published on PM2.5 (or PM10) all-cause or cause-specific mortality. In particular, in the most recent update of the WHO Air Quality Guidelines (WHO 2021), three relevant systematic reviews on short- and long-term exposure to air pollutants and mortality have been conducted (Chen and Hoek 2020 on long-term effects of PM; Huangfu and Atkinson (2020) on long-term effects of NO2; and Orellano et al. (2020) on short-term effects of several pollutants). \\mathrm{M}\_{2.5} \\mathrm{P M}\_{2.5} \\mathrm{P M}\_{10}) \\mathrm{N O}\_{2}\\cdot Below is a short list of the strengths and limitations of the application of a linear (or log-linear) function to estimate the all-cause mortality attributable to air pollution. » Applicability, because mortality statistics on all-cause mortality are generally available worldwide with a greater accuracy than cause-specific mortality. » Effect estimates are robust as they are based on STRENGTHS 30{-40};\\up{\\mathrm{{g}}/\\mathrm{{{m}}^{3}}} » Effect estimates are robust as they are based on several studies. » Effects estimates are all based on studies involving \\mathrm{M}\_{2.5} Health Impact = Exposed population × Background rate of mortality or morbidity × Concentration- Response function,CRF × Change in pollution » The mathematical modeling is relatively simple: » All-cause or natural-cause mortality is influenced by other conditions than chronic diseases, and the percentage of NCDs varies across countries. » The application is difficult outside the exposure LIMITATIONS recommended risk coefficient was based on a meta- » The application is difficult outside the exposure ranges of the original studies; in particular, the use of the log-linear model poses a problem for assessments in any place with high levels of outdoor PM2.5. Extrapolating log-linear model coefficients derived from studies in low-exposure, high-income countries to much greater levels of outdoor PM2.5 results in implausibly large estimates of relative risk and attributable deaths in LMICs. A1.3.2. INTEGRATED EXPOSURE RESPONSE (IER) FUNCTIONS OF THE GBD {\\mathrm{P M}}\_{2.5}. Pope et al. (2009) assessed the shape of the exposureresponse relationship between cardiovascular mortality and fine particulates from cigarette smoke and ambient air pollution in the American Cancer Society cohort. They found that there were substantially increased cardiovascular mortality risks at low levels of active cigarette smoking, and smaller but nevertheless significant excess risks even at the much lower exposure levels associated with secondhand cigarette smoke and ambient air pollution. Based on these findings, Burnett et al. (2014) suggested a more complex shape to describe the association between PM2.5 concentrations and mortality, with no association below some concentration (counterfactual), a near-linear association for low to moderate concentrations, and a diminishing change in risk as concentration increases over the global range of PM2.5. Burnett et al. incorporated information on risk from other sources of PM2.5 such as secondhand and active smoking and exposure to indoor sources of PM2.5 from the burning of biomass for cooking and heating (Pope et al. 2009). Concentrations from these sources are much larger than those observed in cohort studies of ambient air pollution that have been conducted largely in North America and Western Europe (Hoek et al. 2013). The Burnett et al. (2014) approach provided a method to estimate risk over the global range of ambient concentrations. \\mathrm{M}\_{2.5} * * * The GBD project has included ambient air pollution in its evaluation since its 2010 release. For the project, IER functions for fine particulate matter were derived from the pivotal study of Burnett et al. (2014) that considered evidence from different combustion sources. To apply the GBD framework, IER functions were developed that estimate the impact of ambient fine particulate matter on mortality and morbidity within selected disease categories (including cardiovascular and respiratory mortality and lung cancer) prespecified as part of the overall GBD comparative risk assessment project. The GBD project has released several updates since 2010\. The underlying assumptions and the methodology are described in a paper by Burnett et al. (2014) and have been applied in subsequent years by the GBD collaborators. The last report in the GBD series was published in 2020 (GBD 2020). \\mathrm{P M}\_{2.5} Since its introduction, the IERs have been accepted as the state-of-the art model, now used by various organizations, including WHO, to estimate the burden of disease and examine strategies to improve air quality at global, national, and subnational scales for outdoor-air, fine-particulate pollution and household pollution from the use of solid fuels for heating and cooking. The estimates of the IERs continue to evolve, changing with the incorporation of new data and fitting methods. Due to recent studies providing estimates of high levels of fine particulate pollution in China, new estimators based solely on outdoor, fine-particulate air pollution evidence have been proposed which require fewer assumptions than the IER, and yield larger relative risk estimates (Burnett and Cohen 2020; Burnett et al. 2018). The most recent innovation in the GBD approach was the introduction of a new model known as the GEMM (Burnett et al. 2018), based on 41 cohort studies of exposure to only ambient air PM2.5 concentrations in populations predominantly in Europe and North America, but also in Asia. The approach has more flexible parameters A1.3.3. GLOBAL EXPOSURE MORTALITY MODEL (GEMM) \\mathrm{P M}\_{2.5} such that the change in relative risk at higher concentrations declines as concentration increases, thus limiting the magnitude of the relative risk for the most polluted parts of the world where few studies have been conducted. The attributable number of deaths due to PM2.5 exposure worldwide was about twice that predicted by the IER, in part because the GEMM considers natural causes of mortality, specifically, NCDs plus adult lower respiratory infections, and in part because the IER incorporates additional types of exposure, such as active smoking, that have lower relative risks per unit PM2.5 than ambient air pollution (Burnett and Cohen 2020). \\mathrm{P M}\_{2.5} In summary, there are three types of relative risk models proposed for assessing the population mortality burden due to outdoor PM2.5 exposure: linear (or log-linear), the IER approach in GBD, and the GEMM approach. Each of these model specifications has strengths and limitations that have implications depending on the specific analytic objectives and the study area. The work by Burnett and Cohen (2020) provides an illustration of the differences among these models for areas that are at lower outdoor concentrations, and over the global range. \ \ \\{\\mathrm{P M}\_{2.5}} A1.4. METHODS AND INPUT DATA FOR THE HIA IN LAGOS \ mathrm M M\_{10}, In estimating the burden of disease, it is desirable to assess the current exposure of the population to an index pollutant, traditionally PM2.5 and PM10, based on either ground-level monitors, remote-sensing satellites, landuse regression models, chemical transport models, or some combination of the above. Ideally, these concentrations are based on several recent years of complete data (to reduce the influence of an atypical year or season) from monitors that are reasonably representative of local Figure A1.2 illustrates the main steps for calculating the burden of mortality and morbidity in Lagos. A1.4.1. AIR POLLUTION DATA * * * FIGURE A1.2. SCHEMATIC PRESENTATION OF THE MAIN STEPS OF THE HIA aIf modelled data are used, the approach can be used to assess the impact of emission reduction strategies on different health outcomes. population exposure. At a minimum, 1 year of data are necessary for the HIA to make sure that seasonal patterns are incorporated into the annual average (this is the case for the Lagos study). The monitors should not be unduly influenced by local sources such as a nearby highway, factory, or power plant but should rather reflect average exposures over a wide impact area. Typically, ground-based, population-oriented monitors have been averaged across a metropolitan area to characterize air quality in epidemiological studies. These concentrations are then combined with population data to obtain PWEs. We have used the 1-year concentration data of PM2.5 and PM10 measured during the period of the project. The continuous and filter-based monitoring of air pollutants was limited to six sites located across the City of Lagos between August 2020 and July 2021, giving one full year of monitored data. The annual data from the six monitors were used to assign an annual exposure value for the population of each LGA in Lagos State where the monitor was located. For the LGAs without monitors, we have used the results of a dispersion model covering five distinct episodes distributed throughout the monitoring period to derive adjustment factors between the LGAs served with the monitors and those not served with the monitors. For this exercise, data from the recent emission inventory were used as \\mathrm{P M}\_{2.5} \ mathrm P M M\_{10} \\mathrm{P M}\_{2.5} \\mathrm{P M}\_{10} inputs to the dispersion analysis. We first estimated a provisional PWE (population-weighted exposure) for each LGA (Lagos government area) using the results of the dispersion analysis coupled with a high-resolution map of the population density distribution within each LGA to calculate an accurate representation of the LGA-specific PWE. We then derived the PWE for the entire Lagos State, weighing each LGA by its population size. Average annual exposure for Lagos State and each LGA was used in the assessment. This calculation was repeated for both PM2.5 and PM10 exposures, with the latter index being more appropriate to estimate the impact of the Harmattan season. The adjustment factor and LGA PWE estimates were calculated using the following equations: \\begin{array}{r l r}{{lef G A4;d d j u s t m e n t;f a c t o r\ =}}&{{\\frac{P o x;L G;i m e r e t t}{P W x;d i g p e r s i n;m e s t t}}}\ {{}}&{{P W x;d i g p e t m o n;e e s u t r}}\ {{}}&{{}&&{{}{f o r;L G d;w i i l h;;o n i t o r}}}\\end{array} \\begin{array}{c c}{{\\mathcal o n o n.}}&{{l o o s t}}\ {{o n i n o t e r}}&{{=}}\ {{L G2e x p o s e e}}\\end{array}\\begin}{array c{}{{o n o i o o o i g}}\ {{s o a i o o n}o o}\ {{G4\ x x p p o s e}}\\end{array}\\begin{array}{c}{{\ \\begin\\array}array{c{}{{{m o o i t o n n}g}}\ {{s t a t i o n\ o o\ L G4}}\ {{f i n i t r e s t}}\\end{array}}}times\\end{array}\\begin{array}{c}{{\ \\begin{array}{c}{{{d}j i u s t o m e n}}\ {{{}it d i u s h e n t}}\\end{array}}}\ {{\ \ \ \ \ }}\ {{\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ {\ {d d d u s t e n n}}}\ {{\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ * * * In a sensitivity analysis, we used the simplest solution, that is, to assign to the LGA without a monitor the average concentration of the closest LGA (see results in the Supplementary Material). A1.4.2. POPULATION DATA As illustrated in Figure A1.2, the HIA requires input data on demographics and baseline rates for mortality and morbidity. Table A1.1 shows two alternative population TABLE A1.1. ESTIMATES OF LAGOS STATE POPULATION BY AGE GROUP, 2018 | Age(years) | Base case | Sensitivity | | --- | --- | --- | | 0-4 | 1,591,424 | 3,065,496 | | 5-9 | 1,437,766 | 2,769,515 | | 10-14 | 1,292,164 | 2,489,349 | | 15-19 | 1,269,198 | 2,445,056 | | 20-24 | 1,492,935 | 2,875,657 | | 25-29 | 1,529,662 | 2,946,225 | | 30-34 | 1,233,580 | 2,375,555 | | 35-39 | 1,049,921 | 2,021,579 | | 40-44 | 761,198 | 1,465,616 | | 45-49 | 543,296 | 1,045,888 | | 50-54 | 386,546 | 744,286 | | 55-59 | 232,479 | 447,515 | | 60-64 | 168,103 | 323,732 | | 65-69 | 99,570 | 191,740 | | 70-74 | 78,318 | 150,863 | | 75-79 | 42,736 | 82,335 | | 80-84 | 42,969 | 82,824 | | 85+ | 47,982 | 92,471 | | Total | 13,299,845 | 25,615,703 | Source: Author’s own elaboration. compositions by quinquennial age group for Lagos State in 2018, while Table A1.2 presents the population distribution by LGA. Figure A1.3 shows a map of LGA districts. Figure A1.4 depicts the two population distributions and their temporal evolution between 2006, the year of the last national census, and 2018. For the base case, the total population is projected out to 2018, assuming a 3.2  percent mean annual growth (LBS 2019). For each age group, a differential growth is applied. This age-adjusted rate is computed from the national level all-age population growth using the following expression: All-age population growth × μ, where μ is a multiplier equal to the growth in a particular age group, divided by the allage population growth in Nigeria (Figure A1.5). As an example, for ages 0–4 years, the multiplier is 2.3 percent/ 2.7 percent = 0.85; for ages 75–79 years, the multiplier is 3 percent/2.7 percent = 1.11, and so on. The sensitivity case represents the population distribution in 2018 using the approach in LBS (2019)—multiplying each age group in the 2006 census by 1.926 and then adjusting for the annual population growth since 2006. The base case population is about half as large as that of the sensitivity case. Figure A1.4 also shows the LBS 2018 projected population (dotted red line). Compared to the sensitivity case (dash gray curve), the Lagos Bureau of Statistics (LBS) curve is shifted to younger ages. \\texttt{X}\\upmu, A1.4.3. MORTALITY AND MORBIDITY DATA There is a paucity of local data on mortality (and morbidity). Whatever information is currently available is incomplete at best and has not been fully vetted. Regarding hospitalizations, we have evaluated the summary statistics of inpatient admissions (hospitalized patients and their mortality) and outpatient care (emergency room visits and their mortality) for 2017 (see data in the Supplemental Material). These statistics are derived from 184 public health care facilities, but data for the remaining 1,927 care facilities across the state are not available. In addition, for most of the deaths occurring at home, there is no medical certification, and, therefore, the mortality statistics could not be compiled. * * * TABLE A1.2. ESTIMATES OF LAGOS STATE POPULATION BY LGA | LGA | 2006 Population | | 2018 Projected population\* | | | --- | --- | --- | --- | --- | | Census† | LBS‡ | Base Case | Sensitivity | | | Agege | 461,743 | 1,033,064 | 673,840 | 1,507,591 | | Ajeromi-Ifelodun | 687,316 | 1,435,295 | 1,003,027 | 2,094,583 | | Alimosho | 1,319,571 | 2,047,026 | 1,925,702 | 2,987,306 | | Amuwo-Odofin | 328,975 | 524,971 | 480,086 | 766,111 | | Apapa | 222,986 | 522,384 | 325,412 | 762,336 | | Badagry | 237,731 | 380,420 | 346,930 | 555,162 | | Epe | 181,734 | 323,634 | 265,212 | 472,292 | | Eti-Osa | 283,791 | 983,515 | 414,147 | 1,435,282 | | Ibeju/Lekki | 117,793 | 99,540 | 171,900 | 145,263 | | Ifako-Ijaye | 427,737 | 744,323 | 624,214 | 1,086,220 | | Ikeja | 317,614 | 648,720 | 463,507 | 946,703 | | Ikorodu | 527,917 | 689,045 | 770,410 | 1,005,551 | | Kosofe | 682,772 | 934,614 | 996,396 | 1,363,919 | | Lagos Island | 212,700 | 859,849 | 310,402 | 1,254,812 | | Lagos Mainland | 326,700 | 629,469 | 476,766 | 918,609 | | Mushin | 631,857 | 1,321,517 | 922,094 | 1,928,542 | | Ojo | 609,173 | 941,523 | 888,990 | 1,374,002 | | Oshodi-Isolo | 629,061 | 1,134,548 | 918,014 | 1,655,691 | | Shomolu | 403,569 | 1,025,123 | 588,944 | 1,496,003 | | Surulere | 502,865 | 1,274,362 | 733,851 | 1,859,727 | | Lagos State | 9,113,605 | 17,552,942 | 13,299,845 | 25,615,703 | †, National Population Commission, [https://catalog.ihsn.org/index.php/catalog](https://catalog.ihsn.org/index.php/catalog) /3340/download/48521\| ‡ ‡ Lagos Bureau of Statistics population composition (LBS 2019) a Mean annual growth rate is 3.2% (Nigeria National Bureau of Statistics & LBS; the rate is 3.22% according to United Nations World Urbanization Prospects, [https://population.un.org/wup/](https://population.un.org/wup/)) Source: Author’s own elaboration. * * * FIGURE A1.3. MAP OF LAGOS STATE SHOWING LGAS Ifako-Ijaye Agege Ikeja Kosole Ikorodu Alimosho Oshodi-Isolo-Shomolu Epe Surulere Lagos Mainland Ibeju-Lokki Badagry Ojo Apapa Eti-Osa Amuwo-Odofin Ibeju-Lokki Ajeromi-Ifolodun Lagos Island FIGURE A1.4. LAGOS STATE POPULATION IN 2006 AND 2018 Source: Author’s own elaboration. Note: The LBS (2018) curve is biased to younger ages if compared to the sensitivity line composition. Moreover, the curve is shifted up by a fixed factor equal to 1.459, which represents the 12-year total population growth at 3.2 percent. * * * Source: Author’s own elaboration with data from the UN World Population Prospects 2019, [https://population.un.org/wpp](https://population.un.org/wpp). \\mathrm{M}\_{2.5} » Mortality due to NCDs and specific GBD categories, including lower respiratory infections, stroke, COPD, lung cancer, diabetes, and IHD. » Infant mortality (age less than 1 year) from the » Lower respiratory tract infections for children under age 5 (mainly pneumonia). The baseline rate (incidences per 1,000 children is 302, with a 95 percent CI: 160–538) was obtained from the study by McAllister et al. (2019). » Incidence of chronic bronchitis in adults 27 years » Incidence of chronic bronchitis in adults 27 years and older (3.9 cases per 1,000 individuals, based on the rate from HRAPIE, WHO 2013b). » Incidence of restricted activity days in the popula- » RHAs and emergency room visits (including pneumonia, bronchitis, and asthma). The baseline » Incidence of restricted activity days in the population of all ages (19 days per year) was taken from HRAPIE (WHO 2013b). Hospital admissions were subtracted to calculate the net PM attributable restricted activity days. » RHAs and emergency room visits (including * * * TABLE A1.3. LAGOS STATE MORTALITY (BOTH SEXES) BY CAUSE OF DEATH AND AGE, BASE CASE 2018 | Age(years) | Persons | All causes | NCD | IHD | Stroke | COPD | ALRI | LC | DM | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | 0-4 | 1,591,424 | 36,702 | 3,642 | 0 | 64 | 3 | 6,145 | 0 | 0 | | 5-9 | 1437,766 | 1,508 | 247 | 0 | 9 | 0 | 75 | 0 | 0 | | 10-14 | 1,292,164 | 853 | 205 | 0 | 11 | 0 | 36 | 0 | 0 | | 15-19 | 1,269,198 | 1,243 | 284 | 9 | 10 | 5 | 36 | 0 | 6 | | 20-24 | 1,492,935 | 1,976 | 417 | 17 | 26 | 2 | 56 | 1 | 3 | | 25-29 | 1,529,662 | 2,821 | 619 | 20 | 35 | 7 | 85 | 2 | 8 | | 30-34 | 1,233,580 | 3,185 | 727 | 49 | 46 | 8 | 82 | 5 | 15 | | 35-39 | 1,049,921 | 3,797 | 949 | 84 | 76 | 9 | 97 | 7 | 13 | | 40-44 | 761,198 | 3,694 | 1,161 | 116 | 124 | 11 | 100 | 10 | 34 | | 45-49 | 543,296 | 3,507 | 1,326 | 174 | 150 | 18 | 112 | 18 | 63 | | 50-54 | 386,546 | 3,425 | 1,601 | 238 | 226 | 36 | 136 | 29 | 95 | | 55-59 | 232,479 | 2,866 | 1,520 | 220 | 214 | 34 | 129 | 28 | 90 | | 60-64 | 168,103 | 3,178 | 1,875 | 305 | 302 | 59 | 160 | 39 | 122 | | 65-69 | 99,570 | 2,824 | 1,776 | 337 | 295 | 75 | 160 | 41 | 127 | | 70-74 | 78,318 | 3,616 | 2,270 | 482 | 407 | 117 | 233 | 53 | 145 | | 75-79 | 42,736 | 3,025 | 2,129 | 440 | 400 | 101 | 221 | 39 | 144 | | 80-84 | 42,969 | 4,630 | 3,348 | 713 | 618 | 148 | 392 | 42 | 215 | | 85+ | 47,982 | 8,453 | 6,167 | 1,395 | 1,047 | 280 | 857 | 43 | 349 | | Total | 13,299,845 | 91,302 | 30,263 | 4,600 | 4,061 | 914 | 9,112 | 357 | 1,429 | Source: Author’s own elaboration. statistics for the entire Lagos State were based on public hospital data, assuming that the private hospitals had a similar load of patients (2017 data). For RHAs, the incidences in children under 5, who account for 12 percent of the total population and contribute 85 percent of total cases (according to LMoH inpatient records for 2017), are 302 LRI cases per 1,000 children, of which 2.09 percent (range: 0.91–4.79 percent) statistics for the entire Lagos State were estimated based on public hospital data, assuming that the private hospitals had a similar load of patients (2017 data). For RHAs, the incidences in children under 5, who account for 12 percent of the total population and contribute 85 percent of total cases (according to LMoH inpatient records for 2017), are 302 LRI cases per 1,000 children, of which 2.09 percent (range: 0.91–4.79 percent) which 2.09 percent (range: 0.91–4.79 percent) Note: LC = Lung cancer; DM = Diabetes mellitus. Estimates derived from GHDx national hazard rates applied to the projected population based on 2006 census. require hospitalization (McAllister et al. 2019). Further, this increases by 32 percent to include other respiratory illnesses, such as COPD and asthma (according to inpatient statistics from the LMoH inpatient records for 2017). » CHAs and emergency-room visits consist of » CHAs and emergency-room visits consist of disease-specific categories such as IHD, which includes heart attacks, heart failure, and stroke. The baseline statistics for the entire Lagos State * * * TABLE A1.4. LAGOS STATE MORTALITY (BOTH SEXES) BY CAUSE OF DEATH AND AGE, SENSITIVITY CASE 2018 | Age(years) | Persons | All causes | NCD | IHD | Stroke | COPD | ALRI | LC | DM | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | 0-4 | 3,065,496 | 70,698 | 7,015 | 0 | 124 | 7 | 11,837 | 0 | 0 | | 5-9 | 2,769,515 | 2,905 | 476 | 0 | 17 | 0 | 145 | 0 | 0 | | 10-14 | 2,489,349 | 1,643 | 395 | 0 | 22 | 0 | 70 | 1 | 0 | | 15-19 | 2,445,056 | 2,394 | 548 | 17 | 19 | 9 | 69 | 1 | 11 | | 20-24 | 2,875,657 | 3,807 | 804 | 33 | 50 | 3 | 108 | 1 | 6 | | 25-29 | 2,946,225 | 5,433 | 1,192 | 39 | 67 | 13 | 163 | 3 | 16 | | 30-34 | 2,375,555 | 6,133 | 1,401 | 94 | 88 | 16 | 159 | 9 | 28 | | 35-39 | 2,021,579 | 7,312 | 1,827 | 162 | 146 | 17 | 187 | 14 | 26 | | 40-44 | 1,465,616 | 7,112 | 2,235 | 223 | 238 | 21 | 192 | 20 | 66 | | 45-49 | 1,045,888 | 6,752 | 2,552 | 335 | 288 | 35 | 216 | 34 | 122 | | 50-54 | 744,286 | 6,595 | 3,083 | 458 | 436 | 69 | 262 | 55 | 184 | | 55-59 | 447,515 | 5,517 | 2,926 | 424 | 412 | 66 | 248 | 54 | 172 | | 60-64 | 323,732 | 6,120 | 3,611 | 588 | 582 | 114 | 307 | 76 | 235 | | 65-69 | 191,740 | 5,438 | 3,421 | 649 | 568 | 144 | 308 | 79 | 244 | | 70-74 | 150,863 | 6,965 | 4,372 | 928 | 784 | 225 | 449 | 102 | 279 | | 75-79 | 82,335 | 5,828 | 4,102 | 848 | 771 | 194 | 425 | 76 | 277 | | 80-84 | 82,824 | 8,924 | 6,453 | 1,375 | 1,191 | 286 | 755 | 80 | 415 | | 85+ | 92,471 | 16,291 | 11,885 | 2,688 | 2,018 | 540 | 1,651 | 82 | 672 | | Total | 25,615,703 | 175,865 | 58,297 | 8,861 | 7,823 | 1,761 | 17,553 | 687 | 2,752 | Source: Author’s own elaboration. were estimated based on the available public hospital data, assuming that the private hospitals had a similar load of patients (2017 data). For CHAs, we assumed most cases occur among adults, and based on Sub-Saharan Africa data presented in Etyang and Scott (2013) (table S2), the rate is 2.6 times higher than the adult RHA incidence rate (adults account for 15 percent of the all-age RHA cases). were estimated based on the available public hospital data, assuming that the private hospitals had a similar load of patients (2017 data). For CHAs, we assumed most cases occur among adults, and based on Sub-Saharan Africa data presented in Etyang and Scott (2013) (table S2), the baseline times higher than the adult RHA incidence rate (adults account for 15 percent of the all-age RHA cases). \\mathrm{P M}\_{2.5} Note: LC = Lung cancer; DM = Diabetes mellitus estimates derived from GHDx national hazard rates applied to the projected population based on LBS (2006). We also estimated the impact of short-term exposure to PM10 on daily overall mortality due to the Harmattan season. In the specific situation of Lagos, daily population exposure to PM10 has importance and, in some instances, it does not correlate well with that of PM This happens on days when the Harmattan winds blow, between the end of November and mid-March. It is a dry and dusty wind from the North-East originating from the Sahara Desert, and it involves a large size We also estimated the impact of short-term exposure on daily overall mortality due to the Harmattan season. In the specific situation of Lagos, daily popula- has importance and, in some instances, it does not correlate well with that of PM2.5. This happens on days when the Harmattan winds blow, between the end of November and mid-March. It is a dry and dusty wind from the North-East originating from the Sahara Desert, and it involves a large size \\mathrm{P M}\_{10} * * * FIGURE A1.6. LAGOS STATE MORTALITY (BOTH SEXES) BY CAUSE OF DEATH AND AGE, BASE CASE 2018 Note: Estimates derived from GHDx national hazard rates (table A1.3). \\mathrm{P M}\_{10} increase of particles in the air, especially the coarse fraction (that is between 2.5 and 10 microns in diameter). The health effects of this type of source have been suspected (De Longueville et al. 2010) but never well studied. On the other hand, there is ample evidence of the acute health effects of Saharan dust from other locations (Querol et al. 2019), although the overall short-term effect of particles on mortality is much lower in comparison to the overall effect of chronic exposure. For the assessment of the short-term burden on mortality due to the Harmattan season, we applied the short-term ERF for PM10 (2020). increase of particles in the air, especially the coarse fraction (that is between 2.5 and 10 microns in diameter). The health effects of this type of source have been suspected (De Longueville et al. 2010) but never well studied. On the other hand, there is ample evidence of the acute health effects of Saharan dust from other locations (Querol et al. 2019), although the overall short-term effect of particles on mortality is much lower in comparison to the overall effect of chronic exposure. For the assessment of the short-term burden on mortality due to the Harmattan season, we applied the short-term ERF for PM10 from Orellano et al. A1.4.4. EXPOSURE-RESPONSE FUNCTIONS (ERF) The ERFs from the epidemiological literature that quantitatively relate exposure to PM the specific health effect have been reviewed in the first part of the document. The epidemiological studies provide an estimate of the percent change in risk that might be expected per each unit change in air pollution. For example, for ambient air PM 3 30–40 µg/m , current studies of long-term exposure indicate that a 10 μg/m change is expected to result The ERFs from the epidemiological literature that quantitatively relate exposure to PM2.5 to the risk of the specific health effect have been reviewed in the first part of the document. The epidemiological studies provide an estimate of the percent change in risk that might be expected per each unit change in air pollution. For example, for ambient air PM2.5 concentrations below 30–40 µg/m , current studies of long-term exposure 3 indicate that a 10 μg/m change is expected to result 10~\\up{\ up mu\ }mathrm{g/m^{3}} * * * TABLE A1.5. LAGOS STATE MORTALITY (BOTH SEXES) BY CAUSE OF DEATH AND AGE, BASE CASE 2018 | Age(years) | Persons | All causes | NCD | IHD | Stroke | COPD | ALRI | LC | DM | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | 0-4 | 1,591,424 | 38,158 | 3,237 | 0 | 62 | 3 | 7,936 | 0 | 9 | | 5-9 | 1,437,766 | 3,137 | 519 | 0 | 24 | 1 | 216 | 0 | 6 | | 10-14 | 1,292,164 | 1,715 | 402 | 0 | 31 | 1 | 101 | 0 | 8 | | 15-19 | 1,269,198 | 1,174 | 190 | 7 | 7 | 4 | 26 | 0 | 6 | | 20-24 | 1,492,935 | 1,888 | 301 | 13 | 20 | 1 | 42 | 0 | 6 | | 25-29 | 1,529,662 | 2,710 | 449 | 14 | 23 | 4 | 53 | 1 | 10 | | 30-34 | 1,233,580 | 3,044 | 645 | 37 | 34 | 6 | 62 | 2 | 19 | | 35-39 | 1,049,921 | 3,647 | 946 | 64 | 56 | 6 | 73 | 4 | 19 | | 40-44 | 761,198 | 3,542 | 1,107 | 88 | 92 | 8 | 75 | 6 | 36 | | 45-49 | 543,296 | 3,345 | 1,231 | 134 | 118 | 14 | 86 | 13 | 52 | | 50-54 | 386,546 | 3,282 | 1,434 | 199 | 194 | 30 | 116 | 14 | 85 | | 55-59 | 232,479 | 2,720 | 1,562 | 238 | 231 | 37 | 139 | 12 | 103 | | 60-64 | 168,103 | 3,003 | 1,911 | 338 | 335 | 66 | 177 | 9 | 142 | | 65-69 | 99,570 | 2,668 | 1,888 | 403 | 353 | 89 | 191 | 6 | 157 | | 70-74 | 78,318 | 3,397 | 2,391 | 591 | 499 | 139 | 286 | 5 | 184 | | 75-79 | 42,736 | 2,843 | 2,048 | 497 | 453 | 112 | 248 | 3 | 166 | | 80-84 | 42,969 | 4,320 | 3,075 | 772 | 674 | 154 | 425 | 3 | 234 | | 85+ | 47,982 | 8,131 | 5,771 | 1,528 | 1,166 | 298 | 883 | 3 | 382 | | Total | 13,299,845 | 97,724 | 29,107 | 4,923 | 4,373 | 975 | 11,134 | 81 | 1,623 | in an 8  percent increase in the risk of premature death from all natural causes of death (Chen and Hoek 2020). However, for the high levels of PM2.5 pollution recorded in Lagos—well above the range of the concentration levels observed in most of the studies—the best approach has been to apply the IER functions used by GBD to assess the ambient air PM2.5 cause-specific mortality (for example, Croitoru, Chang, and Akpokodje 2020). In this study, we used the most recent IER functions (GBD 2020) as well as the GEMM relationship (Burnett et al. 2018) for the NCDs \\mathrm{P M}\_{2.5} Note: Estimates derived from GHE national hazard rates applied to the projected population based on 2006 census. \\mathrm{P M}\_{2.5} plus lower respiratory illnesses. For infant mortality, we used the novel paper by Heft-Neal et al. (2018). They found that a 10 μg/m3 increase in PM2.5 concentration was associated with a 9.2 percent (95 percent CI: 4–14 percent) rise in infant mortality based on a large study carried out in Africa. Figure A1.7 is a graphical representation of the ERFs that have been used in this work. 10,\\upmu\\mathrm{g/m} The concentration of lead in PM2.5 and PM10 observed in Ikorodu LGA is particularly elevated (see section 2.2) * * * TABLE A1.6. LAGOS STATE MORTALITY (BOTH SEXES) BY CAUSE OF DEATH AND AGE, SENSITIVITY CASE 2018 | Age(years) | Persons | All causes | NCD | IHD | Stroke | COPD | ALRI | LC | DM | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | 0-4 | 3,065,496 | 73,502 | 6,235 | 0 | 120 | 6 | 15,287 | 0 | 18 | | 5-9 | 2,769,515 | 6,043 | 1,001 | 0 | 46 | 1 | 417 | 0 | 12 | | 10-14 | 2,489,349 | 3,305 | 775 | 0 | 60 | 1 | 194 | 0 | 15 | | 15-19 | 2,445,056 | 2,261 | 366 | 13 | 14 | 7 | 51 | 0 | 11 | | 20-24 | 2,875,657 | 3,637 | 580 | 25 | 38 | 2 | 81 | 1 | 11 | | 25-29 | 2,946,225 | 5,219 | 865 | 28 | 45 | 8 | 101 | 2 | 20 | | 30-34 | 2,375,555 | 5,861 | 1,243 | 72 | 66 | 12 | 119 | 5 | 36 | | 35-39 | 2,021,579 | 7,021 | 1,822 | 124 | 107 | 12 | 140 | 8 | 37 | | 40-44 | 1,465,616 | 6,821 | 2,131 | 169 | 178 | 16 | 143 | 12 | 70 | | 45-49 | 1,045,888 | 6,440 | 2,369 | 258 | 227 | 28 | 166 | 25 | 100 | | 50-54 | 744,286 | 6,320 | 2,761 | 383 | 374 | 58 | 224 | 28 | 163 | | 55-59 | 447,515 | 5,237 | 3,006 | 457 | 444 | 71 | 267 | 22 | 198 | | 60-64 | 323,732 | 5,784 | 3,679 | 651 | 644 | 126 | 340 | 18 | 274 | | 65-69 | 191,740 | 5,137 | 3,636 | 777 | 680 | 172 | 369 | 11 | 303 | | 70-74 | 150,863 | 6,544 | 4,606 | 1,138 | 961 | 268 | 550 | 9 | 353 | | 75-79 | 82,335 | 5,478 | 3,947 | 957 | 872 | 216 | 479 | 5 | 319 | | 80-84 | 82,824 | 8,326 | 5,927 | 1,489 | 1,300 | 298 | 819 | 5 | 452 | | 85+ | 92,471 | 15,669 | 11,122 | 2,945 | 2,247 | 574 | 1,701 | 7 | 736 | | Total | 25,615,703 | 178,605 | 56,070 | 9,485 | 8,424 | 1,878 | 21,448 | 157 | 3,127 | (0.15~\\up{up\ \\up{\\mathrm{{g}}}/{\\mathrm{{g m}}}}^{3}) compared to the US EPA 2016 standard (0.15 μg/m3 Lead exposure in children has been linked to severe brain damage, leading to loss of intelligence (IQ), and adverse behavioral outcomes such as learning disabilities, school failure, and conduct disorder (Lanphear et al. 2005; Pew Charitable Trusts 2017; Ruckart et al. 2021). In adults, compared to the US EPA 2016 standard (0.15 μg/m3). Lead exposure in children has been linked to severe brain damage, leading to loss of intelligence (IQ), and adverse behavioral outcomes such as learning disabilities, school failure, and conduct disorder (Lanphear et al. 2005; Pew Charitable Trusts 2017; Ruckart et al. 2021). In adults, Charitable Trusts 2017; Ruckart et al. 2021). In adults, lead exposure can affect the cardiovascular system by increasing the likelihood of high blood pressure and, Source: Author’s own elaboration Note: LC = Lung cancer; DM = Diabetes mellitus. (1.35,\\upmu\\mathrm{g}/\\mathrm{m}^{3} consequently, increasing cardiovascular mortality (Brown et al. 2020; US EPA 1999). To estimate the impact of lead exposure on the Ikorodu population—the LGA with the highest lead exposure exceedance (1.35 µg/m3 compared to the US standard—the air concentration has been converted into blood levels using a conversion factor consequently, increasing cardiovascular mortality (Brown et al. 2020; US EPA 1999). To estimate the impact of lead exposure on the Ikorodu population—the LGA with the highest lead exposure exceedance (1.35 µg/m3 air lead) as compared to the US standard—the air concentration has been converted into blood levels using a conversion factor Based on the estimated blood lead levels, the impact Estimates derived from GHE national hazard rates applied to the projected population based on LBS (2006). * * * TABLE A1.7. NIGERIA MORTALITY RATES (PER 100,000, BOTH SEXES) BY CAUSE OF DEATH AND AGE | Age group | All causes | | NCD | | IHD | | Stroke | | COPD | | ALRI | | LC | | DM | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | GBD | WHO | GBD | WHO | GBD | WHO | GBD | WHO | GBD | WHO | GBD | WHO | GBD | WHO | GBD | WHO | | | 0-4 | 2,306 | 2,398 | 229 | 203 | 0 | 0 | 4 | 4 | 0 | 0 | 386 | 499 | 0 | 0 | 0 | 1 | | 5-9 | 105 | 218 | 17 | 36 | 0 | 0 | 1 | 2 | 0 | 0 | 5 | 15 | 0 | 0 | 0 | 0 | | 10-14 | 66 | 133 | 16 | 31 | 0 | 0 | 1 | 2 | 0 | 0 | 3 | 8 | 0 | 0 | 0 | 1 | | 15-19 | 98 | 92 | 22 | 15 | 1 | 1 | 1 | 1 | 0 | 0 | 3 | 2 | 0 | 0 | 0 | 0 | | 20-24 | 132 | 126 | 28 | 20 | 1 | 1 | 2 | 1 | 0 | 0 | 4 | 3 | 0 | 0 | 0 | 0 | | 25-29 | 184 | 177 | 40 | 29 | 1 | 1 | 2 | 2 | 0 | 0 | 6 | 3 | 0 | 0 | 1 | 1 | | 30-34 | 258 | 247 | 59 | 52 | 4 | 3 | 4 | 3 | 1 | 1 | 7 | 5 | 0 | 0 | 1 | 2 | | 35-39 | 362 | 347 | 90 | 90 | 8 | 6 | 7 | 5 | 1 | 1 | 9 | 7 | 1 | 0 | 1 | 2 | | 40-44 | 485 | 465 | 153 | 145 | 15 | 12 | 16 | 12 | 1 | 1 | 13 | 10 | 1 | 1 | 4 | 5 | | 45-49 | 646 | 616 | 244 | 227 | 32 | 25 | 28 | 22 | 3 | 3 | 21 | 16 | 3 | 2 | 12 | 10 | | 50-54 | 886 | 849 | 414 | 371 | 62 | 51 | 59 | 50 | 9 | 8 | 35 | 30 | 7 | 4 | 25 | 22 | | 55-59 | 1,233 | 1,170 | 654 | 672 | 95 | 102 | 92 | 99 | 15 | 16 | 55 | 60 | 12 | 5 | 39 | 44 | | 60-64 | 1,890 | 1,787 | 1,116 | 1,137 | 182 | 201 | 180 | 199 | 35 | 39 | 95 | 105 | 23 | 5 | 73 | 85 | | 65-69 | 2,836 | 2,679 | 1,784 | 1,897 | 339 | 405 | 296 | 355 | 75 | 90 | 161 | 192 | 41 | 6 | 127 | 158 | Sources: Author’s own compilation of mortality data from GHDx (IHME 2021) and GHE (WHO 2021). Note: Both databases provide similar mortality rates for the selected group of diseases. Lung cancer is a notable exception, for which the WHO estimate is much lower than the GBD value (up to a factor of 15 times smaller for ages 75 and older, and by a factor of 4.5 times smaller when considering all ages). * * * TABLE A1.8. LAGOS STATE MORTALITY (GHDX HAZARD RATES, BOTH SEXES) BY LGA, 2018 | Local Government Area, LGA | All causes | | | NCD | | | Stroke | | | COPD | | | ALRI | | | LC | | | DM | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | Base case | Sensitivity | Base case | Sensitivity | Base case | Sensitivity | Base case | Sensitivity | Base case | Sensitivity | Base case | Sensitivity | Base case | Sensitivity | Base case | Sensitivity | Base case | Sensitivity | Base case | Sensitivity | | | | Agege | 4,626 | 10,350 | 1,533 | 3,431 | 233 | 522 | 206 | 460 | 46 | 104 | 462 | 1,033 | 18 | 40 | 72 | 162 | | | | | | | Ajercomi-Ifriedun | 6,886 | 14,380 | 2,292 | 4,767 | 347 | 725 | 306 | 640 | 69 | 144 | 687 | 1,435 | 27 | 56 | 108 | 225 | | | | | | | Alimosho | 13,220 | 20,509 | 4,382 | 6,799 | 666 | 1,033 | 588 | 912 | 132 | 205 | 1,319 | 2,047 | 52 | 80 | 207 | 321 | | | | | | | Amawo-Odefin | 3,296 | 5,260 | 1,092 | 1,744 | 166 | 265 | 147 | 234 | 33 | 53 | 329 | 525 | 13 | 21 | 52 | 82 | | | | | | | Apapa | 2,234 | 5,234 | 740 | 1,735 | 113 | 264 | 99 | 233 | 22 | 52 | 223 | 522 | 9 | 20 | 35 | 82 | | | | | | | Badagry | 2,382 | 3,811 | 789 | 1,263 | 120 | 192 | 106 | 170 | 24 | 38 | 238 | 380 | 9 | 15 | 37 | 60 | | | | | | | Epe | 1,821 | 3,243 | 603 | 1,075 | 92 | 163 | 81 | 144 | 18 | 32 | 182 | 324 | 7 | 13 | 28 | 51 | | | | | | | Ei-Osa | 2,843 | 9,854 | 942 | 3,266 | 143 | 497 | 126 | 438 | 28 | 99 | 284 | 983 | 11 | 38 | 44 | 154 | | | | | | | Beju/Lakki | 1,180 | 997 | 391 | 331 | 59 | 50 | 52 | 44 | 12 | 10 | 118 | 100 | 5 | 4 | 18 | 16 | | | | | | | Ikako-iijye | 4,285 | 7,457 | 1,420 | 2,472 | 216 | 376 | 191 | 332 | 43 | 75 | 428 | 744 | 17 | 29 | 67 | 117 | | | | | | | Ikjeja | 3,182 | 6,500 | 1,055 | 2,155 | 160 | 327 | 142 | 289 | 32 | 65 | 318 | 649 | 12 | 25 | 50 | 102 | | | | | | Note: LC = Lung cancer; DM = Diabetes mellitus. Estimates derived from GHDx national hazard rates assuming the same age profile in each LGA. Projected population based on 2006 census (base case) and LBS (2006) (sensitivity). * * * TABLE A1.9. LAGOS STATE MORTALITY (GHE HAZARD RATES, BOTH SEXES) BY LGA, 2018 | Local Government Area, LGA | All causes | | | NCD | | | Stroke | | | COPD | | | ALRI | | | LC | | | DM | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | Base case | Sensitivity | Base case | Sensitivity | Base case | Sensitivity | Base case | Sensitivity | Base case | Sensitivity | Base case | Sensitivity | Base case | Sensitivity | Base case | Sensitivity | Base case | Sensitivity | Base case | Sensitivity | Base case | Sensitivity | | | Agege | 4,698 | 10,512 | 1,475 | 3,300 | 249 | 558 | 222 | 496 | 49 | 111 | 111 | 564 | 1,262 | 4 | 9 | 82 | 184 | | | | | | | Ajeromi-ilifeodun | 6,993 | 14,604 | 2,195 | 4,585 | 371 | 776 | 330 | 689 | 74 | 154 | 154 | 840 | 1,754 | 6 | 13 | 122 | 256 | | | | | | | Aimosho | 13,426 | 20,829 | 4,214 | 6,539 | 713 | 1,106 | 633 | 982 | 141 | 219 | 1,612 | 2,501 | 12 | 18 | 235 | 365 | | | | | | | | Amawo-Odofin | 3,347 | 5,342 | 1,051 | 1,677 | 178 | 284 | 158 | 252 | 35 | 56 | 402 | 641 | 3 | 5 | 59 | 94 | | | | | | | | Apapa | 2,269 | 5,315 | 712 | 1,669 | 120 | 282 | 107 | 251 | 24 | 56 | 272 | 638 | 2 | 5 | 40 | 93 | | | | | | | | Badagry | 2,419 | 3,871 | 759 | 1,215 | 128 | 206 | 114 | 183 | 25 | 41 | 290 | 465 | 2 | 3 | 42 | 68 | | | | | | | | Epe | 1,849 | 3,293 | 580 | 1,044 | 98 | 175 | 87 | 155 | 19 | 35 | 222 | 395 | 2 | 3 | 32 | 58 | | | | | | | | Eji-Osa | 2,887 | 10,007 | 906 | 3,142 | 153 | 531 | 136 | 472 | 30 | 105 | 347 | 1,202 | 3 | 9 | 51 | 175 | | | | | | | | Beijin-Lakki | 1,198 | 1,013 | 376 | 318 | 64 | 54 | 57 | 48 | 13 | 11 | 144 | 122 | 1 | 1 | 21 | 18 | | | | | | | | Ikako-jiye | 4,352 | 7,574 | 1,366 | 2,378 | 231 | 402 | 205 | 357 | 46 | 80 | 523 | 939 | 4 | 7 | 76 | 133 | | | | | | | Estimates derived from GHE national hazard rates assuming the same age profile in each LGA. Projected population based on 2006 census (base case) and LBS (2006) (sensitivity). * * * ## FIGURE A1.7. ERFS FOR THE LAGOS HIA 2.2 2.0 GEMM RR and 95% CI 1.9 Chronic Obstructive Pulmonary 2.0 Population at risk: Adults 25+ 1.8 Disease (COPO) RR and 95% CI Health outcome: NCD + LRI deaths **Population at risk: Adults 25+** 1.8 1.7 1.6 1.6 1.5 Relative risk 1.4 1.4 Relative risk1.3 1.2 1.2 1.1 Lower Respirasory Infection (LRI) deaths **Population at risk: All ages** 1.0 1.0 01 02 03 04 05 06 07 08 09 0 100 01 02 03 04 05 06 07 08 09 0 100 2.0 2.0 1.9 Lung Cancers (LC) RR and 95% CI 1.9 Stroke death RR and 95% CI **Population at risk: Adults 25+** Population at risk: Adults 25+ 1.8 1.8 RR by 5-yr age groups: 70–74 yr shown 1.7 1.7 1.6 1.6 1.5 1.5 1.4 1.4 Relative risk Relative risk 1.3 1.5 1.2 1.2 Ischemic Heart Disease (IHD) deaths 1.1 Type 2 Diabetes Mellitius (DM) deaths Population at risk: Adults 25+ 1.1 **Population at risk: Adults 25+** **RR by 5-yr age groups: 70–74 yr shown** 1.0 1.0 01 02 03 04 05 06 07 08 09 0 100 01 02 03 04 05 06 07 08 09 0 100 3.0 2.8 Heft-Neal et al. RR and 95% CI Population at risk: Infants (< 1 year) 2.6 Health outcome: Natural deaths 2.4 2.2 2.0 1.8 Relative risk 1.6 1.4 1.2 1.0 01 02 03 04 05 06 07 08 09 0 100 _Note: Estimates derived from GHDx national hazard rates (table A1.3)._ Air Quality Management Planning for Lagos State of lead exposure on children’s IQ has been estimated (as a decrease in children’s IQ equal to 1.15 points per 1 µg/dl (microgram per deciliter) blood lead increase; Pew Charitable Trusts 2017), as has the impact of lead on cardiovascular mortality in adults (Brown et al. 2020). A PM2.5 counterfactual concentration has been used to estimate the burden of disease. The GBD (2020) study assumed a uniformly distributed value between 2.4 and 5.9 µg/m3 PM2.5, whereas GEMM assumes 2.4 μg/m3, and the same counterfactual is applied for infant mortality. Furthermore, multiple targets have been examined, such as the new WHO Air Quality guideline of 5 µg/m3 or the WHO interim targets (35, 25, 15, 10 µg/m3 PM2.5) to quantify the health benefits that could be achieved from exposure reductions. \\mathrm{P M}\_{2.5} 5.9 ~~\\mathrm{y}\\mathrm{}g/\\mathrm{m^^{33}~~ P M}\_{.5}, 2.4~\\upmu\\mathrm{g/m/^^{3}} 5,\\up{\\mathrm{{g/m}^{\ 2}}} (35,25,15,10,\\up\ \ {mu g}/{^33},{\ !!{M}\_{2.5})} A1.5. RESULTS \\mathrm{P M}\_{2.5} A1.5.1. PM2.5PREMATURE MORTALITY AND MORBIDITY (BASE AND SENSITIVITY SCENARIOS) \\mathsf{P}\\mathsf{M}\_{2.5} Table A1.10 and Figure A1.8 show the estimates of PM2.5 PWE by LGAs in Lagos. The estimation is based on fixed monitor data (for LGAs with such a monitoring station) and the results of the air dispersion analysis for five episodes between August 2020 and July 2021. The overall values for the entire Lagos are 47 µg/m3 and 114 µg/m3 for PM2.5 and PM10, respectively (base case). Only a small difference has been estimated when using the sensitivity population (46 µg/m3 and 116 µg/m3 for PM2.5 and PM10, respectively). The population living in Ikorodu, Shomolu, Mushin, and Oshodi are exposed to particularly high values of ambient pollution (PM2.5 values of 97, 85, 71, and 60 µg/m3, respectively). The alternative calculations, based on the closest monitors, provide similar estimates (see results in the Supplemental Material). \\mathrm{M}\_{2.5} 114,\\up\ \\mathrm{g/m^{3}} \\mathrm{M}\_{2.5} \ \\mathrm{P{}bf M}\_{10}, 116~\\upmu\\mathrm{g}/\\mathrm{m}^{3} \\mathrm{P M}\_{2.5} 6~\\upmu\\mathrm{g}/\\mathrm{m}^{3} summarizes the findings for both the base and sensitivity case populations. For the base case population, the estimated annual mortality attributable to PM2.5 is 15,850 deaths, of which 7,790 are infant deaths, or around 50 percent of the total mortality. In total, 182,400 annual cases of lower respiratory infections in children up to 5 years were estimated, together with 14,700 new cases of chronic bronchitis in adults, 46 million restricted activity days, and 1,490 hospital admissions for cardiovascular and respiratory diseases. The table provides 95 percent CIs around these estimates. Alimosho, Ikorodu, and Oshodi are the LGAs with the greatest impact. The estimates are doubled (Table A1.12 ) when considering the sensitivity population: the annual mortality attributable to PM2.5 is 30,350 deaths (14,890 infant deaths), 349,000 annual cases of lower respiratory infections in children up to 5 years, 28,300 new cases of chronic bronchitis, 88 million restricted days, and 2,840 hospital admissions. In the sensitivity population calculation, the LGAs that had the greatest impact were Alimosho, Mushin, Shomolo, and Oshodi. Figure A1.11 presents the age-specific mortality results for the base case population (top) and sensitivity case population (bottom). The total mortality is calculated as the sum of infant mortality (Heft-Neal et al. 2018), deaths from lower respiratory infections for ages 1–25 (GBD 2020), and adult mortality (ages 25+) according to GEMM. Figure A1.10 shows the attributable cases of premature mortality by cause of death applying the IER functions proposed by GBD in 2020. Mortality results have been adjusted for co-exposure to indoor air pollution, assuming that 40 percent of the population in the following LGAs use solid fuel for cooking purposes: Amuwo- Odofin, Badagry, Epe, Eti-Osa (NCF), Ibekju/Lekki, and Ojo (Croitoru, Chang and Kelly 2020). The adjustment for indoor air pollution follows the GBD 2020 recommended proportional population attributable fraction (PAF) approach (Source: GBD 2020, SI appendix 1, 11). The calculations were done on the base case population and using baseline mortality rates from GHDx-IHME and GHE-WHO. The results were similar using the two databases and indicated that mortality from lower respiratory infections, IHD, and stroke had the greatest impact. * * * TABLE A1.10. ANNUAL PM PWE BY LGA | Local Government Area (LGA) | Closest monitoring station | PM2.5Adj factor | PM10Adj factor | PWE,μg/m3 | | | --- | --- | --- | --- | --- | --- | | PM2.5 | PM10 | | | | | | Agege | Mean of Ikeja & Alimosho | 0.84 | 0.85 | 36 | 97 | | Ajeromi-lfelodun | Lagos Island | 1.02 | 1.02 | 42 | 108 | | Alimosho\* | | | | 46 | 124 | | Amuwo Odofin | Eti-Osa | 1.63 | 1.62 | 48 | 119 | | Apapa | Lagos Island | 0.94 | 0.95 | 39 | 100 | | Badagry | Same as Epe | 0.55 | 0.53 | 16 | 39 | | Epe | Eti-Osa | 0.55 | 0.53 | 16 | 39 | | Eti-Osa\* | | | | 29 | 74 | | Ibeju-Lekki | Same as Epe | 0.55 | 0.53 | 16 | 39 | | Ifako-ljaye | Mean of Ikeja & Alimosho | 0.76 | 0.78 | 33 | 89 | | Ikeja\* | | | | 41 | 106 | | Ikorodu\* | | | | 97 | 171 | | Kosofe | Ikeja | 1.15 | 1.14 | 47 | 120 | | Lagos Island\* | | | | 42 | 105 | | Lagos Mainland\* | | | | 42 | 97 | | Mushin | Lagos Mainland | 1.70 | 1.73 | 71 | 168 | | Ojo | Eti-Osa | 0.83 | 0.86 | 25 | 64 | | Oshodi | Lagos Mainland | 1.43 | 1.45 | 60 | 141 | | Shomolu | Lagos Mainland | 2.03 | 2.05 | 85 | 199 | | Surulere | Lagos Mainland | 0.77 | 0.79 | 32 | 76 | | Lagos State(Base Case population) | | | | 47 | 114 | | Lagos State(Sensitivity population) | | | | 46 | 113 | \\mathrm{P M}\_{2.5},\\mathrm{A d j} \\mathbf{P M}\_{2.5} \\mathbf{P M}\_{10} - LGAs where air monitors are located (mean monitored concentration over period Aug 2020 to Jul 2021). Source: Author’s own elaboration. * * * FIGURE A1.8. AMBIENT AIR QUALITY FOR LAGOS STATE AND LGAS Source: Author’s own elaboration Note: The six LGAs where daily ambient concentrations were monitored during the monitoring campaign between August 2020 and July 2021 are highlighted by the gray boxes along the y-axis on the left. * * * TABLE A1.11. PM2.5 ATTRIBUTABLE HEALTH BURDENS FOR THE BASE CASE POPULATION | Local Government Area(LGA) | Infant Mortality | | Total Mortality\* (all ages) | | Lower Respiratory Infections Children under 5 years | | Onset Chronic Bronchitis Adults over 27 years | | Restricted Activity Days All ages(in thousands) | | Hospital Admissions All ages, cardiovascular and respiratory | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | Deaths | Deaths | 95%CI | Cases | 95%CI | Cases | 95%CI | Cases | 95%CI | Cases | 95%CI | | | | Agege | 320 | 680 | 450-890 | 7,670 | 2,280-14,210 | 710 | 340-880 | 1,840 | 1,660-2,040 | 58 | 2-115 | | | Ayeromi-lilodun | 550 | 1,140 | 720-1,490 | 13,080 | 3,970-23,380 | 1,120 | 550-1,370 | 3,190 | 2,880-3,550 | 102 | 3-201 | | | Alimosho | 1,130 | 2,320 | 1,470-3,020 | 26,730 | 8,220-46,890 | 2,320 | 1,180-2,760 | 6,580 | 5,990-7,310 | 211 | 6-415 | | | Anuwo Odofin | 300 | 600 | 380-780 | 6,950 | 2,160-12,040 | 570 | 290-680 | 1,720 | 1,560-1,910 | 55 | 2-109 | | | Apapa | 170 | 350 | 220-460 | 3,980 | 1,200-7,240 | 350 | 170-430 | 960 | 870-1,070 | 31 | 1-60 | | | Badagry | 70 | 180 | 120-250 | 1,810 | 500-3,810 | 180 | 20-250 | 410 | 370-460 | 13 | 0-25 | | | Epe | 60 | 140 | 90-190 | 1,390 | 380-2,910 | 140 | 50-190 | 310 | 280-350 | 10 | 0-19 | | | Eri-Oba | 160 | 350 | 220-470 | 3,920 | 1,130-7,560 | 360 | 160-470 | 920 | 830-1,030 | 29 | 1-57 | | | Bheja-Lekki | 40 | 90 | 60-120 | 900 | 250-1,890 | 90 | 40-130 | 200 | 180-230 | 6 | 0-13 | | | Ihakol-jaye | 270 | 590 | 370-780 | 6,560 | 1,920-12,370 | 620 | 290-790 | 1,550 | 1,400-1,730 | 49 | 1-97 | | | Ikja | 250 | 510 | 330-670 | 5,860 | 1,770-10,570 | 510 | 250-630 | 1,420 | 1,200-1,580 | 45 | 1-89 | | | Ikorodu | 810 | 1,520 | 1,000-1,860 | 18,210 | 6,710-25,370 | 1,060 | 610-1,190 | 5,170 | 4,730-5,670 | 177 | 6-334 | | | Kosoife | 600 | 1,220 | 780-1,350 | 14,120 | 4,360-24,620 | 1,180 | 600-1,410 | 3,490 | 3,180-3,880 | 112 | 3-220 | | Note: \* Baseline rates obtained from the WHO GHE database for 2018. * * * \\mathsf{P}\\mathsf{M}\_{2.5} Base Case 2018 Population Lagos State Base Case 2018 Population * * * Lagos State Sensitivity Case 2018 Population Source: Author’s own elaboration Note: Adult mortality quantified using GEMM and infant mortality using Heft-Neal et al. (2018). Baseline rates obtained from the WHO GHE database for 2018. * * * | Local Government Area(LGA) | Infant Mortality | | Total Mortality\* (all ages) | | Lower Respiratory Infections Children under 5-years | | Onset Chronic Bronchitis Adults over 27-years | | Restricted Activity Days All ages(in thousands) | | Hospital Autmissions All ages, cardiovascular and respiratory | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | Deaths | Deaths | 95%CI | Cases | 95%CI | Cases | 95%CI | Cases | 95%CI | Cases | 95%CI | Cases | | | Agege | 720 | 1,520 | 960-2,000 | 17,170 | 5,090-31,800 | 1,590 | 750-1,980 | 4,110 | 3,710-4,570 | 130 | 4-258 | | | Ayromi-lobodun | 1,150 | 2,370 | 1,500-3,110 | 27,330 | 8,300-48,830 | 2,340 | 1,140-2,860 | 6,660 | 6,020-7,410 | 212 | 6-419 | | | Almosho | 1,760 | 3,610 | 2,290-4,680 | 41,470 | 12,750-72,750 | 3,590 | 1,830-4,280 | 10,200 | 9,230-11,340 | 327 | 10-644 | | | Amuwo Odofin | 470 | 960 | 610-1,240 | 11,090 | 3,440-19,210 | 900 | 460-1,090 | 2,750 | 2,490-3,050 | 88 | 3-174 | | | Apapa | 390 | 820 | 520-1,070 | 9,330 | 2,800-16,970 | 820 | 390-1,010 | 2,250 | 2,040-2,510 | 71 | 2-142 | | | Badagry | 120 | 290 | 190-400 | 2,900 | 800-6,090 | 290 | 110-410 | 650 | 590-730 | 20 | 1-41 | | | Epe | 100 | 250 | 160-340 | 2,470 | 680-5,180 | 240 | 100-340 | 550 | 500-620 | 17 | 1-34 | | | Eri-Osa | 570 | 1,230 | 770-1,630 | 13,590 | 3,939-26,200 | 1,260 | 560-1,640 | 3,180 | 2,870-3,550 | 100 | 3-199 | | | Ibogie-Lokki | 30 | 80 | 50-100 | 760 | 210-1,590 | 70 | 30-110 | 170 | 150-190 | 5 | 0-11 | | | Ikako-djaye | 480 | 1,030 | 640-1,350 | 11,410 | 3,340-21,530 | 1,080 | 500-1,370 | 2,700 | 2,440-3,010 | 85 | 3-169 | | | Ikaja | 510 | 1,050 | 660-1,370 | 11,970 | 3,610-21,580 | 1,050 | 510-1,280 | 2,900 | 2,630-3,230 | 92 | 3-183 | | | Ikorodu | 1,060 | 1,980 | 1,310-2,420 | 23,770 | 8,760-33,120 | 1,390 | 790-1,560 | 6,740 | 6,170-7,400 | 231 | 7-435 | | Note: \*Baseline rates obtained from the WHO GHE database for 2018. Numbers may not add up due to rounding off errors. * * * \\mathsf{P}\\mathsf{M}\_{2.5} GHDx-IHME baseline mortality rates WHO-GHE baseline mortality rates Source: Author’s own elaboration. Note: Mortality quantified using the IER functions of the GBD (2020). Baseline rates obtained from the WHO GHE database for 2018. A1.5.2. HARMATTAN HEALTH BURDEN Table A1.13 presents the results of attributable mortality from short-term exposure to PM10 during the 2 months of January and February. We have assumed an excess PM10 exposure equal to the difference of the average concentration for January–February and the average of the shoulder months December and March. In  January and February, the excess PM10 concentration was 88 µg/m3 PM10 for the base-case population and 90 µg/m3 for the sensitivity-case population with a total of 250 and 500 premature deaths, respectively. These numbers are not included in the overall impact assessment performed for PM2.5 long-term exposure. \\mathrm{P M}\_{2.5} 88,\\upmu\\mathrm{g}/\\mathrm{m}^{3},\\mathrm{P M}\_{10} \\mathrm{P M}\_{10} A1.5.3. HEALTH BENEFIT ANALYSIS FROM IMPROVEMENTS IN AIR QUALITY (35,\\mathrm{\\up g/m^{3}}), Figure A1.12 shows the benefits of reducing PM2.5 concentration in Lagos. Progressively reaching the different WHO PM2.5 interim targets, IT 1 (35 μg/m3), IT 2 (25 μg/m3), IT 3 (15 μg/m3), IT 4 (10 μg/m3), and the shoulder months December and March. In the WHO air quality guideline (5 μg/m3) would avert 29 percent, 46 percent, 66 percent, 77 percent, and 90 percent of the estimated attributable premature deaths (green curve). \\mathrm{I T};2;(25;\\mathrm{p g}/\\mathrm{m}^{3}),;\\mathrm{I T};3;(15;\\mathrm{u g}/\\mathrm{m}^{3}),;\\mathrm{I T};4;(10;\\mathrm{u g}/\\mathrm{m}^{3}),;\\mathrm{a n} \ 5\ \\upmu\\mathrm{g/m^{3}}} * * * \\mathsf{P}\\mathsf{M}\_{2.5} Lagos State Base case 2018 population Lagos State Sensitivity case 2018 population Source: Author’s own elaboration. * * * TABLE A1.13. PM10 ATTRIBUTABLE SHORT-TERM MORTALITY DUE TO THE HARMATTAN SEASON \\mathsf{P}\\mathbb{M}\_{10} | Local Government Area (LGA) | Population(aged 25+) | | PM$\_{10}$ excess exposure $\\mu$/m$^3$ | Mortality^{†}$ | | | | | --- | --- | --- | --- | --- | --- | --- | --- | | Base Case | Sensitivity | Base Case population | | Sensitivity Case population | | | | | Deaths | 95%CI | Deaths | 95%CI | | | | | | Agege | 314,953 | 704,522 | 70 | 10 | 9-12 | 23 | 19-27 | | Ajeromi-Ifclodun | 468,816 | 978,832 | 91 | 20 | 16-24 | 41 | 34-49 | | Alimosho\* | 900,075 | 1,396,016 | 86 | 36 | 30-43 | 56 | 46-66 | | Amuwo Odofin | 224,393 | 358,016 | 131 | 13 | 11-16 | 22 | 18-26 | | Apapa | 152,098 | 356,252 | 85 | 6 | 5-7 | 14 | 12-17 | | Badagry | 162,155 | 259,436 | 42 | 3 | 3-4 | 5 | 4-6 | | Epe | 123,960 | 220,710 | 42 | 2 | 2-3 | 4 | 4-5 | | Eti-Osa\* | 193,573 | 670,731 | 81 | 7 | 6-9 | 25 | 21-30 | | Ibeju-Lekki | 80,346 | 67,884 | 42 | 2 | 1-2 | 1 | 1-2 | | Ifako-Ijave | 291,758 | 507,608 | 64 | 9 | 7-10 | 15 | 13-18 | | Ikcja\* | 216,643 | 442,409 | 79 | 8 | 7-10 | 16 | 14-19 | | Ikorodu\* | 360,090 | 469,910 | 37 | 6 | 5-7 | 8 | 7-10 | | Kosofe | 465,716 | 637,381 | 90 | 19 | 16-23 | 27 | 22-32 | | Lagos Island\* | 145,082 | 586,394 | 89 | 6 | 5-7 | 24 | 20-29 | | Lagos Mainland\* | 222,841 | 429,281 | 82 | 8 | 7-10 | 16 | 14-19 | | Mushin | 430,987 | 901,239 | 142 | 28 | 23-33 | 59 | 49-70 | | Ojo | 415,515 | 642,093 | 70 | 13 | 11-16 | 21 | 17-25 | | Oshodi | 429,080 | 773,731 | 119 | 23 | 20-28 | 42 | 35-50 | | Shomolu | 275,273 | 699,106 | 168 | 21 | 18-25 | 54 | 45-64 | | Surulere | 343,002 | 869,080 | 64 | 10 | 9-12 | 26 | 22-31 | | Lagos State(Base Case) | 6,216,358 | | 88 | 250 | 210-300 | | | | Lagos State(Sensitivity) | | 11,970,630 | 90 | | | 500 | 420-590 | \\mathbf{P M\_{10}} \\dot{\\mathbf{u}}\\mathbf{g}/\\mathbf{m}^{3} Note: \*LGAs where air monitors are located (mean monitored concentration over August 2020 to July 2021). Source: Author’s own elaboration. \\mathrm{p M}\_{10} †Short-term mortality (based on the ERF by Orellano et al. 2020) during the 2-month period of January and February, assuming an excess PM10 exposure equal to the difference of the average concentration for the months January and February and the average of the shoulder months December and March. * * * **FIGURE A1.12.** HEALTH BENEFITS FOR A REDUCTION IN PM2.5 AIR POLLUTION ACROSS ## LAGOS STATE –10 n –20 –23% –30 –38% –36% (IT#1) –40 –50 –55% (IT#2) –57% –60 –69% –70 –75% (IT#3) –80 –84% % change compared to current burde –86% (IT#4) –90 –95% (WHO AQG 2021) –100% –100 0510 15 20 25 30 35 40 45 50 Rollback (Reduction) in ambient air PM 2.5 concentration, µg/m3 18,000 15,852 (–100%) 16,000 14,000 IT #4 12,239 (–77%) WHO AQG 12,000 2021 14,279 (–90%) 10,000 IT #2 IT #3 8,000 7,307 (–29%) 10,477 (–66%) IT #1 6,000 4,533 (–29%) Averted Premature Deaths 4,000 2,000 05 10 15 20 25 30 35 40 45 50 Rollback (Reduction) in ambient air PM 2.5 concentration, µg/m3 Infant mortality Adult mortality (25+) Mortality (all ages) Morbidity episodes (all ages) _Source: Author’s own elaboration_ _Note: The top figure shows the relative mortality reduction compared to the current state, while the figure below shows the averted deaths for the base-case population_ and GHE baseline mortality rates. The absolute benefit is roughly doubled for the sensitivity-case population. Air Quality Management Planning for Lagos State * * * TABLE A1.14. IMPACT ASSESSMENT OF AIR LEAD CONTAMINATION IN IKORODU | Population scenario | Lead(Pb)air concentration μg/m3 | Children(under6yearsold) | | | | Adults(over40years) | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | Population | Pb blood | | IQ loss | | Cardiovascular deaths | Pb blood | | Pb attributable mortality | | | | μg Pb/dL | Child | Total | μgPb/dL | Deaths | 95%CI | | | | | | | Base Case | 1.35 | 125,499 | 5.4 | 6.21 | 779,349 | 475 | 2.7 | 285 | 191 | 346 | | Sensitivity Case | 1.35 | 163,824 | 5.4 | 6.21 | 1,017,347 | 621 | 2.7 | 373 | 250 | 453 | Source: Author’s own elaboration. A1.5.4. IMPACT OF LEAD EXPOSURE ON CHILDREN IQ AND CARDIOVASCULAR MORTALITY Table A1.14 illustrates the results of the impact assessment of lead contamination in Ikorodu based on measured air contamination (1.35 µg/m3 air lead). We estimated that every child in Ikorodu (125,500 according to the base case and 163,800 according to the sensitivity population) is significantly affected by lead exposure. The calculated loss of intelligence by each child is 6.21 IQ points, which represents a huge physical burden on the current generation and potentially a significant loss of future income. The total loss in IQ points for the two populations is 780,000 and 1,017,000, respectively. Also, the impact of lead on cardiovascular mortality is remarkably high: the attributable premature mortality is 285 and 373 deaths, according to the base case and the sensitivity population, respectively. The estimation of the health burden of disease in Lagos, Nigeria, indicates that air pollution from PM2.5 poses a serious public health hazard, especially among children younger than 5 years. The PWE is high, reaching 47 µg/m3, a value nearly 10 times higher than the new (1.35~\\upmu\\mathrm{g/m^{3}} \\mathrm{M}}{\_}{{2.55} 47;\\upmu\\mathrm{g}/\\mathrm{m}^{3} A1.6. DISCUSSION AND CONCLUSION 5\ \\mumu mathrmmathrm g/m\\mathrm{^33} recommended WHO air quality guideline of 5 µg/m3 (WHO 2021). Urgent action to reach the WHO IT 1 (35 µg/m3) is therefore recommended. The overall impact on mortality across the population of Lagos State is responsible for 15,850 to 30,350 premature deaths per year, with the largest contribution from infant mortality (between 7,800 and 14,900 infant deaths). For adult mortality, the impact is larger for cardiovascular diseases. The impact on morbidity, especially pneumonia and other acute respiratory conditions, in children 0–5 years (between 182,000 and 349,000) is particularly worrisome. Other outcomes were also estimated and they contribute to increasing the overall burden. (35\ \\mathrm\\mumu\\mathrm{g}/\\mathrm{m}^{3}) Two additional critical contributions should be added to the estimates’ loss of life from long-term exposure to PM2.5: (a) the impact of the daily high levels of PM10 during the Harmattan period, particularly during January and February and (b) industrial air pollution in Ikorodu with the relevant lead contamination, which accounts for a sizable loss of intellectual capacity in children (a total of 780,000 to more than 1 million IQ points at the population level) and a high attributable cardiovascular mortality in that particular LGA (285 to 373 premature cardiovascular deaths). The quantified health burdens should be interpreted as conservative estimates because the additional impact from direct exposure to other critical pollutants (for example, gaseous air pollutants such as NO2 and SO2) has not been quantified in this work. A preliminary estimate of the potential attributable burden on mortality from direct NO2 exposure, for example, could add a further 10 percent \\mathrm{N O\_{2}} to the PM2.5 mortality. The adverse health effects from exposure to secondary inorganic aerosols (a component of PM) created through chemical transformation of NO2 and SO2 precursor emissions are already included in the PM2.5 impact assessment. \\mathrm{M}\_{2.5} \\operatorname{a} \\mathrm{N O\_{2}} \\mathrm{S O\_{2}} \\mathrm{P M}\_{2.5} The exposure assessment, one of the most important contributions of this study, is based on an extensive monitoring program of air pollution that has been set up in several locations and with standardized procedures and quality controls. The results of the monitoring program have been coupled with the results of a dispersion model and with population data to estimate PWEs by LGA. In this way, the concentration values are referred to the population, which is the target of the HIA. We have considered a variety of possible outcomes, encompassing both mortality (natural mortality, cause-specific mortality) and several morbidity outcomes. We have addressed not only PM2.5 but also the complementary contributions of daily levels of PM10, mainly attributable to Sahara desert dust, and the lead contamination in Ikorodu. Children are the segment of the population most affected by air pollution as they suffer from extraordinarily high infant mortality, experience frequent episodes of pneumonia and other respiratory disorders, and have to cope with a large limitation of their intellectual capability. It amounts to irreversible damage to the next generation. Finally, we have considered several methodological aspects in our assessment (exposure estimation, choice of the ERFs, alternative demographic assumptions) to overcome the main limitations described further below. \\mathrm{P M}\_{2.5} The HIA for Lagos refers to the most recent period of ambient air pollution monitoring—August 2020 to July 2021. This is the period with the most accurate measurement of air pollution. On the other hand, the other data for the HIA refer to a preceding period (that is, 2018 population data and available health statistics for 2017 and 2018). We believe that the error induced by this choice is minimal because the recent mortality rate has trended lower over the past decade, although at the 2022. This is the period with the most accurate measurement of air pollution. On the other hand, the other data for the HIA refer to a preceding period (that is, 2018 population data and available health statistics for 2017 and 2018). We believe that the error induced by this choice is minimal because the recent mortality rate has trended lower over the past decade, although at the has trended lower over the past decade, although at the same time population growth has been observed. The net effect is that our estimates are on the conservative side. In addition, it should be noted that the measurement period occurred during the COVID-19 pandemic, which has affected Africa and Nigeria as well, with a decrease in economic activity as reflected by the change in the internal gross product, a 3.5 percent drop in 2020 at the national level compared to the previous year when there was no COVID-19. This aspect makes our assessment for 2020–2021 somewhat conservative in comparison to the air pollution data probably experienced in past years. The most relevant uncertainty regarding our work is due to the difficulty in the estimation of the population at risk. Two different sources have been considered in this work because they provide potential extremes of the population size estimate. Assumptions about age distribution across different LGAs, often driven by operational choices, are another source of uncertainty. The difficulties in such estimations stem from the large size of slum settlements that have become a prominent feature of the urban landscape of Sub-Saharan Africa, and from the dynamic nature of this population (Amegah 2021; Thomson et al. 2021). We are confident that our sensitivity choices, though imperfect and leading to a broad spread in the estimates, are the best approach to characterizing the potential size range of the Lagos population. Another concern about the estimates is related to the absence of reliable baseline health data for the entire population. The value of good-quality mortality data for public health is widely acknowledged. While effective civil registration systems remain the “gold standard” source for continuous mortality measurement, in most African countries fewer than 25 percent of deaths are registered, and it appears to be no different in Lagos (Joubert et al. 2012). In addition, only a fraction of the hospital institutions (the public sector) register mortality and morbidity statistics, and a large fraction of health care providers do not release regular information. This difficulty is coupled with the traditional lack of medical certification for persons dying at home. We have used two sources of mortality information related to Nigeria (GBD and WHO) and have scaled down to Lagos, accounting for the differences between national and local age distributions. For hospitalizations, we have used the registrations of the events in the public sector with the strong assumption that the private sector has a proportionally similar load of patients. Finally, it is clear that a source-specific HIA was not performed as a clear partition of PM2.5 exposure data was not available. \\mathrm{P M}\_{2.5} * * * Before comparing the present HIA with other evaluations conducted worldwide and in Africa, it is worth noting the strengths and limitations of the present work. There are only a few examples of HIAs in Africa. Wheida et al. (2018) notably conducted an HIA to quantify the mortality attributable to long-term exposure to PM2.5, NO2, and O3 in Greater Cairo (Egypt). As in Lagos, PM2.5 concentrations vary from 50 to over 100 µg/m3 in the different sectors of the Egyptian megacity, with an average concentration of 75 µg/m3. In the population older than 30 years, 11 percent of the natural mortality could be attributed to PM2.5. No assessment of infant mortality and childhood morbidity was conducted. In Ethiopia, Kumie et al. (2021) performed real-time monitoring of PM2.5 concentrations and assessed the \\mathrm{M}\_{2.5} {mathrm\\mathrm O\_{2}}}\\end{array {\\mathrm{P M}}\_{2.5}, \\mathrm{P M}\_{2.5} {\\bf O}\_{3} 42.4~\\upmu\\mathrm{g}/\\mathrm{m}^{3} \\cdot100,\\upmu\\mathrm{g/m^{2}} 75~\\upmu\\mathrm{g/m^{3}}}\\end{array \\mathrm{M}\_{2.5} 10~\\up{\ up mu\\mathrm{g}m^{3}} health impact in Addis Ababa. After a continuous measurement of 3 years, the annual average PM2.5 concentration was found to be 42.4 µg/m3. The PM2.5 related mortality was estimated at 2,043 premature deaths, assuming a counterfactual equal to 10 μg/m3. Finally, in Ghana, a series of studies are ongoing in Accra to address various sources of air pollution such as waste management (Kanhai et al. 2021) and transportation (Garcia et al. 2021). {\\mathrm{P M}}\_{2.5}. \\mathrm{M}}{\_}{22.5} These studies, however, rely on effect estimates from other parts of the world because data from the African continent are largely deficient due to low access to goodquality health care, the high prevalence of infectious diseases, and different sources of air pollutants. As a TABLE A1.15. COMPARISON OF CURRENT ESTIMATES OF PM2.5 MORTALITY RATES IN LAGOS STATE TO ESTIMATES FROM PREVIOUS WORK BY CROITORU, CHANG AND KELLY (2020) \\mathsf{P M}\_{2.5} | Risk model | Croitoru, Chang,and Akpokodje(2020) | This study | | --- | --- | --- | | IER functions for deaths due to cardiovascular and respiratory plus lung cancer and diabetes | | PM$\_{2.5}$ concentration:47μg/m$^{3}$ based on 1-year,2020-21,measuring campaign | | 2019 IER functions(GBD2020) | | | | Base case population:13.3 million | | | | Mortality rate(per10$^{3}$):38.5 | | | | Sensitivity population:25.6 million | | | | Mortality rate(per10$^{3}$):37.0 | | | | IER functions for deaths due to cardiovascular and respiratory plus lung cancer and diabetes | PM$\_{2.5}$ concentration:68μg/m$^{3}$Population size:24.4 millionGBD2018IER functionMortality rate(per10$^{3}$):45.9 | PM$\_{2.5}$ concentration:68μg/m$^{3}$,same as Croitoru,Chang,and Akpokodje(2020)2019 IER functions(GBD2020) | | Base case population:13.3 million | | | | Mortality rate(per10$^{3}$):47.0 | | | | Sensitivity population:25.6 million | | | | Mortality rate(per10$^{3}$):45.5 | | | | GEMM for NCDand=d lower respiratory illnesses plus Heft-Neal et al.(2018)for infant mortality | | PM$\_{2.5}$ concentration:47μg/m$^{3}$based on 1-year,2020-21,measuring campaign | | Base case population:13.3 million | | | | Mortality rate(per10$^{3}$):119.2 | | | | Sensitivity population:25.6 million | | | | Mortality rate(per10$^{3}$):118.6 | | | :68,\\upmu\\mathrm{g/m^{3}} \\mathrm{P M}\_{2.5} 47,\\upmu\\mathrm{g}/\\mathrm{m}^{3} 10^{5}\ result, the health effects in Africa are likely underestimated (Abera et al. 2021). The paper by Heft-Neal et al. (2018), for example, found that in the African context, a 10 μg/m3 increase in PM2.5 concentration was associated with a 9.2 percent rise in infant mortality. PM2.5 concentrations were responsible for 22 percent of infant deaths in the 30 countries of Africa considered in the study. This was equivalent to 449,000 additional infant deaths in 2015, an estimate that was more than three times higher than previous estimates (Heft-Neal et al. 2018). Finally, the recent work by Fisher et al. (2021) should be noted because they conducted an HIA for air pollution for the entire continent of Africa and indicated that ambient air pollution is increasing across the continent. In the absence of a deliberate intervention, it will likely increase morbidity and mortality, which will diminish economic productivity, impair human capital formation, and undercut development. 10,\\upmu\\mathrm{g}/\\mathrm{m}^{3} \\mathrm{M}\_{2.5} \ \\mathrm{p M}\_{2.5} In 2020, Croitoru, Chang and Kelly published the first HIA of the burden of fine particulate matter in Lagos State. According to this study, in 2018, 11,200 premature deaths (45.9 deaths per 100,000 population) were attributed to exposure to PM2.5 air pollution. The mortality was quantified using the 2017 version of the cause-specific IER functions (GBD 2018). In Table A1.15, we compare our mortality figures with the estimates calculated by Croitoru, Chang and Kelly (2020) using the 2019 IER functions (GBD 2020) for cardiovascular and respiratory mortality, lung cancer, and diabetes deaths. We also provide results based on the relationships of Heft-Neal et al. (2018) for infant mortality, and GEMM (Burnett et al. 2018) for deaths in the broader category of NCDs plus lower respiratory illnesses, for various Lagos State population choices (base case versus sensitivity), with different values of the annual PM2.5 exposure—47 μg/m3 used in this study based on the 1-year measuring campaign 2020–2021 and 68 μg/m3 used in Croitoru, Chang and Kelly (2020). The baseline mortality was estimated using the WHO GHE hazard rates. Our mortality rates are consistent with the results of Croitoru, Chang and Kelly (2020) when assuming the same PM2.5 exposure (68 μg/m3) and using the same impact risk model (GBD IER), but our mortality estimates increase by a factor of 2.5 when \\mathrm{P M}\_{2.5} 44\ \\mathrm{\\mumu/m^{3}} 68,\\upmu\\mathrm{g}/\\mathrm{m}^{3} switching from the IER model to the GEMM and Heft- Neal et al. relationships. This difference is due in part to the size of the baseline mortality used by the different models (Table A1.9), but, more importantly, the difference is related to the shape of the ERFs (Figure A1.7). For instance, the rate of decrease in the health risk at higher exposures using the GEMM relationship is much less than predicted by the IER model. In conclusion, the work illustrates a dramatic situation in Lagos that highlights the large burden of PM air pollution on public health. A future analysis would benefit from greater knowledge about exposure assessment, possibly source-specific, and systematic collection of demographic and health data. Further, it would be useful in follow-up analyses to undertake regional and/or local epidemiologic studies in Lagos so that ERFs would better reflect local conditions. 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The data relevant for the HIA are synthesized in the tables below. | Disease | ICD-10 | Cases | Deaths | Comment | | --- | --- | --- | --- | --- | | Ischemic heart disease(IHD) | 120-25 | 5 | 0 | Ages 15+: four cases | | Stroke | | 842 | 171 | Listed as code 170 | | Chronic obstructive pulmonary disease(COPD) | J40-44 | 12 | 0 | Asthma(J45):159 cases(no deaths) | | Lung cancer | C30-39 | 31 | 1 | Ages 15+:28 cases;1 infant death | | Diabetes | E10-14 | 556 | 35 | Ages 15+:541 cases&35 deaths | | Pneumonia | J12-18 | 1,412 | 46 | Under5:1,221 cases&41 deaths | | Other Acute lower respiratory infections(ALRI) | J20-22 | 276 | 8 | Under5:227 cases&3 deaths | * * * TABLE A2.2. SHARE OF TOTAL INPATIENT HOSPITAL ADMISSIONS BY DISEASE | Disease | Incidences | Deaths | | --- | --- | --- | | Circulatory | 27.0% | 65.5% | | IHD | 0.2% | - | | Stroke | 26.9% | 65.5% | | Respiratory | 55.2% | 21.1% | | COPD | 0.4% | - | | Lung cancer | 1% | 0.4% | | ALRI | 53.9% | 20.7% | | Diabetes | 17.7% | 13.4% | TABLE A2.3. OUTPATIENT HOSPITAL ADMISSIONS, 2017 | Disease | Incidences | Deaths | | --- | --- | --- | | Circulatory | 15.7% | 65.3% | | IHD | 3.3% | - | | Stroke | 12.4% | 65.3% | | Respiratory | 47.3% | 22.4% | | COPD | 1.3% | - | | Lung cancer | 0.01% | 0.5% | | ALRI | 46.0% | 21.9% | | Diabetes | 37.0% | 12.2% | * * * As a comparison to the Lagos data, the relative distribution of deaths for six GBD causes of death at the national level in 2019 (IHME 2021) is reported in the table below. | IHD+Stroke | LRI | COPD | LC | DM | 6-COD Total | | --- | --- | --- | --- | --- | --- | | 38.1% | 49.5% | 4.0% | 1.8% | 6.5% | 349,146 | Note: The mortality ratio CVM (cardiovascular mortality) to LRI (lower respiratory infections) is about 3:1 in Lagos versus 0.77 at the national level. A2.2. SENSITIVITY CALCULATIONS USING AN ALTERNATIVE ASSESSMENT OF THE PWE In the following tables and figures, results of a sensitivity assessment of the exposures are presented: for non-monitored LGA, PM2.5 concentration estimates have been assigned based on the LGA’s proximity to the nearest monitoring station. | Local Government Area (LGA) | Longitude(deg) | Latitude(deg) | Land Area(km²) | Population(all ages) | | PWE,mg/m³ | | | --- | --- | --- | --- | --- | --- | --- | --- | | Base Case | Sensitivity | PM\_{2.5}$ | $PM\_{10}$ | | | | | | Agege | 3.316 | 6.623 | 17.0 | 673,840 | 1,507,591 | 46 | 124 | | Ajeromi-Ifelodun | 3.337 | 6.456 | 13.9 | 1,003,027 | 2,094,583 | 42 | 105 | | Alimosho\* | 3.255 | 6.576 | 137.8 | 1,925,702 | 2,987,306 | 46 | 124 | | Amuwo Odofin | 3.279 | 6.439 | 179.1 | 480,086 | 766,111 | 29 | 74 | | Apapa | 3.371 | 6.435 | 38.5 | 325,412 | 762,336 | 42 | 97 | | Badagry | 2.914 | 6.442 | 443.0 | 346,930 | 555,162 | 29 | 74 | | Epe | 3.973 | 6.553 | 965.0 | 265,212 | 472,292 | 29 | 74 | | Eti-Osa\* | 3.536 | 6.452 | 299.1 | 414,147 | 1,435,282 | 29 | 74 | | Ibeju-Lekki | 3.911 | 6.454 | 653.0 | 171,900 | 145,263 | 29 | 74 | | Ifako-Ijaye | 3.309 | 6.665 | 43.0 | 624,214 | 1,086,220 | 46 | 124 | | Ikeja\* | 3.350 | 6.604 | 49.9 | 463,507 | 946,703 | 41 | 106 | | Ikorodu\* | 3.566 | 6.612 | 345.0 | 770,410 | 1,005,551 | 97 | 171 | | Kosofe | 3.399 | 6.600 | 84.4 | 996,396 | 1,363,919 | 41 | 106 | | Lagos Island\* | 3.392 | 6.454 | 9.3 | 310,402 | 1,254,812 | 42 | 105 | | Lagos Mainland\* | 3.383 | 6.499 | 19.6 | 476,766 | 918,609 | 42 | 97 | | Mushin | 3.347 | 6.530 | 14.1 | 922,094 | 1,928,542 | 42 | 97 | | Ojo | 3.153 | 6.454 | 182.0 | 888,990 | 1,374,002 | 29 | 74 | | Oshodi | 3.314 | 6.542 | 42.0 | 918,014 | 1,655,691 | 42 | 97 | \\mathrm{M}\_{2.5} (\\mathbf{k m}^{2}) \\mathbf{P M}\_{2.5} \\mathbf{P M\_{10}} \\mathrm{E i O O s}{}^{\*} * * * TABLE A2.5. (Continued) | Local Government Area (LGA) | Longitude(deg) | Latitude(deg) | Land Area(km2) | Population(all ages) | | PWE,mg/m3 | | | --- | --- | --- | --- | --- | --- | --- | --- | | Base Case | Sensitivity | PM2.5 | PM10 | | | | | | Shomolu | 3.383 | 6.538 | 14.6 | 588944 | 1496003 | 42 | 97 | | Sunilere | 3.345 | 6.492 | 27.1 | 733851 | 1859727 | 42 | 97 | | Lagos State(Base Case population) | | | 3577 | 13299845 | | 43 | 105 | | Lagos State(Sensitivity population) | | | 3577 | | 25615703 | 42 | 103 | (\\mathbf{d e g}) (\\mathbf{k m}^{2}) \\mathbf{P M}\_{2.5} \\mathbf{P M\_{10}} Source: Author’s own elaboration. FIGURE A2.1. AMBIENT AIR QUALITY IN LAGOS STATE AND LGAS FOR PWE SENSITIVITY ANALYSIS Source: Author’s own elaboration. Note: The six LGAs where daily ambient concentrations were monitored during the monitoring campaign between August 2020 and July 2021 are highlighted by the gray boxes along the y-axis on the left. * * * TABLE A2.6. PM2.5ATTRIBUTABLE HEALTH BURDENS FOR PWE SENSITIVITY ANALYSIS, BASE CASE POPULATION | Local Government Area (LGA) | Infant Mortality | Total Mortality\* (all ages) | | Lower Respiratory Infections Children under 5-years | | Onset Chronic Bronchitis Adults over 27 years | | Restricted Activity Days All ages (in thousands) | | Hospital Admissions All ages, cardiovascular and respiratory | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | | Deaths | Deaths | 95%CI | Cases | 95%CI | Cases | 95%CI | Cases | 95%CI | Cases | 95%CI | | Agege | 400 | 820 | 520-1,060 | 9,350 | 2,870-16,410 | 810 | 410-970 | 2,300 | 2,080-2,560 | | 2-145 | | Ayromi-Ileodum | 550 | 1,140 | 720-1,480 | 12,910 | 3,910-23,160 | 1,110 | 540-1,360 | 3,140 | 2,840-3,490 | 100 | 3-198 | | Alimosho | 1,130 | 2,320 | 1,470-3,020 | 26,730 | 8,220-46,890 | 2,320 | 1,180-2,760 | 6,580 | 9,590-7,310 | 211 | 6-415 | | Ammuo Odofin | 190 | 410 | 260-550 | 4,550 | 1,310-8,760 | 420 | 190-550 | 1,060 | 960-1,190 | 33 | 1-67 | | Aapapa | 180 | 370 | 230-480 | 4,200 | 1,270-7,530 | 340 | 160-430 | 1,020 | 920-1,140 | 33 | 1-64 | | Badagev | 140 | 300 | 190-400 | 3,280 | 9,50-6,330 | 300 | 140-400 | 770 | 690-860 | 24 | 1-48 | | Epe | 100 | 220 | 140-300 | 2,510 | 730-4,840 | 230 | 100-300 | 590 | 530-660 | 18 | 1-37 | | Eir-Osa | 160 | 350 | 220-470 | 3,920 | 1,130-7,560 | 360 | 160-470 | 920 | 830-1,030 | 29 | 1-57 | | Ibque-Lekki | 70 | 150 | 90-200 | 1,630 | 470-3,140 | 150 | 70-200 | 380 | 340-430 | 12 | 0-24 | | Ikako-Jaye | 370 | 760 | 90-980 | 8,660 | 2,660-15,290 | 750 | 380-900 | 2,130 | 1,930-2,370 | 68 | 2-134 | | Ikeya | 250 | 520 | 330-670 | 5,860 | 1,770-10,570 | 510 | 250-630 | 1,420 | 1,200-1,580 | 45 | 1-89 | | Ikorodu | 810 | 1,510 | 1,000-1,860 | 18,210 | 6,710-23,370 | 1,060 | 610-1,190 | 5,170 | 4,730-5,670 | 177 | 6-334 | | Kosife | 530 | 1,100 | 700-1,450 | 12,600 | 3,800-22,710 | 1,100 | 530-1,350 | 3,060 | 2,760-3,400 | 97 | 3-192 | | Lagos Island | 170 | 350 | 220-460 | 4,000 | 1,210-7,170 | 340 | 170-420 | 970 | 880-1,080 | 31 | 1-61 | * * * | Local Government Area (LGA) | Infant Mortality | Total Mortality\* (all ages) | | Lower Respiratory Infections Children under 5-years | | Onset Chronic Bronchitis Adults over 27-years | | Restricted Activity Days All ages (in thousands) | | Hospital Admissions All ages, cardiovascular and respiratory | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | Deaths | Deaths | 95% CI | Cases | 95% CI | Cases | 95% CI | Cases | 95% CI | Cases | 95% CI | | | Aggee | 890 | 1,820 | 1,150-2,360 | 20,930 | 6,430-36,710 | 1,810 | 920-2,160 | 5,150 | 4,660-5,720 | 165 | 5-325 | | Ayeromi-Icelodun | 1,140 | 2,360 | 1,500-3,090 | 26,960 | 8,170-48,360 | 2,310 | 1,120-2,840 | 6,560 | 5,930-7,290 | 209 | 6-413 | | Alimosio | 1,760 | 3,610 | 2,290-4,680 | 41,470 | 12,750-72,750 | 3,590 | 1,830-4,280 | 10,200 | 9,230-11,340 | 327 | 10-644 | | Amuwo Odofin | 300 | 650 | 410-870 | 7,250 | 2,100-13,990 | 670 | 300-480 | 1,700 | 1,530-1,900 | 53 | 2-106 | | Apapa | 420 | 870 | 540-1,120 | 9,840 | 2,980-17,640 | 800 | 380-1,000 | 2,400 | 2,170-2,660 | 76 | 2-151 | | Badagry | 220 | 480 | 300-630 | 5,260 | 1,520-10,140 | 490 | 220-640 | 1,220 | 1,110-1,370 | 39 | 1-77 | | Epe | 190 | 410 | 250-540 | 4,470 | 1,290-8,620 | 410 | 180-540 | 1,050 | 940-1,170 | 33 | 1-66 | | Eir-Osa | 570 | 1,230 | 770-1,630 | 13,590 | 3,930-26,200 | 1,260 | 560-1,640 | 3,180 | 2,870-3,550 | 100 | 3-199 | | Bogju-Lokki | 60 | 130 | 80-170 | 1,380 | 400-2,650 | 130 | 60-170 | 320 | 200-360 | 10 | 0-20 | | Iako-Jipye | 640 | 1,310 | 830-1,700 | 15,080 | 4,630-26,450 | 1,310 | 670-1,560 | 3,710 | 3,360-4,120 | 119 | 4-234 | | Ikja | 510 | 1,050 | 660-1,370 | 11,970 | 3,610-21,530 | 1,050 | 510-1,280 | 2,900 | 2,630-3,230 | 92 | 3-183 | | Ikorodu | 1,060 | 1,980 | 1,310-2,420 | 23,770 | 8,760-33,120 | 1,390 | 790-1,560 | 6,740 | 6,170-7,400 | 231 | 7-435 | | Kosofe | 730 | 1,520 | 960-1,980 | 17,250 | 5,210-31,090 | 1,510 | 730-1,850 | 4,180 | 3,780-4,660 | 133 | 4-263 | * * * \\mathsf{P}\\mathsf{M}\_{2.5} Base Case 2018 Population Lagos State Base Case 2018 Population * * * Lagos State Sensitivity Case 2018 Population Source: Author’s own elaboration. Note: Adult mortality quantified using GEMM and infant mortality using Heft-Neal et al. (2018). Baseline rates obtained from the WHO GHE database for 2018. * * * ## ANNEX 3 # ECONOMIC AND FINANCIAL ASSESSMENT: POLICY, INVESTMENT, AND COST # ASSUMPTIONS The use of “economic” or social cost is a useful concept for assessing activities that have environmental externalities, such as air pollution, because it includes health and other kinds of damage that are not typically reflected in the market. Because con- sumer decisions typically rely on market prices, it is also important for policy makers to ensure that financial costs (reflected in “market prices”) are at appropriate levels to reduce air pollution, for example, by taxing air pollution or subsidizing clean fuels. Cost-effectiveness analysis allows different control measures to be compared based on their cost to reduce air pollution (Naira/US dollar per ton of PM2.5 reduced). The costs for implementing different interventions have been estimated using data from projects in Lagos and elsewhere. Where possible, the proposed interventions have been selected from projects already undertaken in Lagos or in Nigeria. Many potential air quality interventions, such as public transport or the power grid, require public investments while others, such as emissions testing for vehicles or enforcing emissions standards for industry, require regulatory costs. To comply with emissions standards, such as emissions control equipment or clean fuels, there needs to be private costs. These expenditures will be referred to as compliance costs. For some air quality interventions, such as electricity tariffs from additional power sold by the grid or fare revenue from public bus or rail service, there may be additional revenue from improved service. The main benefits of controlling air pollution are the expected reductions in health impacts. Premature mortality attributable to air pollution in Lagos has been estimated in this study at between 15,000 and 30,000 deaths per year, with infants under 1 year accounting for over half of the deaths. The value of premature mortality has been calculated between 1.9 and 3.6 percent of Lagos’ GDP based on lost productivity, Air Quality Management Planning for Lagos State and between 3.7 and 7.2 percent using the VSL. By comparing the costs and benefits of reducing PM2.5 for each intervention, it is possible to (a) estimate how much it would cost to reduce emissions and (b) estimate and compare the cost-effectiveness of different air pollution control measures. \\mathrm{P M}\_{2.5} For the economic and financial analysis, the share of air pollution from the key sectors has relied on the estimates from the emissions inventory and source apportionment 42 work from the study. A3.1. AQM CONTROL STRATEGIES BY SECTOR SOLID WASTE Based on the air pollution monitoring and emissions inventory conducted for the study, the burning of MSW, both collected and uncollected, emerges as a major contributor to PM2.5 air pollution in Lagos. Because such a large share of air pollution in Lagos originates from the open burning of solid waste, a critical policy for Lagos (and LAWMA) is to increase the amount of solid waste that is collected and eliminate the open burning of solid wastes at landfills. Government policies for waste management can help minimize the amount of waste that is generated by ensuring that markets for recyclables and organic material are developed. Collection fees for solid waste could help cover the costs of collecting and disposing of solid waste. Regardless of collection fees, to reduce air pollution from solid waste, it is essential that the municipality collect as much solid waste as possible and ensure that the MSW collected is not burned. \\mathrm{P M}\_{2.5} The remainder of the waste, much of it organic, is left to decompose. Modern sanitary landfills are built to avoid contaminating groundwater and surface water and to capture the CH4 produced from decomposing organic matter. CH4 captured can be used to generate electricity or supplied to other energy users in the form of natu- 43 ral gas. Because CH4 is a powerful GHG, landfills that capture it can earn carbon credits through mitigating the release of CH4. \\mathrm{C H\_{4}} \\mathrm{C H\_{4}} \\mathrm{C H\_{4}} The per capita generation of MSW in Lagos has been estimated at 0.75 kg per day, which translates to 15,000– 18,000 tons of MSW per day for a city of 20–25 million people. Open burning is used as a way for households and enterprises to dispose of uncollected MSW, and open burning occurs at landfills through intentional burning and the spontaneous combustion of waste. It is assumed that through additional investment in collection vehicles and landfills phased in over several years, waste collection could progressively increase from the current rate estimated at 54 percent (PwC 2021) to around 80 percent of total MSW generated. Because available land for landfills is not limitless and the value of land in Lagos is continues to rise as the city grows, it is important to reduce the overall amount of MSW that goes into landfills. Investments in recycling, composting, and incineration would reduce the amount of waste needed to be deposited in landfills. The analysis assumes that the organic fraction of MSW is 50 percent, the combustible fraction is 75 percent (organic 50 percent + paper 15 percent + plastic 10 percent) and that 25 percent of the collected and recyclable MSW (paper and plastic) is recycled. In addition to special handling procedures for hazard- \\mathrm{H\_{4}} * * * TRANSPORT The transport sector is one of several large contributors to PM2.5 air pollution in Lagos. Given its importance to the economy—moving people and goods—and the fact that it will undoubtedly grow, it is essential that air quality policy measures for the transport sector comprise a major part of Lagos’ AQM plan. Among the important measures to reduce PM2.5 emissions from the transport sector are (a) continued expansion and improvement of public transport; (b) improvement in emissions control among vehicle fleets such as trucks, buses, passenger cars, and motorcycles; and (c) the increased supply and guarantee of clean transport fuels in conjunction with stricter emissions standards for vehicle fleets. \\mathrm{M}\_{2.5} \\mathrm{M}}{\_}{22.5} (left\\operatorname a right)) INSPECTION AND MAINTENANCE I&M programs are a prerequisite for implementing vehicle improvement programs. The establishment of LACVIS44 in 2016 was an important development in the capability to monitor and enforce vehicle emission regulations, including the identification of gross polluters, mandatory maintenance, vehicle retirement, and retrofit programs. Currently, the program requires vehicles to be inspected and that they display their emissions certificates on the vehicle or face a fine. Based on experience elsewhere, a large share of vehicle air pollution has been found to come from a small fraction of vehicles, so-called “gross polluters.” In practice, this means that regulation and enforcement of vehicle emissions will be effective if it can control the worst 45 polluters (Krzyzanowski et al. 2005), which can be identified through I&M or roadside inspection. vehicle emissions. While improved vehicle standards cannot immediately replace Lagos’ old and highpolluting fleet, requiring that new vehicles meet stricter standards is an important start, including by providing incentives and penalties. The fixed number of legal ports of entry, and the fact that most secondhand vehicles originate from countries with well-established emission control regimes, imply that targeted efforts to verify and improve the emissions performance of secondhand vehicles are feasible. Although detailed information on the emissions standards of the vehicle fleet in Lagos is not available, limited survey data suggest that most of the fleet is older than 15 years. Although Nigeria agreed in 2018 to establish Euro 4 standards for new vehicle registrations along with 50 ppm fuel sulfur standards, this has not yet occurred. A new vehicle inspection program was established in 46 Lagos in 2016, which is an important step to ensure that vehicles are safe and that emissions control equipment is maintained. Because high vehicle emissions often tend to be concentrated in a small percentage of vehicles, vehicle inspection is important for removing “gross polluters” from the road for repair or scrappage. I&M, combined with improving emission standards for new vehicles, is important for reducing emissions from the vehicle fleet. MINIBUSES/DANFOS Upgrading the vintage of vehicle fleet could significantly reduce PM2.5 emissions. For example, if a Euro 1 vehicle 47 can be replaced by a Euro 4 vehicle, PM2.5 emissions this means that regulation and enforcement of could be reduced ninefold (see Table A3.1). Requiring that danfos be less than 16 years old—Euro 4 equivalent vehicles were introduced in Europe and the US in 2005— would ensure that the emissions control equipment for new and most used vehicles in Nigeria would be at least Euro 4-spec. To achieve the lower emissions from newer vehicles, it is necessary to improve fuel quality. Without lower sulfur fuels, the emissions control equipment (both catalysts and filters) on newer vehicles could be permanently destroyed. * * * TABLE A3.1. ALTERNATIVE VEHICLE TECHNOLOGIES FOR LARGE BUSES | Technology | Purchase cost(US$) | PM2.5 emissions(gPM2.5/km) | | --- | --- | --- | | Baselinea | 200,000 | 0.14 | | Clean diesel | 400,000 | 0.025 | | CNG | 450,000 | 0.009 | | Hybrid(diesel-electric) | 500,000 | 0.012 | | Electric | 750,000 | 0.003 | \\mathbf{P M}\_{2.5} (\\mathfrak{g P M}\_{2,5}/\\mathfrak{k m}) Note:a Baseline assumes Euro 1 diesel buses (properly tuned). [https://www.catf](https://www.catf/) .us/wp-content/uploads/2019/02/CATF\_Pub\_Diesel\_VS\_CNG.pdf. BUSES Many countries and municipalities have attempted to reduce air pollution by converting vehicle fleets to cleaner technologies such as “clean diesel,” natural gas (CNG/LNG), diesel-electric hybrids, or fully electric. The costs of establishing dedicated alternative fuel systems for vehicles include new fuel or motor systems as well as refueling stations. Because of the difficulties of guaranteeing clean diesel fuel free from adulteration, some cities have used this as a reason for moving to alternative fuels such as CNG or electric. Focusing on fleet vehicles such as taxis, buses, or delivery trucks has been a proven approach for alternative fuel vehicles, since this requires the establishment and maintenance of fewer refueling facilities, and conversion and maintenance can be handled by dedicated service personnel. It also allows the refueling facilities to maintain their own fuel quality, such as the ultralow sulfur diesel that is required for advanced catalysts and particulate filters. Given the global trend and falling costs of hybrid and electric vehicles, an evaluation of the costs of such vehicles should be undertaken sooner rather than later, particularly for fleet vehicles such as buses, taxis, and delivery trucks (Mufson and 48 Kaplan 2021). PUBLIC TRANSPORT The expansion of public transport should be considered an important way of improving the efficiency of transport in Lagos and of reducing both air pollution and GHG emissions. With support from international donors, Lagos has invested in both infrastructure and institutions to improve public transport, including BRT, light rail, and ferries. The upgrading and expansion of public transport in Lagos could have a large positive impact on air quality. Public transport investments are large and multi-year and must be justified largely on their transportation benefits rather than on their contribution to improving air quality. Although the air quality benefits of public transport can be large, public transport investments need to be evaluated on their long-term contribution to air quality rather than on their capacity to make an immediate positive impact on air quality. FREIGHT TRANSPORT Investments in alternatives to road transport for freight are under way in Lagos and should help relieve road traffic congestion and reduce air pollution. Major rail infrastructure such as the Lagos-Ibadan portion of the larger Lagos-Kano rail project will connect Apapa seaport and thus reduce the amount of truck traffic in central Lagos. As with BRT and light-rail projects, large investments in rail freight must be justified by their transportation benefits, such as reductions in shipping costs and delivery times. Nonetheless, investment in rail freight from the busy Apapa and Tin Can ports can reduce truck traffic and the air pollution they generate. * * * trucks, or the retrofitting of existing trucks with pollution controls such as catalysts and diesel particulate filters. Either option will likely be costly for truck owners, but the gains are also likely to be large, given the high share of PM2.5 that comes from diesel combustion. \\mathrm{P M}\_{2.} FUEL QUALITY Improved fuel quality can lower PM2.5 emissions through the introduction of more sophisticated emissions control systems on vehicles, such as catalysts and particulate filters that require the use of cleaner fuels. Many of the catalysts and particulate traps that are installed in vehicles that are imported, either new or used, into Nigeria and other countries would quickly become ineffective with Nigeria’s current fuel quality (assumed to be 1,424 ppm sulfur for gasoline and 2,389 ppm sulfur for diesel). Yet establishing stricter standards for transport fuels—gasoline, diesel, and marine fuel oil—has been hampered in Nigeria by fuel smuggling, including from illegal refineries in the Niger delta, and by the delay in the construction of the Dangote refinery. At 650,000 bpd, the Dangote refinery would be the largest in Nigeria and would meet the country’s refined petroleum product needs of around 600,000 bpd. Nigeria currently produces over 2.5 billion bpd of crude oil. In terms of fuel quality, the Dangote refinery is slated to produce Euro 6 standard fuels, meaning ultra-low sulfur diesel and gasoline (10 ppm sulfur). New fuel standards—150 ppm for gasoline and 50 ppm for diesel—were set to be introduced in Nigeria in 2017 but have not yet been implemented. \\mathrm{M}\_{2.5} \ \\mathrm{p M}\_{2.5} \\mathrm{P M}\_{2.5} The current fuel quality in Nigeria does not allow the effective operation of vehicle catalysts beyond Euro 1, a standard that was implemented in Europe in the early a standard that was implemented in Europe in the early 1990s. Properly functioning Euro 5 vehicles can lower emissions of PM Euro 1 vehicles. Reducing the sulfur content of petroleum fuels—gasoline, diesel, and fuel oil (marine)—can lower the production of secondary aerosols such as a standard that was implemented in Europe in the early 1990s. Properly functioning Euro 5 vehicles can lower 2.5 by a factor of 28 compared to Euro 1 vehicles. Reducing the sulfur content of petroleum fuels—gasoline, diesel, and fuel oil (marine)—can lower the production of secondary aerosols such as SOx, which have been estimated at 10 percent of PM2.5 emissions from the transport sector. To guard against fuel adulteration and protect the emissions control equipment in vehicles, it is necessary to ensure that fuel quality at fueling stations is maintained (Table A3.3). Requiring that petroleum products in Lagos meet higher-quality standards will reduce emissions not only from transport but also from other users of diesel fuel such as industry and the backup electricity generators used throughout Lagos State. INDUSTRY Industry is known to be a major contributor to air pollution in Lagos. Several high-polluting industries have moved away from central Lagos in recent years, and the government has sometimes assisted in their relocation. The government’s main role, however, is the monitoring and enforcement of air pollution standards, which may require automatic pollution monitoring equipment at major industrial plants. At the same time, helping industries convert to cleaner technologies or fuels, through training and technical assistance, can be an important way for them to remain competitive and improve their productivity and profitability. “Cleaner production” emerged in the 1980s and 1990s as a strategy for both reducing industrial pollution and facilitating the development of many high-tech and high value-added industries such as information and technology and food processing (World Bank 1998). Nigeria’s power sector is characterized by high technical and financial losses, and the current system cannot provide adequate electricity to the economy. As such, Nigeria has among the highest share of electricity provided by backup generators (“gensets”) in the world. These generators are expensive to operate, noisy, and highly polluting. Gensets in Lagos may account for as much as 40 percent of electricity generation (1,940 GWh) and as much as 90  percent of air pollution from power ELECTRICITY GENERATION generation (chapter 2). Long-term investment in power grid expansion and reliability would eventually reduce genset usage. Improvements to Nigeria’s power sector are critical for the sustainability of the system and the economy. By increasing the supply of electricity, economic losses would be reduced while tariff revenues would increase, generating considerable income to pay for the reforms. Gensets represent one of the least regulated sources of air pollution in Lagos. As noted earlier, improving the quality of diesel and gasoline suppled to Lagos would help reduce emissions from gensets. Currently, there are no emissions standards for electricity gensets in Nigeria. As in other countries, the emissions from such generators should be regulated, requiring (a) emission standards for electricity gensets, (b) improvements in fuel, and (c) the installation of pollution control equipment. OTHER A3.2. FINANCING AQM \\mathrm{M}\_{2.5} There are many options for reducing emissions from other pollution sources. Policies to minimize the amount of resuspended roads, such as paving, could reduce a large fraction of dust emissions. Lower-cost options such as watering or reducing speed limits on unpaved roads can also be effective in the short term and during the dry season. Likewise, policies to reduce crop residue burning, including bans, could be especially effective during the dry season when emissions are at their peak. Subsidizing the use of LPG among low-income households could reduce the use of solid fuels for residential cooking. While a detailed assessment of the costs of reducing emissions from other sectors has not yet been carried out, it is reasonable to assume that selective policies and investments could be effective in reducing the share of PM2.5 emissions by a few percent. PRIVATE FINANCING Access Bank. Access is a publicly listed commercial bank headquartered in Lagos. It has not only issued a green bond but equally invested in two green bonds issued by the Federal Government. (It may also have invested in the North South Power green bond in 2021.) Access Bank is well-positioned to take part in a subnational green bond, given its experience in the issuance and reporting obligations of its own green bond. Capital Assets. A privately held issuing house based in Lagos, Capital Assets acted as the financial adviser to the Federal Government in the issuance of the first and second green bonds in Nigeria, both of which were oversubscribed. Issuing houses are a key part of the process for issuance of capital market instruments. Capital Assets has a ready pool of institutional and other investors interested in green bonds. Nigerian Stock Exchange (NSE). The NSE is the premium exchange for trading of public instruments in Nigeria, with a capitalization of N83 trillion (US$202 billion). The NSE was a key player in the issuance of a green bond by the Federal Government and currently lists four green bonds on its platform. NSE is concerned with the additional reporting obligations associated with green bonds but believes that local capacity can be developed to support issuers in meeting their reporting obligations. MULTILATERAL RESOURCES Resources available through the multilaterals provide an avenue to mobilize additional funds—Figure A3.1 lists commitments by MDBs to climate finance. The MDBs most relevant to the LASG are the African Development Bank (AfDB), the European Investment Bank (EIB), the World Bank Group (WBG), and the Islamic Development Bank (ISDB). Others that are likely to have commitments not listed in figure A3.1 are Africa Finance Corporation (AFC) and International Finance Corporation (IFC). These institutions typically have accreditation with climate funds such as the Global Environment Facility (GEF) and the Green Climate Fund (GCF) and have internal programs designed to provide technical support in developing interventions to address climate issues. Figure A3.2 illustrates contributions to climate financing as of 2019 and shows an increase since the signing of the Paris Agreement in 2015. LASG can access funding for technical support to design relevant interventions or to create a blended approach to funding projects that can address air pollution. Table A3.1 provides an overview of potential funding from the World Bank Group that could be available to Lagos to address climate concerns, many of which would also improve air quality. The ACBP has several focal areas that overlap with the priorities of Lagos State that could address air pollution. FIGURE A3.1. CLIMATE FINANCE COMMITMENTS BY MDBS | CLIMATE FINANCE COMMITMENTS BY MDB | | | --- | --- | | African Development BankTotalUS$3,600millionFor low- and middle-income economiesUS$3,600million | Inter-American Development Bank GroupTotalUS$4,958millionFor low- and middle-income economiesUS$4,417million | | African Development BankTotalUS$7,073millionFor low- and middle-income economiesUS$7,068million | Islamic Development BankTotalUS$466millionFor low- and middle-income economiesUS$464million | | EUROPEAN Bank for Reconstruction and DevelopmentTotalUS$5,002millionFor low- and middle-income economiesUS$3,923million | World Bank GroupTotalUS$18,806millionFor low- and middle-income economiesUS$18,437million | | EUROPEAN Investment BankTotalUS$21,658millionFor low- and middle-income economiesUS$3,558million | | FIGURE A3.2. FUNDING SOURCES FOR CLIMATE FINANCING (INCLUDING PRIVATE SECTOR) * * * TABLE A3.2. WORLD BANK AFRICA CLIMATE BUSINESS PLAN (ACBP) FUNDING WINDOWS | IDA/IBRD | Indicator/commitment | Time period | Relevance to Africa | | --- | --- | --- | --- | | IDA19 | IDAS climate co-benefits share of total commitments will increase to at least 30 percent on average over FY21-23, with half supporting adaptation action. | FY21-23 | Africa share of US$53 billion, pro-rated, would mean US$5.3 billion per year from portfolio (or total of US$15.9 billion) | | WBG | The WBG is stepping up its climate support for Africa With continued strong support for IDA, our fund for the world's poorest countriesa this will provideUS$22.5 billion for Africafor climate adaptation and mitigation for the five years from 2021-25. | FY21-25 | Africa-focused; would be a summation of co-benefits from IDA and IBRD portfolio | | WBG | \[...\] in line with these new climate financing commitments and future direction of our Africa Business Planb more than half of the US$22.5 billion financing will be devoted to supporting adaptation and resilience in Africa. This will amount to about US$12 billion to US$12.5 billion over five years from 2021-25. | FY21-25 | Africa focused; would be a summation of adaptation co-benefits from IDA and IBRD portfolio | | IBRD | Increasing the climate co-benefit target of 28 percent by FY20 to an average of at least 30 percent over FY20-23, with this ambition maintained or increasing to FY30.c | FY20-23,and through 2030 | Bankwide target, no formal Africa target | Alignment with the ACBP. Lagos stands to benefit from support from the ACBP’s city interventions. Sectors such as energy and transport also provide a rationale for Lagos State to sieve its various plans to identify interventions that are consistent with the pollution solution and that could be funded through the commitments in the ACBP. The planned issuance of a green bond could benefit from technical support in the structuring or the provision of a blended financing approach to make the terms of issuance more sustainable. \\mathrm{P M}\_{2.5} A3.3. CONTROL COST ASSUMPTIONS The first step in estimating the cost-effectiveness of each sectoral intervention was to apportion total PM2.5 emissions across the sectors. A second simplifying assumption was that PM2.5 emissions are directly proportional to ambient concentrations. Using the average of PM2.5 source apportionment monitoring data of six sites (figure 10) and the baseline emissions inventory (Table A3.4), the following table apportions PM2.5 emissions (column 2) and ambient concentrations (column 3) to the five sectors (solid waste, industry, transport, power, and other). It is assumed that the cost of policies and actions are perfectly divisible to achieve a given level of air pollution reduction. \\mathrm{M}{}\_{\ ..}\ \\mathrm{P M}\_{2.5} * * * TABLE A3.3. ACTION AREAS IN THE ACBP Action area to support IDA-19 and Corporate climate actions and targets45 | Action area to support IDA-19 and Corporate climate actions and targets45 | | | | --- | --- | --- | | Business element | World Bank instruments for delivery on climate action | Timing | | Delivery of IDA and Corporate commitments | Resilient Cities and Green Mobility | FY21-23; FY24-26 | | CitiesIntegrated planning: multisectoral climate-smart urban and transport plans prepared with up-to-date data for at least five African cities30 cities with integrated, city-based resilience approachTarget of US$2 billion in investment financing for urban resilience-building activitiesGreen mobilitySupport 5 new BRTs in fast-growing African cities(making at least 50% of jobs accessible within an hour of commute)Secure maintenance to make 100,000 km of climate-resilient African roads | | | | Special Areas of Emphasis | | | | Macroeconomic planning and policy | | FY21-23; FY24-26 | | Increase engagement with ministries of finance and planning and other stakeholders on NDCsPromote concrete and systematic policy actions(IDA19)Analytics to inform policy action and design of prior actions in DPFs | | | | Green and Resilient InfrastructureSupports targets of Strategic Directions above, including | | FY21-23; FY24-26 | | Energy renewable energy.battery storage.renewable energy generation capacity | | | | Urbanlow carbon and compact urban planningintegrated, city-based resilience approach | | | Special Areas of Emphasis PUBLIC REGULATION VERSUS PRIVATE COMPLIANCE COSTS to as private compliance costs. Where possible, the net cost (investment minus revenue) of public investments, to as private compliance costs. Where possible, the net cost (investment minus revenue) of public investments, cost (investment minus revenue) of public investments, regulatory costs, and compliance costs have been estimated for each air quality measure. A notable exception mated for each air quality measure. A notable exception is the cost required by industrial enterprises to reduce their air pollution. Aside from the lack of information from industrial enterprises in Lagos, there is a wide range mated for each air quality measure. A notable exception is the cost required by industrial enterprises to reduce their air pollution. Aside from the lack of information from industrial enterprises in Lagos, there is a wide range from industrial enterprises in Lagos, there is a wide range of industrial processes that produce air pollution, making * * * TABLE A3.4. APPORTIONMENT OF PM2.5 EMISSIONS AND AMBIENT CONCENTRATIONS BY SECTOR \\mathsf{P}\\mathsf{M}\_{2.5} | | | | Air pollution reduction scenarios | | | | | --- | --- | --- | --- | --- | --- | --- | | Sectors | Share of PM2.5 emissions(t/yr)-% | Contribution to ambientPM2.5 levels(ug/m3) | 35ug/m3 | 25ug/m3 | 15ug/m3 | 10ug/m3 | | Solid Waste | 26 | 11.7 | -2.5 | -4.9 | -7.5 | -9.0 | | Industry | 20 | 9.0 | -1.2 | -4.0 | -6.5 | -8.0 | | Transport | 20 | 9.0 | -3.7 | -5.0 | -6.3 | -7.6 | | Power | 8 | 3.6 | -1.6 | -3.2 | -3.2 | -3.2 | | Other | 26 | 11.7 | -1.0 | -2.8 | -6.5 | -7.2 | | Total | 100 | 45 | -10.0 | -20.0 | -30.0 | -35.0 | \\mathbf{P M}\_{2.5} \\mathbf{P M}\_{2.5} (\\mathbf{t}/\\mathbf{y}\\mathbf{r})=% 25,\\mathbf{u g/m^{3}} 10,\\mathbf{u g/m^{3}} Source: Author’s own elaboration based on PM source apportionment. a cost assessment of private compliance costs for industry prohibitive. It is also assumed that at least part of the public regulation cost (such as vehicle emissions testing or solid waste collection) can be paid through licensing or user fees. SOLID WASTE Collection and disposal costs for MSW are calculated from waste collection fees charged by private concessionaires. The cost of household waste collection services in Lagos was estimated at US$5.87 per ton in 2014. Assuming this figure has increased to US$10 per ton today, the cost of collecting and disposing of 18,750,000 tons per day (0.75 kg/cap/day times 25 million) is US$73 million per year (Aliu et al. 2014). Composting assumes that the organic fraction (50 percent) collected is composted, with costs based on the EarthCare program in Lagos and the Terra Firma experience in Bangalore India (World Bank 2016b). The assumed cost of building a new sanitary Olusosun-size (100 acres) landfill, at US$500,000 per acre, is US$50 million. If recycling and composting are implemented, less landfill space (and the associated cost) is needed. MSW that is not recycled or composted is assumed to be landfilled or incinerated. For both composting and recycling investments and operating expenses, it is assumed that 80 percent of the costs can be offset through income from the sale of products, such as fertilizer from composting, and paper and plastics from recycling. INDUSTRY The public costs associated with the control of industrial emissions are assumed to be the monitoring and enforcement costs for the regulator, while the private sector will need to invest in pollution control measures to meet the standard. The main regulatory expense is assumed to be the monitoring and enforcement of industrial emission standards, including through the installation of automatic emissions monitoring systems on large industrial sources. Monitoring costs of US$100,000 are assumed for 1,000 enterprises, totaling US$100 million. Compliance costs have not been estimated but would include pollution control equipment, cleaner fuels, or energy efficiency measures. Cleaner production—covering emissions control equipment and cleaner fuels—would likely have a net benefit for industries such as food and beverages, information and technology, and electronics. The payback for energy efficiency measures is typically short for many industrial investments (pumps, motors, boilers, fans) and would lead to reductions in emissions in proportion to reductions in fuel consumption. TRANSPORT Euro 3 and Euro 4 vehicle standards are assumed to be achieved through testing, compliance, and enforcement, phased in over time. More than half of light-passenger vehicles are less than 16 years old and likely therefore to have Euro 4 equipment already installed, both for new and used vehicles. Modest costs are assumed for these vehicles to meet Euro 3 standards—for example, addition of new catalytic converters—but many other vehicles would need to either be retired or retrofitted to comply. Most used vehicles in Nigeria come from Europe, the US, and Japan, all of which have established emissions control standards of Euro 4 or higher. Regulation costs are assumed to include emissions testing for all vehicles, ranging from US$4 (motorcycles) to heavy trucks (US$30), and it is expected that the emissions testing will be done by the current testing service (LCVIS). Compliance costs are assumed to be the expenditures vehicle owners need to make in order to meet Euro 3 and 4 emissions standards. Compliance costs will be greatest for heavy trucks, which are assumed to be among the oldest fleets in Lagos. For passenger vehicles, minivans (including danfos), and motorcycles, private compliance costs are assumed to be the costs of catalytic converters, installation costs, plus the costs of clean fuel. The incremental cost of clean fuels (on average US$0.02–0.03 per liter) would be paid by vehicle owners in the form of higher diesel and gasoline prices. CLEAN FUEL such fuels in sufficient quantity, it is assumed that fuel is imported. The incremental cost for low-sulfur fuels is calculated at US$76 million per year (Miller et al. 2017) with the costs borne by consumers at around 5 percent increase in the cost, or US$0.02–0.03 per liter. Given that all petroleum products are currently imported, the real challenge in obtaining clean fuel in Lagos (and Nigeria in general) is to prevent fuel adulteration. Additional regulatory costs are assumed to ensure that fuel quality is guaranteed, including fines and potential cancellation of retail licenses for violations of fuel quality standards. A program to guarantee fuel quality, as has been implemented in other countries, can help ensure the quality of fuel in the face of numerous incentives to adulterate it (Gwilliam Kojima, and Johnson 2004). ELECTRICITY It is assumed that the majority of emissions from the power sector are from gensets in the residential, commercial, and industrial sectors rather than from the natural, gas-fired power plants on the electric grid. The natural, gas-fired power plants on the electric grid. The cost of power sector reform in Lagos has been calculated at around US$100 million, based on World Bank project costs for increasing power supply in the amount currently provided by gensets. Several efforts are under way to improve the efficiency of Nigeria’s power sector, estimate of the costs of improving the supply and reliability of the grid, measured as additional kWh that are available to consumers, and thus generating additional revenues from electricity sales. The increase in tariff revenue from increased power generation associated with the reforms is calculated at over US$200 million per year, meaning that the reforms would pay for themselves in less than 2 years. estimate of the costs of improving the supply and reliability of the grid, measured as additional kWh that are available to consumers, and thus generating additional revenues from electricity sales. The increase in tariff revenue from increased power generation associated with the reforms is calculated at over US$200 million per year, meaning that the reforms would pay for themselves in * * * TABLE A3.5. COST-EFFECTIVENESS OF SELECTED AIR QUALITY MEASURES | | Public“regulation”cost(US$, millions) | Private“compliance”cost(US$, millions) | PM2.5 reduction potential(t/year) | Cost-effectiveness(US$/t PM2.5 reduced) | | --- | --- | --- | --- | --- | | Solid waste | | | | | | Enhanced MSW collection and new landfills | 83 | — | 3,328 | 24,947 | | Enhanced MSW collection with recycling and composting | 47 | — | 3,328 | 14,130 | | Industry | | | | | | Emissions monitoring and standard enforcement | 95 | ?? | 3,000 | 31,605 | | Transport | | | | | | Vehicle regulation(Euro 3)a | 12 | 149 | 1,200 | 133,725 | | Vehicle regulation(Euro 4)a | 12 | 223 | 2,800 | 69,933 | | Power | | | | | | Power sector reform | 110 | — | 1,152 | 95,833 | | Genset regulation and emissions control | 5 | 246 | 1,152 | 218,229 | \\mathbf{P M}\_{2.5} \\mathbf{P M}\_{2.5} Source: Author’s own elaboration. Note:a Requires fuel quality improvements. COST-EFFECTIVENESS information on the costs of controlling genset emissions. The cost to reduce emissions from backup generators is estimated at one-quarter of the total capital costs of gensets in Lagos (based on genset emissions control costs 50 from the US) or US$280 million. While power sector reform would result in additional revenue from electricity sales that would offset the costs of reform measures, the genset emissions control expenditures would be a net cost to genset owners. \\mathrm{P M}\_{2.5} A3.4. CO-BENEFITS OF CLIMATE CHANGE MITIGATION \\mathrm{P M}\_{2.5} Given the large costs of air pollution in Lagos, it is important that policies to address climate change also look for solutions to reduce ambient air pollution. Fortunately, the sectors targeted in Lagos’ Climate Action Plan— waste, transport, industry, and energy—are precisely those contributing the most to PM2.5 emissions. That said, within each of these priority sectors, different measures to reduce GHG emissions will have varying degrees of success in reducing air pollution. Several measures can reduce both air pollution and GHG emissions (Table A3.6). It is also possible to compare the costs of air pollution to the costs of climate change (Box A3.1). * * * TABLE A3.6. CLIMATE CO-BENEFITS OF SELECTED AIR QUALITY MEASURES | | Avoided CO2 | Avoided CH4 | Avoided black carbon | | --- | --- | --- | --- | | Transport | | | | | Public transport | ✓ | No | ✓ | | Clean fuel | No | No | ✓ | | Euro 3/4 vehicles | No | No | ✓ | | Alternative vehicles(CNG, hybrid,electric) | ✓ | No | ✓ | | Solid waste | | | | | Enhanced collection | ✓ | ✓ | ✓ | | Composting | ✓ | ✓ | ✓ | | Industry | | | | | Energy efficiency | ✓ | No | ✓ | | Pollution controls | No | No | ✓ | | Power | | | | | Sector reform | ✓ | No | ✓ | | Genset emissions control | No | No | ✓ | | Other | | | | | LPG for cooking | ✓ | No | ✓ | | Paving roads | No | No | ✓ | \\mathbf{c O}\_{2} * * * **BOX A3.1.** AIR POLLUTION VERSUS CLIMATE CHANGE COSTS How do the costs of air pollution compare to the costs of climate change? The PMEH study has estimated the health costs of ambient air pollution based primarily on premature deaths attributable to high levels of ambient PM2.5 pollution. While it is difficult to measure the costs of climate change, a notional measure known as the “social cost of carbon” has been devised to reflect the costs of climate impacts such as droughts, floods, and sea level rise. The social cost of carbon (SCC) has been used by policy makers worldwide to attempt to internalize the negative externalities of climate change. In the US, a wide range of SCC values have been used, reflecting assumptions about the extent of climate change damage and the discount rate. 52 Lagos CO2-equivalent emissions have been estimated at 2.64 million tons. 51 If a mid-range global value of US$50/tCO2e is used for the SCC, the cost of climate change for Lagos amounts to US$1.3 billion. The health impact of PM2.5 emissions in Lagos has been estimated at US$2.6–5.0 billion, or more than twice the value of CO2 emissions. Air Quality Management Planning for Lagos State * * * ### ANNEX 4 # METHODOLOGY FOR DEVELOPING AN AQI ## A4.1. WHAT IS AN AQI? Air pollution can be measured and presented in many forms. It includes all the aero- sols and gaseous components, each with its own way of affecting human health to various degrees depending on exposure rate. Some pollutants like CO and O3 can lead to an immediate response and require standard metrics presented on a shorter time scale (for example, 8 hourly) than other pollutants (24 hourly). Their presenta- tion also varies—for example, aerosols are reported as mass fractions, and gases as volumetric fractions of air. An Air Quality Index (AQI) unifies the complicated science of pollution compo- sition, exposure rates-based health severity, ambient standards, measurement, and standard protocols and breaks it down into simple, color-coded bins that give people an instant visual grasp of pollution levels in their surroundings, enabling them to develop the necessary alertness. **FIGURE A4.1.** SCHEMATIC DIAGRAM OF AN AIR QUALITY INDEX Air Quality Management Planning for Lagos State * * * FIGURE A4.2. COLOUR CODING OF AIR QUALITY A4.2. HOW IS AQI CALCULATED? While the methods to monitor air pollution and estimate its health impacts are becoming standardized across the globe, this is not the case for methods used for calculating an AQI. These methods, and the degree of alertness disseminated by health alert systems, vary depending on different countries’ interpretation of thresholds for regulatory purposes and background conditions. This step is primarily driven by predetermined local standards and the feasibility of reaching the lowest possible pollution levels. For example, in a region where dust is naturally present and ubiquitous, it is not possible to reach WHO guidelines for PM10 and PM2.5. The same principle also holds for the formulation of an AQI by individual countries, which notionally mirrors that country’s standards. For example, for PM2.5 presented in the following figure, a concentration of 50 μg/m3 is considered borderline “unhealthy” in the US but “satisfactory” in India. \\mathrm{P M}\_{2.s} 50~\\up{\ mu mathrm g g/m^{{\\mathrm3}}} \ mathrm\\mathrm{P M}{}\_{2.5} Break points and index nomenclatures vary with country. * * * \\mathcal{A}Q I=\\frac{\\mathcal{A}Q\\underline{{I}} _{_{h i}}-\\mathcal{A}Q\\underline{{I}} _{_{h o}}}{\\mathcal{B}P\_{ _{h i}}-\\mathcal{B}P_{ _{h o}}}\*(\\mathcal{C}O N C-B\\boldsymbol{P}_{ _{h o}})+\\mathcal{A}Q\\underline{{I}}_{\_{h o}} where » CONC = concentration of the pollutant » AQI = air quality index for the pollutant » BPhi = breakpoint that is greater than or equal to CONC \\mathrm{B P\_{h i}} » BPlo = breakpoint that is less than or equal to CONC The top of the AQI scale is 500, which means there will be point in the calculation when the AQI value itself will not change between absolute value of 1,000 and 2,000 μg/m3 of PM2.5 or PM10. Once the color code reaches the severe category, there is no change in the AQI value or the alert message. » AQIhi = AQI value corresponding to BPhi » AQIlo = AQI value corresponding to BPlo \\mathrm{B P\_{l o}} number ranges that are under each of the pollutants represent the breakpoints. In this table, all the breakpoints are in μg/m3, except for CO which is listed in mg/m3. \\mathrm{A Q I\_{h i}\ A Q I} \\mathrm{A Q I\_{\\mathrm{l o}}}=\\mathrm{A Q I} Every pollutant has a predefined breakpoint and AQI ranges for each of the color codes. An example for India is presented below. This report reviewed seven methodologies from the US, the EU, the UK, India, China, the Republic of Korea, and Singapore. A summary of parameters used to calculate an AQI by each of these countries is presented below. Like the breakpoint variation in calculating AQI between countries, there is also a significant variation in the use of parameters and time scales. In the case of PM2.5 and PM10, all the countries use 24-hour average concentrations to calculate the AQI. For SO2 and NO2, the most-used time frame is the 24-hour average; and for CO and O3, the most-used time frame is the 8-hour average. In the case of O3, when 8-hour averages reach a certain threshold, the calculators switch to using 1-hour \ \\mathrm{P M}\_{10} \\mathrm{S O\_{2}} \ \\mathrm\ {\\small M}\_{10}, \\mathrm{N O}\_{2}, \\mathrm{M}\_{2.5} \\mathrm{\\mu g/m^{3}} FIGURE A4.4. POLLUTANT PREDEFINED BREAKPOINT AND AQI RANGES FOR INDIA | AQI Category(Range) | PM$\_{10}$24-hr | PM$\_{2.5}$24-hr | NO$\_{2}$24-hr | O$\_{3}$8-hr | CO8-hr(mg/m$^{3}$) | SO$\_{2}$24-hr | NH$\_{3}$24-hr | Pb24-hr | | --- | --- | --- | --- | --- | --- | --- | --- | --- | | Good(0-50) | 0-50 | 0-30 | 0-40 | 0-50 | 0-1.0 | 0-40 | 0-200 | 0-0.5 | | Satisfactory(51-100) | 51-100 | 31-60 | 41-80 | 51-100 | 1.1-2.0 | 41-80 | 201-400 | 0.6-1.0 | | Moderate(101-200) | 101-250 | 61-90 | 81-180 | 101-168 | 2.1-10 | 81-380 | 401-800 | 1.1-2.0 | | Poor(201-300) | 251-350 | 91-120 | 181-280 | 169-208 | 10.1-17 | 381-800 | 801-1200 | 2.1-3.0 | | Very poor(301-400) | 351-430 | 121-250 | 281-400 | 209-748\* | 17.1-34 | 801-1600 | 1201-1800 | 3.1-3.5 | | Severe(401-500) | 430+ | 250+ | 400+ | 748+\* | 34+ | 1600+ | 1800+ | 3.5+ | \\begin{array}{r}{\ \ {\\bf P M}{\\bf.}h{\\bf r}}\\end{array} {\\mathrm O}{}mathrm\_{3}, \\mathbf{N{}}{\\bf{0}}\_{2} {\\mathfrak{s}}0{{}}{}\_{2} (\\mathfrak{m g g}/\\mathfrak{m}^{3}) * * * averages—this happens in the methodologies employed by the US, EU, China, and Singapore. A summary of all the breakpoints and nomenclature for these seven countries is presented below. All the concentrations are presented in μg/m3. An Excel-based AQI calculator is included with this report that will allow for exploring the methodologies and their interpretations. The three steps in using the calculator are: (a) enter concentrations, preferably in μg/m3 units; (b) from the seven countries, select a methodology to use; and (c) click to calculate the AQI. Another variation of the calculator is available in the resource material. That version can utilize larger datasets for multiple pollutants and build AQI trends. The three steps in using the calculator are: (a) enter concentrations, preferably in μg/m3, but could be in other units; (b) from the seven countries, select a methodology to use; and (c) click to calculate the AQI. Another variation of the calculator is available in the resource material. That version can utilize larger datasets for multiple pol- \\mathrm{\\mu g/m^{3}} FIGURE A4.5. SUMMARY OF PARAMETERS FOR ESTIMATING AN AQI FOR COUNTRIES UNDER REVIEW | | | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | PM2.5 | | | PM10 | | | SO2 | | | NO2 | | | CO | | | Ozone | | | | | | 24hr | 8hr | 1hr | 24hr | 8hr | 1hr | 24hr | 8hr | 1hr | 24hr | 8hr | 1hr | 24hr | 8hr | 1hr | 24hr | 8hr | 1hr | | | | 1 | USA | x | | | x | | | x | | | x | | | | x | | | x | x | | 2 | EU | x | | | x | | | | | x | | | x | | | | | | x | | 3 | UK | x | | | x | | | | | x | | | x | | | | | x | | | 4 | India | x | | | x | | | x | | | x | | | | x | | | x | | | 5 | China | x | | | x | | | x | | | x | | | x | | | | x | x | | 6 | S.Korea | | | | x | | | x | | | x | | | | x | | | x | | | 7 | Singapore | x | | | x | | | x | | | | | x | | x | | | x | x | | 8 | | | | | | | | | | | | | | | | | | | | | 9 | | | | | | | | | | | | | | | | | | | | | 10 | | | | | | | | | | | | | | | | | | | | \\mathbf{P M}\_{2.5} \\mathbf{P M}\_{1mathbf00} \ {mathfrak s00}\_{2} \\mathbf{N O\_{2}} * * * FIGURE A4.6. SUMMARY OF BREAKPOINTS AND NOMENCLATURE FOR SEVEN COUNTRIES UNDER REVIEW United States Number of bins | Number of bins | Number of polls | Break Points | | | | | | | | | | | | | | | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | | | | | PM2.5 | PM2.5 | PM2.5 | PM10 | PM10 | PM10 | SO2 | SO2 | SO2 | NO2 | NO2 | NO2 | CO | CO | CO | Ozone | Ozone | Ozone | | | | | 24hr | 8hr | 1hr | 24hr | 8hr | 1hr | 24hr | 8hr | 1hr | 24hr | 8hr | 1hr | 24hr | 8hr | 1hr | 24hr | 8hr | 1hr | | | | | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | | | | | Low | High | col.code | 0 | | 0 | | 0 | | 0 | | 0 | | | 0 | | | 0 | 0 | | | | 1 Good | 0 | 50 | 43 | 12 | | 54 | | | 95 | | | 85 | | | | 5210 | | | 110 | 0 | | 2 Moderate | 50 | 100 | 27 | 35 | | 154 | | | 203 | | | 161 | | | | 11131 | | | 142 | 254 | | 3 Unhealthy for sensitive groups | 100 | 150 | 45 | 55 | | 254 | | | 501 | | | 579 | | | | 14684 | | | 173 | 333 | | 4 Unhealthy | 150 | 200 | 3 | 150 | | 354 | | | 823 | | | 1043 | | | | 18237 | | | 213 | 414 | | 5 Very Unhealthy | 200 | 300 | 13 | 250 | | 424 | | | 1635 | | | 2007 | | | | 35999 | | | 406 | 820 | | 6 Hazardous | 300 | 500 | 9 | 500 | | 604 | | | 2718 | | | 3293 | | | | 59683 | | | 0 | 1226 | \\underline{{\\mathbf{P M}\_{2.5}}} \\mathbf{p M}\_{10} \\mathbf{u g}m^{3} European union | Number of polls18 | | | Break Points | | | | | | | | | | | | | | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | | | | | PM2.524hrug/m³ | PM2.58hrug/m³ | PM2.51hrug/m³ | PM1024hrug/m³ | PM108hrug/m³ | PM101hrug/m³ | SO224hrug/m³ | SO28hrug/m³ | SO21hrug/m³ | NO224hrug/m³ | NO28hrug/m² | NO21hrug/m² | CO24hrug/m³ | CO8hrug/m² | CO1hrug/m² | Ozone24hrug/m³ | Ozone8hrug/m³ | Ozone1hrug/m³ | | | | | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | | | | | AQI | | | | | | | | | | | | | | | | | | | | | | Low | High | col.code | 0 | | | 0 | | | | | 0 | | | 0 | | | | | | 0 | | 0 | 50 | 10 | 10 | | | 20 | | | | | 100 | | | 40 | | | | | | 50 | | 50 | 100 | 43 | 20 | | | 40 | | | | | 200 | | | 90 | | | | | | 100 | | 100 | 200 | 27 | 25 | | | 50 | | | | | 350 | | | 120 | | | | | | 130 | | 200 | 300 | 45 | 50 | | | 100 | | | | | 500 | | | 230 | | | | | | 240 | | 300 | 400 | 46 | 75 | | | 150 | | | | | 750 | | | 340 | | | | | | 380 | | 400 | 500 | 3 | 800 | | | 1200 | | | | | 1250 | | | 1000 | | | | | | 800 | \\underline{{\\mathbf{P M}\_{2.5}}} \\underline{{\\mathbf{P M}\_{10}}}\ \\underline{{\\mathbf{P M}\_{2.5}}} \\underline{{80}}\_{2} {\\mathfrak{S o}}\_{2} \\mathbf{u g}m^{3} \|\\mathbf{u g}m^{3} \|\\mathbf{u g/m^^{3}} United Kingdom Number of bins | 18 | Break Points | | | | | | | | | | | | | | | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | | | | PM2.5 | PM2.5 | PM2.5 | PM10 | PM10 | PM10 | SO2}$ | $SO\_{2}$ | $SO\_{2}$ | $NO\_{2}$ | $NO\_{2}$ | $NO\_{2}$ | CO | CO | CO | Ozone | Ozone | Ozone | | | | 24hr | 8hr | 1hr | 24hr | 8hr | 1hr | 24hr | 8hr | 1hr | 24hr | 8hr | 1hr | 24hr | 8hr | 1hr | 24hr | 8hr | 1hr | | | | AQI | | | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | | Low | High | col.code | 0 | | | 0 | | | | 0 | | | 0 | | | | | 0 | | | 0 | 1 | 43 | 11 | | | 16 | | | | 88 | | | 67 | | | | | 33 | | | 1 | 2 | 43 | 23 | | | 33 | | | | 177 | | | 134 | | | | | 66 | | | 2 | 3 | 43 | 35 | | | 50 | | | | 266 | | | 200 | | | | | 100 | | | 3 | 4 | 27 | 41 | | | 58 | | | | 354 | | | 267 | | | | | 120 | | | 4 | 5 | 44 | 47 | | | 66 | | | | 443 | | | 334 | | | | | 140 | | | 5 | 6 | 45 | 53 | | | 75 | | | | 532 | | | 400 | | | | | 160 | | | 6 | 7 | 46 | 58 | | | 83 | | | | 710 | | | 467 | | | | | 187 | | | 7 | 8 | 3 | 64 | | | 91 | | | | 887 | | | 534 | | | | | 213 | | | 8 | 9 | 53 | 70 | | | 100 | | | | 1064 | | | 600 | | | | | 240 | | | 9 | 10 | 13 | 100 | | | 150 | | | | 1500 | | | 1000 | | | | | 300 | | 1 Good 2 Satisfactory 3 Moderate 4 Poor 5 Very Poor 6 Severe \\underline{{\\mathbf{P M}\_{2.5}}} India \\overline{{\ {\\mathbf{P M}\_{2.5}}}} \|\\mathbf{u g/m^^{3}} \\mathbf{P M}\_{10} \\overline{{\\mathbf{P M}\_{10}}} 80\_{2} \|\\mathbf{u g}m^{3} \\mathbf{u g}m^{3} * * * FIGURE A4.6. (Continued) China | Number of bins6 | Number of polls18 | | | Break Points | | | | | | | | | | | | | | | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | | | | | | | | | | | PM2.5 | PM2.5 | PM2.5 | PM10 | PM10 | PM10 | SO2 | SO2 | SO2 | NO2 | NO2 | NO2 | CO | CO | CO | Ozone | Ozone | Ozone | | | | | | | 24hr | 8hr | 1hr | 24hr | 8hr | 1hr | 24hr | 8hr | 1hr | 24hr | 8hr | 1hr | 24hr | 8hr | 1hr | 24hr | 8hr | 1hr | | | | | | | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | | | | AQI | | | | | | | | | 0 | | | 0 | | 0 | | 0 | | | 0 | 0 | | | | Low | High | col.code | 0 | | | 0 | | | 0 | | | 0 | | | 0 | | | | 0 | 0 | | | 1 Optimal | 0 | 50 | 4 | 35 | | | 50 | | | 50 | | | 40 | | | 2000 | | | | 100 | 0 | | | 2 Good | 50 | 100 | 6 | 75 | | | 150 | | | 150 | | | 80 | | | 4000 | | | | 160 | 0 | | | 3 Light Pollution | 100 | 200 | 22 | 115 | | | 350 | | | 800 | | | 280 | | | 24000 | | | | 265 | 0 | | | 4 Moderate Pollution | 200 | 300 | 3 | 150 | | | 420 | | | 1600 | | | 565 | | | 36000 | | | | 800 | 800 | | | 5 High Pollution | 300 | 400 | 21 | 250 | | | 500 | | | 2100 | | | 750 | | | 48000 | | | | 0 | 1000 | | | 6 Severe Pollution | 400 | 500 | 30 | 500 | | | 600 | | | 2620 | | | 940 | | | 60000 | | | | 0 | 1200 | | \\mathbf{P M}\_{2.5} \\mathbf{P M}\_{2.5} \\mathbf{p M}\_{10} \\mathbf{P M}\_{10} \\mathbb{M O}\_{3} 80\_{2} \\mathbf{u g}m^{3} Korea | Number of bins6 | Number of polls18 | Break Points | | | | | | | | | | | | | | | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | | | | | | | PM2.5 | PM2.5 | PM2.5 | PM10 | PM10 | PM10 | SO2 | SO2 | SO2 | NO2 | NO2 | NO2 | CO | CO | CO | Ozone24hr | Ozone8hr | Ozone1hr | | | | | 24hr | 8hr | 1hr | 24hr | 8hr | 1hr | 24hr | 8hr | 1hr | 24hr | 8hr | 1hr | 24hr | 8hr | 1hr | ug/m³ | ug/m³ | ug/m³ | | | | | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | | | | AQI | Low | High | col.code | | | 0 | | | 0 | | | 0 | | | 0 | | | 0 | | | 1 Good | 0 | 50 | 43 | | | | 30 | | | 54 | | | 48 | | | 2368 | | | 81 | | | 2 Moderate | 50 | 100 | 27 | | | | 80 | | | 135 | | | 96 | | | 10658 | | | 162 | | | 3 Unhealthy for sensitive groups | 100 | 150 | 45 | | | | 120 | | | 271 | | | 241 | | | 14210 | | | 244 | | | 4 Unhealthy | 150 | 250 | 3 | | | | 200 | | | 406 | | | 321 | | | 17763 | | | 609 | | | 5 Very Unhealthy | 250 | 350 | 13 | | | | 300 | | | 1083 | | | 964 | | | 35526 | | | 1015 | | | 6 Hazardous | 350 | 500 | 9 | | | | 600 | | | 2707 | | | 3214 | | | 59210 | | | 1218 | | \\mathbf{p M}\_{10} \\mathbf{P M}\_{10} \\mathbf{P M}\_{10} \ overline\\mathrm{M o}\_{2} \\mathbf{u g}m^{3} \\mathbf{u g/m^{3}} \\mathbf{u g/m^{3}} \\mathbf{u g/m^{3}}}\ Singapore | Number of bins6 | Number of polls18 | Break Points | | | | | | | | | | | | | | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | | | | PM2.5 | PM2.5 | PM2.5 | PM10 | PM10 | PM10 | SO2 | SO2 | SO2 | NO2 | NO2 | NO2 | CO | CO | CO | Ozone | Ozone | Ozone | | | | 24hr | 8hr | 1hr | 24hr | 8hr | 1hr | 24hr | 8hr | 1hr | 24hr | 8hr | 1hr | 24hr | 8hr | 1hr | 24hr | 8hr | 1hr | | | | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | ug/m³ | | | | Low | High | col.code | 0 | | | 0 | | 0 | | | | 0 | | 0 | | 0 | 0 | | | | 0 | 50 | 43 | 12 | | | 50 | | 80 | | | | 0 | | 5000 | | | 118 | 0 | | | 50 | 100 | 27 | 55 | | | 150 | | 365 | | | | 0 | | 10000 | | | 157 | 0 | | | 3 Unhealthy | 100 | 200 | 45 | 150 | | | 350 | | 800 | | | | 1130 | | 17000 | | | 235 | 0 | | 4 Very Unhealthy | 200 | 300 | 3 | 250 | | | 420 | | 1600 | | | | 2260 | | 34000 | | | 785 | 785 | | 5 Hazardous | 300 | 400 | 13 | 350 | | | 500 | | 2100 | | | | 3000 | | 46000 | | | 0 | 980 | | 6 Hazardous | 400 | 500 | 9 | 500 | | | 600 | | 2620 | | | | 3750 | | 57500 | | | 0 | 1180 | \\mathbf{P M}\_{2.5} \|\\mathbf{u g}m/^mathbf{{3}} \\mathbf{u g/m^{3}} * * * FIGURE A4.7. STEPS FOR CALCULATING AQI | Select a nation for required formate | | India | | and click calculate AQI | | | | --- | --- | --- | --- | --- | --- | --- | | | Reqd time. avg | Conc | Unit | AQI | Remarks | Conditional PollutantAverage of worst twoAverage of allPM2.5-24hr300Poor210PoorModerate | | PM2.5 | 24hr | 120.0 | Micro-g/m3 | 300 | Poor | | | 8hr | | Micro-g/m3 | NA | | | | | 1hr | | Micro-g/m3 | NA | | | | | PM10 | 24hr | 130.0 | Micro-g/m3 | 120 | Moderate | | | 8hr | | Micro-g/m3 | NA | | | | | 1hr | | Micro-g/m3 | NA | | | | | SO2 | 24hr | 54.0 | Micro-g/m3 | 68 | Satisfactory | | | 8hr | | Micro-g/m3 | NA | | | | | 1hr | | Micro-g/m3 | NA | | | | | NO2 | 24hr | 34.0 | Micro-g/m3 | 43 | Good | | | 8hr | | Micro-g/m3 | NA | | | | | 1hr | 80.0 | Micro-g/m3 | NA | | | | | CO | 24hr | | Micro-g/m3 | NA | | | | 8hr | 2000.0 | Micro-g/m3 | 100 | Satisfactory | | | | 1hr | 10000.0 | Micro-g/m3 | NA | | | | | Ozone | 24hr | | Micro-g/m3 | NA | | | | 8hr | 80.0 | Micro-g/m3 | 80 | Satisfactory | | | | 1hr | 200.0 | Micro-g/m3 | NA | | | | \\mathrm{M i c r o/g/m^{3}} \\mathrm{P M}\_{2.5} 8\\mathrm{h r} \\mathrm{M i c r o/g/m^{3}} \\mathrm{M i c r o/g/m^{3}} \\mathrm{M i c r o-g/m^{3}} \\mathrm{P M}\_{10} 1\\mathrm{h r} \\mathrm{M e c o,g/m^{3}} \\mathrm{M{C o r o g/m^{3}}} \\mathrm{M i c r o-/g^{3}} 80 \\mathrm{M i c r o/g/m^{3}} \\mathrm{M i c r o g/^{3}} \\mathrm{M i c r o/g/m^{3}} \\mathrm{M i c r o/g/m^{3}} \\mathrm{M i c r o/g/m^{3}} \\mathrm{M i c r o/g^{3}} \\mathrm{M i c r o/g/m^{3}} \\mathrm{M i c r o/g^{3}} \\mathrm{M i c r o/g/m^{3}} \\operatorname{M e r o}g/m^{3} \\mathrm{P M}\_{2.5}\\cdot2,4mathrm,h\\mathrmmathrm{{}r} \\mathrm{M i c r o/g/m^{3}} * * * * * * 1 uMoya-NILU, Who We Are, [http://www.umoya](http://www.umoya/) -nilu.co.za. 2 6-hour mean at each sampling location. 2 6-hour mean at each sampling location. 3 World Health Organization. 4 “Criteria pollutants” are those for which an ambient air quality standard has been established. 5 According to the GBD’s latest assessment, the PM 5 According to the GBD’s latest assessment, the PM attributable mortality is 6.455 million (plus 365,000 for O3). This figure includes 373,000 neonatal disorders (including preterm birth and low-birth weight). 6 The study used a VSL value similar to that in \ mathrm O 7 CH4 has a 100-year global warming potential 28 times that of CO2. \\mathrm{C H\_{4}} \\mathrm{C O\_{2}} 8 The investment by West Africa ENRG would amount to US$125–150 million for a 25 MW wasteto-energy facility that would process 2.5 tons of MSW per day. 9 Many of the catalysts and particulate traps that are 11 ECOWAS directives C/Dir.2/09/20 and C/Dir.1/09/20, respectively. 9 Many of the catalysts and particulate traps that are installed in vehicles that are imported either new or used into Nigeria and other countries would quickly be made ineffective with the current fuel quality (assumed to be 1,424 ppm sulfur for gasoline and 2,389 ppm sulfur for diesel). 10 At 650,000 barrels per day (bpd), the Dangote 10 At 650,000 barrels per day (bpd), the Dangote Refinery would be the largest in Nigeria and meet the country’s refined petroleum product needs of around 600,000 bpd. Nigeria currently produces over 2.5 billion bpd of crude oil. In terms of fuel quality, the refinery is slated to produce Euro 6 standard fuels, meaning ultra-low sulfur diesel and gasoline (10 ppm sulfur). 11 ECOWAS directives C/Dir.2/09/20 and 12 For example, in California, fewer than 15 percent of vehicles are responsible for as much as half of total vehicle emissions. Similarly, in Europe, 3 percent of the fleet have been found to account for 27 percent of emissions. 13 It may also be possible to lease alternative-fuel 13 It may also be possible to lease alternative-fuel vehicles as is being done with fleet vehicles in the US and Europe. Several jurisdictions are currently procuring electric vehicle fleets through leasing and service contracts with vehicle manufacturers and third-party contractors. For example, a county in Maryland, US recently signed an agreement to convert its school bus fleet to electric vehicles over the next 15 years. 14 National Poverty Eradication Program. 14 National Poverty Eradication Program. 15 In Bangkok, two-stroke motorcycles and diesel 15 In Bangkok, two-stroke motorcycles and diesel engines accounted for over 95 percent of motor vehicle particulate matter in the early 2000s. 16 Railway Technology, “Lagos Rail Mass Transit 16 Railway Technology, “Lagos Rail Mass Transit System,” March 13, 2020, [https://www.railway](https://www.railway/) -technology.com/projects/lagosrailmasstransit. 17 [http://www.urbanrail.net/af/lagos/lagos.htm](http://www.urbanrail.net/af/lagos/lagos.htm) 17 [http://www.urbanrail.net/af/lagos/lagos.htm](http://www.urbanrail.net/af/lagos/lagos.htm) 18 Power – 208 Mt CO2e; AFOLU – 136 Mt 18 Power – 208 Mt CO2e; AFOLU – 136 Mt CO2e; Transport – 52 Mt CO2e; Oil and gas – 40 Mt CO2e. 19 Estimates from the emissions inventory suggest that \\mathrm{C O\_{2}e.} 19 Estimates from the emissions inventory suggest that gensets may account for more than 95 percent of power sector emissions of PM2.5. ARIA. 20 A recent study based on satellite measurements \\mathrm{P M}\_{2.5}.,\\mathrm{A R I A} \\mathrm{{C O\_{2}e}} 20 A recent study based on satellite measurements of PM2.5 shows that agricultural field burning is a significant source of PM2.5 in Sub-Saharan Africa, including Nigeria, during the field burning season from November to February. Preliminary data from air quality monitoring from six monitoring stations in Lagos show a dramatic increase in PM2.5 levels during December 2020. To the extent that this increase is associated with seasonal field burning, measures to address biomass burning should be investigated. * * * 21 In Ikeja, studies of household fuel used for cooking show that kerosene (48.6 percent) and LPG (36.3 percent) are the most common, with charcoal (7.1 percent), fuelwood (5.7 percent), and electricity (2.4 percent) providing a minor share. 22 https:/ [www.nesrea.gov.ng/publications-downloads](http://www.nesrea.gov.ng/publications-downloads) 22 https:/ [www.nesrea.gov.ng/publications-downloads](http://www.nesrea.gov.ng/publications-downloads) /laws-regulations/. 23 [https://ngfcp.dpr.gov.ng/media/1070/petroleum](https://ngfcp.dpr.gov.ng/media/1070/petroleum) 23 [https://ngfcp.dpr.gov.ng/media/1070/petroleum](https://ngfcp.dpr.gov.ng/media/1070/petroleum) -act.pdf. 24 The DPR is currently transiting into the Nigerian 24 The DPR is currently transiting into the Nigerian Upstream Petroleum Regulatory Commission (NUPRC) as passed by the Nigerian Petroleum Industry Act of 2021. 25 The Vienna Convention addresses the loss of the O3 25 The Vienna Convention addresses the loss of the O3 layer as a global issue and establishes that all parties should take appropriate measures to avoid impacts on human health and the environment with the modification of the O3 layer. 26 The Montreal Protocol, established in 1987, refers {\\bf O}\_{3} {\\bf O}\_{3} 26 The Montreal Protocol, established in 1987, refers to the substances that deplete the O3 layer and seeks measures for their control for atmospheric protection. The protocol was amended in 1990 (London), 1992 (Copenhagen), 1995 (Vienna), 1997 (Montreal), and 1999 (Beijing). 27 The Stockholm Convention, which entered in force {\\bf O}\_{3} 27 The Stockholm Convention, which entered in force in 2004, aims to protect human health and the environment from the effects of POPs. 28 PCEH claimed that the Climate Change 28 PCEH claimed that the Climate Change Department’s mandate was limited to inventorization and management of the impacts of GHGs and does not permit them to formulate policies for other air pollutants such as CO, NOx, VOCs, and SO2. Climate Change claims that such mandate does indeed lie with them. 29 According to the 2021 Appropriations Act, the 30 The 2021 Appropriations Act identified three projects for air quality under the FMEnv’s budget allocation. These were related to the introduction of smart air quality monitoring, the procurement of analyzers for air monitoring stations, and the implementation of pollution studies for cement. 31 NOSDRA Act 2006 is available at http:// {\\mathrm{N O}}\_{\\mathrm{x}}. \\mathrm{S O\_{2}}. 32 [https://placng.org/i/wp-content/uploads/2019/12](https://placng.org/i/wp-content/uploads/2019/12) /Report-of-the-Senate-Committee-on-Environment -on-National-Oil-Spill-Detection-and-Response -Agency-Act-Amendment-Bill-2017.pdf. 33 [https://www.lasepa.gov.ng/wp-content](https://www.lasepa.gov.ng/wp-content) 33 [https://www.lasepa.gov.ng/wp-content](https://www.lasepa.gov.ng/wp-content) https:/ [www.nesrea.gov.ng/publications-downloads](http://www.nesrea.gov.ng/publications-downloads) /uploads/2020/01/Environmental-Management -Protection-Law-2017-1.pdf. 34 The five strategic goals of AQM are (a) develop [https://ngfcp.dpr.gov.ng/media/1070/petroleum](https://ngfcp.dpr.gov.ng/media/1070/petroleum) 34 The five strategic goals of AQM are (a) develop nationwide ambient air quality standards; (b) develop capacities to assess and determine national, state, or city emissions reduction requirements; (c) establish strategies to achieve the desired emissions reduction; (d) develop strategies to implement and enforce ambient air quality standard; and (e) develop capacity to track and evaluate emissions reduction results. 35 See endnote 25. 35 See endnote 25. 36 See endnote 26. 36 See endnote 26. 37 See endnote 27. 37 See endnote 27. 38 See endnote 5. 38 See endnote 5. 39 Natural cause mortality refers to deaths from all 41 A relative risk is the ratio of health effects (incidence, mortality) between two groups of people exposed to different levels of air pollution. 42 The sectoral shares of PM emissions and \\mathrm{P M}\_{2.5} 42 The sectoral shares of PM2.5 emissions and ambient concentrations that have been used for the economic and financial analysis are waste (26 percent), industry (20 percent), transport (20 percent), power (8 percent), and other (26 percent). As more detailed data and additional source assessment modeling work are completed, the share of pollution from different sources and sectors can be adjusted. 43 See endnote 7. 43 See endnote 7. 44 LACVIS currently has 20 inspection centers with an 45 See endnote 12. * * * 46 The Lagos Computerized Vehicle Inspection Service (LACVIS). [https://lacvis.com.ng](https://lacvis.com.ng/). 47 Currently the majority of danfos are gasoline- 47 Currently the majority of danfos are gasolinepowered, though a shift to diesel is likely to increase as gasoline subsidies are reduced. 48 See endnote 13. 48 See endnote 13. 49 Based on costs from the recent PSRO project in 49 Based on costs from the recent PSRO project in Nigeria, the investment cost to increase the amount of electricity from the current grid in Nigeria is around US$50,000 per GWh. 50 Calculated as gensets producing 1,940 GWh per year in Lagos, an installed capacity of 2,790 MVA, and a total cost of US$1,116 million (US$400/kVA). 51 For example, policy makers in the US have 51 For example, policy makers in the US have calculated the SCC between US$2 and $100 per ton of CO2, reflecting whether only American or total global damages are considered, and whether high versus low discount rates are used. 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