I Ec Assessing the Mortality Burden of Air Pollution in Lima-Callao Final Report | December 14, 2020 prepared for: U.S. Environmental Protection Agency prepared by: Industrial Economics, Incorporated 2067 Massachusetts Avenue Cambridge, MA 02140 617/354-00074 INDUSTRIAL ECONOMICS, INCORPORATED ------- lEc TABLE OF CONTENTS ACKNOWLEDGEMENTS CHAPTER 1 | INTRODUCTION 1.1 Background 1-1 1.2 Analytic Overview 1-2 1.2.1 Research Objectives 1-2 1.2.2 Analytic Steps 1-3 1.3 Report Organization 1-3 CHAPTER 2 | SCENARIO DEVELOPMENT CHAPTER 3 | EMISSIONS ESTIMATION 3.1 Baseline Emissions Inventory 3-1 3.2 Accounting for Non-Compliance 3-3 3.3 Emissions Modeling Results 3-4 CHAPTER 4 | AIR QUALITY DATA AND MODELING 4.1 Air Quality Surfaces 4-1 4.1.1 Monitor Surfaces 4-1 4.1.2 Satellite Surfaces 4-3 4.1.3 Summary of Air Quality Surfaces 4-3 4.2 Air Quality Modeling 4-5 CHAPTER 5 | MORTALITY BURDEN ESTIMATION AND VALUATION 5.1 Overview of Approach 5-1 5.2 Data Inputs 5-2 5.2.1 Population 5-2 5.2.2 Baseline Mortality Incidence 5-2 5.2.3 Health Impacts Functions 5-2 5.2.4 Valuation 5-4 CHAPTER 6 | RESULTS 6.1 Total PM2 5 Attributable Mortality Burden 6-1 6.2 Transport Attributable PM2 5 Mortality Burden 6-2 6.3 PM2 5 Mortality Burden from Non-Compliant Vehicles 6-3 INDUSTRIAL ECONOMICS, INCORPORATED i ------- lEc CHAPTER 7 | SUMMARY OF FINDINGS 7.1 Summary of Findings 7-1 7.2 Uncertainties 7-2 7.3 Next Steps 7-3 REFERENCES APPENDICES Appendix A | Supplemental Emissions Estimation Results Appendix B | Satellite Measurements and Processing Appendix C | Health Impact Estimation Appendix D | District-Level Results INDUSTRIAL ECONOMICS, INCORPORATED ii ------- lEc ACKNOWLEDGEMENTS This report was prepared by Industrial Economics, Incorporated (IEc) for the U.S. Environmental Protection Agency (USEPA) Office of Air Quality Planning and Standards (OAQPS), under USEPA contract EP-D-14-032 as part of EPA's Megacities Partnership program in Lima, Peru. Consistent with the Megacities Partnership approach to air quality management, this project was a collaborative effort between USEPA, IEc, and local partners at the Peruvian Ministry of Environment (MINAM). In particular, IEc acknowledges critical support from Luis Antonio Ibanez Guerrero, Katyuska Baija Paredes, and Guisselle Castillo Coila of MINAM, who provided key data sets, valuable local institutional knowledge, and helpful advice. We also thank USEPA staff including the Lima Megacities Project Manager Paul Almodovar as well as a Richard Baldauf, Ali Kamal, and Ken Davidson for their input on the analysis and helpful feedback on earlier drafts of this report. INDUSTRIAL ECONOMICS, INCORPORATED iii ------- lEc CHAPTER 1 | INTRODUCTION The United States Environmental Protection Agency (USEPA) and the Ministry of the Environment of Peru (MINAM) are collaborating under the USEPA Megacities Partnership to: • Strengthen air quality management in the Lima-Callao region through policy development, community outreach, and stakeholder engagement; • Support air quality monitoring initiatives; and • Build technical capacity in Lima-Callao for scientific and economic analyses and communication planning in support of air quality management plan (AQMP) development. This report presents results from assessments of the current overall mortality burden attributable to concentrations of fine particulate matter (PM2.5) in the region. In addition to estimating total PM2 5-attributable health burden with respect to premature deaths, we employ emissions inventories and reduced-form air quality modeling techniques to analyze the burden attributable to concentrations of PM2.5 associated with emissions from on-road motor vehicles. We further evaluate the burden of a subset of these vehicles—those out of compliance with currently established emissions limits—and highlight the potential benefits associated with increased enforcement and expanded vehicle inspections and maintenance (I&M) programs. 1.1 BACKGROUND The Lima-Callao metropolitan region is home to approximately 10 million people, nearly one-third of Peru's total population. This large and growing population is exposed to significant air pollutant concentrations due to emissions from sources such as motor vehicles. These exposures can be exacerbated by Lima-Callao"s meteorological conditions, primarily the Humboldt ocean current and the Andes Mountains to the east. The Humboldt ocean current carries cold water north from the tip of South America, which lowers atmospheric temperatures and prevents the formation of rain clouds (Thiel et al. 2007). The Humboldt ocean current is also responsible for persistent fog in Lima- Callao. The fog, combined with the obstruction of warmer and more humid air masses from the Amazon, by the Andes, results in frequent air inversions in Lima-Callao. These air inversions trap ambient pollutants at the surface level, causing pollutants to accumulate rather than disperse via coastal winds. As a result, Lima-Callao is ranked among the most polluted cities in Latin America by the World Health Organization (WHO, 2016). INDUSTRIAL ECONOMICS, INCORPORATED 1-1 ------- Transportation sources are responsible for much of the region's air pollution. Despite comprising one-third of the country's population, Lima-Callao is home to roughly two- thirds of Peru's vehicle fleet. In addition to the size of the vehicle fleet, its age plays a significant role in resulting air pollution (MINAM, 2018). According to surveys of registered vehicles from 2016-2019, approximately 34 percent of the on-road vehicles in Lima-Callao are greater than 15 years old. These older vehicles tend to have poor fuel economy and lack the emissions controls required of new vehicles. Based on emissions tests, MINAM partners note that many vehicles are non-compliant with national emissions standards; however, empirical estimates of non-compliance rates are currently limited. To address emissions from the transportation sector, the Government of Peru has passed several laws and regulations concerning emissions standards and vehicle inspection requirements. For example, in 2008, Peru established the National System of Technical Vehicle Inspection, which is responsible for inspecting and testing vehicles for safety and compliance with emissions standards. More recently, Peru adopted the Euro 4 vehicle emissions standards for all new vehicles and is considering implementing Euro 6 standards. Because of the slow rate of turnover in the vehicle fleet—as shown by the prevalence of old vehicles in circulation—additional measures may be needed to address the sector's emissions. In this report, we provide insight into the potential magnitude of these on-road vehicle emissions and associated adverse health outcomes. 1.2 ANALYTIC OVERVIEW In this section, we summarize our analytic approach to estimating the mortality burden associated with PM2.5 concentrations in Lima-Callao. We first define our research objectives and then outline the analytic steps we follow in the remainder of the report. 1.2.1 RESEARCH OBJECTIVES In close consultation with MINAM and USEPA, we developed three research objectives addressed in this report. First, we aim to quantify and value the premature deaths associated with overall ambient PM2.5 concentrations in Lima-Callao. Second, we aim to quantify and value the premature deaths associated with emissions from Lima-Callao's on-road vehicles, to better understand how vehicles contribute to the overall burden of premature deaths. Third, we aim to quantify and value the premature deaths associated with emissions from non-compliant vehicles in the Lima-Callao on-road vehicle fleet, to help MINAM understand the potential health gains from focusing on the non-compliance issue. For each research objective, we consider annual impacts using data that best characterize recent conditions for air quality, population, baseline health, and other relevant data. In Chapter 7, we highlight additional areas of future research that could complement this report. We hope that the analytical framework IEc applied in this analysis will serve as a useful guide for addressing these research topics, including estimating the benefits of specific transportation emissions control measures. INDUSTRIAL ECONOMICS, INCORPORATED 1-2 ------- 1.2.2 ANALYTIC STEPS Our methodology is comprised of five key steps: • Step 1: Scenario development. Define the research objectives and the spatial and temporal scale of our analyses. Specify conditions under a ""busincss-as-usual" or "baseline" scenario and under a "regulatory" scenario in which the proposed regulation is implemented. Based on these scenario definitions, develop baseline and regulatory air quality surfaces. • Step 2: Emissions estimation. Develop or obtain emissions inventories for the transportation sector. Develop estimate of the fraction of non-compliant vehicles. Estimate the excess emissions associated with non-compliant vehicles in the Lima-Callao fleet. (Note: this step is not needed for assessing the total PM2 5- attributable mortality burden.) • Step 3: Air quality modeling. Obtain and process air quality data, such as satellite-based estimates and data from air quality monitors to characterize baseline conditions in Lima-Callao. Use air quality modeling methods to estimate the impact of vehicle emissions on ambient PM2 5 concentrations. • Step 4: Health impact estimation. Quantify premature deaths associated with PM2 5 concentrations using BenMAP-CE and relevant datasets, including population, air quality, baseline mortality incidence, and concentration-response relationships from the epidemiological literature. • Step 5: Valuation. Apply economic valuation estimates to quantified mortality values to characterize the PM2 5-attributable mortality burden in monetary terms. These steps are described in greater detail throughout the report. 1.3 REPORT ORGANIZATION The remainder of this report is organized as follows: • In Chapter 2, we briefly summarize our scenario development efforts, including defining baseline and regulatory air quality conditions needed for estimating PM2 5-attributable mortality burden. • In Chapter 3 we detail our methods for quantifying emissions from the on-road vehicle fleet, including accounting for empirically-derived estimates of non- compliance with emissions standards. • In Chapter 4, we summarize available air quality data in Lima-Callao and methods for estimating changes in air quality stemming from emissions changes. • In Chapter 5, we describe our methods for conducting health impact estimation and valuation using USEPA's BenMAP-CE tool, including summaries of key data inputs such as population, baseline mortality incidence, health impact functions, and valuation estimates. INDUSTRIAL ECONOMICS, INCORPORATED 1-3 ------- lEc • In Chapter 6, we present the results of our health burden analyses, including an all-PM2 5 mortality estimate, a transportation-attributable PM2.5 mortality estimate, and an estimate associated with excess PM2.5 mortality resulting from non- compliant vehicle emissions. • In Chapter 7, we discuss the findings of this research and notable data and methodological limitations. We then provide recommendations for next steps to build upon this collaborative research effort. • In Appendices A-D, we provide supplemental methods discussions and results beyond the primary estimates provided in the main text. INDUSTRIAL ECONOMICS, INCORPORATED 1-4 ------- lEc CHAPTER 2 | SCENARIO DEVELOPMENT In this chapter, we define the scenarios assessed in the remainder of the report to estimate the number of premature deaths attributable to PM2.5 concentrations in Lima-Callao. As described in Chapter 1, a key element of scenario development involves defining temporal and geographic scope. First, we focus on estimating recent mortality burden associated with ambient PM2.5. Therefore, we employ datasets that best characterize conditions in recent years, including air quality, population, and baseline mortality incidence.1 Second, we consider emissions, air quality, and associated health impacts in the Lima-Callao region. We work to employ spatially resolved datasets and report results at fine geographic resolutions (e.g., districts) where data allow.2 Pollutant emissions, air quality, and health impacts outside of Lima-Callao are not considered in our analysis. For regulatory benefit-cost analysis, we typically define business-as-usual and regulatory scenarios. In a forward-looking analysis of a proposed regulation, the business-as-usual scenario reflects conditions as they are now (or are expected to be in the future) without the proposed emissions control measures in place. The regulatory scenario reflects expected conditions now or in the future if the proposed regulation is implemented. In the context of burden analyses—the focus of this report—we similarly define baseline and control scenarios. The baseline scenario reflects observed, recent PM2 5 concentrations in the region. The control scenarios are hypothetical representations of what recent PM2.5 concentrations would be absent contributions from some or all emissions sources. While our baseline scenario is the same across our three analyses, the control scenario differs for each run. These scenarios are summarized in Exhibit 2-1. Importantly, air quality is the only data input that varies across baseline and control scenarios. Our estimates do not account for any effects of the COVID-19 pandemic. To the extent that the virus has affected air quality, population, and baseline death rates in Lima-Callao, these impacts are not quantified in our analysis. In Peru, administrative divisions are geographically resolved, from largest to smallest, into regions, provinces, and districts. Lima and Callao are the names of both a province and a district within a province. This analysis encompasses the Lima and Callao provinces, which are comparable to US states. The districts analyzed are comparable to US counties and range in size from 1 to 2,800 km2. INDUSTRIAL ECONOMICS, INCORPORATED 2-1 ------- lEc EXHIBIT 2-1. DEFINING AIR QUALITY SCENARIOS ANALYSIS BASELINE AIR QUALITY CONTROL AIR QUALITY Total PM2.5 burden Recent characterization of observed PM2.5 concentrations PM2.5 concentrations set to 0 Mg/m3 Transportation sector PM2.5 burden Observed PM2.5 concentrations minus transportation sector contributions Non-compliant vehicles PM2.5 burden Observed PM2.5 concentrations minus contributions from non- compliant vehicles' By comparing the estimated health impacts between baseline and control air quality conditions, we can attribute the mortality burden to various sources. For example, by comparing recent PM2 5 concentrations with a hypothetical scenario where we reduce PM2.5 concentrations by the transportation sector's contribution, we can quantify the mortality burden associated with the sector as a whole. In the following chapters, we explain how we estimate the contributions of transportation sources—and non-compliant vehicle emissions alone—to ambient PM2.5 concentrations. We do not remove all PM2.5-relevant emissions associated with non-compliant vehicles. Rather, we only assess the emissions in excess of comparable vehicles compliant with vehicle emissions standards. INDUSTRIAL ECONOMICS, INCORPORATED 2-2 ------- lEc CHAPTER 3 | EMISSIONS ESTIMATION In this chapter, we explain our data sources and methods for characterizing emissions from the transportation sector in Lima-Callao. While MINAM possesses a comprehensive emissions inventory for on-road vehicles, the inventory assumes that vehicles emit at fixed emissions rates representing "compliant" rates for a given vehicle class (e.g., bus, passenger vehicle), fuel type (e.g., gasoline, diesel), and emissions class (e.g., Euro 2, Euro 6). Yet, inspection data from the Urban Transport Management Office (UTMO) of the Municipality of Lima indicates a significant fraction of the vehicle fleet is out of compliance. This may be due to several factors including the potential tampering of emission control devices, the age of the vehicle fleet, and driving cycles in Lima-Callao. Therefore, the inventory likely underestimates emissions from the vehicle fleet. Although the true rate of non-compliance is not known, it is expected to be significant based on recent data collected by the UTMO. We used these estimates to update the emissions inventory to account for observed rates of non-compliant emissions in the region so that it better reflects the true rate of emissions. 3.1 BASELINE EMISSIONS INVENTORY The calculations in this section detail IEc's implementation of the MINAM transportation sector emissions inventory model. This model generates annual emissions estimates based on the size and composition of the region's on-road vehicle fleet. The model summarizes emissions in tons per year for seven pollutants: PM2.5, NOx, CO, total hydrocarbon, black carbon, SO2 and CO2. Estimates were derived for each combination of vehicle type, emissions class, and fuel type (e.g., Euro 2 diesel automobiles). Additional detail on these vehicle characteristics is summarized in Exhibit 3-1. EXHIBIT 3-1. TRANSPORTATION EMISSIONS INVENTORY DATA ELEMENTS VARIABLE VALUES Fuel type Diesel, high octane gasoline, low octane gasoline, liquified petroleum, natural gas Emissions class Pre-Euro, Euro 2, Euro 3, Euro 4 Vehicle type Automobile, station wagon, pick-up truck, rural truck, panel truck, omnibus, heavy-duty truck, tow truck, motorcycle INDUSTRIAL ECONOMICS, INCORPORATED 3-1 ------- lEc To generate emissions estimates for each vehicle group, the model utilizes information on vehicle counts, average distance driven per year, and emissions factors to calculate annual emissions estimates. First, we multiplied vehicle category counts by the fraction of vehicles with each fuel type to estimate vehicle counts by fuel type and category.4,5 Next, we multiplied vehicle counts by average annual distance for each fuel type and vehicle category to estimate total annual distance driven for all vehicles by fuel type and category.6 We then multiplied the total annual distances by the percentage of vehicles in each Euro level, specific to fuel type and vehicle category, to determine total annual distance by fuel type, vehicle category and Euro emissions level.7 Finally, we applied emissions factors to total distance estimates. Emissions factors reflect the rate of emissions per unit of distance traveled (e.g., 0.05 grams of PM2 5 emitted per kilometer traveled). These steps result in 180 emissions estimates (all combinations of nine vehicle categories, five fuel types, and four Euro levels). Example calculations with hypothetical values are illustrated in Exhibit 3-2. EXHIBIT 3-2. ILLUSTRATIVE EMISSIONS ESTIMATION CALCULATIONS STEP EXAMPLE 1 100,000 automobiles * 30% diesel fuel use = 30,000 diesel automobiles 2 30,000 diesel automobiles * 10,000 km avg. distance = 300,000,000 km traveled by diesel automobiles 3 300,000,000 km * 10% Euro 4 = 30,000,000 km traveled by Euro 4 diesel automobiles 4 30,000,000 km * 0.05 g PM2.5/km = 1,500,000 g = 1.5 tons PM2.5 emitted annually by Euro 4 diesel automobiles 4 Estimates of vehicle category counts are derived from a sum of the Peruvian National Statistical System (INEI) vehicle registry from 2011 to 2016. See https://www.inei.qob.pe/media/MenuRecursivo/publicaciones digitales/Est/Lib1483/cap20/ind20.htm 5 Estimates of the share of vehicles with each fuel type are from the 2012 Ml NAM National Inventory of Greenhouse Gases. See http://infocarbono.minam.gob.pe/annios-inventarios-nacionales-gei/ingei-2012/ 6 Average annual distances are provided by the Climate Change Planning Project. See http://planccperu.org/wp- content/uploads/2016/05/informe final.pdf. These data are supplemented by information on total distance per year for diesel and gas vehicles within the automobile category in Lima from Swisscontact. 7 The portion of the Lima vehicle fleet within each Euro-level are provided by a Nationally Appropriate Mitigation Action (NAAAA) Support Project report. See http://www.transferproiect.org/proiects/transfer-partner-countries/peru/. Vehicles 15 years or older are classified as emitting at the Pre-Euro level, vehicles between the ages of 15 and 12 years are classified as Euro 2, vehicles between the ages of 11 and one years are classified as Euro 3, and vehicles less than 1 year old are classified as Euro 4. INDUSTRIAL ECONOMICS, INCORPORATED 3-2 ------- lEc In the example above, Euro 4 diesel automobiles are estimated to emit 1.5 tons of PM2.5 annually. These estimates would be compiled with the emissions from the 179 other vehicle type, fuel type, emissions class combinations to yield total transportation sector emissions in the region. As noted above, the inventory implicitly assumed perfect compliance in its application of emissions factors - vehicles cannot emit above (or below) the rates established by each Euro class. In the section below, we explain how we adapted these values to account for non-compliance in the vehicle fleet. 3.2 ACCOUNTING FOR NON-COMPLIANCE Empirical evidence demonstrates that many on-road vehicles in Lima-Callao do not comply with emissions limits established by MINAM. In a 2017 analysis of 2,625 vehicles in Lima-Callao conducted by the UTMO, roughly half of vehicles tested as out of compliance with emissions limits.8 Compliance rates, depicted in Exhibit 3-3, varied by vehicle fuel type. EXHIBIT 3-3. COMPLIANCE RATES BY FUEL TYPE 100% 80% 60% 40% 20% 0% Diesel Gasoline GLP GNV ¦ Pass aFail Notes: GLP = liquified petroleum, GNV = natural gas, n = sample size. For diesel vehicles, compliance is determined using an opacity standard and for gasoline, natural gas and liquefied petroleum compliance is based on combined CO, CO2, and HC standards. INDUSTRIAL ECONOMICS, INCORPORATED 3-3 ------- lEc Compliance rates were found to range from 40 percent for liquified petroleum vehicles to 66 percent for gasoline vehicles. We used these data to adjust the emissions inventory estimates to account for emissions rates that likely exceed those quantified in the model's emissions factors. For each fuel type, we divided total annual distances (by vehicle category, fuel type and emissions class) into compliant and non-compliant designations. For compliant vehicles, we applied the conventional emissions factors associated with the vehicle category, fuel type, and emissions class. Non-compliant vehicles, however, were assumed to emit at higher rates (i.e., more pollution per kilometer driven). Given uncertainty in the true emissions rates of non-compliant vehicles, we estimated a lower- and upper-bound emissions estimate using alternative assumptions:9 • Lower bound emissions estimate: Non-compliant vehicles were assumed to emit at one emissions standard older than previously assigned. For example, a non- compliant Euro 4 vehicle was assumed to emit at a rate consistent with Euro 3 emissions factors. • Upper bound emissions estimate: All non-compliant vehicles were assumed to emit at the Pre-Euro level. Notably, IEc only adjusted emissions for PM2.5 and NOx, i.e., the precursors of ambient PM2.5 to account for non-compliance. We were unable to adjust for SO2 emissions as the data were not broken out by Euro-level. However, it is reasonable to assume that compliant and non-compliant vehicles emit comparable SO2 per kilometer traveled (for a given fuel type and vehicle type) as SO2 emissions are a function of distance traveled and sulfur content in fuel—not emissions class. We summed the non-compliant and compliant emissions for each vehicle category and Euro-level to determine annual emission estimates of PM2.5 and NOx for each fuel type. Finally, IEc summed across fuel type to determine estimates of total annual PM2 5 and NOx emissions. 3.3 EMISSIONS MODELING RESULTS The results of emissions inventory model are summarized in Exhibit 3-4. In estimating non-compliance rates, we assumed a random sampling of vehicles and accuracy of the inspection testing method by the UTMO. We explored our assumption that non-compliance corresponded to older Euro standards of emissions using the following sensitivity analysis. IEc assessed the distributions of opacity inspection measurements for diesel vehicles and the distributions of CO measurements for gasoline, natural gas and liquified petroleum vehicles. The percent increase in opacity for compliant to non-compliant diesel vehicles was comparable to the percent increase in PM2.5 emissions for changing from the Euro 4-2 level to the Pre-Euro level. Therefore, IEc determined that accounting for non-compliant vehicles in the inventory model by scaling diesel emissions factors by opacity would likely not change the result significantly. Additionally, the percent increase in CO for compliant to non-compliant gasoline, natural gas and liquified petroleum vehicles was orders of magnitude higher than the percent increase in PM2.5 emissions for changing from the Euro 4-2 level to the Pre-Euro level. IEc determined that scaling gasoline, natural gas and liquified petroleum emissions factors by CO measurements to account for non-compliance would not be appropriate. INDUSTRIAL ECONOMICS, INCORPORATED 3-4 ------- lEc EXHIBIT 3-4. 2018 VEHICLE EMISSIONS ESTIMATES FOR PM2 5, NOx, AND S02 SCENARIO TOTAL VEHICLE EMISSIONS (TONS/YEAR) PM2.5 NOx S02 Full Compliance 4,092 164,038 21,908 Non- Compliance Adjusted Upper Bound 5,176 203,564 21,908 Lower Bound 4,297 175,097 21,908 Implementing the MINAM emissions model assuming perfect compliance results in estimates of PM2.5 emissions of 4,092 tons per year, NOx emissions of 164,038 tons per year, and SO2 emissions of 21,908 tons per year. We found that accounting for non- compliance resulted in a 5 to 26 percent increase in PM2.5 emissions and a 6 to 24 percent increase in NOx emissions (Exhibits 3-5 and 3-6). SO2 emissions were not adjusted and therefore do not vary between the full compliance and non-compliance adjusted scenarios. Appendix A provides greater detail on these estimates, including the share of emissions by vehicle fuel type. EXHIBIT 3-5. ESTIMATED PRIMARY PM25 EMISSIONS, 2018 6,000 5,000 Emissions (tons/year) J\J JJJ 0 0 0 000 000 1,000 0 Full Compliance Upper Bound Lower Bound INDUSTRIAL ECONOMICS, INCORPORATED 3-5 ------- lEc EXHIBIT 3-6. ESTIMATED N0X EMISSIONS, 2018 250,000 200,000 <5 150,000 C o c ¦£ 100,000 50,000 Full Compliance Upper Bound Lower Bound INDUSTRIAL ECONOMICS, INCORPORATED 3-6 ------- CHAPTER 4 | AIR QUALITY DATA AND MODELING In this chapter, we describe our approach to characterizing recent PM2.5 concentrations in Lima-Callao. First, we describe available air quality monitor data and satellite-derived PM data in the region, as well as our approaches to fuse these data sources. Second, we outline our approach to estimating the contribution of transportation sources to ambient PM2.5 concentrations based on the emissions data described in the previous chapter. 4.1 AIR QUALITY SURFACES Several data sources provide estimates of ambient PM2.5 concentrations in Lima-Callao. We describe these air quality surfaces below and weigh the relative benefits and limitations of each data source. 4.1.1 MONITOR SURFACES Monitor data in Lima-Callao were provided by MINAM covering a range of pollutants (PM2.5, PM10, SO2, NO2, O3, and CO) and temporal scales. Pollutant concentrations were summarized at hourly, daily, monthly, and annual timesteps since 2000.10 The monitors analyzed are owned and operated by two separate agencies: MINAM and the National Meteorology and Hydrology Service of Peru (SENAMHI). To best depict the air quality in the Lima-Callao metropolitan area, including "hot spots" of concern to MINAM (e.g., localized high pollutant concentrations in Callao), we used hourly PM2.5 measures from 10 monitors in 2019 and supplemented these values with daily PM2.5 measures from two active samplers in the Callao district.1112 We combined the measures into a single dataset by converting hourly PM2.5 concentrations to daily averages for each 24-hour period.13 Exhibit 4-1 maps these air quality monitor stations in Lima-Callao. Since monitor data provides concentrations at a fixed location, we used the Voronoi Neighborhood Averaging (VNA) method in BenMAP-CE to interpolate PM2 5 concentrations at a 1km x Some pollutants and metrics are only available in select years. 11 Daily monitoring data in Callao are only available for approximately one week of each month in 2019 from the Environmental Assessment and Enforcement Agency (OEFA). 12 Additional monitors provide data in earlier years (e.g., 27 in 2016), but these data risk reflecting older air quality levels and distributions. 13 After converting the hourly data to daily averages, we excluded eight outlier daily values for three monitors. INDUSTRIAL ECONOMICS, INCORPORATED 4-1 ------- lEc lkm grid (not pictured). Hie VNA method calculates an inverse-distance weighted 14 average for each grid cell from the monitors surrounding the grid s center. EXHIBIT4-1. AVAILABLE AIR QUALITY MONITORING STATIONS IN LIMA-CALLAO (2019) : arabayllo Rjfcntf Pi**jrn ~ ~ RicatJo P atma Lurigancho Lnnoqarho * RijTjnrt X Linj/> . r _lkiVicToria * Anita ~ ~ Santiago de Surc6 'San ' V,lla J(ian dp Matja Mir a floret * Triunfo Villa El Salvador ~ Monitor Locations Province Callao Lima See Appendix B in the BenMAP-CE user manual for a detailed discussion of VNA methods: https://www.epa.gov/sites/product!on/files/2015-04/documents/benmap-ce user manual march 2015.pdf INDUSTRIAL ECONOMICS, INCORPORATED 4-2 ------- 4.1.2 SATELLITE SURFACES In areas lacking monitor coverage, satellite data can be useful for estimating ambient surface concentrations of PM2.5. In areas with robust monitoring networks, those data are likely to best represent ground-level ambient concentrations (assuming appropriate quality control procedures are followed); however, satellite data still play an important role by filling gaps between monitored locations and providing information on the spatial distribution of PM2.5 at finer resolutions. Lima-Callao has an established network of monitors; however, coverage is more limited in outer districts. Therefore, we leveraged two satellite-based estimates for the Lima-Callao metropolitan area: an estimated surface from van Donkelaar et al. (2016) and an estimated surface from Shaddick et al. (2017). The van Donkelaar surfaces provide annual estimates of PM2 5 at 0.01° resolution (1km x lkm) for 1998-2016 and the Shaddick surfaces provide annual estimates of PM2.5 at 0.1° (10km x 10km) resolution for 2014 and 2016. The van Donkelaar et al. (2016) and Shaddick et al. (2017) surfaces combined information from satellites, model simulations and ground-level monitors. Their methods are explained in further detail in Appendix B. Notably, both surfaces incorporate monitor data from the WHO Global Ambient Air Quality Database, which contains only one monitor with a directly measured estimate for PM2 5 for Lima-Callao.15 As such, while the surfaces may provide insight into the spatial distribution of air pollution, the magnitude of PM2.5 concentrations may not accurately reflect real-world conditions (as measured by monitors). Therefore, IEc performed additional local calibration of the van Donkelaar and Shaddick surfaces for 2016 using data from twelve SENAMHI and MINSA monitoring stations. Satellite surface calibration was broken into four steps: (1) calculating annual PM2.5 averages at the monitor locations, (2) calculating the ratio between monitor and satellite annual PM2.5 averages, (3) spatially interpolating the ratios to create a calibration surface, and (4) multiplying the calibration surface against the satellite surface to create a locally calibrated air quality surface. These steps, implemented in ArcMap version 10.4.1 using the Spatial Analyst package, are explained in greater detail in Appendix B. 4.1.3 SUMMARY OF AIR QUALITY SURFACES Exhibit 4-2 displays the three final surfaces used to assess the mortality burden of ambient PM2.5 in Lima-Callao. Exhibit 4-2 (a) shows 2019 monitor data interpolated to a lkm x lkm grid. The 2019 monitor surface had an average daily PM2.5 concentration of 33.0 (ig/m3, with a minimum and maximum observed concentration of 16.2 (ig/m3 and 46.9 (ig/m3, respectively. Exhibit 4-2 (b) is the 10km x 10km 2016 Shaddick model surface, locally calibrated using monitor data (see Appendix B). The Shaddick model surface had an annual average PM2.5 concentration of 21.4 (ig/m3, with a minimum and World Health Organization. WHO Global Ambient Air Quality Database (Update 2018); WHO: Geneva, 2018: https://www.who.int/airpollution /data/cities/en/ INDUSTRIAL ECONOMICS, INCORPORATED 4-3 ------- lEc EXHIBIT 4-2. AIR QUALITY SURFACES a) 2019 Monitors 16 Lima-Callao Districts PM2.5 Concentration (ng/m3) < 23.6 23.7 - 28.6 28.7-32.1 32.2 - 34.0 34.1 - 35.9 36.0 - 39.2 ¦ > 39.3 b) 2016 Shaddick Model (locally calibrated) Lurigancho Chadaeayo Lima _ - tfi-VKtona Juan Jo Ma"a Miralaes c) 2016 Van Donkelaar Model (locally calibrated) If# Luiiumidw There are numerous ways one could interpolate between monitors to develop an air quality surface. Exhibit 4-2a was developed using the BenMAP-CE default interpolation procedure, Voronoi Neighborhood Averaging (VNA). The VNA interpolation method may or may not accurately reflect ground-level conditions in Lima-Callao. INDUSTRIAL ECONOMICS, INCORPORATED 4-4 ------- lEc maximum observed concentration of 11.8 (ig/m3 and 36.4 (ig/m3, respectively. Lastly, Exhibit 4-2 (c) is the 1km x 1km van Donkelaar model surface, calibrated using monitor data (see Appendix B). The van Donkelaar model surface had an annual average PM2.5 concentration of 21.6 (ig/m3, with a minimum and maximum observed concentration of 10.5 (ig/m3and 53.5 (ig/m3, respectively. 4.2 AIR QUALITY MODELING In the previous section, we presented data sources and methods used to characterize recent concentrations of PM2.5 in the Lima-Callao region. The resulting air quality surfaces are used to assess the mortality burden associated with all sources of PM2.5. To assess the transport-attributable mortality burden, we needed a means of quantifying the effect of transportation emissions on ambient PM2 5 concentrations. We employed a reduced-form air quality modeling technique employed in past studies commissioned by MINAM: emissions concentration factors (FECs, from the Spanish factor emisidn- concentracion). The FECs used in this report were developed for vehicle emissions in the Valparaiso region of Chile.17 While FECs exist for Lima, they are not specific to the transportation sector. After consultation with MINAM, we elected to use the Valparaiso FECs, as this coastal and mountainous region may be similar to the Lima-Callao Metropolitan area in important meteorological and topographical respects. FECs are modeled by the following equation: 6Cf\_1 Ef FEC' [sEy ~ Cf where FECf is the emission-concentration factor in zone /' for year t in tons/((.ig/ni3). Cf is the ambient concentration of PM2 5 in zone /' for year t in (ig/m3, and Ef is pollutant emissions in zone /' for year t in tons. Pollutant-specific FECs for vehicle emissions are shown in Exhibit 4-3. EXHIBIT 4-3. POLLUTANT-SPECIFIC EMISSION-CONCENTRATION FACTORS (FEC) POLLUTANT FEC (TON/YEAR PER Mg/m3) PM2.5 1,148.106 S02 15,220.700 NOx 18,867.925 17 GreenLab, 2011: https://silo.tips/download/estudio-co-beneficios-de-la-mitigacion-de-gei INDUSTRIAL ECONOMICS, INCORPORATED 4-5 ------- lEc We estimated the total contribution to ambient concentrations from primary PM2 5 from NOx, SO2 and PM2 5 vehicle emissions according to the following equation: °p = (:FEC~) * Ep' where C is the contribution to ambient PM2.5 concentrations of pollutant p, FECP is the emission-concentration factor for pollutant p, and Ep are the primary emissions for pollutant p. We estimated contributions to ambient PM2.5 concentrations for the full compliance, non-compliance upper bound, and non-compliance lower bound scenarios. Finally, we isolated the contribution of non-compliant emissions by subtracting the full compliance total emissions from the non-compliant vehicle emissions, such that: Cnc Cnc adjusted. ^full where C„c are non-compliant emissions, C„c adjusted are total vehicle emissions adjusted for non-compliance, and Cf„n are total vehicle emissions assuming full compliance. This estimated non-compliant emissions rather than the total emissions of non-compliant vehicles. Specifically, a non-compliant vehicle has a portion of emissions that are compliant (i.e. would have still been emitted if they met standards) and a portion of emissions that are in exceedance of the standard. To address the mortality burden of non- compliance, we determined the contribution of only the excess emissions. Results of FEC calculations are shown in Exhibit 4-4. EXHIBIT 4-4. TRANSPORTATION AND NON-COMPLIANT VEHICLE EMISSION CONTRIBUTIONS TO AMBIENT PM2.5 CONCENTRATIONS SCENARIO CONTRIBUTION OF VEHICLE EMISSIONS TO AMBIENT PM2.5 CONCENTRATIONS (Mg/m3) CONTRIBUTION OF NON- COMPLIANT EMISSIONS PM2.5 NOx S02 TOTAL Full Compliance 3.56 8.69 1.44 13.70 N/A Non- Compliance Adjusted Upper Bound 4.51 10.79 1.44 16.74 3.04 Lower Bound 3.74 9.28 1.44 14.46 0.76 Exhibit 4-5 shows the relative contributions of the transport sector (compliant and non- compliant emissions) and other sources to ambient concentrations of PM2.5. We estimated the contribution of other sources, both anthropogenic and non-anthropogenic, by INDUSTRIAL ECONOMICS, INCORPORATED 4-6 ------- lEc subtracting the total vehicle contribution to ambient PM2 5 concentrations from the 2016 annual average of ambient PM2.5 concentrations in Lima-Callao18. The annual average PM2.5 concentration for 2016 was 25.0 (ig/m3. Vehicle emissions contribute between 67 and 58 percent (upper and lower bounds) of ambient PM2.5 concentrations and non-compliant emissions contribute between 12 and 3 percent (upper and lower bounds). EXHIBIT 4-5. CONTRIBUTIONS OF TRANSPORT SECTOR AND OTHER SOURCES TO 2016 AVERAGE PM2 5 CONCENTRATIONS 25.0 20.0 15.0 10.0 5.0 0.0 0.8 3.0 13.7 13.7 10.6 8.3 Lower Upper I Other Sources ¦ Compliant Emissions ¦ Non-compliant Emissions We note that the PM2.5 contributions displayed in Exhibit 4-5 reflect mean effects in the region. While this analysis evaluates transportation emissions impacts and contributions to air pollution at the regional and district level, research has shown that air pollution can be significantly higher within a short distance of large roadways and other transportation facilities, especially within the first 150-300 meters, compared with district-level air pollution concentrations (Karner et al., 2010). Individuals living, working and going to school within this short distance of roadways have increased risks for adverse health effects, including premature mortality (Health Effects Institute 2010). As a result, relying on district-level air quality analyses may underestimate the impacts of transportation sources on the overall mortality burden to the population of Lima-Callao. The annual average was estimated in BenMAP-CE using 2016 monitor data and is population- and spatially-weighted. The other sources category is expected to include both anthropogenic and non-anthropogenic sources of emissions. INDUSTRIAL ECONOMICS, INCORPORATED 4-7 ------- lEc CHAPTER 5 | MORTALITY BURDEN ESTIMATION AND VALUATION In this chapter, we detail our methods for assessing the mortality burden associated with PM2.5 concentrations in Lima-Callao. In total, we performed three health benefits analyses: 1. The total mortality burden of current PM2.5 concentrations in Lima-Callao; 2. The contribution of transportation emissions to the total mortality burden; and 3. The contribution of non-compliant vehicle emissions to the total mortality burden. For these analyses, we used the USEPA's Environmental Benefits Mapping and Analysis Program - Community Edition (BenMAP-CE) version 1.5.2.0, an open-source program that quantifies and values the adverse health effects associated with changes in pollutant concentrations. The remainder of this chapter provides an overview of our approach, including our data sources for key inputs such as population, baseline incidence rates, and concentration-response functions from the epidemiological literature. Finally, we provide an overview of our valuation approach. 5.1 OVERVIEW OF APPROACH We used BenMAP-CE to estimate the impact of PM2.5 concentrations on premature mortality by assessing the difference in the risk of those endpoints under the baseline and control scenarios presented in Chapter 2. BenMAP-CE relies on health impact functions to quantify the change in incidence of adverse health impacts stemming from changes in ambient pollutant concentrations: Ay = y0 ¦ (l — ¦ Pop where Ay is the change in the incidence of the adverse health effect, y0 is the baseline incidence rate for the health effect, beta (/?) is a coefficient derived from a relative risk (RR) estimate associated with a change in exposure (i.e., pollutant concentration) as expressed in concentration-response functions, APM is the change in concentrations of 19 fine particulate matter, and Pop is the exposed population. 19 Based upon the functional form of the underlying concentration-response function, the functional form of the health impact function may differ. A PM may also be replaced by concentrations of other pollutants (e.g., ozone) or conditions (e.g., temperature). INDUSTRIAL ECONOMICS, INCORPORATED 5-1 ------- 5.2 DATA INPUTS We drew upon multiple data sources to parameterize and implement the generic health impact function presented above. These data sources are described below. 5.2.1 POPULATION MINAM provided district-level population data from the Peruvian National Statistical System (INEI) for the period 2005 to 2015, as well as national projected population data from 1950 through 2070 in five-year increments. Both population datasets include age stratification into five-year age bins. To account for population growth since 2015 (the most recent year with district-level population estimates), we projected the 2015 district- level population to the year 2020 by applying age-specific national growth rates. Finally, we formatted these data for use in BenMAP-CE. 5.2.2 BASELINE MORTALITY INCIDENCE To characterize baseline rates of death, we processed data from the Peruvian Ministry of Health (MINSA) National Center for Epidemiology, Prevention and Control of Disease (CNEPCE). These data include counts for a range of mortality and morbidity endpoints from 1986 through 2016. Available mortality data are reported by district, gender, and five-year age increments. For this analysis, we focused on the mortality incidence for the following endpoints: Ischemic Heart Disease (IHD), Acute Lower Respiratory Infection (LRI), Chronic-Obstructive Pulmonary Disease (COPD), Lung Cancer, Cerebrovascular Disease, and Natural Causes (hereafter referred to as Non-Communicable Diseases (NCD) plus LRI). These endpoints were selected to match the health impact functions described in the following section. Prior to use in BenMAP-CE, we converted mortality count data to incidence rates (cases per person per year). First, we formatted the mortality data to align with the level of aggregation in the population dataset (year, district, endpoint, and age group). We then divided the counts by the district- and age-specific population for corresponding years. To minimize variability across years, we estimated incidence rates for a five-year period (2011 to 2015). In some cases, we aggregated incidence rates from various causes to align with the endpoint definitions in the health impact functions (described below). For example, Tapia et al. (2020) reflects respiratory and circulatory mortality. 5.2.3 HEALTH IMPACT FUNCTIONS As described above, health impact functions provide the quantitative framework to estimate changes in health outcomes resulting from changes in pollutant concentrations, incorporating data on population and baseline incidence. These functions are derived from concentration-response relationships published in epidemiological research, which provide insight into the strength of a pollutant's effect on health. For example, a study may suggest that for every 10 (ig/m3 change in PM2 5, we can expect baseline mortality incidence to change by 6 percent. Exhibit 5-1 summarizes our selected health impact functions for assessing mortality burden. INDUSTRIAL ECONOMICS, INCORPORATED 5-2 ------- lEc EXHIBIT 5-1. BENMAP-CE HEALTH IMPACT FUNCTIONS AUTHOR MORTALITY ENDPOINT GROUP AGES Burnett et al. (2018) Non-communicable diseases plus lower respiratory infection (NCD + LRI) 25-99 Cerebrovascular disease 25-99 Chronic obstructive pulmonary disease (COPD) 25-99 Ischemic heart disease (IHD) 25-99 Lung cancer 25-99 Lower respiratory infection (LRI) 25-99 Tapia et al. (2020) All respiratory and circulatory 0-99 Two epidemiological studies provide the concentration-response relationships summarized in Exhibit 5-1. First, we utilized the Global Exposure Mortality Model (GEMM) health impact functions pre-loaded into BenMAP-CE. The GEMM is a family of functions developed by Burnett et al. (2018) to estimate the global burden of disease attributable to PM2.5 exposure over the entire global exposure range. The GEMM consists of risk functions for six mortality endpoints: NCD + LRI, Cerebrovascular Disease, COPD, IHD, Lung Cancer, and LRI.2" Notably, the GEMM is a meta-analytic function developed based on high-quality PM2.5 studies conducted globally. Additionally, the functions are non-linear. That is, the strength of the effect of PM2.5 on premature deaths depends upon the observed PM2.5 concentrations. In general, the functions suggest that the marginal effect of PM2.5 lessens at higher concentrations. Second, we utilized Tapia et al. (2020) estimates of PM2 5-attributable respiratory and circulatory mortality.21 While these estimates are specific to Lima, Peru, the study While age-specific GEMM functions are presented in Burnett et al. (2018), we leverage the all-ages (25-99) functions to capture population-wide effects. Similarly, Burnett et al. (2018) provide estimates with and without a Chinese male cohort included in their meta-analysis. We leverage the GEMM functions with the Chinese male cohort because these estimates are, in part, informed by higher PM2.5 exposure levels experienced by the Chinese cohort. These higher concentrations may be relevant to air quality conditions in Lima-Callao. 21 We identified the Tapia et al. (2020) study by conducting a literature review for PM2.5 epidemiological studies local to Lima-Callao, Peru, or South America using a broad keyword search in Google Scholar and PubMed. We identified 17 potentially relevant papers and abstracts, then narrowed this list to three potential candidates for health impact calculation in BenMAP-CE: Hansel et al. (2018), Tapia et al. (2019) and Tapia et al. (2020). The three studies were selected due to their relevant respiratory and cardiovascular endpoints. Hansel et al. (2018) provided functions that relate PM2.5 exposure to asthma morbidity, including uncontrolled asthma, adverse asthma-related quality of life, health care utilization and missed school days. Tapia et al. (2019) provided functions that relate PM2.5 exposure with cardiorespiratory emergency room visits and Tapia et al. (2020) provided functions that relate PM2.5 exposure with cardiorespiratory mortality. We did INDUSTRIAL ECONOMICS, INCORPORATED 5-3 ------- lEc assesses mortality associated with short-term PM2 5 exposures. As such, we expect the study to understate the total impacts of PM2.5 due to strong empirical evidence that much of the pollutant's effect is associated with its long-term exposure. 5.2.4 VALUATION We valued mortality using a value per statistical life (VSL) estimate from Robinson et al. (2018). The VSL represents individuals" willingness to pay (WTP) for incremental reductions in their annual risk of death. While we are unaware of any WTP studies conducted in Peru, the Robinson et al. provides methods and results for transferring VSL values to countries without primary estimates. The authors synthesize available estimates in other countries and transfer these values to Peru, among many other countries, by accounting for differences in per capita income, a key factor influencing WTP and VSL.22 The resulting value, $1.21 million, is applied to premature deaths to reflect the welfare loss associated with PM2 5-attributable mortality. As we note in the subsequent chapters, this estimate is associated with some uncertainty, and alternative valuation methodologies may result in different estimates. For example, MINAM's analysis of the Euro 6 standards included a range of estimates from $0.14 to $1.6 million (2017$).23 not apply the other functions identified as they are for morbidity endpoints and this report focuses solely on the mortality health burden. 22 . Robinson et al. (2018) extrapolate a Peru-specific VSL from the OECD VSL base ($3 million) using the ratio of gross national income per capita between Peru and OECD countries. 23 . MINAM included a value of $0,138 million derived by "Seminario de Marzi (2017)", which employed a human capital approach for valuing averted deaths. This approach values mortality effects by considering the labor productivity of individuals and is frequently thought to underestimate individual's true willingness to pay to reduce their risk of death. INDUSTRIAL ECONOMICS, INCORPORATED 5-4 ------- lEc CHAPTER 6 | RESULTS In this chapter, we present the results of our mortality burden analyses using the methods and data sources described in previous chapters. First, we present the quantified and valued mortality impacts associated with total PM2.5 concentrations in Lima-Callao. Second, we present results specific to the transportation sector. Finally, we estimate the burden associated with excess emissions from non-compliant vehicles in the region. In Appendix D, we present these results stratified by Lima-Callao District. 6.1 TOTAL PM2. 5 ATTRIBUTABLE MORTALITY BURDEN We estimate that ambient PM2.5 emissions in Lima-Callao result in over 10,000 deaths annually. These results, summarized in Exhibit 6-1, vary depending on the selected health impact function and baseline air quality surface. We highlight the GEMM NCD & LRI results as our preferred estimates. This HIF captures a broader range of air pollution attributable deaths relative to the five cause-specific (5 COD, i.e., cause-of-death) GEMM estimates and the local Tapia et al. (2020) results. Further, the Tapia et al. study only accounts for short-term exposure to PM2.5 and ignores the substantial long-term mortality impacts of air pollution. Results are largely stable across air quality surfaces, with the greatest burden resulting from the 2019 monitor data (12,016 deaths) relative to the Shaddick and van Donkelaar surfaces (10,556 and 10,838, respectively). For the remainder of this report, we present the Shaddick surface and GEMM NCD & LRI results as our primary estimates. We highlight the Shaddick surface results because Shaddick contains more recent data and was generated using a WHO model which builds upon earlier van Donkelaar methods. The model estimates the spatially varying relationship between ground measurements of PM2 5 and factors from the various air quality models (see Appendix B for details). EXHIBIT 6-1. ESTIMATED PM2 5 ATTRIBUTABLE MORTALITY BURDEN PREMATURE DEATHS VAN CAUSE OF MORTALITY MONITORS SHADDICK DONKELAAR GEMM: NCD + LRI 12,016 10,556 10,838 GEMM: 5 COD 7,425 6,517 6,514 Lower respiratory infection 4,022 3,538 3,531 Ischemic heart disease 1,486 1,321 1,338 INDUSTRIAL ECONOMICS, INCORPORATED 6-1 ------- lEc CAUSE OF MORTALITY PREMATURE DEATHS MONITORS SHADDICK VAN DONKELAAR Cerebrovascular disease 987 843 841 Lung Cancer 590 524 508 COPD 339 292 296 Tapia: Respiratory & circulatory (short-term exposure) 1,486 1,245 1,272 Total PM2.5 attributable deaths, estimated at 10,556, represent the annual toll of air pollution in the region. To the extent that air quality, population, and baseline rates of death are relatively comparable over time, we expect that these adverse impacts are likely to occur each year. The costs associated with these deaths amount to $12.7 billion annually (2015$).24 Notably, these costs are irrespective of source: both anthropogenic (e.g., industry, transportation) and non-anthropogenic (e.g., sea salt, crustal dust) sources contribute to the total ambient PM2.5 concentrations in the region. In the following sections, we present the burden associated with transportation sources. 6.2 TRANSPORT ATTRIBUTABLE PM2.5 MORTALITY BURDEN Emissions from on-road vehicles in Lima-Callao result in 5,150 to 6,200 premature deaths each year. These results are summarized in Exhibit 6-2 along with the associated economic costs. It is important to note that the contribution of vehicle emissions to ambient PM2.5 assumes there is some level of non-compliance in meeting emissions standards (i.e. neither estimate represents 100 percent compliance with emissions standards). Additionally, the variance in vehicle emissions contribution is solely due to the variance in non-compliant vehicle emissions. EXHIBIT 6-2. ESTIMATED PM2 5 ATTRIBUTABLE ANNUAL MORTALITY BURDEN, TRANSPORTATION SECTOR VEHICLE EMISSIONS CONTRIBUTION ANNUAL PM2.5 ATTRIBUTABLE DEATHS (SHADDICK) ANNUAL ECONOMIC COSTS (2015$, BILLIONS) Lower bound 5,150 $6.2 Upper bound 6,200 $7.5 24 Using the range of VSL estimates in MINAM's Euro 6 analysis, the total monetized mortality burden in Peru may range from $1.5 billion (VSL = $0.14 million) to $16.9 billion (VSL = $1.61 million). INDUSTRIAL ECONOMICS, INCORPORATED 6-2 ------- lEc The estimated mortality burden for transportation sources amounts to over half of the total PM2.5 mortality burden, reflecting the sector's outsized influence on air pollution in the region. The range in mortality estimates reflects uncertainty in the exact emissions rates of non-compliant vehicles. As summarized in Exhibit 6-2, the economic costs associated with transportation-attributable PM2.5 concentrations are $6.2 to $7.5 billion annually. As we describe in Appendix C, these estimates may understate the burden of transportation sources due to the non-linearity of the GEMM function. Additionally, as noted in Chapter 4, these results may not fully capture local-scale effects, including exposures to significant greater PM2.5 concentrations on or near roadways. 6.3 PM2.5 MORTALITY BURDEN FROM NON-COMPLIANT VEHICLES As discussed in Chapter 3, MINAM emissions measurements suggest roughly half of all on-road vehicles in Lima-Callao are out of compliance with emissions standards. We adjust emissions inventories accordingly and estimate the resulting air quality and health impacts. In total, we find that emissions from non-compliant vehicles in excess of emissions standards are responsible for 248 to 991 deaths annually. These results are presented in Exhibit 6-3. Excess emissions (i.e., above and beyond compliant emission levels) from non-compliant vehicles account for roughly 5 to 16 percent of the transportation mortality burden (comparing Exhibits 6-2 and 6-3). EXHIBIT 6-3. ESTIMATED PM25 ATTRIBUTABLE ANNUAL MORTALITY BURDEN, NON-COMPLIANT EMISSIONS VEHICLE EMISSIONS CONTRIBUTION ANNUAL PM2.5 ATTRIBUTABLE DEATHS (SHADDICK) ANNUAL ECONOMIC COSTS (2015$, MILLIONS) Lower bound 248 $300 Upper bound 991 $1,200 As noted above, the range in mortality estimates reflects uncertainty in the exact emissions rates of non-compliant vehicles. The associated economic costs for non- compliant vehicle emissions amounts to $0.3 to 1.2 billion annually. Policymakers may interpret these estimates as the potential annual benefits that may be achieved through regulatory measures that address the entirety of non-compliant emissions. That is, achieving perfect compliance with vehicle emissions standards would be expected to avoid 248 to 991 deaths annually, resulting in annual benefits of $0.3 to 1.2 billion. While perfect compliance may be infeasible, the mortality burden associated with non- compliant vehicle emissions is sizeable—any policy that materially improves compliance will produce significant public health benefits in Lima-Callao. INDUSTRIAL ECONOMICS, INCORPORATED 6-3 ------- CHAPTER 7 | DISCUSSION AND NEXT STEPS In the previous chapters, we described our analytic approach and findings in detail. In this chapter, we summarize our findings and discuss their implications. We also suggest possible next steps for researchers and MINAM staff 7.1 SUMMARY OF FINDINGS Our mortality burden analyses provide evidence that PM2 5 concentrations in the Lima- Callao region represent a substantial public health concern. Overall, ambient concentrations typically range from 11.8 to 36.4 (ig/m3 on an annual basis, with some "hot spots" likely to experience markedly higher pollution concentrations, particularly at shorter time and distance scales. These values exceed the WHO annual guideline of 10 (.ig/nr\ In total, we estimate that over 10,000 deaths each year result from PM2 5 exposure in Lima-Callao. The economic costs of this loss amount to $12.8 billion USD annually. The transportation sector represents a major contributor to ambient PM2 5—and premature mortality—in the region. We expand upon the overall PM2 5 burden analysis by more closely evaluating the mortality burden associated with PM2 5 concentrations originating from on-road vehicles. We estimate that 14.5 to 16.7 (ig/m3 of ambient PM2 5 concentrations (58 to 67 percent) result from on-road transportation emissions in the region. These emissions result in 5,150 to 6,200 premature deaths annually, equating to $6.2 to $7.5 billion in economic costs. Regulatory interventions in Lima-Callao may lessen the mortality burden associated with transportation emissions. We highlight the role that non-compliant vehicles play in regional emissions by adjusting available emissions inventories using recent MINAM estimates of non-compliance rates (roughly 3 to 12 percent) in the region. We find that 1 to 3 (ig/m3 in the region may be explained by emissions from this subset of vehicles. The wide range in estimates stems from uncertainty in quantifying emissions from these vehicles. Emissions from non-compliant vehicles in excess of federal vehicle emissions standards result in 250 to 990 premature deaths annually, equating to $0.3 to $1.2 billion in economic costs. These costs may be reduced through regulatory measures, such as increased enforcement or enhanced I&M programs. 7.2 UNCERTAINTIES The results presented in this report are accompanied by numerous sources of uncertainty, of which the net effect on our estimates is ambiguous. We attempt to catalogue the major sources of uncertainty in Exhibit 7-1. INDUSTRIAL ECONOMICS, INCORPORATED 7-1 ------- lEc EXHIBIT 7-1. KEY UNCERTAINTIES WITH BURDEN ANALYSES POTENTIAL LIMITATION / SOURCE OF ERROR DIRECTION OF POTENTIAL BIAS FOR ESTIMATED BURDEN Health impacts associated with other pollutants are not quantified. Underestimate. Epidemiological evidence supports a causal relationship between ozone exposure and mortality and morbidity effects. Quantifying and valuing these outcomes would increase the overall burden of air pollution in the region. Morbidity effects are not quantified. Underestimate. Epidemiological evidence supports a causal relationship between PM2.5 exposure and numerous non- fatal respiratory and cardiovascular effects. Quantifying and valuing these outcomes would increase the overall burden. Mortality burden for near-road populations. Underestimate. Epidemiological evidence supports additional health effects to populations living very close to large roadways. Quantifying and valuing these outcomes would increase the overall burden of air pollution in the region. Prevalence of non-compliant vehicles. Unable to determine based on current information. Non- compliance rates are calculated based on a limited sample (n = 2,625) but may be lower or higher in the entire vehicle fleet. Emissions rates for non- compliant vehicles. Unable to determine based on current information. Non- compliance is determined based on tests that do not measure for NOx or PM2.5. Empirical estimates of the effects on these pollutants are not available. We provide two potential assumptions on the emissions from non- compliant vehicles; however, the true emissions may fall above or below these bounds. Air quality modeling approach. Unable to determine based on current information. Alternative air quality models may better characterize the magnitude and spatial distribution of PM2.5 concentrations stemming from one ton of precursor emissions. Concentration-response relationship between PM2.5 and mortality. Unable to determine based on current information. The GEMM function compiles results from many epidemiological studies, some of which find weaker or stronger PM2.5- induced effects. We also do not have an estimate of long- term mortality impacts based on a locally conducted study; the only local study of mortality impacts only assessed those associated with short-term exposures, which will underestimate longer-term mortality impacts. No cessation lag used for premature mortality. Overestimate. If there is a time lag between PM2.5 changes and premature mortality, then benefits occurring in the future should be discounted. Valuation of mortality benefits. Unable to determine based on current information. No primary studies have been conducted in Peru to estimate WTP for mortality risk reductions. MINAM's Euro 6 analysis includes VSL estimates smaller and larger than the estimate employed in this analysis. INDUSTRIAL ECONOMICS, INCORPORATED 7-2 ------- lEc Addressing specific uncertainties, where possible, is an important next step MINAM staff and academic researchers. While the uncertainties presented in Exhibit 7-1 may be deemed acceptable for the types of analyses summarized in this report, addressing one or more of these limitations may be warranted for regulatory benefits analyses. We present our recommended topics for future research in the following section. 7.3 NEXT STEPS As discussed above, our results are accompanied by data and methodological limitations, some of which may be addressed in future analyses by MINAM and academic researchers. We recommend several areas of focus for building upon the methods presented in this report: • Pursue more advanced air quality modeling, such as leveraging photochemical air quality models previously employed in Lima-Callao (see Sanchez-Ccoyllo et al. 2016); • Quantify and value morbidity effects (e.g., onset asthma and exacerbations, respiratory and cardiovascular hospitalizations and emergency room visits); • Consider the impacts of transportation-attributable ozone formation and exposure; • More closely evaluate near roadway exposures (i.e., health impacts resulting from exposure to elevated PM2 5 and other pollutant concentrations near busy, polluted roads and highways). In addition to addressing the methodological limitations outlined above, this report presents a framework that may be expanded to answer related research questions. First, the burden analyses conducted thus far may be adapted to assess the benefits of specific regulatory measures, such as increased enforcement for vehicle emissions standards and enhanced vehicle I&M programs. Second, the burden analyses may be expanded to assess the PM2 5-attributable mortality burden stemming from other sources in Lima-Callao. For example, the results could be stratified further to highlight the mortality burden associated with specific sources within the transportation sector, such as buses or trucks. Additionally, this analysis could be expanded to assess the mortality burden attributed to PM2 5 generated through energy production or chemicals manufacturing, which are two prominent industries within Lima-Callao (MINAM, 2018). Such results could also be expressed at the vehicle level. Understanding the average mortality burden resulting from one non-compliant vehicle, for example, may serve as a useful guide for policymakers in (1) identifying vehicle types for targeted emissions controls and (2) assessing whether emissions control costs would be justified based on the societal costs associated with each vehicle. In addition, evaluations could be made for implementing PM2.5 mitigation strategies to reduce concentrations at the community-level, such as street cleaning, low emission zones, and roadside barriers as part of expanding this work to assess near-road exposures. Additionally, this report presents a framework that could be expanded to include other sectors. Pending emissions data availability and compatibility with air INDUSTRIAL ECONOMICS, INCORPORATED 7-3 ------- lEc quality models, this framework could be applied to other sources, such as industrial point sources. Finally, we hope this report may serve as a conduit for continued technical capacity building for health benefits analysis. Similar analyses conducted under other Megacities efforts have been accompanied by workshops focused on the BenMAP-CE tool, best practices for conducting air pollution benefits analyses, and policy synthesis for advancing local air quality management efforts. We understand that MINAM is currently conducting a parallel analysis using the AirQ+ software. Comparing results and methods would serve to bolster the numbers presented in this report and to improve MINAM capabilities with each tool. Further, engaging additional MINAM staff and relevant stakeholders (e.g., academics, industry experts, municipalities) may serve to (1) disseminate the results more broadly and improve the usefulness of the report, and (2) enhance this study by incorporating alternative data sources and methods recommended by stakeholders. 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Environmental Science and Technolology, 44(14): 5334-44. MINAM. 2018. Avance: Plan de Accion para el Mejoramiento de la Calidad del Aire de Lima-Callao; Version Preliminar: Diagnostico de la Gestion de la Calidad Ambiental del Aire de Lima v Callao. Robinson, L.A., Hammitt, J.K., and O'Keefe, L. 2018. Valuing Mortality Risk Reductions in Global Benefit-Cost Analysis. Guidelines for Benefit-Cost Analysis Project Working Paper No. 7. Sanchez-Ccoyllo, O.R., C.G. Ordonez-Aquino, AG. Munoz, A. Llacza, M.F. Andrade, Y. Liu, W. Reategui-Romero, and G. Brasseur. 2018. Modeling Study of the Particulate Matter in Lima with the WRF-Chem Model: Case Study of April 2016. International Journal of Applied Engineering Research, 13(11): 10129-10141. Shaddick, G., Thomas, M. L., Green, A., Brauer, M., van Donkelaar, A., Burnett, R., Chang, H.H., Cohen, A., Van Dingenen, R., Dora, C. and Gumy, S. 2018. Data integration model for air quality: a hierarchical approach to the global estimation of exposures to ambient air pollution. Journal of the Royal Statistical Society: Series C (AppliedStatistics), 67(1), 231-253. Tapia, V., Steenland, K., Sarnat, S. E., Vu, B., Liu, Y., Sanchez-Ccoyllo, O., Vasquez, V., and Gonzales, G. F. 2019. Time-series analysis of ambient PM 2.5 and cardiorespiratory emergency room visits in Lima, Peru during 2010-2016. Journal of Exposure Science & Environmental Epidemiology, 1-9. Tapia, V., Steenland, K., Vu, B., Liu, Y., Vasquez, V., and Gonzales, G.F. 2020. PM2 5 exposure on daily cardio-respiratory mortality in Lima, Peru, from 2010 to 2016. Environmental Health, 19(63). https://doi.org/10.! 186/sl2940-020-00618-6 INDUSTRIAL ECONOMICS, INCORPORATED R-1 ------- lEc Thiel, M., E.C. Macaya, E. Acuna, W.E. Arntz, H. Bastias, K. Brokordt, et al. 2007. The Humboldt Current System of Northern and Central Chile. Oceanography and Marine Biology 15: 195-344. https://www.researchgate.net/publication/210779714 The Humboldt Current Svste m of Northern and Central Chile Van Donkelaar, A., Martin, R. V., Brauer, M., Hsu, N. C., Kahn, R. A., Levy, R. C., Lyapustin, A., Sayer, A.M. and Winker, D. M. 2016. Global estimates of fine particulate matter using a combined geophysical-statistical method with information from satellites, models, and monitors. Environmental science & technology, 50(7), 3762-3772. INDUSTRIAL ECONOMICS, INCORPORATED R-2 ------- lEc APPENDIX A | SUPPLEMENTAL EMISSIONS ESTIMATION RESULTS In this section, we provide greater detail on estimated pollutant emissions from the transportation sector. In Chapter 3, we summarize emissions estimates by pollutant and compliance assumptions. Below, we further stratify these estimates to show the relative contributions by vehicle fuel type. For PM2.5 and NOx, diesel emissions comprise the majority of total annual emissions (Exhibits A-l, A-2). We determined that high diesel emissions are not caused by a greater share of diesel vehicles in the fleet. Diesel vehicles make up less than 20 percent of the fleet, whereas gasoline vehicles made up greater than 70 percent. Instead, diesel vehicles make up large share (more than 85 percent) of high emitting vehicle types such as pick- up trucks, omnibuses, trucks, and tow trucks. In contrast, gasoline emissions are the largest contributor to SO2 emissions, as seen in Exhibit A-3. Notably, SO2 emissions are assumed to vary with sulfur content in fuels and total fuel consumption. Emissions controls, and thus compliance status, are assumed to not affect SO2 emissions in our model. EXHIBIT A-1. 2018 PM2.5 EMISSIONS BY FUEL TYPE 6,000 5,000 I. 4,000 c o 3,000 c o .t2 2,000 1,000 14 12 5 6 6 Diesel Gas GNV GLP Full Compliance ¦ Upper Bound ¦ Lower Bound Notes: GLP = liquified petroleum, GNV = natural gas. INDUSTRIAL ECONOMICS, INCORPORATED A-1 ------- lEc EXHIBIT A-2. 2018 NOx EMISSIONS BY FUEL TYPE 200,000 ¥ 150,000 1L (D ' > oo c ~ 100,000 00 C O "oo — E 50,000 168,070 153,728 10,813 24,808 13'126 5 453 aii _l_ / 4,008 J 5,082 Diesel Gas GNV Full Compliance ¦ Upper Bound ¦ Lower Bound Notes: GLP = liquified petroleum, GNV = natural gas. EXHIBIT A-3. 2018 S02 EMISSIONS BY FUEL TYPE 25,000 Emissions (tons/year) nj o yi o o o o o o o o o o 5,000 0 I I I 1 Gas Diesel GLP GNV Fuel Type Notes: GLP = liquified petroleum, GNV = natural gas. INDUSTRIAL ECONOMICS, INCORPORATED A-2 ------- lEc APPENDIX B | SATELLITE MEASUREMENTS AND PROCESSING In Chapter 4, we summarize available data sources characterizing air pollution in Lima- Callao, including both monitor and satellite data. In this Appendix, we provide greater detail on the satellite datasets and our methods for ""ground-truthing" these datasets to more closely reflect monitor concentrations. Van Donkelaar et al. (2016) combine information from satellites, model simulations and monitors. Satellites provide global measurements of aerosol optical depth (AOD). The van Donkelaar surfaces combine AOD retrievals from the NASA Moderate Resolution Imaging Spectroradiometer, Multi-angle Imaging SpectroRadiometer and the Sea- Viewing Wide Field-of-View Sensor. Next, model simulations from the GEOS-Chem chemical transport model are used to convert total column AOD to near-surface PM2 5 concentrations. Finally, ground-based monitor measurements from the WHO Global Ambient Air Quality Database25 are used with a GWRto predict and adjust for residual PM2 5 bias in each grid cell from the initial satellite derived values. The Shaddick et al. (2017) surfaces are the result of the Data Integration Model for Air Quality (DIMAQ) developed by the WHO Data Integration Task Force. This model integrates monitor measurements from the WHO Global Ambient Air Quality Database, satellite remote sensing, population estimates, topography, and measures of specific contributors of air pollution from chemical transport models. The same methods as van Donkelaar et al. (2016) are used to combine AOD from multiple satellites with GEOS- Chem chemical transport model simulations to produce estimates of near-surface PM2 5 at 0.1° resolution. DIMAQ goes beyond the methods of van Donkelaar et al. (2016) by using a Bayesian model to estimate the spatially varying relationship between ground measurements of PM2 5 and factors from the GEOS-Chem, TM5, and TM5-FASST chemical models that estimate air quality. Our methods for locally calibrating the satellite surfaces are broken into four steps: (1) calculate annual PM2 5 averages at the monitor locations, (2) calculate the ratio between 2019 monitor and 2016 satellite annual PM2 5 averages, (3) spatially interpolate the ratios to create a calibration surface, and (4) multiply the calibration surface against the satellite surface to create a locally calibrated air quality surface. First, we determine the annual average PM2 5 concentration measured by monitors at each location. A 2016 PM2 5 annual average is available for the ten stations in the SENAMHI World Health Organization. WHO Global Ambient Air Quality Database (Update 2018); WHO: Geneva, 2018: https://www.who.int/airpollution /data/cities/en/ INDUSTRIAL ECONOMICS, INCORPORATED B-1 ------- lEc ground monitoring network. Station information is available in Exhibit 1. Additionally, we use an adjusted 2019 PM2.5 annual average for two MINSA operated stations, "CA- VMP-r and ""CA-VMP-2" as these new stations show a hotspot, or area of higher concentrations, in Callao which is not captured by the SENAMHI monitor network. To include as many monitoring locations as possible, we adjust the 2019 PM2.5 annual average at the Callao stations to 2016 concentrations using a ratio of 2016 to 2019 values. For numerator of the ratio, we use a distance-weighted average of the 2016 annual average from the two closest SENAMHI monitors. For the denominator of the ratio, we use the grid cell value at the Callao monitor locations of a 2019 annual average surface created using data from the ten SENAMHI monitors. We then determine the annual average satellite surface PM2 5 concentration at each location by creating a one kilometer buffer around each station and calculating an area- weighted average within the buffer zone. Second, we calculate a calibration factor for each station, which is equal to the monitor annual average divided by the satellite annual average for each station. A calibration factor greater than one adjusts satellite data upwards and a factor less than one adjusts satellite data downwards. Third, we interpolate the calibration factors across the domain using a Kriging function to create a calibration surface (Exhibit B-l). Finally, we multiply the calibration surface against the original satellite surface to create a locally calibrated final surface. INDUSTRIAL ECONOMICS, INCORPORATED B-2 ------- lEc EXHIBIT B-1. SHADDICK AND VAN DONKELAAR CALIBRATION SURFACES b) 2016 Van Donkelaar Calibration a) 2016 Shaddick Calibration Calibration Factor | 0.60 - 0.75 0.75 - 0.89 ~ 0.89 - 1.02 ^ 1.02-1.13 | 1.13 - 1.39 j Lima-Callao Districts INDUSTRIAL ECONOMICS, INCORPORATED B-3 ------- lEc EXHIBIT B-2. SHADDICK AND VAN DONKELAAR AIR QUALITY SURFACES PRE- AND POST- CALIBRATION a) 2016 Shaddick Model ~ Lima-Callao Districts PM2.5 Concentration (pg/m3) 10.15-16.99 17.00-24.01 24.02-31.30 H 31.31 -43.01 ¦I 43.02 - 59.57 b) 2016 Shaddick Model truthed d) 20 Van Donkelaar Model truthed) INDUSTRIAL ECONOMICS, INCORPORATED B-4 ------- lEc APPENDIX C | HEALTH IMPACT ESTIMATION In this Appendix we discuss additional health impact results, as well as the implication of using a non-linear mortality function. This Appendix will include discussion of the GEMM functions, alternative transport contribution results, alternative non-compliant vehicle contribution results, and the health endpoints not discussed in Chapter 6. GEMM NON-LINEARITY As discussed in Section 5.2.3, we utilized six of the 83 non-linear GEMM functions pre- loaded into BenMAP-CE. It is important to note that these GEMM functions are non- linear with a decreasing marginal relationship between PM2 5 concentration and the mortality hazard ratio (Exhibit C-l). Because we modeled mortality burden analyses by removing the transportation sector's contribution from the high end of PM2.5 concentrations (i.e.. "rolling back" baseline values), we may understate mortality burden due to the lower mortality response per unit change in PiVl > 5 at these higher concentrations. EXHIBIT C-1. BURNETT ET AL. (2018) FIGURE S6 Non Communicable Diseases + Lower Respiratory Infections Ischemic Heart Disease PM? s - ng/m3 PMai-nQ/m3 Chronic Obstructive Pulmonary Disease Lung Cancer 20 40 I PM2 5 • ng/m3 40 PM2 5 - ng/m3 INDUSTRIAL ECONOMICS, INCORPORATED C-1 ------- lEc s a sensitivity analysis, we calculated the contribution of transport emissions and non- compliant vehicle emissions to the total burden of PM2.5 in Lima-Callao by calculating the mortality burden as a percentage of total mortality burden based on the sector's contributions to ambient PM2.5 concentrations. That is, if the sector was responsible for 50 percent of ambient concentrations, we would apportion 50 percent of the total Lima- Callao mortality burden to this sector. Exhibits C-2 and C-3 compare the BenMAP-CE GEMM function results against a direct proportional analysis for the transport and non- compliant vehicle burden contributions. Overall, the GEMM functions may underestimate the contribution of the transport sector and non-compliant vehicles to the total burden of PM2 5. EXHIBIT C-2. TRANSPORT ATTRIBUTABLE PM25 MORTALITY BURDEN CAUSE OF MORTALITY GEMM FUNCTION DIRECT PROPORTIONAL CONTRIBUTION LOWER UPPER LOWER UPPER GEMM: NCD + LRI 5,150 6,200 6,097 7,057 GEAAM: 5 COD 4,221 4,855 3,764 4,357 Lower respiratory infection 2,522 2,855 2,044 2,365 Ischemic heart disease 680 814 763 883 Cerebrovascular disease 534 616 487 563 Lung Cancer 315 368 302 350 COPD 171 201 168 195 INDUSTRIAL ECONOMICS, INCORPORATED C-2 ------- lEc EXHIBIT C-3. NON-COMPLIANT VEHICLE ATTRIBUTABLE PM2.5 MORTALITY BURDEN CAUSE OF MORTALITY GEMM FUNCTION DIRECT PROPORTIONAL CONTRIBUTION LOWER UPPER LOWER UPPER GEAAM: NCD + LRI 248 991 322 1,281 GEAAM: 5 COD 223 890 199 791 Lower respiratory infection 135 541 108 429 Ischemic heart disease 33 133 40 160 Cerebrovascular disease 30 116 26 102 Lung Cancer 16 65 16 64 COPD 9 34 9 35 INDUSTRIAL ECONOMICS, INCORPORATED C-3 ------- lEc APPENDIX D | DISTRICT-LEVEL RESULTS In this Appendix, we highlight the variability in mortality burden results across districts. The tables highlight the importance of the geographic resolution of the selected air quality model when viewing district level results. We compare our primary results (10km x 10km grid, Shaddick) with a finer scale surface (1km x 1km grid, van Donkelaar) and present an average effect across these models. Exhibits D-l through D-5 provide the district level NCD + LRI mortality results for: • Total mortality burden attributed to ambient PM2 5: Exhibit C-l • Transport sector mortality burden: Exhibits C-2 (lower bound) and C-3 (upper bound) • Mortality burden of non-compliant emissions: Exhibits C-4 (lower bound) and C- 5 (upper bound) Overall, while the regional-level differences in mortality burden are negligible across surfaces, we observe notable differences at the district level. Such results may motivate air quality controls be focused in regions with significant mortality burden. INDUSTRIAL ECONOMICS, INCORPORATED D-1 ------- lEc EXHIBIT D-1. DISTRICT-LEVEL TOTAL PM2 5 MORTALITY BURDEN DISTRICT NAME DISTRICT POPULATION (AGES 25-99) SHADDICK VAN DONKELAAR AVERAGE DEATHS DEATHS DEATHS RATE PER 100,000 Overall 6,815,428 10,556 10,838 10,697 157 San Juan De Lurigancho 695,326 1,054 1,009 1,031 148 Comas 361,764 624 707 665 184 San Martin De Porres 488,004 606 654 630 129 Lima 207,527 538 646 592 285 Callao 304,221 535 503 519 171 Villa Maria Del Triunfo 294,591 504 397 451 153 Ate 394,943 370 512 441 112 Santiago De Surco 272,592 354 376 365 134 Carabayllo 189,698 472 247 360 190 Lurigancho 139,146 484 215 349 251 Villa El Salvador 291,540 256 370 313 107 Chorrillos 224,392 350 263 307 137 La Molina 132,567 382 228 305 230 Los Olivos 254,864 277 320 299 117 San Juan De Miraflores 272,025 211 381 296 109 Rimac 120,902 302 285 294 243 Ventanilla 271,339 315 254 285 105 La Victoria 125,163 225 314 270 216 Puente Piedra 210,135 242 289 266 127 Independencia 149,822 253 272 263 175 San Miguel 108,804 217 196 207 190 El Agustino 125,281 165 241 203 162 Miraflores 74,718 127 229 178 238 San Isidro 49,229 197 145 171 348 San Borja 94,488 130 190 160 170 Jesus Maria 61,365 108 174 141 230 Magdalena Vieja 63,935 116 157 136 213 Santa Anita 149,168 91 177 134 90 Brena 60,003 80 144 112 187 Cieneguilla 30,285 166 23 94 311 Magdalena Del Mar 45,329 84 100 92 203 Surquillo 72,492 61 116 89 122 Lince 42,557 69 108 88 208 Bellavista 59,552 66 97 81 137 Lurin 52,254 101 48 75 143 INDUSTRIAL ECONOMICS, INCORPORATED D-2 ------- lEc SHADDICK VAN DONKELAAR AVERAGE DISTRICT POPULATION RATE PER DISTRICT NAME (AGES 25-99) DEATHS DEATHS DEATHS 100,000 Pachacamac 76,225 94 51 73 96 San Luis 42,772 48 75 61 143 La Perla 49,511 33 79 56 113 Chaclacayo 31,232 33 78 55 176 Ancon 27,202 69 38 53 196 Barranco 24,956 38 57 48 191 Carmen De La Legua Reynoso 31,190 48 37 42 135 Santa Rosa 12,157 25 8 17 138 La Punta 3,131 8 8 8 249 Punta Hermosa 5,230 9 2 6 110 Pucusana 10,443 5 4 5 46 Mi Peru* - 6 2 4 NA Punta Negra 5,400 5 3 4 68 San Bartolo 4,845 2 3 3 54 Santa Maria Del Mar 1,113 1 1 1 109 'The Mi Peru district in Callao had zero population in our dataset. Because the Shaddick and van Donkelaar surfaces overlap the population grid (districts), BenMAP apportions incidence results from air quality grid cells into the overlapping districts (including Mi Peru). INDUSTRIAL ECONOMICS, INCORPORATED D-3 ------- lEc EXHIBIT D-2. DISTRICT-LEVEL PM2 5 MORTALITY BURDEN, TRANSPORTATION SECTOR (LOWER BOUND) DISTRICT NAME DISTRICT POPULATION (AGES 25-99) SHADDICK VAN DONKELAAR AVERAGE DEATHS DEATHS DEATHS RATE PER 100,000 Overall 6,815,428 5,150 5,195 5,173 | 76 San Juan De Lurigancho 695,326 509 476 492 | 71 Comas 361,764 299 333 316 | 87 San Martin De Porres 488,004 296 314 305 | 63 Lima 207,527 265 318 292 | 141 Callao 304,221 287 248 267 | 88 Villa Maria Del Triunfo 294,591 242 188 215 | 73 Ate 394,943 178 242 210 | 53 Santiago De Surco 272,592 171 177 174 | 64 Carabayllo 189,698 227 117 172 | 91 Lurigancho 139,146 233 102 168 | 120 Villa El Salvador 291,540 123 173 148 | 51 Chorrillos 224,392 170 123 147 | 65 La Molina 132,567 183 107 145 | 109 Los Olivos 254,864 134 152 143 | 56 Rimac 120,902 149 135 142 | 117 San Juan De Miraflores 272,025 101 179 140 | 51 Ventanilla 271,339 153 120 136 | 50 La Victoria 125,163 111 153 132 | 105 Puente Piedra 210,135 116 136 126 | 60 Independencia 149,822 122 128 125 | 83 San Miguel 108,804 108 100 104 96 El Agustino 125,281 80 113 97 | 77 Miraflores 74,718 62 110 86 | 115 San Isidro 49,229 97 73 85 | 172 San Borja 94,488 63 91 77 | 82 Jesus Maria 61,365 53 88 71 | 115 Magdalena Vieja 63,935 57 80 68 | 107 Santa Anita 149,168 44 83 63 | 43 Brena 60,003 39 72 56 | 93 Magdalena Del Mar 45,329 41 51 46 | 102 Cieneguilla 30,285 80 11 46 | 151 Lince 42,557 34 54 44 | 104 Surquillo 72,492 30 56 43 | 59 Bellavista 59,552 35 49 42 | 70 INDUSTRIAL ECONOMICS, INCORPORATED D-4 ------- lEc DISTRICT NAME DISTRICT POPULATION (AGES 25-99) SHADDICK VAN DONKELAAR AVERAGE DEATHS DEATHS DEATHS RATE PER 100,000 Lurin 52,254 49 24 37 70 Pachacamac 76,225 46 26 36 47 San Luis 42,772 23 36 29 69 La Perla 49,511 18 40 29 59 Chaclacayo 31,232 16 37 26 84 Ancon 27,202 34 18 26 95 Barranco 24,956 19 27 23 91 Carmen De La Legua Reynoso 31,190 24 18 21 67 Santa Rosa 12,157 12 4 8 66 La Punta 3,131 4 4 4 134 Punta Hermosa 5,230 5 1 3 56 Pucusana 10,443 3 2 3 24 Mi Peru - 3 1 2 NA Punta Negra 5,400 2 1 2 36 San Bartolo 4,845 1 2 1 29 Santa Maria Del Mar 1,113 1 1 1 57 'The Mi Peru district in Callao had zero population in our dataset. Because the Shaddick and van Donkelaar surfaces overlap the population grid (districts), BenMAP apportions incidence results from air quality grid cells into the overlapping districts (including Mi Peru). INDUSTRIAL ECONOMICS, INCORPORATED D-5 ------- lEc EXHIBIT D-3. DISTRICT-LEVEL PM2 5 MORTALITY BURDEN, TRANSPORTATION SECTOR (UPPER BOUND) DISTRICT NAME DISTRICT POPULATION (AGES 25-99) SHADDICK VAN DONKELAAR AVERAGE DEATHS DEATHS DEATHS RATE PER 100,000 Overall 6,815,428 6,200 6,238 6,219 91 San Juan De Lurigancho 695,326 607 564 585 84 Comas 361,764 356 394 375 104 San Martin De Porres 488,004 358 381 369 76 Lima 207,527 323 387 355 171 Callao 304,221 355 302 329 108 Villa Maria Del Triunfo 294,591 290 224 257 87 Ate 394,943 211 287 249 63 Santiago De Surco 272,592 205 212 208 76 Carabayllo 189,698 270 140 205 108 Lurigancho 139,146 277 122 200 143 Villa El Salvador 291,540 147 206 177 61 Chorrillos 224,392 206 147 176 79 La Molina 132,567 218 127 173 130 Rimac 120,902 181 162 171 | 67 Los Olivos 254,864 160 183 171 | 142 San Juan De Miraflores 272,025 122 213 167 61 Ventanilla 271,339 184 142 163 60 La Victoria 125,163 135 186 160 128 Puente Piedra 210,135 138 161 150 71 Independencia 149,822 146 152 149 100 San Miguel 108,804 132 123 128 117 El Agustino 125,281 96 134 115 92 Miraflores 74,718 75 134 104 140 San Isidro 49,229 118 89 103 210 San Borja 94,488 76 110 93 98 Jesus Maria 61,365 65 108 86 141 Magdalena Vieja 63,935 69 98 84 131 Santa Anita 149,168 52 99 75 50 Brena 60,003 48 88 68 113 Magdalena Del Mar 45,329 50 63 57 125 Cieneguilla 30,285 95 13 54 180 Lince 42,557 41 67 54 127 Surquillo 72,492 36 68 52 72 Bellavista 59,552 43 60 51 | 86 Lurin 52,254 59 29 44 84 INDUSTRIAL ECONOMICS, INCORPORATED D-6 ------- lEc SHADDICK VAN DONKELAAR AVERAGE DISTRICT POPULATION RATE PER DISTRICT NAME (AGES 25-99) DEATHS DEATHS DEATHS 100,000 Pachacamac 76,225 55 32 43 57 La Perla 49,511 23 49 36 85 San Luis 42,772 28 43 35 72 Chaclacayo 31,232 19 44 31 101 Ancon 27,202 41 22 31 114 Barranco 24,956 23 32 27 109 Carmen De La Legua Reynoso 31,190 29 22 26 82 Santa Rosa 12,157 14 5 10 79 La Punta 3,131 6 5 5 166 Punta Hermosa 5,230 6 2 4 68 Pucusana 10,443 3 3 3 30 Mi Peru - 4 1 2 NA Punta Negra 5,400 3 2 2 44 San Bartolo 4,845 1 2 2 36 Santa Maria Del Mar 1,113 1 1 1 71 'The Mi Peru district in Callao had zero population in our dataset. Because the Shaddick and van Donkelaar surfaces overlap the population grid (districts), BenMAP apportions incidence results from air quality grid cells into the overlapping districts (including Mi Peru). INDUSTRIAL ECONOMICS, INCORPORATED D-7 ------- lEc EXHIBIT D-4. DISTRICT-LEVEL PM2 5 MORTALITY BURDEN, NON-COMPLIANT EMISSIONS (LOWER BOUND) DISTRICT NAME DISTRICT POPULATION (AGES 25-99) SHADDICK VAN DONKELAAR AVERAGE DEATHS DEATHS DEATHS RATE PER 100,000 Overall 6,815,428 248 250 249 | 4 San Juan De Lurigancho 695,326 25 24 25 | 4 Comas 361,764 15 17 16 | 4 San Martin De Porres 488,004 14 15 14 | 3 Lima 207,527 12 15 13 | 6 Callao 304,221 13 11 12 | 4 Ate 394,943 9 12 10 | 4 Villa Maria Del Triunfo 294,591 12 9 10 | 3 Carabayllo 189,698 11 6 9 I 3 Santiago De Surco 272,592 8 9 8 | 4 Lurigancho 139,146 12 5 8 | 6 Villa El Salvador 291,540 6 9 7 I 3 La Molina 132,567 9 5 7 I 3 Chorrillos 224,392 8 6 7 I 5 Los Olivos 254,864 7 7 7 I 3 San Juan De Miraflores 272,025 5 9 7 I 6 Rimac 120,902 7 7 7 I 2 Ventanilla 271,339 7 6 7 I 2 Puente Piedra 210,135 6 7 6 | 5 Independencia 149,822 6 6 6 | 3 La Victoria 125,163 5 7 6 | 4 El Agustino 125,281 4 6 5 | 4 San Miguel 108,804 5 4 5 | 4 Miraflores 74,718 3 5 4 | 5 San Isidro 49,229 5 3 4 | 8 San Borja 94,488 3 4 4 | 4 Jesus Maria 61,365 2 4 3 | 5 Santa Anita 149,168 2 4 3 | 5 Magdalena Vieja 63,935 3 4 3 I 2 Brena 60,003 2 3 3 I 4 Cieneguilla 30,285 4 1 2 I 5 Magdalena Del Mar 45,329 2 2 2 I 7 Surquillo 72,492 1 3 2 I 5 Lince 42,557 2 2 2 I 3 Bellavista 59,552 2 2 2 I 3 INDUSTRIAL ECONOMICS, INCORPORATED D-8 ------- lEc SHADDICK VAN DONKELAAR AVERAGE DISTRICT POPULATION RATE PER DISTRICT NAME (AGES 25-99) DEATHS DEATHS DEATHS 100,000 Lurin 52,254 2 1 2 3 Pachacamac 76,225 2 1 2 2 San Luis 42,772 1 2 1 3 La Perla 49,511 1 2 1 3 Chaclacayo 31,232 1 2 1 4 Ancon 27,202 2 1 1 5 Barranco 24,956 1 1 1 4 Carmen De La Legua Reynoso 31,190 1 1 1 3 Santa Rosa 12,157 1 0 0 3 La Punta 3,131 0 0 0 6 Punta Hermosa 5,230 0 0 0 3 Pucusana 10,443 0 0 0 1 Mi Peru - 0 0 0 NA Punta Negra 5,400 0 0 0 2 San Bartolo 4,845 0 0 0 1 Santa Maria Del Mar 1,113 0 0 0 3 'The Mi Peru district in Callao had zero population in our dataset. Because the Shaddick and van Donkelaar surfaces overlap the population grid (districts), BenMAP apportions incidence results from air quality grid cells into the overlapping districts (including Mi Peru). INDUSTRIAL ECONOMICS, INCORPORATED D-9 ------- lEc EXHIBIT D-5. DISTRICT-LEVEL PM2 5 MORTALITY BURDEN, NON-COMPLIANT EMISSIONS (UPPER BOUND) DISTRICT NAME DISTRICT POPULATION (AGES 25-99) SHADDICK VAN DONKELAAR AVERAGE DEATHS DEATHS DEATHS RATE PER 100,000 Overall 6,815,428 991 1,003 997 | 15 San Juan De Lurigancho 695,326 101 95 98 | 14 Comas 361,764 59 67 63 | 17 San Martin De Porres 488,004 56 59 58 | 12 Lima 207,527 49 59 54 | 26 Callao 304,221 51 46 48 | 16 Villa Maria Del Triunfo 294,591 47 37 42 | 14 Ate 394,943 36 48 42 | 11 Carabayllo 189,698 45 23 34 | 12 Santiago De Surco 272,592 33 35 34 | 18 Lurigancho 139,146 46 20 33 | 24 Villa El Salvador 291,540 24 34 29 | 10 La Molina 132,567 36 21 29 | 13 Chorrillos 224,392 32 24 28 | 21 Los Olivos 254,864 26 30 28 | 11 San Juan De Miraflores 272,025 20 36 28 | 23 Rimac 120,902 28 26 27 | 10 Ventanilla 271,339 29 24 27 | 10 Puente Piedra 210,135 23 28 25 | 20 Independencia 149,822 24 25 25 | 12 La Victoria 125,163 21 28 25 | 16 El Agustino 125,281 15 23 19 | 18 San Miguel 108,804 20 18 19 | 15 Miraflores 74,718 12 21 16 | 22 San Isidro 49,229 18 13 16 | 32 San Borja 94,488 12 17 15 | 16 Jesus Maria 61,365 10 16 13 | 21 Santa Anita 149,168 9 17 13 | 20 Magdalena Vieja 63,935 11 14 12 | 8 Brena 60,003 7 13 10 | 17 Cieneguilla 30,285 16 2 9 | 20 Magdalena Del Mar 45,329 8 9 8 | 28 Lince 42,557 6 10 8 | 19 Surquillo 72,492 6 10 8 | 11 Bellavista 59,552 6 9 8 | 13 INDUSTRIAL ECONOMICS, INCORPORATED D-10 ------- lEc SHADDICK VAN DONKELAAR AVERAGE DISTRICT POPULATION RATE PER DISTRICT NAME (AGES 25-99) DEATHS DEATHS DEATHS 100,000 Lurin 52,254 9 4 7 13 Pachacamac 76,225 9 5 7 9 San Luis 42,772 4 7 6 13 La Perla 49,511 3 7 5 10 Chaclacayo 31,232 3 7 5 16 Ancon 27,202 6 3 5 18 Barranco 24,956 3 5 4 18 Carmen De La Legua Reynoso 31,190 4 3 4 12 Santa Rosa 12,157 2 1 2 13 La Punta 3,131 1 1 1 24 Punta Hermosa 5,230 1 0 1 10 Pucusana 10,443 0 0 0 4 Mi Peru - 1 0 0 NA Punta Negra 5,400 0 0 0 6 San Bartolo 4,845 0 0 0 5 Santa Maria Del Mar 1,113 0 0 0 10 'The Mi Peru district in Callao had zero population in our dataset. Because the Shaddick and van Donkelaar surfaces overlap the population grid (districts), BenMAP apportions incidence results from air quality grid cells into the overlapping districts (including Mi Peru). INDUSTRIAL ECONOMICS, INCORPORATED D-11 ------- |