Ambient Air Pollution Exposure Estimation for the Global Burden of

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Ambient Air Pollution Exposure Estimation for the Global Burden of Disease 2013 Michael Brauer, Greg Freedman, Joseph Frostad, Aaron van Donkelaar, Randall V Martin, Frank Dentener, Rita Van Dingenen, Kara Estep, Heresh Amini, Joshua Schulz Apte, Kalpana Balakrishnan, Lars Barregard, David M Broday, Valery Feigin, Santu Ghosh, Philip K. Hopke, Luke David Knibbs, Yoshihiro Kokubo, Yang Liu, Stefan Ma, Lidia Morawska, José Luis Texcalac Sangrador, Gavin Shaddick, Hugh Ross Anderson, Theo Vos, Mohammad H Forouzanfar, Richard T. Burnett, and Aaron Cohen Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.5b03709 • Publication Date (Web): 23 Nov 2015 Downloaded from http://pubs.acs.org on November 24, 2015

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Environmental Science & Technology

Vos, Theo; Institute for Health Metrics and Evaluation, University of Washington Forouzanfar, Mohammad; Institute for Health Metrics and Evaluation, University of Washington Burnett, Richard; Health Canada, Cohen, Aaron; Health Effects Institute,

ACS Paragon Plus Environment


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Ambient Air Pollution Exposure Estimation for the Global Burden of Disease 2013

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Michael Brauera*, Greg Freedmanb, Joseph Frostadb, Aaron van Donkelaarc, Randall V. Martinc, Frank Dentenerd, Rita van Dingenend, Kara Estepb, Heresh Aminie, Joshua S. Aptef, Kalpana Balakrishnang, Lars Barregardh, David Brodayi, Valery Feiginj, Santu Ghoshg, Philip K. Hopkek, Luke D. Knibbsl, Yoshihiro Kokubom, Yang Liun, Stefan Mao, Lidia Morawskap, José Luis Texcalac Sangradorq, Gavin Shaddickr, H. Ross Andersons, Theo Vosb, Mohammad H Forouzanfarb, Richard T. Burnettt, Aaron Cohenu.

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38

a*

School of Population and Public Health, The University of British Columbia, 3rd Floor – 2206 East Mall, Vancouver BC V6T1Z3 Canada. email: [email protected] tel: 604 822 9585 fax: 604 822 4994 b Institute for Health Metrics and Evaluation, University of Washington, Seattle WA. USA c Department of Physics and Atmospheric Science, Dalhousie University, Halifax NS. Canada d European Commission, Joint Research Centre, Ispra, Italy e Swiss Tropical and Public Health Institute, Basel, Switzerland f Department of Civil, Architectural and Environmental Engineering, University of Texas at Austin, Austin TX. USA g Department of Environmental Health Engineering, Sri Ramachandra University, Chennai, India h Department of Occupational and Environmental Health, University of Gothenburg, Gothenburg, Sweden i Technion-Israel Institute of Technology, Civil and Environmental Engineering, Haifa, Israel j National Institute for Stroke & Applied Neurosciences, Auckland University of Technology, Aukland, New Zealand. k Department of Chemical Engineering, Clarkson University, Potsdam, NY. USA l School of Public Health, The University of Queensland, Brisbane, QLD, Australia m Department of Preventive Cardiology, National Cerebral and Cardiovascular Center, Osaka, Japan n Rollins School of Public Health, Emory University, Atlanta GA. USA o Saw Swee Hock School of Public Health, National University of Singapore, Singapore p School of Chemistry, Physics and Mechanical Engineering, Queensland University of Technology, Brisbane QLD, Australia q National Institute of Public Health, Cuernevaca, Mexico r Department of Mathematical Sciences, University of Bath, Bath, UK. s Population Health Research Institute, St. George's University of London, London, UK t Health Canada, Ottawa, ON. Canada u Health Effects Institute, Boston MA. USA

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Submitted to Environmental Science and Technology

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Word count (including tables and figures): 6996 (text only: 5196)


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Abstract

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Exposure to ambient air pollution is a major risk factor for global disease. Assessment of the

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impacts of air pollution on population health and evaluation of trends relative to other major risk

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factors requires regularly updated, accurate, spatially resolved exposure estimates. We combined

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satellite-based estimates, chemical transport model simulations and ground measurements from

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79 different countries to produce global estimates of annual average fine particle (PM2.5) and

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ozone concentrations at 0.1° × 0.1° spatial resolution for five-year intervals from 1990-2010 and

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the year 2013. These estimates were applied to assess population-weighted mean concentrations

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for 1990 – 2013 for each of 188 countries. In 2013, 87% of the world’s population lived in areas

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exceeding the World Health Organization Air Quality Guideline of 10 µg/m3 PM2.5 (annual

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average). Between 1990 and 2013, global population-weighted PM2.5 increased by 20.4 % driven

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by trends in South Asia, Southeast Asia, and China. Decreases in population-weighted mean

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concentrations of PM2.5 were evident in most high income countries. Population-weighted mean

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concentrations of ozone increased globally by 8.9% from 1990 – 2013 with increases in most

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countries– except for modest decreases in North America, parts of Europe, and several countries

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in Southeast Asia.

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TOC Art

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2 ACS Paragon Plus Environment


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Introduction

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The Global Burden of Disease (GBD) 2013 provided important estimates of the global health

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impacts attributable to ambient air pollution. Ambient particulate matter air pollution (PM2.5,

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particulate matter with aerodynamic diameter 2.5 µm or smaller) was identified as a leading risk

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factor for global disease burden with an estimated 2.9 million attributable deaths in the year

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2013. An additional 217,000 deaths were attributable to long term ozone exposure 1. These two

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pollutants were selected as indicators of exposure to ambient air pollution based on extensive

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epidemiologic and mechanistic evidence indicating independent adverse health impacts2. These

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disease burden estimates built upon those developed previously for the Global Burden of Disease

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2010 (GBD 2010)3 which incorporated novel global air pollution exposure estimates for 1990,

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2005, and 2010 that included both urban and rural areas and that merged data from ground

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measurements, satellite retrievals and chemical transport models (CTM)2. In addition to the

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application of these exposure estimates to the entire global population, their high spatial

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resolution minimized biases due to spatial misalignment between air pollution exposure and

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population information4. Beyond their direct use in the GBD 2010, the PM2.5 exposure estimates

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were combined with CTM simulations to estimate sector-specific contributions to disease burden

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from motor vehicle transportation5,6, and household solid fuel use7,8. Additionally, these

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exposure estimates served as the basis of the World Health Organization air pollution mortality

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estimates9 and were used by the World Bank as indicators of sustainable development10 and to

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estimate the economic damages attributable to air pollution11. The estimates have also been used

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in the U.S. Environmental Protection Agency’s (USEPA) BenMap health and economic impact

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assessment tool12, incorporated into the International Agency for Research on Cancer

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Monograph on the carcinogenicity of outdoor air pollution13. Given the lack of ground


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measurements in many regions of the world, the above analyses were not previously possible

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prior to development of consistent, globally applicable exposure estimates.

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In the context of the most recent update of the Global Burden of Disease (GBD 2013)1 we

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developed updates and revised global exposure estimates for PM2.5 and ozone (O3). We

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incorporated improvements in satellite-based estimation of PM2.514, internally consistent

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emissions inventories spanning this entire period and a substantially increased number of ground

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measurements of particulate matter, compared to those utilized previously. Here we describe the

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methodology used to estimate air pollution exposure for the GBD 20131, provide descriptive

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information on year 2013 concentrations and illustrate trends since 1990, at the country level.

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Methods

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PM2.5

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As in the GBD 2010 estimates, our overall approach to estimate annual average PM2.5

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concentrations for GBD 2013 uses the mean of gridded values of satellite-based and CTM

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estimates at 0.1° x 0.1° resolution, calibrated to available ground measurements. We first provide

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a brief overview of the approach described previously for GBD 2010 and then describe details of

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the updated inputs and methodology used in GBD 2013.

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GBD 2010 estimates

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In the previous GBD (2010) we used satellite-based estimates for PM2.5 for the year 200515

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combined with simulations from the TM5 (Tracer Model, version 5) CTM16 based on year 2005



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emissions, which were the most current available at the time of analysis. The satellite-based

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estimates combine aerosol optical depth (AOD) retrievals (which include the total column

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between surface and the sensor) to estimate near-surface PM2.5 by applying the relationship of

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PM2.5 to AOD simulated by the GEOS-Chem CTM. Our evaluations indicated that both TM5

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and the satellite-estimates provided useful information which contributed to the estimation of

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exposure2. To estimate exposures for 1990 we used a TM5 simulation based on 1990 emissions

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but with the same “standard” meteorology as in the 2005 simulation. For the 1990 satellite-based

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estimates we used the same spatial pattern from the year 2005 estimates and adjusted this to 1990

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based upon the ratio of 2005:1990 simulations from the GEOS-Chem CTM17 that used

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anthropogenic emissions from these years (while again maintaining consistent meteorology and

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natural emissions). For year 2010 estimates, we used 2005 TM5 and satellite-based estimates and

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extrapolated these to 2010 based on the 1990 – 2005 trend following a simple assumption that

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the change proportional to the time period length from 2005 to 2010, relative to 2005 was 1/3 as

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large as the change from 1990 to 2005, relative to 1990 as described by:

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(PM2.5_2010-PM2.5_2005)/PM2.5_2005 = 1/3 * (PM2.5_2005 - PM2.5_1990)/PM2.5_1990

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In addition, we compiled available annual average measurement data for ~2005 from multiple

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sources2. These measurements were used to calibrate the average of the TM5 and satellite-based

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estimates for 2005 at 0.1° x 0.1° grid-cell resolution with a simple prediction equation derived

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for those locations with measurements.

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GBD 2013 estimates

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For GBD 2013, we followed a similar approach using updated inputs. Specifically, we used a

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new series of satellite-based estimates for PM2.5 that included annual estimates for 1998 – 2012

(1)


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and an improved estimation algorithm14. Briefly, satellite-based PM2.5 estimates used aerosol

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optical depth (AOD) retrievals to estimate near-surface PM2.5 by applying the relationship of

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PM2.5 to AOD simulated by the GEOS-Chem CTM. These updated PM2.5 estimates make use of

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both “unconstrained” (as used in GBD 2010) and “optimal-estimation” AOD retrievals, and used

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the MODIS18, MISR19,20 and SeaWiFS21 satellite-borne instruments. In the optimal estimation

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approach, AOD retrievals using observed MODIS top-of-atmosphere reflectance are constrained

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by simulated AOD based on their relative uncertainties. The optimal estimation PM2.5 values

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additionally incorporated vertical profile information from the CALIOP22 satellite instrument to

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inform the relationship between column AOD and ground-level concentrations. These two

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(optimal estimation and unconstrained) sets of estimates were then combined with information

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on temporal variation based on SeaWiFS and MISR to estimate global PM2.5 estimates (50%

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RH) at 0.1° x 0.1° for 2000, 2005, 2010 and 2011 (based on 3 years of retrievals, centered on the

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year of interest). Estimates for 1995 and 1990 were based on the ratio of GEOS-Chem

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simulations that used anthropogenic emissions (from EDGAR23) between 2005 and the

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respective year of interest, but constant meteorology24.

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We also included new TM5-FASST25,26 (FAst Scenario Screening Tool) simulations for 1990,

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2000, and 2010, using an updated set of emissions inventories, and constant meteorological

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inputs and emissions from dust and sea salt. TM5-FASST is a reduced form version of the TM5

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CTM. TM5 is a nested 3-dimensional global atmospheric chemistry transport model, which

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simulates ozone and aerosol components at 1° × 1° resolution16. TM5-FASST emulates the full

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TM5 CTM with a set of linear relationships between emissions in 56 geographically defined

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source regions, and pollutant concentrations in receptor regions. TM5-FASST simulations are

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then sub-allocated based on population density (using the Gridded Population of the World, 6 ACS Paragon Plus Environment


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version 3, GPWv327) to 0.1° x 0.1° grid cells. Here we used a consistent set of emissions

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estimates [ECLIPSE v4a (IEA) 2010, 2000, 1990 emissions; RCP: international aviation and

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shipping (2010); GFED v3.1: Forest fires and savannah burning (2000, 2010)] and “typical”

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(year 2001) meteorology, dust, and sea salt contributions to simulate ambient (50% RH) PM2.5

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concentrations at 0.1° x 0.1 ° for 1990, 2000 and 2010. Estimates for 1995, 2005 and 2011 were

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generated by fitting natural cubic splines to the 1990, 2000 and 2010 estimates at the grid cell

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level.

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To collect updated measurement data for 2010-2013, we used a variety of information sources

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including those used previously2, as well as new data, especially from China and India, where

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available. We sought input from international GBD collaborators, conducted targeted data

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searches and included measurements compiled from a literature search14 and the WHO ambient

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air pollution in cities database9 (Supporting Information). A final database was constructed

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including measurement values, year of annual average (2010-2013 data were targeted), site

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coordinates (if available, or city centroid coordinates if not available), site type (if available),

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data source and whether PM2.5 was measured directly or estimated from a PM2.5:PM10 ratio.

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Given spatial biases in availability of ground measurements, differences in measurement

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approaches between jurisdictions, and absence of details regarding measurement data in some

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instances, the ground measurements were not used in evaluation of the exposure assessment

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methodology but rather incorporated within the approach as an additional source of information.

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For locations where daily values were obtained or where measurement completeness was

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available we retained all sites with >70% valid measurements, as reported in source databases.

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For locations where data were obtained for multiple years at the same location we retained one


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value per location, between the years 2008-2013 in the following order of preference: 2010,

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2011, 2009, 2012, 2008, 2013.

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If multiple monitoring types available at the same site were included in parent databases, these

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values were averaged. Industrial and roadside sites were excluded (where indicated in source

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databases; except in India where these were retained upon the advice of local experts who

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indicated that these sites adequately represented population exposure). In the USEPA database,

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sites indicated as "background surveillance" were designated as background, those indicated as

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"continuous air monitoring program", "population-oriented monitoring", or "exposure studies"

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were designated as population sites. Those sites or measurements identified as "episode

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monitoring," "complaint investigation", or “events,” in the USEPA database were excluded. All

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other sites in the USEPA database were identified as unspecified and retained.

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In locations where no PM2.5 measurements were available, we estimated PM2.5 from

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measurements of PM102,28. We preferentially used locally derived PM2.5:PM10 ratios. In these

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cases ratios from any sites within 50 km where both PM10 and PM2.5 were measured were used.

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These local ratios were only accepted and used to derive PM2.5 estimates for nearby sites if they

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were between 0.2 and 0.8. If local ratios were not available we used a country and monitor-

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specific average if available, followed by a country-specific average. Otherwise, we assumed a

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ratio of 0.5 to estimate PM2.5 from PM10 measurements2.

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In total we included 4073 data points from 3387 unique locations (1,854 [46%] from direct

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measurements of PM2.5) in 79 countries (Figure S1; Supporting Information). Of the 2219 data

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points in which PM2.5 was estimated from PM10, 1,151 (30% of 4073 total) were estimated using

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a locally derived ratio, 590 (15%) were estimated using in-country monitors of the same type, 8 ACS Paragon Plus Environment


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and 309 (8%) were estimated with other in-country monitors. The remaining 169 (4%) were

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estimated with a ratio of 0.5, as in our GBD 2010 estimates. The mean ratio for the estimation

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was 0.61. These individual measurements have unknown accuracy and representativeness, but

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collectively they offer unprecedented ground-based information.

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We then used a regression calibration approach to combine the mean of the satellite-based

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estimates and the TM5-FASST simulations with the collective measurements to produce final

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global estimates at 0.1° x 0.1 °grid-cell resolution. For the regression calibration, we initially

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evaluated a simple regression model analogous to that used in the GBD 2010 estimates:

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Measured ln(PM2.5) = β0 + β1 * ln(fused)

(2)

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where fused is the mean of the satellite-derived and TM5 estimates for each grid cell. We first

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tested whether to apply a single global calibration function or one that varied spatially. Modeling

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with a random effect by 7 aggregated “super-regions” (due to the complete absence of

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measurements in multiple regions) indicated regional variation in slopes but also very poor fits in

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some regions, so a single global calibration function was chosen. Evaluation of model residuals

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indicated no association with population density and addition of population density to the model

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only minimally improved fit. We then evaluated the impact of including available information

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describing the measurements including whether the exact site coordinates were known, whether

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PM2.5 was directly measured or estimated and whether the monitoring site classification was

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known or unspecified. Inclusion of these variables slightly improved the model R2 while slightly

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reducing the residual standard error.

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These two candidate global calibration models (Simple: β0= 0.82, β1 = 0.73, residual standard

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error = 0.43, Multiple R-squared: 0.60, Adjusted R-squared: 0.60; “Advanced”, with additional 9 ACS Paragon Plus Environment

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site parameters: β0= 0.42, β1=0.87, residual standard error = 0.41, Multiple R-squared: 0.64,

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Adjusted R-squared: 0.64) were further evaluated by cross-validation where 10% of the

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measurement sites were randomly selected for model evaluation. This was repeated for a total of

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3 separate sets of 10% testing sites. In all cases the model with additional site parameters

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(“Advanced”, Figure S2; Supporting Information) had a lower RMSE (12.17 vs 11.04; 12.17 vs

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11.04; 10.21 vs 9.15) as well as lower Bayesian and Akaike’s Information Criteria. Therefore,

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we selected the model with additional site parameters (whether coordinates were known, whether

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PM2.5 was directly, and whether the site classification was known) (Figure S2; Supporting

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Information). The relevant terms from this calibration function were then used to adjust the

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gridded values of the mean of the satellite-based and TM5 estimates (“fused”):

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Calibrated PM2.5 = exp[0.41765+(0.86953*ln(fused))]

(3)

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Estimates for 2013 were generated by extrapolating from the 2010-2011 trend of calibrated

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values. Specifically, we assessed the rate of change in concentrations between 2010 and 2011

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and applied this growth rate to an exponential growth function to estimate concentrations in

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2013:

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PM2.5_2013 = PM2.5_2010*exp(rate.of.change *(2013-2010))

(4)

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where, (annual) rate.of.change = ln(PM2.5_2011/ PM2.5_2010). The final PM2.5 estimates used in

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the burden of disease estimation and presented in the Results section are the calibrated, and

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(where applicable) extrapolated values. National level population-weighted mean and 95%

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uncertainty interval concentrations were estimated by sampling 1000 draws of each fused grid

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cell concentration in combination with the calibration parameters and the standard error of the

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calibration function. Gridded exposure concentrations were then aggregated to national level 10 ACS Paragon Plus Environment


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population-weighted means with the corresponding grid cell population value (using the Gridded

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Population of the World, version 3, GPWv327).

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Ozone

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As in GBD 2010, we calculated a running 3-month average (of daily 1 hour maximum values)

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for each grid cell over a full year and selected the maximum of these values. This metric was

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chosen to align with epidemiologic studies of chronic exposure which typically employ a

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seasonal (summer) average, and to account for global variation in the timing of the ozone

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(summer) season2. These estimates were simulated with TM5-FASST at 0.1° x 0.1° for 1990,

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2000 and 2010 using the same emissions datasets and meteorological inputs as for the PM2.5

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simulations. Estimates for 1995, 2005, and 2011 were generated by fitting a natural cubic spline,

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as described previously for PM2.5. As with PM2.5, an exponential growth model for ozone

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concentrations was used to estimate 2013 concentrations from the 2011 estimates. Population

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weighted mean concentrations and 95% uncertainty intervals for each country were estimated as

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described above for PM2.5 and assuming a normal distribution with a standard deviation calculated

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by assuming an uncertainty interval of ± 6% of the estimated concentration. As with GBD 2010

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exposure estimates and given the scarcity of surface ozone measurements throughout the world

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and the challenges in accessing hourly data from available monitoring sites to develop the

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desired metric, we did not utilize surface ozone measurements for developing the global

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estimates.

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Results


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Of the ground measurements, while 79 countries were represented, more than half were from

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high income countries in North America (25%), Western (32%) and Central (12%) Europe.

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Because of a major expansion of China’s air quality monitoring network, 10% of the ground

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measurements were from East Asia, and 5% were from South Asia. Countries in eleven regions

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(Andean Latin America, Australasia, Central Asia, Eastern Europe, High-income Asia Pacific,

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North Africa and Middle East, Southeast Asia, Southern Latin America, Southern Sub-Saharan

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Africa, Tropical Latin America, Western Sub-Saharan Africa) each contributed less than 3% of

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the measurement data, with no measurements at all from the other four regions (Caribbean,

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Central Latin America, East and Central sub-Saharan Africa) of the world. The highest

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measured annual average PM2.5 concentration in the assembled measurement database was 194

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µg/m3 in Shijiazhuang, the capital of Hebei Province in China, while the lowest was 65 µg/m3) concentrations experienced by populations in

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China and India. Fully 87% of the global population resided in areas above the WHO guideline

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of 10 µg/m3, with essentially none of the population of China (0.4%) or India (0.01%) living in

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areas meeting this level.

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Large relative decreases in estimated PM2.5 concentrations between 1990 and 2013 at the 0.1° x

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0.1° grid cell level were evident in the Eastern US., Europe, Russia and in parts of Southeast

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Asia (Figure S3, Supporting Information). In contrast, large relative increases were apparent in

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Western Canada, parts of South America, the Middle East, India and China. Somewhat similar

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patterns were also evident for ozone (Figure S4, Supporting Information)

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The trends in PM2.5 are examined in more detail in Figure 4ab - which display population-

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weighted mean (95% uncertainty interval) distributions of concentrations globally and at the

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country level for the ten most populous countries. Plots including PM2.5 trends all other countries

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are presented in the Supporting Information (Figures S5-S25), along with all of the country-level

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population-weighted estimates for PM2.5 and ozone for 1990, 1995, 2000, 2005, 2010 and 2013)

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(File S1; Supporting Information). Table 1 presents population-weighted estimates and measures

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of change for PM2.5 and ozone for 1990 and 2013 for the world’s ten most populous countries.

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Large proportional increases in mean population-weighted PM2.5 concentrations were apparent in

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India, China, Brazil, Bangladesh, India, China and Pakistan, with decreases observed in the U.S.,

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Indonesia, Russia, Japan, and Nigeria.


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Between 1990 and 2013, decreases in population-weighted mean concentrations of PM2.5 were

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evident in most of high income countries (Figure 4a, Figures S5, S10. S14, S20; Supporting

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Information), in contrast to consistent increases in South Asia (Figure 4a, Figure S8: Supporting

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Information), much of Southeast Asia (Figure S9; Supporting Information), and in China (Figure

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4a). At the country-level, the highest population-weighted mean concentration estimated for

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2013 was Mauritania (70 µg/m3), followed by China (55 µg/m3), Saudi Arabia (54 µg/m3),

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Kuwait (49 µg/m3), Bangladesh (48 µg/m3), India (47 µg/m3), Pakistan and Nepal (46 µg/m3).

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The lowest country-level population-weighted estimates were for several Pacific and Caribbean

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island nations, Australia and Norway (≤ 6 µg/m3). Population-weighted ozone levels also

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increased globally and generally throughout the most heavily populated countries from 1990 –

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2013 (Figure 4b), although to a lesser degree than seen for PM2.5. There were modest decreases

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in population-weighted ozone levels in the U.S., Mexico and Canada as well as parts of Europe,

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likely due to implementation of air quality management programs, and several countries in

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southeast Asia (Indonesia, Malaysia, Singapore) (File S1; Supporting Information),.

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We also compared estimates for the year 2010 using the updated methodology described in this

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manuscript to those reported previously from GBD 20102 for the same year (Figure S26;

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Supporting Information). This is a comparison of methodology, in contrast to the temporal

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comparisons described above. Our updated methods estimated lower (by 50 µg/m3) levels in

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some areas of North Africa, the Middle East and the Gobi Desert, all areas impacted by high

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levels of windblown mineral dust. Slightly higher (~5 – 10 µg/m3) levels were estimated with the

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updated methodology in Brazil and elsewhere in South America with noticeably higher (~30 –

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40 µg/m3) levels in India, Pakistan and Bangladesh. Somewhat lower (~20 – 30 µg/m3) levels

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were also estimated for some areas of eastern China. These differences may be due to the 14 ACS Paragon Plus Environment


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additional temporal coverage incorporated into the current satellite-based and ground

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measurements, as well as the newer emissions information included in the TM5-FASST

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simulations. In North Africa the lower levels reflect changes in mineral dust emissions that were

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driven by changes in local meteorology, and the higher levels in India, Bangladesh and Pakistan

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likely reflect increases in emissions that may not have been represented in the earlier

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methodology that extrapolated 2005 estimates to 2010. In China, the somewhat lower levels

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estimated for GBD 2013 compared to those for GBD 2010 were likely affected by the inclusion

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in the global calibration of substantially more ground measurements in China (Figure S2;

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Supporting Information).

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At the country-level, population-weighted mean estimates for PM2.5 for the year 2010 were very

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similar for most countries between those estimated for GBD 2010 and GBD 2013. Estimates for

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China were noticeably lower for GBD 2013 (54.8 µg/m3) compared to GBD 2010 (72.6 µg/m3),

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as were several countries with high contributions from windblown mineral dust (Saudi Arabia:

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53.8 vs 61.7 µg/m3; Qatar: 40.1 vs 69.0 µg/m3; United Arab Emirates: 40.9 vs 79.5 µg/m3 for

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GBD 2013 vs GBD 2010, respectively). Higher levels were estimated in GBD 2013 for countries

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in South Asia (India: 43.4 vs 32.0; Bangladesh: 45.7 vs 31.1 6; Pakistan: 43.4 vs 38.1; Nepal:

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41.5 vs 32.7 µg/m3 for GBD 2013 vs GBD 2010, respectively).

343 344

Discussion

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We extended previous global estimates of long-term average exposure to PM2.5 and ozone at 0.1

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x 0.1° resolution to support the Global Burden of Disease 2013. Given the increasing emphasis

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within the Global Burden of Disease on country-level (and sub-country) reporting30, we also

348

provided population-weighted estimates of exposure to PM2.5 and ozone for 188 different 15 ACS Paragon Plus Environment

Page 17 of 33


349

countries for the years 1990, 1995, 2000, 2005, 2010 and 2013. As such, these data represent one

350

of the most extensive collections of global air pollution concentration estimates produced to date.

351

We applied improved satellite-based estimates that also incorporated additional information on

352

temporal trends and which have been independently shown to include reduced error compared to

353

those used previously in GBD 201014,15. In addition, we used CTM simulations which

354

incorporated internally consistent emissions trends from 1990 – 2013. Further, we incorporated

355

a substantially larger number of available surface measurements of PM2.5 to calibrate the

356

estimates based on satellite retrievals and CTM simulations. This calibration, which included

357

additional information on the ground measurements (whether the PM2.5 concentration was

358

directly measured, whether the exact site location was known and whether the monitoring site

359

classification was known) resulted in reduced error compared to a simpler form of the

360

calibration, analogous to that used in GBD 2010. In addition, while the calibration we employed

361

here was applicable over the full range of concentrations, our calibration function used in GBD

362

2010 was only valid for concentrations > 10 µg/m3. Given the advances in methodology used to

363

develop these estimates, compared to those previously reported2, we consider these estimates to

364

be more accurate.

365 366

Although these estimates incorporated methodological advancements and updated inputs, they

367

are not without limitations. Given the need to produce similar estimates in future years to support

368

regular updates to the Global Burden of Disease30 and other assessments, we anticipate further

369

enhancements to the methodology to address these limitations. Specifically, ground

370

measurements were underestimated by our estimates in some locations, notably in parts of South

371

America (e.g. Chile), southern Poland and Turkey, and in specific urban areas with high levels of



Page 18 of 33

372

ambient PM2.5 such as Ulaanbaatar, Mongolia (Figure S27; Supporting Information). The same

373

underestimation of ground level measurements in southern Poland and Ulaanbaatar was

374

identified by van Donkelaar et al, who suggested that higher wintertime (and in the case of

375

Ulaanbaatar also nighttime) emissions when satellite retrievals are more limited due to more

376

frequent winter cloud cover (or unavailable at night) are likely to be responsible14. A similar

377

phenomenon may also be contributing to poor agreement in Chile where winter, nighttime wood

378

burning contributes to elevated PM2.5 concentrations31,32. These underestimations of ground

379

measurements in specific locations were also evident in TM5-FASST simulations suggesting that

380

both CTM and satellite-based estimates may fail to accurately estimate ground-level PM2,5 in

381

relatively small areas having very high levels. Improvements in spatial precision of emissions

382

estimates and satellite retrievals and use of regional models may reduce these uncertainties.

383 384

In addition, we used available PM10 measurements to estimate PM2.5 concentrations in locations

385

where PM2.5 measurements were unavailable. Our use of PM2.5:PM10 ratios for the calibration

386

dataset represents a balance between measurement specificity and spatial representativeness. For

387

example in North Africa and the Middle East, South Asia, and high-income Asia Pacific

388

countries, less than 20% of available ground measurements were of PM2.5. Despite increases in

389

the number of available measurements used in our calibration (4073 in this analysis compared to

390

679 in that reported previously2), more than 50% of the ground measurements that were used

391

were estimated from PM10 measurements. Given the importance of these estimates in the

392

calibration, we placed additional importance on using location-specific PM2.5:PM10 ratios to

393

estimate PM2.5 concentrations, for example using ratios from measurements within 50km in 30%

394

of cases and in-country ratios for an additional 23% of cases. Further, our regression calibration


Page 19 of 33


395

included a term to account for the use of direct vs estimated PM2.5 measurements. Van Donkelaar

396

et al., using the same satellite-derived estimates included here, reported 58%, 53% and 65% of

397

explained variability in ground measurements of PM2.5 in North America, Europe and elsewhere,

398

respectively14. In comparison, our candidate simple regression model, which treated directly

399

measured and estimated ground PM2.5 measurements equally, explained 60% of variability in

400

ground measurements, while the (advanced) calibration model that was ultimately used

401

explained slightly more variability (64%). Overall, there is a need for additional PM2.5

402

measurements and greater global coverage. The establishment of a new global network to

403

address these shortcomings and to improve the capability of satellite-based estimates of global

404

particulate matter levels offers the promise of enhanced accuracy and representativeness33. The

405

need for additional air pollution measurements was also highlighted in the recent World Health

406

Assembly Resolution on Air Pollution34.

407 408

High levels of uncertainty in our estimates exist in regions of elevated windblown mineral dust.

409

The latter is partially driven by TM5-FASST use of standard dust contributions that do not align

410

with a specific year and the temporally variable levels of resuspended mineral dust in affected

411

regions. As we observed variability between regions in the function used to calibrate the fused

412

estimates ground measurements, there is a need to improve the incorporation of measurement

413

information. For example, in future work we aim to use the increasing number of surface

414

measurements of PM2.5, especially those in China, and to implement advanced approaches to

415

more directly incorporate ground measurements and other information. Future availability of

416

additional ground measurements may also increase the feasibility of allowing spatially varying

417

calibration functions. This might be achieved by geographically weighted regression35,36 or



Page 20 of 33

418

hierarchical modelling. For the latter, calibration models for different regions may include a

419

global calibration together with regional random effects. Such models sit naturally within a

420

Bayesian framework. Recent advances in computational methods for inference, for example

421

those based on Integrated Nested Laplace Approximations37,38, have increased the feasibility of

422

implementing such complex models. Harmonization of measurement approaches between

423

jurisdictions would also be beneficial.

424

Because we estimated year 2013 concentrations based upon year 2010 concentrations and trends

425

in satellite-based estimates from 2010 to 2011, differences between estimates for 2010 and 2013

426

may be overly influenced by short-term variability in meteorology rather than longer-term trends

427

in emissions, the latter being more stable over the 5-year periods included in our estimates.

428

However, as noted in Methods, the annual satellite-based estimates are 3-year moving averages,

429

centered on the year of interest which should partially mitigate this instability. Future estimations

430

may incorporate a more rigorous evaluation of temporal prediction approaches or include

431

operational forecasts.

432

The updated global exposure estimates presented here represent a further advance in

433

characterizing global population exposure to ambient air pollution for use in burden of disease

434

assessment and other impact analyses. The global coverage allows for estimation of

435

concentrations in areas without extensive ground monitoring, including for example, rural areas

436

with large emissions from household use of solid fuels7. Like our previous GBD 2010 estimates,

437

these are based on PM2.5 mass concentrations. Although there is considerable interest in, and

438

active research concerning, the effects of the myriad constituents of ambient air pollution, ozone

439

and PM2.5 mass concentrations remain the most robust and consistent indicators of health-

440

damaging air pollution from combustion and other major sources39,40. Use of these estimates in 19 ACS Paragon Plus Environment

Page 21 of 33


441

combination with CTM simulations can provide information on sector-specific contributions to

442

ambient concentrations and disease burden to inform air quality management5–8. Improvements

443

in the quality of these estimates and the application of a consistent methodology to evaluate

444

temporal trends in exposure over a 23 year period should inform air quality management

445

strategies and other approaches to mitigate the health impacts of air pollution exposure.

446

Accordingly, we are committed to making these estimates available for others to use and have

447

provided the associated files of country-level population weighted (File S1) and gridded

448

estimates (linked to population data and urbanicity indicators, File S2) and a data dictionary (File

449

S3) in the Supporting Information. Given the evidence indicating the importance of ambient air

450

pollution to global disease burden1,3,9 and the expectation of annual Global Burden of Disease

451

updates beginning in 2015, we anticipate a need for regular updating and improving of these

452

estimates and their use in policy assessments and comparative analyses.

453 454 455

Acknowledgements

456

We thank the following Global Burden of Disease Collaborators for comments on an earlier draft

457

of the manuscript: Kim Yun Jin, Samath Dharmaratne, Maysaa El Sayed Zaki, Jost Jonas,

458

Farshad Pourmalek, Arindam Basu, Michelle Bell, Yousef Khader, Eun-Kee Park, Kingsley

459

Nnnanna Ukwaja, Mark Nieuwenhuijsen, Ivy Shiue, George Thurston, Yuchiro Yano, Dean

460

Hosgood, Awoke Misganaw, Jiang Guohong, Yohannes Adama Melaku, Semaw Ferede Abera,

461

Dietrich Plaß, Sung Kim, Ted Miller, Scott Weichenthal, Elisabete Weiderpass, Ricky Leung,

462

Yoshihiro Kokubo, Hwasin Shin. We also thank Sarah Henderson for assistance with merging

463

data. Figures 1, 2, S1, S3, S4, S26 and S27 were created with QGIS v2.8.2. 20 ACS Paragon Plus Environment


Page 22 of 33

464 465 466


Page 23 of 33


467

Supporting Information

468

Additional figures describing the exposure estimates with the full data set of global gridded and

469

population-weighted country mean estimates along with a data dictionary, R code to create all

470

customized figures in the same style as those included in the manuscript, and a full listing of

471

ground measurement data sources. This information is available free of charge via the Internet at

472

http://pubs.acs.org/.



Page 24 of 33

473 1990

2013

%

1990

2013

%

PM2.5

PM2.5

Change

Ozone

Ozone

Change

Global

26.4

31.8

20.4

57.3

62.4

8.9

China

39.3

54.3

38.0

57.0

64.5

13.2

India

30.2

46.7

54.3

61.5

74.0

20.2

United States

16.4

10.7

-34.5

70.3

67.0

-4.7

Indonesia

21.0

14.8

-29.7

47.3

39.6

-16.1

Brazil

9.7

16.5

70.4

43.4

51.0

17.3

Pakistan

36.5

46.2

26.3

59.0

68.8

16.5

Nigeria

31.0

29.5

-4.7

66.3

67.5

1.9

Bangladesh

29.9

48.3

61.6

59.4

72.0

21.3

Russia

19.7

14.2

-27.6

48.6

48.3

-0.6

Japan

19.4

16.0

-17.5

56.8

60.5

6.7

Country

474 475

Table 1. 1990, 2013 and percent change since 1990 in global population-weighted annual

476

average PM2.5 (µg/m3) and population-weighted seasonal mean 1 hour daily maximum ozone

477

(ppb) concentrations and for the world’s ten most populous countries.

478


Page 25 of 33

479


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480 481 482 483 484 485 486

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Figures

Figure 1. Estimated 2013 annual average PM2.5 concentrations (μg/m3). The PM2.5 estimates are generated from the grid cell average of satellite-based estimates and TM5-FASST simulations and calibrated with a prediction model incorporating surface measurements. White areas indicate no data.


Page 30 of 33

Page 31 of 33


Figure 2. Estimated 2013 seasonal (3-month) hourly maximum ozone concentrations (ppb) from TM5-FASST simulations. Note that the specific 3-month period varies by region to account for spatial differences in seasonal ozone. White areas indicate no data.



Figure 3. Global and regional distributions of population as a function of annual (2013) average ambient PM2.5 concentration for the world’s 10 most populous countries. Plotted data reflect local smoothing of bin-width normalized distributions computed over 400 logarithmically spaced bins; equal-sized plotted areas would reflect equal populations. Dashed vertical lines indicate World Health Organization Interim Targets (IT) and the Air Quality Guideline (AQG).


Page 32 of 33

Page 33 of 33




Figure 4. Global and country-level population-weighted mean (and 95% uncertanty intervals) concentrations for 1990, 1995, 2000, 2005, 2010 and 2013 for the world’s ten most populous countries (a) annual average PM2.5 (µg/m3), (b) 2013 seasonal (3-month) hourly maximum ozone concentrations (ppb) .


Page 34 of 33

Ambient Air Pollution Exposure Estimation for the Global Burden of

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