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Characterization of Natural and Affected Environments
Data integration for the assessment of population exposure to ambient air pollution for global burden of disease assessment. Gavin Shaddick, Matthew Thomas, Heresh Amini, David M Broday, Aaron Cohen, Joseph Frostad, Amelia Green, Sophie Gumy, Yang Liu, Randall V Martin, Annette Prüss-Üstün, Daniel Simpson, Aaron van Donkelaar, and Michael Brauer Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.8b02864 • Publication Date (Web): 29 Jun 2018 Downloaded from http://pubs.acs.org on June 30, 2018
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Environmental Science & Technology
Data integration for the assessment of population exposure to ambient air pollution for global burden of disease assessment Gavin Shaddick1,2, Matthew L. Thomas2,Heresh Amini3,4,5,David Broday6, Aaron Cohen,7,8, Joseph Frostad8, Amelia Green2, Sophie Gumy9, Yang Liu10, Randall V. Martin11,12, Annette Pruss-Ustun9, Daniel Simpson13, Aaron van Donkelaar11, Michael Brauer8,14,* 1
Department of Mathematics, University of Exeter, Exeter, EX4 4QF, UK
2
Department of Mathematical Sciences University of Bath, Bath, BA2 7AY, UK
3
Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel, 4051, Switzerland 4
University of Basel, Basel, 4051, Switzerland
5
Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA 02215, USA 6
Faculty of Civil and Environmental Engineering, The Technion, Haifa, 32000, Israel
7
Health Effects Institute, Boston, MA 02110, USA
8
Institute for Health Metrics and Evaluation, Seattle, WA 98121, USA
9
World Health Organization, Geneva, 1202, Switzerland
10
Department of Environmental Health, Emory University, Rollins School of Public Health, Atlanta, GA 30322, USA 11
Department of Physics and Atmospheric Science, Dalhousie University, Halifax, Nova Scotia, B3H 4R2, Canada 11 13
Harvard-Smithsonian Center for Astrophysics, Cambridge, MA 02138, USA
Department of Statistical Sciences University of Toronto, Toronto, Ontario, M5S 3G3, Canada
14
School of Population and Public Health, The University of British Columbia, Vancouver, BC, V6T 1Z3, Canada *Corresponding author: Michael Brauer, School of Population and Public Health, The University of British Columbia, 3rd Floor – 2206 East Mall, Vancouver BC V6T1Z3 Canada michael.brauer@ubc.ca Abstract word count: 200 Manuscript word count (excluding tables and figures: 5306): 6806 (7000 max)
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Abstract:
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Air pollution is a leading global disease risk factor. Tracking progress (e.g. for Sustainable
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Development Goals) requires accurate, spatially resolved, routinely updated exposure estimates.
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A Bayesian Hierarchical Model was developed to estimate annual average fine particle (PM2.5)
5
concentrations at 0.1° × 0.1° spatial resolution globally for 2010-2016. The model incorporated
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spatially-varying relationships between 6003 ground measurements from 117 countries, satellite-
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based estimates and other predictors. Model coefficients indicated larger contributions from
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satellite-based estimates in countries with low monitor density. Within and out-of-sample cross-
9
validation indicated improved predictions of ground measurements compared to previous
10
(Global Burden of Disease 2013) estimates (increased within-sample R2 from 0.64 to 0.91,
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reduced out-of-sample, global population-weighted Root Mean Squared Error from 23 µg/m3 to
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12 µg/m3). In 2016, 95% of the world’s population lived in areas where ambient PM2.5 levels
13
exceeded the World Health Organization 10 µg/m3 (annual average) Guideline; 58% resided in
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areas above the 35 µg/m3 Interim Target-1. Global population-weighted PM2.5 concentrations
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were 18% higher in 2016 (51.1 µg/m3) than in 2010 (43.2 µg/m3), reflecting in particular
16
increases in populous South Asian countries and from Saharan dust transported to West Africa.
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Concentrations in China were high (2016 population-weighted mean: 56.4 µg/m3) but stable
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during this period.
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Introduction
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Air pollution is a major risk factor for global health. The Global Burden of Disease (GBD)
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estimated that in 2016 ambient fine particulate matter (PM2.5) was the 6th highest ranking risk
22
factor for global mortality1. It was estimated that 4.1 million deaths annually can be attributed to
23
exposure to ambient PM2.5, with an additional 234,000 deaths attributable to exposure to ambient
24
ozone. Given this impact, air pollution has been identified as a global health priority in the
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sustainable development agenda, with metrics related to air pollution exposure and health burden
26
now tracked as components of several Sustainable Development Goals (SDGs)2,3. Assessment
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of the global risks to health and progress in reducing these risks requires accurate estimates of
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population exposures to PM2.5 for all countries of the world, and increasingly also for sub-
29
national regions, for example those incorporated into the recent analyses of the GBD4.
30 31
Traditionally, the information used in analyses of health risks associated with air pollution has
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come from ground monitoring (GM) networks, however there are many regions in which
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monitoring is sparse5, including many low and middle income countries where air pollution
34
levels and disease burdens are high and increasing. Therefore, GM needs to be supplemented
35
with information from other sources to create a comprehensive set of population exposures6.
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Even in areas where there are relatively dense GM networks, the use of information from
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satellite retrievals of aerosol optical depth and land use has been shown to be profitable in filling
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in spatial and temporal gaps in GM networks7,8. In order to assess progress towards meeting the
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SDGs and to facilitate comparisons between countries, it is important that the methods used to
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estimate concentrations, population-exposures and SDG metrics be globally consistent and allow 2
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for assessment of uncertainties. The latter helps prioritize regions where additional information
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on exposure is required and facilitates comparison of health burden between air pollution and
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other risk factors.
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44 45
Estimates of exposures used in previous disease burden assessments were based on a
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combination of satellite-based estimates and chemical transport model simulations9,10.
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Information from these two sources were calibrated to data from GM using a globally
48
standardized calibration function based on linear regression. This enabled the production of
49
estimates at high spatial resolution with global coverage, and the use of a consistent
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methodology over both space and time allowed temporal trends to be examined. However, there
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were a number of limitations with this approach, the most prominent being the use of a single
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global function to calibrate satellite-based and chemical transport model estimates to GM which
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didn’t account for spatial variation in the relationships between GM and information from the
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other two sources.
55 56
While requiring minimal modelling assumptions, the use of a single calibration function tended
57
to underestimate GM in some locations, for example those with high winter-time and/or night-
58
time emissions where satellite retrievals were restricted either at night or seasonally due to more
59
frequent cloud or snow cover. The spatial coherence of biases related to geophysical fields such
60
as PM2.5 composition (van Donkelaar et al.11), together with increases in the number of GM
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locations worldwide, means that spatially-varying calibration functions are now feasible. A
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recent example was described by van Donkelaar et al.11 who used geographically weighted 3
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regression with predictors including simulated PM2.5 composition to allow variation in
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calibration between GM and estimates PM2.5 from satellite remote sensing. This analysis showed
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improvements in the proportion of explained variance (of the GM measurements) compared to
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uncalibrated satellite-based estimates. Under the auspice of the Global Platform on Air Quality
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and Health led by the World Health Organization (WHO)12, a global collaboration between
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research institutions, international, regional and national agencies was initiated with the aim of
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addressing the above mentioned issues and reducing population exposure to air pollution from
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particulate matter.
71 72
In this paper, the methodology used to estimate population level exposures to air pollution for
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recent global disease burden assessments of the GBD (GBD 201513,14 and 20161) and those
74
produced by WHO15 is presented. This methodology allows spatially-varying calibration
75
functions to be fit within a Bayesian hierarchical framework and builds on the developments
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presented in van Donkelaar et al.11 by incorporating geophysical predictors. Comparisons with
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the methods used in previous releases (GBD 2013) of the GBD9 are given, with formal
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evaluation based on the ability to accurately predict GM measurements. A description of
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estimated concentrations and population exposures for 2016 is given together with an
80
examination of recent trends, from 2010 to 2016, at the country level.
81 82
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Materials and Methods
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Ground monitoring (GM) data consisting of annual average PM10 and PM2.5 concentrations from
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6,003 monitors (3,275 PM2.5 and 2,728 PM10 that were used to estimate PM2.5) were used to
86
calculate the city-level averages reported in the WHO ‘Air Pollution in Cities’ database16.(Figure
87
S1). The majority of measurements were recorded in 2013 and 2014 as there is a lag in reporting
88
measurements and, at the time the database was created, limited data were available for 2015.
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Where data were not available for 2014 (2760 monitors), data were used from 2015 (18
90
monitors), 2013 (2155), 2012 (564), 2011 (60), 2010 (375), 2009 (49), 2008 (21) and 2006 (1).
91
For locations measuring only PM10, a locally derived conversion factor was used to estimate
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PM2.5 as described in Brauer et al.9. Monitors with > 75% temporal coverage within a single year
93
were included. A variety of monitoring techniques and operating conditions were used but such
94
information was not available in the database and could not be explicitly included in the
95
modeling.
96
Geophysical satellite-based estimates of annual average surface PM2.5 developed by van
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Donkelaar et al.11, that combine aerosol optical depth retrievals from multiple satellites with
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information from the GEOS-Chem17 chemical transport model, were used at 0.1 ×
99
0.1 resolution (ca. 11 x 11 km resolution at the equator). These were obtained from van
100
Donkelaar et al.18 Estimates of the sum of sulfate, nitrate, ammonium and organic carbon
101
concentrations and the concentrations of mineral dust in PM2.5 simulated using the GEOS-Chem
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chemical transport model, and a measure combining elevation and the distance to the nearest
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urban land surface were also available at the same resolution11. 5
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104 105
A comprehensive set of population data on a high-resolution grid was obtained from the Gridded
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Population of the World (GPW v4) database.19 These data were originally provided at
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0.0417 × 0.0417 resolution and then aggregated to 0.1 × 0.1 resolution to match the grid
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cells of the satellite-based estimates, as described in the supporting information of Forouzanfar et
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al, 201614.
110 111
PM2.5
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In GBD 201010 and GBD 20139 exposure estimates were obtained using a single global function
113
to calibrate available ground measurements to a ‘fused’ estimate of PM2.5 that was calculated for
114
each 0.1 × 0.1 grid cell. This fused estimate comprised the mean of estimates of annual
115
average PM2.5 from remote sensing satellites and from the TM5-FASST chemical transport
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model20. This allowed information from both sources to be utilized, was computationally
117
efficient, reduced the amount of missing data (if any) and provided a simple method of
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smoothing extreme values from either of the sources of data. However, the two estimation inputs
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were different quantities, with different error structures, and it was not possible to interpret the
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individual contributions that the variables made to the model, or to separate their contributions in
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areas where one might be more accurate than the other. In addition, the fused exposure estimates
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underestimated ground measurements in specific locations (see discussion in Brauer et al.,
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20169). This underestimation was largely due to the use of a single, global, calibration function,
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where in reality the relationship between ground measurements and other variables varied
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spatially. Figure S2 shows differences between the global calibration function used in GBD 2013 6
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with those that would be used if the calibration function was fit on a region-by-region basis.
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Van Donkelaar et al.11 used geographically weighted regression (GWR) to allow for spatially-
129
varying calibration functions in this setting. GWR extends the linear regression model by
130
allowing coefficients to vary locally over space and is often used as an exploratory technique to
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examine spatial variation and non-stationarity. It comprises fitting a series of regression models,
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one for each of the GM locations, using data from a set of surrounding points21. The definition of
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‘surrounding points’ may be determined on a measure of distance, e.g. points within a certain
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distance, or by estimating an optimal bandwidth according to pre-specified criteria. Using each
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set of data, weighted regression is performed with the weights determined by the distance of the
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locations from the central point. However, it may be difficult to interpret location-specific
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coefficients and the construction of confidence intervals for coefficients and prediction intervals
138
for the response variable may be problematic22.
139 140
The recently developed Data Integration Model for Air Quality (DIMAQ), the basis for the
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production of the estimated concentrations described in this manuscript, is implemented within a
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Bayesian Hierarchical modelling (BHM) framework23. BHMs provide a flexible framework in
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which to model complex relationships and dependencies in data. Uncertainty can also be
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propagated through the model allowing uncertainty arising from different components, both data
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sources and models, to be incorporated within estimates of uncertainty associated with the final
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estimates. All modeling is based upon annual average concentrations.
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Within DIMAQ, coefficients in the calibration model relating satellite-based estimates with GM
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were estimated for each country. Where data were insufficient within a country, information was
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‘borrowed’ from a higher aggregation (region), and if enough information was still not available,
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from an even higher level (super-region). This was achieved using a hierarchy of random-effects,
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with individual country level estimates based on a combination of information from the country,
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its region and super-region23. The model is designed to produce the most accurate measures of
154
concentrations, and further to that population-exposures, together with valid measures of
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uncertainty on a country-by-country basis. As such, country was selected as the unit of analysis
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as national monitoring protocols may vary (although it is acknowledged that there may also be
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within-country variation in the way that monitoring is performed) and because impact
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assessment and comparative analyses are all conducted at the country level. However, the nature
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of the hierarchy is such that boundary effects may occur between countries (or regions/super-
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regions) where there are substantial differences in the relationships between ground monitors and
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satellite estimates in neighboring countries which may require post-processing in order to
162
produce a smooth map.
163 164
In DIMAQ, modelling was performed on the log-scale with the unit of measurement being a grid
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cell (with ~1.4 million in total, globally). Estimates of exposures were obtained by transforming
166
predictions from the model back to the original scale of ground measurements. The following
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components were included in the model (for further information, see Shaddick et al, 201723):
168 169
1. Continuous explanatory variables: a. Geophysical estimate of PM2.5 (ߤ݃/݉ଷ ) from satellite remote sensing, on the log8
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scale11. b. Estimate of the sum of sulfate, nitrate, ammonium and organic carbon concentrations. simulated using the GEOS-Chem chemical transport model11. c. Estimate of concentrations of mineral dust, simulated using the GEOS-Chem chemical transport model11 d. A measurement of the difference between the elevation at the ground measurement
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location and the mean elevation within the GEOS-Chem simulation grid cell
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multiplied by the inverse distance to the nearest urban land surface, on the log scale11
178 179
e. Estimate of population on the log-scale. 2. Discrete explanatory variables:
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a. Binary variable indicting whether the location of the ground measurement is
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specifically geocoded or whether a city-level centroid geocode was used.
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b. Binary variable indicting whether ground measurement monitoring site type, i.e.
183
industrial, residential etc, is known. Site type characterization may, however, differ by
184
country.
185 186 187 188 189 190
c. Binary variable indicting whether measurement is directly measured PM2.5 or converted from PM10 3. Random Effects: a. Grid cell random effects on the intercept, to allow for multiple ground monitors in a grid cell. b. Country, region and super-region hierarchical random effects for the intercept 9
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c. Country, region and super-region hierarchical random effects for the satellite remote sensing term. d. Country, region and super-region hierarchical random effects for the population. The
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country level random effect allows borrowing of information from neighboring
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countries.
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4. Interactions:
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a. Interactions between the binary variables and the effects of satellite remote sensing
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b. Interactions between the binary variables and the effects of population
199 200
While both the linear regression approach (as used in GBD 20139) and the GWR approach11 only
201
provide an informal analysis of the uncertainty associated with the resulting exposure estimates,
202
DIMAQ produces a posterior distribution for each prediction location, allowing a full assessment
203
of uncertainty.
204 205
Due to both the complexity of the models and the size of the data, notably the number of spatial
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predictions that are required, recently developed techniques that perform ‘approximate’ Bayesian
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inference based on integrated nested Laplace approximations (INLA) were used24. Computation
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was performed using the R interface to INLA (R-INLA25). The code is provided in the
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Supporting Information (File S1).
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The primary output of the DIMAQ is a comprehensive, global, set of exposures and country-
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level population-weighted average exposures for a single year. The model was developed in
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order to provide estimates for 2014, but for subsequent burden of disease calculations, e.g. GBD
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2015 and GBD 2016, and to examine trends, estimates for other years were required. The
215
following is a description of how DIMAQ was used with the fixed set of ground measurements
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together with values of the input variables for each year to produce annual averages for 2010-
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2016.
218 219
Satellite estimates, populations and quantities estimated using the GEOS Chem model were
220
available for each year from 2010-2015. Population estimates for 2000, 2005, 2010, 2015 and
221
2020 were available from GPW version 4. Population values for each grid cell for 2011, 2012,
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2012, 2013 and 2014 were obtained by interpolation (and application to 2016) using natural
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splines with knots placed at 2000, 2005, 2010, 2015 and 2020. To provide PM2.5 concentration
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estimates for 1990, 1995, 2000, 2005 and 2010 we applied the DIMAQ coefficients to historical
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satellite-based PM2.5 estimates for 2000-2010, together with GEOS Chem simulations for 1990
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and 1995.
227 228
These yearly sets of data were used as inputs for DIMAQ, enabling estimates of exposures to be
229
obtained for 2010, 2011, 2012, 2013, 2014 and 2015. Trends presented previously for GBD
230
20139 were based on comparisons of estimated concentrations in 2010 and 2013, the latter being
231
based on an extrapolation of trends in satellite-based estimates from 2010 to 2011. Using this
232
approach, observed differences between estimates for 2010 and 2013 may have been be overly 11
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influenced by short-term variability in meteorology rather than longer-term trends in emissions
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and resulting concentrations. For 2016, estimates of exposures were obtained from predictions
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from locally-varying regression models. For each cell, a LOWESS model26 was fit to the values
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within that grid cell over time, with a constraint placed on the rate of change between 2015 and
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2016 to avoid unrealistic and/or unjustified extrapolation of trends. Measures of uncertainty were
238
obtained by repeating the procedure for the limits of the 95% credible intervals, again on a cell-
239
by-cell basis.
240 241
For GBD 2016, population-weighted mean concentrations, together with 95% uncertainty
242
intervals were calculated for 195 countries and territories, and for sub-national areas in Brazil,
243
China, India, Indonesia, Japan, Kenya, Saudi Arabia, South Africa, Sweden, United Kingdom
244
and the United States (File S2, Supporting Information). There are a number of ways in which
245
uncertainty intervals can be obtained and here we present the results of two approaches: the first
246
to provide consistency with the GBD; and the second utilizing the information that is available
247
within the posterior distributions that are available when using DIMAQ. Consistent with
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previous GBD assessments9,10, in GBD 2016 the population-weighted means were calculated by
249
combining the mean value in each cell with a corresponding estimate of population followed by
250
aggregation to the national or sub-national level. Uncertainty intervals (95%) around these values
251
were obtained by combining the population estimates with concentrations arising from draws
252
from normal distributions constructed to represent the concentrations in each grid cell, where the
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parameters (means and variances) for each cell were estimated based on summary measures of
254
the outputs of DIMAQ. This sampling was repeated 1000 times for each grid cell, with the final 12
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(population-weighted) mean and 95% CI being calculated from the resulting set of 1000
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estimates and then aggregated to sub-national or national-level population-weighted means. In
257
the second approach, uncertainty intervals were obtained by taking samples from the DIMAQ
258
posterior distributions in each grid cell and combining these to produce overall uncertainty
259
intervals for each country that include both within- and between- grid cell uncertainty.
260
Population-weighting is applied to the 2.5% and 97.5% quantiles of the posterior distributions
261
for each grid cell (with the same also applied to the means and medians of the posteriors). As the
262
second approach is based directly on the posterior distributions it more closely reflects the
263
uncertainties present in the outputs of the modelling and therefore produces wider uncertainty
264
intervals for national-level population–weighted mean concentrations.
265 266
Ozone
267
As in GBD 20139, a running 3-month average (of daily 1 h maximum values) was calculated for
268
each grid cell over a full year and the maximum of these values was selected . This metric was
269
chosen to align with epidemiologic studies of chronic exposure which typically employ a
270
seasonal (summer) average, and to account for global variation in the timing of the ozone
271
(summer) season. These estimates were simulated with the TM5-FASST27 reduced-form
272
chemical transport model at 0.1° × 0.1° for 1990, 2000, and 2010 using the same emissions data
273
sets and meteorological inputs as described previously 9. Estimates for 1995, 2005, 2015 and
274
2016 were generated by fitting a natural cubic spline. Uncertainty intervals for each country were
275
estimated by assuming a normal distribution with mean equal to the population-weighted mean
276
concentrations and a standard deviation of ±6% of the estimated concentration. As in GBD 2013, 13
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given the scarcity of surface ozone measurements throughout the world and the challenges in
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accessing hourly data from available monitoring sites to develop the desired metric, surface
279
ozone measurements were not utilized when developing the global estimates. Future iterations
280
will incorporate the recently compiled measurement database of the Tropospheric Ozone
281
Assessment Report28
282 283
Results and Discussion
284
Ground measurements were available for 6003 locations from 117 countries, a substantial
285
improvement compared to previous (GBD 2013) estimates that were based on 4037
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measurements from 79 countries9. Of the 3275 direct PM2.5 measurements, 1046 were from
287
China, 870 from The United States and Canada, 774 from high income countries in Europe, 163
288
from Central, Eastern Europe and Central Asia, 133 from Latin America and the Caribbean, 78
289
from Australasia, 67 from high income Asia Pacific countries, 49 from Sub-Saharan Africa, 31
290
from North Africa and the Middle East, 25 from India, 21 from elsewhere in South Asia and 18
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from elsewhere in Southeast Asia. The highest measured annual average PM2.5 concentration in
292
the GM database was 288 µg/m3 in New Delhi, India while the lowest was 2 µg/m3, in
293
Anchorage, USA. The highest PM2.5 concentration estimated from PM10 measurements was 202
294
µg/m3 in Johannesburg, South Africa with the lowest being 1 µg/m3, in Muonio, Finland and in
295
Kiruna, Sweden.
296 297
Table 1 and Figure 1 show comparisons between the linear regression approach used in GBD
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20139 and the DIMAQ hierarchical approach23 for 2014. Average concentrations from ground
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monitors in each region are shown, together with the within-sample root mean squared error
300
(RMSE). In addition, summaries of out-of-sample validation are shown, in the form of the results
301
from performing cross-validation using 25 combinations of training (80%) and validation (20%)
302
datasets. Validation sets were obtained by taking a stratified random sample, using sampling
303
probabilities based on the cross-tabulation of PM2.5 categories (0-24.9, 25-49.9, 50-74.9, 75-99.9,
304
100+ µg/m3) and GBD super-regions10, resulting in them having the same distribution of PM2.5
305
concentrations and super-regions as the overall set of sites. The following metrics were
306
calculated for each training/evaluation set combination: for model fit - R2 and deviance
307
information criteria (DIC, a measure of model fit for Bayesian models29); for predictive accuracy
308
- RMSE and population weighted root mean squared error (PwRMSE).
309
15
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310 Population weighted
Ground monitor
RMSE (µg/m3)
RMSE (µg/m3)
Average (µg/m3) Region
GBD
GBD
2013
DIMAQ
2013
DIMAQ
23.2
10.9
3.7
6.4
2.7
12.0
5.6
1.7
9.7
6.0
20.4
12.8
5.2
13.9
7.1
42.9
30.6
8.2
20.1
10.8
East
62.4
37.6
16.8
23.6
14.3
Sub-Saharan Africa
50.7
19.6
6.8
38.8
32.3
South Asia
41.2
35.8
17.6
44.8
22.0
Global
27.5
16.9
6.6
23.1
12.1
High income Central Europe, Eastern Europe and Central Asia Latin America and Caribbean Southeast Asia, East Asia and Oceania North Africa / Middle
Table 1: Average concentrations from ground monitors (µg/m3), together with summary measures of predictive ability, globally and by super-region. Results are the (within-sample) root mean squared error (concentration), together with the median values of population weighted 16
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root mean squared error from 25 validation sets (see text for details). A list of countries within each region is provided in the Supporting Information (File S1).
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Table 1 shows the average of ground measurements together with the RMSE (from within-
312
sample validation). Compared to the model used in GBD 2013, DIMAQ resulted in an
313
improvement in the within-sample fit, an increase in R2 from 0.64 to 0.91 and marked
314
improvements in all regions. Globally the RMSE was reduced by 10.3 ߤg/݉ଷ , set against the
315
context of the global ground monitor average of 27.5 ߤg/݉ଷ . The same pattern was seen in out-
316
of-sample predictive ability where, compared to the model used in GBD 2013, DIMAQ showed
317
improved predictions of ground measurements in all super regions as shown by the median
318
values of population weighted root mean squared error (ߤg/݉ଷ ) from 25 validation sets.
319
Globally, population-weighted RMSE was 12.1 µg/m3 compared to 23.1 µg/m3 when using the
320
GBD 2013 model. Figure 1 shows the same pattern in the correlations between predicted and
321
measured values at ground monitoring locations.
322
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323 Figure 1: Comparison of observed and predicted measurements, by super-region, using: (i) the GBD 2013 model, (ii) DIMAQ, for the year 2014. The red line has a slope of one and an intercept of zero. Super-regions are 1: High income (3051 monitors), 2: Southeast Asia, East Asia and Oceania (1312 monitors), 3: Central Europe, Eastern Europe and Central Asia (518 monitors), 4: South Asia (464 monitors), 5: Latin America and Caribbean (308 monitors), 6: North Africa / Middle East (273 monitors) and 7: Sub-Saharan Africa (77 monitors). 324
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Figure 2: Coefficients from DIMAQ based on 2014 data, by country: the effect of (log of) the satellite-based estimates. 325 326
Figure 2 shows the DIMAQ coefficients (each a combination of country-region and super-region
327
fixed and random effects) for the satellite-based estimates for each country using data from 2014,
328
the year for which DIMAQ was developed. The size of the coefficients will reflect a number of
329
different factors with larger coefficients reflecting differences between GMs and estimates from
330
satellites, and thus a greater impact of the satellite-based estimates on the overall estimates.
331
Lower coefficients may be observed where there is little variation (in concentrations) beyond the
332
mean (represented by the intercept) and/or in areas where the magnitude of the estimates from
333
the satellites is large. Countries with higher monitor density, e.g. North America, Western
334
Europe, China, Chile, Argentina, tended to have lower coefficients. In some countries, for
335
example Mongolia and countries in sub-Saharan Africa, relatively higher coefficients for 19
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336
population density indicate importance of this factor in the overall estimates (Supporting
337
Information, Figures S3-4). In areas where there are countries that have no monitoring data that
338
are adjacent to those where measurements are out-of-line with satellite estimates, then calibration
339
functions for neighboring countries may differ more than would be expected. As previously
340
mentioned, due to the nature of the random-effects structure in DIMAQ, this can result in the
341
coefficients for neighboring countries being substantially different, resulting in apparent
342
discontinuities in maps of estimated exposures. In order to produce a smoothed map, the
343
coefficients of the model were smoothed (over continuous space) using a thin plate spline30. The
344
resulting set of coefficients were then used to produce estimated exposures as before (Figure 3).
345
Figure 3: (Smoothed) map of estimated annual average PM2.5 concentrations in 2016 (µg/m3). Map created with QGIS Geographic Information System. QGIS Development Team (v2.18, 2016)31
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346 347
Based on matching the yearly outputs from DIMAQ to estimates of population on a cell-by-cell
348
basis, it was estimated that in 2016, 95% of the world’s population lived in areas that exceeded
349
the WHO guideline of an annual average of 10 µg/m3
350
population resided in areas with PM2.5 concentrations above the WHO Interim Target 1 (IT-1 of
351
35 µg/m3); 69% lived in areas exceeding IT-2 (25 µg/m3); and 85% lived in areas exceeding IT-3
352
(15 µg/m3).
32
. Fifty eight percent of the global
353 354
Population-weighted mean concentrations in 2016 as incorporated into the GBD 2016 estimates
355
were highest in countries in north (e.g. Niger: 204 µg/m3, Egypt: 126 µg/m3, Mauritania: 124
356
µg/m3) and west (e.g. Cameroon: 140 µg/m3, Nigeria: 122 µg/m3, Burkina Faso, 111 µg/m3)
357
Africa and the Middle East (e.g. Saudi Arabia: 188 µg/m3, Qatar: 148 µg/m3, Kuwait: 111
358
µg/m3), due mainly to windblown mineral dust11. The next highest concentrations appeared in
359
South Asia due to combustion emissions from multiple sources, including household use of solid
360
fuels, coal-fired power plants, agricultural and other open burning, and industrial and
361
transportation-related sources. The annual average population-weighted PM2.5 concentrations
362
were 101 µg/m3 in Bangladesh, 78 µg/m3 in Nepal, and 76 µg/m3 in India and Pakistan. The
363
population-weighted annual average concentration in China was 56 µg/m3. Estimates of the
364
annual average population-weighted PM2.5 concentrations were lowest (≤ 8 µg/m3) in Australia,
365
Brunei, Canada, Estonia, Finland, Iceland, New Zealand, Norway, Sweden and several Pacific
366
island nations. Population-weighted mean concentrations and their 95% uncertainty intervals as
367
used in GBD 20161 for all years and countries as well as sub-national areas in Brazil, China, 21
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India, Indonesia, Japan, Kenya, Saudi Arabia, South Africa, Sweden, United Kingdom and the
369
United States are presented in the Supporting Information (File S2). Within countries with sub-
370
national estimates, population-weighted annual average concentrations varied as much as 3-5-
371
fold, for example in India (27.7 µg/m3 in urban Kerala to 147.5 µg/m3 in urban Delhi), China
372
(16.4 µg/m3 in Tibet to 77.0 µg/m3 in Tianjin), Brazil (5.6 µg/m3 in Sergipe to 18.6 µg/m3 in Sao
373
Paulo), Indonesia (9.5 µg/m3 in Maluku to 27.8 µg/m3 in Riau) and Saudi Arabia (82.2 µg/m3 in
374
Tabuk to 238.8 µg/m3 in Eastern Province). Elsewhere within-country variability was lower, for
375
example in the United Kingdom (9.5 µg/m3 in Northern Ireland to 16.0 µg/m3 in Lewisham),
376
Japan (9.5 µg/m3 in Okinawa to 15.0 µg/m3 in Osaka), Kenya (9.7 µg/m3 in Taita-Taveta to 22.9
377
µg/m3 in Busia), Mexico (12.8 µg/m3 in Zacatecas to 28.0 µg/m3 in Tabasco), the United States
378
(5.4 µg/m3 in Alaska to 11.0 µg/m3 in California), and South Africa (21.8 µg/m3 in Eastern Cape
379
to 51.5 µg/m3 in Gauteng).
380 381
As shown by the evaluation, this new (DIMAQ) methodology and its spatially varying
382
calibration resulted in estimates with lower error than other approaches when estimates were
383
compared to GM data. Figure S5 shows, (for 2013 concentrations), the impact of these
384
methodologic changes using exposures previously estimated in GBD 20139. Overall, with
385
DIMAQ increases were seen in population-weighted exposures for most countries, with large
386
increases in countries heavily impacted by windblown mineral dust, e.g. Saudi Arabia, Qatar and
387
Egypt. Only small differences were evident for China, the USA and Western Europe. The global
388
population-weighted mean increased by 39% (from 31.8 µg/m3 using the GBD 2013
389
methodology to 44.2 µg/m3 when using DIMAQ with updated data), mainly driven by large 22
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390
increases in the estimate population-weighted mean concentrations in the highly-populated
391
countries of South Asia (Bangladesh, Pakistan, India).
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392 393
Using the DIMAQ methodology for 2010-2016 allowed for a consistent examination of recent
394
trends. Global population-weighted PM2.5 concentrations were 18% higher in 2016 (51.1 µg/m3)
395
than in 2010 (43.2 µg/m3). This reflects increases in air pollution levels in some of the most
396
populous countries of the world, many of them the result of continuing long term trends but also
397
of note are marked increases in West Africa, as identified by satellite data. Increased levels of
398
particulate matter during late 2015 and early 2016 driven by an episode of wind-blown mineral
399
dust from the Sahara impacted heavily populated countries in the region, such as Nigeria33.
400
Accordingly, a proportion of this global increase may prove to be transient, should similar
401
episodes not affect these heavily populated areas in the future. Figure S6 shows trends in
402
population-weighted PM2.5 concentrations for the 10 most highly populated countries in the
403
world, together with the European Union, from 2010 to 2016.
404 405
Changes between 2010 and 2016 for both PM2.5 and ozone are shown in Table 2. India, Pakistan
406
and Bangladesh, have experienced both high levels and sharply increasing trends in PM2.5
407
exposure, while the decrease seen in Nigeria between 2011 and 2014 is reversed in 2015 due to
408
the extensive dust storm at the end of that year33. Concentrations in the other highly populated
409
countries and the European Union experienced little change over this period, with high levels
410
persisting in China, although with a noteworthy stabilization of concentrations in China over this
411
period, compared to clear increases observed since 19909. Ozone trends for the same period are 23
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412
provided for context although these should be interpreted with caution as they are based upon
413
chemical transport model simulations for 1990, 2000 and 2010; 2016 values and trends merely
414
reflect the pre-2010 trends in emissions.
Location
2010 PM2.5 2016 PM2.5
% change 2010 ozone 2016 ozone % change
GLOBAL
43.2
51.1
18.2
59.6
61.6
3.3
Bangladesh
82.4
101.0
22.6
70.0
74.6
6.6
Brazil
11.0
12.6
14.5
49.6
53.6
8.1
China
58.2
56.3
-1.4
62.8
65.7
4.6
European Union
15.2
14.9
-2.0
59.3
58.6
-1.2
India
64.6
75.8
17.3
72.0
76.6
6.4
Indonesia
14.5
16.7
15.6
42.1
44.0
4.5
Japan
12.3
13.1
6.6
59.9
61.7
3.0
Nigeria
51.5
122.5
137.7
67.5
67.3
-0.3
Pakistan
61.4
75.7
23.3
67.2
69.8
3.9
Russia
16.6
15.5
-6.6
48.2
48.3
0.2
8.6
9.2
6.7
67.6
66.3
-1.9
United States 415
Table 2: Changes in population-weighted concentration of PM2.5 and ozone for the 10 most
416
highly populated countries in the world, together with the European Union between 2010 and
417
2016.
418 419
Annual population-weighed exposures to PM2.5, and ozone for each country, together with 24
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associated measures of uncertainty, can be found in the Supporting Information. Results are
421
given by country and by year (2010-2016).
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422 423
Significant improvements were made in the estimation of global long-term average exposure to
424
PM2.5 when compared to approaches implemented previously. These improvements were
425
facilitated in part by a global collaboration led by the WHO12 and were based on both updated
426
data and methodological advances; more than 6000 ground measurements were used with a
427
model that allowed spatially-varying calibration with satellite-based estimates at 0.1 × 0.1°
428
resolution. The estimates of concentrations using DIMAQ, were used to produce population
429
exposures for the WHO and Global Burden of Disease 2015 and 2016 estimates of disease
430
burden attributable to ambient particulate matter. Compared to earlier exposure estimates, errors
431
in out-of-sample prediction of ground measurements were reduced and the extent of variability
432
explained in ground measurements was increased in all regions of the world. However, as there
433
is still substantial heterogeneity in the magnitude of errors and uncertainties between regions,
434
additional ground monitoring data34, especially in areas currently with limited monitoring, and
435
ongoing improvements to satellite-based estimates35 will be required to reduce errors in large
436
parts of the world. It is important to acknowledge, however, that ground measurement data are
437
included as reported by local agencies and assessed with varying levels of quality assurance. As
438
the exposure estimation methodology for PM2.5 no longer includes direct use of the TM5 or
439
TM5-FASST chemical transport model estimates, the use of satellite-based estimates (which are
440
produced using outputs from the GEOS-Chem model) that are regularly updated along with
441
ground measurements should facilitate regular updating of these exposure estimates as part of 25
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442
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SDG reporting.
443 444
Although these estimates advance those developed previously, they incorporate known
445
limitations. For example, the spatially varying calibration model was built from a specific year of
446
satellite-based estimates and included measurements spanning multiple years. The parameters
447
from this model were then applied to the satellite-based estimates for other years. As it is likely
448
that the relationship between satellite-based estimates and ground monitors would vary over time
449
future iterations will more directly model the relationships between temporally aligned satellite-
450
based estimates and ground monitors.
451 452
Future developments of DIMAQ will also incorporate a continuously varying spatial term, in
453
addition to the country structure, embedded within the model. This will facilitate two lines of
454
improvement, one related to eliminating post-processing smoothing currently performed to
455
produce smoothed maps and secondly allowing within-country variation in the calibration
456
equations. Also, additional information on land-use and topography will be included within the
457
model which should reduce errors and uncertainties. Dependent on computational considerations,
458
the fundamental approach used in DIMAQ is scalable and higher resolution (0.01 x 0.01°) global
459
exposure estimates should be feasible using finer resolution satellite-based estimates11,35. Such
460
estimates would be expected to further reduce errors in relation to ground measurements, reduce
461
spatial misalignment with population density data and improve estimation of disease burden
462
attributable to air pollution at a finer spatial scale. In addition to developments to the spatial
463
component of the model, a temporal component will be added, allowing trends to be modelled 26
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464
directly within the model framework, and allowing dependence between years, rather than being
465
performed on time periods.
466 467
Future developments of DIMAQ will also allow the contributions from individual inputs to be
468
identified. The growing availability of information on source contributions to PM2.5 , for example
469
from simulations36, could allow an assessment of the contribution of different sources to the
470
final estimated concentrations37,38. This would require methodological advances in terms of how
471
multiple, potentially highly correlated, inputs are treated in a coherent manner within DIMAQ.
472
An important aspect of the Bayesian hierarchical modelling approach that is the basis of DIMAQ
473
is the ability to propagate uncertainty from the inputs, through the modelling process, to the final
474
estimates. The method used here, which that is based directly on the posterior distributions that
475
are an output of the DIMAQ Bayesian hierarchical model, includes more of the uncertainties
476
present in the outputs of the modelling than previous approaches and therefore produces wider
477
uncertainty intervals for national -level population–weighted mean concentrations. Future
478
developments will allow a breakdown of the overall uncertainty associated with the final
479
estimates to their individual components, including individual input variables. In addition, such
480
global estimates will benefit from ongoing improvements in understanding of AOD – surface
481
PM2.5 conversion factors, such as those derived from the Surface Particulate Matter Network
482
(SPARTAN) of collocated PM2.5 monitors and sun photometers39.
483 484
Analyses clearly show that the current disease burden attributable to ambient air pollution is
485
dominated by effects of exposure to PM2.5 in comparison to the impacts related to ozone 27
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486
exposure1, and as such less attention has been directed towards evaluation and improvement of
487
ozone exposure estimates. However, as global ozone exposure is increasing and projected to
488
continue to increase as a result of climate change40 there is a need to enhance the estimation of
489
ozone exposures, for example by chemical transport model assimilation and fusion with the
490
newly available global ground measurement database (TOAR)28. Further, at present uncertainty
491
in ozone exposures is assumed to be a uniform standard deviation of ±6% of the estimated
492
concentration given the estimation via a single chemical transport model, without direct
493
calibration to ground measurements. Future estimation of ozone using ground measurements and
494
multiple simulations can also identify areas of greater uncertainty in estimates which may be
495
useful for prioritization of new monitoring. In addition, accumulating research suggests
496
additional health impacts related to nitrogen dioxide (NO2) exposure that is distinct from those
497
currently attributable to PM2.5 and ozone41,42. Global high resolution NO2 exposure estimates43,
498
similar to those already developed for North America44,45, western Europe46 and Australia47
499
which combine satellite retrievals, chemical transport models and land use information have
500
recently become available and should facilitate the potential inclusion of nitrogen dioxide in in
501
future global disease burden assessments.
502 503
Estimates of outdoor air pollution exposure and trends developed with globally consistent
504
methodology have facilitated policy analyses6,41,42 which should ultimately inform strategies to
505
mitigate the health impacts of air pollution exposure and reduce emissions of climate forcing
506
agents. As with prior iterations, we encourage others to use these estimates for relevant impact
507
assessment or epidemiologic analyses and we have provided the DIMAQ code (File S2) along 28
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508
with associated files of national and sub-national population-weighted (File S3) and gridded
509
estimates (linked to population data) (File S4) in the Supporting Information.
Page 32 of 36
510 511
Acknowledgements
512
Annette Pruss-Ustun and Sophie Gumy are staff members of WHO. The authors alone are
513
responsible for the views expressed in this publication and they do not necessarily represent the
514
decisions or stated policy of the World Health Organization. We thank Rita van Dingenen and
515
Frank Dentener of the European Commission Joint Research Centre for the ozone estimates from
516
TM5-FASST.
517 518
Supporting Information
519
Additional figures (Figures S1-S6), list of countries by region and super-region (File S1),
520
DIMAQ code (File S2), and a full listing of population-weighted mean and 95% uncertainty
521
interval PM2.5 and ozone concentration estimates for all countries and selected sub-national
522
entities for 2010-2016 (File S3). Gridded estimates (linked to population data, File S4), and
523
associated metadata (File S5).
29
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