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Global fine particulate matter concentrations from satellite for long-term exposure assessment 1
Aaron van Donkelaar1, Randall V. Martin
1,2, Michael Brauer
3, and Brian L. Boys
1 2
1Department of Physics and Atmospheric Science, Dalhousie University, 6300 Coburg Rd., Halifax, Nova 3
Scotia, Canada, B3H 4R2 4 2Harvard-Smithsonian Center for Astrophysics, Cambridge, Massachusetts, 5
USA. 6 3School of Environmental Health, University of British Columbia, British Columbia, Canada. 7
8
Background: More than a decade of satellite observations offers global information about the trend and 9
magnitude of human exposure to fine particulate matter (PM2.5). 10
Objective: In this study, we developed improved global exposure estimates of ambient PM2.5 mass and 11
trend using PM2.5 concentrations inferred from multiple satellites. 12
Methods: We combined three satellite-derived PM2.5 sources to produce global PM2.5 estimates at about 13
10 km × 10 km from 1998-2012 inclusive. For each source, we related total column retrievals of aerosol 14
optical depth to near-ground PM2.5 using the GEOS-Chem chemical transport model to represent local 15
aerosol optical properties and vertical profiles. We collected 210 global ground-based PM2.5 16
observations from the literature to evaluate our satellite product beyond North America and Europe. 17
Results: Over these 15 years, global population-weighted ambient PM2.5 concentrations increased 18
0.55±0.12 μg/m3/yr (2.1±0.5 %/yr). Increasing PM2.5 in some developing regions drove this global 19
change, despite decreasing PM2.5 in some developed regions. The proportion of population in East Asia 20
living above the World Health Organization (WHO) Interim Target-1 of 35 μg/m3 increased from 51% in 21
1998-2000 to 70% in 2010-2012. In contrast, the North American proportion below the WHO Air Quality 22
Guideline of 10 μg/m3 fell from 62% in 1998-2000 to 19% in 2010-2012. We found significant agreement 23
between satellite-derived and ground-based values outside North America and Europe (r=0.81; n=210), 24
but satellite-derived values are biased low (slope=0.68), implying that true concentrations could be even 25
greater. 26
Conclusions: Satellite observations provide insight into global long term changes in ambient PM2.5 27
concentrations. 28
29
30
31
Page 1 of 28
2
Abbreviations 32
AOD – aerosol optical depth 33
AQG – Air Quality Guideline 34
CEISIN – Center for International Earth Science Information Network 35
GBD – Global Burden of Disease 36
IT – Interim Target 37
MISR – Multiangle Imaging Spectroradiometer 38
MODIS – Moderate Resolution Imaging Spectroradiometer 39
PM2.5 – fine particular matter with diameter less than 2.5 μm 40
SeaWiFS – Sea-viewing Wide Field-of-view Sensor 41
WHO – World Health Organisation 42
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Long-term exposure to fine particulate matter (PM2.5) is associated with morbidity and premature 43
mortality (Dockery et al. 1993; Pope et al. 2009). The Global Burden of Disease (GBD) assessment 44
attributed 3.2 million premature deaths per year to ambient PM2.5 exposure, such that PM2.5 is one of 45
the leading risk factors for premature mortality (Lim et al. 2012). Assessments and indicators of the 46
health effects of long-term exposure to PM2.5, such as the GBD assessment, the WHO assessment 47
(http://www.who.int/gho/phe/outdoor_air_pollution/burden/en/) and the Environmental Performance 48
Index (http://epi.yale.edu), rely on an accurate representation of both magnitude and spatial 49
distribution. Long-term trends in concentration can inform whether appropriate steps are being taken 50
to mitigate health and environmental outcomes, and can motivate additional action. Global monitoring 51
can occur from a single satellite, minimizing artifacts that may result from regional differences in 52
ground-level network design and operation. Satellites also offer one of the few observationally-based 53
sources for long-term PM2.5 concentrations that can represent long-term exposure and detect significant 54
changes in many parts the world. 55
Satellite retrievals of Aerosol Optical Depth (AOD), which provide a measure of the amount of light 56
extinction through the atmospheric column due to the presence of aerosol, have a global data record 57
extending more than a decade. Differing design characteristics between satellite instruments and their 58
retrievals can benefit particular applications. For example, Collection 5 retrievals from the MODIS 59
instrument (Levy et al. 2007) provide relatively frequent (daily) global observation and accurate AOD 60
over dark surfaces, but have uncertain changes in instrument sensitivity with time which could introduce 61
artificial trends. Retrievals from the MISR instrument (Diner et al. 2005; Martonchik et al. 2009) require 62
around 6 days for global coverage, but are accurate for both AOD and trend studies (Zhang and Reid 63
2010). SeaWiFS (Hsu et al. 2013) provides sufficient radiometric stability for trends (Eplee et al. 2011), 64
but is less accurate over land for absolute AOD compared to MODIS or MISR due to the lack of a mid-65
infrared channel (Petrenko and Ichoku 2013). 66
The relationship between AOD and PM2.5 is complicated by the effects of aerosol vertical distribution, 67
humidity, and aerosol composition, which are impacted by changes in meteorology and emissions. One 68
technique of relating AOD to near-surface PM2.5 uses the ratio of PM2.5 to AOD simulated by a chemical 69
transport model. This parameter allows a ground-level PM2.5 estimate to be calculated from satellite 70
AOD retrievals. This approach was first demonstrated using the MISR instrument with the GEOS-Chem 71
chemical transport model (www.geos-chem.org) over the United States for 2001 (Liu et al. 2004), and 72
subsequently extended globally for each of the MODIS and MISR instruments for 2001-2002 at a spatial 73
resolution of about 100 km × 100 km (van Donkelaar et al. 2006). 74
The first long-term mean, global, satellite-derived PM2.5 estimates used this technique to combine 75
filtered values from both MODIS and MISR over 2001-2006 at a spatial resolution of about 10 km × 10 76
km. This dataset demonstrated promising agreement with coincident ground-based observations over 77
North America (r=0.77; slope = 1.07) and globally (r=0.83; slope = 0.86) (van Donkelaar et al. 2010). We 78
hereafter refer to this dataset as Unconstrained (UC), owing to the unrestricted freedom it gave satellite 79
AOD retrievals to represent the total aerosol column. 80
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Improved correlation with ground-based observations for the year 2005 was achieved using optimal 81
estimation (OE) (van Donkelaar et al. 2013). OE constrained AOD retrievals from MODIS top-of-82
atmosphere reflectances based on the relative uncertainties of observational and simulated estimates 83
(van Donkelaar et al. 2013). The PM2.5 estimates produced with this dataset used vertical profile 84
information from the CALIOP satellite instrument to inform the relation of column AOD to ground-level 85
concentrations. 86
Boys et al. (submitted) created a time-series of PM2.5 anomalies by combining AOD from both SeaWiFS 87
and MISR with spatiotemporal information on the PM2.5 to AOD relationship from a GEOS-Chem 88
simulation over 1998-2012 inclusive. In this paper, we extended the OE-based PM2.5 estimates to 2004-89
2010 and combined them with the UC PM2.5 values of van Donkelaar et al. (2010) to produce a global, 90
decadal PM2.5 dataset at approximately 10 km × 10 km, with improved agreement compared to either 91
dataset alone. We then applied the temporal variation based upon SeaWiFS and MISR (Boys et al. 92
submitted) to estimate annual global PM2.5 estimates and trends over 1998-2012 at 10 km × 10 km 93
resolution. 94
95
Materials and Methods 96
97
Production of satellite-derived estimates 98
We first produced a decadal mean PM2.5 estimate over 2001-2010. Following Boys et al. (submitted), we 99
combined retrievals from SeaWiFS (supplemental) and MISR (supplemental) with time-varying GEOS-100
Chem (supplemental) simulated AOD to PM2.5 relationships to infer annual variation in PM2.5 over 1998 101
to 2012 at a spatial resolution of 0.1° x 0.1° (henceforth referred to as SeaWiFS&MISR PM2.5). We then 102
extended both OE and UC to cover the temporal range 2001-2010 by applying to each dataset the ratio 103
of a coincident SeaWiFS&MISR PM2.5 to its decadal mean. We evaluated each extended dataset using 104
ground-based PM2.5 observations over North America. The global MODIS land-cover type product 105
(MOD12; Freidl et al. 2010) was used to determine the relative weighting of each dataset over each land 106
cover type that maximized agreement with ground-level PM2.5 observations following van Donkelaar et 107
al. (2013) to produce an initial global combined decadal mean PM2.5 estimate. 108
We subsequently produced a consistent time series of PM2.5 over 1998-2012. We applied to the initial 109
decadal mean dataset the relative temporal variation of SeaWiFS&MISR PM2.5 to produce monthly 110
satellite-derived PM2.5 estimates over 1998-2012 inclusive. We calculated absolute annual trends for 111
both datasets using a regression of five month box-car filtered (i.e. median of +/- five months from the 112
center date), deseasonalized monthly mean values following Zhang and Reid (2010). We superimposed 113
these trends to create global annual PM2.5 estimates that were consistent in trend with SeaWiFS&MISR 114
and in magnitude with the initial decadal mean. We used a three-year running median to reduce noise 115
in the annual satellite-derived values. All PM2.5 concentrations are given at 35% relative humidity, 116
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except for comparisons involving ground-level measurements outside North America, where the 50% 117
standard is adopted for consistency with the ground-level measurements. 118
Following Evans et al. (2013), we estimated dust-free and sea-salt-free PM2.5 concentrations by 119
removing the simulated relative contribution of these species from total satellite-derived PM2.5. We 120
produced satellite-derived PM2.5 surface area estimates for interpretation of the dust and seasalt-free 121
PM2.5 estimates following a similar approach as PM2.5 mass concentrations, except that the GEOS-Chem 122
model was used to relate AOD to surface area, rather than to mass (supplemental). 123
124
Collection of ground-based observations for evaluation 125
We also collected ground-based PM2.5 observations over Canada and the United States at locations 126
operational for at least 8 years between 2001-2010 (supplemental). We required European sites 127
(supplemental) to be in operation at least 3 years throughout the decade; less than North American 128
locations due to the more recent expansion of this regional network. 129
We collected global ground-based PM2.5 measurements from published values based upon a literature 130
review using the search terms “aerosol” and “PM2.5” in the Thomson Reuters Web of Knowledge, 131
yielding ca. 3500 results. We selected 541 papers for detailed evaluation from this list and in-132
publication citations, and found 342 contained relevant PM2.5 observations. We extracted mean PM2.5, 133
seasonal variation, city, country, site description and geo-coordinates as available. We approximated 134
geo-coordinates using GoogleEarth and in-reference maps at 70 locations. Geocoordinates were not 135
clear for 110 sites; we assumed measurements occurred within 0.1° of city center. When necessary, we 136
approximated seasonal variation from figures. We considered an observational period every third 137
month as sufficient for annual representation. Where possible, we inferred annual mean concentrations 138
for sites without observations every third month using the relative seasonal variation from nearby 139
published values at distances of up to 1°. We excluded industrial, traffic and military studies. We 140
combined observational PM2.5 values at locations within 0.1°, weighted by their temporal coverage, and 141
used only locations that had at least 3 months of direct observation, for a total of 210 ground-based 142
comparison sites outside of Canada, the United States and Europe. 143
We evaluated the combined fifteen year PM2.5 timeseries from MODIS, MISR, and SeaWiFS (henceforth 144
‘combined’) with annual average ground-based PM2.5 observations. We conduct the comparison versus 145
PM2.5 measurements from ground-based monitors on all days (not only days coincident with satellite 146
observations). We included in the evaluation the 110 global comparison sites from the literature 147
without clearly specified geo-coordinates; we conducted evaluations both assuming locations at city 148
center and up to 0.1° away. 149
Gridded population estimates at 2.5’ resolution from the Center for International Earth Science 150
Information Network (CEISIN 2005) at five year intervals starting from 1995, are regridded onto 0.1° x 151
0.1°. Years beyond 2005 are based upon projections. We estimated year-specific population densities 152
using linear interpolation. 153
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154
Results 155
Figure 1 (top panel) shows decadal mean satellite-derived PM2.5 concentrations over North America. 156
Enhancements are visible in the eastern United States and in the San Joaquin Valley of California. Figure 157
1 also shows long-term mean ground-level PM2.5 measured during this period over Canada and the 158
United States and comparison with the satellite-derived estimates. Significant overall agreement is 159
found (slope=1.00, r=0.79; 1σ error=1 μg/m3+14%). Separate comparisons of OE and UC satellite-160
derived estimates with the same ground-level monitors gave similar levels of agreement compared to 161
one another (r=0.72; 1σ error=1 μg/m3+18-21%; not shown). Contributions of OE and UC to the final 162
PM2.5 estimates were approximately equal over most land cover types. 163
Figure 2 (top panel) shows decadal mean satellite-derived PM2.5 concentrations over Europe. PM2.5 is 164
generally higher in Eastern Europe than Western Europe. The Po Valley in Italy is characterized by the 165
highest regional concentrations, with average PM2.5 for some local locations exceeding 35 μg/m3 from 166
2001-2010. Figure 2 also shows available long-term mean ground-level observations which are mostly 167
for the latter part of this period. We find slightly weaker agreement with satellite-derived estimates for 168
Europe than for North America, with slope=0.78, r=0.73 and 1σ error=1 μg/m3+21%. The weaker 169
agreement likely results from the shorter temporal sampling of three years over this region, as 170
illustrated in Table S1 and Table S2 of the supplemental material. A cluster of ground-level monitors in 171
southern Poland contribute to the disagreement with annual mean concentrations above 35 μg/m3. 172
PM2.5 concentrations in Southern Poland near Katowice have pronounced wintertime enhancements 173
(Rogula-Kozlowska et al. 2013) when satellite observations are sparse. 174
Figure 3 (top panel) shows global decadal mean satellite-derived PM2.5. PM2.5 concentrations in large 175
populated regions of northern India and eastern China respectively exceed 60 μg/m3 and 80 μg/m
3. The 176
bottom panels contain the 210 locations of global mean ground-level PM2.5 concentrations outside 177
Canada, the United States and Europe. Significant agreement (r=0.81) exists, but satellite-derived values 178
tend to be lower than ground-level measurements, with an overall slope of 0.68. Some of this 179
underestimate may arise from locations such as Ulaanbataar, Mongolia that experience pronounced 180
enhancement in wintertime and nighttime PM2.5 (World Bank 2011) when satellite observations are 181
limited. Comparison in which the 110 sites with unspecified geo-coordinates are at city center yielded 182
similar, but slightly weaker agreement (r=0.78; slope=0.65). 183
Table 1 provides a summary of population-weighted satellite-derived exposure according to the regions 184
used by the Global Burden of Disease (Lim et al. 2012). Globally, population-weighted PM2.5 exposure 185
between 2001-2010 is 26.4 μg/m3 with large spatial variability (standard deviation of 21.4 μg/m
3). South 186
and East Asia have the highest population-weighted mean exposures at 34.6 and 50.3 μg/m3. 187
Figure 3 (middle) presents global estimates of satellite-derived PM2.5 with dust and sea salt 188
concentrations removed for 2001-2010. Pronounced enhancements remain over China and the Indo-189
Gangetic Plain. North African and Middle Eastern PM2.5 have large relative decreases. Some studies 190
have suggested that the toxicity of particulate matter is more directly related to particle surface area 191
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7
than mass (e.g. Maynard and Maynard 2002; Oberdörster et al. 2005). Interestingly, satellite-derived 192
PM2.5 surface area (supplemental) demonstrates similar spatial patterns as dust and sea salt-free PM2.5. 193
Table 1 summarizes dust and sea salt-free PM2.5 according to GBD region. These components of PM2.5 194
are responsible for about half the population-weighted decadal mean PM2.5 concentrations in Central 195
Asia, North Africa/Middle East and East Sub-Saharan Africa and for three quarters of the concentration 196
in West Sub-Saharan Africa. Dust and sea salt account for 10% of these concentrations in East Asia and 197
20% in South Asia. Dust and sea salt have little influence over European and North American 198
concentrations. 199
Table 1 contains population-weighted PM2.5 trends over 1998-2012 for each GBD region. A 200
corresponding global trend map following Boys et al. (submitted) is in supplemental Figure S1. 201
Statistically significant increasing population-weighted trends include 1.63 ±0.54 μg/m3/yr (3.2±1.1 %/yr) 202
over East Asia and 1.02±0.25 μg/m3/yr (2.9±0.7 %/yr) over South Asia. These trends primarily follow 203
changes in anthropogenic emissions (Klimont et al. 2013) and increasing sulfate-nitrate-ammonium 204
concentrations as described in Boys et al. (submitted). Trends of 0.38±0.21 μg/m3/yr (1.5±0.8 %/yr) in 205
the Middle East are driven by mineral dust (Chin et al. 2014). Statistically significant downward 206
population-weighted trends include -0.33±0.08 μg/m3/yr (-3.3%±0.8 %/yr) over North America and -207
0.25±0.12 μg/m3/yr (-1.9±0.9 %/yr) over Western Europe. The global population-weighted trend is 208
0.55±0.12 μg/m3/yr (2.1± 0.5 %/yr). 209
Figure 4 shows time-series snapshots of PM2.5 over the four large-scale global regions that demonstrate 210
statistically significant trends. Changes are coherent over broad regions. Figure 5 shows local trends for 211
a major city within each region. Evaluation of the satellite-derived PM2.5 trends with available ground-212
level observations near Detroit yields consistent decreasing trends of 0.51-0.54 μg/m3/yr from 2001-213
2010, with a similar C.I. of ±(0.28-0.37) μg/m3/yr. The full 15 year satellite-derived PM2.5 time-series 214
decreases by 0.43±0.12 μg/m3/yr over 1998-2012. Beijing and New Delhi have significant increasing 215
trends over this time period of ca. 2 μg/m3/yr. Kuwait City has an even larger increasing trend of 3.1±0.8 216
μg/m3/yr. 217
Figure 6 gives the cumulative distribution of global annual mean PM2.5 as a function of time, and for the 218
three GBD regions of greatest positive and negative trend magnitude. Table 1 provides the percent of 219
population living in areas where concentrations are below the WHO interim targets (IT3, IT2 and IT1) 220
and guideline (AQG) for 1998-2000 and 2010-2012 for all regions. A small population-weighted global 221
improvement (1%) of those living within AQG is found over the past 15 years, predominantly driven by 222
improvements to air quality in North America which reduced its population living above this target from 223
62% to 19%. Globally, exceedance of IT1 (35 μg/m3) rose by 8% over the same time period, reaching 224
30% by 2010-2012 as driven by increasing PM2.5 concentrations in the heavily populated regions of 225
South and East Asia. The negative bias of satellite values versus ground-based monitors suggests the 226
percentage in living above WHO targets could be even higher. 227
Table 1 also shows the effect on WHO target achievement of population changes over 1998-2012. The 228
effect of population changes on WHO target achievement is less than 25% across all targets for all 229
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regions, and less than ca. 10% in most cases. The number of people living above AQG has increased due 230
to population changes in some regions, accounting for about a quarter of the change seen in Central 231
Asia and South Sub-Saharan Africa from 1998 to 2012. About half the change in Eastern Europe is due 232
to population, although the overall change is small (2%). Population changes contributed to small 233
reductions in population-weighted mean PM2.5 concentrations for regions such as Southeast Asia and 234
North America. 235
236
Discussion 237
A broad community requires globally consistent estimates of long-term PM2.5 exposure and changes 238
over time. For example, this information is used for global burden of disease assessment (Brauer et al. 239
2012; Lim et al. 2012; World Health Organization 2014), for environmental performance indicators 240
(Environmental Performance Index 2014), and for epidemiologic studies of air pollution health effects at 241
global (Anderson et al. 2012; Fleischer et al. 2014) and regional (Chudnovsky et al. 2012; Crouse et al. 242
2012; Vinneau et al. 2013) scales. Satellite retrievals offer the most globally complete observationally-243
based data source of this information, but improvements to these estimates are needed to reduce 244
uncertainties. 245
In this work, we combined the attributes of several recent satellite-derived PM2.5 datasets to improve 246
the accuracy in estimates of long-term exposure and changes in annual concentrations from 1998 to 247
2012. We inferred decadal mean PM2.5 from Unconstrained (van Donkelaar et al. 2010) and Optimal 248
Estimation (van Donkelaar et al. 2013) based approaches utilizing the MODIS and MISR instruments. We 249
then applied the relative temporal variation from SeaWiFS and MISR observations (Boys et al. 250
submitted) to represent the annual variation over 15 years. The resultant combined dataset had 251
significant agreement with 8+ year means of ground-based observations over North America 252
(slope=1.00; r=0.79; 1σ error=1 μg/m3+14%) and 3+ year means over Europe (slope=0.78; r=0.73; 1σ 253
error =1 μg/m3+21%) in non-coincident comparisons that represent both retrieval and sampling induced 254
uncertainties. This performance was better than for any of the individual datasets. We found a 255
noteworthy difference in agreement of satellite-derived PM2.5 with ground-based monitors if they are 256
sampled coincidently (only on the days when the satellite observes) as performed in many previous 257
works or non-coincidently as used here. For example, the coincident correlation of r=0.77 over North 258
America for 2001-2006 previously given in van Donkelaar et al. (2010), drops to r=0.70 when taken non-259
coincidently. The non-coincident comparison used here offers a more rigorous test of satellite sampling 260
bias. 261
A major challenge in evaluating global satellite-derived PM2.5 is the paucity of ground-based 262
measurements. We collected a global dataset of 210 ground-based observations from the literature and 263
used them to evaluate global satellite-derived PM2.5 estimates, including many locations in India and 264
China. Significant agreement was found (r=0.81), although these new monitors revealed that satellite-265
derived PM2.5 is typically lower than ground-based observations (slope=0.68). This underestimate may 266
result from factors such as AOD bias in the MISR retrieval over South and East Asia (Kahn et al. 2009), 267
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9
missing satellite observations during wintertime and/or nighttime enhancements (e.g. Katowice, Poland 268
and Ulaanbaatar, Mongolia), or coarse resolution of either the satellite-derived product or the 269
simulation used to related AOD to PM2.5 which may obscure localized features. The potential 270
underestimate in satellite-derived PM2.5 outside North America and Europe furthermore suggests true 271
PM2.5 concentrations may be even greater than we determined. 272
We found that decade-long populated-weighted ambient PM2.5 concentrations in East Asia are nearly 273
double the global mean of 26.4 μg/m3, and increase at an annual population-weighted rate of 1.63±0.54 274
μg/m3/yr (3.2±1.1 %/yr) between 1998 and 2012. Population-weighted concentrations over western 275
Europe and North America over the same period decreases by 0.25-0.33 ±0.08-0.12 μg/m3/yr (1.9-276
3.3±0.8-0.9 %/yr) in contrast with increases over South Asia (1.02±0.25 μg/m3/yr; 2.9±0.7 %/yr) and the 277
Middle East (0.38±0.21 μg/m3/yr; 1.5±0.8 %/yr). Satellite-derived estimates suggest that 30% of the 278
global population lives in regions above the WHO IT1 standard (35 μg/m3) for PM2.5 in 2010-2012, up 279
from 22% in 1998-2000. We found that most of the changes in exposure were driven by changes in 280
PM2.5 rather than changes in population location. 281
Both the satellite-derived PM2.5 estimates created in and ground-level observations collected for this 282
study are freely available as a public good on our website (http://fizz.phys.dal.ca/~atmos/martin) or by 283
contacting the authors. 284
Further developments to satellite retrievals and simulated aerosol profiles will continue to allow 285
improved representation of global exposures to PM2.5. In particular, higher resolution satellite retrievals 286
may better capture intra-urban variation (Chudnovsky et al. 2012). Recent improvements to MODIS 287
instrument calibration (Levy et al. 2013) may provide an additional data source for trends. 288
289
Acknowledgements 290
This work was supported by Health Canada, the Natural Sciences and Engineering Research Council of 291
Canada, and the National Institutes of Health. Some of the computing facilities used here were provided 292
by the Atlantic Computational Excellence Network. 293
294
295
296
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387
388
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13
Table 1: Population-weighted ambient PM2.5 and trend within GBD regionsa
Region
2001-2010
PM2.5
2001-2010
Dust and
Seasalt-free
PM2.5 1998-2012 PM2.5 Trend
Population in excess of WHO PM2.5 target [%]b
AQG IT3 IT2 IT1
19
98
-20
00
20
10
-20
12
20
10
-20
12
1
99
8-2
00
0 P
op
19
98
-20
00
20
10
-20
12
20
10
-20
12
1
99
8-2
00
0 P
op
19
98
-20
00
20
10
-20
12
20
10
-20
12
1
99
8-2
00
0 P
op
19
98
-20
00
20
10
-20
12
20
10
-20
12
1
99
8-2
00
0 P
op
Ave
Std
Dev Ave
Std
Dev
Ave C.I. Ave C.I.
[μg/m3] [μg/m
3] [μg/m
3/yr] [%]
GLOBAL 26.4 21.4 21.2 19.1 0.55 0.12 2.1 0.5 76 75 75 57 61 60 32 43 42 22 30 30
ASIA PACIFIC, HIGH INCOME 16.8 6.4 15.3 6.0 -0.06 0.14 -0.4 0.8 77 80 80 50 50 49 9 11 10 1 0 0
ASIA, CENTRAL 17.3 5.7 9.7 3.1 0.29 0.17 1.7 1.0 78 84 82 56 69 68 14 18 17 2 2 2
ASIA, EAST 50.3 24.3 45.2 22.5 1.63 0.54 3.2 1.1 95 99 99 86 95 95 67 84 84 51 70 70
ASIA, SOUTH 34.6 15.8 27.8 13.2 1.02 0.25 2.9 0.7 92 100 100 75 98 97 43 78 77 27 52 51
ASIA, SOUTHEAST 11.0 6.4 10.2 6.0 0.30 0.09 2.7 0.8 42 55 56 23 27 28 6 7 7 3 2 2
AUSTRALASIA 3.0 1.0 2.6 0.9 0.01 0.03 0.3 1.0 0 0 0 0 0 0 0 0 0 0 0 0
CARIBBEAN 7.0 2.5 4.7 1.5 -0.02 0.07 -0.3 1.0 15 27 24 2 2 2 1 0 0 0 0 0
EUROPE, CENTRAL 17.8 2.6 16.2 2.7 -0.22 0.26 -1.2 1.5 96 96 97 80 63 63 10 3 3 1 0 0
EUROPE, EASTERN 12.6 3.7 11.2 3.5 -0.04 0.21 -0.3 1.7 66 68 67 28 22 21 2 0 0 0 0 0
EUROPE, WESTERN 13.5 4.6 12.1 4.2 -0.25 0.12 -1.9 0.9 84 66 66 45 27 26 7 3 3 1 0 0
LATIN AMERICA, ANDEAN 6.6 3.7 6.6 3.7 0.09 0.14 1.4 2.1 23 26 26 10 4 4 1 0 0 0 0 0
LATIN AMERICA, CENTRAL 8.5 4.3 7.8 4.3 -0.07 0.07 -0.8 0.8 43 34 34 24 9 9 11 1 0 6 0 0
LATIN AMERICA, SOUTHERN 6.4 2.4 5.4 2.3 0.08 0.09 1.3 1.4 8 8 8 2 1 1 0 0 0 0 0 0
LATIN AMERICA, TROPICAL 5.0 2.6 4.9 2.5 0.01 0.04 0.2 0.8 15 6 6 2 0 0 0 0 0 0 0 0
NORTH AFRICA / MIDDLE EAST 25.5 10.7 11.5 3.6 0.38 0.21 1.5 0.8 93 97 97 72 80 79 35 53 51 15 28 27
NORTH AMERICA, HIGH INCOME 9.9 3.2 9.6 3.3 -0.33 0.08 -3.3 0.8 62 19 20 17 2 2 1 0 0 0 0 0
OCEANIA 2.3 1.1 2.3 1.1 0.09 0.03 3.9 1.3 0 1 0 0 0 0 0 0 0 0 0 0
SUB-SAHARAN AFRICA, CENTRAL 11.4 3.3 9.9 2.7 -0.05 0.09 -0.4 0.8 65 60 59 34 27 26 5 2 2 1 0 0
SUB-SAHARAN AFRICA, EAST 9.8 8.2 5.5 2.4 0.10 0.09 1.0 0.9 32 38 38 19 19 20 8 9 9 3 3 3
SUB-SAHARAN AFRICA, SOUTHERN 5.9 2.0 5.6 1.9 0.09 0.08 1.5 1.4 3 8 7 0 0 0 0 0 0 0 0 0
SUB-SAHARAN AFRICA, WEST 30.8 14.9 7.6 2.9 -0.04 0.29 -0.1 0.9 97 96 95 91 84 84 74 56 55 51 32 32
Abbreviations: Ave, average; Std Dev, standard devation; C.I., confidence interval; AQG, air quality guideline; IT, interim target; Pop, population.
a) Defined in Figure 3.
b) Percent above WHO PM2.5 targets for 1998-2000, 2010-2012, and 2010-2012 using 1998-2000 population distributions.
Page 13 of 28
14
Figure 1: Decadal (2001-2010) mean PM2.5 concentrations over North America. White areas denote
water or missing values. The top panel displays satellite-derived values. The lower right panel contains
averages at ground-based sites in operation at least 8 years during this period. The lower left panel
provides a scatterplot of the two datasets. The 1:1 line is solid. The line of best fit is dash-dot. The
observed 1-σ error is dotted. Ground-based and satellite values are not coincidently sampled to avoid
biasing the data toward clear-sky conditions when satellite retrievals occur. A common, logarithmic
color scale is used for Figures 1-4.
Figure 2: Decadal (2001-2010) mean PM2.5 concentrations over Europe. The top panel displays satellite-
derived values. The lower right panel contains ground-based values in operation at least 3 years during
this period. The lower left panel provides a scatterplot of the two datasets, sampled on the same years
but non-coincidently on a daily basis. The 1:1 line is solid. The line of best fit is dash-dot. The observed
1-σ error is dotted. A common, logarithmic color scale is used for Figures 1-4.
Figure 3: Global decadal (2001-2010) mean PM2.5 concentrations. The top panel displays satellite-
derived PM2.5. The middle panel contains dust and sea-salt free PM2.5. Inset maps display GBD regional
population-weighted mean concentrations. The bottom right panel contains ground-based values in
operation during this period. The lower left panel provides a scatterplot of the two all-species datasets,
sampled on the same years but non-coincidently on a daily basis. The 1:1 line is solid. The line of best
fit is dash-dot. The observed 1-σ error is dotted. A common, logarithmic color scale is used for Figures
1-4.
Figure 4: Three-year running mean of satellite-derived PM2.5 over sample regions of significant trends.
Sub-regions are denoted by boxes with black circles around the city centers highlighted in Figure 5. A
common, logarithmic color scale is used for Figures 1-4.
Figure 5: PM2.5 time-series at the four locations identified in Figure 4. Dots and vertical lines denote
monthly mean and 25th
-75th
percentile. Trend and confidence interval are in the inset. Satellite-derived
(black), ground-level monitor (red), and satellite-derived coincident with ground-level monitor (blue)
PM2.5 are given, as available.
Figure 6: Cumulative distribution of regional, annual mean PM2.5 for 1998-2012. AQG, IT3, IT2, and IT1
refer to the WHO air quality guidelines of 10, 15, 25 and 35 μg/m3.
Page 14 of 28
Figure 1: Decadal (2001-2010) mean PM2.5 concentrations over North America. White areas denote water or missing values. The top panel displays satellite-derived values. The lower right panel contains averages
at ground-based sites in operation at least 8 years during this period. The lower left panel provides a scatterplot of the two datasets. The 1:1 line is solid. The line of best fit is dash-dot. The observed 1-σ
error is dotted. Ground-based and satellite values are not coincidently sampled to avoid biasing the data toward clear-sky conditions when satellite retrievals occur. A common, logarithmic color scale is used for
Figures 1-4.
Page 15 of 28
Figure 2: Decadal (2001-2010) mean PM2.5 concentrations over Europe. The top panel displays satellite-derived values. The lower right panel contains ground-based values in operation at least 3 years during this period. The lower left panel provides a scatterplot of the two datasets, sampled on the same years but non-
coincidently on a daily basis. The 1:1 line is solid. The line of best fit is dash-dot. The observed 1-σ error is dotted. A common, logarithmic color scale is used for Figures 1-4.
Page 16 of 28
Figure 3: Global decadal (2001-2010) mean PM2.5 concentrations. The top panel displays satellite-derived PM2.5. The middle panel contains dust and sea-salt free PM2.5. Inset maps display GBD regional
population-weighted mean concentrations. The bottom right panel contains ground-based values in
operation during this period. The lower left panel provides a scatterplot of the two all-species datasets, sampled on the same years but non-coincidently on a daily basis. The 1:1 line is solid. The line of best fit is
dash-dot. The observed 1-σ error is dotted. A common, logarithmic color scale is used for Figures 1-4. 165x186mm (300 x 300 DPI)
Page 17 of 28
Figure 4: Three-year running mean of satellite-derived PM2.5 over sample regions of significant trends. Sub-regions are denoted by boxes with black circles around the city centers highlighted in Figure
5. A common, logarithmic color scale is used for Figures 1-4.
Page 18 of 28
Figure 5: PM2.5 time-series at the four locations identified in Figure 4. Dots and vertical lines denote monthly mean and 25th-75th percentile. Trend and confidence interval are in the inset. Satellite-derived
(black), ground-level monitor (red), and satellite-derived coincident with ground-level monitor (blue) PM2.5 are given, as available.
Page 19 of 28
Figure 6: Cumulative distribution of regional, annual mean PM2.5 for 1998-2012. AQG, IT3, IT2, and IT1 refer to the WHO air quality guidelines of 10, 15, 25 and 35 µg/m3.
Page 20 of 28
1
Supplemental Material: Unified global fine particulate matter concentrations from satellite for long-term 1
exposure assessment 2
Aaron van Donkelaar, Randall Martin, Brian Boys, and Mike Brauer 3
4
Description of ground-level monitor sources from established networks 5
Established PM2.5 networks provide a robust source of evaluation for satellite-derived PM2.5 6
concentrations due to their long-term observation period and consistent measurement practices. 7
Ground-level Canadian PM2.5 observations were obtained from the National Air Pollution Surveillance 8
network (NAPS; http://www.etc.cte.ec.gc.ca/NAPS/index_e.html), excluding industrial sites. American 9
observations were taken from sites of the Interagency Monitoring of Protected Visual Environments 10
network (IMPROVE; http://vista.cira.colostate.edu/improve/Data/data.htm) and from the 11
Environmental Protection Agency Air Quality System that employ the Federal Reference Method (FRM; 12
http://www.epa.gov/air/data/index.html). PM2.5 measurements at background sites from the European 13
air quality database (Airbase; http://acm.eionet.europa.eu/databases/airbase/) and European 14
Monitoring and Evaluation Programme (EMEP; Torseth et al. 2012) were used over Europe. 15
16
Description of satellite instrumentation 17
As described in van Donkelaar et al. (2010; 2013), the Unconstrained (UC) and Optimal Estimation (OE) 18
PM2.5 datasets use data from the MODIS (MODerate resolution Imaging Spectroradiometer) 19
instruments. UC uses MODIS onboard the Terra satellite, while OE uses MODIS onboard both Terra and 20
Aqua. Both MODIS instruments provide near-daily global AOD coverage in the absence of clouds from a 21
polar orbiting, sun-synchronous orbit. Quality assured collection (version) 5 MODIS AOD at 10 km × 10 22
km over land (Levy et al. 2007) has been validated such that at least two-thirds of its retrievals are 23
within ±(0.05 + 15%) using Aerosol Robotic Network (Holben et al. 2001) measurements of AOD (Remer 24
et al. 2008). Concerns have been raised about drift in MODIS collection 5 over land (Zhang and Reid 25
2010. We use this dataset only for long-term averages (not trends). 26
The MISR (Multi-angle Imaging SpectroRadiometer) instrument onboard the Terra satellite is used for 27
the UC dataset (van Donkelaar et al. 2010) and trends (Boys et al. submitted). MISR observes radiation 28
leaving the top of the atmosphere in four spectral bands (0.446, 0.558, 0.672 and 0.866 μm), each at 29
nine viewing angles (±70.5º, ±60.0º, ±45.6º, ±25.1º and nadir). MISR typically takes 6 to 9 days for 30
complete global in the absence of clouds. The MISR AOD retrieval algorithm (Diner et al. 2005; 31
Martonchik et al. 2002; Martonchik et al. 2009) has been validated such that two-thirds of retrievals fall 32
within the maximum of ±(0.05 or 20%) of ground truth observations (Kahn et al. 2005), and has reliable 33
trend information over land Zhang and Reid 2010. 34
The SeaWiFS (Sea-viewing Wide Field-of-view Sensor) instrument provides near-daily global coverage at 35
8 wavelengths from a sun-synchronous orbit. The Deep Blue algorithm has recently been applied to 36
Page 21 of 28
2
SeaWiFS AOD retrieval at a resolution of 13.5 km (Hsu et al. 2013), providing a well-calibrated retrieval 37
of global AOD from 1998-2010 suitable for trend studies (Hsu et al. 2012). High quality SeaWiFS AOD 38
has been validated such that at least two-thirds of retrievals are within ±(0.05 + 20%) (Sayer et al. 2012). 39
40
Description of the GEOS-Chem chemical transport model 41
The GEOS-Chem chemical transport model (http://geos-chem.org) solves for the spatial and temporal 42
evolution of atmospheric aerosol and gaseous compounds using meteorological data sets, emission 43
inventories, and equations that represent the physics and chemistry of the atmosphere. We used GEOS-44
Chem to relate AOD to PM2.5 mass and surface area, and to provide prior estimates with which to 45
constrain the OE satellite retrievals of AOD. 46
Detailed simulation descriptions are contained within the corresponding publications for UC (van 47
Donkelaar et al. 2010), OE (van Donkelaar et al. 2013) and SeaWiFS&MISR (Boys et al. submitted). A 48
major distinction between these simulations are the assimilated meteorological fields used for UC 49
(GEOS-4), OE (GEOS-5) and SeaWiFS&MISR (MERRA). All fields were provided by the Goddard Earth 50
Observing System and represented current versions of available meteorology at the original time of 51
each publication. All simulations were performed globally at 2° × 2.5°. OE additionally used three 52
nested 1/2° × 2/3° regions overs North America, Europe and eastern Asia. 53
All simulations share a similar treatment of aerosol that include the sulphate-ammonium-nitrate-water 54
system (Park et al. 2004), primary carbonaceous aerosols (Park et al. 2003), secondary organic aerosols 55
(Henze et al. 2008), sea salt (Alexander et al. 2005), and mineral dust (Fairlie et al. 2007). 56
57
Descripton of satellite-derived PM2.5 surface area 58
AOD is more directly related to PM2.5 surface area than PM2.5 mass since light extinction is proportional 59
to particle surface area (not volume) and surface area does not require assumptions about particle 60
densities. Satellite-derived estimates of surface area can, therefore, be readily created following the 61
approaches established for PM2.5 mass. We produced such estimates of surface area by applying to 62
satellite (MODIS, MISR and SeaWiFS) GEOS-Chem simulations of coincident AOD to ground-level surface 63
area of particles with aerodynamic diameter smaller than 2.5 μm which we refer to as PM2.5 surface 64
area. OE, UC, and SeaWiFS&MISR-based surface area was produced using the simulations and methods 65
described for PM2.5 in van Donkelaar et al. (2013) and van Donkelaar et al. (2010), respectively, and 66
combined following the approach outlined in the main manuscript. Figure S2 shows the resultant 67
decadal mean PM2.5 surface area for comparison with Figure 4. 68
69
70
Page 22 of 28
3
71
72
Sub-annual agreement of three-month mean satellite-derived PM2.5 73
Table S1 summarizes the variation in seasonal agreement between the satellite-derived and ground-74
based PM2.5 at approximately 1000 locations in North America. Seasonal agreement varies with 75
expected patterns of AOD retrieval accuracy, with improved agreement during summer months when 76
surface reflectance is better characterized and when seasonal PM2.5 enhancements increase the aerosol 77
signal in satellite observations. We also provide the agreement found when applying GEOS-Chem 78
seasonality to the satellite-derived annual means. Simulated seasonal variation improves monthly 79
satellite-derived PM2.5, particularly in the winter season when satellite retrievals can be inhibited by 80
snow-cover. Seasonal cycles will vary globally, but these results suggest that the impact of snow, cloud 81
and reduced sampling may increase the uncertainty of seasonal decadal mean PM2.5 estimates by up to 82
a factor of two relative to annual mean values. 83
Table S2 evaluates the impact of temporal range on accuracy, comparing mean satellite-derived and 84
ground-based PM2.5 over a varying number of years at ca. 1000 locations in North America. On average, 85
annual performance is degraded significantly from decadal mean values (r=0.68 vs. r=0.79; slope=1.07 86
vs. 1.00; 1σ error = 20% vs. 14%). Errors in long-term exposure assessment increase with decreasing 87
number of measurements from satellite. Sub-annual agreement of three-month running means further 88
increases error by up to a factor of two (supplemental). Significant improvement, however, is found 89
when using as few as three years (r = 0.73; slope = 1.05; 1σ error = 17%), although still well below 90
decadal agreement. As a result, the spatial correlations obtained over European and global regions 91
(Figures 2 and 3) may indicate comparable significance to North America, only reduced by the limited 92
sampling period of available ground-level observations for comparison. 93
94
95
96
Page 23 of 28
4
Figure S1: PM2.5 annual trend over 1998-2012. The intensity of the colorscale provides a measure of statistical significance. Inset gives
population-weighted mean values within GBD-defined regions. Grey areas denote water or missing data.
Page 24 of 28
5
Figure S2: Global decadal (2001-2010) mean PM2.5 surface areas. The inset map displays GBD regional population-weighted mean surface area.
The logarithmic color scale follows that used for Figures 1-4.
Page 25 of 28
6
Table S1: Effect of season on satellite-derived and ground-level PM2.5 agreement over North America,
2001-2010. Mean and standard deviation of year-specific monthly mean agreement is given. The
agreement of simulated seasonality applied to annual mean satellite-derived PM2.5 is also given.
Monthly values represent the center month of a three-month temporal range. Approximately 1000
locations are used.
Satellite Seasonality Simulated Seasonality
Time
Period
1σ error
[% + 1
μg/m3]
Slope Offset Pearson
Coefficient
1σ error
[% + 1
μg/m3]
Slope Offset Pearson
Coefficient
Annual 20 ± 2 1.07 ± 0.10 -1.4 ± 0.7 0.68 ± 0.07 20 ± 2 1.07 ± 0.10 -1.4 ± 0.7 0.68 ± 0.07
January 36 ± 3 1.47 ± 0.27 -4.9 ± 2.4 0.37 ± 0.06 26 ± 3 0.87 ± 0.11 0.6 ± 0.7 0.48 ± 0.04
February 33 ± 4 1.54 ± 0.23 -5.4 ± 2.2 0.45 ± 0.08 24 ± 2 0.91 ± 0.09 0.1 ± 0.7 0.57 ± 0.06
March 28 ± 3 1.49 ± 0.21 -4.7 ± 2.0 0.51 ± 0.08 20 ± 2 0.99 ± 0.10 -0.6 ±0.8 0.65 ± 0.07
April 24 ± 3 1.30 ± 0.18 -2.6 ± 1.4 0.59 ± 0.06 19 ± 2 1.03 ± 0.13 -0.7 ± 0.9 0.66 ± 0.08
May 22 ± 3 1.24 ± 0.18 -2.3 ± 1.5 0.62 ± 0.09 21 ± 3 1.00 ± 0.13 -0.5 ± 0.9 0.62 ± 0.12
June 23 ± 3 1.23 ± 0.18 -2.7 ± 1.5 0.66 ± 0.12 23 ± 4 0.98 ± 0.12 -0.6 ±1.0 0.64 ± 0.15
July 23 ± 3 1.24 ± 0.16 -3.2 ± 1.6 0.68 ± 0.11 24 ± 4 0.94 ± 0.08 -0.4 ±0.9 0.68 ± 0.14
August 24 ± 4 1.14 ± 0.21 -2.3 ± 1.6 0.68 ± 0.11 22 ± 4 0.98 ± 0.10 -0.7 ± 0.8 0.71 ± 0.11
September 25 ± 5 1.06 ± 0.23 -1.5 ± 1.5 0.64 ± 0.10 21 ± 2 1.06 ± 0.16 -0.9 ± 1.0 0.69 ± 0.08
October 30 ± 5 0.94 ± 0.20 -0.7 ± 1.1 0.53 ± 0.10 24 ± 3 1.10 ± 0.17 -1.2 ± 1.1 0.60 ± 0.06
November 34 ± 5 0.95 ± 0.15 -0.7 ± 0.8 0.42 ± 0.08 27 ± 4 1.02 ± 0.11 -0.5 ± 0.7 0.49 ± 0.06
December 37 ± 4 1.05 ± 0.15 -1.3 ± 1.7 0.37 ± 0.07 27 ± 3 0.87 ± 0.10 0.6 ± 0.7 0.45 ± 0.05
Table S2: Effect of temporal range on satellite-derived and ground-level PM2.5 agreement over North
America. Mean and standard deviation of individual temporal comparisons are given (e.g. mean and
standard deviation of annual agreement when temporal range is 1 year). Sites must be active for at
least 80% of the temporal range, resulting in ca. 1000 locations used.
Temporal
Range (yrs)
1σ error
[% + 1 μg/m3]
Slope Offset Pearson
Coefficient
1 20 ± 2 1.07 ± 0.10 -1.4 ± 0.7 0.68 ± 0.07
2 17 ± 2 1.05 ± 0.08 -1.2 ± 0.7 0.72 ± 0.06
3 17 ± 2 1.04 ± 0.08 -1.2 ± 0.7 0.73 ± 0.05
4 15 ± 2 1.02 ± 0.06 -1.0 ± 0.5 0.74 ± 0.04
5 16 ± 1 1.02 ± 0.04 -1.0 ± 0.3 0.75 ± 0.03
6 15 ± 2 1.01 ± 0.04 -0.9 ± 0.3 0.77 ± 0.03
7 15 ± 2 1.00 ± 0.04 -0.8 ± 0.3 0.77 ± 0.03
8 14 ± 1 1.00 ± 0.03 -0.8 ± 0.2 0.78 ± 0.02
9 14 ± 1 1.00 ± 0.02 -0.8 ± 0.2 0.78 ± 0.02
10 14 ± 0 1.00 ± 0.00 -0.7 ± 0.0 0.79 ± N/A
Page 26 of 28
7
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