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ArticleQuantification of Global Primary Emissions of PM2.5, PM10,and TSP from Combustion and Industrial Process Sources

Ye Huang, Huizhong Shen, Han Chen, Rong Wang, Yanyan Zhang, Shu Su, Yuanchen Chen, NanLin, Shaojie Zhuo, Qirui Zhong, Xilong Wang, Junfeng Liu, Bengang Li, Wenxin LIU, and Shu TaoEnviron. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/es503696k • Publication Date (Web): 27 Oct 2014

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Quantification of Global Primary Emissions of PM2.5, PM10, and TSP from 1 Combustion and Industrial Process Sources 2

Ye Huang, Huizhong Shen, Han Chen, Rong Wang, Yanyan Zhang, Shu Su, Yuanchen Chen, 3 Nan Lin, Shaojie Zhuo, Qirui Zhong, Xilong Wang, Junfeng Liu, Bengang Li, Wenxin Liu, Shu 4 Tao* 5

Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, 6 Peking University, Beijing 100871, China. 7 8 * Corresponding author: phone and fax: +86-10-62751938; email: taos@pku.edu.cn 9 10 The authors declare no competing financial interest. 11

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ABSTRACT 12

Emission quantification of primary particulate matter (PM) is essential for assessment of its 13 related climate and health impacts. To reduce uncertainty associated with global emissions of 14 PM2.5, PM10, and TSP, we compiled data with high spatial (0.1°×0.1°) and sectorial (77 15 primary sources) resolutions for 2007 based on a newly released global fuel data product 16 (PKU-FUEL-2007), and an emission factor database. Our estimates for developing countries 17 are higher than those previously reported. Spatial bias associated with large countries could be 18 reduced by using sub-national fuel consumption data. Additionally, we looked at temporal 19 trends from 1960 to 2009 at country-scale resolution. Although total emissions are still 20 increasing in developing countries, their intensities in terms of gross domestic production or 21 energy consumption have decreased. PM emitted in developed countries is finer owing to a 22 larger contribution from non-industrial sources, and use of abatement technologies. In contrast, 23 countries like China, with strong industry emissions and limited abatement facilities, emit 24 coarser PM. The health impacts of PM are intensified in hotspots and cities owing to 25 covariance of sources and receptors. Although urbanization reduces the per person emission, 26 overall health impacts related to these emissions are heightened because of aggregation 27 effects. 28

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30 31 32 33 34 Corresponding author phone and fax: 0086-10-62751938, email: taos@pku.edu.cn 35 The authors declare no competing financial interest. 36 37 Word count for text: 5179 38 Word count for 5 figures: 1800 = 600+300x4 39 Total word count: 6979 40

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INTRODUCTION 42

Particulate matter (PM) in air affects both climate1 and health.2 Frequent occurrences of thick 43 haze in eastern China have raised extensive health concerns in recent years.3,4 Based on the 44 data collected by the American Cancer Society as a part of the Cancer Prevention Study II, 45 Pope et al. estimated that a 10 µg/m3 increase in PM2.5 (particles with aerodynamic size less 46 than 2.5 µm) within a range from 5 to 30 µg/m3 in air could lead to a 6 and 8% increase in 47 cardiopulmonary and lung cancer mortalities, respectively, for people aged 30 or older;2 while 48 in 2005, 376 thousands premature deaths were attributed to exposure to PM2.5 in China.5 49 Additionally, PM in air may cause changes in surface temperature,6 solar radiation,7 cloud 50 properties,8 and precipitation,9 leading to major effects on climate. It was estimated that 51 32-73% of ambient PM were from primary source.10,11 Wildfire, dust, and sea salt were major 52 natural sources. Both health and climate impacts of PM are size dependent. Evidences from 53 both epidemiological and in-vitro studies suggest that fine, especially untrafine particles are 54 more toxic.12,13 For climate impact, finer particles have longer residence time and are more 55 effective in scattering solar radiation.14,15 56

A series of efforts were made to quantify primary emissions of PM from various sources on 57 global,16,17 regional,18,19 and country20-25 scales. These inventories provide essential input for 58 modeling fate, spatiotemporal distributions, and effects of PM. Much effort has been invested 59 to reduce uncertainties in these PM emission inventories, e.g., emissions of PM are compiled 60 for developing and developed countries, using different emission factors (EFs), defined as 61 mass of pollutant emitted per unit fuel consumed or material produced.26 Country and period 62 specific EFs for given sources have been adopted to account for variation in fuels, facility 63 type, operational status, environmental conditions, and other factors through time.20,27-29 The 64 technology division method was developed and applied to describe the impact of abatement 65 technologies.19,24,30-32 Still, source complexity and lack of data lead to large uncertainties in 66 inventories. As one of many examples, primary organic aerosol may evaporated after emitted 67 into the atmosphere,33 causing difficulty in emission estimation. 68

Recently, a global fuel consumption data product (PKU-FUEL-2007) with high spatial 69 (0.1°×0.1°) and source (64 fuel types) resolutions was compiled based on sub-national fuel 70 consumption data for many large countries. Using sub-national instead of national fuel data 71 reduces the spatial bias due to uneven per-person fuel consumption within countries.34 Using 72 this new data product and an EF database with recently published data, 0.1°×0.1° gridded 73 global emission inventories for TSP (total suspended particles), PM10 (particles with 74 aerodynamic size less than 10 µm), and PM2.5 from combustion and industrial process sources 75 were compiled for 2007 in this study (PKU-PM-2007, http://inventory.pku.edu.cn/). Dust and 76 sea salt were excluded in this study because totally different approaches have to be used.35-36 77 Although there is growing concern on ultrafine particles with sizes less than 100 nm, the 78 emission of them is difficult to be quantified at this stage due to data availability. Historical 79 PM emissions from 1960 to 2009 were compiled at country-scale resolution. The uncertainty 80 in these inventories was characterized. The spatial and temporal distributions, changes in 81 emission intensities and fine to coarse PM ratios, and their potential health impacts are 82 discussed. 83

METHODOLOGY 84

Emission estimation. Emissions of TSP, PM10, and PM2.5 from a specific sources were 85 calculated as products of the source strength (quantities of fuel consumed or material 86 produced) and EFs for that particular source; 77 sources were included and listed in Table S1. 87

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In addition to the 65 combustion sources,34 12 material production sources were incorporated, 88 including five processes involved in iron and steel industry (sintering, pig iron production, 89 open hearth furnace, convertor, and arc furnace),37 as well as production of lime, glass, 90 ferroalloy, lead, magnesium, zinc,38 and fertilizers.39 Detailed source information is given in 91 Table S1. For years other than 2007, annual fuel consumptions in individual countries are 92 from Shen et al.,40 except for coal consumptions in energy, industrial, and residential sectors 93 in China41 and several sources associated with industrial processes.38,39 94

EFs. EFs reported in the literature are associated with large uncertainty due to differences 95 among facilities and fuels, environmental and operation conditions, burning stages and so on. 96 Our strategy was to collect as many data as possible, from which distributions of the EFs can 97 be derived for Monte Carlo simulation to characterize uncertainty associated (Table S1). 98 More than 2500 EFs collected from the literature were compiled into a database. For fuels 99 used in transportation, country and time specific EFs were projected, using a series of 100 regression models.42,43 The models for TSP are provided in Table S2, while fixed 101 relationships between PM2.5 and TSP (0.85) and between PM10 and TSP (0.91) were applied.44 102 The EFs for coal combustion in energy and industrial sectors were ash content corrected20,25 103 based on coal ash contents (log normal distributed with a standard deviation (SD) of 0.18 for 104 log transformed ash content) from the literature.30,28,45-49 For all other sources, arithmetic 105 means were used. For the sources with abatement facilities, the EFs for specific technologies 106 (cyclones, wet scrubbers, electrostatic precipitators, or fabric filters) were calculated using 107 their unabated EFs and removal efficiencies (Table S3).18,20,24,25 108

Technology splits. The overall EFs for specific country categories and specific year were 109 different and were derived using technology split method.31 For the energy and industry 110 sectors, penetration rates for pulverized coal-fired boilers, stokers, and various control 111 technologies were taken from Bond et al.31 with slight modification (Tables S4 and S5). 112 Bituminous coal consumed in the residential sector of China was subdivided into chunk coal 113 and honeycomb briquette, each with a different EF.28,47,50-52 For all other countries, use of 114 honeycomb briquette was negligible.54 The fractions of biomass fuels consumed in 115 woodstoves and fireplaces were taken from Bond et al.,31 while ratios of improved woodstove 116 were from Shen et al.40 The fractions of shaft, precalciner, and other kilns were fitted using an 117 S-curve (Table S6).24,54 A large uncertainty (50%) was set to technologies with uniform 118 distributions were assumed. 119

Non-compliance rate. The results of a reliable survey conducted in Jiangsu, China, suggest 120 that SO2 scrubbers perform well, over 90% of the time.55 Considering that PM control 121 facilities are often better maintained than SO2 scrubbers, we assumed a non-compliance rate 122 of 5% (0-10%, uniformly distributed) for all developing countries. 123

Spatial distribution mapping. The spatial distribution mapping of the emissions was 124 conducted based on 0.1°×0.1° grid map of fuel consumption database (PKU-FUEL-2007)34 125 and corresponding EFs. In brief, the fuel inventory was generated based on sub-national fuel 126 data for some countries (county data for China, the United States, and Mexico, 0.5°×0.5° grid 127 data for 36 Europe countries, and provincial/state data for India, Brazil, Canada, Australia, 128 Turkey, and South Africa) and national data for others for most fuels. These data were 129 disaggregated into 0.1°×0.1° grids using total population, rural population, and gross domestic 130 production as proxies, depending fuel categories. Data for wildfire, aviation, shipping, power 131 stations, natural gas flaring, and agricultural waste were from the literature.34 Detailed 132 description can be found in a previous paper.34 For production of cement, lime, coke and 133 aluminum, the geolocations of the sources were directly from the literature.38 Except cement 134 production in China, which was disaggregated based on provincial production56 and industrial 135

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coal consumption as a proxy.34 For other non-combustion industrial processes, activity 136 strengths for all countries from the literature were disaggregated using industrial coal 137 consumptions as proxy.34 138

Uncertainty analysis. A Monte Carlo simulation was conducted to characterize overall 139 uncertainty. Variations in source strengths, EFs, coal ash contents, fractions and efficiencies of 140 control technologies, as well as the non-compliance rate were modeled. Values of these 141 parameters were randomly selected from predefined distributions based on literature 142 values25,40 or those as mentioned in previous parts. Our results are presented as interquartile 143 ranges below. To quantify relative contributions of various parameters to the overall 144 uncertainty, simulations were conducted for each parameters in turn with other parameters 145 fixed. 146

Calculation of Relative Potential Health Effect. As a normalized dimensionless indicator, 147 the Relative Potential Health Effect (RPHE) was proposed by Shen et al. for polycyclic 148 aromatic hydrocarbons.40 A slightly modified method was applied in this study for PM2.5. In 149 brief, the RPHE in a grid was proportional to the total population of the grid and the sum of 150 the emission contributions of the grid and 120 surrounding grids (11×11 grid in total). The 151 contributions of the 25 grids were weighted inversely to distances to the central grid and the 152 global RPHE were normalized. 153

RESULTS AND DISCUSSION 154

Global Emission and Source Profiles. 155

Annual total global emissions of TSP, PM10, and PM2.5 for 2007 were estimated to be 162 156 (123–224), 99 (80–130), and 78 (64–101) Tg, respectively. Of which, anthropogenic sources 157 accounted for 105 (74–158), 55 (42–78), and 40 (31–56) Tg of these fractions. Although our 158 results were broadly similar to those from EDGARv4.216 and HATP_V2,17 relatively large 159 differences were found for individual countries and sectors, reflecting differences in EFs, 160 noncompliance rates, and coal ash contents. For instance, anthropogenic PM10 (2007) and 161 PM2.5 (2008) in developing countries were estimated to be 49.4 and 32.4 Tg in this study, 162 respectively. These values were 4.7 and 12.9% higher than those reported in EDGARv4.2 and 163 HTAP V2, respectively, even though some minor (less than 2% of the total) sources such as 164 solvent and other product use and direct soil emissions in DEGARv4.2 and HTAP V2 were 165 not included in our inventory. 166

The calculated relative variations of the distributions from Monte Carlo simulation, defined as 167 the differences between the 75th and the 25th percentiles divided by medians, were 41.8, 14.6, 168 3.1, 1.2, and 2.1% for EFs, fuel quantities, non-compliance rate, technology split rate, remove 169 efficiency, and ash content, respectively, indicating dominant contribution of EFs. Among the 170 various sources shown in source profiles (Figure S1), wildfires were the largest single source 171 globally, followed by residential biomass burning, power generation, and cement production. 172 In total, biomass burning, including wildfires, residential fuels, and agriculture wastes, 173 accounted for 45.2 (TSP), 59.9 (PM10), and 67.6% (PM2.5) of the total global emission. Power 174 generation contributed 14.0, 9.5, and 6.4% of TSP, PM10, and PM2.5, respectively. Unlike 175 incomplete combustion products, such as black carbon and polycyclic aromatic hydrocarbons, 176 40,57 rather small fractions (0.8-1.4%) of primary PM were from motor vehicles. 177

Per person average annual global emissions of primary TSP, PM10, and PM2.5 from all sources 178 for 2007 were 25.0, 15.6, and 12.4 kg, respectively. Even though per person fuel consumption 179 in developed countries was higher,34 their per person PM emissions were significantly lower 180 (6.6, 5.4, and 4.4 kg) than those in developing countries (18.8, 9.6, and 6.9 kg). This is 181

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primarily because of differences in source profiles and EFs (Figure S2). Strong dependences 182 on biomass fuels, active cement production, and relatively high EFs for anthropogenic sources 183 led to high per person PM emission in developing countries. In China, for example, more than 184 130 million rural households still use wood and crop residues for cooking and heating, 185 contributing greatly to primary PM emission.58 Likewise, 78.5% of cement is produced in 186 developing countries, where emission control facilities are often not installed.25,59 In contrast, 187 major PM emission sources in developed countries, including industrial boilers and motor 188 vehicles are well controlled. 189

Spatial Distribution. 190

Geographical distributions of total and per person anthropogenic emissions of PM2.5 for 2007 191 are shown in Figure 1. The relative contributions from major sectors for various regions are 192 shown as pie charts. Of the total emission of anthropogenic PM2.5, excluding shipping and 193 aviation, 87.7% was from developing countries, with major contributions from China (14.3 194 Tg), India (5.5 Tg), Nigeria (1.6 Tg), Indonesia (1.3 Tg), and Russia (0.99 Tg). In contrast, 195 among-country differences and spatial variations of per person emissions were much less 196 marked. This reflects a correlation between densely populated and hot emission regions. The 197 average per person anthropogenic PM2.5 emission for developed countries (4.4 kg) was also 198 much lower than that for developing countries (6.9 kg). Among other reasons, biomass fuels 199 with very high EFs were extensively used in residential areas of developing countries.60 The 200 penetration rate of fabric filters in coal-fired power stations in China 25 was much lower than 201 that in the United States.61 Spatial distributions of total and per person anthropogenic 202 emissions of primary PM10 and TSP are shown in Figure S3. Although their general patterns 203 are similar to those for PM2.5, differences between developed and developing countries are 204 larger than those for PM2.5, because coal-fired power stations, cement production, and heavy 205 industry in developing countries all emit coarser PM. 206

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Spatial bias caused by significant differences in per person fuel consumption within large 207 countries can be reduced substantially by using sub-national, instead of national fuel 208 consumption data, when population is used as a proxy for disaggregation.34 We confirmed the 209 advantage of this approach by comparing our PM2.5 emissions based on PKU-FUEL-2007 210 with results derived using national fuel consumption data. Relative differences for the 211

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showing the relative contributions from major sectors for various regions. C) Global distributions of per person anthropogenic emissions of PM2.5.

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countries with sub-national fuel data are shown in Figure S4; values ranged from 6 (Brazil) to 212 96% (Mexico) difference in spatial distribution. 213

Our fine spatial resolution allowed us to compare emissions between urban and rural areas for 214 anthropogenic sources in all countries (Table S7). Despite the fact that most industrial and 215 transport sources locate in urban areas, residential fuel consumptions are quite different 216 between rural and urban areas, especially in developing countries. Rural residents rely more 217 on solid fuels, like crop residue, firewood, and coal, emitting large quantities of PM. In 218 contrast, urban residents use cleaner fuels, like natural gas, liquefied petroleum gas, and 219 electricity.62 As a result, per person annual primary PM emission in rural areas are much 220 higher than those in urban areas. Further, this difference in developed countries (12 and 2.8 kg 221 PM2.5 for rural and urban areas) is larger than that in developing countries (8.4 and 4.6 kg 222 PM2.5 for rural and urban areas). Recent rapid economic growth and urbanization in 223 developing countries brought millions of rural residents to cities. In China alone, more than 224 260 million people have moved to urban areas over the past three decades. This trend will 225 continue for years to come.63 Thus, urbanization in developing countries will lead to a 226 significant reduction in PM emission in the future. Our estimates suggest that annual emission 227 of PM2.5 will be reduced by around 3800 ton per million people as rural residents migrate to 228 cities, climbing up the energy ladder. 229

Temporal Variation. 230

Since spatially-resolved fuel consumption data is only available for 2007, annual PM 231 emissions from 1960 to 2009 could only be derived at country-scale resolution in this study. 232 Temporal trends for total PM2.5 emissions from six sectors (left panels) and eight source types 233 (right panels) are shown in Figure S5. We show estimates for the global total (with 234 uncertainty ranges), developed countries, developing countries, and specifically for China, 235 and the United States. Globally, emission of PM2.5 increased continuously from 1960 through 236 the 1980s, and then it was relatively constant until 1996, before decreasing slowly, with strong 237 fluctuations owing to fast changes in economic and energy demands.60 In comparison with 238 EDGARv4.2 (Figure S6), our temporal trends in total anthropogenic PM10 emissions were 239 similar, with significant differences for countries with high emissions, reflecting differences 240 in our technology division and EFs. 241

The overall trends were very different between developed and developing countries (Figure 242 S7). In 1960, anthropogenic PM2.5 emissions were mainly from Europe (32.2%), east Asia 243 (22.6%), North America (18.5%), and south and southeast Asia (12.9%). Post 1960, emissions 244 decreased in Europe and North America, but increased in Asia, making this continent the 245 predominant emission region (66.7%). In developed countries, emissions were reduced in the 246 transportation (gasoline) and industry (coal and processes) sectors. For example, since the 247 1990s, emissions in the United States decreased owing to the implementation of Clean Air 248 Act and other regulations.64,65 In developing countries, increased emissions over the past 50 249 years were related to coal-based industrial activities. In China, for example, coal consumption 250 has increased from 60 MT in 1960 66 to 2118 MT in 2009,60 resulting in enhanced PM 251 emission. Although the number of motor vehicles has risen in recent years in China and other 252 developing countries, 67,68 PM2.5 emission from this source did not show a significant increase, 253 reflecting the rapid implementation of emission regulations and controls. For instance, the 254 China IV standard, equivalent to Euro IV was introduced in Beijing (2008),69 Shanghai 255 (2009),70 Guangzhou (2010),71 and across China (2011) (GB17691-2005 and 256 GB18352.3-2005),72,73 much earlier than in developed countries with respect to its developing 257 status (i.e., per person GDP). Emission histories in other developed and developing countries 258 followed similar trends to those above (Figure S8). 259

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Figure S9 shows a time series of annual emissions of PM10 and TSP for global, developed 260 countries, and developing countries. Slight differences to the trends for PM2.5 were found. In 261 developed countries, emissions of both PM2.5 and PM10 increased before 1970, partly owing 262 to the increase in number of motor vehicles; there was no such trend for TSP, which is 263 primarily of industrial origin.18,24 264

Temporal changes in per person annual PM2.5 emissions from 1960 to 2009 in eight 265 representative countries are shown in Figure S10; we plotted these as totals from all sources, 266 as well as separately for industrial fuel use, industrial processes, and residential fuel 267 consumption. Per person PM2.5 emissions in residential sectors of all eight countries 268 decreased over time, with different rates primarily reflecting replacement of solid fuels (coal 269 and biomass fuels) with cleaner ones (e.g., electricity, liquified petroleum gas, natural gas, and 270 biogas). Additionally, decreases occurred on implementation of centralized heating systems in 271 cities.74 For example, coal consumption in the UK dropped from 48.6 to 24.5 Tg from 1960 to 272 1970,60 while coal used in urban households in China decreased from 65.6 to 37.5 Tg from 273 1995 to 2007.62 For the industrial sector, per person annual PM2.5 emissions in developed 274 countries peaked around 1960–1970, declining continuously since this time because of 275 enforcement of stricter regulations and improvements in abatement technologies.31 A drop in 276 energy demand associated with the shock therapy period60 in the former Soviet Union led to a 277 sharp decrease in PM2.5 emission. Meanwhile, increasing emissions are seen in China and 278 India, the two largest transition economies, because of rapid industralization. 279

Emission Intensity. 280

We calculated emission intensities, i.e., anthropogenic emission (excluding shipping and 281 aviation) per unit of fuel consumption (EIFUEL) or emission per unit of GDP (EIGDP) to 282 characterize how well emission was controlled and their respective fuel efficiencies. Globally, 283 the average EIGDP for PM2.5, PM10, and TSP values for 2007 data were 0.61, 0.84, and1.59 284 g/USD; while their average EIFUEL values were 90.9, 125, and 236 kg/TJ, respectively. Spatial 285 distributions for EIFUEL and EIGDP for PM2.5, PM10, and TSP are shown in Figure S11. Similar 286 spatial distribution patterns were observed for all three PM size categories. In developing 287 countries, both EIFUEL and EIGDP values were significantly higher than those in developed 288 countries. Using PM2.5 as an example, 2007 EIFUEL values for low, lower middle, upper 289 middle, and high income countries75 were 279, 197, 126, and 27.8 kg/TJ; while EIGDP values 290 were 3.5, 1,7, 1,1, and 0.15 g/USD, respectively. The differences in EIFUEL reflect extensive 291 use of biomass fuel and coal in residential sectors, as well as the lack of effective abatement 292 facilities in energy and industry sectors in developing countries.24,25,30 Dominant secondary 293 industries with relatively low energy efficiency and high energy consumption led to relatively 294 low EIGDP values in developing countries. For example, 86.6% of bricks53 and 78.5% of 295 cement38 are produced in developing countries. 296

Temporal trends for EIFUEL and EIGDP for the three PM size fractions for the global total, the 297 four economic categories listed above, as well as China, and the United States are shown in 298 Figure 2. Although emission intensities are significantly different for developing and 299 developed countries; they have all decreased at different rates since the 1960s. 300

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EIGDP decreased faster than EIFUEL for the majority of countries, reflecting the global reduction 301 in energy consumption per unit GDP. In general, emission intensities were negatively 302 correlated with income. There were a few exceptions, e.g., EIFUEL for PM10 and TSP for upper 303 middle income countries, including China, are higher than those for lower-middle and low 304 income countries. This likely reflects emissions from heavy industry in China. In contrast, for 305 PM2.5, even though ElFUEL for China was among the highest before 1986, it decreased at an 306 accelerted pace from 1986 to 2009 from 324 to 152; while values for low and lower middle 307 income countries decreased at a much slower rate. These changes reflect a rapid increase in 308 energy efficiency, penetration of abatement technologies in the industrial sector,25,45 and 309 replacement of solid fuels with cleaner ones in the residential sector.60,62 310

Size Distribution. 311

It is well documented that PM2.5 penetrates more deeply into the lungs compared with coarse 312 particles.76,77 Epidemiological surveys also suggest that PM2.5 is more strongly associated 313

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with health impacts.78 Using the available TSP, PM10, and PM2.5 data, we looked at the size 314 distributions of PM from various sources and countries. Figure 3 shows the relative 315 contributions of the three PM size fractions (PM2.5, PM2.5-10, and PM>10,) to primary PM 316 emission from major sources (A), and from a number of representative countries (B) in 2007. 317 The ratio of these three fractions was 0.485:0.129:0.386 (shown as a red triangle; Figure 3) 318 for all sources/countries. PM2.5 shows the most marked differences, with circles spread from 319 left to right with little vertical change (Figure 3A). The finest PM was from agricultural and 320 residential biomass fuel burning; their contributions to the total PM were above 90%. 321 Contributions of PM2.5 from wildfires, transportation, and residential coal combustion varied 322 from 65 to 88%, while industrial and energy sectors emitted relatively coarse PM, with a 323 PM2.5 fraction below 30%. Such differences should be taken into consideration in formulating 324 abatement strategies to protect human health. Differences among countries were also most 325 pronounced for PM2.5 (Figure 3B). Of the various representative countries, contributions of 326 PM2.5 to total PM were higher than 55% in the United States (67.7%), Germany (67.0%), 327 France (76.9%), Australia (69.5%), and less than 50% in China (31.0%), India (37.7%), South 328 Africa (33.9%), and Egypt (26.9%). Relative contributions of PM2.5 ranged between 32.2 and 329 82.8% in lower middle income countries like Central African Republic (67.7%), Democratic 330 Republic of the Congo (68.3%), Ethiopia (74.6%), and Mozambique (68.2%). These 331 differences were primarily related to different source profiles. The countries with highest 332 PM2.5 fractions were either those dominated by wildfires and deforestation, like Australia and 333 Central African Republic, or those with better emission abatement, like the United States and 334 Germany. Relatively low PM2.5 fractions were found in countries dominated by industrial 335 emissions, such as China. 336

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To examine geographical distribution patterns for PM size, fine particle ratios (Rfine, defined 337 as the ratios of PM2.5 to PM2.5-10) for all sources (except shipping and aviation) were 338 calculated for all grids (Figure S12); a similar map was compiled for the PM10/PM>10 ratio 339 (Figure S13). Although the spatial distributions of PM2.5 (Figure 1) and PM10 (Figure S3) 340 are similar, they are not identical (Figure S12). Relatively high Rfine values in North America 341 and Western Europe reflect the fact that coarse particles are largely removed by abatement 342 facilities.61 While relatively high Rfine values in South America, as well as central and 343 southern Africa are largely due to biomass combustion in both residential60 and 344 wildfire/deforestation79 sectors. Rfine values from southeast Asia were higher than those in 345 China because of biomass burning in both residential and agricultural sectors,34,60 while 346 significant contribution of heavy industry activity resulted relatively coarse PM in China in 347 comparison with other developing countries.24 348

Figure 4 shows the dynamic change in Rfine from 1960 to 2009 for total anthropogenic 349 emissions, as well as those from three other sectors in selected countries. Rfine values showed 350 an increase in most developed countries, since emission of coarser PM is easier to be removed 351 in abatement facilities. It also reflect that the relative contribution of power stations, which 352 emit more coarser PM than industrial processes, had decreased. While Rfine values for 353 developing countries decreased slowly, driven by increasing coal usage in the residential 354 sector from 1960 to 1990. A slow increase in Rfine in China and other developing countries 355

industry coal

other industry

processes

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0%

60%

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China

India

CAR

AustraliaEgypt

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all sources FranceEthiopiaCongo DR

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Figure 3. Relative contributions of PM2.5, PM2.5-10, and PM>10 to total PM in 2007. A) with

respect to various emission sources, and B) with respect to selected countries. The circle areas are proportional to annual emissions. The left and right panels are various sources and various countries or country categories, respectively.

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also reflected increasing abatement efforts. We expect that the relative contribution of 356 ultrafine particles will follow a same trend. Thus, this indicator is an important parameter in 357 health impact assessments, in addition to total emission and exposure. 358

We explored these differences by plotting Rfine against per person GDP for the United States, 359 Germany, China, and India as representative countries (Figure S14). For developed countries, 360 (United States and Germany), Rfine values have continually increased because of advances in 361 abatement technology and the shift toward cleaner energy. While Rfine values in developing 362 countries (China and India) have decreased owing to the rapid growth of heavy industry 363 during recent economic expansion. 364

Potential Health Impact. 365

The spatial covariance of PM emission and population densities intensifies the potential 366 health impact of emissions at the population level in regions with high anthropogenic 367 emissions. By modifying the method proposed by Shen et al.,40 we calculated RPHEs for 368 PM2.5 (Figure 5A) in 2007. The indicator is only a rough estimation of potential impact 369 without taking emission height, source type, PM size, meteorological conditions, and 370 transformation processes into consideration. Compared with the global PM2.5 emission map 371 (Figure 1), the RPHEs in hot emission areas, especially east, south, and southeast Asian 372 regions, were elevated. This is due to the fact that high population density led to strong 373 anthropogenic emissions, and subsequently to heavy exposure of large populations to these 374 emissions. Frequency distributions of normalized grid emissions and RPHEs also showed this 375 trend and the central tendency of RPHE shifted to right, reflecting elevated risk (Figure 5A). 376 In contrast, RPHEs were weak in South America, central/southern Africa, and Australia, 377 where strong emissions occurred in low population density regions related to wildfires and 378 deforestation. 379

Rfin

e R

fine

Year

Power Plants

Anthropogenic

0

3

6

1960 1970 1980 1990 2000 2010

0

1.5

3

1960 1970 1980 1990 2000 2010

Year

Industry

Residential

0

12

24

1960 1970 1980 1990 2000 2010

0

2

4

1960 1970 1980 1990 2000 2010

Brazil Russia GermanyThe United States

ChinaJapan India

The United Kingdom

Figure 4. Temporal trends of Rfine for total anthropogenic emission in selected countries from 1960 to 2009.

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Different sources contributed differently to RPHE as seen in Figure 5B, comparing total 380 emissions, RPHEs, and RPHE/e (RPHE per unit emission) of various sources. Motor vehicles 381 had highest values (6.06×10-14/g) because of their inherent link to people. While the RPHE/e 382 of navigation (2.55×10-16/g), which occurs in remote locations, was extremely low. Similarly, 383 although open burning of biomass (wildfire, deforestation, and agricultural waste) contributed 384 to approximately half of the total PM2.5 emission globally, RPHE/e values for these sources 385 were rather small, since they occurred relatively far away from populated areas. Activities 386 with relatively high RPHE/e values included power stations (3.85×10-14/g), industry 387 (2.75×10-14/g), and residential sources (1.76×10-14/g). In terms of their health effects, highest 388 RPHEs were found for emissions from power stations, cement production, residential crop 389 residue and firewood burning, linked to both their strong emissions and proximity to 390 receptors. 391

Differences in RPHE between developed and developing countries were magnified by higher 392 population density. For anthropogenic sources, the average RPHE of developing countries 393 (0.92) was 12.5 times that of developed countries (0.07), while the difference in average 394 emission was only 7.8 times. Similarly, the average RPHE/e value in developing countries 395

90°N

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uenc

ies (

%)

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RPHE

Emission

RPHE

Emission

RPH

E or

Em

issio

n (%

) R

PHE/eRPHE/e

Emission

RPHE

Figure 5. A) Geographical distribution of the Relative Potential Health Effect (RPHE)

indicator based on PM2.5 and population. The frequency distributions of normalized grid emission and RPHE are shown in the inserted panel. B) Relative contributions of various sources to total PM2.5 emission, RPHE, and RPHE/e (RPHE/emission). Sources are ranked from left to right, with decreasing values of RPHE/e.

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(2.6×10-14/g) was higher than that in developed countries (1.6×10-14/g), because of the 396 differences in source types and population proximity to these sources (e.g., the relatively high 397 contributions from residential solid fuel consumption in developing countries). 398

Based on the high-resolution maps generated in this study, RPHE and RPHE/e were 399 determined separately for urban and rural areas.34 Although average per person emission of 400 PM2.5 in rural areas was higher than that for urban areas, both RPHE and RPHE/e in urban 401 areas (0.73 and 5.7×10-14/g) were higher than for rural areas (0.26 and 9.6×10-15/g). Again, 402 this reflects highly concentrated sources and receptors in cities. This is especially true for 403 developed countries because of their higher urbanization rates. Thus, aggregation effects 404 related to rapid urbanization in the past decades and in the near future, has caused and will 405 continue to cause high health risks, despite reductions in the per person PM2.5 emission. 406 Studies to quantify the influence of urbanization on emissions and their associated health risks 407 using atmospheric transport and population exposure models are urgently needed. 408

Sub-nationally disaggregated fuel consumption data (PKU-FUEL-2007) was used for PM 409 inventories in this study;34 hence, spatial bias in emission data, and their associated health 410 risks (RPHEs) could be calculated with a reduced uncertainty by taking into account uneven 411 spatial distributions of per person fuel consumption and emission. Figure S15 compares 412 spatial distributions of RPHEs derived from both sub-nationally and nationally disaggregated 413 activity data for selected countries: China, the United States, India, Canada, and Australia. 414 The reduction in spatial bias of RPHEs was similar to that for emissions, but was enhanced 415 because emissions from adjacent grids are also taken into account in RPHE calculations. With 416 nationally disaggregated results, the overestimation or underestimation of RPHE were 417 obvious. In the case of China, RPHEs were overestimated in Tibet, Qinhai, northern Inner 418 Mongolia, and Guangdong; while they were underestimated in most other regions, especially 419 in the three northeastern provinces. It appears that the sub-national aggregation method 420 greatly reduced spatial bias, not only for emissions, but also for estimations of their 421 health-related effects. 422

ACKNOWLEDGMENTS 423 Funding for this study was provided by the National Natural Science Foundation of China 424 (41390240, 41130754, and 41390240) and the 111 Program. 425

ASSOCIATED CONTENT 426

Supporting Information. Materials, including our source list, EFs for various sources, 427 regression models for EFs, PM removal efficiency and parameters for technology split, PM 428 source profiles, geographical distributions for total and per person anthropogenic PM 429 emissions, relative differences between sub-nationally and nationally disaggregated emissions, 430 rural and urban anthropogenic PM2.5 emissions for 222 countries/regions, time trends for total 431 PM2.5 emissions from six sectors and eight source types, comparison with other inventories, 432 relative contributions of 12 global regions to PM2.5 emissions, time trends for PM2.5 emission 433 from motor vehicles, time trends for PM10 and TSP from all sources, time trends for per 434 person PM2.5 emissions in eight representative countries, geographical distributions of 435 emission intensity, Rfine and PM10/PM>10 ratios, relationship between Rfine and per person GDP, 436 spatial distributions of RPHEs derived from the sub-nationally and nationally disaggregated 437 activity data are all available free of charge at http://pubs.acs.org. 438

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