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Trends in chemical composition of global and regional population-weighted fine particulate matter estimated for 25 years Chi Li, Randall V Martin, Aaron van Donkelaar, Brian Boys, Melanie Hammer, Jun-Wei Xu, Eloise A. Marais, Adam Reff, Madeleine Strum, David Ridley, Monica Crippa, Michael Brauer, and Qiang Zhang Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b02530 • Publication Date (Web): 11 Sep 2017 Downloaded from http://pubs.acs.org on September 12, 2017

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Environmental Science & Technology

1

Trends

in

chemical

composition

of

global

and

regional

2

population-weighted fine particulate matter estimated for 25 years

3 4

Chi Li1,*, Randall V. Martin1,2, Aaron van Donkelaar1, Brian L. Boys1, Melanie S. Hammer1, Jun-

5

Wei Xu1, Eloise A. Marais3, Adam Reff4, Madeleine Strum4, David A. Ridley5, Monica Crippa6,

6

Michael Brauer7, and Qiang Zhang8

7 8

[1] Department of Physics and Atmospheric Science, Dalhousie University, Halifax, NS, Canada

9

[2] Harvard-Smithsonian Center for Astrophysics, Cambridge, MA, USA

10

[3] School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham,

11

UK

12

[4] U.S. Environmental Protection Agency, Research Triangle Park, NC, USA

13

[5] Department of Civil and Environmental Engineering, Massachusetts Institute of Technology,

14

Cambridge, MA, USA

15

[6] European Commission, Joint Research Centre (JRC), Directorate for Energy, Transport and Climate,

16

Air and Climate Unit, Via E. Fermi 2749, I-21027 Ispra (VA), Italy

17

[7] School of Population and Public Health, The University of British Columbia, Vancouver, BC,

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Canada

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[8] Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System

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Science, Tsinghua University, Beijing 100084, China

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*Correspondence to: Chi Li ([email protected])

22

1

ACS Paragon Plus Environment


23

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TOC/ABSTRACT ART Global population−weighted mean PM2.5 composition 35 Sulfate

Concentration (µg m-3)

OA

BC

Nitrate Dust

Ammonium Sea salt

28

21

14

7

0

24

1990

1995

2000

2005

2010

25

ABSTRACT We interpret in situ and satellite observations with a chemical transport model (GEOS-

26

Chem, downscaled to 0.1˚ × 0.1˚) to understand global trends in population-weighted mean chemical

27

composition of fine particulate matter (PM2.5). Trends in observed and simulated population-weighted

28

mean PM2.5 composition over 1989-2013 are highly consistent for PM2.5 (-2.4 vs. -2.4 %/yr), secondary

29

inorganic aerosols (-4.3 vs. -4.1 %/yr), organic aerosols (OA, -3.6 vs. -3.0 %/yr) and black carbon (-4.3

30

vs. -3.9 %/yr) over North America, as well as for sulfate (-4.7 vs. -5.8 %/yr) over Europe. Simulated

31

trends over 1998-2013 also have overlapping 95% confidence intervals with satellite-derived trends in

32

population-weighted mean PM2.5 for 20 of 21 global regions. Over 1989-2013, most (79%) of the

33

simulated increase in global population-weighted mean PM2.5 of 0.28 µg m-3yr-1 is explained by

34

significantly (p < 0.05) increasing OA (0.10 µg m-3yr-1), nitrate (0.05 µg m-3yr-1), sulfate (0.04 µg m-3yr-

35

1

36

weighted mean PM2.5 over populous regions of South Asia (0.94 µg m-3yr-1), East Asia (0.66 µg m-3yr-1),

37

Western Europe (-0.47 µg m-3yr-1) and North America (-0.32 µg m-3yr-1). Trends in area-weighted mean

38

and population-weighted mean PM2.5 composition differ significantly.

) and ammonium (0.03 µg m-3yr-1). These four components predominantly drive trends in population-

39 40

INTRODUCTION

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Atmospheric aerosols have major roles in air quality,1, 2 visibility3-5 and climate.6-8 Particles with an

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aerodynamic diameter of 2.5 µm or less (PM2.5) are a leading risk factor for global morbidity and

43

mortality.9-12 Not only does over 85% of the world’s current population live where annual estimated

44

PM2.5 is above the World Health Organization (WHO) guideline of 10 µg/m,13, 14 but recent cohort

45

studies also reveal an association of mortality rates with long-term exposure to PM2.5 concentrations at

46

levels below the WHO guideline,15-17 implying the need for further mitigation efforts.18 Changes in 2


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PM2.5 mass concentration are driven by changes in its chemical composition due to variations in

48

emissions and atmospheric processes.1, 19, 20 Understanding trends in PM2.5 components can help inform

49

source contributions and future mitigation efforts. Since the 1980s, regulations in developed regions

50

have dramatically reduced emissions of primary particles and precursor gases, while the rapidly growing

51

economies of developing countries have led to steeply rising energy consumption and pollutant

52

emissions.20-24 Given the established impacts of PM2.5 on human health, improved understanding of how

53

these changes affect trends in global PM2.5 burden and composition is warranted.

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A few regions have long-term in situ measurements of PM2.5 and its chemical composition. These

55

measurements revealed regionally coherent decreases over North America in PM2.5,25-27 ammonium,28-30

56

sulfate,25,

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sulfate,28, 37, 38 nitrate28 and PM2.5.5, 27, 38 SI Table 1 summarizes these reported trends. Measurements in

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these regions, where emissions are relatively well known, offer valuable information to test the

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representation of PM2.5-related atmospheric processes in global models. Over regions without long-term

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in situ data, alternative proxies of PM2.5 provide complementary trend information (SI Table S2), such

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as global trends in satellite observations of columnar aerosol optical depth (AOD).39-41 These satellite

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observations have been related to ground-level PM2.5 by chemical transport modeling42, 43 or statistical

63

models.44, 45 More broadly, models facilitate interpretation of these ground-based and satellite-based

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observations to understand sources and processes affecting these trends, and their relation with chemical

65

composition.19, 46

28-32

nitrate,28,

30

organic aerosol30,

33-35

and black carbon,28,

30, 33, 36

and over Europe in

66

Chemical transport models (CTMs) have been widely used for global characterization of aerosol

67

spatiotemporal variation, with the ability to quantify the contributions from different chemical

68

composition and sources. Recent regional simulations largely reproduced observed aerosol trends over

69

North America and Europe, affirming their value for interpretation.28, 30, 34, 37 But the current generation

70

of global models suffers from relatively coarse resolution that introduces spatial misalignment between

71

population density and modeled PM2.5 that inhibits direct assessment of PM2.5 exposure using models

72

alone. Case studies indicate that mortality estimates due to PM2.5 at a typical resolution of current global

73

simulations could be systematically lower compared to estimates at finer resolutions.47, 48 Thus in this

74

study we combine the attributes of satellite observations at fine resolution with process-level

75

information offered by CTM simulations. We spatially redistribute the CTM-modeled PM2.5

76

composition with satellite-based PM2.5 estimates at 0.1˚ resolution, to produce a 25-year assessment of

77

global population-weighted PM2.5 chemical composition from 1989 to 2013. We evaluate this 3



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78

downscaled simulation versus in situ observations where available. Then we apply this downscaled

79

simulation to investigate global changes in population-weighted mean PM2.5 chemical composition, to

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further understand the intrinsic drivers of trends in PM2.5 exposure.

81 82

MATERIALS AND METHODS

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Observations and complementary data

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We collect long-term observation data about PM2.5 and its chemical composition over North America

85

and Europe, and the global ground-based PM2.5 measurements collected for the Global Burden of

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Disease Study (GBD)14. Population data at 0.1˚ × 0.1˚ resolution are also used for exposure estimation.

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Details on data selection and processing are described in the Supporting Information (SI).

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For model downscaling to a resolution more relevant to population exposure, we use the global

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satellite-based PM2.5 estimates at 0.1˚ × 0.1˚ resolution13 that merged satellite AOD retrievals from 7

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different algorithms inversely weighted by their errors against AERONET, converted AOD to PM2.5

91

using simulated PM2.5-AOD relationships, and then statistically fused (geographically weighted

92

regression) these PM2.5 estimates with ground-based measurements. We use the 5-year average data

93

between 2008-2012 (referred to as Meansat) when more ground-based PM2.5 records (over 4000) are

94

available for statistical calibration.

95

We evaluate simulated trends in global PM2.5 using mostly independent satellite-based PM2.5 trends

96

(referred to as Trendsat). The 1998-2013 annual variations in global PM2.5 were inferred from the

97

SeaWiFS and MISR sensors that have long-term calibration stability.42, 49 PM2.5 trends for 1999-2012

98

from these estimates show unbiased consistency with in situ measurements over the eastern US.42 The

99

absolute concentrations of Trendsat are also calibrated to Meansat to match the 2008-2012 mean while

100

preserving the original trends. The annual mean PM2.5 data to calculate Meansat and Trendsat are obtained

101

directly from the publicly available archive.50 Although Meansat and Trendsat contain some common

102

elements (e.g. both using AOD from SeaWiFS and MISR, and PM2.5-AOD relationship in GEOS-Chem),

103

we attempt to isolate their independent information by focusing on the spatial distribution of absolute

104

concentrations from Meansat, and on temporal variation from Trendsat to evaluate the downscaled

105

simulation.

4


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GEOS-Chem simulation and downscaling

107

We use the GEOS-Chem CTM (version 11-01; http://www.geos-chem.org), including updated emission

108

inventories and meteorological data from a consistent reanalysis (MERRA-2), to simulate the global

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evolution of PM2.5 chemical composition over 1989-2013. More details about the simulation and

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historical emissions are provided in the SI.

111

The current generation of global models does not sufficiently resolve spatial variation in PM2.5 to

112

adequately estimate population exposure.47, 48 Thus we follow Lee et al.51 to downscale the simulation to

113

0.1˚ × 0.1˚ resolution to match the PM2.5 magnitude and spatial variation in the 2008-2012 Meansat. This

114

downscaled simulation (referred to as Simds) does not modify the modeled fraction or the simulated

115

relative temporal variation of PM2.5 composition, since we apply to all years the same scale factors

116

(namely, the 0.1˚ × 0.1˚ spatial map of ratios in the 2008-2012 mean of Meansat versus the simulation).

117

SI Table S3 contains a comparison of annual PM2.5 from Simds versus the global ground-based database

118

collected for GBD. Globally and regionally, fine-scale information from Meansat improves the spatial

119

representation of PM2.5 in Simds compared to the pure simulation.

120

Trend analysis and evaluation

121

We summarize time series as linear trends to aid presentation. Following commonly adopted methods in

122

previous studies (e.g. SI Tables S1 and S2), we analyze data over a long time period (i.e. over ~10 years)

123

to reduce random errors and to detect systematic trends. In addition to trends over 1989-2013, short-term

124

trends over 2002-2013 are included to summarize more recent trends during the periods of denser in situ

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observations, and these over 1998-2013 are also calculated to facilitate comparison with Trendsat. Details

126

on the trend analysis are contained in the SI.

127 128

RESULTS AND DISCUSSION

129

Emission trends

130

SI Figure S2 shows time series of annual area-weighted mean emissions of major aerosol and precursor

131

species over GBD regions, and SI Table S5 lists trends in these regional emissions over 1989-2013.

132

Substantial reductions in anthropogenic and total emissions of SO2, NOx, OC and BC are found over

133

North America, Asia-Pacific and Europe, whereas increases in these species are substantial over Asia.

134

Reductions are dramatic in SO2, OC and BC emissions before ~1996, with a transitional slow down 5



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afterwards over Central Asia, Central Europe and Eastern Europe.23, 24 SO2 emissions over East Asia

136

also show a transition from increasing to decreasing at ~2006.21,

137

increasing or insignificant trends except over Europe where NH3 emissions decrease. Strong inter-

138

annual variation in OC and BC emissions occurs over biomass burning regions such as Eastern Europe

139

(including Siberia), Southeast Asia, Tropical Latin America and Central Sub−Saharan Africa, and in

140

mineral dust emissions from desert regions.

141

In situ trends

142

Figure 1 shows the spatial distribution over North America of annual trends in PM2.5 and its composition

143

from the downscaled simulation (Simds), overlaid with observed trends for two periods. The

144

observations show significant (p < 0.05) decrease in population-weighted mean (PWM) PM2.5 of -0.26

145

µg m-3yr-1 over 1989-2013, increasing in magnitude to -0.44 µg m-3yr-1 over 2002-2013, with larger

146

decreases in the east than in the western interior. The collocated Simds well reproduces the composite

147

PWM trends within 0.06 µg m-3yr-1. Steeper reductions in PWM sulfate, nitrate and ammonium at

148

measurement sites over 2002-2013 than over 1989-2013 are indicated by both the observations and

149

Simds, due to inclusion of more urban sites from EPA_CSN as well as stronger emission reductions in

150

recent years (SI Figure S2). These secondary inorganic aerosols (SIA) largely drive the PM2.5 decreases

151

over North America, with significant decreases (p < 0.05) in PWM concentration over 1989-2013 in

152

both the observations (-0.31 µg m-3yr-1 or -4.3 %/yr) and Simds (-0.28 µg m-3yr-1 or -4.1 %/yr), and more

153

pronounced decreases over 2002-2013 in both the observations and Simds. The Simds shows relatively

154

weaker performance for nitrate trends, similar to recent simulations,28, 53, 54

52

NH3 emissions generally have

30

possibly due to bias in

55

155

simulated HNO3,

uncertainties in NH3 emissions, and non-linear sensitivity of nitrate partitioning

156

to SO2, NOx and NH3 emissions.56, 57 The observed and simulated annual decreases in PM2.5 and SIA are

157

largely consistent with trends in winter and summer (SI Figure S3-S4). Steeper decreases in sulfate and

158

ammonium are observed in summer than in winter, a seasonal variation that is captured by Simds,

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reflecting faster photochemistry in summer that more closely connects emission changes to local

160

concentration changes.58 Meanwhile nitrate generally has higher concentration and stronger absolute

161

decreases in winter when colder temperatures favor its formation.57

162

Observed significant (p < 0.05) reductions in PWM OA partially drive the PM2.5 trends, and are

163

consistent with the collocated Simds (-0.16 vs. -0.11 µg m-3yr-1) over 1989-2013. This reduction in OA

164

reflects different seasonal mechanisms in Simds. Over winter (SI Figure S3), natural OA sources are 6


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weak and simulated OA trends are similar to the annual trends, driven by reductions in anthropogenic

166

OC emissions (SI Figure S2). Over summer (SI Figure S4), observed decreases in OA over the

167

southeastern US are well represented in Simds, due primarily to inclusion of an aqueous formation

168

mechanism of isoprene SOA,59 which yields an SOA decrease driven by reductions in sulfate,34

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consistent with observational evidence.60, 61 Meanwhile over 2002-2013, Simds underestimates observed

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PWM OA trends, driven by weaker wintertime OA trends in the simulation relative to observations (SI

171

Figure S3), due to increasing anthropogenic wood burning62 and OC emissions in the east after 2008.

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BC has significant (p < 0.05) decreases (-2.5 – -4.3 %/yr) in both the observations and Simds

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because of reductions in BC emission, despite small absolute changes. Finally, weak and mostly

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insignificant trends in PWM dust and sea salt are observed and simulated.

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SI Figure S5 shows the spatial distribution of trends in sulfate and PM2.5 over Europe. Significant

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(p < 0.05) reductions in annual PWM sulfate observed over 1989-2013 (-0.15 µg m-3yr-1) and over 2002-

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2013 (-0.12 µg m-3yr-1) are seasonally consistent with Simds. The slower sulfate decline and fewer

178

significant trends over 2002-2013 in both the observations and Simds reflect slower SO2 emission

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reductions (SI Figure S2), consistent with previous investigations.37, 38 The PWM PM2.5 over all sites

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trend downward at p < 0.1 in observations and Simds over 2002-2013, driven mostly by sites over

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western Europe while most eastern sites have insignificant trends. The stronger reductions in observed

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PWM PM2.5 are disproportionately influenced by a single site near the Po Valley of Italy (IT0004R), and

183

the paucity of sites across Europe. Excluding this site reduces the observed annual PWM trends by 75%

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(-0.15 µg m-3yr-1 or -1.4 %/yr), yielding better agreement with the collocated Simds (-0.23 µg m-3yr-1 or -

185

1.6 %/yr).

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SI Table S6 summarizes the network-composite PWM concentrations and trends between Simds

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and in situ data after spatial and temporal collocation. Observed and simulated PWM trends agree within

188

their 95% confidence intervals (CIs) for 33 of the 35 cases. All components except dust and sea salt

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have decreasing and significant trends in observations, which are mostly well reproduced by Simds. The

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importance of SIA and OA in driving PWM PM2.5 trends over North America is apparent in both

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observations and Simds. The Simds underestimates observed OA and its trends in IMPROVE and NAPS,

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which is an unresolved issue in current CTMs, likely due to missing anthropogenic sources and

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uncertainties in SOA modeling.63 The Simds also underestimates PWM BC, partially due to

7



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heterogeneity of BC64 not resolved in Simds. Our estimates of OA and BC contribution to PWM PM2.5

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and its trends likely represent lower bounds.

196

In summary, Simds generally well represents observed trends in PM2.5 and its composition across

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North America and Europe, where emissions are relatively well known. We find greater OA influence

198

than in previous GEOS-Chem simulations30, 42 due to stronger trends in OC emissions,22, 65 and to an

199

aqueous isoprene SOA mechanism.34 The overall consistency of Simds with measurements supports its

200

applicability to represent atmospheric processes to understand the chemical components driving PM2.5

201

trends. We next evaluate the performance of Simds where emissions are less well known.

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Global trends in PM2.5

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SI Figure S6 shows global PM2.5 trends over 1998-2013 in Trendsat and Simds. The two datasets exhibit a

204

high degree of consistency in the spatial distribution of trends and significance. Both datasets show

205

significant (p < 0.1) increases over eastern Africa, India, China and the Middle East, and decreases over

206

North America, western Africa and Europe. Regional mismatches are found from forest fires over boreal

207

Canada and Russia, with little impact on regional PWM trends due to low population. The spatial

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distribution of trends from the two datasets are significantly correlated (r = 0.67, slope = 0.75), with

209

even higher correlation (r = 0.85, slope = 0.76) where both datasets suggest significant trends at p < 0.1.

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Both Trendsat and Simds show significant (p < 0.05) increases in global PWM PM2.5 (0.53 vs. 0.37 µg m-

211

3

yr-1), with overlapping 95% CIs.

212

SI Table S7 lists the annual PWM PM2.5 and trends over 21 GBD regions for 1998-2013. The

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downscaling substantially improves the accuracy of PWM PM2.5 over several regions e.g. South Asia,

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Southern Latin America and Central Asia, where the original simulated PWM PM2.5 underestimates the

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satellite-based estimates by more than a factor of 2. This improvement reinforces the value of bringing

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together the satellite observations and simulation. The annual trends from Trendsat and Simds have

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overlapping 95% CIs for 20 regions (except Oceania with ~0.1% of global population), and consistency

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in their significance (at 90% confidence) for the first 15 regions that comprise 91% of the global

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population. Both datasets also suggest the most pronounced and significant (p < 0.05) increases over

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South Asia (0.97 vs. 0.99 µg m-3yr-1) and East Asia (1.32 vs. 0.86 µg m-3yr-1), in contrast with decreases

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over High-income North America (-0.31 vs. -0.39 µg m-3yr-1), Western Europe (-0.23 vs. -0.23 µg m-3yr-

222

1

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and South Asia drive increases in global PWM PM2.5. Two other developing regions (Southeast Asia

) and Central Europe (-0.23 vs. -0.30 µg m-3yr-1). Increases over the densely populated regions of East

8


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and Southern Sub-Saharan Africa) and two arid regions (Eastern Sub-Saharan Africa & North Africa

225

and Middle East) also show significant and consistent increases (p < 0.1) in both datasets. This overall

226

agreement in regional PWM trends provides further confidence in the ability of Simds to resolve trends

227

on a global scale. We further extend the analysis of Simds to long-term global trends in PM2.5

228

composition in the context of Trendsat.

229

Global trends in PM2.5 composition

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Figure 2 shows trends in PM2.5 mass and composition from Simds over 1989-2013. The spatial pattern of

231

simulated PM2.5 trends is similar (r = 0.81) to that over the shorter 1998-2013 period, with a slightly

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weaker global PWM trend of 0.28 µg m-3yr-1, driven by stronger reductions over Europe and weaker

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increases over Asia in the 1990s. OA makes the largest individual contribution to the increase in global

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PWM PM2.5, with a significant PWM trend of 0.10 µg m-3yr-1, driven by substantial increases (> 0.2 µg

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m-3yr-1) over eastern China and India. This is despite substantial decreases (< -0.2 µg m-3yr-1) in OA

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over less populous tropical biomass burning regions driven by trends in the fire inventory (SI Figure

237

S2). Sulfate and ammonium have similar spatial distribution (r = 0.93) in annual trends, with

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pronounced increases (> 0.1 µg m-3yr-1) over India and China and pronounced decreases (< -0.1 µg m-

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3

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global PWM trends in sulfate (0.04 µg m-3yr-1) and ammonium (0.03 µg m-3yr-1) are also driven by

241

densely populated regions of South and East Asia. The global PWM nitrate trend of 0.05 µg m-3yr-1

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exhibits the most pronounced increases (> 0.1 µg m-3yr-1) over China and northern India, and decreases

243

(< -0.1µg m-3yr-1) over the US and Europe. BC trends have similar spatial distribution (r = 0.78) with

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OA trends, with smaller yet significant contribution (0.02 µg m-3yr-1) to global PWM PM2.5 trends. Dust

245

trends exhibit a dipole with decreases over the western Sahara (< -0.2 µg m-3yr-1) and increases over the

246

eastern Sahara and Arabic Peninsula (> 0.3 µg m-3yr-1); its global PWM trend of 0.03 µg m-3yr-1 reflects

247

these compensating effects. Sea salt trends are minor and insignificant. Trends in PM2.5 composition are

248

similar over the period of satellite observations (SI Figure S7), with largely consistent global PWM

249

trends albeit smaller areas of significant trends.

yr-1) over North America and Europe, driven by trends in regional SO2 emissions (SI Figure S2). The

250

Simds shows an increase in global PWM PM2.5 from 26.7 µg m-3 in 1990 to 32.0 µg m-3 in 2013,

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consistent with the GBD 2013 study (26.4 µg m-3 in 1990 and 31.8 µg m-3 in 2013).14 These trends are

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primarily of anthropogenic origin, consistent with recent studies on PM2.5 air quality20,

253

AOD.

19, 68

42, 66, 67

and -3

SIA and OA account for 79% of the significant increase in global PWM PM2.5 (0.28 µg m yr9



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254

1

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contrast, PM2.5 trends are driven by OA from open fires over tropical rainforests and by mineral dust

256

over North Africa and the Arabic Peninsula. Reddington et al.69 reported recent air quality

257

improvements driven by decreasing OA due to fewer deforestation fires over the Amazon. Boys et al.42

258

attributed the satellite-based PM2.5 trends over the Arabic Peninsula to dust. We further examine the

259

regional driving components based on analysis of regional time series in the next section.

260

Regional trends in populated-weighted mean PM2.5 composition

), and drive PWM PM2.5 trends over densely populated regions (North America, Europe and Asia). In

261

Figure 3 summarizes annual concentrations and trends in PWM PM2.5 and its composition over 21

262

GBD regions from the downscaled simulation and satellite-based estimates. Below we discuss regional

263

trends over 1989-2013.

264

Densely populated regions

265

The regions of South and East Asia have the largest increases in PWM PM2.5, consistent with long-term

266

increases in aerosol loading from satellite AOD39, 40 and visibility observations70, 71 (SI Tables S1 and

267

S2). SIA and OA account for over 90% of these increases. OA is the leading contributor to PWM PM2.5

268

increases over South Asia and the second largest contributor over East Asia. The OA trends over these

269

two regions largely determines its role as the leading contributor to global PWM PM2.5 increases.

270

Over South Asia, OA makes the largest contribution to the annual PWM PM2.5 concentration (38%)

271

and trends (43%), consistent with measurements of high OA fractions in PM2.5,72,

73

272

extensive agricultural and biofuel burning in this region. The residential sector, primarily biofuel

273

burning, accounts for the largest (39%) sectoral fraction of energy consumption in India.21 Lacey et al.74

274

estimated that eliminating residential solid fuel use in India and Bangladesh could avoid ~60 thousand

275

premature deaths from outdoor PM2.5 each year, ~90% of which is associated with OA, illustrating the

276

value of reducing OC emissions across this region.72, 75

reflecting the

277

Over East Asia, nitrate has the largest trends, contributing to 35% of the annual increase in PWM

278

PM2.5, due to continuously increasing NOx and NH3 emissions and to decreasing SO2 emissions since

279

2006 (SI Figure S2). Over 2006-2013, PWM PM2.5 in East Asia insignificantly (p > 0.1) decreases in

280

Trendsat and Simds, despite significantly (p < 0.05) increasing nitrate (0.28 µg m-3yr-1). This recent

281

reversal of PM2.5 trends over China is consistent with other investigations44, 76 (SI Tables S1 and S2),

282

and implies the role of nitrate in driving recent and future PM2.5 trends over China77-79 after recent 10


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reductions in SO2 and sulfate.52 China has made progress in reducing NOx emissions after 2011 as

284

observed by satellite observations,80 but reducing nitrate could also benefit from reductions in NH3

285

emissions.56, 79, 81, 82

286

Southeast Asia also has significant increases in PM2.5 especially over the Mainland (e.g. SI Figure

287

S6). Sulfate and ammonium drive the PWM PM2.5 trends, accounting for over 65% of the simulated

288

PWM PM2.5 increase, consistent with the steady increases in fuel consumption (e.g. 275% increase in

289

coal burning from 1990 to 2003), and in SO2 and NH3 emissions over this region.83, 84 OA accounts for

290

45% of PWM PM2.5, revealing the importance of open and residential burning.85 But OA trends are

291

insignificant due to strong meteorologically driven inter-annual variation of fires.86

292

Three populous regions have significant decreases in PWM PM2.5 in both the simulation and

293

satellite-based estimates: Western Europe, High-income North America and Central Europe. Over the

294

two European regions, more than 80% of PWM PM2.5 trends are explained by SIA, with especially

295

pronounced decreases in the 1990s when SO2 emissions decreased more rapidly (SI Figure S2). Nitrate

296

is a prominent component over Europe that comprises over 25% of PWM PM2.5 concentrations.

297

Simulations and observations also indicate that the nitrate contribution to particle mass over Europe

298

usually is similar to sulfate except during summer.28, 87, 88 Simultaneous reductions in NOx and NH3

299

emissions (SI Figure S2) yield a pronounced nitrate decrease, accounting for 15-19% of PWM PM2.5

300

trends. Over High-income North America, the driving roles of sulfate and OA, accounting for 69% of

301

PWM PM2.5 decreases, are consistent with our previous discussions based on in situ data, and with an

302

extensive body of literature (SI Table S1).

303

For these 6 populated regions, PM2.5 chemical components generally exhibit consistent

304

contributions to PWM PM2.5 trends in all seasons due to seasonally consistent emission changes (SI

305

Figure S8-S11). Dust invasion in spring and summer to South Asia, and in spring to East Asia results in

306

seasonal enhancements in the dust fraction in PWM PM2.5 concentration, but makes insignificant

307

contribution to its trends. East Asia has higher PWM PM2.5 and OA concentrations and increases in

308

winter with intensive coal and wood burning for domestic heating, and in fall with enhanced agricultural

309

burning.89, 90 Europe has the highest PWM PM2.5 in winter driven by higher nitrate. North America has

310

higher PM2.5 in summer associated with OA and sulfate.

11



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311

Arid regions

312

Trends in SIA and OA are weak or variable over the two largest desert regions (North Africa and Middle

313

East, and Western Sub-Saharan Africa), where dust explains most of the annual PWM PM2.5

314

concentrations (> 65%) and trends (> 75%). The opposite regional trends and significance levels are

315

well captured by Simds over 1998-2013, and are consistent with prior trend analysis of dust91, 92 and

316

AOD93,

317

dominant but its importance is less pronounced (accounting for 33% of PWM PM2.5 and 57% of its

318

increase) due to steady increases in SIA, OA and BC associated with anthropogenic emissions (SI

319

Figure S2). Over Southern Sub-Saharan Africa with weaker dust emissions and stronger increases in

320

anthropogenic emissions, the dust contribution is negligible compared to the driving role of ammoniated

321

sulfate.

94

over these two regions (SI Table S2). Over Eastern Sub-Saharan Africa, dust remains

322

Dust exhibits strong meteorologically driven inter-annual variation and seasonality, thus its trends

323

exhibit varying significance levels and contributions to PWM PM2.5 trends in different periods and

324

seasons over these 4 regions (SI Figure S8-S11). For example, the simulation indicates highest dust

325

intensity in winter and lowest in summer over Eastern Sub-Saharan Africa, where dust is the dominant

326

driver of PWM PM2.5 increases in winter, while OA becomes the leading contributor in summer.

327

Tropical biomass burning regions

328

PWM PM2.5 changes over Tropical Latin America and Central Sub-Saharan Africa are driven by OA

329

from biogenic sources and biomass burning. Insignificantly (p > 0.1) decreasing PWM PM2.5 in the

330

satellite-based estimates over 1998-2013 are reproduced by the simulation, consistent with recent

331

decreases in AOD69, 95 (SI Table S2) and burning intensities69, 96, 97 over these regions. Similar to the

332

desert regions, strong inter-annual variation of fire intensity97, 98 leads to variable tendencies in different

333

periods in PWM PM2.5 and OA over these two regions. Trends in OA and PM2.5 over 1989-2013 are

334

more uncertain for regions with PM2.5 dominated by open fire sources, due to weaker constraints on fire

335

emissions from satellite observations in early years (1989-1996).99 The PWM PM2.5 trends are solely

336

driven by OA over Central Sub-Saharan Africa for all seasons, while SIA plays the dominant role over

337

Tropical Latin America in winter (SI Figure S8) and spring (SI Figure S9) when the intensity of burning

338

is low.

12


Page 13 of 27


339

Other regions

340

Insignificant PWM PM2.5 trends are consistently indicated by both the satellite-based estimates and the

341

simulation over High-income Asia Pacific, Southern Latin America and Australasia. Increasing pollutant

342

transport from China5, 100, 101 compensates for the effects of decreasing local emissions (SI Figure S2)

343

and partially explains the insignificant trends over High-income Asia Pacific in recent years. Over the

344

two small dust source regions in the Southern Hemisphere, Simds attributes the insignificant trends in

345

PWM PM2.5 to counteracting effects of different components, i.e., increasing SIA and dust in Southern

346

Latin America and increasing SIA in Australasia counteracts decreasing OA driven by recent decreases

347

in open burning91.

348

The remaining 6 regions, containing 9% of the global population, exhibit less consistency in the

349

magnitude and significance (at p < 0.1) of PWM PM2.5 trends in Trendsat and Simds. Over Central Latin

350

America and Eastern Europe, mismatches in certain years (e.g. 1999-2000 over Central Latin America

351

and 2005-2006 over Eastern Europe) are partially explained by uncertainties in open fire inventories.

352

Over Central Asia and Andean Latin America, the satellite-based estimates suggest significant increases

353

(p < 0.05) in PWM PM2.5 over 1998-2013, consistent with significant SIA increases in the simulation

354

over both regions. Meanwhile the simulation has insignificant trends in total PWM PM2.5 due to

355

compensating effects of other components (e.g. dust over Central Asia and OA over Andean Latin

356

America). The Caribbean and Oceania have strong influences from external transport in Simds. For

357

example, the high dust fraction (31%) in annual PWM PM2.5 over the Caribbean reflects transport from

358

the western African coast in spring and summer.94, 102

359

Contrasts with area-weighted trends

360

Many long-term analyses of regional aerosol composition have focused on area-weighted mean (AWM)

361

or multi-station mean values.19, 26, 28, 32, 37 We find significantly different concentrations and trends in

362

PWM (Figure 3) and AWM (SI Figure S12) PM2.5 composition over the 21 GBD regions. Over

363

populated regions, all PWM trends exceed AWM trends, since higher PM2.5 concentrations generally

364

coexist with higher population densities. For example, PWM PM2.5 concentrations and trends are about

365

twice those of AWM PM2.5 over High-income North America and Eastern Europe. Similarly, SIA

366

generally has higher contributions to PWM PM2.5 and trends than in the AWM case over most regions.

367

In contrast, the contribution of natural dust to PWM PM2.5 concentration is smaller than in the AWM

368

case, especially over regions with both broad desert and populated cities (e.g. Australasia and the 13



Page 14 of 27

369

African arid regions), because the dustiest regions generally have lower population density. Similarly,

370

the contributions of OA to PWM PM2.5 and its trends are also smaller in the PWM cases than in the

371

AWM cases over biomass burning regions (e.g. Tropical Latin America). The global AWM PM2.5 trend

372

of 0.06 µg m-3yr-1 is distinctively weaker than the significant PWM trends of 0.53 µg m-3yr-1 in Trendsat.

373

In summary, population weighting yields significant differences from area weighting, with implications

374

for conclusions about the dominant PM2.5 components and their contributions to PM2.5 trends.

375

Overall, we found that Simds reproduced the significant reductions in PWM concentrations of PM2.5

376

chemical composition across North America and Europe, and exhibited consistency with satellite-based

377

estimates of PM2.5 trends. Globally, population weighting, along with updated emission inventories and

378

OA processes contributed to our finding that OA is the leading contributor to the global increase in

379

PWM PM2.5 over 1989-2013, followed by nitrate and sulfate. Our analysis also identified regional

380

differences in PM2.5 trends and its key drivers, implying the need for ongoing attention to regional

381

variation in emissions and chemistry. Despite the insights from this study, further work is needed in key

382

areas. Finer resolution simulations would better resolve fine-scale and nonlinear processes affecting

383

PM2.5 production and loss. Outstanding issues in current CTMs (e.g. underestimation of OA63,

384

uncertainties in inter-annual variation in open fire emissions, and regional biases in dust103 and nitrate54)

385

continue to warrant attention. Ongoing work to reduce uncertainties in emission inventories, especially

386

beyond North America and Europe would further improve the accuracy of trends in PM2.5 composition

387

and their contributions to PM2.5 trends. Future developments of satellite remote sensing of aerosol

388

properties and accumulation of long-term data in emerging global PM2.5 speciation networks73 would

389

offer valuable observational constraints.

390 391

ASSOCIATED CONTENT

392

Supporting information: Detailed description of literature results, data processing, GEOS-Chem

393

simulation, historical emission inventories and trend analysis method. Seven supplemental tables and 12

394

figures are included to support the main text.

395 396

AUTHOR INFORMATION

397

Corresponding Author: *Phone: +19024832832; Email: [email protected] 14


Page 15 of 27

398


Notes: The authors declare no competing financial interest.

399 400

ACKNOWLEDGEMENT

401

This work was supported by the Natural Science and Engineering Research Council of Canada. Chi Li

402

was partially supported by a Killam Predoctoral Scholarship, and an ACEnet Research Fellowship. We

403

acknowledge

404

(http://views.cira.colostate.edu/fed/DataWizard/), the Environment and Climate Change Canada

405

(http://maps-cartes.ec.gc.ca/rnspa-naps/data.aspx?lang=en) and the World Data Centre for Aerosol

406

(http://ebas.nilu.no) for hosting the in situ data. We also thank the developers of the emission inventories

407

and meteorological data used in this study.

the

Federal

Land

Manager

408

15


Environmental

Database


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788

FIGURE CAPTIONS

789

Figure 1. Spatial distribution of long-term (1989-2013) and short-term (2002-2013) annual trends in

790

PM2.5 and its chemical composition from the downscaled simulation (Simds, background) and in situ

791

observations (symbols, with different shapes representing different networks). The significance (i.e. p

792

value) of derived trends over land is indicated by the opaqueness of the colors. The color scale of trends

793

for PM2.5 (saturated at ±1.0 µg m-3yr-1) and its chemical composition (saturated at ±0.4 µg m-3yr-1)

794

differ. PM2.5 is simulated at 35% RH and its individual components are presented without aerosol water,

795

for consistency with observational protocols. Composite trends (in µg m-3yr-1, with relative trends

796

in %/yr in brackets) in population-weighted mean concentration from all sites are shown for

797

observations (Obs) and spatiotemporally collocated Simds. Trends with >90% significance (p < 0.1) are

798

followed by one asterisk (*), and those with >95% significance (p < 0.05) are followed by two asterisks

799

(**).

800

Figure 2. Annual trends in global PM2.5 and its chemical composition over 1989-2013 from the

801

downscaled simulation. Aerosol water is associated with each chemical component at 35% RH. The

802

significance (i.e. p value) of derived trends over land is indicated by the opaqueness of the colors. The

803

color scale of trends for PM2.5 (saturated at ±1.0 µg m-3yr-1) and its chemical composition (saturated at

804

±0.4 µg m-3yr-1) differ. Global population-weighted mean trends (95% confidence intervals in the

805

square brackets) are shown, and trends with >95% significance (p < 0.05) are followed by two asterisks

806

(**). Boundaries in the maps correspond to the 21 GBD regions in SI Figure S1.

807

Figure 3. Regional and global variations in annual population-weighted mean (PWM) PM2.5 and its

808

composition in the downscaled simulation (Simds), with time series in satellite-based PM2.5 estimates

809

(Trendsat) shown as thick blue lines. Different colors indicate different chemical composition. Aerosol

810

water is associated with each chemical component at 35% RH. The PWM PM2.5 trends (95% confidence

811

intervals in the square brackets) with >90% significance (p < 0.1) are followed by one asterisk (*), and

812

those with >95% significance (p < 0.05) are followed by two asterisks (**). For each region, the pie

813

chart shows the 1989-2013 mean PWM concentrations in each composition with the mean PM2.5

814

concentrations in the middle, and the two bar plots show the trends of each chemical species over 1989-

815

2013 and 1998-2013, respectively. Statistically significant trends (p < 0.1) are presented with filled bars

816

while

insignificant

trends

(p

≥

0.1)

are

24


indicated

by

blank

bars.

Page 25 of 27


817 818

Figure 1. 25



819 820

Figure 2.

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821 822

Figure 3. 27


Trends in Chemical Composition of Global and ... - ACS Publications

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