Seasonal transition in PM10 exposure and ... - ACS Publications

Subscriber access provided by IIT Libraries

Ecotoxicology and Human Environmental Health 10

Seasonal transition in PM exposure and associated all-cause mortality risks in India Pritha Pande, Sagnik Dey, Sourangsu Chowdhury, Palash Choudhary, Sudipta Ghosh, Parul Srivastava, and Babu Sengupta Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.8b00318 • Publication Date (Web): 28 Jun 2018 Downloaded from http://pubs.acs.org on July 3, 2018

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 29

Environmental Science & Technology

1

Seasonal transition in PM10 exposure and associated all-cause mortality risks in India

2 3 4 5 6 7 8 9 10 11 12 13 14

Pritha Pande#, Sagnik Dey*#, Sourangsu Chowdhury#, Palash Choudhary#, Sudipta Ghosh#, Parul Srivastava#, B. Sengupta$

#

Centre for Atmospheric Sciences, Indian Institute of Technology Delhi, Hauz Khas, New Delhi110016, India $

15

Central Pollution Control Board, Delhi-110032 India

16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

*

Corresponding author: Sagnik Dey, [email protected], Phone: + 91-11 26591315

1 ACS Paragon Plus Environment


31

Abstract

32

Lack of a consistent PM10 (particulate matter smaller than 10 µm) database at high spatial

33

resolution hinders in assessing the environmental impact of PM10 in India. Here we propose an

34

alternate approach to estimate PM10 database. Aerosol extinction coefficients at the surface are

35

calculated from mid-visible aerosol optical depth from MERRA-2 reanalysis data using

36

characteristics vertical profiles from CALIOP and then are converted to PM10 mass using aerosol

37

property information and microphysical data. The retrieved PM10 are bias-corrected and

38

evaluated (R2=0.85) against coincident ground-based data maintained under Central Pollution

39

Control Board network. PM10 exposure exceeds Indian annual air quality standard in 72.3%

40

districts. Transition in PM10 exposure from the monsoon (Jun-Sep) to post-monsoon season (Oct-

41

Nov) translates to 1-2% higher all-cause mortality risk over the polluted Indo-Gangetic Basin

42

(IGB). Mortality risk increases in the central to eastern IGB and central India and reduces in

43

Delhi national capital region in the winter (Dec-Feb) relative to the post-monsoon season.

44

Mortality risk decreases by 0.5-1.8% in most parts of India in the pre-monsoon season (Mar-

45

May). Our results quantify the vulnerability in terms of seasonal transition in all-cause mortality

46

risks due to PM10 exposure at district level for the first time in India.

47 48 49 50 51


Page 2 of 29

Page 3 of 29

52


1. Introduction

53

Particulate matter of aerodynamic diameter smaller than 10 µm (PM10) is one of the major

54

criteria pollutants, affecting climate and air quality at global scale1–4. PM10 concentration is

55

highly variable in space and time depending on emission characteristics, meteorology and

56

topography. Major health impacts associated with ambient PM10 exposure include cardio-

57

vascular and respiratory diseases, leading to premature mortality5–9. Two recent estimates10,11 as

58

part of Health Effect Institute study on Public Health and Air Pollution in Asia showed 0.4% and

59

0.15% higher risks for a 10 µg m-3 increase of PM10 based on the PM10 and health data from

60

Chennai10 and Delhi11 respectively. For a regional scale assessment, such epidemiological

61

studies need robust, consistent, long-term PM10 data at high spatial resolution. Aerosol burden is

62

very high in India12 , and continued to increase over a large part of the country13 in contrast to a

63

global decreasing trend14 thereby posing a serious threat to more than 1.2 billion population.

64

Central Pollution Control Board of India maintains a network across the country for air

65

quality monitoring15. The network consists of over 450 operating stations covering 126 cities

66

across the country. However, very few sites have systematic and continuous long-term PM10 data

67

that are useful for epidemiological applications. Moreover, the monitoring is focused on the

68

urban areas leaving a large part of the country (especially the rural areas) unmonitored.

69

Therefore, generation of a PM10 database for the entire country is critically required for better

70

assessment of air quality and health impacts.

71

Utilization of satellite-based aerosol optical depth (AOD) product to infer surface particulate

72

matter has gained importance in recent times to meet the urgent need of continuous and

73

consistent PM data at countries where in-situ data are not adequate in terms of spatial and

74

temporal coverage. Common approach to achieve this is to derive empirical relation between PM



75

(dependent variable) and independent variables (e.g. AOD, vertical distribution, meteorological

76

parameters like temperature, RH, wind, rainfall etc.)16,17. However, such approach has limited

77

applicability, because the regression coefficients developed for a particular region may not be

78

valid for other region and the coincident data of independent variables may not be available at

79

desired temporal and spatial frequency. Recently, a group of scientists18,19 developed a global

80

fine particulate matter (PM2.5) database for epidemiological application by converting satellite

81

AOD product using a conversion factor derived from a chemical transport model. The database

82

was refined at regional scale for India through bias correction against coincident in-situ

83

measurements13 and applied to estimate premature mortality burden20. These databases provide

84

an opportunity to examine the exposure to PM2.5 (within a defined range of uncertainty) at

85

regional scale, where in-situ observations are not available. Further, the premature mortality

86

burden due to chronic exposure to ambient PM2.5 have been estimated at global21,22 and regional

87

scale20,23

88

In addition to chronic exposure, particulate matter has been found to have short-term impacts

89

on human health. These are mostly time-series studies8,24, where relative risk (RR) in all-cause

90

mortality has been determined for a 10 µg m-3 increase in particulate matter. While a majority of

91

the time-series studies globally focused on PM10 and PM2.5 both, limited studies carried out in

92

India had to rely on PM10 only due to non-availability of PM2.5 data at the required space-time

93

interval. The reason for limited number of time-series studies can also be partly attributed to the

94

unavailability of long-term consistent ground-based PM10 database. Approach similar to that of

95

van Donkelaar et al.18,19 (referred to as CTM approach hereafter) can be adopted for estimating

96

PM10, but chemical transport modeling is computationally expensive. Here we propose an

97

alternate physical approach to estimate surface PM10 using the freely available satellite and


Page 4 of 29

Page 5 of 29


98

reanalysis aerosol products and aerosol microphysical properties database. Further, we combine

99

this database with population distribution and the India-specific risk functions attributed to

100

change in ambient short-term PM10 exposure to examine the seasonal transition in all-cause

101

mortality risks from ambient PM10 exposure over India. Vulnerable districts are identified and

102

the implications are discussed.

103 104

2. Methodology

105

2.1 Framework

106

The proposed method is developed based on the physical relations between aerosol physical

107

and optical properties, so that it can be universally applied. It follows three major steps. First, the

108

columnar AOD at mid-visible wavelength is converted to surface extinction coefficient (βext,sfc)

109

using information of aerosol vertical distribution. Next, βext,sfc is converted to aerosol mass using

110

information of aerosol composition and microphysical properties. Finally, PM10 is estimated

111

from total aerosol mass using the size distribution parameters of various individual aerosol

112

species.

113 114

AOD can be theoretically expressed in terms of extinction coefficient (βext) and aerosol layer thickness (z) as25: Z

AOD =

115

∫β

ext

dz

(1).

surface

116

We model exponential decay of βext,sfc with altitude (similar to pressure) as follows:

βext = βext,sfce−Z / H

117

(2),

118

where, H is defined as aerosol scale height. Combining (1) and (2) and solving the equation, we

119

get 5 ACS Paragon Plus Environment


AOD H (1 − e− Z / H )

βext , sfc =

120

Page 6 of 29

(3).

121

From equation 3, βext,sfc is estimated using Z and H from CALIOP (Cloud-Aerosol Lidar with

122

Organizational Polarization)26,27 at 0.5º × 0.5º grid and AOD from Modern-Era Retrospective

123

analysis for Research and Applications Version 2 (MERRA-2) reanalysis data28 interpolated to

124

same resolution from its original 0.5º × 0.625º for the time window covering the CALIOP

125

overpass at around 1:30 pm.

126

In the next step, total aerosol mass (M) is derived from βext,sfc. Theoretically, βext,sfc is related to

127

particle size (effective radius 'r' representing the size distribution), extinction efficiency (Qext),

128

density (ρ) and mass as25

β ext , sfc = πr 2 × Qext ×

M (4πr 3 ρ / 3)

129 130

(4). Rearranging equation (4) and partitioning total mass into individual species, we get: 5

3Qiext , f i M

i =1

4ri ρi

β ext , sfc = ∑

131

(5).

132

Here, we consider five aerosol species - dust, organic matter, maritime aerosol, soot (or black

133

carbon) and water-soluble components (hence i = 5) and fi is the respective mass fraction of the i-

134

th species. Our choice of five aerosol species is based on the previous aerosol studies in India29.

135

Total aerosol mass is then adjusted for particles smaller than 10 µm size using the microphysical

136

database of individual aerosol species. Estimation of f is discussed in the next section.

137 138

2.2 Data and Analysis


Page 7 of 29


139

For AOD and f (equation 5), we use MERRA-2 reanalysis data for the period 2003-2016.

140

MERRA-2 reanalysis data are produced by data assimilation in GEOS-5 earth system model.

141

MERRA reanalysis data have earlier been used to examine monsoon dynamics30,31 in India.

142

MERRA-2 reanalysis further improves upon MERRA, especially for aerosol products as it has

143

assimilated aerosol observations from various ground and space based remote sensing

144

platforms28. MERRA-2 reanalysis improved in handling water cycle and other atmospheric

145

processes that potentially can impact aerosol variability32. Detailed validation of MERRA-2

146

aerosol products is described elsewhere33. In brief, 78% of MERRA-2 AOD overlaps with

147

AERONET AOD28,33. MERRA-2 AOD and AERONET AOD at Kanpur (the lone site in India

148

with 10+ years of data) are well correlated (R2 = 0.72). Comparison of PM2.5 derived from

149

MERRA-2 AOD product using the CTM approach with van Donkelaar et al.18 PM2.5 data reveals

150

significant improvement due to assimilation of satellite data in the reanalysis33. Quantitative

151

comparison against IMPROVE network over the United States also suggests satisfactory results

152

similar to MERRA-Aero offline reanalysis products34. Compared to China, the number of AOD

153

observations that are assimilated in MERRA-2 from India is quite large33, thereby giving us

154

confidence to use it in this work.

155

We analyze CALIOP-measured aerosol vertical profiles35 for the period 2006-2011 for the co-

156

located overpasses with MERRA-2 and generate Z and H statistics representative of every

157

0.5°×0.5° grid. H is calculated from the aerosol vertical distribution profiles following our

158

previous work23. Limited comparison of CALIPSO with MPLNET from Kanpur site36 reveals

159

moderate quality. Microphysical data - r and density of these species are taken from aerosol

160

database (Table 1) used in OPAC37, while Qext at mid-visible is estimated by Mie theory for

161

different diameters of each individual species using the complex refractive index data from



Page 8 of 29

162

OPAC. The mode radii of 'water-soluble' (rwater-soluble) and 'maritime' (rmaritime) aerosols are

163

hygroscopic37 and hence are required to be adjusted for the variation in RH at every grid. We

164

derive empirical relations of rwater-soluble and rmaritime with RH using the OPAC data shown in

165

Table 2 in the following form:

166

r =a × eb×RH

(6),

167

where, a and b are empirical constants. Values of empirical coefficients a and b are 0.02 and

168

0.005, and 1.745 and 0.0009 for 'water-soluble' and maritime aerosols respectively. OPAC

169

database has been used numerous times by scientists to estimate aerosol optical properties in the

170

Indian subcontinent38,39. The mode radii for these hygroscopic species are calculated for every

171

grid for the corresponding RH data from MERRA-2 reanalysis. AOD is also influenced by RH

172

(hygroscopic growth enhances extinction of solar radiation), which is accounted for in the

173

MERRA-2 algorithm28,32. In addition to AOD, MERRA-2 reanalysis data also provide optical

174

depth of individual species (viz. sulfate, black carbon, dust, organic matter and maritime). We

175

use the OPAC optical properties to estimate mass fractions (f) of the individual species at every

176

grid from the fractions of optical depth of individual species to total AOD. Finally, fractions of

177

mass contributed by particles smaller than 10 µm (i.e. PM10) to total mass M are estimated to be

178

91.9% and 96.0% for dust and maritime respectively using the size distribution data. Fractions of

179

mass of particles greater than 10 µm to total mass are negligible for water-soluble (like sulfate)

180

and soot (black carbon) and organic matter.

181 182 183 184

Table 1. Microphysical parameters of individual aerosol species used in the proposed method. Modal radius (r) of water-soluble and maritime aerosols varies with relative humidity (RH).


Page 9 of 29


Aerosol sub-type

r (µm)

ρ (g/cm3)

Real part

Imaginary part

PM10 /SPM

Watersoluble

f (RH)

1.8

1.52

0

0%

Dust

1.9

2.6

1.53

0.0055

91.9%

Organic Matter

0.29

1.39

1.53

0.0030

0%

Soot

0.0118

1.0

1.75

0.44

0%

Maritime

f (RH)

2.2

1.544

0

96.0%

185 186 187

Table 2. RH dependency of r for the hygroscopic species RH (%) 0 (Dry) 50 70 80 90 95

rwater-soluble (µm) 0.0212 0.0262 0.0285 0.0306 0.0348 0.0399

rmaritime (µm) 1.75 2.82 3.17 3.49 4.18 5.11

188 189 190

2.3 Evaluation of the retrieval method

191

The inferred PM10 using the proposed method is first evaluated against CPCB data.

192

Evaluation is done for PM10 concentration for the grid covering the city; therefore, the spatial

193

heterogeneity within the grid may result in some difference. The inferred PM10 concentration

194

shows a low bias relative to the in-situ PM10 data; however, the bias increases linearly with an

195

increase in ground-based PM10 concentration (Figure 1a). The regression reveals statistically

196

significant (at 95% CI) relation between the bias and the in-situ data. We employ percentile9 ACS Paragon Plus Environment


197

based bias correction, where we group inferred and the corresponding CPCB PM10 data for every

198

0.5 percentile and adjust the bias based on linear dependency of the bias relative to the inferred

199

PM10 concentration40,41. The negative bias is corrected for PM10 concentration lower than 102.3

200

µg/m3 (at which point the bias is zero), beyond which the bias is positive and is adjusted in the

201

same way. After employing the bias correction (Figure 1b), the inferred PM10 data reveals a

202

strong correlation with in-situ data (R2 = 0.85, significant at 99% CI following t-test) with a

203

slope of 0.91 (±0.01) for the regression line and an intercept of 8.2 µg m-3. Bias correction

204

allows the bias to remain in the range 15-20% in the inferred PM10 at relatively clean condition

205

(PM10 90th and 25th to 50th percentile category based on

233

PM10 concentration. RRs of the three cities – Hyderabad42 (1.0085; 95% UI is 1.0006-1.0163),

234

Bangalore42 (1.0022; 95% UI is 1.0004-1.0049) and Chennai42 (1.004; 95% UI is 1.002-1.007)

235

are averaged to compute the RR representative of the exposure in the 10th to 25th percentile

236

range. Similarly, RRs for the two cities Mumbai42 (1.002; 95% UI is 1.001-1.003) and

237

Ahmadabad42 (1.0016; 95% UI is 1.0003-1.0062) are averaged to compute the RR representative



238

of exposure in 50th to 75th percentile range. Thus, we have reported RRs (from the literature)

239

representative of PM10 exposure in >90th, 50th to 75th, 25th to 50th and 10th to 25th percentile

240

ranges. For the estimation of RR representative of the exposure in 75th to 90th percentile range,

241

we interpolate the RRs of the exposure in the higher (>90th percentile) and lower (50th to 75th

242

percentile) ranges. We extrapolate RR linearly for the exposure in 90th

255

percentile class, it would result in an increase in all-cause mortality risk by 0.3% (following table

256

3). The change in the opposite direction (i.e. a decrease in exposure) would result in a reduction

257

in all-cause mortality risk by same magnitude. The same margin of change in PM10 exposure in

258

another district belonging to 50th to 75th percentile exposure class would increase the all-cause

259

mortality risk by 0.36% (as the RR is 1.0018). Using this approach, we monitor the enhancement


Page 12 of 29

Page 13 of 29


260

and reduction in all-cause mortality risks due to seasonal transitions in PM10 exposure in India at

261

district level for the first time.

262 263 264 265

Table 3. RR in all-cause mortality due to 10 µg m-3 increase in PM10 exposure for each percentile exposure class and the associated 95% UI. PM10 exposure class (in percentile) 90th

1.0015

1.0007-1.0023

th

266 267

3. Results

268

3.1 Ambient PM10 exposure over India and its seasonal variability

269

Spatial distribution of AOD over India has been studied in the past extensively using both

270

satellite4,29 and in-situ data43. The entire IGB shows large AOD (>0.4) due to a combination of

271

high emission from various anthropogenic sources, transport of dust from arid regions,

272

meteorology and topography. AOD indicates total columnar aerosol load and hence cannot be

273

directly related to PM10. A large fraction (85-95%) of total columnar extinction is contributed by

274

t

275

The distributions of inferred PM10 over India for the period 2003-2016 (Figure 2) reveals a

276

strong seasonality in PM10 exposure with the highest concentration during the winter season

277

followed by the post monsoon season. During the post-monsoon to winter seasons, wind speed

278

remains low, rainfall is scanty and the boundary layer remains mostly stable29. All these

279

meteorological factors in combination favor stagnation of the pollution closer to the surface. 13 ACS Paragon Plus Environment


280

High PM10 concentration (>190 µg m-3) over the IGB can be explained by large emission from a

281

wide range of anthropogenic sources trapped within the valley bounded by high altitude regions

282

in north (Himalayan range) and south (central Indian mountain range)29. Within the IGB, the

283

pollution is carried by the northwesterly wind during the winter before it subsides over the

284

eastern part of the IGB; while it remains subsided over the western IGB in the post-monsoon

285

season29, leading to the change in east-west gradient seen in Figure 2. The southern part of India

286

is much cleaner with PM10 less than 70 µg m-3. During the pre-monsoon season, the

287

northwesterly wind carries dust to the IGB, but the elevated boundary layer facilitates dispersion

288

of the pollution reducing the exposure at the surface29. However, such elevated aerosol layers

289

enhance the aerosol forcing and hence are important from climate point of view44. During the

290

monsoon season, pollution washout by rain is expected to reduce the exposure. This is evident in

291

Figure 2c. PM10 exposure in this season remains in the range 41-100 µg m-3 over most parts of

292

India except over Delhi national capital region where it exceeds 100 µg m-3. Large exposure in

293

many districts, especially in the IGB surrounding the Delhi national capital region suggests that

294

anthropogenic source strength is large enough to replenish the atmosphere with pollutants rapidly

295

during the dry days45. This is worrisome as even the monsoon rain is not enough to cleanse the

296

atmosphere of pollutants to curb the exposure at least below the Indian air quality standard in this

297

part of the country.

298

The annual PM10 exposure statistics in India is summarized in Table 4. 23.9% districts have

299

exposure below the Indian annual standard of 60 µg m-3; amongst which 9.3% come under ‘low’

300

category (exposure in 5th to 10th percentile) and 14.6% in ‘moderate’ category (exposure in 10th


Page 14 of 29

Page 15 of 29


301 302

a

b

c

d

303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331

Figure 2. Mean seasonal satellite-based PM10 concentration (µg m-3) during the (a) winter (Dec‐ Feb), (b) pre‐monsoon (Mar‐May), (c) monsoon (Jun‐Sep) and (d) post‐monsoon (Oct‐Nov) seasons for the period Mar 2003–Feb 2016. ‘White’ color implies absence of PM10 exposure retrieval. Maps are generated in Arc-GIS.

332

to 25th percentile). These districts are primarily in the hilly regions and peninsular and south

333

India (Figure 3). In 24.2% and 24.0% districts, annual PM10 exposure lie in ‘high’ (25th to 50th

334

percentile) and ‘very high’ 50th to 75th percentile) vulnerability category. Annual PM10 exposure

335

lies in the range 135-160 µg m-3 (75th to 90th percentile) and exceeds 160 µg m-3 (90th percentile)

336

in 24.0% and 14.4% districts respectively. The locations of the districts classified into the seven

337

vulnerable classes are shown in Figure 3. 'Low' vulnerable districts are observed in the northeast 15 ACS Paragon Plus Environment


338

India, northern mountainous region and peninsular and south India. Extremely vulnerable

339

districts are located along the east-west transverse of the central IGB and the remaining districts

340

in the IGB are found to be severely vulnerable to ambient PM10 exposure.

341

Since annual PM10 concentration data is available at more than 450 sites across India (Figure

342

S1, see SI), we examine whether annual PM10 exposure map can be derived by simple

343

interpolation. We use krigging to interpolate annual PM10 concentration (left panel of Figure S2)

344

averaged over 4 years (2013-2016) and compare with annual exposure map shown in Figure 3.

345

Interpolated map clearly shows noise and is influenced by the regional distribution of the sites.

346

For example, two red patches in central India where PM10 exposure exceeds 135 µg/m3 are

347

influenced by few urban sites clustered around these regions. We also show the difference in

348

mean annual PM10 exposure estimated by our method (and bias corrected) and ground-based data

349

at these sites (right panel of Figure S1). The bias-corrected PM10 exposure is higher than the

350

ground-based PM10 for the sites in coastal regions and peninsular India. In these sites, the annual

351

exposure is relatively lower than that in the polluted IGB where the inferred exposure is slightly

352

biased low. Part of the bias can be attributed to the difference in sampling days. In most of the

353

monitoring sites, PM10 is manually measured once or twice a week and that too not continuously

354

throughout the year. Annual exposure statistics from any dataset is influenced by the sampling

355

days. Therefore, the large differences in annual exposure (Figure 3 vs. Figure S1) should not be

356

interpreted as poor quality of the inferred data. Rather this comparison highlights the importance

357

of uniform sampling frequency in establishing robust statistics and limitation of existing

358

infrastructure of ground-based monitoring across India. Since most of these sites are set up at

359

urban locations and they do not have continuous data, simple interpolation would not be an


Page 16 of 29

Page 17 of 29


360

effective way to monitor air pollution at national level. They are only useful in the regions where

361

multiple sites are clustered together (e.g. Delhi national capital region).

362 363 364 365

365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393

Figure 3. Classification of Indian districts to 7 vulnerability classes based on annual PM10 exposure (shown as color scale in µg/m3) following Table 4. For details of each district, see the Table S1 in SI. The map is generated in Arc-GIS.

Table 4. Vulnerability assessment at district level for annual ambient PM10 exposure in India. Sufficient data (mainly during the monsoon season) are not available to extract statistics in the remaining 3.8% districts. PM10 (µg m-3)

Percentile

# Districts

160

>90th

9.7%

69.8

Extreme

394 395

3.2 Seasonal transitions in all-cause mortality risks due to PM10 exposure

396

We estimate the changes in all-cause mortality risks due to seasonal changes in PM10

397

exposure in India using India-specific risk functions discussed in section 2.4 (Figure 4). Since the

398

risk functions are valid for the adult population (>25 years), the all-cause mortality risks should

399

be interpreted for the adult population only in each district. Risk of all-cause mortality due to

400

PM10 exposure increases by up to 1.5% in western and central India from the pre-monsoon to

401

monsoon season, while it decreases by almost similar magnitude in the eastern and northeastern

402

India. Due to missing data owing to cloud cover and poor aerosol retrieval quality of CALIOP in

403

presence of thick clouds, we could not calculate PM10 exposure (and therefore RR transitions)

404

during the monsoon season in some of the districts (which are marked by ‘no data available’).

405

Transition from the monsoon to post-monsoon season enhances the all-cause mortality risk

406

almost everywhere except the arid region in the west and parts of peninsular India dominated by

407

rural population. The risk enhances by 1-2% in the IGB and by 0.5-1% in central India. A

408

notable observation is the reduction in risk by~0.5% in the Delhi national capital region and in

409

Southern India in the winter relative to the post-monsoon season. On the other hand, the risk

410

enhances in the rest of the IGB and a large part of India except the southern India with the

411

highest value (in the range 1.4-2%) observed in the northern Bihar, Jharkhand, West Bengal, 18 ACS Paragon Plus Environment

Page 19 of 29


412

northeastern states and several districts in Maharashtra, Madhya Pradesh, Chhattisgarh,

413

Telangana, Odisha and Andhra Pradesh. Throughout India, the risk in all-cause mortality reduces

414

by varying proportion due to PM10 exposure transition from the winter to pre-monsoon season.

415 416 417

b

a 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442

c

d

Figure 4. Seasonal transitions in all-cause mortality risk (in %) due to short-term ambient PM10 exposure from (a) pre-monsoon to monsoon, (b) monsoon to post-monsoon, (c) post-monsoon to winter and (d) winter to pre-monsoon seasons over India. ‘White’ color implies absence of PM10 exposure retrieval in either of the seasons. The maps are generated in Arc-GIS.

443 444 445



446

We carry out a sensitivity study (Figure S2; see SI) to examine the pattern of estimated

447

seasonal transition in RR (as shown in Figure 4) to choice of RR function. For this, we simply

448

average (1.0048 in this case) all the 7 RR estimates from India10,11,42 that are used to generate

449

exposure-based RR function in this study (as shown in Table 3). A single value across India

450

would assume homogeneity in the RR due to short-term changes in PM10 exposure. Normalized

451

frequency distributions of seasonal transition in RR using exposure-based RRs (blue bars in

452

Figure S2) are less skewed compared to the same using single RR values (red bars). This

453

difference may be attributed to the fact that seasonal transition in RR using a single value across

454

the country does not account for spatial heterogeneity in PM10 exposure.

455 456

4. Discussion

457

In this work, we propose an alternate approach to derive surface PM10 using physical

458

relationships between aerosol parameters. Utilizing routinely available satellite-derived and

459

reanalysis aerosol products that can be freely downloaded from the respective data archives, we

460

apply the method in India where poor air quality is the biggest environmental concern46. We

461

demonstrate that simple interpolation of in-situ data is not effective to generate a national level

462

picture. The strength of our approach is that it does not rely on computing resource to integrate

463

satellite aerosol products with chemical transport model outputs18,19. We acknowledge that the

464

reanalysis and satellite products are generated by computationally expensive algorithms, but

465

once the data products are made available routinely to the user community, they can be used

466

quite easily for air pollution study following our methodology. The efficacy of this physical

467

approach may be better understood, if it is applied at other polluted region (e.g. sub-Saharan

468

Africa) lacking in-situ PM10 data at the required spatial resolution. As demonstrated, the bias


Page 20 of 29

Page 21 of 29


469

varies linearly, which implies that exposure tends to be underestimated in polluted condition.

470

This is not unusual as aerosol retrievals tend to be biased low in highly polluted conditions29. We

471

calibrate our retrievals against quality controlled in-situ data to minimize the local bias, so that

472

the seasonal transitions in RRs of all-cause mortality can be quantified more accurately. Without

473

the bias correction, RR estimates would have been biased high for low PM10 concentration and

474

low at high PM10 concentration.

475

These statistics of seasonal transitions in all-cause mortality risk from changes in PM10

476

exposure provide a quantitative assessment of the short-term health impacts of pollution at a

477

district level using risk functions developed for the Indian condition. We feel that India-specific

478

RR function would represent the Indian scenario better than any other RR function developed

479

based on data from the developed countries47. We note that RR may vary as a function of toxicity

480

of the pollutants, but it is difficult to assess toxicity of aerosol components when they are

481

externally or internally mixed. More epidemiological studies in future would address this issue

482

and reduce the uncertainty in RR (Table 3). However, the seasonal transitions in RR (Figure 4)

483

are found to be larger than the existing uncertainty range at many districts, highlighting the

484

importance of formulating season-specific air quality management plan. Largest enhancement in

485

all-cause mortality risk in Delhi NCR due to changes in short-term PM10 exposure is observed

486

during the post-monsoon season. Since meteorology plays a major role in trapping pollution in

487

this season, situation would not improve unless emission is reduced. Information about seasonal

488

transitions in exposure and associated risk would therefore facilitate prioritizing the mitigation

489

measures at the respective vulnerable districts. This would further help in implementing the

490

recommendations made by the steering committee under the Ministry of Health and Family

491

Welfare46 to improve air quality in India. Our results are likely to be highly useful for



492

policymakers as we highlight the vulnerable districts in terms of annual PM10 exposure and map

493

the seasonal transitions in all-cause mortality risks from short-term exposures. In future, utility of

494

such alternate method in estimating PM2.5 exposure will be explored.

495 496

Acknowledgements

497

The present work was supported by grants under the Department of Science and Technology

498

(DST), Government of India network program on 'Climate Change and Human Health'

499

(DST/CCP/NET-2//PR-36/2012) and from the Ministry of Environment, Forest and Climate

500

Change, Govt. of India though a research grant (No. 14/10/2014-CC) under the National

501

Carbonaceous Aerosol Program. CALIOP data are downloaded from Langley Research Centre

502

Data Archive. MERRA 2 reanalysis data are downloaded from the MERRA archive. In-situ data

503

of PM10 are downloaded from Central Pollution Control Board (CPCB) website. Corresponding

504

author acknowledges DST-FIST grant (SR/FST/ESII-016/2014). We thank the anonymous

505

reviewers whose comments helped us improving the earlier version of the manuscript.

506 507

Supporting Information. The supporting information contains two figures (Figures S1-S2) and

508

a table (Table S1) with detailed statistics of seasonal PM10 exposure and associated all-cause

509

mortality risks for each district in India.

510 511

References

512

(1)

513 514

Chowdhury, S.; Dey, S. Air Quality in Changing Climate: Implications for Health Impacts In Climate change and Air pollution, Springer; 2018; pp 9–24.

(2)

Watts, N.; Adger, W. N.; Agnolucci, P.; Blackstock, J.; Byass, P.; Cai, W.; Chaytor, S.;


Page 22 of 29

Page 23 of 29


515

Colbourn, T.; Collins, M.; Cooper, A.; et al. Health and Climate Change : Policy

516

Responses to Protect Public Health. Lancet 2015, 386, 1861–1914.

517

(3)

518 519

Ects in a Warmer World. 2016, 6 (March), 269–274. (4)

520 521

Allen, R. J.; Landuyt, W.; Rumbold, S. T. An Increase in Aerosol Burden and Radiative E

Chowdhury, S.; Dey, S.; Smith, K. R. Ambient PM2.5 exposure and Expected Premature Mortality to 2100 in India under Climate Change Scenarios. Nat. Commun. 2018, 9 (1).

(5)

Jerrett, M.; Burnett, R. T.; Pope, C. A.; Ito, K.; Thurston, G.; Krewski, D.; Shi, Y.; Calle,

522

E.; Thun, M. Long-Term Ozone Exposure and Mortality. N. Engl. J. Med. 2009, 360 (11),

523

1085–1095.

524

(6)

Pope, C. A.; Brook, R. D.; Burnett, R. T.; Dockery, D. W. How Is Cardiovascular Disease

525

Mortality Risk Affected by Duration and Intensity of Fine Particulate Matter Exposure?

526

An Integration of the Epidemiologic Evidence. Air Qual. Atmos. Heal. 2011, 4, 5–14.

527

(7)

Pope, A.; Burnett, R. T.; Thun, M. J.; Calle, E. E.; Krewski, D.; Thurston, G. D. Lung

528

Cancer, Cardiopulmonary Mortality, and Long-Term Exposure to Fine Particulate Air

529

Pollution. 2002, 287 (9).

530

(8)

Stieb, D. M.; Szyszkowicz, M.; Rowe, B. H.; Leech, J. a. Air Pollution and Emergency

531

Department Visits for Cardiac and Respiratory Conditions: A Multi-City Time-Series

532

Analysis. Environ. Health 2009, 8 (2), 25.

533

(9)

Lipsett, M. J.; Ostro, B. D.; Reynolds, P.; Goldberg, D.; Hertz, A.; Jerrett, M.; Smith, D.

534

F.; Garcia, C.; Chang, E. T.; Bernstein, L. Long-Term Exposure to Air Pollution and

535

Cardiorespiratory Disease in the California Teachers Study Cohort. Am. J. Respir. Crit.

536

Care Med. 2011, 184 (7), 828–835.

537

(10)

Balakrishnan, K.; Ganguli, B.; Ghosh, S.; Sankar, S.; Thanasekaraan, V.; Rayudu, V. N.;



538

Caussy, H. Part 1. Short-Term Effects of Air Pollution on Mortality: Results from a Time-

539

Series Analysis in Chennai, India. Res. Rep. Health. Eff. Inst. 2011, No. 157, 7–44.

540

(11)

Rajarathnam, U.; Sehgal, M.; Nairy, S.; Patnayak, R. C.; Chhabra, S. K.; Kilnani;

541

Ragavan, K. V. S. Part 2. Time-Series Study on Air Pollution and Mortality in Delhi. Res.

542

Rep. Health. Eff. Inst. 2011, No. 157, 47–74.

543

(12)

544 545

Dey, S.; Di Girolamo, L. A Decade of Change in Aerosol Properties over the Indian Subcontinent. Geophys. Res. Lett. 2011, 38 (May), 1–5.

(13)

Dey, S.; Di Girolamo, L.; van Donkelaar, A.; Tripathi, S. N.; Gupta, T.; Mohan, M.

546

Variability of Outdoor Fine Particulate (PM2.5) Concentration in the Indian Subcontinent:

547

A Remote Sensing Approach. Remote Sens. Environ. 2012, 127, 153–161.

548

(14)

Mishchenko, M. I.; Geogdzhayev, I. V.; Rossow, W. B.; Cairns, B.; Carlson, B. E.; Lacis,

549

A. A.; Liu, L.; Travis, L. D. Long-Term Satellite Record Reveals Likely Recent Aerosol

550

Trend. Science. 2007, 315 (5818), 1543.

551

(15)

552 553

CPCB. National Ambient Air Quality Status & Trends in India-2010 Central Pollution Control Board. Ministry of Environment, Forest and Climate Change. 2012.

(16)

Chitranshi, S.; Sharma, S. P.; Dey, S. Spatio-Temporal Variations in the Estimation of

554

PM10 from MODIS-Derived Aerosol Optical Depth for the Urban Areas in the Central

555

Indo-Gangetic Plain. Meteorol. Atmos. Phys. 2014, 127, 107–121.

556

(17)

Upadhyay, A.; Dey, S.; Goyal, P.; Dash, S. K. Projection of near-Future Anthropogenic

557

PM2.5over India Using Statistical Approach. Atmos. Environ. 2018, 186 (March), 178–

558

188.

559 560

(18)

van Donkelaar, A.; Martin, R. V.; Brauer, M.; Kahn, R.; Levy, R.; Verduzco, C.; Villeneuve, P. J. Global Estimates of Ambient Fine Particulate Matter Concentrations


Page 24 of 29

Page 25 of 29


561

from Satellite-Based Aerosol Optical Depth: Development and Application. Environ.

562

Health Perspect. 2010, 118 (March), 847–855.

563

(19)

Van Donkelaar, A.; Martin, R. V.; Brauer, M.; Hsu, N. C.; Kahn, R. A.; Levy, R. C.;

564

Lyapustin, A.; Sayer, A. M.; Winker, D. M. Global Estimates of Fine Particulate Matter

565

Using a Combined Geophysical-Statistical Method with Information from Satellites,

566

Models, and Monitors. Environ. Sci. Technol. 2016, 50 (7), 3762–3772.

567

(20)

568 569

Chowdhury, S.; Dey, S. Cause-Specific Premature Death from Ambient PM2.5 Exposure in India: Estimate Adjusted for Baseline Mortality. Environ. Int. 2016, 91, 283–290.

(21)

Wang, H.; GBD-Collaborators. Global, Regional, and National Life Expectancy, All-

570

Cause Mortality, and Cause-Specific Mortality for 249 Causes of Death, 1980-2015: A

571

Systematic Analysis for the Global Burden of Disease Study 2015. Lancet 2016, 388

572

(10053), 1459–1544.

573

(22)

Anenberg, S. C.; Horowitz, L. W.; Tong, D. Q.; West, J. J. An Estimate of the Global

574

Burden of Anthropogenic Ozone and Fine Particulate Matter on Premature Human

575

Mortality Using Atmospheric Modeling. Environ. Health Perspect. 2010, 118 (9), 1189–

576

1195.

577

(23)

578 579

Sources in India. 2018. (24)

580 581

Bell, M. L.; Samet, J. M.; Dominici, F. Time-Series Studies of Particulate Matter. Annu. Rev. Public Health 2004, 25, 247–280.

(25)

582 583

GBD-MAPS Working Group, Burden of Disease Attributable to Major Air Pollution

Seinfeld, J. H.; Pandis, S. N. Atmospheric Chemistry and Physics; From Air Pollution to Climate Change; 1998.

(26)

Omar, A. H.; Winker, D. M.; Kittaka, C.; Vaughan, M. A.; Liu, Z.; Hu, Y.; Trepte, C. R.;



584

Rogers, R. R.; Ferrare, R. A.; Lee, K. P.; et al. The CALIPSO Automated Aerosol

585

Classification and Lidar Ratio Selection Algorithm. J. Atmos. Ocean. Technol. 2009, 26,

586

1994–2014.

587

(27)

Winker, D. M.; Pelon, J.; Coakley, J. A.; Ackerman, S. A.; Charlson, R. J.; Colarco, P. R.;

588

Flamant, P.; Fu, Q.; Hoff, R. M.; Kittaka, C.; et al. The Calipso Mission: A Global 3D

589

View of Aerosols and Clouds. Bull. Am. Meteorol. Soc. 2010, 91 (9), 1211–1229.

590

(28)

Randles, C. A.; da Silva, A. M.; Buchard, V.; Colarco, P. R.; Darmenov, A.; Govindaraju,

591

R.; Smirnov, A.; Holben, B.; Ferrare, R.; Hair, J.; et al. The MERRA-2 Aerosol

592

Reanalysis, 1980 Onward. Part I: System Description and Data Assimilation Evaluation.

593

J. Clim. 2017, 30 (17), 6823–6850.

594

(29)

Dey, S.; Di Girolamo, L. A Climatology of Aerosol Optical and Microphysical Properties

595

over the Indian Subcontinent from 9 Years (2000-2008) of Multiangle Imaging

596

Spectroradiometer (MISR) Data. J. Geophys. Res. Atmos. 2010, 115, 1–22.

597

(30)

Pillai, P. A., Sahai, A. K. Moist Dynamics of Active/break Cycle of Indian Summer

598

Monsoon Rainfall from NCEPR2 and MERRA Reanalysis. Int. J. Climatol. 2013, 34 (5),

599

1429–1444.

600

(31)

601 602

Shah, R.; Mishra, V. Evaluation of the Reanalysis Products for the Monsoon Season Droughts in India. J. Hydrometeorol. 2014, 15 (4), 1575–1591.

(32)

Gelaro, R.; McCarty, W.; Suárez, M. J.; Todling, R.; Molod, A.; Takacs, L.; Randles, C.

603

A.; Darmenov, A.; Bosilovich, M. G.; Reichle, R.; et al. The Modern-Era Retrospective

604

Analysis for Research and Applications, Version 2 (MERRA-2). J. Clim. 2017, 30 (14),

605

5419–5454.

606

(33)

Buchard, V.; Randles, C. A.; da Silva, A. M.; Darmenov, A.; Colarco, P. R.; Govindaraju,


Page 26 of 29

Page 27 of 29


607

R.; Ferrare, R.; Hair, J.; Beyersdorf, A. J.; Ziemba, L. D.; et al. The MERRA-2 Aerosol

608

Reanalysis, 1980 Onward. Part II: Evaluation and Case Studies. J. Clim. 2017, 30 (17),

609

6851–6872.

610

(34)

Buchard, V.; da Silva, A. M.; Randles, C. A.; Colarco, P.; Ferrare, R.; Hair, J.; Hostetler,

611

C.; Tackett, J.; Winker, D. Evaluation of the Surface PM2.5 in Version 1 of the NASA

612

MERRA Aerosol Reanalysis over the United States. Atmos. Environ. 2016, 125, 100–111.

613

(35)

Adams, A. M.; Prospero, J. M.; Zhang, C. CALIPSO-Derived Three-Dimensional

614

Structure of Aerosol over the Atlantic Basin and Adjacent Continents. J. Clim. 2012, 25

615

(19), 6862–6879.

616

(36)

Misra, A.; Tripathi, S. N.; Kaul, D. S.; Welton, E. J. Study of MPLNET-Derived Aerosol

617

Climatology over Kanpur, India, and Validation of CALIPSO Level 2 Version 3

618

Backscatter and Extinction Products. J. Atmos. Ocean. Technol. 2012, 29 (9), 1285–1294.

619

(37)

620 621

Package OPAC. Bull. Am. Meteorol. Soc. 1998, 79 (5), 831–844. (38)

622 623

Hess, M.; Koepke, P.; Schult, I. Optical Properties of Aerosols and Clouds: The Software

Dey, S.; Tripathi, S. N.; Mishra, S. K. Probable Mixing State of Aerosols in the IndoGangetic Basin, Northern India. Geophys. Res. Lett. 2008, 35 (3), 1–5.

(39)

Tripathi, S. N.; Srivastava, A. K.; Dey, S.; Satheesh, S. K.; Krishnamoorthy, K. The

624

Vertical Profile of Atmospheric Heating Rate of Black Carbon Aerosols at Kanpur in

625

Northern India. Atmos. Environ. 2007, 41 (32), 6909–6915.

626

(40)

627 628 629

Mishra, A. K.; Rudich, Y.; Koren, I. Spatial Boundaries of Aerosol Robotic Network Observations over the Mediterranean Basin. 2016, 2259–2266.

(41)

Chowdhury, S.; Dey, S.; Tripathi, S. N.; Beig, G.; Mishra, A. K.; Sharma, S. “Traffic Intervention” Policy Fails to Mitigate Air Pollution in Megacity Delhi. Environ. Sci.



630 631

Policy 2017, 74. (42)

Dholakia, H. H.; Bhadra, D.; Garg, A. Short Term Association between Ambient Air

632

Pollution and Mortality and Modification by Temperature in Five Indian Cities. Atmos.

633

Environ. 2014, 99, 168–174.

634

(43)

635 636

Krishna Moorthy, K.; Suresh Babu, S.; Manoj, M. R.; Satheesh, S. K. Buildup of Aerosols over the Indian Region. Geophys. Res. Lett. 2013, 40, 1011–1014.

(44)

Satheesh, S. K.; Krishna Moorthy, K.; Suresh Babu, S.; Vinoj, V.; Dutt, C. B. S. Climate

637

Implications of Large Warming by Elevated Aerosol over India. Geophys. Res. Lett. 2008,

638

35 (19), 1–6.

639

(45)

640 641

Washout and Recovery over India during Monsoon. Aerosol Air Qual. Res. 2016, 16 (5). (46)

642 643

Chowdhury, S.; Dey, S.; Ghosh, S.; Saud, T. Satellite-Based Estimates of Aerosol

Sagar, A.; Balakrishnan, K.; Guttikunda, S.; Roychowdhury, A.; Smith, K. R. Perspectives | Editorial. 2016, 124 (7), 116–117.

(47)

Pope, C. A., Cohen, A. J., Burett, R. T. Cardiovascular disease and fine particulate matter:

644

Lessons and limitations of an intergrated exposure-response approach. Circulation Res.

645

2018, 122, 1645-1647.

646


Page 28 of 29

Page 29 of 29


259x131mm (220 x 220 DPI)

ACS Paragon Plus Environment

Seasonal transition in PM10 exposure and ... - ACS Publications

Recommend Documents