Seasonal transition in PM10 exposure and associated all-cause

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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

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Environmental Science & Technology

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

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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


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Abstract

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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

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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-

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

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

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

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

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

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

47 48 49 50 51


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1. Introduction

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

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

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

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

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

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

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

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

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

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

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

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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

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

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

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

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

70

assessment of air quality and health impacts.

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

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

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

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



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

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

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

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

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

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

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

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

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

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

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

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

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scale20,23

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

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

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

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

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

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

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

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

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

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


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

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

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

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

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the implications are discussed.

103 104

2. Methodology

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2.1 Framework

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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

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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

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

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

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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

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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

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get 5 ACS Paragon Plus Environment


AOD H (1 − e− Z / H )

βext , sfc =

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(3).

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

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

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

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

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overpass at around 1:30 pm.

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

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

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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.

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

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

137 138

2.2 Data and Analysis


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

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

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

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

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assimilated aerosol observations from various ground and space based remote sensing

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

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

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

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

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

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

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

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

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

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

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confidence to use it in this work.

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

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

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

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

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

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

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



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

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

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

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Table 2 in the following form:

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r =a × eb×RH

(6),

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

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

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

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

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

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

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

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

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

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

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

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

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

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and soot (black carbon) and organic matter.

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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).


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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

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

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

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

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

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

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


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

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

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

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

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

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

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

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

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

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

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

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

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

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



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

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

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

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

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

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

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

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

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

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


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

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district level for the first time.

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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

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3.1 Ambient PM10 exposure over India and its seasonal variability

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

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

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

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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

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

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

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

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


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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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part of the country.

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

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

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


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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

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


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

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

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

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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.

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

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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

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

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

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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

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


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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

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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


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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

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259x131mm (220 x 220 DPI)

ACS Paragon Plus Environment

Seasonal transition in PM10 exposure and associated all-cause

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