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PM2.5 population exposure in New Delhi using a probabilistic simulation framework Arvind Saraswat, Milind Kandlikar, Michael Brauer, and Arun Kumar Srivastava Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.5b04975 • Publication Date (Web): 17 Feb 2016 Downloaded from http://pubs.acs.org on February 18, 2016

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Environmental Science & Technology

PM2.5 population exposure in New Delhi using a probabilistic simulation framework

2 3

Arvind Saraswat

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Institute for Resources Environment and Sustainability, The University of British

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Columbia, Rm 411, 2202 Main Mall, Vancouver, BC V6T 4T1, Canada.

6 7

Milind Kandlikar*

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Liu Institute for Global Issues & Institute for Resources Environment and Sustainability,

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The University of British Columbia, Room 101B, 6476 NW Marine Drive, Vancouver,

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BC V6T 1Z2, Canada.

11 12

Michael Brauer

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School of Population and Public Health, Faculty of Medicine, The University of British

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Columbia, Vancouver, BC V6T 4T1, Canada.

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Arun Srivastava

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School of Environmental Sciences, Jawahar Lal Nehru University, New Delhi 110067,

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India.

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* Phone: 604 822 5918. E-mail: [email protected]

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Abstract: This paper presents a Geographical Information System (GIS) based

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probabilistic simulation framework to estimate PM2.5 population exposure in New Delhi,

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India. The framework integrates PM2.5 output from spatiotemporal LUR models and trip

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distribution data using a Gravity model based on zonal data for population, employment

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and enrollment in educational institutions. Time-activity patterns were derived from a

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survey of randomly sampled individuals (n=1012) and in-vehicle exposure was estimated

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using micro-environmental monitoring data based on field measurements. We simulated

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population exposure for three different scenarios to capture stay-at-home populations

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(Scenario 1), working population exposed to near-road concentrations during commutes

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(Scenario 2), and the working population exposed to on-road concentrations during

ACS Paragon Plus Environment


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commutes (Scenario 3). Simulated annual average levels of PM2.5 exposure across the

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entire city were very high, and particularly severe in the winter months: ~200 µg m-3 in

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November, roughly four times higher compared to the lower levels in the monsoon

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season. Mean annual exposures ranged from 109 µg m-3 (IQR: 97—120 µg m-3) for

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scenario 1, to 121 µg m-3 (IQR: 110—131 µg m-3) and 125 µg m-3 (IQR: 114—136 µ gm-

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3

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annual PM2.5 population exposure to be underestimated by only 11%.

) for scenarios 2 and 3 respectively. Ignoring the effects of mobility causes the average

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1. Introduction

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Air pollution is a growing environmental and public health concern in Indian cities,

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especially in its capital New Delhi. Annual average concentrations of fine particulate

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matter (PM2.5 or particulate matter with aerodynamic diameter ≤2.5µm) in New Delhi are

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considerably higher than the WHO Air Quality Guideline1 of 10 µg m-3. Annual average

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concentrations at six continuous monitoring locations (R.K. Puram, Mandir Marg,

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Punjabi Bagh, IGI Airport, Anand Vihar and Civil Lines) ranged from 125–191 µg m-3 in

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2014.2 In 2010, exposure to PM2.5 contributed to 0.6 million deaths and 17.7 million

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healthy years of life lost in India; exposure to PM2.5 is ranked as the fifth highest risk

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factor for mortality and seventh highest risk factor for overall disease burden in India.3, 4

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While there are multiple sources of air pollution in New Delhi including biomass

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burning, power plants, industry and vehicular traffic, there is no single dominant source

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of PM2.5. Source apportionment studies have shown that primary traffic emissions

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account for about 20% of ambient PM2.5 mass concentration.5, 6 The number of motor

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vehicles in New Delhi has been increasing at 7% per year for the past decade.2 There are

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2.8 million cars, 5.7 million motorized two-wheelers, 81,000 auto-rickshaws, 79,000

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taxicabs and 20,000 buses in New Delhi.2 Transport and land-use policies in New Delhi

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that have tended to discourage walking and bicycling have exacerbated this trend.7 The

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increased number of motor vehicles has led to high traffic volumes and congestion

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related air pollution hotspots8, 9 with significant consequences for public health.10

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Direct measurement of personal exposure is generally infeasible because it is

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prohibitively expensive to gather the large representative samples needed for unbiased

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estimates.11 Exposure modeling is an inexpensive alternative to personal monitoring for

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assessing the magnitude of and variability in population exposure.12, 13 Accurate models

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of exposure require spatially resolved PM2.5 measurements; data collected by New

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Delhi’s regulatory agencies do not adequately capture the spatial heterogeneity of

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particle concentrations since there are only eight monitoring sites in the city. Absence of

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spatial detail can lead to inaccurate estimates of population exposure and impede air



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quality management efforts. The resulting exposure misclassification can also bias

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epidemiologic analyses.14 One alternative is to use spatiotemporal air quality models

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such as land use regression (LUR) models that map ambient concentrations of pollutants.

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LUR models have been widely developed and applied to population exposure assessment

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in urban settings. The spatial variation in LUR output can be combined with time-

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activity based models to develop exposure estimates for a large number of individuals.15-

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To date, studies of population level exposure to air pollutants, including those that use

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LUR models, have typically not accounted for population mobility and the role of

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exposures encountered during transportation.11,

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exposure activity in areas where the vehicle density is high,19 and can contribute

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substantially to total daily exposure20 and consequent health effects. Recent studies show

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that in-vehicle exposure to PM2.5 is associated with cardiovascular effects in healthy

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adults.21 Ignoring mobility can bias the effect estimates since air pollution exposure tends

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to be higher during commutes.22 Further, the choice of travel mode during commute is

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also an important determinant of air pollution exposure for commuters,23 especially in

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places such as New Delhi with high levels of congestion. Modal choice is also strongly

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associated with socioeconomic status (SES), and modes used by low-income commuters

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might result in greater exposure. A population with a lower SES may also be more

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susceptible to adverse health effects of environmental pollutants24 including air

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pollution.25 However, the effect of modal choice needs to be understood relative to total

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exposure since a simple comparison of exposure to pollutants across different modes of

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transport can be misleading.26

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Commuting is generally a high-

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The objective of this study was to quantify the annual average PM2.5 exposure and its

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variability for working and non-working adult sub-populations in New Delhi, with an

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emphasis on assessing the impact of exposure during commutes relative to exposures at

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home and work locations. A probabilistic simulation framework was developed to

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quantify the exposure of individuals in a spatially explicit manner, incorporating

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exposures at home, at work and for different travel modes during commutes to and from


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work. We further estimated population exposure as a function of seasonal and diurnal

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spatiotemporal variations that influence PM2.5 concentrations in Delhi.

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2.0 Materials and Methods

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Exposure to an air pollutant is determined by the concentration of the pollutant and the

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time spent in contact with the pollutant.27 Total exposure thus depends on the sum of

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exposures in different microenvironments as shown in Eq. (1).

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E=∑fi Ci i= h,w,t

(1)

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where, E is the time-weighted average exposure of an individual and fi and Ci are the

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time fraction spent in and concentration in i different microenvironments (h = home; w=

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work; t = travel) respectively. For the purpose of simulation, it was assumed that there

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are three microenvironments that adequately capture an individual’s daily PM2.5

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exposure: home, work and transport.

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Simulation approaches have been used by a number of researchers to model exposure to

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air pollutants.12, 28-31 Only PM2.5 of outdoor origin was considered in this study—in other

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words, the indoor sources of PM2.5 were ignored. The simulation assumes that ambient

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air quality at a specific location, as predicted by an LUR model, is sufficient to estimate

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exposure. A visual representation of the simulation process is provided in Figure 1. A

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detailed description of the simulation algorithm is provided in SI.


124 125 126 127


*Select home zone; select work zone using Gravity model; and select home and work locations

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Use planning data for zones to build a Gravity model for trip generation [Eq. (2)]

*Extract morning and afternoon concentrations at home and work locations using LUR models

*Compute distance between home and work locations and create a distribution for distance

*Assign travel time fraction and generate time spent at work LUR models: 1. Create distributions for morning and afternoon concentrations for major and minor roads 2. Create distributions for kvm ratios for each mode

*Use morning and afternoon concentrations to compute nighttime and average daily concentrations at home locations using diurnal patterns from fixed site monitor (LUR)

*Compute microenvironmental concentrations for home, work and transport microenvironments for March (use kvm ratio for Scenario 3)

For each month, scale microenvironmental concentrations using monthly patterns from fixed site (ITO); and multiply home and work microenvironmenal concentrations by I/O

Compute average exposure for each month and full year [Eq. 1]

Figure 1: A visual representation of the simulation process. *Indicates that the step was repeated 100,000 times.


Create distributions for I/O ratios for summer and nonsummer months

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We implemented three simulation scenarios:

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Scenario 1: Individuals were assumed to spend all their time at home. This scenario may

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be applicable to the very young and old segments of the populations and is a typical

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assumption of epidemiologic studies of long-term exposure to air pollution where

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exposures are estimated based on residential locations.

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Scenario 2: Individuals were assumed to travel for work or educational purposes; in-

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vehicle PM2.5 concentrations during commute were assumed to be identical to the near-

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road PM2.5 concentrations.

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Scenario 3: Individuals were assumed to travel for work or educational purposes; in-

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vehicle PM2.5 concentrations during the commute were estimated using in-vehicle to

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near-road concentration ratios (κvm). This scenario builds on Scenario 2 to incorporate

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differences in exposure based on mode of transport.

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Four datasets, including two primary datasets developed specifically for this simulation

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and two secondary datasets, were used. The secondary datasets included zonal planning

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data used to develop a Gravity model for trip generation, and output from LUR models

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developed previously for Delhi.32 The primary data sets included travel-time survey data

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for New Delhi residents and measurements from an in-vehicle exposure monitoring

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campaign conducted on a busy road in New Delhi.

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2.1 Zonal Planning Data for Home–Work Trip Generation

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We used 16 planning divisions or zones defined by Delhi Development Authority33 for

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trip generation. A smoothed population density domain was created using population

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data obtained in GIS (ArcGIS10). We selected 100,000 points from the population

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density domain; the probability of selection of a point was proportional to the population

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density at that point. This set of points (PTS_POP) was used for selecting home locations

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for each simulated individual. We also selected another set of 100,000 random points

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(PTS_WORK) to represent work locations for each simulated individual. In either case,

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the probability of selection was set to zero for roads and water bodies.



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A home zone for a simulated individual was selected first with the probability of

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selection proportional to the population in that zone. The simulated individual’s home

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location was then selected by randomly sampling from the subset of PTS_POP lying

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within the home zone. A work zone was similarly selected by random sampling with

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probability of selection proportional to the employment and enrollment numbers in that

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zone. If the work zone was different from the home zone, the work zone was redefined

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using a Gravity model.34 Work zones had to be redefined because Eq. 2 becomes

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undefined if home and work zones are the same. Specifically, there could be no instances

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where home and work zones were identical for a simulated individual, which is not

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accurate. To get around this limitation, we used this two-step work zone selection

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process. Probability of selection for the redefined work zone was directly proportional to

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the sum of employment and enrollment numbers, and inversely proportional to the

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distance between the centroid of the work zone and the centroid of the home zone as in

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Eq. (2). Thereafter work locations were selected by randomly sampling from the subset

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of PTS_WORK that lay within the boundaries of the work zone. We then computed the

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distance between the home and work locations (′, ) for each simulated individual

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and a PDF was fitted to ′, . Each individual in the simulation had a unique

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combination of home and work locations. The probability P(i,j) is probability that a

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simulated individual with home zone i will work in zone j is given by:

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Ρ, = Κ

×,

(2)

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Emp(j) is the sum of employment and enrollment numbers in zone j; D(i,j) is the distance

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between centroids of zones i and j; and Κ is a constant.

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2.2 Output From Spatiotemporal LUR Models

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We used morning (0800–1200) and afternoon (1200–1800) PM2.5 concentration maps for

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the month of March (2010) from recently developed LUR models for Delhi.32

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Spatiotemporal LUR models (PM2.5) were developed for morning and afternoon hours

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using data from 39 sites. Independent variables that included concentration data from a

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fixed site monitor located in a residential neighborhood, along with distance from major


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and minor roads, and population density were significantly correlated with PM2.5

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concentrations. Other environmental variables such as proportion of green space and

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socio-economic variables such as caste composition were included but were not found to

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be significant. The models explained more variability in morning (85%) than in the

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afternoon (73%). We extracted morning and afternoon PM2.5 concentrations at home

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locations from the spatiotemporal LUR models. Nighttime concentrations for one month

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(March) were predicted at each home point using the measured diurnal pattern of PM2.5

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at the fixed site monitor in the LUR study, located in a residential neighborhood. These

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concentrations were subsequently scaled using data from a regulatory fixed site monitor

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(at New Delhi’s ITO intersection) to obtain concentrations for months other than March.

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Data from a residential fixed site monitor was available for the duration of the LUR

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study only; data from this monitor was diurnal scaling for the month of March. Since

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data from the LUR study were not available for all months in a year, we used data from a

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regulatory monitor (ITO) for monthly scaling. Since monthly concentrations had to be

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scaled to obtain annual averages, we had to pick a pivot month. March was picked as the

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pivot month for scaling as it was approximately in the middle of the LUR study. Monthly

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patterns at the fixed site (ITO) monitor are shown in Figure S1, SI. PM2.5 data for the

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ITO site was obtained from the Central Pollution Control Board (CPCB) website35. This

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monitoring station is located close to a busy traffic intersection (Figure S3, SI) and

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provides longest record of continuous PM2.5 data in New Delhi. In 2014, annual average

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PM2.5 concentrations at this location (127 µg m-3) were lower than five out of six other

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continuous monitoring stations in New Delhi2.

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It is important to note that PM2.5 concentrations were scaled using data from a regulatory

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monitor as the LUR study only covered the months of February through May. Thus, the

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exposure simulation framework has not been applied to ultrafine and black carbon PM

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because of the inability to scale the pollutant concentrations for want of an annual data

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set from a continuous monitor.

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We extracted morning and afternoon concentrations at the work locations from the

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spatiotemporal LUR models. Indoor concentrations at home and work locations were

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estimated by multiplying the ambient concentrations at those locations by the

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indoor/outdoor PM2.5 concentration (I/O) ratio. The I/O ratio provides a general

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understanding of the steady-state relationship between indoor and outdoor concentrations

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and is influenced by a number of factors including indoor sources, penetration factor, air

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exchange rate, outdoor concentration and seasonality.36 In the absence of indoor sources,

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the I/O ratio will increase with air exchange rates36 and will be less than or equal to

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unity. We used PM2.5 I/O ratio data from a study conducted in urban North India37 which

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reported PM2.5 I/O ratios varying from 0.76 to 0.97. For our study we used uniform

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distributions for PM2.5 I/O ratio: 0.76–0.88 for the winter (November through February)

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months and 0.88–1 for other months. Our ranges are consistent with the reported

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seasonal data for residential locations in north-central India.38 As our interest was in

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exposure to PM2.5 of outdoor origin we did not include the effect of environmental

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tobacco smoke (ETS) and other sources of exposure to PM2.5 of indoor origin.

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To estimate near-road concentration of PM2.5 we extracted average PM2.5 concentrations

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for major and minor roads from morning (0800–1200) and afternoon (1200–1800)

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spatiotemporal LUR model outputs. Since data were close to normally distributed, we

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fitted normal distributions to the extracted values and obtained probability density

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functions (PDFs) for PM2.5 concentrations at major and minor roads during afternoon

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and morning periods. We sampled from these distributions to obtain near-road PM2.5

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concentrations in order to estimate exposure in the transport microenvironment for

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scenarios 2 and 3. This approach allowed the assignment of near-road PM2.5

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concentrations that captured the spatial variation in exposure experienced by a

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commuter.

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2.3 Time-Activity Data

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In addition to unique home and work locations each simulated individual was assigned a

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value for time spent in each microenvironment. Time-activity data were obtained from a


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travel time survey that was conducted in New Delhi during April 2011. The intent of the

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survey was to assess the time spent by participants in different microenvironments, viz.,

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home, work and transport. Six interviewers from a local NGO (Institute for Democracy

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and Sustainability, IDS) were trained to administer a survey instrument (SI) that was

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translated to Hindi prior to use in the field. A half-day training session was conducted for

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the interview team prior to the launch of the survey campaign. The survey instrument

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was administered to a total of 1012 participants in 19 low and middle-income

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neighborhoods identified in consultation with staff at IDS. In each neighborhood, we

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surveyed between 20 and 50 participants, depending on the size of the neighborhood and

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the size of the team of enumerators available that day. We started from a neighborhood

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landmark and surveyed as many participants as possible, without trying to create a

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representative sample of the population. Individuals over the age of 18 years were taken

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as participants in this study. Informed consent was obtained from all participants before

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the interviewers asked survey questions and responses were noted.

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Estimates for travel times were provided by the survey responses; a probability density

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function (PDF) was fitted to the fraction of time in a day used for commuting (Figure S2,

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SI). We also assumed that total travel time was equally split into morning and afternoon

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peak hours39 and less than 10% of travel time was spent on minor roads. The fraction of

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time spent on minor roads was predicted using a uniform distribution with values ranging

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between 0 to 10%. Travel time fractions were assigned to simulated individuals in

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proportion to the distance travelled between home and work locations calculated using

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the Gravity model. This was done by identifying the decile bin of the distance between a

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pair of home and work locations, followed by assigning a randomly sampled value for

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travel time from the corresponding decile bin of the travel time fraction PDF.

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We also assumed that time spent at a work or educational institution followed a normal

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distribution with mean=8 h and standard deviation=0.5 h. This is consistent with data

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from a previous time activity survey in New Delhi.39 We also assumed that commute

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home to work and return occur during morning (0800—1200) and afternoon (1200—

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1800) hours respectively.



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2.4

Ratio of In-vehicle Concentration to Near-Road Concentration, κvm

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In simple terms, the in-vehicle concentration can be expressed as a multiple of a near-

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road ambient term with an associated error term,40 assuming that there are no within-

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vehicle sources like smoking, as shown in Eq. (3).

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Civ,vm = κvm . Cnv + ε vm

(3)

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where Civ,vm is the in-vehicle (iv) PM2.5 concentration for vehicle mode vm; Cnv is the

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near-road PM2.5 concentration; κvm is the ratio of in-vehicle to near-road PM2.5

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concentration for vehicle mode vm where is vm=vehicle mode (car, bus, auto-rickshaw

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(motorized three-wheeler) and motorized two-wheeler) and εvm is an error term. Intra-

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vehicle variability of in-vehicle concentrations is influenced by factors including

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ventilation system, window position, air-conditioning, vehicle speed, road type and a

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time of day effect.40,

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differences in these factors across vehicle types within a mode as well as variability

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across different modes.26 In the simple representation provided in Eq. (3), these

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variations are captured by a probability distribution for κvm.

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Additionally, inter-vehicle variability exists and is based on

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In order to estimate κvm, we measured PM2.5 concentrations for four common modes of

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travel on a busy arterial route (Figure S3, SI). PM2.5 concentrations were measured using

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a personal sampler (37 mm PTFE within a PEM sampler, SKC Inc., connected to a

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personal sampling pump at a flow rate of 4 Lmin-1) in: bus, air-conditioned car,

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motorized two-wheeler and auto-rickshaw. Filters were conditioned for 24 hours in a

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desiccator before pre- and post-weighing (Satorious GD603). Flow calibrations were

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conducted with a TSI® calibrator (4046) before each sampling trip. The sampling

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equipment was enclosed in a backpack with the sampling inlet at breathing height. Trips

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were made using each mode of transportation during morning and afternoon peak hours

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on a fixed route on a busy arterial (Ring Road, Figure S3, SI). This route was chosen


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because it is one of the highest traffic density routes in the city. Each trip was about 50

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km, with six trips for each of the four modes, covering approximately 1200 km in total.

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We extracted average PM2.5 concentrations for the study route from the LUR models.

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We developed subjective prior distributions based on the small sample of six trips per

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mode to develop distributions for κvm ratios for each mode. A subjective prior

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distribution represents one’s scientific knowledge of an uncertain parameter.42 Measured

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concentrations for each mode were divided by the average roadside concentrations

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obtained from LUR models to obtain κvm ratios. We sampled from these distributions in

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proportion to modal share of each mode to assign a value for the κvm ratio to each

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simulated individual.

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3.0

Results

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Out of 1012 survey participants, 30% belonged to the 18–25 years age group, 45% were

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in the 26–40 years age group, 21% were in the 41–65 years age group and 4% were

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above 65 years of age. 56% participants were male and 44% were females. 73% of

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participants belonged to lower income households (monthly income< 20,000 INR) and

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27% participants belonged to households with a monthly income greater than 20,000

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INR. LPG (Liquefied Petroleum Gas) was used as the household cooking fuel for 89% of

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the participants, 7% used kerosene and 4% used solid fuels (wood or coal) –these

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numbers are consistent with 2011 census data43. This provides evidence that indoor

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sources of air pollution (with the exception of smoking) vary by little in the population.

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Median travel times derived from the time-activity survey for the four travel modes

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ranged from 40 to 75 minutes (Table S1).

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The travel time fraction ft was modeled as a beta distribution that was fit to the survey

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response data. The proportion of time that an individual spent at home (fh) was estimated

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as a residual after accounting for travel and work times, and accounted for the bulk of the

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time spent in a microenvironment in a 24 h period. We assumed that individuals spent 8

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h (SD=0.5 h) at work every working day. Table S1 shows geometric mean and geometric



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standard deviation of measured PM2.5 concentrations in the four travel modes, and mean

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and standard deviation for the estimated κvm ratios for each mode. The histogram of

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observed travel time fraction along with corresponding fitted beta density function are

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shown in Figure S2, SI.

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Descriptive statistics for the annual levels of exposure for the three scenarios are

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provided in Table 1. Figure 2(a) shows the cumulative frequency distribution for average

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daily PM2.5 exposures for Scenarios 1, 2 and 3. Figure 2(b) shows micro-environmental

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contributions to average daily exposure for Scenario 1, Scenario 2 and Scenario 3. The

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average annual PM2.5 exposure for Scenario 1 was 109 µgm-3 (IQR: 97–120 µg m-3).

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This scenario is representative of individuals staying at home, i.e., it provides an estimate

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of the exposure for the non-working population. Scenario 2 assumed that all simulated

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individuals commute to work or educational institutions and in-vehicle exposure is same

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as near vehicle exposure, i.e., all commuters at a given point in space and location,

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irrespective of mode, had the same PM2.5 exposure. In other words, this scenario

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assumed that PM2.5 exposure of pedestrians and bicyclists was same as those of car, auto,

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bus or motorized-two-wheeler users on a given road segment at a given time. The

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average annual PM2.5 exposure for Scenario 2 was 121 µg m-3 (IQR: 110–131 µg m-3).

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PM2.5 exposure at home constituted about 60% (IQR: 55–65%) of total daily exposure

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and PM2.5 exposure while commuting constituted about 5% (IQR: 2–7%) of total daily

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exposure.

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364 Scenario

Mean (IQR) of daily exposure (µg m-3) 109(97-120)

% Contribution of transport microenvironment to mean daily exposure (IQR) 0

% Contribution of home microenvironment to mean daily exposure (IQR) 100

Scenario1

Scenario 2

121(110-131)

5 (2–7)

60(55–65)

Scenario 3

125(114-136)

8(4–11)

58(53–63)

365


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366 367


Table 1: Descriptive statistics for mean daily exposure and contributions of transport and home microenvironments to mean daily exposure for Scenarios 1, 2 and 3.

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For Scenario 3, where we assumed that all simulated individuals commute to work or

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educational institutions and in-vehicle exposure was estimated using Eq. (3) average

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annual PM2.5 exposure was 125 µg m-3 (IQR: 114–136 µg m-3). PM2.5 exposure at home

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constituted about 58% (IQR: 53–63%) of total daily exposure and PM2.5 exposure while

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commuting constituted about 8% (IQR: 4–11%) of total daily exposure. Under Scenario

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3, commuters were on an average exposed to PM2.5 concentrations that were twice as

375

high as the average concentration they were exposed to at home. However, because

376

commute times were small relative to the time spent at home and at work, and because

377

PM2.5 concentrations at home and work locations were also high, the contribution of

378

commuting to total exposure was low.

379

380

To assess temporal and spatial variation in exposures we focused on Scenario 1 as it

381

captures the broad patterns of exposure in the city; accounting for exposure during

382

commutes makes little difference to the basic inferences about temporal (seasonal and

383

diurnal scale) and spatial variation in exposures drawn from Scenario 1.

384

385

There was a strong seasonal component to PM2.5 concentrations in New Delhi9 with

386

winter peaks and summer lows and this is reflected in daily population exposure levels

387

averaged on a monthly basis (Figure S4, SI). Figures 2(c) and 2(d) show cumulative

388

frequency distributions for average daily PM2.5 exposures in winter (November through

389

January) and the monsoon season (July through September) respectively. For Scenario 1,

390

average population exposure levels were lowest in July (59, IQR: 49–71 µg m-3), August

391

(53, IQR: 44–63 µg m-3) and September (56, IQR: 47–67 µg m-3), which are also the

392

months for precipitation from the annual monsoon. Population exposures in the winter

393

months (November (195, IQR: 163–232 µg m-3), December (182, IQR: 152–216 µg m-3)

394

and January (157, IQR: 131–187 µg m-3)) were much higher with daily average

395

exposures about four times higher compared to the monsoon season. As seen from

396

Figure 2(d) differences in seasonal averages for daily exposure between scenarios 1 and



397

3 can be as high as 25 µg m-3. During the winter months mean daily population exposure

398

under Scenario 3 was 207 µg m-3, and over 25% of the simulated population experienced

399

exposure levels greater than 189 µg m-3.

400

401

402

403

404

405

406 407 408 409 410

Figure 2: (a) Cumulative frequency distribution (CFD) for average daily PM2.5 exposures for Scenarios 1, 2 and 3; (b) Average daily exposure by microenvironment for Scenario 1, Scenario 2 and Scenario 3; (c) CFD for winter (December, January and February); (d) CFD for monsoon season (July, August and September). The x-axes for the three CFDs are different.


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411

412

In addition to seasonal effects, annual average PM2.5 population exposures also reflect

413

significant diurnal variation in PM2.5 concentrations in New Delhi. Average hourly

414

exposure levels were lowest in the afternoon (1200–1800): ~80 µg m-3, and highest

415

during morning hours (800–1200): ~120 µg m-3 as shown in Figure 3. The box plots

416

show the lower extreme, first quartile, median, third quartile and upper extreme values44,

417

45

418

hotspots, while higher mixing heights in the afternoon46 result in spatially more uniform

419

and lower levels of exposure.32

. During the morning hours, shallower mixing heights result in traffic related pollution

420

421 422

Figure 3: Box plots for average PM2.5 concentrations for morning (0800–1200), afternoon (1200–

423

1800) and nighttime (1800–0800) hours (Scenario 1).

424

425

Modeling results were used to generate zonal PM2.5 exposure profiles (Figure S5, SI).

426

Box plots for annual average daily exposures for residents of each of the 16 zones (A

427

through PII) are shown in Figure 4(a) and range from 89 µg m-3 (Zone PI) to 128 µg m-3

428

(Zone A). The modeling domain covered a small fraction (109 µg m-3) compared

446

to studies in the United States and Europe (~20 µg m-3).12,

447

lowest if we assumed that individuals stay at home (Scenario 1). Exposure at home

448

constituted more than 58% of average daily exposure for commuters (Scenario 2 and 3).

449

Commuters were exposed to higher levels of PM2.5 during travel, about two times more

450

compared with exposures at home. The finding of high levels of exposure during

451

commutes are consistent with those by Apte et al. (2011),49 who found that PM2.5

452

exposures of auto-rickshaw commuters in New Delhi were comparable to daily

453

exposures in high income countries. However, the overall contribution of the transport

454

microenvironment to average daily exposure is small (5–8%) because exposures at work

455

and home are high as well. Consequently, the influence of travel mode on annual average

456

PM2.5 exposure is limited. Ignoring the mobility of the working population resulted in a

457

small underestimation of the annual average PM2.5 exposure: 11% compared to Scenario

458

2 and 15% compared to Scenario 3.


30

PM2.5 exposures were

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459 460 461 462


Figure 4: (a) Box plots for annual average daily PM2.5 exposure for the 16 zones (b) Boxplot for difference between average daily PM2.5 exposures for winter and monsoon months for the 16 zones. The y-axes for the two plots are different.

463

464


465 466 467


Figure 5: Box plots for annual average PM2.5 exposure as a function of distance from nearest major road (Scenario 1).

468

469

These results also show that the lowest PM2.5 exposures occurred during the month of

470

August and the highest exposures occurred during November. Very high levels of

471

exposure during winter are of obvious concern due to adverse health effects of acute

472

exposure.50 Management actions taken to reduce PM2.5 levels in the winter are likely to

473

yield maximum reductions in the average annual PM2.5 population exposure. Zonal

474

variations provide limited additional information about where reductions in PM2.5

475

concentrations might result in the largest public health benefits. Zones A, B, E, G and H

476

had the highest levels of average annual PM2.5 exposure (116–128 µg m-3), and these

477

zones also experience high levels of exposure (192–211 µg m-3) during winter. These

478

levels are plausibly driven by the significant explanatory variables in LUR models:

479

population density and distance from major roads. This research also quantifies the

480

distribution of annual average PM2.5 exposure across New Delhi. Local sources such as

481

traffic emissions do not alone explain the high levels of ambient PM2.5 during winter.5, 6

482

Analyses of satellite data and back-trajectory analyses have shown that high levels of

483

PM2.5 during wintertime episodes are associated with regional smog51, 52 due to burning


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484

of agricultural residue outside New Delhi. It follows therefore that the focus of air

485

pollution improvements in New Delhi should have a regional basis, in contrast to the

486

current focus on local traffic as the primary source of pollution.

487

488

In summary, there are two key implications of this work:

489

•

Seasonal factors are much more important determinants of variation in long-term

490

average exposure compared to spatial factors; this suggests focus on seasonally

491

varying emissions sources (heating, waste burning, agricultural burning, etc.) and

492

overall importance of managing air quality during winter

493

494

•

Exposures are high for the entire population and ignoring mobility results in an underestimation of population exposure but this impact is small (10 –15%).

495 496

The modeling framework developed here has several limitations including those related

497

to: the limitations of LUR models used; the time-activity surveys; and in-vehicle

498

measurements. A key limitation to the use of LUR models in such exposure simulations

499

is one related to validation. While individual parts of the framework are based on

500

empirical observations, the entire simulation is not validated. Given the expense and

501

logistical difficulties in involved in personal exposure measurements, which could be

502

used for evaluation, simulations such as this one provide a cost-effective approach to

503

evaluate exposure determinants. Further, personal exposure measurements are

504

themselves limited with respect to evaluation of population exposure as they are nearly

505

always limited to short-term measurements (24–48 hours) on a small (and not necessarily

506

representative) population subset. Even with these limitations, however, the evaluation of

507

the simulation results in the future would be useful.

508

509

Outputs from the LUR models that underlie our results are driven largely by spatial

510

variation in two variables—population density and proximity to the road network. There

511

are a number of other particulate sources that contribute to poor air quality in New Delhi

512

including small industry sources, informal burning of foliage in the winters as well as the

513

effect of trash burning,53 that were not used as independent variables in the LUR models

514

since these variables were unavailable. Thus, LUR models used to assign concentrations



515

are unable to capture the impacts of these sources on exposure. Additionally, the outputs

516

from LUR models used in the simulation are based on a PM2.5 measurement campaign

517

carried out in Delhi from February through May 2010. In the absence of LUR models

518

developed for the entire year, LUR output was scaled to obtain data for other months

519

using available monitoring data from a regulatory monitoring station. The high degree of

520

temporal correlation (0.84) between PM2.5 concentrations measured at multiple locations

521

and a fixed site in New Delhi32 support the use of scaling.

522

523

Additional limitations also result from the use of modeling (as opposed to direct

524

measurement using personal monitoring) as a way to assess population exposure. The

525

allocation of time-activity patterns for simulated individuals to three activity categories

526

was a necessary modeling simplification. Individuals do more than stay at home and go

527

to work or schools. However, the goal was to simulate exposures at the population levels

528

for which such coarse allocations might be adequate. Additionally, in estimating

529

exposure in home and work microenvironments, we assumed that for a simulated

530

individual, indoor concentrations at work place and residence can be estimated by

531

multiplying ambient outdoor concentrations with I/O ratios derived from work done at a

532

North Indian city close to New Delhi.37,

533

inherent uncertainties in the I/O ratio,36 and exposure estimates can be refined with an

534

improved understanding of the variability in I/O ratios. We also note that the

535

distributions for κvm ratio used in this work are based on a small sample of measurements

536

and measured in-vehicle concentrations are higher compared to another recent study.49

38

This assumption has limitations due to

537

538

Better LUR models, i.e., with a larger number of predictor variables that capture PM2.5

539

sources or their drivers, coupled with a greater number of PM2.5 sampling sites, might be

540

needed to improve the performance of the LUR models at finer spatial scales. The LUR

541

study had used 39 sites for model development. It had a similar number of sites as other

542

LUR models with the advantage that it did not rely only on regulatory sites (which

543

typically are not optimally located to assess the full scale of variation in levels and in

544

land use). Additional sites, especially those that might capture uncharacterized sources

545

such a foliage and trash burning might improve or confirm the optimality of the LUR


Page 22 of 28

Page 23 of 28


546

models. Though a number of regulatory stations have been added recently, the public

547

availability of data from such stations remains poor, and data are hard to access.

548

Widespread availability of such data could help improve the accuracy of LUR based

549

simulation results and help reveal more targeted management options.

550 551 552 553 554

Acknowledgements

555

This work was carried out with the aid of a grant (105407-9906075-078) from the

556

International Development Research Centre, Ottawa, Canada. Information on the Centre

557

is available on the web at www.idrc.ca. The Auto-21 NCE also provided partial funding

558

for this work along with ORSIL# F07-0010. Staff at IDS, New Delhi, provided logistical

559

support related to conducting the time-activity survey. Sumant Srivastava and Himanshu

560

Lal (School of Environmental Sciences, Jawahar Lal Nehru University) provided

561

assistance with fieldwork.



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244x144mm (150 x 150 DPI)


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PM2.5 Population Exposure in New Delhi Using a ... - ACS Publications

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