Elevated Concentrations of Lead in Particulate Matter on the

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Elevated Concentrations of Lead in Particulate Matter on the Neighborhood# Scale in Delhi, India as Determined by Single Particle Analysis Hongru Shen, Thomas M Peters, Gary S. Casuccio, Traci L. Lersch, Roger R. West, Amit Kumar, Naresh Kumar, and Andrew P Ault Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.5b06202 • Publication Date (Web): 14 Apr 2016 Downloaded from http://pubs.acs.org on April 15, 2016

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Environmental Science & Technology

Elevated Concentrations of Lead in Particulate Matter on the Neighborhood-Scale in Delhi, India as Determined by Single Particle Analysis Hongru Shen,1 Thomas M. Peters,2 Gary S. Casuccio,3 Traci L. Lersch,3 Roger R. West,3 Amit Kumar4, Naresh Kumar,5 and Andrew P. Ault1,6* 1

Department of Environmental Health Sciences, University of Michigan, Ann Arbor, Michigan, 48109 2 Department of Occupational and Environmental Health, University of Iowa, Iowa City, Iowa, 52242 3 RJ Lee Group, Inc., Monroeville, Pennsylvania, 15146 4 Society for Environmental Health, Delhi, India 5 Department of Public Health Sciences, University of Miami, Miami, Florida, 33136 6 Department of Chemistry, University of Michigan, Ann Arbor, Michigan, 48109

*Corresponding author: Andrew P. Ault M6116 SPH II 1415 Washington Heights Ann Arbor, Michigan 48109 Tel: 734-763-4212 E-mail: [email protected]

Page 1 ACS Paragon Plus Environment


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Abstract High mass concentrations of atmospheric lead particles are frequently observed in the Delhi,

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India metropolitan area, although the sources of lead particles are poorly understood. In this

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study, particles sampled across Delhi (August – December 2008) were analyzed by computer-

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controlled scanning electron microscopy with energy dispersive x-ray spectroscopy (CCSEM-

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EDX) to improve our understanding of the spatial and physicochemical variability of lead-rich

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particles (> 90% lead). The mean mass concentration of lead-rich particles smaller than 10 µm

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(PM10) was 0.7 µg/m3 (1.5 µg/m3 std. dev.) with high variability (range: 0 – 6.2 µg/m3). Four

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samples (16% of 25 samples) with PM10 lead-rich particle concentrations >1.4 µg/m3 were

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defined as lead events and studied further. The temporal characteristics, heterogeneous spatial

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distribution, and wind patterns of events, excluded regional monsoon conditions or common

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anthropogenic sources from being the major causes of the lead events. Individual particle

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composition, size, and morphology analysis indicate informal recycling operations of used lead-

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acid batteries as the likely source of the lead events. This source is not typically included in

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emission inventories, and the observed isolated hotspots with high lead concentrations could

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represent an elevated exposure risk in certain neighborhoods of Delhi.


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Introduction

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Lead is one of the most hazardous chemical components of particulate matter (PM) due

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to its high toxicity1 and widespread emissions.2 Exposure to atmospheric lead particles has been

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associated with a variety of adverse health effects, including increased blood pressure3 and

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gastrointestinal effects.4 Of particular concern is that lead exposure can impair the nervous

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system and the neurological development of children.5,6 The largest single source of exposure to

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lead particles was from leaded gasoline prior to its phase-out in most countries by the early

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2000’s.7 Since then, the concentration of lead particles has decreased8 with global lead exposures

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now dominated by non-road vehicular and industrial sources.9 As a result, worldwide blood lead

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levels (BLL) have subsequently decreased.10 In the U.S., leaded gasoline has been phased out

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since the mid-1970s, and the national annual maximum three-month average mass concentration

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of lead particles has decreased from 1.54 µg/m3 in 1980 to 0.03 µg/m3 in 2014.11 Since the

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1970s, BLLs above 10 µg/dl (a high BLL according to the guidelines of the Centers for Disease

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Control and Prevention) in the U.S. have declined more than 70%.12,13 Today, the largest single

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source of lead particles in the U.S. is emissions from the burning of aviation fuel, which remains

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leaded to boost the octane of fuel.14 In 2008, the US national ambient air quality standard

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(NAAQS) was lowered from 1.5 to 0.15 µg/m3 of the lead in total suspended particles (TSP)

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based on demonstrated risk from even small amounts of lead in blood.15

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Despite improvement in much of the industrialized world, high mass concentrations of

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lead particles have been reported in cities of developing countries, even after the phase-out of

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leaded gasoline in most locations.16-19 This is particularly true in the Delhi metropolitan area

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(referred to as Delhi hereafter), reported as one of the most air-polluted area with multiple

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sources in India.18,20-23 Even after more than a decade of the leaded gasoline’s phase-out in Delhi



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by the end of 1998, Pant et al. observed mean lead PM2.5 concentration of 0.6 µg/m3 (0.65 µg/m3

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std. dev.) in 2014 winter, with a maximum of 2.5 µg/m3, at a road site. Pant et al. further showed

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enrichment factors of 300-1500 for lead particles compared to continental crustal concentrations,

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indicating substantial contributions from anthropogenic sources to lead particle concentrations.18

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Khillare and Sarkar found the lead in PM10 in one residential area in Delhi ranged from 0.27

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µg/m3 to 0.46 µg/m3.24 Based on a ten-year dataset from 1998 to 2007, Gupta reported that mass

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concentrations of lead particles in Delhi are higher than those in other cities in India, including

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Mumbai and Chennai.25 Moreover, seasonal variations in lead concentrations in Delhi have been

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observed, with higher mass concentrations in winter and lower in monsoon season.18,22,26,27 Kalra

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et al. showed that 36 of (12%) of 300 school children’s BLL in 2006 were ≥ 10 µg/dl, which

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exceeds the CDC recommended BLL threshold, and that high atmospheric concentrations were

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correlated with high BLL levels.16

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While traditional sources of lead are reported in emission inventories (such as vehicles

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and industrial sources), lead emissions from informal sources, such as, used lead-acid battery

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(ULAB) recycling operations, are not typically included in most inventories.28 Recent literature

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suggests that informal ULAB recycling operation may be an important source contributing to

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lead particle emissions in Delhi.29-31 In these facilities, ULABs are recycled in a crude manner,

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separating lead from batteries and melting them on a stove using inefficient combustion methods

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(often open flames of biofuels or coal) in a backyard. These operation units typically do not have

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appropriate engineering controls, and the use of personal protective equipment, such as

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respirators, is rare.29 Poor hygiene and a general lack of awareness of the risks of exposure to

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lead particles place workers at informal ULAB recycling operations at high risk of lead

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poisoning.29 Gupt and Sahay reported that the current deposit refund system for batteries in the


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Delhi market is ineffective and unable to discourage informal ULAB recycling operations.32

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Rajagopalan found that about 88.5% of lead scrap is sold to these informal operations due to

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higher profits, lower storage costs, and a lack of taxes as opposed to government-sanctioned

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ULAB recycling operations.33 Rao et al. found that in India the mean BLLs of battery workers

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(87 mg/dl) was alarmingly higher than those of control groups (6.3 mg/dl) and >1,000 times

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higher than CDC recommended levels.34

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In this study, we employed single-particle microscopic analysis of particles collected

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passively across Delhi to characterize lead particles and help attribute these particles to specific

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sources. Specifically, this paper examines the geographic distribution of lead-rich particles by

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their physicochemical properties (morphology, size, and chemical composition). Our results are

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critically important for assessing differential risk of exposure to lead in the one of the most air-

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polluted cities in the world.

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Experimental Methods

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Delhi Sampling Sites. PM2.5 mass concentration was estimated at 1,576 household locations

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using PM2.5 measured at 113 sites spread across Delhi and surrounding areas.35 Fifty sites were

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selected for passive sampling using an optimized sampling design that captured the maximum

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semivariance of PM2.5 and minimized spatial autocorrelation.36 The detailed methodological

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selection method was described in Kumar et al.37 Two households, with owners who consented

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to participate in the study, were selected within 500 m of each identified sampling site. Each

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household was paid Rs. 500 as an incentive to participate the study. Passive samplers (mounted

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in a shelter)38 were affixed to a railing of a second-floor balcony (~3.5 meters from the ground)

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and deployed at both households simultaneously for approximately two days. The two-day



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sampling period, relatively short for passive sampling in the US, was possible due to the high

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particle concentrations typical of Delhi. The shorter duration sampling allowed us to use local

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meteorology to help identify potential intermittent and/or local sources. The passive samplers

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collected particles through gravitational settling39,40 on to polycarbonate substrates. These

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substrates were chosen because of their smooth surface, which is preferred for the detection of

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particles during the subsequent microscopic analysis, as well as to minimize the interference of

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elements other than carbon and oxygen. It should be noted that the sites were chosen to capture

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the variance in PM2.5 and without knowledge that lead would be observed in the samples.

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Computer-Controlled Scanning Electron Microscopy (CCSEM) Analysis. For optimal spatial

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heterogeneity analysis of metal-containing particles in Delhi, 25 samples (shown in Figure 1) out

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of the 50 outdoor samples were randomly selected and analyzed by Personal SEMTM or PSEM

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(FEI Aspex, Hillsboro, OR).41 Not all samples were analyzed due to funding constraints. The

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location of the samples analyzed are shown in Figure 1, and the sampling start and end times are

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listed in Table S1. The PSEM combines a scanning electron microscope (SEM), a digital scan

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generator and an energy-dispersive X-ray spectroscopy (EDX) system incorporating a silicon

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lithium (Si [Li]) detector under computer control. For PSEM analysis the sample area was

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divided into a matrix of field images, and the sample was scanned for particles at one base

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magnification (0.58 µm/pixel) using the backscattered electron (BE) signal. To detect metal-

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containing particles, a BE image intensity value was set as the threshold to detect high atomic

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number particles while ignoring lower atomic number particles (primarily carbonaceous). Once

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detected, the diameter of the particle was measured using the electron beam to draw a series of

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cords across the particle. The computer then positioned the electron beam on the centroid of the


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particle, and X-rays were acquired (10 seconds for Pb particles; 2 µm particles. Because the SDD

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is more efficient than a Si[Li] detector in processing X-rays, the shorter acquisition times



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provided similar or increased X-ray counts as compared to the PSEM analysis. All parameters

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obtained by IntelliSEM were similar to those of PSEM, except that carbon (C) and cobalt (Co)

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were measured, but Ba was not. Overall, 13,638 particles were analyzed by IntelliSEM for the

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three samples compared to 3,000 particles measured with the PSEM.

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An intercomparison of IntelliSEM and PSEM is given in the Supporting Information.

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Table S1 shows the number of lead-rich particles in each sample analyzed by PSEM and

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IntelliSEM respectively. Table S1 compares the concentrations by size, percentages for the three

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IntelliSEM samples and the analysis of those samples by PSEM.

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K-means clustering. K-means clustering was applied to the PSEM-data following Ault et al.42

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Briefly, the X-ray spectrum for each particle from the PSEM were imported into MatLab 2014a

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(MathWorks, Inc.) as a matrix of relative elemental abundance for each single particle. These

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data were clustered with k-means using 1 to 50 clusters. The fraction of total error, which is the

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ratio of the sum of distance between each particle and its cluster centroid to the sum of distances

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between all particles and the average spectrum, was calculated to determine the number of

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clusters needed. Figure S1 shows that with the increase in the number of clusters, the fraction of

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total error decreases asymptotically. Fifty clusters were chosen as it represented the optimal

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trade-off between error minimization and chemical composition representativeness after

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checking different numbers of clusters. These 50 clusters were identified as mineral dust,43-47

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metal-rich,42,48-50 salts,51-57 carbonaceous (IntelliSEM only),58-62 and biofuel/biomass burning63-66

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based on spectral similarity of the EDX clusters with studies of specific sources, as well as

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ambient studies in multiple locations.42-44,48,50,54,59,60,64,65,67,68 Based on the similarity of elemental

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composition and source, clusters were merged into classes of particles. During the k-means


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clustering for all PSEM-analyzed particles, one particle class containing lead-rich particles, with

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an average atomic percentage of lead per particle equal to 93%, was identified. At greater than

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seven clusters this cluster was always present with the same number of particles and chemical

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composition, indicating a unique and homogeneous particle class. This particle class was

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identified as a lead-rich particle class for the follow up temporal and spatial analysis.

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Lead-rich particles were selected by a search criterion of Pb > 20% from the three

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samples analyzed by IntelliSEM. This criterion was selected to include all the lead-rich particles

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identified by k-means of PSEM data. Additionally, particles with more than 20% lead content

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were identified by confirming the presence of lead L peaks (Lα at 10.55 keV and Lβ at 12.61

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keV). These lead-rich particle data were further clustered using k-means with 1 to 20 clusters,

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shown in Figure S1. Twenty clusters were merged into three particle classes based on their

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chemical composition: Pb, Pb-Cl, and Pb-Cl-K particles. However, the X-ray energies for the S

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Kα (2.31 keV) peak and Pb Mα1 (2.34keV, peaks overlap, which made it difficult for EDX

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analysis to identify whether S is present, especially if only in small concentrations relative to

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Pb.69 To improve the robustness of these data, off-line Gaussian peak fitting was also used to

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deconvolute the S and Pb peaks and determine relative contributions.70 Further discussion on the

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approach taken to handle the overlap of S and Pb peaks is provided in the Supporting

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Information. Overall, 93% of lead-rich particles were determined to have ‘real’ sulfur signals, in

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addition to lead.

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Results and Discussion

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Lead-rich particles in Delhi. Mass and number concentrations of lead-rich particles in PM10 and

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its fine (PM2.5) and coarse (PM10-2.5) components over the sampling period are shown in Figure



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2. The mean mass concentrations of lead-rich particles were 0.7 µg/m3 in PM10, with 0.4 µg/m3

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in PM2.5 and 0.3 µg/m3 in PM10-2.5. This mean mass concentration is consistent with most of the

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previous studied conducted in Delhi, with further details shown in the supporting information.

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As expected, mass concentrations of lead-rich particles in PM10 varied greatly across 25 sites,

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with a range of 0.0-6.2 µg/m3. Within these 25 samples, four samples with mass concentrations

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of lead-rich particles in PM10 > 1.4 µg/m3 were defined as lead events. At least one event

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occurred in three different seasons: monsoon (July - September); post-monsoon (October -

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November); and winter (December - January). The first lead event observed in September during

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the monsoon season was associated with a mass concentrations of lead-rich particles of 4.4

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µg/m3 in PM10, 2.0 µg/m3 in PM2.5, and 2.4 µg/m3 in PM10-2.5. For the event during the post-

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monsoon period, two sequential lead events in early November were observed with elevated

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mass concentrations of lead-rich particles in PM10 = 6.2 and 1.5 µg/m3, respectively (PM2.5 = 3.4

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and 0.9 µg/m3 and PM10-2.5 = 2.8 and 0.6 µg/m3). The fourth lead event was observed in

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December during the winter season. For this event, the concentration of lead-rich particles was

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1.4 µg/m3 in PM10, 0.94 µg/m3 in PM2.5, and 0.42 µg/m3 in PM10-2.5. The sampling sites of these

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four lead events are highlighted as diamond makers (monsoon and post-monsoon seasons) and

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triangle marker (winter season) in Figure 1. The mean mass concentrations of fine lead-rich

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particles across a 25 samples with and without these four lead events were 0.4 and 0.12 µg/m3,

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respectively.

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Elevated number concentrations of lead-rich particles (> 0.2 µm) in PM10, PM2.5 and

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PM10-2.5 were also observed for the corresponding four lead events. Overall, for all 25 samples

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across three seasons, the mean number concentrations of lead-rich particles were 0.8 #/cm3 in

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PM10 (0.7 and 0.03 #/cm3 in PM2.5 and PM10-2.5, respectively). For the three lead events that were


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observed during the monsoon and post-monsoon seasons, number concentrations (of particles >

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0.2 µm) were 3.6, 6.1, and 2.1 #/cm3 in PM2.5 and 0.2, 0.3, and 0.06 #/cm3 in PM10-2.5

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respectively, whereas 1.7 and 0.06 #/cm3 were observed for PM2.5 and PM10-2.5 in the winter lead

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event. These data show that elevated lead concentrations occur frequently (4 out of 25 samples)

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and are primarily associated with particles in the accumulation mode by mass (66%) and number

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(96%).71

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While a seasonal trend in mass concentration for lead particles, with highest in winter

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season and lowest in monsoon season, has been observed in previous Delhi studies,18,22,27,72 the

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four lead events observed in this study were observed as distinct episodes during all three

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seasons. In these four lead events, the mass concentrations of lead-rich particles in fine mode

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were 2-10 times higher than the average winter PM2.5 lead concentrations from Pant et al. (2015)

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of 0.6 µg/m3, despite 3 out of 4 lead events occurring during the lower overall PM2.5

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concentration monsoon and post-monsoon seasons.18 The observation of events during all three

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seasons sampled indicates that the elevated regional PM10 concentrations due to strong

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inversions during winter are unlikely to be the cause of these sporadic events.

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In addition to regional weather patterns, common anthropogenic sources, such as local

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industrial emissions and road dust resuspension, were considered as potential contributors to the

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lead events. Previous studies have shown that lead in fine particles is mainly from industrial

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sources, while the resuspension of road dust contributes to the majority of lead in coarse

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particles. Further, the spatial distribution of lead-rich particles in PM2.5 and PM10-2.5, shown in

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Figure 3, was investigated to determine if local industrial areas in Delhi or road dust were a

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major contributor to these lead events. The four lead events with the highest mass concentrations

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of lead-rich particles in PM2.5 were heterogeneously distributed in either central or the western



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region of the Delhi metropolitan area. Additionally, the lead event samples with elevated fine

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lead-rich particle concentrations were in close proximity to other samples that had low,

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background concentrations, indicating either an intermittent or local source of these lead-rich

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particles.

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Wind speed and direction analysis indicated that local industrial areas in eastern and

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southeastern regions of the Delhi metropolitan area were unlikely to be major contributors to the

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lead events. During the monsoon lead event, winds were either out of the SW to NW (62% of

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sampling time) or calm (20%) and rarely from the ESE to SSE (15%) direction of the power

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plants and industrial sources. In addition, two rain events occurred during this lead event

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indicating that the source of lead-rich particles was likely local or from the northwestern

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quadrant of the metropolitan region, based on wind direction. The sample immediately after the

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monsoon lead event sample (collection starting one day later in close proximity) had winds

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primarily from the NE (79%), as well as rain, resulting in lead concentrations at background

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levels (PM2.5 = 0.28 µg/m3). During the first post-monsoon lead event the winds were mostly

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calm (69%) or from the ESE (14%), while the second post-monsoon lead event had either calm

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conditions (63%) or winds from the W to NW (28%). The sample after the second post-monsoon

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sample was collected in a nearby location and had a low mass concentration of 0.01 µg/m3 lead-

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rich particle in PM2.5. There were fewer calm conditions (47%) for this sample and winds were

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primarily from between the E and SSE (36%), which is the direction of the power plants and

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industrial sources, ruling out their contribution to the prior lead event. The winter lead event had

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winds from between the SW to NW 66% of the sampling time and calm conditions the remaining

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34%. Taken together three of the four lead events had winds primarily from the W or NW and

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the wind direction provided no evidence that power plants or traditional industries were


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important sources. Moreover, neither of these two hotspots were located in industrial clusters as

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evident from the spatial sampling analysis of Kumar et al.’s study.36 Most industrial processes

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that emit lead also co-emit other species, which were not observed in our study, as shown with

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the single particle analysis below.9,48

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The four lead events with elevated concentrations of lead-rich particles in PM10-2.5 show a

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similar heterogeneous spatial distribution and were mainly distributed in either central or western

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Delhi. However, this heterogeneous spatial distribution of coarse lead-rich particles suggests that

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road dust resuspension is unlikely to be a major contributor to these lead-rich particles. While

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road dust from previously deposited vehicular lead emissions should be evenly distributed across

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the city, a recent paper showed that lead contributed less than < 1.2% by mass to road dust

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emissions in Delhi across seven sites.73 Additionally, the samples were collected a decade after

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the leaded gasoline phase-out in Delhi and recent road-side studies have shown vehicles

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contributions leading to a mean of 0.60 µg/m3.18 This further supports that vehicular emissions

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are unlikely to be a major contributor to these events. Moreover, the morphology of road dust

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particles would be expected to be ‘weathered’ and thus should be in irregular shape and mixed

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with other minerals,74,75 which was not observed in our study as shown with the single particle

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analysis discussed below.

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Based on the intermittent and high spatial heterogeneity in the source driving lead events,

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there is a strong possibility of informal ULAB recycling operations contributing to these lead

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events. As noted above, despite 89% of lead recycled from ULABs in Delhi passing through

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informal recycling units, ULAB recycling is not included as a source of atmospheric lead

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particles in emission inventories.29 Though this could be a major contributor to the lead events

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observed in this study, it is challenging to assess the impacts of these informal operations on air



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quality. This is because informal ULAB recycling units can be built or torn down within a short

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period of time in backyards. Due to their illegal and unregulated nature, it is very difficult to

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identify the specific locations of these informal ULAB sources.29 A previous study suggests that

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informal ULAB recycling operations are scattered throughout Delhi, with a greater number of

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sites in the northwest quadrant of the study area. This corresponds with the wind direction of 3

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out of 4 lead events as discussed above.29

299 300

Morphology and composition of lead-rich particles. Single particle analysis with the CCSEM

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analyses was used to gain further insight regarding the source of lead-rich particles. Two distinct

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morphologies were identified: spherical particles; and fractal agglomerates composed of

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spherical subunits. SEM images, elemental maps (C, O, Pb, and Cl), and EDX spectra

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representative of these two particle types are shown in Figure 4. Both particles had high atomic

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lead content (80% for the sphere and 67% for the agglomerate) and showed strong lead peaks in

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their EDX spectra (Pb Lα1 peak at 10.55 keV and Lβ1 at 12.61 keV, Pb Mα1 (2.34 keV), and Pb

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Mβ (2.44 keV)). For the monsoon and post-monsoon lead event samples analyzed by IntelliSEM

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peaks for both Pb (100%) and S (93%) were confirmed as real signals, despite spectral overlap

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(further details in Supporting Information). For the spherical particle in Figure 4a, S, Pb, and Cl

310

were evenly distributed within the particle, whereas C was only detected from the polycarbonate

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substrate, as shown by elemental mapping.

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These particle morphologies are consistent with previous observations of the lead

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particles emitted from high-temperature processes.76 The spherical shape of the lead-rich particle

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in Figure 4a is indicative of high temperature combustion processes.76 When lead that has been

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vaporized moves to a lower temperature portion of the flame, it condenses and forms a spherical


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shape, which is the thermodynamically lowest energy shape.77 During this process, elevated

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gaseous HCl concentration from biofuels are co-emitted and form Cl that is incorporated in some

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lead-rich particles.78 The second particle type, a fractal agglomerate, is composed of many

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submicron spherical primary particles (Figure 4b). While similar to soot particles, which have

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similar morphology and formation processes,79 these fractal agglomerates are much larger. Lead

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is evenly distributed in each of the submicron particles of the fractal agglomerate, suggesting the

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same source as for the submicron particles. The fractal agglomerate in Figure 4b is likely formed

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by coagulation of individual submicron lead-rich particles during cooling.64

324 325

Physicochemical properties. IntelliSEM analysis was conducted to further probe the

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physicochemical properties of the lead-rich particles in the three monsoon and post-monsoon

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lead events. Most of the lead-rich particles detected fit into one of three classes: 47% Pb by

328

number; 34% Pb-Cl; and 20% Pb-Cl-K. The average spectra and digital color histograms of

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these particle classes are shown in Figure 5. The heights of digital color histograms represent the

330

fraction of particles containing a specific element, and different colors represent different atomic

331

percent ranges. For example, 100% of the Pb-Cl particles contain Cl, while about 6% of these

332

particles contain between 10 – 15% Cl (atomic). All three classes had 30 – 100 % Pb content,

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though oxygen was not measured and some lead maybe present as lead oxide.

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The lead content in these particle classes is higher than previous SEM studies of lead-rich

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particles.49 In studies of waste incineration and smelters, particles with high lead content are

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typically present with other trace heavy metals, such as Zn and Cu, thereby eliminating

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incineration as a potential source.48,80 Emissions from informal ULAB recycling operations

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represent a highly likely source for particles with such high lead content, since Pb (or PbO2



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coated Pb) is the primary component of the negative and positive electrodes for most vehicle

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batteries. The presence of S also corroborates emissions from informal ULAB recycling

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operations since sulfuric acid is typically used as the electrolyte in lead acid batteries. Biofuel

342

combustion is typically used to provide the heat necessary to melt and purify the lead. A biofuel

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flame temperature can easily reach 1000°C, which is significantly higher than lead’s melting

344

point of 600°C and will generate vapors that condense.81 As noted above, biofuels commonly

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emit gaseous HCl, which will condense along with volatilized Pb to form Pb-Cl particles.64,80

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Potassium is typically present in biomass burning particles,65,66 which supports the possibility of

347

biofuel combustion leading to K content in the Pb-Cl-K particles.82

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The size distribution and circularity of Pb, Pb-Cl, and Pb-Cl-K particles are shown in

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Figure 6. Pb particles had a size distribution peaking in submicron size range between 0.2-0.4

350

µm (Figure 6a). Pb-Cl particles were also primarily submicrometer with diameters ranging

351

approximately between 0.3-0.5 µm. Pb-Cl-K particles were the largest class of particles, with a

352

mode peak between 0.5-1.5 µm. These data indicate the lead particles are primarily in the

353

atmospherically long-lived accumulation mode.

354

The circularity of submicron and supermicron Pb, Pb-Cl, and Pb-Cl-K particles is shown

355

in Figure 6b. Circularity is a parameter that measures how close the shape of a particle

356

approaches a circle in the SEM micrograph (which implies a sphere in 3 dimensions), with a

357

sphere equal to one. Overall, submicron Pb particles (mode = 0.7), Pb-Cl particles (mode = 0.7),

358

and Pb-Cl-K particles (modes = 0.8) were more spherical than supermicron Pb particles (mode =

359

0.4). This agrees with the majority of submicron particles being spherical and supermicron

360

particles being fractal agglomerates of the spherical subunits.


Page 17 of 33


361

The above chemical and physical properties of lead-rich particles from the single particle

362

analysis further supports the hypothesis that informal ULAB recycling operations are a major

363

contributor to the lead events with elevated concentrations of lead-rich particles observed in

364

Delhi.

365 366

Atmospheric Implications

367

Lead events with mass concentrations of lead-rich particles in PM10 exceeding 1.4 µg/m3,

368

were observed in Delhi and surrounding regions across three seasons (monsoon, post-monsoon,

369

and winter). The presence during all three seasons indicates the observed lead events are not

370

mainly due to low inversion layers in winter, and the heterogeneous spatial distribution suggests

371

an intermittent and/or local source near the hotspots. The lead-rich particles are unlikely to be

372

from power plants or industrial sources based on wind direction and speed analysis. The wind

373

direction and local/intermittent nature of the high lead samples suggests emission from informal

374

recycling operations of ULAB. The physicochemical properties of the individual lead-rich

375

particles during the three lead events in monsoon and post-monsoon seasons support these

376

informal recycling operations as the likely source based on the morphology (spherical and

377

agglomerates of spherical subunits), size (submicron spheres indicating a combustion source),

378

and chemical composition (lead with chlorine and potassium from biofuel combustion and sulfur

379

from the sulfuric acid electrolyte). The presence of Cl could increase the solubility of Pb-Cl

380

particles in human bodies, leading to higher bioavailability of Pb and increased adverse health

381

effects.83 The submicron size of Pb-Cl and some Pb particles may also allow them to penetrate

382

deeper into the lungs, leading to oxidative stress and damage to endothelial cells.71



Page 18 of 33

383

The atmospheric implications of the hotspots of lead-rich particle are that a widely

384

varying lead concentration is likely occurring within different communities but not detected by

385

traditional monitoring sites. This can lead to potentially large lead exposure variation and health

386

risk on a neighborhood scale. Gupt in 2015 estimated that roughly 70 informal recycling sites are

387

operational in Delhi, and approximately 840 workers are employed in these informal

388

operations.29 In addition to the documented dramatic impacts on worker health,29 the findings of

389

this study indicate that exposure in the surrounding neighborhoods is a major public health risk

390

that needs to be addressed. Haefliger et al. reported 18 children’s deaths from a mass lead

391

intoxication through inhalation and ingestion resulting from the informal recycling of ULAB in a

392

suburb community of Dakar, Senegal between November 2007 and March 2008.19 To prevent

393

similar adverse health outcomes in the densely populated Delhi, additional efforts are needed to

394

assess body burden of lead, especially among children and workers living around the identified

395

hotspots, and engage communities in reducing and preventing exposure to lead-rich particles.

396 397

Supporting Information

398

Tables showing the PSEM and IntelliSEM analysis information for lead-rich particles and

399

comparison of mean Pb concentrations with previous studies; normalized S, Cl, K, and Pb peak

400

contents with different acquisition time. Figures showing fraction of total error from k-means

401

clustering; Gaussian deconvolution of Pb and S peaks for PbSO4 and Pb standards. This

402

information is available free of charge via the Internet at http://pubs.acs.org/.

403 404

Acknowledgments


Page 19 of 33


405

This work was funded by NIH grants: ES014004 and P30 ES005604, pro bono analysis by RJ

406

Lee, and startup funds from the University of Michigan. The authors wish to thank the late Shri

407

M.S. Sharma and Mr. Vineet Kumar for coordinating sampling work in Delhi. The RJ Lee Group

408

is gratefully acknowledged for sharing the new user-friendly IntelliSEM Workbench for off-line

409

particle review and for conducting the IntelliSEM analysis. The Michigan Center for Materials

410

Characterization (MC)2 at the University of Michigan is acknowledged for assistance with

411

electron microscopy measurements.

412



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Page 20 of 33

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Figure Captions

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Figure 1: Map of sampling sites across Delhi, India and surrounding area. The black triangles represent three major power plants in Delhi. Sampling sites are randomly masked with a distance of 250 m. Three sampling sites with highest mass concentrations of lead-rich particle are highlighted as diamond markers and further analyzed by IntelliSEM. Another sampling site with elevated mass concentrations of lead-rich particles but were not further analyzed by IntelliSEM are highlighted as triangle marker. Different colors represent the midpoint time for the two-day sampling period. *Note: One green circle and two green diamond markers are overlapping due to close proximity. The background is from ArcGIS 10.1 with the World Street Map basemap (Sources: Esri, DeLorme, HERE, USGS, Intermap, iPC, NRCAN, Esri Japan, METI, Esri China (Hong Kong), Esri (Thailand), MapmyIndia, TomTom).

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Figure 2: Mass concentrations (µg/m3) and number concentrations (#/cm3) of lead-rich particles in PM2.5, PM10-2.5, and PM10 across the sampling period through monsoon (July – September, blue), autumn (October – November, yellow), and winter (December – February, grey) seasons. Average lead-rich particles in PM2.5 with and without the four lead events were plotted with solid and dashed red lines respectively. Mean lead concentration in PM2.5 from Pant et al. (2015) was plotted as black (winter) and grey (summer) lines.

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Figure 3: The spatial maps of mass concentration (µg/m3) of lead-rich particles in a) PM2.5 and b) PM10-2.5 across Delhi and surrounding area. The background is from ArcGIS 10.1 with the World Street Map basemap (Sources: Esri, DeLorme, HERE, USGS, Intermap, iPC, NRCAN, Esri Japan, METI, Esri China (Hong Kong), Esri (Thailand), MapmyIndia, TomTom).

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Figure 4: SEM images, elemental maps, and corresponding EDX spectrum of two representative lead-containing particles: a) spherical particle and b) fractal agglomerate. Elemental maps of C (red), O (green), Pb (purple), and Cl (cyan) are shown for each particle. Gaussian fittings for S (Kα 2.306 keV) and Pb (Mα 2.342 keV and Mβ 2.442 keV) were plotted as gold and purple areas in each particle’s spectrum. *Note C and O was primarily from the polycarbonate substrate.

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Figure 5: Average spectra and digital color histograms for the different particle types observed for lead-rich particles: a) Pb, b) Pb-Cl, and c) Pb-Cl-K particles. Average spectra (left) are shown as average relative areas of 19 elements analyzed by IntelliSEM (C, Na, Mg, Al, Si, P, S, Cl, K, Ca, Ti, Cr, Mn, Fe, Co, Ni, Cu, Zn, and Pb). The heights of digital color histogram (right) represent the fraction of particles containing a specific element and different colors represent different relative area ranges. Note: Carbon (+) was primarily from the polycarbonate substrate. Sulfur (*) reported at low content in lead-rich particles may be false positives due to the close Xray energies associated with S and Pb peaks.

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Figure 6: Size distribution and circularity by normalized counts of Pb, Pb-Cl, and Pb-Cl-K particles from IntelliSEM. Area scaling factors were applied due to multiple magnifications used in the IntelliSEM analysis. Page 26 ACS Paragon Plus Environment

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Figure 1: Map of sampling sites across Delhi, India and surrounding area. The black triangles represent three major power plants in Delhi. Sampling sites are randomly masked with a distance of 250 m. Three sampling sites with highest mass concentrations of lead-rich particle are highlighted as diamond markers and further analyzed by IntelliSEM. Another sampling site with elevated mass concentrations of lead-rich particles but were not further analyzed by IntelliSEM are highlighted as triangle marker. Different colors represent the midpoint time for the two-day sampling period. *Note: One green circle and two green diamond markers are overlapping due to close proximity. The background is from ArcGIS 10.1 with the World Street Map basemap (Sources: Esri, DeLorme, HERE, USGS, Intermap, iPC, NRCAN, Esri Japan, METI, Esri China (Hong Kong), Esri (Thailand), MapmyIndia, TomTom).

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Figure 2: Mass concentrations (µg/m3) and number concentrations (#/cm3) of lead-rich particles in PM2.5, PM10-2.5, and PM10 across the sampling period through monsoon (July – September, blue), autumn (October – November, yellow), and winter (December – February, grey) seasons. Average lead-rich particles in PM2.5 with and without the four lead events were plotted with solid and dashed red lines respectively. Mean lead concentration in PM2.5 from Pant et al. (2015) was plotted as black (winter) and grey (summer) lines.

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Figure 3: The spatial maps of mass concentration (µg/m3) of lead-rich particles in a) PM2.5 and b) PM10-2.5 across Delhi and surrounding area. The background is from ArcGIS 10.1 with the World Street Map basemap (Sources: Esri, DeLorme, HERE, USGS, Intermap, iPC, NRCAN, Esri Japan, METI, Esri China (Hong Kong), Esri (Thailand), MapmyIndia, TomTom).

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Figure 4: SEM images, elemental maps, and corresponding EDX spectrum of two representative lead-containing particles: a) spherical particle and b) fractal agglomerate. Elemental maps of C (red), O (green), Pb (purple), and Cl (cyan) are shown for each particle. Gaussian fittings for S (Kα 2.306 keV) and Pb (Mα 2.342 keV and Mβ 2.442 keV) were plotted as gold and purple areas in each particle’s spectrum. *Note C and O was primarily from the polycarbonate substrate.

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Figure 5: Average spectra and digital color histograms for the different particle types observed for lead-rich particles: a) Pb, b) Pb-Cl, and c) Pb-Cl-K particles. Average spectra (left) are shown as average relative areas of 19 elements analyzed by IntelliSEM (C, Na, Mg, Al, Si, P, S, Cl, K, Ca, Ti, Cr, Mn, Fe, Co, Ni, Cu, Zn, and Pb). The heights of digital color histogram (right) represent the fraction of particles containing a specific element and different colors represent different relative area ranges. Note: Carbon (+) was primarily from the polycarbonate substrate. Sulfur (*) reported at low content in lead-rich particles may be false positives due to the close Xray energies associated with S and Pb peaks.

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Figure 6: Size distribution and circularity by normalized counts of Pb, Pb-Cl, and Pb-Cl-K particles from IntelliSEM. Area scaling factors were applied due to multiple magnifications used in the IntelliSEM analysis.

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TOC Figure 222x132mm (150 x 150 DPI)

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Elevated Concentrations of Lead in Particulate Matter on the

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