Geochemistry of PM2.5 aerosols at an urban site, Varanasi, in the Eastern Indo-Gangetic Plain during pre-monsoon season

Journal Pre-proof Geochemistry of PM2.5 aerosols at an urban site, varanasi, in the Eastern indo-gangetic plain during pre-monsoon season

Manisha Mehra, Felix Zirzow, Kirpa Ram, Stefan Norra PII:

S0169-8095(19)30607-6

DOI:

https://doi.org/10.1016/j.atmosres.2019.104734

Reference:

ATMOS 104734

To appear in:

Atmospheric Research

Received date:

10 May 2019

Revised date:

1 October 2019

Accepted date:

25 October 2019

Please cite this article as: M. Mehra, F. Zirzow, K. Ram, et al., Geochemistry of PM2.5 aerosols at an urban site, varanasi, in the Eastern indo-gangetic plain during pre-monsoon season, Atmospheric Research(2019), https://doi.org/10.1016/j.atmosres.2019.104734

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

© 2019 Published by Elsevier.

Journal Pre-proof Geochemistry of PM2.5 aerosols at an urban site, Varanasi, in the Eastern Indo-Gangetic Plain during pre-monsoon season Manisha Mehra1, Felix Zirzow2, Kirpa Ram1,* [email protected], [email protected] and Stefan Norra2 1 Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi, India-221005 2 Institute of Applied Geosciences, Working Group of Environmental Mineralogy and Environmental System Analysis, Karlsruhe Institute of Technology, Karlsruhe, Germany *

of

Corresponding author.

ro

Abstract

-p

The knowledge of actual morphological features and composition of aerosols are very important to understand atmospheric chemistry, mixing state and radiative forcing on a global scale. Daily

re

and weekly PM2.5 samples were collected during pre-monsoon season (March -May 2015) from an urban site, Varanasi, located in the eastern Indo-Gangetic Plain to study geochemical and

lP

morphological features of aerosols. PM2.5 samples were analyzed for their elemental concentration by means of a high-resolution Inductively Coupled Plasma-Mass Spectrometry

na

(HR-ICP-MS) whereas particle morphology and composition were ascertained by means of a Scanning Electron Microscope (SEM) coupled with an Energy Dispersive X-ray (EDX)

ur

spectroscopy. PM2.5 concentrations ranged between 22.2 and 70.5 µgm-³ in daily samples,

Jo

whereas it varied between 52.0 and 106.2 µgm-³ in weekly samples. PM2.5 concentrations, except in one daily sample, were found to be higher than the 24-hour threshold limit of World Health Organization (WHO) standards (25 µgm-³). Elemental concentration of Pb and Zn were as high as ~2000 ppm and 3700 ppm, respectively highlighting impact of heavy metal pollution in Varanasi. The mass concentration of Al was the highest amongst all the measured elements followed by K, Fe, Zn, Ti, Pb, Mn, As, Cd. Enrichment Factors (EF) < 5 were observed for Fe, Ti and Mn pointing towards their crustal origin. However, an elevated Fe/Al ratio, with a mean value of 0.82, suggests an enrichment of Fe due to anthropogenic emissions. In contrast, EF values were >5 for K, Zn, As, Pb and Cd suggesting anthropogenic sources as a major contributor to these elements at the study site. The cluster analysis suggests that biomass burning emissions and air mass traversing through northern and western parts of India majorly

Journal Pre-proof contributed to high PM2.5 concentration at Varanasi. In contrast, wind patterns did show lower velocities and different directions (from NW India to Eastern parts of India) during the days with relatively lower PM2.5 concentrations. Morphological features of individual particles reveal an irregular, aggregated and flaky morphology whereas the elemental composition revealed the dominance of aluminosilicates, soot and tarball with inclusion of anthropogenic elements (e.g. Pb) at Varanasi. Keywords: PM2.5 aerosols, urban air pollution, Indo-Gangetic Plain, Geochemistry, ICP-MS,

of

SEM-EDX

ro

Introduction

The rapid increase in population and anthropogenic emissions, due to speedy urbanization, has

-p

led to a substantial increase in concentration of atmospheric pollutants in last two decades over South Asia (Lawrence and Lelieveld, 2010). This has further imposed negative impact on air

re

quality and human health (Grahame and Schlesinger, 2010; Pope and Dockery, 2017; Shindell et

lP

al., 2012; WHO, 2006). The degraded air quality is often characterized by high concentration of pollutants (both aerosols and gases) from natural (e.g. dust out breaks, forest-fire events etc.)

na

and/or anthropogenic aerosols (e.g. vehicular, industrial or agricultural waste and biomass burning etc.) (Ram & Sarin, 2012; Ram et al., 2010, 2012). These emissions, under favorable

ur

synoptic and meteorological conditions, can modify visibility, aerosol chemistry, optical properties and radiation budget on a regional as well as global scale (Lawrence & Lelieveld,

Jo

2010; Sinha et al.,2013).

The Indo-Gangetic plain (IGP), extending from Rajasthan in west to Bihar in east, is characterized by emissions from anthropogenic activities, specifically biomass burning emissions during post-monsoon and winter seasons (Bikkina et al., 2017; Gustafsson et al., 2009; Kaskaoutis et al., 2014; Rajput et al., 2011; Ram and Sarin, 2015; Rengarajan et al., 2007; Sharma et al., 2017; Venkataraman et al., 2006). In addition to local emission sources, IGP region also is affected by aerosols originating from distant sources especially during premonsoon season (Chinnam et al., 2006; Ram and Sarin, 2012, 2010; Sharma and Kulshrestha, 2017; Srivastava et al., 2014). This vast heterogeneity in emission sources, coupled with dust storm events during pre-monsoon, led to generation of a complex heterogeneous aerosol system and changes in morphology, composition, size-distribution, mixing state as well as evolution of

Journal Pre-proof aerosols over IGP (Dey et al., 2004; Fu et al., 2012; Pandithurai et al., 2008; Ram et al., 2010; Ram et al., 2016). There are several studies wherein authors have tried to understand aerosol chemical composition as well as their impact on air quality over different parts of India, including IGP (Chinnam et al., 2006; Das et al., 2015; Dey & Tripathi, 2007; Dey et al., 2004; Ram & Sarin, 2012, 2015; Srivastava et al., 2014; Tripathi et al., 2006). However, the presence of mixed emission sources in the region has made characterization and quantification of emissions from specific sources rather a challenging task. It is pertinent to state that the physico-chemical and optical characteristics of aerosols are

of

very different during dust and non-dust events in the IGP (Chinnam et al., 2006; Ram et al.,

ro

2012, 2016; Srivastava et al., 2011) and thus, are likely to have implications on regional radiation budget and climate. In addition, presumed morphological characteristics as well as

-p

mixing state of aerosol for estimating aerosol direct radiative forcing (DRF) using radiative

re

transfer models are likely to add large uncertainties to estimates. For instance, a recent study by Srivastava et al., 2017 reported that assuming dust particles as spherical (chosen as a default

lP

shape while estimating direct radiative forcing (DRF)) in radiative transfer models biases top of atmosphere (TOA) warming over IGP. Furthermore, it is also reported that change in mixing

na

state of aerosols (from external mixing to internal or core shell mixing) results in surface dimming over IGP (Srivastava et al., 2018) which is attributed to an enhancement in extinction

ur

cross-section of aerosol due to change in mixing state over the region. Thus, it becomes imperative to understand chemical composition, morphology and mixing state of aerosols for

Jo

better estimation of aerosol DRF over a region using these models. This can only be achieved by robust studies related to and morphological properties. However, there is paucity of data over IGP on morphology, mixing state and its composition limiting our understanding about estimation of optical properties and associated radiative forcing (Mishra et al., 2008; Mishra and Tripathi, 2008; Srivastava et al., 2018, 2017). In the current study, a campaign was conducted during pre-monsoon (March - June 2015) season with an aim to assess and understand the geochemical behavior of aerosols and their morphological features over eastern IGP. To accord our objectives, PM2.5 samples were analyzed for elemental composition and morphological features within the region. We calculated enrichment factors (EFs) for different elements whereas individual particles were characterized

Journal Pre-proof for morphology and chemical composition through scanning electron microscope coupled with an energy-dispersive X-ray spectrometry (SEM-EDX). 2. Methodology 2.1 Sampling strategy Aerosol sampling was carried out at a representative urban site in Varanasi (25° 18′N, 82° 59′E) in the eastern-IGP (Figure 1). Situated at the western bank of river Ganga, the city comprises a very high population of about 1.2 million inhabitants. Daily PM2.5 samples were collected (n=8)

of

from 11 March 2015 to 18 March 2015 and weekly (n=10) from 19 March 2015 to 29 May 2015.

ro

Sampling was carried on terrace (~10 m above ground) of the Institute of Environment and Sustainable Development (IESD). The sampling site at IESD is located inside the Banaras Hindu

-p

University campus, situated in southern part of the city. Banaras Hindu University is one of the largest campuses in India with mixed residential and commercial sprawl surround the monitoring

re

site.

lP

A Mini Volume Sampler (MVS, Leckel GmbH, Germany) was used for collecting PM2.5 samples (particulate matter with aerodynamic diameter ≤ 2.5 μm) (Chen et al., 2016). The

na

sampler was operated at an average flow rate of 20 liters per hour. All PM2.5 samples were collected on pre-weighed (at room temperature: ~ 25 °C and relative humidity, RH of ~ 45%)

ur

quartz fiber filters (Whatman Inc., Maidstone, UK). Daily PM2.5 samples were collected on circular filters of 25 mm diameter (PMX INLET/25 mm MINI), whereas weekly samples were

Jo

collected on filters with 47 mm diameter (PMX INLET/47-50 mm MINI). Exposed filters were placed into cassettes and were subsequently gravimetrically analyzed in a temperature and humidity-controlled room (with temperature ~25°C and humidity of 45%) using a microbalance (Sartorius SE2-F, Germany) with a precision of 0.1µg to get the aerosol mass deposited on the filters. The PM2.5 concentrations were obtained by dividing the deposited aerosol mass on the filter with total volume of air sampled. 2.3 Elemental Composition Elemental concentrations of Al, K, Ti, V, Mn, Fe, Co, Ni, Ba Cu, Zn, As, Cd, Pb, Sn, Tl, Cs, Sb, Rb, Ga, Sc, Y, Sr were analyzed by a high-resolution Inductively Coupled Plasma Mass Spectrometer (HR-ICP-MS, Axiom, VG Elemental) (Schleicher et al., 2012). For chemical

Journal Pre-proof analyses, half of the 25 mm filters and a quarter of the 47 mm filters were used. These parts of PM2.5 filter samples were cut into small pieces and were digested using a mixture of concentrated HNO3, HF and HClO4 (Merck, Suprapur) and were further diluted with HNO3 (1% v/v). For digestion, 2 ml HNO3 (65% suprapur (spp)) was added to oxidize all the organic components present in aerosols. The volume was reduced with filters still wet following addition of 1.5 ml HF (40% spp) and 0.3 ml HClO4 (70% spp) to the mixture and was heated to dryness. This step was repeated twice with 0.5 ml HF and 0.5 ml HClO4. Subsequently, 2 ml HNO3 (65% spp) was added to the mixture and the volume was reduced to dryness. Thereafter, 2 ml HNO3 (1% spp)

of

was added to the residue and solution was quantitatively transferred with 1% HNO3 into a 10 ml

ro

volumetric flask. In addition, blank filter and reference material (SRM 1649A - urban dust; GXR2 - soil) were also processed simultaneously for assessing recovery and quality control. All

-p

the reported elemental concentrations are corrected for methodological blank values. Certified multi-element standards (CRM-TMDW-A, High Purity Standards, Charleston, SC, USA, and

re

QCP 050–1, Promochem, Wesel, Germany) were used for calibration. Quality control was

lP

performed with the international standard materials GXR-2 (soil) and SRM 1649a (urban dust) acquired from NIST (National Institute of Standards, USA). Recovered concentration of

na

elements from standard material were in average within ±5% of the certified values for the elements K, V, Mn, Fe, Co, Rb, Sr, Cd; within ±10% for the elements Cu, Zn, Cs, Sn, Ba, Pb, within ±15% for the elements Sc, Ni, As, Sb, within ±20% for the elements Li, Ti, and within -

ur

30% for Al and Tl and -50% for Ga, although some of the analyzed elements show larger

Jo

deviations from the certified concentrations. Thus, they can be used to analyze statistical trends but not for comparisons with threshold values. The detailed analytical description of elemental analysis by HR-ICP-MS can be found in a previous study (Schleicher et al.,2012). 2.4 Scanning Electron Microscopy Scanning electron microscope coupled with an Energy-dispersive X-ray spectroscopy (SEMEDX; model: FEI Quanta 650 FEG Instrument, Germany) was used for morphological and elemental characterization of individual particles. Daily PM2.5 samples were used for SEM-EDX analysis. The depositional density of particles even on these samples were very high for SEM analysis thus, aerosols were separated from the filters by attaching an adhesive tape on filter surface. The particles on the adhesive tapes were further used for qualitative analysis by SEM to

Journal Pre-proof understand the morphology, agglomeration of particles, flakiness and roundness etc. whereas EDX spectroscopy provides composition of these particles. The SEM imaging enables an ultrahigh-resolution imaging and analysis of non-conducting specimens without much timeconsuming preparation. The SEM-EDX analysis was performed at the Laboratory for Electron Microscopy (LEM) at the Karlsruhe Institute of Technology, Germany (Schleicher et al., 2012). 2.5 Enrichment Factor and cluster analysis The elemental concentrations obtained from ICP-MS were further used for calculating the

of

Enrichment Factor (EF) as well as for cluster analysis. Both the analysis (EF and cluster analysis) was performed in order to identify probable sources of the elements (i.e. crustal or

ro

anthropogenic sources). EF represents the relative abundance of an element in the atmosphere to

-p

that in the earth’s crust (Eq 1). The EF was calculated using Al as reference element, based on the crustal composition given by Mason and Moore (1967).

re

X [ crustal⁄Al

lP

EF =

X [ 𝑎𝑡𝑚⁄Al atm ]

crustal

]

(1)

The EF has been categorized into different classes (Arimoto et al.,1990; Sutherland et al.,

na

2000) based on the extent of enrichment from anthropogenic activities as follows: EF<5 =crustal elements with no or minimal enrichment; 5 < EF ≤ 10 = significant enrichment due to

ur

anthropogenic activities; 10 < EF ≤ 20= very high enrichment due to anthropogenic activities;

Jo

EF ≥ 20 = extremely high enrichment due to anthropogenic activities. Based on above classification, elements having EF values < 5 are considered to be of crustal origin (crustal elements), whereas those with EF >5 are considered to be of non-crustal/anthropogenic (Anthropogenic elements) origin (Arimoto et al., 1990; Likuku et al., 2013; Sutherland et al., 2000). The cluster analysis was performed using a software package STATISTICA (Release 8.0) by Statsoft, Inc (USA). Before cluster analysis with the Ward method (Ward, 1963), the measured elemental compositions of PM2.5 from ICP-MS were normalized (z-transformation). Finally, a dendrogram was constructed in order to assess the cohesiveness of the clusters, which represent correlations among different elements.

Journal Pre-proof 2.6. Wind trajectory analysis The 5-days air mass back trajectories were performed (at 500 m above ground level) by NOAA Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) for the measurement period (Draxler and Hess, 1998) in order to recognize potential source regions contributing to PM2.5 concentration at the sampling site. The HYSPLIT was run with a combination of archived data and meteorological input from NCEP/NCAR global reanalysis datasets to identify possible transport pathways of aerosols. Furthermore, cluster wind trajectory (CWT) analysis, obtained from a geographic information system (GIS) based software, TrajStat (Wang et al., 2009), was

of

used to assess the impact of synoptic air mass on PM2.5 concentration at Varanasi. In this

ro

method, 5 days air mass back trajectories, obtained from HYSPLIT, were divided into four major clusters of air mass reaching Varanasi. The details of the model can be obtained from

-p

Stohl.,1996.

re

3. Results and Discussion

lP

3.1 Variability in PM2.5

The concentration of PM2.5 observed in both daily (11 March 2015 to 18 March 2015) and

na

weekly (19 March 2015 to 29 May 2015) samples are shown in Figure 2. The box-whisker plots show mean, 25th and 75th percentiles of PM2.5 for daily and weekly samples. The PM2.5 mass

ur

concentration ranged between 22.3 to 70.5 µgm-³ (average 48.2 ± 14.1 µgm-³) in daily samples and between 52.0 to 106.2 µg m-3 (average 74.9 ± 18.3 µgm-³) in weekly samples. The PM2.5

Jo

concentration in daily samples, except one, are within the prescribed 24-hour threshold limit approved by National Ambient Air Quality Standard (NAAQS: 60 µgm-³ for PM2.5). In contrast, PM2.5 mass concentration in weekly samples (except week #4, #6 and #9) are much higher than 24-hour limit of NAAQS. Furthermore, PM2.5 mass concentration in daily (except one) as well as in weekly samples are higher than the prescribed 24-hour limit of World Health Organization (WHO) (i.e. 25 µg m-3 for PM2.5) (Table 1). The temporal variability in PM2.5 concentration in daily as well as weekly samples is presented in Figure 3. The average PM2.5 mass concentration in weekly samples is higher than average mass concentration in daily samples. Relatively higher precipitation rate (as obtained from Tropical Rainfall Monitoring Mission; TRMM)) were observed in a 50 x 50 km grid centering over Varanasi between 8 March to 16 March 2015 (Figure 3) which led to washout of suspended particulate matter and resulted in lower PM2.5

Journal Pre-proof concentration in daily samples at Varanasi. The PM2.5 mass concentration observed in weekly samples at Varanasi are comparable to those reported in earlier study by Sen et al., 2014 and Murari et al., 2015. It is pertinent to state here that the University campus is relatively less influenced from the traffic and other emissions present within the city. Thus, PM2.5 concentrations in the city are expected to be higher than those in the campus. The surface temperature, wind speed and wind direction obtained from an Automatic Weather Station (AWS) installed at air quality monitoring station of Central Pollution Control Board (CPCB), Varanasi (located at a distance of ~9 km from sampling site) was analyzed to

of

comprehend the role of meteorology in observed variability of PM2.5 concentrations (Sen et al.,

ro

2014). The ambient temperature ranged between 26 °C to 32 °C during the entire study period. The maximum and minimum wind speed during weekly and daily measurements were 3.0 and

-p

1.4, 2.8 and 0.8 ms-1 respectively. The wind rose diagram shows that the study site experienced

re

Northerly, Westerly, West-Southwest winds along with the North easterly and East-NE winds during the measurement period. In order to get insight into wind and transport pattern, weekly

lP

sampling period was classified into weeks with high and low PM2.5 concentrations based on the NAAQS 24-hour limit for PM2.5 (i.e. 60 µgm-³). The weeks with PM2.5 concentrations above the

na

NAAQS 24-hour limit were considered as weeks with high PM2.5 concentration and vice versa. Wind rose diagrams were plotted separately for weeks with high and low PM2.5 concentration

ur

(Figure 4a and b). The analysis suggests the dominance of North-Westerly, West-southwest and Northerly winds during weeks experiencing high PM2.5 concentrations. However, a complete

Jo

reversal of the wind direction (from South-easterly to Easterly and East-northeast winds), was observed during weeks with lower concentration of PM2.5 at Varanasi. Furthermore, wind cluster analysis was performed to identify the potential source regions contributing to aerosol mass concentration at Varanasi during weeks with high and low PM2.5. During the days with lower PM2.5 concentrations, the potential air masses were either of primarily local or marine origin (Figure 4c). In contrast, air masses coming from upwind sites, experiencing open fires during pre-monsoon season in northern and western India (discussed in section 3.2) are likely to be the major contributor of aerosol mass during the days experiencing higher PM2.5 concentration (Figure 4d). 3.2 Elemental composition of PM2.5

Journal Pre-proof The mass concentrations of PM2.5 and selected elements (Al, K, Ti, V, Mn, Fe, Co, Ni, Ba Cu, Zn, As, Cd, Pb, Sn, Tl, Cs, Sb, Rb, Ga, Sc, Y, Sr) are presented in Table 1 for both daily and weekly samples. Amongst all analyzed elements, Al was found in the highest concentration with an average concentration of 579 ± 450 ng m-3 in daily and 937 ± 596 ng m-3 in weekly samples. Fe was second most abundant element with an average concentration 458 ± 203 ng m-3 in daily and 707 ± 365 ng m-3 in weekly samples. Apart from these elements, average concentrations of K, Ti, Mn, Zn, Pb, As and Cd were observed to be 838 ± 431 ng m-3, 37.6±14.9 ng m-3, 1.4±6.5 ng m-3, 68.4± 34.5 ng m-3, 39.2 ± 14.7 ng m-3, 3.5 ± 2.3 ng m-3, 2.3 ± 1.9 ng m-3 in daily and

of

1600 ±470 ng m-3, 62.0±34.9 ng m-3, 24.0±9.4 ng m-3, 122 ± 35 ng m-3, 50.6 ± 24.4 ng m-3, 2.8 ±

ro

1.0 ng m-3, 1.9 ± 0.9 ng m-3 in weekly samples respectively. The concentration of Pb in Varanasi is comparable to those reported over other Indian cities like Agra, Chennai and Kolkata

-p

experiencing high traffic load (Das et al., 2015; Srimuruganandam and Shivanagendra, 2011;

re

Varshney et al., 2016).

For weekly samples, the correlation between mass concentrations of measured elements

lP

was further examined, considering Al as an index of geogenic sources. The mass concentration of Fe, Ti and Mn exhibits a linear relationship with Al, suggesting geogenic sources of these

na

elements at the sampling site (with r2 value of 0.95, 0.95, 0.83 respectively) highlighting their crustal origin. On the other hand, poor correlations between Al and K, Zn, As, Cd, Pb

ur

respectively suggest that these elements are influenced by anthropogenic emission sources. This is further, corroborated by the observed EFs for these elements. The EF values of selected

Jo

elements in weekly and daily samples are presented in Figure 5. The EF values for Ti, V, Mn, Co, Ni, Ba were lower than 5 during the study period suggesting major contribution from geogenic sources. In contrast, the EF of K, Cu, As, Cd and Pb were higher than 5 suggesting contributions from anthropogenic emissions. The emissions from fossil fuel/biomass combustion, smelting, sewage sludge and waste incineration as well as extensive use of pesticides are likely to contribute to K, Cd, As, Pb, Zn, Cu at the study site (Councell et al.,2004; Cyrys et al., 2003; Wåhlin et al., 2006). It should be noted that there was no dust storm event during pre-monsoon season of 2015 and thus, transport and contribution of mineral aerosols from the distant sources were minimal.

Journal Pre-proof We have further analyzed elemental ratios of mass concentration of different elements to that of Al in ambient aerosols to decipher the sources of these elements, especially crustal sources. Fe/Al ratio ranged between 0.6 to 2.5 for daily samples whereas the ratio ranged between 0.6 to 1.2 for weekly samples. Notably, Fe/Al ratio, in all daily as well as weekly samples, are found to be higher than reported value for upper continental crust (UCC value of Fe/Al = 0.44; (Mclennan, 2001)) suggesting an enrichment of Fe at the study site. In addition, the observed Fe/Al ratios are also higher than the Fe/Al ratio in the soils of north Indian Plains (as inferred from composition of river sediments) which has a narrow range between 0.55 to 0.63

of

(Sarin and Borole, 1979). Furthermore, a characteristic Fe/Al ratio, in the range of 0.55–0.80, has

ro

been reported for mineral dust derived from desert regions in western India (Kumar & Sarin, 2010). Therefore, the observed elevated Fe/Al ratios, in this study, provide an evidence of Fe

-p

enrichment in aerosols due to sources other than mineral dust and/or soil at Varanasi (in spite of EF<5). An enriched Fe/Al ratio of 1.1 have also been reported in urban particulate matter which

re

was dominated by particles emitted from diesel and fuel oil combustion processes (Desboeufs et

lP

al., 2005). Furthermore, enrichment of Fe in fine-mode aerosols, especially water-soluble Fe, may also be associated with chemical processing of aerosol particles during transport (Kumar

na

and Sarin, 2010). Therefore, an enrichment of Fe is attributed to fossil fuel combustion activities and chemical processing of aerosols in the IGP.

ur

The observed Ti /Al ratio (range: 0.05 to 0.07) was found close to the values reported for UCC (0.05) (Mclennan, 2001) indicating mostly geogenic sources. In contrast, K/Al ratio (range:

Jo

0.85 to 4.41) was found to be much higher than values reported for UCC (0.35) (Mclennan, 2001). Potassium is generally used as tracer for biomass burning emissions. Furthermore, open agricultural residue burning, is a common practice in the states of Punjab and Haryana, located in upwind regions of sampling site during pre-monsoon season after wheat harvest (Kumar et al., 2011; Rajput et al.,2014). We have analyzed the open fire counts retrieved from VIIRS in a rectangular grid covering India and nearby regions of Pakistan, Nepal and China, during the study period. The analysis showed a large number of open fires located in nearby regions and in upwind regions of the sampling site (Figures 4c & d). In addition, air mass back trajectories passed over these regions (Figure 4c & d) and thus, an enrichment of K at Varanasi can be attributed to the open biomass burning activities during pre-monsoon season.

Journal Pre-proof 3.3 Cluster analysis The dendrogram related to sources of elemental constituents of PM2.5 is presented in Figure 6. The first cluster (C1) represents elements (Sn, Zn, Pb, As, Cd, Cu, Cs, Tl) of mostly anthropogenic origin and are also supported by EF values. The elements like Cd, Cu and Zn are generally considered to be of anthropogenic origin and are released by mechanical abrasions of metal structures, tyres and brake linings etc. (Councell et al., 2004; Monaci and Bargagli, 1997; Wåhlin et al., 2006). Few elements in C1 are also emitted on larger scale from insecticide, fungicide bactericide, paints, rubber industry, paint, batteries as well as lubricants, electroplating

of

(Cyrys et al., 2003). Apart from these, possible sources for Cd includes household-waste

ro

combustion, fossil-fuel burning and that for Pb includes fuel combustion, industrial processes

an anti-knocking agent but is phased out now.

-p

and waste incineration (Cyrys et al., 2003). Leaded gasoline was used in the petroleum fuels as

re

The second cluster (C2), was sub-divided into three sub-clusters and exhibit the combination of natural and anthropogenic sources. For example, Antimony (Sb), present alone in

lP

cluster 2.1, has relatively lower concentration in earth’s crust, and is mostly derived from coal combustion and/or automobile exhaust as well as brake abrasion (Banerjee, 2003). Furthermore,

na

the sub cluster (C2.2) consists of V, Ni, Ba and Rb which represent elements of mixed origin (geogenic and anthropogenic). However, the result of cluster analysis for elements in C2.2

ur

appears to be different than the results from EF analysis. For example, the presence of Ba and Rb in aerosols is generally related to geogenic sources, as also suggested by the EF. Whereas, the

Jo

elements such as V and Ni are often indicators of fossil fuels but are also present in sub cluster C2.2 and have EF values < 5 making it difficult to explain with the existing data. However, since the sampling site represents an urban environment, the mixing of crustal elements with vehicular, industrial emissions as well as solid waste burning is likely to happen at study site (Hernandez & Rodriguez, 2012; Reimann et al., 2010). The third cluster, C2.3, contains several elements such as Al, Sr, Ti, Fe, Ga, Y, Mn and thus, points towards geogenic contributions including weathering of rocks and soil (Mclennan, 2001; Reimann et al., 2010). This fact, for some of the elements, is also supported by respective EFs. For example, Al is a ubiquitous and the most abundant metallic element in the earth crust. Similarly, Ti mainly originates from geogenic sources and is widely distributed throughout the

Journal Pre-proof world but has relatively lower abundance (e.g. earth’s crust consists of about 0.6% of Ti only) (Mclennan, 2001). The concentrations of Co, Ga and Mn are rather more influenced by geogenic pathways like weathering or geogenic dust but also have contributions from coal combustion and traffic emissions. Thus, overall, cluster C2.3 is composed of elements with geogenic origin. 3.5 Morphological characteristics of PM2.5: The morphological features such as shape and size of aerosol particles are very important for the estimation of optical properties of aerosols and subsequently, their impact on earth’s radiation

of

balance (Mishra et al., 2008; Mishra et al., 2012; Mishra & Tripathi, 2008). However, an indepth knowledge about the evolution of the aerosol, in terms of their shape, size and chemistry

ro

during the course of time and transport is still lacking over Indian regions. We have used SEM-

-p

EDX spectroscopy technique to understand the specific morphological characteristics of aerosols over Varanasi. The results indicate that aerosol particles over Varanasi have diverse, irregular,

re

spherical, aggregate and flaky shapes (Figure 7). However, a few particles have spheroidal aluminosilicates which might have been generated by high temperature combustion processes in

lP

industries (Pipal et al., 2019). The elemental analysis by EDX Spectroscopy technique shows the dominance of C, O, Al, Si in aerosols. Apart from these abundant elements, Fe and Pb was also

na

found to be present in a few samples. Based on elemental composition analysis by EDX as well as morphological features by SEM, these particles have been classified broadly into three

ur

categories, i.e. aluminosilicates, soot and tarball particles.

Jo

3.5.1 Aluminosilicates and mineral particles The PM2.5 samples at Varanasi are characterized by high content of Al and Si with varying concentration of C, Cu and thus, are dominated by aluminosilicates minerals. These aluminosilicates mostly originate from geogenic sources by wind erosion of crustal surface (Cong et al., 2009; Pipal et al., 2019; Xu et al., 2018). Mineral particles with non-spherical or irregular shape were also observed at Varanasi which may be associated with natural or anthropogenic (such as construction activities) sources in and around the site (Li et al., 2016). However, a few aluminosilicates particles were found to have spheroidal shape as well in this study (Figures 7 a & b). Overall, these particles were found to be rich in Al, Si, O, C, Mg, and S with inclusion of Fe and K (Figure 7c). Furthermore, the presence of K in these samples suggests contribution from biomass burning emission in the area (cf. section 3.2), probably as an external

Journal Pre-proof mixing and/or potash attachment of biomass burning aerosols with mineral dust particles. The elevated K/Al ratios in aerosol samples further corroborates SEM-EDX findings of this study. 3.5.2 Soot particles In comparison to aluminosilicates and mineral particles, soot particles possess typical chain like aggregates and thus, can be easily identified (Figure 7d). These aggregated soot particles are produced by direct emissions from combustion activities, either biomass burning/ fossil fuel or both (Cong et al., 2009; Pipal et al., 2011; Xu et al., 2018). In an earlier study by China et al.,

of

2013, soot particles have been characterized into 4 classes: embedded soot (heavily coated), partly coated (soot voids are filled by coating material), bare soot (no coating) and soot with

ro

inclusions (i.e. soot is mixed but not uniformly coated). The quantification of extent of coating is

-p

not feasible and this classification was completely based on the morphological and visual estimation of coating by SEM-EDX analysis as used in previous studies (China et al.,2013; Xu et

re

al., 2018). Based on morphological characteristics, soot particles in the present study are likely to be partly coated with anthropogenic species, with coating material present in the voids without

lP

completely engulfing the soot spherules. Similar aggregates of soot particles have also been

3.5.3 Tarball particles

na

observed over Pune and Agra (Pipal et al., 2014; Pipal et al., 2019).

ur

Spherical and amorphous tarball particles were also observed at Varanasi (Figure 7e). The composition of tarball was found to be dominated by C and O with trace amount of S and K

Jo

elements, and found to be similar to those of fine mode particles (Pipal et al., 2011; Po´sfai et al., 2004). These particles are likely to be transported to Varanasi from the upwind sites experiencing open biomass burning activities in the pre-monsoon season (discussed in section. 3.2; Figure 4c & d). However, trace amount of Pb, attached with tarball, was also detected in these particles indicating anthropogenic emissions, such as automotive exhaust, smelting and waste incineration processes (Banerjee, 2003). High EF values of Pb also points towards its origin from anthropogenic activities. A recent study concluded that mixture of toxic pollutants such as polyaromatic hydrocarbons (PAHs) and metals in complex environment jointly are likely to increase the toxic potential of PM2.5 thereby, imposing serious effects on human health (Jin et al., 2019).

Journal Pre-proof 4. Conclusions: Daily and weekly PM2.5 samples collected during pre-monsoon season (March - May 2015) from Varanasi in the eastern IGP revealed that PM2.5 concentrations, except one daily sample, exceeded 24-hour threshold limit of WHO. The cluster analysis and wind rose diagrams suggest that biomass burning emissions in northern and western parts of India are major contributors of aerosols at Varanasi during weeks with high PM2.5 concentration. In contrast, relatively lower PM2.5 concentrations were associated with the reversal of wind pattern and air mass from local, central India and marine regions. The elemental composition and their EF values together

of

suggest the presence of a mixed sources (geogenic as well as anthropogenic) for PM2.5 at

ro

Varanasi. In addition, SEM imaging of individual particles reveal irregular, aggregated and flaky shapes whereas EDX analysis revealed the dominance of aluminosilicates, soot and tarball

-p

particles as well as surface attachment of anthropogenic elements in a few particles. Surprisingly,

re

Fe/Al ratio was found higher than reported value of upper continental crust in both daily and weekly samples suggesting an enrichment of Fe due to anthropogenic combustion sources (either

lP

fossil fuel and biomass burning or both) in the region. Furthermore, air mass traversing the region experiencing open biomass burning activities before reaching Varanasi led to K

na

enrichment in PM2.5 aerosols at the study site. In addition, the study highlights that presence of heavy metals (e.g. Pb, Zn) in PM2.5, along with other toxic pollutants such as black carbon and

Jo

Acknowledgements

ur

PAHs, may increase the toxicity of PM2.5 over the region.

KR thanks Department of Science and Technology, Govt. of India for providing financial support under INSPIRE Faculty scheme (No. DST/INSPIRE Faculty Award/2012; IFA-EAS02). Furthermore, we thank CONNECT programme for Participants of Frontiers of Research Program from Alexander von Humboldt Foundation, Berlin. MM acknowledges the Inspire Fellowship (IF150358) from DST, India. More and above we express our gratitude to Claudia Mößner from KIT Institute of Applied Geosciences for supporting ICP-MS analyses and Volker Zibath from KIT Laboratory for electron Microscopy for supporting SEM analyses. References

Journal Pre-proof Arimoto, R., Ray, B.J., Duce, R.A., Hewitt, A.D., Boldi, R., Hudson, A., 1990. Concentrations, sources, and fluxes of trace elements in the remote marine atmosphere of New Zealand. J. Geophys. Res. Atmos. 95, 22389–22405. doi:10.1029/JD095iD13p22389 Banerjee, A.D.K., 2003. Heavy metal levels and solid phase speciation in street dusts of Delhi, India. Environ. Pollut. 123, 95–105. Bikkina, S., Andersson, A., Ram, K., Sarin, M.M., Sheesley, R.J., Kirillova, E.N., Rengarajan, R., Sudheer, A.K., Gustafsson, Ö., 2017. Carbon isotope-constrained seasonality of

of

carbonaceous aerosol sources from an urban location (Kanpur) in the Indo-Gangetic Plain. J.

ro

Geophys. Res. Atmos. 122, 4903–4923. doi:10.1002/2016JD025634

China, S., Claudio, M., Gorkowski, K., Aiken, A.C., Dubey, M.K., Mazzoleni, C., 2013.

re

Commun. 4, 1–7. doi:10.1038/ncomms3122

-p

Morphology and mixing state of individual freshly emitted wildfire carbonaceous particles. Nat.

Chinnam, N., Dey, S., Tripathi, S.N., Sharma, M., 2006. Dust events in Kanpur, northern India:

doi.org/10.1029/2005GL025278

lP

Chemical evidence for source and implications to radiative forcing 33, 1–4.

na

Cong, Z., Kang, S., Dong, S., Zhang, Y., 2009. Individual Particle Analysis of Atmospheric Aerosols at Nam Co, Tibetan Plateau. Aerosol Air Qual. Res. 323–331. doi:

ur

10.4209/aaqr.2008.12.0064

Jo

Councell, T.B., Duckenfield, K.E.A.U., Survey, U.S.G., Drive, S.V., 2004. Tire-Wear Particles as a Source of Zinc to the Environment 38, 4206–4214. doi:10.1021/es034631f Cyrys, J., Stölzel, M., Heinrich, J., Kreyling, W.G., Menzel, N., Wittmaack, K., Tuch, T., Wichmann, H.-E., 2003. Elemental composition and sources of fine and ultrafine ambient particles in Erfurt, Germany. Sci. Total Environ. 305, 143–156. doi:10.1016/S00489697(02)00494-1 Das, R., Khezri, B., Srivastava, B., Datta, S., Sikdar, P.K., Webster, R.D., Wang, X., 2015. Trace element composition of PM 2.5 and PM 10 from Kolkata–a heavily polluted Indian metropolis. Atmos. Pollut. Res. 6, 742–750. doi:10.5094/APR.2015.083

Journal Pre-proof Das, S.K., Chatterjee, A., Ghosh, S.K., Raha, S., 2015. Fog-Induced Changes in Optical and Physical Properties of Transported Aerosols over Sundarbans, India. Aerosol Air Qual. Res. 1201–1212. doi:10.4209/aaqr.2014.11.0287 Desboeufs, K. V, Sofikitis, A., Losno, R., Colin, J.L., Ausset, P., 2005. Dissolution and solubility of trace metals from natural and anthropogenic aerosol particulate matter. Chemosphere 58, 195–203. doi: 10.1016/j.chemosphere.2004.02.025 Dey, S., Tripathi, S.N., 2007. Estimation of aerosol optical properties and radiative effects in the

of

Ganga basin, northern India, during the wintertime. J. Geophys. Res. 112, 1–16.

ro

doi:10.1029/2006JD007267

Dey, S., Tripathi, S.N., Singh, R.P., 2004. Influence of dust storms on the aerosol optical

-p

properties over the Indo-Gangetic basin 109, 1–13. doi:10.1029/2004JD004924

re

Draxler, R.R., Hess, G.D., 1998. An Overview of the HYSPLIT_4 Modelling System for

lP

Trajectories, Dispersion, and Deposition. Aust. Meteorol. Mag. 47, 295–308. Fu, H., Zhang, M., Li, W., Chen, J., Wang, L., Quan, X., Wang, W., 2012. Morphology, composition and mixing state of individual carbonaceous aerosol in urban Shanghai. Atmos.

na

Chem. Phys. 693–707. doi:10.5194/acp-12-693-2012

ur

Grahame, T.J., Schlesinger, R.B., 2010. Cardiovascular health and particulate vehicular emissions: a critical evaluation of the evidence. Air Qual. Atmos. Heal. 3–27.

Jo

doi:10.1007/s11869-009-0047-x

Gustafsson, Ö., Kruså, M., Zencak, Z., Sheesley, R.J., Granat, L., Engström, E., Praveen, P.S., Rao, P.S.P., Leck, C., Rodhe, H., 2009. Brown Clouds over South Asia: Biomass or Fossil Fuel Combustion. Science (80). 323, 495–498. doi:10.1126/science.1164857 Hernandez, H., Rodriguez, R., 2012. Geochemical evidence for the origin of vanadium in an urban environment 5327–5342. doi:10.1007/s10661-011-2343-9 Jin, L., Xie, J., Wong, C.K.C., Chan, S.K.Y., Abbaszade, G., Schnelle-Kreis, J., Zimmermann, R., Li, J., Zhang, G., Fu, P., Li, X., 2019. Contributions of City-Specific Fine Particulate Matter (PM2.5) to Differential In Vitro Oxidative Stress and Toxicity Implications between Beijing and Guangzhou of China. Environ. Sci. Technol. 53, 2881–2891. doi: 10.1021/acs.est.9b00449

Journal Pre-proof Joe H. Ward, 1963. HIERARCHICAL GROUPING TO OPTIMIZE AN OBJECTIVE FUNCTION*. J. Am. Stat. Assoc. 58, 236–244. doi:10.1088/1748-9326/10/7/074007 Kaskaoutis, D.G., Kumar, S., Sharma, D., Singh, R.P., Kharol, S.K., Sharma, M., Singh, A.K., Singh, A., Singh, D., 2014. Effects of crop residue burning on aerosol properties, plume characteristics, and long-range transport over northern India. J. Geophys. Res. Atmos. 5424– 5444. doi: 10.1002/2013JD021357. Kumar, A., Sarin, M.M., 2010. Aerosol iron solubility in a semi-arid region: temporal trend and

of

impact of anthropogenic sources. Tellus B 62, 125–132. doi: 10.1111/j.1600-0889.2009. 00448.x

ro

Kumar, R., Naja, M., Satheesh, S.K., Ojha, N., Joshi, H., Sarangi, T., Pant, P., Dumka, U.C., Hegde, P., Venkataramani, S., 2011. Influences of the springtime northern Indian biomass

-p

burning over the central Himalayas. J. Geophys. Res. Atmos. 116, 1–14.

re

doi:10.1029/2010JD015509

Lawrence, M.G., Lelieveld, J., 2010. Atmospheric pollutant outflow from southern Asia: a

lP

review. Atmos. Chem. Phys. 11017–11096. doi:10.5194/acp-10-11017-2010 Li, W., Shao, L., Zhang, D., Ro, C.-U., Hu, M., Bi, X., Geng, H., Matsuki, A., Niu, H., Chen, J.,

na

2016. A review of single aerosol particle studies in the atmosphere of East Asia: morphology, mixing state, source, and heterogeneous reactions. J. Clean. Prod. 112, 1330–1349. doi:

ur

10.1016/j.jclepro.2015.04.050

Jo

Likuku, A.S., Mmolawa, K.B., Gaboutloeloe, G.K., 2013. Assessment of Heavy Metal Enrichment and Degree of Contamination Around the Copper-Nickel Mine in the Selebi Phikwe Region, Eastern Botswana 1, 32–40. doi:10.13189/eer.2013.010202 Mclennan, S.M., 2001. Relationships between the trace element composition of sedimentary rocks and upper continental crust. Geochemistry Geophys. Geosystems 2. Mishra, S.K., Dey, S., Tripathi, S.N., 2008. Implications of particle composition and shape to dust radiative effect: A case study from the Great Indian Desert. Geophys. Res. Lett. 35, 1–5. doi:10.1029/2008GL036058 Mishra, S.K., Tripathi, S.N., 2008. Modeling optical properties of mineral dust over the Indian Desert. J. Geophys. Res. 113, 1–19. doi:10.1029/2008JD010048

Journal Pre-proof Mishra, S.K., Tripathi, S.N., AGGARWAL, S.G., Arola, A., 2012. Optical properties of accumulation mode, polluted mineral dust: effects of particle shape, hematite content and semiexternal mixing with carbonaceous species. Tellus B 1, 1–18. Monaci, F., Bargagli, R., 1997. Barium and Other Trace Metals as Indicators of Vehicle Emissions. Water. Air. Soil Pollut. 100, 89–98. doi:10.1023/A:1018318427017 Pandithurai, G., Dipu, S., Dani, K.K., Tiwari, S., Bisht, D.S., Devara, P.C.S., 2008. Aerosol radiative forcing during dust events over New Delhi, India Aerosol radiative forcing during dust

of

events over New Delhi, India. J. Geophys. Res. doi:10.1029/2008JD009804

ro

Pipal, A.S., Jan, R., Satsangi, P.G., Tiwari, S., Taneja, A., 2014. Study of Surface Morphology, Elemental Composition and Origin of Atmospheric Aerosols (PM2.5 and PM10) over Agra, India.

-p

Aerosol Air Qual. Res. 1685–1700. doi:10.4209/aaqr.2014.01.0017

re

Pipal, A.S., Kulshrestha, A., Taneja, A., 2011. Characterization and morphological analysis of airborne PM2.5 and PM10 in Agra located in north central India. Atmos. Environ. 45, 3621–

lP

3630. doi: 10.1016/j.atmosenv.2011.03.062

Pipal, A.S., Singh, S., Satsangi, G.P., 2019. Study on bulk to single particle analysis of

ur

10.1016/j.uclim.2018.12.008

na

atmospheric aerosols at urban region. Urban Clim. 27, 243–258. doi:

Po´sfai, M., Gelencse´r, A., Simonics, R., Arato, K., Li, J., Hobbs, P. V, Buseck, P.R., 2004.

Jo

Atmospheric tar balls: Particles from biomass and biofuel burning. J. Geophys. Res. 109, 1–9. doi:10.1029/2003JD004169

Pope, A. I., Dockery, D.W., 2017. Health Effects of Fine Particulate Air Pollution: Lines that Connect Health Effects of Fine Particulate Air Pollution: Lines that Connect. J. Air Waste Manag. Assoc. I 2247. doi:10.1080/10473289.2006.10464485 Rajput, P., Sarin, M.M., Rengarajan, R., Singh, D., 2011. Atmospheric polycyclic aromatic hydrocarbons (PAHs) from post-harvest biomass burning emissions in the Indo-Gangetic Plain: Isomer ratios and temporal trends. Atmos. Environ. 45, 6732–6740. doi: 10.1016/j.atmosenv.2011.08.018

Journal Pre-proof Ram, K., Sarin, M.M., 2015. Atmospheric carbonaceous aerosols from Indo-Gangetic Plain and Central Himalaya: Impact of anthropogenic sources. J. Environ. Manage. 148, 153–163. doi: 10.1016/j.jenvman.2014.08.015 Ram, K., Sarin, M.M., 2012. Carbonaceous aerosols over northern India: Sources and spatiotemporal variability. Proc. Indian Natl. Sci. Acad. 78, 523–533. Ram, K., Sarin, M.M., 2010. Spatio-temporal variability in atmospheric abundances of EC, OC and WSOC over Northern India. J. Aerosol Sci. 41, 88–98. doi: 10.1016/j.jaerosci.2009.11.004

of

Ram, K., Sarin, M.M., Tripathi, S.N., 2012. Temporal trends in atmospheric PM₂.₅, PM₁₀,

ro

elemental carbon, organic carbon, water-soluble organic carbon, and optical properties: impact of biomass burning emissions in the Indo-Gangetic Plain. Environ. Sci. Technol. 46, 686–95.

-p

doi:10.1021/es202857w

re

Ram, K., Sarin, M.M., Tripathi, S.N., 2010. A 1year record of carbonaceous aerosols from an urban site in the Indo-Gangetic Plain: Characterization, sources, and temporal variability. J.

lP

Geophys. Res. Atmos. 115. doi:10.1029/2010JD014188 Ram, K., Singh, S., Sarin, M.M., Srivastava, A.K., Tripathi, S.N., 2016. Variability in aerosol

na

optical properties over an urban site, Kanpur, in the Indo-Gangetic Plain: A case study of haze

ur

and dust events. Atmos. Res. 174–175, 52–61. doi: 10.1016/j.atmosres.2016.01.014 Reimann, C., Äyräs, M.., Chekushin, V., 2010. Environmental Geochemical Atlas of the Central

Jo

Barents Region. Schweizerbart Science Publishers, Stuttgart, Germany. Rengarajan, R., Sarin, M.M., Sudheer, A.K., 2007. Carbonaceous and inorganic species in atmospheric aerosols during wintertime over urban and high-altitude sites in North India. J. Geophys. Res. Atmos. 112, 1–16. doi:10.1029/2006JD008150 Sarin, M.M., Borole, D., 1979. Geochemistry and geochronology of sediments from the Bay of Bengal and the equatorial Indian Ocean Geochemistry and geochronology of sediments from the Bay of Bengal and the equatorial Indian Ocean. Proc. Indian Natl. Sci. Acad. Schleicher, N., Kramar, U., Dietze, V., Kaminski, U., Norra, S., 2012a. Geochemical characterization of single atmospheric particles from the Eyjafjallajökull volcano eruption event

Journal Pre-proof collected at ground-based sampling sites in Germany. Atmos. Environ. 48, 113–121. doi: 10.1016/j.atmosenv.2011.05.034 Schleicher, N., Norra, S., Chen, Y., Chai, F., Wang, S., 2012b. Efficiency of mitigation measures to reduce particulate air pollution — A case study during the Olympic Summer Games 2008 in Beijing, China. Sci. Total Environ. 428, 146–158. doi: 10.1016/j.scitotenv.2012.04.004 Sen, A., Ahammed, Y.N., Arya, B.C., Banerjee, T., Begam, G.R., Baruah, B.P., 2014. Atmospheric Fine and Coarse Mode Aerosols at Different Environments of India and the Bay of

of

Bengal During Winter-2014: Implications of a Coordinated Campaign. MAPAN-Journal Metrol.

ro

Soc. India APAN-Journal Metrol. Soc. India 29, 273–284. doi:10.1007/s12647-014-0109-x Sharma, D., Kulshrestha, U., 2017. Dust storms and their influence on optical and chemical

-p

properties of aerosols along north-western Indo-Gangetic Plains. J. Indian Geophys. Union 526–

re

534.

Sharma, D., Srivastava, A.K., Ram, K., Singh, A., Singh, D., 2017. Temporal variability in

lP

aerosol characteristics and its radiative properties over Patiala, northwestern part of India: Impact of agricultural biomass burning emissions. Environ. Pollut. 231, 1030–1041. doi:

na

10.1016/j.envpol.2017.08.052

Shindell, D., Kuylenstierna, J.C.I., Vignati, E., Dingenen, R. Van, Amann, M., Klimont, Z.,

ur

Anenberg, S.C., Muller, N., Janssens-maenhout, G., Raes, F., Schwartz, J., Faluvegi, G., Pozzoli,

Jo

L., Kupiainen, K., Höglund-isaksson, L., Emberson, L., Streets, D., Ramanathan, V., Hicks, K., Oanh, N.T.K., Milly, G., Williams, M., 2012. Simultaneously Mitigating Near-Term Climate Change and Improving Human Health and Food Security. Science (80-. ). 335, 183–190. Sinha, P.R., Manchanda, R.K., Kaskaoutis, D.G., Kumar, Y.B., Sreenivasan, S., 2013. Seasonal variation of surface and vertical pro fi le of aerosol properties over a tropical urban station Hyderabad, India. J. Geophys. Res. 118, 749–768. doi:10.1029/2012JD018039 Srimuruganandam, B., Shivanagendra, S.M., 2011. Chemical characterization of PM 10 and PM 2.5 mass concentrations emitted by heterogeneous traffic. Sci. Total Environ. 409, 3144–3157. doi: 10.1016/j.scitotenv.2011.04.042

Journal Pre-proof Srivastava, A. K., Bisht, D.S., Ram, K., Tiwari, S., Srivastava, M.K., 2014. Characterization of carbonaceous aerosols over Delhi in Ganga basin: Seasonal variability and possible sources. Environ. Sci. Pollut. Res. 21, 8610–8619. doi:10.1007/s11356-014-2660-y Srivastava, A K, Soni, V.K., Singh, S., Kanawade, V.P., Singh, N., Tiwari, S., Attri, S.D., 2014. An early South Asian dust storm during March 2012 and its impacts on Indian Himalayan foothills: A case study. Sci. Total Environ. 493, 526–534. doi: 10.1016/j.scitotenv.2014.06.024 Srivastava, A.K., Tiwari, S., Devara, P.C.S., Bisht, D.S., Srivastava, M.K., Tripathi, S.N.,

of

Goloub, P., 2011. Pre-monsoon aerosol characteristics over the Indo-Gangetic Basin:

ro

implications to climatic impact. Ann. Geophys. 789–804. doi:10.5194/angeo-29-789-2011 Srivastava, P., Dey, S., Kumar, A., Singh, S., Mishra, S.K., Tiwari, S., 2017. Science of the Total

-p

Environment Importance of aerosol non-sphericity in estimating aerosol radiative forcing in Indo-Gangetic Basin. Sci. Total Environ. 599–600, 655–662. doi:

re

10.1016/j.scitotenv.2017.04.239

lP

Srivastava, P., Dey, S., Srivastava, A.K., Singh, S., Tiwari, S., 2018. Most probable mixing state of aerosols in Delhi NCR, northern India. Atmos. Res. 200, 88–96. doi:

na

10.1016/j.atmosres.2017.09.018

Stohl, A., 1996. Trajectory statistics-A new method to establish source-receptor relationships of

ur

air pollutants and its application to the transport of particulate sulfate in Europe. Atmos. Environ.

Jo

30, 579–587. doi:10.1016/1352-2310(95)00314-2 Sutherland, R.A., Tolosa, C.A., Tack, F.M.G., Verloo, M.G., 2000. E nvironmental C ontamination a n d T Characterization of Selected Element Concentrations and Enrichment Ratios in Background and Anthropogenically Impacted Roadside Areas 438, 428–438. doi:10.1007/s002449910057 Tripathi, S.N., Tare, V., Chinnam, N., Srivastava, A.K., Dey, S., Agarwal, A., Kishore, S., Lal, R.B., Manar, M., Kanwade, V.P., Chauhan, S.S.S., Sharma, M., Reddy, R.R., Gopal, K.R., Narasimhulu, K., Reddy, L.S.S., Gupta, S., Lal, S., 2006. Measurements of atmospheric parameters during Indian Space Research Organization Geosphere Biosphere Programme Land

Journal Pre-proof Campaign II at a typical location in the Ganga basin: 1. Physical and optical properties. J. Geophys. Res. 111, 1–14. doi:10.1029/2006JD007278 Varshney, P., Saini, R., Taneja, A., 2016. Trace element concentration in fine particulate matter (PM2.5) and their bioavailability in different microenvironments in Agra, India: a case study. Environ. Geochem. Health 38, 593–605. doi:10.1007/s10653-015-9745-5 Venkataraman, C., Habib, G., Kadamba, D., Shrivastava, M., Leon, J.F., Crouzille, B., Boucher, O., Streets, D.G., 2006. Emissions from open biomass burning in India: Integrating the inventory

of

approach with high-resolution Moderate Resolution Imaging Spectroradiometer (MODIS)

ro

active-fire and land cover data. Global Biogeochem. Cycles 20. doi:10.1029/2005GB002547 Wåhlin, P., Berkowicz, R., Palmgren, F., 2006. Characterization of traffic-generated particulate

-p

matter in Copenhagen. Atmos. Environ. 40, 2151–2159. doi: 10.1016/j.atmosenv.2005.11.049

re

Wang, Y.Q., Zhang, X.Y., Draxler, R.R., 2009. TrajStat: GIS-based software that uses various trajectory statistical analysis methods to identify potential sources from long-term air pollution

lP

measurement data. Environ. Model. Softw. 24, 938–939. doi: 10.1016/j.envsoft.2009.01.004 WHO, 2006. WHO Air quality guidelines for particulate matter, ozone, nitrogen dioxide and

na

sulfur dioxide. (World Heal. Organ. Geneva). doi:10.1016/0004-6981(88)90109-6

ur

Xu, Z., Shan, W., Qi, T., Gao, J., 2018. Characteristics of individual particles in Beijing before, during and after the 2014 APEC meeting. Atmos. Res. 203, 254–260. doi:

Jo

10.1016/j.atmosres.2018.01.002

of

Journal Pre-proof

Jo

ur

na

lP

re

-p

ro

Figures:

Figure 1: Geographic location of the study site, Varanasi, in the eastern Indo-Gangetic Plain (Source: www.imagenesmi.com) The picture in bottom right inset shows the monitoring site at IESD, BHU (Image source: Google Earth)

Jo

ur

na

lP

re

-p

ro

of

Journal Pre-proof

Figure 2: Box plot showing weekly and daily statistics of PM2.5 mass concentration. The mean is represented by square symbol within the box whereas solid line represents the median values. The lower and upper quartile represents 25th and 75th percentile, respectively. The prescribed

Journal Pre-proof threshold 24-hour limit of PM2.5 of National Ambient Air Quality Standards (NAAQS) by Central Pollution Control Board (CPCB), India and World Health Organization (WHO) are also

na

lP

re

-p

ro

of

shown for reference.

ur

Figure 3: Time series of PM2.5 concentration in weekly and daily samples over Varanasi. The precipitation rate retrieved by TRMM in a 50 × 50 km grid centering over Varanasi during the

Jo

study period are also presented.

lP

re

-p

ro

of

Journal Pre-proof

na

Figure 4: Wind rose for (a) weeks with low PM2.5 concentration (b) weeks with high PM2.5 concentration (Source: www.cpcb.nic.in). Cluster analysis of 5-days air mass back trajectories

ur

arriving at Varanasi at an altitude of 500m agl during (c) weeks with low PM2.5 concentration

Jo

and (d) weeks with high PM2.5 concentration. The percentage values represent contribution of each trajectory cluster to total air mass reaching the study site. Pink dots represent the fire spots observed from VIIRS.

Jo

ur

na

lP

re

-p

ro

of

Journal Pre-proof

Figure 5: Enrichment factor (EF) of the elements analyzed in (a) daily and (b) weekly PM2.5 samples at Varanasi. Horizontal dashed line represents EF=5 whereas vertical dashed line separates crustal and anthropogenic elements. The concentrations of Al in daily samples (D5, D7 and D8) were below detection limit, thus, the EF values for these respective days have not been calculated.

ur

na

lP

re

-p

ro

of

Journal Pre-proof

Jo

Figure 6: Dendrogram of the cluster analysis by Ward's method from detected elements and aerosol mass.

Jo

ur

na

lP

re

-p

ro

of

Journal Pre-proof

re

-p

ro

of

Journal Pre-proof

lP

Figure 7: SEM images of (a) irregular aluminosilicates, (b) spheroidal aluminosilicates, (c) minerogenic particulates, mostly metal oxides of crustal origin, (d) soot particles, (e1) tar ball

na

particles with Pb contamination and (e2) backscattered electron view of the tar ball particle with

Jo

ur

Pb contamination. EDX spectrum of respective cases are provided below SEM images.

Journal Pre-proof

Table.1 Concentrations of PM2.5 (µgm-3) and selected measured elements (ngm-3) in daily and weekly aerosol samples at Varanasi

f o

l a

n r u

Jo BDL: below detection limits

r P

e

o r p

Journal Pre-proof Highlights

   

PM2.5 concentrations higher than the threshold limits of NAAQS and WHO standards in Varanasi. Elemental concentration of Pb and Zn as high as 2.0 g/kg and 3.7 g/kg, respectively highlighting an intensive particulate pollution. SEM-EDX analysis indicate the dominance of aluminosilicates, soot and tarball particles with attachment of anthropogenic elements in a few particles Elevated Fe/Al ratio suggest an enrichment of Fe due to anthropogenic combustion sources in the region.

f o

l a

Jo

n r u

r P

e

o r p