Variation of OC, EC, WSIC and trace metals of PM10 in Delhi, India S.K. Sharma*, T.K. Mandal, Mohit Saxena, Rashmi, A. Sharma, A. Datta and T. Saud

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CSIR-National Physical Laboratory, Dr. K S Krishnan Road, New Delhi-110 012, India

*Corresponding Author: Sudhir Kumar Sharma Radio and Atmospheric Sciences Division CSIR-National Physical Laboratory Dr. K S Krishnan Road, New Delhi-110 012, India Phone: 91-11-45609448 Fax: 91-11-45609310 *e-mail: [email protected] ; [email protected]

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Abstract Variation of organic carbon (OC), elemental carbon (EC), water soluble inorganic ionic components (WSIC) and major & trace elements of particulate matter (PM10) were studied over Delhi, an urban site of the Indo Gangatic Plain (IGP), India in 2010. Strong seasonal variation was noticed in the mass concentration of PM10 and its chemical composition with maxima during winter (PM10: 213.1± 14.9 µg m-3; OC: 36.05 ± 11.60 µg m-3; EC: 9.64 ± 2.56 µg m-3) and minima during monsoon (PM10: 134.7± 39.9 µg m-3; OC: 14.72 ± 6.95 µg m-3; EC: 3.35± 1.45 µg m-3). The average concentration of major and trace elements (Na, Mg, Al, P, S, Cl, K, CA, Cr, Ti, Fe, Zn and Mn) was accounted for ~17% of the PM10 mass. Average values of K+/EC (0.28) and Cl-/EC (0.59) suggest the influences of biomass burning in PM10, whereas, higher concentration of Ca2+ suggests the soil erosion as possible source from the nearby agricultural field. Fe/Al ratio (0.34) indicates mineral dust as a source at the sampling site, similarly, Ca/Al ratio (2.45) indicates that aerosol over this region is rich in Ca mineral compared to average upper continental crust. Positive Matrix Factorization (PMF) analysis quantifies the contribution of soil dust (20.7%), vehicle emissions (17.0%), secondary aerosols (21.7%), fossil fuel combustion (17.4%) and biomass burning (14.3%) to PM10 mass concentration at the observational site of Delhi.

Keywords: PM10, organic carbon, elemental carbon, positive matrix factorization, enrichment factor analysis.

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Introduction Atmospheric aerosols significantly affect the atmospheric chemistry, ambient air quality, visibility and the Earth‟s radiation budget. Over the continents, particulate matter (PM) is produced either by various natural processes or due to several anthropogenic activities. Particulate matter has been extensively studied in recent years due to its potential impacts on air quality and health (Schwartz et al., 1996; Dachs and Eisenreich, 2000; Li et al., 2009; Ram et al., 2012; Sharma et al., 2013a). Several studies have revealed that aerosols, especially fine mode (particulate matter having aerodynamic diameter < 2.5 µm) particles, can lead to serious human health effects like cardiovascular and respiratory disorders (Dockery and Pope, 1994). The particulate air mass has shown significant seasonal variability at various locations in India (Sharma et al., 1995). The variation in PM levels over Delhi could be due to soil dust emission, biomass burning, vehicular emission, industries, photochemical responsible for the formation of inorganic secondary particles and other anthropogenic sources (Sharma et al., 2013b). The variation in inorganic secondary particle is of greater significance from the human health point of view and need to be examined (Sharma et al., 2007; Li et al., 2009). Ambient aerosols consist of organics, mineral dust, metals as well as sea salts and inorganic pollutants. Relative abundance of these components is highly variable both temporally and spatially. Aerosols containing carbonaceous particles have important effect on climate as well as earth atmospheric system (Liousse et al., 1996; Jacobson, 2001). Smoke particles are composed of ~60% OC and ~5–10% EC. EC consisting of a variety of different forms of pure carbon is an important component of PM. The major sources of EC are incomplete combustion of fossil fuel and biomass burning, including forest fires (Cooke et al., 1999; Masielo, 2004). As a primary component of fine PM, EC is associated with adverse effects on public health by absorbing harmful VOCs such as PAHs (Dachs and Eisenreich, 2000). High BC concentration and 3

atmospheric absorption measured during the Indian Ocean Experiment (INDOEX) have been related through trajectory analysis to the source regions in the IGP, central/east coast and south India (Venkataraman et al., 2005). INDOEX made it clear that export of the large scale pollutant from southern Asia was much more substantial than had been expected. The international science teams during INDOEX encountered a surprisingly thick layer of sunlight-absorbing aerosols which cover much of the region of the northern Indian Ocean down to the ITCZ during winter period. The large pollutant could be clearly seen in visible satellite images, and which is responsible for a significant perturbation to the regional atmospheric energy balance. Shortly after INDOEX, this aerosol layer came to be known as the “Asian Brown Clouds” or “ABC”. Biofuel and biomass burning play a disproportionately large role in the emissions of most of the northern hemisphere where fossil fuel burning and industrial processes tend to dominate. This results in polluted air masses which are enriched in carbon containing aerosols. Several researchers have reported carbonaceous aerosols and chemical composition of total suspended particulates (TSP) in the Indian region (Kulshrestha et al., 1998; Venkatraman et al., 2002; Tare et al., 2006; Parasar et al., 2005; Tiwari et al., 2009). The studies suggested that carbonaceous aerosol contributed ~30-35% of the TSP mass over IGP, India during wintertime (Rengarajan et al., 2007; Ram and Sarin 2010) whereas contribution of WSIC was of the order of 15-20% (Tare et al., 2006; Rengrajan et al., 2007). Quantification of the contributions of different type of sources to the ambient concentration of pollutants is one of the major issues of the urban air quality research. Hence, the development and application of improved tools are required for the identification and apportionment of atmospheric aerosols. Receptor modeling offers a method to complete the process by measurements of the pollutant concentrations at a sampling site (Hopke, 1991). Recently, PMF 4

model has been improved significantly and a new approach was developed by Paatero (Paatero and Tapper, 1994; Paatero, 1997), using a least squares approach. PMF solves the problem arising in factor analysis by integrating non-negativity constraints in the optimization process and utilizing the error estimates for each data value as a point-by-point weight (Begum et al., 2004). PMF has been applied successfully worldwide for such studies (Polissar et al., 1998; Kim et al., 2004; Lee and Hopke, 2006; Karanasiou et al., 2009). In view of the importance and sensitivity of carbonaceous and sulphate aerosols properties over Delhi, the present study has been carried out. The study presents the seasonal variability of air mass of PM10, EC, OC, WSIC (cations: Ca2+, Mg2+, Na+ and NH4+; anions Cl¯, NO3¯, and SO42¯) and major (Na, Mg, Al, Ca, Si, K and Fe) and trace elements (P, S, Cl, Cr, Zn and Mn) at an urban location of Delhi for the period of January to December 2010. We have also used PMF model to identify source profiles and apportionment of PM10 mass (in our earlier paper Sharma et al. (2013) reference therein, we have discussed in detail about source apportionment of PM10 mass using PMF model). Using the HYSPLIT model, 7 days back trajectories have also been calculated to understand their flow of pollutants from the distant source region.

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Methodology

2.1

Site description PM10 samples (n = 52) were collected at sampling site of the CSIR-National Physical

Laboratory, New Delhi (28°38′N, 77°10′E; 218 m amsl) (Figure 1). The sampling site represents a typical urban atmosphere, surrounded by huge roadside traffic and agricultural fields in the southwest direction. There are different small, medium and large scale industries in and around Delhi. The total number of registered vehicles in the city was of the order of ~6.35 million in 2010-11 (Delhi Statistical Handbook 2011). This area is under the influence of air mass flow 5

from Northeast to North-west in winter and from Southeast to South-west in the summer (Goyal and Sidhartha, 2002; Sharma et al., 2013a). In addition, Delhi experienced severe fog and haze weather conditions and poor visibility during wintertime. Roadside vehicle, industrial emission and biomass burning etc. could be the major sources of carbonaceous aerosols and several other pollutants. The occasional occurrence of dust storm may contribute the presence of mineral dust significantly to the aerosol loading in summertime (Ram et al., 2010). The temperature of Delhi varied from minimum (monthly Ave: 12.8°C) in winter (November to February) to maximum (monthly Ave: 34.7°C) in summer (March to June). The average rainfall in Delhi during monsoon (July to October) was of the order of 780 mm. Monthly variation of ambient temperature; RH, wind direction and wind speed at the observational site has been given in Figure S-1 (in supplementary information).

2.2

Sampling method PM10 samples were collected (every Wednesday on weekly basis; 4-5 samples in a month) on

quartz fibre filters (that were prebaked at 550 oC at least 5 h before dessicated and sample collection) by using Particle Sampler (APM 460NL, Make: M/s. Envirotech, India) at 10 m height (above ground level). Ambient air was passed through a Whatman Quartz Microfibre filter (QM-A; size: 20 × 25 cm2) at a flow rate of 1.12 m3 min−1 (accuracy ± 2%) for 8 h during the sampling period (1000–1800 h). The QM-A filters were weighed before and after the sampling during the experiment in order to determine the mass of the PM10 collected. The amount of PM10 (μg m−3) was calculated on the basis of the difference between initial and final weights of the QM-A filters measured by a micro balance (M/s.Mettler-Toledo, resolution: ± 1 μg) was determined by dividing the amount of total volume passed during the sampling. After

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collecting samples, filters were stored under dry condition at −20°C in the deep-freezer prior to analysis (Sharma et al., 2012a).

2.3 Chemical analysis Analysis of OC, EC and ionic components have been carried by OC/EC carbon analyzer (Model: DRI 2001A, USA) and Ion Chromatograph (Model: DIONEX-ICS-3000, USA) with conductivity detector respectively. The quantitative elemental analysis of PM10 samples was also carried out using Rigaku ZSX Primus Wavelength Dispersive X-ray Fluorescence Spectrometer (WD-XRF). Blank filters (QM-A) were also analyzed for OC, EC, WSIC and major and trace elements. Ambient NH3 was also measured continuously (> 20 days in a month) during January to December 2010 using a NH3 analyzer (Model: CLD88CYp, M/s. ECO Physics AG, Switzerland) based on the chemiluminescence method (Sharma et al., 2012a, reference therein). Analysis of OC and EC of ambient PM10 samples (n = 52) has been carried out by OC/EC carbon analyzer (Model: DRI 2001A; Make: Atmoslytic Inc., Calabasas, CA, USA) following the USEPA Method „IMPROVE Protocol‟ with negative pyrolysis areas zeroed. The principle of the OC/EC carbon analyzer (DRI 2001A) is based on the preferential oxidation of OC and EC at different temperatures in which the sample is heated to four temperature plateaus (140, 280, 480 and 580 °C) in pure helium and three temperature plateaus (580, 740 and 840 °C) in 98% helium and 2% oxygen. Its function relies on the fact that OC can be volatilized from the sample deposit in a non oxidizing helium atmosphere, while EC must be combusted by an oxidizer. The principal function of the optical component (laser reflectance and transmittance) of the analyzer is to correct for pyrolysis, charring of OC compounds into EC. The thermal optical reflectance (TOR) charring corrections are not necessarily the same, owing to charring of organic vapours within the QM-A filter (Chow et al., 2004). Approximately 0.5 cm2 area of QM-A filter was cut 7

using the proper punch and the values are reported as µg cm−2 as given by the instrumental analysis software. Details of OC and EC analysis of PM10 have been given in Saud et al. (2012). Each filter paper was analyzed triplicate with several blank run to get the representative estimation of OC and EC mass in PM10. The collected filters were extracted for 90 min (using de-ionised water having conductivity 18.2 MΩ) in ultrasonic extractor for the determination of WSIC. The extract was filtered through nylon membrane filter and transferred to polypropylene sample bottles (these bottles were dipped in 2% HNO3 overnight before storage and then again dipped in deionized water overnight to remove any impurities on these bottles. Concentrations of F−, Cl−, NO3− and SO42− were determined by Ion Chromatograph (DIONEX-ICS-3000, USA) using an Ion Pac-AS11-HC analytical column (4 × 250 mm, Dionex, USA) with a guard column (IonPac AG11-HC, 4 × 50 mm, Dionex, USA), ASRS-300 4 mm anion micro-membrane suppressor, 20mM NaOH (50% w/w) as eluent and triple-distilled water as regenerator. After each analytical run, the calibration curves were displayed on the screen, and a visual check was made for linearity and replication. Li+, Na+, NH4+, K+, Ca2+ and Mg2+ were determined by using a separation column (IonPac CS17HC, 4x250 mm, Dionex, USA) with a guard column (IonPac CG17-HC, 4×50 mm, Dionex, USA), suppressor CSRS-300 (4mm, Dionex, USA) and 5mM MSA (methane sulphonic acid) as eluent. The IC system was fitted with a 25 µL sample loop that was used to introduce the sample manually. Chromatography data were collected at 5Hz and chromatograms were processed using the Chromeleon® software. Working standards were prepared from stock standard solutions procured from M/S Dionex. All the standard solutions were filtered using 0.22 µm nylon membrane filters (Millipore) and degassed by ultrasonication. Several blank filters were also analyzed for cations (Li+, Na+, NH4+, K+, Ca2+ and Mg2+) and anions (F−, Cl−, NO3− and SO42−).

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The analytical error (repeatability) was estimated to be 3% based on triplicate (n = 3) analysis. Details of WSIC analysis of PM10 are discussed in our earlier paper Sharma et al. (2012b). The quantitative elemental analysis of PM10 samples was also carried out using Rigaku ZSX Primus Wavelength Dispersive X-ray Fluorescence Spectrometer (WD-XRF). The spectrometer has an Rh-target, end window, 4 kW, sealed X-ray tube as the excitation source and scintillation counter (SC) for heavy elements and flow proportional counter (F-PC) for light elements as detectors. The measurements were made at a temperature of 36.5oC under vacuum at a tube rating of 2.4 kW. The scan was made to identify all elements in the loaded filter in the range F to you (except Si). The measurement conditions for the Kα X-spectral lines of the identified elements (Mg, Al, P, S, Si, Cl, K, Ca, Ti, Cr, Mn, Fe and Zn ) are: RX25 analyzer crystal and FPC detector for Mg, PET analyzer crystal and F-PC detector for Al, Ge analyzer crystal and FPC detector for P, S and Cl, LiF(200) analyzer crystal and F-PC detector for K and Ca, LiF(200) analyzer crystal and SC detector for Ti, Cr, Mn, Fe and Zn. Measurements were made on the blank filter also and correction in the intensities was made for the loaded filters. Data acquisition and quantitative analysis were carried out by using ZSX software (Rigaku Corporation, Japan). Fundamental Parameter method was used for the quantitative analysis.

2.4

Positive matrix factorization (PMF) In the present study, PMF (PMF v3.0; USEPA2008) was used to quantify the contribution of

various emission sources to PM10 mass. PMF is a multivariate factor analysis tool that decomposes a matrix of speciated sample data into two matrices: factor contributions and factor profiles. The PMF v3.0 model requires two input files: one of the measured concentrations of the species and another for the estimated uncertainty of the concentration. The PMF in the details has been described in Paatero and Tapper (1994) and Paatero (1997). 9

A speciated data set can be viewed as a data matrix X of i by j dimensions, in which i number of samples and j chemical species are measured. The aim of multivariate receptor modeling, for example with PMF, is to identify a number of factors p, the species profile f of each source, and the amount of mass g contributed by each factor to each individual sample which is given as: 𝑝

𝑋𝑖𝑗 =

𝑓𝑘𝑗 𝑔𝑖𝑘 + 𝑒𝑖𝑗

(1)

𝑘=1

where eij is the residual for each sample/species. Results are constrained so that no sample can have a negative source contribution. PMF allows each data point to be individually weighed. This feature allows the analyst to adjust the influence of each data point, depending on the confidence in the measurement. For example, data below detection limit can be retained for use in the model, with the associated uncertainty adjusted so these data points have less influence on the solution than measurements above the detection limit. The PMF solution minimizes the object function Q, based upon these uncertainties (u) as follows. 𝒏

𝒎

𝑸= 𝒊=𝟏 𝒋=𝟏

𝑋𝑖𝑗 −

𝟐 𝑝 𝑘=1 𝑔 𝑖𝑘 𝑓 𝑘𝑗

(𝟐)

𝑢𝑖𝑗

where Xij are the measured concentration (in µg m-3), uij are the estimated uncertainty (in µg m3

), n is the number of samples, m is the number of species and p is the number of sources

including in the analysis. The detail descriptions of EPA PMF v3.0 are described in Gugamsetty et al, (2012) and EPA PMF User Guide (2008). In this study, information on chemical properties of 52 PM10 samples has been used as input to the PMF model for total 25 parameters. Categorization of quality of data was based on the signal to noise ratio (S/N) and the percentage of sample method detection limit (MDL). Those 10

species which have S/N ≥ 2 were categorized as strong in data quality. Those with S/N between 0.2 – 2 were categorized as weak in quality. These species are not likely to provide enough variation in concentration and therefore contribute to the noise in the results. Those species with an S/N ratio below 0.2 are classified as bad values and were thus excluded from further analysis.

2.5 Meteorological parameters and Trajectory analysis The meteorological parameters (temperature and relative humidity, wind speed, wind direction and pressure etc) were measured by using sensors of meteorological tower (5 stages tower of 30 m height), which is 100 m away from the observational site in the same campus. Tower measures above mentioned parameters at 5 different layers. We use data available at 10 m height. The meteorological parameters such as temperature (accuracy ± 1oC), relative humidity (RH) (accuracy ± 2%), wind speed (accuracy ± 2%) and wind direction (accuracy ± 2 o) were recorded during the period. Composition of ambient aerosol is influenced by its source region and transport pathway. In order to identify the possible transport pathways of PM10 from their potential sources of origins to Delhi, 7 days backward trajectory calculated using the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model have been traced (Draxler and Rolph, 2003). Air mass back-trajectories for each experimental day for 500 m above the ground level (AGL) during January-December 2010 have been calculated (using GDAS meteorological data). HYSPLIT was run every day starting at 0500 h, IST (Indian Standard Time), at a starting height of 500 m AGL on an hourly basis. This height was chosen to diminish the effects of surface friction and to represent winds in the low boundary layer. The PM10 has the ability to travel long distance therefore 7 days were selected to calculate backward trajectories using HYSPLIT.

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3.

Results and Discussions

3.1

Mass concentration of PM10 Mass concentration of PM10 has varied from 93.4 to 328.8 µg m-3 with a minimum average

value (134.68 µg m-3) during the monsoon and maximum average value (213.09 µg m-3) during the winter season (Table 1). Figure 2 shows the monthly variation in mass concentration of PM10. Higher concentration of PM10 during winter may be due to the combined effect of source strength and lower boundary layer height (Datta et al., 2010). Generally during winter, the meteorology of Delhi is dominated by high pressure centered over Western China causing increased atmospheric stability, which, in turn, allows less general circulation engulfing more stagnant air masses (Datta et al., 2010). Additionally, lack of precipitation during winter also may reduce the potential of wet deposition and associated cleansing mechanisms of the atmosphere. The mass concentrations of PM10 during winter, summer, monsoon and its seasonal differences with a mixing ratio of ambient NH3 are summarized in Table 1. PM10 samples are analyzed for OC, EC, WSIC and major and minor elements. It has been observed that annual average of total carbon (TC = OC + EC) concentration contribute ~ 18% of PM10 mass, whereas, water soluble inorganic ionic species (WSIC: sum of the concentrations of the cations and anions) account for ~ 29% of PM10 mass (Figure S-2, in supplementary information). Abundance of OC and EC concentration shows a linear increment with PM10 mass (r2= 0.68). However, the individual correlation between PM10 mass and WSIC shows seasonal changes (Table S1-S4, in supplementary information). The contribution of unidentified mass (UM), estimated by subtracting TC, WSIC and major & trace elements (Na, Mg, Al, P, S, Cl, K, Si, Ca, Cr, Ti, Fe, Zn and Mn) concentrations from the PM10 mass. The UM and major & trace elements accounts for ~46% and ~17% respectively of total PM10 mass (Figure S-2, in supplementary information). Ram and Sarin (2011) have also estimated 41.4% unidentified mass 12

of PM10 at Kanpur, IGP, India whereas 42.3% and 51.5% UM of TSP at Hissar and Allanhabad of IGI, India (Ram et al., 2012). The unidentified mass of PM10 at the observational site of Delhi could be from carbonate rich minerals, calcium sulphate and alumino-silicates etc (Zhang et al., 2010; Ram et al., 2012). We will describe the detailed results of OC, EC, WSIC and major & trace elements in the following sections.

3.2

Organic carbon and elemental carbon Annual concentration of OC has varied from 9.7 µg m-3 to 69.0 µg m-3 with an average value

of the order of 26.7 ± 9.2 µg m-3 (~15% of PM10 mass), whereas, mass concentration of EC has varied from 1.8 µg m-3 to 13.0 µg m-3 with an average value of 6.1 ± 3.9 µg m-3 (~3% of PM10 mass) (Table 1, Figure S-2; in supplementary information). Concentration of OC in PM10 has been recorded higher (36.05 µg m-3; ~17% of PM10 mass) in winter followed by summer (29.33 µg m-3; ~16% of PM10 mass) and monsoon (14.72 µg m-3; ~11% of PM10 mass). Similarly, EC has followed the similar pattern with maximum in winter (9.64 µg m-3; ~4% of PM10 mass) and minimum in monsoon (3.35 µg m-3; ~3% of PM10 mass). It is to be reminded that TC content accounts for ~18% of PM10 mass at Delhi during 2010 following the pattern of OC and EC with higher concentration during winter (Table 1). Concentrations of OC, EC and TC have varied with the increase of aerosol loading. When we compare the concentration of OC and EC at Delhi with the observation of other locations of India and China, variation pattern is almost similar. Ram and Sarin (2010) have reported that Total Carbonaceous Aerosol (TCA = OM + EC) accounts for ~30-35% of the TSP mass at urban and rural sites of northern India whereas in the present study it is accounted for ~29%. Over an urban site (Beijing) in China, Guinot et al. (2007) have reported that as much as 46% of fine aerosol (< 2.0 µm) mass is composed of carbonaceous aerosols. The concentrations of OC, EC and TC of PM10 mass of present study and 13

the other studies in Delhi and other part of the country is summarized in Table 2. Perrino et al. (2011) reported the more or less similar % contribution of OC (12.0% of PM10 mass) and EC (2.8% of PM10 mass) of PM10 mass (Sharma et al., 2013b) at Delhi whereas Mandal et al. (2013) reported higher % contribution of OC (32.6% of PM10 mass) and EC (9.6% of PM10 mass) at an industrial area of Delhi (Table 2). During the present study, the OC/EC ratio has varied from 3.8 to 5.8 with an average value of 4.38 ± 2.36, whereas, Ram and Sarin (2011) reported the same in the range of 2.9-8.4 with an average value of 6.0 ± 1.3 at Kanpur. However, the OC/EC ratios over Delhi and some of the urban locations of the IGP (Table 2) are relatively higher than those (average: < 4.0) reported for the urban locations in China (Cao et al., 2003; Ho et al., 2007). Such difference in the OC / EC ratio for two different countries might be due to differences in emission sources of carbonaceous aerosols (Ram and Sarin, 2011). OC and EC concentrations at the study site are attributed by the combined effects of traffic emission, biomass burning, wood burning and crop residue burning. Figure 4 shows the scatter plot between OC and EC during the study period (r2= 0.53; at P < 0.05). A significant correlation between OC and EC is usually indicative of their common sources like vehicular traffic (Salma et al., 2004). In contrast, a poor correlation between OC and EC indicates the formation of secondary aerosol under favorable conditions for the gas to particle conversion of VOCs through a photochemical reaction in the atmosphere. Overall, a positive linear trend (r2= 0.53) is observed between OC and EC for an urban site of Delhi especially when OC concentrations are > 50 µg m-3 (Figure 4) and it indicates the influence of vehicular emission.

3.3

Water soluble inorganic ionic components (WSIC)

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Figure 3 shows the monthly variation of WSIC of PM10 mass during January to December 2010 in Delhi. During winter among WSIC, NO3- (12.49 µg m-3) was recorded the highest concentration followed by other ionic compositions e.g., SO42- (10.50 µg m-3) >NH4+ (9.90 µg m3

) > Ca2+ (5.37 µg m-3) > Cl- (4.16 µg m-3) > Na+ (2.72 µg m-3) > K+ (1.55 µg m-3) > and Mg2+

(0.65 µg m-3). Significant seasonal variation was noticed in concentration of particulate SO42-, NO3- and NH4+, whereas, Cl-, Na+, K+ and Mg2+ shows no such variation (Table 1). The assimilation of HNO3 into NH4+ aerosols generally depends on the amount of H2SO4 used in the neutralization with NH3 (Sharma et al., 2012b; 2013a). Thus, the samples containing lower SO42and higher NO3- concentration could be representative of local biogenic NOx emission leading to formation of NO3- or marine masses having high Cl- concentrations whereas the sample containing higher SO42- and lower NO3- concentration could be representative of aerosols transported over a long distance or precursors from a continental air mass whereas (Sharma et al., 2012a). NH4+ generally combines with NO3- and SO42- in atmosphere and forms NH4NO3 and (NH4)2SO4 respectively (Utsunomiya and Wakamatsu, 1996). In (NH4)2SO4, the molar ratio of NH4+ to SO42- is 2:1. If the observed molar ratio is greater than 2, it signifies that excess NH4+ is present and possible may be combined with NO3- or other ions (Sharma et al., 2013a). In the present study, the equivalent molar ratio of NH4+/SO42- is 5.02, which indicates the complete neutralization of atmospheric acid gas H2SO4 and predominant to aerosol formation during winter season. Similarly, the equivalent molar ratio of NH4+/NO3- in the present study is 2.73 indicating formation of NH4NO3 formation in winter. During the study period SO42- and NO3ions also show the significant correlation (Table S-4, in supplementary information) with NH4+ (r2 = 0.50 for SO42- and r2 = 0.54 NO3-) indicating formation of (NH4)2SO4 and NH4NO3 salt (Sharma et al., 2012 a;b). The result also reveals that ambient NH3 has positively correlated with 15

SO42- and NO3¯ ions during winter (r2 = 0.82 with SO42- and r2 = 0.79 with NO3¯) and summer (r2 = 0.73 with SO42- and r2 = 0.69 with NO3¯) at the observational site. The remainder of HNO3 (after neutralization with NH3) and/or H2SO4 may react with mineral dust to form Ca(NO3)2 and/or CaSO4 in the coarse mode (Khoder and Hassan, 2008; Guo et al., 2010; Rastogi and Sarin. 2006). Significant correlation of NO3- with Ca2+ in PM10 samples (Table S1-S4, in supplementary information) indicates the possible association of NO3- with Ca2+ in the coarse mode. Thus, particulate-NH4+ is the major neutralizing agent of the acidic species (SO42- and NO3-) in PM10, whereas, Ca2+ plays an important role in the neutralizing of the acidic species in the coarse mode aerosol over Delhi (Sharma et al., 2013a).

3.4

Source Apportionment In the above sections, chemical characteristics of PM10 have been explained. We have

noticed contrasting seasonal variation in few species of PM10, which could be due to variation of emission sources. In this present section we will try to find the contribution of possible sources to the mass concentration of PM10. Several studies (Novakov et al., 2000; Cheng et al., 2006) have indicated that the ratios of chemical composition of PM10 may also give significant information about their sources. Potassium (K) is generally used as a tracer of crustal dust in the coarse range and soluble K+ for biomass burning in the fine range of particulate matter. It has been used as a key element marker for biomass/wood combustion for TSP, PM10 and PM2.5 (Khare and Baruah, 2010; Shridhar et al., 2010). K+ and NH4+ have also been used as markers for wood burning and agricultural activities (Khare and Baruah, 2010). Fossil fuel (vehicular emission, industrial activities and small scale generator) could be also the dominant sources of OC and EC in mega city. Since higher concentrations of K+ has been recorded, K+/OC and K+/EC ratios may be used to 16

characterize the relative emission from biomass burning and fossil fuel burning. K+/OC ratio exhibits a narrow range of the order of 0.08 to 0.10 for Savana burning (Echalar et al., 1995) and 0.04 to 0.13 for agricultural residue burning (Andreae and Merlet, 2001). On the other hand, fossil fuel emissions have characteristically lower K+/EC ratio (Ram and Sarin 2010). However, a fraction of K+ can be derived from fertilizer too used for agricultural activity. OC/EC, K+/OC, K+/EC, Cl-/EC and SO42-/EC ratios in the particulates at different sites in India and China along with ratios for savannah burning and agricultural waste burning are summarized in Table 2. In the present study, the annual ratio of OC/EC, K+/OC, K+/EC, Cl-/EC and SO42-/EC of PM10 mass is recorded as 4.38, 0.06, 0.28, 0.59 and 1.36 respectively (Table 3). Average values of K+/EC, Cl-/EC ratios are estimated as 0.28 and 0.59, respectively (Table 3) which may give some evidences of the biomass burning as a source of the PM10 aerosols at the observational site. Ratio of elemental Cl-/EC was reported in the range of 0.29 – 0.71 which could be due to for biomass burning at the observational site (Yamasoe et al., 2000; Ferek et al., 1998; Saud et al., 2012). However, this ratio could vary if there is influence of sea salt aerosols, industry etc. Fossil fuel combustion is generally the principal source of anthropogenic sulfate, thus, SO42-/BC ratio of fossil fuel burning is expected to be higher than from biomass burning (Novakov et al., 2000). Since higher concentrations of particulate Ca2+ in PM10 have been recorded throughout the observational period, Ca2+ observed may be from the soil erosion in the nearby agricultural field (Datta et al., 2010). It has also been observed that Fe and Al are significantly correlated with PM10 (r2=0.62 and r2= 0.69 respectively) and Fe/Al ratio is 0.34 (range: 0.20 – 0.53), indicating the dominant source of Fe as a mineral dust. The Fe/Al ratio in north Indian plains has ranged from 0.55 to 0.63 (Sarin et al., 1979). Kumar and Sarin (2009) reported Fe/Al ratio 0.59 for coarse mode particles (PM2.5-10) in a remote high altitude location in western India. Average Ca/Al ratio in PM10 was 17

2.45 (range: 1.94–2.38), whereas, the corresponding ratio in the upper continental crust was 0.38 (McLennan, 2001). This was suggested that mineral dust present in the ambient atmosphere was considerable rich in Ca as compared to average crustal abundance. Kumar and Sarin (2009) reported Ca/Al ratio of the order of 0.73 in fine mode aerosol and 1.74 in coarse mode at an high altitude, remote location of western India. The Thar and Oman Desert are potential sources of mineral aerosol containing minerals enriched with carbonates which can be transported towards this subtropical region at different seasons. During winter and summer, the direction of surface wind is favourable to transport the mineral dust from the That Desert towards the observational site. Average values of K/K+, Ca/Ca2+ and Cl/Cl- ratio in PM10 are estimated as 1.42, 1.63 and 1.08 respectively, which indicate that the vehicular emission, biomass burning, crustal dust and road dust may be the sources of PM10 at the observational site (Pant and Harrison, 2012). We could so far explain the contribution of possible sources to PM10 qualitatively. To quantify the contribution several sources to PM10 over Delhi, we have performed PMF analysis. The goodness of the model fit parameter „Q‟ was evaluated to identify the optimal number of factors and the optimal solution should lie in FPEAK range. The PMF was applied to the analysed data set consisting 25 species and 52 PM10 samples collected during January to December 2010 at Delhi. The model was run in the default robust mode to decrease the influence of extreme values in the PMF solution. In order to determine the optimal number of sources, different numbers of sources were explored by applying a trial and error method. For the final analysis, PMF was applied to the data sets using factors and the resultant change in the Q values was examined. In this study, the theoretical Q value was to be approximately 1300 (i.e., 52 × 25), however, this value was decreased due to several heavily downweighted species and strength of an error constant. Robust Q value was the value for which the impact of outlier was minimized, while true Q value was the value for which the influence of extreme values was not controlled. In 18

the present case, the robust Q values were very close to the true Q values, implying that the model fit the outlier resonabily. It is also important that the range of Q values from the random runs (100 runs in this study) should be adequately small to confirm the achievement of a similar global minimum, and hence outliers are fitted equally well for each random run. In seven factor solution, more than 95% of Q values were quite close to 1060 at the 5% of the error constant showing that the Q value was the global minimum (Figure S-3, in supplementary information). Based on the evaluation of the model result the Q values variations in the model, the seven-factor solution provided the most feasible results (with FPEAK = 0.5). The descriptions of the model and source apportionment of PM10 have been discussed in detail in our previous paper Sharma et al. (2013b). The mass fraction distribution of species was used to identify the sources were soil dust (SD), vehicular emission (VE), sea salt (SS), industrial emission (IE), secondary aerosol (SA), biomass burning (BB) and fossil fuel combustion (FFC) for PM10 mass. Using PMF analysis, we could identify source profiles and contribution of PM10 mass concentrations (Figure 5a; b) and source contributions for all concentration (Figure 6) are respectively. Source 1: Soil dust includes most of the crustal elements and has high concentration of Fe, Ca, Na, Mg, Al and K. These elements are major constituents of airborne soil and road dust and usually contribute to coarse aerosol (Lough et al., 2005). The concentration of Ca of the PM10 is associated with the re-suspension from agricultural fields or bare soils by local winds. In the present study, PMF analysis showed that soil dust has contributed 20.7% of aerosol mass in PM10 at observational site. Crustal elements typically used as tracers for soil and/or crustal resuspension include Al, Si, Ca, Mg, Fe and Na (Begam et al., 2010). A whole array of element tracers has been used in India for identification of this source type include Al, Si, Ca, Ti, Fe, Pb, Cu Cr, Ni, Co and Mg (Khillare et al., 2004; Chelani et al., 2008; Gupta et al, 2010)

19

Source 2: A vehicle exhaust is generally dominated by mainly elemental carbon, Cu, Zn, Ba, Sb, Pb, Mn, Mo and Ni widely used as markers of vehicle sources. In the present study, Zn, Mn and EC have been considered as an indicator of vehicle emission. Furusjo et al. (2007) suggested that the vehicular emissions are associated with high concentration of Cu, Zn and Sb. Cu, Zn, Mn, Sb, Sn, Mo, Ba and Fe are markers of brake wear and can serve as indicators of traffic resuspension (Querol et al., 2008). PMF analysis indicates that vehicle emissions have contributed 17.0% in PM10 mass at Delhi. Internationally, EC (Lee et al., 2008) is used extensively as a marker for diesel exhaust. In India, V, Mn, Co, Pb and Zn are used tracer elements for identification of vehicular emission (Chelani et al., 2008). Vehicular emissions are a major source of PM and research indicates that they contribute 10 to 80 % to PM in cities across India. Comparison of such estimates is made difficult by the fact that the various studies have quantified different vehicular sources (exhausts, re-suspension, abrasion etc). Source 3: Higher concentrations of Na, K and Cl in PM10 mass indicate the possible contribution of sea salt, which is supported by PMF analysis (sea salt ~4.4%). Sievering et al. (1991) suggested that SO2 could react on the sea salt particle to produce SO42- in addition to the direct reaction to the gas phase H2SO4 with NaCl. The use of K offers possible confusion with wood/biomass burning combustion and Cl with coal burning, but a combination of the four elements (Na, K, Cl- and Mg) should provide a reliable signature Source 4: Secondary aerosols are mainly composed of ammonium sulphate and nitrate deriving primarily from the gaseous precursors NH3, SO2 and NOx. The abundance of gaseous NH3, SO2 and NOx are at Delhi (Sharma et al., 2010). Secondary aerosols of PM10 (NO3- and SO42-) are originally from anthropogenic or natural sources being formed in the atmosphere. The key markers of secondary aerosols are NO3-, SO42- and NH4+ and were present in PM10 mass. Present PMF analysis shows that secondary aerosols have contributed to about 21.7% for PM10 20

mass concentrations. SO42- has been used as a marker for coal combustion in some Indian studies whereas NH4+ has been used as a marker as a biomass burning. Source 5: Biomass burning, wood burning and vegetative burning have been characterized as having high concentrations of K+ and SO42- by various source studies (Wu et al., 2007). These sources also could be possible facilitated by regional sources or long range transport. Results show that OC and EC are contributed by traffic emission, biomass burning, and wood burning and crop residue burning in PM10. PMF analysis also shows that biomass burning has contributed 14.3% for PM10 mass in the present study. In India K+ has been used a key marker for biomass/wood combustion for TSP, PM10 and PM2.5 (Shridhar et al., 2010) whereas levoglucosan is the key organic marker (Chowdhury et al., 2007). Biomass burning has been estimated to contribute in the range of 7-20% depending upon season and location. It has been reported to be one of the major sources in Delhi, particularly in winter due to combustion of wood (Sharma et al., 2003). Source 6: The results of the PMF analysis show that industrial emissions accounted for about 4.5% of PM10 mass concentration. A range of tracers has been used for identification of industrial emissions including Cu, Cr, Mn, Ni, Co, Zn etc. The high concentration of Cr, Mn, Zn, and S in PM10 mass at observational site attributed to industrial sources as metal manufacturing plants and storage are located near the sampling site. Begam et al. (2006) used Ni, Pb and S as markers for IE, Song et al. (2006) used Ni, Cr, Fe and Mn and Tauler et al. (2009) used Zn, Fe, Mn and Cd as tracers for IE. Generally Zn, Cu, Mn, S, Ni, Cd, Fe, Mo and Cr are used as a tracer for IE in India (Shridhar et al., 2010). Source 7: The higher concentrations of Al, Cl, Fe, Zn, Cr and SO42- at the sampling site clearly indicate the source of fossil fuel combustion of PM10 mass. Cr and Cd are known to occur at high temperatures during the combustion of coal, oil and refuse. Ni and V are widely used as 21

markers for the combustion of heating fuel (Vallius et al., 2005), while Se and Zn are representative marker species for oil fired power plants and coal combustion, respectively (Lee et al., 2002). PMF analysis shows that fossil fuel burning has contributed 17.4% for PM10 mass in the present study. In international studies, a key marker for coal combustion includes As, Se, Te and SO42- and it has contributed 6 to 30% to PM in different studies (Gupta et al., 2007; Sharma et al., 2007). In Delhi, where three coal fired thermal power plants are situated within the city boundaries, Sharma et al. (2007) attributed ~17% of variance as per PCA results of coal combustion while Srivastava and Jain (2007) attributed ~15% of the variance of PM0.7 fraction of the source. The results of the PMF analysis show that the soil dust (20.7%), vehicle emissions (17.0%), secondary aerosols (21.7%), fossil fuel burning (17.4%), biomass burning (14.3%), industrial emissions (4.5%) and sea salts (4.4%) are the major sources of PM10 mass concentration at the observational site of Delhi. Central Pollution Control Board (CPCB), Delhi had identified seven types of source and reported that SD, VE and SA contributed 23.0%, 14.0% and 22.7% of PM10 mass at Delhi (Table 4).Tiwari et al. (2009) reported that SD contibuted 27.0 % of PM10 mass at Delhi whereas Khillare et al. (2004) estimated as 22.0% SD of coarse particle at Delhi. Chelani et al (2007) reported that SD and VE contributed 17.7% and 23.0% of PM10 mass respectively at Mumbai whereas SD contributed 37.0% of PM10 mass at Kolkata (Gupta et al., 2007). In the present study soil dust contributes to 20.7% of PM10 mass at Delhi which is more or less same to previous study at Delhi whereas the quantifications of other source types were missing in previous study. Gugamsetty et al (2012) used the PMF model and analyzed that the SD (34.0%), VE (24.92%), SA (24.44%) and SS (8.4%) are the major sources of PM10 mass at Shinjung Taiwan. More or less similar source types were also reported at Salamanca, Maxico (Murillo et al., 2012) and other part of the world (Zeng et al., 2010) (Table 4). 22

Lagrangian Integrated Trajectory (HYSPLIT) has also shows air mass parcel from long range transport at the receptor site (Figure 7). A secondary aerosol source in the present study was observed to be composed of higher mass fractions of secondary nitrates and sulphates, namely NO3-, NH4+, and SO42-. During winter season the approaching air mass at the receptor site is mainly of continental type and transported from the IGP, Pakistan, Afghanistan and its surrounding areas. Datta et al. (2010) also reported the long distance source of air mass during winter at Delhi. Sharma et al. (2013a) had also observed the similar trajectories at Delhi during winter 2012. During summer the approaching air mass at the receptor site is mainly from Rajasthan (Thar desert), Gujarat, Pakistan and Arabian Sea whereas during monsoon season its approaching from IGP, Rajasthan, Arabian sea, Bay of Bengal and surrounding areas. During monsoon season winds with the combined effect of continental and marine air mass has flown over the observational site. In all the cases, the air mass backward trajectories support the chemical observations for their origin. Venkataraman et al (2005) has reported high emission of BC aerosols from, IGP, central, east coast and south Indian regions due to extensive use of biomass fuels, particularly wood.

4. Conclusions The present study has demonstrated seasonal variations in concentration of PM10, OC, EC, WSIC and trace metals (major and minor elements) of PM10 mass along with the contribution of different types of sources of PM10 over Delhi PMF during January to December 2010. Average mass concentration of PM10 is being significantly higher (213.1 µg m-3) during winter. Maximum concentration of OC has been recorded during winter (36.05 µg m-3), whereas minimum during monsoon (14.72 µg m-3). The EC has also followed a similar pattern with maximum during winter (9.64 µg m-3) and minimum during monsoon (3.35 µg m-3). During the study period 23

positive correlation of SO42- and NO3- with NH4+ indicates the possibility of the formation of secondary inorganic aerosol [(NH4) 2SO4 and NH4NO3] over Delhi. PMF analysis has quantified that the soil dust (20.7%), vehicle emissions (17.0%), secondary aerosols (21.7%), fossil fuel burning (17.4%) and biomass burning (14.3%) are the major sources of PM10 mass concentration at the observational site. The crustal enrichment factors (EF) analysis also indicates the contribution of soil dust to PM10 mass.

5. Acknowledgements The authors are thankful to the Director, CSIR-NPL, New Delhi and Head Radio and Atmospheric Sciences Division (RASD), CSIR-NPL, New Delhi for their encouragement. The authors also acknowledge Council of Scientific and Industrial Research (CSIR), New Delhi for providing financial support for this study (under CSIR-EMPOWER Project). The authors would like to thank Dr. Thomas John, Scientist, RASD, CSIR-NPL for providing the meteorological datasets. Authors are thankful to the anonymous reviewers for their constructive suggestions to improve the manuscript.

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Caption of tables Table 1: The average concentrations of particulates (PM10), EC, OC, WSIC and trace elements of PM10 over Delhi during 2010. Table 2: Concentrations of OC and EC of PM10 (µg m-3) of present study and at different locations of India Table 3: The average and range (in parenthesis) of OC/EC, K+/OC, K+/EC, Cl–/EC and SO42-/EC ratios in aerosol from Indian and Chinese sites. Table 4: Summary of average contribution (%) of major source types of PM10 mass at mega cities of India and other countries. Caption of Figures Figure 1: Map of sampling location Figure 2: Monthly average variations in air mass of PM10, OC and EC (µg m-3) over Delhi during 2010 Figure 3: Monthly average variations in WSIC of PM10 (µg m-3) over Delhi during 2010 Figure 4: Scatter plot between OC and EC over Delhi during 2010. Figure 5a: PMF source profile of fossil fuel combustion (FFC), soil dust (SD), industrial emissions (IE), biomass burning (BB), vehicle emissions (VE), secondary aerosol (SA) and sea salt (SS) in Delhi for PM10 mass. Fig.5b. Time series of factor contribution to the FFC, SD, IE, BB, VE, SA and SS of PM 10 mass during January to December 2010. Figure 6: Percentage source apportionment of PM10 mass in Delhi, India estimated by PMF. Figure 7: Air parcel back trajectory (using HYSPLIT model) during winter, summer and monsoon period (GDAS meteorological data)

34

N

Km 0

1

Fig. 1 Map of sampling location

35

2

3

4

Fig. 2 Monthly average variations in air mass of PM10, OC and EC (µg m-3) over Delhi during 2010

36

Concentration (µg m-3) Fig. 3 Monthly average variations in WSIC of PM10 (µg m-3) over Delhi during 2010

37

Fig. 4 Scatter plot between OC and EC over Delhi during 2010.

38

SD

IE

BB

VE

SA

SS

Fig.5a. PMF source profile of fossil fuel combustion (FFC), soil dust (SD), industrial emissions (IE), biomass burning (BB), vehicle emissions (VE), secondary aerosol (SA) and sea salt (SS) in Delhi for PM10 mass.

39

% of Species

Mass of Species (µg/µg)

FFC

Factor contribution (average =1) Fig.5b. Time series of factor contribution to the FFC, SD, IE, BB, VE, SA and SS of PM10 mass during January to December 2010.

40

Fig. 6. Percentage source apportionment of PM10 mass in Delhi, India estimated by PMF.

41

AGL (m)

Winter

AGL (m)

Latitude (0N)

Summer

Monsoon

AGL (m)

Longitude (0E)

Time (h)

Fig. 7 Air parcel back trajectory (using HYSPLIT model) during winter, summer and monsoon period (GDAS meteorological data)

42

-3

Table 1 The average concentrations of particulates (PM10), EC, OC, WSIC and trace elements of PM10 (µg m ) over Delhi. _____________________________________________________________________________________________________________ Season Seasonal difference Species _______________________________________________________________ _______________________________ Annual Range Winter (W) Summer (S) Monsoon (M) W-S W-M S-M _____________________________________________________________________________________________________________ Mass 177.9 ± 49.5 93.4–328.8 213.09 185.93 134.68a 27.16** 78.41** 51.25** a a * ** OC 26.7 ± 9.2 9.7–69.0 36.05 29.33 14.72 6.72 21.32 14.61** EC 6.1 ± 3.9 1.8–13.0 9.64a 5.45a 3.35a 4.19* 6.29* 2.10* a Cl 3.24 ± 1.68 1.65–6.59 4.16 3.09 2.47 1.07 1.69 0.62 SO428.27 ± 3.82 2.08–15.70 10.50a 4.44a 9.88a 6.06* 0.63* –5.44* NO36.52 ± 5.36 0.67–17.52 12.49a 2.06a 4.99a 10.43** 7.50** –2.93** + a a a ** ** NH4 4.93 ± 4.39 0.37–14.60 9.90 2.38 2.49 7.52 7.41 –0.11 + Na 3.01 ± 1.09 1.33–5.13 2.72 2.97 3.34 –0.25 –0.62 –0.37 K+ 1.44 ± 0.59 0.62–2.38 1.55 1.21 1.56 0.34 –0.02 –0.36 Mg2+ 0.54 ± 0.18 0.19–0.88 0.65 0.40 0.55 0.25 0.10 –0.15 Ca2+ 4.97 ± 1.39 1.95–6.85 5.37 4.20 5.34 1.17 0.03 –1.14 Na 4.31 ± 1.62 2.12–6.98 6.07 3.28 3.57 2.79 2.50 –0.29 Mg 1.80 ± 0.57 0.71–3.01 1.56 1.87 1.98 –0.31 –0.42 –0.11 Al 2.95 ± 0.93 1.53–4.59 3.47 2.96 2.43 0.51 1.04 0.53 Si 2.06 ± 0.51 1.01–4.22 3.11 1.93 1.14 1.18 1.97 0.79 P 0.48 ± 0.23 0.07–0.92 0.44 0.58 0.41 –0.14 0.03 0.17 S 4.40 ± 1.62 1.76–6.89 3.89 5.18 4.14 –1.29 –0.25 1.04 Cl 3.53 ± 1.13 1.86–5.69 4.43 2.96 3.20 1.47 1.23 –0.24 K 2.04 ± 0.91 0.87–3.55 2.02 2.54 1.57 –0.52 0.45 0.97 Ca 8.11 ± 2.81 2.97–10.95 7.67 7.57 9.11 0.10 –1.44 –1.54 Cr 0.28 ± 0.19 0.13–0.63 0.37 0.14 0.32 0.23 0.05 –0.18 Ti 0.16 ± 0.14 0.03–0.52 0.23 0.06 0.18 0.17 0.05 –0.12 Fe 1.00 ± 0.69 0.30–2.43 0.54 0.96 1.49 –0.42 –0.95 –0.53 Zn 0.51 ± 0.49 0.01–1.42 0.55 0.15 0.84 –0.40 –0.29 –0.69 Mn 0.02 ± 0.01 0.003–0.05 0.014 0.03 0.02 –0.02 –0.01 0.01 * * NH3 (in ppb) 12.75 ± 4.13 0.15–49.16 16.13 10.01 12.11 6.12 4.02 –2.10 _____________________________________________________________________________________________________________ a

Significantly (intra seasonal) different at P < 0.05; *Significant at P < 0.05; **Significant at P < 0.01

43

Table 2 Concentrations of OC and EC of PM10 (µg m-3) of present study and at different locations of India Location

Season

PM10

OC

EC

TC

% OC

% EC

% TC

References

Delhi

Annual

177.9±49.5

26.7±9.2

6.1±3.9

32.8±6.5

15.0

3.4

18.4

Present study

Delhi

Annual

183.0

22.0

5.1

27.1

12.0

2.8

18.8

Perrino et al., 2011

Delhi

Annual

191.4±45.5

25.8±8.3

7.8±3.4

33.6±5.9

15.1

4.1

17.6

Sharma et al., 2013

Delhi

Annual

285.7±26.3

93.0±44.7

27.3±13.4

120.3±57.8

32.6

9.6

42.1

Mandal et al., 2013

Mumbai

Annual

188.7±74.2

35.0±9.7

8.4±3.8

43.5±10.2

18.5

4.5

23.1

Gupta et al. 2012

Kanpur

Annual

203.3±85.2

47.4±11.5

6.1±2.8

53.5±11.2

23.3

3.0

26.3

Ram and Sarin 2011

± Standard deviation

44

Table 3 The average and range (in parenthesis) of OC/EC, K+/OC, K+/EC, Cl–/EC and SO42-/EC ratios in aerosol from Indian and Chinese sites. _____________________________________________________________________________________________________________________________ ________________ Sampling site Time period Season OC/EC K+/OC K+/EC Cl–/EC SO42-/EC References _____________________________________________________________________________________________________________________________ ________________ Delhi Nov-Feb 2010 Winter (W) 3.74 0.04 (0.03-0.06) 0.16 (0.14-0.19) 0.46 (0.27-0.51) 1.08 (0.54-1.20) Present study Delhi

Mar-June 2010

Summer (S)

5.38

0.04 (0.03-0.06)

0.22 (0.13-0.26)

0.54 (0.47-0.60)

0.81 (0.52-1.07)

Present study

Delhi

Jul-Oct 2010

Monsoon (M)

4.39

0.11 (0.06-0.13)

0.47 (0.34-0.50)

0.96 (0.14-1.02)

2.94 (1.10-3.27)

Present study

Delhi

Jan-Dec-2010

Annual

4.38

0.06 (0.04-0.08)

Kanpur Kanpur Allahabad Hissar

Beijing

Oct-2008 Jan-Feb-2007 Dec-2004 Dec-2004

Nov-Oct 1997-98

6.55 8.04 7.20 8.47

0.28 (0.20-0.32)

0.06 (0.02-0.09)

a

0.04 (0.02-0.07)

a

0.05 (0.04-0.06)

a

0.08 (0.04-0.14)

a

0.59 (0.29-0.71)

1.36 (1.05-1.84)

Present study

0.28 (0.15-0.55)

a

0.09

1.53

Ram and Sarin (2011)

0.42 (0.15-0.98)

a

0.19

3.11

Ram and Sarin (2010)

0.44 (0.30-0.69)

a

0.03

2.26

Ram and Sarin (2010)

0.64 (0.28-1.21)

a

0.25

3.34

Rangrajan et al (2007)

0.08-0.10

b

Echalar et al (1995)

0.04-0.13

c

Andreae & Merlet (2007)

0.19-0.21

d

Duan et al (2007)

_____________________________________________________________________________________________________________________________ _______________ Emission sources: abiomass burning, bSavanna burning, cagricultural waste burning, and dwheat straw burning

45

Table 4 Summary of average contribution (%) of major source types of PM10 mass at mega cities of India and other countries. Location

PM10 (µg m-3)

No. of factors

Delhi, India

177.9

7

Delhi, India

-

Delhi, India

Sea salt

Vehicle emission (%) 17.0

Secondary aerosol (%) 21.7

Other combustion/ Industrial emission (%) 36.2 (FFC, BB, IE)

Reference

(%) 4.4

Crustal/soil dust (%) 20.7

7

-

23.0

14.0

22.7

40.3 (other )

CPCB report, 2010

219.0

2

-

27.0

-

-

45.0 (BB and FFC)

Tiwari et al, 2009

Delhi, India

161.0

4

16.0

37.2

23.2

2.6

-

Tiwari et al, 2013

Mumbai, India

114.0

5

15.0

17.7

23.0

-

31.9 (FFC, IE)

Chelani et al., 2008

Kolkata, India

-

5

-

37.0

-

-

35.0 (FFC, IE, Others)

Gupta et al., 2007

Shinjung, Taiwan

39.45

5

8.4

34.0

24.92

24.33

8.35

Gugamsetty et al, 2012

Salamanca, Maxico

89.12

5

-

39.87

30.16

14.42

1.82

Murillo et al 2012

Taiyuan, China

305.0

8

-

12.0

13.0

16.0

30.0

Zeng et al 2010

46

Present Study