Source apportionment of airborne particulates ...

Atmospheric Research 164–165 (2015) 167–187

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Invited review article

Source apportionment of airborne particulates through receptor modeling: Indian scenario Tirthankar Banerjee a,⁎, Vishnu Murari a, Manish Kumar a, M.P. Raju b a b

Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi, India Physics and Dynamics of Tropical Cloud Group, Indian Institute of Tropical Meteorology, Pune, India

a r t i c l e

i n f o

Article history: Received 30 October 2014 Received in revised form 26 March 2015 Accepted 24 April 2015 Available online 6 May 2015 Keywords: Aerosol Indo-Gangetic Plain Particulate Receptor model Source apportionment Tracers

a b s t r a c t Airborne particulate chemistry mostly governed by associated sources and apportionment of specific sources is extremely essential to delineate explicit control strategies. The present submission initially deals with the publications (1980s–2010s) of Indian origin which report regional heterogeneities of particulate concentrations with reference to associated species. Such meta-analyses clearly indicate the presence of reservoir of both primary and secondary aerosols in different geographical regions. Further, identification of specific signatory molecules for individual source category was also evaluated in terms of their scientific merit and repeatability. Source signatures mostly resemble international profile while, in selected cases lack appropriateness. In India, source apportionment (SA) of airborne particulates was initiated way back in 1985 through factor analysis, however, principal component analysis (PCA) shares a major proportion of applications (34%) followed by enrichment factor (EF, 27%), chemical mass balance (CMB, 15%) and positive matrix factorization (PMF, 9%). Mainstream SA analyses identify earth crust and road dust resuspensions (traced by Al, Ca, Fe, Na and Mg) as a principal source (6– 73%) followed by vehicular emissions (traced by Fe, Cu, Pb, Cr, Ni, Mn, Ba and Zn; 5–65%), industrial emissions − (traced by Co, Cr, Zn, V, Ni, Mn, Cd; 0–60%), fuel combustion (traced by K, NH+ 4 , SO4 , As, Te, S, Mn; 4–42%), marine − aerosols (traced by Na, Mg, K; 0–15%) and biomass/refuse burning (traced by Cd, V, K, Cr, As, TC, Na, K, NH+ 4 , NO3 , OC; 1–42%). In most of the cases, temporal variations of individual source contribution for a specific geographic region exhibit radical heterogeneity possibly due to unscientific orientation of individual tracers for specific source and well exaggerated by methodological weakness, inappropriate sample size, implications of secondary aerosols and inadequate emission inventories. Conclusively, a number of challenging issues and specific recommendations have been included which need to be considered for a scientific apportionment of particulate sources in different geographical regions of India. © 2015 Elsevier B.V. All rights reserved.

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Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Regional heterogeneity of airborne particulates . . . . . . . . . . . . . . . Selection of source signature . . . . . . . . . . . . . . . . . . . . . . . 3.1. Crustal elements/soil dust/road dust . . . . . . . . . . . . . . . . 3.2. Vehicular emissions . . . . . . . . . . . . . . . . . . . . . . . . 3.3. Industrial emissions . . . . . . . . . . . . . . . . . . . . . . . . 3.4. Fuel combustion . . . . . . . . . . . . . . . . . . . . . . . . . 3.5. Marine aerosol . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6. Biomass and refuse burning . . . . . . . . . . . . . . . . . . . . Source apportionment and receptor modeling . . . . . . . . . . . . . . . 4.1. Enrichment factors (EFs) . . . . . . . . . . . . . . . . . . . . . . 4.2. Factor analysis (FA) . . . . . . . . . . . . . . . . . . . . . . . . 4.2.1. Exploratory factor analysis: Principle Component Analysis (PCA) 4.2.2. Confirmatory factor analysis . . . . . . . . . . . . . . . . 4.3. Chemical mass balance (CMB) . . . . . . . . . . . . . . . . . . .

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⁎ Corresponding author at: Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi 221005, India. E-mail addresses: [email protected], [email protected] (T. Banerjee).

http://dx.doi.org/10.1016/j.atmosres.2015.04.017 0169-8095/© 2015 Elsevier B.V. All rights reserved.

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4.4.

Hybrid methods . . . . . . . . . . . . . . . . . . . 4.4.1. Constrained Physical Receptor Model (COPREM) 4.4.2. Extended factor analysis models . . . . . . . 5. Temporal pattern of particulate source profile in India . . . . . 5.1. Northern India: Delhi, Kanpur, Agra and Chandigarh . . 5.2. Southern India: Hyderabad, Chennai and Tirupati . . . . 5.3. East & Central India: Kolkata, Jorhat and Durg . . . . . 5.4. Western India: Mumbai, Ahmedabad, Pune and Nagpur . 6. Conclusions and way forward . . . . . . . . . . . . . . . . Acknowledgment . . . . . . . . . . . . . . . . . . . . . . . . Appendix A. Supplementary data . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . .

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1. Introduction Airborne particulates are distinguished as multi-component mixtures originated from a wide range of sources, subsequently evolved through several microphysical processes like nucleation, coagulation and condensation before ultimately scavenging off either through wet or dry deposition (Kumar et al., 2015). Association of airborne particulates with human health, crop yield, regional circulation systems, climate dynamics and many other sectors of earth's system is well established in numerous scientific literatures (Ramanathan and Feng, 2009). Implications of airborne particulate are typically regional as predominant particulate species have comparably lower residence time in contrast to long-lived greenhouse gases. However, trans-boundary movement of particulates amplifies the quantum of impacts to greater distances (Ramanathan and Feng, 2009; Banerjee et al., 2011a; Kumar et al., 2015). Particulate diversity in Indian subcontinent is extremely diverse and complex which requires methodical understanding in terms of composition, morphology and mixing state, size distribution and chemical evolution. Among others, particulate morphology and chemical heterogeneities principally regulate its optical properties, volatilities, interactions and phase transformations in different ways and therefore, efforts have been made over the years to study atmospheric particulates as a function of size, shape and chemical characteristics. Particulate morphology and chemical characteristics are essentially functions of associated sources and therefore, it is extremely essential to be characterized for proper estimation. Source apportionment of airborne particulates essentially quantifies the contribution of individual sources to particulate loading based on source and receptor characteristics and in certain cases, with nature of pollutants. Such may be accomplished either through analyzing specific tracers in bulk filter analysis or by numerical/statistical analysis of specific parameter with prevailing meteorological variables or by coupling emission inventory information with dispersion models. In that way, three principal SA techniques are (a) chemical transport models based upon pollutant composition driven by meteorological variables (Banerjee et al., 2011a; Belis et al., 2013), (b) receptor-oriented models based on analysis of chemical data acquired at receptor sites (Balachandran et al., 2000; Srivastava et al., 2008), and (c) emission inventories and dispersion models (Laupsa et al., 2009; Banerjee et al., 2011b). A number of SA studies for different atmospheric pollutants with some degree of certainty are available in India with majority of studies being conducted using receptor models (RMs) based on monitored particulate concentrations and their source profile. Receptor models are broadly classified into microscopic and chemical methods based on the nature of particle characteristics opted for simulation. Microscopic RM analyzes the morphological features of airborne particulate through optical and scanning electron microscopes which are efficient enough to characterize aerosols in mixing states (Pipal et al., 2011). However, applicability of microscopic RM limits in large-scale as it mostly yields qualitative products (Pant and Harrison, 2012) and confines in identifying inorganic compounds (Shi et al., 2008). In contrast, chemical RM utilizes the chemical composition of airborne particles

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for identification and apportionment of specific sources. The foundation of all receptor based models is mass conservation which may simply be explained by Eq. 1: C ¼ S F þ e

ð1Þ

where, C is the particulate concentration profile in the receptor site, S denotes source contribution which needs to be measured, F is the particulate source profile and e denotes error between the measured and predicted concentrations. However, mass balance equation itself based on certain assumptions is critical for specific RM techniques than for others (Belis et al., 2013). In most of RMs, the system is considered as quasi-stationary representing statistically insignificant variations in source profile with time. Additionally, evolved particulate species are required to be chemically inactive during its aerial transport. For a proper SA, these assumptions are essentially required to be satisfied, except which definite source profiling may not be achieved or RMs may under/ overestimate the individual contribution of particular source (Belis et al., 2013). Additionally, the concept of RMs is based on proper identifications and quantification of specific signatory molecules which virtually establish missing links between sources and receptors. Chemical signatures of specific sources are used to be extremely sensitive and as these signatures gradually evolve with time, may undergo chemical phase transformations and eventually be masked, which critically limits its applications as a tracer. Therefore, selection of the specific RMs for SA studies is extremely important as only few RMs can tolerate deviations of pre-identified assumptions (Watson et al., 2008; Belis et al., 2013). These RMs especially consider selective losses of particular tracers caused by phase transformations and ultimately re-introduce it in the analysis as an error-input. The present submission only synthesizes the results archived in SA research articles and scientific reports published in the Indian context and available in abstract and citation database of peer-reviewed literature. Particulate sources are summarized and quantitatively evaluated to reduce uncertainties in identifying specific airborne particulate sources. A total of 90 research articles originated in a span of 1985– 2014 and available in citation database of peer-reviewed literature were scrutinized. Interestingly, SA studies in India initiated way back in 1985, however, researchers were only aspired for particulate SA studies from 2005 onwards which share 80% (72) of the total scientific publications (90) (Fig. 1a). Until 1990s, there were only 9 publications (10% of total) available typically originated using EF (42%) and FA (33%), while only 3 instances were there when advanced RMs like PCA (17%) and CMB (8%) were in use (Fig. 1b). Unavailability of regional source profile may possibly restrict the use of advanced RM. Since 2000, SA of airborne particulate established itself as a principal research domain (90% of publications) to atmospheric scientists possibly due to raising concern of aerosol induced adverse health impacts and more precisely its association with regional climate change. Since 2010–14, a total of 81 publications were found involving SA of particulates involving many advanced RMs likewise EF (34%), FA (3%), PCA (36%), PMF (15%), UNMIX (3%) and CMB (10%). However, in most of the cases SA


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Fig. 1. (a) Decadal variation of SA studies in India. (b) Chronological pattern of RM applications for SA studies in India.

was only performed for PM10 and SPM and only in few instances, PM 2.5 was considered as a particulate metric. For the entire SA study, most preferred particulate metrics were PM10 (41%) followed by PM2.5 (26%) and SPM (22%). The trend is well comparable to that of European SA case studies (Belis et al., 2013) where significant proportions of SA have been carried out for PM 10 (56%) followed by PM2.5 (37%) and SPM (1%). For both instances, SA for ultrafine particulates was relatively less (India: 10% and Europe: 6%) signifying specific requirement of regional case studies emphasizing on ultrafine particulates, which are supposed to be predominately anthropogenic and carcinogenic in nature. In India, considerable amount of particulate SA was carried out from 2010 and 5 years of research resulted to staggering 44 publications (49% of total). Fig. 2 indicates the geographical distribution of SA studies that have been conducted in India. It is evident that most of SA studies have been performed in and around Delhi, Mumbai, Chennai and Kolkata with some contributions from Hyderabad, Tirupati, Durg, Kanpur, Agra and Chandigarh. A comprehensive analysis on airborne particulate loading and its spatial distribution has also been extensively reviewed. Such meta-analysis clearly indicates the presence of substantial particulate loading especially in the context of Indo-Gangetic Plain (IGP) which requires adequate source segregation for effective implementation of control strategies. Emphases were made to isolate individual species that were characteristics of particulate composition of a specific location. Assignment of specific tracers to precise source categories was also evaluated. Conclusively, a comprehensive review has been made for particulate SA studies in the Indian perspective yielding a quantitative estimation of most relevant particulate sources and there temporal variations.

2. Regional heterogeneity of airborne particulates Airborne particulates emitted from specific sources composed of unique chemical signature molecules and fractional abundance of these molecules essentially serve as an input for receptor models. Airborne particulates include chemical heterogeneity at spatio-temporal levels and therefore, there characteristics are highly region specific (Banerjee et al., 2011b,c; Kumar et al., 2015). Numerous literatures are available concerning chemical speciation of airborne particulate within India. These articles mostly focus on association of trace metals and ionic species with airborne particulates and their multi-temporal variations. Additionally, efforts were also made to understand comparative information of tropospheric aerosol both in rural and urban sectors. However, it was until 2005, when mainstream particulate chemical speciation information were further processed to identify possible particulate sources. Initially during 1960s to 1990s, SA was only conducted through multivariate statistical models based on factor analysis and PCA as speciated emission inventories and source profile information were extremely limited. It was only during the second half of the 20th century that source profiling of airborne particulates has been initiated (Gupta et al., 2007; Gadkari and Pervez, 2008; Patil et al., 2013), before which USEPA speciate database was only available to explore. In the beginning of 2000, Central Pollution Control Board (CPCB) initiated an integrated approach on developing emission inventories and chemical speciation of airborne particulates to assess the contribution from various sources. It essentially helped to develop India-specific particulate source profiles for vehicular as well as non-vehicular sources, refined emissions factors and reliable emission inventories. However, adopted methodologies do pose some irregularities and RM performances somewhat vary in different monitoring cities (under predicted: Delhi, Pune; statistically

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Fig. 2. Geographic distribution of RMs for SA studies in India.

significant prediction: Chennai, Kanpur and Mumbai; CPCB, 2011a) possibly due to some extraneous effect (e.g. background sources) not accounted while modeling or during development of emission inventories. For the following discussions, information related to particulate mass loading, characteristics of spatio-temporal variation, particulate chemical speciation, and presence of specific signature molecules for different source categories were discussed based on available literature. Annual average concentration range of PM10 has been explained for 2008 to 2010 (CPCB, 2011b) with suspended particulate matter (SPM) concentration for year 2010 (CPCB, 2012). Such ambiguity was only due to non-availability of any consistent report for all the concerned regions. Again, due to unavailability of any government published report, annual average concentrations of PM2.5 were only retrieved from peerreviewed research papers. All the concentrations are expressed in terms of microgram per cubic meter unit (Fig. 3).

Mumbai, a coastal city of western peninsula has widely been explored in terms of physio-chemical characterization of airborne particulate (PM2.5: 40–50; PM10: 94–132; SPM: 176–246) for source assessment studies (Kumar et al., 2001; Chelani et al., 2008a, 2008b; Kothai et al., 2008; Herlekar et al., 2012; Gupta et al., 2012). Airborne particulates exhibit seasonal fluctuations with characteristically inverse correlation with an existing boundary layer (Kothai et al., 2008). Gupta et al. (2004) found a significant correlation (0.69–0.89) between TSP and PM10 at three measured sites with PM10 contributing a constant fraction (47%) of the TSP loading. It clearly indicates a common influence of meteorology and regional sources to both kinds of particulates. Efforts were also made to identify relative contribution of PM10 to total particulate loading in industrial (Sharma and Patil, 1992; 85–90%), traffic intersections (Vinod Kumar and Patil, 1994; 75%) and port area (Gupta et al., 2004; 47%), which exhibited differential patterns. Bhanarkar et al. (2005) inventorized annual emission of particulates


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Fig. 3. Spatial variation of airborne particulate mass concentrations in India.

(2001–02: 9.794 Gg y−1; 2010: 3.606 Gg y−1) and toxic metals (2001– 02: 0.375 Gg y−1; 2010: 0.041 Gg y−1) originating from an industrial sector and found thermal power plants responsible for the overall 19% PM and 62% metal emissions. Particulate source profiles for both PM2.5 and PM10 conducted by Patil et al. (2013) indicate higher abundance − of sulfate (SO2− 4 ), nitrate (NO3 ), and organic carbon (OC) in finer particulates compared to coarser ones. Kothai et al. (2008) revealed Fe, Sc, Ti, Si and Ca typically originated from soil and earth crust and associated mostly with PM2.5–10. Additionally, the presence of Cr and Ni in airborne particulates was attributed to hazardous waste disposal and from industrial emissions. Contribution of marine sources to airborne particulate (Na, Mg, K, Cl) was mostly associated with a coarser one (25–54%; Gupta et al., 2012; Kothai et al., 2008 Chelani et al., 2008a, 2008b) while elements (Mn, Cr, Cu) typically having anthropogenic sources were found to be constituents of finer particulates (Kothai et al., 2011). Ambient air quality in terms of size segregated airborne particulates has been studied most extensively in Delhi (PM2.5: 50–130; PM10: 198–259; SPM: 426–576) (Srivastava and Jain, 2007a,b; Gargava et al., 2014; Sharma et al., 2014; Patil et al., 2013; Sen et al., 2014). Seasonal variability of particulate mass concentration exhibits a trend of wintertime

maxima (301–350 μg/m3) and monsoonal minima (51–100 μg/m3) (Sharma et al., 2014). Speciated emission inventory for PM10 developed by Gargava et al. (2014) estimates a staggering 1.4 × 105 kg of daily particulate emission which probably contributes in deteriorating regional environment. Apportionment of particulate sources has also been investigated in a number of instances (Srivastava and Jain, 2007a,b; Srivastava et al., 2008; Shridhar et al., 2010; Khillare and Sarkar, 2012; Patil et al., 2013). Srivastava et al. (2008) found a major proportion of airborne particulate as ≤0.7 μm while vehicular and crustal emissions emerged as principle contributors of fine and coarser particulates, respectively. Interestingly, dominance of vehicular emissions was only found to be confined within PM2.5 in contrast to pre CNG era (2001) when it used to dominate both coarser and finer particulates. Chemical profiles of crustal elements typically earmarked with Si, Al, Fe, Ca, K, Na and Mg (Srivastava et al., 2008), while dominance of Fe was mostly emphasized with coarser particulates. Crustal elements of Delhi region primarily originated from Aravalli hills with ferrogenous quartzite as a main component (Pant and Harrison, 2012) causing high Fe concentration in coarser particulates. Among trace metals, Pb, Cu, V, Cd, Ni, Cr and Zn at the urban site and V at the rural site were reported to be moderately

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enriched (Shridhar et al., 2010). Sulfate was described to be abundant in paved road dust possibly due to chemical binding of secondary aerosols with crustal elements (Kumar et al., 2001). Singh et al. (2011) revealed that Mn, Cr, Cd, Pb, Ni, and Fe mainly associated with finer particulates while organic components like PAH with PM2.5 suggest their anthropogenic origin. In Kolkata (PM2.5: 70–110; PM10: 98–187; SPM: 157–307), airborne particulate mostly reported to be largely affected by regional emissions and prevailing atmospheric conditions. Most of the researches aimed to understand particulate seasonal variability and its association with meteorological parameters (Gupta et al., 2007; Karar and Gupta, 2006). However, meteorological influences were highly location specific as particulates exhibit a poor (Gupta et al., 2006) or an inverse correlation (Karar and Gupta, 2006) with temperature, rainfall, relative humidity and solar radiation or at certain cases exhibit a significant inverse correlation with wind speed (Gupta et al., 2006). Additionally, finer particulates appear to be constant fractions of coarser particulates indicating common influences of meteorology and regional sources (Gupta et al., 2006; Gupta et al., 2007; Karar and Gupta, 2006). In context to very few SA studies conducted in Kolkata, source profiles were mostly adopted from USEPA database. Gupta et al. (2007) identified iron (Fe), SO2− 4 , OC and total carbon (TC) as the most abundant species in road dust while soil dusts were reported to be enriched with OC, TC, − 2− Cl− and SO2− in soil dust 4 . Additionally, presence of both Cl and SO4 was considered to be the effect of aged marine aerosol. Kar et al. (2010) identified elemental compositions of traffic-induced urban aerosol and found a significant spatial deviation representing influence of local sources along with meteorological variables. Additionally, elemental composition of particulates exhibits high levels of Zn, Fe, Cu, Cr, Pb, and Ni with spatial differences most significant for Zn, Ni, Pb, and Cu. Bhattacharjee et al. (2011) analyzed petrol and diesel-run vehicle emissions and identified ultrafine PMs as the major aerosol component. However, unavailability of appropriate source profile information severely reduced application of advanced RMs for SA studies. Till the submission of the manuscript, only two source profile studies (Gupta et al., 2007; Chowdhury et al., 2007) were found to be available for Kolkata. The source profile developed by Gupta et al. (2007) was more specific to road and crustal emissions, while other applied particulate source profile information did not have its origin in India (like as in the case of Chowdhury et al. (2007)), therefore, tracer species were more specific to the sources than to locations. Chennai (PM2.5: 20–90; PM10: 48–70; SPM: 83–174) located in the southern peninsula along the coast of Bay of Bengal, is one of the most comprehensively studied city in respect to airborne particulates (Mohanraj et al., 2011a; Srimuruganandam and Nagendra, 2011, Srimuruganandam and Nagendra, 2012a, 2012b; Guttikunda and Jawahar, 2012; Chithra and Nagendra, 2013). Most of the findings revealed wintertime maxima in both finer and coarser particulates followed by monsoon and summer (Srimuruganandam and Nagendra, 2011; Srimuruganandam and Nagendra, 2012a, 2012b; Chithra and Nagendra, 2013). Distinctively, the minimum particulate concentrations were experienced during summer in comparison to monsoon, owing to better atmospheric dispersibility prevailing during summer and the minimum ventilation coefficients during monsoon. Implications of marine aerosols were also more pronounced during summer (25% of total ionic concentration) compared to monsoon (19%) and winter (20%) (Chithra and Nagendra, 2013). For most of the studies, coarse, fine and ultra-fine particulates exhibit a reasonably good correlation (0.5–0.8). Coarser particulates were reported to be significantly composed of PM2.5 (50%) and PM1 (45%), while PM2.5 comprised of a large fraction of PM1 (80%) (Srimuruganandam and Nagendra, 2011). The PM2.5/PM10 ratio was found to be highly season and location specific indicating variable influences of regional sources and meteorology. Chemical characterization of airborne particulates explained an abundance of crustal elements (Al, Ca, Fe, Mg and K, up to 70%) followed by sea salt (Na and K, 15%) and other trace elements (Zn, Ba, Cd, Ca, Pb,

Cu, Co, Cr, Mn and Ni) (Srimuruganandam and Nagendra, 2012a, 2012b). Coarser particulates were mostly accounted by crustal elements with a definite source signature from paved road dust (Ca), fugitive dust emissions (Al), road dust contamination through vehicular emissions (Cr) and marine aerosol (Na, Mg and K). Ionic constituents also contribute significantly to finer (anions: 20–45%; cations: 35– 50%) and coarser (anions: 40–80%; cations: 25–35%) particulate mass 2+ − loading while, SO2− were mostly reported as dominant 4 , NO3 and Mg species (Srimuruganandam and Nagendra, 2011). Kanpur (PM2.5: 60–150; PM10: 208–211; SPM: 442–493), representative of Indo-Ganga basin in terms of seasonal variability is typically influenced by a number of regional sources viz. industrial emissions, postharvest agricultural-waste burning and fossil-fuel combustion coupled with emissions from brick kilns and textile mills (Ram et al., 2012). Behera et al. (2011) developed a geographic information systembased emission inventory for coarser particulates and revealed total particulate emission of 11 t day−1 predominantly arising from industrial point sources (25%), vehicular emissions (20%), fuel combustions (19%) and road dust re-suspensions (15%). References were available for spatio-temporal variation and chemical speciation of airborne particulate in Kanpur (Sharma and Maloo, 2005; Ram et al., 2012), however, only few studies were further processed to identify associated sources (Shukla and Sharma, 2008; Chakraborty and Gupta, 2010; Behera et al., 2011). Coarser particulates exhibit lowest atmospheric variability during monsoon and highest during summer (Shukla and Sharma, 2008) in contrast to submicron particulate (PM1) which prevailed highest during winter (Chakraborty and Gupta, 2010). Regarding particulate sources, Ram et al. (2012) identified fine-mode particulates as mostly anthropogenic during winter in contrast to that from crustal during summer. Most of particulate speciation studies reveal crustal elements (Al, Ca, Mg, and Fe) as principal constituents of particulates due to persistence of high wind speed and dry soil texture (Shukla and Sharma, 2008; Chakraborty and Gupta, 2010). The contribution of organic carbon is also reported to be pronounced in both finer (30–60%) and coarser particulates (15–50%) (Ram et al., 2012). Most of chemical speciation data reveal inorganic secondary particles as the significant contributor of total particulate loading in Kanpur. Ram et al. (2012) found that the secondary inorganics (combination of − 2− NH+ 4 , NO3 , and SO4 ) contribute 70–80% of the water-soluble inorganic ions and 15–25% to the total aerosol mass, while Shukla and Sharma (2008) held inorganic secondary particles ((NH4)2SO4 and NH4NO3) responsible for 20–25% PM10 composition. Among the submicron particles, inorganic anions contribute 35–40% of PM1 mass loading, 80–90% of which were attributed by nitrate and sulfate (Chakraborty and Gupta, 2010). Agra (PM2.5: 30–150; PM10: 156–185; SPM: 315–523), located in the north-central part of India is the home of the world famous heritage monument Taj Mahal. Temporal variability of a fine and coarse particulate expressed a similar nature of wintertime maxima and monsoonal minima (Kulshrestha et al., 2009; Singh and Sharma, 2012). Kulshrestha et al. (2009) found particulate concentrations to have varied with humidity while it was inversely proportional to temperature and wind speed. The influence of finer particulates to PM10 also varied significantly in different literatures with a contribution extending from 30–70% (urban site) to 40–60% (rural site) (Kulshrestha et al., 2009; Pipal et al., 2011). Numerous references are available regarding speciation of particulates (Kulshrestha et al., 2009; Pipal et al., 2011; Singh and Sharma, 2012; Pachauri et al., 2013) while, source apportionment of particulates was only reviewed recently (Kulshrestha et al., 2009; Singh and Sharma, 2012; Habil et al., 2013; Satsangi et al., 2013; Pachauri et al., 2013). Majority of the source apportionment studies have been conducted using trace element markers (Kulshrestha et al., 2009; Singh and Sharma, 2012; Habil et al., 2013) while in some cases, organic tracers have also been exploited (Satsangi et al., 2013; Pachauri et al., 2013). Pipal et al. (2011) using scanning electron microscopy indicates association of three principal clusters of particles i.e., C, O


rich (PM2.5: 95%; PM10: 72%), Si, Na and Al rich (PM2.5: 3%; PM10: 25%) and S, Fe, K, and Co rich (PM2.5: 1%; PM10: 3%) on the basis of their percentage contribution. Among the ionic species, Ca2+, SO24 − and NO− 3 were most dominant followed by NH+ 4 (Singh and Sharma, 2012; Satsangi et al., 2013). Pachauri et al. (2013) documented a significant association between soluble K+ and OC possibly through regional practices of biomass and waste burning. Satsangi et al. (2013) reported a 2− higher ionic ratio ([NO− 3 ]/[SO4 ]) during winter (1.7 ± 0.4) which was attributed to a combined effect of biomass burning and meteorological conditions. Apart from these evidences, information related to particulate mass loading, chemical speciation and presence of specific trace metals were also available for different Indian cities viz. Varanasi (Murari et al., 2015), Chandigarh (Bandhu et al., 2000), Tirupati (Mouli et al., 2006), Hyderabad (Gummeneni et al., 2011), Dhanbad (Dubey et al 2012), Durg (Gadkari and Pervez 2007), Tiruchirappalli (Mohanraj et al., 2011b), Pune (Yadav and Satsangi 2013), Jorhat (Kahre and Baruah 2010) and Pantnagar (Banerjee et al., 2011a,b,c). 3. Selection of source signature Source apportionment of airborne particulate essentially requires particulate speciation information which is critically analyzed to identify the presence of certain species which are presumed to have evolved from identified sources, transported through atmospheric turbulence and eventually assessed in the receptor site. During the course of SA, these unique species are essentially considered to be chemically isolated and supposed to carry unique identification marks of the respective sources (Fig. 4). A receptor model principally uses these source signature species to identify the perceived source contribution of an area under investigation and eventually establishes a relation between the source and the receptor. Proper identification and further quantification of these individual signature species both in terms of source and receptor are typically most critical step in source apportionment. Selection of

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source specific tracers critically limits the applicability of SA results and hence its inter-comparability. Choice of specific molecular tracers for a particulate source entirely depend on the modeler itself, however, there are some universally accepted tracers available. In a specific case, before selecting an explicit tracer for a definite source, the modeler needs to consider its atmospheric lifetime, chemical stability, phase transformation and model specificity. For the following section, specific tracers identified for particulate source categories and its relevance in context of SA were elaborately discussed. Additionally, efforts were also made to designate few source specific tracers which may turn useful for future studies. 3.1. Crustal elements/soil dust/road dust Soil dust/road dust/crustal elements are often considered to be the most influencing sources of airborne particulates in India. However, for most of the experiments, a mixed profile has been selected to characterize crustal elements (Khare and Baruah, 2010; Kulshrestha et al., 2009; Singh and Sharma, 2012). Being part of earth's crustal component, Si, Al, Fe and Ca are of obvious choice as crustal markers as these component become airborne by winds and other disturbances (Belis et al., 2013). These crustal components are further associated with Na, K and Mg if there are no intrusions of marine aerosol. Additionally, resuspended dusts from paved and unpaved roads accelerated by vehicular activities are reservoirs of crustal elements. The composition of paved road dust is dominated by Si, Ca, Fe, Sn, Al, Fe, K, Mg and Sn possibly due to direct vehicular emissions in the form of exhaust, brake wear and re-suspension of road dust (Srivastava and Jain, 2007a, 2007b). Unpaved road dust profiles somewhat resembles paved road, however, OC and EC in unpaved road dust are typically 40–70% lesser (Patil et al., 2013). In India, selection of tracer species for crustal elements is somewhat inconsistent. Wide ranges of elements have been compiled (Al, Ca, Fe, Mg, Ba, Si, Mg, K, Na, Ni, Mn, Pb, Cu and Zn) as tracer

Fig. 4. Evolution of elementary and organic signature molecules.

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species for crustal elements, where some researchers have included road/re-suspended dust in crustal segments (Srivastava and Jain, 2007a, 2007b; Chakrobarty and Gupta, 2010; Srimuruganandam and Nagendra, 2011) while others have segregated it (Kumar et al., 2001; Gupta et al., 2007; Kothai et al., 2008). Chemical species released from various sources ultimately scavenged off and settled in road side environments. Such appears to be re-released through soil/crustal/road resuspension. Therefore, some SA studies combine the crustal sources/ road dust resuspension with the construction and vehicular activity (Sridhar et al. 2010; Singh et al., 2011; Kulshrestha et al. 2009) while others project them separately (Kulshrestha et al. 2009; Gummeneni et al. 2011). Occasionally the combination of chemical species has also been used to identify separate sources. The presence of Fe with OC and TC is often considered as a tracer for road dust whereas the presence of Cl− and SO24 − with OC and TC marked for soil dust (Gupta et al. 2007). Karar and Gupta (2007) concluded that emission from vehicular activities significantly enriched the road dust by Pb and thus turn Pb as an important tracer for vehicular emissions. Basha et al. (2010) used Pb, Cr and Co as tracers for soil/re-suspended dust while Cd, Cu and Ni for road dust due to vehicular movement. These ambiguities in source profile selection critically restrict inter-comparison of results and subsequently limit policy decisions.

3.2. Vehicular emissions Vehicular emissions are a function of traffic density and vehicle specific emission factors with activity rate. Such emissions characteristically include direct tailpipe emissions of organic and inorganic gases, fuel tank evaporation, wear of brake linings, clutch, tires and resuspensions of road side particulates (Viana et al., 2008; Belis et al., 2013). Due to the nature of pollutants, vehicular emissions are mostly associated with finer particulates. However, it may well be supplemented by elements that were deposited onto the road side and subsequently resuspended with crustal elements and constitute a major fraction of coarser particulates. This makes the emission profiles extremely complex and often forced modeler to consider traffic induced crustal elements as single factor likewise in Kolkata (Karar et al., 2006); Jorhat (Khare and Baruah, 2010) and Agra (Kulshrestha et al., 2009). However, in certain SA studies these two sources were considered separate likewise in Mumbai (Kumar et al., 2001); Nagpur (Pipalatkar et al., 2014); Ahmedabad (Raman et al., 2010) and Chennai (Srimuruganandam and Nagendra, 2012a, 2012b; Srimuruganandam and Nagendra, 2012a, 2012b). Additionally, due to involved complexities in identifying separate molecular markers, vehicular emission again was often supplemented by industrial emissions and considered as a single factor likewise in Delhi (Khillare et al., 2004); Navi Mumbai (Kothai et al., 2008) and Jorhat (Khare and Baruah, 2010). Traffic source profiles typically contain Cu (brake linings), Pb (gasoline additives) and Zn (tire wear). Additionally it may be supplemented with Ba, Fe, Al, K, and Ca from ash fractions of diesel exhausts and markers of brake wear (Belis et al., 2013); Al from wear of pistons (Srimuruganandam and Nagendra, 2012b); Ba from organometallic fuel additives (Srimuruganandam and Nagendra, 2012b); Fe from metal wear in the exhaust system and wear and tear of brake (Karar and Gupta, 2007; Gupta et al., 2007), Mn from additive in unleaded gasoline (Kulshrestha et al., 2009), Ni from combustion of heavy oil (Khare and Baruah, 2010) and Zn from two-stroke engines as it is used as a fueladditive (Kothai et al., 2008). Sometimes combinations of elementary molecules have also been considered as signatory molecules. Elemental species with major proportions of Mg, Fe, Ba, and Zn and trace concentrations of Al, Cr, Mn, and Ca signify emissions from wearing of brake linings while enrichment of Fe, Ba and Cu reflects emission from break pad (Srimuruganandam and Nagendra, 2012a, 2012b). Apart from these evidences, hopanes, steranes, and pyrenes are most characteristically used in RMs (Belis et al., 2013) for diesel exhausts whereas,

coronene and benzo(ghi)perylene are used as molecular markers for gasoline emissions (Pant and Harrison, 2012). In India, most of SA studies have used the elemental species for the identification of vehicular sources while few have also used organic species as molecular tracers. Chowdhury et al. (2007) used both elemental (Si, Al and EC) and organic tracers (n-alkanes, PAHs, hopanes, steranes, and levoglucosan) to identify airborne fine particulate sources in Delhi, Mumbai, Kolkata, and Chandigarh during 2001–02. A combination of anthracene; benzo(a)pyrene; and 1,2,3 cd-pyrene was used for a diesel powered vehicle in Chennai (Mohanraj et al., 2011a). Lead is still in use as the most common signature species for vehicular sources (Balachandran et al., 2000; Khillare et al., 2004; Srivastava and Jain, 2007a, 2007b). Although from 2000 onwards the use of leaded petrol was completely banned, its long residual life still makes it relevant for airborne particulates. Elements of catalytic converters like platinum (Pt), palladium (Pd) and rhodium (Rh) were also correlated in road dust samples (Mathur et al., 2011). Compounds of PAH (BAP, phenanthrene, chrysene) are also found in gasoline and diesel exhaust (Mohanraj et al., 2011a) as well Mn and Cr emitted from brake pad and tire wear (Shridhar et al., 2010). Ambiguities were present in the selection of elemental markers for different SA studies. Some of these confusing selections were combinations of Co, Ni, Cu, Zn, Cd, and Te as tracers of vehicular and industrial emissions (Khare and Baruah, 2010); Mn and Zn as vehicular emission along with resuspension of soil dust (Kulshrestha et al., 2009); and Cr and Co as tracers of vehicular emissions (Kumar et al., 2001; Srivastava et al., 2008, Srivastava et al., 2009). 3.3. Industrial emissions Industrial emissions are typically heterogeneous with emissions associated with different manufacturing processes like petrochemical, metallurgic, ceramic, and pharmaceutical. A series of marker species (Ni, Cr, Co, Cd and As) have been used for particulate SA studies conducted in India. Additionally, different trace elemental markers have also been used to identify specific industrial emissions, most notably Cu, Mn and Ni from ferrous metal processing and steel industries in Mumbai (Kumar et al., 2001); Cr and Zn from metal manufacturing plants in Delhi (Sharma et al., 2014); Pb, Cd and V from battery repair and refuse oil burning plants in Delhi (Shridhar et al., 2010); Cr from electroplating industries of industrial areas of Kolkata (Karar and Gupta, 2007); Zn, Cu and Ni from galvanizing, electroplating and metallurgy industries whereas, Cr from tannery industry in Kolkata (Kar et al., 2010); Pb, Ni, Zn and Cu from industrial emissions in Agra (Kulshrestha et al., 2009); Zn, Pb, Fe, Mn and SO24 − from smelter in Ahmedabad (Raman et al., 2010), and Ba from oil fired power plant (Gupta et al., 2012). Additionally, the first of its kind SA study conducted in India by Negi et al. (1987) differentiated industrial emissions from textile industry (V, Br), oil refinery (S, Cu, Ni, and V) and non-ferrous industry emissions (Zn, Cu, and Mn) for the then Bombay, Bangalore, Nagpur and Jaipur. In India predominantly diverse groups of marker species were considered for industrial emissions. Exemplifying Khare and Baruah (2010) used Cd, Ni and Co as an industrial origin but when complex with Te, Zn and Cu, it was considered under industrial emissions combined with vehicular pollution. Te is mostly considered as a potential marker of fuel combustion emitted from coal. Kulshrestha et al. (2009) used a combination of Ni, Cu, Pb and Zn for industrial emissions in Agra, whereas, a combination of Ni, Cu, Fe and Cr as signatures for construction activities. Sharma et al. (2014) used Cu, Cr, Mn, Ni, Co and Zn as industrial emission tracers for metal manufacturing plants whereas, Zn has also been considered for vehicular tracers. In Mumbai, Kumar et al. (2001) used Cu, Mn and Ni as elemental marker for industrial emissions whereas, Cr was simultaneously considered as having a vehicular source. Choice of such elemental markers for industrial emissions was perplexing as Cu and Mn are generally associated with


vehicular emissions (Pant and Harrison, 2012), while, Cr was generally accepted as a tracer for industries. Such clearly evident the existing overlay of marker selection and may ultimately develop a wide range of indistinct conclusions. 3.4. Fuel combustion Fuel combustion is more a generalized term that includes the combustion of any material that stores potential energy and can be practicably released under combustion. However, for the present analysis, emissions arising only from coal and other petroleum compounds are reviewed. Chemical compositions of coal are significantly varied in different geographical locations which critically limit selection of a specific signature molecule. Most of Indian coal consists of low sulfur content while coal from North-Eastern India typically is composed of high concentration of Te due to its marine origin (Khare and Baruah, 2010). Sulfur is also a major impurity present in coal from north-east India, whereas, in western India coal primary poses As contamination. Universal markers of coal combustion are S, Se, As and SO2− 4 . However, among the different chemical tracers used for coal burning, most prominent are Cr and Cu (Srivastava and Jain, 2007a, 2007b); Ni, V and K (Shridhar et al., 2010); PAH (phenanthrene and anthracene) (Singh et al., 2011); Cd (Khillare et al., 2004); Cd, Se, Pb, and Cl− (Chakraborty and Gupta, 2010); benzo(b)fluoranthene and benzo(a)pyrene (Karar and Gupta, 2007); picene (Chowdhury et al., 2007); Pb and Zn (Negi et al., 1987) and Se and As (Joseph et al., 2012a, 2012b). 3.5. Marine aerosol Marine aerosol regulates substantial proportions of natural aerosols in the coastal environment. Primary marine aerosols are generated mechanically through wind interaction at the ocean surface while secondary aerosols are produced either through gradual nucleation of stable clusters or aqueous phase oxidation of dissolved gases (Odowd and Leeuw, 2007). Marine aerosols can highly be enriched in organic matter characterized by intense biological activity and may also be composed of dimethylsulphide (DMS). Both organics and DMS undergo oxidation and in the presence of humidity convert to acids. The source contributions of marine aerosols are only reported in Mumbai, Chennai and Ahmedabad with significantly varied contributions (PM10: up to 40%; PM2.5: up to 20%). However, apart from coastal cities, the presence of marine aerosols has also been traced in continental cities (like in Delhi), where aged marine aerosol was found to contribute 5% of aerosol loading (Sharma et al., 2014). Inorganic sea salt constitutes dominant mass fraction of coarser aerosol while fine aerosol mostly constitutes of organics and DMS (ODowd and De Leeuw, 2007). Characteristically, marine aerosol is traced by the presence of Na+, K+, Cl− and Mg2+. However, constituents may undergo chemical transformations by reaction of SO24 − and − − NO− 3 which subsequently release Cl and Br to atmosphere. The presence of NO− in finer particulates may also have marine origin through 3 condensation of HNO3 (Srimuruganandam and Nagendra, 2012a, 2012b). Several markers have been used for identifying marine aerosol likewise Na+, K+ and Mg2 + (Kumar et al., 2001); Na+ and K+ (Kothai et al., 2008); Na+, Cl− with Ca2 +, Mg2 +, SO24 − and HCO− 3 (Raman et al., 2010); SO24 −, K+ and Ca2 + (Joseph et al., 2012a, 2012b); and Ca 2 +, K +, and Mg 2 + (Srimuruganandam and Nagendra, 2012a, 2012b). However, selection of some tracers is somewhat contradictory and may well be influenced by other sources like K+ from biomass burning; Mg 2 +, Ca2 + from crustal emissions; Cl − and SO 24 − from fuel combustions. 3.6. Biomass and refuse burning Emissions from biomass and refuse burning are the largest source of primary organic aerosol (POA). It constitutes a large and highly variable

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fraction of fine (17%) and coarse (20–30%) particulates and its chemical compositions, sources and fate have often been utilized as tracers. OA emitted by biomass burning is rich in numerous molecular compounds, however, universally most accepted molecular markers are anhydrosaccharides (mannosan, galactosan and levoglucosan), methoxyphenols and K+ (Simoneit, 2002; Hennigan et al., 2010; Herlekar et al., 2012). The major structural components of biomass are cellulose, hemicellulose and lignin. On ignition (N 300 °C), transglycosylation and disproportionation dominate yielding anhydro sugars and volatile products (Simoneit, 2002) and form levoglucosan (1,6‐anhydro‐b‐D‐glucopyranose), widely accepted as biomass burning tracers since the 1980s. Long atmospheric lifetime also makes it suitable to be used as tracers. Additionally, the ratio of levoglucosan-to-mannosan and galactosan is rather specific to wood types, which allows it to differentiate between the types of wood combustions (Piot et al., 2011). However, references are also available describing significant transformations of levoglucosan by reaction with gas‐phase oxidants (Rudich et al., 2007) or by OH molecules (Hennigan et al., 2010). Apart from levoglucosan, methoxyphenols released from the burning of lignin (Simoneit, 2002) and K+ are also considered as molecular tracers (Khare and Baruah, 2010; Shridhar et al., 2010; Murari et al., 2015). Biomass burning in India is often referred as a combination of cow dung and fuel wood burning, wildfire and post-harvest burning of agricultural residues. In absentia of organic molecular tracers, potassium is the most frequently used inorganic tracer for biomass burning (Shridhar et al., 2010; Khare and Baruah, 2010; Tripathi et al., 2004), however, selection of K+ may create ambiguity for an area intruded by marine aerosol. Multiple variants of organic tracers have been found to be practiced in India viz. levoglucosan, galactosan, mannosan (Herlekar et al., 2012; Chowdhury et al., 2007), anthracene (Mohanraj et al., 2011a) and OC/EC (Chowdhury et al., 2007; Sharma et al., 2014). Chowdhury et al. (2007) used both elemental (Si, Al and EC) and molecular (n-alkanes, polycyclic aromatic hydrocarbons (PAHs), hopanes, steranes, and levoglucosan) markers to identify airborne fine particulate sources in Delhi, Mumbai, Kolkata, and Chandigarh. Khare and Baruah (2010) used NH+ 4 with K to identify the burning emission. Pachauri et al. (2013) found a significant correlation between water soluble K+ and OC in Agra possibly attributed by increased biomass burning emissions. In Hyderabad, Guttikunda et al. (2013) used K as an indicator for biomass burning and accounted 40% of emission of airborne fine particulates. Sharma et al. (2014) studied water soluble + ionic compounds (NH+ 4 and K ) and found 14% source contribution to airborne particulate. Another potential source of particulate emissions is refuse burning which is associated with the emission of Pb, Zn, As, Br, naphthalene, fluoranthene, and dibenz(a,h)anthracene (Chelani et al., 2010; Negi et al., 1987; Bandhu et al., 2000; Karar and Gupta, 2007; Shukla and Sharma, 2008). Refuse burning often combined with the vehicular emission and biomass burning providing relative comparison of SA somewhat critical. Additionally, the chemical nature of waste is largely unexplored in cities, which makes its contribution to total particulate loading highly uncertain. 4. Source apportionment and receptor modeling Airborne particulate chemistry is typically controlled by its constituent species and regional meteorology. Again particulate species is the function of associated sources. Therefore, assessment and possible quantification of particulate sources help to develop conceptual model of source–receptor association. SA is the technique to identify responsible sources of airborne particulates and their contribution based on particulate speciation information in receptor sites. Based on mass balance principle and certain pre-set assumptions, several mathematical and statistical models have been developed which can effectively quantify particulate sources. These models are specifically called as receptor models as multivariate measurement of particulate speciation

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information is used from the receptor site. In that way it differs from source models but essentially complements each other. These RMs may be of microscopic and chemical methods and both pose certain limitations. There are several types of RMs available viz. EF, CMB, multiple linear regression, eigenvector, neural network, aerosol evolution and equilibrium model. A simple RM based on mass conversation principle may be expressed as Eq. (2) Ci j ¼

N X

Sin f jn þ ei j

ð2Þ

n¼1

where, Cij = measured concentration of the jth species in the ith sample; Sin = contribution of the Nth source to the ith sample; fjn = concentration of the jth species in the Nth source; and eij = uncertainty involved in respect to measured and predicted concentration at the receptor site. Within Eq. (2), if composition profiles of the sources (fjn) are known to the modeler through selection of individual tracers, then mass contribution of individual source (Sin) may well be computed using measured receptor concentration (Cij). Watson et al. (2008) re-write the mass balance equation (Eq. (2)) including receptor location (l), particle size fraction (m), transport of pollutant through wind vector (w) and particulate monitoring period (t) as variables which gives Eq. (3) C jtlmw ¼

N X

Smntlw f jnm T jmntlw þ ejtlmw

ð3Þ

direction; and Tjmntlw = changes in fjnm due to transport from source to receptor. The equation is solved including ejtlmw as uncertainty in measurement. The mass balance equation may be solved with different approaches which basically provide subgroups of RMs. Likewise, Eq. (2) is solved through effective-variance least-squares approach referred as CMB, while orthogonal decomposition of datasets to identify individual group of components through loading factors denoted as PCA. The two extremes of receptor models are FA and CMB. While FA is used to explore particulate speciation information only at receptor sites, CMB is more of multiple linear least square method which explores mass fractions of pollutant species in source emissions. However, each RM has some limitations like, FA may sometimes develop negative SA results while multivariate factor analysis like PCA underestimates individual contribution of specific sources and tends to pool down in a mixed profile (Fig. 5). Such limitations were overcame by confirmatory FA (PMF and UNMIX) by pre-setting factor correlation coefficients by the modeler (Wahlin, 2003). However, confirmatory FA is sensitive to pre-set parameters, does not make explicit use of uncertainties and requires large datasets (Watson et al., 2008). CMB is a complete statistical tool that apportions particulate sources more realistically with quantitative uncertainties on source contribution, but requires a complete source profile which may not be available for a specific location.

n¼1

4.1. Enrichment factors (EFs) where, Cjtlmw = measured concentration of the jth species at t sample time in l receptor location with m particle size fraction and w wind direction; fjnm = concentration of the jth species in the Nth source with m particle size fraction; Smntlw = contribution of the Nth source to t sample time l receptor location with m particle size fraction and w wind

Enrichment factor (EF) is used to estimate the origin of the intended species and the extent of anthropogenic activities associated with particulate emission. This is the simplest RM available which indicates the presence or absence of certain emitters and provides basic information

Fig. 5. Different approaches of receptor models with principles and limitations.


related to secondary particulate formation. Enrichment factor compares the relative ratio of elemental composition of intended species with that Cx sample to the correof reference element present in the particulate Cb Cx . sponding ratio in the natural background composition Cb Cx sample C Enrichment factor ¼ b Cx background C

ð4Þ

b

From Eq. (4) it is clearly evident that when EF N 1, local or anthropogenic sources have been considered as a pre-dominant contributor. In most cases when a single particulate source is dominant, EF is performed either through linear regression or by element concentration ratio (Belis et al., 2013). The EF provides limited information about individual sources and mostly unable to quantify individual source contributions for a complex cluster. This reduces the potential use of EF as RM and therefore, should be used in data screening processes or to support assumptions for receptor species and sources (Belis et al., 2013). However, this approach can be very useful when a limited range of information is available. According to available meta-data, in India 27% of SA studies (31) have been conducted with the use of EFs. There are instances when EFs have been used in conjunction with other RMs for data screening (Yadav and Satsangi, 2013; Habil et al., 2013; Pipalatkar et al., 2014). In most of the cases, uses of EFs were helpful to identify the major source categories irrespective of their individual contribution. 4.2. Factor analysis (FA) The principal application of factor analysis (FA) is to reduce the number of variables within a defined dataset. Additionally, it is also used to remove redundancy in a set of correlated variables. FA is often used to explore data patterns, reduction of datasets to a more convenient number and to derive conclusions from the original dataset. It is an important statistical tool that is used to search for realistic solutions using flexible axis and segregate particulate sources based on a series of observations at the receptor site (Wahlin, 2003). Applications of FA for SA studies are always advantageous as information related to source profile are not a necessity but may help in discriminating identical sources (Pant and Harrison, 2012). Additionally, accessibility of common software packages, choice of model dimension and options for axis rotation are reasons for its higher user preference. However, tracers with lower atmospheric residence time may limit its application. Additionally, exploratory factor analysis sometimes underestimates individual contribution of specific sources and tends to pool down in a mixed profile. Therefore, often outcome needs subjective interpretations before eventually drawing conclusions. These limitations have been overcome in confirmatory factor analysis like PMF and UNMIX, where specific parameters may pre-set by the modeler based on theoretical expectation. Although PCA and FA are quite similar in a way both operate linear transformation of datasets, but PCA aims to maximize the variance by minimizing sum of squares while FA depends only on common factors (Belis et al., 2013). In India, the most common way to define FA is Principle Component Analysis. However, Positive matrix factorization and UNMIX are two other RMs that are used extensively. Nearly 58% of SA studies that have been conducted in India are the outcome of FA, among which FA as such has been used for 14%, followed by PCA (34%), PMF (9%) and UNMIX (1%). 4.2.1. Exploratory factor analysis: Principle Component Analysis (PCA) Among multivariate factor analysis, PCA is often used as an exploratory tool which combines FA with a multi-linear regression to quantify particulate source contribution (Viana et al., 2008). The fundamental equation that governs the PCA is mass balance equation (Eq. 2).

177

Particulate chemical speciation subject to PCA forms a complementary set of components which needs to be described as per source profile. PCA uses orthogonal decomposition to identify individual group of components (PCs) which are connected with variables through loading factors. These PCs used to share the entire variability of the datasets while the first PC shares the most (Belis et al., 2013). PCs with maximum variance are interpreted as the most influential source while each succeeding PCs in turn has the highest variance. Within every set of components high correlation exists while among individual PCs minimum or no correlation exists (Singh and Sharma, 2012; Watson et al., 2002). In PCA, loading factors connect individual variables to different components through orthogonal rotations like Varimax. PCA is by far the most common model for SA studies (34%) (Table 1) probably because of its simplistic analytic procedure. However, the physical significance of PCA outcome is often subject to realization and it is virtually dependent on the modeler on how to extrapolate the outcome. Availability of specific tracers or usage of a single tracer for multiple sources again may critically limit its application. PCA principally based on statistical association of data rather than particulate chemical nature and therefore, often used to generalize the information that the datasets originally have (Wahlin, 2003). Moreover, it is based upon the assumption that the dataset is distributed normally which may not be valid for all the cases (Belis et al., 2013). 4.2.2. Confirmatory factor analysis 4.2.2.1. Positive matrix factorization (PMF). Among different FA techniques available for SA of airborne particulates, PMF is the most advanced one. Elaborative mathematical description of the models may be found in works of Paatero and Tappert (1994) and Paatero (1997). PMF is a form of multivariate factor analysis resembling PCA, but excludes all the negative entries which eventually help in the proper explanation of most of environmental phenomena (Paatero and Tappert, 1994). Additionally, despite of entirely depending on statistical association of dataset, PMF uses least squares minimization to compare with input parameters. It essentially distinguishes particulate speciated dataset into different matrices like the number of factor, factor contributions and factor profiles. Such information directly relate it with sources when a stern set of assumptions is fulfilled. Additionally, it treats each data point separately to regulate individual influences based on the confidence in the measurement (USEPA, 2008). Experimental uncertainties are used to consider as input to resolve weighted factorization and allow individual treatment of elements (Paatero and Tappert, 1994; Belis et al., 2013). This is accomplished by minimizing the object function Q depending upon the uncertainties of every observation in Eq. (5)

Q¼

Xp !2 n X m X xi j − k¼1 g ik f k j i¼1 j¼1

ui j

→Q ¼

2 n X m X ei j i¼1 j¼1

ui j

ð5Þ

where, p = number of independent factors; Xij = jth species concentration measured in the ith sample; gik = mass concentration from the kth factor contributing to the ith sample; fkj = jth species mass fraction from the kth factor; uij = estimated uncertainty in the jth species measured in the ith sample (measured uncertainties) and eij = model uncertainties. PMF holds the same advantages that PCA have but it holds the additional advantage of handling missing or below detection level data. PMF is also reported to provide source profiles from the ambient data, but has not been verified against actual profile measurements. However, PMF requires a large dataset, preferably much more than the no. of factors involved and weighing factor associated with each measurement needs to be assigned. In India, PMF shares 9% of total SA applications but from 2010 onwards, 13% of SA have been concluded with PMF (Table 2). However, the optimal number of variables, inability to

178


Table 1 Summary of topical PCA applications for particulate source apportionment. Reference

Location and time frame

Targeted metric

Tracer species used

Sources identified

Habil et al. (2013)

Agra (Jan 2008–May 2009)

PM10, PM2.5, PM5, PM1, PM0.25

Fe, Zn, Cu, Cd, Cr, Mn, Ni, Pb

Yadav and Satsangi (2013)

Pune Urban site (June 2011 and May 2012)

PM10

Cu, Zn, Mn, Fe, Ba, Ca, Co, Cr, K, Na, Ni, Pb, Sr, Cd, Al, Mg

Rd/Id = automobile (37.06%), chalk dust and soil (36.07%), metal processing (15.17%) Rd/Od = vehicular emission and soil dust (47.56%), vehicular wear and tear (33.79%), garbage burning and other activities (17.98%). R/Id = metal enriched soil, vehicular emission, chalk dust and wind blown dust (39.28%), paint, pigments and varnishes (20.92%), incineration activities (25.28%) R/Od = vehicular sources (38.09%), vehicular wear and tear (25.8%), incineration (25.07%). Tire and brake drum abrasion, biomass burning, waste incineration (52.5%), traffic emission, geogenic origin (14.4%) Re-suspended road dust, which includes soil dust mixed with traffic-related particles (30.7%), crustal origin (15.4%), biomass burning and solid waste incineration, crustal emission (13.6%) Gasoline emission, coal combustion, industrial emissions, biomass/wood combustion Ru/Id = cow dung, wood burning and smoking (29%), waste burning (28%), resuspended soil dust (17%) Ru/Od = industrial, refinery emission and resuspended soil (31%), construction and diesel exhaust (27%), anthropogenic activities (18%) Windblown dust, re-suspended dust, dust from paved and unpaved roads, and undisturbed soil, agricultural, and construction activities (55.47%), emission associated different process of vehicular movement (16.90%), industrial process (9.04%), biomass burning (7.34%), secondary inorganic origin (4.55%)

PM2.5

Khillare and Sarkar (2012) Massey et al. (2013)

Delhi 3 sites (December 2008–Nov. 2009) Agra (October 2007 to March 2009)

PM10

PHA

PM10, PM5, PM2.5, PM1,

Pb, Cd, Ni, Fe, Cr, Mn and Cu

Singh and Sharma (2012)

Agra (March 2007–Feb. 2008)

PM10

Na, Mg, Al, Si, S, Ca, Sc, V, Cr, Mn, Fe, Ni, Cu, Zn, As, Br, Rb, Cd, Ba, + − 2− − − Pb, NH+ 4 , K , SO4 , NO3 , F ,Cl

NOTE. R = residential, Rd = road side, Id = indoor, Od = outdoor, Ru = rural.

separate covariant sources, rotational uncertainty and source interpretations need to be improved for better model performances (Viana et al., 2008; Belis et al., 2013). 4.2.2.2. UNMIX. The UNMIX is a multivariate receptor model that uses higher dimensional edges in the dataset, which is useful to identify additional constraints that were needed to run multivariate receptor model (Henry, 2003). UNMIX solves the issue by assuming the positive linear combination of unknown source composition and contribution to samples. It applies singular value decomposition to find out edges in N-dimensional space which is useful to reduce the data dimension. UNMIX also works on break or edge detection technique and generates

source contribution by geometrically driven approach (Henry, 2003; Watson et al., 2008). In edge detection it primarily assumes that some samples at the receptor site contains minimal or no contribution from specific sources, called as missing sources. These sources or edge points identify and differentiate the contribution from the specific sources. Additionally, UNMIX does not require a comprehensive source profile so it can be easily used for the areas where comprehensive source profile information is not available. UNMIX can be expressed as Eq. (6).

Ci j ¼

p p X X l¼1

! U ik Dkl V l j þ εi j

ð6Þ

k¼1

Table 2 Summary of topical PMF applications for particulate source apportionment. Reference


Targeted metric

Tracer species used

Sources identified

Sharma et al. (2014)

Delhi (2010)

PM10

Soil dust (20.7%), vehicular emissions (17.0%), secondary inorganic aerosol (21.7%), sea salt (4.4%), fossil fuel combustion (17.4%), biomass burning (14.3%), industrial emission (4.5%)

Sharma et al. (in press)

Delhi (January 2010 to December 2011)

PM10

Massey et al. (2013)

Agra (October 2007 to March 2009)

Srimuruganandam and Nagendra (2012a, 2012b)

Chennai (November 2008–April 2009)

PM10, PM5, PM2.5, PM1 PM10

Na, Mg, Al, Si, P, S, Cl, K, Ca, Cr, Ti, − Fe, Zn, Mn, NH3, Cl−,SO2− 4 , NO3 , NH+ 4 , Na+, K+, Mg2+, Ca2+, OC, EC Mg, Al, P, S, Si, Cl, K, Ca, Ti, Cr, Mn, Fe, Zn, Li+, Na+, NH 4+, K+, Ca2+, 2− Mg2+, F−, Cl−, NO− 3 , SO4 , OC, EC Pb, Cd, Ni, Fe, Cr, Mn, Cu

PM2.5

Sudheer and Rengarajan (2012)

Ahmedabad (December 2006–January 2007)

PM10 PM2.5

Secondary inorganic aerosol (21.7%), soil dust (20.7%), fossil fuel combustion (17.4%), vehicular emissions (16.8%), biomass burning (13.4%), sea salt (4.6%), industrial emission (5.4%) Industrial or refinery emission, construction for farming and diesel exhaust, cooking, smoking, waste burning, resuspended soil or dust, anthropogenic activities. + 2+ Marine aerosol (40.4%), secondary inorganic aerosol (22.9%), Na+, NH+ , Mg2+, F−, Cl−, 4 , K , Ca − − 2− NO2 , NO3 and SO4 , Ag, Al, As, B, Ba, vehicular emissions (16%), biomass burning (0.7%), tire and brake wear (4.1%), soil (3.4%), other sources (12.7%) Be, Bi, Ca, Cd, Co, Cr, Cu, Fe, Ga, K, Li, Mg, Mn, Mo, Na, Ni, Pb, Rb, Se, Marine aerosol (21.5%), secondary inorganic aerosol (42.1%), Sr, Te, Tl, V, Zn vehicular emissions (6%), biomass burning (14%), tire and brake wear (5.4%), soil (4.3%), other sources (6.8%) Mineral dust (34%), biomass burning (33%). Cd, Pb, Fe, Al, Ca, Mg, Ba, Sr, Cr, + + Cu, Mo, Zn, Ni, Co, Mn, Na , K , Industrial or/and incineration emissions (11%), mineral aerosol − − 2− Ca2+, Mg2+, NH+ or soil dust (10%), coal-based power stations/industrial/vehicular 4 , Cl , NO3 , SO4 , emissions (31%), biomass burning (33%) OC, EC


179

Table 3 Summary of topical UNMIX applications for particulate source apportionment. Reference


Targeted metric

Tracer species used

Sources identified

Chakraborty and Gupta (2010) Tiwari et al. (2013)

Kanpur (July, 2008–May, 2009) Delhi 2008

PM1

3− 2− F−, Cl−, NO− 3 , PO4 , SO4 , As, Ca, Co, Cr, Cd, Mg, Fe, Ni, Pb, Cu, Zn, V, Se,

PM10

Na, Mg, Al, Si, P, S, K, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, As, Br, Sr, Ba, Pb, Cd, Sn and Sb + + 2+ + Cl−, NO3−, SO2− and 4 , Na , NH4 , K , Mg Ca2+

Secondary sources (39%), vehicular emissions (24%), road dust (14%), un-apportioned (12%), coal combustion (11%) Vehicular emissions (60%) followed by crustal elements as a major source

where U, D, and V are n × p, p × p diagonal, and p × m matrices, respectively. εij is the error consisting all the variability in Cij not accounted by first principal components (p). The application of UNMIX requires a large number of variables and a dataset (N100) to draw an eloquent solution (Watson et al., 2008). Additionally, it does not count on uncertainties in ambient measurements and it is unable to process samples with missing data. UNMIX serves excellent in order to distinguish the most influential sources while it underperforms to make agreement between expected and estimated contributions for weaker sources (Henry, 2003). There are only two instances when UNMIX have been applied for SA in India (Table 3). Chakraborty and Gupta (2010) using elemental markers revealed crustal elements as the major sources in Kanpur. Additionally, Tiwari et al. (2013) used PM10 speciation information during 2008 and concluded vehicular emissions (60%) followed by crustal elements as a major source in Delhi.

CMB is based on mass conservation equation assuming that signatory molecules do not undergo chemical transformation from source to receptors. It understands that receptor chemical concentration is a linear sum of source profile abundance and can be computed if appropriate uncertainty estimates are available. It consider particulate source profile abundances, receptor profile with appropriate uncertainty estimates as an input and through multiple linear least squares regression algorithm generate individual source contribution with reasonable uncertainty. However, choices of source profiles should avoid collinearity and possibly from same geographical region, otherwise model applicability may drastically reduce (Watson et al., 1997; Watson et al., 2008). In order to reduce collinearity of the source profiles, it is often advised to merge similar group of sources into one (Belis et al., 2013). CMB in itself is the most advanced receptor model and can be extremely useful where a limited number of monitoring information are available. Availability of complete source profile information effectively decreases the number of required samples, but a small database may possibly increase the level of uncertainties. The model may be expressed as: N X

F in Skn

ð7Þ

=

N

C ik ¼ ∑n¼1 aiN F in Snk

ð8Þ

where, FIin denotes the modified composition of aerosol at source site while Fin is the composition at receptor. However, practical computation of aiN is extremely complex. The uncertainties associated with source profile information have been measured in terms of effective variance weighting and can be represented as Eq. (9) ðωe Þii ¼ 1

4.3. Chemical mass balance (CMB)

C ik ¼

the secondary aerosol formation which sometimes critically limits its application in urban sectors. Chemically similar sources (like crustal emissions and resuspension of road dust from unpaved road) may result to collinearity in absence of specific signatory molecules. Now, as the evolved particulate from source to receptor may undergo chemical transformation or loss due to scavenging, therefore, Winchester and Nifong (1971) introduced coefficient of fractionation (aiN) to Eq. (8)

σ 2i

þ

N X

σ 2in s2n

ð9Þ

n¼1

where σi is the measured uncertainty of the pollutant concentration, xi, and σin is the measured uncertainty of species i emitted by source n. Initiating from 1989, quite a few applications of CMB are found in India. However, CMB was never been the first choice due to limited availability of source profile. During the 1990s inaccessibility of particulate source profile compels modelers to develop a composite source profile based on USEPA. In late 1990s, some efforts were there to develop particulate source speciation (Gupta et al., 2007; Gadkari and Pervez, 2008; Patil et al., 2013, CPCB, 2011a) which eventually helped in effective use of advanced RMs. Until 2005, CMB corresponds to a single application among all SA studies conducted in India while, during the last decade (2005–14) 25% of SA studies (16) that have been originated in India accounts for CMB (Table 4). 4.4. Hybrid methods The concept of hybrid models unifies CMB and non-negative FA and provides better control over the final outcome (Wahlin, 2003). Hybrid methods are basically of two types viz. constrained or expanded RMs and trajectory based RMs.

n¼1

where Cik is the ambient concentration of species I contributed from the Kth source measured at the receptor site and Skn is the source specific contribution from the Kth source (ratio of mass contributed from source N to the total mass collected at receptor cite (Min/mi)). Fin is the actual known source profile and Snk is the source specific contributions that need to be measured based on actual monitored concentration (Cik) at the receptor cite. In practice, the set of linear equations generated by Eq. 7 is solved with variance-weighted least square method using EPA-CMB software. CMB do poses some assumptions which are almost never completely accomplished as particulate species do react with each other and composition of sources may not always be constant. Again, it does not consider

4.4.1. Constrained Physical Receptor Model (COPREM) Originally developed by Wahlin (2003), COPREM is a multivariate RM which uses bilinear modeling and uncertainty weighted data reduction principle with constrains that allow it to provide better source segregation with quantified uncertainties (Wahlin, 2003; Andersen et al., 2007). It initially develops a profile matrix using source vectors and subsequently adds additional constraints to reduce mixing of source profiles. However, as the inventor suggested, introduction of constraints helps to regulate non-physical solution (like negative source profiles) and it also fixes profile component in constant ratio. Therefore, selection of proper constrain is extremely critical and can only be set based on proper information of original source composition. Model provides flexibility of incorporating background information to regulate the

180


Table 4 Summary of topical CMB applications for particulate source apportionment. Reference

Time frame

Targeted matrix fraction

Targeted metric

Sources identified

Pipalatkar et al. (2014)

Nagpur (September to mid February 2009–10)

PM2.5

Na+, NH4+, K+, Ca2+, F−, Cl−, 2− NO− 3 , SO4 , OC, EC, Al, Ba, Cd, Cr, Cu, Fe, Mg, Mn, Ni, Pb, Si, Zn

Guttikunda et al. (2013)

Hyderabad (Nov 2005–Dec 2006) (summer, winter and monsoon)

PM10

Cl−, SO42−, NO3−,NH4+ Na, Mg, Ca, Al, Si, K, Fe, OC, EC

Bengaluru, Chennai, Delhi, Kanpur, Mumbai and Pune Chennai (November 2008–April 2009)

PM10 PM2.5 PM10

R = vehicular emissions (57%), secondary inorganic aerosol (16%), biomass burning (15%), re-suspended dust (6%) C = vehicular emissions (62%), secondary inorganic aerosol (12%), biomass burning (11%), re-suspended dust (10%) I = vehicular emissions (65%), secondary inorganic aerosol (16%), biomass burning (9%), re-suspended dust (7%) Vehicular emissions (30%), vehicular with resuspension dust (30–45%), coal combustion (7–20%) Road dust resuspension (15%), coal combustion (11–36%), open waste burning (N10%). Unpaved road dust, electric arc furnace, wood combustion chulha, wood fired boilers. Diesel exhaust (43–52%), gasoline exhaust (6–16%), paved road dust (0–2.3%), brake lining dust (0.1%), brake pad wear dust (0.1%), marine aerosol (0.1%), cooking (0.8%) Diesel exhaust (44–65%), gasoline exhaust (3–8%), paved road dust (0–2.3%), brake lining dust (0.2%), brake pad wear dust (0.01%), marine aerosol (0.1%), cooking (1.5%) Re suspended dust (40%), vehicular emissions (22%), combustion (12%), industrial (9%), refuse burning (7%) Vehicular emissions (31%), re-suspended dust (26%), combustion (9%), industrial (7%) and refuse burning (6%).

Patil et al. (2013) Srimuruganandam and Nagendra (2012a, 2012b)

PM2.5

PM2.5

Gummeneni et al. (2011)

Hyderabad (June 2004–May 2005)

PM10

39 elements, 12 ions, EC, OC Ag, Al, As, B, Ba, Be, Bi, Ca, Cd, Co, Cr, Cu, Fe, Ga, K, Li, Mg, Mn, Mo, Na, Ni, Pb, Rb, Se, Sr, Te, Tl, V, Zn, Na+, NH4 +, K+, Ca2+, Mg2+, F−, − 2− Cl−, NO− 2 , NO3 , SO4 As, Se, Zn, Cd, Pb, Co, Ni, Fe, B, Mn, Cr, Cu

PM2.5 Note. R = residential, C = commercial, I = industrial.

unwanted mixing of the sources. In that way it resembles both PMF and CMB. However, COPREM requires large datasets with complete particulate source profile information which sometimes limits its application. Likewise, none of the Indian SA studies have been conducted with COPREM, however, references were available globally for satisfactory use of the model (Andersen et al., 2007). 4.4.2. Extended factor analysis models Extended factor analysis model was developed during late 1990s specifically to solve for diverse multilinear and quasi-multilinear problems with the option of including constraints by script language (Viana et al., 2008). Originally developed by Paatero (1999) in the name of Multilinear Engine (ME), it is the background program used to run PMF developed by USEPA (USEPA, 2008). Multilinear problem is represented by a set of equations where each equation approximates one data representing different unknowns (Xie et al., 1999). Unknowns are defined for different data according to the model structure and nonnegativity constraints are included to reduce the possibility of negative apportionment. The model provides a generalized format for several ME models including pure bilinear and trilinear as well as mixed model which improves its flexibility. The ME model can be expressed in sum-of-product form of Eq. (10)

xi ¼ yi þ ei ¼

Ki X

∏f j þ ei

ði ¼ 1; …; MÞ

5. Temporal pattern of particulate source profile in India For the proceeding section, a review has been conducted on published articles on particulate SA using RMs in Indian scenario to understand temporal variation of particulate sources within different geographical region. The intension was to specifically characterize particulate sources both in terms of natural and anthropogenic and possibly quantify them. Although, we wish to draw a pattern of temporal variation in particulate SA for entire India, however, that did not materialize due to scarcity of repetitive SA studies, variation in particulate metric, changes in background concentrations and most significantly, heterogeneities in applied RMs. Therefore, it was rather felt imperative to discuss the regional distribution of particulate sources in terms of experiment site so that a comprehensive overview on the current status may be achieved. Studies have been reviewed and described chronologically in terms of particulate collection episodes so that perspective readers can distinguish gradual modification in particulate sources within a specific region. Interestingly, large heterogeneities have been recognized in terms of regional SA studies within India. Characteristically, 40% of SA studies have been originated in Northern India (N: 43, 40%)

ð10Þ

K¼1

where, the index i signifies the equation which forms the model, each equation corresponds to each measured vale (Xi), M represents the number of equations (sum of number of measured values and number of auxiliary equations, if any), fitted value yi for each data point xi is denoted as sum of product of all factor elements, and Ki indicates the number of product terms in each equation (Xie et al., 1999). The ME model has several advantages like inclusion of heterogeneous datasets like particle speciation, size distributions, meteorological variables, and uncertainties with flexibilities to modify the input according to expectations. ME includes all the advantages for PMF, however, it has been reported to perform better in source resolutions while, PMF provides better uncertainty to particulate apportionment (USEPA, 2008).

Fig. 6. Region wise source apportionment studies in India.


with 22% of apportionments from Delhi itself. Western India (W: 31, 29%) has also been studied extensively representing 29% of the total publications with principal share from Mumbai (14%) (Fig. 6). Both Southern (S: 12, 11%), and East and Central India (E & C: 21, 20%) have been briefly studied in terms of particulate SA, predominately in Kolkata (6%) and Chennai (5%). Such meta-analysis clearly identifies four definite circles (viz. Delhi, Mumbai, Kolkata and Chennai) which share 47% of the total SA studies in aggregate. 5.1. Northern India: Delhi, Kanpur, Agra and Chandigarh Several studies have been undertaken for SA of particulate matter in Delhi. Initiated way back in 1967 through FA, different multivariate FA and advanced RMs (Balachandran et al., 2000; Khillare et al., 2004; Srivastava and Jain, 2007a, b; Sharma et al., in press; Srivastava et al., 2008, 2009; Shridhar et al., 2010; Khillare and Sarkar, 2012; Tiwari et al., 2009, 2013) have been simulated to understand the relative contribution of sources. Coarser particulate speciated data sampled during 1998 were used by Balachandran et al. (2000) for SA and three principal components (PCs) namely vehicular and industrial emission (53.9% of the variance), foundry emissions (19.4% of the variance) and crustal elements (15.7% of the variance) have been identified explaining 90% of the total variance. Khillare et al. (2004) following identical approaches identified two PCs namely vehicular/industrial (60% of the variance) and crustal sources (22% of the variance), explaining 82% of the total variance of SPM speciated data collected during 1997–98. Interestingly, in both instances 53–60% of accounted variances have been attributed to vehicular/industrial emissions signifying the anthropogenic nature of particulates. However, SA by varimax rotated factor matrix using size segregated particulate speciation profile sampled during 2001 revealed contrasting results (Srivastava and Jain, 2007a). Both SPM and PM10.9 were found to have evolved from crustal resuspensions (50%) and construction material (15%). Interestingly, fine particulates (PM1.6) were also seemed to be governed by crustal re-suspension (50%), construction material (13%) and vehicular emissions (11.2%). A similar experiment was carried out by Srivastava and Jain (2007b) during 2001 using advanced receptor model (CMB) and vehicular emissions emerge as a single most influencing source (60–89%) for fine particulates while, both vehicular (24–42%) and crustal (51–73%) emissions identified as prominent sources for coarser particulates. One year (2001–02) fine particulate speciated information was processed through CMB using both elemental (Si, Al and EC) and molecular (n-alkanes, PAHs, hopanes, steranes, and levoglucosan) markers

181

revealed fossil fuel combustion (25–33%) and biomass burning (7– 20%) as the most influential sources (Chowdhury et al., 2007). Particulate speciation data (2003–04) processed by Shridhar et al. (2010) revealed that airborne metals mainly originated from construction and industrial activities within urban regions while, crustal elements were more pronounced in rural sectors. In 2005–06 winter, size segregated airborne particulates were processed for six Delhi sites by Srivastava et al. (2008, 2009) through CMB and PCA. As expected, coarser particulates were found to have originated from crustal sources (64–67%) and vehicular emissions (22–29%). Interestingly, finer particulates revealed an association of vehicular (62–85%) and crustal sources (10–35%), recognizing a predominant anthropogenic origin. Tiwari et al. (2009) processed fine and coarse particulate speciation information collected during 2007 through correlations and factor analyses. SA accounts for 85% of the total variance and recognized dominance of anthropogenic and soil originated particulates. Tiwari et al. (2013) used advanced receptor models (UNMIX and PMF) with coarser particulate speciation information (2008) and concluded with vehicular emissions (60%) followed by crustal elements as principle sources. Such conclusions were in contrast to most other findings like those of Srivastava and Jain (2007a) where crustal elements and road dust resuspensions were designated as principal sources of PM10. Khillare and Sarkar (2012) performed principal component analysis-multiple linear regression and identified contribution of crustal sources (49–65%), vehicular emission (27–31%) and industrial emission (4–21%) for coarser particulates during 2008–09. Sharma et al. (2014) performed SA of coarser particulate speciation information (2010–11) through PMF and quantified secondary aerosols (21.7%), soil dust (20.7%), fossil fuel combustion (17.4%), vehicle emissions (16.8%), and biomass burning (13.4%) as the major contributors to urban PM10 (Fig. 7). Source apportionment of airborne particulates has been conducted thrice in the city of Kanpur (Shukla and Sharma, 2008; Chakraborty and Gupta, 2010; Behera et al., 2011) either by receptor or dispersion models. However, for the present submission SA conducted only by RMs was reviewed. Shukla and Sharma (2008) processed PM10 samples (2000–01) through FA multiple regression and concluded the presence of two potential sources viz. crustal elements (15–47%) and inorganic secondary particles [(NH4)2SO4 and NH4NO3: 21–26%]. Submicron particulates collected during 2008–09 were further processed through PCA and UNMIX (Chakraborty and Gupta, 2010) (Fig. 8). Application of PCA explained almost 94% variance with four distinctive factors viz. vehicular emissions (Cu, Pb, Zn); crustal elements (Ca, Mg, Fe, Pb); inorganic

Fig. 7. Temporal variation (2005–2011) of particulate source profile in Delhi.

182


Fig. 8. Particulate source profile in Kanpur during 2008–09.

− secondary particulates (SO2− 4 , NO3 ) and coal combustions (Cd, Se, Pb). Application of UNMIX recognized the presence of identical factors with contribution from vehicular emissions (24%); road dust (14%); inorganic secondary particulates (39%) and coal combustions (11%). Both SA findings clearly indicate the significance of primary precursor gases like SO2 and NOx, and dominance of NH+ 4 for secondary particulate formation in Kanpur. Agra, situated in north central part of India in the Indo-Gangetic plain has not been found to be strongly influenced by secondary particulates; instead crustal elements, vehicular and industrial emissions and biomass-waste burning were identified as the dominated ones (Kulshrestha et al., 2009; Singh and Sharma, 2012; Habil et al., 2013; Satsangi et al., 2013; Pachauri et al., 2013). The first of its kind SA study was conducted by Kulshrestha et al. (1995) using PCA with particulates collected during 1991–92 revealed crustal elements (34% of variance), industrial activities (20% of variance), biomass burning (10% of variance) and brick kilns emissions (7% of variance) as the major sources. Kulshrestha et al. (2009) collected both airborne fine and coarse particulates (2006–08) and identified resuspension of road dust due to vehicular activities (urban: 38%; rural: 28%); industrial emissions (urban: 18%; rural: 23%); construction activities (rural: 25%); and solid waste dumping and incineration (urban: 20%) as the major sources for PM10 (Fig. 9). Finer particulates were identified to be associated with industrial emissions (urban: 30%; rural: 29%); vehicular emission along with resuspended road dust (urban: 28%; rural: 17%), construction activities (rural: 27%), and solid waste dumping and

Fig. 9. Particulate source profile in Agra during 2006–08.

incineration (Ni, Fe; 22%). Singh and Sharma (2012) following identical approaches characterized sources associated with PM10 (2007–08) as crustal elements (55%), vehicular emissions (17%), industrial emissions (9%), and coal and biomass burning (7%). For both instances, crustal elements associated with road dust resuspensions were reported to be the dominating source for PM10. Habil et al. (2013) identified the association of trace metals with size-segregated airborne particulates (2008–09) and revealed crustal elements and vehicle emissions as the principal sources. There were two other instances when SA has been conducted in Agra based on particle morphological characteristics (Pachauri et al., 2013) and ionic correlations (Satsangi et al., 2013), but are not reviewed. Bandhu et al. (2000) performed SA of particulates collected during 1994–96 in Chandigarh. PCA of particulate-metal data identified crustal elements, industrial activity, vehicular traffic and refuse burning as the major sources within the region. Additionally, Chowdhury et al. (2007) processed fine particulate speciated information through CMB using both elemental and molecular markers and identified fossil fuel combustion (28%) and biomass burning (8%) as the major associated sources in Chandigarh.

5.2. Southern India: Hyderabad, Chennai and Tirupati In India, SA of airborne particulates has been conducted with multiple approaches; however, for cities in southern peninsula (Hyderabad, Chennai and Tirupati), CMB and multivariate analysis were the most preferred ones. Following a direction of the Supreme Court of India, SA was initiated in Hyderabad from 2005 to assess sector wise contribution of emission sources (Guttikunda et al., 2013). Gummeneni et al. (2011) applied CMB on particulate speciated information (2004–05) from traffic corridor and found dominance of crustal elements (40%) in coarser fraction while vehicular emissions (31%) in finer range (Fig. 10). Guttikunda et al. (2013) performed SA in broader perspectives having airborne particulate collected from two urban residential/commercial/ transportation sites and one from control during November, 2005. Through CMB, resuspension of road dust (30–45%) and vehicular exhaust (30%) was identified as a principal (N 60%) contributor for PM10 with minimum seasonal variations. However, fine particulates were found to have originated mostly from road dust resuspension (15%), fuel combustions (b 10–35%) and refuse burning (10%). Chennai has been most intensively studied in terms of particulate temporal variation, whereas only few instances (Mohanraj et al., 2011a; Srimuruganandam and Nagendra, 2012a,b) were there which constitute its associated sources (Fig. 11). Multivariate statistical methods like CMB and PMF were the most preferred methods while PCA has also been performed for PAH analysis. Srimuruganandam and Nagendra (2012a) performed PMF at the urban roadside (2008–09) and found marine (PM10: 40.4%; PM2.5: 21.5%) and secondary aerosol (PM10: 22.9%; PM2.5: 42.1%) as the major contributors followed by vehicular emissions (PM10: 16%; PM2.5: 6%) and biomass burning (PM10: 0.7%; PM2.5: 14%). An identical experiment was carried out by the same group of researchers for different roadside environment and found diesel (PM10: 53–52%; PM2.5: 44–65%) and gasoline exhausts (PM10: 6–16%; PM2.5: 3–8%) as the major particulate contributors, followed by paved road dusts (PM10 = PM2.5: 0–2.3%) (Srimuruganandam and Nagendra, 2012b). Detailed analysis clearly evident crustal elements and vehicular emissions as the principal sources of airborne particulates while certain regional pockets were also reported to be influenced by marine intrusions and secondary aerosols. Mohanraj et al. (2011a) used sequence of organic markers through PCA and identified vehicular emissions inclusive of petrol and diesel-driven engines (62.4%); and biomass and refuse combustion (18.9%) as the principal sources for summer time airborne PAH. However, selection of molecular markers somewhat differs in winters probably due to variations of associated sources. A similar kind of approaches was also made at Coimbatore


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Fig. 10. Particulate source profile in Hyderabad during 2004–05.

and Tiruchirappalli and vehicular emissions emerged as the major PAH contributor. Tirupati, located at the foothills of the Eastern Ghats is a major pilgrimage and cultural city of Andhra Pradesh. Kumar et al. (2008) applied multivariate statistical analysis for PM10 in Tirupati and were able to explain 86.97% of the total variance with major contribution from traffic sources and crustal elements. Conclusively, among eight identified SA studies conducted in southern India, 50% were of PCA origin probably due to unavailability of source profile while it was only during 2008–09 that application of advanced RMs like CMB and PMF was initiated. 5.3. East & Central India: Kolkata, Jorhat and Durg Receptor models have been applied in several instances in parts of East & Central Indian cities most notably in Kolkata, Jorhat and Durg. In Kolkata, five specific references (Karar et al., 2006; Karar and Gupta, 2007; Chowdhury et al., 2007; Gupta et al., 2007, 2008) were available for SA of airborne particulate matter. However, among the five, it appears that four (Karar et al., 2006; Karar and Gupta, 2007; Gupta et al., 2007, 2008) were performed considering single TSP and PM10 data (November, 2003 to November, 2004) for residential (Kasaba) and industrial (Cossipore) sectors. Karar et al. (2006) with the help of PCA and particulate-metal concentrations, concluded solid waste dumping (32%), vehicular emissions (23%) assisted with road dust (15%) and

crustal elements (14%) as the principal sources for residential area while, vehicular emissions induced road dust (23%), industrial (36%) and road dust (45%) as the principal sources for industrial region. Interestingly, the effect of solid waste dumping was obvious for residential site (Kasaba) as it was in fact a municipal solid waste dumping site. Therefore, particulate source profile for this region may not be a true representative of entire Kolkata. Additionally, Karar and Gupta (2007) used organic molecular markers and particulate-metal concentrations for PCA and identified solid waste dumping (36%), vehicular emissions (26%), coal combustion (13%) and crustal elements (4%) as the principal sources for residential area whereas, vehicular (37%) and industrial emissions (18%), and coal combustion (29%) as the principal sources for industrial area. For the same particulate speciation (PM10) information, CMB revealed coal combustion (42%), crustal elements (21%), field burning (7%) and paved road (1%) as the principal sources for residential areas while, vehicular emission (47%), coal combustion (34%), and metal industry (1%) for industrial areas (Gupta et al., 2007) (Fig. 12). Inter-comparison of SA results indicates that for both PCA applications by Karar and Gupta (2007) and Karar et al. (2006), irrespective of variation in marker selections, relatively similar conclusions were achieved. However, that was not the case for CMB where a diverse factor emerges as the most influencing particulate source. Gupta et al. (2008) applied PCA upon ambient concentrations of SO2, NO2, NH3 and PM10 and assessed vehicular and industrial emissions as the principal sources for airborne PM10. Chowdhury et al. (2007) processed fine particulate

Fig. 11. Particulate source profile in Chennai during 2008–09.

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Fig. 12. Particulate source profile in Kolkata during 2003–04.

speciated information for CMB and identified fossil fuel combustion (34–57%) and biomass burning (13–18%) as the major associated sources in Kolkata. Comparative evaluation between all these SA studies clearly indicates discrepancy of associated sources which may either due to uncertainty in marker selections, collinearity of diverse sources, or methodological weakness as also reported by Pant and Harrison (2012). Apart from Kolkata, Khare and Baruah (2010) quantified major sources of airborne fine particulates in Jorhat, Assam through enrichment factor and PCA. The major identified sources include traffic induced crustal sources (38%), fuel combustion (26%), vehicular and industrial emissions (19%), biomass burning (9%) and secondary aerosols (8%). Durg, Chhattisgarh has also been investigated in terms of SA studies (Sharma and Pervez, 2003; Gadkari and Pervez, 2007; Deshmukh et al., 2011). The location corresponds to heavy vehicular pollution coupled with industrial emissions. PM10 apportioned using CMB revealed crustal elements coupled with road dust resuspension as the major sources (Gadkari and Pervez, 2007). Additionally, Deshmukh et al. (2011) applied PCA for one year fine and ultrafine particulate mass and quantified relative contribution of anthropogenic (76.6%) and natural factors (65.9%) to total particulate loading. 5.4. Western India: Mumbai, Ahmedabad, Pune and Nagpur Western India characterized by high industrialized activities has been extensively studied for SA of airborne particulates, with predominate proportions of SA were conducted in Mumbai (Negi et al., 1987; Kumar et al., 2001; Chowdhury et al., 2007; Chelani et al.,

2008a, 2008b; Kothai et al., 2008; Herlekar et al., 2012; Gupta et al., 2012; Joseph et al., 2012a, 2012b). First of its kind SA study was conducted by Negi et al. (1987) and through FA concluded the presence of five potential sources of airborne particulates. Kumar et al. (2001) used particulate speciation information (1991–92) and through factor analysis-multiple regression techniques identified road dust (41%), marine aerosol (15%), vehicular emission (15%), metal industries (6%) and coal combustion (6%) as the prime sources. However, at curbside a specific contribution was mainly associated with road dust (33%), marine aerosol (18%) and vehicular emission (15%). Chelani et al. (2008a, 2008b) used coarse particulate speciated information of metallic species (2001–02) for eleven locations and found vehicular, industrial and crustal elements as dominant sources for normal activity sites (Fig. 13). Single year (2001–02) PM2.5 samples were further processed for the presence of elemental (Si, Al and EC) and molecular (n-alkanes, PAHs, hopanes, steranes, and levoglucosan) markers by Chowdhury et al. (2007). CMB revealed fossil fuel (21–34%; coal, diesel, and gasoline) and biomass burning (8%) as the major contributors for fine particulates. Kothai et al. (2008) through factor analysis-multiple regression techniques identified sea salt (35%), crustal (25%), industrial (14%), vehicular (10%) and fugitive emissions (7%) associated with coarser particulate (2005–06). Joseph et al. (2012a, 2012b) reconstructed PM2.5 mass (2007–08) and subsequently SA through CMB found contributions of organic matter (36–52%), secondary inorganic aerosols (21–27%), crustal material (6–12%), non-crustal material (4–8%) and sea salt (6–11%). Herlekar et al. (2012) collected PM10 at seven sites (2007–08) and analyzed for organic tracers. Coarser particulates were speciated for OC and EC and subsequently for different organic tracers

Fig. 13. Particulate source profile in Mumbai during 1991–2008.


like levoglucosan, hopanes and steranes. The SA results indicated vehicular exhaust, wood combustion and coal combustion as most dominating sources. The first of its kind SA study for airborne particulate over semi-arid urban locations in Ahmedabad was performed by Raman et al. (2010). Suspended particulate (TSP) samples (2000–03) and chemical speciated information were processed through advanced RM like PMF. Airborne regional dust (57.9%) was reported to be the highest contributor followed by calcium carbonate rich dust (19.0%), simultaneously contributing an aggregate of 77% total particulate loading. Additionally, biomass burning/vehicular emissions (8%), secondary nitrate/sulfate (5%) and marine aerosol (4.5%) were also reported to contribute significantly. Results emphasized the principal role of crustal elements in neutralizing acidic species associated with particulates, in contrast to the ammonia dominated neutralization in Europe and North America (Raman et al., 2010). Additionally, SA of chemical species in precipitation samples during south-west monsoon (2000–02) was performed using PMF by Raman and Ramachandran (2011). Crustal material (44.1%) and sea salt (29.8%) were found to be major sources contributing to total dissolved solids. Sudheer and Rengarajan (2012) used particulate-metal speciated information (2006–07) and revealed that 80% and 40–50% of PM2.5 and PM10 are of anthropogenic origin. Airborne particulates have not been extensively source apportioned in Pune. CPCB (2011a) had conducted SA of particulates for different cities using FA and CMB models. Crustal elements (57%) emerged as prominent sources of PM10 in residential sites of Pune followed by construction activities (14.9%), fuel combustions (10.8%) and vehicular emissions (9.8%). However, at kerbside and industrial site, resuspended particulates (49–64%) were found to be most dominating with contribution from fuel burning (8–13%) and vehicular emissions (CPCB, 2011a). Recently, Yadav and Satsangi (2013) used particulate speciation information (2011–12) for enrichment factor analysis. Results were indicative of resuspension of road dust due to traffic, biomass burning, construction activities, and wind-blown dust as the possible sources of airborne particulates. In Nagpur, Pipalatkar et al. (2014) used PM2.5 speciated information and concluded vehicular emissions (57–65%), secondary inorganic aerosol (12–16%) and biomass burning (9–15%) as the prime particulate sources. 6. Conclusions and way forward The present review initially describes the existing status of airborne particulate in different geographical regions within India. Integration of different field research identifies several regional hotspots where air quality has been extensively modified by the presence of tropospheric aerosols. Subsequent efforts were made to identify specific trends of aerosol variation essentially unique to a geographical region. Due to vast geographical distributions and prevailing meteorological implications, large heterogeneities were expected. Additionally, we also intended to identify signature molecules that are considered to carry unique identification marks of the respective sources. The review of existing data and their meta-analysis, submitted in this article identifies a paradigm shift of vision of atmospheric scientists in India. Approach has been changed from a mere identification of particulate loading and its multi-temporal variation to regional–global air quality forecasting, tracking pollution plumes, interacting climate-aerosol and particulate source profiling. In India, source apportionment of airborne particulate has been conducted over the past four decades; however, until 1990 only 9 publications were available. Since 2000, particulate source apportionment has emerged as a popular research domain with a total of 81 publications, 49% of which emerged within 2010– 2014. The basis of selections of signature molecules for specific source categories has also improved. During 1990–00s, combinations of elemental and ionic constituents often ambiguously attributed to a specific particulate source which often results to genuine source collinearity.

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Further, information related to organic molecular tracers and local particulate source profiles were highly inadequate to generate a well validated apportionment. However, marker selection process has been improved only recently with substantial number of citations now available with proper and convincing source apportions. Selection of RMs has also seen a transformation, shifting from enrichment factors and classical factor analysis (during 1980–90s) to more specialized PCA and CMB during 1990–00s, and recently with UNMIX and PMF. However, there is still a lack of long term particulate source profile information which critically limits the applicability of source control technology. Essentially, a need of proper definition and documentation of particulate source profile so that modeled observation may be interrelated and compared was felt. All such information essentially seek more source contribution estimates so that information may be interlinked and processed for better air quality management. The review of existing data of particulate source profiling and their meta-analysis evidenced several abnormalities with widely different conclusions. In most of the cases, ambiguities in selection of particulate source profile critically restrict inter-comparison of results. The most important findings of particulate source profiling may be concluded as: 1. Particulate source profile for different geographical areas was found to be largely incomparable and sometimes confusing. Inconsistency in marker selection, unavailability of definite source profile and multi-site multi-temporal studies critically limit inter-comparability of resultant source profile. 2. Extremely limited use of particulate size fraction information limits the applicability of source profile knowledge to identify relative contribution sources to different segments of particulates. 3. In most of the cases, the absence of particulate source profile and detailed emission inventories limit the applicability of advanced RMs. 4. Limited use of organic molecular markers and gas-to-particle conversion reduces the applicability of source profile results for future studies. Little attention was paid to atmospheric dynamics of sulfate, nitrate and ammonia which subsequently relates it with secondary regional aerosols and local anthropogenic emissions. 5. Characteristically, 71% of SA studies have been originated in Northern and Western India, while both Southern (11%), and Central and Eastern India (20%) have been less extensively studied in terms of particulate SA. 6. Delhi (22%) and Mumbai (14%) have been most extensively studied in terms of SA while, most of the Indo-Gangetic plain and cities in Southern India and Eastern Indian have not been significantly investigated in terms of airborne particulate sources. 7. In Delhi, most of the SA conducted during 1990s for coarser particulates revealed dominance of vehicular and industrial emissions over crustal elements. In contrast SA conducted during 2000s found dominance of crustal elements over vehicular and industrial emissions in Delhi, Agra and Kanpur. 8. In Hyderabad and Chennai, vehicular emissions and road dust resuspensions were predominant contributors for coarser particulates probably due to proximity of monitoring site to a traffic corridor. 9. The SA studies conducted in Kolkata were based on locally-derived emission source profiles and therefore, may not be appropriate to be considered as true representation of regional source. 10. Flanking the Indian peninsula on the western side, cities like Mumbai, coarser particulate mostly originated from road dust resuspension and vehicular emission with varied contributions from marine aerosols. However, for Ahmedabad, airborne regional dust and calcium carbonates rich dust were the sole contributors of coarser particulates. 11. Information related to SA of finer particulates are extremely limited in India and exhibit widely varying conclusions even within individual cities. This may be possibly due to inappropriate particulate

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source profile, limited emission inventories and difference in adopted methodologies. 12. Vehicular and industrial emissions emerge as predominant PM2.5 sources for most of the SA studies conducted in India likewise in Kanpur, Agra, Hyderabad, Chennai and Nagpur. Acknowledgment The present submission is mutually supported by University Grants Commission, New Delhi (F. No. 41-1111/2012, SR) and Department of Science and Technology, New Delhi (F. No. SR/FTP/ES-52/2014). The authors duly acknowledge the guidance and cooperation provided by Director, IESD-BHU and Dean, FESD-BHU. Appendix A. Supplementary data Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.atmosres.2015.04.017. References Andersen, Z.J., Wahlin, P., Raaschou-Nielsen, O., Scheike, T., Loft, S., 2007. Ambient particle source apportionment and daily hospital admissions among children and elderly in Copenhagen. J. Expo. Sci. Env. Epidemiol. 17, 625–636. Balachandran, S., Meena, B.R., Khillare, P.S., 2000. Particle size distribution and its elemental composition in the ambient air of Delhi. Environ. Int. 26, 49–54. Bandhu, H.K., Puri, S., Garg, M.L., Singh, B., Shahi, J.S., Mehta, D., 2000. Elemental composition and sources of air pollution in the city of Chandigarh India, using EDXRF and PIXE techniques. Nucl. Instrum. Meth. B. 160, 126–138. Banerjee, T., Singh, S.B., Srivastava, R.K., 2011a. Development and performance evaluation of statistical models correlating air pollutants and meteorological variables at Pantnagar India. Atmosph. Res. 99, 505–517. Banerjee, T., Barman, S.C., Srivastava, R.K., 2011b. Application of air pollution dispersion modeling for source-contribution assessment and model performance evaluation at Integrated Industrial Estate-Pantnagar. Environ. Pollut. 159 (4), 865–875. Banerjee, T., Srivastava, R.K., 2011c. Assessment of the ambient air quality at the Integrated Industrial Estate-Pantnagar through the air quality index (AQI) and exceedence factor (EF). Asia-Pac. J. Chem. Eng. 6, 64–70. Basha, S., Jhala., J., Thorat., R., Goel, S., Trivedi, R., Shah, K., Menon, G., Gaur, P., Kalpana, H. Mody, Jha, B., 2010. Assessment of heavy metal content in suspended particulate matter of coastal industrial town, Mithapur, Gujarat, India. Atmos. Res. 97, 257–265. Behera, S.N., Sharma, M., Dikshit, O., Shukla, S.P., 2011. GIS-based emission inventory, dispersion modeling, and assessment for source contributions of particulate matter in an urban environment. Water Air Soil Pollut. 218, 423–436. Belis, C.A., Karagulian, F., Larsen, B.R., Hopke, P.K., 2013. Critical review and meta-analysis of ambient particulate matter source apportionment using receptor models in Europe. Atmos. Environ. 69, 94–108. Bhanarkar, A.D., Rao, P.S., Gajghate, D.G., Nema, P., 2005. Inventory of SO2, PM and toxic metals emissions from industrial sources in Greater Mumbai, India. Atmos. Environ. 39, 3851–3864. Bhattacharjee, A., Mandal, H., Roy, M., Chini, T.K., 2011. A preliminary study on the nature of particulate matters in vehicle fuel wastes. Environ. Monit. Assess. 176, 473–481. Chakraborty, A., Gupta, T., 2010. Chemical characterization and source apportionment of submicron (PM1) aerosol in Kanpur Region India. Aerosol Air Qual. Res. 10, 433–445. Chelani, A.B., Gajghate, D.G., Chalapati Rao, C.V., Devotta, S., 2010. Particle size distribution in ambient air of Delhi and its statistical analysis. Bull. Environ. Contam. Toxicol. 85, 22–27. Chelani, A.B., Gajghate, D.G., Devotta, S., 2008a. Source apportionment of PM10 in Mumbai India using CMB model. Bull. Environ. Contam. Toxicol. 190–195. Chelani, A.B., Gajghate, D.G., Devotta, S., 2008b. Source apportionment of PM10 in Mumbai, India using CMB model. Bull. Environ. Contam. Toxicol. 81, 190–195. Chithra, V.S., Nagendra, S.M.S., 2013. Chemical and morphological characteristics of indoor and outdoor particulate matter in an urban environment. Atmos. Environ. 77, 579–587. Chowdhury, Z., Zheng, M., Schauer, J.J., Sheesley, R.J., Salmon, L.G., Cass, G.R., Russell, A.G., 2007. Speciation of ambient fine organic carbon particles and source apportionment of PM2.5 in Indian cities. J. Geophys. Res. 112, 1–14. CPCB, 2011a. Air Quality Monitoring, Emission Inventory and Source Apportionment Study for Indian Cities. Central Pollution Control Board, New Delhi. CPCB, 2011b. Annual Report, 2010–11. Central Pollution Control Board, New Delhi. CPCB, 2012. National Ambient Air Quality Status & Trends in India—2010. Central Pollution Control Board, New Delhi. Deshmukh, D.K., Deb, M.K., Tsai, Y.I., Mkoma, S.L., 2011. Water soluble ions in PM2.5 and PM1 aerosols in Durg City, Chhattisgarh, India. Aerosol Air Qual. Res. 11, 696–708. Dubey, B., Pal, A.K., Singh, G., 2012. Trace metal composition of airborne particulate matter in the coal mining and non-mining areas of Dhanbad Region, Jharkhand India. Atmos. Pollut. Res. 3, 238–246. Gadkari, N., Pervez, S., 2008. Source apportionment of personal exposure of fine particulates among school communities in India. Environ. Monit. Assess. 42, 227–241.

Gadkari, N.M., Pervez, S., 2007. Source investigation of personal particulates in relation to identify major routes of exposure among urban residentials. Atmos. Environ. 41, 7951–7963. Gargava, P., Chow, J.C., Watson, J.G., Lowenthal, D.H., 2014. Speciated PM10 emission inventory for Delhi, India. Aerosol Air Qual. Res. 14, 1515–1526. Gummeneni, S., Yusup, Y.B., Chavali, M., Samadi, S.Z., 2011. Source apportionment of particulate matter in the ambient air of Hyderabad city India. Atmos. Res. 101, 752–764. Gupta, A.K., Karar, K., Ayoob, S., John, K., 2008. Spatio-temporal characteristics of gaseous and particulate pollutants in an urban region of Kolkata, India. Atmos. Res. 87, 103–115. Gupta, A.K., Nag, S., Mukhopadhyay, U.K., 2006. Characterisation of PM10, PM2.5 and benzene soluble organic fraction of particulate matter in an urban area of Kolkata, India. Environ. Monit. Assess. 115, 205–222. Gupta, A.K., Karar, K., Srivastava, A., 2007. Chemical mass balance source apportionment of PM10 and TSP in residential and industrial sites of an urban region of Kolkata India. J. Hazard. Mater. 142, 279–287. Gupta, A.K., Patil, R.S., Gupta, S.K., 2004. A statistical analysis of particulate data sets for Jawaharlal Nehru port and surrounding harbor region in India. Environ. Monit. Assess. 95, 295–309. Gupta, I., Salunkhe, A., Kumar, R., 2012. Source apportionment of PM10 by positive matrix factorization in urban area of Mumbai, India. Sci. World J. http://dx.doi.org/10.1100/ 2012/585791. Guttikunda, S.K., Kopakka, R.V., Dasari, P., Gertler, A.W., 2013. Receptor model-based source apportionment of particulate pollution in Hyderabad India. Environ. Monit. Assess. 185, 5585–5593. Guttikunda, S.K., Jawahar, P., 2012. Application of SIM-air modeling tools to assess air quality in Indian cities. Atmos. Environ. 62, 551–561. Habil, M., Massey, D.D., Taneja, A., 2013. Exposure of children studying in schools of India to PM levels and metal contamination: sources and their identification. Air Qual. Atmos. Health 6, 575–587. Hennigan, C.J., Sullivan, A.P., Collett, J.L., Robinson, A.L., 2010. Levoglucosan stability in biomass burning particles exposed to hydroxyl radicals. Geophys. Res. Lett. 37, 1–4. Henry, R.C., 2003. Multivariate receptor modelling by N-dimensional edge detection. Chemometr. Intell. Lab. 65, 189–197. Herlekar, M., Joseph, A.E., Kumar, R., Gupta, I., 2012. Chemical speciation and source assignment of particulate (PM10) phase molecular markers in Mumbai. Aerosol Air Qual. Res. 12, 1247–1260. Joseph, A.E., Unnikrishnan, S., Kumar, R., 2012a. Chemical characterization and mass closure of fine aerosol for different land use patterns in Mumbai city. Aerosol Air Qual. Res. 12, 61–72. Joseph, A.E., Unnikrishnan, S., Kumar, R., 2012b. Chemical characterization and mass closure of fine aerosol for different land use patterns in Mumbai city. Aerosol Air Qual. Res. 12, 61–72. Kar, S., Maity, J.P., Samal, A.C., Santra, S.C., 2010. Metallic components of traffic-induced urban aerosol, their spatial variation, and source apportionment. Environ. Monitor. Assess. 168, 561–574. Karar, K., Gupta, A.K., 2006. Seasonal variations and chemical characterization of ambient PM10 at residential and industrial sites of an urban region of Kolkata (Calcutta), India. Atmos. Res. 81, 36–53. Karar, K., Gupta, A.K., 2007. Source apportionment of PM10 at residential and industrial sites of an urban region of Kolkata, India. Atmos. Res. 84, 30–41. Karar, K., Gupta, A.K., Kumar, A., Biswas, A.K., 2006. Characterization and identification of the sources of chromium, zinc, lead, cadmium, nickel, manganese, and iron in PM10 particulates at the two sites of Kolkata, India. Environ. Monit. Assess. 120, 347–360. Khare, P., Baruah, B.P., 2010. Elemental characterization and source identification of PM2.5 using multivariate analysis at the suburban site of North-East India. Atmos. Res. 98 148:162. Khillare, P.S., Sarkar, S., 2012. Airborne inhalable metals in residential areas of Delhi, India: distribution, source apportionment and health risks. Atmos. Pollut. Res. 3, 46–54. Khillare, P.S., Balachandran, S., Meena, B.R., 2004. Spatial and temporal variation of heavy metals in atmospheric aerosol of Delhi. Environ. Monit. Assess. 90, 1–21. Kothai, P., Saradhi, I.V., Prathibha, P., Hopke, P.K., Pandit, G.G., Puranik, V.D., 2008. Source apportionment of coarse and fine particulate matter at Navi Mumbai India. Aerosol Air Qual. Res. 8, 423–436. Kothai, P., Saradhi, I.V., Pandit, G.G., Markwitz, A., Puranik, V.D., 2011. Chemical characterization and source identification of particulate matter at an urban site of Navi Mumbai India. Aerosol Air Qual. Res. 11, 560–569. Kulshrestha, A., Satsangi, P.G., Masih, J., Taneja, A., 2009. Science of the total environment metal concentration of PM2.5 and PM10 particles and seasonal variations in urban and rural environment of Agra, India. Sci. Total Environ. 407, 6196–6204. Kulshrestha, U.C., Kumar, N., Saxena, A., Kumari, K.M. And, Srivastava, S.S., 1995. Identification of the nature and source of atmospheric aerosols near the Taj Mahal (India). Environ. Monit. Assess. 34, 1–11. Kumar, A.V., Patil, R.S., Nambi, K.S.V., 2001. Source apportionment of suspended particulate matter at two traffic junctions in Mumbai, India. Atmos. Environ. 35, 4245–4251. Kumar, M., Tiwari, S., Murari, V., Singh, A.K., Banerjee, T., 2015. Wintertime characteristics of aerosols at middle Indo-Gangetic Plain: impacts of regional meteorology and long range transport. Atmos. Environ. 104, 162–175. Laupsa, H., Denby, B., Larssen, S., Schaug, J., 2009. Source apportionment of particulate matter (PM2.5) in an urban area using dispersion, receptor and inverse modelling. Atmos. Environ. 43, 4733–4744. Massey, D., Kulshrestha, A., Masih, J., Taneja, A., 2012. Seasonal trends of PM10, PM5.0, PM2.5 & PM1.0 in indoor and outdoor environments of residential homes located in North-Central India. Build. Environ 47, 223–231.

T. Banerjee et al. / Atmospheric Research 164–165 (2015) 167–187 Mathur, R., Balaram, V., Satyanarayanan, M., Sawant, S.S., Ramesh, S.L., 2011. Anthropogenic platinum, palladium and rhodium concentrations in road dusts from Hyderabad city, India. Environ. Earth Sci. 62, 1085–1098. Mohanraj, R., Solaraj, G., Dhanakumar, S., 2011a. Fine particulate phase PAHs in ambient atmosphere of Chennai metropolitan city, India. Environ. Sci. Pollut. Res. 18, 764–771. Mohanraj, R., Solaraj, G., Dhanakumar, S., 2011b. PM2.5 and PAH concentrations in urban atmosphere of Tiruchirappalli, India. Bull. Environ. Contam. Toxicol. 87, 330–335. Mouli, P.C., Mohan, S.V., Balaram, V., Kumar, M.P., Reddy, S.J., 2006. A study on trace elemental composition of atmospheric aerosols at a semi-arid urban site using ICP-MS technique. Atmos. Environ. 40, 136–146. Murari, V., Kumar, M., Barman, S.C., Banerjee, T., 2015. Temporal variability of MODIS aerosol optical depth and chemical characterization of airborne particulates in Varanasi India. Environ. Sci. Pollut. Res. 22, 1329–1343. Negi, B.S., Sadasivan, S., Mishra, U.C., 1987. Aerosol composition and sources in urban areas in India. Atmos. Environ. 21, 1259–1266. ODowd, C.D., De Leeuw, G., 2007. Marine aerosol production: a review of the current knowledge. Philos. Trans. R. Soc. A 365, 1753–1774. Paatero, P., 1999. The multilinear engine — a table-driven least squares program for solving multilinear problems, including the n-way parallel factor analysis model. J. Comput. Graph. Stat. 8, 1–35. Paatero, P., Tappert, U., 1994. Positive matrix factorization: a non-negative factor model with optimal utilization of error estimates of data values. Environmetrics 5, 111–126. Paatero, P., 1997. Least squares formulation of robust non-negative factor analysis. Chemometr. Intell. Lab. 37, 23–25. Pachauri, T., Singla, V., Satsangi, A., Lakhani, A., Kumari, K.M., 2013. SEM-EDX characterization of individual coarse particles in Agra India. Aerosol Air Qual. Res. 13, 523–536. Pant, P., Harrison, R.M., 2012. Critical review of receptor modelling for particulate matter: a case study of India. Atmos. Environ. 49, 1–12. Patil, R.S., Kumar, R., Menon, R., Kumar, M., Sethi, V., 2013. Development of particulate matter speciation pro fi les for major sources in six cities in India. Atmos. Res. 1–11. Piot, C., Jaffrezo, J.-L., Cozic, J., El Haddad, I., Marchand, N., Personnaz, M.-B., Besombes, J.L., 2011. Caract'erisation chimique des a'erosols en r'egion Rhône-Alpes: 'etude de l'influence des diff'erentes sources d"emissions par une approche multi-traceurs. Pollut. Atmos. 211, 313–318. 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, 3621–3630. Pipalatkar, P., Khaparde, V.V., Gajghate, D.G., Bawase, M.A., 2014. Source apportionment of PM2.5 using a CMB model for a centrally located Indian city. Aerosol Air Qual. Res. 14, 1089–1105. Ram, K., Sarin, M.M., Tripathi, S.N., 2012. Temporal trends in atmospheric PM2.5, PM10, 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, 686–695. Raman, R.S., Ramachandran, S., 2011. Source apportionment of the ionic components in precipitation over an urban region in Western India. Environ. Sci. Pollut. Res. 18, 212–225. Raman, R.S., Ramachandran, S., Rastogi, N., 2010. Source identification of ambient aerosols over an urban region in western India. J. Environ. Monitor. 12, 1330–1340. Ramanathan, V., Feng, Y., 2009. Air pollution, greenhouse gases and climate change: global and regional perspectives. Atmos. Environ. 43, 37–50. Rudich, Y., Donahue, N.M., Mentel, T.F., 2007. Aging of organic aerosol: bridging the gap between laboratory and field studies. Annu. Rev. Phys. Chem. 58, 321–352. Satsangi, A., Pachauri, T., Singla, V., Lakhani, A., Kumari, K.M., 2013. Water soluble ionic species in atmospheric aerosols: concentrations and sources at Agra in the IndoGangetic Plain (IGP). Aerosol Air Qual. Res. 13, 1877–1889. Sen, et al., 2014. Atmospheric Fine and Coarse Mode Aerosols at Different Environments of India and the Bay of Bengal During Winter—2014: Implication of a Coordinated Campaign. MAPAN, Springer (DOI: 10.1007/s12647-014-0109-x). Sharma, M., Maloo, S., 2005. Assessment of ambient air PM10 and PM2.5 and characterization of PM10 in the city of Kanpur, India. Atmos. Environ. 39, 6015–6026. Sharma, R., Pervez, S., 2003. Enrichment and exposure of particulate lead in a traffic environment in India. Environ. Geochem. Health 25, 297–306. Sharma, S.K., Mandal, T.K., Saxena, M., Sharma, A., Datta, A., Saud, T., 2014. Journal of atmospheric and solar-terrestrial physics variation of OC, EC, WSIC and trace metals of PM10 in Delhi India. J. Atmos. Sol-Terr Phy. 113, 10–22. Sharma, S.K., Mandal, T.K., Saxena, M., Rashmi, Rohtash, Sharma, A., Gautam, R., 2014. Urban Climate Source apportionment of PM 10 by using positive matrix factorization at an urban site of Delhi., India. Urban Climate 10, 656–670. Sharma, V.K., Patil, R.S., 1992. Size distribution of atmospheric aerosols and their source identification using factor analysis in Bombay, India. Atmos. Environ. 26, 135–140.

187

Shi, Z., Zhang, D., Ji, H., Hasegawa, S., Hayashi, M., 2008. Modification of soot by volatile species in an urban atmosphere. Sci. Total Environ. 389, 195–201. Shridhar, V., Khillare, P.S., Agarwal, T., Ray, S., 2010. Metallic species in ambient particulate matter at rural and urban location of Delhi. J. Hazard. Mater. 175, 600–607. Shukla, S.P., Sharma, M., 2008. Source apportionment of atmospheric PM10 in Kanpur India. Environ. Eng. Sci. 25, 849–862. Simoneit, B.R.T., 2002. Biomass burning — a review of organic tracers for smoke from incomplete combustion. Appl. Geochem. 17, 129–162. Singh, D.P., Gadi, R., Mandal, T.K., 2011. Characterization of particulate-bound polycyclic aromatic hydrocarbons and trace metals composition of urban air in Delhi, India. Atmos. Environ. 45 (40), 7653–7663. Singh, R., Sharma, B.S., 2012. Composition, seasonal variation, and sources of PM10 from world heritage site Taj Mahal, Agra. Environ. Monit. Assess. 184, 5945–5956. Srimuruganandam, B., Nagendra, S.M.S., 2012a. Source characterization of PM10 and PM2.5 mass using a chemical mass balance model at urban roadside. Sci. Total Environ. 433, 8–19. Srimuruganandam, B., Nagendra, S.M.S., 2012b. Chemosphere application of positive matrix factorization in characterization of PM10 and PM2.5 emission sources at urban roadside. Chemosphere 88, 120–130. Srimuruganandam, B., Nagendra, S.M.S., 2011. Characteristics of particulate matter and heterogeneous traffic in the urban area of India. Atmos. Environ. 45, 3091–3102. Srivastava, A., Gupta, S., Jain, V.K., 2009. Winter-time size distribution and source apportionment of total suspended particulate matter and associated metals in Delhi. Atmos. Res. 92, 88–99. Srivastava, A., Gupta, S., Jain, V.K., 2008. Source apportionment of total suspended particulate matter in coarse and fine size ranges over Delhi. Aerosol Air Qual. Res. 8, 188–200. Srivastava, A., Jain, V.K., 2007a. Size distribution and source identification of total suspended particulate matter and associated heavy metals in the urban atmosphere of Delhi. Chemosphere 68, 579–589. Srivastava, A., Jain, V.K., 2007b. Seasonal trends in coarse and fine particle sources in Delhi by the chemical mass balance receptor model. J. Hazard. Mater. 144, 283–291. Sudheer, A.K., Rengarajan, R., 2012. Atmospheric mineral dust and trace metals over urban environment in Western India during winter. Aerosol Air Qual. Res. 923–933. Tiwari, S., Srivastava, A.K., Bisht, D.S., Bano, T., Singh, S., Behura, S., Padmanabhamurty, B., 2009. Black carbon and chemical characteristics of PM10 and PM2.5 at an urban site of North India. J. Atmos. Chem. 62, 193–209. Tiwari, S., Pervez, S., Cinzia, P., Bisht, D.S., Srivastava, A.K., Chate, D., 2013. Chemical characterization of atmospheric particulate matter in Delhi, India, part II: source apportionment studies using PMF 3.0. Sustain. Environ. Res. 23 (5), 295–306. Tripathi, R.M., Vinod Kumar, A., Manikandan, S.T., Bhalke, S., Mahadevan, T.N., Puranik, V.D., 2004. Vertical distribution of atmospheric trace metals and their sources at Mumbai India. Atmos. Environ. 38, 135–146. USEPA, 2008. EPA positive matrix factorization (PMF) 3.0 fundamentals & user guide. United States Environmental Protection AgencyWashington, DC. Viana, M., Kuhlbusch, T.A.J., Querol, X., Alastuey, A., Harrison, R.M., Hopke, P.K., Hitzenberger, R., 2008. Source apportionment of particulate matter in Europe: a review of methods and results. J. Aerosol Sci. 39, 827–849. Vinod Kumar, A., Patil, R.S., 1994. Particle size distribution of atmospheric aerosols at a traffic junction in Bombay. Proceedings of the Conference on Indian Aerosol Science and Technology, Bhabha Atomic Research Centre, Bombay, 10–12, January 1994 pp. 119–122. Wåhlin, P., 2003. COPREM—a multivariate receptor model with a physical approach. Atmos. Environ. 37, 4861–4867. Watson, J.G., Antony Chen, L.-W., Chow, J.C., Doraiswamy, P., Lowenthal, D.H., 2008. Source apportionment: findings from the U.S. Supersites Program. J. Air Waste Manage. Assoc. 58, 265–288. Watson, J.G., Zhu, T., Chow, J.C., Engelbrecht, J., Fujita, E.M., Wilson, W.E., 2002. Receptor modeling application framework for particle source apportionment. Chemosphere 49, 1093–1136. Watson, J.G., Robinson, N.F., Lewis, C.W., Coulter, C.T., Chow, J.C., Fujita, E.M., Lowenthal, D.H., Conner, T.L., Henry, R.C., Willis, R.D., 1997. Chemical Mass Balance Receptor Model Version 8 (CMB) User's Manual. Reno, NV, US. Winchester, J.W., Nifong, G.D., 1971. Water pollution in Lake Michigan by trace elements from pollution aerosol fallout. Water Air Soil Pollut. 1, 50–64. Xie, Y.L., Hopke, P.K., Paatero, P., Barrie, L.A., Li, S., 1999. Identification of source nature and seasonal variations of Arctic aerosol by the multilinear engine. Atmos. Environ. 33 (16), 2549–2562. Yadav, S., Satsangi, P.G., 2013. Characterization of particulate matter and its related metal toxicity in an urban location in South West India. Environ. Monit. Assess. 185, 7365–7379.