Characterization and source apportionment of organic compounds in PM10 using PCA and PMF at a traffic hotspot of Delhi

Accepted Manuscript Title: CharacterizationThe author names have been tagged as given names and surnames (surnames are highlighted in teal color). Please confirm if they have been identified correctly.–> and source apportionment of organic compounds in PM10 using PCA and PMF at a traffic hotspot of Delhi Authors: Sarika Gupta, Ranu Gadi, S.K. Sharma, T.K. Mandal PII: DOI: Reference:

S2210-6707(17)31604-9 https://doi.org/10.1016/j.scs.2018.01.051 SCS 964

To appear in: Received date: Revised date: Accepted date:

24-11-2017 18-1-2018 30-1-2018

Please cite this article as: Gupta, Sarika., Gadi, Ranu., Sharma, SK., & Mandal, T.K., Characterization and source apportionment of organic compounds in PM10 using PCA and PMF at a traffic hotspot of Delhi.Sustainable Cities and Society https://doi.org/10.1016/j.scs.2018.01.051 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Characterization and source apportionment of organic compounds in PM10 using PCA and PMF at a traffic hotspot of Delhi

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Guru Gobind Singh Indraprastha University, Delhi-110078, India

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Indira Gandhi Delhi Technical University for Women, Delhi-110006, India

CSIR-National Physical Laboratory, Dr. K. S. Krishnan Road, New Delhi-110012, India

*Author for correspondence, E-mail: [email protected]

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OC/EC and organic molecular markers in PM10 have been analyzed. Organic markers of traffic (n-alkanes and PAHs) and cooking (n-alkanoic acids and oleic acid) show higher concentrations over other markers. Seasonal trends of most of the markers indicate higher concentrations in winter due to favorable ambient atmospheric conditions. Source apportionment studies have been performed using PCA and PMF and the source profiles designed by both the models have been compared. Traffic exhaust has been observed as a major contributor for ambient air pollution in the region.

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Sarika Guptaa, Ranu Gadib*, S. K. Sharmac, T.K. Mandalc

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Abstract: Air quality in megacity Delhi is deteriorating with alarming particulate levels. 24 h sampling of PM10 to determine the compositions, seasonal variations and sources of organic molecular markers, was done from January 2015 to December 2015, at the University campus (Indira Gandhi Delhi Technical University for Women- IGDTUW), Delhi, India. The organic fraction of PM10 is composed of a mixture of numerous organic compounds, including alkanes, alkanoic acids, carbonyl compounds, anhydrosugars, and aromatic compounds that serve as specific signatory molecules or organic tracers to their sources. The homologous chains of n-alkanes and n-alkanoic acids showed a typical distribution in the ranges of C12C30. The < C20 homologues generally indicate the dominance of exhausts of kitchen waste 1

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and automobiles. The diagnostic parameters and receptor models have also been used to determine the sources of organic species. A seven-factor source profile solution was provided by Positive Matrix Factorization (PMF) in comparison to 5 factors by Principal Component Analysis (PCA). Seasonal variations were also observed in winter, summer, monsoon and post-monsoon; with comparatively lower concentrations in summer than in winter for most of the organic compounds. Keywords: Organic markers; Seasonal variations; Source profiles; Diagnostic ratios; Principal component analysis; Positive matrix factorization

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1. Introduction Atmospheric particulate matter (PM) is an important constituent because it influences global and regional climate forcing, visibility, composition of atmosphere, biogeochemical cycle and activation of cloud condensation nuclei (CCN) (Poschl, 2005; Andrea & Rosenfeld, 2008) and eventually create negative influence on the biosphere and human well-being by spreading allergies and contagious ailments of respiration, cardiovascular and reproductive systems (Sachdeva and Attri, 2008). In India, PM reflects their origin from anthropogenic emissions from vehicles, coal combustion, industrial setups, biomass and waste burning, fossil fuel combustion, construction activities, and sea salt etc., (Gargava et al., 2004; Chowdhary et al., 2007; Kumar et al., 2009; CPCB, 2010; Goyal et al., 2011; Guttikunda and calori, 2013; Yadav et al., 2013; Pant et al., 2015; Jain et al., 2017). Detailed investigations on the composition of PM explored the fact that a significant fraction could be due to organic compounds which account for up to 70% of the total mass depending upon the locations and source strength (Zheng et al., 2000; Alves et al., 2002; Feng et al., 2006a, b; Oliveira et al., 2007; Fu et al., 2010; Giri et al., 2013; Yadav et al., 2013; Pant et al., 2015). Organic constituents of PM is a mixture of numerous organic compounds, including alkanes, alkanols, alkanoic acids, carbonyl compounds, anhydrosugars, and aromatic compounds that serve as specific signatory molecules or receptor tracers to their sources in conjunction with Volatile Organic Compounds (VOCs) and inorganic species. Organic compounds are characterized by their limited existence, specificity of the source and molecular stability (Simoneit, 1984; 1989). Major factors affecting the characteristics of organic constituents of atmospheric particulates are: source strength, geology and morphology of the area, diversity in flora and fauna, incident solar fluctuation and meteorological parameters (Yadav et al., 2013). In spite of progress and techniques developed in the last decades, understanding the composition, sources, formation pathways and fate of organic compounds in nature, still remains an interesting and challenging task because airborne particulates exhibit seasonal fluctuations and deviations from pre-identified characteristics (Banerjee et al., 2015). PM has been widely evaluated in recent years over the globe as well as in India due to its potential impacts on human well-being and air quality. However, limited scientific studies 2

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have been made to evaluate the composition, characteristics and source profiles of organic molecular markers of PM in India (Sharma et al., 2003; Chowdhary et al., 2007; Herlekar et al., 2012 Fu et al., 2010; Yadav et al., 2013; Gupta et al., 2017). Sharma et al. (2003) in Delhi identified n-Alkanes (C21–C31), polycyclic aromatic hydrocarbons (PAHs) and other organic molecular markers and resolved vehicular emissions, biomass and waste burning as the major contributors to organic species of PM10. Similarly, Gupta et al. (2017) reported the seasonal variation of n-alkanes (C12-C34) in PM10 at a heavy traffic site of Delhi. The results of source apportionment studies using diagnostic tools showed vehicular exhaust and road dust were the major sources of aerosol emissions in Delhi. Chowdhary et al., (2007) reported seasonal variations and source profiling of PM2.5 [using Chemical Mass Balance (CMB) approach] in the megacities of India (Chandigarh, Delhi, Kolkata, and Mumbai) for organic and inorganic compounds. Many source apportionment studies have been done for inorganic species of PM in Delhi (Chowdhary et al., 2007; Srivastava et al., 2008; CPCB, 2010; Sharma et al., 2014b; Jain et al., 2017) but for organic species, there are limited studies in the sampling region of Delhi. The receptor models (PMF and PCA) using organic markers provided more extensive details on the source apportionment of organic aerosols such as biomass burning, secondary organic aerosol, combustion factors and vegetative detritus and their relative contribution, which were not acknowledged by traditional PMF analysis using elements (ion and metal species).

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Previous studies suggested that Organic aerosols are ominously transported to long distances from the Northwest and the Middle East directions during early winter and from Southeast Asia in late winter, but some of the secondary organic aerosols produced from photochemical reactions over the Bay of Bengal (Pavuluri et al., 2010). Hence, a proper identification and quantification of these specific signatory molecules is needed for each region with comprehensive twenty four hourly assessments, since they could be evolved gradually with time or may undergo chemical modifications and ultimately be masked, which critically limits its application as a tracer (Banerjee et al., 2015). Thus, the aim of this study is to evaluate spatial, seasonal and annual trends of organic tracers in atmospheric PM, their compositions and source profiles with the help of receptor modeling, to probably understand their effect on regional climate change and human health.

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2. Depiction of sampling site 2.1 Demography Delhi (28°38′N and 77°20′E), is situated at the bank of river Yamuna; having a geographical area of 1483 Km2 and altitude level is at 216 m above mean sea level (amsl). The city is bounded by diverse climatic zones i.e., the Himalayas from north, central plains from south, the Thar Desert in the west and the Indo-Gangetic plain (IGP) from east, which is the main reason of its semi-arid climate. Being the National Capital Territory (NCT) of India, it is densely populated with the population of 16.8 million (Statistical Abstract of Delhi, 2016). In 3

recent years, Delhi has emerged as most polluted city with drastically high level of atmospheric PM. The IGDTUW campus (sampling location), is situated in the Northern Delhi (Fig. 1), which has huge transport network, i.e. Inter-State Bus Terminus (ISBT) in Kashmere Gate with heavy vehicular traffic road in the vicinity.

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2.2 Climate The seasonal distribution of NCT Delhi follows summer, monsoon, autumn, winter and spring. The average rainfall in Delhi is 714 mm, ¾th of which falls from July to September (Sharma & Dikshit, 2016), but the monsoon season was delayed in year 2015 and originated from South than the usual north-west trend (IMD, 2015). This climate array coupled with the El-Nino effect, which shows enormous increases in temperature of Asian Countries. The maximum temperature ranges were experienced in the summer (41-46oC) and the minimum in winter (4-6oC). It remained hot until October (Table S1; in supplementary information). The year 2015 heat wave has had the highest recorded temperatures since 1995 (Gopalaswami, 2016). The winter months (October-February) were further divided into prewinter (October-November) and winter (December-February); whereas summer months (March-July) into summer (March-May) and late-summer (June and July) (Fig. 2) to develop a better understanding of seasonal variations due to specific climate episodes.

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2.3. Meteorology The meteorological factors like Dew point (td), ambient temperature (t), and Relative Humidity (RH), were noted regularly during the sampling period from India Meteorological Department (www.imd.gov.in). Monthly average values for every month are presented in Table S1 (in supplementary information). Composition and seasonal variation of PM10 is significantly influenced by origin of sources as well as meteorological parameters. High humidity (Table S1) compounded with other meteorological parameters, increase the effect of climate on dispersion of particulate matter. Variation in ambient temperatures and high solar flux, as well as high levels of ozone, SOx and NOx, further encourages photochemical production of aerosol particles in the atmosphere.

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3. Experimental Method 3.1. Sampling PM10 was collected on Quartz fiber filters (QFFs, Pallflex) with a High Volume Sampler (HVS) [APM 460 (Envirotech Instruments)] at the flow rate of 1.35 m3 min-1 at the IGDTUW campus from January 2015 to December 2015. 24 h sampling was carried out two to three times every week during the sampling period. The sampler was positioned on the terrace of Applied Sciences block at the height of 15 m above the ground level. Prior to sampling; the QFFs were baked at 550°C for 5 h to eradicate the organic impurities. The filters were retained in a Secador desiccator (Tarsons) under controlled temperature (25-30°C) and 3540% relative humidity for 24-36 h to avoid hydration of the QFFs. To obtain PM10 4

concentrations, the QFFs were weighed twice, prior to and after the sampling using microbalance with ± 10 µg resolution (M/s. Sartorius). After weighing, aluminum foil was used for wrapping the filter papers properly, to reduce the exposure to sunlight and storage of the wrapped filters under refrigeration (-20°C) will further prevent volatilization of organic compounds, till the completion of extraction and analysis.

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3.2 Analysis of OC and EC A piece of ~0.54 cm2 of the PM10 filter was stamped out and analyzed the concentrations of OC and EC using OC/EC carbon analyzer (model: DRI 2001 A, Atmoslytic Inc., Calabasas, CA, USA) following the USEPA “Improve-A Protocol” with negative pyrolysis areas zeroed (Chow et al. 2004). The analytical technique for quantification of OC and EC concentrations in PM10 are described in detail by Chow et al. (2004). The principle of the instrument is based on the preferential oxidation of OC and EC, first stage of which includes heating of the sample at a standardized temperature programme (140, 280, 480 and 580°C) in environment of pure helium and then at second stage heating is done at elevated temperatures (580, 740, and 840°C) in 98% helium and 2% oxygen. This process leads to the volatilization of OC and EC in a non-oxidizing and oxidizing helium atmosphere respectively. After the pyrolysis, the correction and charring of OC into EC are primarily done by the optical component of the analyzer (Chow et al. 2004). The procedure was repeated thrice for each filter and blank QFFs were also analyzed by the same procedure. The values were computed (μg cm-2) by – Carbon Net software. Calibration of the instrument was done on daily basis before analysis of the exposed filters with the mixture of 5% CH4 + balance helium gas (for OC/EC peak confirmation). Quartz filters have been widely used to collect PM10 for subsequent determination of carbon content by thermal-optical analysis (Chen et al., 2010; Zhu et al., 2014). However, the absorption of gaseous organics could occur during sampling because the quartz filters have a large surface area, which could lead to over estimation of particulate organic carbon. On the other hand, the volatilization of particulate organic carbon from the filter would result in the under estimation of the particulate organic carbon. To eliminate these uncertainties (artefacts) in OC/EC measurement, the OC/EC levels of blank filters were assessed and subtracted from the samples. Analytical procedure of EC and OC of PM10 has been explained in detail in Sharma et al. (2014b).

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3.3. Analysis and Quantification of organic molecular markers by GC/MS A piece of 1 cm2 filter was stamped out from the exposed filter and cut into strips. These strips were extracted with Dichloromethane (DCM) (HPLC grade) using ultrasonic shaking for fifteen minutes; the extraction protocol was repeated thrice to achieve maximum extraction. The extract was concentrated by using a rotary evaporator at 30-40 °C ℃ under gentle vacuum and filtered through a membrane filter (PVDF 0.45 µm). The filtrate was further reduced up to two ml, approximately, under a gentle nitrogen stream and split into two factions, for analysis of non-polar and polar organic compounds. The fraction for the 5

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polar analysis was derivatized using N, O-bis (trimethylsilyl) trifluoroacetamide (BSTFA): trimethylchlorosilane (TMCS) 99:1 [Supelco 33149-U] to change the polar compounds into their trimethylsilyl (TMS) derivatives for analysis of n-alkanoic acids, Diacids, aromatic acids and sterols. 150 µL of BSTFA was added to each vial and immediately capped. The vials were heated in an oven at 70oC for three hours. The samples were then analyzed by GC/MS within eighteen hours after the derivatization procedure. The Gas Chromatograph Mass Spectrometer (Model GCMSQP2010 Plus, Shimadzu, Japan) system fitted with a fused silica capillary Rxi-5Sil MS (Restek Bellefonte, PA, USA) was used for the analysis. The detailed description of analysis and quantification of organic constituents is discussed in our previous paper (Gupta et al., 2017). The identification of compounds was performed on the basis of comparison of distinct fragmentation spectra and chromatographic retention times of organic molecules with the inbuilt Wiley and NIST mass spectral libraries. Authentic standards were used for quantitative estimation. Recovery of the samples and standards was found to be more than 95%. The analysis of the samples was done in triplicate and the results were reproducible with an error of ± 5 %.

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4. Results and Discussion 4.1 PM10 concentration During winter PM10 concentrations was observed to be 369.7±220.1 µg m-3 whereas in summer the average concentration level was 259.1±133.4 µg m-3. The average concentrations of PM10 for the monsoon and post-monsoon seasons were 147.6±111.9 µg m-3 and 243.4±45.9 µg m-3, respectively. The average concentration was found to be lower in the summer than that of winter (Fig. 2). The prime reason for this difference is seasonal dissimilarities in meteorological factors as well as the different potentiality of sources of PM10 for particular season. Substantial seasonal variations in levels of PM have been reported earlier also (Fu et al., 2009; Singh et al., 2011; Yadav et al., 2013; Pant et al., 2015; Gupta et al., 2017). High PM10 concentrations in winter may be due to higher burning/combustion activities with composed climatic conditions and a low mixing height, which does not allow high degree of atmospheric diffusion. During summer, stronger prevailing winds are favorable for long-range transport and dispersion of particles. A significant rise in the average concentration of particles could be seen in Pre-winter (Fig. 2) due to light festivals in the second half of October (Dussehra) and first half of November (Diwali). During Diwali days, PM levels nearly doubles and organic constituents of PM increases more than two times (Sharma and Dikshit, 2016). The higher concentrations in early summer i.e. Second half of March and the first half of May and June were plausibly due to convection re-suspension of dust by Loo-episodes of dry winds originating from Pakistan and Northwest India (Mishra and Sahibata, 2012). The post-monsoonal Crop Residue Burning (CRB) effect also increases the atmospheric PM of Delhi unexpectedly. The particles emitted from the CRB (practice prevalent in Punjab and Haryana) were transported to Delhi by two major wind patterns; Western and North-western upwind disturbance. The monthly variations were evaluated (Fig. 6

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3) with the maximum concentration in November (615.2±54.6 µg m-3) and minimum in July (115.8±9.0 µg m-3). It could be due to the unexpected weather pattern of 2015; the monsoon season was delayed and originated from South than the usual north-west trend (IMD, 2015). This pattern coupled with the El Nino effect and high humidity compounded to create the record high temperatures. Under the National Ambient Air Quality Monitoring Program (NAMP), various atmospheric pollutants like SOx, NOx, TSP and PM10 have been observed by the Central Pollution Control Board (CPCB) at 342 sampling stations all over India. According to CPCB (2010), the influential sources responsible for high levels of PM 10 (200400 µg m-3) were re-suspension of road dust, automobile exhaust and open burning at Delhi; input of re-suspension of dust towards PM10 levels being 20-40% and that of open burning is 25-50%. During a comprehensive study in Delhi (Sharma and Dikshit, 2016), PM levels were found to be 500 µg m-3 in summer; 600 µg m-3 in winter; 25-80 µg m-3 in monsoon. Several studies have reported ambient PM10 concentration and emission sources in the Indian subcontinent; most of these observed remarkably higher concentrations than NAAQS levels (100 µg m-3), WHO levels (50 µg m-3) and European Union air quality annual PM10 (40 µg m-3) irrespective of site type (Fu et al., 2010; Singh et al., 2011; Giri et al.,2013; Trivedi et al., 2014; Pant et al., 2015; Sharma and Dikshit, 2016; Gupta et al., 2017).

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4.2 Organic Carbon (OC) and Elemental Carbon (EC) The average concentrations of OC in summer (30.4±27.3 µg m-3), monsoon (9.4±4.8 µg m-3) and post-monsoon (34.9±13.5 µg m-3) were lower than that in winter (51.8±34.4 µg m-3). EC has followed the same seasonal trend with maximum levels in winter (16.5±5.0 µg m-3). The scientific studies over different regions of India reported similar seasonal behaviour in OC and EC concentrations (Ram and Sarin, 2011; Sharma et al., 2014b; Jain et al., 2017). Sharma et al (2016) reported higher OC and EC levels in winter than summer at a semi-urban site of Delhi with the annual concentrations 22.7±7.3 µg m-3 for OC and 8.7±4.0 µg m-3 for EC. EC acts as a tracer for Primary Organic Carbon (POC) as it comprise exclusively of primary particles while OC also contain secondary particles. EC is much source-specific and directly emitted from combustion and will not change during the atmospheric transport (Turpin and Huntzicker, 1995). In all OC and EC fractions, high temperature OC3 and EC1 has highest concentrations; which indicates emissions from gasoline vehicular exhaust whereas EC2 and OC1 is associated with diesel exhaust (Pant et al., 2015). The OC/EC ratio shows considerable variations from temporal, seasonal and spatial patterns and therefore, this ratio may be influenced by some prominent factors like meteorology, diurnal and seasonal variabilities in emissions and the impact of regional and local sources. It also depends on sampling and analysis technique opted for determination of OC and EC in PM. The OC/EC value in present study was found to be 4.06±3.1; which was in agreement with the ratio (4.38±2.36) reported by Sharma et al (2014b) in Delhi. Lower OC/EC ratio (1.4-5) can be explained by EC inputs from traffic exhaust (Amato et al., 2014). Larger values (between 5 and 12) of OC/EC ratio are generally observed for biomass burning emissions (Szidat et al., 7

2006). Overall, a positive linear trend (r2 = 0.65) is observed between OC and EC for a traffic hotspot; showing the influence of automobile exhaust emission (Fig. 4). The OC, EC and PM10 concentrations were compared in Fig. 5 and a similar pattern has been observed in monthly variations of OC and PM10 which indicate that organic constituents are major portion of particle mass as they show strong correlation.

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The EC tracer method The EC tracer method determined the amount of SOA formation from OC to EC ratio; which signify the extent of SOA formation in a specific region (Turpin and Huntzicker, 1995). As at the sampling site, primary OC from non-combustion sources are negligible, SOA can be estimated using EC as the tracer for primary OC coming from combustion sources: POC = [OC/EC]p × EC The contribution of secondary OC, [OC]s can be estimated as follows: [OC]s = [OC] – [OC]p

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Where [OC]p is the primary organic carbon concentration. Using the equation, the value of secondary OC is found to be 18.4; which clearly indicate the ageing of aerosols and photochemical formation of secondary aerosols. All of these parameters indicate temporal and meteorological variations in anthropogenic emissions.

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4.3 Organic species in PM10 Organic compounds present in urban aerosols from IGDTUW, Delhi constitute n-alkanes, nalkanoic acids, sterols, anhydrosaccharides, n-alkenoic acid, dicarboxylic acids, PAHs and phthalates. These constituents divulge a specific seasonal trend with higher concentrations in winter than summer. Higher concentrations in winter could be attributed to high humidity, low wind speed, effective decrease of mixing heights and lowering of inversion layer, lead to poor diffusion and diminishing the dilution factors resulting in an increase of atmospheric pollutant concentrations (Oliveira et al., 2007). The gas to particle partitioning of organic compounds is highly temperature dependent. There could be decreased in aerosol phase concentrations of organic compounds due to the fact that high summer temperatures stimulate the conversion of particulate to gas phase (Feng et al., 2006a). In winter, long-range transport from origin of sources to other parts may influence the urban ambient atmosphere. Although the sampling site is a traffic hotspot, but other probable sources in the surroundings of the sampling site include Municipal Solid Waste (MSW) burning, construction activities, soil and road dust and fly ash suspension. A significant contribution from consistent sources was additionally observed during all the seasons like construction and demolition activities. Aerosols from Delhi showed the typical distribution of n-alkanes and n-alkanoic acid in the ranges of C12–C30 with other organic tracers like anhydrosugars, PAHs, dicarboxylic acids (Diacids), resinic acids, sterols, and aromatic acids. The average concentrations of organic molecular markers, of PM, collected at different provinces in India (Chowdhary et al., 2007; Fu et al., 2010; Giri et al., 2013; Yadav et al., 2013; Pant et al., 2015; Gupta et al., 2017), are 8

difficult to relate due to heterogeneity with physio-chemical and spatio-temporal characteristics. Therefore, the characteristics and implications of airborne particulates are highly region specific.

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4.3.1 n-Alkanes Alkanes, the most abundant class of organic molecular markers, show the presence of homologues from C12 to C30 in PM10 samples. n-alkanes represented a clear seasonal trend with higher concentrations in winter (198.0±92.1 ng m-3) than in summer (78.1±30.4 ng m-3), monsoon (77.7±18.3 ng m-3) and post-monsoon (110.4±15.4 ng m-3). Due to semi-volatile properties, the gas to particle partitioning and seasonal variations of n-alkanes is exhibiting a temperature dependency. Tong et al. (1995) reported most of the alkane molecules (about 80%) existed in the form of vapor in the summer, while they remain in the particulate phase during winter. The lower levels during summer were due to lesser combustion activities, high proportion of molecules in the vapor phase and favorable diffusion of atmospheric particles, whereas higher concentrations in winter were due to high combustion activities (wood and biomass burning). Some studies have also reported significant seasonal variations in Delhi at various sites; CPCB (2010) at ISBT, Anand Vihar, observed the average concentration levels of n-alkane to be 38.6±21.1 ng m-3 and 4.9±2.7 ng m-3 in winter and summer, respectively. Yadav et al. (2013) reported the mean annual concentration of total n-alkanes to be 517 ± 256 ng m-3, inspite of having comparatively lower vehicular traffic in the vicinity. On the other hand, Pant et al. (2015) at Okhla Industrial area observed very high concentrations of nalkanes in winter (382 ± 137 ng m-3) than summer (48.1 ± 38.9 ng m-3). Similarly, Gupta et al. (2017) at IGDTUW, Delhi observed high values of the average concentration of n-alkanes in winter (187.4±4.3 ng m-3) than summer (56.3±1.1 ng m-3). From Fig. 6 (monthly concentration profiles of n-alkane), it can be illustrated that n-alkanes were most abundant in December (324.0±43.7 ng m-3), due to low ambient temperature, dew point and RH which facilitate the deposition of particles.

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4.3.2 n-alkanoic acids A homologous series of straight chain n-alkanoic acids was detected in the aerosols (C12-C30). These were the most dominant polar organic species of the PM10. A significant seasonal pattern was observed due to high variation in the concentrations of the n-alkanoic acids. The average concentration in winter was reported to be 185.0±111.6 ng m-3, which was comparatively higher than the concentrations reported in summer, monsoon and postmonsoon, 97.7±45.1 ng m-3, 103.1±19.8 ng m-3, 84.5±43.7 ng m-3, respectively. Their molecular distributions represent maxima at C18, C19 and C23 with bimodal pattern of curve. A similar distribution has been reported earlier also (Kawamura et al., 2003; Fu et al., 2008). The sampling site is a University campus with residential hostel facilities and heavy traffic junction nearby. Thus the emissions of n-alkanoic acids were majorly from cooking activities and vehicular traffic, which have better deposition rate in winter months than summer months (Fig. 7). The predominant n-alkanoic acids in kitchen emissions are C14:0, C16:0, and C18:0. The 9

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lower molecular weight n-alkanoic acids (LWAs < C20:0) are ubiquitous and have multiple sources such as fossil-fuel combustion, emission from vascular plants and organic detritus, microbial activity in soil, kitchen emissions and meat cooking (Oliveira et al., 2007; Fu et al., 2010; Giri et al., 2013). The average of winter/summer concentrations ratio of the n-alkanoic acids was 2-3 in the samples. Similar, seasonal drifts have also been reported worldwide in various studies (Feng et al., 2006a, b; Oliveria et al., 2007; Fu et al., 2010; Giri et al., 2013).

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The ratio of C18:0/C16:0 is a qualitative parameter to assess the contribution of different source profiles and origin of n-alkanoic acid in the ambient air (Oliveria et al., 2007). The average values in the present study were generally between 0.5-1.01 with higher values in winter, showing the emissions from cooking operations and road dust (Rogge et al., 2006; Oliveira et al., 2007). A stronger input of vehicular exhaust from car and diesel has also been reported against some values (0.5-0.7) in post-monsoon and summer. The occurrence of unsaturated fatty acids in aerosol load is suggestive of ageing of the aerosols. In urban surroundings, cooking, vehicular exhaust, and biomass burning could also be the foremost man-made sources of these acids (Rogge et al., 1993a, 1996; Fu et al., 2010). Oleic (C18:1) and linoleic (C18:2) acids were found to be the dominant species in Delhi aerosols; with the average concentrations of 24.5± 9.4 and 8.5± 2.5 ng m-3. Oleic acid may serve as a marker for unsaturated organic matter in the atmosphere and an ideal compound for reviewing aerosol chemical reactivity (Rudich et al., 2007). The average value of the ratio of oleic to stearic acid (C18:1/ C18:0) in the samples was 1.12; indicative of high photochemical oxidation of oleic acid and ageing of aerosols in summer due to strong solar radiations (Oliveria et al., 2007).

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4.3.3 Diagnostic ratios of homologous series of n-alkane and n-alkanoic acid Source apportionment of homologous series of organic compounds in the ambient atmosphere can be done by determination of distinct molecular diagnostic parameters. During the course of time, these diagnostic parameters have been developed and frequently reported in many scientific studies to interpret the source profile of homologous series (Bray and Evans., 1961; Rogge et al., 1993 a; Kavouras et al., 1998; Li et al., 2010; Giri et al., 2013; Yadav et al., 2013; Gupta et al., 2017). They are:

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1) Carbon Preference Index (CPI), 2) Carbon number of the most abundant carbon of homologous series (Cmax), 3) Wax content of homologous series (WNA) CPI is used as a tool to recognize emission sources of organic compound series (Rogge et al., 1993 a, Oliveira et al., 2007). The CPI of n-alkanes was calculated according to Bray and Evans (1961) method. The equation has been given in supplementary information (Eq S1). For plant wax n-alkanes, the CPI shows higher values (in the range of 6-10), while the n10

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alkanes releasing from vehicular exhaust and other man-made activities, have CPI index near to unity (Wu et al, 2005). The average CPI ratio of 1.45, (Table 1) in the present study, is indicating that the major inputs were from the diesel vehicular emissions, fly ash, waste burning, and are also in agreement with earlier studies in other urban areas (Fu et al., 2010; Giri et al., 2013; Yadav et al., 2013). All the CPI ratios indicated a dominant input from manmade activities (fossil fuel ignition, petroleum and diesel exhaust) and a minor contribution from biogenic sources. In Monterrey, Mexico the CPI values, for n-alkanes and n-alkanoic acids, were estimated to be less than 1, which indicates emissions were from natural sources (Mancilla et al., 2016) and these findings are very much similar to the present study, where the average CPI value for n-alkanoic acids is 1.1 (Table 1). In contrast, CPI ≤ 1 is indicative of emissions from anthropogenic sources (Gelencser, 2004).The estimation of the relative dominance of homologous chains of organic compounds, expressed as Cmax (most dominant organic species), provide a better perception of the feasible sources that contribute to high levels of organics in PM10. Cmax, at C23 and C25 serves as a signature for vehicular emissions, whereas Cmax ≥ C27 indicates emissions from biogenic sources such as plant epicuticular waxes (Simoneit, 1989). The Calculated Cmax values (Table 1) are at C23 and C25 for n-alkanes and C23 for n-alkanoic acids, a signature for the organic compounds emitting from vehicular exhaust and road dust, whereas Cmax at C18 and C19 for n-alkanoic acids reflect contributions from microbial sources, which are generally found in urban atmosphere (Fang et al., 1999).The plant wax n-alkanes (WNA-al) and wax content of fatty acids (WNA-FAs) are used to estimate the relative dominance of wax content into the aerosols and could be inferred by WNA ratios. These ratios and the percentage contents were calculated by using the same method as Gupta et al. (2017). The equations are given in the supplementary information (Eq. S2). Higher molecular weight odd n-alkanes show origin from epicuticular surfaces of plants. The percentage content of wax alkanes (%WNA-al) was found to be 1.25 %. Percentage content of petrogenic n-alkanes (PNA-al %) can be calculated as: PNA-al (%) =100- WNA-al (%), which was 98.75 %, indicating dominant inputs from automobile emissions. The fraction of waxy fatty acid (%WNA-FAs) in the total homologous series was 7.1 % (Table 1). The higher wax content of n-alkanoic acids was probably due to the wind abrasion of dead leaves. The amount of wax for n-alkanoic acid is five times higher in dead leaves than in green leaves (Rogge et al., 1991).

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4.3.4 Anhydrosugars Among all the Anhydrosugars reported in the atmospheric particulate samples, the monosaccharide Levoglucosan (LG) (1, 6-anhydro-β-D-glucopyranose), derived from dglucose units, was the most abundant. These anhydrosugars are emitted as the products formed during pyrolysis of cellulose and hemicelluloses (Urban et al., 2014). LG is a key tracer for smoke particulates coming from biomass burning and only forms at temperature above 300oC (Wang et al., 2005; Urban et al., 2014). The average concentrations of LG were reported to be 347.3±24.4 ng m-3 and seasonal concentrations were 113.83±37.9 ng m-3 in 11

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winter; 97.2±81.5 ng m-3 in summer (Fig. 8). LG is a very stable compound in ambient air and shows longer half-life during long-range transport (Giri et al., 2013). On the other hand, some of the recent scientific studies revealed that LG undergoes oxidation in the presence of HO., NO3, SO3 (Hoffmann et al., 2010) and these reactions are lower in the winter than the summer season. Thus the higher concentrations of LG are expected during winter (Giri et al., 2013). The winter/summer ratio observed was 1.17. Biomass burning seems to be an important source of the sugars in atmospheric PM at the site under study. But Anhydrosugars production during biomass burning depends on several factors such as the flame temperature, the process duration, the water content, and the quantity of cellulose and hemicelluloses in the vegetation (Simoneit, 2002; Urban et al., 2014). Sugars and sugar alcohols are also emitted from the developing leaves (Fu et al., 2010). This may be the reason for higher values of LG during Monsoon and post-monsoon seasons, i.e., 56.9±54.5 ng m-3 and 79.4±11.4 ng m-3, respectively (Fig. 8). In India, the huge demand of biomass is due to easy availability and low price, in comparison to other fuels. The types of biomass used in India are cow dung, wood, and crop residues (Gadi et al., 2003). Cow dung cakes burning is more prominent than other biomass burning, in rural and urban regions among all social classes of India (Leach, 1987). Mannosan (1, 6-anhydro-β-D-mannopyranose) and Galactosan (1, 6-anhydro-β-Dgalactopyranose) were also reported as other biomass burning markers. The burning of hemicelluloses may generate these anhydrosaccharides (Graham et al., 2002). Their concentrations were much lower as compared to LG. The average concentrations for Mannosan and Galactosan were 37.6±8.3 ng m-3 and 5.2±4.7 ng m-3, respectively. The LG/Mannosan ratio may serve as a signature for types of wood or vegetation contributing to particle emissions. The LG/Mannosan ratio was found to be 25.9 in the present study whereas Pant et al (2015) reported the ratio of 16.07 in Okhla Industrial Area, Delhi. The LG/Mannosan ratio ranging from 19 to 26 seem to be emitted from hardwood smoke (Fine et al., 2004b; Oliveria et al., 2007).

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4.3.5 Polycyclic Aromatic Hydrocarbons (PAHs) PAHs are a class of semi-volatile organic contaminants, formed due to incomplete combustion of fossil fuels and identified as being carcinogenic in humans (Singh et al., 2011). PAHs are organic tracers for traffic hotspot (both diesel and gasoline vehicles), solid fuel combustion, coal combustion, wood combustion, industrial effluents and agricultural burning (Singh et al., 2011; Pant et al., 2015). The total average concentration of PAHs in atmospheric particulates was 288.0±19.4 ng m-3 with lower values in summer than winter. Similar seasonal trends were reported by Sharma and Dikshit (2016) in Delhi, with average concentration of 133 ng m-3 in winter and 18 ng m-3 in summer. Tsai et al. (1995) indicated winter/summer ratios to be significant for seasonal trends. The temporal variation has great influence on the partioning of the gas and particle-phases of PAHs. Pant et al (2015), observed a winter/summer ratio of 15.3 while the ratio observed in our study was 8.02. Singh et al, (2011) reported it to be 2.47 in PM10 in Delhi, while Guo et al (2003) found ratios in 12

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accordance with to the present study, which were 8.6 and 7.5 at two different sites of Hongkong. The lower concentrations in summer are due to higher temperature and wind speed, higher inversion heights and enhanced photodecomposition of PAHs, which further results in increased dispersion of particles with a decrease in overall concentrations. In monsoon, they are washed away by precipitation. Being a traffic hotspot, atmospheric particulate aerosols were loaded by low molecular weight (LMW) PAHs at the site. The dominance by most of the 4 ring PAHs is typical of freshly emitted smoke (Giri et al., 2013). Singh et al. (2011) observed dominance of 4-6 ring PAHs (Indeno (1,2,3,-cd) pyrene (IcdP), Benzo (ghi) perylene (BghiP), Benzo (k) Fluoranthene (BkF), Benzo (b) Fluoranthene BbF, Benzo(a) pyrene (BaP), Chrysene (Chy), Anthracene (Anth) and Pyrene (Pyr) in winter samples of PM10, due to diesel exhaust combustion. Pant et al, (2015) also reported LMW PAHs in their study, indicating the sources to be from traffic emmissions. Reportedly, Anth, methyl- and dimethyl-phenentharenes (Me-phen) are emitted from High Duty Vehicles (HDVs), while species such as Fluorene (Flu), Benzo (b) naphtho(1,2-d) thiophene (BN12T), Phenanthrene (Phe) have been used as markers for diesel exhaust emissions (Rogge et al., 1993; Cecinato et al., 1999; Singh et al., 2011). Cecinato et al., (1999) observed BghiP, IcdP, Pyr were dominant in diesel exhaust emissions, but some studies found BghiP, IcdP and Corene(Cor) as markers for gasoline emissions form traffic (Pant & Harrison., 2013). The 3-6 ring PAHs like Acenapthylene (Acy), Anth, Phe, Pyr, Benzo(e) pyrene (BeP), (BaP), Retene (Ret) are considered to be tracers for wood combustion for residential heating (Singh et al., 2011; Jang et al., 2013; Pant et al., 2015). Species such as Pyr and Chy have been found associated with domestic fuel emissions and coal combustion and BghiP and IcdP with traffic exhaust (Kulkarni and Venkataraman., 2000; Sharma et al., 2007). In Fig. 9, the higher concentration of BghiP indicates very high emissions from industrial as well as automobile exhausts (gasoline traffic emission). Since the site is in the close vicinity to the Inter-state Bus Terminus (ISBT, Kashmere Gate), thus higher concentrations are expected due to wind transport of pollutants. The other sources of BghiP include industrial effluents, municipal waste treatment and aluminium smelting. Previous studies reported Phe as a marker of diesel vehicular exhausts. Comparatively higher concentrations of Phe and Acnp in present study (Fig. 9) is probably due to emissions from heavy duty diesel vehicles like buses,trucks etc.

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4.3.6 Diagnostic ratios for PAHs source profiling The diagnostic ratios could be used to specify the source of origin of PAHs. These organic markersindicate the point sources like gasoline or diesel automobilesas well as relative input from area sources like traffic, residential heating, and wood combustion. The diagnostic ratios for PAHs were calculated and tabulated in Table 2.The diagnostic ratio Flt/ Flt+Pyr was indicative of automobile emissions and B(a)P/ B(a)P+Chy and B(a)P/ B(ghi)P were typical of gasoline emissions, but diagnostic ratio of Phe/ Phe+Anth was suggestive of emissions from biomass burning, especially in winter. This suggests that there were mixed 13

sources for emissions from fossil fuel combustion and biomass burning in the atmospheric PM10 of Delhi.

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4.3.7 Dicarboxylic Acids (diacids) Average concentrations of Dicarboxylic acids (C2-C10) measured inthe PM10 aerosols are presented in Fig. 10. Diacids are secondary products of photo-decomposition of unsaturated FAs, semi-volatile and volatile organic compounds (SVOCs and VOCs) of biogenic and anthropogenic origin, in ambient air, but can also be released from primary sources like biomass burning, traffic exhaust, solid waste combustion and kitchen waste (Wang et al., 2005; Oliveria et al., 2007; Tsai and Kuo., 2013). The molecular composition of diacids serves as a potential marker for origin of source and strength of organic compounds. A previous study using TD-GC/MS technique reported Oxalic acid (C2) as most abundantly present diacid, in the range of C2-C10 in Delhi (Pant et al., 2015). In the present study, Oxalic acid was not found because of the volatility of the trimethylsilyl ester, formed by derivatization or it could be depleted by selective degradation from the atmosphere near ground level (Sempere and Kawamura.,1994). Similar obeservations were reported earlier also, during the use of the derivatizaion technique with BSFTA (Wang et al., 2005). Azelaic acid (C9) was most dominant diacid followed by Benzenedicarboxylic acid/Phthalic acid (Ph) and adipic acid (C6). C9,emitted from various sources in ambient air like biomass burning, and specifically produced from the oxidation reaction of oleic (C18:1) acid having a double bond at a particular (C-9) position (Kawamura et al., 2013; Deshmukh et al., 2016), has been reported to be released from terrestrial plant leaves surfaces (Agarwal et al., 2010; Wang et al., 2012). High concentrations of C9 indicate an enhanced discharge of its precursors during biomass burning and terrestrial vegetation and subsequent oxidation reactions to C9 in the atmosphere of Delhi. Ph is the second most dominant diacid reported in the present study. Primary sources of Ph include emissions from anthropogenic activities like coal combustion, motor vehicle exhaust and secondary sources to be photodegradation of naphthalene and other PAHs (Fine et al., 2004a). It is a noteworthy to mention that Ph and naphthalene both exist in gaseous phase (Schauer et al.,1996). A broad and large peak of Ph may be due to adsorption of Ph on the outer surface of alkaline dust particles in PM10 (Deshmukh et al., 2016). According to Oliveira et al. (2007), the emissions of Ph is not much significant from primary sources and has been proposed as a Secondary Organic Aerosol (SOA) that might show the contribution in the ambient atmosphere. 4.3.8 Diagnostic Mass Ratios of Diacids Table 3 represents the diagnostic mass ratios calculated from the average concentrations of diacids present in Delhi aerosols. The C3/C4 ratio may serve as a diagnostic parameter to assess the rate of photodegradation reactions of organic aerosols as C4 tends to photodecompose into C3. Average C3/C4 ratios in the present study was 0.7. The values were higher than those reported in Raipur aerosols (0.12-0.45) by Deshmukh et al.(2016), but 14

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lower than those at Madras (1.5, Pavuluri et al., 2010). Other countries like China (Wu et al., 2015) and Hongkong (Ho et al., 2006) reported higher values of the C3/C4 ratio (1.1-1.3), indicating higher rates of photochemical transformations of ambient aerosols. Lower C 3/C4 values are typical of freshly produced aerosols from fossil fuel combustion (0.35, Kawamura and Kaplan., 1985), from biomass combustion (0.51, Cong et al., 2015) and from biofuel combustion (0.12-0.45, Deshmukh et al., 2016). The lower ratios, in the present study, suggest the presence of freshly emitted smoke from vehicular exhaust and biomass burning without significant photodegradation. The average values for Ph/C9 ratio was 0.9, which is lower than the values reported in Madras (Pavuluri et al., 2010) and higher than Raipur aerosols (Deshmukh et al., 2016). C3/C4, Ph/C9 and C6/C9 ratios are very significant in evaluation of anthropogenic vs. biogenic contributions of sources of aerosols. C6 and Ph are produced through the photooxidation of cyclohexene and PAHs (Deshmukh et al., 2016) whereas C9 is the degradation product of biogenic unsaturated FAs (Tedetti et al., 2007). The average C6/C9 ratio was 0.9, which was higher than that observed at Raipur (0.10-0.26, Deshmukh et al., 2016). The comparison suggests that ambient aerosols, at the study site, are influenced by mixed biogenic and anthropogenic activities, whereas at Raipur major sources of organic acids were biogenic in origin.

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4.3.9 Resin acids/Terpenoids Resin acids are natural products emitted from softwood smoke (pine and conifers). The representative resinic acid present in aerosol load were, dehydroabietic acid and pimaric acid. The average concentrations of dehydroabietic andpimaric acid were found to be 7.5±2.9 ng m-3 and 7.7±6.1 ng m-3, respectively, in early winter but absent in summer and monsoon samples. This lack of deposition of resinic acids in all seasons except winter was indicative of diverse seasonal atmospheric circulation. Besides softwood combustion, an supplementary source of dehydroabeitic acid at traffic hotspot could be wear and tear of tyres as pine tars are used as additives during manufacturing in tyres. The ratio of dehydroabietic to pimaric acid showed an average value of 0.99 in early winter, which is suggestive of biomass burning (Oros and Simoneit., 2001) but their presence could be related to the wear and tear of automobile parts. Wang et al. (2006) reported a strong correlation between the concentration of β-sitosterol and dehydroabietic acid due to their common sources.

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4.3.10 Sterols Sterols were present as trace components in almost all the samples. The Presence of cholesterol in the PM10 samples could be attributed to the smoke particles released during meat cooking (Rogge et al., 1991). Stigmasterol and β-sitosterol, which are known as phytosterol, were also present in the PM10 samples. Stigmasterol is a tracer used to ascertain cow dung cakes burning (Sheesly et al., 2003; Fu et al., 2010) and β-sitosterol, and stigmasterol is present in terrestrial plant leaves and released to the atmosphere during biomass burning (Simoneit., 2002). The total average concentration of sterols was 24.6±0.6 15

ng m-3. Ergesterol is a directly emitted mycosterol, released into the atmosphere from microorganisms. Some of the samples showed presence of this organic species.

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4.3.11 Aromatic acids Aromatic acids can enhance the formation of new particles and cloud condensation nuclei (CCN) (Zhang et al., 2004). The average concentration of aromatic acids was 19.6±1.8 ng m3 . Benzoic acid is reported as a primary organic contaminant in the exhaust of automobiles (Rogge et al., 1993c; Oliveria et al., 2007; Fu et al., 2010) but some studies also observed its production as a secondary pollutant from photochemical oxidation of aromatic hydrocarbons released from automobile vehicles (Suh et al., 2003). Inspite of having a constant vehicular circulation during all the seasons, benzoic acid is scarcely present in summer samples. The average concentration in early winter (11.6±6.5 ng m-3) was higher than monsoon (6.6±0.4 ng m-3), probably due to more stagnant atmospheric conditions and low inversion layer in colder season. Secondary production of Phthalic acid, as explained above in the text, involved photodegradation of napthalene and other PAHs (Fine et al., 2004a).

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5. Principal Component Analysis (PCA) PCA is a valuable multivariate statistical tool, which reduces the dimensionality of large data sets; that is the extracted number of principal components, needed to explain all the variance of data sets, which are very less than original number of variables. By systematic investigations of the correlation between all variables, PCA extract new variables, called principal components that have most of the information about the data. The same significance is attributed to each variable and the same weight to each subject. The very first component extracted, explain maximum amount of data variance. Each successive component will further explain the maximum amount of remaining unexplained data variance. This creates an orthogonal distribution of components to each other and this kind of regression adjustment of the data to the factors is simple and always stable, no matter how much large data set and many variables are included in the study (Chan & Mozurkewich, 2007). In PCA, following dimensionless standardized equation has been derived from the chemical data: Zij =

̅ Cij − C j

(1)

σj

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where i = 1, 2, 3 …, n samples; j = 1, 2, 3, …m components; Cij is the concentration of component j in sample i; and Cj and σj are the arithmetic mean concentration and the standard deviation for component j, respectively. Source profiles and their respective contributions are then quantified on the basis of principal components loadings of PCA / Absolute Principal Component (APC) method (Thurston and Spengler 1985). Expressions and equations related to absolute principal component score (APCS) have been given in supplementary information (Eq S3 & S4). 16

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In this study, orthogonal distribution with Varimax rotation of PCA/APCS using software package of SPSS (IBM, version 24.0), was performed to categorize the possible sources of organic markers in atmospheric PM of Delhi aerosols. Each of the component or factors extracted by PCA can be identified as emission sources to assess the source profiles of organic markers. APCA extracted five factors of PM10 which explained 68.35% of the data variance (Table S2; in supplementary information). The first factor explains 23.64% of data variance and was dominated by C16-C30 n-alkanes, n-alkanoic acids and high concentration of ACNP and CHY with all PAHs (Fig. 11). Presence of lower n-alkanes and LMW PAHs indicate traffic exhaust from HDVs and diesel cars (Pindado and Perez, 2011; Singh et al., 2011; Choi et al., 2015). These findings are also supported by diagnostic ratios of PAHs (Table 2). The second factor showed association with mixed sources with 19.14% of data variance (Fig. 11). This factor showed dominance of many organic species like higher alkanes (C22-C29), LG and lower acids which point out biomass burning and cooking emissions. But on the other side, the high loadings of LMW PAHs, diacids, oleic acids and Benzopyran-6-one indicate mixed exhaust from biomass, cooking, traffic and secondary organic aerosols. Third factor was rich in higher n-alkanes (C24-C30) and higher n-alkanoic acids (C20-C23) with 12.23% variance. As above mention, the sampling site is a University campus with residential hostel facilities and heavy traffic junction nearby. Thus the emissions were majorly from cooking activities and fossil fuel combustion. Fourth factor represent soil dust and related factors associated with regional as well as long-range transport. It explained 8.12% data variance with presence of OC, EC, n-alkanes, n-alkanoic acids, PAHs, Diacids, phthalates in mid-range concentrations while anhydrosugars were comparatively higher. The high levels of anhydrosugars may be due to their stability and higher life span during longrange transport (Giri et al., 2013). According to Mishra and Sahibata (2012), OC, EC and all the organic constituents of PM increases in their concentrations due to convection resuspension of dust by dry winds originating from Northwest India and Pakistan. Along with this event, the particles emitted from the CRB (practice prevalent in Punjab and Haryana), transported to Delhi by Western and North-western upwind disturbance also raises the PM levels in the city. On the other hand, PAHs and n-alkanes mark the presence of road-dust. Hence, the factor has evidence for combination of road +soil dust. Fifth factor is clearly dominated by very high concentrations of BghiP with total PAHs signifying the presence of traffic exhaust and industrial emissions, municipal waste water treatment facilities and waste incinerators (Irwin, 1997). BghiP biodegrades slowly in the environment (half-life ranges from 600 to 650 days). This component explained 5.21% of variance for the data set. The factor loading for each species has been presented in Table S3. 6. Positive Matrix Factorization (PMF) PMF is a multivariate chemical receptor model based on the concept of factor analysis; which was developed by Paatero at the University of Helsinki, Finland (Paatero, 1997, 1999). The 17

speciated sample data set assume to be influenced by linear combinations of origin of sources emissions, which is distributed as factor contributions by PMF. There is sources (p) affecting a receptor and linear combinations of contribution from these sources give rise to a data matrix X of number of samples (i) by concentration of chemical species (j). By using constraints and the associated adjusted uncertainty, the PMF model minimizes the Q function, which can be written as: p ij−∑ 𝑔 𝑓 k=1 𝑖𝑘 𝑘𝑗

sij

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Where, Xij are the measured organic species concentration, sij are the estimated uncertainty. The EPA PMF 5.0 version has been employed according to the EPA 5.0 user guide, 2014. In this model, more than 50 organic species of PM10 samples has been used in data set as input for the PMF. The signal to noise ratio (S/N ratio) and determination coefficient (r2) of the variables were calculated by a base run of PMF, where S/N ≥ 2 were accepted as strong organic species in data. Organic markers with weak data quality (S/N= 0.2- 2) are implausible to provide source information (Paatero and Hopke, 2003) and bad values are omitted from the analysis (S/N ratio of species are below 0.2). In present study, S/N ratios of organic markers were found to be as high as > 3. Most of the species were well correlated, except for some organic markers like Fluo, 2-Aza Flu, Nap-2-one, and some acids also had r2 values less than 0.6. For extracting factor contributions, 100 runs were performed with boot strapping at a minimum correlation value of 0.6 (with Fpeak 0.1). The most important step in PMF receptor modeling is the identification of accurate number of factors. It is always done on the basis of goodness of fit of the model. Q values were used to obtain quality for the fit for the chemical species and to extract factor solution. The value of Q robust is found to be identical to the value of Q true; showing stable base run and the results were unaffected by any specific event. A seven factors solution was extracted for organic species of PM samples by PMF model and factor contributions have been presented in Fig. 12 and Table S3 (in supplementary information). Also, it is well known that the smallest Q values should achieve the minimum value and thus confirms the fitness of each data point as well as outliers for each random run. At the 7% of the uncertainty error constant, most of the Q values fitted for seven-factor solution, indicating the most sustainable results. The source apportionment of inorganic species in PM10 of Delhi aerosol, using the PMF model, has been discussed in detail in Sharma et al. (2014b). The PMF resolved the following sources: Factor 1: The first factor is characterized by presence of OC, high concentration of lower alkanes (C15-C18), Chrysene and B(ghi) P, directly indicating emission from diesel vehicles (Pindado and Perez, 2011; Wagener et al., 2012) and traffic exhaust. This factor contributes 29.3% to the total factor contributions. Sharma et al (2014b) shows 17% contribution towards vehicular exhaust by PMF analysis. Vehicular exhaust is a major source of PM in the Delhi aerosols (Sharma and Dikshit, 2016). 18

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Factor 2: Second factor contribute 8.4% of PM and the higher concentrations of LG indicate the contributions of biomass burning. LG is a key tracer for smoke particulates coming from biomass burning (Wang et al., 2005; Urban et al., 2014) and it is very stable compound in ambient air even during long-range transport (Giri et al., 2013). Very high percentage of LG indicates stability of the organic marker in the atmosphere. n-alkanoic acids (C16, C18 and C19) and unsaturated acid like oleic acid have primary sources such as cooking. This factor also explains high percentage of diacids and phthalates. These compounds are components of secondary organic aerosols. Diacids are the secondary products of photo-decomposition of unsaturated FAs and VOCs of anthropogenic origin but can also be released from primary sources such as automobile exhaust, biomass burning, solid waste combustion and kitchen waste (Wang et al., 2005; Tsai and Kuo., 2013). But phthalates can easily be leached during manufacturing, storage and usage of plastic materials (Ji et al., 2014). Therefore it can be concluded that this factor represents the secondary organic aerosol along with mixed sources (biomass burning + cooking exhaust + secondary organic aerosol).

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Factor 3: The third factor resolved 8.6% of factor contribution which is from PAHs. This factor includes 50-70% of most of the PAHs considered by the model. PAHs solely are related to incomplete combustion of fossil fuel and indicate anthropogenic emissions from various industries near to sampling site. The same factor has been found and explained as PAHs factor by Pindado and Perez (2011) during PMF analysis in ambient air of Spain aerosol.

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Factor 4: This factor revealed factor contribution of 26.3% due to higher n-alkanes (C24-C30); known as n-alkane factor, showing relation to combustion processes (Pindado and Perez, 2011). Presence of EC, higher alkanes and HMWs PAHs indicate contribution due to motor exhaust.

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Factor 5: The fifth factor was dominated by lower aliphatic alkanes (C18-C23) and n-alkanoic acids. The dominant species among n-alkanoic acids was stearic acid (C18); the sources may be from seed oils and meat cooking (Dutton et al., 2010). These emissions were coming from kitchen emission from nearby food junctions and the hostel kitchen in the University campus.

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Factor 6: 50-80% of C26-C28 aliphatic hydrocarbons were apportioned to this factor. Higher n-alkanes (especially odd numbers C27, C29 and C31) are related to vegetative detritus or biogenic in nature (Rogge et al., 1993; Choi et al., 2015). Presence of galactosan also indicates some biogenic sources. The contribution of this factor towards organic constituents was 13.4% by PMF analysis.

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Factor 7: 20-50% of LMW PAHs were apportioned by this factor. Palmitic, stearic and oleic acids were also explained by the factor, indicating mixed sources of diesel exhaust and cooking exhaust. Diacids were also apportioned by this source. This is definitely a mixed type of source which assigned emissions from diesel vehicles and cooking. This factor contributes 13.5% towards total source apportionment.

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7. Model Comparison Receptor modeling is a multivariate statistical tool used for the identification and apportionment of sources of atmospheric pollutants (Hopke et al., 2006). Pant and Harrison (2013) explained two broad categories of receptor models i.e. microscopic and chemical. Chemical models include various methods such as enrichment factor analysis, CMB analysis, multivariate factor analysis [(PCA) (PMF)], UNMIX, and Multilinear Engine (ME) analysis (Hopke, 1991; Ramadan et al., 2003). The prime requisite is the chemical composition of trace elements, EC/OC and organic markers of atmospheric particles for identification and apportionment of sources of atmospheric PM. Usually, most of the atmospheric studies were performed by using inorganic tracers like Fe, Zn, Pb, Cr, Al and Ni (Chowdhary et al., 2007; Srivastava et al., 2008; Tiwari et al., 2009; 2013; CPCB, 2010; Sharma et al., 2014; Jain et al., 2017) but for organic species, there are very limited studies. From the last two decades, scientists have been focusing on the development of receptor models for source apportionment of organic molecular markers since they are signature of such sources, which are otherwise challenging to be apportioned exclusively on the basis of source apportionment of inorganic tracers, e.g., secondary organic aerosols, vehicular exhaust, biomass burning (Schauer et al., 1996). A high degree of accuracy and confidence can be established by developing an overall approach towards source profiles of both constituents (inorganic and organic).Every receptor model recognizes sources individually, mainly on the basis of input data of the species selected as variables. The present study aims to investigate PM10 data set using two different receptor modeling techniques i.e. PCA and PMF models. Both extracted almost similar sources But PCA extracted only five principal components whereas, PMF showed the contributions from seven factors. The PMF identified vehicular exhaust, biomass burning + cooking emissions + secondary organic aerosol, PAHs factor, n-alkane factor/combustion factor, vegetative detritus and diesel exhaust + cooking exhaust as major sources at sampling site, whereas PCA apportioned vehicular exhaust, biomass burning + cooking emissions + secondary organic aerosol, kitchen exhaust, road + soil dust and industrial emissions as principal factors. The percentage contribution of each source quantified by PCA and PMF were very different. PCA explained the data set with certain level of mixing of sources. The traffic contributions found with PCA (23.64%) were less than that of PMF (29.3%) which may be due to the striking difference of operating methods of PCA and PMF. The latter uses non-negativity of factors loading and score factors and uncertainties of individual data point (Paatero, 1997; Jain, 2017). PMF was able to extract diesel emissions as a separate factor from other traffic exhaust due to the advantage over PCA. PMF justifies all missing and below detection limit values with better analysis of 20

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individual data points. Thus, in PMF, the source profiles as output can directly be matched to the input data matrix without alteration, but the major requisite here is, a large input data set of ambient samples with allocation of weighing factors. It normalizes the data set on the basis of statistical association, which may not be accurate and effective for all data sets. By the overall evaluation of models, it could be inferred that PCA underestimated the contribution of some sources or chemical species. The major disadvantage of PCA is the lower specificity i.e. assigning the single marker for numerous sources or availability of specific organic/inorganic tracers (Belis et al. 2013), but error on the reconstructed concentration matrix could be minimized when the APCS is applied to the data (Cesari et al., 2016). Overall, PMF was a better factor analytical technique than PCA for source apportionment of ambient concentrations of PM10 data set. But PCA could be a powerful and knowledgeable statistical tool when the data has its own limitation to run PMF.

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The average concentrations of OC for PM10 were reported as 41.1±31.9 µg m-3 whereas average concentration of EC was 9.9±5.9 µg m-3. Strong seasonal variations were observed. The lower levels during summer were favored due to negligible combustion activities, high proportion of molecules in the vapor phase and appreciable atmospheric diffusion of particles. However, the higher concentrations in winter were related to high combustion activities (wood and biomass burning) and residential heating. Source apportionment was carried out on the basis of various molecular diagnostic parameters, PCA and PMF models. All the parameters of source apportionment indicated that dominant inputs at the site were from traffic exhaust and less contributions from other anthropogenic activities. Some diagnostic ratios and factors extracted were indicative of mixed type of emissions (from automobiles, soil dust, cooking and biomass burning). PCA extracted five principal components for PM10 (vehicular exhaust, biomass burning + cooking emissions + secondary organic aerosol kitchen exhaust, road + soil dust and industrial emissions ), while PMF extracted seven factors (vehicular exhaust, biomass burning + cooking emissions + secondary organic aerosol, PAHs factor, nalkane factor/combustion factor, vegetative detritus and diesel exhaust + cooking exhaust). The PCA and PMF modeling using organic markers was able to provide more extensive data about the sources of organic aerosols such as traffic exhaust,

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During the study, organic markers associated with PM10 were assessed and quantified to determine the compositions, characteristics, seasonal patterns and source profiles of organic aerosols at a traffic hotspot from January 2015 to December 2015. The major findings are as follows:

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biomass burning, secondary organic aerosol, combustion (PAHs) factors, and vegetative detritus, which were not identified by traditional PMF analysis using elements (ion and metal species). This study will help to design effective control and mitigation strategies to improve ambient air quality by providing information about impact of regional and local sources and their relative contribution.

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The authors are indebted to Prof. Anil K. Tyagi Vice Chancellor, GGSIP University, Delhi and Prof. Nupur Prakash, Vice Chancellor, IGDTUW University, Delhi for their constant inspiration and support. The authors also thankful to Dr. Ajai Kumar, for a great help in analyzing the samples on GC/MS facilities at Advanced Instrumentation Research Facility (AIRF), Jawaharlal Nehru University, Delhi.. The authors also wish to express their sincere gratitude and warm appreciations to Dr. Robin Singh, Indian Pharmacopeia Commission and his team for helping with instrumentation facilities.

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Delhi, the National Capitol Territory, is surrounded by four different climatic zones. The Sampling site, IGDTUW campus, is located in the Northern part of Delhi.

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N

U

SC R

(b) Factor-2

CC E

PT

ED

M

(c) Factor-3

A

(d) Factor-4

36

IP T

(e) Factor-5

M

A

N

U

SC R

(f) Factor-6

CC E

PT

ED

(g) Factor-7

A

Fig 12 Factor profiles of organic species apportioned by PMF

37

Table 1 Summary of diagnostic parameters for n-alkanes and n-alkanoic acids homologues series n-alkanoic acid

1.45

1.1

Cmax

C23 , C25

C18, C19 and C23

WNA (%)

1.15 %

7.1 %.

SC R

CPI

IP T

n-alkanes

N

U

Table 2 Comparison of Diagnostic ratios of PAHs with those reported in literature

Values Singh et from al.,2011 literature

B(a)P/B(a)P+Chy

0.85

0.6

B(a)P/B(ghi)P

0.17

Flt/(Flt+Pyr)

0.45

0.57

0.17-0.4

Gasoline emissions

Caricchia et al., 1999

0.27

0.4-0.7

Automobile emissions

Ravindra et al., 2008

Biomass Burning

Kavouras et al., 1999

ED

M

0.59

0.3

≤ 0.7

A

CC E

Phe/(Phe+Anth)

References

0.73

Point Sources Gasoline emissions

PT

Diagnostic ratios

A

Present Study

38

Guo et al., 2003

Table 3 Comparison of Diagnostic mass ratios of Diacids at various sites Site type

Mass Ratios of diacids

References

C6/C9

Ph/C9

IGDTUW, Delhi

0.6±0.1

0.9±0.7

0.9±0.4

Present study

Raipur, India

0.12-0.45

0.10-0.26

0.69-0.98

Deshmukh et al., 2016

North Pacific Ocean

0.6±0.2 (w)*

2.4±0.7 (w)

10.5±9.7 (w)

Bikkina et al., 2015

1.1±0.3 (s)**

3.6±1.1 (s)

2.8±1.6 (s)

1.5

-

1.0-2.0

*

U

w=winter s = summer

A

CC E

PT

ED

M

A

N

**

SC R

Chennai, India

39

IP T

C3/C4`

Pavuluri et al., 2010