Seasonal Dynamics of Stable Carbon and Nitrogen Isotope Ratio in PM10 Aerosols at a Coastal Urban Site in Mumbai, India

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B. Yadav, Vandana A. Pulhani, A. Vinod Kumar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6194800/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Particulate matter (PM) is a major air pollutant that poses significant risks to human health and the environment, particularly in urban areas with high concentrations of PM. In this study, stable isotopes of carbon (δ 13 C) and nitrogen (δ 15 N), the total carbon to total nitrogen ratio (TC/TN) were estimated in particulate patter (PM 10 ) collected during the year 2022 from Trombay area, a coastal site in Mumbai, India. The results were analysed to identify the possible sources and fate of PM and understand the coastal effects on PM in the sampling area. The results showed that δ 13 C values ranged from − 26.2‰ to -22.4‰, while δ 15 N values ranged from − 2.3‰ to 21.4‰ during the study period. The average values of δ 13 C and δ 15 N was − 24.9 ± 0.9‰ and 9.1 ± 5.6‰, respectively. The TC/TN ratio ranged from 2.8 to 7.5, with an average of 4.8. The TN and TC concentrations varied from 0.1 µg.m − 3 to 2.3 µg.m − 3 and from 0.4 µg.m − 3 to 9.1 µg.m − 3 , respectively. The δ 13 C and δ 15 N values observed indicated fossil fuels and biomass burning, to be the dominant sources of the aerosol. The relative contribution of different sources (vehicular, biomass, coal, marine origin, and continental dust) showed seasonal variations. Systematic change in δ 13 C and δ 15 N values of aerosols from winter to pre-monsoon to monsoon period is noteworthy as it matches with systematically increasing influence of marine winds over the study area. The correlation between parameters reveals the formation of secondary organic and inorganic aerosols and long-range transport history of the aerosols. Particulate matter PM10 δ13C δ15N TC/TN ratio source identification Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1 Introduction Particulate matter (PM), a complex mixture of solid and liquid particles suspended in the air, has been a growing concern for its impact on human health (Brook et al., 2010 ; Carroll & Risch, 1984 ; Gurjar et al., 2008 ; Guttikunda & Gurjar, 2012 ; Kelly & Fussell, 2015 ; Pope & Dockery, 2012 ), as well as climate and hydrological cycle (Ramanathan et al., 2001 ). PM 10 , is a major air pollutant, which includes particles with an aerodynamic diameter of 10 micrometers or less and is known to contain a variety of toxic substances such as heavy metals (Popoola et al., 2018 ), organic compounds (Aggarwal et al., 2013 ; Fazakas et al., 2023 ) and microorganisms (Zhai et al., 2018 ). Many of the Indian city have observed high level of the particulate matter (Pant et al., 2019 ; Satsangi et al., 2011 ; Sharma et al., 2022 ; Vikramahirwar & Bajpai, 2017 ) and Mumbai is no exception to this, with high levels of PM 10 being recorded at various locations in the city (Joseph et al., 2003 ; P. Mangaraj et al., 2024 ). Understanding the sources and transport of atmospheric dust is critical for predicting and mitigating its effects on environmental and human health. Among the various methods used, the stable isotope ratio of carbon and nitrogen (δ 13 C and δ 15 N) are useful tracers for understanding the sources and fate of particulate matter in the atmosphere. The stable carbon and nitrogen isotopic composition of aerosols has advantage compared to chemical tracers, to find the information about the source and aging effect of the aerosols (Bosch et al., 2014 ; Morera-Gómez et al., 2018 ; Sawlani et al., 2019 ; Sen et al., 2018 ) as later are non-conservative and their ratio may get affected during the long-range transport. Air mass back trajectory analysis (Petit et al., 2017 ) provides the geographical location of the potential sources which is adding the particulate loading at the receptor site. However, trajectory analysis does not provide the information regarding the particulate source (e.g., biofuel, fossil fuel, mineral dust etc.) (Sen et al., 2018 ). Stable isotope ratio of carbon (δ 13 C) was first used to identify the urban atmospheric particulate matter source by Court et al., ( 1981 ) and later the carbon emission by biomass burning using δ 13 C along with black carbon to total carbon ratio (Cachier et al., 1985 ); identifying and semi-quantifying the road traffic and industrial particulate matter using isotopes of carbon and lead in Paris city by Widory et al., ( 2004 ). The use of δ 15 N in atmospheric aerosol was initiated by Moore H. ( 1977 ) and subsequently adopted in various studies to find the sources of the particulate matter in the atmosphere (Heaton et al., 1997 ; Russell et al., 1998 ; Widory, 2007 ; Yeatman et al., 2001 ). Thereafter, many studies were carried out using carbon and nitrogen stable isotope ratio (δ 13 C and δ 15 N) of total carbon (TC) and total nitrogen (TN) together to identify the sources and fate of the particulate matter in the atmosphere (Aggarwal et al., 2013 ; Agnihotri et al., 2011 ; Aguilera & Whigham, 2018 ; Bikkina et al., 2019 , 2020 ; Gupta et al., 2017 ; Kirillova et al., 2013 ; Kundu et al., 2010 ; Morera-Gómez et al., 2018 ; Pavuluri et al., 2011; Pavuluri & Kawamura, 2012 ; Sawlani et al., 2019 ; Sharma et al., 2017 , 2022 ) In context of Indian cities, studies (Aggarwal et al., 2013 ; Agnihotri et al., 2011 ; G.K. Singh et al., 2018 , 2021 ; Pavuluri et al., 2011; Sen et al., 2018 ; Sharma et al., 2015 ; Hegde et al., 2016 ; Sawlani et al., 2021 ) reported mainly short term (e.g. winter and/or summer season) measurement of stable carbon and nitrogen isotopic composition. An investigation during December 2009 to January 2011, conducted by Agnihotri et al., ( 2015 ) examined the isotope ratios of carbon and nitrogen in Total Suspended Air Particulate Matter (TSPM) in Goa, India. There are few studies dealing with long-term variation of δ 13 C (Singh et al., 2021 ; Yadav et al., 2022 ), δ 13 C and Δ 14 C (S. Bikkina et al., 2017 ; Kirillova et al., 2013 ) in Indian cities. Studies (Agnihotri et al., 2011 ; Rastogi et al., 2020 ) also reported the isotopic composition of carbon and nitrogen over Bay of Bengal and Arabian Sea, in Indian context. The previous studies in Indian continent with details like measured parameters, fraction of particles measured, duration etc., are given in the supplementary material Table S1 . Therefore, there is scarcity of the data of long-term or annual measurement and interpretation of stable isotopic composition of carbon and nitrogen in PM 10 in Indian context. Mumbai city has various sources of urban particulate matter and the continuous annual monitoring and, isotopic characterization of the particulate matters has not been carried out. In the present study, we have measured the stable isotope ratio of carbon and nitrogen (δ 13 C and δ 15 N) along with the total carbon, total nitrogen in particulate matter (PM 10 ) collected during January to December 2022 from Trombay area, a coastal site which is in the eastern suburbs of Mumbai. These measured parameters and correlations amongst the various parameters were used to understand the source of particulate matters and the coastal effects on PM in the sampling area. 2 Materials and Methods 2.1 A Study area and sampling The city of Mumbai, located on the western coast of India, is one of the most populous cities in the world. The study area Trombay, located in the eastern suburbs of Mumbai, is known for its industrial activities. Apart from Bhabha Atomic Research Centre (BARC), Trombay it is home to a diverse range of industries like, chemical plants, oil refineries, and thermal power stations. Some of the major industries in the area are Tata Power Thermal Power Station, Rashtriya Chemicals and Fertilizers Ltd. (RCF), Hindustan Petroleum Corporation Limited (HPCL) Refinery, Indian Oil Corporation Limited (IOCL) Terminal, National Organic Chemical Industries Limited (NOCIL) and Tata Chemicals. Trombay area is located between two water bodies, in east Mumbai Harbour Bay (distance around 1 km) and in west Arabian Sea (distance around 12.5 km). We have collected particulate matter (PM 10 ) at the terrace (~ 15 m above the ground level) of Modular Laboratories (Lat, long: 9°00'43.4"N 72°55'15.7"E) (Supplementary Fig S1 ) on pre-combusted glass fibre filter paper using high volume air samplers at flow rate of 1.0 m 3 .min − 1 . The continuous sampling was carried out and samples (n = 37) were collected during January 2022 to December 2022. Prior to the isotopic analysis, the filter samples (2 circles, each of ~ 2.8 cm diameters) were placed in closed glass container and stored in the freezer at − 20 0 C temperature. The blank contribution of δ 13 C and δ 15 N from the glass fibre filter paper was determined measuring standard solution of sulfanilamide deposited (0.5, 1.0, 2.0 and 4.0 µmol of N and multiple aliquots) and dried on the glass filter as per the recommended procedure (Agnihotri et al., 2014 ; Avak H. and Fry B., 1999). 2.2 Measurement The elemental (TC and TN) composition, δ 15 N and δ 13 C values were measured using Elemental Analyser (EA; vario PYRO CUBE, Elementary Analysensysteme GmbH, Germany) connected in series with continuous flow Isotope Ratio Mass Spectrometer (IRMS; Isoprime100, Isoprime UK Limited). Two circular pieces of area ~ 2 cm 2 of filters were packed in tin boats as round pellets, and pressed to remove any trapped air. The sample pellets were loaded in auto sampler and dropped in combustion tube where they are combusted at 1150 0 C in presence of oxygen and WO 3 powder as catalyst. The evolved gases from the combustion were passed through the reduction tube filled with metallic copper (Cu) and Ag wool that reduces NO x , SO x into N 2 and SO 2 and removes any Halide (X), respectively. The excess oxygen is also removed by copper in the reduction tube. The gas stream is further passed through drying tube, filled with Sicapent (MgClO 4 ) to remove any moisture in the gas stream (CO 2 , N 2 and SO 2 ). The gases were separated using Temperature Programmable Desorption (TPD) and sent to IRMS system through Thermal Conductivity Detector (TCD) where the percentage composition of these separate gases was estimated. Replicates were analyzed for each sample to check the homogeneity of the sample and precision of the measurement. Stable isotopic compositions are expressed in delta (δ) notation. This is calculated as a ratio of the less abundant or (heavier) atom with respect to naturally more abundant (lighter) atoms in a sample relative to a standard, by following equation: δ 13 C (‰) = [( 13 C/ 12 C) sample /( 13 C/ 12 C) standard − 1] x 1000 δ 15 N (‰) = [( 15 N/ 14 N) sample /( 15 N/ 14 N) standard − 1] x 1000 The measured δ 13 C and δ 15 N values were calibrated and expressed relative to VPDB (Vienna Pee Dee Belemnite) scale and N 2 -air scale, respectively. The replicates of samples were measured to get the precision of the instrument for δ 13 C and δ 15 N measurement. Blank is estimated by plotting δ values of carbon and nitrogen in standard (Sulfanilamide) against 1/nA (i.e., 1/respective IRMS currents in nano Ampere). From the δ 15 N vs. (1/nA) plot, slope and intercepts were evaluated. The blank values of δ 15 N in filter paper was estimated using following equation (Fry, 2007 ); δ 15 N Blank = (Slope/nA Blank ) + Intercept In similar way the blank values of δ 13 C in filter paper were estimated from the slope and intercept of the δ 13 C vs. (1/nA) plot. The external precision of the instrument (EA-IRMS) was estimated by analyzing the working reference standards i.e., Sulfanilamide. The δ 13 C and δ 15 N results were corrected for the blank contribution of carbon and nitrogen from filter paper. The total carbon and nitrogen content in the samples were determined by measuring the peak areas of the carbon and nitrogen signals in the elemental analyser (EA) responses. The peak areas were converted into amounts of carbon and nitrogen using calibration factor measured from laboratory reference standard (sulfanilamide). The calibrated amounts were then divided by the corresponding air volume sampled to obtain the weight per unit volume of air. 3 Result and Discussion The precision of IRMS system (1σ) for δ 13 C and δ 15 N measurement was found to be 0.09‰ and 0.13‰, respectively. Replicate analysis of individual samples for δ 13 C and δ 15 N showed precision better than 0·5‰ and 1.0‰, respectively. The precision (1σ) of EA for C and N measurement were found to be 0.2% and 0.4% respectively. The descriptive statistical analysis of measured parameters is given in the supplementary material (Table S2). During the sampling period from January to December 2022, the δ 13 C and δ 15 N values ranged from − 26.2‰ to -22.4‰ and from − 2.3‰ to 21.4‰, respectively. The average δ 13 C value was − 24.9‰, with a standard deviation (1σ) of 0.9‰. The average δ 15 N value was 9.1‰, with a standard deviation (1σ) of 5.6‰. The other measured parameters, TC/TN ratio, TN, and TC, ranged from 2.8 to 7.5, 0.1 µg.m − 3 to 2.3 µg.m − 3 , and 0.4 µg.m − 3 to 9.1 µg.m − 3 , respectively. The average TC/TN ratio was 4.8, with a standard deviation (1σ) of 1.2. The average TN was 0.9 µg.m − 3 , with a standard deviation (1σ) of 0.7 µg.m − 3 . The average TC was 3.8 µg.m − 3 , with a standard deviation (1σ) of 2.5 µg.m − 3 . The monthly wind rose pattern for the year 2022 is depicted in Fig. 1 . The wind direction is utilized to understand the direction of origin of the primary sources of carbon and nitrogen to aerosol at the sampling site during different months of the study period. The prevailing wind originates predominantly from the South-West, with occasional wind from the North and West-North during April to August. In September, wind blows from both the South-West and North-East directions. From October to January, the wind predominantly comes from the North-East, except in December when there is some contribution from the West-North direction. In February, a mix of wind from the West-North, North, and North-East directions was observed. It is clear from the above wind rose that in different seasons Mumbai has prevalent winds e.g., winter (wind from WN, N and NE), Summer (prevalent wind from SW with some NE), summer (prevalent wind from SW) and post-monsoon (prevalent wind from NE). 3.1 Annual Variation of measured parameters 3.1.1 Annual Variation of TN, TC and TC/TN ratio The variations of TC/TN ratio, TN, and TC in the particulate matter (PM 10 ) observed during the study period are shown in the Supplementary Fig. S2 . The amount of total nitrogen (TN) and total carbon (TC) in the atmosphere was lowest during the monsoon season because of low dust load and the rain wash out. The ratio of total carbon to total nitrogen (TC/TN) varied throughout the study period ( Fig. S2 ). The monthly variation of TN, TC and TC/TN ratio observed during the study period are shown in Fig. 2 . The average TC/TN ratio was higher in February and October than in other months. In February the carbon content was higher than nitrogen but from October onwards, both carbon and nitrogen content increased as compared to other months, but the rate of increase in carbon content was greater than the rate of increase in nitrogen content as shown in Fig. S2 and Fig. 2 . The higher average nitrogen and carbon content having lower TC/TN ratio were found in January and December compared to other months. Agnihotri et al., ( 2011 ) reported significantly lower average values of TC/TN in aerosols over the Bay of Bengal (6.8 ± 12.5) and Indian cities (5.6 ± 2.6) compared to the Arabian Sea (50 ± 10). In January, the prevailing wind direction is mainly NE, while in December; prevailing wind comes from NE along with some minor contribution from NW and N direction. These winds transport aerosols (from Indo Gangetic Plains (IGP) region) with a lower TC/TN ratio (Agnihotri et al., 2011 ; Sen et al., 2018 ; Sharma et al., 2015 ), which is reflected in our measurements. Although the carbon and nitrogen content vary greatly in October, the TC/TN ratio does not vary as much as it does during other months (e.g., May or February) (Fig. 2 ). This is because the wind direction in October is predominantly from the N and NE direction, which has carried the aerosol emitted from IGP region to sampling site. The large variation in nitrogen and carbon content is likely caused due to the input from the burning of firecrackers during Diwali festival, which was celebrated on October 24, 2022. The increasing carbon and nitrogen content has been observed from October to February may be due to; (1) aerosol contribution from firecrackers in October, (2) contribution of aerosol originated in north and northeastern part (IGP region) of India due to burning of biomass and biofuel during the winter season (Agnihotri et al., 2015 ; Sawlani et al., 2021 ; Sen et al., 2018 ), (3) higher residence time of the aerosol due to prevailing metrological conditions (Aggarwal et al., 2013 ; Sharma et al., 2022 ). 3.1.2 Annual Variation of stable carbon and nitrogen isotopic composition (δ 13 C and δ 15 N) The variation of δ 13 C and δ 15 N in the individual samples collected during the study period is shown in Fig. S3. It is observed that there is higher variation in the δ 15 N (-2.33‰ to 21.39‰) values during the study period which may be due to sources with significantly different δ 15 N contributing to the PM 10 . Although the δ 15 N values showed more variation during the study period, about half of the observed values were found to be in the range between ~ 8 to 14‰. The variation in the δ 13 C values (-26.2‰ to -22.4‰) during the study period is less compared to that of δ 15 N variation as shown in Fig. 3 . The lowest value (-2.3‰) of δ 15 N observed during 17–24 May was associated with the highest value (-22.4‰) of the δ 13 C as shown in Fig. S3 . These observation in the month of May indicate the effect of the pre-monsoon shower (on 17th May 2022) and the input of particulate matter having lower δ 15 N and higher δ 13 C value. The rain washes out the existing particulate matter which is having relatively higher δ 15 N and lower δ 13 C value. The fresh input of carbonaceous aerosol with higher δ 13 Cvalue and lowerδ 15 N value is carried by the prevalent wind from S/SW direction as shown in Fig. 1 . These prevailing winds carried the aerosol originated from marine sources, the coal burning from Trombay Thermal Power Station (a coal-based thermal power plant, 1.75 km away) and from fuel oil/fossil fuel burning originated from the different refineries (14 km away) located in SW direction from sampling point. Therefore, considering the δ 13 C values of end-members (marine source: -21.0 ± 1.9‰, coal: -22‰, fossil fuel: -26.05‰) and δ 15 N value of end-members (marine source: 4.9 ± 2.8‰, coal: -5.3‰, fuel oil: -7.5 ± 8.3‰) and associated fractionation, the observed values seem dominantly affected by fuel oil combustion and coal. The δ 15 N value has decreasing trend during the period February to May and the lowest value was found in the month of May (Fig. 3 ). The wind changes its direction from northerly to southerly during the period February to May which results in the different origin of the particulate matter i.e., aerosols from the Arabian sea are transported to land during this period. As it is reported that the δ 15 N found over the Arabian sea has lower values (1.4 ± 3.3‰) (Agnihotri et al., 2015 ) compared to that of the other Indian city (Aggarwal et al., 2013 ; Agnihotri et al., 2015 ; Pavuluri et al., 2010 ) therefore the observed δ 15 N value decreases during the period February to May (discussed in details in next section). The average value of the δ 15 N remains similar during the period June to September with slight variation as shown in Fig. 3 . The value of δ 15 N again increases from September which is related to the change in the wind direction (from SW to NE) along with other factors and sources (discussed in details in next section). The monthly average values of δ 13 C and δ 15 N has shown significant variation throughout the sampling period. The monthly average values for both the δ 13 C and δ 15 N indicate that the PM 10 gets depleted in terms of heavier isotopes ( 13 C and 15 N) in comparison to lighter isotope ( 12 C and 14 N) during January to May. Similarly, PM 10 gets enriched ( 13 C and 15 N) in comparison to lighter isotope ( 12 C and 14 N) during the period September to January. This variation may be attributed to different sources of carbon and nitrogen contributing to the particulate matter and photochemical aging of atmospheric aerosols (Aggarwal et al., 2013 ; Wang et al., 2010 ). 3.2 Seasonal Variation of measured parameters It has been observed in different laboratory and field experiment that during formation of the particulate matter from source material there is isotopic fractionation which depend upon state of material and process through which particles are formed (Martinelli et al., 2002 ; Turekian et al., 1998 ; Widory, 2007 ; Widory et al., 2004 ). Therefore, to relate the particulate matter with their origin during different seasons, it important to understand the value of δ 13 C and δ 15 N in source material and fractionation during particle formation and aging (Wang et al., 2010 ). The results observed during the study period were grouped in 4 categories; winter (January - February), Pre-monsoon/Summer (March - May), Monsoon (June - September) and Post Monsoon (October - December) corresponding to main seasons observed in India (IMD, 2022 ; Singh et al., 2021 ; Yadav et al., 2022 ). This categorization will help in understanding the effect of the different seasons on the particulate matter contribution and sources of the particulate matter. The range and mean (average ± 1σ) values of the different parameters measured during different seasons is given in the Table S3. The observed seasonal changes are very consistent in all the measured parameters (TC, TN, δ 13 C and δ 15 N) as shown in Fig. 4 . Statistical Shapiro-Wilk Test was performed on each set of data to check the normal distribution. Each data set, except δ 13 C, TC and TN measured in summer, followed normal distribution (Supplementary data Table S4). We performed parametric test (ANOVA: Single Factor) and non-parametric test (Kruskal-Wallis test) to check the statistically significant differences of the measured parameters in different seasons. The p values (at the 95% confidence level) of ANOVA (Single Factor) test for δ 13 C, δ 15 N, TC, TN and TC/TN were found to be 7.5 x 10 − 4 , 5.1 x 10 − 6 , 6.0 x 10 − 5 , 1.6 x 10 − 3 and 0.99, respectively. The p values of Kruskal-Wallis test for δ 13 C, δ 15 N, TC, TN and TC/TN were found to be 5.39 x 10 − 4 , 1.06 x 10 − 6 , 2.17 x 10 − 4 , 3.98 x 10 − 4 and 0.89, respectively. Therefore, the observed values of the measured parameters (TC, TN, δ 13 C and δ 15 N) in different seasons have statistically significant difference at the 95% confidence level. The seasonal variation of isotope ratio observed in the present and previous studies is given in the Table 1 . 3.2.1 Seasonal variation of total carbon (TC) and total nitrogen (TN) Total carbon and total nitrogen in PM 10 observed during the winter in present study (Table 1 ) is found to be lower than that reported TC and TN for winter seasons in other Indian cities (Sharma et al., 2015 ; Sen et al., 2018 ). This observation may be attributed to geographically isolated location of the sampling point and proximity to ocean water which also acts as sink for the different pollution and aerosols. Table 1 Comparison of stable carbon and nitrogen isotopic composition during different seasons at different location around the world Region Aerosol Fraction Duration/seasons TC TN δ 13 C TC δ 15 N TN Reference Mumbai, India PM 10 Winter (Jan-Feb, 22) 4.7 ± 1.3 1.3 ± 0.7 -24.0 ± 1.1 15.8 ± 4.4 Present Study PM 10 Summer (Mar-May, 22) 3.5 ± 2.4 0.8 ± 0.7 -24.9 ± 1.0 8.1 ± 6.5 PM 10 Monsoon (Jun-Sept, 22) 1.0 ± 0.5 0.2 ± 0.1 -25.8 ± 0.4 3.5 ± 1.2 PM 10 Post-monsoon (Oct-Dec, 22) 5.6 ± 2.3 1.3 ± 0.6 -24.8 ± 0.5 9.5 ± 1.52 Mumbai, India PM 10 Winter (13–18 Feb, 07) 22 ± 4.7 2.4 ± 1.3 -25.9 ± 0.3 21.3 ± 1.8 (Aggarwal et al., 2013 ) PM 10 Summer (8–14 June, 06) 6.0 ± 1.7 0.8 ± 0.2 -26.5 ± 0.3 20.2 ± 1.2 Goa, India TSPM Summer (Mar-May, 10) 10.0 ± 4.8 1.8 ± 1.0 -24.9 ± 0.9 6.2 ± 2.3 (Agnihotri et al., 2015 ) TSPM Winter (Jan-Feb, 11) 26.3 ± 9.4 5.6 ± 2.8 -24.8 ± 0.9 10.8 ± 2.2 Bhopal, India PM 2.5 Winter (Jan-Feb, 19) 13 ± 10.4 ̶ -25.9 ± 0.5 ̶ (Yadav et al., 2022 ) PM 2.5 Summer (Mar-May, 19) 7.4 ± 3.6 ̶ -26.9 ± 0.3 ̶ PM 2.5 Monsoon (Jun-Sep, 19) 2.8 ± 1.6 ̶ -27 ± 0.3 ̶ PM 2.5 Post-monsoon (Oct-Dec, 19) 21.5 ± 2.3 ̶ -26.4 ± 0.4 ̶ Mysuru, India PM 2.5 Winter (Jan-Feb, 19) 11.8 ± 4.2 ̶ -25.4 ± 0.4 ̶ (Yadav et al., 2022 ) PM 2.5 Summer (Mar-May, 19) 4.8 ± 2.6 ̶ -26.2 ± 0.3 ̶ PM 2.5 Monsoon (Jun-Sep, 19) 1.7 ± 1.1 ̶ -26.8 ± 0.3 ̶ PM 2.5 Post-monsoon (Oct-Dec, 19) 6.9 ± 3.8 ̶ -25.9 ± 0.4 ̶ Prague, Czech Republic PM 2.5 Summer (June-Sep, 16) 2.9 ± 1.1 ̶ −27.2 ± 0.5 ̶ (Vodička et al., 2019 ) PM 2.5 Autumn (Sep-Nov, 17) 5.8 ± 3.5 ̶ −26.1 ± 0.7 ̶ PM 2.5 Winter (Dec, 16-Feb, 17) 14.3 ± 4.4 ̶ −25.5 ± 0.8 ̶ PM 2.5 Spring (Mar-May, 17) 3.9 ± 1.6 −26.6 ± 0.4 ̶ Punjab, India PM 2.5 Summer (May 2018) 23 ̶ -27.4 ± 0.4 ̶ (Singh et al., 2021 ) PM 2.5 Monsoon (Aug 2018) 6.5 ̶ -26.4 ± 0.5 ̶ PM 2.5 Post-monsoon (Oct, 16) 73.4 ̶ -26.5 ± 0.4 ̶ Kanpur, India PM 10 Winter (17 Jan-22 Feb, 07) ̶ ̶ -24.3 ± 0.7 ̶ (S. Bikkina et al., 2017 ) PM 10 Summer (9 Mar-24 May, 07) ̶ ̶ -25.1 ± 0.8 ̶ PM 10 Monsoon (2J un-14 Jun, 07) ̶ ̶ -25.2 ± 0.4 ̶ PM 10 Post-monsoon (16 Oct-7 Dec, 07) ̶ ̶ -23.5 ± 1.1 ̶ Wrocław Poland PM 10 Winter ̶ ̶ −26.1 ̶ (Górka et al., 2014 ) PM 10 Summer ̶ ̶ −27.5 ̶ Debrecen Hungary PM 2.5 Winter ̶ ̶ −25.7 ̶ (Major et al., 2021 ) PM 2.5 Summer ̶ ̶ −26.7 ̶ Okinawa Japan TSPM Winter Dec,09 – Dec, 10 2.0 ± 0.6 1.2 ± 0.6 -22.5 ± 0.62 13.6 ± 1.72 (Kunwar et al., 2016 ) TSPM Summer Jun-Aug, 10 2.1 ± 0.6 0.5 ± 0.6 -22.9 ± 0.69 11.1 ± 1.19 The lowest average value of TC and TN found during monsoon seasons may be attributed to lower residence time of these particles in the atmosphere due to rain washout and lower contribution of re-suspended particles due to damp condition. Higher average values of TC and TN during the post-monsoon and winter season is attributed to aerosol coming from burning of biofuels and biomass in north and north-east Indian states. This is supported by the prevailing wind from N and NE during this period (Fig. 1 ). Similar trend of TC in PM 2.5 is also reported for pre-monsoon, monsoon and post-monsoon seasons from study conducted in other Indian cities (Aggarwal et al., 2013 ; Agnihotri et al., 2015 ; Hegde et al., 2016 ; Singh et al., 2021 ; Yadav et al., 2022 ). The previous study in Mumbai (Aggarwal et al., 2013 ) during winter (13–18 Feb, 2007) have reported TC and TN as 22 ± 4.7 µg m − 3 and 2.4 ± 1.3 µg m − 3 , respectively. The present study reported TC and TN values during winter period (10–21, Feb, 2022) as 4.61 ± 0.83 µg m − 3 and 0.77 ± 0.11 µg m − 3 , respectively. The higher values of TC and TN in previous study (Aggarwal et al., 2013 ) compared to present is observed but the isotope ratio is similar in both the studies. This difference in TC and TN (while same isotope ratio) is attributed to higher input of aerosol at the IIT Mumbai due its closed proximity to heavy road traffic and domestic as well as industrial settlement around the sampling location. While, the sampling point in present study is at isolated location (away from common road traffics and domestic settlements) and closed proximity to marine water bodies (Mumbai Harbour Bay and Arabian Sea). The average values of the TC/TN ratio were found nearly same in all the seasons as the differences between different seasons are not statistically significant at the 95% confidence level (p values: 0.9). The average value of TC/TN ratio was found close to that reported in TSPM from different Indian cities (like Delhi, Bhubaneshwar, Nainital, Nagpur, Anantapur) by (Agnihotri et al., 2011 ). While, TC/TN ratio measured during winter (20 Jan-5 Feb, 2014) by (Sharma et al., 2015 ) from IGP, Indian Himalayan Region (IHR) and Indian desert have values as 1.6 ± 0.6, 1.8 ± 0.5 and 0.9 ± 0.3, respectively. The present study has reported TC/TN ratio for the similar period (18 Jan – 8 Feb, 2022) as 4.17 ± 1.66 which is higher than the reported TC/TN value by (Sharma et al., 2015 ). The average TC/TN ratio at the sampling sites during this campaign was relatively low compared to the ratios reported in the past for other sites in the Indian mainland (Aggarwal et al., 2013 ; Agnihotri et al., 2011 , 2015 ; Sharma et al., 2015 ). 3.2.2 Seasonal variation of stable nitrogen isotopic ratio (δ 15 N) The δ 15 N in the particulate matter from different major sources (end-member) were reported to be in wide range of values, ranging from − 15‰ to 20‰ (Agnihotri et al., 2011 ; Martinelli et al., 2002 ; Pavuluri et al., 2010 ; Sawlani et al., 2019 ; Turekian et al., 1998 ; Widory, 2007 ) as shown in Fig. 5 . The large variation in the δ 15 N value in aerosol is related to variation in the source pool and the different combustion temperature during the burning of biomass and biofuels (Sen et al., 2018 ; Turekian et al., 1998 ). The combustion of fossil fuels like diesel, unleaded gasoline, coal, natural gas, and fuel oil produces the aerosol with δ 15 N values as 4.56 ± 0.76‰, 4.6‰, -5.3‰, 7.7 ± 5.9‰ and − 7.5 ± 8.3‰, respectively. The δ 15 N value is reported in other studies like laboratory combustion of C3 and C4 type vegetation (δ 15 N: 2.0‰ to 19.5‰, (Turekian et al., 1998 )), cow dung cake combustion (δ 15 N: 13.4‰ to 15.5‰, (Pavuluri et al., 2010 )), burning C3 plant matter combustion (δ 15 N: 15‰, (Pavuluri et al., 2010 ). In controlled combustion of typical vegetation which are normally cultivated on tropical soil of India (viz., Eucalyptus, Neem, Arhar, Mustard stem, Babool, Chilly stem, Desi Keekar, Sheesham and Arandi) particulate having average δ 15 N values as 13.2 ± 4.8‰ (Agnihotri et al., 2011 ) are produced. Among these Leguminous plants e.g., Arhar and Mustard stem have shown lower δ 15 N values as 5.2‰ and 7.6‰, respectively. The particle generated from burning of vegetation plants from Piracicaba River basin (C4 plants dominated) and Amazon basin (land cover is primary forest), have δ 15 N values as 10.6 + 2.8 and 11.5 + 2.1, respectively and there was no statistically significant difference in the δ 15 N values for particles generated from combustion C4 and C3 type vegetation (Martinelli et al., 2002 ). It has been found that burning biomass (C3 and C4 plant material) can enrich the emitted particles with the heavier nitrogen isotope ( 15 N) compared to the original plant matter (Turekian et al., 1998 ). This enrichment is attributed to partitioning between gas and particles, potentially involving gaseous nitrogen (like NH3) and organic nitrogen compounds in the particles. The study reported an average increase of 6.6‰ (range: -1.3‰ to 13.1‰) in the δ 15 N ratio of aerosol particles compared to the source vegetation and again no difference was observed between C3 and C4 plants (Turekian et al., 1998 ). The δ 15 N values observed during the different seasons have statistically significant difference at the 95% confidence level (p values < 0.05). The lowest average δ 15 N value (3.52 ± 1.23‰) observed in the current study during monsoon seasons compared to other seasons may be mainly attributed to (i) reduced contribution of aerosol from biomass and biofuel burning and (ii) input of the aerosol from Arabian Sea coming with predominant wind from South-West direction. It is reported that aerosol over Arabian Sea has a clear contrast lower δ 15 N value, compared to that over Bay of Bengal and Indian cities, having an average value of 1.4 ± 3.4‰ (Agnihotri et al., 2011 ). The dominant source of TN during monsoon is liquid fossil fuels and coal as shown in Fig. 5 . The biomass burning have little/no contribution considering the average enrichment of 6.6‰ during particle formation and the end-member values as shown in Fig. 5 . During winter seasons the contribution of biomass to TN is higher compared to liquid fossil fuels as the δ 15 N values during winter are closer to biomass burning as depicted in Fig. 5 . Higher δ 15 N value in winter (δ 15 N: 15.76 ± 4.44‰) compared to that in summer (δ 15 N: 8.11 ± 6.51‰) may be associated with (i) contribution of aerosols from biofuels/biomass (having higher value of δ 15 N) burning from N/NE direction, (ii) longer life time of aerosols in atmosphere during winter, as wet removal is lower in winter compared to summer since relative humidity during winter is lower than relative humidity of summer (Molina et al., 2004 ; Petters et al., 2006 ), (iii) Higher evaporation of inorganic nitrogen in summer (Aggarwal et al., 2013 ) and (iv) Higher contribution of marine aerosol from Arabian sea during the summer (Agnihotri et al., 2015 ; Naqvi et al., 2006 ). The mixing of marine nitrogen species from the adjacent Arabian Sea is responsible for δ 15 N value approaching to its lowest values during pre-monsoon to monsoon period from highest value during winter. Water column denitrification is reported to generate lighter N species ( 14 N) which emanate from the water surface of the ocean (Naqvi et al., 2006 ). Therefore, more than the statistical difference between summer and winter season, systematic decrease in δ 15 N values of aerosols from winter to pre-monsoon to monsoon period is worth mentioning since it matches well with systematically increasing influence of marine winds over the study area. Higher δ 15 N value in winter compared to summer season is also reported in many previous studies in different Indian cities e.g., Mumbai (Aggarwal et al., 2013 ), Nainital (Hegde et al., 2016 ), Goa (Agnihotri et al., 2015 ) (Table 1 ). Pavuluri et al., ( 2010 ) have reported elevated enriched nitrogen (Average δ 15 N value: 24.5‰) in the winter season (during 23 January-6 February, 2007) from Chennai, located on the southeast coast of India, which they have attributed to animal excreta and biofuel/biomass burning. In our measurement the during winter season (δ 15 N: 15.8 ± 4.4‰), we have not observed such high enriched value indicating different source of the nitrogen. Previous study by Aggarwal et al., ( 2013 ) at Indian Institute of Technology Bombay(IITB), Mumbai in winter season (during 13–18 February 2007) have reported the average δ 15 N value in PM 10 as 22.8 ± 1.4‰. In the present study, we have also found the similar average value of δ 15 N (20.6 ± 1.1‰) during the sampling period of 10–21 February 2022. But, during the entire winter season (January – February, 2022), the observed δ 15 N value was found to vary from 10.6‰ to 21.4‰ with an average of 15.8 ± 4.4‰ in Mumbai. Study have reported the average value of δ 15 N as 20.2 ± 1.2‰ during the period June 8–14, 2006 in Mumbai mentioned as summer season, while in the present study we have found δ 15 N value as 4.2‰ for the similar period of the month i.e., June 8–17, 2022 (Aggarwal et al., 2013 ). The significant difference in the observed δ 15 N value in month of June for present study and previous study carried out in Mumbai is be attributed to the rain (South-West monsoon) which starts normally 10 June in Mumbai. It can be seen from the weather history of Mumbai that during the Jun 8–14, 2006 ( https://weatherspark.com/h/m/107286/2006/6/Historical-Weather-in-June-2006-in-Mumbai-India ) there was less rain compared to rain observed during the June 8–14, 2022 ( https://weatherspark.com/h/m/107286/2022/6/Historical-Weather-in-June-2022-in-Mumbai-India ). It is important to mention that the average δ 15 N value for the entire summer season (March – May) was found as 8.11 ± 6.51‰. These finding suggest that it will be erroneous to conclude the δ 13 C and δ 15 N values in particulate matter for entire season based on the short-term measurement of these parameters. Therefore, long term monitoring and measurement is required to have better understanding and predictive modeling about the particulate matter source at any location. 3.2.3 Seasonal variation of stable carbon isotopic ratio (δ 13 C) It is reported that the measured δ 13 C value in particulate matter along with knowledge of end-member values and fractionation, can provide the insight into the source as well atmospheric processes of the carbonaceous aerosol (Kirillova et al., 2013 ). The observed value of δ 13 C of PM 10 from present study along with the end-member (probable primary source) is shown as scattered plot (average ± 1standard deviation) is shown in Fig. 6 . The plants with C3 and C4 metabolism showed distinctly different δ 13 C values ranging − 24‰ to -34‰ (average: -27‰) and − 6‰ to -19‰ (average: -13‰) (Mkoma et al., 2014 ; Smith & Epstein, 1971 ; Turekian et al., 1998 ), respectively which was not observed in δ 15 N. The liquid fossil fuel showed depleted δ 13 C value varying from − 28.6‰ to -26.4‰ (Diesel: -26.5 ± 0.5‰, Regular petrol: -24.5 ± 0.7%, unleaded petrol: -24.2 ± 0.6%, (Widory et al., 2004 )) compared to solid fossil fuel (e.g., coal: -23.6 ± 0.7‰ (Widory, 2006 ), -23.4 ± 1.3‰ (Lim et al., 2022 ), -22.44 ± 0.1‰ (Sawlani et al., 2019 )). The gaseous fossil fuels are strongly depleted in δ 13 C value (-20‰ to -40‰ (Lim et al., 2022 ; Widory, 2006 ). The marine aerosol sources like phytoplankton have δ 13 C value as -20 ± 2.8‰ (Miyazaki et al., 2011 ) while sea salt spray have been reported δ 13 C value as -21 ± 2.0‰ (Cachier et al., 1986 ; Ceburnis et al., 2011 ; Xiao et al., 2018 ). Incomplete combustion of fossil fuels is reported to show depletion/ fractionations of -1.3 ± 0.5‰ in δ 13 C value for the combustion gases (Widory, 2006 ). The particle from fossil fuels like diesel, unleaded gasoline and regular gasoline showed enrichment/fractionations of 1.9 ± 0.4‰, 3.2 ± 1.1‰ and 3.3 ± 0.7‰ respectively. The δ 13 C fractionation in primary particle from coal combustion and natural gas combustion was reported as zero and 11.0 ± 5.0‰, respectively (Martinelli et al., 2002 ; Widory, 2006 ). It has been reported that in controlled laboratory combustion of C3 plants ( Eucalyptus sp . and Colospherum mopane ) and C4 plants ( Cenchriscilliarus sp ., Antephora pubescence and Saccharum officinarum , sugarcane) there is fractionation in the δ 13 C values of 0.5‰ and − 3.5‰ with respect to source vegetation, respectively (Cachier et al., 1986 ; Turekian et al., 1998 ). Considering the δ 13 C value in end-members and fractionation, the average δ 13 C value of -24.89 ± 0.86‰, observed in the present study, seems to represent an intermediate range between aerosols emitted from predominantly biomass burning and fossil fuel combustion (Fig. 6 ) . Similar observation and conclusion is also reported in previous studies (Agnihotri et al., 2015 ; Agnihotri et al., 2011 ; Sen et al., 2018 ; Aggarwal et al., 2013 ) over different cities of India. The δ 13 C values observed during the different seasons have statistically significant difference at the 95% confidence level (p values < 0.05). The highest average δ 13 C value (i.e., greatest 13 C enrichment) was found in winter followed by post-monsoon season and lowest value is observed during monsoon season. This systematic change of average δ 13 C value like seasonal change in δ 15 N value, again indicates the systematic increase of marine influence on aerosol observed over the study area. Similar annual seasonal trend of δ 13 C is also reported at urban European site (Vodička et al., 2022 ), different cities of India (Bhopal and Mysuru: (Yadav et al., 2022 ), Kanpur: (S. Bikkina et al., 2017 )) and Okinawa Island, Japan (Kunwar et al., 2016 ). Previous study at Mumbai during the winter season (13–18 February, 2007) have reported δ 13 C value as -25.9 ± 0.3‰, where the aerosols were strongly influenced by long-range continental and regional transport. Similar δ 13 C value (-25.2 ± 0.5‰) is observed in present study during the period 10–21 February 2022. Like nitrogen isotope ratio, carbon isotope ratio has higher average value (δ 13 C: -24.04 ± 1.08‰) in winter season compared to summer season (δ 13 C: -24.86 ± 0.95‰). As mentioned above, the difference is statistically significant, so the particulate in winter is enriched (Δδ 13 C = δ 13 C winter - δ 13 C summer ) by an average 0.82‰ in δ 13 C value. Previous study at Mumbai also reported enrichment (Δδ 13 C) of 0.60‰ in δ 13 C value during winter compared to summer season (Aggarwal et al., 2013 ). Studies have reported that due to photochemical aging of atmospheric aerosols there is enrichment of 13 C in organic compound (Kawamura & Watanabe, 2004 ; Pavuluri & Kawamura, 2012 ; Wang et al., 2010 ). The mechanism is described as, reaction of OH radical with organic compound which results in breaking of organic compound and release CO 2 /CO, which have more abundance of lighter carbon isotope ( 12 C). Meanwhile, organic compound remaining in the aerosols gets enriched in the 13 C which results higher 13 C/ 12 C ratio i.e. in the enrichment of δ 13 C value (Aggarwal et al., 2013 ). Table 1 summarizes the seasonal variation (winter and summer) of δ 13 C values in different Indian cities along with some study around the world. Previous studies for seasonal changes of δ 13 C value (i.e., enrichment (Δδ 13 C)) between winter and summer seasons have been reported at different locations around the worlds e.g. Prague/Czech Republic (Δδ 13 C: 1.7, (Vodička et al., 2022 )), Wrocław/Poland (Δδ 13 C: 1.4‰, (Górka et al., 2014 )), Lower Silesia/Poland (Δδ 13 C: 0.1 to 2.3‰, (Górka et al., 2020 )), Debrecen/Hungary (Δδ 13 C: 1.0‰, (Major et al., 2021 )), Vavihill/Sweden (Δδ 13 C: 0.2‰, (Martinsson et al., 2017 )), Preila/Lithuania (Δδ 13 C: 0.6‰, (Masalaite et al., 2020 )), Okinawa)/Japan (Δδ 13 C: 0.4‰, (Kunwar et al., 2016 )) and Kanpur/India (Δδ 13 C: 1.1‰, (S. Bikkina et al., 2017 )). It has been reported that these seasonal variation of δ 13 C values depends on the type of sources, aerosol sizes, aging of aerosols and geographical location of the study area (Vodička et al., 2022 ). 3.3 Seasonal variation of aerosol sources based on δ 13 C and δ 15 N values The observations from Fig. 5 indicate that during the winter season δ 15 N value may have dominant contribution from C4 plants. However, it is not true in this case since C4 plants have significantly different carbon isotope ratio (δ 13 C: -13‰ to -20‰, average: -17‰) from the observed value in winter (δ 13 C: -24.04 ± 1.08‰). Also, the main biomass sources in Indian mainland are reported to be from C3 plants (e.g., wood, rice, and wheat) (S. Bikkina et al., 2017 ). This observation highlights the importance of use of dual marker (δ 13 C and δ 15 N) for understanding the source of particulate matter. Therefore, dual isotope isospace of the stable carbon and nitrogen isotopic ratio measured during different seasons along with the end-member values are plotted (Fig. 7 ). Previous studies (using Principal Component Analysis (PCA) based multivariate studies and source apportionment) in Vashi, Navi Mumbai (located at around 10 km in north-east direction from the current sampling location) have identified combustion, soil, and sea salt as main contributor to the coarse fraction of the particulate matter (Kothai et al., 2008 , 2011 ). End-members in the figure are based on the details mentioned in the manuscript and Supplementary Table S5, Table S6 and summarized in Table S7. These end-members are selected based on the aerosol sources at reported in western coast of India by various researchers (Aggarwal et al., 2013 ; Agnihotri et al., 2011 , 2015 , 2020 ; P. Bikkina, et al., 2022 ; Kothai et al., 2008 , 2011 ) and discussed in above sections. They include particles from different sources in that category e.g. biomass (emission from combustion of C3 plants, cow dung etc., (Agnihotri et al., 2011 ; Andersson et al., 2015 ; P. Bikkina et al., 2022 ; Boreddy et al., 2018 ; Smith & Epstein, 1971 )), coal (emission from coal fired thermal power plants (Agnihotri et al., 2011 ; Felix et al., 2012 ; Feng et al., 2020 ; Heaton, 1990 )), vehicular (emission from vehicles/ships running on diesel and petrol (Dai et al., 2015 ; Heaton, 1990 ; Widory, 2007 ; Widory et al., 2004 )), marine (emission of organic matter from marine phytoplankton (P. Bikkina et al., 2022 ; Miyazaki et al., 2011 ) and Dust (fine alluvial particles from IGP and Thar deserts (Agnihotri et al., 2015 , 2020 )). It is clear from the isospace (Fig. 7 ) that during winter, the dominant contributor of TC and TN in aerosols, is biomass and coal fired power plants along with vehicular emission. Other sources e.g., continental dust and marine sources are only minor contributors. The IGP region, also referred as coal belt of India, has a dense network of coal-fired thermal power plants (S. Bikkina et al., 2016 ) contributes dominantly to aerosol in western India during winter, due to prevalent northerly and north-easterly wind. It is important to note that biofuels (particularly cow dung cake) are very common for cooking energy in South Asia (Stone et al., 2010 ; Venkataraman et al., 2020 ). Towards this, it has been also reported that biomass and fossil fuels burning emission is major source of organic aerosols over the IGP (S. Bikkina et al., 2016 ; Gustafsson et al., 2009 ; Rajput et al., 2014 ; Singh et al., 2018 ). Therefore, the emission from biomass (cow dunk and plants residues combustion) and coal combustion is reaching to Mumbai during winter season via northerly and north-easterly wind. Studies (Agnihotri et al., 2020 ; P. Bikkina et at., 2022) had reported that mineral dust from IGP and Thar Desert is also contributing to the aerosol at western coast of India and Arabian Sea. The sampling point is located between two marine water bodies (Mumbai Harbour Bay and Arabian Sea: please refer supplementary Fig. S1 ). Therefore, all the sources (biomass, coal, vehicular, mineral dust and marine) contribute to the aerosol at the sampling location. Previous study (P. Bikkina, Bikkina, & Kawamura, 2022 ) for bulk aerosols collected from offshore waters of the Arabian Sea during winter season has reported same sources. Using isotope mixing model, authors have estimated contribution from different sources as biomass burning (69 ± 5%), vehicle exhaust (8 ± 5%), coal fired power plants (10 ± 6%), continental dust (5 ± 2%) and marine sources (8 ± 5%) to the total carbon and total nitrogen in the aerosol. The observation in present study is also consistent to study (Agnihotri et al., 2015 ) who had reported the biomass combustion, continental dust, marine and vehicular emission as source of aerosol during winter season at Goa, a coastal city situated at ~ 700 km to the south of Mumbai along the west coast of India. During the monsoon season the dominant sources seems as vehicular (burning of liquid fossil fuels) and marine as shown in Fig. 7 . If we compare the pre-monsoon (summer) and post-monsoon, the former seems to have higher contribution from the marine sources. This can be understood by the wind directions during these two seasons. During the pre-monsoon (especially during the April and May), the prevalent wind from south-west may contribute higher marine source. Similarly, there is higher contribution from biomass during winter compared to that in post-monsoon. This is clearly in accordance with the fact that during the winter there is higher biomass burning in north and north-east Indian cities to beat the winter cold. 3.4 Correlation between the measured parameters The correlations amongst the different parameters measured during seasons were estimated using Spearman correlation coefficients (r s ) and statistically significant of correlation was checked at 95% confidence level (i.e., p value ≤ 0.05). The correlation matrix (Spearman correlation coefficients and p-value) for each season as well as for entire study period is given in the Supplementary Tables (S8-S12). A very strong positive correlation (r s = 0.93 and p value < 0.0005) between TC and TN content, measured during the study period, was found during the study period (Supplementary Table S8). This reflects that the dominant source of particulate matter at the sampling location is combustion. This is because combustion processes, such as burning fossil fuels (diesel, petrol, coal, etc.) and biomasses (plant residues and cow dunks cake), release both carbon and nitrogen into the atmosphere. The higher the correlation between carbon and nitrogen content, the more likely it is that combustion is the main source of PM 10 . Other possible sources of PM 10 , such as dust and sea spray, do not typically release as much nitrogen into the atmosphere as carbon. As a result, the correlation between carbon and nitrogen content in PM 10 from these sources is typically weaker. A strong and positive correlation between carbon and nitrogen content in particulate matter also reflects; (i) Carbon and nitrogen can have common dominant sources in particulate matter. For example, combustion processes, such as fossil fuel combustion or biomass burning, can release both carbon-based compounds (e.g., organic carbon) and nitrogen compounds (e.g., nitrogen oxides), (ii) occurrence of atmospheric chemical reactions that transform carbon and nitrogen compounds into secondary organic aerosols (SOA) and secondary inorganic aerosols (Morera-Gómez et al., 2018 ) and (iii) common atmospheric long-range transport history from similar sources i.e. transport of particulate matter from an industrial area and/or urban center, containing both carbon and nitrogen to the sampling location (Aggarwal et al., 2013 ). A week positive correlation (r s = 0.39 and p value = 0.02) between δ 13 C (‰) and δ 15 N (‰), measured during the study period, suggests that, in addition to distant sources such as aerosols from the Arabian Sea (AS) and aerosols from cities in the north and northeast of India, there are also local sources contributing to the aerosol composition. If only the distant sources were contributing to the aerosols, δ 13 C and δ 15 N would have a stronger correlation. As, it is reported that aerosols from the south and southwest direction, originating from the AS, have lower δ 13 C and δ 15 N values compared to aerosols from northern and northeastern regions of India (Agnihotri et al., 2011 ). A moderate positive correlation (r s = 0.53 and p value < 0.001) between δ 13 C and TC content observed in particulate matter during the entire study period, indicates the carbon rich sources with higher δ 13 C values is also contributing to aerosols to some extent. If only high carbon content sources with higher δ 13 C value (e.g., coal) is contributing one would expect better correlation between the δ 13 C and TC content. The deviation from strong positive correlation (r s = ~ 1) in the present study, could be due to a number of factors, including different sources of PM 10 , different ways in which carbon is incorporated into aerosol and different processes that affect the isotopic composition of PM 10 (Agnihotri et al., 2015 ). A strong positive correlation (r s = 0.63 and p value < 0.0005) observed between δ 15 N of aerosols and TN, measured during the study period, is consistent with findings from previous studies conducted in Indian cities (Agnihotri et al., 2015 ; Pavuluri et al., 2010 ). Study (Agnihotri et al., 2015 ) have concluded that the positive correlation (R 2 : 0.58) between δ 15 N and TN in bulk aerosols indicates that δ 15 N values are largely controlled by primary sources dominantly contributing to total nitrogen in aerosols (during winter biomass burning containing heavier nitrogen ( 15 N) and during summer marine emissions containing nitrogen species lighter nitrogen ( 14 N)). Considering the location of the study area (between two marine water bodies) and biomass/biofuels emissions from north and north east direction in winter, similar conclusion will also be valid for present study. The observed correlation also suggests that higher nitrogen inputs in aerosols are associated with higher δ 15 N values in the aerosols, which are dominantly coming from biofuel and biomass. This is because the heavier isotope of nitrogen ( 15 N) is more abundant in biological sources than in non-biological sources. This also supports the biofuels and biomass burning as one of the dominant sources of particulate matter in the study area. During winter seasons, a strong negative correlation (r s = -0.71 and p value = 0.045) between δ 13 C (‰) vs. δ 15 N (‰) is observed indicates the dominant sources contributing with higher δ 15 N and lower δ 13 C e.g., biomass/biofuels and coal as shown in the isospace of dual carbon and nitrogen isotopes (Fig. 7 ). Similarly, a very strong negative correlation (r s = -0.81 and p value < 0.014) between δ 15 N (‰) vs. TN (µg/m 3 ) during winter seasons, again indicates the dominant contributors with high nitrogen content with lower δ 15 N values e.g. (biomass and biofuels (cow dung). A moderate correlation (r s = 0.45 and p value < 0.26) between TC and TN observed indicates the emission from combustion being one of the dominant sources of the aerosols during the winter. Like winter TC and TN and very strong correlation (r s = 0.94 and p value < 0.000) during the post-monsoon and strong correlation (r s = 0.75 and p value = 0.019) during the monsoon season. This indicates that dominant source contributing to TC and TN is not changing during each season. It is important to note that like entire study period, we have observed moderate to very strong positive correlation between TC vs. TN, during different seasons as winter (r s = 0.45 and p value = 0.26), summer (r s = 0.92 and p value = 0.019), monsoon (r s = 0.75 and p value = 0.019) and post-monsoons (r s = 0.94 and p value < 0.000). This again indicates that combustion of biofuels, fossil fuels and biomass as one of the dominant sources of aerosols. 4 Conclusion This study conducted a comprehensive analysis of stable isotope ratio of carbon and nitrogen, as well as elemental composition, in particulate matter (PM 10 ) collected from the Trombay area in Mumbai. The study provides insights into the annual variation of isotopic and elemental composition of PM 10 in the Trombay area, and a better understanding of contributing sources. The annual variation of the measured parameters suggest that they are controlled by several parameters including origins of air mass, wind direction transporting these air mass, contribution of local source and global sources, different metrological parameters etc. The study highlights the importance of long-term (measurement for complete period) monitoring to predict the isotopic value against the short-term (measurement for only few days) study. The wind patterns played a crucial role in transporting aerosols with different δ 13 C, δ 15 N and TC/TN ratios values to the sampling site, resulting in seasonal variations in the composition of PM 10 . The monsoon season exhibited lower TN and TC concentrations due to the washout effect of rain, while the pre-monsoon and post-monsoon periods showed higher TN and TC levels. The higher δ 13 C and δ 15 N values observed during the winter season indicates that the dominant sources of the aerosol are biofuels and biomass burning which are transported from North and North-East India. The seasonal variation of stable carbon and nitrogen isotope ratio indicated the influence of marine on aerosols over the study area. Overall, this research contributes to the understanding of PM 10 pollution in the Trombay area and provides valuable information for future air quality management and mitigation strategies in urban environments. Declarations Acknowledgement Authors acknowledge the Dr. Roopashree Srivastava from RSSD, BARC for providing the metrological parameters and plotting the wind rose diagram. Authors wish to acknowledge Dr. D.K. Aswal, GD, HS&EG for their support and guidance during this study. Authors also acknowledge the use of generative AI tools (ChatGPT and Gemini), for language improvement, including rephrasing and English corrections. These AI tools were used exclusively for language editing, and all intellectual content, analyses, and interpretations are the work of the authors. After using these tools, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication. Funding The authors did not receive support from any organization for the submitted work. Competing Interest The authors have no competing interests to declare that are relevant to the content of this article. Data Availability The complete set of measured data and other parameters (e.g., wind direction, temperature, etc.) measured, are available from the corresponding author upon reasonable request. Ethics Declaration The authors declare that this study was conducted following ethical guidelines and in full compliance with the ethical standards of research in environmental science. No human or animal subjects were involved in this study. 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Dicarboxylic acids, metals and isotopic compositions of C and N in atmospheric aerosols from inland China: Implications for dust and coal burning emission and secondary aerosol formation. Atmospheric Chemistry and Physics , 10 (13), 6087–6096. https://doi.org/10.5194/ACP-10-6087-2010 Widory, D. (2006). Combustibles, fuels and their combustion products: A view through carbon isotopes. Combustion Theory and Modelling , 10 (5), 831–841. https://doi.org/10.1080/13647830600720264 Widory, D. (2007). Nitrogen isotopes: Tracers of origin and processes affecting PM10 in the atmosphere of Paris. Atmospheric Environment , 41 (11), 2382–2390. https://doi.org/10.1016/j.atmosenv.2006.11.009 Widory, D., Roy, S., Le Moullec, Y., Goupil, G., Cocherie, A., & Guerrot, C. (2004). The origin of atmospheric particles in Paris: A view through carbon and lead isotopes. Atmospheric Environment , 38 (7). https://doi.org/10.1016/j.atmosenv.2003.11.001 Xiao, H. W., Xiao, H. Y., Luo, L., Zhang, Z. Y., Huang, Q. W., Sun, Q. Bin, & Zeng, Z. qi. (2018). Stable carbon and nitrogen isotope compositions of bulk aerosol samples over the South China Sea. Atmospheric Environment , 193 , 1–10. https://doi.org/10.1016/j.atmosenv.2018.09.006 Yadav, K., Sunder Raman, R., Bhardwaj, A., Paul, D., Gupta, T., Shukla, D., Laxmi Prasad, S. V., Lokesh, K. S., & Venkatesh, P. (2022). Tracing the predominant sources of carbon in PM2.5 using δ13C values together with OC/EC and select inorganic ions over two COALESCE locations. Chemosphere , 308 , 136420. https://doi.org/10.1016/j.chemosphere.2022.136420 Yeatman, S. G., Spokes, L. J., Dennis, P. F., & Jickells, T. D. (2001). Comparisons of aerosol nitrogen isotopic composition at two polluted coastal sites. Atmospheric Environment , 35 (7). https://doi.org/10.1016/S1352-2310(00)00408-8 Zhai, Y., Li, X., Wang, T., Wang, B., Li, C., & Zeng, G. (2018). A review on airborne microorganisms in particulate matters: Composition, characteristics and influence factors. Environment International , 113 , 74–90. https://doi.org/10.1016/J.ENVINT.2018.01.007 Tables Table 1 : Results of the measured parameters δ 13 C (‰), δ 15 N (‰), TC/TN ratio, N (µg.m -3 ) and C (µg.m -3 ) in PM 10 . Parameter Min Max Average δ 13 C (‰) -26.17 -22.44 -24.87±0.91 δ 15 N (‰) -2.33 21.39 9.06±5.59 TC/TN Ratio 2.76 7.51 4.79±1.21 N (µg.m -3 ) 0.09 2.32 0.9±0.71 C (µg.m -3 ) 0.40 9.12 3.75±2.53 Table 2 : Seasonal variation of the measured parameters Parameter Summer (Mar-May) Monsoon (Jun – Sep) Winter (Nov–Feb) δ 13 C (‰) Range -25.63 to -22.44 -26.17 to -24.87 -25.52 to -22.96 Average -24.86±0.90 -25.73±0.36 -24.20±0.85 δ 15 N (‰) Range -2.33 to 16.10 0.86 to 4.73 6.81 to 21.39 Average 8.11±6.13 3.52±1.16 13.01±4.81 TC/TN ratio Range 3.58 to 6.37 3.18 to 5.79 2.76 to 7.45 Average 4.74±1.05 4.83±0.88 4.48±1.45 TN (µg.m -3 ) Range 0.29 to 1.84 0.09 to 0.34 0.40 to 2.32 Average 0.81±0.66 0.21±0.09 1.45±0.62 TC (µg.m -3 ) Range 0.97 to7.26 0.40 to 1.63 2.07 to 9.12 Average 3.46±2.44 0.97±0.44 5.62±1.72 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6194800","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":435927075,"identity":"d92c4278-d243-4c2b-b99a-c6169f49107c","order_by":0,"name":"V. B. Yadav","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYDACCSDmYWCQM4CLMBOpxZh0LYkbiHaXwe0eww9vag6nb2c/+/ADwy+bxAZ23gP4tdw5Yyw559jh3J096cYSjH1piQ3MfAl4tUjOyDGQ5mFLy91wII2NgbHnsDEDM48BIS3Gv3n+paUbnH9GpBZ+iRwzad42mwSDG0BbGH4cliOsReZYmeXcPhvDnTOeMUskNqTJsRHSwibdvPnGm28S8ub8aYwfPvyx4eHnP4NfCwMDB5KCxDagIQTUAwH7AyTOH8LqR8EoGAWjYOQBAKZdPYKZUTQEAAAAAElFTkSuQmCC","orcid":"","institution":"Bhabha Atomic Research Centre","correspondingAuthor":true,"prefix":"","firstName":"V.","middleName":"B.","lastName":"Yadav","suffix":""},{"id":435927076,"identity":"23bd13e6-5f8b-4c09-a9a9-d42230c62c13","order_by":1,"name":"Vandana A. Pulhani","email":"","orcid":"","institution":"Bhabha Atomic Research Centre","correspondingAuthor":false,"prefix":"","firstName":"Vandana","middleName":"A.","lastName":"Pulhani","suffix":""},{"id":435927077,"identity":"d20b9803-8066-4c6a-a3a9-c9800a31e959","order_by":2,"name":"A. Vinod Kumar","email":"","orcid":"","institution":"Bhabha Atomic Research Centre","correspondingAuthor":false,"prefix":"","firstName":"A.","middleName":"Vinod","lastName":"Kumar","suffix":""}],"badges":[],"createdAt":"2025-03-10 11:08:16","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6194800/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6194800/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79579440,"identity":"4d700c90-d3b2-4364-97fe-85a6f0321762","added_by":"auto","created_at":"2025-03-31 11:40:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":183929,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly wind rose diagram during the period Jan-Dec, 2022\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6194800/v1/8a75c3b3a602ae0ffb420756.png"},{"id":79580691,"identity":"854ca92c-f1c8-46b0-948c-0189e8aeaf5c","added_by":"auto","created_at":"2025-03-31 11:48:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":251731,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly variation of the measured parameters (a): Total Nitrogen (µg.m\u003csup\u003e-3\u003c/sup\u003e), (b): Total Carbon (µg.m\u003csup\u003e-3\u003c/sup\u003e) and (c) ratio of TC/TN. The square within each box represents the mean value. The minima and maxima are shown with horizontal lines (-). The, whiskers represent the 25\u003csup\u003eth\u003c/sup\u003e and 75\u003csup\u003eth\u003c/sup\u003e percentiles and outliers at 99 and 1 percent are shown by crosses (X). The median is represented by the solid line (This description holds for \u003cstrong\u003eFig. 5 and 6,\u003c/strong\u003e as well).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6194800/v1/9f9ae2b13e893fb411f6c76a.png"},{"id":79580690,"identity":"d0b72811-a868-45dd-8308-70cd3c9508aa","added_by":"auto","created_at":"2025-03-31 11:48:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":162140,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly variation of δ\u003csup\u003e15\u003c/sup\u003eN and δ\u003csup\u003e13\u003c/sup\u003eC during the sampling period\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6194800/v1/8ae0ca1a80bee8cfecfac741.png"},{"id":79579446,"identity":"a1e95433-c4d6-4ab3-95f9-e28b9979168f","added_by":"auto","created_at":"2025-03-31 11:40:54","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":255377,"visible":true,"origin":"","legend":"\u003cp\u003eBox plot of the measured in four different seasons\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6194800/v1/4174bfc094ac54f07d323d73.jpeg"},{"id":79579445,"identity":"abf1e2bf-bea9-4739-a8e5-0687bcb57336","added_by":"auto","created_at":"2025-03-31 11:40:54","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":199555,"visible":true,"origin":"","legend":"\u003cp\u003eRange or mean (± 1 standard deviation) of stable nitrogen isotope ratios (δ\u003csup\u003e15\u003c/sup\u003eN) in particulates emitted from typical major sources (end-member) and in atmospheric aerosols (PM\u003csub\u003e10\u003c/sub\u003e) at sampling site. The δ\u003csup\u003e15\u003c/sup\u003eN values are taken from the literature mentioned in the manuscript and Supplementary Table S5.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6194800/v1/3da4788850ee9a79484d687b.png"},{"id":79579444,"identity":"efc73b33-6d11-4f9f-9b49-80110f8005f0","added_by":"auto","created_at":"2025-03-31 11:40:54","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":441415,"visible":true,"origin":"","legend":"\u003cp\u003eRange or mean (± 1 standard deviation) of end-member stable carbon isotope ratios (δ\u003csup\u003e13\u003c/sup\u003eC) in articulates emitted from major typical sources and in atmospheric aerosols (PM\u003csub\u003e10\u003c/sub\u003e) at sampling site. The δ\u003csup\u003e13\u003c/sup\u003eC values are taken from the literature mentioned in the manuscript and Supplementary Table S6.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6194800/v1/2d28018f331183593a679909.png"},{"id":79579447,"identity":"fc70959d-aaf4-4c3f-bbf0-c6136963436e","added_by":"auto","created_at":"2025-03-31 11:40:54","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":101740,"visible":true,"origin":"","legend":"\u003cp\u003eDual isotope isospace plot of δ\u003csup\u003e13\u003c/sup\u003eC versus δ\u003csup\u003e15\u003c/sup\u003eN of TC and TN in PM\u003csub\u003e10\u003c/sub\u003e from study area along with source-emission end-members\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6194800/v1/84d6da1390d4087bc9aa682f.jpeg"},{"id":80659584,"identity":"e84b116a-9873-4c06-9ec4-45fd097ad8bb","added_by":"auto","created_at":"2025-04-15 16:16:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2835629,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6194800/v1/fefdf898-bc98-40d5-8204-b26b1a95b39d.pdf"},{"id":79579448,"identity":"63391e42-56cc-4b78-b9d1-a21593e72562","added_by":"auto","created_at":"2025-03-31 11:40:54","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":606639,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6194800/v1/d6aae5a18eafda2794d30cce.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Seasonal Dynamics of Stable Carbon and Nitrogen Isotope Ratio in PM10 Aerosols at a Coastal Urban Site in Mumbai, India","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eParticulate matter (PM), a complex mixture of solid and liquid particles suspended in the air, has been a growing concern for its impact on human health (Brook et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Carroll \u0026amp; Risch, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1984\u003c/span\u003e; Gurjar et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Guttikunda \u0026amp; Gurjar, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Kelly \u0026amp; Fussell, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Pope \u0026amp; Dockery, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), as well as climate and hydrological cycle (Ramanathan et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). PM\u003csub\u003e10\u003c/sub\u003e, is a major air pollutant, which includes particles with an aerodynamic diameter of 10 micrometers or less and is known to contain a variety of toxic substances such as heavy metals (Popoola et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), organic compounds (Aggarwal et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Fazakas et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and microorganisms (Zhai et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Many of the Indian city have observed high level of the particulate matter (Pant et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Satsangi et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Sharma et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Vikramahirwar \u0026amp; Bajpai, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and Mumbai is no exception to this, with high levels of PM\u003csub\u003e10\u003c/sub\u003e being recorded at various locations in the city (Joseph et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; P. Mangaraj et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUnderstanding the sources and transport of atmospheric dust is critical for predicting and mitigating its effects on environmental and human health. Among the various methods used, the stable isotope ratio of carbon and nitrogen (δ\u003csup\u003e13\u003c/sup\u003eC and δ\u003csup\u003e15\u003c/sup\u003eN) are useful tracers for understanding the sources and fate of particulate matter in the atmosphere. The stable carbon and nitrogen isotopic composition of aerosols has advantage compared to chemical tracers, to find the information about the source and aging effect of the aerosols (Bosch et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Morera-G\u0026oacute;mez et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Sawlani et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Sen et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) as later are non-conservative and their ratio may get affected during the long-range transport. Air mass back trajectory analysis (Petit et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) provides the geographical location of the potential sources which is adding the particulate loading at the receptor site. However, trajectory analysis does not provide the information regarding the particulate source (e.g., biofuel, fossil fuel, mineral dust etc.) (Sen et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStable isotope ratio of carbon (δ\u003csup\u003e13\u003c/sup\u003eC) was first used to identify the urban atmospheric particulate matter source by Court et al., (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1981\u003c/span\u003e) and later the carbon emission by biomass burning using δ\u003csup\u003e13\u003c/sup\u003eC along with black carbon to total carbon ratio (Cachier et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1985\u003c/span\u003e); identifying and semi-quantifying the road traffic and industrial particulate matter using isotopes of carbon and lead in Paris city by Widory et al., (\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The use of δ\u003csup\u003e15\u003c/sup\u003eN in atmospheric aerosol was initiated by Moore H. (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e1977\u003c/span\u003e) and subsequently adopted in various studies to find the sources of the particulate matter in the atmosphere (Heaton et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Russell et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Widory, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Yeatman et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Thereafter, many studies were carried out using carbon and nitrogen stable isotope ratio (δ\u003csup\u003e13\u003c/sup\u003eC and δ\u003csup\u003e15\u003c/sup\u003eN) of total carbon (TC) and total nitrogen (TN) together to identify the sources and fate of the particulate matter in the atmosphere (Aggarwal et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Agnihotri et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Aguilera \u0026amp; Whigham, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Bikkina et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Gupta et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Kirillova et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Kundu et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Morera-G\u0026oacute;mez et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Pavuluri et al., 2011; Pavuluri \u0026amp; Kawamura, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Sawlani et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Sharma et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eIn context of Indian cities, studies (Aggarwal et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Agnihotri et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; G.K. Singh et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Pavuluri et al., 2011; Sen et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Sharma et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Hegde et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Sawlani et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) reported mainly short term (e.g. winter and/or summer season) measurement of stable carbon and nitrogen isotopic composition. An investigation during December 2009 to January 2011, conducted by Agnihotri et al., (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) examined the isotope ratios of carbon and nitrogen in Total Suspended Air Particulate Matter (TSPM) in Goa, India. There are few studies dealing with long-term variation of δ\u003csup\u003e13\u003c/sup\u003eC (Singh et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Yadav et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), δ\u003csup\u003e13\u003c/sup\u003eC and Δ\u003csup\u003e14\u003c/sup\u003eC (S. Bikkina et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Kirillova et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) in Indian cities. Studies (Agnihotri et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Rastogi et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) also reported the isotopic composition of carbon and nitrogen over Bay of Bengal and Arabian Sea, in Indian context. The previous studies in Indian continent with details like measured parameters, fraction of particles measured, duration etc., are given in the supplementary material Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. Therefore, there is scarcity of the data of long-term or annual measurement and interpretation of stable isotopic composition of carbon and nitrogen in PM\u003csub\u003e10\u003c/sub\u003e in Indian context. Mumbai city has various sources of urban particulate matter and the continuous annual monitoring and, isotopic characterization of the particulate matters has not been carried out. In the present study, we have measured the stable isotope ratio of carbon and nitrogen (δ\u003csup\u003e13\u003c/sup\u003eC and δ\u003csup\u003e15\u003c/sup\u003eN) along with the total carbon, total nitrogen in particulate matter (PM\u003csub\u003e10\u003c/sub\u003e) collected during January to December 2022 from Trombay area, a coastal site which is in the eastern suburbs of Mumbai. These measured parameters and correlations amongst the various parameters were used to understand the source of particulate matters and the coastal effects on PM in the sampling area.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 A Study area and sampling\u003c/h2\u003e \u003cp\u003eThe city of Mumbai, located on the western coast of India, is one of the most populous cities in the world. The study area Trombay, located in the eastern suburbs of Mumbai, is known for its industrial activities. Apart from Bhabha Atomic Research Centre (BARC), Trombay it is home to a diverse range of industries like, chemical plants, oil refineries, and thermal power stations. Some of the major industries in the area are Tata Power Thermal Power Station, Rashtriya Chemicals and Fertilizers Ltd. (RCF), Hindustan Petroleum Corporation Limited (HPCL) Refinery, Indian Oil Corporation Limited (IOCL) Terminal, National Organic Chemical Industries Limited (NOCIL) and Tata Chemicals. Trombay area is located between two water bodies, in east Mumbai Harbour Bay (distance around 1 km) and in west Arabian Sea (distance around 12.5 km).\u003c/p\u003e \u003cp\u003eWe have collected particulate matter (PM\u003csub\u003e10\u003c/sub\u003e) at the terrace (~\u0026thinsp;15 m above the ground level) of Modular Laboratories (Lat, long: 9\u0026deg;00'43.4\"N 72\u0026deg;55'15.7\"E) (Supplementary \u003cb\u003eFig S1\u003c/b\u003e) on pre-combusted glass fibre filter paper using high volume air samplers at flow rate of 1.0 m\u003csup\u003e3\u003c/sup\u003e.min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. The continuous sampling was carried out and samples (n\u0026thinsp;=\u0026thinsp;37) were collected during January 2022 to December 2022. Prior to the isotopic analysis, the filter samples (2 circles, each of ~\u0026thinsp;2.8 cm diameters) were placed in closed glass container and stored in the freezer at \u0026minus;\u0026thinsp;20 \u003csup\u003e0\u003c/sup\u003eC temperature. The blank contribution of δ\u003csup\u003e13\u003c/sup\u003eC and δ\u003csup\u003e15\u003c/sup\u003eN from the glass fibre filter paper was determined measuring standard solution of sulfanilamide deposited (0.5, 1.0, 2.0 and 4.0 \u0026micro;mol of N and multiple aliquots) and dried on the glass filter as per the recommended procedure (Agnihotri et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Avak H. and Fry B., 1999).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Measurement\u003c/h2\u003e \u003cp\u003eThe elemental (TC and TN) composition, δ\u003csup\u003e15\u003c/sup\u003eN and δ\u003csup\u003e13\u003c/sup\u003eC values were measured using Elemental Analyser (EA; vario PYRO CUBE, Elementary Analysensysteme GmbH, Germany) connected in series with continuous flow Isotope Ratio Mass Spectrometer (IRMS; Isoprime100, Isoprime UK Limited). Two circular pieces of area\u0026thinsp;~\u0026thinsp;2 cm\u003csup\u003e2\u003c/sup\u003e of filters were packed in tin boats as round pellets, and pressed to remove any trapped air. The sample pellets were loaded in auto sampler and dropped in combustion tube where they are combusted at 1150 \u003csup\u003e0\u003c/sup\u003eC in presence of oxygen and WO\u003csub\u003e3\u003c/sub\u003e powder as catalyst. The evolved gases from the combustion were passed through the reduction tube filled with metallic copper (Cu) and Ag wool that reduces NO\u003csub\u003ex\u003c/sub\u003e, SO\u003csub\u003ex\u003c/sub\u003e into N\u003csub\u003e2\u003c/sub\u003e and SO\u003csub\u003e2\u003c/sub\u003e and removes any Halide (X), respectively. The excess oxygen is also removed by copper in the reduction tube. The gas stream is further passed through drying tube, filled with Sicapent (MgClO\u003csub\u003e4\u003c/sub\u003e) to remove any moisture in the gas stream (CO\u003csub\u003e2\u003c/sub\u003e, N\u003csub\u003e2\u003c/sub\u003e and SO\u003csub\u003e2\u003c/sub\u003e). The gases were separated using Temperature Programmable Desorption (TPD) and sent to IRMS system through Thermal Conductivity Detector (TCD) where the percentage composition of these separate gases was estimated. Replicates were analyzed for each sample to check the homogeneity of the sample and precision of the measurement. Stable isotopic compositions are expressed in delta (δ) notation. This is calculated as a ratio of the less abundant or (heavier) atom with respect to naturally more abundant (lighter) atoms in a sample relative to a standard, by following equation:\u003c/p\u003e \u003cp\u003eδ\u003csup\u003e13\u003c/sup\u003eC (\u0026permil;) = [(\u003csup\u003e13\u003c/sup\u003eC/\u003csup\u003e12\u003c/sup\u003eC) \u003csub\u003esample\u003c/sub\u003e/(\u003csup\u003e13\u003c/sup\u003eC/\u003csup\u003e12\u003c/sup\u003eC) \u003csub\u003estandard\u003c/sub\u003e \u0026minus;\u0026thinsp;1] x 1000\u003c/p\u003e \u003cp\u003eδ\u003csup\u003e15\u003c/sup\u003eN (\u0026permil;) = [(\u003csup\u003e15\u003c/sup\u003eN/\u003csup\u003e14\u003c/sup\u003eN) \u003csub\u003esample\u003c/sub\u003e/(\u003csup\u003e15\u003c/sup\u003eN/\u003csup\u003e14\u003c/sup\u003eN) \u003csub\u003estandard\u003c/sub\u003e \u0026minus;\u0026thinsp;1] x 1000\u003c/p\u003e \u003cp\u003eThe measured δ\u003csup\u003e13\u003c/sup\u003eC and δ\u003csup\u003e15\u003c/sup\u003eN values were calibrated and expressed relative to VPDB (Vienna Pee Dee Belemnite) scale and N\u003csub\u003e2\u003c/sub\u003e-air scale, respectively. The replicates of samples were measured to get the precision of the instrument for δ\u003csup\u003e13\u003c/sup\u003eC and δ\u003csup\u003e15\u003c/sup\u003eN measurement. Blank is estimated by plotting δ values of carbon and nitrogen in standard (Sulfanilamide) against 1/nA (i.e., 1/respective IRMS currents in nano Ampere). From the δ\u003csup\u003e15\u003c/sup\u003eN vs. (1/nA) plot, slope and intercepts were evaluated. The blank values of δ\u003csup\u003e15\u003c/sup\u003eN in filter paper was estimated using following equation (Fry, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2007\u003c/span\u003e);\u003c/p\u003e \u003cp\u003eδ\u003csup\u003e15\u003c/sup\u003eN\u003csub\u003eBlank\u003c/sub\u003e = (Slope/nA\u003csub\u003eBlank\u003c/sub\u003e)\u0026thinsp;+\u0026thinsp;Intercept\u003c/p\u003e \u003cp\u003eIn similar way the blank values of δ\u003csup\u003e13\u003c/sup\u003eC in filter paper were estimated from the slope and intercept of the δ\u003csup\u003e13\u003c/sup\u003eC vs. (1/nA) plot. The external precision of the instrument (EA-IRMS) was estimated by analyzing the working reference standards i.e., Sulfanilamide. The δ\u003csup\u003e13\u003c/sup\u003eC and δ\u003csup\u003e15\u003c/sup\u003eN results were corrected for the blank contribution of carbon and nitrogen from filter paper. The total carbon and nitrogen content in the samples were determined by measuring the peak areas of the carbon and nitrogen signals in the elemental analyser (EA) responses. The peak areas were converted into amounts of carbon and nitrogen using calibration factor measured from laboratory reference standard (sulfanilamide). The calibrated amounts were then divided by the corresponding air volume sampled to obtain the weight per unit volume of air.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Result and Discussion","content":"\u003cp\u003eThe precision of IRMS system (1σ) for δ\u003csup\u003e13\u003c/sup\u003eC and δ\u003csup\u003e15\u003c/sup\u003eN measurement was found to be 0.09\u0026permil; and 0.13\u0026permil;, respectively. Replicate analysis of individual samples for δ\u003csup\u003e13\u003c/sup\u003eC and δ\u003csup\u003e15\u003c/sup\u003eN showed precision better than 0\u0026middot;5\u0026permil; and 1.0\u0026permil;, respectively. The precision (1σ) of EA for C and N measurement were found to be 0.2% and 0.4% respectively. The descriptive statistical analysis of measured parameters is given in the supplementary material (Table S2). During the sampling period from January to December 2022, the δ\u003csup\u003e13\u003c/sup\u003eC and δ\u003csup\u003e15\u003c/sup\u003eN values ranged from \u0026minus;\u0026thinsp;26.2\u0026permil; to -22.4\u0026permil; and from \u0026minus;\u0026thinsp;2.3\u0026permil; to 21.4\u0026permil;, respectively. The average δ\u003csup\u003e13\u003c/sup\u003eC value was \u0026minus;\u0026thinsp;24.9\u0026permil;, with a standard deviation (1σ) of 0.9\u0026permil;. The average δ\u003csup\u003e15\u003c/sup\u003eN value was 9.1\u0026permil;, with a standard deviation (1σ) of 5.6\u0026permil;. The other measured parameters, TC/TN ratio, TN, and TC, ranged from 2.8 to 7.5, 0.1 \u0026micro;g.m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e to 2.3 \u0026micro;g.m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e, and 0.4 \u0026micro;g.m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e to 9.1 \u0026micro;g.m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e, respectively. The average TC/TN ratio was 4.8, with a standard deviation (1σ) of 1.2. The average TN was 0.9 \u0026micro;g.m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e, with a standard deviation (1σ) of 0.7 \u0026micro;g.m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e. The average TC was 3.8 \u0026micro;g.m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e, with a standard deviation (1σ) of 2.5 \u0026micro;g.m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe monthly wind rose pattern for the year 2022 is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The wind direction is utilized to understand the direction of origin of the primary sources of carbon and nitrogen to aerosol at the sampling site during different months of the study period. The prevailing wind originates predominantly from the South-West, with occasional wind from the North and West-North during April to August. In September, wind blows from both the South-West and North-East directions. From October to January, the wind predominantly comes from the North-East, except in December when there is some contribution from the West-North direction. In February, a mix of wind from the West-North, North, and North-East directions was observed. It is clear from the above wind rose that in different seasons Mumbai has prevalent winds e.g., winter (wind from WN, N and NE), Summer (prevalent wind from SW with some NE), summer (prevalent wind from SW) and post-monsoon (prevalent wind from NE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Annual Variation of measured parameters\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1 Annual Variation of TN, TC and TC/TN ratio\u003c/h2\u003e \u003cp\u003eThe variations of TC/TN ratio, TN, and TC in the particulate matter (PM\u003csub\u003e10\u003c/sub\u003e) observed during the study period are shown in the Supplementary \u003cb\u003eFig. S2\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe amount of total nitrogen (TN) and total carbon (TC) in the atmosphere was lowest during the monsoon season because of low dust load and the rain wash out. The ratio of total carbon to total nitrogen (TC/TN) varied throughout the study period (\u003cb\u003eFig. S2\u003c/b\u003e). The monthly variation of TN, TC and TC/TN ratio observed during the study period are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The average TC/TN ratio was higher in February and October than in other months. In February the carbon content was higher than nitrogen but from October onwards, both carbon and nitrogen content increased as compared to other months, but the rate of increase in carbon content was greater than the rate of increase in nitrogen content as shown in \u003cb\u003eFig. S2\u003c/b\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The higher average nitrogen and carbon content having lower TC/TN ratio were found in January and December compared to other months. Agnihotri et al., (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) reported significantly lower average values of TC/TN in aerosols over the Bay of Bengal (6.8\u0026thinsp;\u0026plusmn;\u0026thinsp;12.5) and Indian cities (5.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6) compared to the Arabian Sea (50\u0026thinsp;\u0026plusmn;\u0026thinsp;10). In January, the prevailing wind direction is mainly NE, while in December; prevailing wind comes from NE along with some minor contribution from NW and N direction. These winds transport aerosols (from Indo Gangetic Plains (IGP) region) with a lower TC/TN ratio (Agnihotri et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Sen et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Sharma et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), which is reflected in our measurements.\u003c/p\u003e \u003cp\u003eAlthough the carbon and nitrogen content vary greatly in October, the TC/TN ratio does not vary as much as it does during other months (e.g., May or February) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This is because the wind direction in October is predominantly from the N and NE direction, which has carried the aerosol emitted from IGP region to sampling site. The large variation in nitrogen and carbon content is likely caused due to the input from the burning of firecrackers during Diwali festival, which was celebrated on October 24, 2022. The increasing carbon and nitrogen content has been observed from October to February may be due to; (1) aerosol contribution from firecrackers in October, (2) contribution of aerosol originated in north and northeastern part (IGP region) of India due to burning of biomass and biofuel during the winter season (Agnihotri et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Sawlani et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Sen et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), (3) higher residence time of the aerosol due to prevailing metrological conditions (Aggarwal et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Sharma et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2 Annual Variation of stable carbon and nitrogen isotopic composition (δ\u003csup\u003e13\u003c/sup\u003eC and δ\u003csup\u003e15\u003c/sup\u003eN)\u003c/h2\u003e \u003cp\u003eThe variation of δ\u003csup\u003e13\u003c/sup\u003eC and δ\u003csup\u003e15\u003c/sup\u003eN in the individual samples collected during the study period is shown in \u003cb\u003eFig. S3.\u003c/b\u003e It is observed that there is higher variation in the δ\u003csup\u003e15\u003c/sup\u003eN (-2.33\u0026permil; to 21.39\u0026permil;) values during the study period which may be due to sources with significantly different δ\u003csup\u003e15\u003c/sup\u003eN contributing to the PM\u003csub\u003e10\u003c/sub\u003e. Although the δ\u003csup\u003e15\u003c/sup\u003eN values showed more variation during the study period, about half of the observed values were found to be in the range between ~\u0026thinsp;8 to 14\u0026permil;. The variation in the δ\u003csup\u003e13\u003c/sup\u003eC values (-26.2\u0026permil; to -22.4\u0026permil;) during the study period is less compared to that of δ\u003csup\u003e15\u003c/sup\u003eN variation as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The lowest value (-2.3\u0026permil;) of δ\u003csup\u003e15\u003c/sup\u003eN observed during 17\u0026ndash;24 May was associated with the highest value (-22.4\u0026permil;) of the δ\u003csup\u003e13\u003c/sup\u003eC as shown in \u003cb\u003eFig. S3\u003c/b\u003e. These observation in the month of May indicate the effect of the pre-monsoon shower (on 17th May 2022) and the input of particulate matter having lower δ\u003csup\u003e15\u003c/sup\u003eN and higher δ\u003csup\u003e13\u003c/sup\u003eC value. The rain washes out the existing particulate matter which is having relatively higher δ\u003csup\u003e15\u003c/sup\u003eN and lower δ\u003csup\u003e13\u003c/sup\u003eC value. The fresh input of carbonaceous aerosol with higher δ\u003csup\u003e13\u003c/sup\u003eCvalue and lowerδ\u003csup\u003e15\u003c/sup\u003eN value is carried by the prevalent wind from S/SW direction as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThese prevailing winds carried the aerosol originated from marine sources, the coal burning from Trombay Thermal Power Station (a coal-based thermal power plant, 1.75 km away) and from fuel oil/fossil fuel burning originated from the different refineries (14 km away) located in SW direction from sampling point. Therefore, considering the δ\u003csup\u003e13\u003c/sup\u003eC values of end-members (marine source: -21.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u0026permil;, coal: -22\u0026permil;, fossil fuel: -26.05\u0026permil;) and δ\u003csup\u003e15\u003c/sup\u003eN value of end-members (marine source: 4.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u0026permil;, coal: -5.3\u0026permil;, fuel oil: -7.5\u0026thinsp;\u0026plusmn;\u0026thinsp;8.3\u0026permil;) and associated fractionation, the observed values seem dominantly affected by fuel oil combustion and coal. The δ\u003csup\u003e15\u003c/sup\u003eN value has decreasing trend during the period February to May and the lowest value was found in the month of May (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The wind changes its direction from northerly to southerly during the period February to May which results in the different origin of the particulate matter i.e., aerosols from the Arabian sea are transported to land during this period. As it is reported that the δ\u003csup\u003e15\u003c/sup\u003eN found over the Arabian sea has lower values (1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u0026permil;) (Agnihotri et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) compared to that of the other Indian city (Aggarwal et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Agnihotri et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Pavuluri et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) therefore the observed δ\u003csup\u003e15\u003c/sup\u003eN value decreases during the period February to May (discussed in details in next section).\u003c/p\u003e \u003cp\u003eThe average value of the δ\u003csup\u003e15\u003c/sup\u003eN remains similar during the period June to September with slight variation as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The value of δ\u003csup\u003e15\u003c/sup\u003eN again increases from September which is related to the change in the wind direction (from SW to NE) along with other factors and sources (discussed in details in next section). The monthly average values of δ\u003csup\u003e13\u003c/sup\u003eC and δ\u003csup\u003e15\u003c/sup\u003eN has shown significant variation throughout the sampling period. The monthly average values for both the δ\u003csup\u003e13\u003c/sup\u003eC and δ\u003csup\u003e15\u003c/sup\u003eN indicate that the PM\u003csub\u003e10\u003c/sub\u003e gets depleted in terms of heavier isotopes (\u003csup\u003e13\u003c/sup\u003eC and \u003csup\u003e15\u003c/sup\u003eN) in comparison to lighter isotope (\u003csup\u003e12\u003c/sup\u003eC and \u003csup\u003e14\u003c/sup\u003eN) during January to May. Similarly, PM\u003csub\u003e10\u003c/sub\u003e gets enriched (\u003csup\u003e13\u003c/sup\u003eC and \u003csup\u003e15\u003c/sup\u003eN) in comparison to lighter isotope (\u003csup\u003e12\u003c/sup\u003eC and \u003csup\u003e14\u003c/sup\u003eN) during the period September to January. This variation may be attributed to different sources of carbon and nitrogen contributing to the particulate matter and photochemical aging of atmospheric aerosols (Aggarwal et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Seasonal Variation of measured parameters\u003c/h2\u003e \u003cp\u003eIt has been observed in different laboratory and field experiment that during formation of the particulate matter from source material there is isotopic fractionation which depend upon state of material and process through which particles are formed (Martinelli et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Turekian et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Widory, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Widory et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Therefore, to relate the particulate matter with their origin during different seasons, it important to understand the value of δ\u003csup\u003e13\u003c/sup\u003eC and δ\u003csup\u003e15\u003c/sup\u003eN in source material and fractionation during particle formation and aging (Wang et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe results observed during the study period were grouped in 4 categories; winter (January - February), Pre-monsoon/Summer (March - May), Monsoon (June - September) and Post Monsoon (October - December) corresponding to main seasons observed in India (IMD, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Singh et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Yadav et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This categorization will help in understanding the effect of the different seasons on the particulate matter contribution and sources of the particulate matter. The range and mean (average\u0026thinsp;\u0026plusmn;\u0026thinsp;1σ) values of the different parameters measured during different seasons is given in the Table S3. The observed seasonal changes are very consistent in all the measured parameters (TC, TN, δ\u003csup\u003e13\u003c/sup\u003eC and δ\u003csup\u003e15\u003c/sup\u003eN) as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Statistical Shapiro-Wilk Test was performed on each set of data to check the normal distribution. Each data set, except δ\u003csup\u003e13\u003c/sup\u003eC, TC and TN measured in summer, followed normal distribution (Supplementary data Table S4). We performed parametric test (ANOVA: Single Factor) and non-parametric test (Kruskal-Wallis test) to check the statistically significant differences of the measured parameters in different seasons. The p values (at the 95% confidence level) of ANOVA (Single Factor) test for δ\u003csup\u003e13\u003c/sup\u003eC, δ\u003csup\u003e15\u003c/sup\u003eN, TC, TN and TC/TN were found to be 7.5 x 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e, 5.1 x 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e, 6.0 x 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e, 1.6 x 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e and 0.99, respectively. The p values of Kruskal-Wallis test for δ\u003csup\u003e13\u003c/sup\u003eC, δ\u003csup\u003e15\u003c/sup\u003eN, TC, TN and TC/TN were found to be 5.39 x 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e, 1.06 x 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e, 2.17 x 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e, 3.98 x 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e and 0.89, respectively. Therefore, the observed values of the measured parameters (TC, TN, δ\u003csup\u003e13\u003c/sup\u003eC and δ\u003csup\u003e15\u003c/sup\u003eN) in different seasons have statistically significant difference at the 95% confidence level. The seasonal variation of isotope ratio observed in the present and previous studies is given in the Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Seasonal variation of total carbon (TC) and total nitrogen (TN)\u003c/h2\u003e \u003cp\u003eTotal carbon and total nitrogen in PM\u003csub\u003e10\u003c/sub\u003e observed during the winter in present study (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) is found to be lower than that reported TC and TN for winter seasons in other Indian cities (Sharma et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Sen et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This observation may be attributed to geographically isolated location of the sampling point and proximity to ocean water which also acts as sink for the different pollution and aerosols.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of stable carbon and nitrogen isotopic composition during different seasons at different location around the world\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAerosol\u003c/p\u003e \u003cp\u003eFraction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDuration/seasons\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eδ\u003csup\u003e13\u003c/sup\u003eC\u003csub\u003eTC\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eδ\u003csup\u003e15\u003c/sup\u003eN\u003csub\u003eTN\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eMumbai, India\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWinter (Jan-Feb, 22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-24.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003ePresent Study\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSummer (Mar-May, 22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-24.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMonsoon (Jun-Sept, 22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-25.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePost-monsoon\u003c/p\u003e \u003cp\u003e(Oct-Dec, 22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-24.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMumbai, India\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWinter (13\u0026ndash;18 Feb, 07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-25.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e21.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e(Aggarwal et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSummer (8\u0026ndash;14 June, 06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-26.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGoa, India\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTSPM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSummer (Mar-May, 10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.0\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-24.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e(Agnihotri et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTSPM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWinter (Jan-Feb, 11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.3\u0026thinsp;\u0026plusmn;\u0026thinsp;9.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-24.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eBhopal, India\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWinter (Jan-Feb, 19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13\u0026thinsp;\u0026plusmn;\u0026thinsp;10.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e̶\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-25.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e̶\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e(Yadav et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSummer (Mar-May, 19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e̶\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-26.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e̶\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMonsoon (Jun-Sep, 19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e̶\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e̶\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePost-monsoon\u003c/p\u003e \u003cp\u003e(Oct-Dec, 19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e̶\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-26.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e̶\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eMysuru, India\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWinter (Jan-Feb, 19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e̶\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-25.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e̶\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e(Yadav et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSummer (Mar-May, 19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e̶\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-26.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e̶\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMonsoon (Jun-Sep, 19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e̶\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-26.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e̶\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePost-monsoon\u003c/p\u003e \u003cp\u003e(Oct-Dec, 19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e̶\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-25.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e̶\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003ePrague,\u003c/p\u003e \u003cp\u003eCzech Republic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSummer (June-Sep, 16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e̶\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;27.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e̶\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e(Vodička et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAutumn (Sep-Nov, 17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e̶\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;26.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e̶\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWinter (Dec, 16-Feb, 17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e̶\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;25.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e̶\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpring (Mar-May, 17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;26.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e̶\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePunjab, India\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSummer (May 2018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e̶\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-27.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e̶\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e(Singh et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMonsoon (Aug 2018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e̶\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-26.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e̶\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePost-monsoon (Oct, 16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e̶\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-26.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e̶\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eKanpur, India\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWinter\u003c/p\u003e \u003cp\u003e(17 Jan-22 Feb, 07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e̶\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e̶\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-24.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e̶\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e(S. Bikkina et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSummer\u003c/p\u003e \u003cp\u003e(9 Mar-24 May, 07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e̶\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e̶\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-25.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e̶\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMonsoon\u003c/p\u003e \u003cp\u003e(2J un-14 Jun, 07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e̶\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e̶\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-25.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e̶\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePost-monsoon\u003c/p\u003e \u003cp\u003e(16 Oct-7 Dec, 07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e̶\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e̶\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-23.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e̶\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWrocław\u003c/p\u003e \u003cp\u003ePoland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWinter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e̶\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e̶\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;26.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e̶\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e(G\u0026oacute;rka et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2014\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSummer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e̶\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e̶\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;27.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e̶\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDebrecen\u003c/p\u003e \u003cp\u003eHungary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWinter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e̶\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e̶\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;25.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e̶\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e(Major et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSummer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e̶\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e̶\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;26.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e̶\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOkinawa Japan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTSPM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWinter\u003c/p\u003e \u003cp\u003eDec,09 \u0026ndash; Dec, 10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-22.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e(Kunwar et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTSPM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSummer\u003c/p\u003e \u003cp\u003eJun-Aug, 10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-22.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe lowest average value of TC and TN found during monsoon seasons may be attributed to lower residence time of these particles in the atmosphere due to rain washout and lower contribution of re-suspended particles due to damp condition. Higher average values of TC and TN during the post-monsoon and winter season is attributed to aerosol coming from burning of biofuels and biomass in north and north-east Indian states. This is supported by the prevailing wind from N and NE during this period (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Similar trend of TC in PM\u003csub\u003e2.5\u003c/sub\u003e is also reported for pre-monsoon, monsoon and post-monsoon seasons from study conducted in other Indian cities (Aggarwal et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Agnihotri et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Hegde et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Singh et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Yadav et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe previous study in Mumbai (Aggarwal et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) during winter (13\u0026ndash;18 Feb, 2007) have reported TC and TN as 22\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e and 2.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e, respectively. The present study reported TC and TN values during winter period (10\u0026ndash;21, Feb, 2022) as 4.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.83 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e and 0.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e, respectively. The higher values of TC and TN in previous study (Aggarwal et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) compared to present is observed but the isotope ratio is similar in both the studies. This difference in TC and TN (while same isotope ratio) is attributed to higher input of aerosol at the IIT Mumbai due its closed proximity to heavy road traffic and domestic as well as industrial settlement around the sampling location. While, the sampling point in present study is at isolated location (away from common road traffics and domestic settlements) and closed proximity to marine water bodies (Mumbai Harbour Bay and Arabian Sea).\u003c/p\u003e \u003cp\u003eThe average values of the TC/TN ratio were found nearly same in all the seasons as the differences between different seasons are not statistically significant at the 95% confidence level (p values: 0.9). The average value of TC/TN ratio was found close to that reported in TSPM from different Indian cities (like Delhi, Bhubaneshwar, Nainital, Nagpur, Anantapur) by (Agnihotri et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). While, TC/TN ratio measured during winter (20 Jan-5 Feb, 2014) by (Sharma et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) from IGP, Indian Himalayan Region (IHR) and Indian desert have values as 1.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6, 1.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5 and 0.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3, respectively. The present study has reported TC/TN ratio for the similar period (18 Jan \u0026ndash; 8 Feb, 2022) as 4.17\u0026thinsp;\u0026plusmn;\u0026thinsp;1.66 which is higher than the reported TC/TN value by (Sharma et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The average TC/TN ratio at the sampling sites during this campaign was relatively low compared to the ratios reported in the past for other sites in the Indian mainland (Aggarwal et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Agnihotri et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Sharma et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Seasonal variation of stable nitrogen isotopic ratio (δ\u003csup\u003e15\u003c/sup\u003eN)\u003c/h2\u003e \u003cp\u003eThe δ\u003csup\u003e15\u003c/sup\u003eN in the particulate matter from different major sources (end-member) were reported to be in wide range of values, ranging from \u0026minus;\u0026thinsp;15\u0026permil; to 20\u0026permil; (Agnihotri et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Martinelli et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Pavuluri et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Sawlani et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Turekian et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Widory, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The large variation in the δ\u003csup\u003e15\u003c/sup\u003eN value in aerosol is related to variation in the source pool and the different combustion temperature during the burning of biomass and biofuels (Sen et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Turekian et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). The combustion of fossil fuels like diesel, unleaded gasoline, coal, natural gas, and fuel oil produces the aerosol with δ\u003csup\u003e15\u003c/sup\u003eN values as 4.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76\u0026permil;, 4.6\u0026permil;, -5.3\u0026permil;, 7.7\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9\u0026permil; and \u0026minus;\u0026thinsp;7.5\u0026thinsp;\u0026plusmn;\u0026thinsp;8.3\u0026permil;, respectively. The δ\u003csup\u003e15\u003c/sup\u003eN value is reported in other studies like laboratory combustion of C3 and C4 type vegetation (δ\u003csup\u003e15\u003c/sup\u003eN: 2.0\u0026permil; to 19.5\u0026permil;, (Turekian et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e1998\u003c/span\u003e)), cow dung cake combustion (δ\u003csup\u003e15\u003c/sup\u003eN: 13.4\u0026permil; to 15.5\u0026permil;, (Pavuluri et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2010\u003c/span\u003e)), burning C3 plant matter combustion (δ\u003csup\u003e15\u003c/sup\u003eN: 15\u0026permil;, (Pavuluri et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In controlled combustion of typical vegetation which are normally cultivated on tropical soil of India (viz., Eucalyptus, Neem, Arhar, Mustard stem, Babool, Chilly stem, Desi Keekar, Sheesham and Arandi) particulate having average δ\u003csup\u003e15\u003c/sup\u003eN values as 13.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8\u0026permil; (Agnihotri et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) are produced. Among these Leguminous plants e.g., Arhar and Mustard stem have shown lower δ\u003csup\u003e15\u003c/sup\u003eN values as 5.2\u0026permil; and 7.6\u0026permil;, respectively. The particle generated from burning of vegetation plants from Piracicaba River basin (C4 plants dominated) and Amazon basin (land cover is primary forest), have δ\u003csup\u003e15\u003c/sup\u003eN values as 10.6\u0026thinsp;+\u0026thinsp;2.8 and 11.5\u0026thinsp;+\u0026thinsp;2.1, respectively and there was no statistically significant difference in the δ\u003csup\u003e15\u003c/sup\u003eN values for particles generated from combustion C4 and C3 type vegetation (Martinelli et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). It has been found that burning biomass (C3 and C4 plant material) can enrich the emitted particles with the heavier nitrogen isotope (\u003csup\u003e15\u003c/sup\u003eN) compared to the original plant matter (Turekian et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). This enrichment is attributed to partitioning between gas and particles, potentially involving gaseous nitrogen (like NH3) and organic nitrogen compounds in the particles. The study reported an average increase of 6.6\u0026permil; (range: -1.3\u0026permil; to 13.1\u0026permil;) in the δ\u003csup\u003e15\u003c/sup\u003eN ratio of aerosol particles compared to the source vegetation and again no difference was observed between C3 and C4 plants (Turekian et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e1998\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe δ\u003csup\u003e15\u003c/sup\u003eN values observed during the different seasons have statistically significant difference at the 95% confidence level (p values\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The lowest average δ\u003csup\u003e15\u003c/sup\u003eN value (3.52\u0026thinsp;\u0026plusmn;\u0026thinsp;1.23\u0026permil;) observed in the current study during monsoon seasons compared to other seasons may be mainly attributed to (i) reduced contribution of aerosol from biomass and biofuel burning and (ii) input of the aerosol from Arabian Sea coming with predominant wind from South-West direction. It is reported that aerosol over Arabian Sea has a clear contrast lower δ\u003csup\u003e15\u003c/sup\u003eN value, compared to that over Bay of Bengal and Indian cities, having an average value of 1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u0026permil; (Agnihotri et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The dominant source of TN during monsoon is liquid fossil fuels and coal as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The biomass burning have little/no contribution considering the average enrichment of 6.6\u0026permil; during particle formation and the end-member values as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. During winter seasons the contribution of biomass to TN is higher compared to liquid fossil fuels as the δ\u003csup\u003e15\u003c/sup\u003eN values during winter are closer to biomass burning as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eHigher δ\u003csup\u003e15\u003c/sup\u003eN value in winter (δ\u003csup\u003e15\u003c/sup\u003eN: 15.76\u0026thinsp;\u0026plusmn;\u0026thinsp;4.44\u0026permil;) compared to that in summer (δ\u003csup\u003e15\u003c/sup\u003eN: 8.11\u0026thinsp;\u0026plusmn;\u0026thinsp;6.51\u0026permil;) may be associated with (i) contribution of aerosols from biofuels/biomass (having higher value of δ\u003csup\u003e15\u003c/sup\u003eN) burning from N/NE direction, (ii) longer life time of aerosols in atmosphere during winter, as wet removal is lower in winter compared to summer since relative humidity during winter is lower than relative humidity of summer (Molina et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Petters et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), (iii) Higher evaporation of inorganic nitrogen in summer (Aggarwal et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and (iv) Higher contribution of marine aerosol from Arabian sea during the summer (Agnihotri et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Naqvi et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The mixing of marine nitrogen species from the adjacent Arabian Sea is responsible for δ\u003csup\u003e15\u003c/sup\u003eN value approaching to its lowest values during pre-monsoon to monsoon period from highest value during winter. Water column denitrification is reported to generate lighter N species (\u003csup\u003e14\u003c/sup\u003eN) which emanate from the water surface of the ocean (Naqvi et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Therefore, more than the statistical difference between summer and winter season, systematic decrease in δ\u003csup\u003e15\u003c/sup\u003eN values of aerosols from winter to pre-monsoon to monsoon period is worth mentioning since it matches well with systematically increasing influence of marine winds over the study area.\u003c/p\u003e \u003cp\u003eHigher δ\u003csup\u003e15\u003c/sup\u003eN value in winter compared to summer season is also reported in many previous studies in different Indian cities e.g., Mumbai (Aggarwal et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), Nainital (Hegde et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), Goa (Agnihotri et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Pavuluri et al., (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) have reported elevated enriched nitrogen (Average δ\u003csup\u003e15\u003c/sup\u003eN value: 24.5\u0026permil;) in the winter season (during 23 January-6 February, 2007) from Chennai, located on the southeast coast of India, which they have attributed to animal excreta and biofuel/biomass burning. In our measurement the during winter season (δ\u003csup\u003e15\u003c/sup\u003eN: 15.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4\u0026permil;), we have not observed such high enriched value indicating different source of the nitrogen. Previous study by Aggarwal et al., (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) at Indian Institute of Technology Bombay(IITB), Mumbai in winter season (during 13\u0026ndash;18 February 2007) have reported the average δ\u003csup\u003e15\u003c/sup\u003eN value in PM\u003csub\u003e10\u003c/sub\u003e as 22.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u0026permil;. In the present study, we have also found the similar average value of δ\u003csup\u003e15\u003c/sup\u003eN (20.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u0026permil;) during the sampling period of 10\u0026ndash;21 February 2022. But, during the entire winter season (January \u0026ndash; February, 2022), the observed δ\u003csup\u003e15\u003c/sup\u003eN value was found to vary from 10.6\u0026permil; to 21.4\u0026permil; with an average of 15.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4\u0026permil; in Mumbai.\u003c/p\u003e \u003cp\u003eStudy have reported the average value of δ\u003csup\u003e15\u003c/sup\u003eN as 20.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u0026permil; during the period June 8\u0026ndash;14, 2006 in Mumbai mentioned as summer season, while in the present study we have found δ\u003csup\u003e15\u003c/sup\u003eN value as 4.2\u0026permil; for the similar period of the month i.e., June 8\u0026ndash;17, 2022 (Aggarwal et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The significant difference in the observed δ\u003csup\u003e15\u003c/sup\u003eN value in month of June for present study and previous study carried out in Mumbai is be attributed to the rain (South-West monsoon) which starts normally 10 June in Mumbai. It can be seen from the weather history of Mumbai that during the Jun 8\u0026ndash;14, 2006 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://weatherspark.com/h/m/107286/2006/6/Historical-Weather-in-June-2006-in-Mumbai-India\u003c/span\u003e\u003cspan address=\"https://weatherspark.com/h/m/107286/2006/6/Historical-Weather-in-June-2006-in-Mumbai-India\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) there was less rain compared to rain observed during the June 8\u0026ndash;14, 2022 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://weatherspark.com/h/m/107286/2022/6/Historical-Weather-in-June-2022-in-Mumbai-India\u003c/span\u003e\u003cspan address=\"https://weatherspark.com/h/m/107286/2022/6/Historical-Weather-in-June-2022-in-Mumbai-India\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). It is important to mention that the average δ\u003csup\u003e15\u003c/sup\u003eN value for the entire summer season (March \u0026ndash; May) was found as 8.11\u0026thinsp;\u0026plusmn;\u0026thinsp;6.51\u0026permil;. These finding suggest that it will be erroneous to conclude the δ\u003csup\u003e13\u003c/sup\u003eC and δ\u003csup\u003e15\u003c/sup\u003eN values in particulate matter for entire season based on the short-term measurement of these parameters. Therefore, long term monitoring and measurement is required to have better understanding and predictive modeling about the particulate matter source at any location.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3 Seasonal variation of stable carbon isotopic ratio (δ\u003csup\u003e13\u003c/sup\u003eC)\u003c/h2\u003e \u003cp\u003eIt is reported that the measured δ\u003csup\u003e13\u003c/sup\u003eC value in particulate matter along with knowledge of end-member values and fractionation, can provide the insight into the source as well atmospheric processes of the carbonaceous aerosol (Kirillova et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The observed value of δ\u003csup\u003e13\u003c/sup\u003eC of PM\u003csub\u003e10\u003c/sub\u003e from present study along with the end-member (probable primary source) is shown as scattered plot (average\u0026thinsp;\u0026plusmn;\u0026thinsp;1standard deviation) is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. The plants with C3 and C4 metabolism showed distinctly different δ\u003csup\u003e13\u003c/sup\u003eC values ranging \u0026minus;\u0026thinsp;24\u0026permil; to -34\u0026permil; (average: -27\u0026permil;) and \u0026minus;\u0026thinsp;6\u0026permil; to -19\u0026permil; (average: -13\u0026permil;) (Mkoma et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Smith \u0026amp; Epstein, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e1971\u003c/span\u003e; Turekian et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e1998\u003c/span\u003e), respectively which was not observed in δ\u003csup\u003e15\u003c/sup\u003eN. The liquid fossil fuel showed depleted δ\u003csup\u003e13\u003c/sup\u003eC value varying from \u0026minus;\u0026thinsp;28.6\u0026permil; to -26.4\u0026permil; (Diesel: -26.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u0026permil;, Regular petrol: -24.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7%, unleaded petrol: -24.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6%, (Widory et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2004\u003c/span\u003e)) compared to solid fossil fuel (e.g., coal: -23.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u0026permil; (Widory, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), -23.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u0026permil; (Lim et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), -22.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u0026permil; (Sawlani et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)). The gaseous fossil fuels are strongly depleted in δ\u003csup\u003e13\u003c/sup\u003eC value (-20\u0026permil; to -40\u0026permil; (Lim et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Widory, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe marine aerosol sources like phytoplankton have δ\u003csup\u003e13\u003c/sup\u003eC value as -20\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u0026permil; (Miyazaki et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) while sea salt spray have been reported δ\u003csup\u003e13\u003c/sup\u003eC value as -21\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u0026permil; (Cachier et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1986\u003c/span\u003e; Ceburnis et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Xiao et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Incomplete combustion of fossil fuels is reported to show depletion/ fractionations of -1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u0026permil; in δ\u003csup\u003e13\u003c/sup\u003eC value for the combustion gases (Widory, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The particle from fossil fuels like diesel, unleaded gasoline and regular gasoline showed enrichment/fractionations of 1.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u0026permil;, 3.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u0026permil; and 3.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u0026permil; respectively. The δ\u003csup\u003e13\u003c/sup\u003eC fractionation in primary particle from coal combustion and natural gas combustion was reported as zero and 11.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.0\u0026permil;, respectively (Martinelli et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Widory, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). It has been reported that in controlled laboratory combustion of C3 plants (\u003cem\u003eEucalyptus sp\u003c/em\u003e. and \u003cem\u003eColospherum mopane\u003c/em\u003e) and C4 plants (\u003cem\u003eCenchriscilliarus sp\u003c/em\u003e., \u003cem\u003eAntephora pubescence\u003c/em\u003e and \u003cem\u003eSaccharum officinarum\u003c/em\u003e, sugarcane) there is fractionation in the δ\u003csup\u003e13\u003c/sup\u003eC values of 0.5\u0026permil; and \u0026minus;\u0026thinsp;3.5\u0026permil; with respect to source vegetation, respectively (Cachier et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1986\u003c/span\u003e; Turekian et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). Considering the δ\u003csup\u003e13\u003c/sup\u003eC value in end-members and fractionation, the average δ\u003csup\u003e13\u003c/sup\u003eC value of -24.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86\u0026permil;, observed in the present study, seems to represent an intermediate range between aerosols emitted from predominantly biomass burning and fossil fuel combustion (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Similar observation and conclusion is also reported in previous studies (Agnihotri et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Agnihotri et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Sen et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Aggarwal et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) over different cities of India.\u003c/p\u003e \u003cp\u003eThe δ\u003csup\u003e13\u003c/sup\u003eC values observed during the different seasons have statistically significant difference at the 95% confidence level (p values\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The highest average δ\u003csup\u003e13\u003c/sup\u003eC value (i.e., greatest \u003csup\u003e13\u003c/sup\u003eC enrichment) was found in winter followed by post-monsoon season and lowest value is observed during monsoon season. This systematic change of average δ\u003csup\u003e13\u003c/sup\u003eC value like seasonal change in δ\u003csup\u003e15\u003c/sup\u003eN value, again indicates the systematic increase of marine influence on aerosol observed over the study area. Similar annual seasonal trend of δ\u003csup\u003e13\u003c/sup\u003eC is also reported at urban European site (Vodička et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), different cities of India (Bhopal and Mysuru: (Yadav et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), Kanpur: (S. Bikkina et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)) and Okinawa Island, Japan (Kunwar et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Previous study at Mumbai during the winter season (13\u0026ndash;18 February, 2007) have reported δ\u003csup\u003e13\u003c/sup\u003eC value as -25.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u0026permil;, where the aerosols were strongly influenced by long-range continental and regional transport. Similar δ\u003csup\u003e13\u003c/sup\u003eC value (-25.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u0026permil;) is observed in present study during the period 10\u0026ndash;21 February 2022.\u003c/p\u003e \u003cp\u003eLike nitrogen isotope ratio, carbon isotope ratio has higher average value (δ\u003csup\u003e13\u003c/sup\u003eC: -24.04\u0026thinsp;\u0026plusmn;\u0026thinsp;1.08\u0026permil;) in winter season compared to summer season (δ\u003csup\u003e13\u003c/sup\u003eC: -24.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95\u0026permil;). As mentioned above, the difference is statistically significant, so the particulate in winter is enriched (Δδ\u003csup\u003e13\u003c/sup\u003eC\u0026thinsp;=\u0026thinsp;δ\u003csup\u003e13\u003c/sup\u003eC\u003csub\u003ewinter\u003c/sub\u003e - δ\u003csup\u003e13\u003c/sup\u003eC\u003csub\u003esummer\u003c/sub\u003e) by an average 0.82\u0026permil; in δ\u003csup\u003e13\u003c/sup\u003eC value. Previous study at Mumbai also reported enrichment (Δδ\u003csup\u003e13\u003c/sup\u003eC) of 0.60\u0026permil; in δ\u003csup\u003e13\u003c/sup\u003eC value during winter compared to summer season (Aggarwal et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Studies have reported that due to photochemical aging of atmospheric aerosols there is enrichment of \u003csup\u003e13\u003c/sup\u003eC in organic compound (Kawamura \u0026amp; Watanabe, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Pavuluri \u0026amp; Kawamura, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The mechanism is described as, reaction of OH radical with organic compound which results in breaking of organic compound and release CO\u003csub\u003e2\u003c/sub\u003e/CO, which have more abundance of lighter carbon isotope (\u003csup\u003e12\u003c/sup\u003eC). Meanwhile, organic compound remaining in the aerosols gets enriched in the \u003csup\u003e13\u003c/sup\u003eC which results higher \u003csup\u003e13\u003c/sup\u003eC/\u003csup\u003e12\u003c/sup\u003eC ratio i.e. in the enrichment of δ\u003csup\u003e13\u003c/sup\u003eC value (Aggarwal et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the seasonal variation (winter and summer) of δ\u003csup\u003e13\u003c/sup\u003eC values in different Indian cities along with some study around the world. Previous studies for seasonal changes of δ\u003csup\u003e13\u003c/sup\u003eC value (i.e., enrichment (Δδ\u003csup\u003e13\u003c/sup\u003eC)) between winter and summer seasons have been reported at different locations around the worlds e.g. Prague/Czech Republic (Δδ\u003csup\u003e13\u003c/sup\u003eC: 1.7, (Vodička et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)), Wrocław/Poland (Δδ\u003csup\u003e13\u003c/sup\u003eC: 1.4\u0026permil;, (G\u0026oacute;rka et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2014\u003c/span\u003e)), Lower Silesia/Poland (Δδ\u003csup\u003e13\u003c/sup\u003eC: 0.1 to 2.3\u0026permil;, (G\u0026oacute;rka et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)), Debrecen/Hungary (Δδ\u003csup\u003e13\u003c/sup\u003eC: 1.0\u0026permil;, (Major et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)), Vavihill/Sweden (Δδ\u003csup\u003e13\u003c/sup\u003eC: 0.2\u0026permil;, (Martinsson et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)), Preila/Lithuania (Δδ\u003csup\u003e13\u003c/sup\u003eC: 0.6\u0026permil;, (Masalaite et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)), Okinawa)/Japan (Δδ\u003csup\u003e13\u003c/sup\u003eC: 0.4\u0026permil;, (Kunwar et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)) and Kanpur/India (Δδ\u003csup\u003e13\u003c/sup\u003eC: 1.1\u0026permil;, (S. Bikkina et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)). It has been reported that these seasonal variation of δ\u003csup\u003e13\u003c/sup\u003eC values depends on the type of sources, aerosol sizes, aging of aerosols and geographical location of the study area (Vodička et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Seasonal variation of aerosol sources based on δ\u003csup\u003e13\u003c/sup\u003eC and δ\u003csup\u003e15\u003c/sup\u003eN values\u003c/h2\u003e \u003cp\u003eThe observations from Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e indicate that during the winter season δ\u003csup\u003e15\u003c/sup\u003eN value may have dominant contribution from C4 plants. However, it is not true in this case since C4 plants have significantly different carbon isotope ratio (δ\u003csup\u003e13\u003c/sup\u003eC: -13\u0026permil; to -20\u0026permil;, average: -17\u0026permil;) from the observed value in winter (δ\u003csup\u003e13\u003c/sup\u003eC: -24.04\u0026thinsp;\u0026plusmn;\u0026thinsp;1.08\u0026permil;). Also, the main biomass sources in Indian mainland are reported to be from C3 plants (e.g., wood, rice, and wheat) (S. Bikkina et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This observation highlights the importance of use of dual marker (δ\u003csup\u003e13\u003c/sup\u003eC and δ\u003csup\u003e15\u003c/sup\u003eN) for understanding the source of particulate matter. Therefore, dual isotope isospace of the stable carbon and nitrogen isotopic ratio measured during different seasons along with the end-member values are plotted (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Previous studies (using Principal Component Analysis (PCA) based multivariate studies and source apportionment) in Vashi, Navi Mumbai (located at around 10 km in north-east direction from the current sampling location) have identified combustion, soil, and sea salt as main contributor to the coarse fraction of the particulate matter (Kothai et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). End-members in the figure are based on the details mentioned in the manuscript and Supplementary Table S5, Table S6 and summarized in Table S7.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThese end-members are selected based on the aerosol sources at reported in western coast of India by various researchers (Aggarwal et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Agnihotri et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; P. Bikkina, et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kothai et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and discussed in above sections. They include particles from different sources in that category e.g. biomass (emission from combustion of C3 plants, cow dung etc., (Agnihotri et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Andersson et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; P. Bikkina et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Boreddy et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Smith \u0026amp; Epstein, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e1971\u003c/span\u003e)), coal (emission from coal fired thermal power plants (Agnihotri et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Felix et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Feng et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Heaton, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1990\u003c/span\u003e)), vehicular (emission from vehicles/ships running on diesel and petrol (Dai et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Heaton, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Widory, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Widory et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2004\u003c/span\u003e)), marine (emission of organic matter from marine phytoplankton (P. Bikkina et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Miyazaki et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and Dust (fine alluvial particles from IGP and Thar deserts (Agnihotri et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)).\u003c/p\u003e \u003cp\u003eIt is clear from the isospace (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) that during winter, the dominant contributor of TC and TN in aerosols, is biomass and coal fired power plants along with vehicular emission. Other sources e.g., continental dust and marine sources are only minor contributors. The IGP region, also referred as coal belt of India, has a dense network of coal-fired thermal power plants (S. Bikkina et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) contributes dominantly to aerosol in western India during winter, due to prevalent northerly and north-easterly wind. It is important to note that biofuels (particularly cow dung cake) are very common for cooking energy in South Asia (Stone et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Venkataraman et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Towards this, it has been also reported that biomass and fossil fuels burning emission is major source of organic aerosols over the IGP (S. Bikkina et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Gustafsson et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Rajput et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Singh et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Therefore, the emission from biomass (cow dunk and plants residues combustion) and coal combustion is reaching to Mumbai during winter season via northerly and north-easterly wind. Studies (Agnihotri et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; P. Bikkina et at., 2022) had reported that mineral dust from IGP and Thar Desert is also contributing to the aerosol at western coast of India and Arabian Sea. The sampling point is located between two marine water bodies (Mumbai Harbour Bay and Arabian Sea: please refer supplementary \u003cb\u003eFig. S1\u003c/b\u003e). Therefore, all the sources (biomass, coal, vehicular, mineral dust and marine) contribute to the aerosol at the sampling location. Previous study (P. Bikkina, Bikkina, \u0026amp; Kawamura, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) for bulk aerosols collected from offshore waters of the Arabian Sea during winter season has reported same sources. Using isotope mixing model, authors have estimated contribution from different sources as biomass burning (69\u0026thinsp;\u0026plusmn;\u0026thinsp;5%), vehicle exhaust (8\u0026thinsp;\u0026plusmn;\u0026thinsp;5%), coal fired power plants (10\u0026thinsp;\u0026plusmn;\u0026thinsp;6%), continental dust (5\u0026thinsp;\u0026plusmn;\u0026thinsp;2%) and marine sources (8\u0026thinsp;\u0026plusmn;\u0026thinsp;5%) to the total carbon and total nitrogen in the aerosol. The observation in present study is also consistent to study (Agnihotri et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) who had reported the biomass combustion, continental dust, marine and vehicular emission as source of aerosol during winter season at Goa, a coastal city situated at ~\u0026thinsp;700 km to the south of Mumbai along the west coast of India. During the monsoon season the dominant sources seems as vehicular (burning of liquid fossil fuels) and marine as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. If we compare the pre-monsoon (summer) and post-monsoon, the former seems to have higher contribution from the marine sources. This can be understood by the wind directions during these two seasons. During the pre-monsoon (especially during the April and May), the prevalent wind from south-west may contribute higher marine source. Similarly, there is higher contribution from biomass during winter compared to that in post-monsoon. This is clearly in accordance with the fact that during the winter there is higher biomass burning in north and north-east Indian cities to beat the winter cold.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Correlation between the measured parameters\u003c/h2\u003e \u003cp\u003eThe correlations amongst the different parameters measured during seasons were estimated using Spearman correlation coefficients (r\u003csub\u003es\u003c/sub\u003e) and statistically significant of correlation was checked at 95% confidence level (i.e., p value\u0026thinsp;\u0026le;\u0026thinsp;0.05). The correlation matrix (Spearman correlation coefficients and p-value) for each season as well as for entire study period is given in the Supplementary Tables (S8-S12). A very strong positive correlation (r\u003csub\u003es\u003c/sub\u003e = 0.93 and p value\u0026thinsp;\u0026lt;\u0026thinsp;0.0005) between TC and TN content, measured during the study period, was found during the study period (Supplementary Table S8). This reflects that the dominant source of particulate matter at the sampling location is combustion. This is because combustion processes, such as burning fossil fuels (diesel, petrol, coal, etc.) and biomasses (plant residues and cow dunks cake), release both carbon and nitrogen into the atmosphere. The higher the correlation between carbon and nitrogen content, the more likely it is that combustion is the main source of PM\u003csub\u003e10\u003c/sub\u003e. Other possible sources of PM\u003csub\u003e10\u003c/sub\u003e, such as dust and sea spray, do not typically release as much nitrogen into the atmosphere as carbon. As a result, the correlation between carbon and nitrogen content in PM\u003csub\u003e10\u003c/sub\u003e from these sources is typically weaker. A strong and positive correlation between carbon and nitrogen content in particulate matter also reflects; (i) Carbon and nitrogen can have common dominant sources in particulate matter. For example, combustion processes, such as fossil fuel combustion or biomass burning, can release both carbon-based compounds (e.g., organic carbon) and nitrogen compounds (e.g., nitrogen oxides), (ii) occurrence of atmospheric chemical reactions that transform carbon and nitrogen compounds into secondary organic aerosols (SOA) and secondary inorganic aerosols (Morera-G\u0026oacute;mez et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and (iii) common atmospheric long-range transport history from similar sources i.e. transport of particulate matter from an industrial area and/or urban center, containing both carbon and nitrogen to the sampling location (Aggarwal et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA week positive correlation (r\u003csub\u003es\u003c/sub\u003e = 0.39 and p value\u0026thinsp;=\u0026thinsp;0.02) between δ\u003csup\u003e13\u003c/sup\u003eC (\u0026permil;) and δ\u003csup\u003e15\u003c/sup\u003eN (\u0026permil;), measured during the study period, suggests that, in addition to distant sources such as aerosols from the Arabian Sea (AS) and aerosols from cities in the north and northeast of India, there are also local sources contributing to the aerosol composition. If only the distant sources were contributing to the aerosols, δ\u003csup\u003e13\u003c/sup\u003eC and δ\u003csup\u003e15\u003c/sup\u003eN would have a stronger correlation. As, it is reported that aerosols from the south and southwest direction, originating from the AS, have lower δ\u003csup\u003e13\u003c/sup\u003eC and δ\u003csup\u003e15\u003c/sup\u003eN values compared to aerosols from northern and northeastern regions of India (Agnihotri et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). A moderate positive correlation (r\u003csub\u003es\u003c/sub\u003e = 0.53 and p value\u0026thinsp;\u0026lt;\u0026thinsp;0.001) between δ\u003csup\u003e13\u003c/sup\u003eC and TC content observed in particulate matter during the entire study period, indicates the carbon rich sources with higher δ\u003csup\u003e13\u003c/sup\u003eC values is also contributing to aerosols to some extent. If only high carbon content sources with higher δ\u003csup\u003e13\u003c/sup\u003eC value (e.g., coal) is contributing one would expect better correlation between the δ\u003csup\u003e13\u003c/sup\u003eC and TC content. The deviation from strong positive correlation (r\u003csub\u003es\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;~\u0026thinsp;1) in the present study, could be due to a number of factors, including different sources of PM\u003csub\u003e10\u003c/sub\u003e, different ways in which carbon is incorporated into aerosol and different processes that affect the isotopic composition of PM\u003csub\u003e10\u003c/sub\u003e (Agnihotri et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA strong positive correlation (r\u003csub\u003es\u003c/sub\u003e = 0.63 and p value\u0026thinsp;\u0026lt;\u0026thinsp;0.0005) observed between δ\u003csup\u003e15\u003c/sup\u003eN of aerosols and TN, measured during the study period, is consistent with findings from previous studies conducted in Indian cities (Agnihotri et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Pavuluri et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Study (Agnihotri et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) have concluded that the positive correlation (R\u003csup\u003e2\u003c/sup\u003e: 0.58) between δ\u003csup\u003e15\u003c/sup\u003eN and TN in bulk aerosols indicates that δ\u003csup\u003e15\u003c/sup\u003eN values are largely controlled by primary sources dominantly contributing to total nitrogen in aerosols (during winter biomass burning containing heavier nitrogen (\u003csup\u003e15\u003c/sup\u003eN) and during summer marine emissions containing nitrogen species lighter nitrogen (\u003csup\u003e14\u003c/sup\u003eN)). Considering the location of the study area (between two marine water bodies) and biomass/biofuels emissions from north and north east direction in winter, similar conclusion will also be valid for present study. The observed correlation also suggests that higher nitrogen inputs in aerosols are associated with higher δ\u003csup\u003e15\u003c/sup\u003eN values in the aerosols, which are dominantly coming from biofuel and biomass. This is because the heavier isotope of nitrogen (\u003csup\u003e15\u003c/sup\u003eN) is more abundant in biological sources than in non-biological sources. This also supports the biofuels and biomass burning as one of the dominant sources of particulate matter in the study area.\u003c/p\u003e \u003cp\u003eDuring winter seasons, a strong negative correlation (r\u003csub\u003es\u003c/sub\u003e = -0.71 and p value\u0026thinsp;=\u0026thinsp;0.045) between δ\u003csup\u003e13\u003c/sup\u003eC (\u0026permil;) vs. δ\u003csup\u003e15\u003c/sup\u003eN (\u0026permil;) is observed indicates the dominant sources contributing with higher δ\u003csup\u003e15\u003c/sup\u003eN and lower δ\u003csup\u003e13\u003c/sup\u003eC e.g., biomass/biofuels and coal as shown in the isospace of dual carbon and nitrogen isotopes (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Similarly, a very strong negative correlation (r\u003csub\u003es\u003c/sub\u003e = -0.81 and p value\u0026thinsp;\u0026lt;\u0026thinsp;0.014) between δ\u003csup\u003e15\u003c/sup\u003eN (\u0026permil;) vs. TN (\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e) during winter seasons, again indicates the dominant contributors with high nitrogen content with lower δ\u003csup\u003e15\u003c/sup\u003eN values e.g. (biomass and biofuels (cow dung). A moderate correlation (r\u003csub\u003es\u003c/sub\u003e = 0.45 and p value\u0026thinsp;\u0026lt;\u0026thinsp;0.26) between TC and TN observed indicates the emission from combustion being one of the dominant sources of the aerosols during the winter. Like winter TC and TN and very strong correlation (r\u003csub\u003es\u003c/sub\u003e = 0.94 and p value\u0026thinsp;\u0026lt;\u0026thinsp;0.000) during the post-monsoon and strong correlation (r\u003csub\u003es\u003c/sub\u003e = 0.75 and p value\u0026thinsp;=\u0026thinsp;0.019) during the monsoon season. This indicates that dominant source contributing to TC and TN is not changing during each season. It is important to note that like entire study period, we have observed moderate to very strong positive correlation between TC vs. TN, during different seasons as winter (r\u003csub\u003es\u003c/sub\u003e = 0.45 and p value\u0026thinsp;=\u0026thinsp;0.26), summer (r\u003csub\u003es\u003c/sub\u003e = 0.92 and p value\u0026thinsp;=\u0026thinsp;0.019), monsoon (r\u003csub\u003es\u003c/sub\u003e = 0.75 and p value\u0026thinsp;=\u0026thinsp;0.019) and post-monsoons (r\u003csub\u003es\u003c/sub\u003e = 0.94 and p value\u0026thinsp;\u0026lt;\u0026thinsp;0.000). This again indicates that combustion of biofuels, fossil fuels and biomass as one of the dominant sources of aerosols.\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Conclusion","content":"\u003cp\u003eThis study conducted a comprehensive analysis of stable isotope ratio of carbon and nitrogen, as well as elemental composition, in particulate matter (PM\u003csub\u003e10\u003c/sub\u003e) collected from the Trombay area in Mumbai. The study provides insights into the annual variation of isotopic and elemental composition of PM\u003csub\u003e10\u003c/sub\u003e in the Trombay area, and a better understanding of contributing sources. The annual variation of the measured parameters suggest that they are controlled by several parameters including origins of air mass, wind direction transporting these air mass, contribution of local source and global sources, different metrological parameters etc. The study highlights the importance of long-term (measurement for complete period) monitoring to predict the isotopic value against the short-term (measurement for only few days) study. The wind patterns played a crucial role in transporting aerosols with different δ\u003csup\u003e13\u003c/sup\u003eC, δ\u003csup\u003e15\u003c/sup\u003eN and TC/TN ratios values to the sampling site, resulting in seasonal variations in the composition of PM\u003csub\u003e10\u003c/sub\u003e. The monsoon season exhibited lower TN and TC concentrations due to the washout effect of rain, while the pre-monsoon and post-monsoon periods showed higher TN and TC levels. The higher δ\u003csup\u003e13\u003c/sup\u003eC and δ\u003csup\u003e15\u003c/sup\u003eN values observed during the winter season indicates that the dominant sources of the aerosol are biofuels and biomass burning which are transported from North and North-East India. The seasonal variation of stable carbon and nitrogen isotope ratio indicated the influence of marine on aerosols over the study area. Overall, this research contributes to the understanding of PM\u003csub\u003e10\u003c/sub\u003e pollution in the Trombay area and provides valuable information for future air quality management and mitigation strategies in urban environments.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors acknowledge the Dr. Roopashree Srivastava from RSSD, BARC for providing the metrological parameters and plotting the wind rose diagram. Authors wish to acknowledge Dr. D.K. Aswal, GD,\u0026nbsp;HS\u0026amp;EG for their support and guidance during this study. Authors also acknowledge the use of generative AI tools (ChatGPT and Gemini), for language improvement, including rephrasing and English corrections. These AI tools were used exclusively for language editing, and all intellectual content, analyses, and interpretations are the work of the authors. After using these tools, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors did not receive support from any organization for the submitted work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no competing interests to declare that are relevant to the content of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe complete set of measured data and other parameters (e.g., wind direction, temperature, etc.) measured, are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that this study was conducted following ethical guidelines and in full compliance with the ethical standards of research in environmental science. No human or animal subjects were involved in this study. The authors confirm that all data sources and methodologies are reported transparently and accurately, ensuring reproducibility and scientific integrity.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eVir Bahadur Yadav: Conceptualization, Methodology, Sample collection and analysis, Data processing, Writing (original draft, review \u0026amp; editing) Vandana Ashthana Pulhani: Supervision, Writing (review \u0026amp; editing) Aerattukkara Vinod Kumar: Guidance, editing\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAggarwal, S. G., Kawamura, K., Umarji, G. S., Tachibana, E., Patil, R. S., \u0026amp; Gupta, P. K. (2013). 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Comparisons of aerosol nitrogen isotopic composition at two polluted coastal sites. \u003cem\u003eAtmospheric Environment\u003c/em\u003e, \u003cem\u003e35\u003c/em\u003e(7). https://doi.org/10.1016/S1352-2310(00)00408-8\u003c/li\u003e\n \u003cli\u003eZhai, Y., Li, X., Wang, T., Wang, B., Li, C., \u0026amp; Zeng, G. (2018). A review on airborne microorganisms in particulate matters: Composition, characteristics and influence factors. \u003cem\u003eEnvironment International\u003c/em\u003e, \u003cem\u003e113\u003c/em\u003e, 74\u0026ndash;90. https://doi.org/10.1016/J.ENVINT.2018.01.007\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e: Results of the measured parameters \u0026delta;\u003csup\u003e13\u003c/sup\u003eC (\u0026permil;), \u0026delta;\u003csup\u003e15\u003c/sup\u003eN (\u0026permil;), TC/TN ratio, N (\u0026micro;g.m\u003csup\u003e-3\u003c/sup\u003e) and \u0026nbsp;C (\u0026micro;g.m\u003csup\u003e-3\u003c/sup\u003e) in PM\u003csub\u003e10\u003c/sub\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameter\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMax\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026delta;\u003csup\u003e13\u003c/sup\u003eC (\u0026permil;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-26.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-22.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-24.87\u0026plusmn;0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026delta;\u003csup\u003e15\u003c/sup\u003eN (\u0026permil;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-2.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.06\u0026plusmn;5.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTC/TN Ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.79\u0026plusmn;1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eN (\u0026micro;g.m\u003csup\u003e-3\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.9\u0026plusmn;0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eC (\u0026micro;g.m\u003csup\u003e-3\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.75\u0026plusmn;2.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eSeasonal variation of the measured parameters\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameter\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSummer\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(Mar-May)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMonsoon\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(Jun \u0026ndash; Sep)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWinter\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(Nov\u0026ndash;Feb)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u0026delta;\u003csup\u003e13\u003c/sup\u003eC (\u0026permil;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-25.63 to -22.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-26.17 to -24.87\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-25.52 to -22.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-24.86\u0026plusmn;0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-25.73\u0026plusmn;0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-24.20\u0026plusmn;0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u0026delta;\u003csup\u003e15\u003c/sup\u003eN (\u0026permil;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-2.33 to 16.10\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.86 to 4.73\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.81 to 21.39\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.11\u0026plusmn;6.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.52\u0026plusmn;1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13.01\u0026plusmn;4.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eTC/TN ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.58 to 6.37\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.18 to 5.79\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.76 to 7.45\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.74\u0026plusmn;1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.83\u0026plusmn;0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.48\u0026plusmn;1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eTN (\u0026micro;g.m\u003csup\u003e-3\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.29 to 1.84\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.09 to 0.34\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.40 to 2.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.81\u0026plusmn;0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.21\u0026plusmn;0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.45\u0026plusmn;0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eTC (\u0026micro;g.m\u003csup\u003e-3\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.97 to7.26\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.40 to 1.63\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.07 to 9.12\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.46\u0026plusmn;2.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.97\u0026plusmn;0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.62\u0026plusmn;1.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Particulate matter, PM10, δ13C, δ15N, TC/TN ratio, source identification","lastPublishedDoi":"10.21203/rs.3.rs-6194800/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6194800/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eParticulate matter (PM) is a major air pollutant that poses significant risks to human health and the environment, particularly in urban areas with high concentrations of PM. In this study, stable isotopes of carbon (δ\u003csup\u003e13\u003c/sup\u003eC) and nitrogen (δ\u003csup\u003e15\u003c/sup\u003eN), the total carbon to total nitrogen ratio (TC/TN) were estimated in particulate patter (PM\u003csub\u003e10\u003c/sub\u003e) collected during the year 2022 from Trombay area, a coastal site in Mumbai, India. The results were analysed to identify the possible sources and fate of PM and understand the coastal effects on PM in the sampling area. The results showed that δ\u003csup\u003e13\u003c/sup\u003eC values ranged from \u0026minus;\u0026thinsp;26.2\u0026permil; to -22.4\u0026permil;, while δ\u003csup\u003e15\u003c/sup\u003eN values ranged from \u0026minus;\u0026thinsp;2.3\u0026permil; to 21.4\u0026permil; during the study period. The average values of δ\u003csup\u003e13\u003c/sup\u003eC and δ\u003csup\u003e15\u003c/sup\u003eN was \u0026minus;\u0026thinsp;24.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u0026permil; and 9.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6\u0026permil;, respectively. The TC/TN ratio ranged from 2.8 to 7.5, with an average of 4.8. The TN and TC concentrations varied from 0.1 \u0026micro;g.m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e to 2.3 \u0026micro;g.m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e and from 0.4 \u0026micro;g.m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e to 9.1 \u0026micro;g.m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e, respectively. The δ\u003csup\u003e13\u003c/sup\u003eC and δ\u003csup\u003e15\u003c/sup\u003eN values observed indicated fossil fuels and biomass burning, to be the dominant sources of the aerosol. The relative contribution of different sources (vehicular, biomass, coal, marine origin, and continental dust) showed seasonal variations. Systematic change in δ\u003csup\u003e13\u003c/sup\u003eC and δ\u003csup\u003e15\u003c/sup\u003eN values of aerosols from winter to pre-monsoon to monsoon period is noteworthy as it matches with systematically increasing influence of marine winds over the study area. The correlation between parameters reveals the formation of secondary organic and inorganic aerosols and long-range transport history of the aerosols.\u003c/p\u003e","manuscriptTitle":"Seasonal Dynamics of Stable Carbon and Nitrogen Isotope Ratio in PM10 Aerosols at a Coastal Urban Site in Mumbai, India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-31 11:40:49","doi":"10.21203/rs.3.rs-6194800/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"432ec306-0b75-453a-9681-f97e60198f41","owner":[],"postedDate":"March 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-04-22T17:23:12+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-31 11:40:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6194800","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6194800","identity":"rs-6194800","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}