# Unmasking Emissions: Tracing organic aerosols around a coal-fired power plant in India

Chen Luo, S Sarkar, Fanny Gesmond, Megha Anand, Kalpana Munnuru

Singamshetty, Joyanto Routh

► To cite this version:

Chen Luo, S Sarkar, Fanny Gesmond, Megha Anand, Kalpana Munnuru Singamshetty, et al. Unmasking Emissions: Tracing organic aerosols around a coal-fired power plant in India. Atmospheric Environment, 2025, 360, pp.121428. 10.1016/j.atmosenv.2025.121428. hal-05317389

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# Unmasking Emissions: Tracing organic aerosols around a coal-fired power plant in India

Chen Luo<sup>a, b</sup>, Sayantan Sarkar<sup>b, *, c</sup>, Fanny Gesmond<sup>c, d</sup>, Megha Anand<sup>b</sup>, Kalpana Munnuru Singamshetty<sup>a</sup>, Joyanto Routh<sup>a, ** c</sup>

<figure><img src="image_3.png" /></figure>

## HIGHLIGHTS

* PM<sub>2.5</sub> and PAH levels around a power plant are above national and international standards.

* Clustered trajectory analysis pinpoints the power plant's impact on PAH levels.

* Biomass burning is the dominant emission source overall.

## GRAPHICAL ABSTRACT

<figure><img src="image_4.png" /></figure>

## ARTICLE INFO

**Keywords:**

Thermal power plant

PM<sub>2.5</sub>

PAH

n-Alkane

Levoglucosan

Clustered trajectories

## ABSTRACT

Coal-fired thermal power plants (TPP) are projected to be a major energy source in India for the foreseeable future. Their continued operation and planned expansion will increase emissions, further degrading the air quality in the Indo-Gangetic Plains and undermining long-term sustainability. In this study, we collected for one-year particulate matter (PM<sub>2.5</sub>) at an upwind and downwind site from a super-category TPP and analyzed polycyclic aromatic hydrocarbons (PAH), n-alkanes, and levoglucosan. The annual mean of PM<sub>2.5</sub> level was about 2.5 times the Indian National Ambient Air Quality Standards (NAAQS) (40 µg/m<sup>3</sup>) and 40 times the WHO standard (5 µg/m<sup>3</sup>). The annual mean of benzo[a]pyrene concentration was over five times the NAAQS level (1 ng/m<sup>3</sup>) and 40 times the European Environment Agency reference level (0.12 ng/m<sup>3</sup>, assuming an acceptable risk of additional lifetime cancer risk of 1 in 100,000). Diagnostic PAH ratios and n-alkane indices indicated the primary emission sources. n-Alkanes were mainly derived from petrogenic sources, while pyrogenic sources were

<footnote><sup>a</sup> Department of Thematic Studies – Environmental Change, Linköping University, Linköping, SE-58183, Sweden</footnote>

<footnote><sup>b</sup> School of Civil and Environmental Engineering, Indian Institute of Technology-Mandi, Kamand, Himachal Pradesh, 175075, India</footnote>

<footnote><sup>c</sup> UFR Sciences and Properties of Matter, University of Rennes, Rennes, F-35000, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>** Corresponding author.</footnote>

<footnote>E-mail addresses: sayantan@iitmandi.ac.in (S. Sarkar), joyanto.routh@liu.se (J. Routh).</footnote>
the main contributor to PAHs. Levoglucosan indicated more biomass burning during winter and post-monsoon, with higher emissions at the downwind rural site compared to the upwind suburban site. We also used backward trajectory modeling, including clustering and concentration-weighted trajectories, to identify emission hotspots. They revealed elevated benzo[a]anthracene, benzo[b]fluoranthene, and dibenz[a,h]anthracene levels due to TPP emissions.

# 1. Introduction

India, having become the world's most populous country in 2023, is grappling with some of the worst air quality standards (IQAir, 2021). Of the 100 most polluted cities globally, 83 are in India, underscoring the severity of the air pollution crisis. The country is facing severe health risks due to excessive levels of PM<sub>2.5</sub>, which are further exacerbated by rapidly increasing fossil fuel consumption, expanding energy demands, land-use changes, industrial emissions, and widespread biomass burning (Lelieveld et al., 2023; Pandey et al., 2021). Despite the launch of the National Clean Air Program (NCAP) in 2019 and efforts in air quality monitoring, progress has been limited (IQAir, 2021). Studies have revealed significant spatial heterogeneity of PM<sub>2.5</sub> levels and their associated adverse health impacts and mortality both globally (Bu et al., 2021; Cohen et al., 2017; Ghosh et al., 2012; Yang et al., 2023) and nationally (Conibear et al., 2018; Pandey et al., 2021). In India, the disease burden is particularly high in the Indo-Gangetic Plain (IGP) (Conibear et al., 2018; Pandey et al., 2021). The eastern part of IGP, also known as the lower IGP, is especially vulnerable due to its topographical settings and local meteorological conditions. During winter, significant pollution from the upper and middle IGP is transported and eventually trapped in the densely populated lower IGP. This results in elevated PM levels, unfolding a crisis that demands immediate intervention and mitigation measures (Chawala et al., 2023).

In India, 81 % of PM<sub>2.5</sub> pollution is associated with fuel combustion (Guttikunda and Ka, 2022). Among various sources, the coal-fired thermal power plants (TPP) can contribute up to 20 µg/m<sup>3</sup> of ambient PM<sub>2.5</sub> in the IGP (India's average was 58.1 µg/m<sup>3</sup> in 2021) (Guttikunda and Ka, 2022). Despite the significant contribution of coal-fired TPPs to air pollution, India's energy generation relies heavily on these power plants. The lack of sufficient alternative energy sources means that coal-based TPPs will remain a key player in India's energy landscape in the near future (Joy and Qureshi, 2023; Vig et al., 2023). Moreover, the planned expansion capacity of existing TPPs and numbers will not only further deteriorate the air quality but also result in more severe and detrimental health impacts, potentially leading to a health crisis of unprecedented proportions (Cropper et al., 2021; Dhar et al., 2025; Guttikunda and Jawahar, 2018; Lin et al., 2019). Studies have reported that areas affected by coal-fired TPP experience a higher incidence of health problems and mortality due to increased PM<sub>2.5</sub> levels in the air (Choudhary et al., 2022; Cropper et al., 2021; Guttikunda and Jawahar, 2018; Mishra, 2015).

The Farakka Thermal Power Plant (FTPP) is a super-category facility with a power generation capacity of 2100 MW, located in the Murshidabad district of West Bengal, India. Some environmental problems related to the FTPP have been studied, focusing mainly on soils (Bala Sundar et al., 2008; Ghosh et al., 2012) and physical and chemical characteristics of coal-derived fly ash and their potential use (Rautray et al., 2009; Sarkar et al., 2005). However, to date there has been no study of its impact on ambient air quality. The residents living downwind of FTPP are probably affected by both TPP emissions and pollutants transported from distant sources by prevailing winds, especially from Middle and Upper IGP. This region frequently receives air pollutants transported from the northwest via long-range atmospheric circulation (Behera et al., 2021; Bikkina et al., 2015; Kumar et al., 2010). These external pollutants mix with local emissions, including biomass burning, vehicular exhaust, and other combustion sources, complicating the identification of specific emission sources.

Studies have shown that polycyclic aromatic hydrocarbons (PAH) are mainly emitted from incomplete combustion, with their diagnostic ratios serving as key indicators of coal combustion (Li et al., 2021; Liu et al., 2016; Wang et al., 2016; Wu et al., 2014; Zhang et al., 2008a). Other tracers, such as n-alkanes and levoglucosan (LG), help address the complexity of emission sources and their seasonal variability. n-Alkanes and their indices are used to indicate the prominent petrogenic or biogenic sources (Yang et al., 2023), while levoglucosan is considered almost an exclusive source indicator for biomass burning (Gramlich et al., 2023; Janoszka and Czaplicka, 2019; Li et al., 2023). Thus, a multi-proxy approach combining PAHs, n-alkanes, and LG with dispersion modeling could disentangle the complex emission sources and their spatiotemporal variation in the region.

In this study, we aim to investigate aerosol speciation, especially for organic contaminants, near the coal-fired Farakka TPP to delineate its impact, if any, from other combustion sources. To achieve this, we (1) conducted a year-round seasonal sampling of ambient PM<sub>2.5</sub> at a downwind and an upwind site (relative to the FTPP), (2) analyzed organic tracers, namely n-alkanes and PAHs (and their indices) and LG to investigate their spatiotemporal variations, and (3) identified potential emission sources and hotspots of these contaminants. This study highlights the environmental impacts of, among others, a mega coal-combusting unit like the FTPP. It provides critical data for mitigation strategies, particularly in densely populated, underdeveloped, and socioeconomically diverse regions worldwide. The findings underscore the need for targeted interventions, policy frameworks, and equitable resource allocation to effectively address associated environmental and public health risks.

# 2. Materials and methods

## 2.1. Study site and sampling

The Farakka Thermal Power Plant (FTPP; 24°46′N, 87°53′E) is located in Murshidabad district, West Bengal, with a total installed capacity of 2100 MW (3 × 200 MW, 3 × 500 MW) and a stack height of 275 m (NTPC HYC Report, 2020). The area has four major seasons: winter (December to February), summer/pre-monsoon (March to June), monsoon (July to August), and autumn/post-monsoon (September to November) (GoWB, 2021, 2022). The annual mean temperature is 25.7 °C, and the annual mean precipitations are 1179 mm for Murshidabad district and 1326 mm for the neighboring Malda district. Most rainfall happens in July and August.

Two sampling sites, 88 m above sea level, were selected based on preliminary modeling of the emissions plume (Dhar et al., 2025); one being the downwind site, which was affected by FTPP primarily in winter according to the modeling result, and the other being the upwind site. The downwind site was located in rural Murshidabad district (24°40′N, 87°55′E), about 11 km south of FTPP (24°40′N, 87°55′E). The upwind site was 30 km northeast of the TPP in suburban Malda (25°02′N, 88°08′E) (Fig. 1). A high-volume air sampler (HiVol 3000, Ecotech) was installed on the rooftop of a two-storied school building at each site - Kalachand High School in Old Malda and Baharagachchi High School in Murshidabad district. The samplers were operated at a flow rate of 1.2 m³ min⁻¹, and PM<sub>2.5</sub> was collected for 24 h on a quartz microfilter Grade A filter (8 × 10 inches; QMA, Whatman). Clean filters were heated at 550 °C for 5 h to remove adsorbed organics and were desiccated for 24 h before and after use. The filters were wrapped with
aluminum foil and sealed in polyethylene bags before and after use. After retrieving the loaded filters, they were weighed and stored at -20 °C before analysis. Samples were collected thrice a week (every Monday, Wednesday, and Friday) from 2021 winter (December) to 2022 autumn (November). A total of 106 samples were collected at the upwind site, and 121 were collected at the downwind site.

## 2.2. Analysis of organic tracers

Filters were punched out and extracted on an ASE 350 (Accelerated Solvent Extract, Thermo Fisher Scientific, US) with dichloromethane and methanol (DCM: MeOH = 2:1 v/v mixture) for two cycles of 10 min each, at 100 °C and 1500 psi. The extract volume was reduced on a BÜCHI Syncore Q-101 (BÜCHI, Switzerland) system, coupled with a BÜCHI vacuum controller V-855. The remaining extract was reduced by taking it to dryness under nitrogen, before being redissolved and separated into two fractions with 6 ml glass columns loaded with 500 mg Supelclean LC-NH2 (Sigma-Aldrich). The neutral fraction was eluted with 15 ml dichloromethane (DCM): isopropyl alcohol = 2:1 (v/v), followed by 15 ml of 2 % acetic acid in diethyl ether to separate the acid fraction. The neutral fraction was further separated into alkane and PAH fractions with a low-bleed bilayer cartridge (SPE-SA6, 01.GV6000, Scantec Nordic) by eluting with 5 mL of hexane and DCM: Hex = 1:1 (v/v), respectively. After reducing the volume to ~1 mL, the PAH fraction was taken to dryness under nitrogen, redissolved in 200 µL hexane, and spiked with internal standards (Naphthalene-d8, Phenanthrene-d10 and Perylene-d12, Cambridge Isotope Laboratories) and analyzed on a triple quadrupole GC/MS system (Agilent 8890/7010B GC/TQ; HP-5MS Ultra I column, 30 m × 250 µm × 0.25 µm) in multiple reaction monitoring (MRM) mode (Table S1). The collision energies for the MRM transitions of 16 priority PAHs (DE-PROM 16, LGC Standards, UK) were optimized using MRM workflows and Optimizer software in Agilent Mass Hunter Acquisition (see Table 1 for abbreviations). The injection volume was 0.4 µL. The inlet was maintained at 300 °C and ran in a pulsed splitless mode. The samples were run at a constant flow rate of 1.2 mL/min with helium as a carrier gas. The initial oven temperature was 60 °C with a 1 min hold time, followed by a ramping time of 8 °C/min to 200 °C and a 1 min hold time, and 5 °C/min to 300 °C and a 5 min hold time. The total

run time was 44.5 min. The recoveries were tested by spiking known concentrations of 16 priority PAH standard to a blank filter for extraction as a sample, followed by analysis as mentioned earlier. The BaA, Chr, BbF, BkF, BaP, IndP, DBahA, and BghiP recoveries were above 80 %, with the rest too low to be considered reliable for data analysis/ interpretation. NIST SRM 1649b - Urban dust was used to evaluate the extraction method and analysis. BaP, IndP, and BghiP had a recovery of over 60 %; Flu, Pyr, BaA, Chr, BbF, BkF, and DBahA had a recovery ranging from 89 % to 105 %. Blanks were run with the samples, and blank values were subtracted during quantification.

The n-alkane fractions were spiked with 5α-androstane as internal standard and run on a 7890A GC (column: HP-1MS, 30 m × 250 µm × 0.25 µm) 5975C MSD. The injection volume was 1 µL, and the inlet was maintained at 250 °C in splitless mode. Helium was the carrier gas with a 1.3 mL/min flow rate. The initial column temperature was at 35 °C for 2 min before ramping it to 130 °C at 20 °C/min and then 320 °C at 6 °C/min with a hold time of 6 min. The total run time was 44 min. The recovery of n-triacontane-d62 (Sigma-Aldrich) was 101 % ± 26.1 %. For NIST SRM 1649b standard, the values analyzed were >90 % of the reference values for C20, C26 and C27-alkanes, >80 % for C22 and C24-alkanes, and >75 % for C23, C25 and C29-alkanes.

For levoglucosan analysis, samples were extracted with 10 mL MeOH and ultrasonicated for 30 min before centrifugation at 3000g for 15 min. The supernatant was transferred to a 4 mL vial, from which 200 µL was taken and spiked with 4 ppm of Methyl β-D-xylopyranoside (MXP) before it was taken to dryness under a flow of N gas. 200 µL of (BSTFA + 1 % TMCS):pyridine = 2:1 v/v mixture was added and heated at 70 °C for 1h. After cooling to room temperature, the extract was run on the GC-MS for n-alkanes analysis (1 µL injection volume). The column temperature started at 50 °C and was maintained for 2 min; the temperature was then increased to 120 °C at 30 °C/min with a 10 min holding time, and finally ramped to 225 °C at 6 °C/min. The total run time was 32 min. The recoveries of levoglucosan standard were >85 %.

Diagnostic PAH ratios were used to distinguish PAHs derived from petroleum and/or combustion sources because evaporation and combustion of different source materials and conditions emit distinct PAH isomers (Tobiszewski and Namieśnik, 2012). Likewise, n-alkane indices were used as source indicators. Cmax refers to the maximum carbon

<figure><img src="image_5.png" /><figcaption>**Fig. 1.** Sampling sites near the Farakka power plant (FTTP) in West Bengal, India.</figcaption></figure>
<caption>**Table 1**</caption>

Annual and seasonal concentrations and pollutant indices near the Farakka thermal power plant in West Bengal, India (mean ± standard deviation; median is shown in parentheses). The complete list with concentrations for individual compounds can be found in Table S3.

<table><thead><tr><th rowspan="2"></th><th colspan="2">Annual</th><th colspan="2">Upwind</th><th colspan="2">Post-monsoon</th><th colspan="2">Downwind</th><th colspan="2">Post-monsoon</th></tr><tr><th></th><th>Winter</th><th>Summer</th><th>Monsoon</th><th>Monsoon</th><th>Annual</th><th>Winter</th><th>Summer</th><th>Monsoon</th><th>Monsoon</th></tr></thead><tbody><tr><td>n<br>PM<sub>2.5</sub> (µg/m<sup>3</sup>)</td><td>97<br>105.52 ± 48.61<br>(109.70)</td><td>34<br>139.74 ± 29.77<br>(133.88)</td><td>27<br>105.07 ± 44.11<br>(111.85)</td><td>12<br>45.57 ± 11.23<br>(42.02)</td><td>24<br>87.51 ± 48.84<br>(81.25)</td><td>114<br>109.34 ± 59.22<br>(97.05)</td><td>39<br>153.91 ± 45.48<br>(151.61)</td><td>32<br>96.11 ± 55.75<br>(76.44)</td><td>16<br>47.49 ± 12.93<br>(45.64)</td><td>27<br>97.3 ± 52.15<br>(105.44)</td></tr><tr><td>n<br>Levoglucosan<br>(ng/m<sup>3</sup>)</td><td>46<br>448.36 ± 502.52<br>(219.41)</td><td>17<br>955.48 ± 418.21<br>(946.77)</td><td>11<br>73.61 ± 100.06<br>(39.19)</td><td>9<br>61.46 ± 92.85<br>(25.98)</td><td>9<br>335.37 ± 356.59<br>(139.78)</td><td>49<br>608.8 ± 539.28<br>(497.87)</td><td>18<br>1095.4 ± 484.86<br>(908.75)</td><td>13<br>175.11 ± 86.99<br>(181.78)</td><td>9<br>223.51 ± 207.62<br>(186.42)</td><td>9<br>647.3 ± 428.77<br>(578.56)</td></tr><tr><td>n<br>T-PAHs (ng/m<sup>3</sup>)</td><td>47<br>53.15 ± 47.43<br>(35.46)</td><td>17<br>78.19 ± 52.76<br>(77.79)</td><td>11<br>30.88 ± 23.51<br>(28.01)</td><td>9<br>19.3 ± 6.7<br>(17.66)</td><td>10<br>65.57 ± 52.8<br>(53.94)</td><td>50<br>60.76 ± 42.31<br>(48.32)</td><td>18<br>93.62 ± 39.91<br>(82.41)</td><td>13<br>38.57 ± 18.13<br>(40.62)</td><td>9<br>26.22 ± 15.85<br>(24.7)</td><td>10<br>61.53 ± 44.87<br>(53.18)</td></tr><tr><td>4-ring PAHs<br>(ng/m<sup>3</sup>)</td><td>13.5 ± 13.8<br>(6.73)</td><td>22.3 ± 17.3<br>(18.3)</td><td>6.80 ± 6.04<br>(5.21)</td><td>4.2 ± 1.76<br>(4.11)</td><td>14.3 ± 10.4<br>(12.6)</td><td>16.2 ± 14.4<br>(10.2)</td><td>29.3 ± 14.3<br>(23.9)</td><td>8.11 ± 4.28<br>(9.12)</td><td>4.85 ± 2.9<br>(5.33)</td><td>13.6 ± 11.52<br>(11.6)</td></tr><tr><td>5-ring PAHs<br>(ng/m<sup>3</sup>)</td><td>22.95 ± 21.34<br>(14.90)</td><td>34.71 ± 22.48<br>(34.22)</td><td>12.37 ± 9.31<br>(10.7)</td><td>7.75 ± 3.07<br>(7.21)</td><td>28.3 ± 25.78<br>(21.55)</td><td>26.17 ± 19.31<br>(20.53)</td><td>40.73 ± 17.52<br>(35.29)</td><td>15.58 ± 7.89<br>(15.14)</td><td>9.77 ± 6.22<br>(8.5)</td><td>28.46 ± 21.75<br>(22.3)</td></tr><tr><td>6-ring PAHs<br>(ng/m<sup>3</sup>)</td><td>16.23 ± 12.86<br>(11.55)</td><td>20.4 ± 12.93<br>(20.27)</td><td>11.44 ± 8.14<br>(10.66)</td><td>7.26 ± 2.33<br>(6.75)</td><td>22.49 ± 16.87<br>(19.22)</td><td>17.86 ± 9.57<br>(0.41 ± 0.04)</td><td>22.81 ± 9.14<br>(19.3)</td><td>14.52 ± 6.09<br>(15.54)</td><td>11.39 ± 6.78<br>(10.56)</td><td>19.15 ± 11.93<br>(18.67)</td></tr><tr><td>BaA/(BaA +<br>Chr)</td><td>0.4 ± 0.04<br>(0.41)</td><td>0.41 ± 0.05<br>(0.41)</td><td>0.4 ± 0.04<br>(0.41)</td><td>0.38 ± 0.04<br>(0.39)</td><td>0.41 ± 0.04<br>(0.41)</td><td>0.43 ± 0.07<br>(0.44)</td><td>0.48 ± 0.03<br>(0.48)</td><td>0.38 ± 0.07<br>(0.41)</td><td>0.38 ± 0.06<br>(0.39)</td><td>0.46 ± 0.02<br>(0.46)</td></tr><tr><td>BbF/(BbF +<br>BkF)</td><td>0.43 ± 0.02<br>(0.43)</td><td>0.44 ± 0.01<br>(0.44)</td><td>0.44 ± 0.02<br>(0.43)</td><td>0.42 ± 0.02<br>(0.41)</td><td>0.4 ± 0.01<br>(0.4)</td><td>0.47 ± 0.08<br>(0.46)</td><td>0.5 ± 0.02<br>(0.5)</td><td>0.44 ± 0.03<br>(0.43)</td><td>0.49 ± 0.19<br>(0.43)</td><td>0.45 ± 0.02<br>(0.45)</td></tr><tr><td>IndP/(IndP +<br>BghiP)</td><td>0.5 ± 0.02<br>(0.49)</td><td>0.52 ± 0.02<br>(0.52)</td><td>0.49 ± 0.02<br>(0.48)</td><td>0.49 ± 0.01<br>(0.49)</td><td>0.48 ± 0.01<br>(0.49)</td><td>0.5 ± 0.04<br>(0.50)</td><td>0.53 ± 0.02<br>(0.53)</td><td>0.49 ± 0.04<br>(0.48)</td><td>0.48 ± 0.02<br>(0.48)</td><td>0.49 ± 0.03<br>(0.48)</td></tr><tr><td>T-alkanes (ng/m<sup>3</sup>)</td><td>179.02 ± 182.18<br>(116.79)</td><td>356.13 ± 140.49<br>(303.37)</td><td>55.69 ± 42.2<br>(56.92)</td><td>23.69 ± 24.44<br>(13.87)</td><td>153.39 ± 171.31<br>(101.22)</td><td>206.62 ± 197.74<br>(142.70)</td><td>360.87 ± 212.79<br>(318.15)</td><td>78.12 ± 50.73<br>(55.76)</td><td>72.97 ± 84.84<br>(36.27)</td><td>216.27 ± 167.69<br>(185.61)</td></tr><tr><td>CPI</td><td>2.49 ± 3.5<br>(1.46)</td><td>1.36 ± 0.38<br>(1.29)</td><td>5.33 ± 6.52<br>(3.23)</td><td>2.05 ± 1.42<br>(1.52)</td><td>1.71 ± 0.5<br>(1.74)</td><td>2.47 ± 1.41<br>(1.90)</td><td>1.99 ± 0.56<br>(1.8)</td><td>2.84 ± 1.16<br>(2.42)</td><td>3.63 ± 2.56<br>(2.68)</td><td>1.79 ± 0.17<br>(1.8)</td></tr><tr><td>WNA (ng/m<sup>3</sup>)</td><td>41.72 ± 46.3<br>(34.66)</td><td>74.4 ± 55.46<br>(54.55)</td><td>24.34 ± 18.91<br>(24.2)</td><td>5.17 ± 3.74<br>(5.37)</td><td>38.19 ± 36.65<br>(31.15)</td><td>68.68 ± 71.98<br>(45.92)</td><td>120.57 ± 88.66<br>(89.01)</td><td>32.42 ± 28.1<br>(21.31)</td><td>18.89 ± 9.33<br>(18.02)</td><td>67.23 ± 49.66<br>(63)</td></tr><tr><td>PNA (ng/m<sup>3</sup>)</td><td>137.3 ± 144.8<br>(70.19)</td><td>281.73 ± 102.99<br>(249.2)</td><td>31.35 ± 24.51<br>(23)</td><td>18.52 ± 22.17<br>(8.86)</td><td>115.2 ± 139.37<br>(70.07)</td><td>137.94 ± 136.67<br>(92.2)</td><td>240.3 ± 146.17<br>(211.5)</td><td>45.71 ± 27.89<br>(45.41)</td><td>54.09 ± 81.11<br>(18.26)</td><td>149.04 ± 118.53<br>(123.25)</td></tr><tr><td>WNA%</td><td>28.16 ± 15.28<br>(24.13)</td><td>19.53 ± 8.13<br>(17.86)</td><td>42.52 ± 17.29<br>(40.43)</td><td>27.03 ± 17.6<br>(24.54)</td><td>28.06 ± 8.64<br>(33.23)</td><td>37.03 ± 13.58<br>(33.23)</td><td>32.77 ± 9.3<br>(29.56)</td><td>41.35 ± 13.81<br>(41.83)</td><td>45.09 ± 21.69<br>(47.95)</td><td>31.85 ± 3.24<br>(31.08)</td></tr><tr><td>PNA%</td><td>71.84 ± 15.28<br>(75.87)</td><td>80.47 ± 8.13<br>(82.14)</td><td>57.48 ± 17.29<br>(59.57)</td><td>72.97 ± 17.6<br>(75.46)</td><td>71.94 ± 8.64<br>(66.77)</td><td>62.97 ± 13.58<br>(66.77)</td><td>67.23 ± 9.3<br>(70.44)</td><td>58.65 ± 13.81<br>(58.17)</td><td>54.91 ± 21.69<br>(52.05)</td><td>68.15 ± 3.24<br>(68.92)</td></tr></tbody></table>

number, representing the n-alkane with the highest relative concentration in a sample. The carbon preference index (CPI) is the ratio of total odd carbon number n-alkanes to even carbon number n-alkanes (Herrera-Herrera et al., 2020; Marzi et al., 1993). Waxy n-alkanes (WNA) are the sum of odd n-alkanes subtracting the average of the adjacent higher and lower even n-alkanes; petrogenic n-alkanes (PNA) are obtained by subtracting the WNA value from total n-alkane content (T-alkanes); WNA% and PNA% are WNA and PNA divided by T-alkanes. In the calculations, negative values were replaced by zero. Equations and other details of these calculations can be found in Yang et al. (2023), and references therein. All the indices mentioned above could yield various results for similar sources and show inter-source similarities, which were widely discussed in previous studies (Galarneau, 2008; Tobiszewski and Namieśnik, 2012). Therefore, these indicators are not definitive and should only be considered suggestive of the dominant petrogenic and pyrogenic/combustion sources in the medium.

## 2.3. Air mass transport modeling

Forty-eight-hour backward trajectories were calculated using the Global Data Assimilation System (GDAS 1.0°) with the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT), at the height of 25 m AGL (above ground level) with a starting time at 6:00 UTC, followed by cluster analysis. Directional gradients of source contributions were established by calculating weighted concentration weighted trajectories (WCWT) for PM<sub>2.5</sub>, T-PAH, total n-alkanes, and LG. WCWT is the product of CWT values and an arbitrary weight function, which reveals the ambiguity and provides better visualization (Lin et al., 2022; Polissar et al., 2001; Shan et al., 2023). Meteoinfo with Trajectory Statistics plugin Version 1.5.5 was used. More details about the software and parameters are available at http://www.meteothink.org/.

<footnote>* T-PAHs include naphthalene (NaP), acenaphthylene (Acy), acenaphthene (Ace), fluorene (Flo), phenanthrene (Phe), anthracene (Ant), fluoranthene (Flu), pyrene (Pyr), benz[a]anthracene (BaA), chrysene (Chr), benzo[b]fluoranthene (BbF), benzo[k]fluoranthene (BkF), benzo[a]pyrene (BaP), indeno[1,2,3-cd]pyrene (IndP), dibenz[a,h]anthracene (DBahA) and benzo[ghi]perylene (BghiP).</footnote>
# 3. Results and discussion

## 3.1. Annual and seasonal variation

There was no significant difference between summer and post-monsoon or between the sites for annual and seasonal PM₂.₅ levels (p > 0.05). The annual average of PM₂.₅ was about 2.5 times of the Indian National Ambient Air Quality Standards (NAAQS) (40 µg/m³) (CPCB, 2019), and 20 times the WHO global air quality guideline (5 µg/m³; WHO, 2021). The highest average and median values for T-PAHs were observed in winter, followed by post-monsoon, summer, and monsoon seasons (Fig. 2). The differences between winter and summer, winter and monsoon, and monsoon and post-monsoon were significant (p < 0.05). There was no significant difference between the sites for seasonal and annual T-PAHs. Benzo[a]pyrene (BaP) is commonly used as a representative indicator of the PAH group despite its limitations - such as the potential underestimation of overall risks (WHO, 2021). NAAQS listed an annual average of 1 ng/m³ (CPCB, 2019) and European Environment Agency listed estimated reference levels of 0.12 ng/m³ for BaP, assuming an acceptable risk of additional lifetime cancer risk of 1 in 100, 000 (EEA, 2023). The concentrations observed in this study exceeded the NAAQS value by more than 5 times and the European Environment Agency level by 40 times (European Environment Agency, 2023). The

highest concentration of T-alkanes was observed in winter, followed by post-monsoon, summer, and monsoon seasons (Fig. 2). T-alkanes were higher at the downwind than the upwind site (p < 0.05) during the monsoons; no other difference was noted. The LG level peaked during the winter. This was followed by a sharp decline in level during the post-monsoon, followed by summer, and monsoon. The difference between summer and monsoon was insignificant (p > 0.05). This seasonal trend indicated significant biomass-burning during winter and post-monsoon seasons. The downwind site had higher LG concentrations than the upwind site during summer and monsoon (p < 0.05).

Similar seasonal trends in air pollutants have been reported in other studies (Behera et al., 2021; Ghosh et al., 2024; Mukherjee et al., 2023). During summer, the prevailing winds were from the Bay of Bengal branch of the southwest monsoons. In this case, the IGP did not affect the receptor site (Fig. S1). The same was true for certain days during the post-monsoon season. This resulted in reduced pollution, combined with higher temperature, solar radiation, elevated boundary layer, and increased wind speed - the latter two factors favoring the dispersion of pollutants. However, during the post-monsoon season, the sites, on certain days based on the trajectory modeling, were affected by emissions from the northwest IGP and the northeastern regions (Fig. S1). This explained the significant difference in LG levels between the post-monsoon and summer seasons (Fig. 2). The impact of biomass

<figure><img src="image_6.png" /><figcaption>**Fig. 2.** Seasonal differences of PM₂.₅ (a), T-PAHs (b), T-alkanes (c), and LG (d). Each box includes data from both sites near the Farakka thermal power plant in West Bengal, India. The middle line represents the median. The lower and upper hinges correspond to the 25th and 75th percentiles. The whisker extends to the largest or the smallest value at 1.5 times the interquartile range. To examine if there are statistically significant differences (p < 0.05) between seasons, we conducted Kruskal-Wallis tests to compare multiple groups and unpaired Wilcoxon tests (Mann-Whitney U test) to compare two groups.</figcaption></figure>
burning from the IGP and the northeastern regions, which was
well-documented (Behera et al., 2021; Bikkina et al., 2015; Kumar et al.,
2010), was absent during summer. Due to the mixed wind pattern,
pollutant levels in post-monsoon were intermediate (Fig. 2 and Table 1)
and, in some cases, not significantly different from those in winter or
summer, even though the pollutant concentrations during winter were
consistently higher than those in summer. This suggested that with the
change in the prevailing wind patterns, there was a shift in emission
sources and their strength, which had played a significant role in con-
trolling the pollutant levels and compositions at the receptor sites.

3.2. *Levoglucosan, n-alkanes and the indices*

As depicted in Fig. 3 and Fig. S2, the n-alkane homologous series had a similar pattern for respective sites and seasons, with C<sub>max</sub> being predominantly C<sub>29</sub> alkane (nonacosane). This indicated the substantial input of biogenic n-alkanes all year round for both sites. The predominance of odd/even carbon numbers for C<sub>27</sub>-C<sub>33</sub> indicated epicuticular waxy emissions from leaf abrasion, plant litter, biomass burning, and burning of low-rank coal (Ren et al., 2020; Rogge et al., 1993; Simoneit, 2002). In winter, the upwind site showed a slight enrichment of even-chain n-C<sub>20</sub>, C<sub>22</sub>, C<sub>24</sub>, and C<sub>26</sub> alkanes, which could be attributed to emissions from vehicular exhaust (Yadav et al., 2024).

The lowest CPI value was observed during winter (Fig. 2), which suggests the highest relative contribution of fossil fuel combustion. CPI values for both winter and post-monsoon seasons were significantly lower than summer (p < 0.05), and the latter was primarily affected by plant-based waxes. Similarly, the highest WNA and PNA values were observed during winter, followed by post-monsoon, summer, and monsoon seasons (Fig. 4). However, there was no significant difference between the upwind and downwind sites. WNA% was the lowest during winter, followed by post-monsoon, monsoon, and summer seasons. The opposite was true for PNA%. This means that n-alkanes were derived mainly from petrogenic sources throughout the year, with the lowest percentage of PNA% contribution during summer and monsoon. Similar results were observed in other studies from India (Huma et al., 2016; Shivani et al., 2018; Yadav et al., 2013a, 2013b, 2020). In winter, WNA and WNA% were significantly higher at the downwind site than at the upwind site. During the monsoon season, the fact that WNA was higher

downwind implies more local WNA emissions, as heavy rainfall reduced the long-range transport of pollutants (Seinfeld and Pandis, 2016). Higher LG levels at the downwind site, compared to upwind, during summer and monsoon, corresponded with this inference. During the summer and post-monsoon seasons, local emissions were likely diluted by similar regionally transported pollutants, resulting in comparable values of the abovementioned indices at both sites.

3.3. PAH compositions and diagnostic ratios

The concentrations of 4, 5, and 6-ring PAHs shared a similar seasonal pattern of T-PAH distribution: winter > post-monsoon > summer > monsoon (Fig. S3). Five-ring PAHs dominated during winter and post-monsoon seasons; it was slightly higher than 6-ring PAHs during summer but lower in monsoon. Four-ring PAHs had a higher concentration than 6-ring PAHs during winter, while during other seasons, they had the lowest concentration. Significant differences were absent between the sites during different seasons. Similarly, there was no significant difference between individual PAH levels. The relative abundance in winter was distinctly different from other seasons. The 5-ring PAH/T-PAHs (44 %) were predominant, followed by 4-ring PAH/T-PAHs (29 %), and the lowest value of 6-ring PAH/T-PAHs (26 %). During the post-monsoon season, 5-ring PAHs dominated (42 %), but with a notable increase in 6-ring PAHs (36 %) and a decrease in 4-ring PAHs (21 %). These variations highlighted the seasonal shifts in PAH composition, likely influenced by changes in combustion sources and atmospheric conditions. A similar pattern was found in studies from New Delhi, India (Elzein et al., 2020; Singh et al., 2021). Therefore, this observed pattern of relative abundance of PAH distribution might indicate long-range transport of urban emissions from the northwest in winter and partly during the post-monsoon season. The downwind site had a relatively higher abundance of 4-ring PAHs in winter, 6-ring PAHs in monsoon, and 5-ring PAHs in post-monsoon. In contrast, the relative abundance of 6-ring PAHs in winter and 5-ring PAHs in the monsoon season was higher at the upwind site. It has been argued that 4-ring PAHs are closely associated with coal combustion, which implies that the downwind site was more affected by the TPP in winter. In contrast, the 5 to 6-ring PAHs were more likely derived from vehicular exhaust (Elzein et al., 2020; Fang et al., 2020; Sun et al., 2022). However, the fact that combustion

<figure><img src="image_7.png" /><figcaption>Fig. 3. Median concentrations of n-alkanes (C19-C33) at the upwind and downwind sites across seasons, near Farakka power plant in West Bengal, India.</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>**Fig. 4.** Seasonal variation of WNA (a) and PNA (b) (in ng/m³); (c) is the median contributions of WNA% and PNA% at the upwind and downwind sites across seasons, near the Farakka thermal power plant in West Bengal, India.</figcaption></figure>

sources emit a mixture of PAHs and that the emission sources and intensity vary (Harrison et al., 1996; Khalili et al., 1995; Larsen and Baker, 2003; Ravindra et al., 2008) complicates the differentiation of anthropogenic sources based solely on PAH concentrations and their relative abundances. Fig. 5 showed the dominance of pyrogenic/combustion-derived sources over petrogenic ones for PAH emissions, similar to other studies in India (Kumar et al., 2020; Shankar et al., 2023). Post-monsoon had the second highest BaA/(BaA + Chr) values, followed by summer and monsoon seasons. BbF/(BbF + BkF) in summer and monsoon were higher than post-monsoon. BbF/(BbF + BkF) and BaA/(BaA + Chr) were significantly higher at the downwind site than the upwind during the post-monsoon and winter seasons (p < 0.05). Chrysene was slightly more enriched from coal combustion than BaA (Fu et al., 2010; Hsu et al., 2016).

Long-range transport and local emissions may have masked the expected trends. For example, BaA decay significantly faster than Chr when adsorbed to soot, resulting in definitive shifts of the ratio toward low values (Kim et al., 2009). Therefore, it is likely that the FTPP emission replenished BaA as air parcels traveled downwind. In contrast, air parcels to the upwind site bypassed the FTPP, and BaA levels remained low or unchanged. This replenishment of PAHs might have also resulted in insignificant differences between the post-monsoon and winter seasons. Similarly, the fact that BbF is more enriched in emissions

from coal combustion (Huang et al., 2014; Wang et al., 2016) and that it has a longer half-life than BkF, might have contributed to the higher BbF/(BbF + BkF) ratio downwind (Kim et al., 2009).

### 3.4. Trajectory modeling

Since the change in wind patterns resulted in a shift in emission sources that affected the study sites, it is essential to investigate seasonal variations and differences between clusters (Fig. 6). It is assumed that sample clusters would share similar pollutant signatures derived from the same sources. Trajectories from December 2021 to November 2022 were clustered together. Most air parcels (42%–57%) originated in the Bay of Bengal, traversing the southern part of West Bengal in India and Bangladesh (SC); 33% originated from the northwest (NWC) area, and the remaining from the northeast (NEC). Notably, 85% of NWC occurred in winter, and the remaining 15% during the post-monsoon season. While only 10% and 12% of the SC originated during the winter and post-monsoon seasons, 44% originated during summer and 37% in monsoon. Fifty-six percent of NEC originated during the post-monsoon season, followed by 39% in winter and 6% in summer.

SC had the lowest PM₂.₅ levels, followed by NEC and NWC (Table 2 and Table S4). Significant difference between sites for each cluster (p > 0.05) was absent. NWC and NEC had comparable LG and WNA values,
<figure><img src="image_9.png" /><figcaption>**Fig. 5.** Diagnostic ratios of PAH, including BaA/(BaA + Chr), BbF/(BbF + BkF), IndP/(IndP + BghiP). Cross plot (a) is BaA/(BaA + Chr) vs IndP/(IndP + BghiP); (b) is BaA/(BaA + Chr) vs BbF/(BbF + BkF); (c) is BbF/(BbF + BkF) vs IndP/(IndP + BghiP). Solid and dashed lines represent sample clusters at the downwind and upwind sites near the Farakka power plant in West Bengal, India.</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>**Fig. 6.** Clustered trajectories based on one-year 48 h backward trajectories for the downwind and upwind sites near the Farakka power plant in West Bengal, India.</figcaption></figure>

while SC had low values. WNA for SC and NEC and LG for SC were higher downwind than in the upwind region. This suggested a more significant impact of biomass burning from the northwest and northeast, particularly at the downwind site. CPI in all samples cluster upwind,

implying comparable shares of biogenic and petrogenic n-alkanes. In contrast, the SC downwind had higher CPI than the NWC and NEC, implying input of more petrogenic n-alkanes in those clusters. The CPI values in the NWC downwind were significantly higher than those
<caption>**Table 2**</caption>

The concentration of organic pollutants in the clustered trajectories (mean ± SD) near the Farakka power plant in West Bengal, India.

<table><thead><tr><th rowspan="2"></th><th colspan="3">Upwind</th><th colspan="3">Downwind</th></tr><tr><th>NWC</th><th>SC</th><th>NEC</th><th>NWC</th><th>SC</th><th>NEC</th></tr></thead><tbody><tr><td>n<br>PM<sub>2.5</sub> (µg/m<sup>3</sup>)</td><td>29<br>130.43 ± 42.43<br>(128.44)</td><td>39<br>80.44 ± 39.79<br>(62.38)</td><td>24<br>104.29 ± 44.66<br>(100.73)</td><td>38<br>154.17 ± 61.4 (151.69)</td><td>61<br>77.75 ± 38.07 (71.43)</td><td>11<br>112.35 ± 43.69<br>(120.55)</td></tr><tr><td>n<br>LG (ng/m<sup>3</sup>)</td><td>13<br>857.93 ± 478.04<br>(856.22)</td><td>23<br>120.43 ± 215.75<br>(39.19)</td><td>10<br>670.15 ± 521.89<br>(729.81)</td><td>14<br>1049.95 ± 330.03<br>(912.66)</td><td>29<br>291.07 ± 305.5<br>(199.31)</td><td>6<br>1115.11 ± 740.26<br>(855.86)</td></tr><tr><td>n<br>T-PAHs (ng/m<sup>3</sup>)</td><td>13<br>66.94 ± 40.92 (67.37)</td><td>23<br>32.05 ± 40.93<br>(23.51)</td><td>11<br>80.99 ± 50.3 (79.86)</td><td>14<br>99.95 ± 39.44 (102.5)</td><td>29<br>41.1 ± 34.31 (37.95)</td><td>7<br>63.8 ± 17.25 (63.45)</td></tr><tr><td>4-ring PAHs</td><td>18.93 ± 13.84 (14.1)</td><td>7.94 ± 13.07 (4.7)</td><td>18.66 ± 11.23 (17.77)</td><td>30.25 ± 13.8 (26.97)</td><td>9.75 ± 11.08 (7.58)</td><td>15.03 ± 5.72 (16.44)</td></tr><tr><td>5-ring PAHs</td><td>28.92 ± 17.12 (33.07)</td><td>12.96 ± 16.91 (8.91)</td><td>36.8 ± 25.05 (34.13)</td><td>43.95 ± 17.76 (43.48)</td><td>16.75 ± 15.25 (14.51)</td><td>29.6 ± 9.85 (27.23)</td></tr><tr><td>6-ring PAHs</td><td>18.34 ± 10.39 (20.27)</td><td>10.83 ± 10.4 (7.86)</td><td>25.04 ± 15.34 (25.81)</td><td>24.92 ± 9.48 (24.42)</td><td>14.23 ± 8.52 (14.95)</td><td>18.79 ± 5.85 (17.23)</td></tr><tr><td>BaA/(BaA + Chr)</td><td>0.39 ± 0.05 (0.41)</td><td>0.39 ± 0.04 (0.4)</td><td>0.43 ± 0.03 (0.42)</td><td>0.48 ± 0.03 (0.48)</td><td>0.4 ± 0.07 (0.41)</td><td>0.47 ± 0.02 (0.46)</td></tr><tr><td>BbF/(BbF + BkF)</td><td>0.44 ± 0.02 (0.44)</td><td>0.43 ± 0.03 (0.42)</td><td>0.41 ± 0.02 (0.41)</td><td>0.49 ± 0.02 (0.49)</td><td>0.5 ± 0.04 (0.48)</td><td>0.48 ± 0.03 (0.47)</td></tr><tr><td>IndP/(IndP + BghiP)</td><td>0.5 ± 0.02 (0.51)</td><td>0.49 ± 0.02 (0.49)</td><td>0.51 ± 0.03 (0.5)</td><td>0.52 ± 0.02 (0.52)</td><td>0.46 ± 0.11 (0.43)</td><td>0.51 ± 0.04 (0.49)</td></tr><tr><td>T-alkanes (ng/m<sup>3</sup>)</td><td>334.64 ± 141.82<br>(305.5)</td><td>75.18 ± 150.95<br>(18.24)</td><td>212.22 ± 144.62<br>(220.61)</td><td>361.01 ± 223.11<br>(351.29)</td><td>113.23 ± 145.08<br>(55.76)</td><td>284.71 ± 91.45<br>(285.16)</td></tr><tr><td>CPI</td><td>1.5 ± 0.49 (1.34)</td><td>3.4 ± 4.85 (2.03)</td><td>1.77 ± 0.81 (1.68)</td><td>1.96 ± 0.39 (1.84)</td><td>2.88 ± 1.72 (2.31)</td><td>1.79 ± 0.15 (1.73)</td></tr><tr><td>WNA</td><td>75.71 ± 59.36 (56.72)</td><td>20.57 ± 33.36 (7.06)</td><td>45.77 ± 25.24 (46.52)</td><td>123.1 ± 94.98 (102.02)</td><td>38.97 ± 48.36 (20.28)</td><td>82.91 ± 20.99 (78.62)</td></tr><tr><td>PNA</td><td>258.93 ± 105.53<br>(250.95)</td><td>54.61 ± 119.48<br>(13.76)</td><td>166.45 ± 125.63<br>(178.49)</td><td>237.9 ± 146.02<br>(249.27)</td><td>74.26 ± 107.17<br>(45.11)</td><td>201.8 ± 71.32 (206.54)</td></tr><tr><td>WNA%</td><td>22.64 ± 10.22 (18.97)</td><td>32.16 ± 17.47<br>(34.88)</td><td>26.33 ± 14.05 (17.86)</td><td>33.03 ± 7 (31.33)</td><td>40.67 ± 16.19 (38.71)</td><td>29.98 ± 4.25 (27.71)</td></tr><tr><td>PNA%</td><td>77.36 ± 10.22 (81.03)</td><td>67.84 ± 17.47<br>(65.12)</td><td>73.67 ± 14.05 (82.14)</td><td>66.97 ± 7 (68.67)</td><td>59.33 ± 16.19 (61.29)</td><td>70.02 ± 4.25 (72.29)</td></tr></tbody></table>

upwind, which was also an indicator of more biomass burning/biogenic impact in the downwind sites. Similar results were observed for PNA, but no significant differences existed between the sites. T-alkanes were the lowest for SC downwind; however, it is identical between NWC and NEC upwind. The NWC had higher T-alkane concentrations than the NEC.

The T-PAH of the NWC upwind was equivalent to that of the NEC but higher than that of the SC downwind; NWC had the highest concentration, followed by NEC and SC. IndP, DBahA, and BghiP in SC downwind had higher concentrations than the upwind site. The abundance of these PAHs, along with the enrichment of BbF and BkF, was indicative of vehicular exhaust (gasoline and diesel engines) contributing significantly to the atmospheric contaminant pool, posing health risks to the local population (Harrison et al., 1996; Jiang et al., 2019; Khan et al., 2017; Marr et al., 1999; Miguel et al., 1998). This suggested that both sites were mainly affected by vehicular emissions as the wind blew northwards, which made the impact on the downwind site more severe. The primary contributor to the upwind site might be Malda City. However, for the downwind site, the regional transport of pollutants from Kolkata and other nearby cities in the south might impact the local air quality. The BaA, BbF, and DBahA levels in the NWC were higher in the downwind than in the upwind directions, indicating the significant role of the Farakka power plant in the distribution of pollutants. Such differences between the sites were likely masked, diluted and remained undetected when the winter samples were compared. The NWC grouped samples from the northwest during the winter and post-monsoon seasons, and the differences between PAH distribution increased. The relatively high DBahA cannot be explained solely by the FTPP emission.

PM<sub>2.5</sub> and LG of NWC had weak to moderate positive correlation (r = 0.43 for downwind; r = 0.31 for upwind), indicating similar biomass burning impact on both sites. However, they were not correlated (r < ± 0.2) with T-PAHs or T-alkanes downwind. In the upwind site, PM<sub>2.5</sub> correlated moderately with T-alkanes (r = 0.46) but not T-PAHs; LG correlated with neither compound classes. Transported LG from the middle and upper IGP areas was a common pollutant for both sites.

WCWT showed that the primary pollution sources, including FTPP

and other local sources, lay northwest of the downwind site. Additionally, there was input of long-range transported pollutants, which influenced the distribution of different pollutants (Fig. 7). Local emission sources about 50 km southeast of the receptor site along the India-Bangladesh border also contributed to PM<sub>2.5</sub>. The area includes Berhampore, the seventh largest city in West Bengal, and the administrative center of Murshidabad district. It is a non-industrial city with high transport and traffic density (Ghosh et al., 2018). Prevailing winds transported the vehicular emissions downwind, partly resulting in lower CPI values observed in the SC compared to other clusters and higher concentrations of certain PAHs than those found upwind. WCWT showed that Malda City was potentially the primary source for the upwind site. Besides that, both PM<sub>2.5</sub> and LG had a similar pattern downwind, suggesting that long-range transport of pollutants might have a similar impact on both sites.

## 4. Conclusions

The increase in air pollution levels showed little abatement in the IGP. Coal used in TPP, besides biomass combustion, vehicular traffic, and industrial emissions, contributed to air pollution. PM samples collected year-round at a rural and suburban site near the Farakka TPP highlight significant pollution. These samples indicated the presence of high concentrations of PM<sub>2.5</sub>, reflecting a strong seasonality and the cumulative impact of emissions from coal combustion at the power plant, as well as other regional pollution sources. The average concentration of PM<sub>2.5</sub> was 2.5 times higher than the national limit of 40 µg/m<sup>3</sup>, while exceeding 20 times the WHO global air quality guideline of 5 µg/m<sup>3</sup>. The annual average level of benzo[a]pyrene exceeded 5 times the national value and 40 times the European Environment Agency limit. n-Alkanes were mainly derived from petrogenic sources, while pyrogenic sources were the primary contributor to PAHs. Levoglucosan patterns indicated more biomass burning during winter, followed by post-monsoon, and the downwind/rural site had more local biomass burning emissions than the upwind/suburban site. The trajectory modelling revealed elevated benzo[a]anthracene, benzo[b]
<figure><img src="image_11.png" /><figcaption>Fig. 7. WCWT for PM<sub>2.5</sub>, T-PAHs, T-alkanes and LG. Jenks natural break method was used to visually minimize variation within each class and maximize the difference between classes (thus maps not inter-comparable) (De Smith et al., 2007).</figcaption></figure>

fluoranthene, and dibenz[a,h]anthracene levels due to TPP emissions.

CRediT authorship contribution statement

**Chen Luo:** Writing – review & editing, Writing – original draft, Visualization, Validation, Methodology, Investigation, Formal analysis, Data curation, Conceptualization.
**Sayantan Sarkar:** Writing – review & editing, Validation, Supervision, Resources, Methodology, Conceptual-

ization. **Fanny Gesmond:** Validation, Methodology, Investigation, Data curation. **Megha Anand:** Project administration, Investigation, Data curation. **Kalpana Munnuru Singamshetty:** Writing – review & editing, Validation, Methodology. **Joyanto Routh:** Writing – review & editing, Supervision, Resources, Project administration, Methodology, Funding acquisition, Conceptualization.
Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgment

We sincerely thank the people at Kalachand High School, Old Malda, and Baharagachhi High School, Murshidabad, for their invaluable support in aerosol sample collection and logistics. We also acknowledge the approval of this study by the Indian Institute of Technology-Mandi (IITM/IEC(H)/2022/SDG/P9) and clearance from Etiksprövningsmyn-digheten, Sweden (Dnr 2022-04882-01). This study was made possible by funding from Vetenskapsrådet (Swedish Research Council) (Grant 2020-03605 to Joyanto Routh).

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.atmosenv.2025.121428.

Data availability

Data will be made available on request.

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