Snow Cover as a Medium for Polycyclic Aromatic Hydrocarbons (PAHs) Deposition and a Measure of Atmospheric Pollution in Carpathian Village–Study Case of Zawoja, Poland

Abstract

Snow cover constitutes a medium that can be used as a way of assessing air pollution. The chemical composition of snow layers from the same snowfall event reflects the composition of atmospheric aerosols and dry precipitates, depending on the properties of the adsorbing surface and prevailing weather conditions. Analyzing snow samples provides reliable insights into anthropogenic pollution accumulated in soil and groundwater of different land use type areas, as well as allows the evaluation of the degree and sources of environmental pollution. The aim of the research was to determine the distribution of polycyclic aromatic hydrocarbons in various sites of Zawoja village and identify their possible sources and factors influencing their differentiation. A total of 15 surface snow samples of the same thickness and snowfall origin were collected from different locations in the village in the winter of 2024. The samples were pre-concentrated by solid phase extraction and analyzed by gas chromatography—tandem mass spectrometry. The sampling set was invented, and the extraction procedure and analysis parameters were optimized. A spatial distribution map of PAHs was created. The contamination of ∑16PAHs varied from 710 to 2310 ng/L in melted snow with the highest concentrations detected in Zawoja Markowa by the border of the Babia Góra National Park, which is interpreted mainly as a result of the topographical setting. Medium molecular weight PAHs were the dominant fraction, which, combined with specific PAH ratios, indicate the combustion of biomass and coal as the main source of contamination.

1. Introduction

Polycyclic aromatic hydrocarbons (PAHs) are a group of toxic organic compounds known for their mutagenic, teratogenic, and cancerogenic impact on human health [1,2]. PAHs can be generated from incomplete organic matter combustion of substances, like coal, wood, crude oil, natural gas, or waste. Although PAHs can be derived from natural processes, such as volcanic activity, forest fires, and organic matter diagenesis, anthropogenic sources are predominant [3,4]. Most PAH pollution is generated by fossil fuels and wood combustion in energy plants and for residential heating, industrial processes, such as coal coking and petroleum refining, waste combustion, and motor vehicle emissions [2,5].

PAHs are built of carbon and hydrogen atoms arranged in two or more fused aromatic rings. PAHs’ chemical polycyclic structures make them difficult to break down, and because of their resistant and hazardous nature, they are considered to be persistent organic pollutants (POPs) [2] and listed as priority pollutants by The European and Community and the U.S. Environmental Protection Agency [1]. There are 16 PAHs that have been designated High Priority Pollutants by the Environmental Protection Agency (EPA) and should be analyzed in the environment [6] (Figure 1).

Most of the Carpathian resort villages, despite being surrounded by green areas and forests, are affected by different human activities, such as tourism, road transport, local industry, and household heating, during the winter season. These factors can contribute to local PAH concentration increases. In the winter, when large areas of the village are covered by snow, PAHs and other pollutants are deposited on the snow surface. As the snow melts, these contaminants are transported into water and soil system, which causes the risk of contamination of surface and groundwater as well as soil and affects living organisms. Monitoring the PAH concentration in snow can therefore deliver valuable information on seasonal and spatial changes in PAH deposition and potential risks to the natural environment and human health. The investigation of PAH concentration levels in snow helps to assess the risk of human and environmental exposure to these toxic compounds during winter, when atmospheric conditions and low temperatures may facilitate accumulation of PAHs in the local environment. Moreover, snow acts as a natural filter, capturing PAHs from the air and serving as an indicator of local air quality combustion-related emissions. Monitoring PAH concentrations might be useful for spatial management planning as this information can support decision-making processes, pollution control strategies, and emissions urban development planning to minimize negative environmental impact.

Pollutants, including PAHs, once released into the atmosphere, are transported by air masses and eventually deposited on the surface of soil, vegetation, or into water bodies, such as lakes and rivers. Wind-driven transport and turbulent mixing are key processes in the dispersion of these pollutants throughout the atmosphere [7]. Three types of atmospheric transport onto the surface can be distinguished: wet, dry, and fog (cloud) deposition [8]. Snow scavenging constitutes a significant atmospheric removal mechanism that governs the atmospheric residence time of pollutants and their subsequent deposition into terrestrial and aquatic ecosystems. PAH concentrations in freshly fallen snow can serve as a proxy for assessing atmospheric pollution levels and evaluating the efficiency of snow-mediated scavenging processes for PAHs [9,10]. Studies have demonstrated that snow scavenging exhibits greater efficiency than rain scavenging in removing particulate-bound pollutants from the atmosphere [11,12,13]. Similarly, snow surfaces can serve as a filter for direct measurements of dry deposition [14]. Semi-volatile organic compounds (SVOCs) are subjected to both dry particle deposition and wet scavenging pathways of removal from the atmosphere [15]. Dry deposition consists of the following steps: aerodynamic transport, boundary layer transport, and interactions with the surface, as well as sedimentation as a parallel process that can occur for particles larger than a few tenths of a µm [8]. The efficiency of dry deposition is influenced by the physicochemical properties of the pollutant, prevailing meteorological factors, such as temperature, wind speed, and atmospheric stability, as well as the characteristics of the underlying surface [14]. Wet scavenging can be divided into particle (ice nucleation and rimming) and gas (adsorption) scavenging processes controlled by SVOC gas-particle partitioning in the aerosol, which mainly depends on the substance molecular structure, particulate matter chemical composition, and ambient temperature [15,16,17]. The actual gas scavenging removal of SVOCs from the atmosphere occurs for the substances that reveal minimum interaction with PM and is due to substance dissolution in rain or cloud droplets or sorption to snowflakes or other ice hydrometeors [15,18,19]. Cold temperatures enhance partitioning of gas-phase substances to the snow surface, which, combined with the developed surface areas of snowflakes, makes snow an excellent scavenger of SVOCs from the atmospheric gas phase [20]. Throughout wet scavenging, SVOCs in the gas and particulate phases in the atmosphere are accumulated in the aqueous and particulate phases of precipitation [15]. It has been shown that particle scavenging generally plays a higher role than gas scavenging for hydrophobic SVOCs, such as PAHs [17,21]. However, the process of gas scavenging should not be neglected for light-weight PAHs [11].

Analyses of snow pollution, although not as common as studies on air, soil, or water pollution, have been used for several decades to assess environmental conditions. Researchers have investigated both inorganic [22,23,24,25] and organic pollutants, including PAHs [22,26,27]. Studies have focused on remote sites distant from anthropogenic sources [28,29], mountainous regions [30,31,32,33], as well as urban areas [10,27,34], covering locations from polar [29] to temperate climates [26].

The studied area has been of interest to researchers in terms of pollution assessment in various environmental elements. Most studies have concentrated on heavy metal contamination, which was assessed in soil [35,36], landslide lakes [37] of Babia Góra National Park, and in soil and atmospheric particulate matter from Zawoja village [38]. However, few studies carried out in the investigated region have addressed PAH pollution. Błońska and Lasota [39] used Babia Góra National Park as a sample area to show PAH accumulation in forest soil depending on the location, exposure and presence of decaying wood. Borgulat and Borgulat [40] investigated PAH contamination in spruce and fir needles from nearby areas to assess atmospheric pollution in the mountain villages of Beskid Śląski and Beskid Żywiecki Mountains. The absence of advanced studies on PAH levels in this popular region creates the need for such analyses to evaluate air quality in a typical tourist village in the Beskidy Mountains.

The aim of this study is to develop and validate new research methods based on gas chromatography coupled with mass spectrometry (GC-MS/MS) to detect and quantify PAHs in snow samples and determine their main sources in a typical Carpathian village, using Zawoja as a case study. Due to the unique properties of snow as an environmental matrix capable of accumulating atmospheric pollutants, it is essential to establish precise and reliable analytical methods for monitoring the presence and distribution of PAHs. The methodology includes a detailed characterization and optimization of sample collection, PAH extraction conditions from snow samples, and the development and validation of GC-MS/MS techniques to ensure accurate and repeatable measurements. Another key aspect of this research is assessing the impact of environmental variables, such as temperature or the presence of other organic and inorganic contaminants, on the extraction efficiency and accuracy of analyses. This publication not only aims to enhance knowledge about the PAHs distribution in the environment during the winter season but also to develop analytical tools for monitoring environmental quality and assessing health risks associated with exposure to these pollutants. Additionally, the findings may contribute to a deeper understanding of PAH transport and transformation processes in the environment, which is crucial for future remediation actions and public health protection.

2. Study Area

The study was carried out in Zawoja village (49°34′19.74″; 19°28′2.568″ to 49°41′55.428″ 19°37′9.3″) in southern Poland, near the border with Slovakia (Figure 2). Zawoja is the largest and the longest village in Poland, located at the foothills of Babia Góra Mountain, which encompasses the highest peak (Diablak, 1725 m asl) of the Polish Beskidy Mountain Range. The Babia Góra Massif is protected by Babia Góra National Park. Zawoja stretches for 18 km and covers an area of 128 km² [41], diversified in terms of topography, spatial development, and economic and tourist activities. The village’s altitude ranges from 426 m asl to the summit of Babia Góra at 1725 m asl. The Municipality of Zawoja, as of 31st December 2023 was inhabited by 8855 people [42]. The village is a popular tourist destination, serving as the gateway to Babia Góra hiking trails, and in recent years, the number of visitors entering Babia Góra National Park exceeded 100,000 people annually [43].

The climate in Zawoja is classified as humid continental (Dfb) according to the Köppen–Geiger classification [44,45]. The local climate was characterized using data from 1989 to 2018, recorded by the meteorological station in Zawoja (697 m asl), operated by the Institute of Meteorology and Water Management—National Research Institute (IMGW-PIB). The average annual air temperature during this period was 6.7 °C, with a maximum of 8.2 °C in 2014 and a minimum of 4.8 °C in 1996. Annual precipitation averaged 1262.4 mm, ranging from 917.3 mm in 1993 to 1796.3 mm in 2010. Snow cover was present for an average of 90 days per year, with extremes of 24 days in 2014 and 120 days in 2005. Compared to the 1961–2015 period [46], this data indicates a slight increase in the average temperature (previously 6.4 °C) and a decrease in snow cover duration (previously 104 days). Wind simulation from the Global Wind Atlas, based on a 10-year mesoscale dataset (2008–2017) from the European Centre for Medium-Range Weather Forecasts (ECMWF), shows that winds predominantly come from the west–southwest (W–SW) direction (Figure 3) and during winter months reach an average speed of 1 m/s [47]. The atmosphere in this area is particularly exposed to PAH emissions from various sources, including coal and biomass combustion, tourist activities with the use of numerous ski lifts or mountain hiking trails, and local businesses, such as the wood and furniture industry. During the winter season, the main factor contributing to increased exposure to pollutants, including PAHs, is household heating. According to the 2023 report from the Central Building Emissions Register (Centralna Ewidencja Emisyjności Budynków—CEEB), the following heat sources were identified in the municipality of Zawoja: solid fuels (coal, wood, pellets, or other types of biomass)—4079; other sources (oil heating, electric heating, district heating, system heat)—1308; renewable energy sources (heat pumps for space heating and domestic hot water, solar collectors)—1002; gas—72 [42]. This indicates that the dominant source of heating in the studied village is the combustion of solid fuels. The dominant heating device for solid fuel combustion was a solid fuel boiler (~50%), then a wood stove (~25%).

3. Experimental Part

3.1. Sampling

Fifteen samples were collected at the end of January (30–31 Jan) 2024 after fresh snowfall (Figure 2,

Table 1. Sampling sites.

Sample No	District Name	Coordinates, Altitude (m asl.)	Pollution Factor (PF) 1: I—Distance from the Road, II—Distance from Tourist Attractions, III—Industrial and IV—Commercial Sites, V—Population Density, VI—Distance from the Forest, VII—Altitude	Photo
1	Zawoja Snoza	49°41′43″ N 19°36′41″ E, 630 m asl	I-0, II-0, III-1, IV-0, V-0, VI-2, VII-2 PF = 5 Wasteland, meadow, vicinity of forest, >100 m from the nearest household, 150 m from the sawmill
2	Zawoja Mosorne 650 m asl	49°38′11″ N 19°32′48″ E, 663 m asl	I-0, II-0, III-0, IV-0, V-2, VI-2, VII-1 PF = 5 Meadow, local agriculture, individual dwellings, vicinity of forest, 25 m from the nearest household
3	Zawoja Policzne	49°36′50″ N 19°33′05″ E, 707 m asl	I-5, II-5, III-0, IV-4, V-1, VI-2, VII-0 PF = 17 By the transit main road, in the vicinity of ski lift and restaurant, 55 m from the nearest household
4	Zawoja Ryzowana	49°37′20″ N 19°32′12″ E, 665 m asl	I-2, II-0, III-0, IV-1, V-4, VI-2, VII-1 PF = 10 80 m from the main road, <250 m from the hotel, build up area outside the village centre
5	Zawoja Widły	49°38′06″ N 19°31′33″ E, 606 m asl	I-3, II-0, III-0, IV-3, V-4, VI-4, VII -3 PF = 17 Near the secondary road and the grocery shop, build up area outside the village centre
6	Zawoja Składy	49°37′22″ N 19°31′03″ E, 636 m asl	I-2, II-0, III-0, IV-3, V-2, VI-4, VII-2 PF = 13 15 m from the secondary road, 15 m from the nearest household and forest inspectorate building
7	Zawoja Markowa	49°36′28″ N 19°31′00″ E, 718 m asl	I-2, II-0, III-0, IV-4, V-2, VI-0, VII-0 PF = 8 25 m from the entrance to Babia Góra National Park, 25 m from the secondary road and a car park
8	Zawoja Czatoża	49°36’59″ N 19°30’35″ E, 671 m asl	I-2, II-2, III-0, IV-0, V-3, VI-3, VII-1 PF = 11 100 m from the ski lift, several meters from the secondary road
9	Zawoja Mosorne School	49°38’34″ N 19°32’24″ E, 578 m asl	I-3, II-0, III-0, IV-5, V-5, VI-4, VII-4 PF = 21 20 m from the main road, by the school car park, build up area village centre
10	Zawoja Wełcza, final bus stop	49°38’44″ N 19°30’19″ E, 661 m asl	I-2, II-0, III-0, IV-0, V-2, VI-2, VII-1 PF = 7 20 m from the secondary road, 30 m from the nearest household
11	Zawoja Wełcza PTSM	49°39’07″ N 19°30’45″ E, 628 m asl	I-3, II-3, III-0, IV-3, V-4, VI-4, VII-2 PF = 19 10 m from the secondary road, 30 m from the nearest household, 20 m from the youth tourist shelter and 20 m from the church
12	Zawoja Centrum church	49°39’38″ N 19°33’37″ E, 536 m asl	I-3, II-5, III-0, IV-5, V-5, VI-5, VII-5 PF = 28 Village centre, by the church car park
13	Zawoja Dolna	49°39’56″ N 19°35’09″E, 527 m asl	I-2, II-0, III-3, IV-0, V-3, VI-4, VII-5 PF = 17 Vicinity of PCV window factory, outside of the buildup area
14	Zawoja Podpolice	49°39’15″ N 19°35’28″E, 603 m asl	I-2, II-0, III-0, IV-0, V-0, VI-0, VII-3 PF = 5 Forest
15	Zawoja Przysłup	49°41’10″ N 19°33’55″E, 659 m asl	I-1, II-0, III-0, IV-0, V-2, VI-3, VII-2 PF = 8

¹ Pollution factor estimated as a sum of I + II + III + IV + V + VI + VII using a scale from 0 to 5 points for each category. I—distance from road 0. If > 250 m from the main road, >100 m from the secondary road; 1. If 100–250 m from the main road, 50–100 m from the secondary road; 2. If 50–100 m from the main road, 10–50 m from the secondary road; 3. If 10–50 m from the main road, 5–10 m from the secondary road; 4. If 5–10 m from the main road, <5 m from secondary road; 5. If 0–5 m from the main road; Main road—along the village to Krowiarki pass, secondary road—other roads leading to settlements with a dense population; II—tourist attractions: 0. If >250 m 1. If 100–250 m 2. If 50–100 m 3. If 25–50 m 4. If 10–25 m 5. If <10 m; III—distance from industrial sites: 0. If >250 m 1. If 100–250 m 2. If 50–100 m 3. If 10–50 m 4. If 5–10 m 5. If <5 m; IV distance from commercial sites: 0. If >250 m 1. If 100–250 m 2. If 50–100 m 3. If 10–50 m 4. If 5–10 m 5. If <5 m; V—population density 0. If >100 m to the nearest house, forest meadow; 1. If individual dwellings, 50–100 m to the nearest house; 2. If scattered households, 10–50 m to the nearest house; 3. If scattered households, <10 m to the nearest house; 4. If build up area—districts beyond the center; 5. If built up area—village center. VI—vicinity of the forested/protected areas 0. If <25 m, 1. If 25–50 m, 2. If 50–100 m 3. If 100—250 m 4. If 250–500 m 5 > 500 m; VII—altitude: 0 > 700 m asl, 1. If 660–700 m asl, 2. If 620–660 m asl, 3. If 580–620 m asl, 4. If 540–580 m asl, 5 < 540 m asl.

). The weather conditions preceding sample collection, based on the data provided by Zawoja Station of the Institute of Meteorology and Water Management—National Research Institute, are shown in Figure 4.

To collect a representative sample from each site, a simple field method was invented using the following materials (Figure 5):

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A set of stainless steel tools: a short pipe fragment (H = 5 cm, ∅ = 21 cm), a 30 × 40 cm sheet metal, a shovel, and a funnel;

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Amber glass bottle (prewashed with GC-MS grade dichloromethane and LC-MS grade water).

The pipe was used to collect a snow core, which was then cut off with sheet metal and transferred by shovel and funnel into the amber glass bottle. Depending on the snow structure at the site, one to four snow cores were collected from each sampling spot, yielding a snow volume ranging from 1731 to 6925 cm³.

The sampling sites were distributed to represent the topographical and land-use diversity of the village as well as variations in tourist traffic intensity (Figure 2 and Table 1) Every sampling point was marked on the map with its corresponding coordinates and elevation using the Locus GIS application.

3.2. Reagents and Materials

To avoid contamination of the samples, strict precautions were taken. The extractions were carried out under a fume hood, and chromatographic grade solvents were used for analysis. A solution of 16 PAHs (610 PAH Calibration Mix A) was purchased from Restek (Bellefonte, PA, USA), and deuterated surrogate standards were obtained from Cambridge Isotope Laboratories, Inc. (Tewksbury, MA, USA): anthracene (D10, 98%), acenaphthene (D10, 99%), perylene (D12, 98%), and chrysene (D12, 98%); and Sigma Aldrich (USA): naphthalene (D8, 99%). LC-MS grade deionized water and methanol with >99.9% purity were obtained from Honeywell (Seelze, Germany). GC-MS grade dichloromethane was procured from Sigma Aldrich (St. Louis, MO, USA). The C-18 SPE columns were purchased from J.T. Baker (Deventer, The Netherlands).

3.3. Sample Preparation

After collection, the samples were transported to the laboratory and stored in a cold (0–4 °C) and dark place until analysis, which was performed within 2 weeks. The laboratory analysis included both solid phase extraction (SPE) and gas chromatography coupled with tandem mass spectrometry (GC-MS/MS) (Figure 6).

Solid Phase Extraction (SPE)

The isolation and concentration of analytes were performed using solid phase extraction (SPE) with a 24-position Visiprep™ SPE Vacuum Manifold glass set from SUPELCO and a C-18 (octadecyl-bonded silica) 500 mg sorbent cartridge from J.T. Baker (The Netherlands). The extraction employed methanol with a purity >99.9% (Honeywell, Germany) and LC-MS-grade demineralized water (Honeywell, Germany). Prior to the extraction process, the SPE cartridges were conditioned with 5 mL of methanol and 3 mL of a mixture of demineralized water and methanol, having a solvent composition similar to the analyzed samples. Raw sample volumes ranging from 200 mL to 1 L were enriched with 5% methanol (v/v) to improve PAH recovery and a surrogate internal standard solution containing deuterated PAHs at a concentration of 100 ng/mL, added in an amount equal to 0.1% of the sample volume, to determine analyte recovery, which might vary between the samples. In this study, the following surrogate deuterated PAHs were used to evaluate PAH recoveries in each snow sample: naphthalene—D8, acenaphthene—D10, phenanthrene—D10, chrysene—D12, and perylene—D12. To each of 5 deuterated PAHs, a certain group of PAH compounds was assigned. Therefore, naphthalene—D8 was used for naphthalene, acenaphthene—D10 for acenaphthylene, acenaphthene, and fluorene, phenanthrene—D10 for phenanthrene and anthracene, chrysene—D12 for fluoranthene, pyrene, benzo(a)anthracene, and chrysene, and perylene—D12 for benzo(b)fluoroanthene, benzo(a)pyrene, indeno(1,2,3-cd)pyrene, dibenzo(a,h)anthracene, and benzo(g,h,i)perylene. The final results were corrected using the surrogate recovery rate. The analytes were concentrated and purified via solid-phase extraction (SPE) at a flow rate of 5–6 mL/min. The cartridges were dried for 3 min under reduced pressure. Analytes were eluted from the sorbent bed with 2 mL of DCM (in four portions of 0.5 mL each) into pre-weighed chromatographic vials. The extracted samples were analyzed immediately by GC-MS/MS.

3.4. GC-MS/MS Analysis

Samples were analyzed in dynamic multiple reaction monitoring (dMRM) mode. The dMRM windows were determined according to the retention times of the analytes from the 16 PAH mix and 5 deuterated PAH solution. To identify PAHs for most of the compounds, one target and one qualifier transition ion were monitored (

Table 2. GC-MS/MS—retention times, QQQ transitions used for detection of targeted compounds and internal standards and obtained limits of detection (LOD) and quantification (LOQ).

Compound	Rt (min)	Transition	Qualifier	LOD (ng/L)	LOQ (ng/L)
Nph-d8	2.871	136 → 136	-	<0.06	<0.06
Nph	2.883	128 → 102	128 → 127	<0.06	<0.06
Ace	4.012	152 → 150	152 → 151	<0.06	<0.06
Ace-d10	4.086	162 → 160	-	<0.06	<0.06
Acy	4.124	154 → 152	153 → 152	<0.06	<0.06
Fl	4.637	166 → 165	166 → 163	<0.06	<0.06
Ph	6.331	178 → 176	178 → 152	<0.06	0.06
Ph-d10	6.346	188 → 188	-	<0.06	<0.06
Ant	6.387	178 → 176	178 → 152	0.06	0.09
Flt	9.312	202 → 200	202 → 201	<0.06	0.18
Py	10.168	202 → 200	202 → 201	<0.06	0.16
B[a]A	16.193	228 → 226	228 → 224	0.98	1.18
Chr-d12	16.438	240 → 236	118 → 116	0.24	1.76
Chr	16.550	228 → 226	228 → 224	0.30	0.90
B[b]F	16.460	252 → 250	250 → 248	<0.06	0.46
B[k]F	19.510	252 → 250	250 → 248	<0.06	0.69
Benzo[a]/[e]pyrene	20.952	252 → 250	250 → 248	1.50	3.65
Per-d12	21.350	264 → 260	-	0.98	1.22
I [cd]P	24.252	276 → 274	138 → 124	0.63	4.00
D[ah]A	24.291	278 → 276	125 → 124	1.28	4.42
B[ghi]P	24.809	276 → 274	274 → 272	0.46	3.65

), and quantification was carried out based on target ion responses.

After the optimization, the following set up and parameters were used for GC-MS/MS analysis:

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GC Column: DB-EUPAH capillary column: 20 m × 180 μm × 0.14 μm, 5% phenyl methyl siloxane (Agilent 121–9627);

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Liner: 800 μL Splitless, double taper, Ultra Inert (Agilent 5190–3983);

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Carrier gas: Ultrapurified helium 99,999%, 1 mL/min;

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Injection type: pulsed splitless, 70 psi until 1.9 min, then 22.292 psi;

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Inlet temperature: 325 °C;

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Oven temperature: 70 °C (1,16 min), 70 °C/min to 180 °C (0 min), 7 °C/min to 230 °C (6 min), 40 °C/min to 280 °C (5 min), 25 °C/min to 330 °C (5 min);

-

Injection volume: 2.5 µL;

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Mass spectrometer quadrupole temperature: 150 °C;

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Mass spectrometer source temperature: 340 °C.

Separate calibration curves were created for each of the 16 PAH compounds and 5-D PAHs in the range of 0.06–1000 ng/mL concentrations. The limit of detection (LOD) and limit of quantification (LOQ) values were calculated in Mass Hunter Agilent Software based on the signal to noise ratio using the RMS (root mean square) noise algorithm. The LOD values were found to be from <0.06 to 1.5 ng/L, and the LOQ values were <0.06–4.42 ng/L depending on the individual compound (Table 2). The final concentration was obtained as a mean of 2 injections by use of internal standard method calculation.

4. Results and Discussion

4.1. PAH Concentrations

The concentrations of PAHs measured in snow at each site are given in

Table 3. (A) Concentrations of PAHs in Zawoja snow samples (ng/L). (B) Concentrations of PAHs in various regions—literature results (ng/L).

(A)
		1		2		3		4		5		6		7		8		9	10	11		12		13		14		15
Nph		55.3		90.3		17.7		111.8		99.6		91.8		66.1		62.2		56.3	91.2	50.3		37.1		37.6		151.3		35.4
Acy		14.3		13.9		11.6		10.4		33.2		14.8		15.9		14.3		14.7	13.2	5.7		16.0		13.9		105.6		11.2
Ace		6.5		4.2		2.7		2.2		4.1		2.8		3.1		2.1		3.6	2.9	3.3		3.9		23.3		16.3		2.6
Fl		17.7		17.0		13.1		12.0		23.9		17.5		17.4		16.3		17.3	15.3	5.8		20.1		32.8		25.4		12.6
Ph		153.2		159.1		137.0		126.2		219.6		184.6		514.8		175.1		164.1	163.8	41.9		164.8		224.2		231.7		117.8
Ant		6.4		7.8		7.5		5.0		13.6		8.9		10.1		9.5		8.2	6.6	2.6		145.3		21.2		17.1		5.6
Flt		134.3		168.6		165.8		143.5		226.2		174.9		687.8		185.0		175.1	195.6	36.7		122.3		274.5		218.5		121.7
Pyr		107.5		142.3		139.6		119.7		186.9		143.6		466.9		151.3		147.8	162.6	36.5		90.8		259.9		230.0		99.9
B[a]A		36.7		53.3		64.5		48.4		74.6		51.5		66.8		54.9		56.7	72.0	24.0		36.0		99.6		72.7		39.4
Chr		71.1		83.2		110.3		87.3		121.5		80.9		122.6		86.4		99.8	126.1	30.6		68.1		120.6		99.0		63.6
B[b]F		48.7		55.6		80.0		59.7		86.2		50.5		73.5		51.6		73.1	98.6	34.0		54.2		83.4		69.8		43.9
B[k]F		11.8		16.0		26.4		19.7		33.3		16.7		28.7		18.5		25.8	16.4	8.9		14.8		37.8		20.1		13.1
B[a]P		34.4		46.5		65.8		44.3		73.6		45.3		63.1		43.9		58.6	73.7	32.9		40.4		104.3		82.4		40.0
I[cd]P		43.9		60.8		84.6		61.7		98.7		48.9		84.1		56.2		78.9	99.2	44.5		53.7		106.8		82.6		51.0
D[ah]A		13.3		20.1		28.4		19.7		33.1		16.9		29.0		19.7		25.6	31.1	13.4		16.2		38.7		25.2		16.8
B[ghi]P		29.6		42.7		60.2		43.7		72.8		43.1		59.7		39.6		57.6	68.1	32.8		39.4		82.6		72.9		35.9
∑PAH		785		981		1015		915		1401		992		2310		987		1063	1246	404		923		1561		1521		710
∑L-PAH		253		292		190		268		394		320		627		279		264	293	110		387		353		548		185
∑M-PAH		350		447		480		399		609		451		1344		478		479	556	128		317		755		620		325
∑H-PAH		182		242		345		249		398		221		338		230		320	397	166		219		454		353		201
∑carcPAH		260		335		460		341		521		310		468		331		419	527	188		283		591		452		268
(B)
	Ankara, Turkey		Wangdu, China		Patriot Hills, South Pole, Antarctica		Fairbanks, Alaska, USA	Mt. Tateyama, Japan	Ivanovo, Russia		Arkhangelsk, Russia		Khabarovsk, Russia		Bolshekhekhtsirsky State Nature Reserve, Station 12, Russia		Fildes Peninsula, King George Island, Antarctica	Redo Lake, Pyrenees, Spain	Minnesota, USA	Minnesota, USA	Punta Indren Glacier, Italian Alps		Punta Indren Glacier, Italian Alps		Kuopio, Finland		Luleå, Sweden		Umeå, Sweden
SM	I		II		III		IV	V	VI		VII		VIII		VIII		NA	NA	IX	IX	X		X		XI		XII		XII
Nph	NA		0.2		231.4		450	NA	152		10		NA		NA		94.75	NA	NA	NA	2.47		4.73		NA		60		80
Acy	ND		0.26		NA		34	NA	NA		ND		NA		NA		1.61	NA	5	02.2	1.6		0.97		NA		NA		NA
Ace	ND		0.18		NA		13	NA	NA		2.3		NA		NA		11.69	NA	211	27	2.97		2.4		NA		NA		NA
Fl	79.9		0.5		NA		71	NA	107		8.3		2.39		5.33		13.49	0.039	364	44	2.14		1.64		BDL		NA		NA
Ph	678		4.67		78.4		377	NA	394		73.4		19.96		46.51		14.14	0.26	3590	515	9.47		4.2		6600		410		1260
Ant	ND		0.7		51.6		1.9	NA	21		2		1.81		1.83		1.5	0.004	300	43	0.61		4.32		1400		NA		NA
Flt	85.4		5.23		96.0		125	3.93	588		55.5		4.96		05.4		1.83	0.12	1890	677	2.14		1.2		3800		490		1760
Pyr	71.6		3.38		67.1		53	3.35	267		46.8		6.54		4.97		2.23	0.13	3115	491	1.9		0.94		3000		580		2310
B[a]A	85.4		1.33		ND		3.7	2.81	NA		09.7		0.74		0.72		0.6	0.013	976	65	0.57		0.09		10,600		NA		NA
Chr	64.7		2.39		35.1		29	2.44	1		10.6		2.63		1.57		0.78	0.007	1420	211	0.57		0.06		600		NA		NA
B[b]F	29.9		1.45		D		12	2.51	176		5.7		1.59		0.97		0.63	0.007	2085 (b + k)	499 (b + k)	0.50		0.1		BDL		270		1100
B[k]F	NA		1.28		67.2		7.0	0.77	111		3.1		0.69		0.50		0.63	ND	905	179	0.38		NA		200		NA		NA
B[a]P	32.7		01.4		23.5		4.8	0.86	6		3.5		1.74		1.27		ND	0.014	905	179	0.07		NA		BDL		NA		NA
I[cd]P	24.4		2.35		47.4		3.6	0.03	NA		ND		0.03		ND		ND	0.06	871	169	0.09		0.06		NA		130		370
D[ah]A	ND		0.64		43.3		n.d.	0.17	5		ND		ND		0.08		ND	ND	186	32	0.02		0.03		BDL		NA		NA
B[ghi]P	23.8		1.92		D		9.4	0.89	7		02.3		0.20		0.50		ND	ND	736	145	0.17		0.007		BDL		210		800
∑PAH	1176		27.5		741.0		1194	17.8	1835		233		43.0		69.3		143.9	0.7	17,580	3320	24.3		20.4		26,200		2720		9640
∑L-PAH	757.9		6.5		361.4		946.9	0	674		96		24.7		53.7		137.2	0.3	4470	631.1	18.7		18.1		8000		90		140
∑M-PAH	307.1		12.3		198.2		222.7	12.5	856		122		14.9		12.3		5.4	0.3	7401	1444	4.7		2.1		18,000		1540		5460
∑H-PAH	110.8		08.7		181.4		24.8	05.2	305		15		4.3		3.3		1.3	0.08	2698	525	1.2		0.2		200		1090		4040
Ref.	[26]		[48]		[29]		[49]	[32]	[50]		[27]		[51]		[51]		[52]	[30]	[21]	[21]	[53]		[53]		[54]		[55]		[55]

SM—sampling method; ND—not detected; BDL—below detection limit; D—detected, not quantified; NA—not available.

A. All 16 investigated PAH compounds were detected and quantified.

The Σ16 PAHs ranged from 404 at site 11 to 2310 ng/dm³ at site 7 (Figure 7), with a mean for all the samples equal to 1161 ng/L. Individual PAH concentrations ranged from 2,1 (Ace) at site 8 to 687.8 (Flt) ng/dm³ at site 7 (Table 3A; Figure 8). The results showed low concentrations of acenaphthylene, acenaphthene, and Fluorene, which may be attributed to their volatile nature and high concentrations of phenanthrene, fluoranthene, and pyrene (Table 3A, Figure 8). The accumulation of Flt and Pyr is typically linked to the combustion of automobile fuel, coal, and biomass [50,56]. The dominant fraction of PAHs consisted of medium-weight PAHs (Table 3A, Figure 7) To show the susceptibility of the sampling sites to pollution sources a pollution factor (PF) parameter was created. The pollution factor includes variables, like distance from the road (I), forest (VI), tourist attractions (II), industrial (III) and commercial (IV) facilities, population density (V), and altitude (VII). It was calculated as the sum of all variables, each assigned a score from 0 to 5 (Table 1). The values of PF ranged from 5 (sites 1, 2, and 14) to 28 (site 12).

The highest value of Σ16 PAHs was observed in sample 7 (PF = 8), which was an unexpected result. This sample was collected at the border of Babia Góra National Park, near a forested area and far from major roads or industrial facilities, where significantly lower PAH concentrations were anticipated. The elevated PAH levels in sample 7 were likely influenced by the local topography, situated at the foot of the Babia Góra Massif (on its northern side) and on a northeast-facing slope, which, in combination with the surrounding forest, limits wind flow and air circulation. Another potential source of pollution may be the nearby popular tourist trail leading to the Markowe Szczawiny Mountain shelter and further to Babia Góra’s summit, which generates both tourist and vehicular traffic.

Elevated concentrations were also recorded in sample 5 (1401 ng/L, Table 3A), suggesting that nearby infrastructure, such as a grocery store and a parking area, could be contributing to local pollution through intensified vehicle use. Similarly, the high Σ16 PAH concentration in sample 14 (1521 ng/L, Table 3A), which also exhibited elevated PAH concentrations (1561 ng/L, Table 3A). These findings suggest that proximity to industrial infrastructure should be given greater consideration when assessing potential exposure to PAH pollution.

In contrast, the lowest Σ16 PAH concentration (404 ng/L, Table 3A) was detected in sample 11 (PF = 17), collected from a valley site near a local road and built-up area. Despite the location’s apparent vulnerability to emission sources, the low PAH levels may be attributed to snowmelt and runoff processes. This sample was collected in the early afternoon under temperatures above 0 °C, coinciding with the warmest point of the day. Two additional samples from higher elevations—sites 1 and 15 (PF = 5 and 8, respectively), located further from the village center—also showed low Σ16 PAH concentrations (785 and 710 ng/L, respectively; Table 3A). These values align with expectations and previous pollution exposure assessments.

The remaining sites exhibited Σ16 PAH concentrations close to the average across all samples (915–1246 ng/L, Table 3A). The Σ16 PAH values measured in this study are comparable to findings from previous research conducted in other regions (Table 3B). Similar concentrations have been reported in urban areas, such as Ankara (1176 ng/L), Fairbanks (1194 ng/L), and Ivanovo (1835 ng/L) [26,49,50]. A comparable distribution of low, medium, and high molecular weight PAHs (ΣL, ΣM, and ΣH PAHs) has also been reported in Minnesota [21].

4.2. PAHs Sources

It should be considered that, due to the longer accumulation time of PAHs in the snow cover compared to the air and its dependence on atmospheric conditions, it is extremely difficult to reliably determine the exact sources of PAHs. Therefore, to differentiate PAHs originating from environmental samples, diagnostic ratios between individual PAHs were introduced [1,57,58,59]. To reduce distorting factors, such as variations in volatility, water solubility, and adsorption, ratio calculations are usually limited to PAHs of the same molecular mass [58]. Anthropogenic input and/or combustion are often concluded based on the increase in the proportion of the “kinetically” less stable PAHs isomers [57,58]. Among the most common ratios used for provenance determination are Ant/Ph, Flt/Pyr, and low molecular weight (LMW) PAHs/ high molecular weight PAHs [10,57]. Moreover, to deduce petroleum or combustion sources additional ratios of relative stability in the environment are employed: IcdP/(IcdP + BghiP) and Flt/(Flt + Pyr) [60]. The results of PAH diagnostic ratios for investigated samples are presented in the

Table 4. (A) Diagnostic ratios and toxic equivalent factors for investigated PAHs in Zawoja. (B) Limit values of diagnostic ratios with interpretation of PAH sources.

(A)
Site	∑COMB/ ∑PAH	Ant/ (Ant + Ph)	Flt/ (Flt + Pyr)	IcdP/ (IcdP + Bghi)	BaP/ BghiP	∑LMW/ ∑HMW	BaA/ (BaA + Chr)	BaPTEQ	BaPMEQ
1	0.66	0.04	0.56	0.60	1.15	0.48	0.34	63	75
2	0.68	0.05	0.54	0.59	1.09	0.42	0.39	87	101
3	0.79	0.05	0.54	0.58	1.09	0.23	0.37	122	142
4	0.69	0.04	0.55	0.59	1.01	0.41	0.36	85	100
5	0.70	0.06	0.55	0.58	1.01	0.39	0.38	139	161
6	0.66	0.05	0.55	0.53	1.05	0.48	0.39	80	93
7	0.72	0.02	0.60	0.58	1.06	0.37	0.35	121	138
8	0.70	0.05	0.55	0.59	1.11	0.40	0.39	84	95
9	0.73	0.05	0.54	0.58	1.02	0.33	0.36	110	129
10	0.74	0.04	0.55	0.59	1.08	0.31	0.36	137	162
11	0.70	0.06	0.50	0.58	1.00	0.37	0.44	58	69
12	0.56	0.47	0.57	0.58	1.03	0.72	0.35	75	89
13	0.75	0.09	0.51	0.56	1.26	0.29	0.45	179	200
14	0.62	0.07	0.49	0.53	1.13	0.56	0.42	135	156
15	0.72	0.05	0.55	0.59	1.12	0.35	0.38	73	84
(B)
PAH ratio			Value			PAH Source			Reference
∑COMB/∑PAH			~1			Combustion			[56]
Ant/(Ant + Ph)			<0.1			Petrogenic			[58]
			>0.1			Pyrogenic
Flt/(Flt + Pyr)			<0.4			Petrogenic			[58]
			0.4–0.5			Fossil fuel combustion
			>0.5			Grass, wood, coal combustion
IcdP/(IcdP + Bghi)			<0.2			Petrogenic			[58]
			0.2–0.5			Petroleum combustion
			>0.5			Grass, wood and coal combustion
BaP/BghiP			<0.6			Non traffic emissions			[61]
			>0.6			Traffic emissions
∑LMW/∑HMW			<1			Pyrogenic			[62]
			>1			Petrogenic
BaA/(BaA + Chr)			<0.2			Petrogenic			[58]
			>0.35			Combustion

∑PAH—sum of all 16 PAHs; ∑LMW—sum of two- and three-ring PAHs; ∑HMW—sum of four- and five-ring PAHs; ∑COMB—sum of Flt, Pyr, BaA, BbF, BkF, BaP, DBahA, BghiP, and IP.

A.

Combustion and petroleum sources are commonly distinguished using PAHs with molecular masses 178 and 202 u. For mass 178 u, an Ant/(Ant + Ph) ratio <0.10 usually indicates petroleum, while >0.10 combustion [1,57] (Table 4B). The obtained results for Zawoja, except for sample 12, were below 0.10 and ranged from 0.02 to 0.09 (Table 4A), suggesting either an origin from petroleum or combustion of lignite/brown coal or No 2 fuel oil [58]. These ratios resemble the values obtained for urban air [58]. The only sample with extremely elevated values collected from the vicinity of the main church parking lot (site 12, Ant/(Ant + Ph) = 0.47) might indicate the presence of bituminous coal combustion.

Another indicator based on mass 202 u is the Flt/(Flt + Py) ratio. A value of 0.50 for this ratio is considered a transition point between petroleum samples (<0.50) and kerosene, grass, wood, and coal combustion samples. Ratios between 0.40 and 0.50 are characteristic of liquid fossil fuels, and >0.50 indicate grass, wood, and coal combustion [58] (Table 4B). For the majority of the samples, the ratio exceeded Flt/(Flt + Py) = 0.50 (Table 4A), which leads to the conclusion that the origin of PAHs is related to the combustion of solid fossil fuels and biomass.

PAHs of molecular masses 228 and 276 u are less commonly used as PAH source indicators [58], as generally present in significant amounts only in higher fractions of petroleum [63,64] and likely in bitumen and coal [65]. Yunker et al. (2002) [58] attributed a BaA/(BaA + Chry) ratio > 0.20 to petroleum, 0.20–0.35 to petroleum or combustion, and >0.35 to combustion (Table 4B). Given that, the results of BaA/(BaA + Chr) for Zawoja ranged between 0.34 and 0.45, suggesting a mixed source of PAHs (Table 4A).

For the IcdP/IcdP + BghiP) ratio, values < 0.20 are interpreted as petroleum combustion, between 0.20 and 0.50 as liquid fossil fuel combustion, and >0.50 as coal, grass, and wood combustion [58] (Table 4B).

The aforementioned would suggest the origin of Zawoja PAHs (values 0.53–0.60, (Table 4A) from biomass and coal combustion. High values obtained for the BaP/BghiP ratio combined with Flt/(Flt + Py) indicate that coal and biomass combustion are the primary sources of PAHs [66].

PAHs typically produced from combustion processes are represented by the sum of: Flt, Pyr, BaA, BbF, BkF, BaP, DBahA, BghiP, and IcdP [67]. All compounds are HMW and contain between 4 and 6 rings. It is claimed that ratios of ∑COMB/∑16PAHs >0.50 indicate that PAHs originated from combustion activities [68] (Table 4A). The ratio ∑COMB/∑PAHs for Zawoja samples varied from 0.56 at site 12 to 0.79 at site 3 (Table 4A). This would confirm the higher impact of vehicle transport at those sites (e.g., church parking lot at 12). All sample values of the ∑LMW/∑HMW PAH ratio were < 1, which indicates pyrolytic sources [62] (Table 4A,B). In conclusion, the dominant source of PAHs, according to the selected coefficients, is pyrogenic and comes from biomass and coal combustion (Figure 9).

4.3. PAH Toxicity

Some of the PAHs are believed to have carcinogenic and mutagenic effects on living organisms. Among them, BaP is considered especially hazardous and belongs to the 1st hazard class, which causes cancer [69]. The maximum permissible concentrations for snow cover have not been established so far; therefore, water quality standards were applied in this study. The European Union (EU) established an environmental quality standard for BaP in inland surface waters, specifying an annual average value of 0.17 ng/L and a maximum allowable concentration of 270 ng/L, as outlined in Directive 2013/39/EU of the European Parliament and Council [70]. The BaP equivalent toxicity (BaP_TEQ) was calculated using the toxic equivalency factor (TEF) method [71]. The toxic equivalency of a mixture is presented as the sum of the concentrations of individual compounds (Ci) multiplied by their respective TEF:

$${BaP}_{TEQ}=\sum (C_i \ast {TEF}_i)$$

where TEFi values are as follows: Ph (0.001), Ant (0.01), BaA (0.1), Flt (0.001), Pyr (0.01), Chr (0.01), BbF (0.1), BkF (0.1), BaP (1), DahA (1), BghiP (0.01), and IcdP (0.1) [70].

BaP_MEQ (mutagenic equivalents) were calculated as follows [72]:

$${BaP}_{MEQ}=\sum (C_i \ast {TEF}_i)$$

(BaP_MEQ)_Σ8PAH = [BaA] × 0.082 + [Chry] × 0.017 + [BbF] × 0.25 + [BkF] × 0.11 + [BaP] × 1 + [IcdP] × 0.31 + [DahA] × 0.29 + [BghiP] × 0.19

The obtained values for BaP_TEQ and BaP_MEQ (Table 4A; Figure 10) ranged from 58 to 179 and 69 to 200, respectively, with the highest results observed at site 13, which might be caused by the pollution from the nearby small factory producing PVC windows and doors. Based on the obtained BaP_TEQs, the toxicity of snow cover can be represented in the following order into two groups (I—more hazardous, II—less hazardous):

-

Group I: 13 > 5 > 10 > 14 > 3 > 7 > 9;

-

Group II: 2 > 4 > 8 > 6 > 12 > 15 > 1 > 11.

Similarly, BaP_MEQs values can give the following group order of the samples:

-

Group I: 13 > 10 > 5 > 14 > 3 > 7 > 9;

-

Group II—2 > 4 > 8 > 6 > 12 > 15 > 1 > 11.

The spatial hazard of carcinogenicity and mutagenicity is shown in Figure 10. Yu et al. [73] and Ashayeri et al. [74] used a level of 600 µg/kg of TEQ in sediment as a threshold for environmental safety. None of the obtained values for Zawoja exceeded this limit. Nonetheless, compared to the BaP_TEQ values from snow cover obtained by Levshina et al. [51], ranging between 0.89 and 40.62 ng/L, Zawoja’s results were 2–4 times higher. The authors, however, mentioned that the data from Khabarovsk were more than two orders of magnitude lower than from the clean and polluted areas of the city of Novokuznetsk. In addition, values of BaP_TEQ of Zawoja samples (Table 4A) ranged from 58 to 179 ng/L and, compared with the river waters, showed high correlation with the values obtained for the Euphrates waters, 73.4 ng/L–171.3 ng/L, which according to the authors, were recognized as elevated and alarming [75].

Furthermore, the levels of potential ecological risk produced by specific PAHs were evaluated using a risk quotient (RQ), which was calculated according to equations given by Na et al. [52]:

RQ = C_PAHs/C_QV

RQ_NCs = C_PAHs/C_QV(NCs)

RQ_MPCs = C_PAHs/C_QV(MPCs)

$${\mathrm{R}\mathrm{Q}}_{\sum \mathrm{P}\mathrm{A}\mathrm{H}\mathrm{s}}={\sum }_{i=1}^{16}{RQ}_{\mathrm{i}}({\mathrm{RQ}}_i\ge 1)$$

$${\mathrm{R}\mathrm{Q}}_{\sum \mathrm{P}\mathrm{A}\mathrm{H}\mathrm{s}\left(\mathrm{N}\mathrm{C}\mathrm{s}\right)}={\sum }_{i=1}^{16}{RQ}_{i\left(\mathrm{N}\mathrm{C}\mathrm{s}\right)}({\mathrm{RQ}}_{\mathrm{i(NCs)}}\ge 1)$$

$${\mathrm{R}\mathrm{Q}}_{\sum \mathrm{P}\mathrm{A}\mathrm{H}\mathrm{s}\left(\mathrm{M}\mathrm{P}\mathrm{C}\mathrm{s}\right)}={\sum }_{i=1}^{16}{RQ}_{i\left(\mathrm{M}\mathrm{P}\mathrm{C}\mathrm{s}\right)}({\mathrm{RQ}}_{\mathrm{i(MPCs)}}\ge 1)$$

where RQ: risk quotient; C_PAHs: concentration of PAHs in environmental object, C_QV: risk standard value of PAHs, NCs: negligible/minimum concentration, MPCs: maximum permissible concentration; C_QV(NCs): the normative value of the minimum concentration of PAHs generating negligible risk; C_QV(MPCs): the normative value of the maximum concentrations of PAHs given the maximum environmental risk (ng/L). The risk levels values were adopted from [52].

For individual PAHs:

• RQ_NCs = 0 means no risk,
• RQ_NCs ≥ 1 and RQ_MPCs < 1 means moderate risk,
• RQ_MPCs ≥ 1 means high risk.

Regarding RQ_∑PAHs, values are interpreted as follows:

• RQ_ΣPAHs(NCs) = 0 means no risk;
• 1 ≤ RQ_ΣPAHs(NCs) < 800 and RQ _ΣPAHs(MPCs) = 0 means low risk;
• RQ_ΣPAHs(NCs) ≥ 800 and RQ_ΣPAHs(MPCs) = 0 means moderate risk;
• RQ_ΣPAHs(NCs) < 800, RQ_ΣPAHs(MPCs) ≥ 1, means moderate risk;
• RQ_ΣPAHs(NCs) ≥ 800, RQ_ΣPAHs(MPCs) ≥ 1 means high risk.

Currently, there are no standardized values for PAH pollutants in snow cover to assess environmental risk. Therefore, in accordance with [52] and [50], correspondence values of C_QV(NCs) and C_QV(MPCs) for water bodies were applied (

Table 5. Environmental risk quotient values for PAHs present in Zawoja snow samples.

Site	PAH	Nap	Acy	Ace	Flu	Phe	Ant	Fla	Pyr	B[a]A	Chry	B[b]F	B[k]F	B[a]P	I[1,2,3-I[c,d]P	D[a,h]A	B[ghi]P	∑PAHs
	CQV(NCs) (ng/L) *	12	0.7	0.7	0.7	3	0.7	3	0.7	0.1	3.4	0.1	0.4	0.5	0.4	0.5	0.3
	CQV(MPCs) (ng/L) *	1200	70	70	70	300	70	30	70	10	340	10	40	50	40	50	30
1	RQ(NCs)	4.6	20.4	9.2	25.4	51.1	9.2	44.8	153.5	366.7	20.9	486.8	29.5	68.9	109.8	26.5	99.6	1526.8
	RQ(MPCs)	0.05	0.20	0.09	0.25	0.51	0.09	4.48	1.54	3.67	0.21	4.87	0.29	0.69	1.10	0.27	1.00	12.99
2	RQ(NCs)	7.5	19.9	6.1	24.3	53.0	11.1	56.2	203.2	533.1	24.5	556.3	39.9	93.0	151.9	40.2	142.2	1962.5
	RQ(MPCs)	0.1	0.2	0.1	0.2	0.5	0.1	5.6	2.0	5.3	0.2	5.6	0.4	0.9	1.5	0.4	1.4	19.4
3	RQ(NCs)	1.5	16.6	3.8	18.7	45.7	10.8	55.3	199.4	644.9	32.4	800.0	66.1	131.5	211.4	56.8	200.7	2495.6
	RQ(MPCs)	0.01	0.17	0.04	0.19	0.46	0.11	5.53	1.99	6.45	0.32	8.00	0.66	1.32	2.11	0.57	2.01	19.41
4	RQ(NCs)	9.3	14.8	3.1	17.2	42.1	7.1	47.8	171.0	483.6	25.7	596.7	49.2	88.5	154.2	39.3	145.6	1895.3
	RQ(MPCs)	0.09	0.15	0.03	0.17	0.42	0.07	4.78	1.71	4.84	0.26	5.97	0.49	0.89	1.54	0.39	1.46	20.3
5	RQ(NCs)	8.3	47.4	5.8	34.2	73.2	19.4	75.4	267.0	745.9	35.7	862.4	83.3	147.2	246.7	66.2	242.8	2960.8
	RQ(MPCs)	0.08	0.47	0.06	0.34	0.73	0.19	7.54	2.67	7.46	0.36	8.62	0.83	1.47	2.47	0.66	2.43	32.66
6	RQ(NCs)	7.6	21.1	4.0	25.0	61.5	12.6	58.3	205.2	515.1	23.8	505.4	41.7	90.6	122.3	31.9	143.8	1870.0
	RQ(MPCs)	0.08	0.21	0.04	0.25	0.62	0.13	5.83	2.05	5.15	0.24	5.05	0.42	0.91	1.22	0.32	1.44	18.69
7	RQ(NCs)	5.5	22.7	4.4	24.9	171.6	14.5	229.3	667.0	668.3	36.1	735.4	71.8	126.2	210.2	58.0	199.0	3244.9
	RQ(MPCs)	0.06	0.23	0.04	0.25	1.72	0.14	22.93	6.67	6.68	0.36	7.35	0.72	1.26	2.10	0.58	1.99	44.03
8	RQ(NCs)	5.2	20.4	2.9	23.3	58.4	13.6	61.7	216.2	548.6	25.4	516.1	46.3	87.8	140.6	39.4	132.1	1937.9
	RQ(MPCs)	0.05	0.20	0.03	0.23	0.58	0.14	6.17	2.16	5.49	0.25	5.16	0.46	0.88	1.41	0.39	1.32	21.71
9	RQ(NCs)	4.7	21.0	5.1	24.7	54.7	11.8	58.4	211.1	567.4	29.3	730.9	64.5	117.2	197.3	51.3	192.0	2341.5
	RQ(MPCs)	0.05	0.21	0.05	0.25	0.55	0.12	5.84	2.11	5.67	0.29	7.31	0.64	1.17	1.97	0.51	1.92	25.99
10	RQ(NCs)	7.6	18.8	4.1	21.9	54.6	9.4	65.2	232.3	720.0	37.1	986.5	66.1	147.4	248.0	62.2	227.0	2908.1
	RQ(MPCs)	0.08	0.19	0.04	0.22	0.55	0.09	6.52	2.32	7.20	0.37	9.86	0.66	1.47	2.48	0.62	2.27	32.12
11	RQ(NCs)	4.2	8.2	4.7	8.4	14.0	3.7	12.2	52.1	240.2	9.0	339.6	22.3	65.8	111.2	26.7	109.2	1031.6
	RQ(MPCs)	0.04	0.08	0.05	0.08	0.14	0.04	1.22	0.52	2.40	0.09	3.40	0.22	0.66	1.11	0.27	1.09	9.22
12	RQ(NCs)	3.1	22.8	5.5	28.7	54.9	207.6	40.8	129.7	360.2	20.0	541.8	37.0	80.8	134.3	32.3	131.3	1831.0
	RQ(MPCs)	0.03	0.23	0.06	0.29	0.55	2.08	4.08	1.30	3.60	0.20	5.42	0.37	0.81	1.34	0.32	1.31	17.83
13	RQ(NCs)	3.1	19.9	33.3	46.8	74.7	30.2	91.5	371.4	996.3	35.5	834.1	94.4	208.6	266.9	77.4	275.3	3459.5
	RQ(MPCs)	0.03	0.20	0.33	0.47	0.75	0.30	9.15	3.71	9.96	0.35	8.34	0.94	2.09	2.67	0.77	2.75	38.67
14	RQ(NCs)	12.6	150.9	23.3	36.3	77.2	24.5	72.8	328.5	726.8	29.1	697.6	50.3	164.7	206.6	50.5	242.9	2894.9
	RQ(MPCs)	0.13	1.51	0.23	0.36	0.77	0.24	7.28	3.29	7.27	0.29	6.98	0.50	1.65	2.07	0.50	2.43	32.48
15	RQ(NCs)	2.9	16.0	3.7	17.9	39.3	7.9	40.6	142.7	393.5	18.7	439.3	32.8	80.1	127.6	33.5	119.6	1516.2
	RQ(MPCs)	0.03	0.16	0.04	0.18	0.39	0.08	4.06	1.43	3.94	0.19	4.39	0.33	0.80	1.28	0.34	1.20	16.30

* Standard values of CQV(NCs) negligible and CQV(MPCs) maximum risk for individual PAHs present in water. Values classified as high risk are presented in bold font.

).

The results of calculated environmental risks in the case of PAH contamination of snow cover for the studied area are given in Table 5. The obtained values displayed a high level of environmental risk for Zawoja village. All of the individual PAHs showed at least moderate environmental risk, while the bolded ones were classified as high risk (Table 5). The main contribution is made by B[b]F, B[a]A, I [1,2,3-c,d]P, B[ghi]P, Pyr, BaP, Fla (in order with decreasing RQ values; Table 5), which for the majority of the sites, were classified as highly risky to the environment. Moreover, in all investigated sites, RQ_∑PAHs revealed a high environmental risk with the maximum values recorded at sites 7 and 13 (>3000 ng/L) and the minimum at site 11. The environmental risks for Zawoja align closely with the values obtained from the snow samples in the city of Ivanovo [50], which indicates that the level of anthropogenic pressure in the winter season in this resort village is exceptionally high. Based on measurements conducted in this work and total snowfall in Zawoja during the 2023/2024 winter season (362.2 mm), the annual PAH load deposited by snow and subsequently released into rivers and soil was calculated. It ranged from 146.5 µg L-1 year-1 for sample 10 to 837.6 µg L-1 year-1 for sample 7, with an average value of 406.5 µg L-1 year-1 for all samples from Zawoja.

5. Conclusions

This study was conducted to characterize the composition of PAHs present in snow. The developed method, compared to standard atmospheric pollution assessment techniques, is cost-effective (e.g., Radiello air passive sampler cost of USD 350 per unit), user-friendly (in terms of sampling, accessibility, and minimal matrix interferences), and serves as a relatively reliable indirect alternative for assessing air quality during the winter season when snow cover is present. Furthermore, the proposed procedure allows for the simultaneous measurement of atmospheric pollution and the subsequent transfer of contaminants into soil and groundwater. However, when using snow cover as a deposition medium, special precautions must be taken, as pollutant concentrations and source identification are highly dependent on weather conditions. To ensure sample homogeneity and prevent analyte loss, sampling should be conducted immediately after fresh snowfall and under stable weather conditions (i.e., windless conditions and temperatures below 0 °C). In the analytical phase of the study, the application of solid-phase extraction for concentration and purification, a specialized high-resolution PAH column, pulsed injection, and the dMRM GC-MS/MS mode resulted in low limits of detection and quantification. This approach enabled the detection and quantification of all 16 PAHs in each sample. This represents a significant advantage of the developed method, as most previous studies have reported incomplete results for PAHs in snow samples due to concentrations falling below the LOD of the applied analytical techniques.

The studied village, characterized by its mountainous location, low population density, extensive forest cover (66% of the municipality area) [42], and the absence of large industrial facilities in the vicinity, would be expected to exhibit minimal environmental contamination. However, analysis of polycyclic aromatic hydrocarbons in the snow cover revealed relatively high levels of contamination, particularly from high molecular weight (HMW) PAHs, comparable to those observed in urban areas. Notably, higher PAH concentrations did not necessarily coincide with the areas most exposed to traffic, tourism, or the highest population density. This suggests that topographical features and vegetation cover play a significant role in the deposition of PAHs. Diagnostic ratio analysis indicated that the predominant source of PAHs is pyrogenic, primarily originating from biomass and coal combustion. This finding aligns with data provided by the mayor of the municipality [42],who reported that the majority of households rely on solid fuels, including coal, wood, and other forms of biomass, for heating. Additionally, areas with higher visitor traffic exhibited an increased contribution of petroleum-derived PAHs, likely from vehicle emissions. An attempt was made to assess the potential environmental and biological risks associated with the detected PAH concentrations and their distribution. The calculated toxicity (BaP_TEQ), mutagenicity (BaP_MEQ) coefficients, and risk quotient (RQ) values indicated alarming pollution levels. These findings warrant attention from local authorities to implement measures aimed at improving environmental quality.