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Article

A Reliable Medium for Monitoring Atmospheric Deposition near Emission Sources by Using Snow from Agricultural Areas

by
Jiayang Liu
1,2,
Zaijin Sun
3,*,
Wenkai Lei
2,4,
Jingwen Xu
3,
Qian Sun
2,
Zhicheng Shen
3,
Yixuan Lyu
2,
Huading Shi
3,
Ying Zhou
1,
Lan Zhang
4,
Zefeng Wu
5 and
Yuepeng Pan
2,*
1
Key Laboratory of Beijing on Regional Air Pollution Control, School of Environmental Science and Engineering, Beijing University of Technology, Beijing 100124, China
2
Key Laboratory of Atmospheric Environment and Extreme Meteorology, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
3
Technical Centre for Soil, Agriculture and Rural Ecology and Environment, Ministry of Ecology and Environment, Beijing 100012, China
4
The Analysis and Test Center, Capital Normal University, Beijing 100048, China
5
Shifang Environmental Protection Engineering Co., Ltd., Anyang 455000, China
*
Authors to whom correspondence should be addressed.
Atmosphere 2025, 16(1), 26; https://doi.org/10.3390/atmos16010026
Submission received: 22 October 2024 / Revised: 16 December 2024 / Accepted: 26 December 2024 / Published: 29 December 2024
(This article belongs to the Section Biosphere/Hydrosphere/Land–Atmosphere Interactions)
Browse Figures
Figure 1
<p>The specific location of the area of study in China (<b>a</b>) and snow sampling sites surrounding factories and agricultural fields (<b>b</b>).</p> "> Figure 2
<p>The concentrations of elements in the snow at Anyang for all the samples (<b>a</b>) and for the sites near the factories versus the background farmlands (<b>b</b>). Note: from top to bottom, the three lines in the box plot indicate the upper quartile (Q3), median, and lower quartile (Q1), respectively, and the circular dots indicate outliers in the data, defined as values less than Q1 − 1.5 × IQR or greater than Q3 + 1.5 × IQR, with IQR being the interquartile spacing, the same as below.</p> "> Figure 3
<p>Enrichment factors of metal elements in snow.</p> "> Figure 4
<p>The spatial distribution of Cd concentrations across the entire area studied (<b>a</b>) and surrounding specific factories (<b>b</b>).</p> "> Figure 5
<p>A time series analysis of PM<sub>2.5</sub> and PM<sub>10</sub> (<b>a</b>) and a rose diagram of the wind direction and speed (<b>b</b>) in December in Anyang.</p> "> Figure 6
<p>The relationship between the concentration of Cd in snow and distance from the factories.</p> "> Figure 7
<p>A clustered tree map of the samples (<b>a</b>) and the spatial distribution of the sampling sites (<b>b</b>).</p> "> Figure 8
<p>The spatial distribution of Cd concentration in the snow versus PM<sub>2.5</sub> emissions (<b>a</b>) and the SO<sub>4</sub><sup>2−</sup> concentration in the snow versus the SO<sub>2</sub> satellite column concentration in the air (<b>b</b>).</p> "> Figure 9
<p>The correlation of Cd concentration with the production capacity (<b>a</b>) and total permitted emissions (<b>b</b>) of the factories. Note: the Cd concentration is the mean value across the two sampling sites closest to the emission sources along the upwind and downwind directions to each of the factories G, J, M, N and Q.</p> ">
Versions Notes

Abstract

:
Atmospheric deposition is an important source of heavy metal in soil and the use of dust collection cylinders is a traditional monitoring method. This method has limitations in agricultural areas because polluted soil particles may become resuspended and eventually deposited into these cylinders, leading to overestimates in the amount of atmospheric deposition in soil. To address this concern, we propose that frequent snowfall can help suppress local soil dust resuspension and that fresh snow can serve as an efficient surrogate surface when collecting atmospheric deposition samples. To investigate the rationality of this method, 52 snow samples were collected from sites surrounding smelting plants in Anyang, an industrial region of North China. The results revealed that the concentration of cadmium in the melted snow ranged between 0.03 and 41.09 μg/L, with mean values three times higher around the industrial sites (5.31 μg/L) than background farmlands (1.54 μg/L). In addition, the cadmium concentration in the snow from sites surrounding the factories was higher in the north than in the south because of prevailing winds blowing from the southwest. Moreover, snow samples from sites with high concentrations of cadmium and sulfate can be categorized into different groups via the clustering method, conforming to the spatial distribution of particulate matter emissions and sulfur dioxide satellite column concentrations. Finally, a positive correlation was found between the cadmium content in the snow and the production capacity (R2 = 0.90, p < 0.05) and total permitted emissions (R2 = 0.69, p > 0.05) of the nearby factories. These findings demonstrate that snow is a reliable medium for documenting atmospheric dry deposition associated with specific industrial emissions.

1. Introduction

The accumulation of heavy metal in soil poses a serious threat to crop growth and the safety of agricultural products [1] and has, therefore, attracted considerable attention from both the scientific community and the public. The 2014 National Soil Pollution Situation Bulletin indicated that the overall soil conditions in China were not optimal, with a national excess point rate of 19.4% in arable soil; 82.4% of this excess point rate was due to heavy metal contamination, with cadmium (Cd) as the primary heavy metal pollutant [2].
The main sources of heavy metal pollution in agricultural soil include wastewater irrigation [3], chemical fertilizer application [4], solid waste accumulation [5] and atmospheric deposition [6]. In recent decades, with the gradual implementation of national environmental policies, the use of chemical fertilizer, sewage, and livestock manure has decreased annually, resulting in a decreasing trend in the input flux of heavy metal in agricultural soil [7]. However, the contribution from atmospheric deposition remains largely uncontrolled. With the deterioration in air quality, atmospheric deposition has become an important external source of heavy metal in soil in agricultural areas such as the North China Plain [8], the Songnen Plain in Heilongjiang [9], and the Yangtze River Delta [10]. Therefore, systematic monitoring of heavy metal fluxes in agricultural soil due to atmospheric deposition is essential for assessing the status of heavy metal pollution in soil.
The use of dust collection cylinders is a traditional method for monitoring dry and wet atmospheric deposition and these cylinders are in wide use due to the simplicity of their operation [11]. However, this method has certain limitations when applied in agricultural areas, particularly in northern regions, where the soil is bare during cold seasons. Exposed soil can be easily lifted into the air by strong westerly winds and deposited into dust collection cylinders. If the monitored soil is already contaminated with heavy metal, the collected samples may include resuspended soil particles, leading to overestimates in atmospheric heavy metal deposition.
The scientific hypothesis proposed in this study is that frequent snowfall in northern regions during the winter can help suppress local soil dust resuspension and fresh snow can serve as an effective surrogate surface when collecting atmospheric deposition samples. Therefore, collecting snow samples for heavy metal analysis can provide a systematic assessment of the actual flux in atmospheric deposition and its contribution to the input of heavy metal in agricultural soil. The objectives of this study are the following: (1) to analyze the concentration and enrichment factors of heavy metal in snow samples to gain insight into the pollution characteristics of heavy metal in the area of study; (2) to compare the concentration of elements in snow between areas upwind and downwind of key smelters and investigate whether snow samples can capture the atmospheric signal of dry deposition; and (3) to perform cluster analyses of all the sampling sites, based on the chemical composition data obtained and verify whether snow samples can reflect emission signals from surrounding industrial emissions on a regional scale. Based on these analyses, our hypothesis that fresh snow can be used as a reliable medium for monitoring dry atmospheric deposition near emission sources is tested.

2. Materials and Methods

2.1. The Study Area and Sampling Sites

In this study, Anyang was selected as the target region, which hosts a significant number of nonferrous metal smelting industries, with an annual Cd emission of 2.78 t, rendering it a typical metallurgical industrial city in North China. Sixteen factories emitting heavy metals, including those in the cement manufacturing industry, iron and steel manufacturing industry, electric power industry, industrial boiler industry and other industrial sectors, were identified as the focus of this study (Figure 1).
Two principles were considered in the selection of sampling sites surrounding the target factories, namely, sampling direction and sampling distance. According to dominant winds in the region, a north–south-prevailing wind direction was considered in the selection of sampling sites. In addition, to capture the atmospheric deposition signal from emission sources, the Gaussian plume model was employed to calculate sampling distances based on the height of each stack [12]. The sampling distance can be obtained as follows:
x max = ( H 2 / C z 2 ) 1 / ( 2 − n ) ,
where xmax is the maximum ground-level concentration at a certain distance (m), H is the source height (m), Cz is the turbulent diffusion coefficient along the vertical direction and n is the atmospheric turbulence coefficient.
According to the above deployment principles, 32 snow sampling sites were established around the factories according to different transmission distances. Seven pairs of surface (0–3 cm/0–5 cm) and subsurface (3–6 cm/5–10 cm) snow samples were collected. In addition, 13 snow samples were collected from background agricultural fields far from the factories (Figure 1). Detailed information is provided in Table 1.

2.2. Sample Collection and Snowfall Analysis

Between 13 and 15 December 2023, most areas of Anyang experienced heavy snowfall. The cumulative snow depth during this period ranged between 10 and 20 cm (equivalent to 15–30 mm of precipitation). In the districts Long’an and Yindu, the average snow depth was 10 cm, while in Tangyin county, the average snow depth reached 12 cm. During the following week, the temperature in Anyang remained below 0 °C, which facilitated the retention of atmospheric deposition material in the snow.
Finally, fifty-two snow samples were collected between 22 and 23 December 2023 from the sampling sites listed in Table 1. Near large factories, with multiple chimneys emitting exhaust, snow was collected from two layers at depths of 0–3 cm and 3–6 cm (a snow depth of less than 10 cm). In deeper snow (>10 cm), samples were collected from layers at depths of 0–5 cm and 5–10 cm. At other sampling sites, surface snow samples were collected at depths ranging between 0 and 5 cm. A clean polytetrafluoroethylene (PTFE) shovel was used for sample collection in the field. Once the snow samples melted in the laboratory, 20–30 mL was poured into a PET bottle for analysis. Prior to analysis, 1% nitric acid was added to the samples to adjust the pH to approximately 1 [11].
The concentrations of 25 elements (Ca, Na, Mg, Al, K, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Mo, Cd, Sb, Ba, Be, Tl, Pb, Th, U, and Ag) present in the snow samples were analyzed via an inductively coupled plasma mass spectrometer (ICP–MS 7500ce, Agilent, Tokyo, Japan). Instrument tuning was performed prior to measurement to ensure the R-values of the instrumental standard curves were all above 0.9999 [13]. Each sample was tested three times during measurement. The stability of the instrument was ensured by the relative standard deviation (RSD) values of the internal standard elements (72Ge, 103Rh, 115In, and 209Bi). The internal standard tubes were filled with 1 μg/L internal standard solution (Part# 5183-4680, Agilent) and the samples were retested if the RSD value of each element was less than 3% [14]. The detection limit of each metal element in the samples is shown in Table S1. The recovery of each metal element ranged between 80 and 120%. The concentration of inorganic ions in the water-soluble components (F−, Cl−, SO42−, NO3−, NO2−, NH4+, K+, Ca2+, Na+, and Mg2+) in the samples were determined via ion chromatography. Each sample was tested three times [6].

2.3. The Determination of Enrichment Factors (EFs)

Enrichment factor analysis can quantify an element's degree of enrichment in the snow. In this study, Al was used as the referent element in the following formula:
EF   = ( C i / C Al ) sample ( C i / C Al ) background ,
where ( C i ) sample is the concentration of the measured element i, ( C i ) background is the concentration of element i in the crust, ( C Al ) sample is the concentration of the element Al in the sample, ( C Al ) background is the concentration of the element Al in the crust [15] and EF is the enrichment factor corresponding to each element.

2.4. Statistical Analysis

The descriptive statistical analyses (mean, maximum and minimum values) in this study were performed via Microsoft Excel, 2021 (Microsoft, 2021). The software Origin (Origin 2021) and R (version 4.3.1) were used for data visualization. Cluster analysis was performed via R software (version 4.3.1). The spatial distribution was mapped via ArcGIS (ArcGIS 10.8).

2.5. Other Data Sources

The PM2.5 and PM10 air quality concentration data used in this study were obtained from the China Air Quality Online Monitoring and Analysis Platform (https://www.aqistudy.cn/historydata, accessed on 10 February 2024), with a temporal resolution of 1 h. Meteorological data, such as wind direction and speed, were obtained from the National Meteorological Information Centre (https://data.cma.cn/, accessed on 11 February 2024), with a temporal resolution of 1 h. The SO2 satellite column concentration data were obtained from vertical SO2 column concentration data from the Sentinel-5P/TROPOMI satellite (https://dataspace.copernicus.eu, accessed on 15 January 2024). The PM2.5 emission data used in this study were obtained from the Multiresolution Emission Inventory of China in 2020 (MEIC), which was developed and maintained by Tsinghua University (http://meicmodel.org/, accessed on 20 April 2024).

3. Results and Discussion

3.1. The Concentrations and Enrichment Characteristics of Metal Elements in the Snow

Figure 2a shows the concentrations of the 25 metal elements in the snow samples collected in this study. As shown, there was a variation of six orders of magnitude (the removal of anomalous sampling site J1S), with Ca (9749 μg/L) as the highest concentration obtained and Th (0.04 μg/L) the lowest. Notably, the concentration of elements in the surface snow was 3–10 times greater than in the subsurface snow, as shown in Figure S1. This is because the surface layer of snow, which remains exposed over a longer period than deep snow, is an open system, continuously receiving dry-deposited heavy metals emitted from nearby factories. Notably, as shown in Figure 2b, the average concentration of Cd in the snow nearer to the factories (5.31 μg/L) was three times greater than in the snow from the background farmlands (1.54 μg/L). Importantly, this finding also suggests that snow has a better scavenging effect on pollutants emitted from factories, considering the snow samples from the background farmland sites may represent the initial level of pollutants in the snow prior to precipitation.
Compared to the elemental composition of snow from other regions (Table 2), significantly higher concentrations of Cd and Sb were measured in this study. In particular, the Cd concentration is the highest reported to date, 10–102 orders of magnitude higher than that reported in other studies. Notably, relatively high heavy metal concentrations (e.g., Ni and Cu), previously reported in Harbin (China), Moscow (Russia), the Kola Peninsula (Russia) and Thessaloniki (Greece), can be attributed to coal combustion [16,17,18] and the smelting industry [16,19]. Similarly, the elevated concentrations of Cd, Sb and Pb observed in this study are linked to emissions from smelting and coal combustion activities in the region [8,20].
To assess the extent to which the metals detected in the snow were influenced by human activities, Al was used as the referent element to calculate the enrichment factor (EF) of each element in the snow (Figure 3). The EF values of elements such as K, Ca, Na, Mg, Mn, Ni, Cu, Mo and Ba ranged between 10 and 102, indicating moderate enrichment and suggesting that these elements are influenced by anthropogenic activities. In contrast, Zn, As, Se, Ag, Cd, Sb, Tl, and Pb yielded EF values ranging between 102 and 105, indicating high enrichment and a significant anthropogenic influence. In this study, the EFs of Cd and Sb were significantly greater than those reported in other studies [21,22,23], which can be attributed to the high intensity of coal burning, smelting and transport activities in the region. Considering that both the absolute concentration and relative enrichment of Cd in the snow were greater in this study than in previous reports, the following sections focus on Cd as a typical heavy metal.
Table 2. A comparison of the heavy metal concentrations in the snow studied and the corresponding literature (μg/L).
Table 2. A comparison of the heavy metal concentrations in the snow studied and the corresponding literature (μg/L).
SiteVCrMnFeCoNiCuZnAsMoCdSbPbReference
Anyang,
China		Average1.771.3461.12481.540.491.253.9185.334.320.175.3113.4523.32This study
Min-Max0.08–9.070.16–6.033.80–272.6512.58–2657.620.02–3.720.18–4.680.57–20.315.91–521.140.21–24.570.01–0.540.03–41.098.05–22.531.17–186.04This study
Surface2.632.29110.72956.740.782.006.0495.617.720.247.8413.6335.99This study
Subsurface0.460.4719.53123.920.130.611.7128.391.250.100.7011.554.94This study
Niihama, Japan0.03////0.41////0.11//[24]
Central Pyrenees,
Spain		//0.509.01/0.060.062.72////1.92[25]
Tianjin, China0.340.9313.63106.000.171.251.9622.101.37/0.66/0.17[26]
Tyumen, Russia0.15 7.3020.20/0.954.6813.200.510.050.100.101.34[27]
Moscow, Russia0.920.6611.0093.000.284.605.2016.00/0.260.070.380.35[16]
Lake Saint-Charles,
Canada		/0.4027.90263.300.100.800.804.80/0.200.03/0.20[28]
Sivas, Turkey//14.93//4.936.0213.15//3.71/13.53[29]
Poznań, Poland/0.40///3.772.0313.200.71/0.08/4.93[30]
Northwestern,
Russia		/3.4015.90/18.30702.00525.0046.702.20/0.18/8.60[19]
Valday, Russia//6.7045.50/2.403.3041.00//0.06/1.60[31]
Thessaloniki,
Greece		/1.10–16.801.80–16.607.80–580.00/0.80–16.1955.50–42.1010.00–262.000.06–2.50/0.15–3.76/11.90–24.00[17]
Harbin, China12.9015.10197.00300.004.0910.5024.30151.0022.80/2.07/25.30[18]
Northeastern China0.700.9022.0062.00/1.801.3023.003.00/0.10/2.50[32]
Southern
Tibetan Plateau		1.761.9942.67497.370.702.745.2818.73//0.02/2.14[33]
Beijing, China//1.16–54.62///N.D.-9.633.91–71.33N.D.-3.36//0.55–2.86/[34]
Hungary/0.55////2.87/1.14/0.04/2.18[35]

3.2. The Spatial Distribution of Cd Concentrations in the Snow

Figure 4a shows the spatial distribution of Cd concentrations in the snow in the area studied with other heavy metals shown in Figure S2. Overall, a high Cd concentration was found in the central part of the area studied, especially in the sampling sites around the factories G, N, O, J, K, and L. This is mainly due to the large number of chemical and smelting factories in the area, resulting in significant local emissions and deposition. Note that a severe air pollution event occurred in the target region after snowfall and before sampling. In particular, between 19 and 20 December, the hourly mean values of PM2.5 and PM10 were 120 μg/m3 and 140 μg/m3, respectively, and the maximum values reached 248 μg/m3 and 271 μg/m3 (Figure 5a). This can be explained by the unfavorable meteorological conditions, under which the weather system remained stable (with an average wind speed of 1.7 m/s), and the shallow near-surface boundary layer, resulting in temperature inversion [36]. These conditions reduced the ability of pollutants to disperse and facilitate the accumulation of metals in the atmosphere and their subsequent dry deposition in the snow.
A close inspection of the sites surrounding the factories revealed the concentration of Cd in the snow was higher in the north (S2N) than in the south (S2S), of which factory S is an example (Figure 4b). Similar spatial features were also found around industrial factories A, B, C, F, etc. (Figure S3). This phenomenon can be explained via the wind rose diagram in Figure 5b. Notably, the prevailing winds during the period studied were southerly winds, which carried heavy metals emitted from the factories to the northern observation sites.
To investigate the relationship between the Cd concentration in the snow and the distance from emission sources, several sampling sites were selected upwind and downwind of typical factories along the prevailing wind direction. As shown in Figure 6, the Cd concentration in the snow decreased as the distance from the source increased, particularly around factory S. This pattern was also observed around factories Q and JKL as a cluster (Figure S4). The underlying reason for this pattern is that particulate heavy metals such as Cd, after emission from chimneys, diffuse outward due to atmospheric turbulence and their concentrations decrease with the distance [37,38,39]. As a result, the concentration of Cd and other heavy metals is relatively high near emission sources and decreases with distance, at which point the concentration reduces by more than half within 1 km (Figure 6).

3.3. Spatial Clustering by the Chemical Composition of the Snow

Based on the chemical composition of the snow (F−, Cl−, SO42−, NO3−, NO2−, NH4+, K+, Ca2+, Na+, Mg2+, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Mo, Cd, Sb, Ba, Be, Tl, Pb, Th, U and Ag), dendrograms were obtained from hierarchical clustering via Euclidean distance and Ward methods. As shown in Figure 7, the sampling sites can be divided into three clusters reflecting the spatial distribution and type of factory selected.
Cluster 1 includes a single sampling site (green, GIS) located around factories G, H and I (in the central part of the area studied). These factories are involved in power generation and steel production, both of which can emit significant amounts of Cd [40].
Cluster 2 comprises eight sampling sites (red) located around factories B, C, D and F (northwest of the study area). The factories in this cluster represent a mixed industrial source and are focused mainly on cement production and other industrial processes. Cement production is a major contributor to SO2 emissions in the Henan Province [41], which are released at the clinker calcination stage and can chemically react in the atmosphere to form SO42− [42].
Cluster 3 encompasses twenty-two sampling sites (blue) distributed around factories J, K, L, M, N, O, S and A (mainly in the southern and northeastern parts of the study area). The factories in this cluster mainly represent other industrial sources, e.g., the manufacture of basic chemical raw material, industrial furnaces and metal smelting, with Cd as a typical pollutant [43].
To interpret the above cluster results, we further analyzed the spatial distribution of Cd and sulfate (SO42−) in the snow, as well as the atmospheric particulate matter and SO2 in the air (Figure 8). As shown, the sampling site G1S in Cluster 1 presented the highest Cd concentration, which coincided with areas with high PM2.5 emissions. Similarly, the Cd concentration in the snow from Cluster 3 was also relatively high and was located within the high-PM2.5 emission zone (Figure 8a). The elevated Cd concentration in the snow in these clusters could be attributed to significant emissions from steel smelting, coal combustion in power plants, and other industrial production activities in the area. Similarly, Cluster 2 showed a relatively high concentration of SO42− in zones with a high SO2 satellite column concentration (Figure 8b). This finding is confirmed by previous observations [44]. This spatial consistency between the chemical composition of the snow and atmospheric precursors suggests the snow samples performed well in capturing dry-deposited materials and could serve as effective indicators of emissions from local industries.
To further assess whether snow samples can reflect the emission intensity of factories, we selected five factories located within high-PM2.5 emission zones for subsequent analysis. The relationships between the Cd concentration in the snow and the production capacity and emission permits of these factories are shown in Figure 9. As a result, high positive correlations between the Cd concentration and both the production capacity (R2 = 0.90, p < 0.05) and total permitted emissions (R2 = 0.69, p > 0.05) of the factories were found. A similar positive relationship was found for other heavy metals, as shown in Figure S5. These findings further demonstrate that atmospheric deposition signals captured in fresh snow can effectively reflect the emission intensity of nearby factories.

4. Conclusions

To test whether frequent snowfall can help suppress local soil dust resuspension and if fresh snow can serve as an efficient surrogate surface when collecting atmospheric deposition samples, we collected 52 samples of snow in Anyang, China. The absolute concentrations and enrichment factors of Cd and Sb in the snow samples were significantly greater in this study than in previous reports, likely because of high-intensity coal combustion, smelting, and transport activities in the region. The Cd concentration in the snow samples surrounding the factories was influenced by the dominant southern wind direction, with higher values at the northern sites and lower values at the southern sites. In addition, the Cd concentration in the snow samples decreased as the distance from the factories increased. A cluster analysis based on the chemical composition of the snow revealed sampling sites near the factories could be divided into two major subregions with high concentrations of Cd and SO42−, which were consistent with the distributions of PM2.5 emissions and SO2 satellite column concentration in each subregion. We also found that the Cd concentration in the snow was better correlated with the production capacity and total permitted amount of emissions from nearby factories. This study highlights the usefulness of fresh snow as a surrogate surface for monitoring the atmospheric dry deposition of pollutants associated with specific industrial emissions, particularly in agricultural areas where the soil is already contaminated with heavy metal.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/atmos16010026/s1: Figure S1: Comparison of metal concentrations in surface snow and subsurface snow; Figure S2: Spatial distribution of heavy metal concentrations across the entire study area; Figure S3: Spatial distribution of Cd concentrations near factories; Figure S4: Relationships between the Cd concentration and distance from factories; Figure S5: Correlation of concentrations of heavy metals with production capacity (left column) and total permitted amount of emissions (right column); and Table S1: Detection limit for each element (μg/L).

Author Contributions

Conceptualization, Y.P. and Z.S. (Zaijin Sun); methodology, Y.P. and Q.S.; software, Z.S. (Zhicheng Shen); validation, Y.P. and Y.Z.; formal analysis, Y.L.; investigation, W.L.; resources, J.X.; data curation, L.Z.; writing—original draft preparation, J.L.; writing—review and editing, Y.P.; visualization, J.L.; supervision, Y.Z.; project administration, H.S.; funding acquisition, Z.S. and Z.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by The National Key Research and Development Program of China, grant number 2022YFC3704800.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors.

Acknowledgments

We acknowledge Ailin Wu and the staff at the Shifang Environmental Protection Engineering Co., Ltd. for their help in the field with snow sampling.

Conflicts of Interest

The authors declare no competing financial interest.

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Figure 1. The specific location of the area of study in China (a) and snow sampling sites surrounding factories and agricultural fields (b).
Figure 1. The specific location of the area of study in China (a) and snow sampling sites surrounding factories and agricultural fields (b).
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Figure 2. The concentrations of elements in the snow at Anyang for all the samples (a) and for the sites near the factories versus the background farmlands (b). Note: from top to bottom, the three lines in the box plot indicate the upper quartile (Q3), median, and lower quartile (Q1), respectively, and the circular dots indicate outliers in the data, defined as values less than Q1 − 1.5 × IQR or greater than Q3 + 1.5 × IQR, with IQR being the interquartile spacing, the same as below.
Figure 2. The concentrations of elements in the snow at Anyang for all the samples (a) and for the sites near the factories versus the background farmlands (b). Note: from top to bottom, the three lines in the box plot indicate the upper quartile (Q3), median, and lower quartile (Q1), respectively, and the circular dots indicate outliers in the data, defined as values less than Q1 − 1.5 × IQR or greater than Q3 + 1.5 × IQR, with IQR being the interquartile spacing, the same as below.
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Figure 3. Enrichment factors of metal elements in snow.
Figure 3. Enrichment factors of metal elements in snow.
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Figure 4. The spatial distribution of Cd concentrations across the entire area studied (a) and surrounding specific factories (b).
Figure 4. The spatial distribution of Cd concentrations across the entire area studied (a) and surrounding specific factories (b).
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Figure 5. A time series analysis of PM2.5 and PM10 (a) and a rose diagram of the wind direction and speed (b) in December in Anyang.
Figure 5. A time series analysis of PM2.5 and PM10 (a) and a rose diagram of the wind direction and speed (b) in December in Anyang.
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Figure 6. The relationship between the concentration of Cd in snow and distance from the factories.
Figure 6. The relationship between the concentration of Cd in snow and distance from the factories.
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Figure 7. A clustered tree map of the samples (a) and the spatial distribution of the sampling sites (b).
Figure 7. A clustered tree map of the samples (a) and the spatial distribution of the sampling sites (b).
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Figure 8. The spatial distribution of Cd concentration in the snow versus PM2.5 emissions (a) and the SO42− concentration in the snow versus the SO2 satellite column concentration in the air (b).
Figure 8. The spatial distribution of Cd concentration in the snow versus PM2.5 emissions (a) and the SO42− concentration in the snow versus the SO2 satellite column concentration in the air (b).
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Figure 9. The correlation of Cd concentration with the production capacity (a) and total permitted emissions (b) of the factories. Note: the Cd concentration is the mean value across the two sampling sites closest to the emission sources along the upwind and downwind directions to each of the factories G, J, M, N and Q.
Figure 9. The correlation of Cd concentration with the production capacity (a) and total permitted emissions (b) of the factories. Note: the Cd concentration is the mean value across the two sampling sites closest to the emission sources along the upwind and downwind directions to each of the factories G, J, M, N and Q.
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Table 1. Detailed information about the sampling sites of snow in Anyang, Henan Province.
Table 1. Detailed information about the sampling sites of snow in Anyang, Henan Province.
Sampling SiteRegionLongitude (°E)Latitude (°N)Type of SnowFactories Nearby
A1N (0–3 cm)YDQ113.995236.0569Surface snowA
A1N (3–6 cm)YDQ113.995236.0569Subsurface snowA
A1S (0–3 cm)YDQ114.000136.0449Surface snowA
A1S (3–6 cm)YDQ114.000136.0449Subsurface snowA
B1NYDQ114.082436.1073Surface snowB
B1SYDQ114.079636.0958Surface snowB
C1NYDQ114.075636.1829Surface snowC, D
C1SYDQ114.074236.1509Surface snowC, D
C2N (0–3 cm)YDQ114.065336.1749Surface snowC, D
C2N (3–6 cm)YDQ114.065336.1749Subsurface snowC, D
C2S (0–3 cm)YDQ114.073036.1560Surface snowC, D
C2S (3–6 cm)YDQ114.073036.1560Subsurface snowC, D
F1NYDQ114.204436.1392Surface snowF
F1SYDQ114.201136.1308Surface snowF
G1NYDQ114.283036.1604Surface snowG, H, I
G1SYDQ114.275636.0839Surface snowG, H, I
J1NLAQ114.282836.0358Surface snowJ, K, L
J1SLAQ114.277636.0173Surface snowJ, K, L
J2N (0–5 cm)LAQ114.273536.0404Surface snowJ, K, L
J2N (5–10 cm)LAQ114.273536.0404Subsurface snowJ, K, L
J2SLAQ114.284536.0097Surface snowJ, K, L
J3N (0–5 cm)LAQ114.271536.0476Surface snowJ, K, L
J3N (5–10 cm)LAQ114.271536.0476Subsurface snowJ, K, L
J3SLAQ114.282736.0224Surface snowJ, K, L
M1NLAQ114.293936.0140Surface snowM
M1SLAQ114.291135.9967Surface snowM
M2NLAQ114.293236.0101Surface snowM
M2SLAQ114.292736.0017Surface snowM
N1N (0–5 cm)LAQ114.305736.0277Surface snowN, O
N1N (5–10 cm)LAQ114.305736.0277Subsurface snowN, O
N1SLAQ114.303136.0130Surface snowN, O
Q1NYDQ114.321436.1831Surface snowQ
Q1SYDQ114.321336.1706Surface snowQ
Q2NYDQ114.323036.1804Surface snowQ
Q2SYDQ114.321536.1730Surface snowQ
S1NTYX114.347735.8581Surface snowS
S1STYX114.341535.8423Surface snowS
S2NTYX114.346735.8546Surface snowS
S2STYX114.339335.8471Surface snowS
LONGANQU *LAQ114.117336.0473Surface snow/
YINDUQU *YDQ114.228536.1605Surface snow/
TANGYINXIAN *TYX114.307535.8255Surface snow/
GD1 *LAQ114.480935.9804Surface snow/
GD2 *YDQ114.298936.0662Surface snow/
GD3 *TYX114.243436.0328Surface snow/
GD4 *TYX114.346036.0013Surface snow/
GD5 *LAQ114.285535.9718Surface snow/
GD6 *LAQ114.366535.9658Surface snow/
GD7 *LAQ114.328135.9036Surface snow/
GD8 *LAQ114.117336.0473Surface snow/
GD9 *TYX114.228536.1605Surface snow/
GD10 *TYX114.307535.8255Surface snow/
Note: the sampling depths for the surface and subsurface snow are 0–3 cm, 0–5 cm, 3–6 cm and 5–10 cm; YDQ denotes the Yindu district in Anyang; LAQ denotes the Long’an district in Anyang; TYX denotes Tangyin county in Anyang; * indicates the sampling sites located in the surrounding farmlands or background areas; A and D are from cement manufacturing industries; B, C, F, J, K, L, M, N, O, and Q are from other industrial sectors (including smelters); G, H and I are from power and steel manufacturing industries; and S is from the industrial boiler industry.
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Liu, J.; Sun, Z.; Lei, W.; Xu, J.; Sun, Q.; Shen, Z.; Lyu, Y.; Shi, H.; Zhou, Y.; Zhang, L.; et al. A Reliable Medium for Monitoring Atmospheric Deposition near Emission Sources by Using Snow from Agricultural Areas. Atmosphere 2025, 16, 26. https://doi.org/10.3390/atmos16010026

AMA Style

Liu J, Sun Z, Lei W, Xu J, Sun Q, Shen Z, Lyu Y, Shi H, Zhou Y, Zhang L, et al. A Reliable Medium for Monitoring Atmospheric Deposition near Emission Sources by Using Snow from Agricultural Areas. Atmosphere. 2025; 16(1):26. https://doi.org/10.3390/atmos16010026

Chicago/Turabian Style

Liu, Jiayang, Zaijin Sun, Wenkai Lei, Jingwen Xu, Qian Sun, Zhicheng Shen, Yixuan Lyu, Huading Shi, Ying Zhou, Lan Zhang, and et al. 2025. "A Reliable Medium for Monitoring Atmospheric Deposition near Emission Sources by Using Snow from Agricultural Areas" Atmosphere 16, no. 1: 26. https://doi.org/10.3390/atmos16010026

APA Style

Liu, J., Sun, Z., Lei, W., Xu, J., Sun, Q., Shen, Z., Lyu, Y., Shi, H., Zhou, Y., Zhang, L., Wu, Z., & Pan, Y. (2025). A Reliable Medium for Monitoring Atmospheric Deposition near Emission Sources by Using Snow from Agricultural Areas. Atmosphere, 16(1), 26. https://doi.org/10.3390/atmos16010026

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