Spatial Distribution and Migration of Heavy Metals in Dry and Windy Area Polluted by Their Production in the North China
Inner Mongolia Key Laboratory of Soil Quality and Nutrient Resources, Key Laboratory of Agricultural Ecological Security and Green Development at Universities of Inner Mongolia Autonomous, Hohhot 010018, China
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Authors to whom correspondence should be addressed.
Processes 2024, 12(1), 160; https://doi.org/10.3390/pr12010160
Submission received: 23 November 2023
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Revised: 27 December 2023
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Accepted: 3 January 2024
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Published: 9 January 2024
Abstract
:We explored the migration and distribution of heavy metal pollution in a dry and windy area in northern China. We collected soil, atmospheric deposition, and water samples, and measured heavy metal concentrations. Cu, Zn, As, and Pb in the 0–10 cm soil layer had a fan-shaped distribution, consistent with their atmospheric deposition fluxes. This indicates that the distribution of these heavy metals was driven by strong winds. The concentration of Cd in the river increased from 0.257 mg/L upstream to 0.460 mg/L downstream, resulting in the same distribution trends as soil near the river. Surface runoff may therefore drive Cd migration. The concentration of Pb in the river exceeded the pollution threshold, resulting in accumulation in the 5–10 cm soil layer. Atmospheric deposition fluxes were consistent with the soil distribution results, and principal component analysis showed that the contribution of surface runoff was high. This suggests that the migration of Pb and Cr is driven by both wind and surface runoff. Six heavy metals showed different migration behaviors, suggesting specific control strategies should be implemented for individual heavy metals.
1. Introduction
Heavy metal pollution resulting from expanding mining and industry development has received considerable attention in recent decades [1]. China, one of the world’s biggest producers and consumers of heavy metals, is suffering from heavy metal pollution around mining areas [2,3]. In one study, of 1672 soil samples from 70 mining areas surveyed in China, 33.4% exceeded safety thresholds according to the Survey Bulletin of Soil Pollution in 2014 [4]. Cao et al. (2022) reported heavy metal pollution in soil from mining covering 41,600 ha [5]. Heavy metals accumulate in the soil, damaging the health of the ecosystem and nearby habitats, and the organisms therein [6]. To minimize soil pollution from heavy metals, it is essential to understand the factors driving their distribution and migration.
The distribution of soil heavy metals tends to follow a ring, spreading outward from the mining area [7,8,9,10], and the concentrations of heavy metals in the soil decrease with increasing distance from mining and production areas. Compared with Cu, Zn, and Pb, concentrations of As and Cr show an one- to two-fold greater decline in soil when the distance from the mining area increases from 20 m to 100 m [10]. Zn can be transported two- to six-fold further than Pb and Cd [8]. However, Gao et al. (2017) found that the distributions of Cu, Zn, Cr, Pb, and Cd involve two peaks along a distance gradient from mining areas, with the first at 0–3 km and the second at ~2 km, followed by a decline to the background value [9].
Many studies have shown that wind and surface runoff are key drivers of the distribution of heavy metals in soil [11,12,13]. Transportation of contaminated soil particles by wind leads to a considerable accumulation in soil near mining and production areas [14]. One study showed that Cu, Zn, and Pb accumulate more in soil downwind from mining-affected areas [15]. Hu et al. (2018) [10] also showed that wind direction in mining areas determines As and Cd accumulation patterns. Cu, Cr, and Zn migrate much further than Cd along the prevailing wind direction [16]. Li et al. (2018) also found that Zn migration is more dependent on wind than Cd migration [17].
Heavy metals dissolved in water are easily transported from mining and production areas to other uncontaminated areas through surface runoff [18]. Concentrations of heavy metals in the soil near surface runoff are higher than in other affected soils in mining areas [11]. Concentrations of Cu, Zn, As, Pb, and Cd in soil decline along the river from upstream to downstream [19]. The distance of Cd migration through surface runoff is twice that of Pb [20]. Similarly, Jiang et al. (2018) found that Cd is more easily transported via surface runoff than Cu [21].
Compared with wind-driven migration of heavy metals, runoff migration is well-documented in the literature, especially in southern China, which has a rainy climate [11,22,23]. Western Inner Mongolia is rich in heavy metal reserves and many companies are working on their mining and production. The climate in this region is windy. In general, production areas are near rivers because levigation is expensive. Thus, soils near production areas are associated with heavy metal pollution risks from both wind- and surface-runoff pathways. Unlike in southern China, soil pH in western Inner Mongolia is high, mostly >8.0. The solubility of heavy metals in these soils is much lower than in more acidic soils in southern China. Few studies have focused on these areas. In the present study, we aimed to (1) determine the distributions of heavy metals in areas that are windy, near rivers, and with high-pH soil, and (2) estimate the contributions of wind and runoff pathways to the migration of heavy metals in these areas.
2. Materials and Methods
2.1. Study Area
The studied production area, abandoned for 9 years, is located in Urat Houqi, Bayannur City, Inner Mongolia Autonomous Region (N 41°16′50″–41°18′2″, S 106°35′50″–106°36′49″; Figure 1A). It has a temperate continental monsoon climate with an annual mean temperature of 3.8 °C and an average rainfall of 96 mm. The dominant wind direction is northwest, with an average wind speed of 5.1 m/s. Saline-alkali soil and gray-brown desert soil are present. Water chemical types were SO4•CI–Ca•Na•Mg, and the water was brackish. The terrain is high in the south and low in the north. A river crosses the studied area from south to north.
2.2. Data Collection and Processing
2.2.1. Soil Sampling and Analysis
The grid sampling method was employed in the study area, with each sampling grid covering an area of 50 m × 50 m. One sample was taken from each grid, and the nine drills mixed with one drill sampling method was used. Soil samples were divided into three layers of 0–5 cm, 5–10 cm, and 10–20 cm, and 675 soil samples (Figure 1B) were collected, avoiding roads and mountains to ensure that the samples were representative. Samples were immediately sealed in polyethylene plastic bags to avoid contamination and labeled. The latitude and longitude of each sampling point were recorded using a GPS device and any relevant environmental information pertaining to the sample site was recorded.
Air-dried soil samples were screened at 0.15 mm and digested by mixed acid. Firstly, 0.1500 g of air-dried soil sample was placed in a microwave digestion tube, 0.5 mL of ultrapure water, and 2 mL of HF (40%; Tianjin Xinbote Chemical, Tianjin, China), before being incubated overnight (>12 h). Secondly, 6 mL of HCl (31%; Tianjin Xinbote Chemical) and 2 mL of HNO3 (68%; Tianjin Xinbote Chemical) were added and digestion was performed for 2 h at 120 °C, 150 °C, and 200 °C. After the digestion was complete, the tubes were taken out and placed in the acid extractor. One mL of HClO4 (70%; Tianjin Xinbote Chemical) was added into each tube to remove the acid until 1 drop of liquid remained. The acid extractor temperature was set to 190 °C and run for at least 3 h. Using HClO4 can reduce the evaporation time of acids. Finally, after cooling, the sample was rinsed with nitric acid (volume ratio 2%) in a 50 mL volumetric flask for filtration. The sample to be tested was filtered through a 0.22 μm filter. The concentrations of Cu, Zn, As, Pb, Cd, and Cr in solution were determined on AAS (ATS-986, Beijing Pu analysis general instrument company, Beijing, China) and ICP-MS (ICAPRQ, Thermo Scientific, Waltham, MA, USA), respectively.
2.2.2. Atmospheric Deposition Sampling and Analysis
Ambient air determination using the dustfall-Gravimetric method was employed for atmospheric deposition, and sample collection took place every 2 months from December 2020 to October 2021. Twelve PVC cylinders were placed in the studied area to collect the atmospheric deposition of heavy metals caused by wind (Figure 1C). Two cylinders were included at each determination point. Sampling points S4, S7, and S9 were 25 m from the production area. Sampling points S3, S5, S6, S8, S10, S11, and 12 were 50 m away. Sampling points S1 and S2 were 100 m and 150 m away, respectively. Samples were taken every 2 months. The pretreatment process for samples was performed according to GB/T 15265-1994 [24]. Analysis methods were conducted as described above for soil samples. Dry samples were used for analysis.
2.2.3. Water Sampling and Analysis
River sampling points are shown in Figure 1D. River water was sampled using a polyethylene bottle from 50 m downstream from the study area to 1 km away. A volume of 20 mL of water was sampled at each site. The sampling depth was 0–30 cm and the sampling time was June 2021. The pH of the water sample was adjusted to <2 by adding concentrated HNO3 to maintain the solubility of heavy metals [25]. After filtering through a 0.22 μm membrane, samples were determined by ICP-MS.
2.2.4. Data Analysis
Microsoft Office Excel 2010 (Microsoft, Washington, DC, USA) was used for statistical analysis. ArcMap 10.5 (Environmental Systems Research Institute, Redlands, CA, USA) was used for digital visualization processing. Origin 2021 (OriginLab, Northampton, MA, USA) was used for plotting and principal component analysis.
3. Results
3.1. Spatial Distribution of Heavy Metals
The spatial distributions of Cu, Zn, As, Pb, Cd, and Cr along the soil profile in the studied area are shown in Figure 2. The mean concentrations of Cu, Zn, As, Pb, Cd, and Cr in the top 0–5 cm soil layer were 257.2 mg/kg, 101.5 mg/kg, 18.6 mg/kg, 106.0 mg/kg, 0.3 mg/kg, and 60.0 mg/kg, respectively. With increasing soil depth, the concentrations of Cu, Zn, As, and Pb decreased by 23.2%, 10.4%, 23.4%, and 39.6% in the 5–10 cm soil layer, respectively. While the concentration of Cd showed the greatest increase (253.9%), Cr did not show a large increase (1.9%). Compared with the 0–5 cm soil layer, the concentrations of Cu, Zn, As, Pb, and Cr decreased by 35.2%, 20.6%, 31.0%, 47.5%, and 1.2% in the 10–20 cm soil layer, respectively. The concentration of Cd increased by 169.23%. Concentrations of Cu, Zn, As, and Pb showed the same trends along soil depth, consistently decreasing with increasing soil depth. Relatively high concentrations of Cu, Zn, As, Pb, Cd, and Cr were separately distributed in different subareas, with Cu, Zn, As, and Pb showing a similar pattern. Relatively high concentrations of Cu were mainly distributed in soil around 500 m southeast of the production area in the 0–10 cm soil layer. Cu, Zn, As, and Pb displayed a similar spatial distribution pattern, with relatively high concentrations in the southeast of the mining area in the 0–10 cm layer. Relatively high concentrations of Cu, Zn, As, and Pb were distributed in soil 200 m away from the studied area in the 10–20 cm soil layer. Relatively high concentrations of Cd and Cr were observed in the 5–10 cm layer of soil. The mean value of Cd was ordered 5–10 cm > 10–20 cm > 0–5 cm. Relatively high concentrations of Cd were distributed 1200 m north of the studied area in the 5–20 cm soil layer. Relatively high concentrations of Cr were distributed 150 m southwest of the studied area in the 0–20 cm soil layer. Overall, heavy metals migrated upwind and downwind under the influence of wind direction, and the concentrations of heavy metals first increased and then decreased with increasing distance, resulting in a fanning-out pattern.
3.2. Atmospheric Deposition of Heavy Metals
3.2.1. Concentrations of Heavy Metals in Atmospheric Deposition
Figure 3 shows heavy metal concentrations in samples of atmospheric deposition. The ranges were 32.3–592.3 mg/kg for Cu, 19.0–526.1 mg/kg for Zn, 1.1–23.1 mg/kg for As, 22.6–286.0 mg/kg for Pb, 0.02–0.44 mg/kg for Cd, and 19.1–119.3 mg/kg for Cr. Concentrations of Cu, Zn, As, Pb, and Cd were highest at S4, and the concentration of Cr was highest at S7. Concentrations of Cu, Zn, As, Pb, Cd, and Cr were lowest at S1. The concentration at S4 was almost three times higher than that at S1. The highest Cu concentrations were measured at S4, S5, and S6, while the highest concentrations of Zn were at S4, S7, and S10. Except for S1, S2, and S11, concentrations of As and Pb were similar. Cd concentrations were higher at S7 and S8, and concentrations of Cr were the same at S4–S12. Concentrations of Cu, Zn, As, Pb, Cd, and Cr were highest in January–February, and lowest in November–December. Concentrations of Cu, Zn, Pb, Cd, and Cr declined in the order January–February > March–April > September–October > July–August > May–June > November–December. Concentrations of As declined in the order January–February > March–April > July–August > September–October > May–June > November–December. The mean concentrations of heavy metals were ordered Cu > Zn > Pb > Cr > As > Cd.
3.2.2. Heavy Metal Fluxes in Atmospheric Deposition
The annual deposition fluxes of Cu, Zn, As, Pb, Cd, and Cr are presented in Table 1. Atmospheric deposition fluxes of Cu ranged from 17.04 to 221.59 mg/m2/year. The highest atmospheric deposition flux Cu concentration was at S4 (221.59 mg/m2/year) and the lowest was at S1 (17.04 mg/m2/year). Annual atmospheric deposition fluxes of Zn ranged from 12.33 at S1 to 202.49 mg/m2/year at S4, and the range of As was from 1.62 to 10.56 mg/m2/year at S4. Annual deposition fluxes of Zn at S5 and S7 were similar. Annual deposition fluxes of Cd were highest at S4 and S7 (0.16 mg/m2/year). Annual deposition fluxes of Pb and Cr were 12.81–122.08 mg/m2/year and 7.50–50.83 mg/m2/year, respectively. The annual deposition flux of Pb at S4 was ten times that at S1.
Table 2 shows the deposition fluxes of heavy metals in the studied area every 2 months from 2020 to 2021. The deposition fluxes of heavy metals were highest in January–February and lowest in November–December. The deposition fluxes of Cd varied slightly throughout the year. The deposition fluxes of Cu, Zn, As, Pb, and Cr from March to April were the second highest, and only less than those in January–February. The deposition fluxes of Cu and Zn in July–August were higher than in September–October. Except for Cd, the deposition fluxes of heavy metals in May–June were higher than in November–December.
3.3. Heavy Metals in the River
The concentrations of heavy metals in surface runoff in the study area are shown in Figure 4. The concentration ranges of Cu, Zn, As, Pb, Cd, and Cr were 0.01–0.10 mg/L, 0.03–0.08 mg/L, 0.01–0.02 mg/L, 0.00–0.85 mg/L, 0.26–0.46 mg/L, and 0.04–0.06 mg/L in surface runoff, with mean concentrations of 0.03 mg/L, 0.05 mg/L, 0.01 mg/L, 0.25 mg/L, 0.35 mg/L, and 0.05 mg/L, respectively. Concentrations of Cu were highest at P19 and lowest at P2. The highest concentrations of Zn and As were approximately twice the lowest concentrations. At P1, the concentration of Pb was highest and the concentration of Cd was lowest. The concentration of Cr was highest at P9 and lowest at P2, and the highest concentration was 1.5 times that of the lowest concentration. The concentration of Pb in some of the surface runoff and the concentration of Cd in all surface runoff exceeded the standard applicable to industrial water (GB 3838-2002) [26]. Concentrations of Cu, Zn, and As in surface runoff were lower than the industrial water standard (GB 3838-2002). Concentrations of Cu, Zn, As, and Cd downstream from the mining area were higher than those upstream, but the Pb concentration displayed the opposite trend, and the concentration of Cr was not significantly different upstream and downstream.
3.4. Principal Component Analysis of Heavy Metals
The results of soil principal component analysis (PCA) in the production area are shown in Table 3 and Figure 5. Kaiser–Meyer–Olkin (KMO) results for 0–5 cm, 5–10 cm, and 10–20 cm soil layers were 0.805, 0.700, and 0.741, respectively, meeting the conditions of KMO > 0.5. Bartlett spherical test results showed that values for 0–5 cm, 5–10 cm, and 10–20 cm soil layers were 0. Bartlett spherical test result values < 0.05 indicate that soil data for the production area met the factor analysis conditions and could be used for PCA. According to the results, two PCs were eventually determined for soil in the study area, and the variance interpretation rates of 0–5 cm, 5–10 cm, and 10–20 cm soil layers were 62.57%, 18.18%, 53.78%, 17.07%, 58.53%, and 17.10%, respectively. Cumulative explanations accounted for 80.75%, 70.85%, and 75.63% of variance, respectively. Both PC1 and PC2 account for >50% of cumulative variance and can, therefore, be used to explain the source and migration pathways of elements. For the 0–5 cm soil layer, the characteristic of PC1 was 3.754, the load coefficients of the orthogonal rotation factor of Cu, Zn, As, Pb, Cr, and Cd were all > 0.4, and the characteristic of PC2 was 1.091. The load coefficients of the orthogonal rotation factors of Cu, Zn, As, Pb, Cr, and Cd were low, and negative for Zn, As, and Pb. For the 5–10 cm soil layer, the characteristic of PC1 was 3.227, the load coefficients of the orthogonal rotation factors of Cu, Zn, As, Pb, Cr, and Cd were all > 0.4, and the characteristic of PC2 was 1.024. The load coefficients of the orthogonal rotation factors of Cu, Zn, As, Pb, Cr, and Cd were low, and values for Cu and As were negative. In the 10–20 cm soil layer, the characteristic of PC1 was 3.512, the load coefficients of the orthogonal rotation factors of Cu, Zn, As, Pb, Cr, and Cd were all > 0.4, and the characteristic value of PC2 was 1.026. Therefore, PC1 was interpreted as atmospheric deposition and PC2 as surface runoff.
4. Discussion
4.1. Heavy Metal Distribution Patterns
In this study, Cu, Zn, As, and Pb in the 0–10 cm soil layer showed a fan-shaped distribution with a direction from northwest to southeast. The same distribution of heavy metals was observed in a mining-affected area of Chifeng City, Inner Mongolia Autonomous Region [10]. The fan-shaped distribution of heavy metals was partly due to strong winds in these areas, with the prevailing wind direction determining the direction of the fan shape. This distribution pattern increases remediation costs because the area is large and the pollution is dispersed. The presence of multiple heavy metals also increases the difficulty of remediation. By contrast, the horizontal distribution of the same metals showed a very different pattern in a mining area of Fujian Province in southern China, being almost uniformly distributed outwards from the mining area [27]. A similar distribution pattern of Pb and As was reported by Xie et al. (2022) [28] in a mining area of Hunan Province, also in southern China. These results show that winds are not the determining drivers of the horizontal distributions of Cu, Zn, As, and Pb in southern China.
In the present study, Cd was distributed 1200 m north of the production area and downstream along the river. This indicates that surface runoff rather than wind drove Cd transport in the studied area. However, many previous studies showed that the horizontal distribution of Cd in a mining area in northern China was similar to that of Cu [10,14]. The distribution of Cd was influenced by surface runoff in northern China. By contrast, the distribution of Cd in the soil of a mining area was uniform in all directions following an outward diffusion in southern China [17,28]. The solubility of Cd is higher than that of Cu, Zn, and As, leading to the above phenomenon. Cr hardly moves away from the production area, consistent with studies conducted in Baotou City in the Inner Mongolia Autonomous Region and Jiaozuo City in Henan Province [14,17], as well as Hunan Province in southern China [28,29]. These results indicate that the Cr migration capacity is lower than that of Cu and Zn and related to the weight of Cr-loaded particles and the valence state. Cr in alkaline soil usually exists in the form of Cr6+ [30].
Our study showed that concentrations of Cu, Zn, As, and Pb in a vertical distribution were negatively correlated with depth, consistent with the results of previous studies in northern China [8,9], but contrary to the results of studies at 0–20 cm soil depth in southern China [31]. This is due to differences in precipitation and soil pH between locations. In this study, the concentration of Cd in the vertical distribution of the 0–20 cm soil layer was positively correlated with soil depth. The distribution of Cd in northern China with surface runoff near mining areas is similar to that in southern China [32]. Cd showed an opposite vertical distribution to Cu, Zn, As, and Pb, possibly due to high mobilization in low-pH soils [33]. Contaminated soil is often acidic, with soil pH reaching 2.24 in this study. This facilitates Cd leaching in dry areas. Our results showed that the vertical distribution of Cr was mainly concentrated in the surface layer (0–10 cm), consistent with results for mining areas in northern China [9]. This is related to the low solubility of Cr and the low weight of attached particles [14].
Accumulated heavy metals in the topmost soil layer pose a high potential risk of secondary pollution through wind erosion, particularly in northern areas where strong winds often occur in winter and spring. Low precipitation cannot leach heavy metals such as Cu, Zn, As, and Pb out of the topmost soil layer in this study area, hence they are retained in this layer. Therefore, we speculate that the spatial distribution of heavy metals in northern China is driven by wind because the concentrations of heavy metals vary along the prevailing wind direction [13].
4.2. Contribution of Atmospheric Deposition to Heavy Metal Distribution
In this study, the relatively high fluxes and concentration distributions of Cu, Zn, As, Pb, and Cr in atmospheric deposition were consistent with the distribution patterns of Cu, Zn, As, Pb, and Cr in soil in the studied area. The extent of heavy metals in soil affected by atmospheric deposition can be determined [21]. According to the PCA results, PC1 was interpreted as atmospheric deposition, and the above six elements in the 0–5 cm and 5–20 cm soil layers (excluding Cd in the 5–20 cm layer) all migrated through atmospheric deposition. The contribution of atmospheric deposition was >50% of the total source, making it the main migration path for Cu, Zn, As, Pb, and Cr. Anaman et al. (2022) [19] studied the migration paths of heavy metals in the soil of mining areas in Hunan Province and found that Cu, Zn, As, and Pb were jointly affected by atmospheric deposition and surface runoff. These results are inconsistent with the results of our study, presumably due to more surface runoff near the southern mining area. Wang et al. (2018) [16] studied the migration of heavy metals in the soil of a mining area in Youxi County, Fujian Province, and showed that the migration mode of Cr was atmospheric deposition, consistent with the migration path of Cr in our study. Zhang et al. (2021) [34] found that finer soil particles are more capable of enriching and mobilizing Cr than coarse soil particles. Only finer particles can migrate through atmospheric deposition, hence the main migration path of Cr in soil is atmospheric deposition in both southern and northern China. Numerous studies have shown that atmospheric deposition and surface runoff are equally important in the migration paths of heavy metals in southern China [19]. In northern China, precipitation is lower than in the south, and wind frequency is higher. Thus, atmospheric deposition dominates the migration of heavy metals.
4.3. Contribution of Surface Runoff Transport to Heavy Metal Distribution
Surface runoff transport is an important pathway for heavy metal migration, especially in rainy and low-pH soil areas, and for mobile metals [35]. The main migration path of Cd and Pb in the soil of typical mining areas in the north of China is surface runoff [36]. Our PCA results indicated that Cr and Cd migrate through surface runoff. Both sediment and water samples are commonly used to probe the aquatic environment. The former can reflect the accumulation of heavy metals over a long period of time, while the latter can explain the migration of heavy metals [37,38]. Therefore, our results show that the concentration of Cd in the middle and downstream of surface water was higher than that upstream, and the relatively high concentration distribution of Pb in water was consistent with the high concentration distribution in soil. Chen et al. (2018) [11] analyzed the migration paths of Cd and Pb in typical lead–zinc mining areas in Guangdong Province, and the results showed that surface runoff of tailings reservoirs is the main route of heavy metal migration, consistent with our results. Acidic conditions (pH 3) facilitate the release and migration of Cd and Pb from soil, slag, and tailings from a residual state to a non-residual state, and Pb and Cd from a residual state to an exchange state [36]. Although the average soil pH in the studied area was ~8, production reduced soil pH in some places to <3.0 in some cases. This promotes the migration of heavy metals through surface runoff. A soil leaching experiment showed that the migration ability of Cd is stronger than that of Pb [39]. This result could explain why Cd migrates farther than Pb in surface runoff at this study site.
5. Conclusions
The distribution of soil heavy metals is mainly determined by the local wind direction in dry and windy areas. The contribution of atmospheric deposition to migration was >50%, especially for Cu, Zn, and As. The migration path of Cd was surface runoff, while the migration of Pb and Cr involved both atmospheric deposition and surface runoff. This study provides useful knowledge for the control of heavy metal pollution in these areas.
Author Contributions
W.B. carried out the experiments, performed data analysis, and wrote the manuscript. Z.S. guided the experiments. W.W. helped with the final manuscript preparation. M.H. supervised the research. H.L. guided the conception of the study, analysis of the data, and the revision of the manuscript. All authors contributed to the article and approved the submitted version. All authors have read and agreed to the published version of the manuscript.
Funding
This work was supported by the Project Fund (2019ZD001).
Data Availability Statement
All data are contained within the article.
Conflicts of Interest
The authors have no conflicts of interest to declare.
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Figure 1.
Map of the studied area (A) and the locations of sample sites ((B) soil; (C) atmospheric deposition; (D) water).
Figure 1.
Map of the studied area (A) and the locations of sample sites ((B) soil; (C) atmospheric deposition; (D) water).
Figure 2.
Spatial distributions of heavy metals ((A–C) Cu; (D–F) Zn; (G–I) As; (J–L) Pb; (M–O) Cd; (P–R) Cd) in different soil layers in the studied area.
Figure 2.
Spatial distributions of heavy metals ((A–C) Cu; (D–F) Zn; (G–I) As; (J–L) Pb; (M–O) Cd; (P–R) Cd) in different soil layers in the studied area.
Figure 3.
Heavy metal concentrations in atmospheric deposition ((A) Cu; (B) Zn; (C) Pb; (D) As; (E) Cd; (F) Cr).
Figure 3.
Heavy metal concentrations in atmospheric deposition ((A) Cu; (B) Zn; (C) Pb; (D) As; (E) Cd; (F) Cr).
Figure 4.
Heavy metal concentrations in the river ((A) Cu; (B) Zn; (C) As; (D) Pb; (E) Cd; (F) Cr).
Figure 5.
Biplots of the principal component analysis of heavy metals.
Table 1.
Atmospheric deposition fluxes of heavy metals (mg/m2/year).
| Heavy Metal | Sampling Point | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| S1 | S2 | S3 | S4 | S5 | S6 | S7 | S8 | S9 | S10 | S11 | S12 | |
| Cu | 17.04 | 31.99 | 52.31 | 221.59 | 193.91 | 163.45 | 135.30 | 101.42 | 120.21 | 83.32 | 66.36 | 85.67 |
| Zn | 12.33 | 21.59 | 46.20 | 202.49 | 143.97 | 109.07 | 141.45 | 80.31 | 64.41 | 103.79 | 47.86 | 55.73 |
| As | 1.62 | 2.04 | 4.78 | 10.56 | 9.56 | 6.96 | 8.64 | 7.61 | 6.58 | 4.94 | 2.78 | 3.62 |
| Pb | 12.81 | 20.96 | 51.39 | 122.08 | 108.41 | 69.97 | 90.74 | 68.45 | 73.67 | 55.59 | 36.58 | 42.65 |
| Cd | 0.02 | 0.04 | 0.06 | 0.16 | 0.14 | 0.09 | 0.16 | 0.14 | 0.10 | 0.08 | 0.07 | 0.08 |
| Cr | 7.50 | 11.03 | 15.94 | 50.83 | 46.68 | 39.14 | 38.85 | 28.82 | 34.64 | 31.08 | 25.69 | 23.27 |
Table 2.
Atmospheric deposition fluxes of heavy metals (mg/m2/60 days).
| Heavy Metal | Date | Sampling Point | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| S1 | S2 | S3 | S4 | S5 | S6 | S7 | S8 | S9 | S10 | S11 | S12 | ||
| Cu | November 2020–December 2020 | 1.6 | 4.2 | 6.0 | 25.5 | 22.5 | 20.5 | 22.3 | 14.0 | 17.6 | 13.4 | 8.0 | 11.3 |
| January 2021–February 2021 | 3.8 | 6.5 | 10.5 | 55.7 | 50.6 | 43.9 | 28.6 | 20.9 | 24.1 | 15.6 | 13.8 | 21.5 | |
| March 2021–April 2021 | 3.2 | 6.2 | 10.3 | 38.8 | 32.8 | 26.3 | 23.2 | 18.2 | 18.7 | 16.9 | 13.4 | 16.5 | |
| May 2021–June 2021 | 2.5 | 4.8 | 7.2 | 32.0 | 28.6 | 23.7 | 22.8 | 14.8 | 20.6 | 13.4 | 9.5 | 11.6 | |
| July 2021–August 2021 | 3.0 | 5.0 | 8.3 | 35.0 | 29.3 | 24.0 | 18.5 | 16.0 | 21.2 | 11.8 | 10.5 | 12.3 | |
| September 2021–October 2021 | 2.9 | 5.3 | 10.1 | 34.6 | 30.2 | 24.9 | 20.0 | 17.5 | 18.2 | 12.2 | 11.1 | 12.4 | |
| Zn | November 2020–December 2020 | 0.9 | 2.7 | 5.2 | 23.3 | 17.5 | 13.2 | 21.6 | 10.6 | 8.5 | 13.5 | 7.0 | 7.7 |
| January 2021–February 2021 | 3.2 | 4.7 | 9.4 | 49.5 | 44.6 | 27.9 | 27.9 | 16.9 | 13.3 | 20.0 | 9.2 | 11.5 | |
| March 2021–April 2021 | 2.9 | 4.9 | 9.2 | 41.5 | 24.1 | 20.2 | 25.1 | 16.1 | 12.0 | 19.9 | 9.1 | 10.3 | |
| May 2021–June 2021 | 15 | 2.9 | 6.5 | 25.3 | 18.4 | 14.8 | 21.8 | 11.4 | 9.6 | 16.2 | 7.3 | 7.9 | |
| July 2021–August 2021 | 1.8 | 2.9 | 7.5 | 32.6 | 18.9 | 15.5 | 23.4 | 12.2 | 10.4 | 17.7 | 7.57 | 8.78 | |
| September 2021– October 2021 | 2.0 | 3.6 | 8.4 | 30.5 | 20.4 | 17.6 | 21.7 | 13.2 | 10.8 | 16.5 | 7.8 | 9.5 | |
| As | November 2020–December 2020 | 0.1 | 0.2 | 0.5 | 1.5 | 1.3 | 0.9 | 1.3 | 1.1 | 0.6 | 0.6 | 0.3 | 0.5 |
| January 2021–February 2021 | 0.5 | 0.4 | 1.2 | 2.2 | 2.0 | 1.5 | 1.6 | 1.5 | 1.6 | 1.3 | 0.6 | 0.7 | |
| March 2021–April 2021 | 0.4 | 0.5 | 1.1 | 1.9 | 1.7 | 1.3 | 1.6 | 1.4 | 1.3 | 1.0 | 0.6 | 0.7 | |
| May 2021–June 2021 | 0.1 | 0.3 | 0.6 | 1.7 | 1.5 | 0.9 | 1.4 | 1.1 | 0.8 | 0.6 | 0.4 | 0.5 | |
| July 2021–August 2021 | 0.3 | 0.4 | 0.7 | 1.7 | 1.6 | 1.3 | 1.4 | 1.3 | 1.1 | 0.8 | 0.5 | 0.6 | |
| September 2021–October 2021 | 0.2 | 0.4 | 0.7 | 1.7 | 1.5 | 1.0 | 1.4 | 1.3 | 1.2 | 0.7 | 0.4 | 0.6 | |
| Pb | November 2020–December 2020 | 1.1 | 2.8 | 6.3 | 15.7 | 15.0 | 8.7 | 11.8 | 8.4 | 9.1 | 6.9 | 4.6 | 5.3 |
| January 2021–February 2021 | 3.4 | 4.4 | 12.2 | 26.9 | 24.5 | 15.9 | 20.2 | 17.7 | 15.5 | 11.6 | 7.5 | 8.9 | |
| March 2021–April 2021 | 2.7 | 4.1 | 11.2 | 24.6 | 22.9 | 13.6 | 18.8 | 13.2 | 13.9 | 11.5 | 7.4 | 8.0 | |
| May 2021–June 2021 | 1.6 | 2.9 | 6.9 | 16.5 | 14.7 | 9.9 | 12.4 | 8.7 | 9.9 | 7.3 | 5.4 | 6.5 | |
| July 2021–August 2021 | 1.9 | 3.3 | 7.3 | 18.9 | 15.3 | 9.3 | 13.3 | 9.95 | 12.1 | 7.7 | 5.7 | 6.7 | |
| September 2021–October 2021 | 2.0 | 3.5 | 7.7 | 19.6 | 16.0 | 12.7 | 14.3 | 10.5 | 13.3 | 10.6 | 6.0 | 7.3 | |
| Cd | November 2020–December 2020 | 0.00 | 0.00 | 0.01 | 0.02 | 0.02 | 0.01 | 0.02 | 0.02 | 0.01 | 0.01 | 0.01 | 0.01 |
| January 2021–February 2021 | 0.01 | 0.01 | 0.01 | 0.03 | 0.03 | 0.02 | 0.03 | 0.02 | 0.02 | 0.01 | 0.01 | 0.01 | |
| March 2021–April 2021 | 0.00 | 0.01 | 0.01 | 0.03 | 0.02 | 0.02 | 0.03 | 0.03 | 0.02 | 0.01 | 0.01 | 0.01 | |
| May 2021–June 2021 | 0.00 | 0.00 | 0.01 | 0.02 | 0.02 | 0.01 | 0.02 | 0.02 | 0.02 | 0.01 | 0.01 | 0.01 | |
| July 2021–August 2021 | 0.00 | 0.00 | 0.01 | 0.02 | 0.02 | 0.02 | 0.02 | 0.02 | 0.02 | 0.01 | 0.01 | 0.01 | |
| September 2021–October 2021 | 0.00 | 0.01 | 0.01 | 0.03 | 0.02 | 0.02 | 0.03 | 0.03 | 0.02 | 0.01 | 0.01 | 0.01 | |
| Cr | November 2020–December 2020 | 1.0 | 1.4 | 2.3 | 6.5 | 6.0 | 5.0 | 5.6 | 3.5 | 4.8 | 4.5 | 3.1 | 3.1 |
| January 2021–February 2021 | 1.4 | 2.3 | 3.0 | 11.2 | 10.5 | 8.8 | 8.7 | 5.8 | 7.4 | 6.0 | 5.1 | 5.1 | |
| March 2021–April 2021 | 1.4 | 2.1 | 3.1 | 9.7 | 9.1 | 7.0 | 6.7 | 5.7 | 6.1 | 5.9 | 5.2 | 4.3 | |
| May 2021–June 2021 | 1.0 | 1.6 | 2.2 | 6.9 | 6.3 | 5.6 | 6.1 | 3.7 | 5.0 | 4.7 | 3.4 | 3.2 | |
| July 2021–August 2021 | 1.4 | 1.7 | 2.6 | 7.6 | 6.7 | 5.8 | 5.5 | 4.6 | 5.5 | 4.6 | 4.2 | 3.6 | |
| September 2021–October 2021 | 1.2 | 1.9 | 2.7 | 9.1 | 8.0 | 6.9 | 6.3 | 5.5 | 5.8 | 5.4 | 4.7 | 4.0 | |
Table 3.
Principal component analysis results.
| Heavy metal | Soil layer (cm) | |||||
| 0–5 | 5–10 | 10–20 | ||||
| KMO | Bartlett | KMO | Bartlett | KMO | Bartlett | |
| 0.805 | 0.000 | 0.700 | 0.000 | 0.741 | 0.000 | |
| Components | ||||||
| PC1 | PC2 | PC1 | PC2 | PC1 | PC2 | |
| Cu | 0.445 | 0.060 | 0.253 | −0.554 | 0.444 | −0.188 |
| Zn | 0.468 | −0.073 | 0.462 | 0.031 | 0.467 | 0.194 |
| As | 0.448 | −0.104 | 0.455 | −0.179 | 0.445 | −0.368 |
| Pb | 0.425 | −0.260 | 0.479 | −0.236 | 0.428 | −0.443 |
| Cr | 0.059 | 0.923 | 0.260 | 0.725 | 0.283 | 0.550 |
| Cd | 0.445 | 0.248 | 0.468 | 0.282 | 0.351 | 0.541 |
| Eigenvalue | 3.754 | 1.091 | 3.227 | 1.024 | 3.512 | 1.026 |
| Contribution rate % | 62.57 | 18.18 | 53.78 | 17.07 | 58.53 | 17.10 |
| Cumulative contribution rate % | 62.57 | 80.75 | 53.78 | 70.85 | 58.53 | 75.63 |
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Bao, W.; Wan, W.; Sun, Z.; Hong, M.; Li, H. Spatial Distribution and Migration of Heavy Metals in Dry and Windy Area Polluted by Their Production in the North China. Processes 2024, 12, 160. https://doi.org/10.3390/pr12010160
AMA Style
Bao W, Wan W, Sun Z, Hong M, Li H. Spatial Distribution and Migration of Heavy Metals in Dry and Windy Area Polluted by Their Production in the North China. Processes. 2024; 12(1):160. https://doi.org/10.3390/pr12010160
Chicago/Turabian StyleBao, Weimin, Weifan Wan, Zhi Sun, Mei Hong, and Haigang Li. 2024. "Spatial Distribution and Migration of Heavy Metals in Dry and Windy Area Polluted by Their Production in the North China" Processes 12, no. 1: 160. https://doi.org/10.3390/pr12010160
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