Distribution Of Heavy Metals In Soils Research Articles

Spatiotemporal interpretable mapping framework for soil heavy metals

Mapping the spatiotemporal distribution of soil heavy metals is a prerequisite for soil pollution prevention and control. However, current mapping methods make it difficult to balance robustness and interpretability. In this study, we proposed a spatiotemporal interpretable mapping framework for soil heavy metals and conducted a case study of soil cadmium (Cd) in cropland of the Poyang Lake region in China. The results showed that the average soil Cd concentration increased significantly (p < 0.01) from 0.13 mg/kg in 1983 to 0.19 mg/kg in 2010. Soil Cd in 1983 was mainly controlled by human activities and topography, whereas it was jointly driven by human activities, topography, and climate in 2010. In addition, the framework revealed the spatial distribution of soil Cd and their uncertainties at a 12.5-m resolution in 1983 (R2 = 0.94) and 2010 (R2 = 0.97). The top three covariates in 1983 were nighttime-light, PM10, and land surface temperature (day), whereas those in 2010 were mean annual minimum temperature, gross domestic product, and industrial enterprises, with Shapley additive explanation (SHAP) values of 0.44, 0.42, and 0.27, and 0.31, 0.11, and 0.09 mg/kg, respectively. By integrating machine learning, variable and model selection, and the SHAP module, the proposed framework achieves a balance between the robustness and interpretability in mapping the spatiotemporal distribution of soil Cd, going beyond the limitations of the classical statistical “smoothing effect” and the artificial intelligence “black box.” Our study emphasizes the importance of considering time, space, models, variables, and mechanisms in digital soil mapping, which will enable more accurate and scientific predictions of soil contaminants across time and space.

Identifying interactive effects of spatial drivers in soil heavy metal pollutants using interpretable machine learning models

The accurate identification of spatial drivers is crucial for effectively managing soil heavy metals (SHM). However, understanding the complex and diverse spatial drivers of SHM and their interactive effects remains a significant challenge. In this study, we present a comprehensive analysis framework that integrates Geodetector, CatBoost, and SHapley Additive exPlanations (SHAP) techniques to identify and elucidate the interactive effects of spatial drivers in SHM within the Pearl River Delta (PRD) region of China. Our investigation incorporated fourteen environmental factors and focused on the pollution levels of three prominent heavy metals: Hg, Cd, and Zn. These findings provide several key insights: (1) The distribution of SHM is influenced by the combined effects of various individual factors and interactions within the source–flow–sink process. (2) Compared with the spatial interpretation of individual factors, the interaction between Hg and Cd exhibited enhanced spatial explanatory power. Similarly, interactions involving Zn mainly demonstrated increased spatial explanatory power, but there was one exception in which a weakening was observed. (3) Spatial heterogeneity plays a crucial role in determining the contributions of environmental factors to soil heavy metal concentrations. Although individual factors generally promote metal accumulation, their effects fluctuate when interactions are considered. (4) The SHAP interpretable method effectively addresses the limitations associated with machine-learning models by providing understandable insights into heavy metal pollution. This enables a comparison of the importance of environmental factors and elucidates their directional impacts, thereby aiding in the understanding of interaction mechanisms. The methods and findings presented in this study offer valuable insights into the spatial heterogeneity of heavy metal pollution in soil. By focusing on the effects of interactive factors, we aimed to develop more accurate strategies for managing SHM pollution.

Quantifying the mitigating effect of organic matter on heavy metal availability in soils with different manure applications: A geochemical modelling study

Manure is one of the main sources of heavy metal (HM) pollution on farmlands. It has become the focus of global ecological research because of its potential threat to human health and the sustainability of food systems. Soil pH and organic matter are improved by manure and play pivotal roles in determining soil HM behavior. Geochemical modeling has been widely used to assess and predict the behavior of soil HMs; however, there remains a research gap in manure applications. In this study, a geochemical model (LeachXS) coupled with a pH-dependent leaching test with continuously simulations over a broad pH range was used to determine the effects and pollution risks of pig or cattle manure separate application on soil HMs distribution. Both pig and cattle manure applications led to soil pH reduction in alkaline soils and increased organic matter content. Pig manure application resulted in a potential 90.5–156.0 % increase in soil HM content. Cattle manure did not cause significant HM contamination. The leaching trend of soil HMs across treatments exhibited a V-shaped change, with the lowest concentration at pH = 7, gradually increasing toward strong acids and bases. The dissolved organic matter-bound HM content directly increased the HM availability, especially for Cu (up to 8.4 %) after pig manure application. However, more HMs (Cr, Cu, Zn, Ni) were in the particulate organic matter-bound state than in other solid phases (e.g., Fe-Al(hydr) oxides, clay minerals), which inhibited the HMs leaching by more than 19.3 % after cattle manure application. Despite these variations, high HM concentrations introduced by pig manure raised the soil contamination risk, potentially exceeding 40 times at pH ±1. When manure is returned to the field, reducing its HM content and mitigating possible pollution is necessary to realize the healthy and sustainable development of circular agriculture.

Effects of drip and flood irrigation on soil heavy metal migration and associated risks in China

BackgroundDifferent irrigation methods have variable effects on the physiochemical properties of soils. The predicted widespread replacement of flood irrigation with drip irrigation will alter the micro-environment of the associated soils in terms of migration, transformation, morphology, and toxicology of heavy metals and pesticides. The dynamics of heavy metals in soil under drip irrigation have only been investigated with regard to the use of sewage and reclaimed water; studies comparing these patterns across different irrigation methods are scarce. PurposeHere, we aimed to investigate the effects of drip and flood irrigation on heavy metal distribution in soil and the related risks in three typical agricultural systems in China. MethodsWe used fixed-point sampling, digestion analytical methods, descriptive analyses, one-way analysis of variance (ANOVA), Nemerow pollution index analyses, and correlation analyses to investigate the different effects of drip and flood irrigation on heavy metals (Cd, Cr, Cu, Pb, and Zn) in soils from Hebei, Xinjiang, and Ningxia Provinces, China. Results and DiscussionAnalyses of soil samples collected from drip-irrigated fields revealed significant differences in the concentrations of soil heavy metals at depths of 0–60 cm. Drip irrigation had a greater effect on heavy metal content in the soil surface than flood irrigation. In addition, heavy metals likely accumulated at the edges of the wetting fronts following prolonged irrigation. In comparison to drip irrigation, flood irrigation elevated pollution levels in the soil. The amount of fertiliser affected the leaching and migration of heavy metals in the soils by altering the physiochemical characteristics of the drip irrigation solution. ConclusionDrip irrigation alters the dynamics of heavy metals in soil by influencing their migration and accumulation patterns. This effect can be attributed to the methods of adding water and fertilisers to the soil under drip irrigation. Altering these variables can impact the level of heavy metals that bioaccumulate in crops. This study provides a scientific basis for reducing heavy metal pollution under drip irrigation.

Potential Ecological Risk Assessment of Soil Heavy Metals in Fengdong New District Based on Information Diffution Model

The large-scale construction of new districts has led to severe soil heavy metal pollution. Therefore, taking Fengdong New District as the target research area, the descriptive statistics of heavy metal content characteristics and Kriging interpolation analysis have been conducted, and the potential ecological risk index and information diffusion theory were further combined to create an information diffusion model based on risk assessment. Finally, the pollution degree, ecological risk, and risk occurrence probability of Pb, Cu, Cd, and Hg were discussed. The findings revealed that the average concentrations of the four heavy metals far exceeded the background value of soil heavy metals by a factor of 1.943 (Pb), 1.419 (Cu), 3.074 (Cd), and 3.567 (Hg), respectively. Moreover, the distribution of soil heavy metals showed strong variability(CV>65%)owing to human interference. The distribution of Pb and Cu pollution were predominantly influenced by industrial production and land development for construction purposes, whereas industrial activities, agricultural practices, and transportation served as the primary sources of Cd contamination. On the other hand, industrial construction emerged as the major factor contributing to Hg pollution. The average values of individual potential ecological risk index for heavy metals of 9.716 (Pb), 7.095 (Cu), 92.292 (Cd), and 142.469 (Hg), coupled with the regional comprehensive potential ecological risk index (RI) average of 251.573, signified that the region was overall characterized by a relatively high potential ecological risk status. The overall potential ecological risk for Pb and Cu in the region were mild, whereas Cd and Hg posed moderate to high risks, indicating that Cd and Hg were the dominant driving factors behind regional heavy metal pollution. The evaluation results of the information diffusion model based on the potential ecological risk indicated that the probability ranking of different levels of comprehensive potential ecological risk was as follows:slightly high (38.98%) > moderate (38.55%) > high (5.89%) > slight (5.15%) > extremely high (3.56%). The exceeding probabilities of potential ecological risk levels for Cd and Hg were significantly higher than those for Pb and Cu. The exceeding probability of different pollution levels of Hg were slight (94.89%), moderate (66.85%), slightly high (23.62%), high (3.9%), and extremely high (2%), of which only the surpassing probability of the slight level was lower than that of Cd. The prediction error of pollution probability of each potential ecological risk level was less than 5%, demonstrating the reliability of the information diffusion model based on the risk assessment. This research will provide technical reference and support for the monitoring and management of potential ecological risks from soil heavy metals in limited sample data regions.

Identification of sources and analysis of spatial distribution of soil heavy metals in northern China coal mining areas.

The mining and utilization of coal resources has not only promoted rapid economic development but also poses a potential threat to the ecological environment. The purpose of this study is to clarify the effects both of mining and land use types on the spatial distribution and particular sources of heavy metals in soil, using inverse distance weighted (IDW) and the Positive Matrix Factorization (PMF) model. A total of 99 topsoil and profile soil samples across different land use types and mining conditions were collected. The contamination of soil with Cd, Pb, and Hg in the research area was most severe, with the coefficient of variation (CV) of Hg being the largest, while also being heavily influenced by human activities. Severely polluted regions were mainly distributed in the center of the coal mining area, as well as near the highway. The contents of heavy metals for various land use patterns were ranked as follows: forestland > farmland > bare land > grassland > building land. Hg, Cd, Pb, Cr, and Zn had showed migration in the 0-60cm depth range, and the enrichment factors (EFs) of Cd, Pb, Hg, and As in the soil profile were the most significant. The PMF demonstrated that the contributions of industrial activities and atmospheric deposition, transportation and mining activities, agricultural activities, and natural sources accounted for 31.25%, 28.13%, 22.24%, and 18.38%, respectively. The migration and deposition of atmospheric particulate matter from coal mining, transportation, and coal combustion under winds triggered heavy metal contamination in semi-arid areas of northern China. This phenomenon has important implications for the prevention and reduction of heavy metal pollution through various effective measures in coal-mining cities in northern China.

Spatial Distribution, Risk Assessment and Sources of Heavy Metals in Roadside Soils Exposed to the Zhengzhou-Kaifeng Intercity Railway in Huanghuai Plain, China

ABSTRACT Heavy metal accumulation in soil can seriously harm human health, and it is necessary to identify the accumulation status and access the potential risks for local pollution control and sustainable economic development. This study evaluated the pollution level, spatial distribution, potential risk and sources of soil heavy metals along the Zhengzhou-Kaifeng intercity railway and compared pollution characteristics in north side soils with south side soils of the railway. A total of 260 soil samples were collected from a section along the railway. In practice, only the average Zn and Pb contents in soils were slightly higher than their corresponding risk screening values. The heavy metal enrichment in the north side soils was marginally lower than that in the south side soils. The spatial distribution of soil heavy metals except Pb could be mainly influenced by the different land use types. The geoaccumulation index and potential ecological risk of a single heavy metal indicated that Cd was the major contaminant with moderate pollution and high ecological risks in the south side soils and none to moderate pollution and moderate ecological risks in the north side soils. However, the mean multimetal potential ecological risk values suggested that the north side soils were at low ecological risks and the south side soils were at moderate ecological risks. The comprehensive non-carcinogenic risks and total carcinogenic risks for adults were low and acceptable, respectively. Pearson correlation analysis, PCA, and APCS-MLR analyses identified that the contributions of natural sources, mixed sources of industrial and traffic activities, agricultural activities, and other sources were 57.49%, 21.44%, 12.67% and 8.40%, respectively, and the major soil pollution Cd was mainly related to mixed sources of industrial and traffic activities. Therefore, continuous soil heavy metal monitoring is essential to elucidate the long-term railway operation effect on soil heavy metal accumulation.

Analysis of Influencing Factors of Heavy Metals and Non-Point Source Pollution in Typical Areas of Tethys Himalayan Tectonic Domain

Due to their potential toxicity and non-degradability, heavy metals pose water and soil quality and safety challenges, impacting crop growth and the ecological environment. The contamination of heavy metals (HMs) and non-point source pollution from agriculture and pastoral presents significant ecological and environmental challenges, necessitating prioritized prevention and mitigation. In this study, 44 water samples and 55 soil samples from Gangba County, a typical agricultural and pastoral area in the Tethys Himalaya tectonic domain, served as research objects. We employed various methods, including the inverse distance weighting, ecological risk assessment model, redundancy analysis, and geographical detector modeling, to investigate the spatial distribution and pollution attributes of arsenic (As), chromium (Cr), cadmium (Cd), lead (Pb), nickel (Ni), nitrogen (N), phosphorus (P), and potassium (K). Our analysis considered the impact of soil physicochemical properties on heavy metals (HMs), elucidating factors influencing their spatial distribution. Results indicated that 65.46% of soil As in the study area exceeded the screening value, while the concentrations of the eight selected elements in water remained below the standard limit. Simultaneously, the study area exhibited low overall ecological risk and minimal HM pollution. Furthermore, As and Pb were primarily linked to human activities and the environment, while Cd, Cr, and Ni were predominantly associated with natural processes. Additionally, factors, such as per capita net income, mean annual temperature, mean annual precipitation, geomorphic type, organic matter, geology type, and soil texture (sand, silt, and clay) constituted primary controlling factors influencing the spatial distribution of HMs in soil. Therefore, for effective prevention and control of HMs and non-point source pollution in agriculture and pastoral, arsenic should be the primary monitoring target, with careful consideration given to the application rates of fertilizers containing N, P, and K to facilitate sustainable development of the ecological environment.

Risk Assessment of Heavy Metal Distribution in Soil of a Typical Tourist Island in South China

Risk Assessment of Heavy Metal Distribution in Soil of a Typical Tourist Island in South China

Spatial distribution prediction of soil heavy metals based on sparse sampling and multi-source environmental data

Predicting the precise spatial distribution of heavy metals in soil is crucial, especially in the fields of environmental management and remediation. However, achieving accurate spatial predictions of soil heavy metals becomes quite challenging when the number of soil sampling points is relatively limited. To address this challenge, this study proposes a hybrid approach, namely, Light Gradient Boosting Machine plus Ordinary Kriging (LGBK), for predicting the spatial distribution of soil heavy metals. A total of 137 soil samples were collected from the Shengli Coal-mine Base in Inner Mongolia, China, and their heavy metal concentrations were measured. Leveraging environmental covariates and soil heavy metal data, we constructed the predictive model. Experimental results demonstrate that, in comparison to traditional models, LGBK exhibits superior predictive performance. For copper (Cu), zinc (Zn), chromium (Cr), and arsenic (As), the coefficients of determination (R²) from the cross-validation results are 0.65, 0.52, 0.57, and 0.63, respectively. Moreover, the LGBK model excels in capturing intricate spatial features in heavy metal distribution. It accurately forecasts trends in heavy metal distribution that closely align with actual measurements. Environmental ImplicationThis study introduces a novel method, LGBK, for predicting the spatial distribution of soil heavy metals. This method yields higher-precision predictions even with a limited number of sampling points. Furthermore, the study analyzes the spatial distribution characteristics of Cu, Zn, Cr, and As in the grassland coal-mine base, along with the key environmental factors influencing their spatial distribution. This research holds significant importance for the environmental regulation and remediation of heavy metal pollution.

Characteristics and Sources of Heavy Metal Pollution in Cropland near a Typical Lead–Zinc Processing Plant in Xieping Village, Hui County, China

Metal beneficiation activities may cause soil pollution in the surrounding cropland, making it crucial to conduct heavy metal pollution assessment and source analysis of the cultivated land in mining areas for the protection of the ecological environment and human health. In this study, a total of 205 surface soil samples (0–20 cm) were collected on site from the Tianheba cropland near the lead–zinc concentrator in Xieping Village, Huixian County, Gansu Province, China; their pH values and their SOM, Zn, As, Cd, Cr, Hg, and Pb contents were determined. Based on the data, we used the Kriging spatial interpolation, the Nemero index, the index of geoaccumulation, and the PMF model to analyze the characteristics of the spatial distribution of soil heavy metals in the region, the degree of contamination, the sources, and the contribution rate. The results indicated that the heavy metals with contamination levels in the Tianheba cropland were Cd, Zn, Pb, Hg, As, and Cr in descending order, with the average concentrations of Cd (0.39 mg/kg), Zn (122 mg/kg), Pb (30.4 mg/kg), and Hg (0.07 mg/kg) being significantly higher than the background values of the Gansu soils. The soil in the region as a whole was heavily polluted; it was heavily polluted with Cd, moderately polluted with Zn, and mildly polluted with Pb. Hg had a larger value in the index of geoaccumulation. By analyzing the spatial distribution and sources of the soil metals, it was found that the cropland contaminated with heavy metals in Tianheba was distributed around the mineral processing plant and the infiltration area of the canal. The PMF model revealed three pollution sources: the industrial source related to mining activities, the fertilizer application source, and the natural source. This study provides a scientific basis for the precise management of heavy metal pollution in the area.

Spatial Distribution of Soil Heavy Metals and Regional Control Strategies in China at Province Level

It is important to understand the spatial distribution characteristics and health risks of soil heavy metals for the implementation of soil pollution control measures in different levels and regions. Based on the data of 706 core studies in the last 20 years, the spatial distribution characteristics, accumulation degree, and health risks of soil heavy metals in China were analyzed at the provincial level. The results showed that the soil heavy metals had obvious spatial differences on the provincial scale, with an overall trend of "high in the south and low in the north and high in the east and low in the west." The content of heavy metals in the soil of agricultural land and construction land was high, and the rate of exceeding the standard was higher than that of other land types. Soil heavy metal concentrations in most areas of China were higher than the regional background values and were highly cumulative. The accumulation indices were:Cd(1.80)>Pb(0.23)>Cu(0.17)>Zn(-0.05)>As(-0.56)>Cr(-0.69), with more than 85% of the provincial soils reaching moderate levels of Cd pollution. Non-ferrous metal resource-based provinces such as Yunnan, Guizhou, Guangxi, Hunan, and Jiangxi generally had higher soil heavy metal levels than those in other provinces, and local children faced higher cancer risks. Soil pollution in coastal areas such as Fujian, Zhejiang, Jiangsu, and Tianjin mainly originated from industrial production and urbanization construction. High intensity agricultural utilization was an important cause of soil heavy metal accumulation in Henan, Shandong, and Anhui.

Distribution Characteristics of Heavy Metals in Different Particle Size Fractions of Chinese Paddy Soil Aggregates

In recent years, the migration and transformation of heavy metals (HMs) in soil has become a hot issue. Soil particle size has an important effect on the environmental behavior of HMs in soil. The distribution of HMs in soil is strongly affected by the size of a soil aggregate. In this study, paddy samples in both cultivated and uncultivated soils were collected from Anhui Province, China. The soils were sieved into six particle size fractions (diameters of &gt;4000, 4000–2000, 2000–1000, 1000–250, 250–53, and &lt;53 μm) and the wet digestion method was used to determine the concentrations of Cd, Cr, Pb, and As in the soil aggregates. The results showed that the surface soils were characterized by the largest proportion of coarsest aggregates of &gt;4000 μm particles. Moreover, the concentrations of Cd, Cr, Pb, and As were elevated in cultivated paddy soils compared to uncultivated soils. The accumulation of HMs in all paddy soils increased with decreasing particle size. Although the smallest particle size fractions accounted for only 5.65–17.28%, they provided the highest distribution factor (DF) of Cr (1.35), As (1.25), Cd (1.28), and Pb (1.38). The highest contributions of HMs came from the coarser fractions (&gt;2000 μm); however, for Cr, Pb, Cd, and As, the mass loading of &lt;53 μm particles was up to 15.90%, 14.41%, 15.21%, and 15.70%, respectively. The highest content of HMs was found in the finest aggregate, with a pattern of decreasing with increasing aggregate size. In addition, the leachability behaviors of different HMs in different particle size aggregates were different. The leachability behaviors of Cr and Pb in the coarse particle size fractions were the highest, while the leachability behaviors of As and Cd in the less than 250 μm particle size fractions were 10–100 times higher than those of Cr and Pb. A correlation analysis showed that the particle distributions of Fe, TOC, and Mn had significant influences on the distributions of Pb, Cd, As, and Cr. This study provides a theoretical basis for the prevention, treatment, and remediation of HMs pollution in soil.

Ecological risks and sensitivity distribution of soil heavy metals in Altay Prefecture, Xinjiang, Northwest China

Ecological risks and sensitivity distribution of soil heavy metals in Altay Prefecture, Xinjiang, Northwest China

Improving the mapping accuracy of soil heavy metals through an adaptive multi-fidelity interpolation method

Soil heavy metal pollution poses a serious threat to environmental safety and human health. Accurately mapping the soil heavy metal distribution is a prerequisite for soil remediation and restoration at contaminated sites. To improve the accuracy of soil heavy metal mapping, this study proposed an error correction-based multi-fidelity technique to adaptively correct the biases of traditional interpolation methods. The inverse distance weighting (IDW) interpolation method was chosen and combined with the proposed technique to form the adaptive multi-fidelity interpolation framework (AMF-IDW). In AMF-IDW, sampled data were first divided into multiple data groups. Then one data group was used to build the low-fidelity interpolation model through IDW, while the other data groups were treated as high-fidelity data and used for adaptively correcting the low-fidelity model. The capability of AMF-IDW to map the soil heavy metal distribution was evaluated in both hypothetical and real-world scenarios. Results showed that AMF-IDW provided more accurate mapping results compared with IDW and the superiority of AMF-IDW became more evident as the number of adaptive corrections increased. Eventually, after using up all data groups, AMF-IDW improved the R2 values for mapping results of different heavy metals by 12.35–24.32%, and decreased the RMSE values by 30.35%–42.86%, indicating a much higher level of mapping accuracy relative to IDW. The proposed adaptive multi-fidelity technique can be equally combined with other interpolation methods and provide promising potential in improving the soil pollution mapping accuracy.

Quantitative analysis of the contribution of sources, diffusion pathways, and receptor attributes for the spatial distribution of soil heavy metals and their nested structure analysis in China

Investigation of heavy metal pollution degree, pollution sources, and spatial distribution structure is crucial for the country's soil pollution prevention, but relevant research is lacking. In this study, As, Cd, Cr, Cu, Pb and Zn in the national scope are taken as research objects. Among them, Cd has the highest pollution level. Four sources were quantitatively allocated as soil type, mining and dressing industry, GDP, and NDVI, which accounted for 92.93, 97.81, 99.30 and 96.24 % of Cr, Cd, Zn and As contamination, respectively. In addition, according to the geographical detector, the spatial distribution of As was affected by three diffusion pathways, whose influence degree were 0.822–0.947, especially the slope. Cadmium was primarily affected by both receptor attributes and diffusion pathways, with an influence degree of 0.010–0.175, especially soil water content and slope; Cr and Pb were affected by receptor attributes, with an influence degree of 0.886–0.986 and 0.007–0.288, respectively, especially for soil water content and soil organic carbon; Cu and Zn were affected by receptor attributes, with an influence degree of 0.182–0.823 and 0.002–0.150, respectively, especially for soil texture. There are two spatial distribution structures with nested scales in east–west and north–south directions. The large spatial structure has a more significant impact on the spatial distribution of heavy metals, especially in the east–west direction. Overall, the mining and dressing industry is the main source in Hunan, Yunnan, and Liaoning, where many mines exist and mining activities are frequent. GDP was the main source in Shanghai and Zhejiang areas, where the economy is developed. NDVI was the main source in Guangdong and Anhui areas, where agriculture is relatively developed. These results provide a basis for determining remediation and prevention objectives in soil pollution remediation and prevention in the national scope.

Spatial distribution and ecological risk assessment of heavy metals in alpine grasslands of the Zoige Basin, China

Heavy metals elements are not only related to the functions of ecosystems but also affect human health. To understand the characteristics of heavy metals in the topsoil of the Zoige alpine basin, a total of 252 grass and topsoil samples were collected in May and September 2014. The results showed that only Cd and Pb highly exceeded their background values (BV); in May and September, Cd was 2.02- and 1.55-fold higher than its BV, respectively, and Pb was 2.35- and 2.17-fold above its BV, respectively. The sources of Cd and Pb were homologous. In addition, the comprehensive potential ecological risk index was less than 150, indicating that heavy metal pollution in the study area is currently low. The spatial interpolation indicated that Cd and Pb pollution might be related to tourism and transportation, but the low biological absorption coefficient for all heavy metals showed that heavy metal absorption ability of forage was low and would not impact yak breeding. Finally, the soil was lightly contaminated by Cd and Pb due to the rapid development of the animal husbandry and tourism. The spatial variation of heavy metal in the basin is dominated by structural factors, and the random factors also have an effect on spatial distribution of As, Cd, Cu and Ni. The random factors such as overgrazing can exert an influence on physical structure and the circulation of nutrient substances of meadow soil through livestock grazing and trampling, ultimately affecting the content and distribution of soil heavy metals.

Analysis of driving factors of spatial distribution of heavy metals in soil of non-ferrous metal smelting sites: Screening the geodetector calculation results combined with correlation analysis

Heavy metals (HMs) discharged from smelting production may pose a major threat to human health and soil ecosystems. In this study, the spatial distribution characteristics of HMs in the soil of a non-ferrous metal smelting site were assessed. This study employed the geodetector (GD) by optimizing the classification condition and supplementing the correlation analysis (CA). The contribution of driving factors, such as production workshop distributions, hydrogeological conditions, and soil physicochemical properties, to the distribution of HMs in soil in the horizontal and vertical dimensions was assessed. The results showed that the main factors underlying the spatial distribution of As, Cd, Hg, Pb, Sb, and Zn in the horizontal direction were the distance from the sintering workshop (the maximum q value of that factor, q=0.28), raw material yard (q=0.14), and electrolyzer (q=0.29), while those in the vertical direction were the soil moisture content (q=0.17), formation lithology (q=0.12), and soil pH (q=0.06). The findings revealed that the CA is a simple and effective method to supplement the GD analysis underlying the spatial distribution characteristics of HMs at site scale. This study provides useful suggestions for environmental management to prevent HMs pollution and control HMs in the soil of non-ferrous metal smelting sites.

An interpolation method incorporating the pollution diffusion characteristics for soil heavy metals - taking a coke plant as an example

The existing spatial interpolation methods in the prediction of soil heavy metal distribution are generally based on spatial auto correlation theory, rarely considering the pollution patterns. By contrast, in polluted sites, heavy metals have a strong heterogeneity even within a very small area, which is not exactly in line with auto correlation theory. This contradiction may lead to inaccuracy in spatial prediction. Atmospheric diffusion and deposition are one of the main sources of soil heavy metal pollution caused by coal-related production activities. To improve the prediction accuracy, the diffusion patterns of pollutants were considered in this paper by integrating Geodetector, Co-Kriging (COK), and partition interpolation. Geodetector was used to identify the main driving factors of soil pollution, based on which, the main driving factors were used as covariates introduced into the interpolation method (COK). Specifically, the amount of particulate matter deposition obtained by a pollutant diffusion model (AERMOD) was used as a covariate. For comparison, the distances to quenching, coke oven, and ammonium sulfate section were also used as covariates. Compared with the Ordinary Kriging method, the method COK-AERMOD established here decreased the root mean square error values of As (2.05 reduced to 1.89), Cd (0.18 reduced to 0.16), Cr (19.07 reduced to 12.97), Cu (6.92 reduced to 4.72), Hg (0.32 reduced to 0.28), Ni (16.92 reduced to 16.10), Pb (18.29 reduced to 16.62), and Zn (159.68 reduced to 153.66). This method in this paper is informative for the interpolation of soil elements in contaminated areas with known pollution source and diffusion patterns.

A statistical approach to study the spatial heavy metal distribution in soils in the Kushk Mine, Iran

AbstractThe present study was conducted for the spatial distribution and concentration evaluation of heavy metals, including Cu, Cd, Mn, Fe, Zn, and Pb, within 102 soil samples collected from Kushk Mine in Bafgh, Iran. This work employed hierarchical clustering analysis (HCA), principal component analysis (PCA), and spatial distribution patterns, to perform element distribution evaluation within the area. The distributions of heavy metals in the entire area were exhibited in the form of maps. The average concentrations of Cd, Cu, Mn, Fe, Zn, and Pb were found to be 0.39, 0.26, 5.3, 4.1, 51.9, and 40.9 mg.kg−1, respectively. Based on the PCA and HCA findings, the heavy metals were divided into two groups. The first group included Pb, Cd, Zn, and Cu. In the first group, altered threshold‐surpassing anthropogenic and lithogenic pollution was found to be the main factor accounting for Pb and Zn. The second group involved Fe and Mn, which could be impacted by either anthropogenic and lithogenic factors. Furthermore, the geo‐statistical results demonstrated higher contents of the heavy metals in the south of the mine and in the vicinity of the mine tailings. It may be concluded from the results that the heavy metal contents of the area are impacted by anthropogenic and lithogenic factors.