A practical method for identifying key factors in the distribution and formation of heavy metal pollution at a smelting site

A remote sensing analysis method for soil heavy metal pollution sources at site scale considering source-sink relationships

Conventional methods for identifying soil heavy metal (HM) pollution sources are limited to area scale, failing to accurately pinpoint sources at specific sites due to the spatial heterogeneity of HMs in mining areas. Furthermore, these methods primarily focus on existing solid waste polluted dumps, defined as "direct pollution sources", while neglecting existing HM pollution hotspots generated by historical anthropogenic activities (e.g., mineral development, industrial discharges), defined as "potential pollution sources". Addressing this gap, a novel remote sensing analysis method is proposed to identify both direct and potential pollution sources at site scale, considering source-sink relationships. Direct pollution sources are extracted using a supervised classification algorithm on high-resolution multispectral imagery. Potential pollution sources depend on the spatial distribution of HM pollution, mapped using a machine learning model with hyperspectral imagery. Additionally, a source identification algorithm is developed for gridded pollution source analysis. Validated through a case study in a cadmium (Cd)-polluted mine area, the proposed method successfully extracted 21 solid waste polluted dumps with an overall accuracy approaching 90 % and a Kappa coefficient of 0.80. Simultaneously, 4167 HM pollution hotspots were identified, achieving optimal inversion accuracy for Cd (Rv2 = 0.91, RMSEv = 4.27, and RPDv = 3.02). Notably, the spatial distribution patterns of these identified sources exhibited a high degree of similarity. Further analysis employing the identification algorithm indicated that 3 polluted dumps and 258 pollution hotspots were pollution sources for a selected high-value point of Cd content. This innovative method provides a valuable methodological reference for precise prevention and control of soil HM pollution.

Enhancing zinc biofortification and mitigating cadmium toxicity in soil-earthworm-spinach systems using different zinc sources

Cadmium (Cd) pollution poses significant threats to soil organisms and human health by contaminating the food chain. This study aimed to assess the impact of various concentrations (50, 250, and 500 mg·kg-1) of zinc oxide nanoparticles (ZnO NPs), bulk ZnO, and ZnSO4 on morphological changes and toxic effects of Cd in the presence of earthworms and spinach. The results showed that Zn application markedly improved spinach growth parameters (such as fresh weight, plant height, root length, and root-specific surface area) and root morphology while significantly reducing Cd concentration and Cd bioconcentration factors (BCF-Cd) in spinach and earthworms, with ZnO NPs exhibiting the most pronounced effects. Earthworm, spinach root, and shoot Cd concentration decreased by 82.3 %, 77.0 %, and 75.6 %, respectively, compared to CK. Sequential-step extraction (BCR) analysis revealed a shift in soil Cd from stable to available forms, consistent with the available Cd (DTPA-Cd) results. All Zn treatments significantly reduced Cd accumulation, alleviated Cd-induced stress, and promoted spinach growth, with ZnO NPs demonstrating the highest Cd reduction and Zn bioaugmentation efficiencies compared to bulk ZnO and ZnSO4 at equivalent concentrations. Therefore, ZnO NPs offer a safer and more effective option for agricultural production and soil heavy metal pollution management than other Zn fertilizers.

Source apportionment of soil PTE in a northern industrial county using PMF model: Partitioning strategies and uncertainty analysis

Positive matrix factorization (PMF) has commonly been applied for source apportionment of potentially toxic elements (PTE) in agricultural soil, however, spatial heterogeneity of PTE significantly undermines the accuracy and reliability of PMF results. In this study, a representative industrial-agricultural hub in North China (Xuanhua district, Zhangjiakou City) was selected as the research subject, multiple partition processing (PP) strategies and uncertainty analyses were integrated to advance the PMF modeling and associated algorithm mechanisms were comparatively discussed. Specifically, we adopted three methods to split the research area into several subzones according to industrial density (PP-1), population density (PP-2), and the ecological risk index (PP-3) respectively, to rectify the spatial bias phenomenon of PTE concentrations and to achieve a more interpretable result. Our results indicated that the obvious enrichment of Cd, Pb, and Zn was found in the agricultural soil, with Hg and Cd accounted for 83.49% of the overall potential ecological risk. Combining proper PP with PMF can significantly improve the modelling accuracy. Uncertainty analysis showed that interval ratios of tracer species (Cd, Pb, Hg, and Zn) calculated by PP-3 were consistently lower than that of PP-1 and PP-2, indicating that PP-3 coupled PMF can afford the optimal modeling results. It suggested that natural sources, fertilizers and pesticides, atmosphere deposition, mining, and smelting were recognized as the major contributor for the soil PTE contamination. The contribution of anthropogenic activities, specifically fertilizers and pesticides, and atmosphere deposition, increased by 1.64% and 5.91% compared to PMF results. These findings demonstrate that integration of proper partitioning processing into PMF can effectively improve the accuracy of the model even at the case of soil PTE contamination with high heterogeneity, offering support to subsequently implement directional control strategies.

Available heavy metals concentrations in agricultural soils: Relationship with soil properties and total heavy metals concentrations in different industries

Heavy metal (HM) pollution in agricultural soils has arisen sharply in recent years. However, the impact of main factors on available HMs concentrations in agricultural soils of the three main industries (smelting, chemical and mining industry) is unclear. Herein, soil properties (pH, cation exchange capacity (CEC) and texture (sand, slit, clay)), total and available concentrations were concluded based on the results of 165 research papers from 2000 to 2023 in Web of Science database. In the three industries, the correlation and redundancy analysis were used to study the correlation between main factors and available concentrations, and quantitatively analyzed the contribution of each factor to available concentrations with gradient boosting decision tree model. The results showed that different factors had varying degrees of impact on available metals in the three main industries, and the importance of same factors varied in each industry, as for soil pH, it was most important for available Pb and Zn in the chemical industry, but the total concentrations were most important in the smelting and mining industry. There was no significant correlation between total and available concentrations. Soil properties involved in this paper (especially soil pH) were negatively correlated with available concentrations. This study provides effective guidance for the formulation of soil pollution control and risk assessment standards based on industry classification in the three major industrial impact areas.

Driving factors for distribution and transformation of heavy metals speciation in a zinc smelting site

Heavy metal pollution at an abandoned smelter pose a significant risk to environmental health. However, remediation strategies are constrained by inadequate knowledge of the polymetallic distribution, speciation patterns, and transformation factors at these sites. This study investigates the influence of soil minerals, heavy metal occurrence forms, and environmental factors on heavy metal migration behaviors and speciation transformations. X-ray diffraction analysis revealed that the minerals associated with heavy metals are mainly hematite, franklinite, sphalerite, and galena. Sequential extraction results suggest that lead and zinc are primarily present in the organic-sulfide fractions (F4) and residual form (F5) in the soil, accounting for over 70% of the total heavy metal content. Zinc displayed greater instability in carbonate-bound (16%) and exchangeable (2%) forms. The migration and diffusion patterns of heavy metals in the subsurface environment were visualized through the simulation of labile state heavy metals, demonstrating high congruence with groundwater pollution distribution patterns. The key environmental factors influencing heavy metal stable states (F4 and F5) were assessed by integrating random forest models and redundancy analysis. Primary factors facilitating Pb transformation into stable states were available phosphorus, clay content, depth, and soil organic matter. For Zn, the principal drivers were Mn oxides, soil organic matter, clay content, and inorganic sulfur ions. These findings enhance understanding of the distribution and transformation of heavy metal speciation and can provide valuable insights into controlling heavy metal pollution at non-ferrous smelting sites.

Source identification and driving factor apportionment for soil potentially toxic elements via combining APCS-MLR, UNMIX, PMF and GDM

The contamination and quantification of soil potentially toxic elements (PTEs) contamination sources and the determination of driving factors are the premise of soil contamination control. In our study, 788 soil samples from the National Agricultural Park in Chengdu, Sichuan Province were used to evaluate the contamination degree of soil PTEs by pollution factors and pollution load index. The source identification of soil PTEs was performed using positive matrix decomposition (PMF), edge analysis (UNMIX) and absolute principal component score-multiple line regression (APCS-MLR). The geo-detector method (GDM) was used to analysis drivers of soil PTEs pollution sources to help interpret pollution sources derived from receptor models. Result shows that soil Cu, Pb, Zn, Cr, Ni, Cd, As and Hg average content were 35.2, 32.3, 108.9, 91.9, 37.1, 0.22, 9.76 and 0.15 mg/kg in this study area. Except for As, all are higher than the corresponding soil background values in Sichuan Province. The best performance of APCS-MLR was determined by comparison, and APCS-MLR was considered as the preferred receptor model for soil PTEs source distribution in the study area. ACPS-MLR results showed that 82.70% of Cu, 61.6% of Pb, 75.3% of Zn, 91.9% of Cr and 89.4% of Ni came from traffic-industrial emission sources, 60.9% of Hg came from domestic-transportation emission sources, 57.7% of Cd came from agricultural sources, and 89.5% of As came from natural sources. The GDM results showed that distance from first grade highway, population, land utilization and total potassium (TK) content were the main driving factors affecting these four sources, with q values of 0.064, 0.048, 0.069 and 0.058, respectively. The results can provide reference for reducing PTEs contamination in farmland soil.

Geochemical fractionation and potential release behaviour of heavy metals in lead‒zinc smelting soils

The lack of understanding of heavy metal speciation and solubility control mechanisms in smelting soils limits the effective pollution control. In this study smelting soils were investigated by an advanced mineralogical analysis (AMICS), leaching tests and thermodynamic modelling. The aims were to identify the partitioning and release behaviour of Pb, Zn, Cd and As. The integration of multiple techniques was necessary and displayed coherent results. In addition to the residual fraction, Pb and Zn were predominantly associated with reducible fractions, and As primarily existed as the crystalline iron oxide-bound fractions. AMICS quantitative analysis further confirmed that Fe oxyhydroxides were the common dominant phase for As, Cd, Pb and Zn. In addition, a metal arsenate (paulmooreite) was an important mineral host for Pb and As. The pH-stat leaching indicted that the release of Pb, Zn and Cd increased towards low pH values while release of As increased towards high pH values. The separate leaching schemes were associated with the geochemical behaviour under the control of minerals and were confirmed by thermodynamic modelling. PHREEQC calculations suggested that the formation of arsenate minerals (schultenite, mimetite and koritnigite) and the binding to Fe oxyhydroxides synchronously controlled the release of Pb, Zn, Cd and As. Our results emphasized the governing role of Fe oxyhydroxides and secondary insoluble minerals in natural attenuation of heavy metals, which provides a novelty strategy for the stabilization of multi-metals in smelting sites.

Determination of high-risk factors and related spatially influencing variables of heavy metals in groundwater

Identifying high-risk factors (heavy metals (HMs) and pollution sources) by coupling receptor models and health risk assessment model (HRA) is a novel approach within the field of risk assessment. However, this coupled model ignores the contribution of spatial differentiation to high-risk factors, resulting in the assessment being subjective. Taking Dongting Plain (DTP) as an example, a coupling framework by jointly using the positive matrix factorization model (PMF), HRA, Monte Carlo simulation, and geo-detector was developed, aiming to identify high-risk factors in groundwater, and further explore key environmental variables influencing the spatial heterogeneity of high-risk factors. The results showed that at least 82.86 % of non-carcinogenic risks and 97.41 % of carcinogenic risks were unacceptable for people of all ages, especially infants and children. According to the relationships among HMs, pollution sources, and health risks, As and natural sources were defined as high-risk HMs and sources, respectively. The interactions among Holocene thickness, oxidation-reduction potential, and dissolved organic carbon emerged as the primary drivers of spatial variability in high-risk factors, with their combined explanatory power reaching up to 74%. This proposed framework provides a scientific reference for future studies and a practical reference for environmental authorities in developing effective pollution management measures.

Spatial distribution of rare earth elements and their impact factors in an area with a high abundance of regolith-hosted deposits

Despite the widespread occurrence of regolith-hosted rare earth elements (REEs) across South China, their spatial distribution characteristics in soils and their impact factors remain largely uncertain. This knowledge gap impedes the exploration of regolith-hosted REE deposits and the assessment of the environmental risks associated with REEs. To address this issue, 180 soil samples were collected from Meizhou City, Guangdong Province, a region known for its high abundance of regolith-hosted REEs. Subsequently, the correlations between REE enrichment/fractionation and various factors, i.e., topography, climate conditions, land use, and landform were analysed using the geo-detector method. The results revealed a highly uneven spatial distribution of REEs and their fractionation features with some regions displaying distinct spatial patterns. Elevation was the dominant factor influencing this distribution, and showed strong correlations with the concentrations of REEs, light REEs (LREEs) and heavy REEs (HREEs); the LREE/HREE ratio; and the positive Ce anomaly (δCe). The negative Eu anomaly (δEu) showed a good correlation with rock type. The enrichment and fractionation of REEs indicated a coupling among the abovementioned factors. For REE enrichment, areas with elevations of 138-148 m, precipitation levels of 1553-1574 mm, annual average land surface temperatures of 30.4-30.5 °C, leaf area index values of 22-29 and surface cutting degree of 21.5-29.9 m showed the highest average abundance within each type (scope) of the predominant factors. These findings highlight the key factors affecting REE distribution, thereby aiding the efficient utilization of regolith-hosted REE resources and the evaluation of their environmental risks.

Understanding the driving mechanisms of site contamination in China through a data-driven approach

China currently faces significant environmental risks stemming from contaminated sites. The driving mechanism of site contamination, influenced by various drivers, remain obscured due to a dearth of quantitative methodologies and comprehensive data. Here, we used a data-driven causality inference approach to construct an interpretable random forest (RF) model. Results show that: (1) the trained RF model demonstrated remarkable predictive accuracy for identifying contaminated sites, with an accuracy rate of 0.89. In contrast to conventional correlation analysis, the RF model excels in discerning the key drivers through non-linear and genuine causal relationships between these drivers and site contamination. (2) Among the 25 potential drivers, we identified 18 key drivers of site contamination. These drivers encompass a broad spectrum of factors, including production and operational data, pollutant control level, site protection capability, pollutant characteristics, and physical-geographical conditions. (3) Each key driver exerts varying impacts on site pollution, with diverse directions, intensities, and underlying patterns. The partial dependence plots (PDPs) illuminate the role of each key driver, its critical value contributing to site pollution, and the interplay between these drivers. The key drivers facilitate the realization of three primary contamination processes: uncontrolled release, effective migration, and persistent accumulation. In light of our findings, environmental managers can proactively prevent site contamination by regulating single, dual, and multiple key drivers to disrupt critical pollution processes. This research offers valuable insights for devising targeted strategies and interventions aimed at mitigating environmental risks associated with contaminated sites in China.

Arsenic pollution cycle, toxicity and sustainable remediation technologies: A comprehensive review and bibliometric analysis

Arsenic pollution and its allied impacts on health are widely reported and have gained global attention in the last few decades. Although the natural distribution of arsenic is limited, anthropogenic activities have increased its mobility to distant locations, thereby increasing the number of people affected by arsenic pollution. Arsenic has a complex biogeochemical cycle which has a significant role in pollution. Therefore, this review paper has comprehensively analysed the biogeochemical cycle of arsenic which can dictate the occurrence of arsenic pollution. Considering the toxicity and nature of arsenic, the present work has also analysed the current status of arsenic pollution around the world. It is noted that the south of Asia, West-central Africa, west of Europe and Latin America are major hot spots of arsenic pollution. Bibliometric analysis was performed by using scopus database with specific search for keywords such as arsenic pollution, health hazards to obtain the relevant data. Scopus database was searched for the period of 20 years from year 2003-2023 and total of 1839 articles were finally selected for further analysis using VOS viewer. Bibliometric analysis of arsenic pollution and its health hazards has revealed that arsenic pollution is primarily caused by anthropogenic sources and the key sources of arsenic exposure are drinking water, sea food and agricultural produces. Arsenic pollution was found to be associated with severe health hazards such as cancer and other health issues. Thus considering the severity of the issue, few sustainable remediation technologies such as adsorption using microbes, biological waste material, nanomaterial, constructed wetland, phytoremediation and microorganism bioremediation are proposed for treating arsenic pollution. These approaches are environmentally friendly and highly sustainable, thus making them suitable for the current scenario of environmental crisis.

Soil heavy metal source apportionment and environmental differentiation study in Dulan County, Qinghai Province, using geodetector analysis

Elucidating material sources and investigating the impact of various environmental factors on material source accumulation are important for the environmental restoration of the Qinghai-Tibet Plateau. This study was conducted within the Borhan Buda Mountain Range of Dulan County, Qinghai Province, China, where 6274 surface soil samples were collected. The geoaccumulation index was employed to assess the levels of heavy metals, including As, Cr, Cu, Hg, Ni, Pb, Sb, Sn, and Zn, in the soil. A comprehensive approach combining principal component analysis (PCA) and geodetector analysis was employed to assess the spatial variation in soil heavy metal origins and the factors that influence them. The findings indicate that the mean concentrations of Pb exceed the background values for the soil in Qinghai Province, with Hg exhibiting low pollution, whereas the other elements showed no contamination. PCA indicated that the soil elements in the Borhan Buda Mountain Range were influenced by four sources, all attributed to the geological background. Geodetector analysis of the factor contributions suggested that geological factors had the strongest explanatory power for the four material sources. Furthermore, the risk detection results demonstrated significant variations in the material source contributions among most subregions when considering three environmental factors in pairs. Interaction detection revealed that the combined influence of two environmental factors on material source contributions was greater than that of the individual factors. Additionally, ecological detection demonstrated significant differences in material source contributions one, two, and three between land cover types and geological backgrounds, whereas no significant differences were observed in material source four between land cover types and geological backgrounds. This study offers valuable insights into the sources of soil elements in Dulan County and the influence of environmental factors on these sources, thereby contributing an essential knowledge base for the protection and management of soil heavy metals in the region.

Machine learning combined with Geodetector quantifies the synergistic effect of environmental factors on soil heavy metal pollution

The critical prerequisite for the prevention and control of soil heavy metal (HM) pollution is the identification of factors that influence soil HM accumulation. The dominant factors have been individually identified and apportioned in existing studies. However, the accumulation of soil HMs results from a combination of multiple factors, and the influence of a single factor is less than the interaction of multiple parameters on soil HM pollution. In this study, we employed Geodetector to delve into the interaction effect of the influencing factors on the variations of soil HMs. We performed partial dependence plot to depict how these factors interact with each other to affect the HM content. We found that both individually and interactively, pH and agricultural activities significantly impact soil HM content. Except for Hg and Cu, the pairs with the most significant interaction effects all involve pH. For Pb, As and Zn, interaction with pH has the most significant driving force compared to the other factors. For Cu, Hg, and Ni, all environmental factor interactions increased their explanatory power, while for Cr, the single most significant driver decreased its driving power when interacting with other factors. Additionally, the study area exhibited a widespread prevalence of changes in HM concentration being governed by the synergistic effect of two factors. For the response of HMs to the interaction of pH and fertilizer, soil HM concentration was sensitive to pH, while fertilizer had less effect. These results provide a dependable method of investigating the interaction of environmental factors on soil HM content and put forth efficacious and potent tactical measures for soil HM pollution prevention and control based on the interaction type.

Assessment of potential health risks from heavy metal pollution of surface water for drinking in a multi-industry area in Mali using a multi-indices approach

The Niger River, Bamako's population's primary drinking water source, is threatened by human activities. This study examines the Niger River pollution trend using heavy metals pollution indexes and Bamako's population's non-carcinogenic and carcinogenic related health risks. Parameters were monitored at fifteen sampling locations in low and high flow seasons. pH (7.30-7.50) and fluoride (0.15-0.26 mg/L) were within the normal drinking water range. Among seven heavy metals (copper, zinc, cadmium, nickel, iron, manganese, and lead), the latter three were above the drinking water standard. The degree of contamination was negative, pointing to better water quality. However, the heavy metal evaluation index (HEI) was below the mean (5.88), between the mean and twice the mean, indicating a low and medium degree of pollution. Besides, heavy metal pollution indexes (HPI) were above the standard value (100), explaining a low-medium pollution level. High values of HPI could be explained by the industrial units' intensive activities coupled with the runoff effect. The hazard index (HI) indicated a low and medium non-carcinogenic health risk for adults and children. The probability of cancer risk (PCR) of nickel showed a cancer risk. Therefore, the river was polluted with trace elements and could not be used for drinking water without any treatment.

Investigation and comparative analysis of ecological risk for heavy metals in sediment and surface water in east coast estuaries of India

The sediments and surface water from 8 stations each from Dhamara and Paradeep estuarine areas were sampled for investigation of heavy metals, Cd, Cu, Pb, Mn, Ni, Zn, Fe, and Cr contamination. The objective of the sediment and surface water characterization is to find the existing spatial and temporal intercorrelation. The sediment accumulation index (I_sed), enrichment index (I_En), ecological risk index (I_EcR) and probability heavy metals (p-HMI) reveal the contamination status with Mn, Ni, Zn, Cr, and Cu showing permissible (0 ≤ I_sed ≤ 1, I_En ˂ 2, I_EcR ≤ 150) to moderate (1 ≤ I_sed ≤ 2, 40 ≤ R_f ≤ 80) contamination. The p-HMI reflects the range from excellent (p-HMI = 14.89-14.54) to fair (p-HMI = 22.31-26.56) in off shore stations of the estuary. The spatial patterns of the heavy metals load index (I_HMc) along the coast lines indicate that the pollution hotspots are progressively divulged to trace metals pollution over time. Heavy metal source analysis coupled with correlation analysis and principal component analysis (PCA) was used as a data reduction technique, which reveals that the heavy metal pollution in marine coastline might originate from redox reactions (FeMn coupling) and anthropogenic sources.

Three-dimensional distribution characteristics of multiple pollutants in the soil at a steelworks mega-site based on multi-source information

Soil pollution at steelworks mega-sites has become a severe environmental issue worldwide. However, due to the complex production processes and hydrogeology, the soil pollution distribution at steelworks is still unclear. This study scientifically cognized the distribution characteristics of polycyclic aromatic hydrocarbons (PAHs), volatile organic compounds (VOCs), and heavy metals (HMs) at a steelworks mega-site based on multi-source information. Specifically, firstly, 3D distribution and spatial autocorrelation of pollutants were obtained by interpolation model and local indicators of spatial associations (LISA), respectively. Secondly, the characteristics of horizontal distribution, vertical distribution, and spatial autocorrelations of pollutants were identified by combining multi-source information such as production processes, soil layers, and properties of pollutants. Horizontal distribution showed that soil pollution in steelworks mainly occurred in the front end of the steel process chain. Over 47% of PAHs and VOCs pollution area were distributed in coking plants and over 69% of HMs in stockyards. Vertical distribution indicated that HMs, PAHs, and VOCs were enriched in the fill, silt, and clay layers, respectively. Spatial autocorrelation of pollutants was positively correlated with their mobility. This study clarified the soil pollution characteristics at steelworks mega-sites, which can support the investigation and remediation of steelworks mega-sites.

Physiological and biochemical response of P. fortunei to Mn exposure

Fast-growing woody plants with metal tolerance are considered as potential candidates for phytoremediation. P. fortunei is widely distributed in China. Herein, the Mn tolerance ability and physiological and biochemical response of P. fortunei to Mn were explored in this study. Results showed that a low concentration of Mn exposure was favorable for the growth of P. fortunei, while it was inhibited in high Mn exposure. P. fortunei showed high tolerance to Mn (10 mmol/L). The microstructure of P. fortunei organs revealed that the Mn tolerance of P. fortunei was related to the compartmentalization of the cell wall. The subcellular distribution of Mn in P. fortunei showed that Mn was mainly stored in the cell wall fraction (39%-90%). Under Mn exposure, the proportion of pectate and protein-integrated Mn increased by 5%-29% in P. fortunei. The changes of function groups (-CH₃ and -COOH) in P. fortunei might be related to the reduction of Mn toxicity in plant cells in the way of chelation. Additionally, P. fortunei leaves resisted Mn toxicity by increasing the activities of CAT and SOD under low Mn concentration exposure, but it might be destroyed under excessive Mn concentration exposure. P. fortunei might be used as a candidate plant for low concentration Mn tailing restoration.

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.