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

Distinguishing the contributions of different smelting emissions to the spatial risk footprints of toxic elements in soil using PMF, Bayesian isotope mixing models, and distance-based regression

Toxic element pollution of soils emanating from smelting operations is an escalating global concern due to its severe impact on ecosystems and human health. In this study, soil samples were collected and analyzed to quantify the risk contributions and delineate the spatial risk footprints from smelting emissions for 8 toxic elements. A comprehensive health risk contribution and delineation framework was utilized, consisting of Positive matrix factorization (PMF), spatial interpolation, an advanced Bayesian isotope mixing model via Mixing Stable Isotope Analysis in R (MixSIAR), and distance-based regression. The results showed that the mean concentrations of As, Cd, Cu, Hg, Pb, and Zn exceeded the background levels, indicating substantial contamination. Three sources were identified using the PMF model and confirmed by spatial interpolation and MixSIAR, with contributions ranked as follows: industrial wastewater discharge and slag runoff from the smelter site (48.9 %) > natural geogenic inputs from soil parent materials (26.7 %) > atmospheric deposition of dust particles from smelting operations (24.5 %). Among the identified sources, smelter runoff posed the most significant risk, accounting for 97.9 % of the non-carcinogenic risk (NCR) and 59.9 % of the carcinogenic risk (CR). Runoff also drove NCR and CR exceedances at 7.8 % and 4.7 % of sites near the smelter, respectively. However, atmospheric deposition from smelting emissions affected soils across a larger 0.8 km radius. Although it posed lower risks, contributing just 1.1 % to NCR and 22.6 % to CR due to the limited elevation of toxic elements, deposition reached more distant soils. Spatial interpolation and distance-based regression delineated high NCR and CR exposure hotspots within 1.4 km for runoff and 0.8 km for deposition, with exponentially diminishing risks at further distances. These findings highlight the need for pathway-specific interventions that prioritize localized wastewater containment and drainage controls near the smelter while implementing broader regional air pollution mitigation measures.

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.

Quantitative Tracing the sources and Human Risk Assessment of Complex Soil Pollution in an Industrial Park

Pollution in industrial parks has long been characterized by complex pollution sources and difficulties in identifying pollutant origins. This study focuses on a typical industrial park consisting of 11 factories (F1-F11) including organic pigment, inorganic pigment, and chemical factories in Hunan Province, China, here, a total of 327 sample points were surveyed. Eight pollutants (Mn, Cd, As, Co, NH3-N, l,1,2-Trichloroethane, chlorobenzene, and petroleum hydrocarbons) were classified as contaminants of concern (COCs). This study assessed the contributions of driving factors to the distribution of COCs in the soil. Pollutant source apportionment was conducted using positive matrix factorization (PMF) and random forest (RF). The results revealed that the main factors driving pollution are groundwater migration, non-compliant emissions, leaks during production, and interactions among pollutants. The primary pollution sources were four chemical factories and an inorganic pigment factory. Source 5 demonstrates significant correlations with TCA (29.6%), CB (30%), and As (31.6%). Two chemical factories (F7 and F10) are the most significant pollution source with a risk assessment contribution rate of more than 60%. The present study sheds some light on the contamination characteristics, source apportionment and source-health risk assessment of COCs in industrial park. By utilizing the proposed research framework, decision-makers can effectively prioritize and address identified pollution sources.

Spatial distribution, chemical fractionation and risk assessment of Cr in soil from a typical industry smelting site in Hunan Province, China

The closure or relocation of many industrial enterprises has resulted in a significant number of abandoned polluted sites enriched in heavy metals to various degrees, causing a slew of environmental problems. Therefore, it is essential to conduct research on heavy metal contamination in the soil of industrial abandoned sites. In this study, soils at different depths were collected in a smelting site located in Hunan Province, China, to understand the Cr distribution, speciation and possible risks. The results revealed that the high-content Cr and Cr(VI) contamination centers were mainly concentrated near S1 (Sample site 1) and S5. The longitudinal migration law of chromium was relatively complex, not showing a simply uniform trend of decreasing gradually with depth but presenting a certain volatility. The vertical distribution characteristics of chromium and Cr(VI) pollution suggest the need for attention to the pollution from chromium slag in groundwater and deep soil layers. The results of different speciation of Cr extracted by the modified European Community Bureau of Reference (BCR) method showed that Cr existed primarily in the residual state (F4), with a relatively low content in the weak acid extraction state (F1). The correlation analysis indicated that Cr was affected by total Cr, pH, organic matter and total carbon during the longitudinal migration process. The RSP results revealed that the smelting site as a whole had a moderate level of pollution. Soil at depths of 2-5 m was more polluted than other soil layers. Consequently, it is necessary to treat the site soil as a whole, especially the subsoil layer (2-5 m). Health risk assessment demonstrated that the soil chromium pollution was hazardous to both adults and children, and the probability of carcinogenic and non-carcinogenic risk was relatively high in the latter group. As a result, children should be a group of special concern regarding the assessment and remediation of soil contaminated with Cr. This study can provide some insight into the contamination characteristics, ecological and health risks of chromium in contaminated soils and offer a scientific basis for the prevention and control of chromium pollution at abandoned smelting sites.

Quantitative heterogeneous source apportionment of toxic metals through a hybrid method in spatial random fields

Toxic metals in soils pose hazards to food security and human health. Accurate source apportionment provides foundation for pollution prevention. In this study, a novel hybrid method that combines positive matrix factorization, Bayesian maximum entropy and integrative predictability criterion is proposed to provide a new perspective for exploring the heterogeneity of pollution sources in spatial random fields. The results suggest that Cd, As and Cu are the predominant pollutants, with exceedance rates of 27%, 12% and 11%, respectively. The new method demonstrates superiority in predicting toxic metals when combined major and all sources as auxiliary information., with the improvements of 44% and 46%, respectively, Although the major sources identified with the hybrid method are the primary contributors to the accumulation of toxic metals (e.g. coal combustion for Hg, traffic emission for Pb and Zn, industrial activities for As, agricultural activities for Cd and Cu and natural sources for Cr and Ni), the impact of nonmajor sources on toxic metal sin specific regions should not be ignored (e.g. industrial activities on Ni, Pb and Zn in the north and natural sources on Cd, Cu, As, Pb and Zn in the south). For better pollution control, specific local sources should be considered.

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.

Analysis of soil pollution characteristics and influencing factors based on ten electroplating enterprises

The electroplating industry encompasses various processes and plating types that contribute to environmental pollution, which has led to growing public concern. To investigate related soil pollution in China, the study selected 10 sites with diverse industrial characteristics distributed across China and collected 1052 soil samples to determine the presence of industrial priority pollutants (PP) based on production process and pollutant toxicity. The factors influencing site pollution as well as proposed pollution prevention and control approaches were then evaluated. The results indicate the presence of significant pollution in the electroplating industry, with ten constituents surpassing the risk screening values (RSV). The identified PP consist of Cr(VI), zinc (Zn), nickel (Ni), total chromium (Cr), and petroleum hydrocarbons (C10-C40). PP contamination was primarily observed in production areas, liquid storage facilities, and solid zones. The vertical distribution of metal pollutants decreased with soil depth, whereas the reverse was true for petroleum hydrocarbons (C10-C40). Increase in site production time was strongly correlated with soil pollution, but strengthening anti-seepage measures in key areas can effectively reduce the soil exceedance standard ratio. This study serves as a foundation for conceptualizing site repair technology in the electroplating industry and offers a reference and methodology for pollution and source control in this and related sectors.

Differentiating environmental scenarios to establish geochemical baseline values for heavy metals in soil: A case study of Hainan Island, China

Soil heavy metal distributions exhibit regional heterogeneity due to the complex characteristics of parent materials and soil formation processes, emphasizing the need for appropriate regional standards prior to assessing soil risks. This study focuses on Hainan Island and employs the Multi-purpose Regional Geochemical Survey dataset to establish heavy metal geochemical baseline and background values for soil using an iterative method. Geographical detector analysis reveals that parent materials are the primary factor influencing heavy metal distribution, followed by soil types and land use. Heavy metal geochemical baseline values are established for the island's three environments and administrative regions. Notably, a universal geochemical baseline value cannot adequately represent regional variations in heavy metal distribution, with parent materials playing a crucial role in various scenarios. Locally applicable values based on parent material are the most representative for Hainan Island. This study provides a reference framework for developing region-specific environmental baseline values for soil heavy metal assessments.

Distribution, sources, and risk analysis of heavy metals in sediments of Xiaoqing River basin, Shandong province, China

The accumulation of heavy metals in river sediment poses a major threat to ecological safety. The Xiaoqing River originates in western Jinan, with higher population density and per capita gross domestic product (GDP) in its basin compared to the Shandong province average. This study analyzed the spatial characteristics, ecological risk, human health risk, and contamination sources of heavy metals by collecting sediment samples from Xiaoqing River. We use the methods such as geo-accumulation index (Igeo), ecological risk assessment based on the interval number sorting method, and health risk assessment to evaluate the risk of heavy metals in sediments. The research finding suggests heavy metals including Pb, As, Ni, and Cr are low ecological risks, while Hg and Cd have reached high and extreme ecological risks. Correlation analysis and principal component analysis were used to analyze the correlation and sources of different heavy metals. The six heavy metals were categorized into three groups. Factor 1, comprising Hg, Cr, and Pb, was identified as a mixed source with a contribution rate of 37.76%. Factor 2 is an agricultural source and comprises Ni, Cd, and As with a contribution rate of 27.05%. Factor 3 includes Pb and Ni contributing to 15.30% as a natural source. This study offers valuable insights for the prevention of heavy metal pollution, as well as promoting sustainable urban development.

Synchronous stabilization of Pb, Zn, Cd, and As in lead smelting slag by industrial solid waste

In order to prevent heavy metal (HM) pollution from lead smelting slag (LSS) to the surrounding environment, this work investigated the feasibility, influencing factors, and mechanisms of using industrial solid waste such as fly ash (FA), oil sludge pyrolysis residue (PR), and steel slag (SS) as remediation amendments. The results demonstrated that the stabilization process was influenced by the material dosage, water content, and LSS particle size. Compared to single materials, the combination amendment PR2FA1 (with a mass ratio of PR to FA as 2:1) exhibited the best stabilization effect, simultaneously reducing the leaching concentrations of As, Zn, Cd, and Pb in LSS to 0.032, 0.034, 0.002, and 0.014 mg/L, respectively. The pH value of the leachate remained between 8 and 9, which met the requirements of surface water quality class IV (GB3838-2002). Through morphological analysis, microscopic characterization, and simulated solution adsorption experiments, it was determined that the stabilization process of HMs was controlled by various mechanisms, including electrostatic attraction, physical adsorption, ion exchange, and chemical precipitation. PR2FA1 had more active components, and its fine-porous structure provided more active sites, resulting in good stabilization performance for As, Zn, Cd, and Pb. Furthermore, cost analysis showed that PR2FA1, as an environmentally friendly material, could generate profits of 157.2 ¥/ton. In conclusion, the prepared PR2FA1 not only addressed the HMs pollution from lead smelting slag to the surrounding environment but also achieved the safe and resourceful disposal of hazardous waste-oil sludge. Its excellent performance in stabilizing HMs and cost-effectiveness suggested promising commercial applications.

Pollution threshold assessment and risk area delineation of heavy metals in soils through the finite mixture distribution model and Bayesian maximum entropy theory

Pollution threshold and high-risk area determination for heavy metals is important for effectively developing pollution control strategies. Based on heavy metal contents in 3627 dense samples, an integrated framework combining the finite mixture distribution model and Bayesian maximum entropy (BME) theory was proposed to assess pollution thresholds, contamination levels and risk areas in an uncertain environment for soil heavy metals. The results showed that the average heavy metal contents were in the order Zn > Cr > Pb > Cu > Ni > As > Cd > Hg, with strong/moderate variation, and the corresponding pollution thresholds were 158.39, 84.29, 47.84, 49.75, 28.95, 18.01, 0.49 and 0.16 mg/kg, respectively. The thresholds were consistently greater than the Zhejiang Province backgrounds but lower than the national risk screening values, except for Cd. Approximately 27.9% of the samples were classified as contaminated at various levels, and they were distributed in the northern, northwestern and eastern regions of the study area. Additionally, 3.73%, 5.34% and 8.22% of the total area were classified as at-risk areas under confidence levels of 95%, 90% and 75%, respectively, through BME theory. The findings provide a reasonable classification system and suggestions for heavy metal pollution management and control.