Soil Heavy Metal Pollution Research Articles

Long-Term Trends and Ecological Risks of Heavy Metal Accumulation in Cultivated Land of Songnen Plain, China

Heavy metal pollution in agricultural soils poses a serious threat to food security. Therefore, it is crucial to conduct risk assessments and issue early warnings about high levels of metal contamination for the sustained prosperity of agriculture. To assess the risks, identify the sources, quantify the amounts, and determine the extent of pollution from seven heavy metals, as well as to provide early warnings, 78 soil samples were collected from farmed lands in the Songnen Plain of Jilin Province. The average concentrations of Zn, Cu, Mn, Pb, Cd, Ni, and As were found to be 2.05, 1.5, 0.2, 1.09, 2.68, 1.53, and 1.17 times higher than the background values of Chinese soils, respectively. Source analysis indicated that toxic Pb originates from vehicle exhaust emission, while Cd, Cu, and Ni are attributed to industrial activities. Zn and As are likely associated with agricultural practices, and Mn predominantly stems from natural environmental sources. The geo-accumulation index suggests relatively high, accumulation levels for Zn, Cu, Mn, and Pb. Meanwhile, the single-factor pollution index indicates elevated pollution levels of Zn, Cu, and Cd. Potential ecological risk assessment reveals that certain areas within Changchun and Baicheng cities exhibit higher ecological risks. Notably, Cd has the highest potential ecological risk index (RI) of the seven heavy metals and warrants special attention. By analyzing annual pollutant accumulations, predictions can be made about the heavy metal content in four cities within the Songnen Plain, enabling the issuance of early warnings regarding soil heavy metal risks. The findings suggest that without proactive measures to mitigate heavy metal accumulation in soils, Changchun and Songyuan will reach severe pollution levels by 2031 and 2029, respectively.

Riparian Soil Heavy Metal Contamination and Pollution Assessment and Management Planning Integrating Multiple Indices, Statistical and Geospatial Approaches.

This research assesses heavy metal contamination within the riparian zone of the Danro River, a tributary of the Ganges River basin in India, particularly impacted by sand mining activities. The study conducted analyses on major and trace elements in soil samples, focusing on those identified as ecologically hazardous by the Water Framework Directive of India. Utilizing a combination of indices (Enrichment Factor, Pollution Load Index, and Index of geo-accumulation) and statistical techniques such as Principal Component Analysis (PCA), the investigation aimed to evaluate contamination severity, ecological risks, and pollution sources. Results revealed arsenic concentrations ranging from 0.00-0.54 mg/kg to 117-136 mg/kg, and ecological risks for cadmium exceeding 30. PCA identified three dominant factors explaining over 95% of variance. This study also employed the Analytic Hierarchy Process (AHP) method to assess land use suitability. Results unveiled that chromium and nickel predominantly stemmed from natural origins, while arsenic, cadmium, lead, and zinc exhibited a mixed origin. While most sites displayed low to moderate contamination, south-western portion of the basin demonstrated significantly elevated copper concentrations. Cadmium emerged as a particular concern, posing downstream ecological risks alongside chromium, nickel, and zinc, surpassing established thresholds. Further examination using PCA analysis pinpointed three primary pollution sources: traffic emissions, industrial activities, and natural processes. The research concludes by proposing a novel approach for remediation, including the Miyawaki technique alongside traditional methods like electrokinetic remediation and soil leaching. Policy suggestions advocate for collaborative efforts between economic entities and governments to promote sustainable practices that minimize heavy metal pollution.

Conservative mechanism through various rapeseed (Brassica napus L.) varieties respond to heavy metal (Cadmium, Lead, Arsenic) stress

IntroductionHeavy metal soil pollution is a global issue that can be efficiently tackled through the process of phytoremediation. The use of rapeseed in the phytoremediation of heavy metal-contaminated agricultural land shows great potential. Nevertheless, its ability to tolerate heavy metal stress at the molecular level remains unclear.MethodsHere, with 7-day seedlings as raw materials, we investigated physiological and biochemical indexes, analyzed the transcriptome sequencing for different treated materials (control, 50×, and 100×), combined with the results of transcriptome and proteome sequencing of the near-isogenic lines (F338 and F335) to reveal the response mechanism to heavy metal stress. Due to oxidative stress response caused by heavy metal stress, there are heavy effects on the emergence of rapeseeds and the growth of seedlings. Although rapeseed can alleviate oxidative stress by enhancing the enzyme activity, especially peroxidase in the oxidation system, this process has its limits. Rapeseed plants activate antioxidase, transport enzymes, and biological regulation to cope with heavy metal stress. Among these responses, peroxidase, ABC transporters, and abscisic acid are particularly significant in this process.Results and discussionBased on this study, we identified a breeding material with high adsorption capacity for heavy metals, which contributed to the research on resistance breeding in rapeseed. The results of this study may be useful to alleviate heavy metal soil pollution and tackle edible oil shortages in China.

Characteristics, Health Risks, and Source Analysis of Heavy Metal Pollution in Surface Soil in Shanxian County

Soil heavy metal （HM） pollution is a prominent global environmental problem. Understanding the risk characteristics and quantitative analysis of potential sources of soil HM pollution is of great significance for accurate prevention and control, scientific management, and safe utilization of soil resources. In the surface soil of Shanxian County, the contents of eight HMs, such as As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn, were collected and identified in 330 surface soil samples. The HM pollution degree was evaluated using the accumulation index and enrichment index. The carcinogenic and non-carcinogenic risks of HMs were evaluated using the health risk model designated by the Ministry of Ecology and Environment. The sources of HMs in topsoil were analyzed using correlation analysis, principal component analysis, cluster analysis, absolute principal component score-multiple linear regression （APCS-MLR）, and other qualitative and quantitative methods. The results showed that all eight HM elements in the surface soil samples were lower than the background values and the screening values of agricultural land soil pollution risk in Heze City. According to Igeo and EF evaluation, only approximately 5% of Hg samples were slightly polluted, and the other seven HMs were non-polluted or slightly polluted. The carcinogenic risk and non-carcinogenic risk of As for adults and children in surface soil were at tolerable levels, and the carcinogenic risk and non-carcinogenic risk of the other seven HMs for adults and children could be ignored. The main sources of Cd, Cu, Zn, Ni, and As in the surface soil were natural background sources； the main sources of Cr and Hg were agricultural breeding sources and industrial coal sources, respectively； and the main pollution sources of Pb included other unknown sources and natural background sources. The average contribution rates of natural background sources, agricultural breeding sources, industrial coal sources, and other unknown sources were 51.69%, 27.18%, 6.83%, and 14.31%, respectively, indicating that the main sources of heavy metals in the surface soil of Shanxian County were natural background sources, followed by agricultural breeding sources.

The Impact of Different Fertilization Methods on Soil Heavy Metal Pollution and Restoration Methods

The Impact of Different Fertilization Methods on Soil Heavy Metal Pollution and Restoration Methods

Distribution and Source-specific Ecological Risk Analysis of Soil Heavy Metals in South Hunan Province

To identify the spatial distribution patterns and assess the ecological risks associated with soil heavy metal pollution in the southern region of Hunan Province, a total of 362 surface soil samples were collected from the studied area. This study employed multivariate statistics and geographic information systems （GIS） to investigate the spatial distribution pattern of soil metals （Cd, Hg, As, Pb, Zn, Ni, Mn, Tl, and Sb）. Furthermore, the pollution sources and source-specific ecological risk of heavy metals were quantified by combining the positive matrix factorization （PMF） model and the comprehensive ecological risk index model. The results suggested that the mean concentration of soil metals in the study area exceeded the mean background values. Specifically, Cd, Hg, Pb, and Sb showed significant pollution levels and substantial spatial variability, with their concentrations reaching the background values by factors of 8.31, 9.12, 5.09, and 2.79, respectively. The spatial distribution of these heavy metals exhibited discernible patterns, with a higher concentration observed in western Chenzhou and a noticeable trend of increasing concentrations from the central region towards the periphery. The clusters with high concentrations of heavy metals were predominantly situated near industrial zones engaged in black metal smelting and processing and nonferrous metal mining and processing enterprises. Source apportionment indicated that the primary sources of soil heavy metals in the area were industrial activities associated with black metal mining and metallurgy （29.06%）, atmospheric deposition （22.05%）, nonferrous metal mining and processing （20.65%）, a combination of industrial and transportation sources （14.73%）, and natural sources （13.50%）. Moreover, the source-ecological risk assessment model specifically identified Cd and Hg as crucial elements requiring prioritized control measures to mitigate soil ecological risks. Industrial activities, including black metal smelting and processing and atmospheric deposition sources, were identified as the priority sources for health risks in the study region. These results provide an important theoretical basis for the development of effective strategies aimed at managing and diminishing the risks associated with heavy metal contamination in the southern region of Hunan Province.

Organic Amendments Enhance the Remediation Potential of Economically Important Crops in Weakly Alkaline Heavy Metal-Contaminated Bauxite Residues

Heavy metal (HM) pollution in soil has emerged as a global concern. This study introduces a novel approach to ameliorate HM-contaminated bauxite residues (BRs) characterized by weak alkalinity and low nutrient levels. By cultivating economically important crops, this method aims to enhance the remediation of heavy metal-contaminated BR while simultaneously promoting economically important crop production. Using a pot experiment, we investigated the effects of four organic amendments (peat, cow dung, bagasse, and microbial fertilizer) on the growth and BR properties of four economically important crops (castor, ramie, sugarcane, and cassava). The application of these organic amendments obviously reduced the BRs pH by 0.6–2.22%. Organic amendment applications significantly increased the soil organic matter (SOM) content and cation exchange capacity (CEC) by 14.35 to 179.94% and 6.87 to 12.14%, respectively. Additionally, the use of organic amendments enhanced BR enzyme activity, with microbial fertilizer demonstrating a substantial increase in BR invertase activity from 131.49 to 687.61%. Superoxide dismutase (SOD) activity and malondialdehyde (MDA) content remarkably increased, whereas catalase (CAT) activity did not show significant differences. HM content analysis in different plant parts revealed HMs primarily found in the plant roots. Organic amendments mitigate the transfer of HMs from roots to shoots, thereby reducing HM content in the available parts of economically important crops. The pot experiment results demonstrated the effectiveness of the four combinations in achieving both the repair and production objectives. These combinations include planting castor and ramie with cow dung, sugarcane with peat, and cassava with bagasse. These findings underscore the feasibility of cultivating economically important crops in HM-contaminated BRs, enhancing BR quality, and augmenting farmers’ incomes. This study provides a scientific basis for mine remediation and reclamation using BRs.

Assessment of geo-accumulation index and contamination factors of zinc (Zn) and lead (Pb) in urban soils of Annaba city

This study assesses the geo-accumulation index (Igeo) and contamination factors (Cf) of zinc (Zn) and lead (Pb) in urban soils of Annaba, Algeria. Soil pollution is a significant environmental issue affecting human health and ecosystems, with heavy metals like Pb and Zn being of particular concern due to their toxicity even at low concentrations. The research involved collecting 25 soil samples from various sites in Annaba, characterized by industrial and urban activities. pH measurements indicated alkaline soils, with values ranging from 7.2 to 7.9. Analysis revealed Pb concentrations between 26.93 and 218.30 mg/kg and Zn concentrations from 103.95 to 713.2 mg/kg. The contamination factor (Cf) indicated moderate to severe pollution, particularly for Zn, which was identified as the most contaminating metal. The geo-accumulation index showed that most sampling sites fell into moderate to heavily contaminated categories for Zn. The findings highlight the urgent need for remediation measures to mitigate heavy metal pollution and protect soil quality in Annaba, emphasizing the importance of monitoring and management strategies to address this environmental challenge.

Effects of Distiller's Grains Biochar and Lactobacillus plantarum on the Remediation of Cd-Pb-Zn-Contaminated Soil and Growth of Sorghum-Sudangrass.

Soil contamination with heavy metals is a significant environmental issue that adversely affects plant growth and agricultural productivity. Biochar and microbial inoculants have emerged as a promising approach to solving this problem, and previous studies have focused more on the remediation effects of single types of materials on heavy metal soil pollution. This study examined the impact of both standalone and combined applications of distiller's grains biochar, Lactobacillus plantarum thallus, and the bacterial supernatant on the availability of cadmium (Cd), lead (Pb), and zinc (Zn) in soil, its physicochemical features, and its enzyme activities; this study also examined the growth, physiological and biochemical characteristics, and heavy metal accumulation of Sorghum-sudangrass. The findings suggest that the application of distiller's grains biochar, Lactobacillus plantarum thallus, and the bacterial supernatant can improve the soil's physical and chemical properties and enhance soil enzyme activity while reducing the availability of heavy metals in the soil. Furthermore, the addition of these materials promoted plant growth, increased stress resistance, and significantly decreased the accumulation of heavy metals in the plants. A thorough analysis of the results shows that applying 0.025% Lactobacillus plantarum thallus along with 4.4% distiller's grains biochar produced the best results.

Ensemble learning-assisted quantitative identifying influencing factors of cadmium and arsenic concentration in rice grain based multiplexed data.

Rapid and accurate prediction of rice Cd (rCd) and rice As (rAs) bioaccumulation are important for assessing the safe utilization of rice. Currently, there is lack of comprehensive and systematic exploration of the factors of rCd and rAs. Herein, ensemble learning (EL) was first used to analysis the 23 factors in 8 categories (heavy metal pollution characteristics, soil properties, geographical characteristics, meteorological factors, socio-economic factors, environmental factors, rice type, and nutrient element) in typical regions of China based on the results of 193 research papers from 2000 to 2024 in Web of Science database. Three machine learning methods were used to predict rCd and rAs concentrations and identify the key factors in each region, and explored the mechanism of Cd and As uptake in rice. The results showed that there were large differences in the factors affecting rice enrichment for the same heavy metal in different regions. For Cd, rice type (48.30 %), soil characteristics (28.14 %), and environmental factors (61.30 %) were the most important factors in Central South, East China, and Southwest China, respectively. For As, soil properties (34.01 %) and geographical characteristics (50.22 %) had the greatest influence in Central South and East China, respectively. Our study provided valuable insights into the prediction of rCd and rAs, thus contributing to ensuring food safety and preventing Cd and As exposure-associated health risks.

Spatial Distribution, Pollution Assessment, and Source Analysis of Soil Heavy Metals Under Different Land Use Types in the Southern Area of Laizhou Bay

To ascertain the status of the surface soil heavy metal pollution in the southern area of Laizhou Bay, 263 surface soil samples were collected from seven different land use types in the study area and the contents and pH of eight soil heavy metals （As, Cr, Ni, Cu, Zn, Cd, Pb, and Hg） were determined. The pollution assessment was carried out using the geo-accumulation index method, Nemerow comprehensive index method, and potential ecological risk index method. Then, the source of heavy metal pollution was discussed using correlation analysis and the source analysis was carried out through positive matrix factorization （PMF）. The results showed that the surface soil in the study area was weakly alkaline； As, Ni, Zn, and Hg slightly exceeded the background values of Weifang and the spatial distribution of heavy metals showed a trend of low in the north and high in the south. The results of the geo-accumulation index indicated that the study area was pollution free and the geo-accumulation index of different land types was arranged in the order of As>Ni>Zn>Cr>Pb>Hg>Cu>Cd. The results of the Nemerow comprehensive pollution index indicated that the study area was lightly polluted and the Nemerow comprehensive pollution index of different land types was arranged in the order of town>grassland>forest>agricultural land>vegetable land>industrial land>mudflat. The results of the potential ecological risk index indicated that the study area belonged to low ecological risk and the potential ecological risk index of different land types was arranged in the order of agricultural land>town>forest land>grassland>vegetable land>industrial land>mudflat. The results of PMF analysis indicated that the heavy metal pollution sources in the study area mainly included natural sources, transportation sources, agricultural sources, and industrial sources, with contribution rates of 38.06%, 26.38%, 26.21%, and 9.35%, respectively.

Research on Plant-amendment Combined Remediation Technology of Heavy Metals Contaminated Soil

With the rapid development of industrialization and urbanization, the problem of heavy metal pollution in soil is becoming more and more serious, posing a serious threat to the ecological environment and human health. Phytoremediation technology is valued for its environmental friendliness and cost-effectiveness. In order to provide a scientific basis for the remediation of heavy metal contaminated soil and the promotion of the safe, efficient and sustainable use of heavy metal contaminated cultivated soil, We reviewed the application of plants, amendments, and plant-amendment combinations in the remediation of heavy metal contaminated soils, and The effects of different plants and amendments and their combined application on heavy metal contaminated soil were discussed.

PREDICTING URBAN SOIL POLLUTION USING MACHINE LEARNING ALGORITHMS

Different cities of the world are facing heavy metal pollution in soils at different levels. Previous studies have found that heavy metal concentrations in urban soils tend to increase with increasing levels of urbanization, indicating a link between heavy metal content in soils and urban expansion. Thus, understanding this relationship and considering factors related to urbanization to create reliable predictions of heavy metal distribution in soils can contribute to effective management of urban health. This study examines the sources, distribution, and environmental effects of heavy metals. These elements accumulate in soil due to vehicle emissions, tire and brake wear, and abrasion of road surfaces, which carry significant environmental and health risks. The presence of heavy metals in road soil can detrimentally affect plant growth, enter the food chain, and pose a direct threat to human health when contaminated soil is ingested, or dust particles are inhaled. In this study, a random forest (RF) machine learning model was applied to predict the extent of heavy metals in soil along highways. The results showed that the RF model has high accuracy in predicting the spatial distribution of heavy metals in soil.

Synergizing carbon sequestration mechanisms during the remediation of Cr(VI) by nano zero-valent iron loaded biochar (nZVI-BC)

Applying nano zero-valent iron loaded biochar (nZVI-BC) to remediate soil heavy metal pollution has been frequently tested in current studies. However, little knowledge has been disclosed regarding the effect of nZVI-BC on soil organic carbon (SOC) mineralization/sequestration. In this study, the dual effect of nZVI-BC addition on Cr(VI) removal and SOC mineralization/sequestration and the underlying mechanisms were explored, based on a 90 days laboratory soil incubation. The results confirmed that nZVI-BC significantly improved the Cr(VI) removal rate while markedly reducing the cumulative CO2 emissions. Following the 90 days incubation, nZVI-BC addition notably increased the contents of Fe-oxides, Fe-bound organic carbon (Fe-OC), and SOC. Pearson correlation and PLS-SEM analyses indicated that the enhanced Fe-OC content, resulting from increased Fe-oxides, was a critical abiotic mechanism for improved SOC preservation. Additionally, the observed decrease in enzymatic activity and the shift in bacterial community from unstable carbon-dominant taxa (e.g., Proteobacteria and Actinobacteria) to stable carbon-dominant taxa (e.g., Firmicutes) in the nZVI-BC treatments suggested that SOC bio-mineralization was also inhibited. Overall, this study demonstrates that nZVI-BC not only aids in heavy metal remediation but also promotes long-term SOC preservation.

Entropy-informed multi-stage sampling design for soil pollution mapping in heavy metal contaminated areas

The accuracy of soil heavy metal pollution mapping is heavily reliant on the sampling strategies utilized in both the preliminary and detailed survey stages of site investigations. This study introduces an entropy-informed multi-stage sampling design (EIMSD) method that leverages preliminary survey data as background information and utilizes relative entropy to progressively select sampling points in detailed surveys. Results indicate that the EIMSD method outperforms the grid sampling design (GSD) and conventional sampling design (CSD) methods across both hypothetical and real-world study areas. This superiority is evidenced by a notable rise in R2 values, ranging from 6.4% to 60.4% and a decrease in RMSE values from 16.7% to 54.0% relative to GSD, and a similar trend with an increase in R2 values from 6.7% to 44.1% and a reduction in RMSE values from 16.5% to 39.7% when compared to CSD. This study also investigates the optimal configurations for EIMSD, focusing on the number of detailed sampling points per stage (Nadd) and the ratio of preliminary-to-detailed survey sample sizes (Np/Nd) when the sum of Np and Nd is held constant. Our findings highlight that adding one detailed sampling point per stage (Nadd = 1) is the most effective. For areas with strong spatial variability, a larger Np/Nd value of approximately 3/2 is recommended, whereas a ratio close to 1 is apt for areas with moderate variability. Conversely, for areas with weak variability, a smaller Np/Nd value of about 2/3 is advised. EIMSD provides a more detailed and accurate map of soil heavy metal contamination, facilitating more targeted and effective remediation strategies.

Source apportionment and driving factor identification for typical watersheds soil heavy metals of Tibetan Plateau based on receptor models and geodetector

The identification and quantification of soil heavy metal (HM) pollution sources and the identification of driving factors is a prerequisite of soil pollution control. In this paper, the Sabaochaqu Basin of the Tuotuo River, located in the Tibetan Plateau and the headwater of the Yangtze River, was selected as the study area. The soil pollution was evaluated using geochemical baseline, and the source apportionment of soil HMs was performed using absolute principal component score-multiple linear regression (APCS-MLR), edge analysis (UNMIX) and positive matrix decomposition (PMF). The driver of the source factor was identified with the geodetector method (GDM). The results of pollution evaluation showed that the HM pollution of soil in the study area was relatively light. By comparison, UNMIX model was considered to be the preferred model for soil HMs quantitative distribution in this study area, followed by PMF model. The UNMIX model results show that source 1 (U-S1) was dominated by As, with a contribution rate of 53.31%; source 2 (U-S2) was dominated by Cd and Zn, whose contribution rates are 50.35% and 46.60% respectively; source 3 (U-S3) was dominated by Pb, with a contribution rate of 45.58%; source 4 (U-S4) was dominated by Cr, Cu, Hg and Ni, with contribution rates of 60.58%, 60.07%, 51.58% and 56.45%, respectively. The GDM results showed that the main driving factors of U-S1 were distance from lake (explanatory power q = 0.328) and distance from wind channel (q = 0.168), which were defined as long-distance migration sources. The main driving factors of U-S2 were parent material type (q = 0.269) and distance from Tuotuo river (q = 0.213), which were defined as freeze-thaw sources. The main driving factors of U-S3 were distance from town (q = 0.255) and distance from county road (Yanya Line) (q = 0.221), which were defined as human activity sources. The main drivers of U-S4 were V (q = 0.346) and Sc (q = 0.323), which were defined as natural sources. The GDM results of the 3 models were generally consistent with the analytical results of similar types of sources, especially the results of PMF model and Unmix model can basically verify each other. The research results can provide important theoretical reference for the analysis of HM sources in the soil of high-cold and high-altitude regions.

Distribution, assessment, and causality analysis of soil heavy metals pollution in complex contaminated sites: a case study of a chemical plant.

To effectively prevent and control pollution from heavy metals (HMs) in urban soils, it is essential to thoroughly understand the contamination status of contaminated sites. In this study, the contamination status and sources of six HMs (As, Cu, Cr, Ni, Pb, Cd) in the soil of a decommissioned chemical plant in southern China were comprehensively analyzed. The results indicated that the average concentration of HMs followed the sequence: Cr > Pb > Cu > Ni > As > Cd. Heavy metal accumulation in the upper soil layer was predominantly observed in industrial zones and low-lying areas, with notable variations in concentration along the vertical profile. Certain sections of the site exhibited severe HM contamination, particularly with Cu levels exceeding the background value by 46.77times. Cd presented significant ecological risks in specific areas, with an average Ecological Index of 96.09. Carcinogenic and non-carcinogenic risks were identified at three and six sampling points, respectively, with sampling point S103 demonstrating both types of risks. The causes of HM contamination were primarily attributed to anthropogenic activities. Horizontal dispersion was mainly influenced by production operations and topographical features, while vertical distribution was predominantly affected by the permeability characteristics of the strata. The causality analysis incorporating production activities and topographical factors provides novel perspectives for understanding sources of contamination at contaminated sites. The study outcomes can offer guidance for the assessment and surveying of urban industrial pollution sites.

Soil health assessment of dressing and smelting slag field based on heavy metal pollution-buffer-fertility three aspects

The soil health of heavy metals in dressing and smelting slag field varies soil physicochemical properties. This study proposed a new soil health index based on heavy metal pollution-buffer-fertility for dressing and smelting slag field. Consequently, spatial distribution of soil physicochemical properties and heavy metals were varied, and correlated to each other. Soil buffer function and fertility played a much more important role in soil health in the dressing and smelting slag field located in Gejiu city, which can result in that soil health indexes were higher than those in Huili county, although the soil heavy metal pollution in the former was severer than that in the latter. Maximum values of soil health indexes for dressing and smelting slag field in Gejiu city were 3.84, 0.61, and 1.75 corresponding to additive, multiplicative, and maximum value composite methods, which were higher than those in Huili county with 2.25, 0.61, and 0.17. The former’s high value is concentrated in southeastern regions and low value in some western areas, the latter’s high value occurred in southeastern districts and low value in northwestern places. So this study unveils a novel perspective on the soil health consequences associated with soil heavy metal pollution-buffer-fertility three aspects.

Revealing the synergistic spatial effects in soil heavy metal pollution with explainable machine learning models

The identification of factors that affect changes in the heavy metal content of soil is the basis for reducing or preventing soil heavy metal pollution. In this research, 16 environmental factors were selected, and the influences of soil heavy metal spatial distribution factors and the synergy amongst space factors were evaluated using a geographic detector (GD) and the extreme gradient boosting (XGBoost)-Shapley additive explanations (SHAP) model. Three heavy metal elements, namely, Cd, Cu and Pb, in the study region were examined. The following results were obtained. (1) XGBoost demonstrated high accuracy in predicting the spatial distributions of soil heavy metals, with each heavy metal having an R2 value of over 0.6. (2) Geological type map (Geomap) and enterprise density considerably affected the concentrations of Cd, Cu and Pb in soil in the GD and XGBoost-SHAP models. In addition, cross-detection revealed strong explanatory power when natural and human factors were combined. (3) Under the same geological background, the different trends of gross domestic product effects on heavy metals indicated that pollution control measures were effective in economically developed areas, and the economy and the environment could be balanced. Meanwhile, the interaction between the normalised difference vegetation index and enterprise density showed that vegetation could alleviate heavy metal pollution in the region. This study supports strategic decision-making, serving as a reference for the global management of soil heavy metal contamination, sustainable ecological development and assurance of people’s health and well-being.

Characteristics and Risk Assessment of Heavy Metal Contamination in Arable Soils Developed from Different Parent Materials

This study analyzes the heavy metal pollution in cultivated soils developed from different parent materials in Yunnan Province and assesses their risk levels. The results show significant regional differences in soil heavy metal pollution, greatly influenced by the type of parent material. Cadmium (Cd) pollution is most severe in multiple parent material soil regions, particularly in areas with carbonate and purple rocks, exhibiting a high pollution risk. Other heavy metals such as zinc (Zn), copper (Cu), and lead (Pb) also show varying degrees of enrichment in different parent material zones, posing potential pollution risks. The soil pollution levels of heavy metals were classified using the geo-accumulation index method. It was found that soils developed from carbonate rocks and purple rocks have the most severe heavy metal pollution, while soils from quartzitic rocks, acidic crystalline rocks, and basalt exhibit relatively lower pollution levels. By analyzing the characteristics of heavy metal pollution in different parent materials, this study provides a scientific basis for regional soil pollution management and sustainable agricultural development.