Effects of natural factors on the spatial distribution of heavy metals in soils surrounding mining regions

Mapping soil cadmium content using multi-spectral satellite images and multiple-residual-stacking model: Incorporating information from homologous pollution and spectrally active materials

Soil cadmium (Cd) contamination significantly threatens ecosystems and human health. Traditional geochemical investigation, although accurate, is impractical for wide-area and frequent monitoring applications. Multi-spectral satellite images combined with the homologous pollution information (HPI) and the spectral and content information of soil organic matter (SOMSCI) is an unconventional and promising approach for large-scale, dynamic soil heavy metal (SHM) monitoring. Based on a novel Multiple-Residual-Stacked (MRS) machine-learning framework, the study estimated the soil Cd content in Yueyang City, China, during the past decade (2014-2023) using Landsat 8 images. Within it, three feature construction methods and four models were employed. The experimental results indicate that the XGB-MRS model incorporating HPI and SOMSCI significantly improved the estimation performance (RPD exceeded 90 %, R2, RMSE, and MAE exceeded 40 %). Moreover, against 243 ground samples during 2016-2022, the average overall estimation accuracy exceeded 80 %, validating the model's robustness and practicality. Furthermore, the descending order of contribution in the modelling is environmental auxiliary variables (55 %), HPI and SOMSCI (26 %), and spectral information (19 %). The fertilizer usage has direct (up to 2 years) and delayed (3-5 years) effects on soil Cd accumulation. Overall, our study provides a scalable framework for monitoring global SHM pollution using open-source multi-spectral satellite data.

Machine learning combined with geodetector to predict the spatial distribution of soil heavy metals in mining areas

An accurate understanding of the spatial distribution of soil heavy metals (HMs) is crucial for the effective prevention of soil pollution and remediation strategies. Traditional machine learning models often overlook the spatially stratified heterogeneity inherent to environmental data, which can impair predictive accuracy. Therefore, we combined the Geodetector model (GDM) with machine learning models. The factor detection results were used to screen covariates to consider the local spatial heterogeneity of model features. The interaction detection results were used to construct spatially stratified covariates to consider the spatially stratified heterogeneity of model features. The results showed that covariate screening largely avoided the introduction of redundant features. The constructed spatially stratified covariates improved the predictive performance of the model (both the R-squared (R2) and root mean square error (RMSE) of different models were optimized). Among these, the XGB model exhibited the best performance. Analysis of the factors influencing Pb and Cr revealed that the interaction between pH and NDVI was the main determinant of Pb spatial distribution (q = 0.3516, XGB Importance Score = 93). In contrast, the interaction between DEM and pH (q = 0.7156, XGB Importance Score = 121) as well as the distance to waste piles (q = 0.6390, XGB Importance Score = 66), were the main driving factors for the spatial distribution of Cr. The current work provides an improved approach for interrogating the factors that influence HM distribution in soil. This study offers valuable insights into the spatial distribution of soil HMs. The proposed methodology can be applied in future soil pollution assessments and environmental management strategies, thus contributing to more precise pollution prevention and remediation efforts.

Applications of geographically weighted machine learning models for predicting soil heavy metal concentrations across mining sites

The accurate prediction of soil heavy metal contamination is crucial for the effective environmental management of abandoned mining areas. However, conventional machine learning models (CMLMs) often fail to account for the spatial heterogeneity of soil contamination, which limits their predictive accuracy. This study evaluated the performance of geographically weighted machine learning models (GWMLMs) in predicting soil Cd and Pb concentrations in abandoned mines in the Republic of Korea. We compared two GWMLMs (Geographically Weighted Random Forest and Geographically Weighted Extreme Gradient Boosting) with four CMLMs (Random Forest, Gradient Boosting, Light Gradient Boosting, and extreme Gradient Boosting). The data used in this study included soil samples from six abandoned mining sites with various geographical and soil input variables. The results showed that the GWMLMs consistently outperformed the CMLMs in predicting heavy metal contamination. For Cd predictions, GWMLMs exhibited on average 0.02 lower root mean square error and mean absolute error values, with a 0.26 increase in R2 values compared to CMLMs. Similarly, for Pb predictions, the GWMLMs showed 0.18 and 0.13 lower root mean square error and mean absolute error values, respectively, and a 0.17 increase in R2 relative to the CMLMs. The findings demonstrate the usefulness of GWMLMs for predicting the spatial distribution of soil heavy metals. SHapley Additive exPlanations analysis exhibited elevation and distance from abandoned mining sites as the most influential factors in predicting both Cd and Pb concentrations. This study highlights the value of GWMLMs that incorporate spatial heterogeneity into CMLMs for enhancing prediction accuracy and providing crucial insights for environmental management in mining-impacted regions.

Spatial distribution and migration characteristics of heavy metals at an abandoned industrial site in the Southwest of China

The rapid acceleration of global industrialization has rendered heavy metal contamination at abandoned industrial sites a severe challenge, particularly in geologically complex and fragile karst regions of Southwest China, posing significant threats to ecosystems and public health. However, existing research lacks a comprehensive understanding of the spatial distribution and migration mechanisms of heavy metals in this region. In this study, 523 soil samples and 30 groundwater samples were collected, and the pollution levels were systematically assessed using the Geo-Accumulation Index, Single Pollution Index, and Nemerow Integrated Pollution Index. Horizontal and vertical spatial heterogeneity was explored through Moran's I and Voronoi polygon analysis. Furthermore, 3D geological modeling and groundwater flow simulations were employed to investigate the influence of hydrogeological conditions on contaminant migration. The results indicate elevated concentrations of Cd, Hg, Pb, and As in the surface layer, with concentrations initially decreasing and then increasing with depth, likely due to the presence of discontinuous clay layers. Moran's I revealed significant clustering effects at depths of 0.2 m and 4 m, while Voronoi analysis confirmed vertical heterogeneity. This study provides a scientific basis for pollution assessment and targeted remediation in karst regions.

Metal-bearing airborne particles from mining activities: A review on their characteristics, impacts and research perspectives

The presence of various contaminants in airborne dusts from metal mining sites poses obvious risks to human health and the environment. Yet, few studies have thoroughly investigated the properties of airborne particles in terms of their morphology, size distribution and chemical composition, that are associated with health effects around mining activities. This review presents the most recent knowledge on the sources, physicochemical characteristics, and health and environmental risks associated with airborne dusts from various mining and smelting operations. The literature reviewed found only one research on atmospheric dust associated with hydrometallurgical plants compared to a larger number of pyrometallurgical processes/smelters studies. In addition, there are relatively few works comparing the distribution of metals between the fine and coarse size fractions around mining sites. Our analysis suggests that (i) exposure pathways of metal(loid)s to the human body are defined by linking concentration data in human biosamples and contaminated samples such as soils, drinking water and food, and (ii) chitosan and its derivatives may serve as an environmentally friendly and cost-effective method for soil remediation, with removal rates for metal(loid)s around 70-95 % at pH 6-8, and as dust suppressants for unpaved roads around mining sites. The specific limit values for PM and metal(loid)s at mining sites are not well documented. Despite the health risks associated with fine particles around mining areas, regulations have tended to focus on coarse particles. While some air quality agencies have issued regulations for occupational health and safety, there is no global alignment or common regulatory framework for enforcement. Future research priorities should focus on investigating PM and secondary inorganic aerosols associated with hydrometallurgical processes and dust monitoring, using online metal(loid)s analysers to identify the driving parameters in the deposition and resuspension process.

Characterization and source apportionment of heavy metal pollution in soil around red mud disposal sites using absolute principal component scores-multiple linear regression and positive matrix factorization models

In recent years, industrial waste and agrochemicals have reduced soil fertility and productivity, significantly impacting food security and ecosystems. In China, areas near red mud deposits from the aluminum industry show severe heavy metal contamination. This study examines agricultural soil near a red mud site in Shanxi Province, analyzing Cd, Cr, Hg, Ni, Pb, As, Cu, and Zn levels and distribution. Geostatistical methods and GIS are utilized to assess heavy metal pollution using the single factor index, the Nemerow integrated index, and the Hakanson potential ecological risk index. Absolute Principal Component Scores-Multiple Linear Regression (APCS-MLR) and Positive Matrix Factorization (PMF) models are used for quantitative analysis of pollution sources. Research indicates that the average concentrations of eight heavy metals exceed the natural background values of Shanxi, placing them at a severe pollution level with moderate ecological risk. Specifically, indices for As, Pb, and Cr are 3.79, 3.38, and 3.26, indicating severe pollution; Cd, Cu, and Hg at 2.36, 2.62, and 3.00 suggest moderate pollution; Ni at 1.87 shows mild pollution, while Zn at 0.97 is not polluted. Hg presents the highest ecological risk with a coefficient of 120.00, followed by Cd (70.69) and As (37.92). Spatial analysis shows significant correlations among Pb, Zn, Cu, and Ni, while Cr, Cd, Hg, and As show greater variability and weaker correlations. Both models identify five main sources: industrial activities, agricultural fertilizers, red mud leachate, energy combustion, and natural geological backgrounds, with respective contribution rates in the APCS-MLR model at 27.7%, 24.6%, 18.1%, 15.2%, and 14.4%, and in the PMF model at 29.2%, 21.5%, 16.9%, 16.7%, and 15.7%. This study offers a scientific basis for controlling soil pollution in the region, filling a literature gap.

Invasive Amaranthus spp. for heavy metal phytoremediation: Investigations of cadmium and lead accumulation and soil microbial community in three zinc mining areas

Amaranthus spp. are a group of strongly invasive and vigorous plants, and heavy metal phytoremediation using alien invasive Amaranthus spp. has been a popular research topic. In this study, the bioconcentration factor (BCF) and translocation factor (TF) of Amaranthus spp. were evaluated, focusing on the accumulation potential of cadmium (Cd) and lead (Pb) by plants from three different zinc mining areas, namely Huayuan (HYX), Yueyang (LYX), and Liuyang (LYX). The HYX area has the most severe Cd contamination, while the LYX area has the most apparent Pb contamination. The results showed that Amaranthus spp. had a strong Cd and Pb enrichment capacity in low-polluted areas. To elucidate the underlying mechanisms, we used high-throughput sequencing of 16S rRNA and internal transcribed spacer (ITS) regions to analyze rhizosphere bacterial and fungal communities in three areas. The results showed significant differences in the structure, function, and composition of microbial communities and complex interactions between plants and their microbes. The correlation analysis revealed that some key microorganisms (e.g., Amycolatopsis, Bryobacterium, Sphingomonas, Flavobacterium, Agaricus, Nigrospora, Humicola) could regulate several soil factors such as soil pH, organic matter (OM), available nitrogen (AN), and available phosphorus (AP) to affect the heavy metal enrichment capacity of plants. Notably, some enzymes (e.g., P-type ATPases, Cysteine synthase, Catalase, Acid phosphatase) and genes (e.g., ZIP gene family, and ArsR, MerR, Fur, NikR transcription regulators) have been found to be involved in promoting Cd and Pb accumulation in Amaranthus spp. This study can provide new ideas for managing heavy metal-contaminated soils and new ways for the ecological resource utilization of invasive plants in phytoremediation.

Improved mapping of heavy metals in agricultural soils using machine learning augmented with spatial regionalization indices

The accurate spatial mapping of heavy metal levels in agricultural soils is crucial for environmental management and food security. However, the inherent limitations of traditional interpolation methods and emerging machine-learning techniques restrict their spatial prediction accuracy. This study aimed to refine the spatial prediction of heavy metal distributions in Guangxi, China, by integrating machine learning models and spatial regionalization indices (SRIs). The results demonstrated that random forest (RF) models incorporating SRIs outperformed artificial neural network and support vector regression models, achieving R2 values exceeding 0.96 for eight heavy metals on the test data. Hierarchical clustering for feature selection further improved the model performance. The optimized RF models accurately predicted the heavy metal distributions in agricultural soils across the province, revealing higher levels in the central-western regions and lower levels in the north and south. Notably, the models identified that 25.78 % of agricultural soils constitute hotspots with multiple co-occurring heavy metals, and over 6.41 million people are exposed to excessive soil heavy metal levels. Our findings provide valuable insights for the development of targeted strategies for soil pollution control and agricultural soil management to safeguard food security and public health.

Root-soil-microbiome interaction in the rhizosphere of Masson pine (Pinus massoniana) under different levels of heavy metal pollution

Heavy metal pollution of the soil affects the environment and human health. Masson pine is a good candidate for phytoremediation of heavy metal in mining areas. Microorganisms in the rhizosphere can help with the accumulation of heavy metal in host plants. However, studies on its rhizosphere bacterial communities under heavy metal pollution are still limited. Therefore, in this study, the chemical and bacterial characteristics of Masson pine rhizosphere under four different levels of heavy metal pollution were investigated using 16 S rRNA gene sequencing, soil chemistry and analysis of plant enzyme activities. The results showed that soil heavy metal content, plant oxidative stress and microbial diversity damage were lower the farther they were from the mine dump. The co-occurrence network relationship of slightly polluted soils (C1 and C2) was more complicated than that of highly polluted soils (C3 and C4). Relative abundance analysis indicated Sphingomonas and Pseudolabrys were more abundant in slightly polluted soils (C1 and C2), while Gaiella and Haliangium were more abundant in highly polluted soils (C3 and C4). LEfSe analysis indicated Burkholderiaceae, Xanthobacteraceae, Gemmatimonadaceae, Gaiellaceae were significantly enriched in C1 to C4 site, respectively. Mantel analysis showed that available cadmium (Cd) contents of soil was the most important factor influencing the bacterial community assembly. Correlation analysis showed that eight bacterial genus were significantly positively associated with soil available Cd content. To the best of our knowledge, this is the first study to investigate the rhizospheric bacterial community of Masson pine trees under different degrees of heavy metal contamination, which lays the foundation for beneficial bacteria-based phytoremediation using Masson pines in the future.

Spatial diffusion of potentially toxic elements in soils around non-ferrous metal mines

This study focuses on the diffusion patterns of principal ore-forming elements (Pb and Zn) and associated elements (Cd, Cu, Cr, and As) in lead-zinc ore. Sampling points in upwind and downwind directions of lead-zinc ore areas at various densities (1 N/km2 - 4 N/km2) were categorized. This study analyzed the statistical relationship between the content of PTEs in the soil around lead-zinc ore and the source strength and dominant wind direction, constructed one-dimensional and two-dimensional diffusion model, and simulated the EER scope caused by PTEs. The findings indicate that: (1) concerning source strength, the content of PTEs in soils of high-density ore aggregation areas is significantly higher than in low-density ore aggregation areas. However, the impact of source strength decreases with decreasing ore grade, with a difference in Pb content of 1.71 times among principal ore-forming elements and almost consistent Cd content among associated elements. (2) Regarding the transport pathways, for most PTEs, the inverse proportion coefficients downwind are higher than upwind, approximately 1.18-3.63 times, indicating greater migration distances of PTEs downwind due to atmospheric dispersion. (3) By establishing a two-dimensional risk diffusion model, the study simulates the maximum radius of risk diffusion (r = 5.7 km), the 50% probability radius (r = 3.1 km), and the minimum radius (r = 0.8 km) based on the maximum, median, and minimum values statistically obtained from the EER. This study provides a scientific basis for implementing preventive measures for PTEs accumulation in soil within different pollution ranges. Different risk prevention and control measures should be adopted for PTEs accumulation in soil within the three ranges after cutting off pollution sources. Subsequent research should further investigate the impact and contribution of atmospheric transmission and surface runoff on the diffusion of PTEs in areas with high risk near lead-zinc ore.

Machine learning-driven source identification and ecological risk prediction of heavy metal pollution in cultivated soils

To overcome challenges in assessing the impact of environmental factors on heavy metal accumulation in soil due to limited comprehensive data, our study in Yangxin County, Hubei Province, China, analyzed 577 soil samples in combination with extensive big data. We used machine learning techniques, the potential ecological risk index, and the bivariate local Moran's index (BLMI) to predict Cr, Pb, Cd, As, and Hg concentrations in cultivated soil to assess ecological risks and identify pollution sources. The random forest model was selected for its superior performance among various machine learning models, and results indicated that heavy metal accumulation was substantially influenced by environmental factors such as climate, elevation, industrial activities, soil properties, railways, and population. Our ecological risk assessment highlighted areas of concern, where Cd and Hg were identified as the primary threats. BLMI was used to analyze spatial clustering and autocorrelation patterns between ecological risk and environmental factors, pinpointing areas that require targeted interventions. Additionally, redundancy analysis revealed the dynamics of heavy metal transfer to crops. This detailed approach mapped the spatial distribution of heavy metals, highlighted the ecological risks, identified their sources, and provided essential data for effective land management and pollution mitigation.

Health Risk Assessment of Heavy Metals in Agricultural Soils Based on Multi-Receptor Modeling Combined with Monte Carlo Simulation

The spatial characteristics, pollution sources, and risks of soil heavy metals were analyzed on Hainan Island. The results showed that the heavily polluted points accounted for 0.56%, and the number of mildly and above polluted points accounted for 15.27%, respectively, which were mainly distributed in the northern part of the study area. The principal component analysis-absolute principal component score-multiple linear regression (APCS-MLR) and the positive matrix factorization (PMF) revealed four sources of heavy metals: agricultural pollution sources for cadmium, (Cd), industrial and mining pollution sources for arsenic, (As), transportation pollution sources for zinc and lead (Zn and Pb), and natural pollution sources for chromium, nickel, and copper (Cr, Ni, and Cu). The human health risk assessment indicated that the average non-carcinogenic risk (HI) for both adults and children was within the safe threshold (<1), whereas Cr and Ni posed a carcinogenic risk (CR) to human health. In addition, the total non-carcinogenic risk (THI) indicated that heavy metals posed a potential non-carcinogenic risk to children, while the total carcinogenic risk (TCR) remained relatively high, mainly in the northern part of the study area. The results of the Monte Carlo simulation showed that the non-carcinogenic risk (HI) for all heavy metals was <1, but the total non-carcinogenic risk index (THI) for children was >1, indicating a potential health risk above the safe threshold. Meanwhile, nearly 100% and 99.94% of the TCR values exceeded 1 × 10-4 for children and adults, indicating that Cr and Ni are priority heavy metals for control. The research results provide the necessary scientific basis for the prevention and control of heavy metals in agricultural soils.

Analysis of driving factors for potential toxic metals in major urban soils of China: a geodetetor-based quantitative study

Potential toxic metal (PTM) is hazardous to human health, but the mechanism of spatial heterogeneity of PTM at a macro-scale remains unclear. This study conducts a meta-analysis on the data of PTM concentrations in the soil of 164 major cities in China from 2006 to 2021. It utilizes spatial analysis methods and geodetector to investigate the spatial distribution characteristics of PTMs. The geographic information systems (GIS) and geodetector were used to investigate the spatial distribution characteristics of PTMs, assess the influence of natural factors (NFs) and anthropogenic factors (AFs) on the spatial heterogeneity of PTMs in urban soils, and identified the potential pollution areas of PTMs. The results indicated that the pollution levels of PTMs in urban soils varied significantly across China, with higher pollution levels in the south than in the north. Cd and Hg were the most severely contaminated elements. The geodetector analysis showed that temperature and precipitation in NFs and land use type in AFs were considered as the main influencing factors, and that both AF and NF together led to the PTM variation. All these factors showed a mutually enhancing pattern which has important implications for urban soil management. PTM high-risk areas were identified to provide early warning of pollution risk under the condition of climate change.

Integrated framework to assess soil potentially toxic element contamination through 3D pollution analysis in a typical mining city

Soil potentially toxic elements (PTEs) pollution of contaminated sites has become a global environmental issue. However, given that previous studies mostly focused on pollution assessment in surface soils, the current status and environmental risks of potentially toxic elements in deeper soils remain unclear. The present study aims to cognize distribution characteristics and spatial autocorrelation, pollution levels, and risk assessment in a stereoscopic environment for soil PTEs through 3D visualization techniques. Pollution levels were assessed in an integrated manner by combining the geoaccumulation index (Igeo), the integrated influence index of soil quality (IICQs), and potential ecological hazard index. Results showed that soil environment at the site was seriously threatened by PTEs, and Cu and Cd were ubiquitous and the predominant pollutants in the study area. The stratigraphic models and pollution plume simulation revealed that pollutants show a decreasing trend with the deepening of the soil layer. The ranking of contamination soil volume is as follows: Cu > Cd > Zn > As > Pb > Cr > Ni. According to the IICQs evaluation, this region was subject to multiple PTE contamination, with more than 60% of the area becoming seriously and highly polluted. In addition, the ecological hazard model revealed the existence of substantial ecological hazards in the soils of the site. The integrated potential ecological risk index (RI) indicated that 45.7%, 10.13%, and 4.15% of the stereoscopic areas were in considerable, high, and very high risks, respectively. The findings could be used as a theoretical reference for applying multiple methods to integrate evaluation through 3D visualization analysis in the assessment and remediation of PTE-contaminated soils.

Quantification of sources and potential risks of cadmium, chromium, lead, mercury and arsenic in agricultural soils in a rapidly urbanizing region of southwest China: the case of Chengdu

Rapid urbanization a major factor affecting heavy metal contamination on suburban agricultural soils. In order to assess the dynamic contamination of heavy metals in soil from agricultural land bordering a rapidly urbanizing area and the transfer of human health risks from contaminants in this process, 186 and 293 soil samples from agricultural land in suburban Chengdu were collected in September 2008 and September 2017, respectively. Several indicators, such as the integrated pollution index (PI) and the potential ecological risk index (RI), were employed for analyzing the heavy metal contamination levels, and the APCS-MLR receptor model were applied for analyzing the heavy metal sources. As a result, mean concentrations for five elements did not exceed the national soil pollution risk screening values in the two periods mentioned above. Nemerow's composite contamination index revealed an increase in soil contamination of arable land after 10 years of urbanization, with 3.75 and 1.02% of light and moderate sample plots, respectively, by 2017. The assessment for potential ecological risk indicated an increased level of eco-risk to high for most of the sample plots. Based on the APCS-MLR model, the origin and contribution to the five elements varied considerably between the two periods mentioned above. Among them, soil Pb changed from "industrial source" to "transportation source," soil Cr changed from "natural source" to "transportation source," and As and Hg changed from "industrial source" to "transportation source." As and Hg were associated with agricultural activities in both periods, and Cd was derived from industrial activities in both periods. The study suggests that inhalation has become a major contributor to non-cancer health risks in urbanization, unlike intake routes in previous periods, and that the increase in cancer risk is mainly due to children's consumption of agricultural products with As residues. The change in the main source of As to "transportation" also indicates a decrease in air quality during urbanization and the development of the transportation industry. This study provides a reference for the governments of rapidly urbanizing cities to formulate relevant highway and agricultural policies to safeguard the health of the people based on the current situation.

Ecological risk and spatial distribution, sources of heavy metals in typical purple soils, southwest China

The identification and quantification of the ecological risks, sources and distribution of heavy metals in purple soils are essential for regional pollution control and management. In this study, geo-accumulation index (Igeo), enrichment factor (EF), pollution index (PI), potential ecological risk index (RI), principal component analysis (PCA) model and geographical detector (GD) were combined to evaluate the status, ecological risk, and sources of heavy metals (HMs) in soils from a typical purple soil areas of Sichuan province. The results showed that the average contents of As, Cd, Cr, Cu, Hg, Ni, Pb and Zn in purple soil were 7.77, 0.19, 69.5, 27.9, 0.077, 30.9, 26.5 mg/kg and 76.8 mg/kg, and the Igeo, EF and RI of topsoil Hg and Cd in designated area was the highest, and the average contents of Hg and Cd in topsoil were obviously greater than respective soil background value in Sichuan province and purple soil. The hot spots for the spatial distribution of 8 HMs were mainly focused in the southwest and northeast of the designated area, and there were also significant differences for 8 HMs distribution characteristics in the profile soil. Cu comes from both anthropogenic and natural sources, Zn, Ni and Cr mainly come from natural sources, but As, Pb, Hg and Cd mainly derived from human activities. GD results showed that soil texture (X18), altitude (X4), total nitrogen (TN), clay content (X3), sand content (X2) and silt content (X1) had the greatest explanatory power to 8 HMs spatial differentiation.This study provides a reference for understanding the status and influencing factors of HM pollution in typical purple soil, and lays a theoretical foundation for the environmental treatment of purple soil in China.

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.

Application and mechanism of carbonate material in the treatment of heavy metal pollution: a review

Among the many heavy metal pollution treatment agents, carbonate materials show strong flexibility and versatility by virtue of their high adsorption capacity for heavy metals and the characteristics of multiple and simple modification methods. It shows good potential for development. This review summarizes the application of carbonate materials in the treatment of heavy metal pollution according to the research of other scholars. It mainly relates to the application of surface-modified, activated, and nano-sized carbonate materials in the treatment of heavy metal pollution in water. Natural carbonate minerals and composite carbonate minerals solidify and stabilize heavy metals in soil. Solidification of heavy metals in hazardous waste solids is by MICP. There are four aspects of calcium carbonate oligomers curing heavy metals in fly ash from waste incineration. The mechanism of treating heavy metals by carbonate in different media was discussed. However, in the complex environment where multiple types of pollutants coexist, questions on how to maintain the efficient processing capacity of carbonate materials and how to use MICP to integrate heavy metal fixation and seepage prevention in solid waste base under complex and changeable natural environment deserve our further consideration. In addition, the use of carbonate materials for the purification of trace radioactive wastewater and the safe treatment of trace radioactive solid waste are also worthy of further exploration.

Wide Riparian Zones Inhibited Trace Element Loss in Mining Wastelands by Reducing Surface Runoff and Trace Elements in Sediment

The diffusion of trace elements in mining wastelands has attracted widespread attention in recent years. Vegetation restoration is an effective measure for controlling the surface migration of trace elements. However, there is no field evidence of the effective riparian zone width in mining wastelands. Three widths (5 m, 7.5 m, and 10 m) of Rhododendron simsii/Lolium perenne L. riparian zones were constructed in lead-zinc mining wastelands to investigate the loss of soil, cadmium (Cd), copper (Cu), arsenic (As), lead (Pb), and zinc (Zn). Asbestos tiles were used to cut off connections between adjacent plots to avoid hydrological interference. Plastic pipes and containers were used to collect runoff water. Results showed that more than 90% of trace elements were lost in sediment during low coverage and heavy rainfall periods. Compared with the 5 m riparian zone, the total trace element loss was reduced by 69-85% during the whole observation period in the 10 m riparian zone and by 86-99% during heavy rain periods in the 10 m riparian zone, which was due to reduction in runoff and concentrations of sediment and trace elements in the 10 m riparian zone. Indirect negative effects of riparian zone width on trace element loss through runoff and sediment concentration were found. These results indicated that the wide riparian zone promoted water infiltration, filtered soil particles, and reduced soil erosion and trace element loss. Riparian zones can be used as environmental management measures after mining areas are closed to reduce the spread of environmental risks in mining wastelands, although the long-term effects remain to be determined.

An integrated framework for source apportionment and spatial distribution of mercury in agricultural soil near a primary ore mining site

Mercury (Hg) is a global environmental concern that affects both humans and ecosystem. The comprehensive understanding of sources and dynamics is crucial for facilitating targeted and effective control strategies. Herein, a robust approach integrating Multivariate Statistics, Geostatistics, and Positive Matrix Factorization (PMF) was employed to quantitatively elucidate the distribution and sources of Hg in agricultural lands. Results indicated elevated Hg concentrations in the land with 74.46% of soils, including 84.85% of topsoil, 69.70% of subsoil, and 67.31% of deepsoil, exceeding risk screening value. Geoaccumulation Index of Hg in soil surpassed level Ⅱ with more than 50% of Hg in the residual fraction regardless of the layer or location. The levels of Hg in surface water for irrigation exhibited a negative correlation with the distance from the mine and a positive correlation with that in sediment (R2>0.78, p < 0.01), suggesting the downstream migration and remobilization from sediment. Source apportion revealed that human activities as primary contributors despite high variability across locations and soil layers. Contributions to downstream soil Hg from Natural Background (NB), Primary Ore Mining (OM), Agricultural Practices (AP), and Wastewater Irrigation (WI) were 15.5%, 83.1%, 1.3%, and 0.1%, respectively. A reliable approach for source apportionment of Hg in soil was suggested, demonstrating potential applicability in the risk management of Hg-contaminated sites.

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 IMPLICATION: This 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.

Effects of elevation and geomorphology on cadmium, lead and chromium enrichment in paddy soil and rice: A case study in the Xiangtan basin of China

The distributions of heavy metals in paddy fields and rice along river valleys were studied to explore the key factors affecting the accumulation of heavy metals in the upstream terraces and downstream plains. Results from 975 sampling sites showed that elevation, growing season and soil organic matter (OM) had significant effects on the content of Cd and Pb in topsoil and rice. The content of Cd (0.47-0.66 mg kg-1) and Pb (49.9-68.6 mg kg-1) in paddy fields with low elevation (30-60 m) in the downstream plains was significantly higher than the content of Cd (0.29-0.38 mg kg-1) and Pb (43.9-56.3 mg kg-1) in the upstream terraces with high altitude (60-90 m). In the double-rice production area, late rice generally produced grains with higher Cd and Pb content than early rice. Soil Cd was positively increased with the content of OM, especially in the downstream plains. When elevation was used for principal component analysis, plains with low elevation were grouped together with high content of total and soluble Cd, OM and Pb in soil, as well as high content of Cd and Pb in late rice. Altitude is one of the key factors affecting Cd content in rice. Although content of Cr (93.7-138.0 mg kg-1) was significantly higher than that of Cd and Pb in soil, content of Cr was lower than that of Cd in rice. These results indicate that paddy fields with elevation of 30-60 m in the downstream plains had high risk to produce late rice with Cd and Pb content exceeding the food safety standard 0.2 mg kg-1, which may be resulted from the driving force of runoff on soil soluble Cd and Pb from terraces to alluvial plains in river valleys.

Identifying driving factors of soil heavy metal at the mining area scale: Methods and practice

Identifying driving factors is of great significance for understanding the mechanisms of soil pollution. In this study, a data processing method for driving factors was analyzed to explore the genesis of Arsenic (As) pollution in mining areas. The wind field that affects the atmospheric diffusion of pollutants was simulated using the standard k-ε model. Machine learning and GeoDetector methods were used to identify the primary driving factors. The results showed that the prediction performances of the three machine learning models were improved after data processing. The R2 values of random forest (RF), support vector machine, and artificial neural network increased from 0.45, 0.69, and 0.24 to 0.55, 0.76, and 0.52, respectively. The importance of wind increased from 20.85% to 26.22%. The importance of distance to the smelter plant decreased from 43.26% to 33.19% in the RF model. The wind's driving force (q value) increased from 0.057 to 0.235 in GeoDetector. The average value of historical atmospheric dust reached 534.98 mg/kg, indicating that atmospheric deposition was an important pathway for As pollution. The outcome of this study can provide a direction to clarify the mechanisms responsible for soil pollution at the mining area scale.

Health Risks for Consumers of Forest Ground Cover Produce Contaminated with Heavy Metals

BACKGROUND

The activity of heavy metal (HM) mining and processing industries causes soils contamination with HM. The metals could be transferred from contaminated soils to edible plants and fungi. This study aimed to assess the content of Cd, Pb, Hg, As, and Ni in berries and edible mushrooms collected in the forests located near Miasteczko Slaskie zinc smelter and in the Lubliniec region, in the context of consumers' health risk.

METHODS

The ET-AAS method was used to determine the content of Cd, Pb, Ni, and As. Mercury concentration was determined using the CV-AFS method.

RESULTS

The studies showed high levels of Cd and Pb in the examined products. A statistically significant impact of the distance from the smelter on the Cd concentration in the berries was observed. Total non-cancer health risk from the combined exposure of adults to all HM in mushrooms and berries was significant when consuming the most heavily contaminated produce. The risk to children was significant, even when consuming products with moderate levels of the metals. Ingestion of Cd by children with mushrooms was related to a high cancer risk. The uncertainty of the results was determined.

CONCLUSIONS

It is recommended to take action to increase awareness among residents of the areas adjacent to the forests regarding the existing health risk and to take possible measures to reduce exposure to HM.

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.

Impacts of land use/cover and slope on the spatial distribution and ecological risk of trace metals in soils affected by smelting emissions

The soil contamination around smelting sites shows high spatial heterogeneity. This study investigated the impacts of distance, land use/cover types, land slopes, wind direction, and soil properties on the distribution and ecological risk of trace metals in the soil around a copper smelter. The results demonstrated that the average concentrations of As, Cd, Cu, Pb, and Zn were 248.0, 16.8, 502.4, 885.6, and 250.2 g mg kg-1, respectively, higher than their background values. The hotspots of trace metals were primarily distributed in the soil of smelting production areas, runoff pollution areas, and areas in the dominant wind direction. The concentrations of trace metals decreased with the distance to the smelting production area. An exponential decay regression revealed that, depending on the metal species, the influence distances of smelting emissions on trace metals in soil ranged from 450 to 1000 m. Land use/cover types and land slopes significantly affected trace element concentrations in the soil around the smelter. High concentrations of trace metals were observed in farmland, grassland, and flatland areas. The average concentrations of trace metals in the soil decreased in the order of flat land > gentle slope > steep slope. Soil pH values were significantly positively correlated with Cd, Cu, Pb, Zn, and As, and SOM was significantly positively correlated with Cd, Pb, and Zn in the soil. Trace metals in the soil of the study area posed a significant ecological risk. The primary factors influencing the distribution of ecological risk, as determined by the Ctree analysis, were land slope, soil pH, and distance to the source. These results can support the rapid identification of high-risk sites and facilitate risk prevention and control around smelting sites.

Connotation, status, and governance of land ecological security in China's new urbanization: recent advances and future prospects

The rapid development of China's new urbanization has created favorable conditions for economic growth and social development. Urbanization includes population urbanization and land urbanization, among which land urbanization leads to land ecological security problems. At present, there is a lack of comprehensive understanding of land ecological security in China's new urbanization construction. This paper aims to fill the gap by systematically combing relevant literature on the connotation, status, and governance of land ecological security in China's new urbanization. Literature review shows that China's land ecological security is still at a low level, and the new urbanization construction has significant impacts on land ecological security. Land contamination is the most critical factor threatening land ecological security, and there are differences in the levels of land contamination and types of pollutants in different new urbanization construction forms. According to an example of land ecological security governance with enterprises as the main body and multiple subjects cooperating, the governance of land ecological security needs to integrate a variety of different subjects to coordinate governance. Future research directions should focus on the construction of land ecological security assessment index system, development of land contamination multi-level control technology, and construction of multi-subject collaborative governance model with "government-enterprise-social organization-residents."

Distribution of cadmium and lead in soil-rice systems and their environmental driving factors at the island scale

Toxic elements, such as Cd and Pb are of primary concern for soil quality and food security owing to their high toxicity and potential for bioaccumulation. Knowledge of the spatial variability of Cd and Pb in soil-rice systems across the landscape and identification of their driving factors are prerequisites for developing appropriate management strategies to remediate or regulate these hazardous contaminants. Considering the role of rice (Oryza sativa) as a dietary staple in China, this study aimed to examine the distribution patterns and drivers of Cd and Pb in tropical soil-rice systems across Hainan Island. To achieve this goal, 229 pairs of representative paddy soil and rice samples combined with a set of environmental covariates at the island scale were systematically analyzed. Arithmetic mean values (AMs) of Cd and Pb in rice were 0.080 and 0.199 mg kg-1, and exceeded the standard limits by 27.1% and 22.7%, respectively. We found that the AMs of Cd and Pb concentrations in paddy soil were 0.294 and 43.0 mg kg-1. Additionally, Cd in 29.26% of soil samples and Pb in 11.35% of soil samples exceeded the risk screening value for toxic elements. The enrichment factor generally showed that soil Cd and Pb on Hainan Island were both moderately enriched. Results obtained from both Spearman's correlation and stepwise regression analyses suggest that the concentrations of soil Cd and Pb are significantly influenced by the soil Na and Fe concentrations. Specifically, an increment of 1 g kg-1 in soil Na caused a rise of soil Cd and Pb by 57.1 mg kg-1 and 34.4 mg kg-1, respectively, while an increase of 1 g kg-1 in soil Fe resulted in a rise by 25.0 mg kg-1 and 14.5 mg kg-1. Similarly for rice grains, an increment of 1 g kg-1 in soil Ca resulted in a rise of rice Pb by 30.8 mg kg-1, whereas an increase of 1 g kg-1 in soil Mg led to a decrease in rice Pb by 14.8 mg kg-1. However, no significant correlation between soil Se and rice Cd concentrations was found. Furthermore, the result of geographically weighted regression revealed that the impacts of soil Na, Ca, Fe, and Mg on rice Cd were more significant in the western region, whereas the effects of soil Na and Fe on rice Pb were stronger in the northeastern region. This study provides new insights for the identification of factors influencing the distribution and accumulation of Cd and Pb in tropical island agroecosystems.

Spatial distribution characteristics and risk assessment of soil heavy metal pollution around typical coal gangue hill located in Fengfeng Mining area

The pollution of heavy metals in soil caused by exposed coal gangue and its prevention and control has become a hot issue restricting the green mining of coal in China. Nemerow integrated pollution index (NIPI), potential ecological risk index (RI) and human health risk assessment model were used to evaluate the pollution and risk of heavy metals (Cu, Cr, As, Pb) in the soil around the typical coal gangue hill in Fengfeng mining area of China. The results show that: firstly, the accumulation of coal gangue leads to the enrichment of four heavy metals in the surrounding shallow soil, and NIPI and RI were 1.0-4.4 and 21.63-91.28, respectively. The comprehensive pollution level of heavy metals in soil reached the warning line and above, and the potential ecological risk level reached slightly and above. When the horizontal distance exceeded 300 m, 300 m and 200 m, respectively, the influence of coal gangue hill on the heavy metal content in shallow soil, the comprehensive pollution level of heavy metals and the potential ecological risk level basically disappeared. In addition, based on the potential ecological risk assessment results and main risk factors, the ecological risk configuration of the study area was divided into five categories: "strong ecological risk + As," "intermediate ecological risk + As + Cu," "intermediate ecological risk + As + Cu or Pb," "minor ecological risk + As + Cu" and "minor ecological risk + As + Cu or Pb." The hazard index (HI) and total carcinogenic risk (TCR) of shallow soil polluted by heavy metals in the study area were 0.24-1.07 and 0.41 × 10-4-1.78 × 10-4, respectively, which posed non-carcinogenic and carcinogenic risks to children, but the risks were controllable. This study will help to take strategic measures to accurately control and repair the heavy metal pollution in the soil around the coal gangue hill and provide a scientific basis for solving the safe use of agricultural land and realizing the construction of ecological civilization.

Investigating metal pollution in the food chain surrounding a lead-zinc mine (Northwestern Iran); an evaluation of health risks to humans and animals

The current study aims to evaluate the health risk of heavy metals for humans and animals in the Angouran mining complex (northwest of Iran). Twenty-five plant species and their corresponding soils (natural soils) were collected along with mine tailings samples. The carcinogenic and non-carcinogenic risks of heavy metals (Zn, Pb, Cd, Cr, and Co) for humans using the hazard quotient (HQ) and hazard index (HI) were evaluated. Moreover, the health risk caused by forage feeding to grazing ruminants (cow and sheep) and the risk associated with animal products consumption by humans in the soil-plant-animal transfer system were assessed. The value of HI in natural soils (rangeland use) was less than one (HI < 1), while regarding tailings, the HQ via oral ingestion and the HI were greater than one (HI & HQ > 1). The range of total carcinogenesis risk in natural soils exceeded the target risk (Risk < 10-6) and for tailings, it showed the probability of cancer risk, 1 person per 3636 populations, which is much higher than the acceptable or tolerable range (10-4 < Risk < 10-6). Regarding the animal health risk, the content of Pb and Cd in most of the animal organs was higher than the control values. In turn, dietary exposure to Pb and Cd is worrying for residents due to exceeding the provisional tolerable weekly intake (PTWI). This comprehensive study suggests the necessity of risk assessment of mining sites in Iran and immediate control measures to diminish pollutants.

Forage accumulation of potentially toxic elements (PTEs) from soils around Kilembe copper mine, Western Uganda

Past copper mining within Kilembe valley between 1956-1982 left behind mine tailings rich in potentially toxic elements (PTEs). This study was conducted to assess the concentrations of PTEs in soils and the potential uptake by forage. Tailings, soils and forage were collected and analyzed using ICP-MS. The study established that over 60% of grazed plots contained high concentrations of Cu, Co, Ni and As. Copper in 35%, Co in 48% and Ni in 58% of forage soil plots exceeded the thresholds for agricultural soils. Bio-accumulation of Zn and Cu, was observed. Zinc in 14% of guinea grass (Panicum maximum), 33% coach grass (Digitalia Scarulum) and in 20% of elephant grasses (Penisetum perpureun) exceeded thresholds of 100-150 mg kg-1. Copper (Cu) concentrations in 20% of Penisetum perpureun and 14% of Digitalia Scarulum exceeded grazing thresholds of 25 mg kg-1. Containment of tailing erosion should be explored to control erosion of tailings into grazing areas.

Spatial response relationship between mining and industrial activities and eco-environmental risks in mineral resource-based areas

Mining and industrial activities in mineral resource-based areas are important sources of potentially toxic elements (PTEs) in the soil, which lead to spatial heterogeneity in regional eco-environmental risks. In this study, we analysed the spatial response relationship between mining and industrial activities and eco-environmental risks using Anselin local Moran's I index and bivariate local Moran's I index. The results showed that the proportions of moderate, moderate to strong, and strong pollution of PTEs in the study area reached 30.9%. The high clusters of PTEs ranged from 5.4 to 13.6%, and were mainly distributed around cities. The influence of different types of metal mines on eco-environmental risks was nonferrous metal mines > precious metal mines > ferrous metal mines. In turn, that of different pollution enterprises was manufacturing industry > other industries > power and thermal industries. Our research demonstrates that there was a significant spatial response relationship between densities of mines and enterprises and eco-environmental risk level. High-density metal mines (5.3/100 km2) and high-density pollution enterprises (10.3/100 km2) resulted in the local high risk. Consequently, this study provides a basis for regional eco-environmental risk management of mineral resource-based areas. With the gradual depletion of mineral resources, high-density pollution enterprise area should be paid more attention to, which would pose a threat not only to the environment but also to population health.

Potential roles of the rhizospheric bacterial community in assisting Miscanthus floridulus in remediating multi-metal(loid)s contaminated soils

Phytoremediation technology is an important approach applied to heavy metal remediation, and how to improve its remediation efficiency is the key. In this study, we compared the rhizospheric bacterial communities and metals contents in Miscanthus floridulus (M. floridulus) of four towns, including Huayuan Town (HY), Longtan Town (LT), Maoer Village (ME), and Minle Town (ML) around the lead-zinc mining area in Huayuan County, China. The roles of rhizospheric bacterial communities in assisting the phytoremediation of M. floridulus were explored. It was found that the compositions of the rhizospheric bacterial community of M. floridulus differed in four regions, but majority of them were heavy metal-resistant bacteria that could promote plant growth. Results of bioconcentration factors showed the enrichment of Cu, Zn, and Pb by M. floridulus in these four regions were significantly different. The Zn enrichment capacity of ML was the strongest for Cu and stronger than LT and ME for Pb. The enrichment capacity of LT and ML was stronger than HY and ME. These bacteria may influence the different heavy metals uptake of M. floridulus by altering the soil physiochemical properties (e.g., soil peroxidase, pH and moisture content). In addition, co-occurrence network analysis also showed that LT and ML had higher network stability and complexity than HY and ME. Functional prediction analysis of the rhizospheric bacterial community showed that genes related to protein synthesis (e.g., zinc-binding alcohol dehydrogenase/oxidoreductase, Dtx R family transcriptional regulators and ACC deaminase) also contributed to phytoremediation in various ways. This study provides theoretical guidance for selecting suitable microorganisms to assist in the phytoremediation of heavy metals.

Application potential of Vaccinium ashei R. for cadmium migration retention in the mining area soil

Despite numerous reports on phytoremediation of heavy metals contaminated soil, there are few reports on plant retention of heavy metals in the mining area slope. This study was the first of its kind to explore the cadmium (Cd) retention capacity of the blueberry (Vaccinium ashei Reade). Firstly, we investigated the stress response of blueberry to different soil Cd concentrations (1, 5, 10, 15, 20 mg/kg) to assess its potential for phytoremediation by pot experiments. The results showed that the blueberry biomass exposed to 10 and 15 mg/kg Cd was significantly increased compared with the control (1 mg/kg Cd); the blueberry crown increased by 0.40% and 0.34% in 10 and 15 mg/kg Cd-contaminated soil, respectively, compared with control; the blueberry heigh did not even change significantly in each treatment group; the total chlorophyll content, peroxidase and catalase activity of blueberry were enhanced in 5-20 mg/kg Cd treatments. Furthermore, the Cd contents of blueberry in the root, stem and leaf increased significantly as the Cd concentration of soil increased. We found that more Cd accumulated in blueberry root: the bioaccumulation concentration factor was root > stem > leaf for all groups; the residual-Cd (Cd speciation) in soil increased by 3.83%-411.11% in blueberry-planted versus unplanted groups; blueberry improved the Cd-contaminated soil micro-ecological environment including soil organic matter, available K and P, as well as microbial communities. Then, to investigate the effect of blueberry cultivation on Cd migration, we developed a bioretention model and revealed that soil Cd transport along the model slope was significantly weakened by blueberry cultivation, especially at the bottom of the model. In a word, this research suggests a promising method for the phytoremediation of Cd-contaminated soil and the reduction of Cd migration in mining areas.

Spatiotemporal patterns of soil heavy metal pollution risk and driving forces of increment in a typical industrialized region in central China

Excessive enrichment of soil heavy metals seriously damages human health and soil environment. Exploring the spatiotemporal patterns and detecting the influencing factors are conducive to developing targeted risk management and control. Based on the soil samples of Co, Cr, Cu, Mn, Ni, Pb, Zn, and Cd collected in one typical industrialized region in China from 2016 to 2019, this study analyzed the spatiotemporal pattern of geo-accumulation risk and potential ecological risk based on the spatiotemporal ordinary kriging (STOK) prediction, and probed the driving forces of heavy metal increments with the random forest (RF) regression model. The risk assessment revealed that soils were seriously contaminated by Pb, Cd, and Cu, moderately contaminated by Zn and Mn, and uncontaminated by Co, Cr, and Ni; more than 30% of areas had moderate to high potential ecological risks. From 2016 to 2019, soil heavy metal contents increased in more than 50% of regions and the growth rates of accumulations were ranked as Co (65%) > Ni (56%) > Mn (43%) > Pb (40%) > Cr (36%) > Zn (31%) > Cu (23%) > Cd (3%). High contents and increases of heavy metals in soils near industrial lands are higher. Smelter (24%), mine (20%), and factory (12%) were the major contributing factors for these heavy metal increments, followed by transportation (6%) and population (5%). The results indicated that the management of industrial discharge and contaminated soils should be strengthened to prevent the worsening soil heavy metal pollution in the study area.

Phytoremediation Potential of Native Plant Species in Mine Soils Polluted by Metal(loid)s and Rare Earth Elements

Mining activity has an adverse impact on the surrounding ecosystem, especially via the release of potentially toxic elements (PTEs); therefore, there is an urgent need to develop efficient technologies to remediate these ecosystems, especially soils. Phytoremediation can be potentially used to remediate contaminated areas by potentially toxic elements. However, in soils affected by polymetallic contamination, including metals, metalloids, and rare earth elements (REEs), it is necessary to evaluate the behavior of these toxic elements in the soil-plant system, which will allow the selection of the most appropriate native plants with phytoremediation potential to be used in phytoremediation programs. This study was conducted to evaluate the level of contamination of 29 metal(loid)s and REEs in two natural soils and four native plant species (Salsola oppositifolia, Stipa tenacissima, Piptatherum miliaceum, and Artemisia herba-alba) growing in the vicinity of a Pb-(Ag)-Zn mine and asses their phytoextraction and phytostabilization potential. The results indicated that very high soil contamination was found for Zn, Fe, Al, Pb, Cd, As, Se, and Th, considerable to moderate contamination for Cu, Sb, Cs, Ge Ni, Cr, and Co, and low contamination for Rb, V, Sr, Zr, Sn, Y, Bi and U in the study area, dependent of sampling place. Available fraction of PTEs and REEs in comparison to total concentration showed a wide range from 0% for Sn to more than 10% for Pb, Cd, and Mn. Soil properties such as pH, electrical conductivity, and clay content affect the total, available, and water-soluble concentrations of different PTEs and REEs. The results obtained from plant analysis showed that the concentration of PTEs in shoots could be at a toxicity level (Zn, Pb, and Cr), lower than toxic but more than sufficient or natural concentration accepted in plants (Cd, Ni, and Cu) or at an acceptable level (e.g., V, As, Co, and Mn). Accumulation of PTEs and REEs in plants and the translocation from root to shoot varied between plant species and sampling soils. A. herba-alba is the least efficient plant in the phytoremediation process; P. miliaceum was a good candidate for phytostabilization of Pb, Cd, Cu, V, and As, and S. oppositifolia for phytoextraction of Zn, Cd, Mn, and Mo. All plant species except A. herba-alba could be potential candidates for phytostabilization of REEs, while none of the plant species has the potential to be used in the phytoextraction of REEs.

Soil potentially toxic element pollution at different urbanization intensities: Quantitative source apportionment and source-oriented health risk assessment

Potentially toxic element (PTE) pollution of urban soils has become the focus of social concern, but the differences of the sources, pollution levels and source-oriented human health risks (HHR) of PTE in urban soils among different urban intensity areas is rarely known. This study explored a comprehensive scheme that combined positive matrix factorization model and source-oriented assessment to quantitatively assess the priority pollution sources and HHR in urban soils from areas with different urbanization intensities. All the average values for PTE concentrations, except for Cr, were higher than their corresponding background values. The contributions made by the four sources (atmospheric deposition, agricultural activities, traffic activities, and natural sources) were relatively similar (22.29-29.89%) in the low urbanization intensity (LUI) area, whereas traffic activities and atmospheric deposition made the greatest contributions in the medium urbanization intensity (MUI) (29.12%) and the high urbanization intensity (HUI) (38.97%) areas, respectively. The geo-accumulation index results revealed that Cd was the most polluting element and the HUI area had the highest pollution levels. The content-oriented assessment of HHR demonstrated that the non-carcinogenic risks were acceptable, but the carcinogenic risks were unacceptable. According to the source-oriented HHR assessment, among the anthropogenic activities, atmospheric deposition contributed the most to carcinogenic risk of children in all areas, and atmospheric deposition, traffic activities and agricultural activities contributed the most to the carcinogenic risk of adults in HUI, MUI and LUI, respectively. This suggest that control measures need to be tailored to the appropriate urbanization intensity to effectively curb PTE pollution caused by anthropogenic activities.

Migration, accumulation, and risk assessment of potentially toxic elements in soil-plant (shrub and herbage) systems at typical polymetallic mines in Northwest China

In grassland systems of the semi-arid mining area, the migration, accumulation, and bioavailability of potentially toxic elements (PTEs) are important ecological and health risk issues. Thirty-eight pairs of topsoil (0-20 cm) and plant samples were collected around Baiyin City and in Dongdagou stream valley to investigate the migration of PTEs in soils, transfer of PTEs in soil-plant (shrub and herbage) systems, and assess the risk in soils and plants. The total concentrations of PTE (Hg, As, Cu, Zn, Cd, and Pb) were analyzed following digestion in mixture acid solution, and bioavailable PTE was extracted with a strong chelating agent (DTPA-TEA-CaCl₂). The transfer factor (TF) and bioaccumulation factor (BCF) were calculated to examine the migration of PTEs in soil-plant. Hazard quotient (HQ) and total hazard index (THI) were calculated to assess the risk and migration of PTEs in soils. The results showed that PTEs in soils and plants of study area exceeded the soil background value and Hygienic Standard for Feeds. Correlation among the total Hg, As, Cu, Zn, Cd, and Pb in soils of Dongdagou stream valley was significant at p < 0.01. A good correlation was exhibited between PTEs in root/aboveground parts of plants and DTPA-soil extractable. Difference of TF and BCF was existed between Dongdagou stream valley and around Baiyin City. Hg, Cu, Zn, Cd, and Pb were mainly accumulated in soils near the mining area. The calculated THI exceeded 1, and As and Pb were the major risk factors. The ability to absorb and transfer Hg, As, Cu, and Pb of plants was lower in more serious polluted area. As had a stronger migration capacity in study area. PTEs in soils had an adverse health effect for residents, and PTEs in plants may cause toxicity to cattle and sheep.

Prediction of the concentration of antimony in agricultural soil using data fusion, terrain attributes combined with regression kriging

Potentially toxic elements in agricultural soils are primarily derived from anthropogenic and geogenic sources. This study aims to predict and map antimony (Sb) concentration in soil using multiple regression kriging in two distinct modeling approaches, namely Sb prediction using data fusion coupled with regression kriging (scenario 1) and Sb prediction using data fusion, terrain attributes, and regression kriging (scenario 2). Cubist regression kriging (cubist_RK), conditional inference forest regression kriging (CIF_RK), extreme gradient boosting regression kriging (EGB_RK) and random forest regression kriging (RF_RK) were the modeling techniques used in the estimation of Sb concentration in agricultural soil. The validation results suggested that in scenario 1, EGB_RK was the optimal modeling approach for Sb prediction in agricultural soil with root mean square error (RMSE) = 1.31 and mean absolute error (MAE) = 0.61, bias = 0.37, and high coefficient of determination R² = 0.81. Similarly, the EGB_RK was also the optimal modeling approach in scenario 2, with the highest R² = 0.76, RMSE = 0.90, bias = 0.06, and MAE = 0.48 values than the other regression kriging modeling approaches. The cumulative assessment suggested that the EGB_RK in scenario 2 yielded optimal results compared to the respective modeling approach in scenario 1. The uncertainty propagated by the modeling approaches in both scenarios indicated that the degree of uncertainty during the modeling process was distributed across the study area from a low to a moderate uncertainty level. However, cubist_RK in scenario 2 exhibited some elevated spots of uncertainty levels. As a result, the combination of data fusion, terrain attributes, and regression kriging modeling approaches produces optimal results with a high R² value, minimal errors as well as bias. Furthermore, combining terrain attributes with data fusion is promising for reducing model error, bias and yielding high-accuracy predictions.

Effects of anthropogenic and natural environmental factors on the spatial distribution of trace elements in agricultural soils

The concentrations of trace elements in agricultural soils directly affect the ecological security and quality of agricultural products. A comprehensive study aimed at quantitatively analyze the effects of anthropogenic and natural environmental factors on the spatial distribution of heavy metals (HMs) and selenium (Se) in agricultural soils in a typical grain producing area of China. Factors considered in this study were parent rock, soil physicochemical properties, topography, precipitation, mine activity, and vegetation. Results showed that the median values of Zn, Cd, Cr, and Cu of 111 topsoil samples exceeded the background values of Guangxi province but were lower than the relevant national soil quality standards, and 85% of soil samples were classified as having rich Se levels (0.40 -3.0 mg kg^-1). The potential ecological risk index of soil heavy metals as a whole was low, with Cd in 9% of the samples posing moderate ecological risk. The concentrations of heavy metals and Se were relatively high in soils from shale rock. Soil properties, mainly Fe₂O₃ and Mn played a dominant role on soil HMs and Se concentrations. Based on GeoDetector, we found that the interaction effects of two factors on the spatial differentiation of soil HMs and Se were greater than their sum effect. Among the factors, Mn enhanced the explanatory power of the model the most when interacting with other factors for soil Zn; the greatest interactive effect was between distance from mining area and Mn for Cd (q = 0.70); Fe₂O₃ significantly promoted the spatial differentiation of soil Cr, Cu and Se when interacting with other factors (q > 0.50). These findings contribute to a better understanding of the factors that drive the distribution of HMs and Se in agricultural soils.

Spatial and temporal distribution and influencing factor analysis of the malignant tumor mortality rate around the mining area

Mining activities can threaten residents' health even lives. Integrating spatial empirical Bayesian smoothing, joinpoint regression and spatiotemporal scanning methods, we analyzed aggregations and possible factors of four tumor mortality rates at township and village scales from 2012 to 2016 in Suxian district of Hunan Province, China. Results indicate: (1) Mortality rates were ranked: lung cancer > liver cancer > gastric cancer > colorectal cancer. (2) Lung cancer had a higher five-year mortality rate in the middle; relative risk (RR) of death from lung cancer from 2012 to 2015 in Xujiadong Village was 7.48. Liver cancer had a higher five-year mortality rate in the Middle West; RR in areas centered on Nanta Street with a radius of 9.87 km from 2015 to 2016, was 1.83. Gastric cancer had a higher five-year mortality rate in the east; RR in Xujiadong Village from 2012 to 2014 was 6.9. Five-year mortality rate of colorectal cancer was higher in the northwest; RR in regions centered on Huangcao Village with a radius of 12.11 km in 2016, was 2.88. (3) Pollution from ore mining and smelting, heavy metal and non-metallic, and mine transportation were the main possible factors. This research provides a method reference for studying spatiotemporal patterns of disease in China even the world.

Variation in Road Dust Heavy Metal Concentration, Pollution, and Health Risk with Distance from the Factories in a City-Industry Integration Area, China

Road dust samples around three typical factories, F1, F2, and F3, in the National Zhengzhou Economic and Technology Development Zone (ZETZ), China, were collected to study the variation in heavy metal concentration (As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn), pollution, and health risk with distance from the factories. The results indicated that the concentrations of all the elements near F1 were higher than near both F2 and F3. Apart from Co, Mn, and Cu in some dust samples, all the element concentrations were higher than the corresponding background values (BCs), to varying degrees. The spatial distributions of the heavy metals surrounding the factories followed the normal distribution. The peak values of element concentrations occurred at 300~400 m away from the factories, except for Hg, which continued increasing more than 500 m away from the factories. The fluctuation curves of the pollution load index value calculated according to the BCs for F1, F2, and F3 all had two peaks, a "small peak" and a "large peak", appearing at about 30 m and 300 m, respectively. For the hazard index and the total carcinogenic risk, the peak values all appeared at 400 m, with the curves following the normal distribution. Exposure to road dust containing non-carcinogenic and carcinogenic elements around F1 was greater than around F2 or F3. In conclusion, our results provide a reference for pursuing effective prevention of dust heavy metal pollution around modern manufacturing factories.

Responsive change of crop-specific soil bacterial community to cadmium in farmlands surrounding mine area of Southeast China

In arable soils co-influenced by mining and farming, soil bacteria significantly affect metal (Cadmium, Cd) bioavailability and accumulation. To reveal the soil microecology response under this co-influence, three intersection areas (cornfield, vegetable field, and paddy field) were investigated. With a similar nutrient condition, the soils showed varied Cd levels (0.31-7.70 mg/kg), which was negatively related to the distance from mining water flow. Different soils showed varied microbial community structures, which were dominated by Chloroflexi (19.64-24.82%), Actinobacteria (15.49-31.96%), Acidobacteriota (9.46-20.31%), and Proteobacteria (11.88-14.57%) phyla. A strong correlation was observed between functional microbial taxon (e. g. Acidobacteriota), soil physicochemical properties, and Cd contents. The relative abundance of tolerant bacteria including Vicinamibacteraceae, Knoellia, Ardenticatenales, Lysobacter, etc. elevated with the increase of Cd, which contributed to the enrichment of heavy metal resistance genes (HRGs) and integration genes (intlI), thus enhancing the resistance to heavy metal pollution. Cd content rather than crop species was identified as the dominant factor that influenced the bacterial community. Nevertheless, the peculiar agrotype of the paddy field contributed to its higher HRGs and intlI abundance. These results provided fundamental information about the crop-specific physiochemical-bacterial interaction, which was helpful to evaluate agricultural environmental risk around the intersection of farmland and pollution sources.

The impact of chromium ion stress on plant growth, developmental physiology, and molecular regulation

In recent years, heavy metals-induced soil pollution has increased due to the widespread usage of chromium (Cr) in chemical industries. The release of Cr into the environment has reached its peak causing hazardous environmental pollution. Heavy metal-induced soil pollution is one of the most important abiotic stress affecting the dynamic stages of plant growth and development. In severe cases, it can kill the plants and their derivatives and thereby pose a potential threat to human food safety. The chromium ion effect on plants varies and depends upon its severity range. It mainly impacts the numerous regular activities of the plant's life cycle, by hindering the germination of plant seeds, inhibiting the growth of hypocotyl and epicotyl parts of the plants, as well as damaging the chloroplast cell structures. In this review article, we tried to summarize the possible effects of chromium-induced stress on plant growth, developmental physiology, biochemistry, and molecular regulation and provided the important theoretical basis for selecting remedial plants in chromium-induced contaminated soils, breeding of low toxicity tolerant varieties, and analyzing the mechanism of plant resistance mechanisms in response to heavy metal stress.

Machine learning can identify the sources of heavy metals in agricultural soil: A case study in northern Guangdong Province, China

Source tracing of heavy metals in agricultural soils is of critical importance for effective pollution control and targeting policies. It is a great challenge to identify and apportion the complex sources of soil heavy metal pollution. In this study, a traditional analysis method, positive matrix fraction (PMF), and three machine learning methodologies, including self-organizing map (SOM), conditional inference tree (CIT) and random forest (RF), were used to identify and apportion the sources of heavy metals in agricultural soils from Lianzhou, Guangdong Province, China. Based on PMF, the contribution of the total loadings of heavy metals in soil were 19.3% for atmospheric deposition, 65.5% for anthropogenic and geogenic sources, and 15.2% for soil parent materials. Based on SOM model, As, Cd, Hg, Pb and Zn were attributed to mining and geogenic sources; Cr, Cu and Ni were derived from geogenic sources. Based on CIT results, the influence of altitude on soil Cr, Cu, Hg, Ni and Zn, as well as soil pH on Cd indicated their primary origin from natural processes. Whereas As and Pb were related to agricultural practices and traffic emissions, respectively. RF model further quantified the importance of variables and identified potential control factors (altitude, soil pH, soil organic carbon) in heavy metal accumulation in soil. This study provides an integrated approach for heavy metals source apportionment with a clear potential for future application in other similar regions, as well as to provide the theoretical basis for undertaking management and assessment of soil heavy metal pollution.

Contamination and Health Risk Assessment of Heavy Metals in Soil and Ditch Sediments in Long-Term Mine Wastes Area

The ecological and health risks posed by wastes discharged from mining areas to the environment and human health has aroused concern. 114 soil samples were collected from nine areas of long-term mine waste land in northwestern Yunnan to assess the pollution characteristics, ecological and health risks of heavy metals. The result revealed that the geo-accumulation indexes were Cd (4.00) > Pb (3.18) > Zn (1.87) > Cu (0.25). Semi-variance analysis revealed that Cd and Cu showed moderate spatial dependency, whereas Pb and Zn showed strong spatial dependency. Cd posed an extreme potential ecological risk. Slopes and ditches were extreme potential ecological risk areas. Non-carcinogenic risk to children from Pb and Carcinogenic risk to adult and children from Cd was non-negligible and direct ingestion was the major source. This study provided a scientific basis for policymakers in management and exposure reduction.

Heavy Metal Sources, Contamination and Risk Assessment in Legacy Pb/Zn Mining Tailings Area: Field Soil and Simulated Rainfall

This study investigated heavy metal(HM) soil pollution and evaluated the risk and sources at a legacy tailings pond's area in Meizhou, China. Result shows that HM accumulation in soil, particularly Cd, Pb, and Zn, were serious. Zn and Cd in tailing soil and all studied elements in field soil had a significant release potential. Four HM sources were identified by positive matrix factorization (PMF) model: cinder and vehicle emissions (11.3%), natural sources (16.3%), tailings pond and human activities (32.8%), tailings pond (39.7%). The soil was severely polluted with Cd, Pb, and Zn, which posed a high potential environmental risk near surrounding area. Column leaching tests showed that large quantities of HMs were released from the tailings soil during simulated rainfall with different pH. This study indicates that the study area has been severely polluted and continues to have a great risk of HM pollution under natural conditions.

Pollution characteristics of soil heavy metals around two typical copper mining and beneficiation enterprises in Northwest China

In order to investigate the situation of heavy metal pollution in the heavy metal industry in Gansu Province, a large copper mining province, two large and typical copper mining and beneficiation enterprises with differences in topographic features, climatic conditions, and soil types were selected as the target of this study based on similar ore types and beneficiation processes. Around these two enterprises, geochemical baselines of the six heavy metals were established, while the degree of local soil heavy metal pollution and potential hazards to humans were assessed based on statistical analysis, single-factor and multi-factor index analysis, and health risk evaluation models. In addition, Spearman's correlation analysis and hierarchical cluster analysis were used to explore the intrinsic association between each heavy metal in the two mining industries to reveal the pattern of soil heavy metal pollution in the copper mining and beneficiation industry and to propose targeted measures to improve and prevent soil heavy metal pollution. The results showed that the heavy metal pollution in the soil around Shengxi Mining Co., Ltd. of Subei County (SX enterprise) was higher than that around Yangba Copper Co., Ltd. of Gansu Province (YB enterprise), but the two enterprises had similar patterns of pollution, with an overall medium level of pollution. The carcinogenic and non-carcinogenic risks for children and adults were within acceptable limits for both enterprises. Besides, the correlation between the different heavy metals to similarity in their sources of contamination and the different degrees of association between the soil heavy metals of the two enterprises due to their environmental characteristics.

Identification priority source of soil heavy metals pollution based on source-specific ecological and human health risk analysis in a typical smelting and mining region of South China

An in-depth understanding of the ecological and health risks posed by heavy metals originating from various pollution sources is critical for foresighted soil-quality management. Based on 220 grid samples (2 × 2 km) analyzed for eight heavy metals (Cd, Hg, As, Pb, Cr, Ni, Cu, and Zn) in the Chenshui (CS) watershed of Hunan Province, China, we applied an integrated approach for identifying and apportioning pollution sources of soil heavy metals and exploring their source-specific pollution risks. This approach consists of three sequential steps: (1) source identification by combining the positive matrix factorization model with geostatistical analysis; (2) quantification of ecological, carcinogenic, and non-carcinogenic risks in a source-specific manner; (3) prioritization of sources in a holistic manner, considering both ecological risks and human health risks. Cd (68.0%) and Hg (13.3%) dominated the ecological risk in terms of ecological risk index; As dominated the non-carcinogenic health risk in terms of total hazard index (THI; adults: 84.8%, children: 84.7%) and the carcinogenic health risk in terms of total carcinogenic risk index (TCRI; adults: 69.0%, children: 68.8%). Among three exposure routes, oral ingestion (89.4-95.2%) was the predominant route for both adults and children. Compared with adults (THI = 0.41, TCRI = 7.01E-05), children (THI = 2.81, TCRI = 1.22E-04) had greater non-carcinogenic and carcinogenic risks. Four sources (F1-4) were identified for the CS watershed: atmospheric deposition related to coal-burning and traffic emissions (F1, 18.0%), natural sources from parent materials (F2, 34.3%), non-ferrous mining and smelting industry (F3, 37.9%), and historical arsenic-related activity (F4, 9.8%). The F3 source contributed the largest (45.2%) to the ecological risks, and the F4 source was the predominant contributor to non-carcinogenic (52.4%) and carcinogenic (64.6%) risks. The results highlight the importance of considering legacy As pollution from abandoned industries when developing risk reduction strategies in this region. The proposed methodology for source and risk identification and apportionment formulates the multidimensional concerns of pollution and the various associated risks into a tangible decision-making process to support soil pollution control.

Comparative analysis unveils the cadmium-induced reproductive toxicity on the testes of Pardosa pseudoannulata

Cadmium (Cd) pollution is one of the most serious heavy metal pollutions in the world, which has been demonstrated to cause different toxicities to living organisms. Cd has been widely suggested to cause reproductive toxicity to vertebrates, yet its reproductive toxicity to invertebrates is not comprehensive. In this study, the wolf spider Pardosa pseudoannulata was used as a bioindicator to evaluate the male reproductive toxicity of invertebrates under Cd stress. Cd stress had no effect on the color, size and length of testis. However, Cd significantly increased the contents of catalase, glutathione peroxidase and malondialdehyde, the antioxidants in the testis of P. pseudoannulata. Then we analyzed the transcriptome of testis exposed to Cd, and identified a total of 4739 differentially expressed genes (DEGs) compared to control, with 2368 up-regulated and 2371 down-regulated. The enrichment analysis showed that Cd stress could affect spermatogenesis, sperm motility, post-embryonic development, oxidative phosphorylation and metabolism and synthesis of male reproductive components. At the same time, the protein-protein interaction network was constructed with the generated DEGs. Combined with the enrichment analysis of key modules, it revealed that Cd stress could further affect the metabolic process in testis. In general, the analysis of testicular damage and transcriptome under Cd stress can provide a novel insight into the reproductive toxicity of Cd on rice filed arthropods and offer a reference for the protection of rice filed spiders under Cd pollution.