Process, influencing factors, and simulation of the lateral transport of heavy metals in surface runoff in a mining area driven by rainfall: A review

Fissure channeling caused anomalous uranium concentration in groundwater downstream of a uranium mine tailings impoundment site

Widespread uranium mining resulted in large amounts of radioactive wastes. While impoundment has been commonly used to store U-laden wastes, information has been lacking on the secondary contamination potential. We conducted a field-scale study at a prototype uranium mine tailings impoundment site in China to understand the abnormally high concentrations (0.90-36.7 μg/L) of uranium recently observed in the downstream groundwater. Based on on-site hydrogeological measurements and modeling analysis, fracture channels were identified in the aquifer. To consider the fissure channeling flow in the solute transport modeling, a modified discrete-continuum coupled modeling approach was established, which considers the flow dynamics within both the matrix domain and the preferential flow through the main fractures. The model was not only able to simulate the observed uranium concentrations at various locations, but also to reveal the U release rate from the impoundment. Moreover, the contaminant transport modeling supported that the abnormal uranium concentrations were attributed to the channeling flow in the aquifer fractures, which was confirmed through geological analysis. In addition to valuable field data, this work offered an effective modeling approach for assessing contaminant transport through fissure channeling and the associated environmental impact of waste disposal facilities in complex hydrogeological settings.

Heavy metal pollution migration and its ecological impact on microbial communities in the karst region of Guangxi

This study examines the factors driving heavy metal pollution in karst soils and assesses the associated environmental risks. Using a representative karst depression in Guangxi as a case study, we measured soil concentrations of arsenic (As), cadmium (Cd), lead (Pb), and zinc (Zn). These concentrations were analyzed in relation to soil physicochemical properties, morphology, and geological factors to understand pollution dynamics and migration patterns. The findings highlight As as the dominant contaminant, primarily existing in a highly mobile, reduced form. Further analysis shows that its mobility is strongly influenced by soil pH and available phosphorus (AP), emphasizing the interplay between soil chemistry and pollutant behavior. The study area, dominated by limestone strata, experiences changes in soil oxidative conditions due to rainfall, which significantly influence As accumulation and migration. The presence of abundant iron and manganese oxides further amplifies these effects. According to the Hakanson index, As contamination in surface soils presents a high ecological risk to the surrounding environment. Microbial analysis reveals a positive correlation between Proteobacteria and both the total concentration and chemical forms of As. These bacteria demonstrate resistance to As and contribute to increased residual As content, thereby reducing its toxicity and mobility. Additionally, Proteobacteria thrive in As-contaminated soils with high pH and low nutrient levels, highlighting their potential for bioremediation. This positions Proteobacteria as a promising microbial group for managing As pollution in the region.

Effects of geographical and soil factors on soilś arsenic levels: a case study in typical arsenic-contaminated paddy fields based on machine learning

Heavy metal pollution in agricultural land has emerged as a contemporary environmental issue of prominent concern. The concentration of heavy metals in soil is influenced not only by inherent soil properties but also by geographical factors. Moreover, the identification of its influencing factors is challenging because of the intricate interactive effects among them. Previous studies primarily focused on single-factor identification and spatial distribution characterization, neglecting the characteristics and spatial features of soil heavy metal concentration under the interactive effects of geographical factors and soil properties. This study assessed the influence of geographical factors, soil properties, and their interactive effects on the spatial distribution of soil arsenic (As), in a typical arsenic-contaminated paddy field area by employing machine learning, analysis of variance, and spatial analysis methods. The findings show that the prediction performance (R2) of the random forest model for soil As concentration was 0.596, and the primary factors influencing the distribution of soil As are elevation, roads, rivers, soil pH, and cation exchange capacity (CEC). Moreover, the interactive effect between elevation and soil CEC had a significant effect on soil As (p < 0.05), exhibiting spatially homogeneous characteristics. The interactive effect between rivers and both soil pH and soil CEC exhibited spatially heterogeneous effects on soil As (p < 0.1). Additionally, the interactive effect between roads and soil pH affected soil As (p < 0.05), with spatially homogeneous characteristics. By identifying the main influencing factors of As in paddy soil, this study further explores the variation characteristics of soil As concentration under the interactive effects of geographical factors and soil properties. These insights can serve as a valuable reference for the precise prevention of As pollution in paddy field area.

Lead source apportionment and climatic impacts in rural environmental justice mining communities

After a sequence of natural disasters in Gila County, Arizona, USA environmental justice (EJ), mining areas, community members raised concerns about metal(loid)s exposure and origin. To address these concerns, non-residential sediments (0-2 cm, 2-15 cm, and 15-30 cm), household soil (0-2 cm), and indoor and outdoor dust samples were analyzed for metal(loid)s concentration and Pb isotopes via inductively coupled plasma mass spectrometry. To identify the potential sources of Pb, 37 studies were considered, and 21 different Pb isotopic ratios were documented and compared. Spearman's correlation and principal component analysis were used to investigate the co-occurrence of metal(loid)s associated with Pb. The results demonstrated a clear association (p < 0.05) between Pb and mining activity in households and non-residential locations as well as a co-occurrence with As, Cd, Cu, Mo, Sb, and Zn at 0-2 cm and in non-residential with As, Cd, and Zn at 2-15 cm and 15-30 cm. The outdoor household dust was impacted by a mixture of Pb sources and was associated with metal(loid)s coming from mining, wildfire, lead based-paint and landfill, whereas indoor Pb dust was associated mainly with metal(loid)s coming from geogenic sources. Further, 66% of town/city sediment samples across depth and 53.8% of outdoor dust samples were aligned with mining fingerprint and 30.1% of outdoor dust and 25% of household soil samples were aligned with the wildfire Pb isotopic ratio/fingerprint. The Positive Matrix Factorization model illustrates flood's ability to remobilize metal(loid)s from neighboring mine sites to the households' locations. Currently there is no established Pb isotopic ratio composition for wildfires in Arizona; this study lays the foundation for understanding the complex relationship between the myriads of lead sources in our environment, wildfires, and flooding.

Simulating the effects of drip and flood irrigation on the leaching, migration, and redistribution of heavy metals in contaminated soil

BACKGROUND

The extensive adoption of drip irrigation globally has adverse effects on the ecological integrity of soil, leading to significant changes in the distribution of heavy metals and other pollutants across various soil layers. However, the mechanisms underlying such changes are still unclear.

PURPOSE

This study explores the effects of different irrigation methods (drip and flood irrigation) on the heavy metal behaviors in contaminated soils, including leaching, migration, and redistribution within agricultural practices.

MATERIALS AND METHODS

The soil column leaching experiments simulating drip and flood irrigation were performed. The correlation tests and one-way analysis of variance were used for data analysis.

RESULTS

Compared to flood irrigation, drip irrigation resulted in higher leaching; as high as twice of heavy metal redistribution across soil columns. The average changes in heavy metal concentration before and after drip irrigation and flood irrigation were 16.3 % and 3.3 %, respectively. The soil redox potential and contact time were the main factors affecting the migration of heavy metals during the initial stages of irrigation implementation; however, hydrodynamic factors were also influential.

CONCLUSION

Drip irrigation may pose a serious threat to food safety in the areas with considerable heavy metal pollution due to increased heavy metal leaching and the accumulation of heavy metals near the soil surface, in particular at lower water volume.

Influence of sewage sludge compost on heavy metals in abandoned mine land reclamation: A large-scale field study for three years

Using sewage sludge compost (SSC) for abandoned mine land reclamation supports ecological sustainability, but the environmental behavior of heavy metals in this process lacks systematic field validation. Here we analyzed the dynamic changes in heavy metal composition in topsoil, surface runoff, and subsurface infiltration after large-scale reclamation. Results show that SSC application promoted plant growth by 2-4 times, enhanced the physicochemical structure of the topsoil, and increased the levels of organic matter and inorganic nutrients. Most heavy metals exhibited higher retention in SSC-treated areas compared to non-SSC areas; nonetheless, they remained within low toxicity risk levels overall. Surface runoff from areas with high SSC content exhibited elevated concentrations of heavy metals. In the 2020-M225 sample, Cd, Cu, Pb, and Zn concentrations were at least 1.5 times that of M0. Mixing application of SSC further mitigated the subsurface migration of Cr, Cu, Pb, and Zn compared to S120, with concentrations of As, Cr, Pb, and Zn in 2020-M225 being less than 1/10 of those in M0. Correlation analysis demonstrates that SSC regulated topsoil pH and the contents of organic matter, phosphorus, and Fe and Al (hydr)oxides, which synergistically enhanced the adsorption and complexation of most toxic heavy metals, thereby reducing their migratory pollution over time. This study suggests that practical SSC application (up to 225 t/ha) results in long-term effects on heavy metals characterized by in-situ multi-effect stabilization, rather than increasing overall environmental risks, and provides a technological foundation for ensuring the safe use of SSC in mine reclamation.

Impacts of non-ferrous metal mining on soil heavy metal pollution and risk assessment

Mining activities significantly contribute to soil contamination, posing risks to the environment and human health. This study evaluates the environmental and health impacts of four non-ferrous mining types, which have been rarely examined globally. It highlights gaps in existing datasets from selected mining sites and sampling practices, correlating soil pollutants with atmospheric variables. Overall, the geoaccumulation index (Igeo) of all mine soil types were in the order Hg > Cd > As>Cu > Pb > Zn > Ni > Cr > Mn > Co indicating that Hg and Cd are the main metals posing risks from soil pollution associated with all mining activities. Notably, the highest As contamination (Igeo: class 6) occurs in copper mines in China, Russia, and Portugal, and in lead‑zinc mines in Sweden and Mexico. Cd contamination (Igeo: class 6) was most severe in lead‑zinc mine soils across Tunisia, China, Ireland, Spain, Slovenia, Mexico, France, North Macedonia, Bulgaria, and Egypt, while uranium mines in the USA showed notably lower levels of Cd contamination (Igeo: class 2). Hg contamination levels were higher in gold mine-associated soils in Iran, China, Myanmar, Brazil and Nigeria. About half of the sites faced the highest ecological risks from lead‑zinc mining areas in Vietnam, Tunisia, and Sweden, while copper mines in Cyprus, China, and Portugal had only 8 % in that category. Gold mines in China, Nigeria, and Brazil showed considerable risks from As and Hg, whereas uranium mines in Germany and Bulgaria had a lower ecological risk, due to better environmental management. The primary exposure route for heavy metals is ingestion, with the hazard index (HI) for non-carcinogenic impacts being acceptable for most elements, except for As. Carcinogenic risks are notably present in Brazil, Spain, Slovenia, Mexico, China, and the UK. Hence, this review underscores the urgent need to address heavy metal pollution from global metal mining and offers policy recommendations for effective environmental management and restoration efforts.

Interactions between riverine sediment organic matter molecular structure and microbial community as regulated by heavy metals

Heavy metals (HMs) exert a profound influence on soil carbon storage potential. The microbially-mediated association between HM content and carbon structure in riverine sediments remains unclear in lotic ecosystems. We investigated the spatiotemporal variations of HMs content, carbon content and microbial communities in riverine surface sediments, and further explored the chemical structure of sediment organic carbon (OCsed), the molecular composition of dissolved organic matter (DOM), and their interactions with microorganisms. The spatial-temporal variations in the chemical structure of OCsed, excluding O-alkyl C, were minimal, whereas the molecular composition of DOM underwent substantial fluctuations with seasons and sites. Significantly positive correlations were observed between Cu, Zn, Pb, and OCsed content. However, within a certain content range, HMs can promote the mineralization risk of OCsed, as reflected in their ability to increase the proportion of unstable O-alkyl C and decrease the proportion of stable carbon fractions (aromatic C, alkyl C, and phenolic C). Additionally, appropriate contents of HMs also improved the abundance and diversity of bacteria and fungi. Bacteria consumed more stable OC under HMs enrichment, whereas fungi increased the consumption of DOM fractions (condensed aromatic hydrocarbons and amino sugars). Our findings contribute to the understanding of the molecular mechanisms of carbon storage in HM-rich riverine sediments.

Assessment of soil heavy metal pollution: a case study of the abandoned mine of Ichmoul, Algeria

This study aimed to assess and evaluate heavy metal contamination in the soil and sediment surrounding the Ichemoul lead mine northeast of Algeria. Soil and sediment samples were analyzed to determine the pH, particle size, organic matter (OM) content, and heavy metal (HM) concentration. The total HM concentration was determined by digestion in a mixture of strong acids. Flame atomic absorption spectrophotometry (FAAS) was used to determine the copper (Cu), lead (Pb), and zinc (Zn) contents in the obtained solutions. Major elements were analyzed by X-ray fluorescence spectrometry (XRF). X-ray diffraction (XRD) was used to determine the mineralogy of processing tailings, lead concentrates inside the abandoned plant, and the soil surrounding the mine. The potential environmental contamination was assessed by comparing the concentrations of Cu, Zn, and Pb with the geochemical background and using the following pollution indices: enrichment factor (EF), geoaccumulation index (Igeo), and Nemerow pollution index (NPI). Most soil samples had Cu, Pb, and Zn concentrations significantly exceeding local and regional background values. Spearman correlation, variance coefficient (VC), and HM spatial distribution suggested anthropic contamination in this area due to the storage of ore-rich sulfide minerals and ore extraction and processing. The EF showed that the soil was significantly enriched in Pb. The Igeo and NPI showed that the soil near the old abandoned plant was severely contaminated. The mineralogical and chemical composition of the concentrate showed the presence of galena, anglesite, barite, and chalcopyrite, with 78% of the lead as a product of the flotation processes prevalent at that time. Its presence under weathering processes has contributed significantly to the soil contamination surrounding the treatment plant with heavy metals, especially Pb. The chemical composition of ore processing waste indicated a deficiency in heavy metals, so it does not provide an environmental risk. The spatial interpolation results of the HMs indicate that high concentrations of these elements are closer to sources of contamination. The hotspots with high HMs concentrations are limited and localized due to the carbonate environment, neutral to alkaline pH, and fine soil fractions.

A novel graph convolutional neural network model for predicting soil Cd and As pollution: Identification of influencing factors and interpretability

Soil pollution caused by toxic metals poses serious threats to the ecological environment and human well-being. Accurately predicting toxic metal concentrations is critical for safeguarding soil environmental security. However, the distribution of soil toxic metal concentrations often exhibits significant spatial heterogeneity and intricate correlations with other environmental influencing factors, posing substantial challenges to accurate prediction. This study delves into the prospective application of a novel graph convolutional neural network model, namely DistNet-GCN. By capitalizing on the spatial relationships among sampling points, this model endeavors to predict cadmium (Cd) and arsenic (As) concentrations in soil. The distinctive feature of this model resides in its capacity to mimic the transmission process of relationships between soil Cd/As concentrations and the environmental influencing factors within a local spatial scope by integrating the powerful ability of GCN to extract the inter-node dependencies in complex networks. Subsequently, it extracts the critical features of the dataset from a spatial relationship graph structure by taking the spatial positions of sampling points as network nodes, the concentrations of toxic metals as node labels, and environmental factors as node attributes. In comparison with traditional models, the DistNet-GCN model achieves the highest prediction accuracy for soil Cd and As concentrations. Specifically, the R2 values reach 0.91 and 0.94 respectively, which signify improvements of 21.33 % and 9.30 % over those of Multiple Linear Regression (MLR). The outcome of the interpretability analysis shows that the urban human activities, mining operation, pH, and soil organic matter (SOM) are the most important environmental factors affecting the spatial distribution of soil Cd/As concentrations in the study area. Additionally, the local spatial autocorrelation findings reveal that the Moran's I values for Cd and As are 0.796 and 0.897, respectively, which validate the structural soundness and rationality of the DistNet-GCN model. This study enlightens a novel approach of soil Cd/As concentrations prediction by integrating spatial graph structures into the deep learning models and is significant for uncovering the complex correlations between toxic metal concentrations in soil and various environmental factors.

Natural factor-based spatial prediction and source apportionment of typical heavy metals in Chinese surface soil: Application of machine learning models

Predicting the natural distribution of heavy metals (HMs) in soil is important to understand the potential risk of pollution. However, suitable technologies are still lacking for wide scale due to the large spatial heterogeneity. In this study, we developed machine learning models for predicting natural contents of five typical HMs in soil, including As, Cd, Cr, Hg and Pb. It was found that the optional random forest (RF) model had the best performance with the R2 up to 0.64. Based on this model, potential distribution of the five HMs explored that elevated contents were mainly concentrated in the southwest and south central of China. Feature analysis illustrated that importance of natural factors followed the order of geological attributes > soil properties > climatic conditions > ecological functions. In particular, lithology of the parent material dominated the content of metals, with the contributions of 18-25%. Moreover, soil properties of pH, cation exchange capacity, profile depth of soil and vegetation coverage had different influences on HMs, due to the variability in the properties of different HMs. This study developed a mapping relationship between natural factors and soil HMs by data science method, which may provide instructive information for pollution control and planning decisions.

Priority sources identification and risks assessment of heavy metal(loid)s in agricultural soils of a typical antimony mining watershed

Heavy metal(loid) (HM) pollution in agricultural soils has become an environmental concern in antimony (Sb) mining areas. However, priority pollution sources identification and deep understanding of environmental risks of HMs face great challenges due to multiple and complex pollution sources coexist. Herein, an integrated approach was conducted to distinguish pollution sources and assess human health risk (HHR) and ecological risk (ER) in a typical Sb mining watershed in Southern China. This approach combines absolute principal component score-multiple linear regression (APCS-MLR) and positive matrix factorization (PMF) models with ER and HHR assessments. Four pollution sources were distinguished for both models, and APCS-MLR model was more accurate and plausible. Predominant HM concentration source was natural source (39.1%), followed by industrial and agricultural activities (23.0%), unknown sources (21.5%) and Sb mining and smelting activities (16.4%). Although natural source contributed the most to HM concentrations, it did not pose a significant ER. Industrial and agricultural activities predominantly contributed to ER, and attention should be paid to Cd and Sb. Sb mining and smelting activities were primary anthropogenic sources of HHR, particularly Sb and As contaminations. Considering ER and HHR assessments, Sb mining and smelting, and industrial and agricultural activities are critical sources, causing serious ecological and health threats. This study showed the advantages of multiple receptor model application in obtaining reliable source identification and providing better source-oriented risk assessments. HM pollution management, such as regulating mining and smelting and implementing soil remediation in polluted agricultural soils, is strongly recommended for protecting ecosystems and humans.

Investigating the environmental dynamics of emerging pollutants in response to global climate change: Insights from bibliometrics-based visualization analysis

The environmental dynamics of emerging pollutants were profoundly influenced by global climate change, attracting widespread attention to this complex interaction. However, single studies or reviews were insufficient to grasp, clarify, and predict the evolutionary characteristics and coupling patterns of emerging pollutants under global climate change. Here, 2389 research articles collected from the Web of Science Core Collection database for the period 2000-2023 were analyzed using systematic bibliometric visual analysis software. Results suggested a rapid growth trend in this field study, particularly accelerating after 2015. The United States, China, the United Kingdom, and Spain led in the volume of publications, forming a multidisciplinary research network centered on environmental science. Wastewater treatment, personal care products, pharmaceuticals, and heavy metals were identified as current research hotspots, with climate change emerging as the most prominent keyword. Research focus gradually shifted from single pollutants to multi-pollutant composite effects, from local issues to global-scale assessments, and from phenomenon description to mechanism analysis and risk evaluation. It is concluded that climate change is reshaping the environmental behaviors and ecological risks of emerging pollutants, and multidisciplinary, multi-scale research methods are urgent need. Future research is suggested to strengthen interdisciplinary collaboration, integrate climate and pollutant migration models, and investigate impacts of extreme climate events in depth.

Whole-tree harvesting improves the ecosystem N, P and K cycling functions in secondary forests in the Qinling Mountains, China

Forest ecosystem nutrient cycling functions are the basis for the survival and development of organisms, and play an important role in maintaining the forest structural and functional stability. However, the response of forest nutrient cycling functions at the ecosystem level to whole-tree harvesting remains unclear. Herein, we calculated the ecosystem nitrogen (N), phosphorus (P), and potassium (K) absorption, utilization, retention, cycle, surplus, accumulation, productivity, turnover and return parameters and constructed N, P, and K cycling function indexes to identify the changes in ecosystem N, P, and K cycling functions in a secondary forest in the Qinling Mountains after 5 years of five different thinning intensities (0% (CK), 15%, 30%, 45%, and 60%). We showed that the ecosystem's N, P, and K cycling parameters varied significantly and responded differently to thinning treatments. As the thinning intensity increased, the N, P, and K cycling function indexes increased by 5%~232%, 32%~195%, and 104%~233% compared with CK. Whole-tree harvesting promoted ecosystem N and P cycling functions through two pathways: (a) directly regulated litter biomass, indirectly affected soil nutrient characteristics, and then regulated ecosystem N and P cycling functions; (b) directly regulated plant productivity, indirectly affected plant and soil nutrient characteristics, and then regulated ecosystem N and P cycling functions. In contrast, whole-tree harvesting mainly indirectly affected the plant and soil nutrient characteristics by directly adjusting the plant productivity, and promoting the ecosystem K cycling function. Furthermore, N and P cycling functions were mainly regulated by understory plant productivity while tree and herb nutrient characteristics were key driving factors for K cycling functions. These findings indicated that whole-tree harvesting significantly improved the ecosystem N, P and K cycling functions, and reveals varied regulatory mechanisms, which may aid in formulating effective measures for sustainable forest ecosystem nutrient management.

Heavy metal contamination assessment and source attribution in the Vicinity of an iron slag pile in Hechi, China: Integrating multi-medium analysis

Heavy metals, such as mercury, cadmium, and nickel, may contaminate human inhabited environments, with critical consequences for human health. This study examines the health impacts of heavy metal pollution from an iron slag pile in Hechi, China, by analyzing heavy metal contamination in water, sediment, soil, and crops. Here, the Nemerow pollution index (NI) indicated severe pollution at most sampling sites, the mean NI of groundwater, and surface water had reached 594.13 and 26.79, respectively. Bioaccumulation of mercury (Hg), cadmium (Cd), and nickel (Ni) was noted in crops, cucumbers showed comparatively lower risk levels. Logarithmic surface water-sediment partition coefficient calculations indicated that heavy metals such as chromium (Cr), ferrum (Fe), zinc (Zn), copper (Cu), Ni, arsenic (As), and lead (Pb) tend to accumulate in sediments. There was a high risk in groundwater (67.48-6590.54) and surface water (13.73-2500.85). Variably influenced by rainfall, these metals can be diluted and mobilized from surface water and sediments, thereby changing the contamination levels and ecological risks. Probabilistic health risk assessments indicated that health risks were higher in children than in adults, the mean total carcinogenic risk values of soil, groundwater, and surface water, were 6.79E-04, 4.20E-06, and 1.15E-6 for children, respectively. Moderate soil pollution is the main health hazard. A Positive Matrix Factorization model attributed over 60% of the pollution to slag stacking. Biotechnologies, solidification/stabilization techniques, field management, and institutional controls, driven by principles of green, low-carbon, and economic efficiency may mitigate. These findings contribute to the management of heavy metal pollution in iron slag pile areas.

Deciphering the driving factors and probabilistic health risks of potentially toxic elements in arid surface water: Insights from the Tarim River Basin

Potentially toxic elements (PTEs) in surface water in arid areas pose a serious threat to environmental safety and human health within a basin. It is important to determine the factors controlling PTEs and to assess the likelihood that they will pose a risk to human health in order to support the development of environmental protection and risk management strategies. In this study, a structural equation model and Bayesian method were combined to discuss the distribution and probabilistic health risks of PTEs in surface water in arid area, and the Tarim River Basin was taken as a case study. The results show that the average concentrations of As, Co, Cu, and Ni in the surface water in the Tarim River Basin ranged from 0.04 to 2.92 μg/L, which do not exceed the international standard values. However, the maximum value of As (19.20 μg/L) exceeded both the recommended drinking water standards and the Chinese irrigation water standards. Spatially, the high As concentrations were distributed in the upper reaches of the Kashgar River, and the high Co, Cu and Ni concentrations were distributed in reservoirs and lakes on the main stream of the Tarim River. The concentrations of the PTEs in the surface water in the basin were not only affected by random anthropogenic factors such as traffic discharge, agricultural activities and mining industry, but were also directly and indirectly influenced by climatic factors. The results of the probabilistic health risk assessment showed that the 95th percentile the total hazard index for infants exceeded the allowable value of 1, and the total carcinogenic risk of PTEs exposure in four age groups was at the notable level. In this study, we conducted a comprehensive analysis of the controlling factors and health risks associated with PTEs in surface water in the Tarim River Basin, and the findings are expected to provide a scientific basis for regional water environment management and safety control.

Non-phytoremediation and phytoremediation technologies of integrated remediation for water and soil heavy metal pollution: A comprehensive review

Since the 1980s, there has been increasing concern over heavy metal pollution remediation. However, most research focused on the individual remediation technologies for heavy metal pollutants in either soil or water. Considering the potential migration of these pollutants, it is necessary to explore effective integrated remediation technologies for soil and water heavy metals. This review thoroughly examines non-phytoremediation technologies likes physical, chemical, and microbial remediation, as well as green remediation approaches involving terrestrial and aquatic phytoremediation. Non-phytoremediation technologies suffer from disadvantages like high costs, secondary pollution risks, and susceptibility to environmental factors. Conversely, phytoremediation technologies have gained significant attention due to their sustainable and environmentally friendly nature. Enhancements through chelating agents, biochar, microorganisms, and genetic engineering have demonstrated improved phytoremediation remediation efficiency. However, it is essential to address the environmental and ecological risks that may arise from the prolonged utilization of these materials and technologies. Lastly, this paper presents an overview of integrated remediation approaches for addressing heavy metal contamination in groundwater-soil-surface water systems and discusses the reasons for the research gaps and future directions. This paper offers valuable insights for comprehensive solutions to heavy metal pollution in water and soil, promoting integrated remediation and sustainable development.

Spatial patterns of toxic elements in stream sediment transportation at a hilly mine area

The spatial pattern of toxic metals plays a major role in watershed diffuse metal non-point source pollution, particularly during stream sediment transportation at hills mines. This study investigated a typical hilly mine area to quantitatively analyze the characteristics, sensitivities, and influencing factors of toxic elements transported in stream sediments through field research and Geodetector models. The results showed that the spatial patterns of toxic elements in stream sediment transportation at the hills mine area were significantly influenced by water erosion and sulfate. Water erosion and sulfate promoted the transport differences of stream sediment metals from upstream to downstream at the hills mine area. Arsenic, cadmium, mercury, and antimony in the stream sediments at the hills mine exhibited higher coefficients of variation (101 % to 397 %) than those in plain and basin topographies. Potential ecological risks of arsenic and cadmium were assessed as high-risk levels, at 19 % and 64 %, respectively. Metal import in the midstream sediments of the hills mine area was accelerated by strong water erosion. Sulfate and dissolved organic matter (DOM) were highly enriched in stream sediments, with sulfate showing a strong correlation with toxic metals (24 %). Positive responses were observed between arsenic, mercury, antimony, and sulfate in sediments, with sensitivities of 41 %, 25 %, and 16 %, respectively, while cadmium was associated with DOM, with a sensitivity of 46 %. Importantly, water erosion interactions with functional type of mine significantly influenced on the spatial transportation patterns of toxic metals in stream sediments. The interactive influences of sulfate combined with bicarbonate on arsenic, mercury, and antimony and bicarbonate combined with DOM on cadmium were enhanced compared to individual factors (>20 %). This study elucidates the spatial patterns of metals during stream sediment transportation in hills mine and offers the novel insights for developing effective watershed metal management strategies in hilly mine environments.

Prediction of heavy metal ion distribution and Pb and Zn ion concentrations in the tailing pond area

The pollution caused by tailings ponds has resulted in ecological damage, with soil contamination significantly impacting the daily lives of residents in the vicinity of mining areas and the future development of mining areas. This study assesses the transport status of heavy metal pollution in tailings areas and predicts its impact on future pollution levels. This study focused on lead-zinc tailing ponds, exploring the spatial and chemical distribution characteristics of heavy metals based on the distributions of Pb, Zn, As, Cu, Cr, Cd, Hg, and Ge ions. The concentrations of the major heavy metal ions Pb and Zn in tailings ponds were predicted via the exponential smoothing method. ① The total accumulation of Pb and Zn in the mine tailings ranges from 936.74~1212.61 mg/kg and 1611.85~2191.47 mg/kg, much greater than the total accumulation of the remaining six heavy metals. The total accumulation of associated heavy metal Cu was high, and the lowest total heavy metals were Hg and Ge at only 0.19 mg/kg and 1.05 mg/kg. ② The analyses of soil heavy metal chemical forms reveal that the heavy metals Pb and Zn had the highest exchangeable state content and state ratio and the strongest transport activity in the industrial plaza and village soils. Pb and Zn are the heavy metals with the greatest eco-environmental impacts in the mining area. ③ The predicted results show that the soil concentrations of the heavy metals Pb and Zn around the tailings area in 2026 are 1.335 and 1.191 times the predicted time starting values. The concentrations of the heavy metals Pb and Zn at the starting point of the forecast are already 3.34 and 3.02 times the upper limits of the environmental standard (according to environmental standards for gravelly grey calcium soils). These results have significant implications for heavy metal pollution risk management.

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.

Migration and Accumulation Simulation Prediction of PPCPs in Urban Green Space Soil Irrigated with Recycled Water: A Review

The presence of pharmaceuticals and personal care products (PPCPs) in reclaimed water introduces an ongoing challenge as they infiltrate green space soils during irrigation, leading to a gradual buildup that poses considerable ecological risks. The simulation and forecasting of PPCPs accumulation in soil are pivotal for proactive ecological risk management. However, the majority of research efforts have predominantly concentrated on the vertical transport mechanisms of PPCPs in the soil, neglecting a holistic perspective that integrates both vertical and lateral transport phenomena, alongside accumulation dynamics. To address this gap, this study introduces a comprehensive conceptual model that encapsulates the dual processes of vertical and lateral transport, coupled with accumulation of PPCPs in the soil environment. Grounded in the distinctive properties of green space soils, we delve into the determinants governing the vertical and lateral migration of PPCPs. Furthermore, we consolidate existing simulation methodologies for contaminant transport, aiming to establish a comprehensive model that accurately predicts PPCPs accumulation in green space soils. This insight is critical for deducing the emission threshold of reclaimed water necessary for the protection of green space soils, informing the formulation of rational irrigation strategies, and anticipating future environmental risks. It provides a critical theoretical basis for more informed decision-making in the realm of urban water reuse and pollution control.

Mercury sources, transport, and transformation in rainfall-runoff processes: Mercury isotope approach

Mercury (Hg) in runoff water poses significant ecological risks to aquatic ecosystems that can affect organisms. However, accurately identifying the sources and transformation processes of Hg in runoff water is challenging due to complex natural conditions. This study provides a comprehensive investigation of Hg dynamics in water from rainfall to runoff. The Hg isotope fractionation in water was characterized, which allows accurate quantification of Hg sources, transport, and transformations in rainfall-runoff processes. Δ200Hg and corrected Δ199Hg values can serve as reliable tracers for identifying Hg sources in the runoff water and the variation of δ202Hg can be explained by Hg transformation processes. During runoff migration processes, Hg from rainfall is rapidly absorbed on the land surface, while terrestrial Hg entering the water by the dissolution process becomes the primary component of dissolved mercury (DHg). Besides the dissolution and adsorption, microbial Hg(II) reduction and demethylation of MeHg were dominant processes for DHg in the runoff water that flows through the rice paddies, while photochemical Hg(II) reduction was the dominant process for DHg in the runoff water with low water exchange rates. Particulate Hg (PHg) in runoff water is dominantly originated by the terrestrial material and derived from the dissolution and adsorption process. Tracking sources and transformations of Hg in runoff water during the rainfall-runoff process provides a basis for studying Hg pollution in larger water bodies under complex environmental factors.

Innovative graph neural network approach for predicting soil heavy metal pollution in the Pearl River Basin, China

Predicting soil heavy metal (HM) content is crucial for monitoring soil quality and ensuring ecological health. However, existing methods often neglect the spatial dependency of data. To address this gap, our study introduces a novel graph neural network (GNN) model, Multi-Scale Attention-based Graph Neural Network for Heavy Metal Prediction (MSA-GNN-HMP). The model integrates multi-scale graph convolutional network (MS-GCN) and attention-based GNN (AGNN) to capture spatial relationships. Using surface soil samples from the Pearl River Basin, we evaluate the MSA-GNN-HMP model against four other models. The experimental results show that the MSA-GNN-HMP model has the best predictive performance for Cd and Pb, with a coefficient of determination (R2) of 0.841 for Cd and 0.886 for Pb, and the lowest mean absolute error (MAE) of 0.403 mg kg-1 for Cd and 0.670 mg kg-1 for Pb, as well as the lowest root mean square error (RMSE) of 0.563 mg kg-1for Cd and 0.898 mg kg-1 for Pb. In feature importance analysis, latitude and longitude emerged as key factors influencing the heavy metal content. The spatial distribution prediction trend of heavy metal elements by different prediction methods is basically consistent, with the high-value areas of Cd and Pb respectively distributed in the northwest and northeast of the basin center. However, the MSA-GNN-HMP model demonstrates superior detail representation in spatial prediction. MSA-GNN-HMP model has excellent spatial information representation capabilities and can more accurately predict heavy metal content and spatial distribution, providing a new theoretical basis for monitoring, assessing, and managing soil pollution.

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

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

Effects of water-table changes following rainfall events on arsenic fate and transport in groundwater-surface water mixing zones

This study investigated the effects of groundwater-surface water (GW-SW) interactions on the fate and transport of arsenic (As) following rainfall events and subsequent water-table changes in GW-SW mixing zones, comprising the riparian and hyporheic zones, near an abandoned gold mine. During the dry and wet periods, stream conditions changed from flow-through to gaining, respectively. Water-table changes caused by rainfall events controlled flow paths between riparian zones and the stream, affecting spatiotemporal variation in the redox and pH conditions of the aquatic environment. Subsequently, the fate and transport of As in GW-SW mixing zones was responsive to variations in redox and pH conditions. Through the oxidative dissolution of As-bearing sulfide minerals and the reductive dissolution of iron (Fe) oxides with adsorbed As, As was released into the groundwater in the riparian zones and transported to the stream and streambed along the baseflow discharge. However, As was also immobilized in the sediment through adsorption onto Fe-oxides and coprecipitation with calcium (Ca) and zinc (Zn), suggesting that the sediment acts as a sink-and-source of As in aquatic environments. Therefore, water-table changes and GW-SW interactions could play an important role in the fate and transport of As in aquatic environments, specifically groundwater-riparian-streambed-stream systems. The findings of this study will provide scientific insights into the mechanisms of As in aquatic environments, aiding in improved decision-making to ensure safe and sustainable water management in response to future climate change.

Migration patterns of heavy metals from solid waste stockpile soils by native plants for ecological restoration in arid and semi-arid regions of Northwest China

Ecological remediation with native plants is the main measure to control the pollution of solid waste in Northwest China. However, the heavy metal transport characteristics of these native plants are still unidentified. This study analyzed the distribution of 16 heavy metals in native plants in the desulfurization gypsum yard (DGY), the gangue yard (GY) and the fly ash yard (FAY). The results showed that the soil contained many heavy metals in high concentrations. For instance, As concentrations were comparable to the global soil background values, whereas Cr and Mn concentrations in the area were 2-3 times greater than the global soil background values. The content of heavy metals in the plant root system increased first, then decreased as the distance from the yard increased. Ni, Pb, and Cd migrated well in Artemisia frigida Willd and Artemisia sieversiana Ehrhart ex Willd, with A. sieversiana showing a particularly strong migration in GY. A. sieversiana, on the other hand, was more successful at migrating Cd at DGY and had a similar capability for Mg migration in all three locations. Festuca rubra L was potentially suitable for planting in GY for Ni removal. In conclusion, the migration patterns of different heavy metals were not alike for plants in the three landfills. The results provided a basis for plant selection for ecological restoration in arid and semi-arid regions.

Heavy metal migration dynamics and solid-liquid distribution strategy in abandoned tailing soils

The tailings soil originating from an abandoned sulfur-iron mine in Sichuan Province, China, exhibits elevated concentrations of heavy metals (HMs) and possesses limited soil conservation capacity. Variability soil particle size fractions (PSFs) contributes to an increased risk of HMs ion migration. Existing research on HMs behavior has focused on the bulk soil scale, resulting in a dearth of comprehensive information concerning different particle sizes and colloid scales. We collected soil samples from upstream source (XWA), migration path (XWB), and downstream farmland (XWC) of an abandoned tailing and categorized into sand, silt, clay, colloid and dissolved, respectively. The investigation primarily aimed to elucidate the solid-liquid distribution trade-off strategies of soil HMs along migration pathway. Results show that PSFs composition predominantly influences HMs solid-liquid distribution. In the mining area, large particles serve as the principal component for HMs enrichment. However, along the migration pathway, the proportion of highly mobile fine particles increases, shifting HMs from solid to liquid phase. Furthermore, inorganic elements such as Mg, Al, and Fe influence on HMs distribution within PSFs through various reactions, whereas organic matter and glomalin-related soil protein (GRSP) also exert regulatory roles. Increasing the proportion of large particles can reduce the risk of HMs migration.

Water and sediment chemistry drivers of chlorophyll-a dynamics within a Ramsar declared floodplain pan wetland system

Floodplain pans are hydrologically dynamic in nature and characterised by variables such as chlorophyll-a (chl-a), water, and sediment chemistry over their hydroperiods. The present study investigated the spatio-temporal variations in water and sediment physico-chemical, and chlorophyll-a concentration characteristics of six floodplain pans found in the Ramsar declared Makuleke wetlands, Kruger National Park, South Africa. The water and sediment physico-chemical variable values were generally elevated during the high-water period, whereas chlorophyll-a concentrations varied across pans and hydroperiod. Benthic chl-a concentration significantly varied across pans with concentrations ranging from 161 to 1036.2 mg m2. The two-way ANOVA showed significant differences in benthic chl-a concentration among hydroperiods, and no significant differences were observed in pelagic chl-a across pans and hydroperiods. Generally, pelagic and benthic chl-a concentration increased as water and sediment chemistry variables increased. Furthermore, three sediment variables, i.e. pH, calcium, and magnesium, and water conductivity were found to be significant in structuring benthic chlorophyll-a dynamics in pans. However, none of the sediment and water variables had a significant effect on pelagic chl-a. Hydroperiod had a significant effect on influencing chl-a concentration, with high and low water level periods being characterised by low and high chl-a concentration, respectively. The n-MDS results showed strong overlaps in chl-a biomass among the Makuleke floodplain pans across hydroperiods. The increasing chl-a concentration in these floodplain pans due to potential bioturbation effects as a result of large mammals could potentially lead to eutrophication, which in turn could affect the system's primary productivity and aquatic biota. Therefore, it is important to establish a continuous monitoring programme on these pans to inform sound management decisions.

Simulation of vertical migration behaviors of heavy metals in polluted soils from arid regions in northern China under extreme weather

Heavy metal migration behaviors and mechanisms in soils are important for pollution control and remediation. However, there are few related studies in arid areas under extreme weather patterns. In this study, we developed a one-dimensional continuous point source unsaturated solute transport model, and utilized Hydrus-1D to simulate the transport of Cu, As and Zn, in the pack gas zones of soils within the impact areas of two typical mining areas in Inner Mongolia. The results show that the soil has a significant interception capacity, with a short heavy metal vertical migration distance of ≤100 cm. Soil texture and heavy metal sorption affinity are two key factors that influence heavy metal transport. In soils with high contents of sands but low contents of clays, heavy metals have large mobility and thus migrate deeper and are more evenly distributed in the soil profile. The migration of different heavy metals in the same soil also varies considerably, with large migration depth for metals having low binding affinities onto soils. Scenario analysis for extreme drought and rainfall shows that, rainfall amount and intensity are positively correlated with heavy metal transport depth and negatively correlated with the peak concentration. Increasing rainfall/intensity results in a more uniform distribution of heavy metals, and lower profile concentrations owing to enhanced horizontal dispersion of surface runoff. When the total amount and intensity of rainfall remain constant, continuous or intermittent rainfall only affects the transport process but has almost no effect on the final pollutant concentration redistribution in the soil. These results provide theoretical data for estimating the degree of heavy metal pollution, and help design control and remediation strategies for polluted soils.

Potentially toxic elements fluxes in 210Pb-dated sediment cores from a large coastal lagoon (southern Gulf of Mexico) under environmental stress

Terminos Lagoon (TL), in the southern Gulf of Mexico, has been under intensive anthropogenic pressure (e.g., oil-industry development) since the 1970s. Historical changes in flux ratios of potentially toxic elements (PTEs; As, Cd, Cr, Cu, Ni, Pb, V, Zn) were, for the first time, assessed inside TL by using 210Pb-dated sediment cores. Sediments showed minor enrichments for Cd, Ni, Pb, and V. However, according to international benchmarks, the As, Cr, Cu, and Ni concentrations could pose a risk for benthic biota. Sedimentary processes involved in the accumulation of PTEs were identified through a chemometric approach. Increments in PTEs flux ratios concur with the recent (⁓50 years) and extensive land-use changes, particularly the transport and deposit of materials delivered by rivers. These findings are expected to be used in managing this crucial natural resource, the larger Mexican coastal lagoon ecosystem, to mitigate the effects of global change.

The leaching behavior of heavy metal from contaminated mining soil: The effect of rainfall conditions and the impact on surrounding agricultural lands

Contaminated mining soils could lead to heavy metal pollution of surrounding farmlands under rainfall conditions. With the aids of sequential extraction, batch leaching, and dynamic leaching experiments, this study was carried out to investigate the characteristics of heavy metals in contaminated mining soils, understand their leaching behavior under different rainfall conditions, and evaluate the potential effects on surrounding farmlands. The results indicated that the concentrations of heavy metals (Cr, Ni, Cu, Zn, As, Cd, and Pb) in the contaminated mining soils were several or even twenty times higher than their corresponding background values, and Cd, Zn, Cu and Pb had considerable proportions (>50 %) in mobile forms. The leaching amounts of heavy metals from the contaminated mining soils had positive correlation with their contents in acid soluble form, and showed strong dependence on rainfall pH conditions. Acid rainfalls (pH = 4.32) can greatly increase the average annual release of Cd, Zn, Cu and Pb from mine soils in the study area, with increments ranging from 72.4 % (Pb) to 85.9 % (Cd) compared to those under alkaline conditions (pH = 7.42). The leaching of heavy metals was well fitted by two-constant, pseudo second-order and parabolic equations, indicating that their multi-layer sorption/desorption behavior on soil surface was dominated by chemical processes and their release was controlled by the diffusion within the soil pore channels. The two-column leaching experiment showed that the metal-rich leachate can lead to obvious increments of heavy metals in non-residual fractions (in particular Cd in acid soluble form) in surrounding farmlands, which would significantly raise the potential ecological risk associated with heavy metals. These findings indicate the importance of contaminated mining soils as a long-term source of heavy metals and the needs for mitigating the releases of toxic elements, especially in areas with heavy acid precipitation.

Influence of landform, soil properties, soil Cd pollution and rainfall on the spatial variation of Cd in rice: Contribution and pathway models based on big data

Landform, soil properties, soil cadmium (Cd) pollution and rainfall are the important factors affecting the spatial variation of rice Cd. In this study, we conducted big data mining and model analysis of 150,000 rice-soil sampling sites to examine the effects by the above four factors on the spatial variation of rice Cd in Hunan Province, China. Specifically, the variable coefficient of rice Cd in space was significantly correlated with the partition scale according to the logistic fitting. The improved random forest results suggested that elevation (DEM) and pH were the two most important factors affecting the spatial variation of rice Cd, followed by relief, soil Cd content and rainfall. Typically, variance partitioning analysis (VPA) revealed that both the soil property and the interactive effects between the soil property and Cd pollution were the principal contributors to the rice-Cd variation, with the respective contributing rates of 30.5 % and 29.0 %. Meanwhile, the partial least square-structural equation modelling (PLS-SEM) elucidated 4 main paths of specific indirect effects on rice-Cd variation. They were landform → physicochemical property → soil acidity → rice-Cd variation, landform → soil acidity → rice-Cd variation, physicochemical property → soil acidity → rice-Cd variation, and soil texture → soil acidity → rice-Cd variation. This work can provide a general guidance for scientific zoning, accurate prediction and prevention of Cd pollution in paddy fields.

Spatiotemporal dynamics and modeling of thiacloprid in paddy multimedia systems with the effect of wetting-drying cycles

The widespread presence of thiacloprid (THI), a neonicotinoid, raises concerns for human health and the aquatic environment due to its persistence, toxicity, and bioaccumulation. The fate of THI in paddy multimedia systems is mainly governed by irrigation practices, but the potential impacts remain poorly documented. This study investigated the effects of water management practices on THI spatiotemporal dynamics in paddy multimedia systems by combining soil column experiments and a non-steady-state multimedia model. The results indicated the wetting-drying cycle (WDC) irrigation reduced THI occurrences in environmental phases (i.e., soil, interstitial water, and overlying water) and accelerated the THI loss through the THI aerobic degradation process. THI occurrences in the soil and water phases decreased from 18.8% for conventional flooding (CF) treatment to 9.2% for severe wetting-drying cycle (SW) treatment after 29 days, while the half-lives shortened from 11.1 days to 7.3 days, respectively. Meanwhile, the WDC decreased THI outflow from leakage water, which reduced the THI risk of leaching. There was no significant difference in THI plant uptake and volatilization between CF and WDC treatments. The mean proportions of THI fate in paddy multimedia systems followed the order: THI degradation (57.7%), outflow from leakage water (25.5%), occurrence in soil (12.4%), plant uptake (3.4%), occurrence in interstitial water (0.7%), occurrence in overlying water (0.3%), volatilization (<0.1%) after 29 days. The sensitivity analysis identified the soil organic carbon partition coefficient (KOC) as the most sensitive parameter affecting THI's fate. In addition, the topsoil layers of 0-4 cm were the main sink of THI, holding 67% of THI occurrence in the soil phase. The THI occurrence in interstitial water was distributed evenly throughout the soil profile. These findings made beneficial theoretical supplements and provided valuable empirical evidence for water management practices to reduce the THI ecological risk.

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

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

Exploring the accumulation capacity of dominant plants based on soil heavy metals forms and assessing heavy metals contamination characteristics near gold tailings ponds

Heavy metal contamination of soil commonly accompanies problems around gold mine tailings ponds. Fully investigating the distribution characteristics of heavy metals and the survival strategies of dominant plants in contaminated soils is crucial for effective pollution management and remediation. This study aims to investigate the contamination characteristics, sources of heavy metals (As, Cd, Pb, Hg, Cu, Zn, Cr, and Ni) in soils around gold mine tailings ponds areas (JHH and WZ) and to clarify the form distribution of heavy metals (As, Cd, Pb, Hg) in contaminated plots as well as their accumulation and translocation in native dominant plants. The results of the study showed that the concentrations of As, Pb, Cd, Cu, and Zn in soil exceeded the national limits at parts of the sampling sites in both study areas. The Nemerow pollution index showed that both study areas reached extreme high pollution levels. Spatial analysis showed that the main areas of contamination were concentrated around metallurgical plants and tailings ponds, with Cd exhibiting the most extensive area of contamination. In the JHH, As (74%), Cd (66%), Pb (77%), Zn (47%) were mainly from tailings releases, and Cu (52%) and Hg (51%) were mainly from gold ore smelting. In the WZ, As (42%), Cd (41%), Pb (73%), Cu (47%), and Zn (41%) were mainly from tailings releases. As, Cd, Pb, and Hg were mostly present in the residue state, and the proportion of water-soluble, ion-exchangeable, and carbonate-bound forms of Cd (19.93%) was significantly higher than that of other heavy metals. Artemisia L. and Amaranthus L. are the primary dominating plants, which exhibited superior accumulation of Cd compared to As, Pb, and Hg, and Artemisia L. demonstrated a robust translocation capacity for As, Pb, and Hg. Compared to the concentrations of other forms of soil heavy metals, the heavy metal content in Artemisia L correlates significantly better with the total soil heavy metal concentration. These results offer additional systematic data support and a deeper theoretical foundation to bolster pollution-control and ecological remediation efforts in mining areas.

A critical review on the migration and transformation processes of heavy metal contamination in lead-zinc tailings of China

The health risks of lead-zinc (Pb-Zn) tailings from heavy metal (HMs) contamination have been gaining increasing public concern. The dispersal of HMs from tailings poses a substantial threat to ecosystems. Therefore, studying the mechanisms of migration and transformation of HMs in Pb-Zn tailings has significant ecological and environmental significance. Initially, this study encapsulated the distribution and contamination status of Pb-Zn tailings in China. Subsequently, we comprehensively scrutinized the mechanisms governing the migration and transformation of HMs in the Pb-Zn tailings from a geochemical perspective. This examination reveals the intricate interplay between various biotic and abiotic constituents, including environmental factors (EFs), characteristic minerals, organic flotation reagents (OFRs), and microorganisms within Pb-Zn tailings interact through a series of physical, chemical, and biological processes, leading to the formation of complexes, chelates, and aggregates involving HMs and OFRs. These interactions ultimately influence the migration and transformation of HMs. Finally, we provide an overview of contaminant migration prediction and ecological remediation in Pb-Zn tailings. In this systematic review, we identify several forthcoming research imperatives and methodologies. Specifically, understanding the dynamic mechanisms underlying the migration and transformation of HMs is challenging. These challenges encompass an exploration of the weathering processes of characteristic minerals and their interactions with HMs, the complex interplay between HMs and OFRs in Pb-Zn tailings, the effects of microbial community succession during the storage and remediation of Pb-Zn tailings, and the importance of utilizing process-based models in predicting the fate of HMs, and the potential for microbial remediation of tailings.

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

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

The multi-media environmental behavior of heavy metals around tailings under the influence of precipitation

Precipitation can lead to significant leaching of heavy metals from abandoned tailings，resulting in a decline in the quality of the surrounding environment. This study aimed to simulate and quantify the migration patterns and fate of heavy metals in tailings caused by precipitation in various environmental media (tailings, air, water, soil, and sediments) using leaching tests, source apportionment, and a fugacity model. Results revealed that the average contents of Cd, Cu, As, Pb, Zn, and Cr in the un-weathered tailings were 3.43, 495.56, 160.70, 138.94, 536.57, and 69.52 mg/kg, respectively. The ecological risk factors in the tailings as well as in sediments and soils, were in the following order: Cd >Cu >As >Pb >Zn >Cr. A fugacity model based on the mass-balance methods was established, achieving a good agreement between simulation and measured values. The total amounts of Cd, Cu, As, Pb, and Zn leached from abandoned tailings over the 30-year evaluation period were estimated to be 1.09, 62.44, 0.16, 0.94, and 102.12 t, respectively. Soil and sediments are important reservoirs for heavy metals. The sum of the As, Cd, Cu, Pb, and Zn storage capacities in the soil and sediment accounted for 77.28%, 75.63%, 73.94%, 69.39%, and 57.80% of the total storage capacity, respectively. This study could provide the means for the establishment of a targeted pollution control plan, a guide for restoration projects, and will aid in controlling pollution risk and improving the surrounding environment.

Impacts of the steel industry on sediment pollution by heavy metals in urban water system

The impact of the steel industry on sediment heavy metal (HM) pollution in urban aquatic environments was investigated in a major iron ore-producing area (Ma'anshan) in China. The concentrations of Cd, Cr, Cu, Ni, Pb, and Zn were 9.68 ± 3.56, 170.31 ± 82.40, 90.62 ± 19.54, 30.61 ± 6.72, 125.43 ± 63.60, and 1276.59 ± 701.90 mg/kg in the steel industry intruded upon sediments and 4.63 ± 1.41, 87.60 ± 10.96, 52.67 ± 19.99, 37.49 ± 6.17, 35.84 ± 11.41, and 189.02 ± 95.57 mg/kg in the control area, respectively. Comparing with the local soil background (0.08 mg/kg for Cd, 62.6 mg/kg for Cr, 19.3 mg/kg for Cu, 28.1 mg/kg for Ni, 26.0 mg/kg for Pb, and 58.0 mg/kg for Zn), significantly higher levels of Cd, Cr, Cu, Pb, and Zn were detected in the steel industry affected sediments. The enrichment factor and principal component analysis indicated that the heavy metals (HMs), except for Ni, were primarily derived from anthropogenic inputs, particularly from steel industrial activities. Multiple risk assessment models suggested that the sediments affected by industrial activities showed significant toxic effects for Cd, Cr, Pb, and Zn, with Cd being the main contributor to sediment toxicity. However, the alkaline nature of the sediments (pH = 7.85 ± 0.57) and the high proportion of residual fraction Cd (61.09% ± 26.64%) may help to reduce the toxic risks in the sediments. Effective measures to eliminate tinuous thethe continous input of Cd and Zn via surface runoff are crucial.

Static and dynamic source identification of trace elements in river and soil environments under anthropogenic activities in the Haraz plain, Northern Iran

Unsustainable human activities have disrupted the natural cycle of trace elements, causing the accumulation of chemical pollutants and making it challenging to determine their sources due to interwoven natural and human-induced processes. A novel approach was introduced for identifying the sources and for quantifying the contribution of trace elements discharge from rivers to soils. We integrated fingerprinting techniques, soil and sediment geochemical data, geographically weighted regression model (GWR) and soil quality indices. The FingerPro package and the state-of-the-art tracer selection techniques including the conservative index (CI) and consensus ranking (CR) were used to quantify the relative contribution of different upland sub-watersheds in trace element discharge soil. Our analysis revealed that off-site sources (upland watersheds) and in-site sources (land use) both play an important role in transferring trace elements to the Haraz plain (northern Iran). The unmixing model's results suggest that the Haraz sub-watersheds exhibit a higher contribution to trace elements transfer in the Haraz plain, and therefore, require greater attention in terms of implementing soil and water conservation strategies. However, it is noteworthy that the Babolroud (adjacent to Haraz) exhibited a better performance of the model. A spatial correlation between certain heavy metals, such as As and Cu, and rice cultivation existed. Additionally, we found a significant spatial correlation between Pb and residential areas, particularly in the Amol region. Our result highlights the importance of using advanced spatial statistical techniques, such as GWR, to identify subtle but critical associations between environmental variables and sources of pollution. The methodology used comprehensively identifies dynamic trace element sourcing at the watershed scale, allowing for pollutant source identification and practical strategies for soil and water quality control. Tracer selection techniques (CI and CR) based on conservatives and consensus improve unmixing model accuracy and flexibility for precise fingerprinting.

Identifying the potential soil pollution areas derived from the metal mining industry in China using MaxEnt with mine reserve scales (MaxEnt_MRS)

Identifying the potential soil pollution areas derived from the metal mining industry usually requires extensive field investigation and laboratory analysis. Moreover, the previous studies mainly focused on a single or a few mining areas, and thus couldn't provide effective spatial decision support for controlling soil pollution derived from the metal mining industry at the national scale. This study first conducted a literature investigation and web crawler for the relevant information on the metal mining areas in China. Next, MaxEnt with mine reserve scales (MaxEnt_MRS) was proposed for spatially predicting the probabilities of soil pollution derived from the metal mining industry in China. Then, MaxEnt_MRS was compared with the basic MaxEnt. Last, the potential soil pollution areas were identified based on the pollution probabilities, and the relationships between the soil pollution probabilities and the main environmental factors were quantitatively assessed. The results showed that: (i) MaxEnt_MRS (AUC = 0.822) obtained a better prediction effect than the basic MaxEnt (AUC = 0.807); (ii) the areas with the soil pollution probabilities higher than 54% were mainly scattered in the eastern, south-western, and south-central parts of China; (iii) GDP (45.7%), population density (30.1%), soil types (15.5%), average annual precipitation (3.9%), and land-use types (3.1%) contributed the most to the prediction of the soil pollution probabilities; and (iv) the soil pollution probabilities in the areas with all the following conditions were higher than 54%: GDP, 7600-2612670 thousand yuan/km²; population density, 152-551 people/km²; precipitation, 924-2869 mm/year; soil types, Ferralisols or Luvisols; and land-use types, townland, mines, and industrial areas. The above-mentioned results provided effective spatial decision support for controlling soil pollution derived from the metal mining industry at the national scale.

Anthropogenic processes drive heterogeneous distributions of toxic elements in shallow groundwater around a smelting site

Smelting activities have a far-reaching influence on the quality of soil and groundwater, while most studies have neglected the information on the pollution characteristics of groundwater. The hydrochemical parameters of shallow groundwater and the spatial distributions of toxic elements were investigated in this study. Correlations analysis and groundwater evolution revealed that the major ions were primarily determined by silicate weathering and calcite dissolution process, and anthropogenic processes had a significant effect on groundwater hydrochemistry. Almost 79%, 71%, 57%, 89%, 100%, and 78.6% of samples exceeded the standards of Cd, Zn, Pb, As, SO₄^2-, and NO₃^-, and their distribution is closely related to the production process. Analysis of soil geochemistry indicated that the relatively mobile forms of toxic elements strongly influence the origin and concentration in shallow groundwater. Besides, rainfall with high magnitude would lead to a decrease of toxic elements in shallow groundwater, whereas the area once stacked waste residue was the opposite. It is recommended to strengthen risk management of the limited mobility fraction while devising a plan for waste residue treatment in accordance with the local pollution conditions. The research on controlling the mechanism of toxic elements in shallow groundwater, along with sustainable development in the study area and other smelting zones may benefit from this study.

Modeling transport and fate of heavy metals at the watershed scale: State-of-the-art and future directions

A predictive understanding of the source-specific (e.g., point and diffuse sources) land-to-river heavy metal (HM) loads and HM dynamics in rivers is essential for mitigating river pollution and developing effective river basin management strategies. Developing such strategies requires adequate monitoring and comprehensive models based on a solid scientific understanding of the watershed system. However, a comprehensive review of existing studies on the watershed-scale HM fate and transport modeling is lacking. In this review, we synthesize the recent developments in the current generation of watershed-scale HM models, which cover a wide range of functionalities, capabilities, and spatial and temporal scales (resolutions). Existing models, constructed at various levels of complexity, have their strengths and weaknesses in supporting diverse intended uses. Additionally, current challenges in the application of watershed HM modeling are covered, including the representation of in-stream processes, organic matter/carbon dynamics and mitigation practices, the issues of model calibration and uncertainty analysis, and the balance between model complexity and available data. Finally, we outline future research requirements regarding modeling, strategic monitoring, and their combined use to enhance model capabilities. In particular, we envisage a flexible framework for future watershed-scale HM models with varying degrees of complexity to accommodate the available data and specific applications.

Leaching Mechanism and Health Risk Assessment of As and Sb in Tailings of Typical Antimony Mines: A Case Study in Yunnan and Guizhou Province, Southwest China

The weathering and leaching of mining tailings have released large amounts of antimony (Sb) and arsenic (As), causing serious pollution in the surrounding soil, water, and sediments. To understand the leaching characteristics of Sb and As in mining tailings, Zuoxiguo and Qinglong mining tailings were collected for analysis. The average content of Sb in Zuoxiguo and Qinglong tailings was 5902.77 mg/kg and 1426.43 mg/kg, respectively, while that of As was 412.53 mg/kg and 405.26 mg/kg, respectively, which exceeded the local background value. Furthermore, the concentrations of Sb in the leachate of Zuoxiguo and Qinglong increased with time; the average Sb concentration in the leachate of Zuoxiguo and Qinglong was 1470.48 μg/L and 70.20 μg/L, respectively, while that of the As concentration was 31.20 μg/L and 6.45 μg/L, respectively. This suggests that the concentrations of Sb and As in the leachate of Zuoxiguo are both higher than those in the leachate of Qinglong and that the pH of the leachate of Zuoxiguo and Qinglong significantly changed within the first day under different initial pH conditions, and tended to be between 6 and 8, after one day. The results of the average health risk index showed that As in the leachate from Zuoxiguo and Qinglong for children was 5.67 × 10^-4 and 9.13 × 10^-5, respectively, and 4.43 × 10^-4 and 7.16 × 10^-5, respectively, for adults. As in the leachate from Zuoxiguo poses serious carcinogenic risks for residents, and in the study area, As poses a serious threat to human health. Therefore, the local government must manage As in these areas.