Research on health risk assessment of heavy metals in soil based on multi-factor source apportionment: A case study in Guangdong Province, China

Exploring the environmental risks and seasonal variations of potentially toxic elements (PTEs) in fine road dust in resource-based cities based on Monte Carlo simulation, geo-detector and random forest model

The environmental pollution caused by mineral exploitation and energy consumption poses a serious threat to ecological security and human health, particularly in resource-based cities. To address this issue, a comprehensive investigation was conducted on potentially toxic elements (PTEs) in road dust from different seasons to assess the environmental risks and influencing factors faced by Datong City. Multivariate statistical analysis and absolute principal component score were employed for source identification and quantitative allocation. The geo-accumulation index and improved Nemerow index were utilized to evaluate the pollution levels of PTEs. Monte Carlo simulation was employed to assess the ecological-health risks associated with PTEs content and source orientation. Furthermore, geo-detector and random forest analysis were conducted to examine the key environmental variables and driving factors contributing to the spatiotemporal variation in PTEs content. In all PTEs, Cd, Hg, and Zn exhibited higher levels of content, with an average content/background value of 3.65 to 4.91, 2.53 to 3.34, and 2.15 to 2.89 times, respectively. Seasonal disparities were evident in PTEs contents, with average levels generally showing a pattern of spring (winter) > summer (autumn). PTEs in fine road dust (FRD) were primarily influenced by traffic, natural factors, coal-related industrial activities, and metallurgical activities, contributing 14.9-33.9 %, 41.4-47.5 %, 4.4-8.3 %, and 14.2-29.4 % to the total contents, respectively. The overall pollution and ecological risk of PTEs were categorized as moderate and high, respectively, with the winter season exhibiting the most severe conditions, primarily driven by Hg emissions from coal-related industries. Non-carcinogenic risk of PTEs for adults was within the safe limit, yet children still faced a probability of 4.1 %-16.4 % of unacceptable risks, particularly in summer. Carcinogenic risks were evident across all demographics, with children at the highest risk, mainly due to Cr and smelting industrial sources. Geo-detector and random forest model indicated that spatial disparities in prioritized control elements (Cr and Hg) were primarily influenced by particulate matter (PM10) and anthropogenic activities (industrial and socio-economic factors); variations in particulate matter (PM10 and PM2.5) and meteorological factors (wind speed and precipitation) were the primary controllers of seasonal disparities of Cr and Hg.

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.

Health impact assessment of the surface water pollution in China

China suffers from severe surface water pollution. Health impact assessment could provide a novel and quantifiable metric for the health burden attributed to surface water pollution. This study establishes a health impact assessment method for surface water pollution based on classic frameworks, integrating the multi-pollutant city water quality index (CWQI), informative epidemiological findings, and benchmark public health information. A relative risk level assignment approach is proposed based on the CWQI, innovatively addressing the challenge in surface water-human exposure risk assessment. A case study assesses the surface water pollution-related health impact in 336 Chinese cities. The results show (1) between 2015 and 2022, total health impact decreased from 3980.42 thousand disability-adjusted life years (DALYs) (95 % Confidence Interval: 3242.67-4339.29) to 3260.10 thousand DALYs (95 % CI: 2475.88-3641.35), measured by total cancer. (2) The annual average health impacts of oesophageal, stomach, colorectal, gallbladder, and pancreatic cancers added up to 2621.20 thousand DALYs (95 % CI: 2095.58-3091.10), revealing the significant health impact of surface water pollution on digestive cancer. (3) In 2022, health impacts in the Beijing-Tianjin-Hebei and surroundings, the Yangtze River Delta, and the middle reaches of the Yangtze River added up to 1893.06 thousand DALYs (95 % CI: 1471.82-2097.88), showing a regional aggregating trend. (4) Surface water pollution control has been the primary driving factor to health impact improvement, contributing -3.49 % to the health impact change from 2015 to 2022. It is the first city-level health impact map for China's surface water pollution. The methods and findings will support the water management policymaking in China and other countries suffering from water pollution.

The critical role of organic matter for cadmium-lead interactions in soil: Mechanisms and risks

Understanding the interaction mechanisms between complex heavy metals and soil components is a prerequisite for effectively forecasting the mobility and availability of contaminants in soils. Soil organic matter (SOM), with its diverse functional groups, has long been a focal point of research interest. In this study, four soils with manipulated levels of SOM, cadmium (Cd) and lead (Pb) were subjected to a 90-day incubation experiment. The competitive interactions between Cd and Pb in soils were investigated using Fourier transform infrared spectrometer (FTIR), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and X-ray adsorption near-edge structure (XANES) analysis. Our results indicate that Pb competed with Cd for adsorption sites on the surface of SOM, particularly on carboxyl and hydroxyl functional groups. Approximately 22.6 % of Cd adsorption sites on humus were occupied by Pb. The use of sequentially extracted exchangeable heavy metals as indicators for environment risk assessments, considering variations in soil physico-chemical properties and synergistic or antagonistic effects between contaminants, provides a better estimation of metal bioavailability and its potential impacts. Integrating comprehensive contamination characterization of heavy metal interactions with the soil organic phase is an important advancement to assess the environmental risks of heavy metal dynamics in soil compared to individual contamination assessments.

Different "nongrain" activities affect the accumulation of heavy metals and their source-oriented health risks on cultivated lands

"Nongrain" production on cultivated land is one of the primary environmental issues in China. Different "nongrain" activities may introduce different pollution sources to the local environment, leading to variations in heavy metal contents in soil, which can profoundly impact national food security. In this study, three typical "nongrain" regions (Nanxun (NX), Xiaoshan (XS) and Lin'an (LA)) with intensive aquaculture, tea planting and flower (seedling) growth on cultivated land around the Hangzhou metropolitan area were selected to address the spatial heterogeneity of accumulation levels, sources and source-oriented health risks of heavy metals in soil. The results showed that Hg was the main pollutant in NX and XS, while Cd and As were the major contaminants in LA. Aquiculture and sericultural industries (37.43%), natural sources (23.59%) and industrial activities (38.99%) were the major sources in NX; atmospheric deposition (37.73%), flower and seedling planting (23.49%) and metal-related industries (35.16%) were the major sources in XS; and atmospheric deposition (28.06%), excessive application of fertilizers and pesticides during tea planting (43.47%) and natural sources (28.47%) were the major sources in LA. The major risk population, area, exposure route and hazardous elements were children, LA, ingestion and As and Cr, respectively. From the perspective of source-based health risk assessment, in addition to natural sources that are difficult to intervene in, industrial activities, especially leather and wood process industries, metal-related industries and excessive fertilizer and pesticide application during tea planting contributed the most to the total health risk, which explained 67%, 41% and 42%, respectively, of the total risk in NX, XS and LA. High health risks are present in sources with heavy loadings of hazardous heavy metals (As and Cr); thus, to protect human health, the corresponding high-risk anthropogenic pollution sources in different "nongrain" areas need to be controlled.

Improving prediction of soil heavy metal(loid) concentration by developing a combined Co-kriging and geographically and temporally weighted regression (GTWR) model

The study of heavy metal(loid) (HM) contamination in soil using extensive data obtained from published literature is an economical and convenient method. However, the uneven distribution of these data in time and space limits their direct applicability. Therefore, based on the concentration data obtained from the published literature (2000-2020), we investigated the relationship between soil HM accumulation and various anthropogenic activities, developed a hybrid model to predict soil HM concentrations, and then evaluated their ecological risks. The results demonstrated that various anthropogenic activities were the main cause of soil HM accumulation using Geographically and temporally weighted regression (GTWR) model. The hybrid Co-kriging + GTWR model, which incorporates two of the most influential auxiliary variables, can improve the accuracy and reliability of predicting HM concentrations. The predicted concentrations of eight HMs all exceeded the background values for soil environment in China. The results of the ecological risk assessment revealed that five HMs accounted for more than 90% of the area at the "High risk" level (RQ ≥ 1), with the descending order of Ni (100%) = Cu (100%) > As (98.73%) > Zn (95.50%) > Pb (94.90%). This study provides a novel approach to environmental pollution research using the published data.

Application of the partial least square regression method in determining the natural background of soil heavy metals: A case study in the Songhua River basin, China

The "background" is an essential index for identifying anthropogenic inputs and potential ecological risks of soil heavy metals. However, the lithology of bedrock can cause significant spatial variation in the natural background of soil elements, posing considerable difficulties in estimating background values. In this study, an attempt was made to calculate the natural background through regression analysis of soil chemical composition, and reasonably evaluate the impact of lithology. A total of 1771 surface soil samples were collected from the Songhua River Basin, China, for chemical composition analysis, and the partial least square regression (PLSR) method was employed to establish the relationship between heavy metals (As, Hg, Cr, Cd, Pb, Cu, Zn, and Ni) and soil chemical composition/environmental parameters (SiO2, Al2O3, TFe2O3, MgO, CaO, K2O, Na2O, La, Y, Zr, V, Sc, Sr, Li and pH). The result shows that As, Cr, Pb, Cu, Zn, and Ni have significant linear relationships with soil chemical composition. Each of these six heavy metals obtained 1771 regression background values; some were higher than the uniform background value obtained from the boxplot, while others were lower. The regression background values recognized not only subtle anthropogenic inputs and potential ecological risks in low-background regions but also spurious contamination in high-background areas. All these indicate that the PLSR method can effectively improve the determination accuracy of the natural background of soil heavy metals. More attention should be paid to the serious anthropogenic inputs appearing in some places of the study area.

Incorporating environmental capacity considerations to prioritize control factors for the management of heavy metals in soil

Heavy metals (HMs) pollution threatens food security and human health. While previous studies have evaluated source-oriented health risk assessments, a comprehensive integration of environmental capacity risk assessments with pollution source analysis to prioritize control factors for soil contamination is still lacking. Herein, we collected 837 surface soil samples from agricultural land in the Nansha District of China in 2019. We developed an improved integrated assessment model to analyze the pollution sources, health risks, and environmental capacities of As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn. The model graded pollution source impact on environmental capacity risk to prioritize control measures for soil HMs. All HMs except Pb exceeded background values and were sourced primarily from natural, transportation, and industrial activities (31.26%). Approximately 98.92% (children), 97.87% (adult females), and 97.41% (adult males) of carcinogenic values exceeded the acceptable threshold of 1E-6. HM pollution was classified as medium capacity (3.41 kg/hm2) with mild risk (PI = 0.52). Mixed sources of natural backgrounds, transportation, and industrial sources were identified as priority sources, and As a priority element. These findings will help prioritize control factors for soil HMs and direct resources to the most critical pollutants and sources of contamination, particularly when resources are limited.

Predictions of heavy metal concentrations by physiochemical water quality parameters in coastal areas of Yangtze river estuary

Due to the degradation-resistant and strong toxicity, heavy metals pose a serious threat to the safety of water environment and aquatic ecology. Rapid acquisition and prediction of heavy metal concentrations are of paramount importance for water resource management and environmental preservation. In this study, heavy metal concentrations (Cr, Ni, Cu, Pb, Zn, Cd) and physicochemical parameters of water quality including Temperature (Temp), pH, Oxygen redox potential (ORP), Dissolved oxygen (DO), Electrical conductivity (EC), Electrical resistivity (RES), Total dissolved solids (TDS), Salinity (SAL), Cyanobacteria (BGA-PE), and turbidity (NTU) were measured at seven stations in the Yangtze river estuary. Principal Component Analysis (PCA) and Spearman correlation analysis were employed to analyze the main factors and sources of heavy metals. Results of PCA revealed that the main sources of Cr, Ni, Zn, and Cd were steel industry wastewater, domestic and industrial sewage, whereas shipping and vessel emissions were typically considered sources of Pb and Cu. Spearman correlation analysis identified Temp, pH, ORP, EC, RES, TDS, and SAL as the key physicochemical parameters of water quality, exhibiting the strongest correlation with heavy metal concentrations in sediment and water samples. Based on these results, multiple linear regression as well as non-linear models (SVM and RF) were constructed for predicting heavy metal concentrations. The results showed that the results of the nonlinear model were more suitable for predicting the concentrations of most heavy metals than the linear model, with average R values of the SVM test set and RF test set being 0.83 and 0.90. The RF model showed better applicability for simulating the concentration of heavy metals along the Yangtze river estuary. It was demonstrated that non-linear research methods provided efficient and accurate predictions of heavy metal concentrations in a simple and rapid manner, thereby offering decision-making support for watershed managers.

Source-based health risk assessment of heavy metal contamination in soil: a case study from a polymetallic mining region in Southeastern Hubei, Central China

To conduct a precise health risk assessment of heavy metals (HMs) in soil, it is imperative to ascertain the primary sources of potential health risks. In this study, we conducted comprehensive measurements of HMs, specifically focusing on the accumulation of Cu, Cd, Sb, Zn, and Pb in local soil, which may pose threats to environmental quality. To achieve our objective, we employed a method that combines positive matrix factorization with a health risk assessment model to quantify the health risks associated with specific sources. The results obtained from the geo-accumulation index indicate that the majority of HMs found in the local soil are influenced by anthropogenic activities. Among these sources, local industrial-related activities contributed the largest proportion of HMs to the soil at 34.7%, followed by natural sources at 28.7%, mining and metallurgy-related activities at 28.2%, and traffic-related activities at 8.40%. Although the non-carcinogenic and carcinogenic risks associated with individual HMs were found to be below safety thresholds, the cumulative health risks stemming from total HMs exceeded safety limits for children. Moreover, the unacceptable health risks for children originating from industrial-related activities, natural sources, and mining and metallurgy-related activities were primarily concentrated in proximity to mining sites and industrial areas within the local region. This investigation furnishes valuable insights that can aid governmental authorities in formulating precise control policies to mitigate health threats posed by soils in polymetallic mining areas.

Uncertainty Evaluation of Soil Heavy Metal(loid) Pollution and Health Risk in Hunan Province: A Geographic Detector with Monte Carlo Simulation

Research on soil heavy metal(loid) pollution and health risk assessment is extensive, but a notable gap exists in systematically examining uncertainty in this process. We employ the Nemerow index, the health risk assessment model, and the geographic detector model (GDM) to analyze soil heavy metal(loid) pollution, assess health risks, and identify driving factors in Hunan Province, China. Furthermore, the Monte Carlo simulation (MCS) method is utilized to quantitatively evaluate the uncertainties associated with the sampling point positions, model parameters, and classification boundaries of the driving factors in these processes. The experimental findings reveal the following key insights: (1) Regions with high levels of heavy metal(loid) pollution, accompanied by low uncertainty, are identified in Chenzhou and Hengyang Cities in Hunan Province. (2) Arsenic (As) and chromium (Cr) are identified as the primary contributors to health risks. (3) The GDM results highlight strong nonlinear enhanced interactions among lithology and other factors. (4) The input GDM factors, such as temperature, river distance, and gross domestic product (GDP), show high uncertainty on the influencing degree of soil heavy metal(loid) pollution. This study thoroughly assesses high heavy metal(loid) pollution in Hunan Province, China, emphasizing uncertainty and offering a scientific foundation for land management and pollution remediation.

Spatial distribution, source identification, and risk assessment of heavy metals in riparian soils of the Tibetan plateau

Riparian soils in the lower sections of the Lhasa River were chosen as the research focus, to examine the characteristics and sources of heavy metals in riparian soils of high-cold regions. To investigate the influence of various factors on the geographical distribution of heavy metals, three horizontal and one vertical profiles were considered. The geoaccumulation index, prospective ecological risk index, and enrichment factor were used to evaluate the extent of soil contamination. Correlation analysis and the positive-matrix-analysis receptor model were used to quantitatively examine the sources of the elements. According to the soil-evaluation, the topsoil was more polluted than the deep soil. Overall, the soil was slightly degraded and posed minor ecological concern. Cd was the primary contributor to the overall contamination, with moderate and considerable risk levels at certain locations. Five sources were identified for the six heavy metals. Transportation and agricultural production were the principal sources of Cd. Ni and Cr were mostly connected to agricultural practices and weathering of parent-soil materials. Pb and Zn were mostly related to geological history, geothermal development, and traffic pollution. Mineral resource development has had a major impact on Cu. Non-carcinogenic risk index of each heavy metal and their total value were <1, indicating they are not harmful to human health. The riparian soil of the Lhasa River Basin contains heavy metals from various sources; therefore, it is important to monitor these heavy metals. This study provides a scientific foundation for the safe utilization and classification of soils in high cold regions.

Health risk assessment of heavy metal pollution and its sources in agricultural soils near Hongfeng Lake in the mining area of Guizhou Province, China

Background

Accelerated modern industrial processes, extensive use of pesticides and fertilizers and remaining issues of wastewater irrigation have led to an increasingly severe composite pollution of heavy metals in arable land. Soil contamination can cause significant damage to ecological environments and human health. Mineral resource mining can result in varying degrees of heavy metal pollution in surrounding water systems and soil. As a plateau lake, Hongfeng Lake has a fragile watershed ecosystem. Coupled with the rapid development of the current socio-economy and the ongoing activities of mining, urbanization and agricultural development, the water and soil environment of the lake and arable land are facing serious heavy metal pollution. Therefore, the situation warrants attention.

Methods

This study focused on characterizing soil types and conducted sampling and laboratory testing on the farmland soil in Hongfeng Lake. The integrated Nemero comprehensive pollution assessment and potential ecological pollution assessment methods were used to evaluate the heavy metal pollution status. The APCS-MLR model was employed to explore the sources of heavy metal pollution. In addition, the human health risk model was used to analyze the association between heavy metal content in cultivated land and human health risks.

Results

The single-factor pollution of each element was ranked in descending order: Hg > As > Pb > Cr > Cd, with Hg being the main pollutant factor. The entire area was subjected to mild pollution according to the pollution index. Pollution source analysis indicated two main pollution sources. Hg, As, Pb and Cr pollution mainly resulted from Source 1 (industrial and natural activities), accounting for 71.99%, 51.57%, 67.39% and 68.36%, respectively. Cd pollution was mainly attributed to Source 2 (agricultural pollution source), contributing 84.12%. The health risk assessment model shows that heavy metals posed acceptable carcinogenic risks to humans rather than non-carcinogenic risks. As was the main non-carcinogenic risk factor, while Cr was the main carcinogenic risk factor, with higher risks in children than adults.

Conclusion

Our study identified the heavy metal pollution in farmland soil in Hongfeng Lake, evaluated and analyzed the pollution sources and identified the heavy metal elements in cultivated lands that have the greatest impact on human health risks. The aim of this study is to provide a scientific basis for soil heavy metal pollution control.

Changes of heavy metal concentrations in farmland soils affected by non-ferrous metal smelting in China: A meta-analysis

Long-term human smelting activities have resulted in substantial heavy metals (HMs) pollution of farmland soils around smelting sites, and the safety of farmland products is critical for human health. The current study focuses on HMs in farmland soils surrounding a single smelter, therefore the impact of smelting on a national scale needs to be investigated further. This study was based on 116 papers and 1143 sets of relevant data for meta-analysis, and a hierarchical mixed-effects model was used to quantify the changes of HMs concentrations in farmland soils affected by non-ferrous metal smelting on a national scale, as well as their relationships with relevant explanatory variables in China. Results showed that: (i) non-ferrous metal smelting substantially increased farmland soils HMs concentrations (323%), with each HM concentration increasing in the following order: Cd (2753%) > Pb (562%) > Hg (455%) > Zn (228%) > Cu (158%) > As (107%) > Ni (52%); (ii) the highest increase of HMs in vegetable fields (361%), but not significant in comparison to other farmland categories, and the increase of Pb, Zn, Cu and As concentrations were significantly different in different types of smelting areas; (iii) the increase of Hg was significantly higher in the northern region than in the southern region, and the opposite increase of Cu; (iv) the soil depth from 0 to 40 cm was significantly affected by smelting, and the increase of multiple HMs were significantly positively correlated with soil pH and negatively correlated with distance; (v) the other explanatory variables (farmland category and soil organic matter) were not significantly related to the effect of smelting. The results can provide some reference for protecting and restoring farmland soils around smelting areas.

Heavy metals contamination, receptor model-based sources identification, sources-specific ecological and health risks in road dust of a highly developed city

The present study quantified Ni, Cu, Cr, Pb, Cd, As, Zn, and Fe levels in road dust collected from a variety of sites in Tangail, Bangladesh. The goal of this study was to use a matrix factorization model to identify the specific origin of these components and to evaluate the ecological and health hazards associated with each potential origin. The inductively coupled plasma mass spectrometry was used to determine the concentrations of Cu, Ni, Cr, Pb, As, Zn, Cd, and Fe. The average concentrations of these elements were found to be 30.77 ± 8.80, 25.17 ± 6.78, 39.49 ± 12.53, 28.74 ± 7.84, 1.90 ± 0.79, 158.30 ± 28.25, 2.42 ± 0.69, and 18,185.53 ± 4215.61 mg/kg, respectively. Compared to the top continental crust, the mean values of Cu, Pb, Zn, and Cd were 1.09, 1.69, 2.36, and 26.88 times higher, respectively. According to the Nemerow integrated pollution index (NIPI), pollution load index (PLI), Nemerow integrated risk index (NIRI), and potential ecological risk (PER), 84%, 42%, 30%, and 16% of sampling areas, respectively, which possessed severe contamination. PMF model revealed that Cu (43%), Fe (69.3%), and Cd (69.2%) were mainly released from mixed sources, natural sources, and traffic emission, respectively. Traffic emission posed high and moderate risks for modified NIRI and potential ecological risks. The calculated PMF model-based health hazards indicated that the cancer risk value for traffic emission, natural, and mixed sources had been greater than (1.0E-04), indicating probable cancer risks and that traffic emission posed 38% risk to adult males where 37% for both adult females and children.

Distribution and relationship of antibiotics, heavy metals and resistance genes in the upstream of Hanjiang River Basin in Shiyan, China

The upstream basin of Hanjiang River is an important water source for the middle route of China's South-to-North Water Diversion Project. The quality of water and soil in the Hanjiang River have enormous biological and environmental impacts, and resistant genetic contamination has emerged, but only few studies are concerned the correlation between heavy metals and metal resistance genes (MRGs). In this study, 8 antibiotics and 19 heavy metals were analyzed, the results showed that the highest antibiotic content was tetracycline, with mean concentrations of 43.201 µg/kg and 0.022 µg/L. Mn was the highest heavy metal in soil with a content of 1408.284 µg/kg, and in water was Zn with a content of 10.611 µg/L. We found that the most abundant antibiotic resistance genes (ARGs) and metal resistance genes (MRGs) in the study area were bacA and arsT genes, coding for resistance mechanisms to bacitracin and arsenic, respectively. The data showed that heavy metals had a greater impact on antibiotic resistance genes than antibiotics, and the correlation between resistance genes was significantly positive. This work expands our understanding of the correlations of antibiotics, heavy metals, and resistance genes in the Hanjiang River, indicating that more attention should be paid to the effects of resistance genes and the quality of water.

Dynamic crosstalk between silicon nanomaterials and potentially toxic trace elements in plant-soil systems

Agricultural soil pollution with potentially toxic trace elements (PTEs) has emerged as a significant environmental concern, jeopardizing food safety and human health. Although, conventional remediation approaches have been used for PTEs-contaminated soils treatment; however, these techniques are toxic, expensive, harmful to human health, and can lead to environmental contamination. Nano-enabled agriculture has gained significant attention as a sustainable approach to improve crop production and food security. Silicon nanomaterials (SiNMs) have emerged as a promising alternative for PTEs-contaminated soils remediation. SiNMs have unique characteristics, such as higher chemical reactivity, higher stability, greater surface area to volume ratio and smaller size that make them effective in removing PTEs from the environment. The review discusses the recent advancements and developments in SiNMs for the sustainable remediation of PTEs in agricultural soils. The article covers various synthesis methods, characterization techniques, and the potential mechanisms of SiNMs to alleviate PTEs toxicity in plant-soil systems. Additionally, we highlight the potential benefits and limitations of SiNMs and discusses future directions for research and development. Overall, the use of SiNMs for PTEs remediation offers a sustainable platform for the protection of agricultural soils and the environment.

Source-specific risk judgement and environmental impact of potentially toxic elements in fine road dust from an integrated industrial city, North China

The contamination of potentially toxic elements (PTEs) in road dust of large industrial cities is extremely serious. Determining the priority risk control factors of PTE contamination in road dust is critical to enhance the environmental quality of such cities and mitigate the risk of PTE pollution. The Monte Carlo simulation (MCS) method and geographical models were employed to assess the probabilistic pollution levels and eco-health risks of PTEs originating from different sources in fine road dust (FRD) of large industrial cities, and to identify key factors affecting the spatial variability of priority control sources and target PTEs. It was observed that in FRD of Shijiazhuang, a typical large industrial city in China, more than 97% of the samples had an INI > 1 (INI_mean = 1.8), indicating moderately contaminated with PTEs. The eco-risk was at least considerable (NCRI >160) with more than 98% of the samples, mainly caused by Hg (E_{i (mean)} = 367.3). The coal-related industrial source (NCRI_(mean) = 235.1) contributed 70.9% to the overall eco-risk (NCRI_(mean) = 295.5) of source-oriented risks. The non-carcinogenic risk of children and adults are of less importance, but the carcinogenic risk deserves attention. The coal-related industry is a priority control pollution source for human health protection, with As corresponding to the target PTE. The major factors affecting the spatial changes of target PTEs (Hg and As) and coal-related industrial sources were plant distribution, population density, and gross domestic product. The hot spots of coal-related industrial sources in different regions were strongly interfered by various human activities. Our results illustrate spatial changes and key-influencing factors of priority source and target PTEs in Shijiazhuang FRD, which are helpful for environmental protection and control of environmental risks by PTEs.

Spatial distribution, sources, and risk assessment of potentially toxic elements in cultivated soils using isotopic tracing techniques and Monte Carlo simulation

Potentially toxic elements (PTEs) in cultivated lands pose serious threats to the environment and human health. Therefore, improving the understanding of their distinct sources and environmental risks by integrating various methods is necessary. This study investigated the distribution, sources, and environmental risks of eight PTEs in cultivated soils in Lishui City, eastern China, using digital soil mapping, positive matrix factorisation (PMF), isotopic tracing, and Monte Carlo simulation. The results showed that Pb and Cd are the main pollutants, which posed higher ecological risks in the study area than the other PTEs. Natural, mining, traffic, and agricultural sources were identified as the four determinants of PTE accumulation via a PMF model combined with Pearson correlation analysis, showing that their contribution rates were 22.6 %, 45.7 %, 15.2 %, and 16.5 %, respectively. Stable isotope analysis further confirmed that local mining activities affected the HM accumulation. Additionally, non-carcinogenic and carcinogenic risk values for children were 3.18 % and 3.75 %, respectively, exceeding their acceptable levels. We also identified that mining activities were the most important sources of human health risks (55.7 % for adults and 58.6 % for children) via Monte Carlo simulations coupled with the PMF model. Overall, this study provides insights into the PTE pollution management and health risk control in cultivated soils.

High-resolution risk mapping of heavy metals in soil with an integrated static-dynamic interaction model: A case study in an industrial agglomeration area in China

Heavy metal pollution of soils in industrial agglomeration areas is an increasing concern worldwide. In this study, we traced the sources of heavy metal emissions using a positive matrix factorization (PMF) model. Accordingly, we proposed a novel static-dynamic risk interaction model incorporating multiple risk-related factors to quantify the spatial interaction of emission sources and the probability of accumulation of heavy metals on a large scale. This model was further classified using the Jenks optimization technique to predict the spatial distribution of high-risk hotspots. Our results determined four primary emission sources of heavy metals: industrial (35.01 %), natural (28.61 %), agricultural (26.07 %), and traffic (10.31 %) sources. Five levels were classified by the integrated risk coefficient (IRC), namely, from extremely high to extremely low risk. The extremely high- and high-risk hotspots constituting 41.52 % of the total area of the Zhenhai District, with IRC values ranging from 0.221 to 0.413, were mainly generated by multiple sources linked to PMF-based factors. This quantitative evaluation framework can generate a high-resolution spatially distributed pollution risk map at the grid scale (1 km), which can provide a relatively precise basis for policymaking for point-to-point soil pollution management.

Pollution Profiles, Source Identification and Health Risk Assessment of Heavy Metals in Soil near a Non-Ferrous Metal Smelting Plant

Heavy metal pollution related to non-ferrous metal smelting may pose a significant threat to human health. This study analyzed 58 surface soils collected from a representative non-ferrous metal smelting area to screen potentially hazardous heavy metals and evaluate their health risk in the studied area. The findings demonstrated that human activity had contributed to the pollution degrees of Cu, Cd, As, Zn, and Pb in the surrounding area of a non-ferrous metal smelting plant (NMSP). Cu, Cd, As, Zn, Pb, Ni, and Co pollution within the NMSP was serious. Combining the spatial distribution and Spearman correlations with principal component analysis (PCA), the primary sources of Cd, As, Pb, and Zn in surrounding areas were related to non-ferrous metal smelting and transportation activities. High non-cancer (THI = 4.76) and cancer risks (TCR = 2.99 × 10^-4) were found for adults in the NMSP. Moreover, heavy metals in the surrounding areas posed a potential cancer risk to children (TCR = 3.62 × 10^-6) and adults (TCR = 1.27 × 10^-5). The significant contributions of As, Pb, and Cd to health risks requires special attention. The construction of a heavy metal pollution management system will benefit from the current study for the non-ferrous metal smelting industry.