Multivariate receptor models and robust geostatistics to estimate source apportionment of heavy metals in soils

Spatial and ecological health impacts of potentially toxic elements in road dust from long-term mining activities: A case study of the Bayan Obo deposit

The long-term impacts of mining activities at the Bayan Obo deposit on potentially toxic elements (PTEs) in road dust remain insufficiently understood. This study aims to enrich knowledge in this area by investigating the spatial and eco-health impacts of PTEs in both bulk road dust (BRD) and resuspended road dust (RRD) from mining. An integrated approach combining Monte Carlo simulations with multiple statistical and geostatistical methods was used to quantify mining-related impacts. The findings revealed that Cd was the most polluted element. Concentrations of Cd, Mo, Pb and Zn were notably higher near the mine and decreased with increasing distance, with mining activities directly contributing over 20 % to these PTEs. Moderate and considerable eco-risks were identified for BRD and RRD, respectively, primarily driven by Cd and Mo, with higher risks closer to the mine. While non-carcinogenic risks were negligible, carcinogenic risks for adults required attention. Mining-related sources accounted for over 30 % of eco-risks but less than 10 % of health risks. This research integrates multiple methods, providing a more comprehensive understanding of the spatial and eco-health impacts of mining activities on PTEs in road dust. These findings offer critical insights and guidance for managing similar environmental challenges in other mining regions.

Quantification of an integrated approach to heavy metal source apportionment and probabilistic health risk assessment in the black soil region of central Jilin Province, China

The northeast black soil region is critical for grain production in China but has experienced significant heavy metals (HMs) contamination due to intensive agriculture. This study investigates the levels of Cr, Ni, Cu, Zn, Cd, As, Pb, and Hg in agricultural soils within the black soil region of central Jilin Province. Enrichment factor (EF) and geo-accumulation index (Igeo) indicate that Ni and Cr are significantly affected by human activities, with notable pollution levels. The Positive Matrix Factorization (PMF) model identifies four primary pollution sources: coal combustion, traffic emissions, and soil parent material (24.70 %); fertilizers and pesticides (24.50 %); mining (27.81 %); and organic fertilizers combined with soil parent material (22.99 %). The potential ecological risk assessment results reveal a generally low potential ecological risk in the study area, although Hg and Cd contribute notably to the overall risk. The human health risk assessment (HHRA) results show that non-carcinogenic risk for all populations are below the threshold of 1, while the average carcinogenic risk for all populations exceed the acceptable threshold of 1E-6. Children face higher non-carcinogenic and carcinogenic risks compared to adults. By integrating the PMF results with potential ecological and health risk assessments, it was found that coal combustion and mining activities contribute most significantly to potential ecological and health risks, respectively. This study investigates the pollution characteristics, sources, and ecological and health risks of HMs in agricultural soils in the black soil region. The findings offer valuable insights for policymakers in developing effective environmental management strategies.

Assessing natural background concentrations of chemical elements in urban soils: A case study in Benevento (Italy)

The main objective of this study is to propose a method to determine, as accurately as possible, the natural background content of a chemical element in urban soils, identify potential sources, and quantify emissions from human activities. To achieve this, 156 topsoil samples were taken from the surface horizon of the soil (first 20 cm) and analysed for 25 elements using a combination of inductively coupled plasma atomic emission spectrometry (ICP-AES) and inductively coupled plasma mass spectrometry (ICP-MS), after aqua regia digestion. The concentration data obtained were rigorously analysed using multivariate statistical analysis methods, including compositional data analysis (CoDA), clustering and dimension reduction techniques. This analysis separated the data into distinct populations, each characteristic of a natural or anthropogenic phenomenon. The ProUCL 5.2.0 software package was then used to calculate the natural background levels of each element for each data population. The background of some elements, including Co and Tl, exceeds the threshold values imposed in Italy by environmental law in some areas. These findings, together with the use of specific indices, allowed us to precisely define the degree of potentially toxic elements enrichment and the potential ecological risk of the studied area, thus providing valuable information for decisions on urban planning and environmental policy and potentially influencing future strategies for managing urban soil health.

Contamination and source-specific health risk assessment of soil heavy metals in the middle and upper reaches of the Heihe River Basin of China

Anthropogenic activities drive heavy metal contamination in soil, making source-specific apportionment essential for managing health risks in rapidly urbanizing areas. This study focuses on the novel task of quantifying health risks from specific sources of heavy metal contamination and visualizing the spatial patterns of human activities' impact on heavy metal contamination and health risks. It combined multiple analytical techniques, including pollution indices, health risk assessments, and bivariate local indicators of spatial association analysis. Additionally, the absolute principal component score-multiple linear regression model, integrated with a human health risk assessment, was employed to quantify health risks and evaluate the contributions of specific sources. Results revealed that Cd and As were at moderate contamination levels, while Zn, Cu, and Ni showed low contamination. Despite generally low contamination levels, moderately to heavily contaminated areas were identified in the southern region correlated with human activities. Although both non-carcinogenic and carcinogenic risks were low for both children and adults, Cr and As were still the main contributors to health risks, primarily through ingestion, with children being at a greater risk compared to adults. The health risks were primarily linked to four sources: traffic and mining, natural sources, agricultural activities, and industrial sources. Industrial (children: 27.47%; adults: 31.96%) and agricultural activities (children: 27.11%; adults: 24.01%) were the primary contributors to non-carcinogenic risks, while the carcinogenic risks were mainly contributed by agricultural activities (children: 40.21%; adults: 40.14%). Therefore, controlling industrial and agricultural activities is crucial to safeguarding public health during sustainable regional development.

High-accuracy spatial prediction of soil pollutants and their speciation in strong human-affected areas

Strong human activities greatly challenge the high-accuracy spatial prediction of soil pollutants and their speciation. This study first determined three auxiliary variables of soil total arsenic (TA) in a typical strong human-affected area, namely in-situ portable X-ray fluorescence (PXRF) TA calibrated by robust geographically weighted regression (RGWR), atmospheric deposition information simulated by atmospheric diffusion model (AERMOD), and land-use types. Then, robust residual cokriging with the above three auxiliary variables (RRCoK-RCPXRF/AD/LUT) was proposed to spatially predict soil TA. Finally, RGWR-robust ordinary kriging (RGWR-ROK) with the RRCoK-predicted soil TA was proposed to spatially predict soil As(III). The results show that: (i) RGWR obtained a higher spatial calibration accuracy (RI = 64.78%) for in-situ PXRF TA than the basic geographically weighted regression and traditionally-used ordinary least squares; (ii) The effect of auxiliary variables and model robustness on the prediction accuracy of soil TA is significant (RI > 14.33%); (iii) RRCoK-RCPXRF/AD/LUT achieved a higher prediction accuracy (RI = 58.87%) for soil TA than the other six traditional models; and (iv) RGWR-ROK achieved a higher prediction accuracy (RI = 55.26%) for soil As(III) than the other three traditional models. Therefore, this study provided a cost-effective solution for high-accuracy spatial prediction of soil pollutants and their speciation in strong human-affected areas.

Source-oriented health risk assessment of soil potentially toxic elements based on Monte Carlo simulation in the upper reaches of Wei River Basin, China

The natural environment and public health are gravely threatened by the enrichment of soil potentially toxic elements (PTEs). To explore the contamination level, sources and human health risks posed by PTEs, high-density soil sampling was carried out in the upper Wei River region (UWRR). The results demonstrated that the pollution risk and ecological risk in UWRR as a whole were at a low level, but there were moderate or higher ecological risks of Hg and Cd in some areas. Source analysis of soil PTEs was conducted via absolute principal component score multiple linear regression (APCS-MLR) and positive matrix factorization (PMF) receptor models. APCS-MLR identified three potential sources, while the source division of PMF model was more detailed, which identified four potential sources: mining, coal combustion, machinery manufacturing and agricultural sources, with contribution percentages of 31%, 3%, 37% and 29% respectively. According to the probabilistic human health risk assessment (HHRA), the non-carcinogenic risk for adults was negligible, while that for children cannot be negligible. There were total carcinogenic risks for all populations, but the risk level was acceptable. The total cancer risk for children surpassed 1E-04 by 31.29%, implying a significant carcinogenic risk. Machinery manufacturing was found to be the most significant anthropogenic source of health concerns. This study offers an illustration of probabilistic risk assessment based on sources. The results of the study are favorable to provide new perspectives and scientific reference for soil PTE risk assessment and pollution control.

A spatial source-oriented and probability-based risk-assessment framework for heavy metal and PAH contamination of urban soils in Guangzhou, China

The identification and quantification of high-risk hotspots for soils contaminated by heavy metals (HMs) and polycyclic aromatic hydrocarbons (PAHs) remains a challenge due to their various sources and heterogeneous sink properties in urban soil systems. In this study of 221 soil samples from Guangzhou, China, a novel framework combining Bivariate local Moran's I (BLMI), positive matrix factorization (PMF), human health risk (HHR) assessment, Monte Carlo simulation (MCS), and a newly developed spatial risk model were proposed to conduct probabilistic source-oriented HHR assessment, high-risk hotspot quantification, and risk formation mechanism elaboration. Study results indicate that traffic emissions are the largest contributor of HMs (47.6 %) and PAHs (40.2 %), but not always the largest contributor of HHR. Agricultural or urban green-space management activities of HM, and mixed source of PAH, are the largest contributors of non-carcinogenic risk (NCR, 48.7 % and 51.1 %, respectively), while mixed source of HM and traffic emissions of PAH are the largest contributors of carcinogenic risk (CR, 53.9 % and 71.2 %, respectively). The probability of risk exceeding safe threshold levels is < 5.0 % for NCR and > 90.0 % for CR. High-risk hotspots were identified in the mid-west and south of the city, making up 15.0 % of the total Guangzhou area. Risk mechanisms were deduced from the spatial heterogeneity and inter-dependence of emission sources and soil sink, based on source-sink theory. Our findings provide a new framework for precisely identifying risk sources and target areas, thereby alleviating HHR associated with co-occurring HMs and PAHs in urban soil systems.

A comprehensive approach for quantifying source-specific ecological and health risks of potentially toxic elements in agricultural soil

Given the pollution prevalence of potentially hazardous elements (PTEs) in agricultural soils worldwide, it is crucial to establish a comprehensive approach to accurately assess soil contamination, and quantitatively allocate sources and source-specific risks. In the study, soil contamination was assessed through environmental capacity based on the local geochemical baseline established using PTE contents of the subsoil. The sources of PTEs were quantified through positive matrix factorization (PMF) and GIS mapping. Ecological risk (ER) and human health risk (HHR) models based on PMF were used to evaluate source-specific ER and HHR. Taking Jieyang City as an example, obvious contamination of As, Pb, Cd, Zn and Hg was observed in agricultural soils, and 94.40% of sites had high-to-medium capacity for local PTE contamination. Four sources were apportioned including agricultural activities (17.36%), industrial activities (20.49%), natural sources (34.60%) and traffic emissions (27.55%). The study area was at moderate ER level (121.21) with industrial activities contributing the most (41.26%). The carcinogenic risks (3.21E-05 for children and 1.42E-05 for adults) were within the tolerable range, and non-carcinogenic risks (7.08E-01 for children and 7.70E-02 for adults) were not significant. Agricultural activities were the largest source to the carcinogenic (47.17% for children and 46.31% for adults) and non carcinogenic risks (53.55% for children and 53.03% for adults). Therefore, industrial activities and agricultural activities were the priority control sources to reduce ecological risk and protect human health, respectively.

Soil type data provide new methods and insights for heavy metal pollution assessment and driving factors analysis

Assessing heavy metal pollution and understanding the driving factors are crucial for monitoring and managing soil pollution. This study developed two modified assessment methods (NIPIt and NECI) based on soil type-specific background values and pollution indices, and combined them with the receptor model to evaluate pollution status. Additionally, a structural equation model was used to analyze the driving factors of soil heavy metal pollution. Results showed that the average NIPIt and NECI were 1.48 and 0.92, respectively, indicating a low pollution risk level. In some areas, Cd and Hg were the primary heavy metals contributing to pollution risk, with their highest average concentrations exceeding soil type-specific background values by 2.06 and 2.04 times, respectively. Additionally, in black soils, meadow soils, and chernozems, heavy metals primarily originated from natural sources, accounting for 48.92 %, 45.98 %, and 45.58 %, respectively. In aeolian soils, agricultural sources were predominant, contributing 43.38 %. Soil pH and organic matter were key soil properties affecting NECI and NIPIt, with direct effects of 0.36 and -0.19, respectively. This study aims to provide new methods and insights for the comprehensive assessment and driving factors analysis of soil heavy metal pollution, with the goal of enhancing pollution monitoring and reducing risk.

Source to risk receptor transport and spatial hotspots of heavy metals pollution in peri-urban agricultural soils of the largest megacity in China

The traditional concentration-based health risk assessment of heavy metal (HMs) pollution in soil has often overlooked the initial loading and toxicity differences of HMs from various sources. This oversight hinders effective identification of the risky source, complicating precise risk management of soil HMs pollution. This study applied a source-oriented health risk assessment framework that integrates source profiling, exposure risk assessment, and spatial cluster analysis. Taking the Shanghai City, the largest megacity in China as a case, the findings revealed that overall environmental quality of peri-urban agricultural soil in Shanghai remains good, though 3.03 % of Cd concentrations exceeded the national reference standards. Industrial & traffic activities, primarily contributing Hg, Cd, and Pb, accounted for the highest proportion (44.3 %) of total metal concentrations and posed the greatest non-cancer risk (54.6 % for children and 53.1 % for adults). Notably, natural activities, mainly contributing Cr, ranked only third in concentration contribution (26.55 %) but induced the highest cancer risk (58.55 % for children and 57.08 % for adults). These findings suggest that sources with lower concentration contributions may still pose significant health risk. Integrating source apportionment with health risk assessment can more precisely identify the risky source and target areas for mitigating the human health hazards.

Potential ecological risk assessment for trace metal(loid)s in soil surrounding coal gangue heaps based on source-oriented

Coal is the predominant energy source in China, resulting in coal gangue. We used the absolute principal component score multiple linear regression (APCS-MLR) model and the geo-detector method (GDM) for determining the potential ecological risk, apportioning sources, and identifying driving factors for trace metal(loid)s (TMs) in soil surrounding coal gangue heaps. The average contents for the concerned TMs (Cd, Hg, As, Pb, Cr, Cu, Ni, and Zn) in the soil of interest were 0.48, 0.18, 11.0, 36.0, 129, 99.2, 68.3 and 141 mg/kg, respectively. Potential ecological risk indicated that the soil was primarily within the "Moderate risk" level, and Cd was the primary pollutant. "The number of coal gangue units" and "the distance between the sampling point and the coal gangue heap" were the key driving factors included in the geo-detector method. Combining APCS-MLR model and GDM, the source apportionment was enhanced in terms of accuracy and reliability. Natural, mining, and unrecognized sources contributed 41.1 %, 39.2 %, and 19.7 % of the TM distribution, respectively. Considering the relationship between TMs, their sources, and corresponding potential ecological risks, mining sources (mainly affected by gangue accumulation) presented a primary linkage with Cd, and its contribution to potential ecological risk was the highest, accounting for 58.2 %. Therefore, further research should focus on effectively managing and controlling the potential ecological risks originating from mining sources and Cd.

Source-oriented stochastic health risk assessment of toxic metals in soil via a hybrid model and Monte Carlo simulation

Toxic metal contamination in soils poses significant hazards to the environment and human health; thus, quantitative assessment of the sources and risks of metal contaminants are urgently needed. A hybrid model that integrates the positive matrix factorization (PMF) and random forest (RF) methods was proposed to quantify the sources of toxic metals in soils by combining diverse environmental variables (source proxies) in this study. In addition, a health risk assessment and Monte Carlo simulations were integrated to estimate the source-oriented stochastic health risk. The results suggested that, except for Ni, which exhibited moderate contamination, other toxic metals (As, Cd, Cr, Hg and Pb) presented slight contamination. Four sources (agricultural activities loaded heavily by As, atmospheric deposition loaded heavily by Hg and Pb, natural sources and mining activities loaded heavily by Cr and Ni, and industrial activities loaded heavily by Cd) were defined and explained 23.44 %, 26.65 %, 30.13 % and 19.78 % of the total variance in toxic metals, respectively. The principal route of exposure (i.e., ingestion), the population at highest risk (i.e., children), and the most hazard-inducing metals (i.e., As and Cr) were determined. Agricultural activities and the combination of natural sources and mining activities demonstrated certain degrees of noncarcinogenic risk to children, with exceedance ratios of 2.20 % and 2.56 %, respectively. Additionally, the combination of natural sources and mining activities demonstrated probabilities for significant carcinogenic risk to adults and children of 0.59 % and 3.76 %, respectively. To reduce the health risks of toxic metals in soils and to protect food and ecological safety, strict regulations should be established to control the discharge of waste from mining and agricultural activities.

A comprehensive study of source apportionment, spatial distribution, and health risks assessment of heavy metal(loid)s in the surface soils of a semi-arid mining region in Matehuala, Mexico

BACKGROUND

This study investigates the contamination level, spatial distribution, pollution sources, potential ecological risks, and human health risks associated with heavy metal(loid)s (i.e., arsenic (As), copper (Cu), iron (Fe), manganese (Mn), lead (Pb), and zinc (Zn)) in surface soils within the mining region of Matehuala, located in central Mexico.

OBJECTIVES

The primary objectives are to estimate the contamination level of heavy metal(loid)s, identify pollution sources, assess potential ecological risks, and evaluate human health risks associated with heavy metal(loid) contamination.

METHODS

Soil samples from the study area were analysed using various indices including Igeo, Cf, PLI, mCd, EF, and PERI to evaluate contamination levels. Source apportionment of heavy metal(loid)s was conducted using the APCS-MLR and PMF receptor models. Spatial distribution patterns were determined using the most efficient interpolation technique among five different approaches. The total carcinogenic risk index (TCR) and total non-carcinogenic index (THI) were used in this study to assess the potential carcinogenic and non-carcinogenic hazards posed by heavy metal(loid)s in surface soil to human health.

RESULTS

The study reveals a high contamination level of heavy metal(loid)s in the surface soil, posing considerable ecological risks. As was identified as a priority metal for regulatory control measures. Mining and smelting activities were identified as the primary factors influencing heavy metal(loid) distributions. Based on spatial distribution mapping, concentrations were higher in the northern, western, and central regions of the study area. As and Fe were found to pose considerable and moderate ecological risks, respectively. Health risk evaluation indicated significant levels of carcinogenic risks for both adults and children, with higher risks for children.

CONCLUSION

This study highlights the urgent need for monitoring heavy metal(loid) contamination in Matehuala's soils, particularly in regions experiencing strong economic growth, to mitigate potential human health and ecological risks associated with heavy metal(loid) pollution.

Experimental study on the adsorption and interaction of P and Cd in polluted sediment from Dongting Lake

A series of experiments was performed to elucidate the effects of the adsorption and interaction of different concentrations of P and Cd on the availability of P and Cd. First, the sediments before and after maturation were subjected to X-ray diffraction (XRD), scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS) and Fourier transform infrared (FTIR) spectroscopy. The results revealed that the composition and main components of the sediment were unchanged by maturation. The adsorption results fitted by the Freundlich equation revealed that the maximum concentration of Cd in the sediment changed from 979.12 mg/L to 980.92 mg/L and 1215 mg/L after the addition of 1 mg/L and 2 mg/L P, respectively. The maximum concentration of P in the sediments increased from 397.57 mg/L to 403.19 mg/L and 422.89 mg/L after the addition of Cd concentrations of 5 mg/L and 50 mg/L, respectively. A batch experiment was subsequently performed with multiple groups of P and Cd at the same concentration. The results revealed that the content of available Cd was the highest when the content of P was 180 mg/kg and that the content of available P was the highest when the content of Cd was 1 mg/kg. However, when the concentrations of P and Cd exceeded a certain level, Cd3(PO4)2 precipitated. Finally, the experimental results were reverified by XPS, and the results revealed that the contents of P and Cd in the sediments increased through adsorption and precipitation between P and Cd.

Application of time series and multivariate statistical models for water quality assessment and pollution source apportionment in an Urban River, New Jersey, USA

Water quality monitoring reveals changing trends in the environmental condition of aquatic systems, elucidates the prevailing factors impacting a water body, and facilitates science-backed policymaking. A 2020 hiatus in water quality data tracking in the Lower Passaic River (LPR), New Jersey, has created a 5-year information gap. To gain insight into the LPR water quality status during this lag period and ahead, water quality indices computed with 16-year historical data available for 12 physical, chemical, nutrient, and microbiological parameters were used to predict water quality between 2020 and 2025 using seasonal autoregressive moving average (ARIMA) models. Average water quality ranged from good to very poor (34 ≤ µWQI ≤ 95), with noticeable spatial and seasonal variations detected in the historical and predicted data. Pollution source tracking with the positive matrix factorization (PMF) model yielded significant R2 values (0.9 < R2 ≤ 1) for the input parameters and revealed four major LPR pollution factors, i.e., combined sewer systems, surface runoff, tide-influenced sediment resuspension, and industrial wastewater with pollution contribution rates of 23-30.2% in the upstream and downstream study areas. Significant correlation of toxic metals, nutrients, and sewage indicators suggest similarities in their sources.

Identifying heavy metal sources and health risks in soil-vegetable systems of fragmented vegetable fields based on machine learning, positive matrix factorization model and Monte Carlo simulation

Urban fragmented vegetable fields offer fresh produce but pose a potential risk of heavy metal (HM) exposure. Thus, this study investigated HM sources and health risks in the soil-vegetable systems of Chongqing's central urban area. Results indicated that Cd was the primary pollutant, with 28.33 % of soil samples exceeding the screening value. Amaranth was particularly problematic, exceeding thresholds for Cd, Hg, and Cr, and both amaranth and celery showed significantly higher HM accumulation (p < 0.05). The HM pollution level in the soil-vegetable system was moderate or above. The sources of HMs identified via Positive matrix factorization (PMF) model included agricultural activities (18.19 %), natural soil parent material (25.88 %), mixed metal smelting and transportation (30.72 %), and coal combustion (25.21 %). Furthermore, evaluations using the Random Forest (RF) model revealed an intricate interaction of factors influencing the presence of HMs, where enterprise density, population density, and road density played significant roles in HMs accumulation. Monte Carlo assessments revealed higher non-carcinogenic risks for children (Pb, As) and greater carcinogenic risks for adults (Cd). Therefore, the issue of HM pollution in soils and vegetables from fragmented fields in industrial urban areas need attention, given the potential for elevated health risks with long-term vegetable consumption.

Source-specific ecological and human health risk analysis of topsoil heavy metals in urban greenspace: a case study from Tianshui City, northwest China

Soil contamination of heavy metals in urban greenspaces can exert detrimental impacts on ecological biodiversity and the health of inhabitants through cross-media migration-induced risks. Here, a total of 72 topsoil samples were collected from greenspaces in the popular tourist city of Tianshui, ranging from areas with parks, residential, road, industrial and educational soils. The study aimed to evaluate an integrated source-specific ecological and human health risk assessment of heavy metals. Among the analyzed heavy metals, except Cr (mean), all exceeded the local background values by 1.30-5.67-fold, and Hg, Cd, Pb and As were the metals with large CV values. The Igeo and CF results showed Hg, Cd, As and Pb exhibited significantly high pollution levels and were the primary pollution factors. The mean PLI values indicated moderate pollution in educational (2.21), industrial (2.07), and road (2.02) soils but slight pollution in park (1.84) and residential (1.39) greenspaces. The Igeo, CF, and PLI results also revealing that these heavy metals are more likely to be affected by human activity. Four primary source factors were identified based on PMF model: coal combustion (25.57%), agricultural sources (14.49%), atmospheric deposition (20.44%) and mixed sources (39.50%). In terms of ecological risk, the mean IRI values showed considerable risks in educational soils (287.52) and moderate risks in road (215.09), park (151.27) and residential (136.71) soils. And the contribution ratio of atmospheric deposition for park, residential, road, industrial and educational greenspaces were 57.72%, 65.41%, 67.69%, 59.60% and 75.76%, respectively. In terms of human health risk, the HI (below 1) and CR (below 1.00E-04) for adults from soils of all land use types was negligible. However, children have more significant non-carcinogenic and carcinogenic hazards especially in residential soils, the HI (above 1) and CR (above 1.00E-04) revealed the significance of regarding legacy As contamination from coal combustion when formulating risk mitigation strategies in this area. The proposed method for source and risk identification makes the multifaceted concerns of pollution and the different relevant risks into a concrete decision-making process, providing robust support for soil contamination control.

Analysis of heavy metal and arsenic sources in mangrove surface sediments at Wulishan Port on Leizhou Peninsula, China, using the APCS-MLR model

Mangrove forests are sources and sinks for various pollutants. This study analyzed the current status of heavy metal and arsenic (As) pollution in mangrove surface sediments in rapidly industrializing and urbanizing port cities. Surface sediments of mangroves at Wulishan Port on the Leizhou Peninsula, China, were analyzed using inductively coupled plasma emission spectrometry (ICP-AES) and inductively coupled plasma mass spectrometry (ICP-MS) for the presence of Cr, Pb, Ni, Zn, Cd, Cu, As, and Hg. The Pollution load index, Nemerow pollution index, and Potential ecological risk index were employed to evaluate the pollutant. Multivariate statistical methods were applied for the qualitative analysis of pollutant sources, and the APCS-MLR receptor model was used for quantification. This study indicated the following results: (1) The average content of eight pollutants surpassed the local background level but did not exceed the Marine Sediment Quality standard. The pollution levels across the four sampling areas were ranked as Ⅲ > Ⅳ > Ⅰ > Ⅱ. The area Ⅱ exhibited relatively lower pollution levels with the grain size of the sediments dominated by sand, which was not conducive to pollutant adsorption and enrichment. (2) The factor analysis and cluster analyses identified three primary sources of contamination. As, Cr, Ni, and Pb originated from nearby industrial activities and their associated wastewater, suggesting that the primary source was the industrial source. Cd, Cu, and Zn stem from the cement columns utilized in oyster farming, alongside discharges from mariculture and pig farming, establishing a secondary agricultural source. Hg originated from ship exhaust burning oil and vehicle emissions in the vicinity, representing the third traffic source. (3) The APCS-MLR receptor model results demonstrated industrial, agricultural, and traffic sources contributing 47.19 %, 33.13 %, and 13.03 %, respectively, with 6.65 % attributed to unidentified sources.

Predictive analysis and risk assessment of potentially toxic elements in Beijing gas station soils using machine learning and two-dimensional Monte Carlo simulations

Gas stations not only serve as sites for oil storage and refueling but also as locations where vehicles frequently brake, significantly enriching the surrounding soil with potentially toxic elements (PTEs). Herein, 117 topsoil samples from gas stations were collected in Beijing to explore the impact of gas stations on PTE accumulation. The analysis revealed that the average Pollution Index (PI) values for Cd, Hg, Pb, Cu, and Zn in the soil samples all exceeded 1. The random forest (RF) model, achieving an AUC score of 0.95, was employed to predict PTE pollution at 372 unsampled gas stations. Additionally, a Positive Matrix Factorization (PMF) model indicated that gas station operations and vehicle emissions were responsible for 70 % of the lead (Pb) enrichment. Probabilistic health risk assessments showed that the carcinogenic risk (CR) and noncarcinogenic risk (NCR) for PTE pollution to adult females were the highest, at 0.451 and 1.61E-05 respectively, but still within acceptable levels. For adult males at contaminated sites, the Pb-associated CR and NCR were approximately twice as high as those at uncontaminated sites, with increases of 107 % and 81 %, respectively. This study provides new insights for managing pollution caused by gas stations.

Distribution characteristics, sources and risk assessment of heavy metals in the surface sediments from the largest tributary of the Lancang River in the Tibet Plateau, China

Angqu, positioned in the eastern expanse of the Tibet Plateau, claims the title of the largest tributary to the Lancang River. In October and December of 2018, in the sediment of Angqu, a comprehensive investigation was conducted on nine heavy metals-arsenic (As), manganese (Mn), chromium (Cr), cadmium (Cd), lead (Pb), mercury (Hg), copper (Cu), zinc (Zn), and nickel (Ni). This investigation aimed to scrutinize the spatial and temporal distribution patterns of these metals, assess the pollution status and ecological risks associated with the sediments, and delve into the sources contributing to their presence. The research results indicate that the average concentrations of As, Hg, and Cd in Angqu sediments exceed the soil background values of Tibet, while the concentrations of other heavy metals are below the soil background values of Tibet. Notably, arsenic poses potential ecological risks. In Angqu sediments, the concentrations of Mn, Cu, Ni, and Pb are generally higher in the wet season, but the seasonal variations of heavy metals in Angqu sediments are not significant. The sediments in the Angqu Basin are predominantly affected by mercury Hg, Cd, and As, with varying degrees of pollution at different sampling points. In the main stream of Angqu (City section), Hg pollution has reached above a moderate level, whereas As pollution near the tributary is only slightly polluted. The analysis of heavy metal sources reveals that there are five primary contributors to heavy metals in surface sediments of Angqu: parent material, agricultural activities, groundwater, atmospheric deposition, and other unidentified sources. Mn, Cr, Pb, and Ni are mainly derived from soil parent material, accounting for more than 50%. About 60.82% of As comes primarily from groundwater. Zn and Cd are mainly sourced from agricultural activities, accounting for 41.25% and 34.33%, respectively. Additionally, 20.6% of Hg originates from atmospheric deposition.

High-density sampling of soil heavy metals in the upper Bailang River basin: contamination characteristics, sources, and source-oriented health risk assessment

Heavy metals (HMs) seriously harm soil environment and threaten crop quality and human health. The aim of the study was to investigate the characteristics, quantify the sources and assess the risks of HMs in soil of upper Bailang River Basin (UBRB). The results indicated that the soils in UBRB were at a non-polluted level and posed a low ecological risk to the environment as a whole. The main pollutants were Ni and Cr obtained by indices Pi and Igeo. Based on the consideration of toxicity, the fuzzy comprehensive evaluation model and Ei index revealed that Hg and Cd were dominating pollutants and ecological risk factors of soil in UBRB. The positive matrix factorization model ascertained five potential sources of soil HMs, namely, plastic processing, energy activities, parent material, transportation and agriculture mixed source and industrial manufacturing, with contribution rates of 17%, 7%, 15%, 29% and 32%, respectively. Natural source primarily determined the non-carcinogenic risk for all populations, accounting for about 43% of the total risk. Industrial manufacturing mainly determined the carcinogenic risk, accounting for about 45%. For adults, the risk was acceptable for most of the sample points. For children, potential non-carcinogenic risks were present in 13.19% of the sample sites, which were mainly located in the west, and unacceptable carcinogenic risks were present in 57.21% of the sample sites, which were mainly concentrated in the western and central parts.

Bayesian multivariate receptor model and convolutional neural network to identify quantitative sources and spatial distributions of potentially toxic elements in soils: A case study in Qingzhou City, China

Determining sources and spatial distributions of potentially toxic elements (PTEs) is a crucial issue of soil pollution survey. However, uncertainty estimation for source contributions remains lack, and accurate spatial prediction is still challenging. Robust Bayesian multivariate receptor model (RBMRM) was applied to the soil dataset of Qingzhou City (8 PTEs in 429 samples), to calculate source contributions with uncertainties. Multi-task convolutional neural network (MTCNN) was proposed to predict spatial distributions of soil PTEs. RBMRM afforded three sources, consistent with US-EPA positive matrix factorization. Natural source dominated As, Cr, Cu, and Ni contents (78.5 %∼86.1 %), and contributed 37.1 %, 61.0 %, and 65.9 % of Cd, Pb, and Zn, exhibiting low uncertainties with uncertainty index (UI) < 26.7 %. Industrial, traffic, and agricultural sources had significant influences on Cd, Pb, and Zn (30.2 %∼61.9 %), with UI < 39.3 %. Hg originated dominantly from atmosphere deposition (99.1 %), with relatively high uncertainties (UI=87.7 %). MTCNN acquired satisfactory accuracies, with R2 of 0.357-0.896 and nRMSE of 0.092-0.366. Spatial distributions of As, Cd, Cr, Cu, Ni, Pb, and Zn were influenced by parent materials. Cd, Hg, Pb, and Zn showed significant hotspot in urban area. This work conducted a new approach exploration, and practical implications for soil pollution regulation were proposed.

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

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

Assessment of the migration characteristics and source-oriented health risks of heavy metals in the soil and groundwater of a legacy contaminated by the chlor-alkali industry in central China

The chlor-alkali industry (CAI) is crucial for global chemical production; however, its operation has led to widespread heavy metal (HM) contamination at numerous sites, which has not been thoroughly investigated. This study analysed 122 soil and groundwater samples from a typical CAI site in Kaifeng, China. Our aim was to assess the ecological and health risks, identify the sources, and examine the migration characteristics of HMs at this site using Monte Carlo simulation, absolute principal component score-multiple linear regression (APCS-MLR), and the potential environmental risk index (Ei). Our findings revealed that the exceedance rates for Cd, Pb, Hg, and Ni were 71.96%, 45.79%, 49.59%, and 65.42%, respectively. Mercury (Hg) displayed the greatest coefficient of variation across all the soil layers, indicating a significant anthropogenic influence. Cd and Hg were identified as having high and extremely high potential environmental risk levels, respectively. The spatial distributions of the improved Nemerow index (INI), total ecological risk (Ri), and HM content varied considerably, with the most contaminated areas typically associated with the storage of raw and auxiliary materials. Surface aggregation and significant vertical transport were noted for HMs; As and Ni showed substantial accumulation in subsoil layers, severely contaminating the groundwater. Self-organizing maps categorized the samples into two different groups, showing strong positive correlations between Cd, Pb, and Hg. The APCS-MLR model suggested that industrial emissions were the main contributors, accounting for 60.3% of the total HM input. Elevated hazard quotient values for Hg posed significant noncarcinogenic risks, whereas acceptable levels of carcinogenic risk were observed for both adults (96.60%) and children (97.83%). This study significantly enhances historical CAI pollution data and offers valuable insights into ongoing environmental and health challenges.

Source-specific ecological risk analysis and critical source identification of heavy metal(loid)s in the soil of typical abandoned coal mining area

Long-term coal mining activities in abandoned coal mining areas have resulted in the migration of large quantities of heavy metals into the surrounding soil environment, posing a threat to the regional ecological environment. This study focuses on the surface soil collected from a typical abandoned coal mining area. Methods such as the pollution index (PI) and potential ecological risk index (RI) were used to comprehensively evaluate the pollution levels and ecological risks of soil heavy metals. Geostatistical analysis and the APCS-MLR model were used to quantify the sources of soil heavy metals, and Nemerow integrated ecological risk (NIRI) model was coupled to apportion the ecological risks from different pollution sources. The results indicate that the average concentrations of Cd, As, and Zn are 4.58, 2.44, and 1.67 times the soil background values, respectively, while the concentrations of other heavy metals are below the soil background values. The soil of study area is strongly polluted by heavy metals, with the pollution level and ecological risk of Cd being significantly higher than those of other heavy metals. The NIRI calculation results show that the overall comprehensive ecological risk level is considerable, with sample points classified as relatively considerable, moderate, and low at 60.53 %, 36.84 %, and 2.63 %, respectively. The sources of soil heavy metals can be categorized into four types: traffic activities, natural sources, coal gangue accumulation, and a combined source of coal mining and agricultural activities, with contribution rates of 35.3 %, 36.1 %, 19.5 %, and 9.1 %, respectively. The specific source ecological risk assessment results indicate that coal gangue accumulation contributes the most to ecological risk (36.4 %) and should be prioritized for pollution control, with Cd being the priority control element for ecological risk. The findings provide theoretical support for the refined management of soil heavy metal pollution in abandoned coal mining areas.

Spatial-temporal variation and source analysis of heavy metals in different land use types in Beilun District (2015 and 2022)

The soil environment plays an important role in urban ecosystems. To study the heavy metal contamination of soil in Beilun District, Ningbo, we collected soil samples from 60 points in urban and peri-urban areas of Beilun District and analyzed the spatiotemporal variation and sources of heavy metal pollution in various land-use types. The results shown that the heavy metal contents in 2015 and 2022 were higher than the background soil values of Ningbo city, and there was an accumulation of heavy metals over these 7 years. The contents of heavy metals in green belts and woodland in 2022 were higher than those in 2015, while there was no significant change in agricultural land. The heavy metal contents in both years were mainly in the order green belts > agricultural land > woodland. The spatiotemporal distribution of heavy metal content showed that heavy metal pollution in Beilun District was concentrated in five industrial areas, and there was a trend toward the disappearance of highly polluted points. But the single-factor pollution index, pollution load index (PLI), and geoaccumulation index (Igeo) indicated that there was no significant heavy metal pollution in Beilun District, and individual elements at specific points showed slight pollution. The source analysis results showed that the main source of Hg is chemical, As is mainly derived from agricultural, Cr, Ni and Cu are mainly derived from natural, the main sources of Zn and Cd are electroplating and machinery activities, and the main source of Pb is traffic. These results specify a reference for future investigation on urban soil heavy metals, and the source apportionment results provide a scientific foundation for subsequent soil heavy metal pollution treatment.

Source apportionment of soil PTE in a northern industrial county using PMF model: Partitioning strategies and uncertainty analysis

Positive matrix factorization (PMF) has commonly been applied for source apportionment of potentially toxic elements (PTE) in agricultural soil, however, spatial heterogeneity of PTE significantly undermines the accuracy and reliability of PMF results. In this study, a representative industrial-agricultural hub in North China (Xuanhua district, Zhangjiakou City) was selected as the research subject, multiple partition processing (PP) strategies and uncertainty analyses were integrated to advance the PMF modeling and associated algorithm mechanisms were comparatively discussed. Specifically, we adopted three methods to split the research area into several subzones according to industrial density (PP-1), population density (PP-2), and the ecological risk index (PP-3) respectively, to rectify the spatial bias phenomenon of PTE concentrations and to achieve a more interpretable result. Our results indicated that the obvious enrichment of Cd, Pb, and Zn was found in the agricultural soil, with Hg and Cd accounted for 83.49% of the overall potential ecological risk. Combining proper PP with PMF can significantly improve the modelling accuracy. Uncertainty analysis showed that interval ratios of tracer species (Cd, Pb, Hg, and Zn) calculated by PP-3 were consistently lower than that of PP-1 and PP-2, indicating that PP-3 coupled PMF can afford the optimal modeling results. It suggested that natural sources, fertilizers and pesticides, atmosphere deposition, mining, and smelting were recognized as the major contributor for the soil PTE contamination. The contribution of anthropogenic activities, specifically fertilizers and pesticides, and atmosphere deposition, increased by 1.64% and 5.91% compared to PMF results. These findings demonstrate that integration of proper partitioning processing into PMF can effectively improve the accuracy of the model even at the case of soil PTE contamination with high heterogeneity, offering support to subsequently implement directional control strategies.

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.

Health risk assessment of soil heavy metals in a typical mining town in north China based on Monte Carlo simulation coupled with Positive matrix factorization model

The accumulation of heavy metals (HMs) in soil caused by mineral resource exploitation and its ancillary industrial processes poses a threat to ecology and public health. Effective risk control measures require a quantification of the impacts and contributions to health risks from individual sources of soil HMs. Based on high-density sampling, soil contamination risk indexes, positive matrix factorization (PMF) model, Monte Carlo simulation and human health risk analysis model were applied to investigate the risk of HMs in a typical mining town in North China. The results showed that As was the most dominant soil pollutant factor, Cd and Hg were the most dominant soil ecological risk factors, and Cr and Ni were the most dominant health risk factors in the study area. Overall, both pollution and ecological risks were at low levels, while there were still some higher hazard areas located in the central and south-central part of the region. According to the probabilistic health risk assessment (HRA), children suffered greater health risks than adults, with 21.63% of non-carcinogenic risks and 53.24% of carcinogenic risks exceeding the prescribed thresholds (HI > 1 and TCR>1E-4). The PMF model identified five potential sources: fuel combustion (FC), processing of building materials with limestone as raw materials (PBML), industry source (IS), iron ore mining combined with garbage (IOG), and agriculture source (AS). PBML is the primary source of soil HM contamination, as well as the major anthropogenic source of carcinogenic risk for all populations. Agricultural inputs associated with As are the major source of non-carcinogenic risk. This study offers a good example of probabilistic HRA using specific sources, which can provide a valuable reference for strategy establishment of pollution remediation and risk prevention and control.

Ecological risk assessment of heavy metal(loid)s in riverine sediments along the East China Sea: A large-scale integrated analysis

This study comprehensively assesses spatial distribution, pollution levels, and potential sources of heavy metal(loid)s in surface sediments across multiple river systems along the coastal area of the East China Sea. Copper in Qiantang River and Xiangshan Bay showed higher concentations and exceeded the threshold effect value, while the higher content of Lead was mainly found in the Saijiang River, Oujiang River, and Minjiang River. Heavy metal(loid)s in the alluvium of Qiantang River, Jiaojiang River, and Yangtze River showed low to moderate pollution levels, with Cd posing the highest ecological risk, followed by Hg. Meanwhile, Qiantang River, Jiaojiang River, Yangtze River, and Oujiang River exhibited considerable to moderate ecological risks and low toxic risk. PMF model analysis results reveal that concentrations of Cr, Ni, and As were closely related with natural geogenic input (36.56 %), while industrial and traffic activities (48.77 %) were primary source of Cu, Pb, Zn, and Hg, and main source of Cd was agricultural emissions (14.67 %).

Source specific sedimentary response towards the differential anthropogenic impacts in terms of potentially toxic elements in an urban river

To investigate the interplay between varying anthropogenic activities and sediment dynamics in an urban river (Turag, Bangladesh), this study involved 37-sediment samples from 11 different sections of the river. Neutron activation analysis and atomic absorption spectrometry were utilized to quantify the concentrations of 14 metal(oid)s (Al, Ti, Co, Fe, As, Cd, Cr, Cu, Fe, Hg, Mn, Ni, Pb, Zn). This study revealed significant toxic metal trends, with Principal coordinate analysis explaining 62.91 % of the variance from upstream to downstream. The largest RSDs for Zn(287 %), Mn(120 %), and Cd(323 %) implies an irregular regional distribution throughout the river. The UNMIX-model and PMF-model were utilized to identify potential sources of metal(oid)s in sediments. ∼63.65-66.7 % of metal(oid)s in sediments originated from anthropogenic sources, while remaining attributed to natural sources in both models. Strikingly, all measured metal(oid)s' concentrations surpassed the threshold effect level, with Zn and Ni exceeding probable effect levels when compared to SQGs.

Pollution sources and risk assessment of potentially toxic elements in soils of multiple land use types in the arid zone of Northwest China based on Monte Carlo simulation

The concentration of potentially toxic elements (PTEs) in soils of different land-use types varies depending on climatic conditions and human. Topsoil samples were collected in Northwest China to investigate PTE pollution and risk in different land uses, and thereby estimate the risk of various pollution sources. The results showed that human activity had an impact on PTE concentrations in the study area across all land use types, with farmland, grassland, woodland, and the gobi at moderate pollution levels and the desert at light pollution levels. Different PTE sources pose different risks depending on the land-use type. Apart from deserts, children are exposed to carcinogenic risk from a variety of sources. A mixed natural and agricultural source was the main source of public health risk in the study area, contributing 38.7% and 39.0% of the non-carcinogenic and 40.7% and 35.5% of the carcinogenic risks, respectively. Monte Carlo simulations showed children were at a higher health risk from PTEs than adult s under all land uses, which ranked in severity as farmland > woodland > grassland > gobi > desert. As and Ni has a higher probability of posing both a non-carcinogenic and a carcinogenic risk to children. Sensitivity analysis showed that the contribution of parameters to the assessment model of PTEs exhibited the following contribution pattern: concentration > average body weight > ingestion rate > other parameters. The PTEs affecting the risk assessment model were not common among different land use types, where the importance distribution pattern of each parameter was basically the same in woodland, grassland, and farmland, and Ni contributed the most to carcinogenic risk. However, Cr contributed the most to the carcinogenic risk in the desert and gobi.

Source identification and driving factor apportionment for soil potentially toxic elements via combining APCS-MLR, UNMIX, PMF and GDM

The contamination and quantification of soil potentially toxic elements (PTEs) contamination sources and the determination of driving factors are the premise of soil contamination control. In our study, 788 soil samples from the National Agricultural Park in Chengdu, Sichuan Province were used to evaluate the contamination degree of soil PTEs by pollution factors and pollution load index. The source identification of soil PTEs was performed using positive matrix decomposition (PMF), edge analysis (UNMIX) and absolute principal component score-multiple line regression (APCS-MLR). The geo-detector method (GDM) was used to analysis drivers of soil PTEs pollution sources to help interpret pollution sources derived from receptor models. Result shows that soil Cu, Pb, Zn, Cr, Ni, Cd, As and Hg average content were 35.2, 32.3, 108.9, 91.9, 37.1, 0.22, 9.76 and 0.15 mg/kg in this study area. Except for As, all are higher than the corresponding soil background values in Sichuan Province. The best performance of APCS-MLR was determined by comparison, and APCS-MLR was considered as the preferred receptor model for soil PTEs source distribution in the study area. ACPS-MLR results showed that 82.70% of Cu, 61.6% of Pb, 75.3% of Zn, 91.9% of Cr and 89.4% of Ni came from traffic-industrial emission sources, 60.9% of Hg came from domestic-transportation emission sources, 57.7% of Cd came from agricultural sources, and 89.5% of As came from natural sources. The GDM results showed that distance from first grade highway, population, land utilization and total potassium (TK) content were the main driving factors affecting these four sources, with q values of 0.064, 0.048, 0.069 and 0.058, respectively. The results can provide reference for reducing PTEs contamination in farmland soil.

Quantitative assessment of ecological risk from pollution source based on geostatistical analysis and APCS-MLR model

The safety of human health and agricultural production depends on the quality of farmland soil. Risk assessment of heavy metal pollution sources could effectively reduce the hazard of soil pollution from various sources. This study has identified and quantitatively analyzed pollution sources with geostatistical analysis and the APCS-MLR model. The potential ecological risk index was combined with the APCS-MLR model which has quantitatively calculated the source contribution. The results revealed that As, Cr, Cd, Pb, Zn, and Cu were enriched in soil. Geostatistical analysis and the APCS-MLR model have apportioned four pollution sources. The Mn and Ni were attributed to natural sources; As and Cr were from agricultural activities; Cu and Zn were originated from natural sources; Cd and Pb were derived from atmospheric deposition. Atmospheric deposition and agricultural activities were the largest contributors to ecological risk of heavy metals in soil, which accounted for 56.21% and 36.01% respectively. Atmospheric deposition and agricultural activities are classified as priority sources of pollution. The combination of source analysis receptor model and risk assessment is an effective method to quantify source contribution. This study has quantified the ecological risks of soil heavy metals from different sources, which will provide a reliable method for the identification of primary harmfulness sources of pollution for future studies.

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

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

Integrated assessment of the pollution and risk of heavy metals in soils near chemical industry parks along the middle Yangtze River

Industrialization in riparian areas of critical rivers has caused significant environmental and health impacts. Taking eight industrial parks along the middle Yangtze River as examples, this study proposes a multiple-criteria approach to investigate soil heavy metal pollution and associated ecological and health risks posed by industrial activities. Aiming at seven heavy metals, the results show that nickel (Ni), cadmium (Cd), and copper (Cu) exhibited the most significant accumulation above background levels. The comprehensive findings from Pearson correlation analysis, cluster analysis, principal component analysis, and industrial investigation uncover the primary sources of Cd, arsenic (As), mercury (Hg), and lead (Pb) to be chemical processing, while Ni and chromium (Cr) are predominantly derived from mechanical and electrical equipment manufacturing. In contrast, Cu exhibits a broad range of origins across various industrial processes. Soil heavy metals can cause serious ecological and carcinogenic health risks, of which Cd and Hg contribute to >70 % of the total ecological risk, and As contributes over 80 % of the total health risk. This study highlights the importance of employing multiple mathematical and statistical models in determining and evaluating environmental hazards, and may aid in planning the environmental remediation engineering and optimizing the industry standards.

Spatio-temporal trends of mercury levels in alluvial gold mining spoils areas monitored between rainy and dry seasons in the Peruvian Amazon

Artisanal and small-scale gold mining (ASGM) in the Amazon has degraded tropical forests and escalated mercury (Hg) pollution, affecting biodiversity, ecological processes and rural livelihoods. In the Peruvian Amazon, ASGM annually releases some 181 tons of Hg into the environment. Despite some recent advances in understanding the spatial distribution of Hg within gold mine spoils and the surrounding landscape, temporal dynamics in Hg movement are not well understood. We aimed to reveal spatio-temporal trends of soil Hg in areas degraded by ASGM.,. We analyzed soil and sediment samples during the dry and rainy seasons across 14 ha of potentially contaminated sites and natural forests, in the vicinities of the Native community of San Jacinto in Madre de Dios, Peru. Soil Hg levels of areas impacted by ASGM (0.02 ± 0.02 mg kg-1) were generally below soil environmental quality standards (6.60 mg kg-1). However, they showed high variability, mainly explained by the type of natural cover vegetation, soil organic matter (SOM), clay and sand particles. Temporal trends in Hg levels in soils between seasons differed between landscape units distinguished in the mine spoils. During the rainy season, Hg levels decreased up to 45.5% in uncovered soils, while in artificial pond sediments Hg increased by up to 961%. During the dry season, uncovered degraded soils were more prone to lose Hg than sites covered by vegetation, mainly due to higher soil temperatures and concomitantly increasing volatilization. Soils from natural forests and degraded soil covered by regenerating vegetation showed a high capacity to retain Hg mainly due to the higher plant biomass, higher SOM, and increasing concentrations of clay particles. Disturbingly, our findings suggest high Hg mobility from gold mine spoil to close by sedimentary materials, mainly in artificial ponds through alluvial deposition and pluvial lixiviation. Thus, further research is needed on monitoring, and remediation of sediments in artificial to design sustainable land use strategies.

Characteristics, source analysis, and health risk assessment of potentially toxic elements pollution in soil of dense molybdenum tailing ponds area in central China

The issue of potentially toxic elements (PTEs) contamination of regional soil caused by mining activities and tailings accumulation has attracted wide attention all over the world. The East Qinling is one of the three main molybdenum mines in the world, and the concentration of PTEs such as Hg, Pb and Cu in the slag is high. Quantifying the amount of PTEs contamination in soil and identifying potential sources of contamination is vital for soil environmental management. In the present investigation, the pollution levels of 8 PTEs in the Qinling molybdenum tailings intensive area were quantitatively identified. Additionally, an integrated source-risk method was adopted for resource allocation and risk assessment based on the PMF model, the ecological risk, and the health risk assessment model. The mean concentrations of Cu, Ni, Pb, Cd, Cr, Zn, As, and Hg in the 80 topsoil samples ranged from 0.80 to 13.38 times the corresponding background values; notably high levels were observed for Pb and Hg. The source partitioning results showed that PTEs were mainly affected by four pollution sources: natural and agricultural sources, coal-burning sources, combined transport and mining industry sources, and mining and smelting sources. The health risk assessment results revealed that the risks of soil PTEs for adults are acceptable, while the risks for children exceeded the limit values. The obtained results will help policymakers to obtain the sources of PTEs of tailing ponds intensive area. Moreover, it provides priorities for the governance of subsequent pollution sources and ecological restoration.

Spatial distribution prediction of soil heavy metals based on sparse sampling and multi-source environmental data

Predicting the precise spatial distribution of heavy metals in soil is crucial, especially in the fields of environmental management and remediation. However, achieving accurate spatial predictions of soil heavy metals becomes quite challenging when the number of soil sampling points is relatively limited. To address this challenge, this study proposes a hybrid approach, namely, Light Gradient Boosting Machine plus Ordinary Kriging (LGBK), for predicting the spatial distribution of soil heavy metals. A total of 137 soil samples were collected from the Shengli Coal-mine Base in Inner Mongolia, China, and their heavy metal concentrations were measured. Leveraging environmental covariates and soil heavy metal data, we constructed the predictive model. Experimental results demonstrate that, in comparison to traditional models, LGBK exhibits superior predictive performance. For copper (Cu), zinc (Zn), chromium (Cr), and arsenic (As), the coefficients of determination (R²) from the cross-validation results are 0.65, 0.52, 0.57, and 0.63, respectively. Moreover, the LGBK model excels in capturing intricate spatial features in heavy metal distribution. It accurately forecasts trends in heavy metal distribution that closely align with actual measurements. ENVIRONMENTAL IMPLICATION: This study introduces a novel method, LGBK, for predicting the spatial distribution of soil heavy metals. This method yields higher-precision predictions even with a limited number of sampling points. Furthermore, the study analyzes the spatial distribution characteristics of Cu, Zn, Cr, and As in the grassland coal-mine base, along with the key environmental factors influencing their spatial distribution. This research holds significant importance for the environmental regulation and remediation of heavy metal pollution.

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

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

A study of environmental pollution and risk of heavy metals in the bottom water and sediment of the Chaohu Lake, China

Most of the existing research for heavy metals in water at present is focusing on surface water. However, potential environmental risk of heavy metals in the bottom water of lakes cannot be ignored. In this study, the content, distribution, and speciation of nine heavy metals (As, V, Cr, Co, Ni, Cu, Zn, Cd, and Pb) in the bottom water and sediment of Chaohu Lake were studied. Some pollution assessment methods were used to evaluate the environmental effect of heavy metals. Positive matrix factorization was conducted to investigate the potential sources of heavy metals in sediment. The contents of heavy metals in the bottom water of Chaohu Lake mean that its environmental pollution can be ignored. In sediment, Cd and Zn have showed stronger ecological risk. pH and redox potential are more likely to affect the stability of heavy metals in the bottom water of Chaohu Lake during the dry reason. Industrial sources (16%) are no longer the largest source of heavy metal pollution; traffic sources (33.6%) and agricultural sources (23.4%) have become the main sources of pollution at present. This study can provide some support and suggestions for the treatment of heavy metals in lakes.

Source and risk assessment of heavy metals in mining-affected areas in Jiangxi Province, China, based on Monte Carlo simulation

In recent years, heavy metal contamination of soils has become a major concern in China due to the potential risks involved. To assess environmental pollution and human health risks in a typical heavy metal polluted site in Jiangxi Province, a thorough evaluation of the distribution, pollution levels, and sources of heavy metals in soils of the Yangmeijiang River watershed was conducted in this study. Positive matrix factorization and Monte Carlo simulation were used to evaluate the ecological and human health risks of heavy metals. The research findings indicate that heavy metal pollution was the most severe at the depth of 20-40 cm in soils, with local heavy metal pollution resulting from mining and sewage irrigation. The high-risk area accounted for 91.11% of the total area. However, the pollution level decreased with time due to sampling effects, rainfall, and control measures. Leaf-vegetables and rice were primarily polluted by Cd and Pb. The main four sources of heavy metals in soils were traffic emission, metal smelting, agricultural activities and natural sources, mining extraction, and electroplating industries. Heavy metals with the highest ecological risk and health risk are Cd and As, respectively. The non-carcinogenic and carcinogenic risks of children were 7.0 and 1.7 times higher than those of adults, respectively. Therefore, children are more likely to be influenced by heavy metals compared to adults. The results obtained by the risk assessments may contribute to the identification of specific sources of heavy metals (e.g., traffic emissions, metal smelting, mining excavation, and electroplating industries). Additionally, the environmental impacts and biotoxicity associated with various heavy metals (e.g., Cd and As) can also be reflected. These outcomes may serve as a scientific basis for the pollution monitoring and remediation in the mining-affected areas.

Spatial distribution and source identification of potentially toxic elements in Yellow River Delta soils, China: An interpretable machine-learning approach

Identifying the driving factors and quantifying the sources of potentially toxic elements (PTEs) are essential for protecting the ecological environment of the Yellow River Delta. In this study, data from 201 surface soil samples and 16 environmental variables were collected, and the random forest (RF) and Shapley additive explanations (SHAP) methods were then combined to explore the key factors affecting soil PTEs. An innovative t-distributed random neighbor embedding-RF-SHAP model was then constructed, based on the absolute principal component score and multivariate linear regression model, to quantitatively determine PTE sources. Although average PTE concentrations did not exceed the risk control values, PTE distributions exhibited significant differences. It was found that sodium, soil organic matter, and phosphorus contents were the three most important factors affecting PTEs, and human activities and natural environmental factors both influence PTE contents by altering the soil properties. The proposed model successfully determined PTE sources in the soil, outperforming the original linear regression model with a significantly lower RMSE. Source analysis revealed that the parent material was the main contributor to soil PTEs, accounting for more than half of the total PTE content. Industrial and agricultural activities also contributed to an increase in soil PTEs, with average contributions of 19.91 % and 17.44 %, respectively. Unknown sources accounted for 10.83 % of the total PTE content. Thus, the proposed model provides innovative perspectives on source parsing. These findings provide valuable scientific insights for policymakers seeking to develop effective environmental protection measures and improve the quality of saline-alkali land in the Yellow River Delta.

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.

Risk assessment of heavy metals in agricultural soil based on the coupling model of Monte Carlo simulation-triangular fuzzy number

Soils in areas wherein agriculture and mining coexist are experiencing serious heavy metal contamination, posing a great threat to the ecological environment and human health. In this study, heavy metals (As, Cd, Cr, Cu, Ni, Pb, and Zn) in agricultural soil samples from mining areas were analyzed to explore pollution status, bioavailability, potential sources, and ecological/health risks. Particularly, the coupling model of Monte Carlo simulation-triangular fuzzy number (MCS-TFN) was established to quantify ecological/health risks accurately. Results showed that Cd was heavily enriched in soil and had the highest bioavailability based on both geo-accumulation index (Igeo) and chemical speciation analysis. Pollution sources apportioned with the absolute principal component score-multiple linear regression (APCS-MLR) model demonstrated that heavy metals were mainly derived from agricultural activities, followed by mining activities and natural sources. The MCS-TFN ecological risk assessment classified Cd into the high-risk category with a probability of 40.96%, whereas other heavy metals were categorized as the low risk. Cd was regarded as the major pollutant for the ecosystem. Moreover, the MCS-TFN health risk assessment indicated that As showed high noncarcinogenic risk (0.07% probability) and moderate carcinogenic risk (1.87% probability), and Cd presented low carcinogenic risk (80.19% probability). As and Cd were identified as the main heavy metals that pose a threat to human health. The MCS-TFN risk assessment is superior to the traditional deterministic risk assessment since it can obtain the risk level and the corresponding probability, and significantly reduce the uncertainty in risk assessment.

Ecological and health risk assessment of heavy metals in agricultural soils from northern China

Soil pollution by heavy metals can cause continuing damage to ecosystems and the human body. In this study, we collected nine fresh topsoil samples and 18 maize samples (including nine leaf samples and nine corn samples) from agricultural soils in the Baiyin mining areas. The results showed that the order of heavy metal concentrations (mg/kg) in agricultural soils was as follows: Zn (377.40) > Pb (125.06) > Cu (75.06) > Ni (28.29) > Cd (5.46) > Hg (0.37). Cd, Cu, Zn, and Pb exceeded the Chinese risk limit for agricultural soil pollution. The average the pollution load index (4.39) was greater than 3, indicating a heavy contamination level. The element that contributed the most to contamination and high ecological risk in soil was Cd. Principal component analysis (PCA) and Pearson's correlation analysis indicated that the sources of Ni, Cd, Cu, and Zn in the soil were primarily mixed, involving both industrial and agricultural activities, whereas the sources of Hg and Pb included both industrial and transportation activities. Adults and children are not likely to experience non-carcinogenic impacts from the soil in this region. Nonetheless, it was important to be aware of the elevated cancer risk presented by Cd, Pb, and especially Ni. The exceedance rates of Cd and Pb in corn were 66.67% and 33.3%, respectively. The results of this research provide data to improve soil protection, human health monitoring, and crop management in the Baiyin district.

Combined use of positive matrix factorization and 13C15N stable isotopes to trace organic matter-bound potential toxic metals in the urban mangrove sediments

Coastal sediments act as sinks of sediment organic matter (SOM) and metals because of their special land-sea location and depositional properties. However, there are few reports on the correlation between the sources of organic matter (OM) and associated potential toxic metals (PTMs). In this study, we combined CN stable isotope analysis and positive matrix factorization to identify the matter and metal sources of OM and glomalin-related soil protein (GRSP) in an estuary under several decades of urbanization. The results of the positive matrix factorization (PMF) reveal a correlation between the sources of total sediment metals and the sources of OM-related metals. The sources of both SOM-bound PTMs and GRSP-bound PTMs are significantly related to the sources of total PTMs. OM sources were elucidated through 13C-15 N stable isotopes, and the potential sources of different types of OM differed. In addition, there is a significant correlation between OM-associated PTMs and organic matter sources. Interestingly, the functional groups of SOM were mainly influenced by multiple PTM sources but no OM source, while the functional groups of GRSP were regulated by a single metal source and OM source. This study deepened the understanding of the coupling between PTMs and SOM. The possibility of combined use of positive matrix factorization and 13C-15 N stable isotope tracing of metals as well as the sources of each metal fractions has been evaluated, which will provide new insights for the transportation of PTMs.

Integrative risk assessment method via combining geostatistical analysis, random forest, and receptor models for potentially toxic elements in selenium-rich soil

Revealing the spatial features and source of associated potentially toxic elements (PTEs) is crucial for the safe use of selenium (Se)-rich soils. An integrative risk assessment (GRRRA) approach based on geostatistical analysis (GA), random forest (RF), and receptor models (RMs) was first established to investigate the spatial distribution, sources, and potential ecological risks (PER) of PTEs in 982 soils from Ziyang City, a typical natural Se-rich area in China. RF combined with multiple RMs supported the source apportionment derived from the RMs and provided accurate results for source identification. Then, quantified source contributions were introduced into the risk assessment. Eighty-three percent of the samples contain Cd at a high PER level in local Se-rich soils. GA based on spatial interpolation and spatial autocorrelation showed that soil PTEs have distinct spatial characteristics, and high values are primarily distributed in this research areas. Absolute principal component score/multiple line regression (APCS/MLR) is more suitable than positive matrix factorization (PMF) for source apportionment in this study. RF combined with RMs more accurately and scientifically extracted four sources of soil PTEs: parent material (48.91%), mining (17.93%), agriculture (8.54%), and atmospheric deposition (24.63%). Monte Carlo simulation (MCS) demonstrates a 47.73% probability of a non-negligible risk (RI > 150) caused by parent material and 3.6% from industrial sources, respectively. Parent material (64.20%, RI = 229.56) and mining (16.49%, RI = 58.96) sources contribute to the highest PER of PTEs. In conclusion, the GRRRA method can comprehensively analyze the distribution and sources of soil PTEs and effectively quantify the source contribution to PER, thus providing the theoretical foundation for the secure utilization of Se-rich soils and environmental management and decision making.

Prioritization of control factors for heavy metals in groundwater based on a source-oriented health risk assessment model

Heavy metals (HMs) in groundwater seriously threaten ecological safety and human health. To facilitate the effective management of groundwater contamination, priority control factors of HMs in groundwater need to be categorized. A total of 86 groundwater samples were collected from the Huangpi district of Wuhan city, China, during the dry and wet seasons. To determine priority control factors, a source-oriented health risk assessment model was applied to compare the pollution sources and health risks of seven HMs (Cu, Pb, Zn, Cr, Ni, As, and Fe). The results showed that the groundwater had higher As and Fe contents. The sources of HM pollution during the wet period were mainly industrial and agricultural activities and natural sources. During the dry period, origins were more complex due to the addition of domestic discharges, such as sewage wastewater. Industrial activities (74.10% during the wet period), agricultural activities (53.84% during the dry period), and As were identified as the priority control factors for groundwater HMs. The results provide valuable insights for policymakers to coordinate targeted management of HM pollution in groundwater and reduce the cost of HM pollution mitigation.

Processing toxic metal source proxies appropriately for better spatial heterogeneity source apportionment

Soil toxic metals have strong spatial heterogeneity, and their sources vary among regions. Thus, this study integrated the Catreg and geographically weighted regression (GWR) models to quantitatively extract the main source proxies (numerical and categorical variables were analyzed simultaneously) for different toxic metals and analyze the spatial heterogeneity of the distributions of these sources. Pb, Cd and Hg were the predominant toxic metals in soil. Of the samples with Pb, Cd and Hg, 84.12 %, 68.03 % and 41.57 % exceeded the background values, and 5.36 %, 6.42 % and 5.43 % were moderately contaminated according to the geoaccmulation index, respectively. Industrial activities, with relative importance values of 17.82 %, 31.54 % and 26.51 % for Cd, Hg and Pb, respectively, were the predominant source of these metals especially, in their high-content cluster areas (central urban areas). Soil available phosphorus was another important factor (relative importance values of 13.03 %, 13.41 % and 25.55 % for Cd, Hg and Pb, respectively), and agricultural activities (especially the overuse of phosphoric fertilizers) were identified as an anthropogenic source of these toxic metals. Soil parent material had the greatest influence on As and Cr, with relative importance values of 19.88 % and 19.09 %, respectively, especially in their high-content accumulation area (the eastern coastal area), indicating that these toxic metals mainly come from natural sources. Slope had important impacts on toxic metal accumulation (relative importance values of 17.48 %, 21.22 %, 12.40 % and 16.13 % for Cd, Hg, Cr and As, respectively) by influencing industrial distribution and pollutant migration. By changing the soil adsorption capacity, soil organic matter (explaining 13.01 % of Pb) and soil pH (explaining 14.58 % of As and 12.40 % of Cr) strongly influenced toxic metal accumulation. This study highlights the benefits of the integrated Catreg-GWR model for analyzing multiple spatially heterogeneous environmental data types (numerical and categorical variables), providing a potential foundation for local pollution prevention.