An integrated approach for quantifying source apportionment and source-oriented health risk of heavy metals in soils near an old industrial area

Spatiotemporal variability of groundwater chemistry, source identification and health risks in the southern Chinese Loess Plateau

Groundwater pollution of the loess plateau regions has become a global concern due to its vulnerability to natural and anthropogenic influences. In this study, 146 water samples were investigated to identify the spatiotemporal variability in groundwater chemistry, pollution sources and nitrate health risks in two interconnected river basins of a typical loess region. The results showed that except for bicarbonate, spatiotemporal variability of hydrochemical components in Malian River Basin (ML) was generally greater than that in Upper Jinghe River basin (JH-U) due to the hydrogeological conditions, and the hydrochemical facies in two river basins transformed from SO4·Cl and Cl·SO4 types to HCO3 and HCO3·SO4 types. The results of integrated-weight quality index (IWQI) showed that 77.8 % (1970s), 33.3 % (2004), 34.3 % (2015) of samples in ML exceeded the standard limits of Class IV groundwater quality, displaying a high pollution level with an improvement trend, while groundwater quality in JH-U indicated a very low pollution level with a deterioration trend. The geogenic source was identified as a main factor affecting groundwater quality, with contributions of 59.2 % and 48.7 % in JH-U and ML (2015), respectively. The anthropogenic sources including agricultural activities (20.7 % and 21.8 % in JH-U and ML) and coal mining activities (20.1 % and 29.5 % in JH-U and ML) also played a role in affecting groundwater quality. The nitrate health risk assessment demonstrated that 39.1 % and 20.3 % of groundwater samples (2015) significantly exceeded the standard threshold (Hazard Index = 1), implying a higher health risk to children than adults, and the nitrate health risk in ML was obviously greater than that in JH-U. This study provides novel insight into the spatiotemporal variability in groundwater chemistry, quality and health risk in loess regions under the influence of geogenic and anthropogenic factors.

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 identification of heavy metals and metalloids in soil using open-source Tellus database and their impact on ecology and human health

The presence of heavy metals and metalloids (metal(loid)s) in the food chain is a global problem, and thus, metal(loid)s are considered to be Potentially Toxic Elements (PTEs). Arsenic (As), lead (Pb), mercury (Hg), and cadmium (Cd) are identified as prominent hazards related to human health risks throughout the food chain. This study aimed to carry out a source attribution for metal(loid)s in shallow topsoil of north-midlands, northwest, and border counties of the Republic of Ireland, followed by an assessment of the potential ecological and human health risks. The positive Matrix Factorization (PMF) was used for source characterization of PTEs, followed by the Monte Carlo simulation method, used for a probabilistic model to evaluate potential human health risks. The mean concentrations of prioritized metal(loid)s in the topsoil range in the order of Pb (28.83 mg kg-1) > As (7.81 mg kg-1) > Cd (0.51 mg kg-1) > Hg (0.11 mg kg-1) based on the open-source Tellus dataset. This research identified three primary sources of metal(loid) pollution: geogenic sources (36 %), mixed sources of historical mining and natural origin (33 %), and anthropogenic activities (31 %). The ecological risk assessment showed that Ireland's soil exhibits low-moderate pollution levels however, concerns remain for Cd and As levels. All metal(loid)s except Cd showed acceptable non-carcinogenic risk, while Cd and As accounted for high to moderate potential cancer risks. Potato consumption (if grown on land with elevated metal(loid) levels), Cd concentration in soil, and bioaccumulation factor of Cd in potatoes were the three most sensitive parameters. In conclusion, metal(loid)s in Ireland present low to moderate ecological and human health risks. It underscores the need for policies and remedial strategies to monitor metal(loid) levels in agricultural soil regularly and the production of crops with low bioaccumulation in regions with elevated metal(loid) levels.

Driving mechanism of groundwater quality and probabilistic health risk quantification in the central Yinchuan Plain

The environmental changes from climatic, terrestrial and anthropogenic drivers can significantly influence the groundwater quality that may pose a threat to human health. However, the driving mechanism of groundwater quality and potential health risk still remains to be studied. In this paper, 165 groundwater samples were analyzed to evaluate the groundwater quality, driving mechanism, and probabilistic health risk in the central Yinchuan Plain by applying fuzzy comprehensive evaluation method (FCEM), redundance analysis (RDA) and Monte Carlo simulation. The results showed that hydrochemical evolution of groundwater were strongly influenced by water-rock interaction, evaporation and human activities. While 55.2% of groundwater samples reached the drinking water quality standard (Class I, II and III), 44.8% of samples exceeded the standard limits of Class III water quality (Class IV and V), indicating a high pollution level of groundwater. Mn, TDS, NH4+, NO3-, Fe, F-, NO2-, As were among major indicators that influence the groundwater quality due to the natural and anthropogenic processes. The RDA analysis revealed that climatic factors (PE: 10.9%, PRE: 1.1%), GE chemical properties (ORP: 20.7%, DO: 2.4%), hydrogeological factors (BD: 16.5%, K: 4.1%), and terrestrial factors (elevation: 1.2%; distanced: 5.6%, distancerl: 1.5%, NDVI: 1.2%) were identified as major driving factors influencing the groundwater quality in the study area. The HHRA suggested that TCR values of arsenic in infants, children and teens greatly exceeded the acceptable risk threshold of 1E-4, indicating a high cancer risk with a basic trend: infants > children > teens, while TCR values of adults were within the acceptable risk level. THI values of four age groups in the RME scenario were nearly ten times higher than those in the CTE scenario, displaying a great health effect on all age groups (HQ > 1). The present study provides novel insights into the driving mechanism of groundwater quality and potential health hazard in arid and semi-arid regions.

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 remote sensing analysis method for soil heavy metal pollution sources at site scale considering source-sink relationships

Conventional methods for identifying soil heavy metal (HM) pollution sources are limited to area scale, failing to accurately pinpoint sources at specific sites due to the spatial heterogeneity of HMs in mining areas. Furthermore, these methods primarily focus on existing solid waste polluted dumps, defined as "direct pollution sources", while neglecting existing HM pollution hotspots generated by historical anthropogenic activities (e.g., mineral development, industrial discharges), defined as "potential pollution sources". Addressing this gap, a novel remote sensing analysis method is proposed to identify both direct and potential pollution sources at site scale, considering source-sink relationships. Direct pollution sources are extracted using a supervised classification algorithm on high-resolution multispectral imagery. Potential pollution sources depend on the spatial distribution of HM pollution, mapped using a machine learning model with hyperspectral imagery. Additionally, a source identification algorithm is developed for gridded pollution source analysis. Validated through a case study in a cadmium (Cd)-polluted mine area, the proposed method successfully extracted 21 solid waste polluted dumps with an overall accuracy approaching 90 % and a Kappa coefficient of 0.80. Simultaneously, 4167 HM pollution hotspots were identified, achieving optimal inversion accuracy for Cd (Rv2 = 0.91, RMSEv = 4.27, and RPDv = 3.02). Notably, the spatial distribution patterns of these identified sources exhibited a high degree of similarity. Further analysis employing the identification algorithm indicated that 3 polluted dumps and 258 pollution hotspots were pollution sources for a selected high-value point of Cd content. This innovative method provides a valuable methodological reference for precise prevention and control of soil HM pollution.

Pollution area identification, receptor model-oriented sources and probabilistic health hazards to prioritize control measures for heavy metal management in soil

Identifying the primary source of heavy metals (HMs) pollution and the key pollutants is crucial for safeguarding eco-health and managing risks in industrial vicinity. For this purpose, this investigation was carried out to investigate the pollution area identification with soil static environmental capacity (QI), receptor model-oriented critical sources, and Monte Carlo simulation (MCS) based probabilistic environmental and human health hazards associated with HMs in agricultural soils of Narayanganj, Bangladesh. The average concentration of Cr, Ni, Cu, Cd, Pb, Co, Zn, and Mn were 98.67, 63.41, 37.39, 1.28, 23.93, 14.48, 125.08, and 467.45 mg/kg, respectively. The geoaccumulation index identified Cd as the dominant metal, indicating heavy to extreme contamination in soils. The QI revealed that over 99% of the areas were polluted for Ni and Cd with less uncertain regions whereas Cr showed a significant portion of areas with uncertain pollution status. The positive matrix factorization (PMF) model identified three major sources: agricultural (29%), vehicular emissions (25%), and industrial (46%). The probabilistic assessment of health hazards indicated that both carcinogenic and non-carcinogenic risks for adult male, adult female, and children were deemed unacceptable. Moreover, children faced a higher health hazard compared to adults. For adult male, adult female, and children, industrial operations contributed 48.4%, 42.7%, and 71.2% of the carcinogenic risks, respectively and these risks were associated with Ni and Cr as the main pollutants of concern. The study emphasizes valuable scientific insights for environmental managers to tackle soil pollution from HMs by effectively managing anthropogenic sources. It could aid in devising strategies for environmental remediation engineering and refining industry standards.

Systematic assessment of source identification and ecological and probabilistic health risks of potentially toxic elements (PTEs) in soils of a typical coal mining area in Guanzhong region

Mining activities may cause the accumulation of potentially toxic elements (PTEs) in surrounding soils, posing ecological threats and health dangers to the local population. Therefore, a comprehensive assessment using multiple indicators was used to quantify the level of risk in the region. The results showed that the mean values of the nine potentially toxic elements in the study area were lower than the background values only for Cr, and the lowest coefficient of variation was 17.1 % for As, and the spatial distribution characteristics of the elements indicate that they are enriched by different factors. The elements Hg and Cd, which have substantial cumulative features, are the key contributors to ecological risk in the study region, which is overall at moderate risk. APCS-MLR model parses out 4 possible sources: mixed industrial, mining and transportation sources (53.98 %), natural sources (24.56 %), atmospheric deposition sources (12.60 %), and agricultural production sources (8.76 %). The probabilistic health risks show that children are more susceptible to health risks than adults; among children, the safety criteria (HI < 1 and CR < 10-4) were surpassed by 29.29 % of THI and 8.58 % of TCR. According to source-orientated health hazards, the element Ni significantly increases the risk of cancer. Mixed sources from industry, mining, and transportation are important sources of health risks. The results of this research provide some scientific references for the management and decrease of regional ecological and health risks.

Pollution characteristics and source apportionment of heavy metal(loid)s in soil and groundwater of a retired industrial park

Heavy metal(loid)s (HMs) pollution has become a common and complex problem in industrial parks due to rapid industrialization and urbanization. Here, soil and groundwater were sampled from a retired industrial park to investigate the pollution characteristics of HMs. Results show that Ni, Pb, Cr, Zn, Cd, and Cu were the typical HMs in the soil. Source analysis with the positive matrix factorization model indicates that HMs in the topsoil stemmed from industrial activities, traffic emission, and natural source, and the groundwater HMs originated from industrial activities, groundwater-soil interaction, groundwater-rock interaction, and atmosphere deposition. The sequential extraction of soil HMs reveals that As and Hg were mainly distributed in the residue fraction, while Ni, Pb, Cr, Zn, Cd, and Cu mainly existed in the mobile fraction. Most HMs either in the total concentration or in the bioavailable fraction preferred to retain in soil as indicated by their high soil-water partitioning coefficients (Kd), and the Kd values were correlated with soil pH, groundwater redox potential, and dissolved oxygen. The relative stable soil-groundwater circumstance and the low active fraction contents limited the vertical migration of soil HMs and their release to groundwater. These findings increase our knowledge about HMs pollution characteristics of traditional industrial parks and provide a protocol for HMs pollution scrutinizing in large zones.

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.

Field application of hydroxyapatite and humic acid for remediation of metal-contaminated alkaline soil

The quality of soil is essential for ensuring the safety and quality of agricultural products. However, soils contaminated with toxic metals pose a significant threat to agricultural production and human health. Therefore, remediation of contaminated soils is an urgent task, and humic acid (HA) with hydroxyapatite (HAP) materials was applied for this study in contaminated alkaline soils to remediate Cd, Pb, Cu, and Zn. Physiochemical properties, improved BCR sequential extraction, microbial community composition in soils with superoxide dismutase (SOD), peroxidase (POD), and chlorophyll content in plants were determined. Among the studied treatments, application of HAP-HA (2:1) (T7) had the most significant impact on reducing the active forms of toxic metals from soil such as Cd, Pb, Cu, and Zn decreased by 18.59%, 9.12%, 11.83%, and 3.33%, respectively, but HAP and HA had a minor impact on metal accumulation in Juncao. HAP (T2) had a beneficial impact on reducing the TCleaf/root of Cd, Cu, and Zn, whereas HAP-HA (T5) showed the best performance for reducing Cd and Cu in EFleaf/soil. HAP-HA (T5 and T7) showed higher biomass (57.3%) and chlorophyll (17.9%), whereas HAP (T4) showed better performance in POD (25.8%) than T0 in Juncao. The bacterial diversity in soil was increased after applying amendments of various treatments and enhancing metal remediation. The combined application of HAP and HA effectively reduced active toxic metals in alkaline soil. HAP-HA mixtures notably improved soil health, plant growth, and microbial diversity, advocating for their use in remediating contaminated soils.

Source-oriented risk assessment of heavy metal(loid)s in agricultural soils around a multimetal smelting area near the Yellow River, China

Heavy metal(loid) (HM) contamination in agricultural soils, particularly in areas severely impacted by smelting industries, has attracted worldwide attention. In this study, agricultural soils were collected in a flourishing multimetal smelting area near the Yellow River in central China. By an integrated approach encompassing the positive matrix factorization model, ordinary kriging interpolation and hierarchical clustering analysis (PMF-OK-HC), a total of four major sources and their mass contributions were identified, namely, soil parent material (56.6%), industrial waste and Mo smelting (24.0%), metal smelting and traffic emissions (12.8%), and coal combustion (6.7%). On this basis, the health risk of HMs was evaluated by Monte Carlo simulations and showed that a higher risk, with a higher proportion of exceeding-thresholds risk, was observed for children than for adults in terms of both noncarcinogenic and carcinogenic risks. Exposure pathways of oral ingestion in children could result in a higher attributed risk than other pathways. Furthermore, source-oriented risk assessment (SORA) revealed that the sources of coal combustion, industrial waste and Mo smelting had the highest contributions to noncarcinogenic and carcinogenic risks. Overall, for effective environmental management in agricultural soil, the framework of SORA was verified as an effective tool in the identification of the priority control of HMs and their sources.

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.

A hydrochemical and isotopic approach for source identification and health risk assessment of groundwater arsenic pollution in the central Yinchuan basin

Arsenic contamination of groundwater is becoming a major global issue as it can severely affect the safety of drinking water and human health. In this paper, 448 water samples were investigated to study the spatiotemporal distribution, source identification and human health risk of groundwater arsenic pollution in the central Yinchuan basin by applying a hydrochemical and isotopic approach. The results showed that arsenic concentrations in groundwater ranged from 0.7 μg/L to 26 μg/L with a mean of 2.19 μg/L, and 5.9% of samples were above 5 μg/L, indicating the arsenic pollution of groundwater in the study area. High arsenic groundwater was mainly distributed in the northern and eastern areas along the Yellow river. The main hydrochemistry type of high arsenic groundwater was HCO3·SO4-Na·Mg, and the dissolution of arsenic-bearing minerals in sediment, irrigation water infiltration and aquifer recharge from the Yellow river were the main sources of arsenic in groundwater. The arsenic enrichment was dominantly controlled by the TMn redox reaction and the competitive adsorption of HCO3-, and the influence of anthropogenic activities was limited. The health risk assessment suggested that the carcinogenic risk of As for children and adults greatly exceeded the acceptable risk threshold of 1E-6, displaying a high carcer risk, while the non-carcinogenic risks of As, F-, TFe, TMn and NO3- in 2019 were largely higher than the acceptable risk threshold (HQ > 1). The present study provides insight into the occurrence, hydrochemical processes and potential health risk of arsenic pollution in groundwater.

Source apportionment and human health risk of PAHs accumulated in edible marine organisms: A perspective of "source-organism-human"

Most PAHs produced by human activities can be absorbed and accumulated by edible organisms and pose a potential hazard to human health. However, the source apportionment and human health risk of PAHs accumulated in edible organisms remains largely unknown. Therefore, we conducted source analysis and health risk assessment based on the PAH concentrations in ten marine fish from coastal areas of Guangdong, China. Results showed that the pollution of PAHs in fish organisms was at "Minimally polluted" level, and that all marine fish had the ability to accumulate PAHs. Risk assessment indicated Carcinogenic risk of PAHs in four populations was at a "Cautionary risk" level, with urban children suffered the highest risk. Petroleum pollution, Coal and biomass combustion, and Marine transport emissions were identified as the main anthropogenic sources for PAHs in organisms, and Marine transport emissions accounted for the highest Carcinogenic risk. The Acceptable daily intake for all populations were far below their actual daily intake without causing "Cautionary risk". Our findings provide new insights into the source apportionment and health risk of PAHs from a "source-organism-human" perspective, and suggested that joint management of three anthropogenic sources would be an effective way to prevent the health risks of PAHs.

A global meta-analysis of the correlation between soil physicochemical properties and lead bioaccessibility

Soil physiochemical properties play a vital role in bioaccessibility-based health risk assessment as it can determine the bioaccessibility and the true risk of potentially toxic elements in soil. However, the effects of soil properties on bioaccessibility still remains unclear. In this paper, 17 of the 1454 literatures with 474 samples were identified, screened and reviewed for exploring the correlation between soil physicochemical properties and lead bioaccessibility (BAc_Pb) through a meta-analysis approach. Five soil physicochemical parameters including pH, SOM, Clay, CEC and T-Pb were systematically analyzed using Principal component analysis, Pearson correlation analysis and survival analysis. The results showed that pH of simulated gastric juice is a major source of heterogeneity of the correlation between soil pH and BAc_Pb. In the gastric phase, the effect of alkaline soil on high BAc_Pb (BAc >50%) is more sensitive, and the effect of acidic soil on low BAc_Pb (BAc <50%) is more sensitive. However, in the small intestinal phase, soil pH displays little impacts on BAc_Pb in acidic, alkaline and neutral soils. Although three principal components explained 66.2% and 64.9% of the total variance of the urban, agricultural, and mining soils in gastric and small intestinal phases, respectively, there was no strong evidence that soil type can influence the BAc_Pb. The results of present study provide insights into the correlation between soil properties and BAc_Pb, and prediction of the bioaccessibility and bioavailability of Pb in different types of soil.

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.