Health risk assessment of heavy metal(loid)s in park soils of the largest megacity in China by using Monte Carlo simulation coupled with Positive matrix factorization model

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

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

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.

Identifying high-risk volatile organic compounds in residences of Chinese megacities: A comprehensive health-risk assessment

Indoor volatile organic compounds (VOCs) pose considerable health hazards. However, research on hazardous VOCs in Chinese residences has been conducted on a limited spectrum. This study used Monte Carlo simulations with data from Beijing, Shanghai, and Shenzhen to assess VOC health risks in Chinese homes. We identified high-risk VOCs and analyzed the impact of geographic location, age group, activity duration, and inhalation rate on VOC exposure, including lifetime risks. Formaldehyde, acrolein, naphthalene, and benzene posed the highest risks. Notably, acrolein made the leading contribution to non-cancer risks across all megacities. Naphthalene had elevated cancer and non-cancer risks in Shenzhen. This study highlights the need to investigate acrolein and naphthalene, which are currently unregulated but pose substantial health risks. The cumulative cancer risk (TCR) decreases from adults to children, while the cumulative non-cancer risk (HI) is higher for children. In all cities, the average TCR for adults exceeds the tolerable threshold of 10-4, and the average HI values surpass the safety threshold of 1. Nearly 100 % of the population faces a lifetime cancer risk above 10-4, and over 71 % face a non-cancer risk exceeding 10 (tenfold the benchmark). This study underscores the critical need for developing control strategies tailored to VOCs.

Salinity influence correction for zinc ion seawater quality criteria and ecological risk assessment in Chinese seas

The increasing prevalence of zinc pollution in marine ecosystems, primarily from industrial sources, has become a global environmental concern. This study addresses zinc toxicity in Chinese coastal waters, emphasizing the importance of considering environmental factors like salinity and temperature in establishing water quality criteria (WQC). Data collected from various marine regions underwent meticulous analysis, incorporating salinity corrections to derive more precise criteria values. The short-term criteria for the Bohai Sea, Yellow Sea, East China Sea, and South China Sea were 94.0, 77.6, 84.2, and 118 μg/L under the salinity correction, respectively, and the long-term criteria was 4.10 μg/L. Ecological risk assessments employing diverse methodologies revealed varying levels of risk across sea areas, underscoring the nuanced nature of zinc pollution's impact on marine ecosystems. Greater acute and chronic risk of zinc ions observed in the Yellow Sea region. These findings underscore the imperative need for tailored management strategies to protect local marine life from the environmental threats posed by zinc.

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.

Which pollutants and sources should be prioritized for control in multi-pollutants complex contaminated areas?

Risk assessment and source identification of multi-pollutants are essential for accurate control of soil contamination. However, complexity in pollutant properties and diversity in source types raise challenges to the target. Therefore, this study constructed a hierarchical ecological risk quantification method combined with risk ranking, risk of single pollutant using potential affected fraction (PAF), and joint risk of multi-pollutants employing msPAF. Taking regional contamination in South China as a case, the risk ranking was determined, while single and joint effects showed msPAF reaching 79.4 %, with risk as heavy metals (HMs) > per- and polyfluoroalkyl substances (PFASs) > polycyclic aromatic hydrocarbons (PAHs). Meanwhile, an integrated source apportionment method was established from three layers by principal component analysis to classify source types, multiple linear regression of distance to identify key sources, and positive matrix factorization to track omitted sources. Consequently, key sources were captured, with 80.8 %-93.2 % contribution of farmland and electroplating to three main HMs, 52.2 %-69.4 % contribution of roads to three main PAHs, and 71.1 %-73.2 % contribution of electroplating to two main PFASs. Further, omitted sources were tracked with contribution of 31.2 %-84.1 % to eight pollutants. The established methods can identify control targets, including high-risk pollutants and their key sources.

Hazard profiles, distribution trends, and sources tracing of rare earth elements in dust of kindergartens in Beijing

Children, the most vulnerable group in urban populations, are susceptible to the effects of pollution in urban environments. It is significant to evaluate the influence of rare earth elements (REEs) from kindergartens dust (KD) in Beijing on children's health. This study collected surface dust from 73 kindergartens in 16 districts of the mega-city of Beijing, and the concentrations of 14 REEs in KD, including La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, and Lu, were detected. The contamination levels, source apportionment, and health exposure risk of REEs were comprehensively investigated. The results indicate that the contamination levels of 14 REEs are within the acceptable range. Nevertheless, Eu, Ce, La, Pr, Nd, Gd, and Sm show high enrichment due to anthropogenic influence. Besides, KD is rich in light rare earth elements (LREEs) (90.97 mg kg-1) compared to heavy rare earth elements (HREEs) (8.65 mg kg-1). The distribution parameter patterns of REEs suggest that complicated anthropogenic sources influence the enrichment of REEs in KD. The main sources of REEs in KD include natural sources (40.64%), mixed high-tech industries and construction (33.89%), and mixed coal-fired, historical industrial, and transportation sources (26.47%). The primary pathway for daily intake of REEs in children is through ingestion, which presents a low but not negligible health risk. This study provides guidance for the effective risk management of REEs in KD.

Determining priority control factors for heavy metal management in urban road dust based on source-oriented probabilistic ecological-health risk assessment: A study in Xi'an during peak pollution season

Urban road dust (URD) is essential for transporting heavy metals (HMs), which can be a major danger to both the environment and human health. Moreover, URD has the potential to be carried into bodies of water, leading to contamination of the aquatic ecosystem. A study was conducted in Xi'an, a city in northwestern China known for high air pollution levels, during January 2024 - a period characterized by peak pollution due to frequent low wind speeds and temperature inversions. The research investigated the presence of 10 types of HMs (Cu, Zn, Cd, Cr, Pb, As, Ni, Hg, Co, and Mn) in URD. Findings revealed elevated levels of Cu, Zn, Cd, Cr, Pb, As, and Hg in URD compared to background levels. Hg showed the most significant contamination (moderate to heavy), followed by moderate contamination of Cd, and lower levels of As, Zn, and Cu. The main sources of HMs were traffic (58.2%), mixed natural and industrial (30.3%), and industrial (11.5%). The ecological risk in the area was deemed to be very high, primarily because of Hg and Cd. Based on probabilistic health risk assessments, it was determined that non-carcinogenic risks were deemed acceptable for all groups. Nevertheless, the possibility of carcinogenic risks should not be disregarded. Strategies for controlling ecological-health risks prioritize mixed natural and industrial sources, with a focus on Hg, Cd, and As in URD. The results offer a foundation for policymakers to create specific control strategies.

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.

Ecotoxicological, ecological, and human health risks of total carbohydrates and some inorganic pollutants on the Nile Delta region along the Egyptian Mediterranean Coast

This is one of few studies dealing with the potential impact of total carbohydrates (TCHO), and some inorganic pollutants (F, B, As, V, Se) on human health. Additionally, the latter pollutants toxicological and ecological effects on the Egyptian Mediterranean Coast, especially, the Nile Delta region, were investigated. Both F (0.18 ± 0.09 mg/g) and As (2.47 ± 5.39 μg/g) were of lower concentrations compared to previous reports. Values of all ecological and ecotoxicity indices, particularly, the risk quotient (RQ), showed that arsenic had the most adverse biological effects on three trophic levels (algae, invertebrates, and fish). Children and adults non-carcinogenic hazard index (HI) values were <1, revealing that sediments in the studied area would pose no risk to humans. However, arsenic carcinogenic risk (CR) values exceeded the maximum permissible limits, implying risk to children and adults. These findings could anticipate toxic impacts of polluted effluents on the Nile Delta region.

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.

Chemical speciation and health risk assessment of potentially toxic elements in playground soil of bell metal commercial town of Eastern India

Contaminated playground soils can expose players to harmful pollutants, increasing the risk of respiratory, skin, and gastrointestinal issues and potentially impacting long-term health and development. This study investigated the chemical forms and the human health risks associated with potentially toxic elements (PTEs) found in playground soil samples from Khagra, a historic town known for its bell metal industry, located in the Murshidabad district of eastern India. Sequential extraction techniques were employed to analyze the distribution of PTEs such as As, Cd, Co, Cu, Mn, Pb, Ni, Sn, and Zn among different fractions: exchangeable (F1), bound to carbonate phase (F2), bound to iron and manganese oxides (F3), bound to organic matter (F4), and residual (F5). The playground soil showed the highest contamination with Sn, with an IPOLL value of 3.14, indicating moderate to heavy contamination, while Cd, Cu, Mn, Pb, and Zn exhibit moderate contamination. The mean concentration of PTEs in all fractions (F1-F5) follows the order: Fe > Zn > Cu > Mn > Pb > Sn > Ni > Co > As > Cd. The maximum affinity of PTEs and their percentages are as follows: Fe (F5, 80.6%), As (F5, 55.31%), Cd (F5, 48.8), Co (F5, 64.9%), Mn (F3, 44%), Ni (F5, 53.2%), Pb (F3, 44.7%), Zn (F3, -43.19%), Sn (F3, 55%), Cu (F5 -42.18). As, Cd, Co, Cu, Fe, and Ni have a high affinity for F5, indicating geogenic source, while Mn, Pb, Sn, and Zn have a high affinity for F3, indicating anthropogenic source. Fe-Mn oxide partition was dominant for nearly all PTEs due to elevated sorption of cations onto Fe-Mn oxides at high pH. The risk assessment code for Cd, Cu, Mn, Ni, Sn, and Zn in playground soil is categorized under moderate risk, below 30%, while other elements showed no risk. Also, mobility factors were calculated for each PTEs, suggesting the degree of mobility that PTEs can easily migrate and be taken up, absorbed, or adsorbed by the human body. The mobility factor in playground soil was higher for Sn (59.89%) followed by Mn (54.24%) > Pb (52.91%) > Zn (52.01%) > Cd (39.49%) > Ni (33.20%) > As (30.39%) > Co (26.56%) > Cu (21.24%) > Fe (11.20%). Risk hazard quotients for children and adults were found to follow the order: Pb (0.263; 0.040), Cu (0.098; 0.015) > As(0.056; 0.008) > Mn (0.045; 0.009) > Zn(0.36; 0.05) > Cd(0.006; 0.001) > Ni (0.004; 0.001) > Co (0.001; 0.0). PTEs detected in the environment result from atmospheric deposition from small-scale metallurgical industries (bell metal and brass), coal and oil combustion, civil works, municipal waste incineration, and fugitive emissions from road dust. The human non-carcinogenic health risk for PTEs from ingestion and dermal contact was higher than that from inhalation. In the context of carcinogenic risk, As shows the highest health risk of 2.51E-05, followed by Cd (1.02E-09) and Co (8.14E-09). This study uniquely assesses the chemical speciation of PTEs in playground soils, revealing their geogenic and anthropogenic sources, and evaluates associated health risks. Policy intervention is vital for monitoring and remediating PTEs in playgrounds to protect children's health.

Source-specific probabilistic health risk assessment of dust PAHs in urban parks based on positive matrix factorization and Monte Carlo simulation

Understanding the health risks of polycyclic aromatic hydrocarbons (PAHs) in dust from city parks and prioritizing sources for control are essential for public health and pollution management. The combination of Source-specific and Monte Carlo not only reduces management costs, but also improves the accuracy of assessments. To evaluate the sources of PAHs in urban park dust and the possible health risks caused by different sources, dust samples from 13 popular parks in Kaifeng City were analyzed for PAHs using gas chromatograph-mass spectrometer (GC-MS). The results showed that the surface dust PAH content in the study area ranged from 332.34 µg·kg-1 to 7823.03 µg·kg-1, with a mean value of 1756.59 µg·kg-1. Nemerow Composite Pollution Index in the study area ranged from 0.32 to 14.41, with a mean of 2.24, indicating that the overall pollution warrants attention. Four pollution sources were identified using the positive matrix factorization (PMF) model: transportation source, transportation-coal and biomass combustion source, coke oven emission source, and petroleum source, with contributions of 33.74%, 25.59%, 22.14%, and 18.54%, respectively. The Monte Carlo cancer risk simulation results indicated that park dust PAHs pose a potential cancer risk to all three populations (children, adult male and adult female). Additionally, the cancer risk for children was generally higher than that for adult males and females, with transportation sources being the main contributor to the carcinogenic risk. Lastly, sensitivity analyses results showed that the toxic equivalent concentration (CS) is the parameter contributing the most to carcinogenic risk, followed by Exposure duration (ED).

Optimizing health risk assessment for soil trace metals under low-precision sampling conditions: A case study of agricultural soil

Cost limitations often lead to the adoption of lower precision grids for soil sampling in large-scale areas, potentially causing deviations in the observed trace metal (TM) concentrations from their true values. Therefore, in this study, an enhanced Health Risk Assessment (HRA) model was developed by combining Monte Carlo simulation (MCS) and Empirical Bayesian kriging (EBK), aiming to improve the accuracy of health risk assessment under low-precision sampling conditions. The results showed that the increased sampling scale led to an overestimation of the non-carcinogenic risk for children, resulting in potential risks (the maximum Hazard index value was 1.08 and 1.64 at the 500 and 1000 m sampling scales, respectively). EBK model was suitable for predicting soil TM concentrations at large sampling scale, and the predicted concentrations were closer to the actual value. Furthermore, we found that the improved HRA model by combining EBK and MCS effectively reduced the possibility of over- or under-estimation of risk levels due to the increasing sampling size, and enhanced the accuracy and robustness of risk assessment. This study provides an important methodology support for health risk assessment of soil TMs under data limitation.

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.

Do the residual metals in multiple environmental media surrounding mines pose ecological and health risks? A case of an abandoned mining area in central south China

Despite effective mining environmental regulations, residual metal pollution persists, leading to significant ecological harm and posing substantial risks to human well-being. This study employed multiple-criteria methods to investigate the ecological and health risks caused by metals in multiple environmental media (e.g., arable soil, indoor dust, PM10, homegrown vegetables, and rice) around abandoned mine areas (MA) in central south China. The study also aimed to identify predominant risk factors and the main exposure pathway. The findings revealed that metal levels and risks in the environmental media surrounding the MA were significantly higher than those in the control areas (away from abandoned mines, CA). This indicates that the accumulation of metals in the environmental media surrounding the MA was attributed to the previous mining activities. Variations in metal content were observed among different environmental media in MA, with Cd from mining source being the primary pollutant in arable soil, indoor dust, PM10, and vegetables, while As from agricultural source was the main pollutant in rice. Additionally, the consumption of Cd-contaminated vegetables and As-contaminated rice emerged as the primary routes of health hazards for the local population, leading to significant non-carcinogenic and carcinogenic risks. Consequently, it is imperative for the government and mining companies to promptly establish risk control and remedial strategies for mitigating residual metal levels in multiple environmental media surrounding the MA.

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.

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.

Sources and health risks of heavy metals in kindergarten dust: The role of particle size

Particle size effects significantly impact the concentration and toxicity of heavy metals (HMs) in dust. Nevertheless, the differences in concentrations, sources, and risks of HMs in dust with different particle sizes are unclear. Therefore, guided by the definition of atmospheric particulate matter, dust samples with particle sizes under 1000 μm (DT1000), 100 μm (DT100), and 63 μm (DT63) from Beijing kindergartens were collected. The concentrations of HMs (e.g., Cd, Pb, Zn, Ni, Cr, Ba, Cu, V, Mn, Co, and Ti) in dust samples with different particle sizes were measured. Besides, the differences in HM concentrations, contamination levels, sources, and source-oriented health risks in dust samples of different particle sizes were systematically explored. The results show that the concentrations of Mn, V, Zn, and Cd gradually increase with decreasing dust particle sizes, the concentrations of Ba and Pb show a decreasing trend, and the concentrations of Cr, Cu, Ni, and Co display an increasing and then decreasing trend. The degree of contamination of HMs in dust of different particle sizes varies, with Cd being the most dominant contaminant. Compared with DT1000 and DT63, DT100 is the most polluted. In addition, the sources of HMs in DT1000, DT100, and DT63 become more single with decreasing particle size, which may be mainly due to the particle-size effect inducing the redistribution of HMs in different sources. Notably, the potential health risk is higher in DT100 than in DT1000 and DT63. The highest contribution of industrial sources to the health risk is found in DT100, which is mainly caused by highly toxic chromium (Cr). This work emphasizes the importance of considering particle size in risk assessment and pollution control, which can provide a theoretical basis for precise management of HMs pollution in dust.

Fate and potential ecological risk of rare earth elements in 3000-year reclaimed soil chronosequences

The introduction of anthropogenic inputs into natural systems may lead to enduring alterations in the innate characteristics of Rare Earth Elements (REEs). Against this backdrop, the evolutionary processes and environmental drivers of REEs in soil remain uncertain. A 3000-year soil chronosequence with uniform parent material was established in reclaimed farmland along the Yangtze River, reconstructing, for the first time, the dynamic processes of REE accumulation and fractionation over a long-time scale. Analysis of 122 soil samples showed REE concentrations ranging from 146.00 to 216.56 μg/g. Based on reclamation duration, three significant stages of REE evolution were identified: natural leaching, rapid accumulation, and stable accumulation with differentiation. Reclaimed soil after 3000 years exhibited a 14.1 % increase in REE concentrations compared to fresh sediments, attributed to anthro -pedogenic processes. Moreover, Heavy Rare Earth Elements (HREEs) accumulated faster than Light Rare Earth Elements (LREEs), particularly in deeper soils (60-100 cm), where HREE concentrations rose by 34.3 %, mainly due to acidic environments promoting HREE fixation. Additionally, the potential ecological risk posed by REEs heightened with reclamation duration, with HREEs exhibiting a sensitivity of 83 % to 94 %. Our findings stress the urgency of carefully monitoring exogenous REEs introduced through anthropogenic activities, particularly HREEs.

Research on pollution identification and safety thresholds based on the olfactory effect on a halogenated hydrocarbon contaminated site

To effectively address odor control issues at sites contaminated with halogenated hydrocarbons, it is essential to establish an odor risk prediction system for evaluating potential risks that may impact future planning. This research focuses on a representative halogenated hydrocarbon-contaminated site, examining the spatial and temporal distribution characteristics of key pollutants in soil gas. By analyzing odor contribution rates, the study identifies significant odorants in soil gas, which enables the derivation of both probabilistic and deterministic safety thresholds for soil and groundwater based on olfactory effects. The findings indicate that 1,1-dichloroethylene, vinyl chloride, chloroform, and 1,1-dichloroethane are prevalent throughout the contaminated site, displaying elevated concentration levels and substantially influencing the overall contamination extent. These substances are highlighted as critical pollutants requiring attention. Correlation analysis (P < 0.05) reveals a strong relationship between the concentrations of vinyl chloride, 1,1-dichloroethane, and chloroform with groundwater depth and air temperature. Additionally, the analysis of odor activity values (OAV) identified 1,1-dichloroethene, 1,4-dichlorobenzene, chlorobenzene, chloroform, and vinyl chloride as key olfactory factors at the site. The corresponding probabilistic safety thresholds are 0.68, 1.65, 0.50, 7.87, and 3.72 mg kg-1 for soil, and 9.29, 3.46, and 1.09, 69.55, and 47.01 mg L-1 for groundwater, respectively. Among them, the odor risks of chlorobenzene and 1,1-dichloroethylene warrant more attention than soil contamination risks; regarding 1,4-dichlorobenzene, it is recommended to concurrently consider odor risks during human health risk assessment; as for vinyl chloride and chloroform, their odor risks can be largely eliminated based on human health-oriented pollution management.

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

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

Soil pollution identification and human health risk assessment of soil heavy metals in an abandoned mine area in the Republic of Korea

In this study, the Geo-accumulation index (Igeo), Human Health Risk Assessment (HRA), and Ecological Risk Index (ERI) were utilized to examine the risks associated with the soils at the DaeyangYeongseong mine. Brassica juncea and Raphanus sativus were employed in the ecological toxicity test. In all soil samples, the mean Igeo value of arsenic measured 3.15, and cadmium measured 6.63, indicating a very high level of heavy metal contamination. The carcinogenic risk of cadmium and arsenic for adults was 4.30×10-3 and 1.43×10-5, respectively. For children, these values were 3.92 × 10-2 and 1.33 ×10-4, exceeding the acceptable level (1×10-6). In all soils, cadmium showed extremely high ecological risk levels, and arsenic had extremely high risk levels in 34.8% of the total area. This was also confirmed in toxicity assessments using plants. Therefore, arsenic and cadmium were found to be the main causes of soil contamination and ecological risk.

Determination of contamination, source, and risk of potentially toxic metals in fine road dust in a karst region of Southwest China

Understanding the pollution situation of potentially toxic metals (PTMs) in fine road dust (FRD) in emerging industrialized cities and identifying priority control factors is crucial for urban environmental management, resident health protection, and pollution control. This study conducted a comprehensive investigation on PTMs pollution in FRD in Zunyi, a representative emerging industrialized city in the karst region of southwestern China. The average contents of Ni, Cr, Mn, Cu, Zn, Ba, Pb, V, and Co in the FRD were 43.2, 127.0, 1232.1, 134.4, 506.6, 597.8, 76.1, 86.8, and 16.2 mg kg-1, respectively, which were obviously higher than the corresponding background levels of the local soil except for V and Co. The comprehensive pollution level of the determined PTMs in the FRD was very high, primarily caused by Zn and Cu. The sources of PTMs in Zunyi FRD were traffic, industrial, construction, and natural sources, accounting for 38.0, 23.7, 21.9, and 16.4% of the total PTMs content, respectively. The PTMs in Zunyi FRD exhibited a low to moderate overall ecological risk level, mainly contributed by Cu and traffic source. The cancer risks of PTMs in Zunyi FRD were high for all populations. The non-carcinogenic risk of PTMs in Zunyi FRD was acceptable for adults, but cannot be ignored for children. According to the source-specific probabilistic health risk estimation results, the priority control source is industrial source and the priority control PTM is Cr. Local governments need to give more attention to the carcinogenic risks and health hazards posed by PTMs in the FRD.

Spatiotemporal variations and risk assessment of estrogens in the water of the southern Bohai Sea: A comprehensive investigation spanning three years

The ubiquitous and adverse effects of estrogens have aroused global concerns. Natural and synthetic estrogens in 255 water samples from the southern Bohai Sea were analyzed over three years. Total estrogen concentrations were 11.0-268 ng/L in river water and 1.98-99.7 ng/L in seawater, with bisphenol A (BPA) and 17α-ethynylestradiol (EE2) being the predominant estrogens, respectively. Estrogen showed the highest concentrations in summer 2018, followed by spring 2021 and spring 2019, which was consistent with the higher estrogen flux from rivers during summer. Higher estrogen concentrations in 2021 than in 2019 were driven by the higher level of BPA, an additive used in personal protective equipment. Estrogen exhibited higher concentrations in the southern coast of the Yellow River Delta and the northeastern coast of Laizhou bay due to the riverine input and aquaculture. Estrogens could disturb the normal endocrine activities of organisms and edict high ecological risks (90th simulated RQT > 1.0) to aquatic organisms, especially to fish. EE2 was the main contributor of estrogenic potency and ecological risk, which requires special concern. This is the first comprehensive study of estrogen spatiotemporal variations and risks in the Bohai Sea, providing insights into the environmental behavior of estrogens in coastal regions.

Pinus eldarica (L.) bark as urban atmospheric trace element pollution bioindicator: pollution status, spatial variations, and quantitative source apportionment based on positive matrix factorization receptor model

In this study, a total of 180 Pinus eldarica bark samples were collected from different regions of Hamedan megacity, Iran, in 2023, and contents of Cd, Cr, Cu, Mn, Ni, Pb, and Zn in the samples were determined using ICP-OES. The results illustrated that the average contents of all the analyzed elements were greater than those in the background contents, which presumably demonstrated anthropogenic sources of these potentially toxic elements (PTEs). The greatest concentrations of the analyzed PTEs for different functional areas were observed in specimens collected from commercial or industrial areas, indicating the impact of human entries. The I-geo values were in the range of "unpolluted to moderately polluted" to "moderately to heavily polluted", PI showed "moderate to very high pollution", and PLI reflected high to very high pollution levels for the whole study area. Additionally, the cumulative mean value of ecological risk (RI) was found to be 152, demonstrating moderate ecological risk across the study area. The results of positive matrix factorization (PMF) showed that the PTE contamination in the air of Hamedan could mainly have an anthropogenic origin (82.7%) and that the traffic emissions as the primary pollution source (33.6%) make the highest contribution to the PTE pollution and ecological risks in the study area. In residential areas, demolition and construction activities could be considered the main sources of PTEs, while in commercial and industrial areas traffic emissions and industrial emissions, could be regarded as the main sources of such pollution, respectively. In conclusion, this study provides a useful approach to identifying the sources and contributions of the toxic elements in different functional areas and can inform future endeavors that aim at managing and controlling metal element pollution.

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.

Source-oriented health risk assessment of groundwater nitrate by using EMMTE coupled with HHRA model

Conventional concentration-oriented approaches for nitrate risk diagnosis only provide overall risk levels without identifying risk values of individual sources or sources accountable for potential health risks. Therefore, a hybrid model combining the end-member mixing model tool on Excel™ (EMMTE) with human health risk assessment (HHRA) was developed to assess the source-oriented health risks for groundwater nitrate, particularly in the Poyang Lake Plain (PLP) region. The results indicated that the EMMTE and the Bayesian stable isotope mixing model (MixSIAR) exhibited remarkable consistency in source apportionment of groundwater nitrate. The source contribution of groundwater nitrate in PLP was related to land use types, hydrogeological conditions, and soil properties. Notably, manure and sewage sources, contributing up to 53.4 %, represented the largest nitrate pollution sources, with a significant contribution of soil nitrogen and nitrogen fertilizers. The non-carcinogenic risk for four potential sources was below the acceptable threshold of 1. Given the factors including rainfall dilution and economic development, attention should be directed towards mitigating the health risks posed by manure and sewage. This study can verify the efficacy of EMMTE in source apportionment and offer valuable insights for decision-makers to regulate the largest sources of nitrate contamination and enhance groundwater management efficiency.

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

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

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.

Health Risk Assessment of Heavy Metal(loid)s in the Overlying Water of Small Wetlands Based on Monte Carlo Simulation

Heavy metal(loid) (HM) contamination is a significant threat to wetland ecosystem. However, contamination risks of HMs in overlying water of small wetlands, which are easily ignored because of their minor occupancy in an overall area, are nearly unknown. A total of 36 water samples containing six HMs were collected from the urban and rural small wetlands of Urumqi in China, and the contamination levels and probabilistic health risks caused by HMs were assessed using the Nemerow pollution index (NPI) and the health risk assessment model introduced by the US EPA. The results revealed that the average concentration of Hg in the urban and rural small wetlands surpassed the Class II thresholds of the Environmental Quality Standards for Surface Water (GB 3838-2002) by factors of 3.2 and 5.0 times, respectively. The overall contamination levels of HMs in the small wetlands fall into the high contamination level. Results of a health risk assessment indicated that non-carcinogenic health risk of the investigated HMs are found to be lower than the acceptable range for adults, but higher than the acceptable range for children. Meanwhile, As falls into the low carcinogenic risk level, whereas Cd falls into the very low carcinogenic risk level. Overall, HMs in rural small wetlands showed relatively higher contamination levels and probabilistic health risks than that of urban small wetlands. In addition, As was identified as the dominant health risk factor in the overlying water of small wetlands in the study area. Findings of this study provide scientific support needed for the prevention of HM contamination of small wetlands in arid zones.

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

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

Pollution and Health Risk Assessment of Potentially Toxic Elements in Groundwater in the Kǒnqi River Basin (NW China)

Potentially toxic elements (PTEs) pose a significant threat to the groundwater system and human health. Pollution and the potential risks of PTEs in groundwater in the Kǒnqi River Basin (KRB) of the northwest arid zones of China are still unknown. A total of 53 groundwater samples containing eight PTEs (Al, As, Cd, Cu, Mn, Pb, Se, and Zn) were collected from the KRB, and the pollution levels and probabilistic health risks caused by PTEs were assessed based on the Nemerow Index (NI) method and the health risk assessment model. The results revealed that the mean contents of Al, As, and Mn in the groundwater surpassed the Class III threshold of the Standard for Groundwater Quality of China. The overall pollution levels of the investigated PTEs in the groundwater fall into the moderate pollution level. The spatial distributions of contents and pollution levels of different PTEs in the groundwater were different. Health risk assessment indicated that all the investigated PTEs in groundwater in the KRB may pose a probabilistic non-carcinogenic health risk for both adults and children. Moreover, As may pose a non-carcinogenic health risk, whereas the non-carcinogenic health risk posed by the other seven PTEs in groundwater will not have the non-carcinogenic risks. Furthermore, As falls into the low carcinogenic risk level, whereas Cd falls into the very low carcinogenic risk level. Overall, As was confirmed as the dominant pollution factor and health risk factor of groundwater in the KRB. Results of this study provide the scientific basis needed for the prevention and control of PTE pollution in groundwater.

Characteristics and correlation analysis of heavy metal distribution in China's freshwater aquaculture pond sediments

The concentration of heavy metals (HMs) in aquaculture pond sediments significantly affects aquatic food safety and environmental quality. The contamination characteristics, drivers and potential sources of HMs in typical bulk freshwater aquaculture pond sediments in major provinces of China were systematically investigated using a variety of methods and models. Specifically, 130 surface sediment samples were collected from the study area, and the geoaccumulation index (Igeo) and potential ecological risk index (RI) were used to jointly evaluate the characteristics of the HMs. Spearman's correlation and redundancy analysis revealed the main drivers of the HMs. Additionally, the positive matrix factorization (PMF) model and absolute principal component score-multiple linear regression (APCS-MLR) model were used to identify the sources of HMs. The results revealed that the pond sediments were safe for fish culture in most of the study areas. Aquafeed protein content is an important driver of HM concentrations in sediments. The total organic carbon (TOC) content, percentage of clay particles, and pH of the aquaculture pond sediments determined the sediment HMs enrichment abilities as 13.6 %, 52 %, and 9.8 %, respectively. Cd, a significantly enriched pollutant, posed a greater ecological risk than the other five HMs (Cr, Cu, Zn, As, and Pb). Three sources of HMs were identified, including agricultural activity (e.g., aquafeeds, pesticides, and fertilizers), industrial production, and natural sources, with contributions of 44.29 %, 36.66 %, and 19.05 %, respectively. This study provides a scientific basis for minimizing the input and accumulation of HMs in freshwater aquaculture pond sediments, and this can provide insights into the prevention and control of the ecological risks posed by HMs.

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.

Evaluation of the prediction effectiveness for geochemical mapping using machine learning methods: A case study from northern Guangdong Province in China

This study compares seven machine learning models to investigate whether they improve the accuracy of geochemical mapping compared to ordinary kriging (OK). Arsenic is widely present in soil due to human activities and soil parent material, posing significant toxicity. Predicting the spatial distribution of elements in soil has become a current research hotspot. Lianzhou City in northern Guangdong Province, China, was chosen as the study area, collecting a total of 2908 surface soil samples from 0 to 20 cm depth. Seven machine learning models were chosen: Random Forest (RF), Support Vector Machine (SVM), Ridge Regression (Ridge), Gradient Boosting Decision Tree (GBDT), Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), and Gaussian Process Regression (GPR). Exploring the advantages and disadvantages of machine learning and traditional geological statistical models in predicting the spatial distribution of heavy metal elements, this study also analyzes factors affecting the accuracy of element prediction. The two best-performing models in the original model, RF (R2 = 0.445) and GBDT (R2 = 0.414), did not outperform OK (R2 = 0.459) in terms of prediction accuracy. Ridge and GPR, the worst-performing methods, have R2 values of only 0.201 and 0.248, respectively. To improve the models' prediction accuracy, a spatial regionalized (SR) covariate index was added. Improvements varied among different methods, with RF and GBDT increasing their R2 values from 0.4 to 0.78 after enhancement. In contrast, the GPR model showed the least significant improvement, with its R2 value only reaching 0.25 in the improved method. This study concluded that choosing the right machine learning model and considering factors that influence prediction accuracy, such as regional variations, the number of sampling points, and their distribution, are crucial for ensuring the accuracy of predictions. This provides valuable insights for future research in this area.

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.

Controlling factors and sources-specific ecological risks associated with toxic metals in core sediments from cascade reservoirs in Southwest China

Toxic metals (TMs) in reservoir sediments pose significant risks to ecosystem security and human safety, yet their presence in the cascade reservoirs of the Lancang River remains understudied. This research examined TMs in core sediments from the Manwan (MW) and Dachaoshan (DCS) cascade reservoirs, aiming to elucidate contamination characteristics, controlling factors, and source-specific ecological risks. The study revealed that the concentrations of As, Cd, Cr, Cu, Hg, Ni, and Zn in the MW Reservoir (37.3, 0.54, 95.1, 44.0, 0.09, 44.8, and 135.7 mg/kg) were notably higher compared to the DCS Reservoir (14.6, 0.30, 82.6, 31.0, 0.08, 36.6, and 108.7 mg/kg). While both reservoirs demonstrated elevated contamination levels of Cd and Hg, the MW Reservoir also exhibited high levels of As, whereas the DCS Reservoir showed relatively high levels of Pb. Mining activities in upstream metal deposits significantly correlated Cd, Hg, and Zn in the MW Reservoir with sulfur. In both reservoir sediments, Cr and Ni displayed a greater affinity for iron oxides, while As, Cd, Cu, Hg, and Zn showed more affinity with manganese oxides. Ecological risk index (RI) values in half of the sediments from the MW Reservoir ranged from 300 to 600, denoting a significant ecological risk. Conversely, in the DCS Reservoir, 93.3 % of the sediments exhibited RI values between 150 and 300, signifying a moderate ecological risk. Source-oriented ecological risks highlighted the need for particular attention to Cd from anthropogenic sources in the MW Reservoir. These findings underscore the importance of implementing measures for TM contamination prevention and control, contributing to strategic planning for sustainable water resource management in the Lancang-Mekong River.

Sources apportionments of heavy metal(loid)s in the farmland soils close to industrial parks: Integrated application of positive matrix factorization (PMF) and cadmium isotopic fractionation

Understanding the source identification and distribution of heavy metal(loid)s in soil is essential for risk management. The sources of heavy metal(loid)s in farmland soil, especially in areas with rapid economic development, were complicated and need to be explored urgently. This study combined geographic information system (GIS) mapping, positive matrix factorization (PMF) model and cadmium (Cd) isotope fingerprinting methods to identify heavy metal(loid) sources in a typical town in the economically developed Yangtze River Delta region of China. Cd, As, Cu, Zn, Pb, Ni and Co in different samples were detected. The results showed that Cd was the most severely contaminated element, with an exceedance rate of 78.0 %. GIS mapping results indicated that the hotspot area was located in the northeastern area with prolonged operational histories of electroplating and non-ferrous metal smelting industries. The PMF model analysis also identified emissions from smelting and electroplating enterprises as the main sources of Cd in the soil, counted for 49.28 %, followed by traffic (25.66 %) and agricultural (25.06 %) sources. Through further isotopic analysis, it was found that in soil samples near the industrial park, the contribution of electroplating and non-ferrous metal smelting enterprises to cadmium pollution was significantly higher than other regions. The integrated use of various methodologies allows for precise analysis of sources and input pathways, offering valuable insights for future pollution control and soil remediation endeavors.

Innova4Health: an integrated approach for prevention of recurrence and personalized treatment of Major Depressive Disorder

Background

Major Depressive Disorder (MDD) is a prevalent mental health condition characterized by persistent low mood, cognitive and physical symptoms, anhedonia (loss of interest in activities), and suicidal ideation. The World Health Organization (WHO) predicts depression will become the leading cause of disability by 2030. While biological markers remain essential for understanding MDD's pathophysiology, recent advancements in social signal processing and environmental monitoring hold promise. Wearable technologies, including smartwatches and air purifiers with environmental sensors, can generate valuable digital biomarkers for depression assessment in real-world settings. Integrating these with existing physical, psychopathological, and other indices (autoimmune, inflammatory, neuroradiological) has the potential to improve MDD recurrence prevention strategies.

Methods

This prospective, randomized, interventional, and non-pharmacological integrated study aims to evaluate digital and environmental biomarkers in adolescents and young adults diagnosed with MDD who are currently taking medication. The study implements a sensor-integrated platform built around an open-source "Pothos" air purifier system. This platform is designed for scalability and integration with third-party devices. It accomplishes this through software interfaces, a dedicated app, sensor signal pre-processing, and an embedded deep learning AI system. The study will enroll two experimental groups (10 adolescents and 30 young adults each). Within each group, participants will be randomly allocated to Group A or Group B. Only Group B will receive the technological equipment (Pothos system and smartwatch) for collecting digital biomarkers. Blood and saliva samples will be collected at baseline (T0) and endpoint (T1) to assess inflammatory markers and cortisol levels.

Results

Following initial age-based stratification, the sample will undergo detailed classification at the 6-month follow-up based on remission status. Digital and environmental biomarker data will be analyzed to explore intricate relationships between these markers, depression symptoms, disease progression, and early signs of illness.

Conclusion

This study seeks to validate an AI tool for enhancing early MDD clinical management, implement an AI solution for continuous data processing, and establish an AI infrastructure for managing healthcare Big Data. Integrating innovative psychophysical assessment tools into clinical practice holds significant promise for improving diagnostic accuracy and developing more specific digital devices for comprehensive mental health evaluation.

Comprehensive insight into mercury contamination in atmospheric, terrestrial and aquatic ecosystems surrounding a typical antimony-coal mining district

This study comprehensively investigated mercury (Hg) contents of various environmental compartments in a typical antimony-coal mining area with intensive industrial activities over the past 120 years to analyze Hg environmental behaviors and evaluate Hg risks. The total mercury (THg) contents in river water, sediments, soils, PM10, dust falls, vegetables and corns were 1.16 ± 0.63 µg/L, 2.01 ± 1.64 mg/kg, 1.87 ± 3.88 mg/kg, 7.87 ± 18.68 ng/m3, 13.01 ± 14.53 mg/kg, 0.30 ± 0.34 mg/kg and 3.11 ± 0.51 µg/kg, respectively. The δ202Hg values in soils and dust falls were - 1.58 ∼ 0.12‰ and 0.25 ∼ 0.30‰, respectively. Environmental samples affected by industrial activities in the Xikuangshan (XKS) presented higher THg and δ202Hg values. Binary mixing model proved that atmospheric deposition with considerable Hg deposition flux (0.44 ∼ 6.40, 3.12 ± 2.20 mg/m2/y) in the XKS significantly contributed to Hg accumulations on surface soils. Compared with soils, sediments with more frequent paths and higher burst probabilities presented higher dynamic Hg risks. Children were faced higher health risk of multiple Hg exposure than adults. Furthermore, the health risk of THg by consuming leaf vegetables deserved more attention. These findings provided scientific basis for managing Hg contamination.

Integrated assessment of potentially toxic elements in soil of the Kangdian metallogenic province: A two-point machine learning approach

The accumulation of potentially toxic elements in soil poses significant risks to ecosystems and human well-being due to their inherent toxicity, widespread presence, and persistence. The Kangdian metallogenic province, famous for its iron-copper deposits, faces soil pollution challenges due to various potentially toxic elements. This study explored a comprehensive approach that combinescombines the spatial prediction by the two-point machine learning method and ecological-health risk assessment to quantitatively assess the comprehensive potential ecological risk index (PERI), the total hazard index (THI) and the total carcinogenic risk (TCR). The proportions of copper (Cu), cadmium (Cd), manganese (Mn), lead (Pb), zinc (Zn), and arsenic (As) concentrations exceeding the risk screening values (RSVs) were 15.03%, 5.1%, 3.72%, 1.24%, 1.1%, and 0.13%, respectively, across the 725 collected samples. Spatial prediction revealed elevated levels of As, Cd, Cu, Pb, Zn, mercury (Hg), and Mn near the mining sites. Potentially toxic elements exert a slight impact on soil, some regions exhibit moderate to significant ecological risk, particularly in the southwest. Children face higher non-carcinogenic and carcinogenic health risks compared to adults. Mercury poses the highest ecological risk, while chromium (Cr) poses the greatest health hazard for all populations. Oral ingestion represents the highest non-oncogenic and oncogenic risks in all age groups. Adults faced acceptable non-carcinogenic risks. Children in the southwest region confront higher health risks, both non-carcinogenic and carcinogenic, from mining activities. Urgent measures are vital to mitigate Hg and Cr contamination while promoting handwashing practices is essential to minimize health risks.

Historical construction, quantitative source identification and risk assessment of heavy metals contamination in sediments from the Pearl River Estuary, South China

Historical reconstruction of heavy metals (HMs) contamination in sediments is a key for understanding the effects of anthropogenic stresses on water bodies and predicting the variation trends of environmental state. In this work, eighteen sediment cores from the Pearl River Estuary (PRE) were collected to determine concentrations and geochemical fractions of HMs. Then, their potential sources and the relative contributions during different time periods were quantitatively identified by integrating lead-210 (210Pb) radioisotope dating technique into positive matrix factorisation (PMF) method. Pollution levels and potential ecological risks (PERs) caused by HMs were accurately assessed by enrichment factors (EF) based on establishment of their geochemical baselines (GCBs) and multiparameter evaluation index (MPE). HMs concentrations generally showed a particle size- and organic matter-dependent distribution pattern. During the period of 1958-1978, HMs concentrations remained at low levels with agricultural activities and natural processes being identified as the predominant sources and averagely contributing >60%. Since the reform and opening-up in 1978, industrial and traffic factors become the primary anthropogenic sources of HMs (such as Cu, Zn, Cd, Pb, Cr, and Ni), averagely increasing from 22.1% to 28.1% and from 11.6% to 23.4%, respectively. Conversely, the contributions of agricultural and natural factors decreased from 37.0% to 28.5% and from 29.3% to 20.0%, respectively. Subsequently, implementation of environmental preservation policies was mainly responsible for the declining trend of HMs after 2010. Little enrichment of sediment Cu, Zn, Pb, Cr and Ni with EFs (0.15-1.43) was found in the PRE, whereas EFs of Cd (1.16-2.70) demonstrated a slight to moderate enrichment. MPE indices of Cu (50.7-252), Pb (52.0-147), Zn (35.5-130), Ni (19.6-71.5), Cr (14.2-68.8) and Cd (0-9.90) highlighted their potential ecological hazards due to their non-residual fractions and anthropogenic sources.

Risk-based imprecise post-remediation soil quality objectives

While risk-based contaminated land management is an essential component of sustainable remediation, uncertainty is an unavoidable aspect of risk assessment, since most of the parameters that influence risk are typically affected by uncertainty. Uncertainty may be of different origins; i.e., stochastic or epistemic. Stochastic (or aleatoric) uncertainty arises from random variability related to natural processes, while epistemic uncertainty arises from the incomplete/imprecise nature of available information. But the latter is rarely considered in risk assessments, with the result that risk-based soil quality objectives are almost invariably presented as precise (unique) threshold values. In this paper it is shown: (i) how the joint treatment of stochastic and epistemic uncertainty in risk assessment can lead to soil quality objectives presented as intervals rather than precise values and (ii) how this provides an upper risk-based safeguard for post-remediation monitoring values. The proposed method is illustrated by a real case of soils contaminated by arsenic located in the North-East of France. At this site steel manufacturers have gradually filled up a small valley with slag and dust, over more than a century. These materials are enriched in various metal(loid)s, including arsenic and lead. As the environmental authority has asked for a conversion of the site to other uses that may involve access by the general public, an investigation of human health risk was performed based on a sampling campaign and chemical characterizations including various types of extractions and an analysis of bioaccessibility. While further investigations are required to improve the bioaccessibility model, the human health risk presented herein shows how partial or imprecise information can be incorporated in the analysis while taking into account underlying uncertainties.

Human health risks of heavy metal(loid)s mediated through crop ingestion in a coal mining area in Eastern China

The heavy metal(loid)s (HMs) in soils can be accumulated by crops grown, which is accompanied by crop ingestion into the human body and then causes harm to human health. Hence, the health risks posed by HMs in three crops for different populations were assessed using Health risk assessment (HRA) model coupled with Monte Carlo simulation. Results revealed that Zn had the highest concentration among three crops; while Ni was the main polluting element in maize and soybean, and As in rice. Non-carcinogenic risk for all populations through rice ingestion was at an "unacceptable" level, and teenagers suffered higher risk than adults and children. All populations through ingestion of three crops might suffer Carcinogenic risk, with the similar order of Total carcinogenic risk (TCR): TCRAdults > TCRTeenagers > TCRChildren. As and Ni were identified as priority control HMs in this study area due to their high contribution rates to health risks. According to the HRA results, the human health risk was associated with crop varieties, HM species, and age groups. Our findings suggest that only limiting the Maximum allowable intake rate is not sufficient to prevent health risks caused by crop HMs, thus more risk precautions are needed.

Determination of high-risk factors and related spatially influencing variables of heavy metals in groundwater

Identifying high-risk factors (heavy metals (HMs) and pollution sources) by coupling receptor models and health risk assessment model (HRA) is a novel approach within the field of risk assessment. However, this coupled model ignores the contribution of spatial differentiation to high-risk factors, resulting in the assessment being subjective. Taking Dongting Plain (DTP) as an example, a coupling framework by jointly using the positive matrix factorization model (PMF), HRA, Monte Carlo simulation, and geo-detector was developed, aiming to identify high-risk factors in groundwater, and further explore key environmental variables influencing the spatial heterogeneity of high-risk factors. The results showed that at least 82.86 % of non-carcinogenic risks and 97.41 % of carcinogenic risks were unacceptable for people of all ages, especially infants and children. According to the relationships among HMs, pollution sources, and health risks, As and natural sources were defined as high-risk HMs and sources, respectively. The interactions among Holocene thickness, oxidation-reduction potential, and dissolved organic carbon emerged as the primary drivers of spatial variability in high-risk factors, with their combined explanatory power reaching up to 74%. This proposed framework provides a scientific reference for future studies and a practical reference for environmental authorities in developing effective pollution management measures.

Spatial variability of heavy metals concentrations in soil of auto-mechanic workshop clusters in Nsukka, Nigeria

The indiscriminate disposal of spent engine oils and other hazardous waste at auto mechanic workshops clusters in Nsukka, Enugu State, Nigeria is an environmental concern. This study examines the concentration of heavy metals in the soil inside the workshop cluster and in the unpolluted soil outside the workshop cluster at approximately 100 m. Ten sampling points were randomly selected from within the cluster and another ten from outside the cluster. Using a hand-held Global Positioning System, the coordinates of the selected points were established and used to create a digital map. Soil samples at depths of 0-30 cm and 30-60 cm, were analyzed for Cu, Fe, Zn, Pb, As and Cd using Spectrophotometer. Moisture content determination and particle size analysis were also done on the samples. Spatial variability of heavy metals concentrations of the studied site was also mapped with ArcGIS 10.2.2 using interpolation methods. Results showed that the soil ranged from sandy loam to sandy clay loam. Cadmium and Zinc had the lowest and highest concentration, respectively, in the studied area. Comparing the concentrations of heavy metals in soils within and outside the auto mechanic cluster revealed notable differences across various depths (0-30 cm and 30-60 cm). The analysis results for soil samples within the cluster exhibited concentration levels (mg/kg) ranging from 0.716-0.751 (Cu), 2.981-3.327 (Fe), 23.464-30.113 (Zn), 1.115-1.21 (Pb), 2.6-2.912 (As), and 0.133-0.365 (Cd) demonstrating a variation pattern in the order of Zn > Fe > As > Pb > Cu > Cd. Conversely, for soil samples outside the cluster, concentration levels (mg/kg) ranged from 0.611-0.618 (Cu), 2.233-2.516 (Fe), 12.841-15.736 (Zn), 0.887-0.903 (Pb), 1.669-1.911 (As), and 0.091-0.091 (Cd). To assess the disparity in heavy metal concentration levels between samples collected within and outside the clusters, ANOVA test was performed. The test showed significant difference in heavy metal concentrations between samples within and outside the auto mechanic cluster (p < 0.05), implying auto mechanic activities significantly impact heavy metal levels within the cluster compared to outside areas. The assessment of soil pollution utilized indices including the Geo-accumulation Index (Igeo), Contamination factor (Cf), and anthropogenic metal concentration (QoC). Zinc, Cadmium, and Arsenic showed the highest contamination factors, indicating significant soil contamination likely due to anthropogenic activities. The concentrations of the metals analyzed were within WHO permissible limits while the metals concentrations were also observed to decrease as depth was increased. Using ArcGIS 10.2.2, spatial maps showing heavy metal distribution were developed, with the Kriging method proving superior. This study suggests that heavy metal levels in the soil at the area be monitored on a regular basis.

Anthropogenic processes drive spatiotemporal variability of sulfate in groundwater from a multi-aquifer system: Dilution caused by mine drainage

The water quality evolution of surface and groundwater caused by mining activities and mine drainage is a grave public concern worldwide. To explore the effect of mine drainage on sulfate evolution, a multi-aquifer system in a typical coal mine in Northwest China was investigated using multi-isotopes (δ34SSO4, δ18OSO4, δD, and δ18Owater) and Positive Matrix Factorization (PMF) model. Before mining, the Jurassic aquifer was dominated by gypsum dissolution, accompanied by cation exchange and bacterial sulfate reduction, and the phreatic aquifers and surface water were dominated by carbonate dissolution. Significant increase in sulfate in phreatic aquifers due to mine drainage during the early stages of coal mining. However, in contrast to common mining activities that result in sulfate contamination from pyrite oxidation, mine drainage in this mining area resulted in accelerated groundwater flow and enhanced hydraulic connections between the phreatic and confined aquifers. Dilution caused by the altered groundwater flow system controlled the evolution of sulphate, leading to different degrees of sulfate decrease in all aquifers and surface water. As the hydrogeochemical characteristic of Jurassic aquifer evolved toward phreatic aquifer, this factor should be considered to avoid misjudgment in determining the source of mine water intrusion. The study reveals the hydrogeochemical evolution induced by mine drainage, which could benefit to the management of groundwater resources in mining areas.

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

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

Determining priority control toxic metal for different protection targets based on source-oriented ecological and human health risk assessment around gold smelting area

Determining the priority control source and pollutant is the key for the eco-health protection and risk management around gold smelting area. To this end, a case study was conducted to explore the pollution characteristics, source apportionment, ecological risk and human health risk of toxic metals (TMs) in agricultural soils surrounding a gold smelting enterprise. Three effective receptor models, including positive matrix factorization model (PMF), ecological risk assessment (ERA), and probabilistic risk assessment (PRA) have been combined to apportion eco-human risks for different targets. More than 95.0% of samples had a Nemerow pollution index (NPI) > 2 (NPImean=4.27), indicating moderately or highly soil TMs contamination. Four pollution sources including gold smelting activity, mining source, agricultural activity and atmosphere deposition were identified as the major sources, with the contribution rate of 17.52%, 44.16%, 13.91%, and 24.41%, respectively. For ecological risk, atmosphere deposition accounting for 30.8% was the greatest contributor, which was mainly loaded on Hg of 51.35%. The probabilistic health risk assessment revealed that Carcinogenic risks and Non-carcinogenic risks of all population were unacceptable, and children suffered from a greater health risk than adults. Gold smelting activity (69.2%) and mining source (42.0%) were the largest contributors to Carcinogenic risks and Non-carcinogenic risks, respectively, corresponding to As and Cr as the target pollutants. The priority pollution sources and target pollutants were different for the eco-health protection. This work put forward a new perspective for soil risk control and management, which is very beneficial for appropriate soil remediation under limited resources and costs.