Source apportionment of heavy metals in farmland soil with application of APCS-MLR model: A pilot study for restoration of farmland in Shaoxing City Zhejiang, China

Spatial distribution, risk assessment, and source apportionment of soil heavy metals in a karst county based on grid survey

Soil in karst areas commonly exhibits characteristics of heavy metal enrichment. Accurate identification of soil heavy metal distribution, risks, and sources are crucial for preventing soil heavy metal pollution in karst areas. In this study, 2467 topsoil samples (0-20 cm) and 620 subsoil samples (150-200 cm) were collected using a grid-based sampling method in Tianyang County. Statistics, geo-statistics, correlation analysis, principal component analysis, and the absolute principal component-multiple linear regression model were utilized to analyze the content, spatial distribution and sources of heavy metals. The geo-accumulation index and the potential ecological risk index were employed to assess the ecological risks of heavy metals in the topsoil, with the subsoil content as baseline. The results showed that the study area's soil exhibited high heavy metal content, significantly exceeding Chinese background values. The content of heavy metals in the karst area's soil was notably higher than that in the non-karst area. The fitted semi-variogram models and the spatial distribution map revealed that the heavy metals' content was generally dominated by the geological background. As, Cr, Cu, Hg, Ni, Pb, and Zn displayed low levels of pollution in the topsoil and posed low ecological risk, with over 90 % of samples classified as unpolluted and low risk. Cd exhibited high levels of pollution and ecological risks, with 52.28 % of samples classified as polluted and 60.81 % classified as moderate to high risk. For Hg, despite only 6.94 % of samples showing polluted, the ecological risks were not negligible, with 40.65 % of samples in moderate to high risk. Natural source and anthropogenic source contribute to the heavy metals on average by 81.49 % and 18.51 %, respectively. This study provides a reference for the risk assessment of soil heavy metals, and its findings offer valuable scientific insights for the prevention of heavy metal pollution in the study area.

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

BACKGROUND

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSION

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

Residual mulch-film characteristics affect heavy metal migration of different soil layers in the subtropical croplands of China

In recent years, as the abundance of residual mulch film (RMF) in agricultural soil continues to increase, whether the adsorption capacity of its surface affects the migration of heavy metals is a topic of current interest for scholars. Herein, this study investigated the distribution of RMF abundance and metal concentration in different soil layers of 75 plastic-mulching croplands in subtropical China; meanwhile, we also explored the associations of RMF characteristics with metal concentration. The results showed that land type, film mulching amount, and film mulching time were the main factors affecting RMF abundance, distribution, and particle size composition. The highest abundance of RMF was found in the garden soils (910 n·kg-1) with more than 15 years mulching period and more than 19.5 kg hm-2 of annual mulch amount. The lowest abundance of RMF was occurred in the group of field and conservation agricultural land (237 n·kg-1). Moreover, the concentrations of metals in soil, especially Cd, Cr, Cu, and Pb, were closely related to the extent of RMF contamination in the soil environment. In the 0-10 cm and 10-20 cm soil layers, microplastic abundance exhibited a negative correlation with Cr and Cu concentrations and a positive correlation with Pb concentration. Based on the above findings, it is demonstrated that RMF significantly influences the mobility of metals in soil via adsorption processes, with potential synergistic effects between RMF and heavy metals posing a heightened risk to the soil environment.

A methodological study on the identification of ecological security change processes and zoning control strategies -- Based on the perspective of sustainable development

Ecological security (ES) is a crucial indicator for assessing the sustainable development of a region. Currently, most studies on ES primarily focus on process analysis, and the integration of environmental variability into the development of tailored control strategies for regions with varying ecological quality is overlooked. Therefore, in this study, we identified regional ES change processes, employed an optimized system to calculate the ecological security index (ESI), and identified ecological corridors (ECs) through the Minimum Constrained Resource (MCR) model to determine zoning strategies for typical arid regions, using the Ningxia region in the Yellow River Basin of China as an example. The findings showed that (1) from 2006 to 2020, the ESI values of most regions were between 0.2 and 0.4, with small but consistent increases in the ESI values over the years. (2) The proportion of regions with high ES ratings increased by 9.08 % across all districts and counties, and the center of gravity of ES shifted in a north-south and east-west direction. (3) The ESI exhibited a strong positive spatial correlation, characterized by spatial diffusion and spillover effects in most regions. (4) The ECs were predominantly distributed in a north-south direction, involving a total of 20 districts and counties. Based on the principles of sustainable development, we developed a model for the dynamic identification and zoning control of regional ES, aiming to provide a practical framework for effective ecological restoration and protection measures. Additionally, the strategies and methodologies presented in this study serve as important references for similar regions worldwide to facilitate the zoning control of ES, highlighting the broader significance and applicability of the study.

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.

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

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

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 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.

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

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

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

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

Risk assessment and source apportionment of available atmospheric heavy metal in a typical sandy area reservoir in Inner Mongolia, China

This study evaluated dry and wet deposition of atmospheric heavy metals (HMs) in a sandy area of Inner Mongolia, China, with the Dahekou Reservoir, Xilin Gol League, adopted as the study area. Monthly monitoring of atmospheric HM dry and wet deposition was conducted over one year (2021 to 2022) at 12 monitoring points, producing 144 dry and wet deposition samples, respectively. The sample contents of eight HMs (Cr, Ni, Pb, Cu, Zn, Mn, As, and Cd) were determined to estimate the fluxes of available forms of heavy metal (AHM) in dry and wet deposition. The potential ecological index (Eri), risk assessment coding (RAC), and ratio of secondary phase to primary phase (RSP) were used to evaluate the impact of atmospheric HM dry deposition on ecological security. Correlation analysis, principal component analysis, and the absolute principal component scores-multiple linear regression (APCS-MLR) receptor model were used to quantitatively analyze the sources of AHMs in atmospheric dry and wet deposition. The results showed that the study area experienced annual dry and wet deposition fluxes of AHMs of 1712.59 kg and 534.97 kg, respectively. Atmospheric heavy metal dry deposition over the entire year presented a strong ecological risk, with Cd contributing most to this risk. Risk assessment of HM speciation showed that the greatest risks of migration and transformation were for Cd and Pb. The APCS-MLR receptor model identified five and three sources of dry and wet deposition, respectively, in order of proportion of total contribution of: natural wind and sand > road traffic and coal combustion > mineral mining > other human activities > industrial soot.

Comparison of antimony and arsenic behaviour at the river-lake junction in the middle of the Yangtze River Basin

As typical metalloid toxic elements widely distributed in environmental media, the geochemical behaviour of antimony (Sb) and arsenic (As) affects environmental safety. We selected the surface waters and sediments at the river-lake junction of Dongting Lake as the research objects, analysed the concentration and chemical partitioning of Sb and As, assessed its contamination and ecological risk levels, and discussed its sources and potential influencing factors. The concentrations of dissolved Sb and As in surface waters were low (< 5.46 µg/L), and the concentrations of Sb and As in surface sediments were 2.49-22.65 mg/kg and 11.10-136.34 mg/kg, respectively. Antimony and As in sediments were mainly enriched in the fraction of residues, but the proportion of As in bioavailability was significantly higher than that of Sb. Although the contamination level of Sb was higher than that of As, the risk assessment code (RAC) showed that the ecological risk level of As was higher than that of Sb. Rainwater erosion and mining activities (in the midstream of Zijiang River) were the main contaminated sources of Sb, while As was affect mainly by rainwater erosion. The contamination and ecological risk of Sb in the inlet of the Zijiang River should receive considerable attention, while those of As in the inlet of the Xiangjiang River should also be seriously considered. This study highlights the need for multi-index-based assessments of contamination and ecological risk and the importance of further studies on the environmental behaviour of metalloids in specific hydrological conditions, such as river-lake junctions.

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

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

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

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

Ecological Risk and Pollution Assessment of Heavy Metals in Farmland Soil Profile with Consideration of Atmosphere Deposition in Central China

Heavy metals (HMs) in agricultural land have caused serious environmental problems, resulting in severe contamination of the food chain and posing potential health threats. This study aims to investigate the pollution levels and potential ecological risks of HMs in farmland soils in central China, taking into account atmospheric deposition. Several indices were used to assess the status of HMs and compare surface soil with deeper soil. Descriptive statistics, Pearson correlation, and UMAP clustering methods were utilized to identify the characteristics of HMs. Additionally, stepwise linear regression models were employed to quantify the contributions of different variables to the potential ecological risks of HMs. The results showed that the average content of Zn in surface soil (289.41 ± 87.72 mg/kg) was higher than in the deeper soil (207.62 ± 37.81 mg/kg), and similar differences were observed in the mean values of related Igeo (1.622 ± 0.453 in surface soil and 1.183 ± 0.259 in deeper soil) and PEI (0.965 ± 0.292 in surface soil and 0.692 ± 0.126 in deeper soil) indices. This indicates that surface soil is more heavily polluted. The UMAP results confirmed the high variability of HMs in the surface soil, while PCA results suggested the importance of pollution and ecological risk indices. The stepwise linear model revealed that different variable structures contribute differently to the risk. In conclusion, Cr and Zn were found to be the major contaminants in the local farmland soil, with higher concentrations in the surface soil. The geoaccumulation and total potential ecological risk were classified as low risk. High variability of HMs was observed in the surface soil. Therefore, HM-related pollution indices and ecological risk indices are important for assessing the contamination status of local HMs. The local potential ecological risk can be attributed to specific heavy metals, each of which can have different effects on the local ecological risk.

Speciation characteristics, ecological risk assessment, and source apportionment of heavy metals in the surface sediments of the Gomishan wetland

Metal contamination is one of the worldwide environmental issues. The main aim of this study was to evaluate the concentration, probable environmental risk, and source of investigated elements (Al, As, Co, Cr, Cu, Fe, Mn, Ni, and Zn) in sediments and water of the Gomishan wetland. Sediment contamination indices revealed sediments were solely polluted by As. The potential ecological risk index (RI), toxic risk index (TRI), and chemical speciation assessments indicated no major ecological hazards for investigated metals. Correlation analysis and principal component analysis (PCA) indicated that all studied metals in the Gomishan wetland sediments derived from natural sources. HPI, and HEI indices showed that the water quality in terms of hazardous components was inappropriate for aquatic life.

Source-sink response analysis of heavy metals and soil pollution assessment in non-ferrous metal industrial agglomeration areas based on decision unit

Soil heavy metals (HMs) pollution is a worldwide concern. In this study, decision units based on "source-sink relationship" were established using multi-source data. The source-sink response of heavy metals in agricultural soils at the regional scale was analyzed using machine learning methods, receptor models, and geospatial analysis. The comprehensive pollution risk score (CRS) was proposed by integrating a variety of key evaluation indicators to evaluate the pollution degree of each decision unit. We divided the study area into 193 decision units, the proportions of sites with concentrations of Cd, Hg, As, Pb, and Cr exceeding the most stringent risk screening values were 16.4 %, 2.2 %, 4.0 %, 7.6 %, and 0.2 %, respectively. Agricultural activities (livestock manure, fertilizer, sewage irrigation), industrial activities (rare earth ore and tungsten‑molybdenum mining and smelting), and soil parent material are the dominant pollution sources of HMs in the study area. The risk of contamination of each element is ranked from largest to smallest according to the CRS as Cd > Hg > Pb > As > Cr. The western and southwestern water pollution decision units are the areas with the highest risk of soil HMs contamination. This quantitative evaluation framework can provide a relatively accurate decision basis for soil pollution management.

Human activities contributing to the accumulation of high-risk trace metal(loid)s in soils of China's five major urban agglomerations

Rapid urbanization has accelerated the accumulation of trace metal(loid)s (TMs) in soils, but the relationship between this accumulation and human activities remains largely unknown. Therefore, based on 775 published literatures (2001-2020), this study aimed to identify the influence of human activities on TM accumulation. Results showed that all soil TM concentrations were higher than their corresponding Chinese soil background values. The pollution risk assessment indicated that the soil TMs in the study area were at moderate levels, and the value of Pollution load index was 2.10. According to the assessment of health risks, the non-carcinogenic risks for adults were at the "Negligible risk" level; while the carcinogenic risk was not negligible for all populations, with children being more susceptible than adults. Meanwhile, six high-risk TMs were identified based on the grading of Contaminating factors (CF ≥ 3) and contribution to health risk (≥ 75%), including four high pollution risk TMs (Cd, Hg, Cu, and Pb) and two high health risk TMs (Cr and As) . In addition, in accordance with the results of the Random forest model, the accumulation of soil high-risk TMs was closely related to influencing factors associated with human activities. The accumulation of Hg and Cr among five major urban agglomerations had the same influencing factors (the number of industrial companies and the amount of industrial wastewater discharge for Hg; the amount of pesticide application and highway mileage for Cr). However, there were significant differences in the factors influencing the accumulation of the other four high-risk TMs (including Cd, As, Cu and Pb), due to the different characteristics of each urban agglomeration. Our results provide new insights into the relationship between human activities and soil TM accumulation.

Toxic effects of lead (Pb), cadmium (Cd) and tetracycline (TC) on the growth and development of Triticum aestivum: A meta-analysis

The environmental issue of lead (Pb), cadmium (Cd), and tetracycline (TC) contamination in cereal crops has become a growing concern worldwide. An in-depth understanding of this issue would be of importance to promote effective management strategies for heavy metals and antibiotics worldwide. The present study was conducted to assess the toxic effects of heavy metals (Cd, Pb) and antibiotics (TC) on Triticum aestivum (T. aestivum, common wheat) based on studies conducted in the past 22 years. Data pertaining to the growth and development of T. aestivum were extracted and analyzed from 89 publications spanning from 2000 to 2022. Our results showed that Pb, Cd and TC significantly reduced growth and development by 11 %, 9 %, and 5 %, respectively. Additionally, significant accumulation of Cd (42 %) and Pb (17 %) was observed in T. aestivum samples, although there was little change in TC accumulation, which showed limited absorption, accumulation, and translocation of TC in wheat plants. Pb had the greatest impact on the yield of T. aestivum, followed by Cd, while TC had no apparent effect. Furthermore, exposure to Cd, Pb and TC reduced the photosynthetic rate due to chlorophyll reduction, with Cd having the most pronounced effect (58 %), followed by Pb (37 %) and TC (8 %). Cd exposure also significantly enhanced gaseous exchange (37 %) compared to TC and Pb, which reduced gaseous exchange by 4 % and 10 %, respectively. However, the treatments with TC (>50-100 mgL-1), Pb (>1000-2000 mg L-1) and Cd (>500-1000 mg L-1) increased the defense system of T. aestivum samples by 38 %, 15 %, and 11 %, respectively. The obtained findings have significant implications for risk assessment, pollution prevention, and remediation strategies to address soil contamination from Pb, Cd and TC in farmland.

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

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

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

Background

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

Methods

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

Results

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

Conclusion

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

Interpretation of the coupling mechanism of ecological security and urbanization based on a Computation-Verification-Coupling framework: Quantitative analysis of sustainable development

In recent decades, China's rapid urbanization has produced numerous economic benefits while simultaneously creating substantial risks to ecological security. China's 14th Five-Year Plan and the United Nations Sustainable Development Goals (SDGs) have recently explicitly called for the coordinated development of ecological security and urbanization. Given this context, it is important to explore the mechanism by which ecological security and urbanization are coupled and coordinated to promote sustainable development. In this study, an index of the relationship between ecological security and urbanization was established via high-resolution data, and a "Computation-Verification-Coupling" (CVC) framework was constructed. The accuracy of the ecological security index was verified using a linear regression model, and the coordination level between ecological security and urbanization was analyzed via a coupled coordination model (CCM). The results revealed a steady increase in the ecological security index from 2010 to 2020; the proportion of the area above the medium level increased from 63.1 % to 74.1 %. The urbanization index in core counties exhibited rapid growth, with level V urbanized areas expanding from 5.5 % to 9.9 %. The ecological security verification model produced a coefficient of determination (R²) of 0.75685, indicating a satisfactory degree of predictive capability. From 2010-2020, the coupled coordination improved, with the high coordination area accounting for 48.8 % and the extreme discoordination area decreasing from 1.8 % to 1.0 %. Coordinated development exhibited a stable progression, characterized by a cyclical evolution from initial coupling to antagonistic coupling and finally to coordinated development. This framework can be used not only to investigate the relationship between ecological security and urbanization but also to provide a quantifiable measure of progress toward achieving the SDGs.

Assessment of heavy metal contamination of an electrolytic manganese metal industrial estate in northern China from an integrated chemical and magnetic investigation

Heavy metal concentrations (Al, V, Mn, Fe, Co, Ni, Cu, Zn, and Pb) and the magnetic properties of soil and sediment samples in/around an electrolytic manganese metal (EMM) industrial estate in northern China were investigated. Potential enrichment of Mn, Zn, and Pb was found in/around the core area of the EMM industrial estate; however, the pollution load index (PLI) values did not indicate severely polluted levels. For adults, all hazard index (HI) values of noncarcinogenic risks in the soil samples were below the safe level of 1.00. For children, none of the HI values exceeded the safe level, except Mn (HI = 1.23) in one industrial estate sample. The particle size of magnetic materials was mostly in the range of stable single-domain, and coarser ferrimagnetic phases enhanced the magnetic parameters in the industrial estate soils. Highly positive correlations were found between magnetic parameters, heavy metal concentrations, and PLI values, demonstrating that the magnetic parameters are an efficient proxy for assessing heavy metal contamination. Enrichment of Mn, Zn, and Pb was mainly derived from the EMM industry. The data showed that the EMM industrial estate under cleaner production had limited adverse impacts on the adjacent environment from the perspective of heavy metal contamination.

Source apportionment and potential ecological risk assessment of heavy metals in soils on a large scale in China

The properties and sources of soil heavy metals (Pb, Zn, Cu, Cd, As, Hg, Cr, and Ni) need to be comprehensively analyzed to take effective steps to control and reduce soil pollutants. In this research, 416 soil samples were collected on a large scale in China. Two receptor models (PCA/MLR and PMF) were utilized to identify pollutant sources and quantify the contributions. The means of soil heavy metals (Zn, Cu, As, Hg, Cr, and Ni) were lower than the corresponding screening values and intervention values. Cd was greater than the intervention value, while Pb was between the screening value and the intervention value. Source apportionments suggested that mine sources were the most polluted (64.28%), followed by traffic sources (38.98%), natural sources (11.41-39.58%), industrial sources (9.8-18.65%), and agricultural sources (2.79-14.51%). Compared to the PCA/MLR model, the PMF model had a better effect in evaluating soil heavy metal pollution. It gave corresponding weights according to the data concentration and its uncertainty, which made the result reasonable. The ecological risk assessment indicated that Cd posed a significant risk, while Hg caused a mild risk and the other six heavy metals posed a low risk. The spatial distribution of ecological risk suggested that severe risk points were mainly distributed in the central area, while high-risk points were distributed in the southern region. The SRI method was developed to link pollution sources and their potential ecological risks and indicated better applicability to the PMF model. The study findings could provide guidelines for monitoring the main sources and reducing the pollution of soil heavy metals.

Quantitative Source Apportionment of Potentially Toxic Elements in Baoshan Soils Employing Combined Receptor Models

Arable soils are crucial for national development and food security; therefore, contamination of agricultural soils from potentially toxic elements (PTEs) is a global concern. In this study, we collected 152 soil samples for evaluation. Considering the contamination factors and using the cumulative index and geostatistical methods, we investigated the contamination levels of PTEs in Baoshan City, China. Using principal component analysis, absolute principal component score-multivariate linear regression, positive matrix factorization, and UNMIX, we analyzed the sources and quantitatively estimated their contributions. The average Cd, As, Pb, Cu, and Zn concentrations were 0.28, 31.42, 47.59, 100.46, and 12.36 mg/kg, respectively. The Cd, Cu, and Zn concentrations exceeded the corresponding background values for Yunnan Province. The combined receptor models showed that natural and agricultural sources contributed primarily to Cd and Cu and As and Pb inputs, accounting for 35.23 and 7.67% pollution, respectively. Industrial and traffic sources contributed primarily to Pb and Zn inputs (47.12%). Anthropogenic activities and natural causes accounted for 64.76 and 35.23% of soil pollution, respectively. Industrial and traffic sources contributed 47.12% to pollution from anthropogenic activities. Accordingly, the control of industrial PTE pollution emissions should be strengthened, and awareness should be raised to protect arable land around roads.

Source Apportionment and Model Applicability of Heavy Metal Pollution in Farmland Soil Based on Three Receptor Models

The identification of the source of heavy metal pollution and its quantification are the prerequisite of soil pollution control. The APCS-MLR, UNMIX and PMF models were employed to apportion pollution sources of Cu, Zn, Pb, Cd, Cr and Ni of the farmland soil in the vicinity of an abandoned iron and steel plant. The sources, contribution rates and applicability of the models were evaluated. The potential ecological risk index revealed greatest ecological risk from Cd. The results of source apportionment illustrated that the APCS-MLR and UNMIX models could verify each other for accurate allocation of pollution sources. The industrial sources were the main sources of pollution (32.41~38.42%), followed by agricultural sources (29.35~31.65%) and traffic emission sources (21.03~21.51%); and the smallest proportion was from natural sources of pollution (11.2~14.42%). The PMF model was easily affected by outliers and its fitting degree was not ideal, leading to be unable to get more accurate results of source analysis. The combination of multiple models could effectively improve the accuracy of pollution source analysis of soil heavy metals. These results provide some scientific basis for further remediation of heavy metal pollution in farmland soil.

Source apportionment and source-specific risk evaluation of potential toxic elements in oasis agricultural soils of Tarim River Basin

As rapidly developing area of intensive agriculture during the past half century, the oases in the source region of the Tarim River have encountered serious environmental challenges. Therefore, a comparative analysis of soil pollution characteristics and source-specific risks in different oases is an important measure to prevent and control soil pollution and provide guidance for extensive resource management in this area. In this study, the concentration of potential toxic elements (PTEs) was analyzed by collecting soil samples from the four oases in the source region of the Tarim River. The cumulative frequency curve method, pollution index method, positive matrix factorization (PMF) model, geographical detector method and health risk assessment model were used to analyze the pollution status and source-specific risk of potential toxic elements in different oases. The results showed that Cd was the most prominent PTE in the oasis agricultural soil in the source region of the Tarim River. Especially in Hotan Oasis, where 81.25% of the soil samples were moderately contaminated and 18.75% were highly contaminated with Cd. The PTEs in the Hotan Oasis corresponded to a moderate level of risk to the ecological environment, and the noncarcinogenic risk of soil PTEs in the four oases to local children exceeded the threshold (TH > 1), while the carcinogenic risk to local residents was acceptable (1E-06 < TCR < 1E-04). The research results suggested that the Hotan Oasis should be the key area for soil pollution control in the source region of the Tarim River, and agricultural activities and natural sources, industrial sources, and atmospheric dust fall are the priority sources that should be controlled in the Aksu Oasis, Kashgar Oasis and Yarkant River Oasis, respectively. The results of this study provide important decision-making support for the protection and management of regional agricultural soil and the environment.

Distribution characteristics and potential ecological risk assessment of heavy metals in soils around Shannan landfill site, Tibet

At present, sanitary landfill is mainly used for domestic waste treatment in Shannan City, Tibet. However, there are few studies on heavy metals in the soil around the landfill in Shannan city. Therefore, the surrounding soil of Luqionggang landfill in Shannan City, Tibet Autonomous Region, is taken as the research object. In the study, the geo-accumulation index method, Nemerow comprehensive pollution index method and potential ecological risk index method are mainly used to evaluate the pollution and risk of heavy metals in the soil around the landfill site. The main results are as follows: The average pH value of the soil around the landfill site is 9.37, belonging to the strong alkaline range. The average values of heavy metals Hg and Ni in soil exceeded the background content, and the average contents of other heavy metals Cu, Pb, Zn, Cr, As and Cd did not exceed the background content. The average content of these eight heavy metals did not exceed the screening value of the national soil environmental quality standard. In the horizontal direction, the average content of heavy metal elements Cu, Cr, Cd, Hg and Ni is relatively high in the west. The average content of heavy metals As, Zn and Pb in the north, east and south is slightly higher than that in the west. And the farther away from the landfill, the less the soil is affected by heavy metals. The evaluation results of geo-accumulation index show that heavy metal Hg is the most affected. The average value of the comprehensive pollution index is 2.969, which is between 2 and 3, belonging to the moderate pollution level. And the west side of the landfill (downstream area) is greatly affected. The evaluation results of potential ecological hazard pollution index show that the potential risk index of single pollutants of heavy metals Cu, Pb, Zn, Cr, Ni, As and Cd belongs to low ecological hazard level, and the potential risk index of single pollutants of heavy metal Hg belongs to relatively heavy ecological hazard level. On the whole, the total potential risk coefficient belongs to medium pollution hazard degree. According to the correlation analysis, there is no significant correlation between heavy metal elements As and Hg and the other six heavy metal elements. In addition, the pollution source of heavy metal As may be mainly soil forming factors and the pollution source of Hg may be mainly human factors.

Delineating and identifying risk zones of soil heavy metal pollution in an industrialized region using machine learning

The ability to control the risk of soil heavy metal pollution is limited by the inability to accurately depict their spatial distributions and to reasonably delineate the risk zones. To overcome this limitation and develop machine learning methods, a hybrid data-driven method supported by random forest (RF) and fuzzy c-means with the aid of inverse distance weighted interpolation was proposed to delineate and further identify risk zones of soil heavy metal pollution on the basis of 577 soil samples and 12 environmental covariates. The results indicated that, compared to multiple linear regression, RF had a better prediction performance for As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn, with the corresponding R² values of 0.86, 0.85, 0.78, 0.85, 0.84, 0.78, 0.79 and 0.76, respectively. The relative concentrations (predicted concentrations divided by risk screening values) of Cd (17.69), Cr (1.38), Hg (0.31), Pb (6.52), and Zn (8.24) were relatively high in the north central part of the study area. There were large differences in the key influencing factors and their contributions among the eight heavy metals. Overall, industrial enterprises (21.60% for As), soil pH (31.60% for Cd), and population (15.50% for Cr) were the key influencing factors for the heavy metals in soil. Four risk zones, including one high risk zone, one medium risk zone, and two low risk zones were delineated and identified based on the characteristics of the eight heavy metals and their influencing factors, and accordingly discriminated risk control strategies were developed. In the high risk zone, it will be necessary to strictly control the discharge of heavy metals from the various industrial enterprises and mines by the adoption of cleaner production practices, centralizedly treat the domestic wastes from residents, substantially reduce the irrigation of polluted river water, and positively remediate the Cd, Cr, and Ni-polluted soil.

Risk assessment, geochemical speciation, and source apportionment of heavy metals in sediments of an urban river draining into a coastal wetland

Thirty sediment samples were collected from the Gohar Rood River (Iran) to assess the elemental concentrations, origins, and probable environmental risks in the riverine system. In this study, fifteen elements were analyzed by inductively coupled plasma mass spectrometry (ICP-MS). Cr at all sites were exceeded the SEL (Severe Effect Level) value. Zn, Mn, Co, and Cr showed a moderate level of contamination, based on pollution index (PI), modified pollution index (MPI), and enrichment factor (EF). The modified hazard quotient (mHQ) represented low to extreme severity of pollution for some elements. The multi-linear regression of the absolute principal component score model indicated that largest contributors of Zn, Cu, Pb, Sb, and Mo to the riverine sediment were from agricultural runoff, domestic, and municipal sewage. Based on the modified BCR (the European Community Bureau of Reference) fractionation scheme, Mn, Co, and Zn indicated a medium to high risk to the local environment.

Source Apportionment of Heavy Metals Based on Multiple Approaches for a Proposed Subway Line in the Southeast Industrial District of Beijing, China

Apportioning the sources of heavy metals (HMs) in soil is of great importance for pollution control. A total of 64 soil samples from 13 sample points at depths of 0-21 m were collected along a proposed subway line in the southeast industrial district of Beijing. The concentrations, distribution characteristics, and sources of eight HMs were investigated. The results showed that the concentrations of Hg, Cd, Cu, Pb, As, and Zn in the topsoil (0-2 m) exceeded the Beijing soil background values. Three sources were identified and their respective contribution rates calculated for each of the HMs using multiple approaches, including correlation analysis (CA), top enrichment factor (TEF), principal component analysis (PCA), and positive matrix factor (PMF) methods. As (63.11%), Cr (61.67%), and Ni (70.80%) mainly originated from natural sources; Hg (97.0%) was dominated by fossil fuel combustion and atmospheric deposition sources; and Zn (72.80%), Pb (69.75%), Cu (65.36%) and Cd (53.08%) were related to traffic sources. Multiple approaches were demonstrated to be effective for HM source apportionment in soil, whilst the results using PMF were clearer and more complete. This work could provide evidence for the selection of reasonable methods to deal with soils excavated during subway construction, avoiding the over-remediation of the soils with heavy metals coming from natural sources.

Pollution Characteristics, Sources, and Health Risk Assessments of Potentially Toxic Elements in Community Garden Soil of Lin'an, Zhejiang, China

The characteristics, sources and risk assessment of heavy metal pollution in community garden soil of Lin'an District were evaluated. The 28 soil samples from community garden were collected for determination of 7 heavy metal elements. The Geostatistical analysis, Spearman correlation coefficient, Principal component analysis and PMF model have explored sources of heavy metal pollution. The health risk assessment model has assessed ecological risk of heavy metals. The results revealed that average concentration of As, Cd, Cr, Cu, Ni, Pb and Zn were 16.0, 0.158, 76.1, 34.6, 45.8, 20.9 and 166 mg kg^-1, respectively. Whereas As, Cd, Cr, Cu, Ni and Zn were higher than background values. The spatial distribution of heavy metal pollution in the southwest of the study area was higher than northeast. The pollution sources of Cd, Cu, Ni and Zn in the study area were due to agricultural activities (42.9%), Cr and Pb were from traffic sources (36.2%), and As was domestic pollution (20.9%) according to Spearman correlation coefficient, Principal component analysis and PMF model. The non-carcinogenic risks of As (5.39), Cr (3.53) and Ni (2.07) have a value of 1, which indicated significant risk. The potentially toxic elements have not exceeded maximum threshold of USEPA, with regard to carcinogenic risk, while As (3.37E-05) and Cr (5.74E-05) have exceeded the safety range. It is concluded that soils of community gardens are facing pollution problem due to potentially toxic elements which require environmental monitoring of the soil to reduce risk of human health.

Source Analysis of Heavy Metal Pollution Using UNMIX and PMF Models in Soils along the Shuimo River in Urumqi, China

Eight kinds of heavy metals in soil within 0-2 km from the banks of Shuimo River in Urumqi were analyzed by using an X-ray fluorescence spectrometer and national standard detection methods. Unmix and PMF models are comprehensively used to analyze potential pollutant sources and contribution rates. Soil samples are sampled in three layers of 0-20, 20-40, and 40-60 cm, and each group of sample points in each layer is 5 m, 1 km, and 2 km away from the riverbank, respectively. Only the average concentration of Mn in each layer of soil is lower than the background value, according to the analytical results, while the average concentration of other heavy metals surpasses the background value. The highest proportion of exceeding the background value is Ni in the 40-60 cm soil layer, up to 1.92 times. Unmix and PMF models are used to analyze pollutants' source quantity and contribution rate, respectively. The results show that the two models can identify two pollution sources at the three soil layers, and their contribution rates are similar, and each index of the analysis results of the two models is within the required range of model reliability. By comparing with the Pearson correlation coefficient and distribution map of heavy metal concentration in surface soil, it is concluded that Zn, Pb, Cr, and Cu are mainly from industrial sewage and air pollution from coal combustion, while As, Mn, Ni, and V are mainly from agricultural pollution and light industrial pollution. In future research, it is necessary to investigate the change of heavy metal concentration in detail from the time dimension to further quantitatively calculate the potential pollutant source and contribution rate.

Pollution Risk Assessment and Sources Analysis of Heavy Metal in Soil from Bamboo Shoots

In order to investigate the pollution situation and sources analysis of heavy metals in bamboo shoot soil in Guangdong Province, a total of 175 soil samples were collected at 46 sites. Atomic fluorescence spectrophotometer and inductively coupled plasma mass spectrometry were used to determine the content of five heavy metals: lead (Pb), cadmium (Cd), arsenic (As), mercury (Hg), and chromium (Cr). In addition, the soil environmental quality was evaluated through different index methods, including single-factor pollution, Nemeiro comprehensive pollution, geoaccumulation, and potential ecological risk. Furthermore, the correlation coefficients were also discussed. The results showed that the soils collected were acidic or slight alkaline. The maximum content of Pb and As from some areas exceeded the standard limit value. The coefficient of variation value from six areas exceeded 100%. The index method mentioned above confirmed that the soil within study areas was divided into three pollution levels: no, slightly, and mild. Additionally, there was a very significant correlation between pH and Pb, Hg; the correlation between heavy metal As and Pb, Cr also reached a very significant level. The principal component analysis results show that PC1 accounts for 39.60% of the total variance, which includes Pb, Cd, and As. PC2 mainly includes Hg and Cr.

Pollution Characteristics and Health Risk Assessment of Heavy Metals in Agricultural Soils over the Past Five Years in Zhejiang, Southeast China

Heavy metal contamination in agricultural soils has attracted increasing attention in recent years. In this study, 1999 agricultural soil samples were collected from 11 cities in Zhejiang Province from 2016 to 2020, and the spatial and temporal variation characteristics of 3 of the most important heavy metals, i.e., lead (Pb), cadmium (Cd), and chromium (Cr) were analyzed. The results showed that Cd had a slightly higher sample over-standard rate of 12.06%. Spatial distribution and temporal trends showed that the Pb concentrations overall increased from 2016 to 2020 and mainly accumulated in southern Zhejiang. In addition, multiple exposure routes were evaluated for human health risks. Children are more susceptible to the adverse effects of heavy metals in agricultural soils, and oral ingestion was the major exposure route. Cr poses higher human health risks to humans than Pb and Cd in agricultural soils. Therefore, more rigid environmental monitoring and related soil remediation counter-measures for some sites with high concentrations of heavy metals are necessary to limit the potential threat to human health.

Contamination, Source Apportionment, and Health Risk Assessment of Heavy Metals in Farmland Soils Surrounding a Typical Copper Tailings Pond

Tailings resulting from mining and smelting activities may cause soil heavy-metal pollution and harm human health. To evaluate the environmental impact of heavy metals from tailings on farmland soils in the surrounding area, heavy metals (As, Cd, Cr, Cu, Ni, Pb, and Zn) in tailings and farmland soils in the vicinity of a typical copper tailings pond were analyzed. Contamination status, potential sources, and health risks for farmland soils were investigated. The results showed that the tailings contained a high concentration of Cu (1136.23 mg/kg). The concentrations of Cd and Cu in the farmland soils exceeded the soil quality standard. The geoaccumulation index (I_geo) indicated that the soils were moderately polluted by Cu and Cd, and slightly polluted by Ni, Cr, and Zn. The absolute principal component scores-multiple linear regression (APCS-MLR) model was applied for source apportionment. The results showed that tailings release is the main source of soil heavy-metals contamination, accounting for 35.81%, followed by agricultural activities (19.41%) and traffic emission (16.31%). The health risk assessment suggested that the children in the study region were exposed to non-carcinogenic risks caused by As, while the non-carcinogenic risk to adults and the carcinogenic risk to both adults and children were at acceptable levels. It is necessary to take effective measures to control heavy-metal contamination from tailings releases to protect humans, especially children, from adverse health risks.

Health risk assessment of PM2.5 heavy metals in county units of northern China based on Monte Carlo simulation and APCS-MLR

The key areas of China's urbanization process have gradually shifted from urban areas to county-level units. Correspondingly, air pollution in county towns may be heavier than in urban areas, which has led to a lack of understanding of the pollution situation in such areas. In view of this, 236 PM_2.5 filter samples were collected in Pingyao, north of the Fen-Wei Plain, one of the most polluted areas in China. Monte Carlo simulation was used to solve the serious uncertainties of traditional HRA, and the coupling technology of absolute principal component score-multiple linear regression (APCS-MLR) and health risk assessment (HRA) is used to quantitatively analyze the health risks of pollution sources. The results showed that PM_2.5 concentration was highest in autumn, 3.73 times the 24 h guideline recommended by the World Health Organization (WHO). Children were more susceptible to heavy metals in the county-level unit, with high hazard quotient (HQ) values of Pb being the dominant factor leading to an increased non-carcinogenic risk. A significant carcinogenic risk was observed for all groups in autumn in Pingyao, with exposure to Ni in the outdoor environment being the main cause. Vehicle emissions and coal combustion were identified as two major sources of health threats. In short, China's county-level population, about one-tenth of the world's population, faces far more health risks than expected.

Heavy Metal Sources, Contamination and Risk Assessment in Legacy Pb/Zn Mining Tailings Area: Field Soil and Simulated Rainfall

This study investigated heavy metal(HM) soil pollution and evaluated the risk and sources at a legacy tailings pond's area in Meizhou, China. Result shows that HM accumulation in soil, particularly Cd, Pb, and Zn, were serious. Zn and Cd in tailing soil and all studied elements in field soil had a significant release potential. Four HM sources were identified by positive matrix factorization (PMF) model: cinder and vehicle emissions (11.3%), natural sources (16.3%), tailings pond and human activities (32.8%), tailings pond (39.7%). The soil was severely polluted with Cd, Pb, and Zn, which posed a high potential environmental risk near surrounding area. Column leaching tests showed that large quantities of HMs were released from the tailings soil during simulated rainfall with different pH. This study indicates that the study area has been severely polluted and continues to have a great risk of HM pollution under natural conditions.

Receptor model-oriented sources and risks evaluation of metals in sediments of an industrial affected riverine system in Bangladesh

Toxic metals in river sediments may represent significant ecological concerns, although there has been limited research on the source-oriented ecological hazards of metals in sediments. Surface sediments from an industrial affected Rupsa River were utilized in this study to conduct a complete investigation of toxic metals with source-specific ecological risk assessment. The findings indicated that the average concentration of Ni, Cr, Cd, Zn, As, Cu, Mn and Pb were 50.60 ± 10.97, 53.41 ± 7.76, 3.25 ± 1.73, 147.76 ± 36.78, 6.41 ± 1.85, 59.78 ± 17.77, 832.43 ± 71.56 and 25.64 ± 7.98 mg/kg, respectively and Cd, Ni, Cu, Pb and Zn concentration were higher than average shale value. Based on sediment quality guidelines, the mean effective range median (ERM) quotient (1.29) and Mean probable effect level (PEL) quotient (2.18) showed medium-high contamination in sediment. Ecological indexes like toxic risk index (20.73), Nemerow integrated risk index (427.59) and potential ecological risk index (610.66) posed very high sediment pollution. The absolute principle component score-multiple linear regression (APCS-MLR) and positive matrix factorization (PMF) model indicated that Zn (64.21%), Cd (51.58%), Cu (67.32%) and Ni (58.49%) in APCS-MLR model whereas Zn (49.5%), Cd (52.7%), Cu (57.4%) and Ni (44.6%) in PMF model were derived from traffic emission, agricultural activities, industrial source and mixed sources. PMF model-based Nemerow integrated risk index (NIRI) reported that industrial emission posed considerable and high risks for 87.27% and 12.72% of sediment samples. This work will provide a model-based guidelines for identifying and assessing metal sources which would be suitable for mitigating future pollution hazards in Riverine sediments in Bangladesh.

Nationwide review of heavy metals in municipal sludge wastewater treatment plants in China: Sources, composition, accumulation and risk assessment

Systematically analyzing the problem of heavy metals in the municipal sludge, a meta-analysis of nine metals was undertaken to distinguish the sources and sinks of those with the impact of their accumulation on the environment. Municipal sludge was rich in N, P and K nutrients, was found to contain heavy metals comprising the descending order Zn > Mn > Cu > Cr > Pb > Ni > As > Cd > Hg. The forms, in which heavy metals accumulated in geographical regions, were characterized. The geographical distribution of heavy metals in the sludge showed a significant difference, with higher accumulation in Eastern and Southern regions, however, the risk evaluations showed the higher risk of heavy metals accumulation in Eastern and Western regions. Agricultural, industrial and traffic activities, and storm water pipeline sediments were identified as the main sources of heavy metals in the sludge. The correlation analysis elucidated the role of the total organic carbon in the accumulation of heavy metals in sludge. Municipal sludge is endowed with resource properties due to the detection of heavy metal contents thresholds in household products and its own resource-attributable enrichment behavior, which requires deduction of environmental risks.

Ecological and human health risk assessment of heavy metals based on their source apportionment in cropland soils around an e-waste dismantling site, Southeast China

An accurate understanding of soil heavy metal (HM) pollution characteristics and source apportionment, and a recognition of the major factors influencing ecological and human health risks (HHRs) are essential for soil HM pollution control and remediation. In this study, 212 surface soils (0-20 cm) and 15 profile soils (0-100 cm) were collected from cropland soils around an e-waste dismantling site in Taizhou city, Zhejiang Province, China. Spatial analysis was used to evaluate the pollution characteristics of HMs (Cd, Cu, Pb, Zn, Cr and Ni). Principal component analysis (PCA) and positive matrix factorization (PMF) were also conducted to quantify their source contributions. A modified source-oriented HHR assessment integrated source-oriented ecological risk and source-oriented HHR assessment was developed to describe the major factors that influenced HHR. Results showed that 94.81 %, 88.21 %, 36.79 % and 47.17 % of Cd, Cu, Pb and Zn, respectively, in surface soils exceeded their screening values in the soil environmental quality standard for agricultural soils (GB 15618-2018). Spatial analysis indicated that high values of Cd, Cu, Pb and Zn were distributed near the e-waste dismantling site. The results of PCA and PMF showed that the primary sources of HMs in the study area are e-waste dismantling activities, natural sources and atmospheric deposition, which contribute 27 %, 46 % and 27 % of HM pollutants, respectively. The results of source-oriented ecological risk and HHR assessment indicated that e-waste dismantling activities and natural sources were primary sources for ecological risk and HHR. However, source-oriented HHR assessment may underestimate the contribution of e-waste dismantling activities by ignoring HM pollution levels. The modified source-oriented HHR assessment highlights that e-waste dismantling activities were major factor that affect noncarcinogenic risk. This study could provide important data support for subsequent environmental remediation of soil HM pollution in cropland soils around e-waste dismantling sites.

APCS-MLR model: A convenient and fast method for quantitative identification of nitrate pollution sources in groundwater

Nitrate (NO₃^-) contamination in groundwater has diverse sources and complicated transformation processes. To effectively control NO₃^- pollution in groundwater systems, quantitative and accurate identification of NO₃^- sources is critical. In this work, we applied hydrochemical characteristics and isotope analysis to determine NO₃^- source apportionment. For the first time, the NO₃^- source contributions were calculated using hydrochemical indicators combined with multivariate statistical model (PCA-APCS-MLR). The results interpret that chemical fertilizers (58.11%) and natural sources (22.69%) were the primary NO₃^- sources in the vegetable cultivation area (VCA) which were rather close to the estimation by Bayesian isotope mixing model (SIAR). In particular, the contributions of chemical fertilizers in the VCA differed by only 3.79% between the two methods. Compared with previous approaches e.g. SIAR, the key advantage of the proposed PCA-APCS-MLR model is that it only requires the hydrochemical indicators which can be easily measured. A series of complicated experiments including measurement of isotope data of NO₃^- in groundwater, monitoring of in-situ pollution source information and calculation of isotopic enrichment factor can be simply avoided. The PCA-APCS-MLR model offers a much more convenient and faster method to determine the contribution rates of NO₃^- pollution sources in groundwater.

The sources risk assessment combined with APCS/MLR model for potentially toxic elements in farmland of a first-tier city, China

With the rapid economic development, potentially toxic elements (PTEs) are continuously migrating, transforming, and enriching in farmland through atmospheric deposition and other media, posing threats to food security and human health. At present, there are few quantitative studies on the health risks of PTEs sources in farmland. In this study, absolute principal component score-multiple linear regression (APCS-MLR) receptor model was used to quantify the pollution sources of PTEs in farmland in Suzhou of Yangtze River Delta Economic Zone, China. Combined with geoaccumulation index (I_geo) and health risk assessment model, the source risk of PTEs was further quantified. The results show that Cd has reached the level of unpolluted to moderate polluted (0 < I_geo < 1); the total hazard index (THI) and total carcinogenic risk (TCR) index of PTEs are acceptable for adults, but not for children (THI > 1, TCR > 1 × 10^-4). The results of APCS-MLR source apportionment were industrial sources (25.65%), agricultural sources (20.00%), traffic sources (16.81%), and domestic pollution sources (9.71%). The I_geo values of all pollution sources were less than 0, and no ecological risk was caused. The contribution patterns of pollution sources to THI and TCR in adults and children are similar. Industrial pollution sources pose the greatest non-carcinogenic risk to humans, accounting for 47.35% and 47.26% of adults and children, respectively; for carcinogenic risks, domestic pollution sources contribute the most among all identified pollution sources, accounting for 27.71% and 27.73% of adults and children, respectively. In general, this study emphasizes the need to strengthen the supervision of industrial pollution sources and domestic pollution sources in the study area to reduce the health risks to children.

Health Risk Assessment of Heavy Metals in Groundwater of Hainan Island Using the Monte Carlo Simulation Coupled with the APCS/MLR Model

Groundwater is a significant component of water resources, but drinking groundwater with excessive heavy metals (HMs) is harmful to human health. Currently, quantitative source apportionment and probabilistic health risk assessment of HMs in groundwater are relatively limited. In this study, 60 groundwater samples containing seven HMs were collected from Hainan Island and analyzed by the coupled absolute principal component scores/multiple linear regression (APCS/MLR), the health risk assessment (HRA) and the Monte Carlo simulation (MCS) to quantify the pollution sources of HMs and the health risks. The results show that the high-pollution-value areas of HMs are mainly located in the industry-oriented western region, but the pollution level by HMs in the groundwater in the study area is generally low. The main sources of HMs in the groundwater are found to be the mixed sources of agricultural activities and traffic emissions (39.16%), industrial activities (25.57%) and natural sources (35.27%). Although the non-carcinogenic risks for adults and children are negligible, the carcinogenic risks are at a high level. Through analyzing the relationship between HMs, pollution sources, and health risks, natural sources contribute the most to the health risks, and Cr is determined as the priority control HM. This study emphasizes the importance of quantitative evaluation of the HM pollution sources and probabilistic health risk assessment, which provides an essential basis for water pollution prevention and control in Hainan Island.

Apportionment and Spatial Pattern Analysis of Soil Heavy Metal Pollution Sources Related to Industries of Concern in a County in Southwestern China

Soil heavy metal pollution is frequent around areas with a high concentration of heavy industry enterprises. The integration of geostatistical and chemometric methods has been used to identify sources and the spatial patterns of soil heavy metals. Taking a county in southwestern China as an example, two subregions were analyzed. Subregion R1 mainly contained nonferrous mining, and subregion R2 was affected by smelting. Two factors (R1F1 and R1F2) associated with industry in R1 were extracted through positive matrix factorization (PMF) to obtain contributions to the soil As (64.62%), Cd (77.77%), Cu (53.10%), Pb (75.76%), Zn (59.59%), and Sb (32.66%); two factors (R2F1 and R2F2) also related to industry in R2 were extracted to obtain contributions to the As (53.35%), Cd (32.99%), Cu (53.10%), Pb (56.08%), Zn (67.61%), and Sb (42.79%). Combined with PMF results, cokriging (CK) was applied, and the z-score and root-mean square error were reduced by 11.04% on average due to the homology of heavy metals. Furthermore, a prevention distance of approximately 1800 m for the industries of concern was proposed based on locally weighted regression (LWR). It is concluded that it is necessary to define subregions for apportionment in area with different industries, and CK and LWR analyses could be used to analyze prevention distance.

Source apportionment and risk assessment for available occurrence forms of heavy metals in Dongdahe Wetland sediments, southwest of China

Urban wetland ecosystems are easily influenced by heavy metals (HMs) because of their functional properties. In this study, absolute principal component scores-multivariate linear regression (APCS-MLR) and positive matrix factorization (PMF) receptor models were applied for the source apportionment of available occurrence forms of heavy metals (AHMs) of surface sediments in a typical urban wetland of Dianchi Lake, southwest of China. The risk assessment was conducted to evaluate the potential ecological/human health risks of HMs. Results indicated that Zn, Pb, and Cr were the major pollutants affected by anthropogenic activities in sediments and their concentrations were significantly exceeding the background value. Most of the highly AHMs-polluted area was close to the river in wetland, and the concentration distribution of all AHMs were generally low in the southwest and high in the northeast. Both APCS-MLR and PMF models identified three comparable classes of potential sources, namely (1) agricultural fertilizer/insecticide, atmospheric deposition, and traffic emissions; (2) natural transitions; and (3) industrial and sewage wastes. Moreover, the comparison results implied that the PMF model was more feasible for quantifying AHMs sources in wetland sediments since it is capable to analyze one more source, namely plant maintenance and waterfowl feeding, and has higher accuracy in predicting the concentrations of AHMs. In addition, the risk assessment model revealed that all these HMs were within the acceptable ranges of ecological and carcinogenic/non-carcinogenic human health risks. Among these, ingestion was the major exposure pathway of HMs from local areas, followed by dermal exposure and oral or nasal inhalation. However, children were more easily exposed to HMs than adults by ingestion due to their hand-to-mouth behaviors. This study aims to assess the HM pollution status in a plateau urban wetland, and provides a practical case for modeling source apportionment and risk assessment of HMs in wetland sediments.

Eutrophication and sediment-water exchange of total petroleum hydrocarbons and heavy metals of Hashilan wetland, a national heritage in NW Iran

The heavy metal(loid)s concentrations in water and sediments were analyzed in the Hashilan wetland to assess the spatial distribution, pollution status, fate, partitioning, and ecological risk and also to identify the heavy metal(loid)s sources in sediments using PMF (Positive Matrix Factorization) and APCs-MLR (absolute principal component score-multiple linear regression) receptor models. According to the pollution indices, (Ni, Cu, Cr, Mo), and (Zn, Cr, and Cu) are considered the most important pollutants in sediments and water, respectively. Ni, Cr, and Cu are the main contributors to ecological risks in sediments of some stations. The potential ecological risk assessment proposed low ecological risk in water of the study area. Higher distribution coefficient (Kp) values of Ni, Cr, Mn, Cu, Co, Pb, As, and Zn indicated the majority of these heavy metals present in the sediments; whereas, the majority of Cd concentration occurs in water. PMF and APCs-MLR results indicated the natural sources were the main factors affecting the concentrations of Ni, Cr, Zn, Al, Co, Fe, Pb, As, Cd and somewhat Cu. Mixed natural and agricultural activities are the main sources of Mo, and somewhat Cu. According to the results, there is low pollution of TPH (total petroleum hydrocarbons) in the sediment samples. Also, phosphate (PO42-) and nitrate (NO3-) concentrations were below the recommended permissible limits at all sampling sites except the S8 station for NO3-.

Practical limitations of bioaugmentation in treating heavy metal contaminated soil and role of plant growth promoting bacteria in phytoremediation as a promising alternative approach

Bioaugmentation, the addition of cultured microorganisms to enhance the currently existing microbial community, is an option to remediate contaminated areas. Several studies reported the success of the bioaugmentation method in treating heavy metal contaminated soil, but concerns related to the applicability of this method in real-scale application were raised. A comprehensive analysis of the mechanisms of heavy metal treatment by microbes (especially bacteria) and the concerns related to the possible application in the real scale were juxtaposed to show the weakness of the claim. This review proposes the use of bioaugmentation-assisted phytoremediation in treating heavy metal contaminated soil. The performance of bioaugmentation-assisted phytoremediation in treating heavy metal contaminated soil as well as the mechanisms of removal and interactions between plants and microbes are also discussed in detail. Bioaugmentation-assisted phytoremediation shows greater efficiencies and performs complete metal removal from soil compared with only bioaugmentation. Research related to selection of hyperaccumulator species, potential microbial species, analysis of interaction mechanisms, and potential usage of treating plant biomass after treatment are suggested as future research directions to enhance this currently proposed topic.

Receptor model-based source apportionment and ecological risk of metals in sediments of an urban river in Bangladesh

Metal accumulation (As, Cd, Cr, Cu, Fe, Hg, Mn, Ni, Pb, and Zn) in Korotoa River sediment was studied in order to determine the metal content, distribution, sources, and their possible ecological impacts on the riverine ecosystem. Our study found significant spatial patterns of toxic metal concentration and principal coordinate analysis (PCoA) accounted for 45.2% of spatial variation from upstream to downstream. Metal contents were compared to sediment quality standards and found all studied metal concentrations exceeded the Threshold Effect Level (TEL) whereas Cr and Ni surpassed probable effect levels. All metal concentrations were higher than Average Shale Value (ASV) except Mn and Hg. The positive matrix factorization (PMF) and absolute principal component score-multiple linear regression models (APCS-MLR) were applied to identify promising sources of metals in sediment samples. Both models identified three potential sources i.e. natural source, traffic emission, and industrial pollution, which accounted for 50.32%, 20.16%, and 29.51% in PMF model whereas 43.56%, 29.42%, and 27.02% in APCS-MLR model, respectively. Based on ecological risk assessment, pollution load index (7.74), potential ecological risk (1078.45), Nemerow pollution index (5.50), and multiple probable effect concentrations quality (7.73) showed very high contamination of toxic metal in sediment samples.