An integrated method for source apportionment of heavy metal(loid)s in agricultural soils and model uncertainty analysis

Identifying interactive effects of spatial drivers in soil heavy metal pollutants using interpretable machine learning models

The accurate identification of spatial drivers is crucial for effectively managing soil heavy metals (SHM). However, understanding the complex and diverse spatial drivers of SHM and their interactive effects remains a significant challenge. In this study, we present a comprehensive analysis framework that integrates Geodetector, CatBoost, and SHapley Additive exPlanations (SHAP) techniques to identify and elucidate the interactive effects of spatial drivers in SHM within the Pearl River Delta (PRD) region of China. Our investigation incorporated fourteen environmental factors and focused on the pollution levels of three prominent heavy metals: Hg, Cd, and Zn. These findings provide several key insights: (1) The distribution of SHM is influenced by the combined effects of various individual factors and interactions within the source-flow-sink process. (2) Compared with the spatial interpretation of individual factors, the interaction between Hg and Cd exhibited enhanced spatial explanatory power. Similarly, interactions involving Zn mainly demonstrated increased spatial explanatory power, but there was one exception in which a weakening was observed. (3) Spatial heterogeneity plays a crucial role in determining the contributions of environmental factors to soil heavy metal concentrations. Although individual factors generally promote metal accumulation, their effects fluctuate when interactions are considered. (4) The SHAP interpretable method effectively addresses the limitations associated with machine-learning models by providing understandable insights into heavy metal pollution. This enables a comparison of the importance of environmental factors and elucidates their directional impacts, thereby aiding in the understanding of interaction mechanisms. The methods and findings presented in this study offer valuable insights into the spatial heterogeneity of heavy metal pollution in soil. By focusing on the effects of interactive factors, we aimed to develop more accurate strategies for managing SHM pollution.

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

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

Distribution, risk evaluation, and source allocation of cesium and strontium in surface soil in a mining city

Radioactive nuclides cesium (Cs) and strontium (Sr) possess long half-lives, with 135Cs at approximately 2.3 million years and 87Sr at about 49 billion years. Their persistent accumulation can result in long-lasting radioactive contamination of soil ecosystems. This study employed geo-accumulation index (Igeo), pollution load index (PLI), potential ecological risk index (PEPI), health risk assessment model (HRA), and Monte Carlo simulation to evaluate the pollution and health risks of Cs and Sr in the surface soil of different functional areas in a typical mining city in China. Positive matrix factorization (PMF) model was used to elucidate the potential sources of Cs and Sr and the respective contribution rates of natural and anthropogenic sources. The findings indicate that soils in the mining area exhibited significantly higher levels of Cs and Sr pollution compared to smelting factory area, agricultural area, and urban residential area. Strontium did not pose a potential ecological risk in any studied functional area. The non-carcinogenic health risk of Sr to the human body in the study area was relatively low. Because of the lack of parameters for Cs, the potential ecological and human health risks of Cs was not calculated. The primary source of Cs in the soil was identified as the parent material from which the soil developed, while Sr mainly originated from associated contamination caused by mining activities. This research provides data for the control of Cs and Sr pollution in the surface soil of mining city.

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

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

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

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

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

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

Distribution patterns, risk assessment and potential sources of heavy metals in sediment in the Qiongzhou Strait, China

Risk assessment and pollutant source analysis are crucial tools for the management and protection of coastal ecosystems. The distribution patterns, risk assessment, and potential sources of heavy metals (Cd, Cr, Fe, Ni, Cu, Zn, As, Hg, and Pb) in surface sediment were analyzed in the Qiongzhou Strait, China, in summer and autumn of 2022. Heavy metals in autumn showed higher ecological risk than that in summer. Seasonal shifts in ocean currents may result in variations in heavy metal accumulation and dispersion. Cd and Hg were the priority heavy metals found, and according to the Positive Matrix Factorization results, the study area contains five sources of pollution, with natural sources, shipping-related activities, and industrial activities being the primary contributors. This study indicated that pollutants from adjacent areas should be considered for managing the environmental quality of Qiongzhou Strait.

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.

Hydrogeochemical characteristics, stable isotopes, positive matrix factorization, source apportionment, and health risk of high fluoride groundwater in semiarid region

Chronic exposure to high fluoride (F-) levels in groundwater causes community fluorosis and non-carcinogenic health concerns in local people. This study described occurrence, dental fluorosis, and origin of high F-groundwater using δ2H and δ18O isotopes at semiarid Gilgit, Pakistan. Therefore, groundwater (n = 85) was collected and analyzed for F- concentrations using ion-chromatography. The lowest F- concentration was 0.4 mg/L and the highest 6.8 mg/L. F- enrichment is linked with higher pH, NaHCO3, NaCl, δ18O, Na+, HCO3-, and depleted Ca+2 aquifers. The depleted δ2H and δ18O values indicated precipitation and higher values represented the evaporation effect. Thermodynamic considerations of fluorite minerals showed undersaturation, revealing that other F-bearing minerals viz. biotite and muscovite were essential in F- enrichment in groundwater. Positive matrix factorization (PMF) and principal component analysis multilinear regression (PCAMLR) models were used to determine four-factor solutions for groundwater contamination. The PMF model results were accurate and reliable compared with those of the PCAMLR model, which compiled the overlapping results. Therefore, 28.3% exceeded the WHO permissible limit of 1.5 mg/L F-. Photomicrographs of granite rocks showed enriched F-bearing minerals that trigger F- in groundwater. The community fluorosis index values were recorded at > 0.6, revealing community fluorosis and unsuitability of groundwater for drinking.

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.

Risk assessment and strontium isotopic tracing of potentially toxic metals in creek sediments around a uranium mine, China

The contamination of creek sediments near industrially nuclear dominated site presents significant environmental challenges, particularly in identifying and quantifying potentially toxic metal (loid)s (PTMs). This study aims to measure the extent of contamination and apportion related sources for nine PTMs in alpine creek sediments near a typical uranium tailing dam from China, including strontium (Sr), rubidium (Rb), manganese (Mn), lithium (Li), nickel (Ni), copper (Cu), vanadium (V), cadmium (Cd), zinc (Zn), using multivariate statistical approach and Sr isotopic compositions. The results show varying degrees of contamination in the sediments for some PTMs, i.e., Sr (16.1-39.6 mg/kg), Rb (171-675 mg/kg), Mn (224-2520 mg/kg), Li (11.6-78.8 mg/kg), Cd (0.31-1.38 mg/kg), and Zn (37.1-176 mg/kg). Multivariate statistical analyses indicate that Sr, Rb, Li, and Mn originated from the uranium tailing dam, while Cd and Zn were associated with abandoned agricultural activities, and Ni, Cu, and V were primarily linked to natural bedrock weathering. The Sr isotope fingerprint technique further suggests that 48.22-73.84% of Sr and associated PTMs in the sediments potentially derived from the uranium tailing dam. The combined use of multivariate statistical analysis and Sr isotopic fingerprint technique in alpine creek sediments enables more reliable insights into PTMs-induced pollution scenarios. The findings also offer unique perspectives for understanding and managing aqueous environments impacted by nuclear activities.

Heavy metal pollution in agricultural soils from surrounding industries with low emissions: Assessing contamination levels and sources

The potential for heavy metal (HM) pollution in agricultural soils adjacent to industries with elevated HM emissions has long been recognized. However, industries with relatively lower levels of HM emissions, such as alumina smelting and glass production, may still contribute to the pollution of surrounding agricultural soils through continuous, albeit low-level, emissions. Despite this, this issue has not garnered adequate attention thus far. Therefore, this study aimed to assess the extent of HM pollution in agricultural soils adjacent to an alumina smelting and a glass production factory, identifying contamination levels and potential sources through the analysis of input fluxes, isotope fingerprints, and receptor models. Results showed moderate cadmium (Cd) contamination in surface soil, exceeding standards at a rate of 86.36 %. Further analysis revealed that atmospheric deposition was the primary route for Cd input in both paddy fields (89.20 %) and dryland soils (91.61 %). Additionally, the δ114/110Cd values in surface soils indicated that dust played a role in influencing Cd levels in distant surface soils, while raw materials and slags were identified as primary sources near the factory. Industrial sources were considered the primary contributors of Cd in soil accounting for approximately 73.38 % and 82.67 %, respectively, according to the positive matrix factorization model (PMF) and absolute principal component scores-multiple linear regression model (APCS-MLR). Overall, this study underscores the importance of monitoring HMs from industries with relatively low emissions and provides a scientific basis for effectively managing HMs pollution in agricultural soils, ensuring the preservation of agricultural soil quality.

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

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

Assessing heavy metal pollution in sediments from the northern margin of Chinese mangrove areas: Sources, ecological risks, and health impacts

With the rapid economic development of coastal cities, the discharge of substantial amounts of heavy metal pollutants poses a serious hazard to mangroves; however, the potential sources of heavy metals and the resulting health risks are not fully understood. In this study, we analyzed the contents, sources, and ecological and health risks of heavy metal contamination in mangrove sediments from the northern margin of China. The accumulation of heavy metals in mangroves was primarily driven by five potential sources, namely agricultural (33.5 %), natural sources (21.3 %), industrial (19.1 %), aquaculture (14.3 %), and traffic (11.8 %). The assessment of health risks using a probabilistic approach demonstrated that noncarcinogenic risks were within acceptable limits for all populations. It was worth noting that both noncarcinogenic and carcinogenic risks were greater in children than in adults. Analysis of source-oriented health risks revealed that agricultural sources and As and Cd were priority sources and elements of pollution requiring attention.

Response of trace elements in urban deposition to emissions in a northwestern river valley type city: 2010-2021

Anthropogenic activities release significant quantities of trace elements into the atmosphere, which can infiltrate ecosystems through both wet and dry deposition, resulting in ecological harm. Although the current study focuses on the emission inventory and deposition of trace elements, their complex interactions remain insufficiently explored. In this study, we employ emission inventories and deposition data for eight TEs (Cr, Mn, Ni, Cu, Zn, As, Cd, Pb) in Lanzhou City to unveil the relationship between these two aspects. Emissions in Lanzhou can be roughly divided into two periods centered around 2017. Preceding 2017, industrial production constituted the primary source of TEs emissions except for As; coal combustion was the primary contributor to Cr, Mn, and As emissions; waste incineration played a significant role in As, Zn, and Cd emissions; biomass combustion influenced Cr and Cd emissions; and transportation sources were the predominant contributors to Pb and Cu emissions. With the establishment of waste-to-energy plants and the implementation of ultra-low emission retrofits, emissions from these sources decreased substantially after 2017. Consequently, emissions from industrial production emerged as the main source of TEs. The deposition concentrations of Cr, Mn, Ni, Cu, and Pb followed a similar trend to the emissions. However, Cd and As exhibited lower emissions and a less pronounced response relationship. Moreover, Zn concentrations fluctuated within a narrow range and showed a weaker response to emissions. The consistent changes in emissions and TEs deposition concentrations signify a shift in deposition pollution in Lanzhou city from Coal-fired pollution to that driven by transportation and industrial activities. Within this transition, the industrial production process offers significant potential for emission reduction. This insight provides a crucial foundation for managing TEs pollution and implementing strategies to prevent ecological risks.

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

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

Combining Cd and Pb isotope analyses for heavy metal source apportionment in facility agricultural soils around typical urban and industrial areas

Facility agriculture enhances food production capabilities. However, concerns persist regarding heavy metal accumulation resulting from extensive operation of this type of farming. This study integrated the total content, five fractions, and isotope composition of Cd and Pb in intensively farmed soils in regions characterized by industrialization (Shaoguan, SG) and urbanization (Guangzhou, GZ), to assess the sources and mechanisms causing metals accumulation. We found significantly more severe Cd/Pb accumulation and potential mobility in SG than GZ. Cd displayed higher accumulation levels and potential mobility than Pb. The distinct isotopic signals in SG (-0.54 to 0.47‰ for δ114/110Cd and 1.1755 to 1.1867 for 206Pb/207Pb) and GZ (-0.86 to 0.12‰ for δ114/110Cd and 1.1914 to 1.2012 for 206Pb/207Pb) indicated significant differences in Cd/Pb sources. The Bayesian model revealed that industrial activities and related transportation accounted for over 40% and approximately 30%, respectively, of the average contributions of Cd/Pb in SG. While urban-related (26.6%) and agricultural-related (26.3%) activities primarily contributed to Cd in GZ. The integration of δ114/110Cd and 208Pb/206Pb has further enhanced the regional contrast in sources. The present study established a comprehensive tracing system for Cd-Pb, providing crucial insights into the accumulation and distribution of these metals in facility agricultural soils.

Concentration and sources of persistent organic pollutants within the vicinity of a scrap-iron smelting plant: Seasonal pattern and health risk assessment

Polychlorinated biphenyls (PCBs) are a class of ubiquitous and significant synthetic organic chemicals that pose deleterious threats to the environment and human health. This study examined the concentration, indoor-outdoor and seasonal change, sources, and health effects of PCBs in particulate-bound dust near a scrap iron recycling plant. PCBs levels were determined in samples using gas chromatograph mass spectrometer. The results indicated that 5 Cl atoms PCB constituted the majority of PCBs (41% overall), contributing 43% during the rainy season and 39% during the dry season. Dioxin-like PCBs (DLPCBs) contributed 38% during the rainy season and 33% during the dry season. In addition, DLPCB accounted for 26% and 40% of indoor and outdoor PCB emissions, respectively. Iron and steel production were identified as the highest identified contributing sources, accounting for 76% of PCB emissions in the rainy season, while plastic combustion had the highest contribution in the dry season, accounting for 44% of PCB emissions. Incremental Lifetime Cancer Risk assessment showed ingestion as the main exposure pathway for children and adults during the two seasons (74.42% and 58.24%, respectively), followed by dermal exposure, while inhalation had the least contribution. A multifaced approach involving relevant agencies, the industry, and the community is required to reduce exposure.

Source characteristics of polycyclic aromatic hydrocarbons and polychlorinated biphenyls in surface soils of Shenyang, China: A comparison of two receptor models combined with Monte Carlo simulation

The surface soil concentrations of 16 PAHs and 15 PCBs were simultaneously determined by gas chromatography-tandem mass spectrometry in 21 locations of urban areas of Shenyang. The average concentrations of PAHs and PCBs were 26.40 ± 34.68 mg/kg and 48.03 ± 27.47 μg/kg, respectively. Factor analysis with nonnegative constraints (FA-NNC) and absolute principal component score with multiple linear regression (APCS-MLR) model were used to explore and evaluate the sources of PAHs and PCBs in the study area. The results of FA-NNC showed that PAHs in soils were mainly from traffic emissions (49.64%), coal combustion (46.88%) and petrogenic source (3.49%). The PCBs in soils were mainly from commercial and high temperature combustion mixed sources (20.3%), combustion and industry emission mixed sources (21.1%), electrical equipment sources (22.2%) and traffic emission sources (36.4%). The results of APCS-MLR were consistent with those of FA-NNC. The uncertainty of FA-NNC and APCS-MLR model was analyzed by Monte Carlo simulation method. The results revealed the reliability of the two receptor models on source apportionment. The estimated carcinogenic risks indicated that the risks of PAHs in soils exceed the acceptable range (10-6-10-4), while the risks of PCBs were below the acceptable risk level of 10-6.

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

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

Machine learning combined with Geodetector quantifies the synergistic effect of environmental factors on soil heavy metal pollution

The critical prerequisite for the prevention and control of soil heavy metal (HM) pollution is the identification of factors that influence soil HM accumulation. The dominant factors have been individually identified and apportioned in existing studies. However, the accumulation of soil HMs results from a combination of multiple factors, and the influence of a single factor is less than the interaction of multiple parameters on soil HM pollution. In this study, we employed Geodetector to delve into the interaction effect of the influencing factors on the variations of soil HMs. We performed partial dependence plot to depict how these factors interact with each other to affect the HM content. We found that both individually and interactively, pH and agricultural activities significantly impact soil HM content. Except for Hg and Cu, the pairs with the most significant interaction effects all involve pH. For Pb, As and Zn, interaction with pH has the most significant driving force compared to the other factors. For Cu, Hg, and Ni, all environmental factor interactions increased their explanatory power, while for Cr, the single most significant driver decreased its driving power when interacting with other factors. Additionally, the study area exhibited a widespread prevalence of changes in HM concentration being governed by the synergistic effect of two factors. For the response of HMs to the interaction of pH and fertilizer, soil HM concentration was sensitive to pH, while fertilizer had less effect. These results provide a dependable method of investigating the interaction of environmental factors on soil HM content and put forth efficacious and potent tactical measures for soil HM pollution prevention and control based on the interaction type.

Agricultural abandoned lands as emission sources of dust containing metals and pesticides in the Sonora-Arizona Desert

This investigation examines the transport of metal- and pesticide-polluted dust emitted by one of the most relevant agricultural areas of Northwestern Mexico. In the contaminated area, an excessive water extraction of the aquifer and seawater intrusion caused the abandonment of fields, which are pollutant-loaded dust emitters. We used air mass forward trajectories (HYSPLIT) model to obtain particle trajectories in the wind and the use of banned pesticides as geochemical tracers for dust transported by wind. Fifty dust samples from 10 agriculture fields and 26 roof dust of a city close to the agricultural area were analyzed for their contents of zirconium, lead, arsenic, zinc, copper, iron, manganese, vanadium, and titanium, by portable X-ray fluorescence. Nine pesticides were analyzed in the roof dust and agricultural soil samples by gas chromatography. Results show that the distribution of metals was significantly different between active and abandoned fields. Arsenic-lead-copper was mainly concentrated in abandoned fields, while zinc-iron-manganese-titanium was dominant in active fields. Two potential sources of metal contamination were found by principal component analysis (PCA): (I) a mixture of traffic and agricultural sources and (II) a group related to agricultural activities. The occurrence of banned pesticides in dust deposited on roofs collected at nearby cities confirms the atmospheric transport from the agricultural area. The HYSPLIT results indicated that the dust emitted from agricultural fields can reach up to the neighboring states of Sonora, Mexico, and the USA. The impacts that these emissions can have on human health should be studied in future research.

Resourcelized conversion of poultry feces to ordered carbon with electron poor/rich microregions for water purification induced by peroxymonosulfate

For the sustainable reutilization of poultry feces (PF) to reduce environmental pollution, we present a novel approach for converting PF into a highly effective catalyst, consisting of trace copper (Cu) and sulfur (S) linked with ordered graphitized carbon (CS/CPF) for wastewater purification. Raman and EPR results verified that the disorderly organic matters in PF are transformed into orderly graphene structures that complexed with Cu to form large numbers of electron-poor/rich microregions on CS/CPF surface. The electrons from electron-rich organic pollutants can be directly captured by dissolved oxygen (DO) to produce abundant reactive oxygen species due to the enhanced electron polarization via the construction of Cu-S-C bond bridge on CS/CPF surface, which greatly enhance the removal efficiency of pollutants. CS/CPF achieves 100% removal for 2,4-dichlorophenoxyacetic acid (2,4-D) in just 10 min after adding trace peroxymonosulfate (PMS), keeping efficient catalytic activity after continuous reactions for 240 h. This strategy offers a practical and sustainable solution for the efficient resource recovery of poultry feces.

Appraisal of contamination, source identification and health risk assessment of selected metals in the agricultural soil of Chakwal, Pakistan

Contamination of metals in agricultural soil is a serious global threat but there are limited reports related to their risks in major agronomic areas. The current study is aimed to assess the distribution of selected macroelements and essential/toxic trace metals (Ca, Mg, Na, K, Sr, Li, Ag, Fe, Zn, Co, Cu, Mn, Cd, Cr, Pb and Ni) in the agricultural soil of Chakwal, Pakistan, in order to appraise their contamination status, source identification and probable human health risks. Quantification of the metals was performed by AAS employing aqua regia digestion method. Among the selected metals, dominant mean concentrations were observed for Ca (48,285 mg/kg) and Fe (30,120 mg/kg), followed by Mg (9171 mg/kg), K (973.3 mg/kg), Mn (399.0 mg/kg) and Na (368.9 mg/kg). The correlation study indicated strong mutual relationships among the metals as well as physicochemical properties. Multivariate analysis (PCA/CA) of the metal levels revealed their diverse anthropogenic sources in the soil. Various pollution indices indicated extremely high contamination/enrichment of Cd, followed by moderate enrichment/contamination of Ag in the soil. The HQ values for most of the metals manifested insignificant non-cancer risks. The average CR value of Cr was exceeding the safe limit (1.0E-06) for both ingestion and inhalation exposure, indicating a considerable lifelong cancer risk for the population. The results of this study will provide a better understanding related to the contamination of agricultural soil and its effects on human health and to promote effective actions to reduce the soil pollution.

An integrated overview of metals contamination, source-specific risks investigation in coal mining vicinity soils

Heavy metals in soil are harmful to natural biodiversity and human health, and it is difficult to estimate the effects accurately. To reduce pollution and manage risk in coal-mining regions, it is essential to evaluate risks for heavy metals in soil. The present study reviews the levels of 21 metals (Nb, Zr, Ag, Ni, Na, K, Mg, Rb, Zn, Ca, Sr, As, Cr, Fe, Pb, Cd, Co, Hg, Cu, Mn and Ti) in soils around Barapukuria coal-mining vicinity, Bangladesh which were reported in literature. An integrated approach for risk assessments with the positive matrix factorization (PMF) model, source-oriented ecological and health hazards were applied for the study. The contents of Rb, Ca, Zn, Pb, As, Ti, Mn, Co, Ag, Zr, and Nb were 1.63, 1.10, 1.97, 14.12, 1.20, 3.13, 1.22, 3.05, 3.85, 5.48, and 7.21 times greater than shale value. About 37%, 67%, 12%, and 85% of sampling sites posed higher risks according to the modified contamination factor, Nemerow pollution index, Nemerow integrated risk index, and mean effect range median quotient, respectively. Five probable metal sources were computed, including industrial activities to coal mining (17%), agricultural activities (33%), atmospheric deposition (19%), traffic emission (16%), and natural sources (15%). Modified Nemerow integrated risk index reported that agricultural activities, industrial coal mining activities, and atmospheric deposition showed moderate risk. Health hazards revealed that cancer risk values computed by the PMF-HHR model with identified sources were higher than the standard value (1.0E-04) for children, adult male, and female. Agricultural activities showed higher cancer risks to adult male (39%) and children (32%) whereas traffic emission contributed to female (25%). These findings highlight the ecological and health issues connected to potential sources of metal contamination and provide useful information to policymakers on how to reduce such risks.

Source identification and health risk assessment of heavy metals with mineralogy: the case of soils from a Chinese industrial and mining city

Understanding the precise sources of heavy metals (HMs) in soil and the contribution of these sources to health risks has positive effects in terms of risk management. This study focused on the HMs in the soil of five land uses in an industrial and mining city. The sources of HMs in soils were identified, and the soil mineralogical characteristics and health risks of HMs were discussed. The results showed that the HMs (Cu, Zn, Ni, Cd, Pb) found in the soil of the five land uses were affected by human activities. For example, the Cu in grassland, gobi beach, woodland, green belt, and farmland is 22.3, 3.5, 22.5, 16.7, and 21.3 times higher than the soil background values in Gansu Province, respectively. The Positive Matrix Factorization model (PMF) results revealed that traffic emissions and industrial and agricultural activities were the primary sources of HMs in the soil, with industrial sources accounting for the largest share at 55.79%. Furthermore, various characteristics proved that the studied HMs were closely related to smelting products. Concentration-oriented health risk assessments showed that HMs in the different soil types held non-carcinogenic and carcinogenic risks for children and adults. Contamination source-oriented health risk assessments of children and adults found that industrial activities controlled non-carcinogenic and carcinogenic risks. This study highlighted the critical effects of smelting on urban soil and the contribution of pollution sources to health risks. Furthermore, this work is significant in respect of the risk control of HMs in urban soils.

Heavy metals distribution characteristics, source analysis, and risk evaluation of soils around mines, quarries, and other special areas in a region of northwestern Yunnan, China

In this study, based on the assessment of soil heavy metals (HMs) pollution using relevant indices, a comprehensive approach combined network environ analysis (NEA), human health risk assessment (HHRA) method and positive definite matrix factor (PMF) model to quantify the risks among ecological communities in a special environment around mining area in northwest Yunnan, calculated the risk to human health caused by HMs in soil, and analyzed the pollution sources of HMs. The integrated risks for soil microorganisms, vegetations, herbivores, and carnivores were 2.336, 0.876, 0.114, and 0.082, respectively, indicating that soil microorganisms were the largest risk receptors. The total hazard indexes (HI_T) for males, females, and children were 0.542, 0.591, and 1.970, respectively, revealing a relatively high and non-negligible non-carcinogenic risks (NCR) for children. The total cancer risks (TCR) for both females and children exceeded 1.00E-04, indicating that soil HMs posed carcinogenic risks (CR) to them. Comparatively, Pb was the high-risk metal, accounting for 53.76%, 57.90%, and 68.09% of HI_T in males, females, and children, respectively. PMF analysis yielded five sources of pollution, F1 (industry), F2 (agriculture), F3 (domesticity), F4 (nature), and F5 (traffic).

An integrated approach to identify the source apportionment of potentially toxic metals in shale gas exploitation area soil, and the associated ecological and human health risks

Public awareness of the potential environmental risks of shale gas extraction has increased in recent years. However, the status and environmental risks of potentially toxic metals (PTMs) in shale gas field soil remain unclear. A total of 96 topsoil samples were collected from the first shale gas exploitation area in China. The sources of nine PTMs in the soils were identified using positive matrix factorization and correlation analysis, and the ecological and human health risks of toxic metals from different sources under the two land use types were calculated. The results showed that mean pollution load index (PLI) values for farmland (1.18) and woodland (1.40) indicated moderate pollution, As, Cd and Ni were the most serious contaminants among all nine PTMs. The following four sources were identified: shale gas extraction activities (43.90%), nature sources (31.90%), agricultural and traffic activities (17.55%) and industrial activities (6.55%). For ecological risk, the mean ecological risk index (RI) values for farmlands (161.95) and woodlands (185.27) reaching considerable risk. The contribution ratio of shale gas extraction activities for farmlands and woodlands were 5.70% and 8.90%, respectively. Regarding human health risk, noncarcinogenic risks for adults in farmlands and woodlands were negligible. Industrial activities, agricultural and traffic activities were estimated to be the important sources of health risks. Overall, shale gas extraction activities had little impact on the ecological and human health risk. This study provides scientific evidence regarding the soil contamination potential of shale gas development activities.

Source analysis and health risk assessment of heavy metals in agricultural land of multi-mineral mining and smelting area in the Karst region - a case study of Jichangpo Town, Southwest China

In the Karst region of Southwest China, the content of soil heavy metals is generally high because of the geological background. Moreover, Southwest China is rich in mineral resources. A large number of mining and smelting activities discharge heavy metals into surrounding soil and cause superimposed pollution, which has drawn widespread concern. Due to the large variation coefficients of soil heavy metals in the Karst region, it is particularly essential to select appropriate analysis methods. In this paper, Jichangpo in Puding County, a Karst area with multi-mineral mining and smelting, is selected as the research object. A total of 368 pieces of agricultural topsoil in the study area are collected. The pollution level of heavy metals in agricultural soil is evaluated by the geological accumulation index (I_geo) and enrichment factor (EF). Absolute Factor Score/Multiple Linear Regression (APCS/MLR), geographic information system (GIS), self-organizing mapping (SOM), and random forest (RF) are used for the source allocation of soil heavy metals. Finally, the combination of APCS/MLR and health risk assessment model is adopted to evaluate the risks of heavy metal sources and determine the priority-control source. The results show that the average values of soil heavy metals in the study area (Cd, Hg, As, Pb, Cr, Cu, Zn, and Ni) exceed the background values of corresponding elements in Guizhou Province. Three sources of heavy metals are identified by combining APCS/MLR, GIS, SOM, and RF. Zn (63.47%), Pb (55.77%), Cd (58.98%), Hg (32.17%), Cu (14.41%), and As (5.99%) are related to lead-zinc mining and smelting; Cr (98.14%), Ni (90.64%), Cu (76.93%), Pb (43.02%), Zn (35.22%), Cd (28.97%), Hg (22.44%), and As (5.84%) are mixed sources (natural and agricultural sources); As (88.17%), Hg (45.39%), Cd (12.04%), Cu (8.66%), and Ni (6.72%) are related to the mining and smelting of coal and iron. The results of health risk assessment show that only As poses a non-carcinogenic risk to human health. 3.31% of the sampling points of As have non-carcinogenic risks to adults and 10.22% to children. In terms of carcinogenic risks, As, Pb, and Cr pose carcinogenic risks to adults and children. Combined with APCS/MLR and the health risk assessment model, the mining and smelting of coal and iron is the priority-control pollution source. This paper provides a comprehensive method for studying the distribution of heavy metal sources in areas with large variation coefficients of soil heavy metals in the Karst region. Furthermore, it offers a theoretical basis for the management and assessment of heavy metal pollution in agricultural land in the study area, which is helpful for researchers to make strategic decisions on food security when selecting agricultural land.

Effects of multi-heavy metal composite pollution on microorganisms around a lead-zinc mine in typical karst areas, southwest China

Heavy metal pollution poses a serious hazard to the soil bacterial community. The purpose of this study is to understand the characteristics of soil heavy metal pollution in lead-zinc mines in karst areas and the response of Pb, Zn, Cd, and As-induced composite pollution to soil microorganisms. This paper selected soil samples from the lead-zinc mining area of Xiangrong Mining Co., Ltd., Puding County, Guizhou Province, China. The soil in the mining area is contaminated by multiple heavy metals such as Pb, Zn, Cd and As. The average levels of Pb, Zn, Cd and As in the Pb-Zn mining soil were 14.5, 7.8, 5.5 and 4.4 times higher than the soil background in this area, respectively. Bacterial community structures and functions were analyzed using 16 S rRNA high-throughput sequencing technology and the PICRUSt method. A total of 19 bacterial phyla, 34 classes and 76 orders were detected in the tested soil. At the phylum level, the Proteobacteria are the dominant flora of the soil in the tailings reservoir area of the lead-zinc mine, respectively GWK1 (49.64%), GWK2 (81.89%), GWK3 (95.16%); and for the surrounding farmland soil, the Proteobacteria, Actinobacteriota, Acidobacteriota, Chloroflexi and Firmicutes are the most abundant in five bacterial groups. RDA analyses revealed that the heavy metal pollution of the lead-zinc mining area has a significant impact on the diversity of soil microorganisms. As the distance from the mining area increased, the heavy metal comprehensive pollution and potential risk value decreased, and the bacterial diversity increased. Additionally, various types of heavy metals have different effects on bacterial communities, and soil heavy metal content will also change the bacterial community structure. Proteobacteria positively related to Pb, Cd, and Zn, therefore, Proteobacteria were highly resistant to heavy metals. PICRUSt analysis suggested that heavy metals significantly affect the metabolic function of microorganisms. Microorganisms might generate resistance and enable themselves to survive by increasing the transport of metal ions and excreting metal ions. These results can be used as a basis for the microbial remediation of heavy metal-contaminated farmland in mining areas.

Spatial prediction and influencing factors identification of potential toxic element contamination in soil of different karst landform regions using integration model

The prediction of contamination distribution of potentially toxic elements (PTEs) in soils of Guangxi province, China and the identification of their controlling factors pose great challenges due to diverse bedrock types, intense leaching and weathering, and discontinuous terrain distributions. Herein, we integrated the random forest (RF) and empirical Bayesian kriging (EBK) to interpret and predict complex PTEs contamination distribution from three different karst landform regions (fenglin, fengcong, isolated peak plain) in Guangxi province. The modeling results are compared with the commonly used ordinary kriging and regression-kriging. In this study, our developed RF-EBK model combines the advantages of the RF and EBK model to promote the prediction accurately and efficiently. In this study, it was shown that the integration RF-EBK model exhibited desirable for Cd and As concentrations, with R² of 0.89 and 0.83, respectively. The average RMSE and MAE of integration RF-EBK model decreased by 39% and 44%, respectively, relative to the regression-kriging with the second highest accuracy. Furthermore, the modeling results showed that approximately 41.96% and 18.96% of total area was classified as Cd and As polluted and above regions (I_geo >0) in Guangxi province, respectively. Higher Cd concentration was observed in the soil of fenglin and fengcong regions than that in isolated peak plain region due to the secondary enrichment and parent rock inheritance, while the As concentration exhibited no significant difference among the three regions. The modeling results indicated that the elevated Cd concentration might be associated with soil CaO concentration and alkaline soil environment, whereas As concentration tended to be increased with the elevating Fe₂O₃ concentrations in weakly acidic soil environment. This result confirmed the applicability and effectiveness of integration model in predicting complex spatial patterns of soil PTEs and identifying their controlling factors.

Human health-risk assessment of heavy metal-contaminated soil based on Monte Carlo simulation

Soil contamination soils of by heavy metals (HMs) poses serious threats to the soil environment and enters the human body through exposure pathways such as ingestion and skin contact, posing a threat to human health. The purpose of this study was to analyze the sources and contributions of soil HMs, and to quantitatively assess the human health risks of soil HMs to different populations (i.e. children, adult females and adult males), and to analyze the human health risks caused by various sources of sensitive populations. 170 topsoil (0-20 cm) were collected from Fukang, Jimsar and Qitai on the northern slope of Tianshan Mountains in Xinjiang, China, and the contents of Zn, Cu, Cr, Pb and Hg were determined. This study used the Unmix model and a health-risk assessment (HRA) model to assess the human health risks of five HMs. The results showed that: (1) The mean values of Zn and Cr were lower than the background values of Xinjiang, the mean values of Cu and Pb were slightly higher than the background values of Xinjiang but lower than the national standard, and the mean value of Hg and Pb was higher than the background value of Xinjiang and the national standard. (2) The sources of soil HMs in the region were mainly traffic, natural, coal, and industrial sources. Moreover, the HRA model combined with Monte Carlo simulation showed similar trends in the health-risk status of all population groups in the region. Probabilistic HRA revealed that noncarcinogenic risks were acceptable for all populations (HI < 1) while carcinogenic risks were high (children: 77.52%; female: 69.09%; male: 65.63%). For children, carcinogenic risk from industrial and coal sources exceeded the acceptable threshold by 2.35 and 1.20 times, respectively, and Cr was the main element contributing to human carcinogenic risk. These findings suggest that carcinogenic risks from coal-based Cr emissions cannot be ignored, and the study area should aim to control Cr emissions from industrial sources. The results of this study provide support for the prevention of human health risks and the control of soil HMs pollution across different age groups.

A comprehensive method of source apportionment and ecological risk assessment of soil heavy metals: A case study in Qingyuan city, China

The study combined multiple models to provide a deeper understanding to soil heavy metal contamination and source information, which are essential for controlling pollution and reducing human health risks. In this study, the agricultural soils were collected from the Qingyuan City of China as an example. The multiple models (APCS/MLR, PMF, and GDM) were used to identify and quantitatively apportion the main sources of heavy metal pollution in the area. The results showed that Cu (56.4 %), Ni (70.9 %), B (44.5 %), and Cr (72.8 %) were associated with natural sources, such as soil parent material and soil-forming processes. However, Pb (41.2 %), Zn (61.8 %), Hg (67.0 %), and Cd (69.6 %) were associated with agricultural activities, atmospheric deposition, vehicle exhaust emissions, and vehicle tires, while Mo, Se, and Mn were possibly derived from natural sources, including rock weathering and soil parent materials. Additionally, the network of environmental analysis revealed that soil microbes are far more sensitive to soil heavy metal pollution than herbivores, vegetation, and carnivores. This study can serve as a guideline for reducing the ecological and health risks associated with heavy metals in soil by controlling their preferential sources. Environmental implication Combining multiple models is more effective approach to wide understanding of heavy metal contamination and source information, which is essential for controlling pollution and reducing human health risks. Based on multiple models (APCS/MLR, PMF, and GDM) and network environ analysis, a comprehensive method for apportioning soil heavy metal sources and assessing ecological risk had been provided. Further, the present study can be a guideline for reducing ecological and health risks by heavy metals in soil by controlling preferential sources.

Source-oriented probabilistic health risk assessment of soil potentially toxic elements in a typical mining city

Identifying potential sources of soil potentially toxic elements (PTEs) and developing source-oriented health risk assessments in typical mining cities are key for pollution prevention and risk management. To this end, a case study was conducted to explore the pollution characteristics, potential sources, and human health risks of PTEs in Daye City, China. Indices, including the pollution factor (PF), pollution load index (PLI), and geo-accumulation index (I_geo), were applied to assess PTE pollution. Cd had the highest value among the detected PTEs, and 82.93% of the sampling sites had moderate pollution levels, with the highest mean I_geo value for Cd (2.30). Four potential sources were determined. Cr and Ni originated mainly from natural sources. Zn (91.5%) was exclusively and then Cd (33.1%) was moderately derived from industrial activities. The mixed source of various mineral exploitation smelting, and coal-fired traffic emissions leaded to the accumulation of As, Cd, and Pb. Cu was associated with Cu-related mining and smelting activities. The probabilistic health risk assessment indicated that the non-carcinogenic risks for populations were negligible. Overall, this work provides scientific information for environmental managers to manage soil PTE pollution through the effective management of anthropogenic sources with limited resources and costs.

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

Environmental problems caused by heavy metal pollution in soil have attracted widespread attention worldwide. Identifying and quantifying the heavy metal pollution sources and risks is crucial for subsequent soil management. In this study, an integrated source-risk method for source apportionment and risk assessment based on the PMF model, the geodetector model and the health risk assessment model (HRA) was proposed and applied. Analysis of Hg, As, Pb, Cd, Cu, Ni, Cr, and Zn in 208 topsoils showed that the average contents of eight heavy metals were 1.87-5.86 times greater than corresponding background values, among which Cd and As were relatively high, which were higher than the specified soil risk screening values, high-value areas of heavy metals are mainly concentrated in the central part of the study area. The source apportionment showed that the accumulation of heavy metals was affected by five sources: atmospheric deposition (16.3 %), natural sources (33.1 %), industrial activities dominated by metal mining (15.1 %), industrial activities dominated by metal smelting (12.6 %) and traffic sources (22.9 %). The results of the health risk assessment showed that the carcinogenic risks (adult: 4.74E-05, children: 7.41E-05) of heavy metals in soil to the study population were both acceptable, the non-carcinogenic risk of adult (THI = 0.277) was within the limit, while the non-carcinogenic risk of children (THI = 1.70) was higher than the limit value. Ingestion (89.5 %-95.9 %) contributed the greatest health risk among all exposure routes. Source 3 (arsenic-related industrial activities dominated by metal mining) contributed the most to the HI and CRI of adults and children (all above 50 %), therefore, in the formulation stage of soil management strategy in this area, priority should be given to the control and management of this pollution source. These results can provide more detailed support for environmental protection departments to propose targeted soil pollution control measures.

Evaluating heavy metal pollution risks and enzyme activity in soils with intensive hazelnut cultivation under humid ecological conditions

In order to promote sustainable agriculture and ensure food security, it has become more vital to identify the causes of soil pollution in agricultural areas. This study was carried out in order to determine the danger of heavy metal contamination in hazelnut production areas and to take the appropriate actions in accordance with the study's findings. In this context, the main objectives of this study were to (i) determine some physical, chemical, and biological properties and heavy metal concentrations of different soils in intensive hazelnut cultivation areas under humid ecological conditions; (ii) reveal the heavy metal pollution risks of these areas by their enrichment factor, contamination factor, geo-accumulation index, degree of contamination, pollution load index, and potential ecological risk index; (iii) analyze the quality of soils contaminated with heavy metals by their total enzyme activity index and the geometric mean of enzymatic activities; and (iv) explore the correlation between heavy metals and soil enzyme activity indices. According to our results, the average concentrations of heavy metals in the study area ranked as Fe > Mn > Zn > Cr > Ni > Cu > Co > Pb > Cd. Based on EF, the area was evaluated as between deficiency to low enrichment and moderate enrichment for all elements except for Cd. When the parameters used to assess the risk of heavy metal contamination were evaluated, it was determined that the risk of contamination of other elements in the study area, except Cd, was low. Finally, analyzing the heavy metals and soil enzyme activity indices shows that there is a negative correlation between Ni and GMea and TEI.

A comprehensive framework for identifying contributing factors of soil trace metal pollution using Geodetector and spatial bivariate analysis

The accurate identification of pollution sources is important for controlling soil pollution. However, the widely used Positive matrix factorization (PMF) model generally relies on knowledge and experience to accurately identify pollution sources; thus, this method faces significant challenges in objectively identifying soil pollution sources. Herein, we established a comprehensive source analysis framework using factor identification and geospatial analysis, and revealed the factors contributing to trace metal(loid) (TM) pollution in soil in the Pearl River Delta (PRD), China. Using the PMF model, we initially considered that the PRD may be affected by natural, atmospheric, traffic and industrial, and agricultural sources. Moreover, Geodetector model detected the relationship between TMs and 12 environmental variables based on the strong spatial "source-sink" relationship of pollutants. The parent material and digital elevation model were the key factors predicting the accumulation of Cr, Ni, and Cu. Industries and roads were the most important determinants of Pb, Zn, and Cd, whereas atmospheric deposition was more important for Hg accumulation. The accumulation of As was found to be closely related to agricultural activities such as the application of chemical fertilizers and pesticides. The spatial autocorrelation between soil TM pollution and environmental variables further supports this hypothesis. Overall, the obtained results showed that proposed approach improved the accuracy of source apportionment and provided a basis for soil pollution control.

An approach to quantify heavy metals and their source apportionment in coal mine soil: a study through PMF model

Mining activities in the opencast coal mines contaminate the surrounding soil by releasing coal dust containing heavy metals (HMs). The objective of the present study was to quantify the concentration of HMs like Fe, Cu, Mn, Ni, Cr, Zn, and Co in soil on profile and distance basis in the vicinity of the coal mines. This research also proposed the synthesis application of positive matrix factorization (PMF) model for the quantitative assessment of pollution sources. The results showed that the soil was more affected due to the presence of Cr in mining areas., and the contamination factor (C_f) of Cr was high at the edge of coal mine. It was observed from the study that C_f of the HMs was decreased with the increase in distance from the mine edge. The application of PMF model demonstrated that the contributions of Zn (4.2%), Ni (16.8%), and Mn (100%) were maximum in the pollution. The study concluded that soil contamination is inexorable due to opencast coal mining activities, and it can be mitigated by developing a green belt or through the process of ecological restoration and phytoremediation.

Heavy metal levels in the soil near typical coal-fired power plants: partition source apportionment and associated health risks based on PMF and HHRA

In this study, five priority metals recommended by the Ministry of Ecology and Environment of China (MEEC) were investigated. In the Bijie region of Guizhou Province, three typical coal-fired power plants were chosen as the research locations. A combination of 24 soil samples was obtained at various distances and depths from the point source of contamination. The authors found that the average contents of As, Cd, Cr, Ni, and Pb were 14.15, 1.52, 16.80, 40.71, and 53.00 mg kg^-1, respectively, with Cd and Pb pollution prominent. In another, soil heavy metal (SHM) content tends to increase or decrease dependently with the increase of sampling distance and depth, with total concentrations ranging from 77.14 to 157.33 mg kg^-1. Combining PCA and PMF models, the number of source factors was determined more clearly and accurately using PCA, and the Q-value of PMF was used for validation. The PCA-PMF indicated that the primary anthropogenic sources were transportation-related activities and emissions from coal combustion. The health risks of SHMs under three different exposure routes were then assessed using the HHRA. The findings showed the five HMs in order of non-carcinogenic risk were As > Pb > Cr > Ni > Cd. The comprehensive non-carcinogenic risk for children under the oral intake route around plant B and C was greater than 1, pointing to a potential health risk to children from the soils. The carcinogenic risk of HM was less than 1.00E-04 for both single-factor and multifactor under all three exposure routes, which is below the tolerable limit.

Responses of diversity and arsenic-transforming functional genes of soil microorganisms to arsenic hyperaccumulator (Pteris vittata L.)/pomegranate (Punica granatum L.) intercropping

Intercropping of arsenic (As) hyperaccumulator (Pteris vittata L.) with crops can reduce the As concentration in soil and the resulting ecological and health risks, while maintaining certain economic benefits. However, it is still unclear how As-transforming functional bacteria and dominant bacteria in the rhizosphere of P. vittata affect the microbial properties of crop rhizosphere soil, as well as how As concentration and speciation change in crop rhizosphere soil under intercropping. This is of great significance for understanding the biogeochemical cycle of As in soil and crops. This study aimed to use high-throughput sequencing and quantitative polymerase chain reaction (qPCR) to analyze the effects of different rhizosphere isolation patterns on the bacterial diversity and the copy number of As-transforming functional genes in pomegranate (Punica granatum L.) rhizosphere soil. The results showed that the abundance of bacteria in the rhizosphere soil of pomegranate increased by 16.3 %, and the soil bacterial community structure significantly changed. C_Alphaproteobacteria and o_Rhizobiales bacteria significantly accumulated in the rhizosphere of pomegranate. The copy number of As methylation (arsM) gene in pomegranate rhizosphere soil significantly increased by 63.37 %. The concentrations of nonspecifically sorbed As (F1), As associated with amorphous Fe (hydr)oxides (F3), and the total As (FT) decreased; the proportion of As (III) in pomegranate rhizosphere soil decreased; and the proportion of As (V) increased in pomegranate rhizosphere soil. c_Alphaproteobacteria and o_Rhizobiales accumulated in crop rhizosphere soil under the intercropping of P. vittata with crops. Also, the copy number of As methylation functional genes in crop rhizosphere soil significantly increased, which could reduce As (III) proportion in crop rhizosphere soil. These changes favored simultaneous agricultural production and soil remediation. The results provided the theoretical basis and practical guidance for the safe utilization of As-contaminated soil in the intercropping of As-hyperaccumulator and cash crops.

An insight into source apportionment of metals in superficial sediments from the Tien Hai nature reserve of the Red River delta, Vietnam

In the present study, concentrations of 23 metallic elements (Al, Fe, K, Mg, Ti, Mn, V, Cr, Co, Ni, Cu, Zn, Ga, As, Sr, Zr, Mo, Cd, Ba, W, Hg, Pb and U) were determined in superficial sediments from 20 sites within the Tien Hai nature reserve of the Red River delta. An integrated approach including correlation analysis (CA), principal components analysis (PCA) and positive matrix factorization (PMF) was combined to identify and quantify the sources of these metals. The results indicated that the correlation analysis was able to identify both allochthonous and authigenous origin of metals while the remaining methods were unable to identify these origins. With the help of CA, the PMF model performed better at the source identification of metals than the PCA. The results of PMF demonstrated that weathering products formed on different parent rocks were the biggest contributor to natural sources of metals in the superficial sediments, while agricultural activities were regarded as the major contributor to anthropogenic sources of metals. These findings could provide a scientific basis for environmental protection authority to prevent and control the sediment metal pollution. Accordingly, application of organic amendments and reduction of inorganic fertilizer should be encouraged to minimize metal accumulation in sediments.

Comprehensive assessment of heavy metals in soil-crop system based on PMF and evolutionary game theory

The traditional assessment of farmland environmental quality usually focuses on soil heavy metals, but ignores agricultural produce safety. It is urgent to comprehensively assess the effects of farmland environmental quality based on soil quality and produce safety. To fill this gap, the comprehensive assessment method was improved based on previous studies, which was used to assess the pollution level of heavy metals in soil-crop system of Shenyang, Liaoning Province, Northeast China. In addition, this study also made a comprehensive analysis of pollution sources based on positive matrix factorization (PMF) model, and discussed soil-crop system income stability by evolutionary game theory. The mean concentrations of As, Cd, Cr, Hg, Pb, Cu, Zn, and Ni in soil exceeded the corresponding Shenyang soil background values (5.68 %, 14.36 %, 57.61 %, 7.86 %, 30.32 %, 5.21 %, 211.72 %, 171.88 %). The results showed that about 28.28 % of paired soil-crop points were polluted by heavy metals, especially rice-soil points. Furthermore, heavy metals in crops may be transmitted less from soil and more from other environmental media. The PMF analysis results showed that there were six pollution sources in study area, and the major contributor of pollution were agricultural activities, traffic-related activities, and industrial activities. In farmland environment protection, the only stable strategy is soil-crop system, and soil-crop system is better than the benefits of single soil or crop from the perspective of benefits. This study provides a scientific and reliable method to combine soil quality with produce safety to assess the risk of heavy metals in farmland.

Insights into site-specific influences of emission sources on accumulation of heavy metal(loid)s in soils by wheat grains

Excessive accumulation of heavy metal(loid)s in agricultural environment usually originates from anthropogenic activities. Both large diversities of emission sources and complexity of plant accumulation challenge the understanding of the site-specific effects of emission sources on heavy metal(loid)s in wheat grains. Herein, both soil samples and wheat grain samples (n = 80) were collected from the farmland of Jiyuan City, China. Soil and grain burdens of heavy metal(loid)s were determined by inductively coupled plasma mass spectrometry (ICP-MS) and/or X-ray fluorescence spectrometry (XRF). The quotients (Q) were developed to indicate relative impacts of industrial plants and traffic to soil sites. Principal component analysis-absolute principal component scores-multivariate linear regression (PCA-APCS-MLR) analysis was conducted to reveal the source contributions to heavy metal(loid)s in grains, considering Q values, soil, and wheat grain data. Results showed that contributions of main sources and factors drastically varied with soil sites, and usually overlapped to different extents. For grain Cd and grain Pb, natural soil silicate (0.066/0.104 mg/kg) and iron-bearing minerals (- 0.044/ - 0.174 mg/kg) contributed to high extents, while metal smelting activities (0.018/0.019 mg/kg) and agronomic activities (- 0.017/ - 0.019 mg/kg) unexpectedly posed low or moderate contributions. The pH-mediated availability of soil Cd (0.035 mg/kg) and the sand-dust weather (0.028 mg/kg) also made considerable contributions to grain Cd. For grain As, both natural soil iron-bearing (- 0.048 mg/kg) and silicate minerals (- 0.013 mg/kg) made negative contributions. The results benefit to the decision-making of pollution remediation of farmland soils in the regional scales.

Distribution characteristics, source identification, and risk assessment of heavy metals in surface sediments of the salt lakes in the Ordos Plateau, China

Salt lakes considerably affect the regional climate, environment, and ecology of semiarid regions characterized by low rainfall and high evaporation. However, under the stresses of global change and human disturbance, anthropogenic pollution is the primary factor threatening the lake's ecological environment. Surface sediment samples collected from four salt lakes in the Ordos Plateau were used to investigate the salinity, concentration, pollution status, potential sources of heavy metals, and influencing factors. The surface sediments of Beida Pond and Gouchi Pond were weakly alkaline (pH < 9) due to the presence of Na₂SO₄, whereas those of Chaigannaoer and Hongjiannao were strongly alkaline (pH > 9) due to the presence of Na₂CO₃. The concentration range of Cr, Ni, Cu, Zn, As, Cd, and Pb in the sediment samples collected from the salt lakes in the Ordos Plateau followed the order of Cr > Zn > Ni > Pb > Cu > As > Cd. The Cr concentration values were higher in Chagannaoer and Hongjiannao; however, the Ni, Cu, and Zn values were higher in Beida Pond and Gouchi Pond. The geoaccumulation index (I_geo) and enrichment factor (EF) consistently indicated that Cr posed the greatest potential ecological risk and that Ni, Cu, and Zn pollution was more severe in Beida Pond and Gouchi Pond than in Chagannaoer or Hongjiannao. However, the ecological risk index and potential ecological risk value indicated that these heavy metals posed low risks to the environment. The risk assessment code (RAC) revealed that Pb and Cr exhibited no mobility and had low potential bioavailability risk. Meanwhile, Zn, Ni, and As were categorized as medium risk. Cu had the highest mobility and was categorized as high risk. Principal component analysis for the four salt lakes revealed that the source of Ni, Cu, Zn, and Cd might be associated with water-soluble elements associated with aqueous migration, while the source of Cr, Pb, and As might be the lithospheric minerals carried by dust storms. Pearson's correlation analysis indicated that clay minerals were the primary adsorbers of Ni, Cu, Zn, and Cd. Moreover, pH was identified as the main environmental factor controlling the distribution of heavy metals in the salt lakes.

Contamination characteristics, source identification, and source-specific health risks of heavy metal(loid)s in groundwater of an arid oasis region in Northwest China

Heavy metal(loid)s accumulation in groundwater has posed serious ecological and health concerns worldwide. Source-specific risk apportionment is crucial to prevent and control potential heavy metal(loid)s pollution in groundwater. However, there is very limited comprehensive information on the health risk apportionment for groundwater heavy metal(loid)s in arid regions. Thus, the Zhangye Basin, a typical arid oasis region in Northwest China, was selected to investigate the contamination characteristics, possible pollution sources, and source-specific health risks of groundwater heavy metal(loid)s. The heavy metal pollution index (HPI), the Nemerow index (NI), and the contamination degree (CD) were adopted to assess the pollution level of heavy metal(loid)s; then source-specific health risk was apportioned integrating the absolute principal component scores-multiple linear regression (APCS-MLR) with health risk assessment. Noticeable accumulation of Mn, Fe, and As was observed in this region with especially Fe/As in 12.68%/2.11% of the samples revealing significant enrichment. Approximately 3.5% of the groundwater samples caused moderate or higher pollution level based on the HPI. The APCS-MLR model was more physically applicable for the current research than the positive matrix factorization (PMF) model. Industrial-agricultural activity factor (12.56%) was the major source of non-cancer (infants: 59.15%, children: 64.87%, teens: 64.06%, adults: 64.02%) and cancer risks (infants: 77.36%, children: 77.35%, teens: 77.40%, adults: 77.41%). Industrial-agricultural activities should be given priority to control health risks of heavy metal(loid)s in groundwater. These findings provide fundamental and significant information for mitigating health risks caused by heavy metal(loid)s in groundwater of typical arid oasis regions by controlling priority sources.

Applications of dynamic simulation for source analysis of soil pollutants based on atmospheric diffusion and deposition model

Existing receptor-model-based source apportionment methods failed to derive source contributions to accumulation of soil heavy metals (SHMs). In this research, a dynamics-simulation-based source apportionment approach (DSSA) was developed by integrating mathematical models of source release, diffusion and deposition pathway, and receptor accumulation, to quantify accumulative contributions of SHMs. The case study was carried out in a complex industrialized region in southeast China to investigate pollution situation of SHMs (Zn, Pb, Ni, As, Cd, and Cr). The results showed that SHMs distributions were affected by seasonal variation and near-surface meteorology, which could be sequenced by correlation coefficient as temperature (0.968) > humidity (0.552) > precipitation (0.389) > wind speed (0.386). The source categories and corresponding contribution rates were identified as: i) battery plant to Zn (72.32%) and Pb (71.73%), ii) traffic to Ni (64.55%), iii) traffic and agriculture to Cd (43.26%, 41.63%), iv) agriculture to As (75.30%) and Cr (60.05%), which was similar to the results of positive matrix factorization (PMF). Furthermore, DSSA could illustrate SHMs migration process from source to receptor. The uncertainty analysis further proved the distinct advantages of DSSA. The results of this research could predict pollutant enrichment and could provide new perspective for environment and public health management.

Apportionment and location of heavy metal(loid)s pollution sources for soil and dust using the combination of principal component analysis, Geodetector, and multiple linear regression of distance

The accurate identification of sources for soil heavy metal(loid) is difficult, especially for multi-functional parks, which include multiple pollution sources. Aiming to identify the apportionment and location of heavy metal(loid)s pollution sources, this study established a method combining principal component analysis (PCA), Geodetector, and multiple linear regression of distance (MLRD) in soil and dust, taking a multi-functional industrial park in Anhui Province, China, as an example. PCA and Geodetector were used to determine the type and possible location of the source. Source apportionment of individual elements is achieved by MLRD. The detection results quantified the spatial explanatory power (0.21 ≤ q ≤ 0.51) of the potential source targets (e.g., river and mining area) for the PCA factors. A comparative analysis of the regression equation (Model 1 and Model 3) indicated that the river (0.50 ≤ R² ≤0.78), main road (0.47 ≤ R² ≤ 0.81), and mine (0.14 ≤ R² ≤ 0.92) (p < 0.01) were the main sources. Different from the traditional source apportionment methods, the current method could obtain the exact contributing sources, not just the type of source (e.g., industrial activities), which could be useful for pollution control in areas with multiple sources.

Characteristics, Health Risk Assessment, and Transfer Model of Heavy Metals in the Soil-Food Chain in Cultivated Land in Karst

Heavy metal(loid) contamination of farmland is a crucial agri−environmental problem that threatens food safety and human health. In this study, we examined the contamination levels of heavy metals (As, Pb, Cd, Hg, Cr) in farmland and foods (rice, maize, and cabbage) in the core of Asia’s largest karst region and assessed the potential health risks of consumption of these three foods. In addition, we developed a predictive transfer model of heavy metals in the soil−food chain through multiple regression equations. The results reveal that the soil heavy metals in the study area showed high accumulation characteristics, and the average concentration exceeded the national background value by 1.6−130 times, among which Cd pollution was the most serious. The order of contamination of the three soils in the study area was cabbage land > maize land > rice land. The order of potential risk of toxic elements in all three soils was Cd > Hg > As > Pb > Cr. The results of the risk assessment of agricultural consumption indicated a high carcinogenic and noncarcinogenic risk for the local population. The top contributor to carcinogenic risk was Cr, followed by As. Cd is the major noncarcinogenic contributor in maize and cabbage, and the noncarcinogenic contribution in rice is mainly caused by As. The risk was higher in children than in adults and was the highest for rice consumption. In addition, the predictive transfer model showed that the Cd levels in the three foods showed sufficient predictability and reasonable simulations of Cd concentrations in rice, maize, and cabbage throughout the study area. It could allow decision-making on the need for remediation strategies to reduce the risk of metal contamination of agricultural land in potentially high−risk areas of karst.

Dominant roles of torrential floods and atmospheric deposition revealed by quantitative source apportionment of potentially toxic elements in agricultural soils around a historical mercury mine, Southwest China

Hg pollution in soils surrounding the Wanshan mercury mine (WMM), once the largest Hg-producing center in China, has been confirmed, neglecting other potential toxic elements (PTEs). Better understanding of the sources and transport pathways of soil PTEs remains insufficient. To response these limitations, eight soil PTEs (As, Cd, Cr, Cu, Hg, Ni, Pb and Zn) at two typical agricultural sites, namely AZ and WW that are located near and far from the WMM, respectively, were systemically investigated. The results showed that AZ exhibited significantly higher concentrations of all the PTEs in the surface soil than WW (p < 0.01). Hg and Cd were recognized as the priority control PTEs, with their average concentrations of 21.54 and 1.21 mg kg^-1 at AZ, and 15.79 and 0.48 mg kg^-1 at WW. Those affected PTEs tended to enrich in near-river areas. Atmospheric deposition contributed more to soil Hg than did regular irrigation, but these two sources could not explain the considerable soil Hg accumulation. Three sources, including natural sources, hydraulic transport (torrential floods and regular irrigation) and atmospheric deposition, were identified and quantified based on the positive matrix factorization model, statistical methods and various auxiliary information. Hydraulic transport (mainly torrential floods) dominated the soil Hg input, which could explain 83.8% and 69.8% of the soil Hg input at AZ and WW, respectively. Atmospheric deposition dominated the soil Cd input, explaining 44.3% and 59.9% of the soil Cd input at AZ and WW, respectively. More attention should be given to the safe utilization of agricultural land and long-term monitoring of atmospheric deposition of Hg and Cd. This study could provide insights to prevent PTE diffusion along the above dominant transportation pathways while developing similar mine regions.

Distribution and ecological risk assessment of trace elements in the paddy soil-rice ecosystem of Punjab, Pakistan

Trace elements (TEs) contamination of agricultural soils requires suitable criteria for regulating their toxicity limits in soil and food crops, which depends on their potential ecological risk spanning regional to global scales. However, no comprehensive study is available that links TE concentrations in paddy soil with ecological and human health risks in less developed regions like Pakistan. Here we evaluated the data set to establish standard guidelines for defining the hazard levels of various potentially toxic TEs (such as As, Cd, Co, Cu, Cr, Fe, Mn, Ni, Pb, Se, Zn) in agricultural paddy soils of Punjab, Pakistan. In total, 100 topsoils (at 0-15 cm depth) and 204 rice plant (shoot and grain) samples were collected from five ecological zones of Punjab (Gujranwala, Hafizabad, Vehari, Mailsi, and Burewala), representing the major rice growing regions in Pakistan. The degree of contamination (C_d) and potential ecological risk index (PERI) established from ecological risk models were substantially higher in 100% and 97% of samples, respectively. The positive matrix factorization (PMF) model revealed that the elevated TEs concentration, notably Cd, As, Cr, Ni, and Pb, in the agricultural paddy soil was attributed to the anthropogenic activities and groundwater irrigation. Moreover, the concentration of these TEs in rice grains was higher than the FAO/WHO's safe limits. This study provided a baseline, albeit critical knowledge, on the impact of TE-allied ecological and human health risks in the paddy soil-rice system in Pakistan; and it opens new avenues for setting TEs guidelines in agro-ecological zones globally, especially in underdeveloped regions.