Health risk assessment of heavy metals based on source analysis and Monte Carlo in the downstream basin of the Zishui.

In this study, stone coal mines in the lower reaches of the Zijiang River were adopted as the research object. To analyze the spatial distribution, sources, and health risks of heavy metals in the surrounding soil of stone coal mines, 82 topsoil samples were collected in the study area, and the contents of 8 heavy metals including Cd, Hg, As, Cr, Pb, Cu, Ni, and Zn were determined. The spatial distribution of heavy metals was analyzed using ArcGIS, and the pollution sources of heavy metals were identified using Positive matrix factorization (PMF). Then, Monte Carlo and health risk assessment models were used to evaluate the health risks of different populations. The results showed that the average content of heavy metals followed the order of Zn > Cr > Pb > Cu > Ni > As > Cd > Hg, and the contents of all heavy metals were higher than the soil background values of Hunan Province. The high-value areas of heavy metals content were mostly concentrated in the central region close to areas with a notable concentration of stone coal mines. PMF identified four pollution sources, namely, mining activities (26.9%), atmospheric deposition (18.8%), natural sources (32.8%) and agricultural sources (21.5%). The carcinogenic and non-carcinogenic risks for children were higher than those for adults, with As and Cd posing higher carcinogenic risks to children. Based on the source of health risks, it was determined that the health risks could be primarily attributed to agricultural sources, and As was the main heavy metal causing health risks. This study provides theoretical support for treating heavy metal pollution in mining basins.

Nonionic surfactant Tween 80-facilitated bacterial transport in porous media: A nonmonotonic concentration-dependent performance, mechanism, and machine learning prediction.

The surfactant-enhanced bioremediation (SEBR) of organic-contaminated soil is a promising soil remediation technology, in which surfactants not only mobilize pollutants, but also alter the mobility of bacteria. However, the bacterial response and underlying mechanisms remain unclear. In this study, the effects and mechanisms of action of a selected nonionic surfactant (Tween 80) on Pseudomonas aeruginosa transport in soil and quartz sand were investigated. The results showed that bacterial migration in both quartz sand and soil was significantly enhanced with increasing Tween 80 concentration, and the greatest migration occurred at a critical micelle concentration (CMC) of 4 for quartz sand and 30 for soil, with increases of 185.2% and 27.3%, respectively. The experimental results and theoretical analysis indicated that Tween 80-facilitated bacterial migration could be mainly attributed to competition for soil/sand surface sorption sites between Tween 80 and bacteria. The prior sorption of Tween 80 onto sand/soil could diminish the available sorption sites for P. aeruginosa, resulting in significant decreases in deposition parameters (70.8% and 33.3% decrease in KD in sand and soil systems, respectively), thereby increasing bacterial transport. In the bacterial post-sorption scenario, the subsequent injection of Tween 80 washed out 69.8% of the bacteria retained in the quartz sand owing to the competition of Tween 80 with pre-sorbed bacteria, as compared with almost no bacteria being eluted by NaCl solution. Several machine learning models have been employed to predict Tween 80-faciliated bacterial transport. The results showed that back-propagation neural network (BPNN)-based machine learning could predict the transport of P. aeruginosa through quartz sand with Tween 80 in-sample (2 CMC) and out-of-sample (10 CMC) with errors of 0.79% and 3.77%, respectively. This study sheds light on the full understanding of SEBR from the viewpoint of degrader facilitation.

Accurate Prediction of Soil Heavy Metal Pollution Using an Improved Machine Learning Method: A Case Study in the Pearl River Delta, China.

In traditional soil heavy metal (HM) pollution assessment, spatial interpolation analysis is often carried out on the limited sampling points in the study area to get the overall status of heavy metal pollution. Unfortunately, in many machine learning spatial information enhancement algorithms, the additional spatial information introduced fails to reflect the hierarchical heterogeneity of the study area. Therefore, we designed hierarchical regionalization labels based on three interpolation techniques (inverse distance weight, ordinary kriging, and trend surface interpolation) as new spatial covariates for a machine learning (ML) model. It was demonstrated that regional spatial information improved the prediction performance of the model (R2 > 0.7). On the basis of the prediction results, the status of HM pollution in the Pearl River Delta (PRD) region was evaluated: cadmium (Cd) and copper (Cu) were the most serious pollutants in the PRD (the point overstandard rates are 18.77% and 12.95%, respectively). The analysis of index importance and bivariate local indicators of spatial association (LISA) shows that the key factors affecting the spatial distribution of heavy metals are geographical and climatic conditions [namely, altitude, humidity index, and normalized vegetation difference index (NDVI)] and some industrial activities (such as metal processing, printing and dyeing, and electronics industry). This study develops a novel approach to improve existing spatial interpolation techniques, which will enable more precise and scientific soil environmental management.

Long-term variations in external phosphorus inputs and riverine phosphorus export in a typical arid and semiarid irrigation watershed.

Excessive phosphorus (P) along with drained water from farmland in the arid and semiarid watersheds when entering into water bodies brings about serious environmental problems in the aquatic ecosystem. It is critical to explore variations in watershed P balance and the relationship between anthropogenic P input and riverine total phosphorus (TP) export in a typical irrigation watersheds. In this study, long-term anthropogenic P variations in Ulansuhai Nur watershed (UNW), a typical irrigation watershed in Yellow River basin, was investigated using a quantitative Net Anthropogenic Phosphorus Input (NAPI) budget model. The results showed that annual NAPI exhibited a significant upward trend with a multi-year average of 2541.6 kg P km-2 yr-1 in the UNW. Hotspots for watershed NAPI were discovered in Linhe and Hangjin Houqi counties. Chemical P fertilizers and livestock breeding were two dominated sources of NAPI. Annual riverine TP export showed a significantly declined trend with a net decrease of 80.6%. The export ratio of watershed NAPI was 0.6%, lower than those reported for other watersheds worldwide. There was a significant positive linear correlation between NAPI and riverine TP export from 2005 to 2009. However, after 2009, riverine TP export exhibited a decreased trend with increasing watershed NAPI, which was attributed to environmental treatment measures. By reconstructing riverine TP export without the impact of pollution treatment measures, annual average reduction amount of riverine TP export from 2009 to 2019 was estimated to be 237.2 ton, 47.2% and 52.8% of which were attributed to the point and nonpoint sources measures. This study not only widens the application scope of NAPI budget method, but also provides useful information of nutrient management and control in the arid and semiarid irrigation watershed.

Pollution and risk assessment of potentially toxic elements in soils from industrial and mining sites across China.

Potentially toxic elements in soils (SPTEs) from industrial and mining sites (IMSs) often cause public health issues. However, previous studies have either focused on SPTEs in agricultural or urban areas, or in a single or few IMSs. A systematic assessment of the pollution and risk levels of SPTEs from IMS at the national scale is lacking. Here, we obtained SPTE (As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn) concentrations from IMSs across China based on 188 peer-reviewed articles published between 2004 and 2022 and quantified their pollution and risk levels using the pollution index and risk assessment model, respectively. The results indicated that the average concentrations of the eight SPTEs were 4.42-270.50 times the corresponding background values, and 19.58% of As, 14.39% of Zn, 12.79% of Pb, and 8.03% of Cd exceeded the corresponding soil risk screening values in these IMSs. In addition, 27.13% of the examined IMS had one or more SPTE pollution, mainly distributed in the southwest and south central China. On the examined IMSs, 81.91% had moderate or severe ecological risks, which were mainly caused by Cd, Hg, As, and Pb; 23.40% showed non-carcinogenic risk and 11.70% demonstrated carcinogenic risk. The primary exposure pathways of the former were ingestion and inhalation, while that for the latter was ingestion. A Monte Carlo simulation also confirmed the health risk assessment results. As, Cd, Hg, and Pb were identified as priority control SPTEs, and Hunan, Guangxi, Guangdong, Yunnan, and Guizhou were selected as the key control provinces. Our results provide valuable information for public health and soil environment management in China.

Environmental quality standards for agricultural land in China: What should be improved on derivation methodology?

Soil pollution has caused increasingly widespread attention in China. The environmental risk threshold of pollutants is a yardstick to measure soil environmental quality. For decades, plenty of research on soil environmental quality standards (SEQSs) has been carried out, providing scientific basis for the investigation and supervision of soil environmental quality. This paper summaries the development of SEQSs in China, the corresponding influencing factors and methodology of SEQSs derivation. In the current version of SEQSs (GB15618-2018), the thresholds of soil pollutants are derived by the methods of environmental risk assessment, which are more methodologically scientific than geochemical method and ecological effect method used in the previous version (GB15618-1995). Abundant toxicology data on related species is required for risk assessment of soil pollution using extrapolation; however, basic toxicological data is insufficient and few valid data is available at present. Besides, the inadequate consideration on influencing factors for the derivation of soil pollutant threshold would affect the scientificity and rationality of SEQSs, such as biotic factors (species type, test endpoint etc.) and abiotic factors (aging effect, leaching effect, synergistic or antagonistic effects of elements etc.). These problems should be paid close attention in future research on soil environmental quality standards. The contents summarized in this review may provide reference for decision-making on supervision of soil environmental quality and point out important directions for future studies.

Soil bacteria, genes, and metabolites stimulated during sulfur cycling and cadmium mobilization under sodium sulfate stress.

Sodium sulfate stress is known to improve cadmium (Cd) mobilization in soil and microbial sulfur oxidation, Cd resistance, and the accumulation of stress tolerance-associated metabolites has been correlated with increased soil Cd availability and toxicity. In this study, aerobic soil microcosms with Cd-contamination were stimulated with sodium sulfate to investigate its effects on soil microbial community structure, functional genes, and associated metabolite profiles. Metagenomic analysis revealed that sulfur oxidizing and Cd-resistant bacteria carried gene clusters encoding sox, dsr, and sqr genes, and znt, czc, and cad genes, respectively. Exposure to sodium sulfate resulted in the reprogram of soil metabolites. In particular, intensification of sulfur metabolism triggered an up-regulation in the tricarboxylic acid (TCA) cycle, which promoted the secretion of carboxylic acids and their precursors by soil bacteria. The accumulation of organic acids induced in response to high sodium sulfate dosages potentially drove an observed increase in Cd mobility. Pseudomonas and Erythrobacter spp. exhibited a high capacity for adaptation to heavy metal- or sulfur-induced stress, evident by an increased abundance of genes and metabolites for sulfur cycling and Cd resistance. These results provide valuable insights towards understanding the microbial mechanisms of sulfur transformation and Cd dissolution under saline stress.

Accumulation risk and source apportionment of heavy metals in different types of farmland in a typical farming area of northern China.

The types of land used for farmland can greatly influence the source and accumulation risk of heavy metals in soil. However, the apportioning quantitatively the source of soil heavy metals has been studied insufficiently, especially in terms of different types of farmland. In this study, a total of 252 soil samples were taken from dry land, paddy fields and greenhouse fields in the Jinyuan district of Taiyuan city, China, to assess the accumulation risk of heavy metals (As, Cd, Cr, Cu, Hg, Ni, Pb and Zn). The results were then integrated, and source apportionment was evaluated by geospatial analysis, multivariate statistical analysis and positive matrix factorization (PMF). Cr, Cd and Hg were the dominant pollutants in the studied area. Accumulation risk by Cd and Cu was more severe in greenhouse fields than in dry land or paddy fields, whereas As, Hg and Pb had relatively higher accumulation in paddy fields than in dry land or greenhouse fields. Hg was derived mainly from coal combustion by atmospheric precipitation for the three types of farmland. Long-term irrigation using sewage is the main reason for the accumulation of Cu and Ni in dry land soil, Cu and Zn in paddy field soil and Zn in greenhouse soil. Cd in dry land, Cd and Pb in paddy fields and Cd, Cu, Ni and Pb in greenhouse fields were primarily added to soil through fertilization. Sewage irrigation and fertilization were the dominant sources of heavy metals for paddy field (31.3%) and greenhouse field (33.1%), respectively.

Application of a self-organizing map and positive matrix factorization to investigate the spatial distributions and sources of polycyclic aromatic hydrocarbons in soils from Xiangfen County, northern China.

The concentrations of 16 priority polycyclic aromatic hydrocarbons (PAHs) were measured in 128 surface soil samples from Xiangfen County, northern China. The total mass concentration of these PAHs ranged from 52 to 10,524ng/g, with a mean of 723ng/g. Four-ring PAHs contributed almost 50% of the total PAH burden. A self-organizing map and positive matrix factorization were applied to investigate the spatial distribution and source apportionment of PAHs. Three emission sources of PAHs were identified, namely, coking ovens (21.9%), coal/biomass combustion (60.1%), and anthracene oil (18.0%). High concentrations of low-molecular-weight PAHs were particularly apparent in the coking plant zone in the region around Gucheng Town. High-molecular-weight PAHs mainly originated from coal/biomass combustion around Gucheng Town, Xincheng Town, and Taosi Town. PAHs in the soil of Xiangfen County are unlikely to pose a significant cancer risk for the population.

Diurnal variation in the urban thermal environment and its relationship to human activities in China: a Tencent location-based service geographic big data perspective.

The main factor of the formation and deterioration in China's urban thermal environment is human activity, which is difficult to describe and measure. A new perspective on the effect of human activity on the urban thermal environment can be obtained by examining the interaction between location-based service (LBS) data and the urban thermal environment in China. However, relevant research is still limited. In this study, we used Tencent LBS data, Terra/Aqua MODIS land surface temperature (LST) data, and land use data to investigate the relationship between LBS and the urban thermal environment, specifically the LST and surface urban heat island intensity (SUHII) across China and its provinces. Our results showed that (1) in summer, the heat island effect was an issue in 94% of the urban areas in China, which was worse during the day. The high- and low-value periods of LBS data on a given day coincided with the acquisition times of MODIS LST products during the day and at night, respectively. (2) During both the day and at night, there was a significant connection between LBS data and the urban thermal environment in China. The highest correlation coefficient (r) between LBS data and the LST could reach 0.55 (p < 0.01) at the provincial level, and the highest correlation coefficient (r) between LBS data and the SUHII could reach 0.78 (p < 0.01) at the provincial level. (3) The urban thermal environment diurnal difference and LBS data exhibited a significant relationship. The ΔLBS diurnal differences were significantly positively related to the SUHII diurnal differences in China. The overall study findings revealed that LBS data constitute an important parameter to represent the human activity intensity when investigating the formation of the urban thermal environment in China.

Sorption of cadmium by layered double hydroxides: Performance, structure-related mechanisms, and sequestration stability assessment.

Layered double hydroxides (LDHs) have been recognized to have great potential for the treatment of heavy metals in wastewater and soil through various mechanisms. Isomorphic substitution is an important mechanism for the sorption of heavy metal cations with LDH reconstruction and highly stable product formation. However, sorption performance, structure-related relationships, and, more importantly, stability are still poorly understood. In this study, a series of LDHs with different structures were synthesized to evaluate their cadmium (Cd) sorption performance and stability concerning the isomorphic substitution mechanism. Divalent cation types in the LDH lattice determined the Cd sorption capacity as well as the isomorphic substitution possibility, following the order of hydroxide solubility of divalent cations (MII): Ca2+>Mg2+>(Cd2+) > Ni2+>Zn2+. In addition, CaAl-LDH exhibited a super-high Cd sorption capacity of 625.0 mg g-1. Cd sorption by LDHs with different interlayer anion types and divalent/trivalent cation molar ratios varied due to crystallite size-related MII release through cation-exchange/isomorphic substitution. Coexisting cations (e.g., Zn2+, Ni2+, Mg2+) influence the sorption performance of MII-LDH mainly through isomorphic substitution mechanism, largely depending on the solubility of MII(OH)2 with a trend of stable product formation. Furthermore, Mg2.9Cd0.1AlCl-LDH was fabricated, and limited Cd dissolution without destruction of the LDH structure was observed under various conditions. For example, only 7.69%, 2.16% and 0.96% of Cd was released from as-prepared Mg2.9Cd0.1AlCl-LDH in NaCl solution (0.02 mol L-1, pH 5), soil extract, and soil matrix, respectively. The very low leaching of Cd from Cd-containing LDHs indicated the high stability of LDH-sorbed Cd via isomorphic substitution and feasible practical application in Cd sequestration in wastewater treatment and soil remediation.

Identifying driving factors of soil heavy metal at the mining area scale: Methods and practice.

Identifying driving factors is of great significance for understanding the mechanisms of soil pollution. In this study, a data processing method for driving factors was analyzed to explore the genesis of Arsenic (As) pollution in mining areas. The wind field that affects the atmospheric diffusion of pollutants was simulated using the standard k-ε model. Machine learning and GeoDetector methods were used to identify the primary driving factors. The results showed that the prediction performances of the three machine learning models were improved after data processing. The R2 values of random forest (RF), support vector machine, and artificial neural network increased from 0.45, 0.69, and 0.24 to 0.55, 0.76, and 0.52, respectively. The importance of wind increased from 20.85% to 26.22%. The importance of distance to the smelter plant decreased from 43.26% to 33.19% in the RF model. The wind's driving force (q value) increased from 0.057 to 0.235 in GeoDetector. The average value of historical atmospheric dust reached 534.98 mg/kg, indicating that atmospheric deposition was an important pathway for As pollution. The outcome of this study can provide a direction to clarify the mechanisms responsible for soil pollution at the mining area scale.

[Enrichment Characteristics, Source Apportionment, and Risk Assessment of Heavy Metals in the Industrial and Mining Area of Northern Guangdong Province].

In order to understand the heavy metal pollution of the industrial and mining area in northern Guangdong Province, topsoil samples (0-20 cm) from 209 sites in study area were collected, and the concentrations of As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn were analyzed. The source and distribution characteristics of Cd, Cr, Hg, Ni, Pb, As, Cu, and Zn in soil samples were analyzed using the geographic information system (GIS) and principal component analysis (PCA), and the geo-accumulation index and potential ecological hazard index were used to evaluate their pollution status and ecological risk. The results showed that:① Except Ni, all seven heavy metal elements exceeded the national soil background value but were below the pollution risk screening value. The soil environment as a whole was relatively clean. ② The spatial distribution of heavy metals in soil differed. The contents of As and Pb showed northwest-southeast zonal distribution. The contents of Cd, Cu, Hg, and Zn generally decreased from the middle to the surrounding, which was consistent with the industrial and mining enterprise locations. However, the spatial distribution of Cr and Ni had no direct relationship with the location of pollution sources such as industrial enterprises. ③ The eight heavy metals could be identified as three principal components (PCs). PC1 (Cd, Cu, Pb, and Zn) was mainly affected by human activities such as lead-zinc deposit dressing, traffic emissions, and agricultural production. However, PC2 (Cr and Ni) was a natural source, mainly affected by soil parent material. In addition, PC3 (As and Hg) was mainly affected by industrial activities such as non-ferrous metal smelting and thermal power generation. ④ According to the geo-accumulation index method, the risk degree of the eight heavy metals was:Cd>As>Zn>Hg>Pb>Cu>Cr=Ni. The contents of Cr and Ni in soil were at a no-risk level; the contents of As, Cu, Hg, Pb, and Zn were at a low-risk level; and the content of Cd was at a high-risk level. Most potential ecological hazards for single elements in the study area were at a mild risk level. In contrast, a small proportion of the surface soils in areas of intense industrial activity were subject to substantial levels of heavy metal stress and require further attention.

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.

Source, environmental behavior and potential health risk of rare earth elements in Beijing urban park soils.

Rare earth elements (REEs) have been increasingly diffused to the environment due to their extensive use and application in industries, agriculture, and high-tech devices, which have been regarded as emerge pollutants. However, the study concerning REEs in urban soils is still limited. Therefore, the objectives of this study were to investigate the potential source and risk of REEs in urban environment. We analyzed the concentration and distribution of REEs in urban park soils, and performed a combination of micro geochemical method and random forest method to characterize the pollution sources of REEs. The results showed that the ΣREE concentrations in Beijing urban park soils ranged from 117.19 to 198.09 mg/kg. Spatial distribution indicated that the high concentrations of REEs were mainly concentrated in the west of Beijing near an industrial area. The geochemical parameters, micro spherules and random forest results confirmed the anthropogenic pollution sources from industry and traffic. Risk assessment showed that the average daily doses of total REEs for children and adults were far below the reference threshold with values of 0.08 and 0.02 µg/kg/day, respectively. Our study has exhibited that though the reconstruction of parks from abandoned industrial sites showed an accumulation of REEs, the health risk of REEs for human beings are negligible.

Challenges and opportunities for improving the environmental quality of cadmium-contaminated soil in China.

Considering the soil cadmium pollution problem, the Chinese government proposed to estimate the costs and practicality "to completely improve the soil quality by the middle of this century". This study analyzed the challenges in achieving this goal using biophysical data from 10 typical demonstration soil phytoextraction projects. The current annual phytoextraction efficiency was determined as 14.8-490 g ha-1 a-1 at 319 RMB g-1 cadmium. A total of 798 billion RMB and 5 years were required for remediation of cadmium contamination, which was 22 times the investment in soil remediation during 2016-2022. The break-even point of phytoextraction projects was 29 years. The heavy financial burden was considered the primary challenge in improving the environmental quality of such soil. The cost could be reduced by 5.5-35.3 % through optimization measures such as resourcefulness of hyperaccumulator harvests, large-scale breeding, and mechanized management. The break-even point could be shortened to 6-15 years by intercropping/rotating crops, contributing to the goal. Active exploration of phytoextraction efficiency-more efficient accumulators, optimized agronomic measures-is worth practicing.

[Source Apportionment and Potential Ecological Risk Assessment of Soil Heavy Metals in Typical Industrial and Mining Towns in North China].

To understand the status of heavy metals in soils of typical industrial and mining towns and quantitatively analyze the potential sources, the contents of seven heavy metals (Cd, As, Pb, Cr, Cu, Ni, and Zn) in 150 surface soils in Xuanhua District, Zhangjiakou City, Hebei Province were collected and examined. The geoaccumulation index and potential ecological risk index methods were used to evaluate the heavy metal pollution status and potential ecological risk. Principal component analysis (PCA) and the positive matrix factorization (PMF) model were used to comprehensively analyze the pollution sources of seven heavy metals, and geostatistics was used to identify the high contribution areas of potential sources. The results revealed that:① the average values of heavy metals in the study area ranged from 0.23-103.34 mg·kg-1, among which the average contents of Cd, Pb, Cu, and Zn were higher than the soil background value of Hebei Province. ② The results of the geoaccumulation and potential ecological risk indices demonstrated that the degree of pollution of the seven heavy metals was in the following order:Cd>Pb>Cu>Zn>Ni>As>Cr, the content of Cd in 16% sites was above a moderate pollution level, and the potential ecological risk of heavy metals in more than 95% sites was at a light risk level. ③ The main sources of accumulation of the seven heavy metals in the study area were combined sources of industry and traffic, natural sources, and agricultural sources, with their contribution rates of 33.1%, 48.7%, and 18.2%, respectively. Among them, Cd, Pb, Cu, and Zn were primarily affected by the combined sources of industry and transportation; Cr, Ni, and As were mainly affected by natural sources, whereas Cd and some As were affected by agricultural sources. The organic combination of PCA, PMF model, and geostatistical methods confirmed the results of each analysis, which increased the reliability of the analytical results of heavy metal sources.

Transforming approach for assessing the performance and applicability of rice arsenic contamination forecasting models based on regression and probability methods.

Probability models are preferred over regression models recently in contamination evaluation but lacking proper performance comparison between two model types. Linear regression, logistic regression, XGBoost-based regression, and probability models were built considering soil arsenic and certain soil physicochemical properties of 287 samples to predict arsenic in rice grains. The outputs of all models were binarily classified uniformly for comparison. The complex algorithm-based models--XGBoost-based regression (R2 =0.046 ± 0.036) and probability models (cross-entropy = 0.697 ± 0.020)-did not surpass the simple linear regression (R2 =0.046 ± 0.031) and logistic regression models (cross-entropy = 0.694 ± 0.021). Accuracy, sensitivity, specificity, precision, and F1 score showed that the probability models exhibit no advantage on regression models, although the indicators above did not serve as proper scoring rules for the probability model. When discretizing the contaminant concentration in grains for probabilistic modeling, the limit concentration was considered as the splitting point but not the structure of the datasets, which would reduce the inherent advantage of the probability model. When predicting the contamination of crops, the probability model cannot eliminate the regression model, and simple but robust algorithm-based models are preferred when the quality and quantity of the dataset are undesirable.

Redox dependence of manganese controls cadmium isotope fractionation in a paddy soil-rice system under unsteady pe + pH conditions.

Flooding in paddy soils alters the soil redox of manganese (Mn) and produces elevated concentrations of soluble Mn that can reduce cadmium (Cd) uptake by rice. To better understand the fates of Mn and Cd, along with changes in soil redox conditions, we conducted microcosm incubations in paddy soil covering the reduction to oxidation to re-reduction phases. The extractable Cd concentration decreased rapidly during the reduction phases but increased upon oxidation, and Cd availability largely depended on soil pH, Eh, pe + pH, and the extractable Mn concentration. Exogenous Mn can promote Cd binding with Fe-Mn(oxyhydro)oxides. A trade-off effect between the soil-extractable Cd and Mn concentrations across changes in pH, Eh, pe + pH was identified, and attaining an optimal pe + pH value of 6.8 was targeted. Furthermore, to provide insights into how the redox status of Mn changes to alter Cd mobilization in a paddy soil-rice system, Cd isotope ratios across the paddy soil-rice tissue continuum were investigated using planted rhizobox experiments under different irrigation regimes. The heavy Cd isotopes from the soil to liquid-phase (Δ114/110Cdextract-soil = 0.40-0.82‰) and from the soil to rice grain (Δ114/110Cdgrain-soil = 0.84-0.89‰) were preferentially enriched. Light isotopes were likely to be enriched in Cd bound to Fe/Mn-oxides, a process that was promoted by increased Mn availability. These results suggest that Cd isotopes are systematically fractionated within the paddy soil-rice system, which is caused by the unsteady soil redox, and the stabilization of Cd in the bound soil pool such as Fe-Mn(oxyhydro)oxides-Cd under reducing conditions could be developed as a Cd retention mechanism in paddy soils.

Prediction of high-risk areas of soil heavy metal pollution with multiple factors on a large scale in industrial agglomeration areas.

Heavy metals in soil are a great threat to ecosystems and human health. The rapid development of industrialization has created a serious risk of heavy metal pollution in soil. The study took the industrial-intensive Dahetan subbasin as the typical area. The factors and interactions that affected the distribution of soil heavy metals (Cd, Hg, As, Pb and Cr) in the typical area were explored based on the Geodetector model. The analysis results were extended to predict high-risk areas of heavy metal pollution in the soil in the Xiangjiang River basin. The results showed that Cd, As and Pb were significantly affected by local industrial and mining activities, and Hg and Cr were primarily affected by natural factors, such as pH and soil type. Compared to a single factor, the interaction between factors had a greater impact on the concentration of heavy metals. The high-risk areas of soil heavy metal pollution in the Xiangjiang River basin were primarily concentrated in the upper reaches and middle reaches.Significant overlapping of high-risk areas of multiple heavy metals occurred in the west, middle and south of the basin. The spatial visualization of the high-risk areas was realized, and the influence of several factors was integrated via layer superposition. This study proposes a new idea to predict the high-risk areas of soil pollution in large-scale areas to provide a reference for the regional prevention and control of soil pollution.