Elucidating the differentiation of soil heavy metals under different land uses with geographically weighted regression and self-organizing map

Groundwater hydrochemistry, source identification and health assessment based on self-organizing map in an intensive mining area in Shanxi, China

The Changzhi Basin in Shanxi is renowned for its extensive mining activities. It's crucial to comprehend the spatial distribution and geochemical factors influencing its water quality to uphold water security and safeguard the ecosystem. However, the complexity inherent in hydrogeochemical data presents challenges for linear data analysis methods. This study utilizes a combined approach of self-organizing maps (SOM) and K-means clustering to investigate the hydrogeochemical sources of shallow groundwater in the Changzhi Basin and the associated human health risks. The results showed that the groundwater chemical characteristics were categorized into 48 neurons grouped into six clusters (C1-C6) representing different groundwater types with different contamination characteristics. C1, C3, and C5 represent uncontaminated or minimally contaminated groundwater (Ca-HCO3 type), while C2 signifies mixed-contaminated groundwater (HCO3-Ca type, Mixed Cl-Mg-Ca type, and CaSO4 type). C4 samples exhibit impacts from agricultural activities (Mixed Cl-Mg-Ca), and C6 reflects high Ca and NO3- groundwater. Anthropogenic activities, especially agriculture, have resulted in elevated NO3- levels in shallow groundwater. Notably, heightened non-carcinogenic risks linked to NO3-, Pb, F-, and Mn exposure through drinking water, particularly impacting children, warrant significant attention. This research contributes valuable insights into sustainable groundwater resource development, pollution mitigation strategies, and effective ecosystem protection within intensive mining regions like the Changzhi Basin. It serves as a vital reference for similar areas worldwide, offering guidance for groundwater management, pollution prevention, and control.

The hidden threat of heavy metal leaching in urban runoff: Investigating the long-term consequences of land use changes on human health risk exposure

This study evaluated the potential effects of long-term land use and climate change on the quality of surface runoff and the health risks associated with it. The land use change projection 2030 was derived from the main changes in land use from 2009 to 2019, and rainfall data was obtained from the Long Ashton Research Station Weather Generator (LARS-WG) model. The Long-Term Hydrological Impact Assessment (L-THIA) model was then utilized to calculate the rate of runoff heavy metal (HM) pollutant loading from the urban catchment. It was found that areas with heavy development posed a significantly greater public health risk associated with runoff, with higher risks observed in high-development and traffic areas compared to industrial, residential, and commercial areas. Additionally, exposure to Lead (Pb), Mercury (Hg), and Arsenic (As) was found to contribute significantly to overall non-carcinogenic health risks for possible consumers of runoff. Carcinogenic risk values of As, Cadmium (Cd), and Pb were also observed to increase, particularly in high-development and traffic areas, by 2030. This investigation offers important insight into the health risks posed by metals present in surface runoff in urban catchment areas under different land use and climate change scenarios.

Pollution sources and risk assessment of potentially toxic elements in soils of multiple land use types in the arid zone of Northwest China based on Monte Carlo simulation

The concentration of potentially toxic elements (PTEs) in soils of different land-use types varies depending on climatic conditions and human. Topsoil samples were collected in Northwest China to investigate PTE pollution and risk in different land uses, and thereby estimate the risk of various pollution sources. The results showed that human activity had an impact on PTE concentrations in the study area across all land use types, with farmland, grassland, woodland, and the gobi at moderate pollution levels and the desert at light pollution levels. Different PTE sources pose different risks depending on the land-use type. Apart from deserts, children are exposed to carcinogenic risk from a variety of sources. A mixed natural and agricultural source was the main source of public health risk in the study area, contributing 38.7% and 39.0% of the non-carcinogenic and 40.7% and 35.5% of the carcinogenic risks, respectively. Monte Carlo simulations showed children were at a higher health risk from PTEs than adult s under all land uses, which ranked in severity as farmland > woodland > grassland > gobi > desert. As and Ni has a higher probability of posing both a non-carcinogenic and a carcinogenic risk to children. Sensitivity analysis showed that the contribution of parameters to the assessment model of PTEs exhibited the following contribution pattern: concentration > average body weight > ingestion rate > other parameters. The PTEs affecting the risk assessment model were not common among different land use types, where the importance distribution pattern of each parameter was basically the same in woodland, grassland, and farmland, and Ni contributed the most to carcinogenic risk. However, Cr contributed the most to the carcinogenic risk in the desert and gobi.

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

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

Revealing soil microbial ecophysiological indicators in acidic environments laden with heavy metals via predictive modeling: Understanding the impacts of black diamond excavation

Appraising the activity of soil microbial community in relation to soil acidity and heavy metal (HM) content can help evaluate it's quality and health. Coal mining has been reported to mobilize locked HM in soil and induce acid mine drainage. In this study, agricultural soils around coal mining areas were studied and compared to baseline soils in order to comprehend the former's effect in downgrading soil quality. Acidity as well as HM fractions were significantly higher in the two contaminated zones as compared to baseline soils (p < 0.01). Moreover, self-organizing and geostatistical maps show a similar pattern of localization in metal availability and soil acidity thereby indicating a causal relationship. Sobol sensitivity, cluster, and principal component analyses were employed to enunciate the relationship between the various metal and acidity fractions with that of soil microbial properties. The results indicate a significant negative impact of metal bioavailability, and acidity on soil microbial activity. Lastly, Taylor diagrams were employed to predict soil microbial quality and health based on soil physicochemical inputs. The efficiency of several machine learning algorithms was tested to identify Random Forrest as the best model for prediction. Thus, the study imparts knowledge about soil pollution parameters, and acidity status thereby projecting soil quality which can be a pioneer in sustainable agricultural practices.

Potential toxic heavy metals in village rainwater runoff of antimony mining area, China: Distribution, pollution sources, and risk assessment

The potential toxic heavy metal runoff from antimony mining areas poses a serious threat to the water environment and the health of residents in the village. The study found that the average concentrations of As, Sb, Cr, Pb, and Cd in the runoff were 0.1237, 0.1148, 0.0332, 0.0140, and 0.0013 mg/L, which were higher than the normal average concentrations in the water environment of 0.018, 0.0009, 0.05, 0.012, and 0.000013 mg/L, respectively.Sb and As are severely polluted, while Cd, Pb, and Cr have lower pollution levels. The coefficients of variation for As, Sb, Cr, Pb, and Cd range from 0.079 to 1.051, with Sb showing exceptionally high variability. Heavy metal elements Pb, Cd, and Sb accumulate in the southeastern area of the village, with Sb concentrations decreasing from the southeast to the northwest. As is mainly distributed in the northeastern part of the village, while Cr is primarily found in the central-western region. Source analysis indicates that As and Sb originate from mining and industrial activities, dust deposition, and domestic sewage. Cr comes from the natural geological background and metal product industry, Pb from lead-acid batteries, industrial activities, and gasoline additives, and Cd from fertilization in residential green areas and pesticide use. Health risk analysis reveals that the hazard index (HI) values for As and As in the water environment are 1.49 and 2.31, respectively, both exceeding 1, posing a serious threat to the health of village residents. The HI values for Pb, Cr, and Cd elements are all below 1, indicating lower risks. This study identified that Sb in the antimony ore area and its associated metal element As are the main elements leading to potential heavy metal pollution in the runoff of village residential areas, providing direction for subsequent water environment restoration work.

WITHDRAWN: Trace metals translocation from soil to plants: Health risk assessment via consumption of vegetables in the urban sprawl of a developing country

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Analysis of soil fertility and toxic metal characteristics in open-pit mining areas in northern Shaanxi

The study specifically focused on the Hongliulin mining area, where a total of 40 soil samples were meticulously collected and analyzed from within a 1000 m radius extending from the tailings dam. The findings revealed that soil pH within the 0-1000 m range generally leaned towards the alkaline side. In terms of soil nutrient content, encompassing factors such as soil organic matter (SOM), total nitrogen (TN), total phosphorus (TP), total potassium (TK), alkali nitrogen (AK), available phosphorus (AP), and quick-acting potassium (AK), the variations fell within the following ranges: 2.23-13.58 g/kg, 0.12-0.73 g/kg, 0.18-1.15 g/kg, 9.54-35.82 g/kg, 2.89-6.76 mg/kg, 3.45-11.25 mg/kg, and 5.86-130.9 mg/kg. Collectively, these values indicate relatively low levels of soil nutrients. Within the 0-500 m range of soil samples, the average concentrations of Cd, Hg, Pb, and As were 0.778, 0.198, 24.87, and 17.92 mg/kg, respectively. These concentrations exceeded the established soil background values of Shaanxi Province and emerged as the primary pollutants in the study area. Within this same range, the mean values of eight toxic metals (Pi) were ranked in the following descending order: 1.726 (Hg), 1.400 (As), 1.129 (Cr), 1.109 (Pb), 0.623 (Zn), 0.536 (Cd), 0.309 (Cu), and 0.289 (Ni). With the exception of Hg, As, Cr, and Pb, which exhibited slight pollution, the other toxic metals were found to be within acceptable pollution limits for this sampling range, in line with the results obtained using the geo-accumulation index method. The average potential ecological risk index for the eight toxic metals in the study area stood at 185.0, indicating a moderate overall pollution level. When assessing individual elements, the proportions of ecological risk attributed to Hg, As, Pb, and Cd were 34.57%, 27.44%, 25.11%, and 23.11%, respectively. This suggests that the primary potential ecological risk elements in the study area are Hg and As, followed by Cd and Pb. Notably, toxic metals Hg and Pb, as well as As and Pb, exhibited significant positive correlations within the sampling area, suggesting a common source. An analysis of the relationship between soil physicochemical properties and toxic metals indicated that soil pH, SOM, TN, and TP were closely linked to toxic metal concentrations. The toxic metal elements in the research area's soil exhibit moderate variability (0.16 < CV < 0.36) to high variability (CV > 0.36). Within the range of 0-200 m, the CV values for Cd and Hg exceed 1, indicating a high level of variability. The coefficient of variation for SOM, TP, AP, AK and TK is relatively high with the of 2.93, 2.36, 2.36, 21.01, 7.54. The soil in the sampling area has undergone significant disturbances due to human activities, resulting in toxic metal pollution and nutrient deficiencies.

With spatial distribution, risk evaluation of heavy metals and microplastics to emphasize the composite mechanism in hyporheic sediments of Beiluo River

This study aimed to assess the hazardous impacts of heavy metals (HMs) enrichment on the surface of microplastics (MPs) in the hyporheic zone. The present work analyzed the spatial distribution and risk evaluation of HMs (V, Cr, Mn, Co, Ni, Cu, Zn, As, Cd, and Pb) and MPs and the mechanism of HMs enrichment on MPs in the sediments. The highest rates of contamination were for Cd, Pb, and As. The main types of MPs were fiber, blue, and a size smaller than 500 µm. The lower reaches of the Beiluo River had the most serious HMs and MPs pollution, especially BL-10 (HMs: CF-Cd, 41.91; EF-Cd, 50.87; Igeo-Cd, 4.80; RI, 1291; PN, 29.83; MPs: abundance, 890 ± 18 items/kg). Meanwhile, the principal component analysis showed that natural, industrial activities, and agricultural production and transportation were primary HMs sources in sediments, and Cd, Co, and Pb were the main enriched metals on the surface of MPs. More importantly, regarding the interaction mechanism of these composite pollutants, we concluded that electrostatic adsorption and biofilm mediation were the main mechanisms of the synergistic effect. Overall, our findings provide a theoretical basis for further research on the ecotoxicity of composite pollutants in aquatic environments.

Development of a new method framework to estimate the nonlinear and interaction relationship between environmental factors and soil heavy metals

The intricate and multifaceted nature of soil system profoundly influences the highly complex and often nonlinear changes that soil heavy metals (HM) undergo. Spatial heterogeneity, location and scale variability, and the interaction and superposition among environmental drivers challenged researchers to determine the sophisticated nature of soil HMs changes at the regional scale. This study aims to develop a new method framework and selects Ningbo as the case study to apportion the environmental factors responsible for soil HMs pollution that include Cd, Cr, Pb, Hg, As, Cu, Zn and Ni, focusing on nonlinearity and interaction. We harnessed the Random Forest model to apportion the environmental drivers of soil HM change. The directionality and shape of the nonlinear relationship between HMs and their individual contributors were derived by Partial Dependence Plots. The interactions of multiple drivers were quantitatively assessed by the Conditional Inference Tree. Our results demonstrated that soil HMs in the study area varied spatially. Soil HMs pollution was mitigated by natural factors and anthropogenic factors. The main influencing factors were pH, soil parent material type, enterprise activities, and agricultural application. The effects of some factors on soil HMs showed a monotonic linear trend, but some have apparent threshold effects. The direction of influence on soil HMs will shift when pH and phosphate fertilizer reach a specific value. The addition of enterprises in the area would rarely have an impact on the HMs pollution once it reached around 2 per km2 because of the industrial agglomeration. Soil HM concentrations were mainly from multi-pollutants and were governed by a combination of environmental factors. Our study provided managers and policymakers with site-specific and definite guidelines for preventing and controlling soil HM pollution.

Impacts of land use/cover and slope on the spatial distribution and ecological risk of trace metals in soils affected by smelting emissions

The soil contamination around smelting sites shows high spatial heterogeneity. This study investigated the impacts of distance, land use/cover types, land slopes, wind direction, and soil properties on the distribution and ecological risk of trace metals in the soil around a copper smelter. The results demonstrated that the average concentrations of As, Cd, Cu, Pb, and Zn were 248.0, 16.8, 502.4, 885.6, and 250.2 g mg kg-1, respectively, higher than their background values. The hotspots of trace metals were primarily distributed in the soil of smelting production areas, runoff pollution areas, and areas in the dominant wind direction. The concentrations of trace metals decreased with the distance to the smelting production area. An exponential decay regression revealed that, depending on the metal species, the influence distances of smelting emissions on trace metals in soil ranged from 450 to 1000 m. Land use/cover types and land slopes significantly affected trace element concentrations in the soil around the smelter. High concentrations of trace metals were observed in farmland, grassland, and flatland areas. The average concentrations of trace metals in the soil decreased in the order of flat land > gentle slope > steep slope. Soil pH values were significantly positively correlated with Cd, Cu, Pb, Zn, and As, and SOM was significantly positively correlated with Cd, Pb, and Zn in the soil. Trace metals in the soil of the study area posed a significant ecological risk. The primary factors influencing the distribution of ecological risk, as determined by the Ctree analysis, were land slope, soil pH, and distance to the source. These results can support the rapid identification of high-risk sites and facilitate risk prevention and control around smelting sites.

Source apportionment and migration characteristics of heavy metal(loid)s in soil and groundwater of contaminated site

The rapid industrial growth has generated heavy metal(loid)s contamination in the soil, which poses a serious threat to the ecology and human health. In this study, 580 samples were collected in Henan Province, China, for source apportionment, migration characterization and health risk evaluation using self-organizing map, positive matrix factorization and multivariate risk assessment methods. The results showed that samples were classified into four groups and pollution sources included chromium slag dump, soil parent rock and abandoned factory. The contents of Cr, Pb, As and Hg were low in Group 1. Group 2 was characterized by total Cr, Cr(Ⅵ) and pH. The enrichment of total Cr and Cr(Ⅵ) in soil was mainly attributed to chromium slag dump, accounting for more than 84.0%. Group 3 was dominated by Hg and Pb. Hg and Pb were primarily attributed to abandoned factory, accounting for 84.7% and 70.0%, respectively. Group 4 was characterized by As. The occurrence of As was not limited to one individual region. The contribution of soil parent rock reached 83.0%. Furthermore, the vertical migration of As, Hg, Pb and Cr(Ⅵ) in soil was mainly influenced by medium permeability, pH and organic matter content. The trends of As, Pb, and Hg with depth were basically consistent with the trends of organic matter with depth, and were negatively correlated with the change in pH with depth. The trends of Cr(Ⅵ) with depth were basically consistent with the changes in pH with the depth. The content of Cr(Ⅵ) in the deep soil did not exceed the detection limits and Cr(Ⅵ) contamination occurred in the deep aquifer, suggesting that Cr(Ⅵ) in the deep groundwater originated from the leakage of shallow groundwater. The assessment indicated that the non-carcinogenic and carcinogenic risks for children and adults could not be neglected. Moreover, children were more susceptible than adults.

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.

Distribution, source investigation, and risk assessment of topsoil heavy metals in areas with intensive anthropogenic activities using the positive matrix factorization (PMF) model coupled with self-organizing map (SOM)

Over the past decade, heavy metal (HMs) contamination in soil environments has become severe worldwide. However, their resulting ecological and health risks remained elusive across a variety of soil ecosystems due to the complicated distributions and sources. This study investigated the HMs (Cr, As, Cu, Pb, Zn, Ni, Cd, and Hg) in areas with multi-mineral resources and intensive agricultural activities to study their distribution and source apportionment using a positive matrix factorization (PMF) model coupled with self-organizing map (SOM). The potential ecological and health risks were assessed in terms of distinct sources of HMs. The results disclosed that the spatial distribution of HM contaminations in the topsoil was region-dependent, primarily located in areas with high population intensity. The geo‑accumulation index (Igeo) and enrichment factor (EF) values collectively displayed that the topsoils were severely contaminated by Hg, Cu, and Pb, particularly in residential farmland areas. The comprehensive analysis combined with PMF and SOM identified both geogenic and anthropogenic sources of HMs including natural, agricultural, mining, and mixed sources (caused by multi-anthropogenic factors), accounting for 24.9%, 22.6%, 45.9%, and 6.6% contribution rates, respectively. The potential ecological risk was predominantly due to the enrichment of Hg, followed by Cd. The non-carcinogenic risks were mostly below the acceptable risk level, while the potential carcinogenic health risks caused by As and Cr should be paid prime attention to, particularly for children. In addition to the 40% geogenic sources, agricultural activities contributed to 30% of the non-carcinogenic risk, whereas mining activities contributed to nearly half of the carcinogenic health risks.

Assessing pollution and health risks from chromite mine tailings contaminated soils in India by employing synergistic statistical approaches

Potentially toxic elements (PTEs) contamination in the agricultural soil can generate a detrimental effect on the ecosystem and poses a threat to human health. The present work evaluates the PTEs concentration, source identification, probabilistic assessment of health hazards, and dietary risk analysis due to PTEs pollution in the region of the chromite-asbestos mine, India. To evaluate the health risks associated with PTEs in soil, soil tailings and rice grains were collected and studied. The results revealed that the PTEs concentration (mainly Cr and Ni) of total, DTPA-bioavailable, and rice grain was significantly above the permissible limit in site 1 (tailings) and site 2 (contaminated) as compared with site 3 (uncontaminated). The Free ion activity model (FIAM) was applied to detect the solubility of PTEs in polluted soil and their probable transfer from soil to rice grain. The hazard quotient values were significantly higher than the safe (FIAM-HQ < 0.5) for Cr (1.50E+00), Ni (1.32E+00), and, Pb (5.55E+00) except for Cd (1.43E-03), Cu (5.82E-02). Severity adjustment margin of exposure (SAMOE) results denote that the PTEs contaminated raw rice grain has high health risk [Cr_SAMOE: 0.001; Ni_SAMOE: 0.002; Cd_SAMOE: 0.007; Pb_SAMOE: 0.008] for humans except for Cu. The Positive matrix factorization (PMF) along with correlation used to apportion the source. Self-organizing map (SOM) and PMF analysis identified the source of pollution mainly from mines in this region. Monte Carlo simulation (MCS) revealed that TCR (total carcinogenic risk) cannot be insignificant and children were the maximum sufferers relative to adults via ingestion-pathway. In the spatial distribution map, the region nearer to mine is highly prone to ecological risk with respect to PTEs pollution. Based on appropriate and reasonable evaluation methods, this work will help environmental scientists and policymakers' control PTEs pollution in agricultural soils near the vicinity of mines.

The spatial analysis, risk assessment and source identification for mercury in a typical area with multiple pollution sources in southern China

Mercury (Hg) has always been a research hot spot because of its high toxicity. This study conducted in farmland near rare earth mining area and traffic facilities, which considered multiple pollution sources innovatively. It not only analyzed Hg spatial characteristics using inverse distance weighting and self-organizing map (SOM), but also assessed its pollution risk by potential ecological risk index (Er) as well as geoaccumulation index (Igeo), and identified the pollution sources with positive matrix factorization. The results showed that there was no heavy Hg pollution in most farmland, while a few sampling sites with Hg pollution were close to highway, railway station and petrol station in Xinfeng or in the farmland of Anyuan, which were divided into the cluster with highest Hg concentration in SOM. The vehicle exhaust emission and pesticide as well as fertilizer additions significantly contributed to the local Hg pollution. Besides, there was moderate pollution and high ecological risk in Anyuan assessed by Igeo and Er, respectively. In contrast, Xinfeng had the moderate and considerable ecological risks in a larger scale. The enriched Hg might harmed not only the nearby ecological environment, but also the human health when it entered human body through food chain. The three factors that contributed to mercury concentration in this area according to positive matrix factorization were natural source, traffic source and agricultural source, respectively. This study about Hg pollution in the typical area would provide scientific evidence for the particular treatment of Hg pollution from various pollution sources like traffic source, agricultural source, etc.

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

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

Pollution and health risk assessment of mine tailings contaminated soils in India from toxic elements with statistical approaches

The rapid mining activities of mica mines in Giridih district, India, have led to toxic metal pollution of agricultural soil. This is a key concern for environmental risk and human health. 63 top soil samples were collected at a distance of 10 m (Zone 1), 50 m (Zone 2), and 100 m (Zone 3) from near 21 mica mines with agriculture fields. The mean concentration of total and bio-available toxic elements (TEs - Cr, Ni, Pb, Cu, Zn, and Cd) was higher in zone 1 across three zones. The Positive matrix factorization model (PMF) and Pearson Correlation analysis were used to identify waste mica soils with TEs. Based on PMF results, Ni, Cr, Cd, and Pb were the most promising pollutants and carried higher environmental risks than the other TEs. Using the self-organizing map (SOM), zone 1 was identified as a high-potential source of TEs. Soil quality indexes for TEs risk zone 1 were found to be higher across three zones. Based on the health risk index (HI), children are more adversely affected than adults. Monte Carlo simulations (MCS) model and sensitivity analysis of total carcinogenic risk (TCR), children were more affected by Cr and Ni than adults through ingestion exposure pathways. Finally, a geostatistical tool was developed to predict the spatial distribution patterns of TEs contributed by mica mines. In a probabilistic assessment of all populations, non-carcinogenic risks appeared to be negligible. The fact that there is a TCR can't be ignored, and children are more likely to develop it than adults. Mica mines with TEs contamination were found to be the most significant anthropogenic contributor to health risks based on source-oriented risk assessment.

Possible Sources of Trace Metals in Obese Females Living in Informal Settlements near Industrial Sites around Gauteng, South Africa

Trace metals have been reported in the literature to be associated with obesity. Exposure to some trace metals such as Mn, Cr, Ni, Cd, and Pb may pose a serious health risk to individuals living around a polluted environment. The present study assessed the levels of trace metals in the blood of obese females living around industrial areas in Gauteng, South Africa. The study was carried out using a mixed method approach. Only females with a BMI ≥ 30.0 were considered. A total of 120 obese females participated in the study (site 1: 40-industrial area, site 2: 40-industrial area, and site 3: 40-residential area), aged 18-45 and not in menopause. Blood samples were analysed for trace metals content using inductively coupled plasma mass spectrometry (ICP-MS). The mean concentrations of trace metals were in the order Pb > Mn > Cr > Co > As > Cd (site 1), Pb > Mn > Co > As > Cd (site 2), and Mn > Cr > Co > As > Pb > Cd (site 3). The blood Mn from site 1 ranged from 6.79 µg/L-33.99 µg/L, and the mean differences obtained from the participants from different sites were significant (p < 0.01). The blood levels of Mn, Pb, Cr, Co, As, and Cd were above the recommended limits set by the WHO in some of the participants. The present study noted, among others, closeness to industrial areas, lifestyle decisions such as the use of tobacco products by their partners indoors, and the method used for cooking as factors that might have accounted for the blood levels of Mn, Pb, Cd and Co. The study showed that there is a need for constant monitoring of the levels of trace metals in the blood of those living in these areas.

Assessment of water quality, trace metal pollution, source apportionment and health risks in the groundwater of Chakwal, Pakistan

Groundwater quality evaluation is the main concern in the regions like Chakwal where it is major source of water for drinking and irrigation due to low storage capacity of the surface water and lack of proper irrigation system. The aim of the present study was to evaluate various physicochemical parameters (pH, EC, TDS, DO, TA, TH and chlorides) and selected essential/toxic trace metal concentrations (Na, K, Ca, Mg, Sr, Li, Ag, Zn, Fe, Cu, Co, Mn, Cr, Cd, and Pb) in order to explore their distribution, correlation, spatial variations and health risk assessment. Average concentration of some trace metals (Co, Cd and Pb) and physicochemical parameters (EC, TDS, and alkalinity) were found to exceed the national/international standards. Multivariate methods of analysis showed strong associations among Fe-Li-K, Sr-Mg-Ca, Cd-Mn, Cu-Zn, Ag-Co, and Cr-Pb-Na which were significantly contributed by anthropogenic activities. Irrigation water quality index exhibited intermediate suitability of the groundwater for irrigation purpose. Health risk evaluation of the trace metals revealed significant non-carcinogenic risks for Cd, Co and Pb (HQ_ing > 1) especially for children. Similarly, significant carcinogenic risk was found to be associated with Pb and Cr which exceeded the safe limit, suggesting the lifetime carcinogenic risk associated with these metals in the groundwater. The present health risk problems should be considered on top priority and immediate actions should be taken to safeguard the water quality in the study area.

A spatial distribution - Principal component analysis (SD-PCA) model to assess pollution of heavy metals in soil

With the rapid development of urbanization, heavy metal pollution of soil has received great attention. Over-enrichment of heavy metals in soil may endanger human health. Assessing soil pollution and identifying potential sources of heavy metals are crucial for prevention and control of soil heavy metal pollution. This study introduced a spatial distribution - principal component analysis (SD-PCA) model that couples the spatial attributes of soil pollution with linear data transformation by the eigenvector-based principal component analysis. By evaluating soil pollution in the spatial dimension it identifies the potential sources of heavy metals more easily. In this study, soil contamination by eight heavy metals was investigated in the Lintong District, a typical multi-source urban area in Northwest China. In general, the soils in the study area were lightly contaminated by Cr and Pb. Pearson correlation analysis showed that Cr was negatively correlated with other heavy metals, whereas the spatial autocorrelation analysis revealed that there was strong association in the spatial distribution of eight heavy metals. The aggregation forms were more varied and the correlation between Cr contamination and other heavy metals was lower. The aggregation forms of Mn and Cu, Zn and Pb, on the other hand, were remarkably comparable. Agriculture was the largest pollution source, contributing 65.5 % to soil pollution, which was caused by the superposition of multiple heavy metals. Additionally, traffic and natural pollution sources contributed 17.9 % and 11.1 %, respectively. The ability of this model to track pollution of heavy metals has important practical significance for the assessment and control of multi-source soil pollution.

Distribution, sources, and pollution levels of toxic metal(loid)s in an urban river (Ichamati), Bangladesh using SOM and PMF modeling with GIS tool

Indexical assessment coupled with a self-organizing map (SOM) and positive matrix factorization (PMF) modeling of toxic metal(loid)s in sediment and water of the aquatic environment provides valuable information from the environmental management perspective. However, in northwest Bangladesh, indexical and modeling assessments of toxic metal(loid)s in surface water and sediment are still rare. Toxic metal(loid)s were measured in sediment and surface water from an urban polluted river (Ichamati) in northwest Bangladesh using an atomic absorption spectrophotometer to assess distribution, pollution levels, sources, and potential environmental risks to the aquatic environment. The mean concentrations (mg/kg) of metal(loid)s in water are as follows: Fe (871) > Mn (382) > Cr (72.4) > Zn (34.2) > Co (20.8) > Pb (17.6) > Ni (16.7) > Ag (14.9) > As (9.0) > Cu (5.63) > Cd (2.65), while in sediment, the concentration follows the order, Fe (18,725) > Mn (551) > Zn (213) > Cu (47.6) > Cr (30.2) > Ni (24.2) > Pb (23.8) > Co (9.61) > As (8.23) > Cd (0.80) > Ag (0.60). All metal concentrations were within standard guideline values except for Cr and Pb for water and Cd, Zn, Cu, Pb, and As for sediment. The outcomes of eco-environmental indices, including contamination and enrichment factors and geo-accumulation index, differed spatially, indicating that most of the sediment sites were moderately to highly polluted by Cd, Zn, and As. Cd and Zn content can trigger ecological risks. The positive matrix factorization (PMF) model recognized three probable sources of sediment, i.e., natural source (49.39%), industrial pollution (19.72%), and agricultural source (30.92%), and three possible sources of water, i.e., geogenic source (45.41%), industrial pollution (22.88%), and industrial point source (31.72%), respectively. SOM analysis identified four spatial patterns, e.g., Fe-Mn-Ag, Cd-Cu, Cr-Pb-As-Ni, and Zn-Co in water and three patterns, e.g., Mn-Co-Ni-Cr, Cd-Cu-Pb-Zn, and As-Fe-Ag in sediment. The spatial distribution of entropy water quality index values shows that the southwestern area possesses "poor" quality water. Overall, the levels of metal(loid) pollution in the investigated river surpassed a critical threshold, which might have serious consequences for the river's aquatic biota and human health in the long run.

Impacts of land uses on spatio-temporal variations of seasonal water quality in a regulated river basin, Huai River, China

Land use impacts from agriculture, industrialization, and human population should be considered in surface water quality management. In this study, we utilized an integrated statistical analysis approach mainly including a seasonal Mann-Kendall test, clustering analysis, self-organizing map, Boruta algorithm, and positive matrix factorization to the assessment of the interactions between land use types and water quality in a typical catchment in the Huai River Basin, China, over seven years (2012-2019). Spatially, water quality was clustered into three groups: upstream, midstream, and downstream/mainstream areas. The water quality of upstream sites was better than of mid-, down-, and mainstream. Temporally, water quality did not change significantly during the study period. However, the temporal variation in water quality of up-, down-, and mainstream areas was more stable than in the midstream. The interactions between land use types and water quality parameters at the sub-basin scale varied with seasons. Increasing forest/grassland areas could substantially improve the water quality during the wet season, while nutrients such as phosphorus from cropland and developed land was a driver for water quality deterioration in the dry season. Water area was not a significant factor influencing the variations of ammonia nitrogen (NH₃-N) and total phosphorus (TP) in the wet or dry season, due to the intensive dams and sluices in study area. The parameters TP, and total nitrogen (TN) were principally linked with agricultural sources in the wet and dry seasons. The parameters NH₃-N in the dry season, and chemical oxygen demand (COD_Cr) in the wet season were mainly associated with point source discharges. Agricultural source, and urban point source discharges were the main causes of water quality deterioration in the study area. Collectively, these results highlighted the impacts of land use types on variations of water quality parameters in the regulated basin.

Machine learning based on the graph convolutional self-organizing map method increases the accuracy of pollution source identification: A case study of trace metal(loid)s in soils of Jiangmen City, south China

Rapid economic development and industrialization may include environmentally harmful human activities that cause heavy-metal accumulation in soils, ultimately threatening the quality of the soil environment and human health. Therefore, accurate identification of pollution sources is an important weapon in efforts to control and prevent pollution. The self-organizing map (SOM) method is widely used in pollution source identification because of its capacity for visualization of high-dimensional data. The SOM ignores the graph structure relationship among chemical elements in soils; the SOM analysis of pollution sources has high uncertainty. Here, we propose a new analysis method, i.e., the graph convolutional self-organizing map (GCSOM), which uses a graph convolutional network (GCN) to extract the graph structure relationship among the chemical elements in soils, then performs data visualization using an SOM. We compared the performances of GCSOM and SOM, then assessed the pollution source characteristics of trace metal(loid)s (TMs, mostly heavy metals) in Jiangmen City using the GCSOM. Our experimental results showed that the GCSOM is superior to the SOM for identification of TM sources, while the TMs in the soil of Jiangmen originate from three main sources: agricultural activities (mainly in Taishan City, Jiangmen), traffic emissions (mainly in Xinhui and Pengjiang Districts), and industrial activities (mainly in Xinhui District). The risk assessment indicated that the risk of all TMs was within threshold.

Analysis of water quality indexes and their relationships with vegetation using self-organizing map and geographically and temporally weighted regression

Natural vegetation has been proved to promote water purification in previous studies, while the relevant laws has not been excavated systematically. This research explored the relationships between vegetation cover and water quality indexes in Liaohe River Basin in China combined with self-organizing map (SOM) and geographically and temporally weighted regression (GTWR) innovatively and systematically based on the distributing heterogeneity of water quality conditions. Results showed that the central and northeast regions of the study area had serious organic and nutrient pollution, which needed targeted treatment. And SOM verified that high vegetation coverage with retention potential of organic and inorganic pollutants as well as nutrients improved water quality to some degree, while the excessive discharges of pollutants still had serious threats to nearby water environment despite the purification function of vegetation. GTWR indicated that the waterside vegetation was beneficial for dissolved oxygen increasing and contributed to the decreasing of organic pollutants and inorganic pollutants with reducibility. Natural vegetation also obsorbed nutrients like TN and TP to some degree. However, the retential potential of nitrogen and organic pollutants became not obvious when there were heavy pollution, which demonstrated that pollution sources should be controlled despite the purification function of vegetation. This study implied that natural vegetation purified water quality to some degree, while this function could not be revealed when there was too heavy pollution. These findings underscore that the pollutant discharge should be controlled though the natural vegetation in ecosystem promoted the purification of water bodies.

Elucidating the spatial determinants of heavy metals pollution in different agricultural soils using geographically weighted regression

Intensive human activities caused massive socio-economic and land-use changes that directly or indirectly resulted in excessive accumulation of heavy metals in agricultural soils. The goal of our study was to explore the spatial determinants of heavy metals pollution for agricultural soil environment in Sunan economic region of China. We applied geographically weighted regressions (GWR) to measure the spatially varying relationship as well as conducted principal component analysis (PCA) to incorporate multiple variables. The results indicated that our GWR models performed well to identify the determinants of heavy metal pollution in different agricultural soils with relatively high values of local R². Heavy metal pollution in Sunan economic region was crucially determined by accessibility, varying agricultural inputs as well as the composition and configuration of agricultural landscape, and such impacts exhibited significantly heterogeneity over space and farming practices. For the both agricultural soils, the major variance proportion for our determinants can be grouped into the first four factors (82.64 % for cash-crop soils and 73.065 for cereal-crop soils), indicating the incorporation and interactions between variables determining agricultural soil environment. Our findings yielded valuable insights into understanding the spatially varying 'human-land interrelationship' in rapidly developing areas. Methodologically, our study highlighted the applicability of geographically weighted regression to explore the spatial determinants associated with unwanted environmental outcomes in large areas.

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.

Soil heterogeneity influence on the distribution of heavy metals in soil during acid rain infiltration: Experimental and numerical modeling

Acid rain is a global environmental problem that mobilizes heavy metals in soils, while the distribution and geochemical fraction of heavy metals during acid rain infiltration in heterogeneous soils are still unclear. In this study, we performed column experiments to investigate the distribution and geochemical fraction of Cu, Pb, Ni and Cd in heterogeneously layered soils during continuous acid rain infiltration. Chloride ion used as a conservative tracer was found to be uniformly distributed during acid rain infiltration, showing insignificant preferential flow effects in the column. In contrast, however, the distribution of heavy metals was highly non-uniform, especially in the silty soil at the lower part of the column, indicating a heterogeneous distribution of adsorption capacity. In addition, in the control experiments with neutral rain infiltration, uniform distribution of heavy metals was observed, indicating that the heterogeneous distribution of adsorption coefficient during acid rain infiltration was mainly caused by different pH buffering capacities. A numerical model considering water flow and solute transport was developed, where the average water-solid distribution coefficient (K_d) in Layer 2 was only 1.5-12.5% of that in Layer 1 during acid rain infiltration. The model could predict the variation of heavy metal concentrations in soil with the majority of error less than 35%, confirming that different K_d induced the heterogeneous distribution of heavy metals. In addition, the geochemical fraction of heavy metals in the upper coarse sand layer remained stable, while the acid-extractable fractions in the lower loam and silt loam layer gradually increased. Our findings suggest that soil heterogeneity, especially chemical heterogeneity affected by rainfall acidity, has an important influence on the infiltration, migration and geochemical fraction of heavy metals in soils. This study could help guide the risk assessment of heavy metal-contaminated sites that were polluted by acid rain or landfill leachate.

Exploration of pristine plate-tectonic plains and mining exposure areas for indigenous microbial communities and its impact on the mineral-microbial geochemical weathering process in ultramafic setting

Heavy metal release from harsh ultramafic settings influences microbial diversity and function in soil ecology. This study aimed to determine how serpentine mineralosphere bacterial assemblies and their functions differed in two different plate-tectonic plains and mining exposure sites under heavy metal release conditions. The results showed that the Proteobacteria, Actinobacteria, Cyanobacteria, Planctomycetes, and Chloroflexi were the most abundant bacterial groups among all the sites. The log10-based LDA scores highlighted that some specific groups of bacterial assemblies were enriched in plate-tectonic plains and mining activity areas of the serpentine mineralosphere. Functional prediction revealed that the abundance of heavy metal (Cr and Ni) resistance and biogeochemical cycles involving functional KEGG orthology varied in samples from plate-tectonic plains and mining activity sites. The bipartite plot showed that the enrichment of the biogeochemical cycle and heavy metal resistance functional genes correlated with the abundance of serpentine mineralosphere bacterial groups at a 0.005% confidence level. The co-occurrence network plot revealed that the interconnection pattern of the indigenous bacterial assemblies changed in different plate-tectonic plains and mining exposure areas. Finally, this study concluded that due to heavy metal release, the variation in bacterial assemblies, their functioning, and intercommunity co-occurrence patterns were clarified the synergetic effect of mineral-microbial geochemical weathering process in serpentine mining areas.

Mechanisms and influencing factors of yttrium sorption on paddy soil: Experiments and modeling

High-technology rare earth elements (REEs) as emerging contaminants have potentially hazardous risks for human health and the environment. Investigating the sorption of REEs on soils is crucial for understanding their migration and transformation. This study evaluated the sorption mechanisms and influencing factors of the rare earth element yttrium (Y) on paddy soil via integrated batch sorption experiments and theoretical modeling analysis. Site energy distribution theory (SEDT) combined with kinetics, thermodynamics, and isotherm sorption models were applied to illustrate the sorption mechanism. In addition, the effects of phosphorus (P), solution pH, particle size of soil microaggregates, and initial Y content on the sorption processes were evaluated by self-organizing map (SOM) and Boruta algorithm. The sorption kinetic behavior of Y on paddy soil was more consistent with the pseudo-second-order model. Thermodynamic results showed that the Y sorption was a spontaneous endothermic reaction. The generalized Langmuir model well described the isotherm data of Y sorption on heterogeneous paddy soil and soil microaggregates surface. The maximum sorption capacity of Y decreased with increasing soil particle size, which may be related to the number of sorption sites for Y on paddy soil and soil microaggregates, as confirmed by SEDT. The heterogeneity of sorption site energy for Y was the highest in the original paddy soil compared with the separated soil microaggregates. The SOM technique and Boruta algorithm highlighted that the initial concentration of Y and coexisting phosphorus played essential roles in the sorption process of Y, indicating that the addition of phosphate fertilizer may be an effective way to reduce the Y bioavailability in paddy soil in practice. These results can provide a scientific basis for the sustainable management of soil REEs and a theoretical foundation for the remediation of REEs-contaminated soils.

Machine learning can identify the sources of heavy metals in agricultural soil: A case study in northern Guangdong Province, China

Source tracing of heavy metals in agricultural soils is of critical importance for effective pollution control and targeting policies. It is a great challenge to identify and apportion the complex sources of soil heavy metal pollution. In this study, a traditional analysis method, positive matrix fraction (PMF), and three machine learning methodologies, including self-organizing map (SOM), conditional inference tree (CIT) and random forest (RF), were used to identify and apportion the sources of heavy metals in agricultural soils from Lianzhou, Guangdong Province, China. Based on PMF, the contribution of the total loadings of heavy metals in soil were 19.3% for atmospheric deposition, 65.5% for anthropogenic and geogenic sources, and 15.2% for soil parent materials. Based on SOM model, As, Cd, Hg, Pb and Zn were attributed to mining and geogenic sources; Cr, Cu and Ni were derived from geogenic sources. Based on CIT results, the influence of altitude on soil Cr, Cu, Hg, Ni and Zn, as well as soil pH on Cd indicated their primary origin from natural processes. Whereas As and Pb were related to agricultural practices and traffic emissions, respectively. RF model further quantified the importance of variables and identified potential control factors (altitude, soil pH, soil organic carbon) in heavy metal accumulation in soil. This study provides an integrated approach for heavy metals source apportionment with a clear potential for future application in other similar regions, as well as to provide the theoretical basis for undertaking management and assessment of soil heavy metal pollution.

The potential ecological risk assessment of soil heavy metals using self-organizing map

Heavy metal pollution control zoning aiming at the health maintenance of watershed soil ecosystem has become an important means of soil environmental protection and governance. Based on the self-organizing map (SOM), this study classifies the data sets of eight heavy metals such as Co, Cd, Zn, Cr, Cu, Pb, Ni, and Tl in 354 samples, calculates the potential ecological risk value of soil heavy metals in combination with the potential Hakansom Risk index (HRI), and uses the geographic information system (GIS) for visualization. In the research results, SOM has divided five soil ecological risk categories. The highest average ecological risk value of 85.95 is found in cluster IV, which is clustered and distributed in urban development areas in the upper reaches of the river. The average ecological risk values of cluster I and cluster V are relatively close at 79.64 and 79.19, respectively. Cluster I and cluster V are distributed in the north of the river in a linear and cluster manner, respectively, and are located on a concave bank with a relatively gentle slope. The average ecological risk of soil pollution in cluster II is 77.59, which is linearly distributed on both banks of the river. The ecological risk of soil pollution in cluster III is the lowest (74.39), mainly scattered in the south of rivers with less human activities. The study further identified the environmental factors that affect the soil ecological risk value in different cluster units and put forward the classified and differentiated management and control strategies for different cluster units. The research shows that SOM can cluster the data sets of heavy metals with high sensitivity and low threshold through competitive learning to effectively provide the distribution information of abnormal soil ecological risk areas. This information is helpful for urban environmental management departments and planning departments to take targeted management and recovery measures to avoid the health risks related to soil heavy metal pollution.

Multivariate statistical analysis of potentially toxic elements in the sediments of Quanzhou Bay, China: Spatial relationships, ecological toxicity and sources identification

In this paper, the spatial distribution, pollution degree, ecological toxicity and possible sources of seven potentially toxic elements (PTEs) collected from the surface sediments of Quanzhou Bay (QZB) were analyzed by obtaining concentration measurements. The results indicated that the areas with high Cu, Pb, Zn and Hg concentrations were mainly located in the Luoyang River estuary, while the areas with high contents of Cd and As appeared in the Luoyang River estuary area and in the southern part of QZB, respectively. The contamination indices showed that the Cd pollution degree was slight to serious, while other elements were slightly enriched. The calculation results of the potential ecological risk index (RI) and toxic risk index (TRI) indicated that Cd was the main element posing ecological risk among the PTEs of sediments in QZB, followed by Hg. Moreover, in approximately 30% of the surveyed sites, PTEs exhibited low toxicity to aquatic ecosystems. Finally, the self-organizing map (SOM) and positive matrix factorization (PMF) model were used to determine the PTEs sources. Natural sources, industrial emissions, and the combustion of fossil fuels were three main sources for PTEs in the surface sediments of QZB. This study provides a reference for assessing sediment pollution and managing marine pollution in QZB.

Spatial distribution characteristics and evaluation of soil pollution in coal mine areas in Loess Plateau of northern Shaanxi

The ecological environment in Loess Plateau of Northern Shaanxi is fragile, so the soil pollution caused by the exploitation of coal resources cannot be ignored. With Shigetai Coal Mine in Loess Plateau of Northern Shaanxi as the object of study for field survey and sampling, the content of heavy metals in soil is analyzed, the environmental pollution in the research area is evaluated by the single factor pollution index method, comprehensive pollution index method and potential ecological risk index method, and the spatial distribution characteristics of heavy metals are discussed by the geostatistics method. According to the study results, the average contents of heavy metals Hg, Cd, Pb and Cr are 2.03, 1.36, 1.11 and 1.23 times of the soil background values in Shaanxi Province respectively and the average contents of other heavy metals are lower than the soil background values in Shaanxi Province; Hg and Cd show moderate variation while As, Pb, Cr, Zn, Ni and Cu show strong variation; the skewness coefficients and kurtosis coefficient of Cd, As and Cu in the soil within the research area are relatively high, and these elements are accumulated in large amounts. Single factor pollution index (Pi) and potential ecological risk index (E) indicate that heavy metal Hg is the main pollution factor and mainly distributed in the east and north of the research area. The comprehensive index of potential ecological risk (RI) of the research area is 1336.49, showing an extremely high ecological risk, and the distribution characteristics of potential ecological risk are consistent with that of potential ecological risk index (E) of Hg. The results of ecological risk warning show that Hg is in a slight warning status, while Cd, Pb and Cr are in a warning status. The areas with high ecological risk warning values are mainly distributed in the east and north, and the whole research area shows relatively obvious zonal distribution law. The soil is disturbed greatly during the coal mining, so the ecological governance of the mine area shall adapt to the local natural conditions and regional environmental characteristics and follow the principle of “adjusting governance measures based on specific local conditions and classifications”. An environmentally sustainable governance manner shall be adopted to realize the protection of the ecological environment and high-quality development of coal resources.

Soil Heavy Metal Content and Enzyme Activity in Uncaria rhynchophylla-Producing Areas under Different Land Use Patterns

In this study, we investigated the content of soil heavy metals, the level of heavy metal pollution and the characteristics of soil enzyme activity under three different land use patterns of Uncaria rhynchophylla base, forestland and wasteland in Jianhe County, Qiandongnan Prefecture, Guizhou Province, revealing the intrinsic correlation between heavy metal content and soil enzyme activity to reveal the relationship between soil enzyme activity and heavy metal content under different land use patterns in the Uncaria rhynchophylla production area. The results showed that soil Cd and Hg contents in Uncaria rhynchophylla base both exceeded the national soil background value. The single pollution index indicated that Cd had the greatest contribution to P_n, and the comprehensive pollution index (P_n) demonstrated no heavy metal pollution in the soil of Uncaria rhynchophylla-producing areas. Under different land use patterns, the enzyme activity was forestland > wasteland > Uncaria rhynchophylla base, and catalase and acid phosphatase activities presented significant spatial differences (p < 0.05). The correlation between soil enzyme activity and heavy metal content was uncertain due to the changes in land use patterns and heavy metal species. The proportions of positive correlation and negative correlation between soil enzyme activity and heavy metals in Uncaria rhynchophylla base were 50%, respectively. In the forestland, soil enzyme activity was positively correlated with heavy metals, while in the wasteland, soil enzyme activity was negatively correlated with heavy metals. This study revealed that the changes in heavy metal content should be focused on for the soil quality in Uncaria rhynchophylla-producing areas under different land use patterns. The results of the study provide some basic theoretical references for the improvement of soil quality in the production area of Uncaria rhynchophylla under different land use practices.

Potential hot spots contaminated with exogenous, rare earth elements originating from e-waste dismantling and recycling

Dismantling and recycling e-waste has been recognized as a potential emission source of rare earth elements (REEs). However, the presence of REEs in typical regional soils has yet to be studied. Given the potential health implications of such soil contamination, it is vital to study the characteristics, spatial distribution, and pollution level of REEs caused by e-waste dismantling as well as determine the influencing mechanism. This study focused on Guiyu Town as an example site, which is a typical e-waste dismantling base. From the site, 39 topsoil samples of different types were collected according to grid distribution points. Soil profiles were also collected in the dismantling and non-dismantling areas. The REE characteristic parameters showed that the REE distribution was abnormal and was affected by multiple factors. The results of the integrated pollution index showed that approximately 61.5% of soil samples were considered to be lightly polluted. Spatial distribution and correlation analysis showed that hot spots of REE-polluted soil coincided with known, main pollution sources. Moreover, there was a significant negative correlation (p ≤0.05) between the REE concentration and the distance from the pollution source. E-waste disassembly and recycling greatly affect the physical and chemical properties of the surrounding soil as well as downward migration areas. In the disassembly area, REE accumulated more easily in the surface layer (0-20 cm). Geographical detector results showed that distance factor was the main contribution factor for both light rare earth elements (LREE) and heavy rare earth element (HREE) (q = 34.59% and 53.33%, respectively). REE distribution in soil was nonlinear enhanced by different factors. Taken together, these results showed that e-waste disassembling and recycling not only directly affected the spatial distribution of REEs, but that their distribution was also affected by land use type and soil properties.

Spatial distribution and potential ecological risk of metal(loid)s in cultivated land from Xianjia Town in Fujian, Southeast China

Metal(loid)s in cultivated land become an important issue with respect to human health and food security. However, it remains challenging to identify metal(loid) pollution characteristics due to varying environmental settings at the local scale. In this study, the geographic information system and categorical regression model were applied to analyze the spatial distribution and influencing factors of metal(loid)s in cultivated land using 90 sampling sites in Xianjia Town, Southeast China. The pollution levels and ecological risks of five metal(loid)s-Cd, Pb, Cr, Hg, and As-were further investigated using the single pollution index (PI), Nemerow comprehensive pollution index (PN), and potential ecological risk index (RI). The results indicate that the cultivated soils were affected by Cd and Pb pollution, with 3.06 and 6.30 times higher average concentrations than the soil environment background values (SEBV) of Fujian Province, respectively. Based on the CATREG model, crop type had a great impact on Pb and Hg contents. Cr contents were higher in rice fields, while Hg and As concentrations were higher in turmeric fields. Cr and Hg contents under five crop types did not exceed the SEBV of Fujian Province. The average Pb contents in rice fields were 1.25 and the Cd contents in vegetable fields 1.09 times higher than the average value in sampled soils. According to the RI, 63.66% of the sampling points were at medium to high risk. These findings enhance our understanding of the metal(loid)s pollution characteristics and their ecological risks in cultivated land at the local scale.

Impacts of Land Use on Surface Water Quality Using Self-Organizing Map in Middle Region of the Yellow River Basin, China

The Yellow River is one of the most important water sources in China, and its surrounding land use affected by human activities is an important factor in water quality pollution. To understand the impact of land use types on water quality in the Sanmenxia section of the Yellow River, the water quality index (WQI) was used to evaluate the water quality. A self-organizing map (SOM) was used for clustering analysis of water quality indicators, and the relationship between surface water quality and land use types was further analyzed by redundancy analysis (RDA). The results showed that WQI values ranged from 82.60 to 507.27, and the highest value was the sampling site S3, whose water quality grade was "Likely not suitable for drinking", mainly polluted by agricultural non-point sources ammonia nitrogen pollution. SOM clustered the sampling sites into 4 groups according to the water quality indicators, the main influencing factors for different groups were analyzed and explored in more depth in relation to land use types, suggesting that surface water quality was significantly connected with the proportion of land use types at the watershed scale in the interpretation of water quality change. The negative impact of cropland on surface water quality was greater than that of other land use types, and vegetation showed a greater positive impact on surface water quality than other land uses. The results provide evidence for water environment conservation based on land use in the watershed.

pH and pCl Operational Parameters in Some Metallic Ions Separation with Composite Chitosan/Sulfonated Polyether Ether Ketone/Polypropylene Hollow Fibers Membranes

The development of new composite membranes is required to separate chemical species from aggressive environments without using corrective reagents. One such case is represented by the high hydrochloric acid mixture (very low pH and pCl) that contains mixed metal ions, or that of copper, cadmium, zinc and lead ions in a binary mixture (Cu-Zn and Cd-Pb) or quaternary mixture. This paper presents the obtaining of a composite membrane chitosan (Chi)-sulfonated poly (ether ether ketone) (sPEEK)-polypropylene hollow fiber (Chi/sPEEK/PPHF) and its use in the separation of binary or quaternary mixtures of copper, cadmium, zinc, and lead ions by nanofiltration and pertraction. The obtained membranes were morphologically and structurally characterized using scanning electron microscopy (SEM), high resolution SEM (HR-SEM), energy dispersive spectroscopy analysis (EDAX), Fourier Transform InfraRed (FTIR) spectroscopy, thermogravimetric analysis, and differential scanning calorimetry (TGA-DSC), but also used in preliminary separation tests. Using the ion solutions in hydrochloric acid 3 mol/L, the separation of copper and zinc or cadmium and lead ions from binary mixtures was performed. The pertraction results were superior to those obtained by nanofiltration, both in terms of extraction efficiency and because at pertraction, the separate cation was simultaneously concentrated by an order of magnitude. The mixture of the four cations was separated by nanofiltration (at 5 bars, using a membrane of a 1 m² active area) by varying two operational parameters: pH and pCl. Cation retention could reach 95% when adequate values of operational parameters were selected. The paper makes some recommendations for the use of composite membranes, chitosan (Chi)-sulfonated poly (ether ether ketone) (sPEEK)-polypropylene hollow fiber (Chi/sPEEK/PPHF), so as to obtain the maximum possible retention of the target cation.

Quantitative source apportionment and associated driving factor identification for soil potential toxicity elements via combining receptor models, SOM, and geo-detector method

Quantitative source apportionment of soil potential toxicity elements (PTEs) and associated driving factor identification are critical for prevention and control of soil PTEs. In this study, 421 soil samples from a typical area in southeastern Yunnan Province of China were collected to evaluate the pollution level of soil PTE using pollution factors, pollution load index, and enrichment factors. Positive matrix factorization (PMF), absolute principal component score/multiple line regression (APCS/MLR), edge analysis (UNMIX) and self-organizing map (SOM) were applied for source apportionment of soil PTEs. The geo-detector method (GDM) was used to identify the driving factor to PTE pollution sources, which assisted in source interpretation derived from receptor models. The results showed that the geometric mean of As, Cd, Cu, Cr, Ni, Pb, and Zn were 94.94, 1.02, 108.6, 75.40, 57.14, 160.2, and 200.3 mg/kg, which were significantly higher than their corresponding background values (P < 0.00). Particularly, As and Cd were 8.71 and 12.75 times higher than their corresponding background values, respectively. SOM yielded four clusters of soil PTEs: AsCd, PbZn, CrNi, and Cu. APCS/MLR was regarded as the preferred receptor model for source apportionment of soil PTEs due to its optimal performance. The results of ACPS/MLR revealed that 36.64% of Pb and 38.30% of Zn were related to traffic emissions, Cr (92.64%) and Ni (82.51%) to natural sources, As (85.83%) and Cd (87.04%) to industrial discharge, and Cu (42.78%) to agricultural activities. Distance to road, lithology, distance to industries, and land utilization were the respective major driving factor influencing these four sources, with the q values of 0.1213, 0.1032, 0.2295 and 0.1137, respectively. Additionally, GDM revealed that nonlinear interactions between anthropogenic and natural factors influencing PTEs sources. Based on these results, comprehensive prevention and control strategies should be considered for pollution prevention and risk controlling.

Interactive influences of meteorological and socioeconomic factors on ecosystem service values in a river basin with different geomorphic features

Ecosystem service value (ESV) is influenced by land use and land cover (LULC), and is closely related to natural conditions and human activities. However, the interactions between human and natural systems and ESV remain unclear, especially concerning widely discussed meteorological and socioeconomic factors. In this study, three periods of LULC patterns (2000, 2010, and 2020) in the Haihe River Basin, northern China, were collected to determine the relationship between changes in LULC and ESV over time. Natural and socioeconomic data associated with ESV were obtained and the structural equation model was used to decouple interactions between these factors. Results showed that the total value of regional ecosystem services has decreased as cultivated land shrunk and artificial surfaces increased over the past two decades. The ESV was significantly decreased in the middle of the basin. The direct effects of meteorological factors and socioeconomic factors on ESV were positive (0.094) and negative (-0.203), respectively. The indirect effect of socioeconomic factors on ESV through meteorological and LULC factors was 0.149. Structural equation modeling demonstrated that under the dominance of LULC, interactions between natural and socioeconomic factors affected ESV in a complex manner. These results implied that identifying the direct and indirect effects of economic development and human activities on ESV could guide and implement effective land management policies.

Spatial changes of nutrients and metallic contaminants in topsoil with multi-geostatistical approaches in a large-size watershed

Appropriate assessment on concerned soil contaminants spatially is of importance for decision-makers and stakeholders to make efficient mitigation countermeasures. In this study, we applied multiple geostatistical approaches to explore soil nutrient and metallic contaminant distributions in a large river watershed in Thailand, and to compare their performances in predicting spatial distribution patterns of the concerned soil contaminants under suitable application scenarios. The total carbon, nitrogen and phosphorous in surface soils over the whole watershed were measured with their maximum concentrations up to 131.47, 9.24, 5.33 g·kg^-1, respectively, while the concentrations of eight metallic elements (Cu, Zn, Pb, Cd, Hg, As, Cr, and Ni) were 933.00, 6862.50, 373.00, 6.22, 1.15, 178.53, 761.11, and 372.44 mg·kg^-1, respectively. It was found that the conditional interpolation approaches such as land use stratified inverse distance weighted and land use stratified original kriging provided better boundary details than original interpolations, with substantially reduced root mean square errors (up to 28% for nutrients and 54% for specific metals) and mean relative errors (up to 38% for nutrients and specific metals respectively) in predicting the spatial patterns of soil nutrients and several land use specific metals (Cu, Zn, Cd, and Pb). The global accuracies were equivalent or higher than those of geographically weighted regression. Nonetheless, the prediction accuracy for Cr, Ni, As, and Hg could not be improved using the land use stratified interpolation because their sources and pathways were not significantly correlated with land use types in the watershed, as reflected by the results of analysis of variance with post hoc test (p ≤ 0.05) and principal component analysis.

Potentially toxic elemental contamination in Wainivesi River, Fiji impacted by gold-mining activities using chemometric tools and SOM analysis

Potentially toxic element (PTE) contamination in Wainivesi River, Fiji triggered by gold-mining activities is a major public health concern deserving attention. However, chemometric approaches and pattern recognition of PTEs in surface water and sediment are yet hardly studied in Pacific Island countries like Fijian urban River. In this study, twenty-four sediment and eight water sampling sites from the Wainivesi River, Fiji were explored to evaluate the spatial pattern, eco-environmental pollution, and source apportionment of PTEs. This analysis was done using an integrated approach of self-organizing map (SOM), principle component analysis (PCA), hierarchical cluster analysis (HCA), and indexical approaches. The PTE average concentration is decreasing in the order of Fe > Pb > Zn > Ni > Cr > Cu > Mn > Co > Cd for water and Fe > Zn > Pb > Mn > Cr > Ni > Cu > Co > Cd for sediment, respectively. Outcomes of eco-environmental indices including contamination and enrichment factors, and geo-accumulation index differed spatially indicated that majority of the sediment sites were highly polluted by Zn, Cd, and Ni. Cd and Ni contents can cause both ecological and human health risks. According to PCA, both mixed sources (geogenic and anthropogenic such as mine wastes discharge and farming activities) of PTEs for water and sediment were identified in the study area. The SOM analysis identified three spatial patterns, e.g., Cr-Co-Zn-Mn, Fe-Cd, and Ni-Pb-Cu in water and Zn-Cd-Cu-Mn, Cr-Ni and Fe, Co-Pb in sediment. Spatial distribution of entropy water quality index (EWQI) values depicted that northern and northwestern areas possess "poor" to "extremely poor" quality water. The entropy weights indicated Zn, Cd, and Cu as the major pollutants in deteriorating the water quality. This finding provides a baseline database with eco-environmental and health risk measures for the Wainivesi river contamination.

Potentially toxic elemental contamination in Wainivesi River, Fiji impacted by gold-mining activities using chemometric tools and SOM analysis

Potentially toxic element (PTE) contamination in Wainivesi River, Fiji triggered by gold-mining activities is a major public health concern deserving attention. However, chemometric approaches and pattern recognition of PTEs in surface water and sediment are yet hardly studied in Pacific Island countries like Fijian urban River. In this study, twenty-four sediment and eight water sampling sites from the Wainivesi River, Fiji were explored to evaluate the spatial pattern, eco-environmental pollution, and source apportionment of PTEs. This analysis was done using an integrated approach of self-organizing map (SOM), principle component analysis (PCA), hierarchical cluster analysis (HCA), and indexical approaches. The PTE average concentration is decreasing in the order of Fe > Pb > Zn > Ni > Cr > Cu > Mn > Co > Cd for water and Fe > Zn > Pb > Mn > Cr > Ni > Cu > Co > Cd for sediment, respectively. Outcomes of eco-environmental indices including contamination and enrichment factors, and geo-accumulation index differed spatially indicated that majority of the sediment sites were highly polluted by Zn, Cd, and Ni. Cd and Ni contents can cause both ecological and human health risks. According to PCA, both mixed sources (geogenic and anthropogenic such as mine wastes discharge and farming activities) of PTEs for water and sediment were identified in the study area. The SOM analysis identified three spatial patterns, e.g., Cr-Co-Zn-Mn, Fe-Cd, and Ni-Pb-Cu in water and Zn-Cd-Cu-Mn, Cr-Ni and Fe, Co-Pb in sediment. Spatial distribution of entropy water quality index (EWQI) values depicted that northern and northwestern areas possess "poor" to "extremely poor" quality water. The entropy weights indicated Zn, Cd, and Cu as the major pollutants in deteriorating the water quality. This finding provides a baseline database with eco-environmental and health risk measures for the Wainivesi river contamination.

Integrated assessment of the impact of land use types on soil pollution by potentially toxic elements and the associated ecological and human health risk

The impact of land use type on the content of potentially toxic elements (PTEs) in the soils of the Qinghai-Tibet Plateau (QTP) and the associated ecological and human health risks has drawn great attention. Consequently, in this study, top- and subsurface soil samples were collected from areas with four different land uses (i.e., cropland, forest, grassland, and developed area) and the total contents of Cr, Cd, Cu, Pb and Zn were determined. Geostatistical analysis, self-organizing map (SOM), and positive matrix factorization (PMF), ecological risk assessment (ERA) and human health risk assessment (HRA) were applied and used to classify and identify the contamination sources and assess the potential risk. Partial least squares path modeling (PLS-PM) was applied to clarify the relationship of land use with PTE contents and risk. The PTE contents in all topsoil samples surpassed the respective background concentrations of China and corresponding subsurface concentrations. However, the ecological risk of all soil samples remained at a moderate or considerable level across the four land use types. Developed area and cropland showed a higher ecological risk than the other two land use types. Industrial discharges (32.8%), agricultural inputs (22.6%), natural sources (23.7%), and traffic emissions (20.9%) were the primary PTE sources in the tested soils, which indicate that anthropogenic activities have significantly affected soil PTE contents to a greater extent than other sources. Industrial discharge was the most prominent source of non-carcinogenic health risk, contributing 37.7% for adults and 35.2% for children of the total risk. The results of PLS-PM revealed that land use change associated with intensive human activities such as industrial activities and agricultural practices distinctly affected the PTE contents in soils of the Qinghai-Tibet Plateau.

Hydrochemistry and source apportionment of boron, sulfate, and nitrate in the Fen River, a typical loess covered area in the eastern Chinese Loess Plateau

Fen River Basin (FRB) is water-deficient and strongly influenced by human activities in the eastern Chinese Loess Plateau. The spatio-temporal variation and controlling factors of hyrochemistry and quality, sources of high boron, sulfate, and nitrate of surface waters in FRB were unclear. Major ions, δ¹¹B, δ¹⁵N, and δ¹⁸O in surface waters in dry season and wet season of FRB were analyzed and correlation analysis (CA), principal component analysis (PCA), self-organizing map (SOM), forward model, and Bayesian isotope mixing model (MixSIAR) were used to solve above problems. Results showed that average riverine δ¹¹B, δ¹⁵N, and δ¹⁸O of FRB was 7.8‰, 11.2‰, and 1.3‰ (1SD), respectively. Dissolved solutes ranked midstream > downstream > upstream with water type of Na ⁺-Cl^-, Ca²⁺-Mg²⁺-Cl^-, and Ca²⁺-HCO₃^-, respectively. Low dissolved solutes were in forest areas while high values were in cropland and city areas. SOM analysis indicated that hydrochemistry was both influenced by natural (upstream) and pollutional input (midstream and downstream) and variation between dry season and wet season was minor. The abnormally high boron concentrations were mainly from silicate weathering (43%) and evaporites dissolution of loess (32%), urban and industrial input contributed 15% of riverine boron. High SO₄^2- (207 ± 267 mg/L, 1SD) was mainly from sulfates. δ¹⁵N and δ¹⁸O analysis indicated that nitrification was the primary N cycling process. Further, MixSIAR showed that NO₃^- was mainly from municipal sewage (∼67%) and the total contribution of chemical fertilizer and soil nitrogen was ∼30% with slightly higher values in upstream and wet season. Influenced by land-use types, evaporite dissolution, and anthropogenic input, water quality below midstream was worse and strict sewage reduction policies must be developed. This study highlights the significant influence of evaporite dissolution of loess and anthropogenic input (urban and industrial input for B and sewage for NO₃^-) on hydrochemistry and water quality.

Heavy Metal Pollution and Soil Quality Assessment under Different Land Uses in the Red Soil Region, Southern China

The influences of different land uses associated with human activities on soil quality and the redistribution of heavy metal in soil have been widely concerned. Surface soil samples were obtained to assess comprehensive soil quality in a typical red soil region of southern China, combining the heavy metal pollution evaluation with fertility evaluation. It can be learned from the results that the overall level of soil fertility was at medium and lower level, and soil heavy metal pollution risk in the study area in a few regions had reached warning line and slight pollution line, and there was a risk of potential pollution. TOPSIS evaluation results showed that the comprehensive soil quality was mainly good quality and moderate quality, accounting for 31.7% and 29.0% of the total land area, respectively. Positive matrix factorization (PMF) model results showed that transportation source contributes a lot in terms of Cd and Pb. As for Cr, natural source contributes 53.8%. In terms of Cu and Zn, agriculture source contributes 50.7% and 38.7%, respectively. In a word, the comprehensive soil quality assessment in red soil region of southern China provides an important basis for the scientific management and sustainable utilization of soil resources.

Ranking of Basin-Scale Factors Affecting Metal Concentrations in River Sediment

River sediments often contain potentially harmful pollutants such as metals. Much research has been conducted to identify factors involved in sediment concentrations of metals. While most metal pollution studies focus on smaller scales, it has been shown that basin-scale parameters are powerful predictors of river water quality. The present study focused on basin-scale factors of metal concentrations in river sediments. The study was performed on the contiguous USA using Random Forest (R.F.) to analyze the importance of different factors of the metal pollution potential of river sediments and evaluate the possibility of assessing this potential from basin characteristics. Results indicated that the most important factors belonged to the groups Geology, Dams, and Land cover. Rock characteristics (contents of K2O, CaO, and SiO2) and reservoir drainage area were strong factors. Vegetation indices were more important than land cover types. The response of different metals to basin-scale factors varied greatly. The R.F. models performed well with prediction errors of 16.5% to 28.1%, showing that basin-scale parameters hold sufficient information for predicting potential metal concentrations. The results contribute to research and policymaking dependent on understanding large-scale factors of metal pollution.

Multivariate statistical analysis of potentially toxic elements in soils under different land uses: Spatial relationship, ecological risk assessment, and source identification

The Qinghai-Tibet Plateau is one of the most fragile and susceptible areas to climate change and human disturbances in the world. Here, a total of 48 soil samples were obtained from areas of different land uses within a typical basin in eastern Qinghai-Tibet, China. The selected potentially toxic elements (PTEs, including Cd, Cr, Cu, Pb, and Zn) contents were analyzed to explore their spatial patterns, ecological risks, and then the effects of land use types on these elements were assessed by self-organizing map (SOM) and random forest regression (RFR) models, and the main sources were revealed using positive matrix factorization (PMF) model. Results showed that mean concentrations of selected PTEs in surface soils were higher than local background values and those of subsurface soils. The low-degree ecological risk was obtained with comparatively high risks in the north and south of the study area. The results of the SOM and RFR models revealed that land use types affected the redistribution of PTEs in surface soil. The PMF model demonstrated that these PTEs were mainly derived from natural sources (46.7%), traffic emissions (31.2%), and industrial and agricultural inputs (22.1%). Natural sources were the essential contributors for these soil PTEs, especially for Cr. In addition to natural sources, traffic sources made great contributions for Cd, Pb, and Zn elements, while the enrichment of Cu was mainly related to industrial and agricultural activities.

Prediction of nickel concentration in peri-urban and urban soils using hybridized empirical bayesian kriging and support vector machine regression

Soil pollution is a big issue caused by anthropogenic activities. The spatial distribution of potentially toxic elements (PTEs) varies in most urban and peri-urban areas. As a result, spatially predicting the PTEs content in such soil is difficult. A total number of 115 samples were obtained from Frydek Mistek in the Czech Republic. Calcium (Ca), magnesium (Mg), potassium (K), and nickel (Ni) concentrations were determined using Inductively Coupled Plasma Optical Emission Spectroscopy. The response variable was Ni, while the predictors were Ca, Mg, and K. The correlation matrix between the response variable and the predictors revealed a satisfactory correlation between the elements. The prediction results indicated that support vector machine regression (SVMR) performed well, although its estimated root mean square error (RMSE) (235.974 mg/kg) and mean absolute error (MAE) (166.946 mg/kg) were higher when compared with the other methods applied. The hybridized model of empirical bayesian kriging-multiple linear regression (EBK-MLR) performed poorly, as evidenced by a coefficient of determination value of less than 0.1. The empirical bayesian kriging-support vector machine regression (EBK-SVMR) model was the optimal model, with low RMSE (95.479 mg/kg) and MAE (77.368 mg/kg) values and a high coefficient of determination (R² = 0.637). EBK-SVMR modelling technique output was visualized using a self-organizing map. The clustered neurons of the hybridized model CakMg-EBK-SVMR component plane showed a diverse colour pattern predicting the concentration of Ni in the urban and peri-urban soil. The results proved that combining EBK and SVMR is an effective technique for predicting Ni concentrations in urban and peri-urban soil.