Heavy Metal Pollution Risk Research Articles

Combining spatial autocorrelation with artificial intelligence models to estimate spatial distribution and risks of heavy metal pollution in agricultural soils

Information on spatial distribution and potential sources of heavy metals in agricultural lands is very important for human health and food safety. In this study, pollution degree of lead (Pb), cadmium (Cd), and nickel (Ni) in Yüksekova Plain, located on the border in the southeastern part of Turkey, was evaluated by geoaccumulation index (Igeo), modified contamination factor (mCdeg), and Nemerow pollution index (PINemerow) combined with spatial autocorrelation using deep learning algorithms. A total of 304 soil samples were collected from two different depths (0-20 and 20-40cm) in the study area, which covered 17.5 thousand ha land. Covariates were determined for spatial distribution models of Pb, Cd, and Ni by factor analysis (FA). Spatial distribution models for surface soils were developed using pedovariables (silt, sand, clay lime, organic matter, electrical conductivity, pH, Ca, and Na) determined by the FA and Igeo and mCdeg values by artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models. The estimation success of models for different depths was assessed by root mean square error (RMSE), mean absolute percent error (MAPE), and Taylor diagrams. The RMSE and MAPE values showed a strong correlation between heavy metal contents and the covariates. The RMSE values of ANN-Ni0-20, ANN-Ni20-40, ANN-Pb0-20, ANN-Cd0-20, and ANN-Cd20-40 models (0.01240, 0.07257, 0.0039, 0.00045, 0.00044, and 0.04607, respectively) confirmed the success of the models. Likewise, the MAPE values between 0.2 and 8.5% indicated that all models were very good predictors. In addition, the Taylor diagrams showed that the estimation performance of ANFIS and ANN models are compatible. The IgeoNi and IgeoPb values in both models at both depths indicated that strongly to extremely polluted (4-5) areas are quite high in the study area, while the IgeoCd values revealed that unpolluted areas are widespread. The mCdeg index value showed a moderate to high contamination at the first depth, while very high contamination at the second depth in most of the study area. Spatial distribution of PINemerow revealed that moderate pollution (2-3) is common in both soil depths of the study area. The PINemerow of subsurface layer was between 0.91 and 1 (warning limit class) in a small part of the study area. The results showed that vertical mobility of heavy metals is closely related to pedovariables. In addition, the ANN and ANFIS models are capable of exhibiting the heterogeneity in the spatial distribution pattern of high variation in the data. Thus, the locations with extreme contamination have been accurately determined. The pollution indices calculated considering the commonly used international reference values revealed that heavy metal pollution in some part of the study area reached the detrimental levels for human health and food safety. The results suggested that the pollution indices were more successful than simple heavy metal concentrations in interpreting the pollution risk levels. High-resolution spatial information reported in this study can help policy makers and authorities to reduce heavy metal emissions of pollutants or, if possible, to eliminate the pollution.

Spatial distribution and pollution evaluation in dry riverbeds affected by mine tailings

The objective of this study was to evaluate the level of pollution, sources and potential risk of heavy metals (Zn, Cu, Mn, Cd, Cr, Ni, Fe and Pb) and arsenic (As) in four dry riverbeds affected by mine tailing, which drain into one of the biggest coastal lagoon of Europe (Mar Menor). El Beal, La Carrasquilla, Las Matildes and Ponce dry riverbeds sediments were sampled along its course (20, 18, 13, 19 samples were collected, respectively), and total/soluble metal(loid)s, water soluble ions, nitrogen, and organic/inorganic carbon contents were analyzed. Spatial distribution, principal component analysis (PCA), hierarchical cluster analysis (HCA), contamination factor (Cf), pollution load index (PLI) and potential ecological risk index (RI) were used to identify the possible sources of metal(loid)s and to assess the sediment pollution status. The results showed that the mean total concentrations of As, Cu, Cd, Mn, Zn and Pb exceeded the natural background levels of the study area, with the highest values located close to the mining areas. Correlation and cluster analysis identified that Cd and Zn were associated mainly with anthropogenic activities for all riverbeds, while Cr and Ni come from parent. PLI graded the four riverbeds as contaminated by heavy metals, while RI manifested that 100% of samples located in El Beal, La Carrasquilla and Las Matildes had a significantly high ecological risk. Therefore, this study suggests that mine wastes are the main source of metal(loids) contamination in the dry riverbeds, which results can be used to design actions and measures to reduce the environmental impact of metal(loid)s in the Mar Menor coastal lagoon.

Distribution, risk assessment, and quantitative source apportionment of heavy metals in surface sediments from the shelf of the northern South China Sea

The concentration of heavy metals (Cu, Pb, Zn, Cr, Co, and Ni) from 421 sediment samples from the shelf of the northern margin of the South China Sea (SNSCS) was analyzed. The heavy metal content and calculated potential ecological indicators (Eri < 40 and RI < 160) indicate that there is low potential ecological risk of heavy metal pollution in the SNSCS. The mean effects range-median quotient (M-ERM-Q) and hazard quotient (HQ) values of sediment toxicological characteristics indicate that heavy metals are a potential toxicological risk. The high-risk area is mainly distributed in the southwest of the nearshore SNSCS. The positive matrix factorization (PMF) analysis results showed that major contributors to heavy metal pollution were natural sources and anthropogenic activities in the SNSCS. The government should pay particular attention to the monitoring of heavy metals in the nearshore southwest of the SNSCS.

Distribution Characteristics and Pollution Risk of Heavy Metals in River Sediment of Suzhou Water Network Area, China

The average contents of the heavy metals Cd, Cu, Cr, As, Ni, Pb, and Zn in the sediment of sampling points in the Suzhou water network area were 1.4, 127.4, 83.2, 18.2, 51.7, 145.1, and 350.7 mg·kg-1, respectively, which were 13.7, 5.7, 1.1, 1.7, 1.9, 5.5, and 5.6 times the background values, respectively. The proportions of points exceeding the risk screening values of the GB 15618-2018 standard were 100.0%, 97.3%, 38.4%, 83.6%, 97.3%, 90.4%, and 100.0%, respectively. The pollution degree of single heavy metal elements was evaluated using the improved ground accumulation index method. The pollution degree of the seven heavy metal elements in the sediment of the Suzhou water network area was in the order of Cd>Cu>Pb>Zn>As>Cr>Ni. Among them, Cd showed extremely strong pollution, Cu and Pb showed intense to extremely strong pollution, Zn showed strong pollution, As showed moderate to intense pollution, and Cr and Ni showed moderate pollution. Cd, Pb, Cu, and Zn in the sediment samples of the ancient city area, northwest area, southwest area, and east area were the heavy metal elements with high pollution contributions. The potential ecological risk degree of heavy metals was ranked as northwest>southwest>ancient city area>east. Correlation analysis and principal component analysis showed that Cd, Cu, Cr, As, Ni, Pb, and Zn may have come from anthropogenic factors such as tail gas emissions, the use of chemical fertilizers and pesticides, and the pollutant emissions of the electronic manufacturing industry.

Understanding urban groundwater pollution in the Upper Gangetic Alluvial Plains of northern India with multiple industries and their impact on drinking water quality and associated health risks

Udham Singh Nagar district is having various industries like paint, textile, sugar, petrochemical, paper and automobile which produces different kind of contaminants including heavy metals in the environment and pollute groundwater. Therefore, in the present study, fifty groundwater samples were collected from this region and analyzed for 7 physicochemical parameters (pH, electrical conductivity, total dissolved solids, bicarbonate, chloride, sulphate, nitrate), 4 metals (calcium, magnesium, sodium, potassium) along with 6 heavy metals (Fe, Mn, Zn, Pb, Cd, Cr). Heavy metal pollution level in the groundwater was assessed by calculating and analysing heavy metal pollution indices (heavy metal pollution indices -HPI, m-HPI; heavy metal evaluation index- HEI) and risk assessment parameters (hazard quotient - HQ and hazard index - HI). Multivariate statistical analysis was also done to identify the sources of heavy metals and categorize groundwater quality. Concentration of Fe, Mn and Cr in 14%, 16% and 18% water samples exceeded the desirable limit. Piper diagram suggested that majority of groundwater samples were Ca–Mg–HCO3 type. HPI calculation indicated that no groundwater sample fall under high category of pollution. HEI and m-HPI indicated that 90% and 82% groundwater samples were found suitable for potable purpose respectively while on the basis of m-HPI, 3 groundwater samples were found under the unacceptable category. Non-carcinogenic hazards were observed which might be due to high chromium content. According to principal component analysis (PCA) and cluster analysis (CA), geogenic, anthropogenic and industrial activities were responsible for groundwater pollution. The values of HQ for Cr for both adult (13 samples) and younger people (9 sampling sits) surpass the unity. HI values indicated that groundwater samples from 14 sites were not suitable for drinking purpose. This is the first attempt of analyzing heavy metal pollution and health risk in Udham Singh Nagar district of Uttarakhand.

ASSESSMENT OF HEAVY METAL POLLUTION INDEX OF WATER-BASED DRILLING FLUID WASTE

The prevalence of heavy metal pollution in oil and gas producing areas, Niger-Delta Nigeria, due to drilling activities in some oil and gas sites has resulted in the pollution of an immeasurable area of land and water. This study appraised the level of environmental menace caused by heavy metals in water-based drilling fluid waste. It involved an oil and gas site where drilling activities were taking place and settlement with no record of drilling activities served as control. In the drilling sites, two composite drilling fluid waste samples were collected before and after treatment and analyzed for cadmium, chromium, iron, nickel, lead, and zinc using flame atomic absorption spectrophotometry. The measured concentrations of the heavy metals in drilling fluid wastes were then used to determine the pollution and ecological risk posed by heavy metals. The thermal desorption treatment method was used. Their concentrations before and after treatment of the drilling fluid wastes were in the order Fe &gt; Cr &gt;Zn &gt; Cd &gt; Ni &gt; Pb and Fe &gt; Cr &gt; Ni &gt; Zn &gt; Pb &gt; Cd respectively with Cr, Fe, Ni, and Pb having their concentrations higher than permissible levels in water after treatment and responsible for most of the total potential ecological risk. The PLI (Pollution Load Index) before and after treatment were 0.0463 and 0.0017 respectively. There was a drastic decrease in potential pollution levels after treatment. Also, all the several pollution indices studied revealed that the drilling sites were contaminated with Cd and Cr. This reveals that drilling sites cause further risk of heavy metal pollution. The correlation analysis of heavy metals assessed is between 0.9 - 1.0 suggesting that all the heavy metals assessed showed high positive correlation coefficients at (p &lt; 0.05) and likely originated from the same source of drilling activities in the study area.

Spatiotemporal patterns of soil heavy metal pollution risk and driving forces of increment in a typical industrialized region in central China.

Excessive enrichment of soil heavy metals seriously damages human health and soil environment. Exploring the spatiotemporal patterns and detecting the influencing factors are conducive to developing targeted risk management and control. Based on the soil samples of Co, Cr, Cu, Mn, Ni, Pb, Zn, and Cd collected in one typical industrialized region in China from 2016 to 2019, this study analyzed the spatiotemporal pattern of geo-accumulation risk and potential ecological risk based on the spatiotemporal ordinary kriging (STOK) prediction, and probed the driving forces of heavy metal increments with the random forest (RF) regression model. The risk assessment revealed that soils were seriously contaminated by Pb, Cd, and Cu, moderately contaminated by Zn and Mn, and uncontaminated by Co, Cr, and Ni; more than 30% of areas had moderate to high potential ecological risks. From 2016 to 2019, soil heavy metal contents increased in more than 50% of regions and the growth rates of accumulations were ranked as Co (65%) > Ni (56%) > Mn (43%) > Pb (40%) > Cr (36%) > Zn (31%) > Cu (23%) > Cd (3%). High contents and increases of heavy metals in soils near industrial lands are higher. Smelter (24%), mine (20%), and factory (12%) were the major contributing factors for these heavy metal increments, followed by transportation (6%) and population (5%). The results indicated that the management of industrial discharge and contaminated soils should be strengthened to prevent the worsening soil heavy metal pollution in the study area.

Heavy Metal Pollution from Listvenitization: In Case of Alakeçi (Bayramiç-Çanakkale/West Türkiye)

This study aimed to investigate the risk of element/heavy metal pollution caused by listvenitization. In this context, the heavy metal pollution risk of listvenite-derived soils in the region where listvenitized ultrabasic rocks are present as a result of hydrothermal alterations in the vicinity of Alakeçi (Bayramiç Çanakkale/Western Türkiye) was investigated with pollution index, geoaccumulation index and integrated pollution risk parameters. For this purpose, Cu, Zn, Pb element concentrations of 350 soil samples collected from the field were determined, and Pollution Index (PI) and Geo-accumulation Index (Igeo) parameters for each element and Integrated Pollution Index (IPI) parameters for each sampling point were calculated. In addition, distribution maps of PI, Igeo and IPI parameters were plotted. When the site is considered in terms of IPI parameter, it has been determined that the site has medium and high pollution risk. When the field is considered in terms of PI and Igeo parameters, a remarkable level of pollution has been detected in the field, especially by Ni, Co and As elements. When the distribution maps of the PI, Igeo and IPI parameters are examined, it has been determined that the pollution risk is higher than the other areas, especially in the areas where hydrothermal alteration is intense and in the tectonic line areas. Although listvenitizations and listvenite zones are important target areas especially for epithermal gold mineralizations, this study has shown that listvenitization areas are also areas at risk of heavy metal pollution. Therefore, listvenitization zones are areas that should be investigated in terms of heavy metal pollution risk as well as epithermal gold mineralization potentials.

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

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

Accumulation of Metals in the Environment and Grazing Livestock near A Mongolian Mining Area.

The Mongolian economy is supported by rich deposits of natural resources, such as copper, coal, and gold. However, the risk of heavy metal pollution to livestock and human have been recently discussed. This research collected various samples from soil and animal (sheep, goat, horse, cow, and camel), blood and organs (kidney and liver) in the Mongolian countryside. These samples were processed, and the concentration of metals was quantified using inductively coupled plasma-mass spectrometry (ICP/MS). As previously reported, arsenic was found at high levels of accumulation in soil. Selenium is another concern, as median concentration in one area exceeded the maximum allowable level. Cadmium and selenium were found to be highly accumulated in animal kidney. This research revealed the current pollution level in Mongolia based on evaluation of soil and animals. The concentration in animals could not indicate that animals had severe effects because of heavy metal exposure. However, kidney is eaten in Mongolia, and so there is a direct connection to human health, and this research suggested the possible risks posed by each edible animal. In particular, evaluation of metals in livestock is rare in Mongolia. This result can contribute to animal and human health in Mongolian communities.

Pollution Assessment and SSD-Based Ecological Assessment of Heavy Metals in Multimedia in the Coast of Southeast China

Heavy metals in the ocean exist in various media and assessing heavy metal pollution in the multimedia in seawater is important for proposing effective strategies to protect marine ecosystem health. However, comprehensive coastal pollution assessments and SSD-based assessments of heavy metals have been limited from an international perspective. This study discusses the distribution, sources, interactions, associated environmental factors, and potential ecological risks related to heavy metal pollution. To attain this objective, several tools and models were considered. The partition coefficient between sediment and water was used to understand the ability of heavy metals to be adsorbed from seawater by sediment. The water quality index was applied to evaluate the comprehensive heavy metal pollution at different sampling sites. The species diversity index was calculated by calculating the concentration of chlorophyll a. The geo-accumulation and potential ecological risk indexes were employed for the sediments' general pollution characteristic of heavy metals. Species sensitivity distribution was used for ecological risk evaluation. The results indicated that heavy metal pollution and ecological risk (Cu, Pb, Zn, Cd, As, Cr and Hg) are not serious, and the pollution conditions remain safe. Only Cu, Pb, and Hg concentrations in seawater exceed the Nation Class I Water Quality Standard. The concentrations of heavy metals showed significant spatial characteristics. Fisheries activities and industrial effluent discharges were identified as the main anthropogenic sources. This study provides a comprehensive assessment of heavy metals in multi-media, and the results will provide valuable information for nearshore ecological management and risk avoidance.

Leaching characteristics and solidification strategy of heavy metals in solid waste from natural graphite purification.

The tailings and fluorine-containing sludge were produced during the physical and chemical purification of natural crystalline graphite, containing heavy metals in different occurrence forms. To evaluate the threat of different heavy metals to the environment, this work uses the modified sequential extraction method (BCR) to study the presence of heavy metals in two solid wastes and their dissolution characteristics in different environments. The results show that the pollution risk of heavy metals in graphite tailings to the environment is ranked as Mn > Cr > Ni > Zn, and the pollution risk of Mn in fluorine-containing sludge is higher than that of Cr. This is because the Mn in the two solid wastes mainly exists in the form of weak acid extraction. The leaching number of heavy metals in the two solid wastes is directly proportional to the soaking time and soaking temperature, and inversely proportional to the pH value and the solid-to-liquid ratio. The number of heavy metals dissolved in solid waste landfills is significantly higher than that of acid rain and surface water environments. Based on the above results and the distribution of graphite solid waste, solidification agent was suggested to prevent heavy metal dissolution and reduce environmental risks.

Risk Assessment of Heavy Metal Pollution in Farmland Soils at the Northern Foot of the Qinling Mountains, China.

To provide scientific basis for the prevention and control of heavy metal pollution, a field investigation, sample collection and analysis of the heavy metal content in farmland soils at the northern foot of the Qinling Mountains were conducted. Based on the comparative analysis of the single pollution index method, the Nemerow comprehensive pollution index method, the geological accumulation index method, the potential ecological hazard index method, and the geological accumulation index method were used to comprehensively analyze and evaluate the risk of soil heavy metal pollution. The results showed that the heavy metal pollution of farmland soil at the northern foot of the Qinling Mountains was severe, among which Hg and Cr pollution was relatively obvious. Taking the soil screening values of agricultural land as the standard, the quantity of element Hg in agricultural soils at the northern foot of the Qinling Mountains was higher than the relevant screening value. In the two sample sites investigated, the intensity of the heavy metal accumulation index in Baoqizhai Village was Hg > Cr > Cu > As > Pb, and in Dayangyu Village it was Cr > Cu > As > Pb. Among them, in Baoqizhai Village it shows the heavy pollution caused by Hg (Igeo= 3.42) and the light pollution caused by Cr (Igeo < 1) in the two areas. Hg is mostly affected by mining activities and its atmospheric subsidence. At the same time, Cr is mainly derived from the weathering of rock parent material and is also affected by anthropogenic factors to a certain extent. The accumulation of heavy metals in the farmland soil around the northern foot of the Qinling Mountains was relatively high, posing a threat to the surrounding soil environment. Therefore, it is urgent to control farmland soil environmental pollution.

Variation in Road Dust Heavy Metal Concentration, Pollution, and Health Risk with Distance from the Factories in a City-Industry Integration Area, China.

Road dust samples around three typical factories, F1, F2, and F3, in the National Zhengzhou Economic and Technology Development Zone (ZETZ), China, were collected to study the variation in heavy metal concentration (As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn), pollution, and health risk with distance from the factories. The results indicated that the concentrations of all the elements near F1 were higher than near both F2 and F3. Apart from Co, Mn, and Cu in some dust samples, all the element concentrations were higher than the corresponding background values (BCs), to varying degrees. The spatial distributions of the heavy metals surrounding the factories followed the normal distribution. The peak values of element concentrations occurred at 300~400 m away from the factories, except for Hg, which continued increasing more than 500 m away from the factories. The fluctuation curves of the pollution load index value calculated according to the BCs for F1, F2, and F3 all had two peaks, a "small peak" and a "large peak", appearing at about 30 m and 300 m, respectively. For the hazard index and the total carcinogenic risk, the peak values all appeared at 400 m, with the curves following the normal distribution. Exposure to road dust containing non-carcinogenic and carcinogenic elements around F1 was greater than around F2 or F3. In conclusion, our results provide a reference for pursuing effective prevention of dust heavy metal pollution around modern manufacturing factories.

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

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

Dissolved Heavy Metal Pollution and Assessment of a Karst Basin around a Mine, Southwest China.

Karst water quality is one of the most important environmental issues in karst areas. The study's purpose was to investigate dissolved heavy metal pollution and health risk assessment in karst water basins around mines. River water and groundwater samples were analyzed by principal component analysis, correlation analysis, water quality index, hazard quotient, and hazard index. Median concentrations of dissolved heavy metals in the Sidi River were similar to the world average with a slightly alkaline characteristic. The concentrations of most dissolved heavy metals in river water were higher than those in groundwater. The concentrations of Zn, Pb, and Cd around the mine exceeded the limits of drinking water indicators. The poor water quality samples with high water quality index values were distributed around the mine. Lead (Pb), Zn, As, Cd, and Cr were potentially threatening metals in the study area. The pollution level of dissolved heavy metals in the Sidi River was at a medium level compared with other rivers worldwide. Principal component analysis and correlation analysis showed that Cu, Pb, Zn, Cd, Mn, Fe, As, and Sr mainly came from mine drainage; Ca2+, Mg2+, and Cr mainly came from the contribution of carbonate rocks; Na+ and K+ were related to local human agricultural activities. The concentrations of dissolved heavy metals in groundwater were affected by karst aquifers. The results of this study can provide a data reference for water resources prevention and human health protection in the Sidi River's karst basin and similar karst basins.

Vertical distribution and health risk assessment of heavy metals in soils around tin ore areas in Yunnan, China

ABSTRACT To assess heavy metal pollution and human health risks, 481 samples from 59 soil sampling points were collected from different soil layers at an underground depth of 10 m in a tin ore area in Yunnan, China. The soil around the mining area was polluted by heavy metals to varying degrees, and high concentrations of Cd, Pb, As, and Cu posed serious ecological risks. Owing to leaching and infiltration of heavy metals, the lower soil samples were also severely polluted as much as the surface soil. The heavy metals mainly accumulated in the soil layers at depths of 0 − 6 m. Meanwhile, the concentration decreased with an increase in soil depth, after which it stabilised. Residents of the area are currently at danger of being diagnosed with non-carcinogenic disorders, while exhibiting a significantly elevated risk of carcinogenicity; children are at a higher risk than adults. In addition, As was the primary threatening factor to the health of residents.

Regional metal pollution risk assessment based on a big data framework: A case study of the eastern Tianshan mining area, China

With the increasing maturity of big data technology, its application in the field of environmental assessment has become an important issue. The mining of mineral resource can affect the balance in the ecological environment surrounding mining areas and the normal life activities of humans through groundwater. To solve this problem, big data-based methods were used to evaluate the pollution risk in mining areas in this article. Based on a spatial big data management framework, in this paper, high-risk areas in eastern Tianshan were quantitatively analyzed and delineated via a new comprehensive heavy metal pollution assessment system. A distributed storage environment, unstructured management method and spatial index coding were used to uniformly manage spatial data in vector and raster formats retrieved from different sources. A system involving 18 geological environment indicators was used to evaluate the risk level in the study area and in delineated areas with a high risk of heavy metal pollution. The results indicated that the proposed framework could efficiently store and process spatial data and realize environmental pollution risk assessment in a big data environment. In addition, some of the high-pollution risk areas identified based on the assessment results were consistent with actual mine locations. Overall, the results show that integrating multi-source geological characteristics through a comprehensive evaluation system based on big data can have a positive effect on improving the accuracy of heavy metal pollution risk assessment. Heavy metal pollution assessment can provide a reference for environmental monitoring and governance of the eastern Tianshan region and offer new solutions to large-scale heavy metal pollution assessment.

Effect of Biochar Addition on Mechanism of Heavy Metal Migration and Transformation in Biogas Residue Aerobic Compost

In this study, biogas residue fermented by biogas engineering was used as compost raw material, and different quality biochar was added in the composting process to explore the effect of biochar on the transformation of heavy metals in the composting process. The composting process was comprehensively analyzed with the potential ecological risk assessment of heavy metals and redundancy analysis. The addition of 10.0% biochar during composting had a strong passivation effect on exchangeable Cu and Cd, with passivation rates of 11.75 and 63.89%, respectively; the addition of 2.5 and 7.5% biochar had strong passivation ability for exchangeable Zn and Pb, and the passivation rates were 15.26 and 45.02%, respectively. At the end of composting, the potential ecological risk indexes of each treatment were T4 (10.0% biochar) &gt; T3 (7.5% biochar) &gt; T2 (5.0% biochar) &gt; T1 (2.5% biochar) &gt; CK (no biochar added). The risk of heavy metal pollution during the aerobic composting of biogas residue was low, which significantly reduced secondary pollution during the composting process.

Spatial Differentiation and Influencing Factor Analysis of Soil Heavy Metal Content at Town Level Based on Geographic Detector

Geographic detectors can quickly detect spatial stratified heterogeneity and quantitatively reveal the intensity of driving factors of heavy metal content, which is of great significance for the prevention, control, and remediation of soil heavy metal pollution. In order to reveal the spatial differentiation and influencing factors of soil heavy metal content on the town-scale, 788 topsoil samples were collected from a town in the hinterland of Chengdu Plain. Soil heavy metal (Cd, Hg, As, Cu, Pb, Cr, Zn, and Ni) pollution risk assessments were carried out by using the geo-accumulation index method. Additionally, based on the geographic detector model, 15 factors such as soil properties, topography, soil forming factors, and distance were taken as independent variables, and the contents of each heavy metal element were taken as dependent variables to explore the spatial differentiation and influencing factors of heavy metal content in soils. The results showed that:the average contents of Hg, As, Pb, Cr, Cu, Ni, and Zn in the study area were 1.06-1.93 times the background value of Chengdu, and the content of Cd was lower than the background value; among them, Hg reached the light pollution level, and the other seven heavy metals were at the non-pollution level. The spatial distribution of eight heavy metals was significantly different, the correlation among the elements was significant, and a significant correlation was found between most heavy meals with soil properties; however, the correlation with distance factor and topographic factor was relatively weak. The factor detection showed that TP, TK, pH, TOC, elevation, and distance from the railway had the most significant explanatory power for the heavy metal contents. Interaction detection showed that the interaction between soil properties and other factors was the dominant factor for the spatial variation in heavy metals, and elevation, distance from residential area, distance from railways, and distance from industrial areas were also important factors. Risk detection showed that Hg had the most significant difference in the subregion of elevation and distance from railway, whereas the other seven heavy metals had the most significant difference in the sub-regions of influencing factors of soil properties. The spatial distribution of heavy metals varied significantly in soil at the town-scale, which was closely related to soil properties, topography, and human activities in the study area.