Geochemical characteristics and health risks of heavy metals in agricultural soils and crops from a coal mining area in Anhui province, China.

Soil contamination by heavy metals (HMs) in mining areas is a major issue because of its significant impact on the environmental quality and physical health of residents. Mining of minerals used in energy production, particularly coal, has led to HMs entering the surrounding soil through geochemical pathways. In this study, a total of 166 surface soil and 100 wheat grain samples around the Guobei coal mine in southeast China were collected, and trace metal levels were determined via inductively coupled plasma mass spectrometry (ICP-MS). The average HMs (Ni, As, Cr, Cu, Pb, Cd, and Zn) concentrations were lower than the screening values in China (GB 15618-2018) but higher than the soil background values in the Huaibei Bozhou area of Anhui Province (except Zn), indicating HMs enrichment. Based on the geoaccumulation index (Igeo) and ecological risk index (IER), Cd pollution levels were low, while for the other metals the samples were pollution-free, and therefore no ecological risk warning was issued for the mining area. Both Cr and Pb had a higher noncarcinogenic health risks for adults and children. The lifetime carcinogenic risks (LCR) of Cr, Pb, and Cd were within acceptable levels. A positive matrix factorization (PMF) model identified two factors that could explain the HMs sources: factor 1 for Zn, Cd, and Pb, factor 2 for Ni, As, Cr, and Cu. Furthermore, HMs enrichment was observed in surface soil and the Carboniferous-Permian coal seams in the Guobei coal mine, which may suggest that coal mining is an important source for HMs enrichment in surface soil. Overall, this study provides a theoretical basis for undertaking the management and assessment of soil HMs pollution around a coal mine.

Heavy Metal Concentration Estimation for Different Farmland Soils Based on Projection Pursuit and LightGBM with Hyperspectral Images.

Heavy metal pollution in farmland soil threatens soil environmental quality. It is an important task to quickly grasp the status of heavy metal pollution in farmland soil in a region. Hyperspectral remote sensing technology has been widely used in soil heavy metal concentration monitoring. How to improve the accuracy and reliability of its estimation model is a hot topic. This study analyzed 440 soil samples from Sihe Town and the surrounding agricultural areas in Yushu City, Jilin Province. Considering the differences between different types of soils, a local regression model of heavy metal concentrations (As and Cu) was established based on projection pursuit (PP) and light gradient boosting machine (LightGBM) algorithms. Based on the estimations, a spatial distribution map of soil heavy metals in the region was drawn. The findings of this study showed that considering the differences between different soils to construct a local regression estimation model of soil heavy metal concentration improved the estimation accuracy. Specifically, the relative percent difference (RPD) of As and Cu element estimations in black soil increased the most, by 0.30 and 0.26, respectively. The regional spatial distribution map of heavy metal concentration derived from local regression showed high spatial variability. The number of characteristic bands screened by the PP method accounted for 10-13% of the total spectral bands, effectively reducing the model complexity. Compared with the traditional machine model, the LightGBM model showed better estimation ability, and the highest determination coefficients (R2) of different soil validation sets reached 0.73 (As) and 0.75 (Cu), respectively. In this study, the constructed PP-LightGBM estimation model takes into account the differences in soil types, which effectively improves the accuracy and reliability of hyperspectral image estimation of soil heavy metal concentration and provides a reference for drawing large-scale spatial distributions of heavy metals from hyperspectral images and mastering soil environmental quality.

Assessment and machine learning prediction of heavy metals fate in mining farmland assisted by Positive Matrix Factorization.

The accurate pollutant prediction by Machine Learning (ML) is significant to efficient environmental monitoring and risk assessment. However, application of ML in soil is under studied. In this study, a Positive Matrix Factorization (PMF) assisted prediction method was developed with Support Vector Machine (SVM) and Random Forest (RF) for heavy metals (HMs) prediction in mining farmland. Principal Component Analysis (PCA) and Redundancy Analysis (RDA) were selected to pretreat data. Experiment results illustrated Cd was the main pollutant with heavy risks in the study area and Pb was easy to migrate. The method effects of HMs total concentration predicting were PMF > Simple > PCA > PCA - PMF, and RF predicted better than SVM. Data pretreatment by RDA prior inspection improved the model results. Characteristic HMs Tessier fractions prediction received good effects with average R value as 0.86. Risk classification prediction performed good in Cd, Cu, Ni and Zn, however, Pb showed weak effect by simple model. The best classifier method for Pb was PMF - RF method with relatively good effect (Area under ROC Curve = 0.896). Overall, our study suggested the combination between PMF and ML can assist the prediction of HMs in soil. Spatial weighted attribute of HMs can be provided by PMF.

Incorporating environmental capacity considerations to prioritize control factors for the management of heavy metals in soil.

Heavy metals (HMs) pollution threatens food security and human health. While previous studies have evaluated source-oriented health risk assessments, a comprehensive integration of environmental capacity risk assessments with pollution source analysis to prioritize control factors for soil contamination is still lacking. Herein, we collected 837 surface soil samples from agricultural land in the Nansha District of China in 2019. We developed an improved integrated assessment model to analyze the pollution sources, health risks, and environmental capacities of As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn. The model graded pollution source impact on environmental capacity risk to prioritize control measures for soil HMs. All HMs except Pb exceeded background values and were sourced primarily from natural, transportation, and industrial activities (31.26%). Approximately 98.92% (children), 97.87% (adult females), and 97.41% (adult males) of carcinogenic values exceeded the acceptable threshold of 1E-6. HM pollution was classified as medium capacity (3.41 kg/hm2) with mild risk (PI = 0.52). Mixed sources of natural backgrounds, transportation, and industrial sources were identified as priority sources, and As a priority element. These findings will help prioritize control factors for soil HMs and direct resources to the most critical pollutants and sources of contamination, particularly when resources are limited.

Soil pollution identification and human health risk assessment of soil heavy metals in an abandoned mine area in the Republic of Korea.

In this study, the Geo-accumulation index (Igeo), Human Health Risk Assessment (HRA), and Ecological Risk Index (ERI) were utilized to examine the risks associated with the soils at the DaeyangYeongseong mine. Brassica juncea and Raphanus sativus were employed in the ecological toxicity test. In all soil samples, the mean Igeo value of arsenic measured 3.15, and cadmium measured 6.63, indicating a very high level of heavy metal contamination. The carcinogenic risk of cadmium and arsenic for adults was 4.30×10-3 and 1.43×10-5, respectively. For children, these values were 3.92 × 10-2 and 1.33 ×10-4, exceeding the acceptable level (1×10-6). In all soils, cadmium showed extremely high ecological risk levels, and arsenic had extremely high risk levels in 34.8% of the total area. This was also confirmed in toxicity assessments using plants. Therefore, arsenic and cadmium were found to be the main causes of soil contamination and ecological risk.

Geochemical baseline establishment, pollution level and health risk assessment of soil heavy metals in the upper Xiaowen River Basin, Shandong Province, China.

This study analyzed the distribution and content of eight heavy metals (Cu, Pb, Zn, Ni, Cr, As, Cd, and Hg) in 221 surface soil samples from the upper reaches of the Xiaowen River. Environmental geochemical baselines were established for the eight heavy metals, and the pollution status was assessed on the basis of these baselines and the soil background value of Weifang City. The calculation results of Nemerow pollution index and the potential ecological hazard index (PEHI)-Ri showed that the overall pollution degree and ecological hazard in the study area were at a slight level. 49% (calculated by baseline value) and 24% (calculated by background value of Weifang City) samples were at moderate or above pollution level. 9% (calculated by baseline value) and 42% (calculated by background value) samples were at the level of moderate potential ecological hazards or above. According to the calculation results of Igeo and PEHI-Ei, the main pollutant in the study area was Hg, followed by Cd. 3% (calculated by baseline value) and 12% (calculated by background value) of Hg samples were at moderate or above contamination levels. 5% (calculated by baseline value) and 38% (calculated by background value) of Hg samples were at the level of strong potential ecological hazard or above. The western, central, and eastern parts of the study area were mainly the primary areas of pollution and ecological hazards. The non-carcinogenic risk was at an acceptable level, the carcinogenic risk was at a tolerable level, and the main risk pathway was oral intake, with Cr being the main contributor. Source apportionment indicated that soil heavy metals primarily originate from soil parent material, transportation, agricultural fertilization, and industrial emissions (waste gas, waste water and solid waste).

Identification of sources and analysis of spatial distribution of soil heavy metals in northern China coal mining areas.

The mining and utilization of coal resources has not only promoted rapid economic development but also poses a potential threat to the ecological environment. The purpose of this study is to clarify the effects both of mining and land use types on the spatial distribution and particular sources of heavy metals in soil, using inverse distance weighted (IDW) and the Positive Matrix Factorization (PMF) model. A total of 99 topsoil and profile soil samples across different land use types and mining conditions were collected. The contamination of soil with Cd, Pb, and Hg in the research area was most severe, with the coefficient of variation (CV) of Hg being the largest, while also being heavily influenced by human activities. Severely polluted regions were mainly distributed in the center of the coal mining area, as well as near the highway. The contents of heavy metals for various land use patterns were ranked as follows: forestland > farmland > bare land > grassland > building land. Hg, Cd, Pb, Cr, and Zn had showed migration in the 0-60 cm depth range, and the enrichment factors (EFs) of Cd, Pb, Hg, and As in the soil profile were the most significant. The PMF demonstrated that the contributions of industrial activities and atmospheric deposition, transportation and mining activities, agricultural activities, and natural sources accounted for 31.25%, 28.13%, 22.24%, and 18.38%, respectively. The migration and deposition of atmospheric particulate matter from coal mining, transportation, and coal combustion under winds triggered heavy metal contamination in semi-arid areas of northern China. This phenomenon has important implications for the prevention and reduction of heavy metal pollution through various effective measures in coal-mining cities in northern China.

Impact of different industrial activities on heavy metals in floodplain soil and ecological risk assessment based on bioavailability: A case study from the Middle Yellow River Basin, northern China.

Understanding the impact of different industrial activities on heavy metals and conducting scientific ecological risk assessments are critical to the management of heavy metal pollution. The present study compared soils affected by different industrial activities in three types of industrial cities (coal city, oil-gas city, and economic city) to control samples and examined the ecological risk based on bioavailability in the Middle Yellow River Basin. The findings revealed that the impact characteristics of different industrial activities on soil heavy metals in the research area were different. Both coal-based and oil-gas industry activities had a minor impact on soil heavy metals, whereas economic industry activities in the southern part had a major impact, as evidenced by significant enrichment of Cd, Hg, Cu, Pb, and Zn. In principal component analysis, the soil heavy metals affected by economic industry activities designated a distinct source from the control samples, particularly the anthropogenic sources represented by Hg and Cd. In the context of heavy metals in chemical form, three types of industrial activities all had an effect on bioavailability (0.72-24.27%) and could increase migratory activity in the environment. Furthermore, both traditional and improved assessments, based on total content and bioavailability, showed a low ecological risk near coal cities and oil-gas cities in the middle and northern parts, while there was a medium-high ecological risk near economically developed cities in the south, particularly Tianshui, Baoji, Qishan, Xianyang, Xi'an, and Tongchuan. In comparison, improved risk assessment based on bioavailability tends to not only compensate for an overestimation in traditional risk assessment from the perspective of total content, but additionally achieve a more reasonable, effective, and advanced assessment of heavy metal risks in scientific research. The outcome of this study has significance for the ecological conservation and high-quality development of the Yellow River Basin.

Spatial patterns and the associated factors for breast cancer hospitalization in the rural population of Fujian Province, China.

BACKGROUND: Despite the known increasing incidence of breast cancer in China, evidence on the spatial pattern of hospitalization for breast cancer is scarce. This study aimed to describe the disparity of breast cancer hospitalization in the rural population of Southeast China and to explore the impacts of socioeconomic factors and heavy metal pollution in soil. METHODS: This study was conducted using the New Rural Cooperative Medical Scheme (NRCMS) claims data covering 20.9 million rural residents from 73 counties in Southeast China during 2015-2016. The associations between breast cancer hospitalization and socioeconomic factors and soil heavy metal pollutants were evaluated with quasi-Poisson regression models and geographically weighted Poisson regressions (GWPR). RESULTS: The annual hospitalization rate for breast cancer was 101.40/100,000 in the studied area and the rate varied across different counties. Overall, hospitalization for breast cancer was associated with road density (ß = 0.43, P = 0.02), urbanization (ß = 0.02, P = 0.002) and soil cadmium (Cd) pollution (ß = 0.01, P = 0.02). In the GWPR model, a stronger spatial association of Cd, road density and breast cancer hospitalization was found in the northeast regions of the study area while breast cancer hospitalization was mainly related to urbanization in the western regions. CONCLUSIONS: Soil Cd pollution, road density, and urbanization were associated with breast cancer hospitalization in different regions. Findings in this study might provide valuable information for healthcare policies and intervention strategies for breast cancer.

Estimation of the distribution patterns of heavy metal in soil from airborne hyperspectral imagery based on spectral absorption characteristics.

Though soil is widely known as one of the most valuable resources for the world, its quality is going to be lower because of unsustainable economic development and social progress. Therefore, it is important for us to monitor and evaluate the quality of soil, especially its heavy metal contents which is too scarce to identify in soil spectra easily but poisonous enough to affect human health in a long run. Most of the existing estimation methods have based the characteristic bands on statistical analysis to a large extent, which is hard to accurately explain the retrieval mechanism. In this paper, the absorption characteristics of heavy metal are studied based on the soil spectra, and the distribution pattern is mapped in a large-scale continuous space, for environmental monitoring and further decision support. Taking Yitong County, China as the study area. After spectra continuum removal, the heavy metal contents were estimated by 11 features including the absorption depth, absorption area, and band ratio around 2200 nm, which showed the best performance. For arsenic (As), the best model yields Rp2 value of 0.8474, and the RMSEP value is 36.1542 (mg/kg). It is concluded that As is adsorbed by organic matter, clay minerals, and iron/manganese oxides in soil, and the adsorption of As by first two components is greater than that of the last. For airborne spectra after continuum removal, combining the spectral absorption characteristic parameters and the highly correlated bands is more accurate than using the spectral absorption characteristic parameters or bands alone. AdaBoost is presented for the heavy metal estimation, and the fitting ability of the method is found to be stronger than that of the traditional classical methods, with the Rp2 values of 0.6242 and the RMSEP value of 43.6481 (mg/kg). In summary, these results will provide a prospective basis for the rapid estimation of soil heavy metals, the risk assessment of soil heavy metals and soil environmental monitoring in a large scale.

Effects of heavy metals on microorganisms and enzymes in soils of lead-zinc tailing ponds.

Taking the soil around the lead-zinc tailings pound in the upper reaches of the Hanjiang River in Shaanxi Province as the research object, with tailings pond as the center, seven different belt zones were divided outwards, the contents of Pb, Cu, Zn, V, Ni, Cd in soil were analyzed, as well as soil basic respiration (SBR), microbial biomass carbon (MBC), microbial metabolic quotient (MMQ), and the activities of catalase, urease, cellulase, invertase and neutral phosphatase were also determined. The purpose was to reveal the intrinsic relationship between soil microbial, enzyme activities and heavy metal pollution, and to establish the characterization system of enzyme activities, soil heavy metal pollution degree, as well as microbial parameters. The results showed that: (1) The potential ecological risk index of six heavy metals was ranked as Cd > Cu > Pb > Ni > Zn > V. Cd was a high potential ecological risk, Cu was a medium potential ecological risk, and Zn, Pb, V and Ni were low potential ecological risk. The comprehensive evaluation result of Hakanson's potential ecological hazard index showed that, Zone I was of high potential risk level, Zone II, III and IV were of medium risk level, and Zone V, VI and VII were of low level. (2) Microbial biomass carbon (MBC) had a significant negative correlation or extremely significant negative correlation with 6 heavy metals, and microbial metabolic quotient (MMQ) had a significant positive correlation or extremely significant positive correlation with 6 heavy metals. MBC and MMQ were effective microbiological indexes to measure the quality status of soil, while SBR was not. (3) Catalase, cellulase, sucrase and neutral phosphatase activity had significant negative correlation with the contents of 6 heavy metals, and they could replicate the pollution degree of substantial metals in the soil. However, urease had no significant correlation with the contents of 6 heavy metals, which could not reflect the pollution degree of soil heavy metals.

Risk assessment, spatial distribution, and source identification of heavy metals in surface soils in Zhijin County, Guizhou Province, China.

Zhijin County is a typical mineral resource-based city in Southwest China. The problem of heavy metals (HM) in the soil in Zhijin County must be considered during regional economic and ecological development. A total of 2436 soil samples (0‒20 cm depth) were collected to analyze the soil pH, organic matter content, and HM spatial distribution and sources. The HM concentrations in the surface soil were found to be higher than the national surface soil background values. Absolute principal component sore-multivariate linear regression (APCS-MLR) showed that the HM sources in the surface soil of Zhijin County were industrial and agricultural activities (48.09%), natural sources (34.47%), and atmospheric deposition (17.43%); 65.53% of HM were produced by anthropogenic activities, which were mainly associated with the mineral industry. The impact of anthropogenic pollution decreased in the following order: paddy field (66.45%) > rainfed cropland (65.91%) > barren land (61.98%) > garden land (61.82%) > forest land (59.11%) > grassland (53.31%). The potential ecological risk of surface soil is moderate, while low-risk areas were mainly distributed in mountainous regions in the north, southwest, and east. The study emphasizes the source and risk assessment of HM in the surface soil of Zhijin County. The results can be used for environmental management planning, decision-making, and risk assessment.

Source apportionment and ecological and health risk mapping of soil heavy metals based on PMF, SOM, and GIS methods in Hulan River Watershed, Northeastern China.

Heavy metals in agricultural soils not only affect the food security and soil security, but also endanger the human health through the food chain. Based on the incorporation of index analysis, positive matrix factorization (PMF), self-organizing map (SOM), and geostatistical methods, this research performed the assessment of source apportionment and ecological and health risks of soil heavy metals in Hulan River Watershed, Northeastern China. According to the Pollution Load Index (PLI), 83.08% of the soil samples were slightly or mildly polluted, and 1.54% of the soil samples were severely polluted. The ecological risk index (EI) showed that about 80.77% and 60.77% of the soil samples were beyond the low risk level for Hg and Cd, respectively. In this research, the non-carcinogenic and carcinogenic risk indices for children were higher than adult males and adult females. Four potential sources were revealed based on the PMF and SOM analysis including atmospheric deposition and industrial emission; transportation source; agricultural source; and a combination of agricultural, industrial, and natural sources. Considerable and high ecological risk from Hg existed in the area close to the coal steam-electric plant, and considerable and high ecological risk from Cd existed in the Hulan River estuary area. The eastern part of the study area experienced higher non-carcinogenic and carcinogenic risks for adults and children than the western part of the study area. The source apportionment and ecological and health risk mapping provide important role in reducing pollution sources. Zonal pollution control and soil restoration measures should be performed in the areas with high ecological and health risks.

Nitrogen and Phosphorus Fertilizer Increases the Uptake of Soil Heavy Metal Pollutants by Plant Community.

Soil heavy metal pollution is widespread around the world. Compared with hyperaccumulation plants, non-hyperaccumulator plant communities have many advantages in the remediation of heavy metals pollution in soil. The application of nitrogen (N) and phosphorus (P) is inexpensive and convenient, which can promote the growth of plant. N and P fertilizer might increase plant community remediation of heavy metal polluted soils. In our study, the effects of N and P fertilizer on remediation of soil Cd, Cu, Pb pollution by plant community were studied through a greenhouse experiment. Our results indicated that addition of N, P and N + P fertilizer increased plant community aboveground biomass. Simultaneously, addition of N and P fertilizer increased the accumulation of heavy metals in aboveground of the plant community and accelerated plants absorption soil heavy metals. Among them, N fertilizer had the best effect. Our results provide an inexpensive method for remediation heavy metal pollution of contaminated farmland, abandoned land and mine tailings, etc.

Study on the Hyperspectral Retrieval and Ecological Risk Assessment of Soil Cr, Ni, Zn Heavy Metals in Tailings Area.

The large-scale rapid monitoring of heavy metal pollution has become a hot topic due to increasing contamination of Tailings soil by heavy metal. In order to explore the possibility of using soil spectrum to estimate the content of heavy metals in soil and realize the rapid monitoring of soil heavy metals in the Yangshanchong tailings area in Tongling, China. The spectral reflectance of soil and the content of heavy metals (Cr, Ni, Zn) in soil were determined. The optimal bands of Cr, Ni and Zn elements in soil appeared at 467 nm, 467 nm and 468 nm respectively, and the maximum correlation coefficients were - 0.716, - 0.685 and - 0.630. The inversion model of element Cr constructed under the Reciprocal Transformation Second Derivative has a better effect, and its determination coefficient R2 is 0.613; It is better to construct the model of elements Ni and Zn in the form of Reciprocal Transformation First Derivative, and their determination coefficients R2 are 0.724 and 0.603, respectively. The results of the single factor index method showed that the pollution degree of heavy metal elements in the soil in the study area is Ni > Zn > Cr; the Nemerow comprehensive pollution index method showed that the three elements in the study area were polluted to varying degrees, and the comprehensive pollution index was in order Ni > Zn > Cr; Comprehensive potential ecological hazard index evaluation, the pollution degree and ecological risk of the study area were low.

Heavy metal pollution and health risk assessment of agricultural land in the Southern Margin of Tarim Basin in Xinjiang, China.

The study aimed to analyze the level of heavy metal pollution in the agricultural lands in the southern margin of Tarim Basin, Xinjiang, and its risks to human health. A total of 1765 soil samples were collected from the Cele-Ruoqiang area. The contents of soil As, Cd, Cr, Cu, Hg, Ni, Pb and Zn were analyzed using the pollution load index method. The results showed that soil was slightly contaminated with Hg and Cd; the multivariate statistical analysis indicated that Cr, Ni, As, Cu, Zn, and Cd were formed mostly by geochemical genesis, whereas Hg and Pb contents by the geological origin and human activities. The health risk assessment showed that there is no unacceptable non-carcinogenic health risk. As levels posed carcinogenic risk, however, they were within human tolerance. Thus, this study provides a scientific basis for local agricultural production safety and prevention and control of soil heavy metal pollution.

Soil heavy metal pollution source analysis based on the land use type in Fengdong District of Xi'an, China.

The soil environment imposes a great influence on human health. Soil heavy metal pollution caused by human activities is an important part of environmental problems in urban areas. Due to an inadequate infrastructure, imperfect management, and intensive human activities, the sources of heavy metals in urban fringe areas are often more complicated than those in other areas, such as mining areas and agricultural irrigation areas. To solve this problem, the first step is to locate the source of pollution. However, the traditional methods of source analysis, such as principal component analysis and positive matrix factorization, always require correlations between elements. This study examined the Hg, Cd, Pb, and Cu contents in the Fengdong District of Xi'an, China, and found that these elements are not correlated in this area. Hence, traditional source analysis methods are not applicable in the study area. In response to this problem, this research proposed a new source analysis method based on Pearson's correlation analysis. The Nemerow index, geoaccumulation index, and ecological risk index were adopted to evaluate soil heavy metal pollution in the study area. Via comparison to the actual situation, it was concluded that the geoaccumulation index is more suitable for source analysis in this area. Through Pearson's correlation analysis, it was found that the geoaccumulation index is significantly correlated with the various land use types. Among them, transportation land exerted a greater impact on Pb pollution, and industrial land exerted a significant impact on the Hg distribution. The Cu distribution was related to construction land, while the Cd distribution was mainly related to urban land and cultivated land. In addition, the demolition of residential areas and abandoned farmlands imposed significant effects on Pb and Cd pollution, respectively.

Spatial Distribution and Source Apportionment of Agricultural Soil Heavy Metals in a Rapidly Developing Area in East China.

We collected 682 topsoil samples (0-20cm) from agricultural lands of Luhe County in East China, and analyzed the spatial distribution patterns and potential sources of four major heavy metals. High Pb and Cr were mainly in the southeast adjacent to the Yangtze River, and Cd were characterized by an increasing trend from northwest to southeast, while high Hg mainly occurred in the areas near downtown. Spatially-continuous sources dominated the soil heavy metal concentrations. Contributions of spatially-continuous natural source (soil parent material) to Cr and Cd were 97.0% and 77.7%, respectively, whereas contributions of spatially-continuous anthropogenic source such as diffuse pollution to Pb and Hg were 75.7% and 86.7%, respectively. The distance to factories was the most influential anthropogenic factor for localized anomaly patterns of Pb, Cd, and Cr, while the intensive agricultural land uses associated with the rapid urban expansion were particularly relevant to the anomaly patterns of Hg.

Distribution, accumulation, and potential risks of heavy metals in soil and tea leaves from geologically different plantations.

Risk assessment regarding heavy metals in tea is crucial to ensure the health of tea customers. However, the effects of geological difference on distribution of heavy metals in soils and their accumulation in tea leaves remain unclear. This study aimed to estimate the impacts of geological difference on distribution of cadmium (Cd), lead (Pb), thallium (Tl), mercury (Hg), arsenic (As), antimony (Sb), chromium (Cr), nickel (Ni), and manganese (Mn) in soils and their accumulation in tea leaves, and further evaluate their health risks. 22 soils and corresponding young tea leaves (YTL) and old tea leaves (OTL), from geologically different plantations, were sampled and analyzed. Results showed that heavy metals concentrations in soils, derived from Permian limestone and Cambrian weakly mineralized dolomite, were obviously greater than those from Silurian clastic rock. The geological difference controlled the distribution of soil heavy metals to a large extent. Contents of Cd, Tl, and Mn in tea leaves mainly depended on their contents in soils. Soil Hg, Pb, As, and Sb contents may not be the only influencing factors for their respective accumulation in tea leaves. More attentions should be paid to soil acidification of tea plantations to ensure the tea quality security. Target hazard quotients (THQ) of Cd, Pb, Tl, Hg, As, Sb, Cr, and Ni and hazard index (HI) via tea intake were below one, indicating no human health risk. The non-mineralized Silurian area was less at risk of heavy metals accumulation in tea leaves than the Cambrian metallogenic belt and the Permian Cd-enriched zone. This study could provide an important basis to understand and mitigate the potential risks of heavy metals in tea.

Ecological security and health risk assessment of soil heavy metals on a village-level scale, based on different land use types.

Land use affects the accumulation of heavy metals in soil, which will endanger ecological safety and human health. Taking the village as an administrative unit, the ecological safety and health risks of heavy metals, namely, Cr, Cu, Zn, and Pb in soils in the Houzhai River Watershed of Guizhou Province, China, were evaluated based on land use types by the Hakanson potential ecological risk methods and human health risk model. Results showed that the spatial heterogeneity of Cu and Zn was greatly affected by primary structural factors, and Cr and Pb were interfered by both structural factors and human activities. The geo-accumulation index of the heavy metals showed a light pollution in the study area. The comprehensive potential ecological risk of heavy metal in the area was divided into three levels: slight, moderate, and intense, and it is spatially high in the northwest and low in the southeast. Both non-carcinogenic risk and carcinogenic risk of the heavy metals to the human body are not significant and are acceptable. The risks of children are higher than adults, and direct intake is the primary route of exposure in the area. The potential ecological risk and human health risk of soil heavy metals are relatively obviously affected by digital elevation data and normalized vegetation index. The study has certain reference value for the prevention and control of regional soil heavy metal risk.