Almond shell-derived biochar decreased toxic metals bioavailability and uptake by tomato and enhanced the antioxidant system and microbial community

The effectiveness of almond shell-derived biochar (ASB) in immobilizing soil heavy metals (HMs) and its impact on soil microbial activity and diversity have not been sufficiently studied. Hence, a pot study was carried out to investigate the effectiveness of ASB addition at 2, 4, and 6 % (w/w) on soil biochemical characteristics and the bioavailability of Cd, Cu, Pb, and Zn to tomato (Solanum lycopersicum L.) plants, as compared to the control (contaminated soil without ASB addition). The addition of ASB promoted plant growth (up to two-fold) and restored the damage to the ultrastructure of chloroplast organelles. In addition, ASB mitigated the adverse effects of HMs toxicity by decreasing oxidative damage, regulating the antioxidant system, improving soil physicochemical properties, and enhancing enzymatic activities. At the phylum level, ASB addition enhanced the relative abundance of Actinobacteriota, Acidobacteriota, and Firmicutes while decreasing the relative abundance of Proteobacteria and Bacteroidota. Furthermore, ASB application increased the relative abundance of several fungal taxa (Ascomycota and Mortierellomycota) while reducing the relative abundance of Basidiomycota in the soil. The ASB-induced improvement in soil properties, microbial community, and diversity led to a significant decrease in the DTPA-extractable HMs down to 41.0 %, 51.0 %, 52.0 %, and 35.0 % for Cd, Cu, Pb, and Zn, respectively, as compared to the control. The highest doses of ASB (ASB6) significantly reduced the metals content by 26.0 % for Cd, 78.0 % for Cu, 38.0 % for Pb, and 20.0 % for Zn in the roots, and 72.0 % for Cd, 67.0 % for Cu, 46.0 % for Pb, and 35.0 % for Zn in the shoots, as compared to the control. The structural equation model predicts that soil pH and organic matter are driving factors in reducing the availability and uptake of HMs. ASB could be used as a sustainable trial for remediation of HMs polluted soils and reducing metal content in edible plants.

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).

Spatial distribution prediction of soil heavy metals based on sparse sampling and multi-source environmental data

Predicting the precise spatial distribution of heavy metals in soil is crucial, especially in the fields of environmental management and remediation. However, achieving accurate spatial predictions of soil heavy metals becomes quite challenging when the number of soil sampling points is relatively limited. To address this challenge, this study proposes a hybrid approach, namely, Light Gradient Boosting Machine plus Ordinary Kriging (LGBK), for predicting the spatial distribution of soil heavy metals. A total of 137 soil samples were collected from the Shengli Coal-mine Base in Inner Mongolia, China, and their heavy metal concentrations were measured. Leveraging environmental covariates and soil heavy metal data, we constructed the predictive model. Experimental results demonstrate that, in comparison to traditional models, LGBK exhibits superior predictive performance. For copper (Cu), zinc (Zn), chromium (Cr), and arsenic (As), the coefficients of determination (R²) from the cross-validation results are 0.65, 0.52, 0.57, and 0.63, respectively. Moreover, the LGBK model excels in capturing intricate spatial features in heavy metal distribution. It accurately forecasts trends in heavy metal distribution that closely align with actual measurements. ENVIRONMENTAL IMPLICATION: This study introduces a novel method, LGBK, for predicting the spatial distribution of soil heavy metals. This method yields higher-precision predictions even with a limited number of sampling points. Furthermore, the study analyzes the spatial distribution characteristics of Cu, Zn, Cr, and As in the grassland coal-mine base, along with the key environmental factors influencing their spatial distribution. This research holds significant importance for the environmental regulation and remediation of heavy metal pollution.

Mechanisms of microbial-driven changes in soil ecological stoichiometry around gold mines

In this study, we used soils with different pollution and nutrient levels (non-polluted S1, highly polluted low-nutrient S2, and highly polluted high nutrient S3) around the gold mine tailing ponds, and combined with metabolic limitation modeling and macro-genomics approaches, aiming to investigate the relationship between soil microbial composition and soil eco-chemometrics characteristics under heavy metal stress. The results showed that heavy pollution resulted in reduced SOC, TN, microbial biomass, and with C- and P- acquisition (BG, CBH, ALP) as well as nitrogen limitation of soil microbial metabolism in soils (S2, S3). Further analysis by macrogenomics showed that heavy metal contamination led to an increase in α-microbial diversity and altered the composition of microbial communities in the soil. The cycling of C, N, and P nutrients was altered by affecting the relative abundance of Anaeromyxobacter, Steroidobacter, Bradyrhizobium, Acidobacterium, Limnochorda (predominantly in the Ascomycetes and Acidobacteria phyla), with the most pronounced effect on the composition of microorganisms synthesizing C-acquiring enzymes, and heavy metals and pH were the main influences on ecological stoichiometry. The results of this study are useful for understanding the sustainability of ecological remediation in heavy metal contaminated areas and for developing ecological restoration strategies.

Source identification and exposure risk management for soil arsenic in urban reclamation areas with high background levels: A case study in a coastal reclamation site from the Pearl River Delta, China

Urbanization involving the excavation and reuse of arsenic-bearing geological materials may pose human health risks. We investigated the distribution and sources of soil arsenic at a coastal reclamation site in the Pearl River Delta, China, and proposed risk management strategies. Analysis of 899 soil samples revealed an average of 58.97 mg/kg arsenic, with a maximum of 1450 mg/kg, mainly in fill material obtained from a local island. Integrative analysis combining reclamation history, regional geology, and bedrock mineralogy conclusively identified hydrothermally altered arsenic-bearing sulfide minerals within extensively fractured bedrock as the primary source of arsenic. Physical weathering and anthropogenic rock blasting produced discrete arsenic-rich particles that were directly transported into soils during land reclamation and accumulated to potential hazardous levels. Oral, dermal, and inhalation pathways were identified as primary exposures for future populations. Integrated engineering and institutional controls, coupled with long-term monitoring, were recommended to mitigate risks. The results highlight the importance of identifying specific geogenic and anthropogenic sources that contribute to heavy metal enrichment of soils in reclaimed areas where native bedrock naturally contains elevated level of metals, supporting evidence-based best practices for risk management and future land use.

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.

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.

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.

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.

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.

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.

Inversion of soil heavy metals in metal tailings area based on different spectral transformation and modeling methods

The exploitation of mineral resources has seriously polluted the environment around mines, notably in terms of heavy metal contamination of tailings pond soil. Hyperspectral remote sensing, as opposed to conventional on-site sampling and laboratory analysis, offers a potent tool for effective monitoring the content of soil heavy metals. Therefore, we investigated the inversion models of heavy metal content in metal tailings area based on measured hyperspectral and multispectral data. Hyperspectral and its transformation, as well as the simulated Landsat8-OLI multispectral were used for model inversion respectively. Stepwise Multiple Linear Regression (SMLR), Partial Least Squares Regression (PLSR) and Back Propagation Neuron Network (BPNN) were established to study the spectral inversion of eight heavy metals (Cu, Cd, Cr, Ni, Pb, Zn, As, and Hg). The direct inversion models were established on the basis of correlation analysis and the adjust coefficient of determination (Adjust_R2) and Root Mean Square Error (RMSE) were used for model evaluation. Then the best combination of spectral transformation and inversion model were explored. The model inversion results suggested that: (1) Hyperspectral transformation can generally improve the model accuracy, especially the second derivative spectral, based on which the training Adjust_R2 of Hg SMLR and PLSR models are as high as 0.795 and 0.802. (2) The BP neural network inversion based on the denoised hyperspectrum demonstrate that both the training and testing Adjust_R2 of Cd, Ni and Hg models are all greater than 0.5, indicating good applicability in practical extrapolation. (3) Both the training and testing Adjust_R2 of Cu and Hg PLSR models based on simulated R_Landsat8-OLI multispectral are greater than 0.5, and Hg has lower RMSE and lager Adjust_R2 with training and testing Adjust_R2 values of 0.833 and 0.553 respectively. (4) Multispectral remote sensing detection and mapping of Hg contamination were realized by the optimal simulation model of Hg. Hence, it is feasible to simulate the multispectral with hyperspectral data for investigating heavy metal contamination.

Physical properties of natural deep eutectic solvent and its application in remediation of heavy metal lead in soil

At present, solvent extraction is an effective method to remove heavy metals from soil, which has certain practical significance. The physical properties such as density, viscosity and conductivity of NADESs with different proportions synthesized based on the double solid components of glycolic acid (GA) and L-proline (L-PRO) and the physical properties of NADESs aqueous solution at the lowest eutectic point (3:1) were studied. The extraction effect of NADESs on soil heavy metal Pb2+ under different conditions was studied. The results showed that under the conditions of atmospheric pressure of 101.33 kPa, the lowest eutectic melting point, DESs concentration of 0.6 mol·L-1, extraction temperature of 313.15 K and extraction time of 4 h, the extraction rate of Pb2+ by NADESs was 95.28%. In addition, the internal structure of DESs was characterized by IR and NMR, which indicated that intermolecular hydrogen bonds were formed. and the interaction between DESs and Pb2+ was analyzed by quantum chemical calculation, which showed that the hydroxyl group of GA was more likely to form coordination bond with Pb2+, and chelation occurred between them. This kind of DESs provides a new idea for the removal of heavy metals in soil.

Improving the mapping accuracy of soil heavy metals through an adaptive multi-fidelity interpolation method

Soil heavy metal pollution poses a serious threat to environmental safety and human health. Accurately mapping the soil heavy metal distribution is a prerequisite for soil remediation and restoration at contaminated sites. To improve the accuracy of soil heavy metal mapping, this study proposed an error correction-based multi-fidelity technique to adaptively correct the biases of traditional interpolation methods. The inverse distance weighting (IDW) interpolation method was chosen and combined with the proposed technique to form the adaptive multi-fidelity interpolation framework (AMF-IDW). In AMF-IDW, sampled data were first divided into multiple data groups. Then one data group was used to build the low-fidelity interpolation model through IDW, while the other data groups were treated as high-fidelity data and used for adaptively correcting the low-fidelity model. The capability of AMF-IDW to map the soil heavy metal distribution was evaluated in both hypothetical and real-world scenarios. Results showed that AMF-IDW provided more accurate mapping results compared with IDW and the superiority of AMF-IDW became more evident as the number of adaptive corrections increased. Eventually, after using up all data groups, AMF-IDW improved the R² values for mapping results of different heavy metals by 12.35-24.32%, and decreased the RMSE values by 30.35%-42.86%, indicating a much higher level of mapping accuracy relative to IDW. The proposed adaptive multi-fidelity technique can be equally combined with other interpolation methods and provide promising potential in improving the soil pollution mapping accuracy.

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 (β = 0.43, P = 0.02), urbanization (β = 0.02, P = 0.002) and soil cadmium (Cd) pollution (β = 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.

Quantitative analysis of the contribution of sources, diffusion pathways, and receptor attributes for the spatial distribution of soil heavy metals and their nested structure analysis in China

Investigation of heavy metal pollution degree, pollution sources, and spatial distribution structure is crucial for the country's soil pollution prevention, but relevant research is lacking. In this study, As, Cd, Cr, Cu, Pb and Zn in the national scope are taken as research objects. Among them, Cd has the highest pollution level. Four sources were quantitatively allocated as soil type, mining and dressing industry, GDP, and NDVI, which accounted for 92.93, 97.81, 99.30 and 96.24 % of Cr, Cd, Zn and As contamination, respectively. In addition, according to the geographical detector, the spatial distribution of As was affected by three diffusion pathways, whose influence degree were 0.822-0.947, especially the slope. Cadmium was primarily affected by both receptor attributes and diffusion pathways, with an influence degree of 0.010-0.175, especially soil water content and slope; Cr and Pb were affected by receptor attributes, with an influence degree of 0.886-0.986 and 0.007-0.288, respectively, especially for soil water content and soil organic carbon; Cu and Zn were affected by receptor attributes, with an influence degree of 0.182-0.823 and 0.002-0.150, respectively, especially for soil texture. There are two spatial distribution structures with nested scales in east-west and north-south directions. The large spatial structure has a more significant impact on the spatial distribution of heavy metals, especially in the east-west direction. Overall, the mining and dressing industry is the main source in Hunan, Yunnan, and Liaoning, where many mines exist and mining activities are frequent. GDP was the main source in Shanghai and Zhejiang areas, where the economy is developed. NDVI was the main source in Guangdong and Anhui areas, where agriculture is relatively developed. These results provide a basis for determining remediation and prevention objectives in soil pollution remediation and prevention in the national scope.

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.

A Monte Carlo simulation-based health risk assessment of heavy metals in soils of an oasis agricultural region in northwest China

In recent years, heavy metal contamination of soils has been increasing, posing a major threat to food security, human health, and soil ecosystems. This study analyzed the spatial characteristics, contamination sources, risks of heavy metals by collecting topsoil samples from farmland in an oasis agricultural region in northwest China. The results found that soil heavy metals in farmland were at a moderate contamination level. The PMF model classifies soil heavy metals as fertilizer and pesticide sources dominated by As and Mn with 27.8 %, mixed sources of transport and agricultural sources dominated by Cu, Zn, Cd and Pb with 26.9 %, metal processing sources dominated by Cr and Ni with 22.6 %, and the combined pollution sources of Ti, V, Cr, Mn, Fe, As, Pb dominated by natural sources and fuel combustion. The noncarcinogenic and carcinogenic risks values from the ingestion route were higher for children than for adults. The non-carcinogenic risk of heavy metals to adults in the southwestern and central regions of the study area was >1 × 10^-4. The carcinogenic risk was >1 in all adults, but >1 in children in the central and southwestern study areas. Monte Carlo simulation takes into account the parameters and their distributions that affect the health risk assessment model by combining the uncertainty assessment with the health risk, which will reduce the uncertainty of the health risk assessment. The results showed that conventional deterministic risk assessment may overestimate health risk outcomes. In addition, As has a 1.85 % probability of non-carcinogenic risk to children, and an 85.3 % probability of total non-carcinogenic risk for children for all heavy metals. 69.5 % and 11.4 % probability of carcinogenic risk for children and adults respectively for Ni, and 96.4 % and 52.1 % probability of total carcinogenic risk, suggesting that Ni is a priority control heavy metal.

Source apportionment of soil heavy metals: A new quantitative framework coupling receptor model and stable isotopic ratios

Tracing the source of heavy metals in soils is crucial for reversing the worrisome situation of heavy metal contamination. In this study, the origins of heavy metal pollution in soil were examined in a primary electronic waste treatment and disposal hub in China, using a synergistic source apportionment framework consisting of the positive matrix factorization (PMF) model and the Bayesian stable-isotope analysis mixing model (MixSIAR). Industrial activity is significant to heavy metal contamination in both industrial park and farmland soils, however, the contribution varied through PMF model (industrial park, 64.2%; farmland, 35.6%). In the industrial park, Pb was identified as the major pollutant in the soils, and the local children suffered from noncarcinogenic risks. Moreover, the contribution of Pb contamination sources were allocated more accurately (electronic waste dismantling, 25.1%; industrial production, 23.7%; vehicle exhaust from leaded gasoline, 9.1%; vehicle exhaust from unleaded gasoline, 20.2%; natural process, 21.9%) using MixSIAR for the first time. The main soil contaminants in surrounding farmland were Cd, Cu, and Zn. The variations in heavy metal pollution sources in soils were found to be associated with local policies and regulations, such as the phasing out of leaded gasoline and the conversion of industrial park from electronic waste demolition switched to production and storage. The identification of the source of heavy metals in soil will support targeted reduction of the associated emissions, which can immediately help alleviating soil contamination and control human health risks.

Merrillii plantation region of southeastern China

Torreya grandis (Torreya grandis cv. Merrillii) is a unique nut tree species in China. Currently, researches on Torreya grandis focus on nuts quality and yield, while few works are related to the soil quality of Torreya grandis plantation. In this study, the typical Torreya grandis production areas of Zhuji, Shengzhou, Keqiao and Dongyang cities along the Kuaiji Mountain were selected. A total of 121 topsoil samples (0-20 cm) were collected based on a grid of 1 km × 1 km. The results indicated that the average concentrations of Cd, Cr, Cu, As, Ni and Pb in soils were 0.12, 49.01, 27.95, 14.28, 26.97 and 40.28 mg kg^-1, respectively. The concentrations of six heavy metals all exceeded the background values, and there were different degrees of pollution levels. The results of Moran's I indicated that the spatial high-high clusters of soil heavy metals were mainly distributed in Zhuji and the junction of Shengzhou and Keqiao. The partial least squares path analysis of structural equation modeling (PLS-SEM) showed that OM and soil nutrients had extremely significant effects on soil heavy metals. Sources identification of principle component analysis (PCA) and positive matrix factorization model (PMF) revealed that agricultural activities, natural factors and mining were the main sources of soil heavy metals. The human health risks caused by soil heavy metals pollution were generally acceptable based on Monte Carlo simulation method. For the heavy-metal polluted area, management measures should be considered in order to protect human health.

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.

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

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

A novel interpolation method to predict soil heavy metals based on a genetic algorithm and neural network model

To improve the prediction accuracy of soil heavy metals (HMs) by spatial interpolation, a novel interpolation method based on genetic algorithm and neural network model (GANN model), which integrates soil properties and environmental factors, was proposed to predict the soil HM content. Eleven soil HMs (Cu, Pb, Zn, Cd, Ni, Cr, Hg, As, Co, V and Mn) were predicted using the GANN model. The results showed that the model had a good prediction performance with correlation coefficients (R²) varying from 0.7901 to 0.9776. Compared with other traditional interpolation methods, including inverse distance weighting (IDW), ordinary kriging (OK), universal kriging (UK), and spline with barriers interpolation (SBI) methods, the GANN model had a relatively lower root mean square error value, ranging from 0.0497 to 77.43, suggesting that the GANN model might be a more accurate spatial interpolation method and the soil properties together with the environmental geographical factors played key roles in prediction of soil HMs.

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.

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.

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.

Spatial distribution and ecological risk assessment of heavy metals in karst soils from the Yinjiang County, Southwest China

BACKGROUND

Soil heavy metals (HMs) under different land-use types have diverse effects, which may trigger the ecological risk. To explore the potential sources of HMs in karst soils, the spatial distribution and geochemical behavior of HMs based on different land-use types are employed in this study.

METHODS

Soil samples (n = 47) were collected in three suites of karst soil profiles from the secondary forest, abandoned cropland and shrubland in Yinjiang, Southwest China. The concentrations of Ni, Mn, Cr, Pb, Cd and Mo were determined to give a comprehensive understanding of the possible sources of these HMs and evaluate the potential ecological risk in Yinjiang County.

RESULTS

The mean concentrations of HMs in all profiles followed the same order: Mn > Cr > Ni > Pb > Mo > Cd. Meanwhile, the concentrations of most HMs roughly increased with the depth. Additionally, the concentrations of HMs were mostly correlated with soil pH and SOC, rather than with clay and silt proportions. By contrast, with the enrichment factors (EF), geo-accumulation (Igeo) and potential ecological risk index (PERI) of HMs in soil under different land-use types, the results indicated that these HMs exhibited non-pollution (Igeo < 0) and no ecological risk (PERI < 30) to human health in soils of Yinjiang County.

CONCLUSIONS

The distribution of HMs is dominated by weathering in the karst area, and the effects of agricultural inputs on the enrichment of soil HMs in Yinjiang County are limited. This further state that the arrangement of the local agricultural structure is reasonable.

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.

Source apportionment of soil heavy metals using robust spatial receptor model with categorical land-use types and RGWR-corrected in-situ FPXRF data

High-density samples are usually a prerequisite for obtaining high-precision source apportionment results in large-scale areas. In-situ field portable X-ray fluorescence spectrometry (FPXRF) is a fast and cheap way to increase the sample size of soil heavy metals (HMs). Moreover, categorical land-use types may be closely associated with source contributions. However, the above information has rarely been incorporated into the source apportionment. In this study, robust geographically weighted regression (RGWR) was first used to correct the spatially varying effect of the related soil factors (e.g., soil water and soil organic matter) on in-situ FPXRF in an urban-rural fringe of Wuhan City, China, and the correction accuracy of RGWR was compared with those of the traditionally non-spatial multiple linear regression (MLR) and basic GWR. Then, the effect of land-use types on HM concentrations was partitioned using analysis of variance (ANOVA). Last, based on the robust spatial receptor model (i.e., robust absolute principal component scores/RGWR [RAPCS/RGWR]), this study proposed RAPCS/RGWR with categorical land-use types and RGWR-corrected in-situ FPXRF data (RAPCS/RGWR_LU&FPXRF), and its performance was compared with those of RAPCS/RGWR with none or one kind of auxiliary data. Results showed that (i) the performances of the correction models for in-situ FPXRF data were in the order of RGWR > GWR > MLR, and the relative improvement of RGWR over MLR ranged from 52.6% to 70.71% for each HM; (ii) categorical land-use types significantly affected the concentrations of soil Zn, Cu, As, and Pb; (iii) the highest estimation accuracy for source contributions was obtained by RAPCS/RGWR_LU&FPXRF, and the lowest estimation accuracy was obtained by basic RAPCS/RGWR. It is concluded that land-use types and RGWR-corrected in-situ FPXRF data are closely associated with the source contribution, and RAPCS/RGWR_LU&FPXRF is a cost-effective source apportionment method for soil HMs in large-scale areas.

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.

Policy adjustment impacts Cd, Cu, Ni, Pb and Zn contamination in soils around e-waste area: Concentrations, sources and health risks

Pollution control policies (PCP) have been implemented in some e-waste dismantling areas in China to curb metal contamination since 2012. We investigated the effects of policy intervention on the concentrations, sources and health risks of heavy metals in soils. Post-implementation, among Cd, Cu, Ni, Pb and Zn, Pb levels declined while the Cd, Cu, Ni and Zn concentrations in soils were not impacted. Changes in their pollution indices and health risks were also similar. After the PCP, the contribution of traffic emission significantly decreased, while natural and industrial contribution increased due to the heighten background input and relocation of small e-waste dismantling workshops. Risk assessment showed that total cancer risk of five metals also slightly increased. Thus, policy intervention might be effective in controlling the release of some metals from e-waste dismantling. However, the performance of control measures varied depending on both source emission and geochemical properties of the metals. This study reveal the ongoing need of stricter supervision, targeted emission reduction and more-effective soil remediation actions to alleviate soil contamination from e-waste dismantling.

A novel extracellular enzyme stoichiometry method to evaluate soil heavy metal contamination: Evidence derived from microbial metabolic limitation

Heavy metal contaminates have become a significant threat to soil ecosystems due to their chronicity and universality in soil. Soil microbial metabolism plays a vital role in biogeochemical cycles and soil functions. However, the response of microbial metabolism to heavy metal contamination in soil remains elusive despite potentially offering important insight into the health and ecological consequences of soil ecosystems under such contamination. This study used extracellular enzyme stoichiometry models to identify the response of microbial metabolism to various heavy metal contaminants, while also revealing potential implications of heavy metal contaminates in soil ecosystems. Results showed that microbial metabolism was restricted by soil carbon (C) and phosphorus (P) within a heavy metal polluted area in Northwest China. Heavy metal stress significantly increased microbial C limitation while decreasing microbial P limitation. However, microbial C and P limitations both responded consistently to different heavy metals (i.e., Cd, Pb, Zn, and Cu). Heavy metals had the greatest effect on microbial C limitation (i.e., 0.720 of the total effects) compared to other soil properties, and soil with the lowest heavy metal concentration exhibited the lowest microbial C limitation, and vice versa. These results indicated that microbial metabolic limitation can robustly and sensitively reflect the degree of heavy metals pollution in soil. Additionally, increased microbial C limitation caused by heavy metal contaminants could potentially escalate C release by promoting soil C decomposition as well as increasing investments in enzyme production and the maintenance of metabolic processes. Consequently, potential C loss induced by heavy metal pollution on soil ecosystems may be extensive and significant. Generally, our results suggest the usefulness of extracellular enzyme stoichiometry as a new method from which to evaluate heavy metal soil pollution, while microbial metabolic limitation could potentially be a promising indicator.

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.

Plants mitigate restrictions to phosphatase activity in metal contaminated soils

Soil anthropogenic contaminants can limit enzymatic nutrient mineralization, either by direct regulation or via impacts on the microbial community, thus affecting plant growth in agricultural and non-agricultural soils. The impact on phosphatase activity of mixing two contaminated, post-industrial rail yard soils was investigated; one was vegetated and had high phosphatase function, the other was barren and had low enzymatic function. The two soils had different abiotic properties, including contaminant load, vegetation cover, soil aggregate size distribution, and phosphatase potential. An experimental gradient was established between the two soils to systematically vary the abiotic properties and microbial community composition of the two soils, creating a gradient of novel ecosystems. The time dependence of extracellular phosphatase activity, soil moisture, and organic matter content was assessed along this gradient in the presence and absence of plants. Initially, mixtures with higher percentages of functional, vegetated soil had higher phosphatase activities. Phosphatase activity remained unchanged through time (65 days) in all soil mixtures in unplanted pots, but it increased in planted pots. For example, in the presence of plants, phosphatase activity increased from 0.6 ± 0.1 to 2.4 ± 0.3 μmol•h^-1•g_{dry soil}^-1 from day one to day 65 in the 1:1 functional:barren soil mixture. The presence of plants also promoted moisture retention. Inoculation of poorly functioning soil with 10% of the functional soil with its microbial community did not, over 65 days, revitalize the poorly functioning soil. The findings showed that abiotic limitations to enzymatic activity in barren brownfield soils could be mitigated by establishing primary production but not by the addition of enzymatically active microbial communities alone.

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.

Tracing geochemical pollutants in stream water and soil from mining activity in an alpine catchment

This research developed a method of tracing major water chemical parameters (WCP) and soil heavy metals (HM) to identify the processes of mining pollution in topographically complex landscapes. Ninety-nine spatially distributed water samples were collected to characterise the hydrochemical characteristics of an alpine river in north-west China. Sixty river WCP and fifty-six soil HM samples from areas near mining sites were then used to analyse the mining pollution process. Geographical and mining activity characteristics were derived from topographic and mine site information. The occurrence of sulphates (SO₄^2-) and nitrates (NO₃^-) in river water were highly correlated (up to 0.70), providing strong evidence of pollution from nearby mining activities. Levels of arsenic and cadmium were high in first and fifth order streams, where mining activities were most concentrated. The modelling results showed that geographical patterns and mining activity account for predicting HM distribution, and WCP can be reasonable predictors to trace soil mining pollution. This research can help improve the accuracy of predicting the mining pollution process.

Assessing heavy metal concentrations in earth-cumulic-orthic-anthrosols soils using Vis-NIR spectroscopy transform coupled with chemometrics

Soil visible and near infrared (Vis-NIR) has become an applicable and interesting technique to predict soil properties because it is a fast, cost-effective, and non-destruction technique. This study presents an application of diffuse reflectance spectroscopy (DRS) and chemometric techniques for evaluating concentrations of heavy metals in earth-cumulic-orthic-anthrosols soils. 44 soil samples of 0-30 cm were collected from three representative agriculture areas (Fufeng, Yangling, and Wugong transects with 16, 10, and 18 samples, respectively) and analyzed for Cr, Mn, Ni, Cu, Zn, As, Cd, Hg, and Pb by Vis-NIR spectroscopy (350-2500 nm). Average levels of Cr, Mn, Ni, Cu, Zn, As, Cd, Hg, and Pb were 17.95, 274, 12.77, 7.29, 15.81, 7.51, 0.40, 12.58, and 21.05 mg kg-1, respectively. Twenty-four preprocessing methods were extracted sensitive bands. Partial least squares regression (PLSR) used to obtain effective bands and predict soil heavy metals concentrations. The accuracy of the predictive models were assessed in terms of coefficient of determination (R²), the root mean squared error (RMSE), standard error (SE) and the ratio of performance to deviation (RPD). The results revealed that excellent predictions for Hg(Rv² = 0.99, RPD = 8.59, RMSEP = 0.12, SEP = 0.13), Cr (Rv² = 0.97, RPD = 5.96, RMSEP = 0.10, SEP = 0.10), Ni (Rv² = 0.93, RPD = 3.74, RMSEP = 0.13, SEP = 0.13), Pb (Rv² = 0.97, RPD = 5.57, RMSEP = 0.10, SEP = 0.01), and Cu (Rv² = 0.92, RPD = 3.38, RMSEP = 0.08, SEP = 0.08). Models for As (Rv² = 0.87, RPD = 2.58), Mn (Rv² = 0.80, RPD = 2.09), and Cd (RPD = 2.77) had Rv² < 0.9 and RPD<3.0, not excellent predictions. For the element of Zn, although Rv2 = 0.91, RPD = 3.13, the offset had too much deviation, and it cannot be considered an excellent model. Therefore, a combination of spectroscopic and chemometric techniques can be applied as a practical, rapid, low-cost and quantitative approach for evaluating soil physical and chemical properties in Shaanxi, China.

Hyperspectral indirect inversion of heavy-metal copper in reclaimed soil of iron ore area

To explore a rapid detection technology for detecting heavy metals in soil based on hyperspectral data, this paper took an iron mine in Daye Country, Hubei Province, as the research area, used a FieldSpec4 portable ground object spectrometer to obtain soil spectral reflectance and combine the measured data, and used three spectral transformation methods: first-order differential, second-order differential, and continuum removal. We studied the indirect hyperspectral inversion of heavy metals in reclaimed soils in the iron mine area by using three models: partial least squares regression, support vector machine, and back propagation (BP) neural network. The results show that spectral transformation can effectively highlight the position of spectral characteristic bands and improve the correlation between spectral curves and iron (Fe) element concentration. The partial least squares regression model based on first-order differential had the highest inversion accuracy for Fe element concentration in the study area, R² and RMSE were 0.88 and 0.53, respectively. The correlation analysis of soil elements showed that the highest correlation coefficient between Cu and Fe was 0.81. We selected the copper (Cu) element with the largest correlation coefficient with the Fe element as an example and realized the indirect prediction of soil Cu concentration using a linear regression model and BP neural network model. Among them, the model based on BP neural network is better, R² was 0.82, RMSE was 0.62, compared with the direct method, the model R² increased by about 0.2, and the root mean square error decreased by about 0.1. The effect of the indirect method was better than that of the direct method. We selected the optimum statistical interpolation method for spatial analysis of Fe and Cu concentrations in the soil of the study area and further demonstrated the feasibility of the indirect inversion method of heavy metals in the soil of iron mine areas based on hyperspectral data. These results provide a theoretical basis and new ideas for the application of near-earth sensing technology in soil and for efficient detection of heavy metals in iron ore areas.

Prediction of heavy metals in soils of an arid area based on multi-spectral data

With the rapid and extensive development of industry and agriculture, the soil environment inevitably becomes contaminated with heavy metals, thus creating adverse environmental conditions for flora and fauna. The traditional methods for combining field sampling with laboratory analysis of soil heavy metals are limited not only because they are time-consuming and expensive, but also because they are unable to obtain adequate information about the spatial distribution characteristics of heavy metals in soil over a large area. Three hundred and ninety-four soil samples (Gobi and farmland) were collected in an arid area in Jiuquan in Northwest China and analyzed for elements concentrations. Based on these measured concentrations, as well as rapid and environmentally friendly remote sensing (multi-spectral data), stepwise multiple linear regression (SMLR) and partial least-squares regression (PLS) were combined to predict concentrations and distributions of heavy metals in the soils of the study area. Furthermore, laboratory data were used to assess the accuracy of the prediction results. Obtained results suggest that the SMLR and PLS models were able to predict the metals contents in the study area. The concentrations of Cr, Ni, V and Zn could be predicted by two regression models, while those of Cu and Mn were predicted more accurately when they were attached to the SMLR model. The spatial distribution of heavy metals derived from the two models is consistent with measured values, indicating that it is reasonable to predict the concentrations of heavy metals in the soil of the study area using the multi-spectral data.

Differences in the risk assessment of soil heavy metals between newly built and original parks in Jiaozuo, Henan Province, China

Differences in the concentrations of heavy metals between newly built and original parks remain incompletely understood. In this work, two newly built and four original parks in Jiaozuo, China, were taken as research objects. Using the geo-accumulation index, ecological risk assessment index and human health risk model, differences in heavy metal pollution between the two types of parks were determined. In the surveyed region, five heavy metals, namely, Zn, Cu, As, Ni and Co, polluted the environment. Serious As pollution was discovered, and respective As concentrations in the soils of newly built and original parks were 5.9 and 3 times the background value. The concentrations of Zn, Mn, As, Pb, Cr and Ni in newly built parks were higher than those in the four original parks, although the concentrations of Cu and Co between the two types of parks were not significantly different. The carcinogenic and non-carcinogenic risks of these metals in newly built parks (adults: 4.27E-05, 1.08; children: 2.53E-04, 8.94) were greater than those in original parks (adults: 2.57E-05, 0.67; children: 1.52E-04, 5.39), and newly built parks posed higher potential risk than original parks. Therefore, the concentration of heavy metals in soil must be assessed before former industrial sites are transformed into parks.

Application of modified receptor model for soil heavy metal sources apportionment: a case study of an industrial city, China

As we all know, geochemical data usually contain outliers and they are heterogeneous, which will severely affect the use of receptor models based on classical estimates. In this paper, an advanced modified RAPCS-RGWR (robust absolute principal component scores-robust geographically weighted regression) receptor model was introduced to analyze the pollution sources of eight heavy metals (Cd, Hg, As, Pb, Ni, Cu, Zn) in a city of southern China. The results showed that source identification and source apportionment are more consistent by this advanced model even though the soil types and farming patterns are diverse. Moreover, this model decreased the occurrence of negative values of the source contribution. For these reasons, the pollution sources were classified into five types by the new model in the study area: agricultural sources, industrial sources, traffic sources, comprehensive sources, and natural sources. (1) The contributions of agricultural sources to Cr and Ni were 243.36% and 242.61%, respectively; (2) the contribution of industrial sources to Cd was 79.25%; (3) the contribution of traffic sources to Cu was 100.31%; (4) the contributions of comprehensive sources to Hg, Pb, and Zn were 253.90%, 242.31%, and 93.32%, respectively; and (5) the contribution of natural sources to As was 208.21%. Overall, the RAPCS-RGWR receptor model improved the validity of the receptor models. It is of great realistic significance to understand and popularize the advanced model in soil source apportionment in agricultural land.

Effects of organic-inorganic amendments on the cadmium fraction in soil and its accumulation in rice (Oryza sativa L.)

Cadmium (Cd) stress is a serious concern in agricultural soils worldwide, and increasing accumulation and subsequent transfer to humans via the food chain can have potentially harmful effects. In this study, field experiments were conducted to examine the uptake and translocation of Cd in rice, changes in the soil Cd speciation, and the subsequent effect on Cd accumulation in rice under combined organic (farmyard manure and crop straw) and inorganic (sepiolite, lime, and calcium-magnesium phosphate) soil amendments. The results showed that farmyard manure combined with sepiolite or lime and straw combined with lime or calcium-magnesium phosphate reduced the Cd translocation from the rice roots to the straw and the grains, significantly decreasing the Cd accumulation in brown rice. In addition, straw combined with sepiolite, lime, or calcium-magnesium phosphate reduced the Cd accumulation in brown rice but increased the Cd translocation from the roots to the straw by 37.8-279.3% compared with the control. Organic-inorganic amendments also decreased the soil exchangeable Cd and increased the organic-bound Cd by more than 40%. Fe-Mn oxide-bound Cd also increased but varied with growth. Cd accumulation in brown rice showed a significant positive relationship with soil exchangeable Cd at 90 days after transplantation, while at 30 days, the increase in Fe-Mn oxide- and organic-bound Cd was found to be the key factor in reducing the Cd accumulation in rice. These findings suggest that straw (under rice-rape rotation) and farmyard manure (under rice-wheat rotation) combined with sepiolite or lime are widely applicable as agronomic control techniques aimed at lowering Cd pollution.

Predicting future contents of soil heavy metals and related health risks by combining the models of source apportionment, soil metal accumulation and industrial economic theory

Enriched and bio-refractory soil heavy metals (SHMs) originate from the underground mineral, which supplies energy and materials for the development of economy and industry. Investigating soil metal contents and their adverse health impacts is the principal concern associated metal contaminated industrial areas, including both current assessments and future projections. In this research, we create a novel spatiotemporal model of SHMs prediction and risk characterization for future by citing a rigorous theory of industrial economics, and time series of activity intensity changes of various pollution sources are forecasted. The dynamic change of source contributions is quantitatively resolved and the mean SHMs concentrations are estimated by classical formulas for heavy metal accumulation. Human health risk in the future is described in a manner of time series. The results of the case study show that contribution rates of the five sources of the six metals change continuously over time. Pb, Cd and As assume the highest growth rates (400%, 500% and 165%), while Zn, Ni, Cr possesses relatively lower growth (< 130%), compared to their corresponding background values. Health risk of local sensitive population (children) is estimated at exceeding threshold in 2022 (non-carcinogenic) and 2012 (carcinogenic), and the upward trend will continue. Traffic emission, agriculture and household garbage are identified as major risky sources in the coming decades at the studied area, and improvement measures are recommended. Although a degree of uncertainties exists, the overall tendency is a conservative bias for chemical risk. Additionally, this paper is the first to explore a methodology of predicting future SHMs and associated human health risk, based on industrial economics and temporal source apportionment.

Spatial distribution and source apportionment of heavy metals in soil from a typical county-level city of Guangdong Province, China

The contents of ten heavy metals (Cr, Hg, As, Pb, Ni, Cd, Ti, Cu, Zn and V) in 413 topsoil samples from Puning City, Guangdong Province, China were investigated. Obvious enrichment of Hg, As, Pb, Cd and Zn were presented, and the contents of Hg and As in 5.8% and 3.4% samples respectively were higher than the guideline values recommended by the Chinese Environmental Quality Standard for Soils. Chromium and V were presented no enrichment and no pollution. According to one-way analysis of variance, the mean contents of Hg, Pb, Cu and Zn in land for construction were significantly higher than farmland and natural vegetation, but the land use had no obvious effect on other heavy metals. Furthermore, the potential sources of ten heavy metals were identified and apportioned in combination with geostatistics, correlation analysis and positive matrix factorization model. The results were following as: a) Pb, Zn and Cu mainly origin from vehicle emission and atmosphere deposition, and the hotspots approximately distributed in the areas of intensive traffic and near main roads; b) Hg and Cd were derived to industrial activities related to pharmaceutical industries, the textile and dyeing industries and e-waste recycling industries, and high-value areas were mainly concentrated in the northeast of the urban area where the industrial parks have been distributed; c) Soil parent material (Jurassic shale) was the main source of Cr, Ni, V and Ti; d) As mainly came from agricultural inputs such as pesticides or herbicides, livestock and fertilizers. Meanwhile, the contributions of four sources were 33.08%, 24.04%, 27.11% and 15.77% of the total contribution, respectively.

An innovative expression model of human health risk based on the quantitative analysis of soil metals sources contribution in different spatial scales

Toxicity of heavy metals from industrialization poses critical concern, and analysis of sources associated with potential human health risks is of unique significance. Assessing human health risk of pollution sources (factored health risk) concurrently in the whole and the sub region can provide more instructive information to protect specific potential victims. In this research, we establish a new expression model of human health risk based on quantitative analysis of sources contribution in different spatial scales. The larger scale grids and their spatial codes are used to initially identify the level of pollution risk, the type of pollution source and the sensitive population at high risk. The smaller scale grids and their spatial codes are used to identify the contribution of various sources of pollution to each sub region (larger grid) and to assess the health risks posed by each source for each sub region. The results of case study show that, for children (sensitive populations, taking school and residential area as major region of activity), the major pollution source is from the abandoned lead-acid battery plant (ALP), traffic emission and agricultural activity. The new models and results of this research present effective spatial information and useful model for quantifying the hazards of source categories and human health a t complex industrial system in the future.

Risk assessment and source analysis of soil heavy metal pollution from lower reaches of Yellow River irrigation in China

The level of concentration of heavy metal in soil is detrimental to soil quality. The Heigangkou-Liuyuankou irrigation area in the lower-reach of Yellow River irrigation, as home to a large population and a major site to agricultural production, is vulnerable to heavy metal pollution. This study examined soil quality in Heigangkou-Liuyuankou irrigation areas of Kaifeng, China. Pollution in soil and potential risks introduced by heavy metal accumulation were assessed using Nemerow, Geoaccumulation, and Hakanson's ecological risk indices. Statistics and Geographic Information Systems (GIS) were used to model and present the spatiotemporal changes of the pollution sources and factors affecting the levels of pollution. The heavy metals found in the sampled soil are Cr, Ni, Cu, Zn, Cd, Pb, As, and Hg. Among them, Cd is more concentrated than the others. The southwestern region of the studied area confronts the most serious heavy metal pollution. There exist spatial disparities of low concentrations of different heavy metals in the study area. Hg and Cd are found to pose the highest potential ecological risks. However, their risk levels are not the same across the study area. Levels concentration of Ni, Cu, Zn, Cd, Pb, As, and Hg in soil are highly correlated. In combination, they post an additional threat to the ecological environment. Transportation, rural settlements, and water bodies are found to be the major sources of Cr, Ni, Cu, Zn, Cd, Pb, and Hg pollution in the soil; among the major sources, transportation is the most significant factor.

Study on the prediction of soil heavy metal elements content based on visible near-infrared spectroscopy

The estimation of soils heavy metal content can reflect the impending surroundings of surface, which lays theoretical foundation for using covered vegetation to monitor environment and investigate resource. In this study, the contents of Cr, Mn, Ni, Cu, Zn, As, Cd, Hg and Pb in 44 soil samples were collected from Fufeng County, Yangling County and Wugong County, Shaanxi Province and were used as data sources. ASD FieldSpec HR (350-2500nm), and then the NOR, MSC and SNV of the reflectance were pretreated, the first deviation, second deviation and reflectance reciprocal logarithmic transformation were carried out. The optimal spectroscopy estimation model of nine heavy metal elements of Cr, Mn, Ni, Cu, Zn, As, Cd, Hg and Pb was established by regression method. Comparing the diffuse reflectance characteristics of different heavy metal contents and the effect of different pretreatment methods on the establishment of soil heavy metal spectral inversion model. The results of chemical analysis show that there was a serious Hg pollution in the study area, and the Cd content was close to the critical value. The results show that: (1) NOR, MSC and SNV were adopted for the acquisition of visible near-infrared. Combining differential transformation can improve the information of heavy metal elements in the soil, and use the correlation band energy Significantly improve the stability and predictability of the model. (2) The modeling accuracy of the optimal model of nine heavy metal spectra of Cr, Mn, Ni, Cu, Zn, As, Cd, Hg and Pb by PLSR method were 0.70, 0.79, 0.69, 0.81, 0.86, 0.58, 0.55, 0.99, 0.62. (3) The optimal estimation model of different elements using different treatment methods has better stability and higher precision, and can realize the rapid prediction of nine kinds of heavy metal elements in this region.

Space-time quantitative source apportionment of soil heavy metal concentration increments

Assessing the space-time trends and detecting the sources of heavy metal accumulation in soils have important consequences in the prevention and treatment of soil heavy metal pollution. In this study, we collected soil samples in the eastern part of the Qingshan district, Wuhan city, Hubei Province, China, during the period 2010-2014. The Cd, Cu, Pb and Zn concentrations in soils exhibited a significant accumulation during 2010-2014. The spatiotemporal Kriging technique, based on a quantitative characterization of soil heavy metal concentration variations in terms of non-separable variogram models, was employed to estimate the spatiotemporal soil heavy metal distribution in the study region. Our findings showed that the Cd, Cu, and Zn concentrations have an obvious incremental tendency from the southwestern to the central part of the study region. However, the Pb concentrations exhibited an obvious tendency from the northern part to the central part of the region. Then, spatial overlay analysis was used to obtain absolute and relative concentration increments of adjacent 1- or 5-year periods during 2010-2014. The spatial distribution of soil heavy metal concentration increments showed that the larger increments occurred in the center of the study region. Lastly, the principal component analysis combined with the multiple linear regression method were employed to quantify the source apportionment of the soil heavy metal concentration increments in the region. Our results led to the conclusion that the sources of soil heavy metal concentration increments should be ascribed to industry, agriculture and traffic. In particular, 82.5% of soil heavy metal concentration increment during 2010-2014 was ascribed to industrial/agricultural activities sources. Using STK and SOA to obtain the spatial distribution of heavy metal concentration increments in soils. Using PCA-MLR to quantify the source apportionment of soil heavy metal concentration increments.

Source apportionment and health risk assessment of heavy metals in soil for a township in Jiangsu Province, China

Human activities contribute greatly to heavy metal pollution in soils. Concentrations of 15 metal elements were detected in 105 soil samples collected from a typical rural-industrial town in southern Jiangsu, China. Among them, 7 heavy metals-lead, copper, zinc, arsenic, chromium, cadmium, and nickel-were considered in the health risk assessment for residents via soil inhalation, dermal contact, and/or direct/indirect ingestion. Their potential sources were quantitatively apportioned by positive matrix factorization using the data set of all metal elements, in combination with geostatistical analysis, land use investigation, and industrial composition analysis. Furthermore, the health risks imposed by sources of heavy metal in soil were estimated for the first time. The results indicated that Cr, Cu, Cd, Pb, Ni, and Co accumulated in the soil, attaining a mild pollution level. The total hazard index values were 3.62 and 6.11, and the total cancer risks were 9.78 × 10^-4 and 4.03 × 10^-4 for adults and children, respectively. That is, both non-carcinogenic and carcinogenic risks posed by soil metals were above acceptable levels. Cr and As require special attention because the health risks of Cr and As individually exceeded the acceptable levels. The ingestion of homegrown produce was predominantly responsible for the high risks. The potential sources were apportioned as: a) waste incineration and textile/dyeing industries (28.3%), b) natural sources (45.4%), c) traffic emissions (5.3%), and d) electroplating industries and livestock/poultry breeding (21.0%). Health risks of four sources accounted for 23.5%, 32.7%, 7.4%, and 36.4% of the total risk, respectively.

Effects of natural factors on the spatial distribution of heavy metals in soils surrounding mining regions

Various studies have shown that soils surrounding mining areas are seriously polluted with heavy metals. Determining the effects of natural factors on spatial distribution of heavy metals is important for determining the distribution characteristics of heavy metals in soils. In this study, an 8km buffer zone surrounding a typical non-ferrous metal mine in Suxian District of Hunan Province, China, was selected as the study area, and statistical, spatial autocorrelation and spatial interpolation analyses were used to obtain descriptive statistics and spatial autocorrelation characteristics of As, Pb, Cu, and Zn in soil. Additionally, the distributions of soil heavy metals under the influences of natural factors, including terrain (elevation and slope), wind direction and distance from a river, were determined. Layout of sampling sites, spatial changes of heavy metal contents at high elevations and concentration differences between upwind and downwind directions were then evaluated. The following results were obtained: (1) At low elevations, heavy metal concentrations decreased slightly, then increased considerably with increasing elevation. At high elevations, heavy metal concentrations first decreased, then increased, then decreased with increasing elevation. As the slope increased, heavy metal contents increased then decreased. (2) Heavy metal contents changed consistently in the upwind and downwind directions. Heavy metal contents were highest in 1km buffer zone and decreased with increasing distance from the mining area. The largest decrease in heavy metal concentrations was in 2km buffer zone. Perennial wind promotes the transport of heavy metals in downwind direction. (3) The spatial extent of the influence of the river on Pb, Zn and Cu in the soil was 800m. (4) The influence of the terrain on the heavy metal concentrations was greater than that of the wind. These results provide a scientific basis for preventing and mitigating heavy metal soil pollution in areas surrounding mines.