Application of stochastic model to assessment of heavy metal(loid)s source apportionment and bio-availability in rice fields of karst area

Identifying heavy metal sources and health risks in soil-vegetable systems of fragmented vegetable fields based on machine learning, positive matrix factorization model and Monte Carlo simulation

Urban fragmented vegetable fields offer fresh produce but pose a potential risk of heavy metal (HM) exposure. Thus, this study investigated HM sources and health risks in the soil-vegetable systems of Chongqing's central urban area. Results indicated that Cd was the primary pollutant, with 28.33 % of soil samples exceeding the screening value. Amaranth was particularly problematic, exceeding thresholds for Cd, Hg, and Cr, and both amaranth and celery showed significantly higher HM accumulation (p < 0.05). The HM pollution level in the soil-vegetable system was moderate or above. The sources of HMs identified via Positive matrix factorization (PMF) model included agricultural activities (18.19 %), natural soil parent material (25.88 %), mixed metal smelting and transportation (30.72 %), and coal combustion (25.21 %). Furthermore, evaluations using the Random Forest (RF) model revealed an intricate interaction of factors influencing the presence of HMs, where enterprise density, population density, and road density played significant roles in HMs accumulation. Monte Carlo assessments revealed higher non-carcinogenic risks for children (Pb, As) and greater carcinogenic risks for adults (Cd). Therefore, the issue of HM pollution in soils and vegetables from fragmented fields in industrial urban areas need attention, given the potential for elevated health risks with long-term vegetable consumption.

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.

Spatial prediction and influencing factors identification of potential toxic element contamination in soil of different karst landform regions using integration model

The prediction of contamination distribution of potentially toxic elements (PTEs) in soils of Guangxi province, China and the identification of their controlling factors pose great challenges due to diverse bedrock types, intense leaching and weathering, and discontinuous terrain distributions. Herein, we integrated the random forest (RF) and empirical Bayesian kriging (EBK) to interpret and predict complex PTEs contamination distribution from three different karst landform regions (fenglin, fengcong, isolated peak plain) in Guangxi province. The modeling results are compared with the commonly used ordinary kriging and regression-kriging. In this study, our developed RF-EBK model combines the advantages of the RF and EBK model to promote the prediction accurately and efficiently. In this study, it was shown that the integration RF-EBK model exhibited desirable for Cd and As concentrations, with R² of 0.89 and 0.83, respectively. The average RMSE and MAE of integration RF-EBK model decreased by 39% and 44%, respectively, relative to the regression-kriging with the second highest accuracy. Furthermore, the modeling results showed that approximately 41.96% and 18.96% of total area was classified as Cd and As polluted and above regions (I_geo >0) in Guangxi province, respectively. Higher Cd concentration was observed in the soil of fenglin and fengcong regions than that in isolated peak plain region due to the secondary enrichment and parent rock inheritance, while the As concentration exhibited no significant difference among the three regions. The modeling results indicated that the elevated Cd concentration might be associated with soil CaO concentration and alkaline soil environment, whereas As concentration tended to be increased with the elevating Fe₂O₃ concentrations in weakly acidic soil environment. This result confirmed the applicability and effectiveness of integration model in predicting complex spatial patterns of soil PTEs and identifying their controlling factors.

Derivation and validation of soil cadmium thresholds for the safe farmland production of vegetables in high geological background area

Excessive dietary intake of cadmium (Cd) poses toxicity risks to human health, and it is therefore essential to establish accurate and regionally appropriate soil Cd thresholds that ensure the safety of agricultural products grown in different areas. This study investigated the differences in the Cd accumulation in 32 vegetable varieties and found that the Cd content ranged from 0.01 to 0.24 mg·kg^-1, and decreased in the order of stem and bulb vegetables > leafy vegetables > solanaceous crops > bean cultivars. A correlation analysis and structural equation model showed that pH, soil organic matter, and the cation exchange capacity had significant effects on Cd accumulation in the vegetables and explained 72.1 % of the variance. In addition, species sensitivity distribution (SSD) curves showed that stem and bulb vegetables were more sensitive to Cd than other types of vegetables. Using the Burr Type III function for curve fitting, we derived Cd thresholds of 6.66, 4.15, and 1.57 mg·kg^-1 for vegetable soils. These thresholds will ensure that 20 %, 50 %, and 95 % of these vegetable varieties were risk-free, respectively. The predicted threshold of soil Cd was more than twice that of China's current National Soil Quality Standard (GB 15618-2018) for Cd values. Therefore, soil scenarios and cultivars should be considered comprehensively when determining farmland soil thresholds. The present results provide a new model for setting soil Cd criteria in high geological background areas.

Estimation of heavy metal soil contamination distribution, hazard probability, and population at risk by machine learning prediction modeling in Guangxi, China

Due to superposition of diverse pollution sources, soil heavy metal concentrations have been detected to exceed the recommended maximum permissible levels in many areas of Guangxi province, China. However, the heavy metal contamination distribution, hazard probability, and population at risk of heavy metals in the entire Guangxi province remain largely unclear. In this study, machine learning prediction models with different standard risk values determined according to land use types were used to identify high-risk areas and estimate populations at risk of Cr and Ni based on 658 topsoil samples from Guangxi province, China. Our results showed that soil Cr and Ni contamination derived from carbonate rocks was relatively serious in Guangxi province, and that their co-enrichment during soil formation was associated with Fe and Mn oxides and alkaline soil environment. Our established model exhibited excellent performance in predicting contamination distribution (R² > 0.85) and hazard probability (AUC>0.85). Pollution of Cr and Ni exhibited a pattern of decreasing gradually from the central-west areas to the surrounding areas with the polluted area (I_geo>0) of Cr and Ni accounting for approximately 24.46% and 29.24% of total area in Guangxi province, respectively, but only 10.4% and 8.51% of total area was classified as Cr and Ni high-risk regions. We estimated approximately 1.44 and 1.47 million people were potentially exposed to the risk of Cr and Ni contamination, which were mainly concentrated in the Nanning, Laibin, and Guigang. These regions are main heavily-populated agricultural regions in Guangxi, and thus heavy metal contamination localization and risk control in these regions are urgent and essential from the perspective of food safety.

A hybrid data-driven framework for diagnosing contributing factors for soil heavy metal contaminations using machine learning and spatial clustering analysis

The efficacy of source apportionment is often limited by a lack of information on natural and anthropogenic contributing factors influencing soil heavy metal (HM) contaminations. To overcome this limitation and develop the data mining methods, a novel hybrid data-driven framework was proposed to diagnose the contributing factors in an industrialized region in Guangdong Province, China, mainly using a combination of naive Bayes (NB), random forest (RF), and bivariate local Moran's I (BLMI) on the basis of the multi-source big data. The medium industry types of enterprises from the freely available Baidu point of interest data were successfully classified, and then the 250 contaminating enterprises as a contributing factor were identified by the optimized NB classifier. The quantitative contributions of the nine contributing factors for the As, Cd, and Hg concentrations were determined by the optimized RF. The twelve spatial clustering maps between the three HM concentrations and the four key contributing factors were generated by BLMI, explicitly revealing their mutual interactions and internal effects and also intuitively showing the "high-high" areas and their distributions. This framework can obtain rich information on contributing factors such as medium industry types, contribution rates, spatial clusters, and spatial distributions.

Priority Soil Pollution Management of Contaminated Site Based on Human Health Risk Assessment: A Case Study in Southwest China

The human risk assessment model can serve as a tool for regional contaminated site comprehensive management. However, site-specific risk assessment is still seldom applied in China as a basis for making decisions on risk management actions. In this study, a total of 112 soil samples were collected from ten polluted sites in Southwest China. The human health risk assessment method was used to assess the risk of Cd, As, Cu, Pb, Cr, Zn, and Hg pollution. According to the findings, the average concentrations of As, Zn, and Pb in contaminated sites are substantially higher than those of Cu, Cd, Cr, and Hg. Further studies conclusively showed the soil at contaminated site in its present condition pose risk to human health to potential future receptors. The main contribution of non-cancer and cancer risks was through incidental soil and dust ingestion. The priority control site remediation order is Region VI > Region V > Region IV > Region III > Region VII > Region X > Region IX > Region VIII > Region I > Region II. Finally, management recommendations are made, including reducing pollutant intake, implementing a stringent monitoring scheme, utilizing bioremediation, and strengthening the implementation of relevant laws. This study provides a case for the comprehensive evaluation of soil pollution at contaminated sites in China.