Bioavailability (BA)-based risk assessment of soil heavy metals in provinces of China through the predictive BA-models

Anthropogenic impacts on regional leaching risks posed by trace metal(loid)s in the soil of an industrial city

The leaching risks associated with trace metal(loid)s (s) in regional soil are complex due to the intricate interplay between pollution levels and soil properties. A Kd-based regional leaching risk assessment method was developed to assess the leaching risks posed by soil TMs. The random forest model was used to identify the effects of the soil environment on the soil Kd and the leaching risks. The reliability of the established method was successfully validated by field monitoring data (R2 = 0.84). The mean total soil groundwater risk was 1.59, the high mobility of Cd contributed the most to total risks. High-risk areas were mainly located in the farmland and forestland around a smelter and areas with severe soil acidification. The high mobility and moderate contamination of TMs resulted in the highest leaching risks. Furthermore, soil acidification and the conversion of farmland to forestland would increase the leaching risk by 33.5 % and 46.4 %, respectively, while urban expansion would reduce the leaching risk by 60.3 %. The Kd-based leaching risk assessment method provided a critical framework for decision-makers to efficiently identify high-risk areas on a regional scale, facilitating a deeper understanding of how anthropogenic activities influenced the leaching risks of TMs in soil.

Quantifying the contributions of factors to bioaccessible Cd and Pb in soil using machine learning

The bioaccessibility of cadmium (Cd) and lead (Pb) in the gastrointestinal tract is crucial for health risk assessments of contaminated soils. However, variability in In vitro analytical conditions and soil properties introduces bias and uncertainty in predictions. This study employed three in vitro methods to measure Cd and Pb bioaccessibility during the gastric and gastrointestinal phases, using soil samples incubated for one year. Twelve machine learning models were tested, with Random Forest chosen for its superior performance, achieving R² values between 0.74 and 0.82 in the test set. Key experimental conditions, including Cl⁻ concentration and extraction pH, were identified among the top five factors influencing bioaccessibility. Despite identical incubation conditions, bioaccessible Cd and Pb varied significantly, sometimes by several orders of magnitude, across soil types. Soil properties such as fine particle percentage (<1 μm) and pH were crucial, while MnO₂ content had a greater effect on Pb due to its geochemical behavior. Incorporating aging time into the model improved predictions, explaining 3.6-7.5 % of the variation, with the potential for a greater influence over longer contact times. This study emphasizes the importance of experimental conditions and soil-specific factors in accurately predicting heavy metal bioaccessibility in contaminated soils.

Source-specific soil heavy metal risk assessment in arsenic waste mine site of Yunnan: Integrating environmental and biological factors

This study quantified heavy metal (HM) pollution risks in mining site soils to provide targeted solutions for environmental remediation. Focusing on As waste mine sites in Yunnan, we utilised multiple indices and a positive matrix factorisation model to assess and quantify ecological health risks. Our ecological risk assessment distinguished between environmental and biological factors. This study demonstrated that As and Pb are the most impactful contaminants in environmental and biological contexts, respectively. Notably, the quantification of ecological risk sources indicated that agricultural sources were the main environmental influencers, accounting for 58.45 % of the total impact. Consequently, Cu from agricultural sources has become a primary environmental HM target, replacing As. In the quantification of health risk sources, mining and smelting activities predominantly contributed to health risks, contributing 23 % and 39.81 % of the Non-Carcinogenic Risk and 47.98 % and 42.96 % of the Carcinogenic Risk, respectively. The representative pollution source elements As and Cd were consistent with the health risk assessment results. This study refined the ecological risk assessment framework by distinguishing between environmental and biological factors, providing crucial insights into the rehabilitation of mine sites and formulation of effective environmental management strategies.

24-Epibrassinolide Enhanced Plant Antioxidant System and Cadmium Bioavailability Under Soil Cadmium Stress

Soil cadmium pollution poses significant environmental risks, prompting global concern. Previous studies have demonstrated that 24-epibrassinolide (Brs) can enhance plant photosynthesis, thereby potentially improving the efficiency of soil cadmium remediation by increasing biomass. Therefore, this study investigated the use of Brs to enhance Cd remediation by willow and alfalfa. After four months, we analyzed soil physicochemical properties, plant physiological and biochemical responses, biomass, Cd fractionation, plant Cd concentrations, and bioaccumulation factor (BCF). Willow and alfalfa cultivation without Brs increased soil pH and carbonates, reduced the exchangeable Cd fractionation, and increased Cd bound to Fe-Mn oxides and organic matter (p < 0.05). Conversely, Brs application increased soil total acids, increasing the bioavailable Cd (p < 0.05). Willow grown for four months accumulated Cd in leaves, stems, and roots at concentrations of 141.83-242.75, 45.91-89.66, and 26.73-45.68 mg kg-1, respectively, with leaf BCF ranging from 14.53 to 24.88. After five months, leaves of willow planted in Cd-contaminated soil (9.65 mg kg-1) contained 187.90-511.23 mg kg-1 Cd, with BCFs of 19.25-52.38. Brs also increases plant biomass by improving photosynthesis, detoxification, and antioxidant defenses. Treatments with Brs and willow extracted 1.57-1.81 times more Cd (0.56-1.37 mg pot-1) than without Brs (0.31-0.87 mg pot-1). This study offers guidelines for Cd phytoremediation and highlights an effective strategy to enhance Cd accumulation.

Using machine learning to predict soil lead relative bioavailability

Although the relative bioavailability (RBA) can be applied to assess the effects of Pb on human health, there is no definition and no specific data of Pb-RBA to different soil sources and endpoints in vivo. In this study, we estimated the Pb-RBA from different soil sources and endpoints based on machine learning. The Pb-BAc and Pb-RBA in soils were found to be mostly in the range of 20-80 %, which is different from the USEPA Pb-RBA of 60 % in soils. The mean Pb-RBA for different biological endpoints in vivo predicted using the RF model were 49.94 ± 18.65 % for blood; 60.15 ± 26.62 %, kidney; 60.90 ± 21.51 %, liver; 50.70 ± 17.56 %, femur; and 62.89 ± 16.64 % as a combined measure. Pb-RBA of shooting range soils was 88.21 ± 16.92 % (mean), spiked/aged soils 77.11 ± 14.05 % and certified reference materials 73.70 ± 20.31 %; agricultural soil 68.28 ± 18.93 %, urban soil 64.36 ± 21.82 %, mining/smelting soils 53.99 ± 17.66 %, and industrial soils 47.71 ± 20.35 %. This study is first to define the Pb-RBA according to various soil sources and endpoints in vivo with the objective of providing more accurate Pb-RBA data for soil lead risk assessment.

Novel insight into deriving remediation goals of arsenic contaminated sites with multi-media-equivalent dose and local exposure parameters

The remediation goal (RG) for arsenic (As) calculated by the traditional method is approximately 0.45 mg·kg-1, significantly lower than the background values. This poses significant challenges for the management of As-contaminated sites. The present study focused on a typical glassworks site with an As contamination level of up to 298 mg·kg-1, predominantly existing as As (III), with a carcinogenic risk level as high as 8.6 × 10-5. We developed a novel method known as multi-media-equivalent dose (MMED), incorporating local exposure parameters, and investigated the impacts of site-specific bioaccessibility (from 6.9 % to 51.5 %) on the results. The RG of arsenic calculated via MMED was 34.4 mg·kg-1 and 54 mg·kg-1 when bioaccessibility was considered. Integrating with five exposure parameters across 31 provinces, the provincial remediation goals (PRGs) ranged from 15.1 to 31.7 mg·kg-1. The RG calculated using the new method were more aligned with the practical conditions of managing As-contaminated sites, with potential for broader implementation across various provinces.

Source-risk and uncertainty assessment of trace metals in surface sediments of a human-dominated seaward catchment in eastern China

Current total concentration-based methods for source attribution and risk assessment often overestimate metal risks, thereby impeding the formulation of effective risk management strategies. This study aims to develop a framework for source-specific risk assessment based on metal bioavailability in surface river sediments from a human-dominated seaward catchment in eastern China. Metal bioavailability was quantified using chemical fractionation results, and source apportionment was conducted using the positive matrix factorization (PMF) model. Risk assessment integrated these findings using two indices: the Potential Ecological Risk Index (PERI) and the Mean Probable Effect Concentration Quotient (mPEC-Q), with uncertainty addressed via Monte Carlo simulations. Results indicated that average total concentrations of Cu, Pb, Zn, Cr, Hg, Cd, and As exceeded their respective background levels by 1.63 to 15.00 times. The residual fraction constituted the majority, accounting for 53.84 % to 77.79 % of total concentrations, suggesting significant natural origins. However, source apportionment revealed a predominant contribution from anthropogenic activities, including industrial smelting, agricultural practices, and atmospheric deposition. The contributions were found to vary between 5.35 % and 40.03 % when the total concentration was adjusted to bioavailable content. Total concentration-based PERI/mPEC-Q assessments indicated high/moderate risk levels, decreasing to considerable/low risk levels with bioavailability adjustment. Hg and Cd were identified as priority metals. Further incorporating source appointment parameters into the risk assessment, industrial smelting was identified as the primary contributor, accounting for 66.06 % of total risk by total concentration and 65.63 % by bioavailability. This underscores the role of bioavailability in mitigating risk overestimation. Monte Carlo simulations validated industrial smelting as a major risk contributor. This study emphasizes the importance of considering bioavailability in the source-risk assessment of sediment-metals, crucial for targeted risk management in urbanized catchment areas.

Utilizing machine learning to evaluate heavy metal pollution in the world's largest mangrove forest

The world's largest mangrove forest (Sundarbans) is facing an imminent threat from heavy metal pollution, posing grave ecological and human health risks. Developing an accurate predictive model for heavy metal content in this area has been challenging. In this study, we used machine learning techniques to model sediment pollution by heavy metals in this vital ecosystem. We collected 199 standardized sediment samples to predict the accumulation of eleven heavy metals using ten different machine learning algorithms. Among them, the extremely randomized tree model exhibited the best performance in predicting Fe (0.87), Cr (0.89), Zn (0.85), Ni (0.83), Cu (0.87), Co (0.62), As (0.68), and V (0.90), achieving notable R2 values. On the other hand, the random forest outperformed for predicting Cd (0.72) and Mn (0.91), whereas the decision tree model showed the best performance for Pb (0.73). The feature attribute analysis identified FeV, CrV, CuZn, CoMn, PbCd, and AsCd relationships resembled with correlation coefficients among them. Based on the established models, the prediction of the contamination factor of metals in sediments showed very high Cd contamination (CF ≥ 6). The Moran's I index for Cd, Cr, Pb, and As were 0.71, 0.81, 0.71, and 0.67, respectively, indicating strong positive spatial autocorrelation and suggesting clustering of similar contamination levels. Conclusively, this research provides a comprehensive framework for predicting heavy metal sediment pollution in the Sundarbans, identifying key areas needing urgent conservation. Our findings support the adoption of integrated management strategies and targeted remedial actions to mitigate the harmful effects of heavy metal contamination in this vital ecosystem.

Water-extractable metals as indicators of wheat metal accumulation: Insights from Cd, Pb, Mn, Cu, and Zn

There is a long-standing debate over the effectiveness of chemical extraction methods in assessing soil metal phytoavailability. This study addresses the limitations of widely-used chemical extraction methods and presents the water-extractable pool as a more reliable indicator based on wheat pot experiments using homogenized agricultural soil amended with lime materials, phosphate, and biochar. Over 120 days' pot experiments, Cd accumulation in whole wheat plants and tissues exhibited positive relationships with water-extractable Cd concentrations at heading and maturity stage (Spearman's rho: 0.521-0.851; P < 0.05), revealing that the water-extractable pool instead of other pools better indicates wheat metal accumulation. Water-extractable metal concentrations are effective in assessing phytoavailability of metals primarily in ionic forms in soil solution (e.g, Zn, Cd), but less reliable for metals strongly complexed with dissolved organic matter (DOM) or sensitive to redox conditions. It demonstrated that water-extractable metal concentrations and chemical forms are key factors, fundamentally determined by metal properties and impacted by environmental factors. This study clarifies a more direct link between chemical extraction and plant metal uptake mechanisms. Given the extensive application of chemical extraction methods over several decades, this study will help advance soil metal risk assessment and remediation practices.

Optimal biochar selection for cadmium pollution remediation in Chinese agricultural soils via optimized machine learning

Biochar is effective in mitigating heavy metal pollution, and cadmium (Cd) is the primary pollutant in agricultural fields. However, traditional trial-and-error methods for determining the optimal biochar remediation efficiency are time-consuming and inefficient because of the varied soil, biochar, and Cd pollution conditions. This study employed the machine learning method to predict the Cd immobilization efficiency of biochar in soil. The predictive accuracy of the random forest (RF) model was superior to that of the other common linear and nonlinear models. Furthermore, to improve the reliability and accuracy of the RF model, it was optimized by employing a root-mean-squared-error-based trial-and-error approach. With the aid of the optimized model, the empirical categories for soil Cd immobilization efficiency were biochar properties (60.96 %) > experimental conditions (19.6 %) ≈ soil properties (19.44 %). Finally, this study identified the optimal biochar properties for enhancing agricultural soil Cd remediation in different regions of China, which was beneficial for decision-making regarding nationwide agricultural soil remediation using biochar. The immobilization effect of alkaline biochar was pronounced in acidic soils with relatively high organic matter. This study provides insights into the immobilization mechanism and an approach for biochar selection for Cd immobilization in agricultural soil.

Research on aquatic microcosm: Bibliometric analysis, toxicity comparison and model prediction

Recently, aquatic microcosms have attracted considerable attention because they can be used to simulate natural aquatic ecosystems. First, to evaluate the development of trends, hotspots, and national cooperation networks in the field, bibliometric analysis was performed based on 1841 articles on aquatic microcosm (1962-2022). The results of the bibliometric analysis can be categorized as follows: (1) Aquatic microcosm research can be summarized in two sections, with the first part focusing on the ecological processes and services of aquatic ecosystems, and the second focusing on the toxicity and degradation of pollutants. (2) The United States (number of publications: 541, proportion: 29.5%) and China (248, 13.5%) are the two most active countries. Second, to determine whether there is a difference between single-species and microcosm tests, that is, to perform different-tier assessments, the recommended aquatic safety thresholds in risk assessment [i.e., the community-level no effect concentration (NOECcommunity), hazardous concentrations for 5% of species (HC5) and predicted no effect concentration (PNEC)] were compared based on these tests. There was a significant difference between the NOECcommunity and HC5 (P < 0.05). Moreover, regression models predicting microcosm toxicity values were constructed to provide a reference for ecological systemic risk assessments based on aquatic microcosms.

Indirect Estimation of Heavy Metal Contamination in Rice Soil Using Spectral Techniques

The rapid growth of industrialization and urbanization in China has led to an increase in soil heavy metal pollution, which poses a serious threat to ecosystem safety and human health. The advancement of spectral technology offers a way to rapidly and non-destructively monitor soil heavy metal content. In order to explore the potential of rice leaf spectra to indirectly estimate soil heavy metal content. We collected farmland soil samples and measured rice leaf spectra in Xushe Town, Yixing City, Jiangsu Province, China. In the laboratory, the heavy metals Cd and As were determined. In order to establish an estimation model between the pre-processed spectra and the soil heavy metals Cd and As content, a genetic algorithm (GA) was used to optimise the partial least squares regression (PLSR). The model's accuracy was evaluated and the best estimation model was obtained. The results showed that spectral pre-processing techniques can extract hidden information from the spectra. The first-order derivative of absorbance was more effective in extracting spectral sensitive information from rice leaf spectra. The GA-PLSR model selects only about 10% of the bands and has better accuracy in spectral modeling than the PLSR model. The spectral reflectance of rice leaves has the capacity to estimate Cd content in the soil (relative percent difference [RPD] = 2.09) and a good capacity to estimate As content in the soil (RPD = 2.97). Therefore, the content of the heavy metals Cd and As in the soil can be estimated indirectly from the spectral data of rice leaves. This study provides a reference for future remote sensing monitoring of soil heavy metal pollution in farmland that is quantitative, dynamic, and non-destructive over a large area.