A hybrid framework for delineating the migration route of soil heavy metal pollution by heavy metal similarity calculation and machine learning method

A city-level dataset of heavy metal emissions into the atmosphere across China from 2015-2020

The absence of nationwide distribution data regarding heavy metal emissions into the atmosphere poses a significant constraint in environmental research and public health assessment. In response to the critical data deficiency, we have established a dataset covering Cr, Cd, As, and Pb emissions into the atmosphere (HMEAs, unit: ton) across 367 municipalities in China. Initially, we collected HMEAs data and covariates such as industrial emissions, vehicle emissions, meteorological variables, among other ten indicators. Following this, nine machine learning models, including Linear Regression (LR), Ridge, Bayesian Ridge (Bayesian), K-Neighbors Regressor (KNN), MLP Regressor (MLP), Random Forest Regressor (RF), LGBM Regressor (LGBM), Lasso, and ElasticNet, were assessed using coefficient of determination (R2), root-mean-square error (RMSE) and Mean Absolute Error (MAE) on the testing dataset. RF and LGBM models were chosen, due to their favorable predictive performance (R2: 0.58-0.84, lower RMSE/MAE), confirming their robustness in modelling. This dataset serves as a valuable resource for informing environmental policies, monitoring air quality, conducting environmental assessments, and facilitating academic research.

Analysis of soil fertility and toxic metal characteristics in open-pit mining areas in northern Shaanxi

The study specifically focused on the Hongliulin mining area, where a total of 40 soil samples were meticulously collected and analyzed from within a 1000 m radius extending from the tailings dam. The findings revealed that soil pH within the 0-1000 m range generally leaned towards the alkaline side. In terms of soil nutrient content, encompassing factors such as soil organic matter (SOM), total nitrogen (TN), total phosphorus (TP), total potassium (TK), alkali nitrogen (AK), available phosphorus (AP), and quick-acting potassium (AK), the variations fell within the following ranges: 2.23-13.58 g/kg, 0.12-0.73 g/kg, 0.18-1.15 g/kg, 9.54-35.82 g/kg, 2.89-6.76 mg/kg, 3.45-11.25 mg/kg, and 5.86-130.9 mg/kg. Collectively, these values indicate relatively low levels of soil nutrients. Within the 0-500 m range of soil samples, the average concentrations of Cd, Hg, Pb, and As were 0.778, 0.198, 24.87, and 17.92 mg/kg, respectively. These concentrations exceeded the established soil background values of Shaanxi Province and emerged as the primary pollutants in the study area. Within this same range, the mean values of eight toxic metals (Pi) were ranked in the following descending order: 1.726 (Hg), 1.400 (As), 1.129 (Cr), 1.109 (Pb), 0.623 (Zn), 0.536 (Cd), 0.309 (Cu), and 0.289 (Ni). With the exception of Hg, As, Cr, and Pb, which exhibited slight pollution, the other toxic metals were found to be within acceptable pollution limits for this sampling range, in line with the results obtained using the geo-accumulation index method. The average potential ecological risk index for the eight toxic metals in the study area stood at 185.0, indicating a moderate overall pollution level. When assessing individual elements, the proportions of ecological risk attributed to Hg, As, Pb, and Cd were 34.57%, 27.44%, 25.11%, and 23.11%, respectively. This suggests that the primary potential ecological risk elements in the study area are Hg and As, followed by Cd and Pb. Notably, toxic metals Hg and Pb, as well as As and Pb, exhibited significant positive correlations within the sampling area, suggesting a common source. An analysis of the relationship between soil physicochemical properties and toxic metals indicated that soil pH, SOM, TN, and TP were closely linked to toxic metal concentrations. The toxic metal elements in the research area's soil exhibit moderate variability (0.16 < CV < 0.36) to high variability (CV > 0.36). Within the range of 0-200 m, the CV values for Cd and Hg exceed 1, indicating a high level of variability. The coefficient of variation for SOM, TP, AP, AK and TK is relatively high with the of 2.93, 2.36, 2.36, 21.01, 7.54. The soil in the sampling area has undergone significant disturbances due to human activities, resulting in toxic metal pollution and nutrient deficiencies.

Source-specific probabilistic contamination risk and health risk assessment of soil heavy metals in a typical ancient mining area

Heavy metal pollution (HMP) from mining operations severely threatens soil ecosystems and human health. Identifying the sources of soil heavy metals (HMs) and assessing source-specific risks are critical for developing effective risk mitigation strategies. In this study, a combination of methodologies including PMF, Monte Carlo analysis, soil pollution risk index, and a human health risk assessment model were utilized to investigate soil HM risks in a typical ancient mining area in Daye City, China, considering both environmental pollution and human health impacts. Cu emerged as the most significant soil pollution risk, whereas As posing the highest health risk. About 48.44 % of the multi-element integrated soil pollution risk has escalated to the heavy level. Furthermore, around 22.42 % of the non-carcinogenic risk (NCR) and 9.53 % of the carcinogenic risk (CR) exceeded unacceptable thresholds (THI > 1 for NCR and TCR > 1E-4 for CR). The PMF model identified four distinct sources: the smelting industry, traffic emissions, a combination of agricultural and natural factors, and mining activities. The mixed agricultural and natural source significantly impacted health risks, contributing 42.17 % to NCR and 53.88 % to CR, followed by the mining source, contributing 31.67 % to NCR and 24.07 % to CR. Interestingly, the mining source contributed the highest soil pollution risk at 42.45 %, while the mixed agricultural and natural source exhibited the lowest at 16.33 %. Furthermore, the study explored source-specific risk components by evaluating the contributions of different sources to specific elements. The mining source was identified as the focus for soil HMP control, followed by the mixed agricultural and natural source. Overall, this study provided an in-depth analysis of soil heavy metal risks in mining areas from the source apportionment perspective, which broadened the research framework of soil heavy metal source analysis and risk assessment, potentially providing scientific guidance for managing regional soil HMP.

Processing toxic metal source proxies appropriately for better spatial heterogeneity source apportionment

Soil toxic metals have strong spatial heterogeneity, and their sources vary among regions. Thus, this study integrated the Catreg and geographically weighted regression (GWR) models to quantitatively extract the main source proxies (numerical and categorical variables were analyzed simultaneously) for different toxic metals and analyze the spatial heterogeneity of the distributions of these sources. Pb, Cd and Hg were the predominant toxic metals in soil. Of the samples with Pb, Cd and Hg, 84.12 %, 68.03 % and 41.57 % exceeded the background values, and 5.36 %, 6.42 % and 5.43 % were moderately contaminated according to the geoaccmulation index, respectively. Industrial activities, with relative importance values of 17.82 %, 31.54 % and 26.51 % for Cd, Hg and Pb, respectively, were the predominant source of these metals especially, in their high-content cluster areas (central urban areas). Soil available phosphorus was another important factor (relative importance values of 13.03 %, 13.41 % and 25.55 % for Cd, Hg and Pb, respectively), and agricultural activities (especially the overuse of phosphoric fertilizers) were identified as an anthropogenic source of these toxic metals. Soil parent material had the greatest influence on As and Cr, with relative importance values of 19.88 % and 19.09 %, respectively, especially in their high-content accumulation area (the eastern coastal area), indicating that these toxic metals mainly come from natural sources. Slope had important impacts on toxic metal accumulation (relative importance values of 17.48 %, 21.22 %, 12.40 % and 16.13 % for Cd, Hg, Cr and As, respectively) by influencing industrial distribution and pollutant migration. By changing the soil adsorption capacity, soil organic matter (explaining 13.01 % of Pb) and soil pH (explaining 14.58 % of As and 12.40 % of Cr) strongly influenced toxic metal accumulation. This study highlights the benefits of the integrated Catreg-GWR model for analyzing multiple spatially heterogeneous environmental data types (numerical and categorical variables), providing a potential foundation for local pollution prevention.

Identification of potentially contaminated areas of soil microplastic based on machine learning: A case study in Taihu Lake region, China

Soil microplastic (MP) pollution has recently become increasingly aggravated, with severe consequences being generated. Understanding the spatial distribution characteristics of soil MPs is an important prerequisite for protecting and controlling soil pollution. However, determining the spatial distribution of soil MPs through a large number of soil field sampling and laboratory test analyses is unrealistic. In this study, we compared the accuracy and applicability of different machine learning models for predicting the spatial distribution of soil MPs. The support vector machine regression model with radial basis function (RBF) as kernel function (SVR-RBF) has a high prediction accuracy (R² = 0.8934). Among the six ensemble models, random forest (R² = 0.9007) could better explain the significance of source and sink factors affecting the occurrence of soil MPs. Soil texture, population density, and MPs point of interest (MPs-POI) were the main source-sink factors affecting the occurrence of soil MPs. Furthermore, the accumulation of MPs in soil was significantly affected by human activity. The spatial distribution map of soil MP pollution in the study area was drawn based on the bivariate local Moran's I model of soil MP pollution and the normalized difference vegetation index (NDVI) variation trend. A total of 48.74 km² of soil was in an area of serious MP pollution, mainly concentrated in urban soil. This study provides a hybrid framework that includes spatial distribution prediction of MPs, source-sink analysis, and pollution risk area identification, providing scientific and systematic methods and techniques for pollution management in other soil environments.

Seaweed polysaccharide relieves hexavalent chromium-induced gut microbial homeostasis

Heavy metals released in the environment pose a huge threat to soil and water quality, food safety and public health. Additionally, humans and other mammals may also be directly exposed to heavy metals or exposed to heavy metals through the food chain, which seriously threatens the health of animals and humans. Chromium, especially hexavalent chromium [Cr (VI)], as a common heavy metal, has been shown to cause serious environmental pollution as well as intestinal damage. Thus, increasing research is devoted to finding drugs to mitigate the negative health effects of hexavalent chromium exposure. Seaweed polysaccharides have been demonstrated to have many pharmacological effects, but whether it can alleviate gut microbial dysbiosis caused by hexavalent chromium exposure has not been well characterized. Here, we hypothesized that seaweed polysaccharides could alleviate hexavalent chromium exposure-induced poor health in mice. Mice in Cr and seaweed polysaccharide treatment group was compulsively receive K2Cr2O7. At the end of the experiment, all mice were euthanized, and colon contents were collected for DNA sequencing analysis. Results showed that seaweed polysaccharide administration can restore the gut microbial dysbiosis and the reduction of gut microbial diversity caused by hexavalent chromium exposure in mice. Hexavalent chromium exposure also caused significant changes in the gut microbial composition of mice, including an increase in some pathogenic bacteria and a decrease in beneficial bacteria. However, seaweed polysaccharides administration could ameliorate the composition of gut microbiota. In conclusion, this study showed that seaweed polysaccharides can restore the negative effects of hexavalent chromium exposure in mice, including gut microbial dysbiosis. Meanwhile, this research also lays the foundation for the application of seaweed polysaccharides.