Effects of anthropogenic and natural environmental factors on the spatial distribution of trace elements in agricultural soils

Identifying interactive effects of spatial drivers in soil heavy metal pollutants using interpretable machine learning models

The accurate identification of spatial drivers is crucial for effectively managing soil heavy metals (SHM). However, understanding the complex and diverse spatial drivers of SHM and their interactive effects remains a significant challenge. In this study, we present a comprehensive analysis framework that integrates Geodetector, CatBoost, and SHapley Additive exPlanations (SHAP) techniques to identify and elucidate the interactive effects of spatial drivers in SHM within the Pearl River Delta (PRD) region of China. Our investigation incorporated fourteen environmental factors and focused on the pollution levels of three prominent heavy metals: Hg, Cd, and Zn. These findings provide several key insights: (1) The distribution of SHM is influenced by the combined effects of various individual factors and interactions within the source-flow-sink process. (2) Compared with the spatial interpretation of individual factors, the interaction between Hg and Cd exhibited enhanced spatial explanatory power. Similarly, interactions involving Zn mainly demonstrated increased spatial explanatory power, but there was one exception in which a weakening was observed. (3) Spatial heterogeneity plays a crucial role in determining the contributions of environmental factors to soil heavy metal concentrations. Although individual factors generally promote metal accumulation, their effects fluctuate when interactions are considered. (4) The SHAP interpretable method effectively addresses the limitations associated with machine-learning models by providing understandable insights into heavy metal pollution. This enables a comparison of the importance of environmental factors and elucidates their directional impacts, thereby aiding in the understanding of interaction mechanisms. The methods and findings presented in this study offer valuable insights into the spatial heterogeneity of heavy metal pollution in soil. By focusing on the effects of interactive factors, we aimed to develop more accurate strategies for managing SHM pollution.

Influence of geographical location on the distribution of heavy metals in dairy cattle feeds sourced from two South African provinces

The contamination of feed and food by heavy metals represents a significant concern for the health of both animals and humans. This study investigates the impact of geographical location on heavy metal distribution in dairy cattle feeds sourced from Free State and Limpopo, South Africa (SA). A total of 70 feed samples (40 from Free State and 30 from Limpopo) were collected from 2018 to 2019 and analyzed for heavy metals, including cadmium (Cd), arsenic (As), copper (Cu), zinc (Zn), lead (Pb), and chromium (Cr), using inductively coupled plasma mass spectrometry (ICP-MS). Our findings revealed the presence of Cr, Cu, and Zn in the feeds, but at levels below the FAO/WHO permissible limits. Additionally, As, Cd, and Pb concentrations in the feeds were below the Limit of Detections (LODs). Generally, Cr concentrations (0.032-0.454 mg/kg) identified in the Free State samples were lower than those found in Limpopo (0.038-1.459 mg/kg), while the levels of Cu (0.092-4.898 mg/kg) and Zn (0.39-13.871 mg/kg) recorded in the Free State samples were higher than those from Limpopo [(0.126-3.467 mg/kg) and (0.244-13.767 mg/kg), respectively]. According to independent sample t-tests, Cu and Zn levels were substantially higher (p ≤ .05) in Free State feeds compared to Limpopo, while Limpopo feeds exhibited significantly higher (p ≤ .05) Cr concentrations than Free State feeds. Despite the low recorded heavy metal levels, regular monitoring of these elements in cow diets across all SA provinces is essential for ensuring the well-being of animals and humans.

Spatial distribution and main drivers of soil selenium in Taihu Lake Basin, Southeast China

Selenium (Se) is an essential micronutrient that is both hazardous and beneficial to living organisms. However, few studies have examined soil Se distribution and its driving mechanisms on a large basin scale. Thus, multivariate statistics, geostatistics, boosted regression trees, and structural equation models were used to investigate the spatial distribution, driving factors, and multivariate interactions of soil Se based on 1753 topsoil samples (0-20 cm) from the Taihu Lake Basin. The results indicated that the soil Se concentration ranged from 0.12 to 57.26 mg kg-1, with a mean value of 0.90 mg kg-1. Overall, the spatial pattern of soil Se gradually decreased from south to north with approximately 1.06% of the soil contaminated with Se. Moisture index (MI), soil moisture (SM), and ≥ 0 ℃ accumulative temperature (AAT0) were the main determinants of soil Se accumulation. Additionally, the substantial effect of SM∩AAT0 on soil Se concentrations demonstrated that climate-soil interactions largely governed the spatial pattern of soil Se. The Se-enriched and Se-contaminated soils occurred mainly in regions with high precipitation, MI, SM, AAT0, and soil organic matter. This study provides a theoretical basis and practical guidance for the remediation of soil Se contamination and the sustainable development of Se-enriched agriculture.

Spatial distribution and source identification of potentially toxic elements in Yellow River Delta soils, China: An interpretable machine-learning approach

Identifying the driving factors and quantifying the sources of potentially toxic elements (PTEs) are essential for protecting the ecological environment of the Yellow River Delta. In this study, data from 201 surface soil samples and 16 environmental variables were collected, and the random forest (RF) and Shapley additive explanations (SHAP) methods were then combined to explore the key factors affecting soil PTEs. An innovative t-distributed random neighbor embedding-RF-SHAP model was then constructed, based on the absolute principal component score and multivariate linear regression model, to quantitatively determine PTE sources. Although average PTE concentrations did not exceed the risk control values, PTE distributions exhibited significant differences. It was found that sodium, soil organic matter, and phosphorus contents were the three most important factors affecting PTEs, and human activities and natural environmental factors both influence PTE contents by altering the soil properties. The proposed model successfully determined PTE sources in the soil, outperforming the original linear regression model with a significantly lower RMSE. Source analysis revealed that the parent material was the main contributor to soil PTEs, accounting for more than half of the total PTE content. Industrial and agricultural activities also contributed to an increase in soil PTEs, with average contributions of 19.91 % and 17.44 %, respectively. Unknown sources accounted for 10.83 % of the total PTE content. Thus, the proposed model provides innovative perspectives on source parsing. These findings provide valuable scientific insights for policymakers seeking to develop effective environmental protection measures and improve the quality of saline-alkali land in the Yellow River Delta.

Distribution and transport of contaminants in soil through mining processes and its environmental impact and health hazard assessment: A review of the prospective solutions

Environmental regulations were concerned with support in reaction to the enormous ecological harm caused by mining in the past. Because mining, dumping, and tailings can generate waste and radioactive consequences, society must develop methods for successfully treating mining waste from mine dumps, tailings, and abandoned mines. Strict policies associated with environmental regulations to avoid the possible dangers caused by garbage and radioactivity. Several characteristics, including background contamination from natural sources related to mineral deposits, contamination from industrial activities in three-dimensional subsurface space, a problem with long-term remediation following mine closure, a problem with secondary contaminated areas near mine sites, land use conflicts, and abandoned mines, distinguish it. Reusing and recycling mine waste occasionally results in cost-effective advantages in the mining sector by offsetting natural resource requirements and reducing the volume of garbage materials. These benefits stem from recycling and reusing mining waste, which can lower the amount of garbage that must be managed. This review focuses on realistic strategies for anticipating mining exploration control and attempts to examine those methods in-depth. Management strategies for limiting the environmental impact of mining dumps, stockpiles, and tailings were discussed. The environmental assessment was also mentioned to carry out specific control and take preventive actions.

Soil heavy metal source apportionment and environmental differentiation study in Dulan County, Qinghai Province, using geodetector analysis

Elucidating material sources and investigating the impact of various environmental factors on material source accumulation are important for the environmental restoration of the Qinghai-Tibet Plateau. This study was conducted within the Borhan Buda Mountain Range of Dulan County, Qinghai Province, China, where 6274 surface soil samples were collected. The geoaccumulation index was employed to assess the levels of heavy metals, including As, Cr, Cu, Hg, Ni, Pb, Sb, Sn, and Zn, in the soil. A comprehensive approach combining principal component analysis (PCA) and geodetector analysis was employed to assess the spatial variation in soil heavy metal origins and the factors that influence them. The findings indicate that the mean concentrations of Pb exceed the background values for the soil in Qinghai Province, with Hg exhibiting low pollution, whereas the other elements showed no contamination. PCA indicated that the soil elements in the Borhan Buda Mountain Range were influenced by four sources, all attributed to the geological background. Geodetector analysis of the factor contributions suggested that geological factors had the strongest explanatory power for the four material sources. Furthermore, the risk detection results demonstrated significant variations in the material source contributions among most subregions when considering three environmental factors in pairs. Interaction detection revealed that the combined influence of two environmental factors on material source contributions was greater than that of the individual factors. Additionally, ecological detection demonstrated significant differences in material source contributions one, two, and three between land cover types and geological backgrounds, whereas no significant differences were observed in material source four between land cover types and geological backgrounds. This study offers valuable insights into the sources of soil elements in Dulan County and the influence of environmental factors on these sources, thereby contributing an essential knowledge base for the protection and management of soil heavy metals in the region.

Distribution of cadmium and lead in soil-rice systems and their environmental driving factors at the island scale

Toxic elements, such as Cd and Pb are of primary concern for soil quality and food security owing to their high toxicity and potential for bioaccumulation. Knowledge of the spatial variability of Cd and Pb in soil-rice systems across the landscape and identification of their driving factors are prerequisites for developing appropriate management strategies to remediate or regulate these hazardous contaminants. Considering the role of rice (Oryza sativa) as a dietary staple in China, this study aimed to examine the distribution patterns and drivers of Cd and Pb in tropical soil-rice systems across Hainan Island. To achieve this goal, 229 pairs of representative paddy soil and rice samples combined with a set of environmental covariates at the island scale were systematically analyzed. Arithmetic mean values (AMs) of Cd and Pb in rice were 0.080 and 0.199 mg kg-1, and exceeded the standard limits by 27.1% and 22.7%, respectively. We found that the AMs of Cd and Pb concentrations in paddy soil were 0.294 and 43.0 mg kg-1. Additionally, Cd in 29.26% of soil samples and Pb in 11.35% of soil samples exceeded the risk screening value for toxic elements. The enrichment factor generally showed that soil Cd and Pb on Hainan Island were both moderately enriched. Results obtained from both Spearman's correlation and stepwise regression analyses suggest that the concentrations of soil Cd and Pb are significantly influenced by the soil Na and Fe concentrations. Specifically, an increment of 1 g kg-1 in soil Na caused a rise of soil Cd and Pb by 57.1 mg kg-1 and 34.4 mg kg-1, respectively, while an increase of 1 g kg-1 in soil Fe resulted in a rise by 25.0 mg kg-1 and 14.5 mg kg-1. Similarly for rice grains, an increment of 1 g kg-1 in soil Ca resulted in a rise of rice Pb by 30.8 mg kg-1, whereas an increase of 1 g kg-1 in soil Mg led to a decrease in rice Pb by 14.8 mg kg-1. However, no significant correlation between soil Se and rice Cd concentrations was found. Furthermore, the result of geographically weighted regression revealed that the impacts of soil Na, Ca, Fe, and Mg on rice Cd were more significant in the western region, whereas the effects of soil Na and Fe on rice Pb were stronger in the northeastern region. This study provides new insights for the identification of factors influencing the distribution and accumulation of Cd and Pb in tropical island agroecosystems.

Spatial distribution, sources, and risk assessment of potentially toxic elements in cultivated soils using isotopic tracing techniques and Monte Carlo simulation

Potentially toxic elements (PTEs) in cultivated lands pose serious threats to the environment and human health. Therefore, improving the understanding of their distinct sources and environmental risks by integrating various methods is necessary. This study investigated the distribution, sources, and environmental risks of eight PTEs in cultivated soils in Lishui City, eastern China, using digital soil mapping, positive matrix factorisation (PMF), isotopic tracing, and Monte Carlo simulation. The results showed that Pb and Cd are the main pollutants, which posed higher ecological risks in the study area than the other PTEs. Natural, mining, traffic, and agricultural sources were identified as the four determinants of PTE accumulation via a PMF model combined with Pearson correlation analysis, showing that their contribution rates were 22.6 %, 45.7 %, 15.2 %, and 16.5 %, respectively. Stable isotope analysis further confirmed that local mining activities affected the HM accumulation. Additionally, non-carcinogenic and carcinogenic risk values for children were 3.18 % and 3.75 %, respectively, exceeding their acceptable levels. We also identified that mining activities were the most important sources of human health risks (55.7 % for adults and 58.6 % for children) via Monte Carlo simulations coupled with the PMF model. Overall, this study provides insights into the PTE pollution management and health risk control in cultivated soils.