Spatial-temporal variation, source apportionment and risk assessment of lead in surface river sediments over ∼20 years of rapid industrialisation in the Pearl River Basin, China

Accurate prediction of spatial distribution of soil potentially toxic elements using machine learning and associated key influencing factors identification: A case study in mining and smelting area in southwestern China

Accurate prediction of spatial distribution of potentially toxic elements (PTEs) is crucial for soil pollution prevention and risk control. Achieving accurate prediction of spatial distribution of soil PTEs at a large scale using conventional methods presents significant challenges. In this study, machine learning (ML) models, specially artificial neural network (ANN), random forest (RF), and extreme gradient boosting (XGB), were used to predict spatial distribution of soil PTEs and identify associated key factors in mining and smelting area located in Yunnan Province, China, under the three scenarios: (1) natural + socioeconomic + spatial datasets (NS), (2) NS + irrigation pollution index (IPI) datasets, (3) NS + IPI + deposition (DEPO) datasets. The results highlighted the combination of NS+IPI+DEPO yielded the highest predictive accuracy across ML models. Particularly, XGB exhibited the highest performance for As (R2 =0.7939), Cd (R2 =0.6679), Cu (R2 =0.8519), Pb (R2 =0.8317), and Zn (R2 =0.7669), whereas RF performed the best for Ni (R2 =0.7146). The feature importance and Shapley additive explanation (SHAP) analysis revealed that DEPO and IPI were the pivotal factors influencing the distribution of soil PTEs. Our findings highlighted the important role of DEPO in spatial distribution prediction of soil PTEs, which has often been ignored in previous studies.

Pollution control performance of solidified nickel-cobalt tailings on site: Bioavailability of heavy metals and microbial response

There has been increasing attention given to nickel-cobalt tailings (NCT), which pose a risk of heavy metal pollution in the field. In this study, on site tests and sampling analysis were conducted to assess the physical and chemical characteristics, heavy metal toxicity, and microbial diversity of the original NCT, solidified NCT, and the surrounding soil. The research results show that the potential heavy metal pollution species in NCT are mainly Ni, Co, Mn, and Cu. Simultaneous solidification and passivation of heavy metals in NCT were achieved, resulting in a reduction in biological toxicity and a fivefold increase in seed germination rate. The compressive strength of the original tailings was increased by 20 times after solidification. The microbial diversity test showed that the abundance of microbial community in the original NCT was low and the population was monotonous. This study demonstrates, for the first time, that the use of NCT for solidification in ponds can effectively solidification of heavy metals, reduce biological toxicity, and promote microorganism diversity in mining areas (tended to the microbial ecosystem in the surrounding soil). Indeed, this study provides a new perspective for the environmental remediation of metal tailings.

Accelerated export and transportation of heavy metals in watersheds under high geological backgrounds

The geological background level of metals plays a major role in mineral distribution and watershed diffuse heavy metal (HM) pollution. In this study, field research and a distributed hydrological model were used to analyze the distribution, sources, and pollution risk of watershed HMs in sediments with high geological HM backgrounds. Study showed that the mineral distribution and landcover promoted the transport differences of watershed HMs from upstream to the estuary. And the main sources of Co, Ni, and V in the estuarine sediments were natural sources. Sources of Pb and Zn were dominated by anthropogenic sources, accounting for 76% and 64% of their respective totals. The overall ecological risk of anthropogenically sourced HMs was dominated by Pb (46.6%), while the contributions of Co and Ni were also relatively high, accounting for 35.70% and 33.40%. Moreover, redundancy analysis showed that HM variations in the sediments were most sensitive to soil erosion and mineralizing rock distribution. The spatial patterns of watershed HMs from natural sources were significantly influenced by P loading, precipitation, and forest distribution. This combination of experiments and model improves the understanding of watershed HM variation and provides a new perspective for formulating effective watershed HM management strategies.