During the natural cooling process of magnesium slag stockpiles in the open air, the phase transformation of gamma-dicalcium silicate (γ-C₂S) induces a powdering phenomenon, resulting in the generation of a large amount of PM10 and PM2.5 dust. Based on the dust emission model of stockpiles and the Gaussian dispersion model, combined with the Monte Carlo simulation method, this study conducted a quantitative assessment of the environmental risk of heavy metals (Pb, Cd, Hg, As, Cr(VI)) in dust to the surrounding residential areas. The results show that the enrichment degree of heavy metals in PM2.5 is significantly higher than that in PM10. At a downwind distance of 1000 m, the exceedance multiples of Cr(VI), As, and Cd reach 131.5, 23.6, and 51.8 times, respectively. The total carcinogenic risk (9.2 × 10⁻⁷) and total non-carcinogenic hazard quotient (0.15) in the residential area are below the limits, but the contribution rates of As and Cd are relatively high. Sensitivity analysis further reveals that the moisture content of the stockpile, dust removal rate, and distance are the key control parameters affecting the environmental risk. Based on the research findings, it is recommended to increase sprinkling frequency, install windbreak nets, and promote magnesium slag utilization to effectively control dust risks.

Groundwater nitrate (NO₃⁻) pollution is a critical environmental challenge with direct implications for human health. In this work, we propose a comprehensive analytical framework that integrates multi-model intercomparison, interpretable machine learning techniques, and quantitative health risk evaluation to tackle the pressing groundwater nitrate governance dilemmas in Handan City, a representative urban area in North China. Based on 157 groundwater samples and 17 hydrochemical parameters, comparative analysis of three state-of-the-art machine learning algorithms showed that the Light Gradient Boosting Machine (LightGBM) algorithm outperformed all counterparts, delivering the optimal predictive performance (R² = 0.753, RMSE = 3.67). SHapley Additive exPlanations (SHAP) analysis identified F⁻, Ca²⁺, Cl⁻, K⁺, total hardness, and Mg²⁺ as dominant factors influencing groundwater NO₃⁻ concentrations, reflecting the combined effects of carbonate dissolution, nitrification, and anthropogenic inputs. Subsequently, we performed a health risk assessment based on the standard methodological framework issued by the United States Environmental Protection Agency (USEPA), and the results indicated that children were the most vulnerable group, with hazard quotient (HQ, a non-carcinogenic risk indicator) values reaching 1.07 in the western mountainous region, exceeding the safety threshold (HQ > 1). These findings clarify the pollution mechanisms and spatial heterogeneity, and provide targeted policy guidance for groundwater protection as well as the safeguarding of public health.

Surfactant-enhanced soil washing is a promising strategy for the remediation of organochlorine pesticide (OCPs) contaminated sites. In this study, we constructed a comprehensive evaluation framework integrating efficient parameter optimization, effluent recovery and ecological restoration assessment. Among the 14 evaluated washing agents, the non-ionic surfactant Triton X-100 exhibited superior solubilization capacity for highly hydrophobic OCPs. Under an optimal dosage of 2.0%, Triton X-100 achieved near-complete extraction of γ-chlordane and over 75% removal of mirex in both moderately and severely contaminated soils. Powdered activated carbon (PAC) demonstrated exceptional selective adsorption performance, significantly outperforming activated carbon fiber (ACF). The optimal PAC dosages (20 g/L) could extract over 90% of OCPs from the soil washing effluents, facilitating potential washing agent recycling. Furthermore, community-level physiological profiling (BIOLOG-AWCD) revealed distinct ecological trajectories post-washing. While nitrogen and phosphorus (N/P) bio-stimulation successfully restored and even surpassed the microbial diversity in moderately contaminated soils, it only partially alleviated the ecological vulnerability in severely contaminated soils (Simpson index < 0.45). These findings underscore that while surfactant-enhanced soil washing combined with selective adsorption constitutes a powerful physicochemical remediation cycle, restoring heavily degraded microhabitats necessitates an integrated approach coupling bio-stimulation with phytoremediation.

China is the world’s largest producer and consumer of mineral resources. While large-scale development of metal mineral resources has supported rapid national economic development, it has also caused serious heavy metal pollution in soils in mining areas. This paper systematically reviews the latest research progress on heavy metal pollution in soils in mining areas, focusing on Chinese metal mining areas, covering pollution characteristics, source identification, risk assessment, and sustainable remediation technologies. Meta-analysis shows that smelting sources contribute more than 50% of soil Cd in southwestern Pb-Zn mining areas, making them priority targets for risk management. This review aims to provide theoretical support and decision-making references for precise source tracing, scientific assessment, and green remediation of heavy metal pollution affecting soils in Chinese metal mining areas.