Chinese Journal of Environmental Engineering•2026•DOI: 10.12030/j.cjee.202508050
This study investigated the spatial distribution and ecological risk of heavy metals (As, Cd, Cr, Cu, Ni, Pb, Zn) in soil beneath an informal waste dump in a pastoral area of Baingoin County, Nagqu City, Tibet, a high-altitude cold region with frequent freeze-thaw cycles. A total of 55 soil samples were collected from surface (0 cm), middle (10-30 cm), and deep (50 cm) layers. Single-factor index (Pi), geo-accumulation index (Igeo), Nemerow index (PN), and risk assessment code (RAC) were employed to evaluate contamination levels and potential ecological risks, while Kriging interpolation was used to map spatial distribution. Results showed that average concentrations of all seven heavy metals exceeded local background values. Horizontally, high-concentration zones were mainly located at five points within the dump. Vertically, Cd, Cu, Pb, and Zn were significantly enriched in the surface layer, whereas Ni exhibited higher concentrations in deeper layers, indicating downward migration driven by freeze-thaw processes. All evaluation methods identified Cd as the primary pollutant. Speciation analysis revealed that heavy metals were predominantly in the residual fraction, with Ni having the highest weak-acid-extractable fraction (5.55%), indicating strong mobility and potential biological toxicity. This study fills a gap in systematic research on informal waste dumps in high-altitude ecologically fragile areas and provides a case reference for environmental management and remediation of such sites in cold regions.
Journal of Environmental Engineering Technology•2026•DOI: 10.13205/j.hjgc.202607001
Co-combustion of municipal solid waste (MSW) and sewage sludge (SS) offers a promising route for synergistic waste management, yet pollutant release dynamics and environmental trade-offs remain inadequately characterized. This study systematically investigated the combustion behavior, pollutant emissions, and environmental impacts of MSW-SS blends at 850, 950, and 1050 °C with varying SS mass fractions (0–100%). Machine learning models, particularly artificial neural networks (ANN), were optimized to predict pollutant generation, and SHAP analysis identified key influencing factors. Results demonstrated that combustion temperature and blending ratio significantly affected burnout efficiency, with temperature exerting a more pronounced effect. An SS proportion of 20% yielded favorable combustion performance. Among pollutants, N2O and C2H4 emissions were significantly influenced by temperature, blending ratio, and their interaction, indicating high sensitivity to operating conditions. CO and C6H6 were primarily affected by blending ratio, while C7H8 responded to both temperature and blending ratio. N2O and CH4 were predominantly released during the initial combustion stage; elevated temperatures markedly suppressed N2O formation, and co-combustion generally reduced CH4 emissions. A 20% SS blend effectively reduced SO2 emissions, and NO synergistic reduction was optimal at 950 °C. Emissions of CO, C2H4, C6H6, and C7H8 exhibited antagonistic behavior under co-combustion. The ANN model accurately predicted pollutant concentrations, with combustion temperature, volatile matter, and fixed carbon content identified as critical factors. Environmental impact assessment revealed that higher temperatures reduced global warming potential (GWP) and photochemical ozone creation potential (POCP), while lower MSW proportions decreased POCP but increased GWP and acidification potential (AP). Integrating combustion performance, pollutant release, and environmental impacts, an SS proportion of 20% is recommended for optimized co-combustion.