Landfill leachate contains a range of organic and inorganic pollutants, including per-/polyfluoroalkyl substances (PFASs), which can infiltrate into surrounding soil and groundwater through leaching processes, and can pose a threat to human health via food chains and drinking water processes. Thus, the transport of PFASs in landfill leachate is a research hotspot in environmental science. This study investigates the complexation and adsorption mechanisms between humic substances and PFASs in landfill leachate at the molecular level. Experimental results demonstrate that the binding constant logK_sv of humic substances with PFASs correlates positively with specific ultraviolet absorbance (SUVA₂₅₄), absorbance ratio (A₂₅₀/A₃₆₅), humification index (HIX), and fluorescence index (FI), while it exhibits a negative correlation with the biological index (BIX). These findings indicate that high aromaticity is a prerequisite for molecular interactions between humic substances and PFASs, with polar functional groups further facilitating the interaction. Molecular-level analysis revealed that humic substances undergo complexation and adsorption with PFASs through hydrophobic interactions, van der Waals forces, hydrogen bonding, ionic bonding, and covalent bonding, by functional groups such as hydroxyl, aliphatic C-H bonds, aromatic C=C double bonds, amides, quinones, and ketones. Future efforts should focus on enhanced co-regulation and mitigation strategies addressing the combined pollution of PFASs and humic substances in landfill leachate.

Hydrochemical data were utilized in this study to elucidate the hydrogeochemical characteristics and genesis of high-arsenic groundwater in the North China Plain, employing both traditional hydrogeochemical analysis and unsupervised machine learning techniques. The findings indicate that the predominant hydrochemical types of groundwater in the study area are HCO₃-Ca·Na and SO₄·Cl-Na·Ca. The primary mechanism influencing groundwater chemistry has been identified as rock weathering. The unsupervised machine learning framework incorporates various methods, such as principal component analysis (PCA), non-negative matrix factorization (NMF), machine learning models (gradient boosting trees and random forests), and cluster analysis to explore the characteristics and genesis of groundwater hydrochemical types within the study area. This study demonstrated that the formation mechanism of high-arsenic groundwater results from multiple interacting factors.

The remediation of contaminated sites necessitates robust and objective sustainability assessment frameworks to guide decision-making, yet prevailing methods often rely on qualitative or semi-quantitative metrics susceptible to subjectivity. This study establishes a comprehensive, fully quantitative evaluation system integrating environmental, economic, and social dimensions, comprising 13 objective indicators derived from Life Cycle Assessment (LCA), economic documentation, and publicly accessible social data—including nighttime light intensity, Point of Interest (POI) data, and social media sentiment analysis. The system employs the Analytic Hierarchy Process (AHP) for weight assignment, ensuring methodological rigor and expert consensus. Validated through three case studies of remediated contaminated sites in Shandong Province, China, the framework reveals distinct sustainability profiles: Site 1 achieved the highest composite score (0.1030), demonstrating balanced performance across all dimensions, whereas Sites 2 and 3 yielded negative scores (−0.2490 and −0.1069, respectively), reflecting trade-offs between remediation efficiency, secondary environmental impacts, and socio-economic outcomes. The key findings underscore the dominance of environmental health indicators—notably Disability-Adjusted Life Years (DALYs)—in overall weighting and highlight the critical influence of remediation technology selection on lifecycle impacts. The study validates the utility of a quantitative, multi-criteria approach in identifying optimal remediation strategies, facilitating cross-project comparability, and supporting the transition from cost-centric remediation toward value-driven, sustainable redevelopment.

Per- and polyfluoroalkyl substances (PFASs) presented in landfill leachate can be transported to groundwater and soil, posing risks to human health in food and water. However, the occurrence characteristics of PFASs in landfill leachate and the influence paths of abiotic and biotic factors have not been fully discussed. Our study found that the detection rate of PFASs in the leachate samples was 100%; ΣPFASs ranged from 1328 ng/L to 37,447 ng/L; and the average ΣPFASs was 9593 ng/L. Most of the physical and chemical indexes in the landfill leachate showed a significant positive correlation with PFASs concentration, with the main physical and chemical factors including TOC, sodium ion, chromium ion, and chloride ion. Moreover, dissolved organic matter had a very important influence on the environmental behavior of PFASs. The degree of dissolved organic matter (DOM) humification promoted the transportation of PFBA and PFBS from the landfill waste to leachate, while microbial DOM inhibited the transportation of ∑₁₃ PFCAs. The microorganisms in the leachate were mainly Firmicutes, Campylobacterota and Proteobacteria, among which there was a negative correlation between PFBS and Proteobacteria, indicating that PFBS was highly toxic to Proteobacteria and would inhibit the growth of Proteobacteria in leachate. Firmicutes and Campylobacterota had little influence on PFASs. However, Synergistota, and Halanaerobiaeota, which had a low abundance, both positively correlated with the various PFASs. This result may imply that these rare microphyla are the main microphyla driving the transformation of PFASs in leachate. Microorganisms in leachate indirectly affected the occurrence of PFASs, mainly by influencing the environmental factors in leachate. Therefore, abiotic factors are important factors affecting PFASs in the landfill leachate. In summary, PFASs pollution management in landfills should be enhanced by regulating abiotic factors to control PFASs in leachate.