Heavy metal(loid) hazards in landfill groundwater: An assessment using self-organizing maps and 2D Monte Carlo modeling - Vesković, Jelena; Onjia, Antonije (Journal of hazardous materials, 2025)
Landfill-derived heavy metal(loid) (HM) contamination of groundwater is a pressing environmental issue, yet comprehensive assessments in such settings remain limited. This study investigated HM pollution, hydrogeochemistry, irrigation suitability, and corrosion/scaling potential in groundwater surrounding the Vinča municipal solid waste landfill, one of the largest in Southeast Europe. Concentrations of Fe and Mn exceeded drinking water standards in 50 % and 77 % of samples, respectively, while other metals remained within permissible limits. The dominant hydrochemical facies was Ca-Mg-HCO3, shifting toward Ca-Mg-SO4-Cl near the landfill. Irrigation quality was generally acceptable, with sodium adsorption ratio (SAR) and sodium percentage (%Na) ranging from 0.42 to 14.1 and 5.8–84.9 %. Scaling indices indicated a tendency toward scale formation, particularly in the northeastern sector. Self-organizing maps (SOM) classified samples into four distinct clusters, corresponding to different HM sources. The heavy metal pollution index (HPI) suggested low to moderate pollution, primarily driven by Cd and Hg. Combined two-dimensional Monte Carlo simulation (2D MCS) and multiple linear regression identified threshold concentrations for Cd (1.8 μg/L) and Hg (0.11 μg/L) necessary to maintain HPI values within the low-pollution category. This integrated approach offers a framework for establishing protective concentration limits to improve groundwater management. [Display omitted] •Landfill-derived heavy metal(loid)s pose significant risks to groundwater.•Elevated Fe and Mn contents were detected in groundwater near the Vinča landfill.•Groundwater quality ranged from excellent to good for irrigation purposes.•Predominant scaling tendencies were observed in groundwater chemistry.•Thresholds for Cd and Hg were derived using 2D Monte Carlo combined with regression.