Driven by the rapid advancement of the economy and urbanization, substantial alterations in land use patterns have taken place, exerting certain impacts on groundwater. This study examines the land use changes in Zhengzhou’s central urban area from 2000 to 2020 and projects these changes to 2030 using the PLUS model. It optimizes the groundwater vulnerability assessment methodology from two key aspects, namely the evaluation indicators and the associated weights, to enhance its suitability for the study area. This study employs a multi-indicator and dual-method validation approach to verify the groundwater vulnerability assessment results, ensuring the accuracy and reliability of the findings. Urban, rural, and construction lands increased significantly, while paddy fields, drylands, and forests decreased. The 2030 prediction suggests a continuation of these trends. The groundwater vulnerability in 2020 correlated strongly with the groundwater quality, particularly with chloride ions (AUC = 0.804, Spearman’s rho = 0.83). The 2030 projection indicates a minimal change in the vulnerability distribution but anticipates an increase in high- and very-high-vulnerability areas, particularly in regions with land use changes, potentially increasing the groundwater contamination risk. This suggests the need for targeted groundwater protection policies to mitigate contamination risks.

Nanoscale zero-valent iron (nZVI) has been broadly applied in the remediation of heavy metals pollution. In this research, the toxicity characteristic leaching procedure (TCLP), the in vitro gastrointestinal (IVG) method, and the diffusive gradients in thin-films (DGT) technique were used to evaluate the effects of heavy metals in soil with remediation by nZVI. The results indicate that, compared with the dose of 0.5 g·L⁻¹, the nZVI in the dose of 1.0 g·L⁻¹ can remove the heavy metals in the soil. The leaching toxicities of the heavy metals (Cr, Cu, Zn, Pb) showed apparent decreases after the remediation by nZVI. In the gastric phase, the highest bioaccessibility values of the Cr, Cu, Zn, Pb were decreased by 27.2, 31.7, 11.7, and 20.1%, respectively. Moreover, in the gastric phase, the highest bioaccessibility values of the Cr, Cu, Zn, Pb were decreased by 5.5, 1.29, 8.0, and 7.3%, respectively. The availabilities of the heavy metals were also reduced. The above results show that the nZVI effectively reduced the heavy metal pollution in the soil.

This study evaluates the accuracy of various Geographic Information System interpolation methods in predicting the stratified spatial distribution of organic pollutants (Benzene, Total Petroleum Hydrocarbons [TPH], and Methyl Tert-butyl Ether [MTBE]) in groundwater at a petrochemical-contaminated site. Given the limitations of traditional monitoring methods in predicting spatial distribution, this study focuses on the spatial computational prediction of volatile organic compound concentrations at a former petrochemical industrial site. Three interpolation methods—Inverse Distance Weighting (IDW), Radial Basis Function (RBF), and Ordinary Kriging (OK)—were applied and evaluated. Prediction accuracy was assessed using leave-one-out cross-validation, with performance quantified through key metrics: Root Mean Square Error, Coefficient of Determination, and Spearman’s Rank Correlation Coefficient. Results demonstrate significant variations in optimal prediction methods depending on pollutant type and depth stratum. For pollutants predominantly enriched in shallow and middle layers (Benzene, TPH), OK yielded the highest accuracy and stability. Conversely, for predictions of pollutants primarily concentrated in deeper layers, RBF achieved superior performance. IDW consistently underperformed across all strata and pollutants. All interpolation methods generally exhibited systematic overestimation of pollutant concentrations (mean cross-validation error > 0). Through a hierarchical evaluation of the accuracy and interpolation effectiveness of these methods, this study develops a more accurate modeling framework to describe the composite groundwater contamination patterns at petrochemical sites. This study systematically evaluates the spatial prediction accuracy of various non-aqueous phase liquid species under differing groundwater-table depths, identifies the most robust interpolation method, and thereby provides a benchmark for enhancing predictive fidelity in subsurface contaminant mapping.