Microplastics (MPs) are abundant in soil and the subsurface environment. They can co-transport with pathogens or act as vectors for pathogens, potentially causing severe ecological harm. The interaction of MPs with pathogens is an important topic. To describe the origins and features of MPs in the subsurface environment, we evaluated relevant studies conducted in the laboratory and field groundwater habitats. We explore the interactions between pathogens and microplastics from three perspectives including the respective physicochemical properties of microplastics and pathogens, external environmental factors, and the binding between microplastics and pathogens. The effects of some interaction mechanisms and environmental factors on their co-transport are discussed. The key factors affecting their interaction are the particle size, specific surface area, shape and functional groups of MPs, the zeta potential and auxiliary metabolic genes of pathogens, and the hydrophobicity of both. Environmental factors indirectly affect MPs and the interaction and co-transport process of pathogens by changing their surface properties. These findings advance our knowledge of the ecological behavior of MPs–pathogens and the associated potential health hazards.

Pump-and-treat (P&T) techniques are among the most commonly applied methods for groundwater contamination remediation. This study develops a groundwater flow model and a solute transport model using MODFLOW and RT3D to design and optimize P&T strategies for a polluted chemical park in northern China. Five scenarios were analyzed to evaluate the effects of well placement and pumping rates on remediation performance. Simulation results indicate that the existing number of extraction wells is inadequate to meet remediation targets. While increasing the pumping rate of individual wells accelerates remediation, it also increases the total extracted water volume. Similarly, higher extraction rates in heavily contaminated areas shorten remediation times but lead to greater water extraction. The findings demonstrate that numerical modeling can effectively optimize P&T designs, enabling the minimization of water extraction volumes while achieving remediation objectives within the required timeframe.

This study develops an integrated framework for groundwater pollution source identification by coupling Principal Component Analysis (PCA), Positive Matrix Factorization (PMF), and the Mantel test, with the Qujiang River Basin as a case study. The framework enables a full-process assessment, encompassing qualitative identification, quantitative apportionment, and spatial validation of pollution drivers. Results indicate that groundwater chemistry is primarily influenced by three categories of sources: natural rock weathering, agricultural and domestic activities, and industrial wastewater discharge. Anthropogenic sources account for 73.7% of the total contribution, with mixed agricultural and domestic inputs dominating (38.5%), followed by industrial effluents (35.2%), while natural weathering contributes 26.3%. Mantel test analysis further shows that agricultural and domestic pollution correlates strongly with intensive farmland distribution in the midstream area, natural sources correspond to carbonate outcrops and higher elevations in the upstream, and industrial contributions cluster in downstream industrial zones. By integrating PCA, PMF, and Mantel analysis, this study offers a robust and transferable framework that improves both the accuracy and spatial interpretability of groundwater pollution source identification. The proposed approach provides scientific support for regionalized groundwater pollution prevention and control under complex hydrogeological settings.

Groundwater pollution risk assessment is a useful tool for groundwater pollution prevention and control. However, it is difficult to accurately quantify groundwater flow and contaminant fluxes in karst areas and different types of karst areas have different hydrogeological characteristics. Therefore, the assessment of groundwater pollution risk in karst areas must use different assessment indicator systems. This study developed a new methodology that modified the vulnerability assessment model PLEIK, determined pollutant fluxes considering hydrogeological conditions, and revised parameter weights using the random forest method. The resulting PLEIKD-RF model was used to assess the risk of groundwater contamination in the southwestern karst region and its validity was verified. The results showed that the groundwater pollution risk in the region was low, with 65.64% of the low and relatively low risk areas located in the middle and high mountainous regions. 11.81% of the high and relatively high risk areas were sporadically located in the western and central regions, which were mainly controlled by the distribution of the pollution sources and the karst development. The accuracy of the results of groundwater pollution risk assessment in the study area was 71.87% as verified by the horizontal difference method. The results of the sensitivity analysis indicated that accurate, detailed, and representative data on the protective layer, surface water-groundwater interactions, and pollution source loads would improve the accuracy of groundwater pollution risk zoning. This assessment method provided a reference for similar assessments and the results provide a basis for the protection and management of groundwater resources in the region.