The determination of changes in hydrogeological properties (e.g., permeability and specific storage) of aquifers disturbed by mining activity is significant to groundwater resource and ecological environment protection in coal mine areas. However, such parameters are difficult to continuously measure in situ using conventional hydrogeological methods, and their temporal changes associated with coal mining are not well understood. The response of well water level to Earth tides provides a unique probe to determine the in situ hydrogeological parameters and their variations. In this study, the tidal responses of well water level were employed to characterize the changes in hydrogeological parameters of the overburden aquifer induced by longwall mining in a coalfield, northwest China. Based on the long-term hourly recorded water level data, two analytical models were used to determine the temporal changes of permeability and specific storage of the overburden aquifer. The results showed that the hydrogeological parameters changed with the longwall coal face advance. When the longwall coal face approached the wells, the aquifer permeability increased several to dozens of times, and the response distance ranged from 80 m to 300 m. The specific storage decreased before the coal face reached wells and recovered after the coal face passed. The results of this study indicate that the hydrogeological parameter changes induced by coal mining are related to the location of the well relative to the coal face and the stress distribution in the overburden aquifer. This study revealed the changes in permeability and specific storage associated with the mining disturbance which could have great significance for quantitative assessment of the impact of mining on overburden aquifer.

Karst groundwater systems are characterized by highly heterogeneous flow networks, resulting in complex responses of groundwater levels to precipitation variability. The Jinan Spring Basin, one of the most representative karst spring systems in northern China, has experienced substantial changes in spring discharge due to variations in precipitation, groundwater exploitation, and hydrogeological conditions. However, the temporal scales at which precipitation signals control groundwater-level fluctuations and the mechanisms governing their transmission within the karst aquifer remain poorly understood. In this study, daily precipitation data from 30 meteorological stations and groundwater-level records at Baotu Spring during 2016–2018 were analyzed using global wavelet spectrum (GWS) and wavelet transform coherence (WTC) approaches. The dominant precipitation cycles, scale-dependent precipitation–groundwater relationships, and phase-derived groundwater response lags were quantified to reveal the hydrological response characteristics of the Jinan karst system. Three prominent precipitation periods were identified at 17.37, 29.22, and 330.57 days. Groundwater responses presented clear temporal-scale dependence, with short-period signals showing rapid but localized responses, while intermediate and long-period signals demonstrated stronger and more persistent coherence. The percentage of significant coherence area (PASC) increased from 23.50% at the 0–17.37 day scale to 64.28% at the 29.22–330.57 day scale, indicating that accumulated precipitation rather than individual rainfall events exerts the dominant control on groundwater-level variations. The spatial distribution of response lags revealed that rapid responses (17.37 days) mainly occurred in the southern recharge areas, reflecting preferential recharge through well-developed karst conduits. Intermediate responses (29.22 days) showed a progressive increase in lag time from south to north, indicating the influence of regional groundwater flow and aquifer storage. Long-period responses (330.57 days) were locally enhanced near major faults, suggesting structural controls on deeper groundwater circulation. This study reveals that precipitation signals in the Jinan Spring Basin are transmitted through multiple groundwater circulation pathways with distinct temporal characteristics. The identified multi-scale response patterns provide new insights into the internal structure and hydrological functioning of karst aquifers and offer scientific support for sustainable management of spring water resources.

Comprehensive monitoring of per- and polyfluoroalkyl substances (PFASs) in drinking water sources is challenging because sampling intensity, analytical coverage, reporting limits, and well construction vary among monitoring programs. We developed a machine learning pipeline to characterize environmental and pollution source susceptibility to PFAS occurrence in municipal and public supply groundwater wells across California. The assembled covariate inventory contained 7951 public supply wells; 7938 wells contributed 60,744 first-classifiable well–compound records for the eight-compound modeling analysis. Occurrence was defined as a concentration greater than 2 ng/L at the first-classifiable test for each well–compound pair. Eight compounds met prespecified support criteria: PFOS, PFHxS, PFOA, PFBS, PFHxA, PFPeA, PFBA, and PFHpA. Separate binary classifiers were evaluated using five spatially blocked folds and a forward temporal test for first-tested wells from 2024 onward. Across the eight compounds, calibrated Extra Trees achieved a mean spatial ROC-AUC of 0.722 and a forward temporal ROC-AUC of 0.669. Mean PR-AUC was 0.350 for spatial validation (mean positive rate, 16.2%) and 0.223 for forward temporal validation (mean positive rate, 9.6%), which indicates moderate discrimination that supports ranking wells for confirmatory sampling but not classifying individual wells. The spatial ensemble generated 1 km screening surfaces for PFOS, PFOA, and PFHxS with explicit model support and extrapolation diagnostics. These surfaces represent susceptibility under a typical public supply well scenario and are intended to prioritize confirmatory monitoring, not establish contamination at unsampled wells, attribute occurrence to specific facilities, or validate 1 km positional accuracy.