IntroductionGroundwater is the principal source of drinking and irrigation water in semi-arid hard-rock regions, where natural hydrogeochemical processes and anthropogenic activities strongly influence water quality. However, comprehensive studies integrating hydrochemical characterization, multivariate statistical analysis, geochemical modelling, irrigation suitability assessment, and geospatial analysis remain limited for the crystalline aquifers of southern India.MethodsThis study presents an integrated framework to evaluate groundwater quality, hydrogeochemical evolution, and irrigation suitability in Warangal District, Telangana, India. A total of 58 groundwater samples collected during the pre-monsoon season were analyzed for physicochemical parameters. Groundwater quality was assessed using the Water Quality Index (WQI), irrigation water quality indices, Piper and Gibbs diagrams, ionic ratio analysis, PHREEQC saturation index modelling, GIS-based spatial interpolation, and multivariate statistical techniques, including Pearson correlation, Principal Component Analysis (PCA), Factor Analysis (FA), and Hierarchical Cluster Analysis (HCA).ResultsGroundwater exhibited hydrochemical variability, with pH ranging from 6.52 to 8.95, electrical conductivity from 0.26 to 3.29 dS m-1, and total dissolved solids from 166.4 to 2105.6 mg L-1. The dominant Na–HCO3 hydrochemical facies indicates that groundwater evolution is mainly controlled by rock–water interaction, silicate weathering, and ion exchange. WQI indicated that most groundwater samples were poor to unsuitable for drinking, whereas the majority remained suitable for irrigation despite localized salinity and sodium hazards. Combined PCA, FA, HCA, ionic ratio analysis, and PHREEQC modelling identified mineral weathering, ion exchange, and localized agricultural activities as the principal controls on groundwater evolution.Discussion and ConclusionThe novelty of this study lies in integrating hydrochemical, geospatial, statistical, and geochemical modelling into a unified framework that identifies groundwater quality hotspots, distinguishes geogenic and anthropogenic influences, and provides a scientific basis for groundwater monitoring, irrigation management, and sustainable water-resource planning in semi-arid hard-rock aquifers.
Compound hydroclimatic extremes challenge water management worldwide due to their complex and nonlinear hydrological impacts. Drought-to-flood (DtF) events occur when prolonged dry periods are followed by rapid wet anomalies with distinct impacts from regular floods. Such events occur across diverse hydroclimatic settings; in heterogeneous snow-fed basins, DtF behaviour is governed by precipitation variability, antecedent soil-moisture conditions, snow processes, and, where present, reservoir regulation. This study investigates DtF transitions across six sub-catchments of the Narayani River Basin, Nepal, using monthly precipitation (17 stations), streamflow (6 gauges), remote sensing soil moisture, and historical drought–flood records. Historical DtF events were fitted with monthly Standardized Precipitation Index (SPI) signals, and sensitivity analysis of three ΔSPI thresholds (1.0, 1.25, and 1.5) identified 1.25 as the optimal criterion for distinguishing meaningful DtF transitions. The calibrated ΔSPI framework identifies DtF events and integrates ΔSPI–ΔSSI regression with multivariate Principal Component Analysis (PCA) of ΔSPI, ΔSSI, and ΔSWDI to investigate coupled hydroclimatic controls and process-level DtF dynamics. Results reveal pronounced spatial contrasts across hydroclimatic regimes. Highly snow-dominated Budhi Gandaki exhibits weak or opposing ΔSPI–ΔSSI responses during snow storage, followed by strengthened rainfall–runoff coupling during snowmelt (R2 up to 0.96). Moderately snow-influenced sub- catchments display mixed rainfall–runoff responses (R2 = 0.02–0.79), reflecting varying contributions of snowmelt and rainfall, whereas rainfed reservoir- regulated Tadi Khola and Rapti show weak pre-monsoon responses during reservoir filling (R2 = 0.001–0.96) and shift to strong monsoon coupling (R2 = 0.75–1.00) during reservoir release. PCA shows that PC1 explains 40%–100% of the joint ΔSPI–ΔSSI–ΔSWDI variance, with contrasting loading directions between precipitation, streamflow, and soil-moisture during decoupled periods, but increasingly aligned ΔSPI–ΔSSI loadings during stronger hydrological coupling. These patterns indicate that elevation and snow storage-melt timing regulate snow-influenced transitions, while antecedent soil-moisture conditions and reservoir operation control DtF behaviour in regulated systems, highlighting that precipitation anomalies alone are insufficient to explain observed transitions. The proposed framework provides a robust and transferable tool for detecting compound hydroclimatic extremes and supports early warning systems, reservoir management, and climate-resilient water planning in snow-affected, data-scarce mountain basins.
Plastic pollution in landfills presents a significant environmental challenge, as macroplastics fragment into persistent and harmful micro- and nanoplastics. Despite growing interest in plastitrophs, we know relatively little about how environmental conditions shape their community assembly and function. Among possible determinants, light exposure is likely consequential but remains poorly characterized. We examined how ambient light versus dark incubation conditions affect plastitrophic potential using 36 modified Winogradsky columns seeded with landfill soil from Portland, Connecticut United States of America. We incubated the columns with or without polyethylene (PE) sheets added, over 12 months, and we sampled across four stratified layers for 16S rRNA gene sequencing and targeted metagenomic analysis of PE strips. Light exposure promoted the enrichment of photoautotrophic Cyanobacteriota and Chloroflexota, while dark conditions favored anaerobic Bacillota and Bacteroidota. Metagenomic analysis revealed strong enrichment of light-responsive pathway genes, including circadian rhythm mechanisms, phototransduction, and pigment biosynthesis. In the replicated 16S dataset, species richness and Shannon diversity did not differ significantly between light and dark or between plastic and soil. Higher diversity under light appeared only in the smaller, exploratory metagenomic dataset (n = 3 per condition). Putative plastizyme genes, identified by name matching to curated databases, showed higher abundance under light, with oxidoreductases showing the strongest increase. Our findings reveal that light exposure enriches plastitrophic communities, light-responsive pathway genes and degradation-associated functional potential. This work provides foundational insight into how natural environmental factors such as light shape the genetic potential for oxidative transformation of polyethylene in terrestrial plastispheres.
IntroductionAir pollution has become a critical global health and environmental concern due to rapid urbanisation and industrialisation. Making accurate prediction of air quality has became crucial for effective prevention and control. Machine learning offers a robust approach by capturing complex pollutant interactions, enabling precise and timely air pollution prediction. However, existing approaches lack temporal-based chronological splitting, which aids in addressing bias in the data. Further, these AQI prediction methods are inadequate in handling missing data prior to imputation.MethodsThe proposed framework aims to integrate pre-processing and AQI prediction on Central Pollution Control Board dataset of India. Further, this work ensures the statistical validity of the imputation and adopts strict chronological validation on the benchmarking models to predict AQI. The dataset is formally analysed using statistical mechanisms such as MCAR, MNAR, and MAR, and the imputation methods such as mean, KNN, MICE, GAIN, and linear interpolation. Feature engineering is used to increase the number of features from 12 to 24 by adding rolling, lag, and temporal features. The data is split into 80:20, in strictly chronological order for the prediction models such as CatBoost, XGBoost, LightGBM, and Random Forest, using the metrics MAE, R2, and RMSE.ResultsAmong the four models, CatBoost achieves the strongest overall performance, with the MAE of 10.94, RMSE of 16.93, and R2 of 0.96. Season-stratified evaluation reveals that summer is the most predictable season, with the RMSE of 11.25 and an R2 of 0.92. DiscussionThe integration of imputation methods and adoption of strict chronological validation for AQI prediction establishes a temporally validated benchmark across multiple cities in India. This enables policymakers to implement season-specific emission controls, equips with timely health awareness and supports environmental agencies in monitoring and regulating pollution.
Existing research on emerging-contaminant removal has focused on urban water treatment plants or single contaminant classes under laboratory conditions. However, resource-limited mountainous rural water supplies, where conventional pollutants and trace emerging contaminants—per- and polyfluoroalkyl substances (PFAS) and antibiotics—co-occur as a composite pollution burden, cannot be adequately managed by the existing infrastructure. This study adopts a rural-systems perspective, evaluating a full-scale before-after upgrade at a small mountainous rural plant in northeastern Chongqing to determine whether cost-appropriate process intensification can simultaneously resolve conventional noncompliance and reduce emerging-contaminant loads. We quantified the simultaneous removal of five PFAS and nine antibiotics and examined potential surrogate relationships between emerging contaminants and routine organic indicators. The upgraded process achieved effluent turbidity (0.22 ± 0.03 NTU) and reduced total PFAS from 10.8-11.3 to 3.06 ± 0.60 ng/L (72.9%), compared with 10.7% removal by the conventional process. Long-chain PFAS removal exceeded 83%, whereas short-chain compounds reached 53%–60%. Total antibiotic removal improved from 23.4% to 82.6%. After the upgrade, the total concentrations of PFAS and antibiotics showed positive correlations with UV absorbance at 254 nm, dissolved organic carbon, and permanganate index along the treatment train. These findings provide preliminary, site-specific evidence and a reference for similar rural water supply scenarios, although broader validation across multiple sites and seasons is warranted.
