Mining activities significantly alter environmental conditions by increasing atmospheric aerosol loads and degrading water quality. This study employs Google Earth Engine (GEE) to analyze spatiotemporal anomalies in Aerosol Optical Depth (AOD), PM₂.₅ concentrations, chlorophyll-a levels, and turbidity in Tonk, Rajasthan, between 2016 and 2022. Data from Copernicus Atmosphere Monitoring Service (CAMS) and Landsat 8/9 Surface Reflectance (SR) were used to compute anomalies based on deviations from the 2000–2015 baseline. Results indicate a strong correlation between mining density and AOD (r = 0.82), PM₂.₅ (r = 0.69), chlorophyll-a (r = 0.76), and turbidity (r = 0.88), highlighting significant environmental degradation. Increased AOD and PM₂.₅ values were observed near active mining sites, confirming mining-induced dust emissions. Water quality analysis revealed elevated chlorophyll and turbidity anomalies, likely due to mining runoff, sedimentation, and nutrient enrichment leading to eutrophication. The findings emphasize the need for sustainable mining practices, stricter regulatory measures, and enhanced pollution monitoring to mitigate environmental degradation in the region.
Landslides present a critical hazard in the Himalayas, where steep topography, intense rainfall, and tectonic activity converge to destabilize slopes. Accurate delineation of high-susceptibility zones is essential to safeguard lives, infrastructure, and ecosystems. Here, we construct a comprehensive Landslide Susceptibility Map (LSM) for Uttarakhand, a landslide-prone state in northern India, by integrating advanced ensemble machine learning (ML) with explainable AI. Our analysis comprises 35 geo-environmental variables, ranging from historical landslide inventories and remote sensing data to GIS-based geomorphological, hydrological, and anthropogenic layers. We evaluate six ML models (Logistic Regression, Support Vector Machine, Random Forest, Extra Trees, Gradient Boosting, and eXtreme Gradient Boosting) before consolidating them into a stacking ensemble (SE), achieving an Area Under the Curve (AUC) of 0.987 on the training set and 0.979 on the test set. Across models, false-negative rates were low; Extra Trees minimized missed events (FNR = 3.5 %) but with a high false-positive rate (23.6 %), whereas XGBoost and the SE achieved a better sensitivity-specificity balance (FNR = 5.6 and 5.5 %, respectively) with comparatively lower false positives, favoring operational use. Spatial transferability to Sikkim was strong (Uttarakhand test accuracies 0.864-0.917; Sikkim 0.905-0.971), with XGBoost yielding the highest Sikkim test accuracy (0.971) and ensemble approaches (GB, XGBoost, SE) all exceeding 0.96, highlighting robust generalization across different Himalayan regions. Our ensemble model surpasses all individual models and classifies the study area into five susceptibility zones (very low to very high), with 18.20 % of Uttarakhand, particularly in Pithoragarh, Chamoli, and Rudraprayag districts, falling under high-susceptibility zones. Further interpretability is provided by SHapley Additive exPlanations (SHAP), which highlight key drivers of slope failure, including slope angle, fault proximity, and rainfall. Our findings highlight the value of combining robust ML techniques with geoscientific data, thereby enhancing hazard assessments and informing disaster risk reduction across the Himalayas and similarly vulnerable terrains worldwide.
The Vietnamese Mekong Delta (VMD), a cornerstone of national food security, is increasingly affected by salinity intrusion arising from the combined influences of upstream hydropower development, climate change, and sea-level rise. Despite growing attention to this issue, the long-term hydrological mechanisms shaping these changes remain insufficiently understood. This study examines freshwater–salinity dynamics along the Co Chien River over the period 2000–2024, applying nonparametric Mann–Kendall (MK) tests and Sen’s slope estimators to identify spatio-temporal trends, alongside a comparative assessment of hydrological variability between coastal and inland zones. Spearman correlation analysis is used to distinguish the relative contributions of climatic variability and upstream hydrological regulation. The findings indicate a pronounced landward shift of the salinity boundary, with inland monitoring stations exhibiting relative increases in minimum salinity (Smin) exceeding 3
Agricultural smoke from rice residue fires is now regarded as a dominant source of India’s autumn haze. Yet, the degree to which yield-driven intensification amplifies this pollution remains uncertain. To assess this environmental issue, there is an urgent need to analyze long-term datasets and to develop fine-grained fire-severity mapping. This study conducted a 25-year, satellite- and ancillary-data-driven reconstruction (2000–2024) of paddy cultivation, burn severity, and Aerosol Optical Depth (AOD) in Punjab’s Malwa region to close that knowledge gap. Multispectral Landsat scenes processed in Google Earth Engine yielded annual Differenced Normalised Burn Ratio (dNBR) layers that were cross-validated with MODIS active fire detections (κ = 0.79, overall accuracy = 0.86). District-level crop statistics were merged with MODIS MAIAC AOD fields to create a district-year panel dataset on which trend tests and Pearson correlation were performed. The results showed that while planted area expanded by only 21
Changes in land use over space and time are key drivers of water pollution. However, current studies on landuse-water-quality relationships in small watersheds are insufficient to support regional development. Comparative research across large-scale watersheds can better inform water environmental protection, yet such studies remain limited. This study analyzes data from 100 sampling sites across four major watersheds in Zhejiang Province. Using multivariate statistical methods and redundancy analysis, it investigates the effects of land use patterns on water quality across seasons and spatial scales. Results reveal pronounced spatial and temporal heterogeneity among watersheds. In the Qiantang River Basin, pH remains relatively stable, while other indicators vary considerably. Reduced downstream flow, particularly during the dry season, promotes the accumulation of pollutants. During the wet season, water quality in the Feiyun and Ou River Basins is more strongly influenced by geogenic processes related to land use. The Feiyun River Basin, dominated by forests and grasslands, is susceptible to rainfall-induced erosion. In the Ou River Basin, land reclamation alters hydrodynamics and salinity, and precipitation intensification further intensifies land-use impacts. At the spatial scale, the 2000m buffer exerts the strongest influence on water quality in the Feiyun, Ou, Yong, and Jiao River Basins, likely due to longer runoff pathways integrating multiple pollution sources. In contrast, in the Qiantang River Basin, the 500-m buffer is more influential during the dry season, while larger buffers dominate in the wet season. Overall, this study provides a scientific basis for watershed-specific land-use planning and water-quality protection, emphasizing policies tailored to distinct spatiotemporal dynamics.
