Improper solid waste management (SWM) remains a critical environmental and public health challenge in peri-urban regions of developing countries, driven by weak infrastructure, socio-economic disparities and inadequate monitoring. This study investigates the spatial–temporal dynamics and typology of roadside waste dumping in the peri-urban zones of Thanjavur, Tamil Nadu, using geo-tagged field surveys integrated with GIS-based hotspot analysis. A total of 318 dumping sites were identified in January 2025, increasing sharply to 1,325 sites by mid-2025, representing an approximately 4.17-fold increase. Kernel density estimation revealed a transition from localized clusters to widespread, high-intensity hotspots, alongside the emergence of new dumping zones. Waste composition analysis indicates a significant increase and diversification, including construction debris, plastics and hazardous waste such as biomedical and e-waste. Individual waste-type analysis showed that C and D waste incidents increased from 117 to 323, while plastic waste increased from 21 to 141 instances. Overall C and D waste occurrences rose from 144 to 422 cases, nearly tripling during the study period. Biomedical waste exhibited the most alarming growth, increasing tenfold from 1 to 10 instances. The findings highlighted the escalating environmental and health risks. Hence, the research work emphasizing the need for integrated geospatial monitoring, improved waste management systems and targeted policy and community-based interventions.
North Bihar, situated in the eastern Indo-Gangetic floodplain, is characterized by fertile alluvial soils deposited by major Himalayan rivers such as the Kosi, Gandak, Bagmati and Mahananda. While these river systems support agriculture and regional livelihoods, they also expose the region to numerous geotechnical hazards due to weak alluvial deposits, shallow groundwater conditions, dynamic river morphology and recurrent hydrometeorological events. This study presents a quantitative risk assessment of major geotechnical hazards in North Bihar including soil liquefaction, high groundwater table, land subsidence, riverbank erosion, flood-induced geotechnical failures, seismic hazards, embankment instability and river migration. A structured Probability–Severity (P–S) framework was adopted to evaluate hazard risks. Probability values were determined through a systematically designed, questionnaire-based assessment, while severity was quantified using a multi-criteria scoring approach that considered structural damage, economic losses, service disruption, human safety and environmental impacts. The combined probability and severity scores were integrated through a risk assessment matrix to develop a region-specific Risk Assessment Code (RAC) for floodplain environments. Results indicate that liquefaction, embankment instability, flood-driven erosion and river migration constitute the most critical hazards, with high occurrence probabilities and severe consequences. Most districts of North Bihar fall within High to Extremely High risk categories, particularly along the Kosi and Gandak floodplains. The proposed RAC framework provides a practical tool for geotechnical risk evaluation, infrastructure planning and disaster mitigation in floodplain regions.
Dengue fever, a mosquito-borne viral disease transmitted mainly by Aedes aegypti, remains a serious public health concern in tropical and subtropical regions. This study investigates the first major dengue outbreak in Diphu town, Karbi Anglong district, Assam, which recorded a sharp surge in cases during 2022-2023. Geospatial techniques using ArcGIS 10.3 were applied to map the spatial distribution of dengue cases across municipal wards, while temporal analysis identified seasonal infection patterns. A household survey conducted between November 2022 and January 2025 among 360 respondents assessed community awareness, preparedness and response. Results revealed that the outbreak was concentrated in densely populated areas, especially ward no. 3 (18.9%), with peak incidence during the monsoon and post-monsoon months (June-November). The survey showed low awareness and preventive practices during the 2022 outbreak, with modest improvement in 2023. Poor drainage, stagnant water accumulation, inadequate waste disposal and limited access to health facilities emerged as key contributing factors. Despite increased awareness, significant gaps persist in community preparedness and environmental management. The study underscores the importance of proactive dengue awareness campaigns, improved urban sanitation and drainage systems and the development of public health measures to enhance resilience and prevent future dengue outbreaks in hill towns like Diphu. The findings contribute to achieving the Sustainable Development Goals (SDGs), particularly SDG 3 (Good Health and Well-being), while supporting SDG 11 (Sustainable Cities and Communities) through evidence-based urban health planning and vector control strategies.
Urban informal settlements in rapidly urbanising Indian cities carry the highest disaster risk and the fewest adaptive resources, yet disaster-risk-reduction research has concentrated on physical vulnerability and has largely overlooked the cognitive and behavioural factors that shape household resilience. This study reinterprets the growth-versus-fixed mindset construct as a quantifiable determinant of disaster resilience and builds a composite Psychosocial Disaster Resilience Index (PDRI) for slum households in the tier-II city of Amritsar, Punjab, which was affected by floods in 2023 and by record-breaking floods in 2025 on the Sutlej-Beas-Ravi floodplain. A simulated household population (n = 512), calibrated against Census 2011 slum parameters and National Disaster Management Authority (NDMA) flood records, incorporates multi-hazard exposure, nine mindset attributes and socio-economic assets. Flooding was the dominant hazard and 90% of households experienced at least one flood in the preceding five years. A Random-Forest model identified growth mindset as the strongest determinant of preparedness (cross-validated R² = 0.70), ahead of income, education and prior disaster experience. The PDRI correlated strongly and negatively with postflood recovery time (r = -0.75). Strengthening growth mindset, hope and self-efficacy therefore constitute a low-cost, high-leverage disaster-risk-reduction strategy aligned with priorities 1 and 4 of the Sendai Framework.
Groundwater recharge in hard-rock semi-arid terrains depends more on how rainfall is distributed through the year than on its total amount, yet most drought and recharge indices rely primarily on rainfall totals. This study addresses that gap for Kovilpatti region, Tamil Nadu, a crystalline-aquifer, rain-shadow region utilising 125 years (1901-2025) of India Meteorological Department gridded daily rainfall data. A dynamic Recharge-Stress Index (dRRSI) was built from four indicators, SPI-12 deficit, maximum dry-spell length, precipitation concentration index and rainy-day deficit, combined through entropy weighting, which assigned 84% of total weight to rainfallstructure variables. Trend and change-point analysis identified a significant increasing rainfall trend and an abrupt regime shift in 1987 (+25.5% mean annual rainfall), corroborated by an independent rainfall typology. Eight forecasting models were benchmarked at 1, 3, 6 and 12-month lead times. The skill was strong at short horizons (Nash-Sutcliffe efficiency above 0.75 up to three months) but collapsed below the climatological baseline by 12 months. SHAP attribution showed forecasts shift from short-term memory toward rainfall-structure variables as horizon lengthens. Three uncertainty quantification methods all under-covered their nominal 90% target. These findings suggest that recharge stress in such systems is genuinely forecastable only over the short-to-medium term.
