Seasonal Dynamics of Drinking Water Quality in a Marginalized Rural Village in Odisha, India: A Water Quality Index-Based Assessment

Despite significant advancements in improving access to safe drinking water, marginalized communities, particularly those relying on rivers and handpumps, continue to face challenges related to water contamination. This study provides comprehensive seasonal water quality assessments from the river and handpump in Barapita, a marginalized village in Odisha, India, using a novel Targeted Parameter Adjustment Strategy (TPAS). TPAS ranks pollutants by their Water Quality Index (WQI) contribution, then iteratively lowers the top contributor(s) to World Health Organization (WHO) permissible levels—recomputing WQI after each step—until the score falls below the “Good” (< 50) threshold, efficiently channelling resources towards the parameter whose incremental correction most improves overall water quality. When full compliance with all WHO guidelines is unrealistic, TPAS provides a practical route to safer water. The use of Hierarchical Cluster Analysis (HCA) enhanced understanding of the contamination sources, grouping parameters into clusters linked to geogenic and anthropogenic sources. We employed sensitivity analysis to identify the parameters influencing water quality during the four distinct seasons: winter, pre-monsoon, monsoon, and post-monsoon. This innovative combination of TPAS, HCA, and sensitivity analysis provides deeper insights into seasonal water quality variations, providing tailored interventions in each season to improve drinking water quality in the village. The findings revealed significant seasonal variations, with the monsoon season exhibiting the highest WQI values due to runoff-induced contamination, while pre-monsoon values were lowest due to reduced anthropogenic interference. Iron, turbidity, colour, and nitrates were identified as critical parameters influencing water quality. The study’s adaptive approach effectively reduced WQI to acceptable levels by sequentially targeting the most impactful parameters, demonstrating the feasibility of low-cost, community-focused interventions in rural contexts.