Novel framework for river health assessment: principal component-based water quality index and causal inference through regression discontinuity design.

Rivers in industrial regions face significant pressure from anthropogenic activities, often resulting in degraded water quality. This study investigates long-term water quality dynamics of the Damodar River in West Bengal, India, using a multi-index approach and advanced statistics. Six established Water Quality Indices (WQIs) were computed using monthly data from 2014 to 2024 across ten monitoring sites. To reconcile the inconsistencies among individual indices, Principal Component Analysis (PCA) was applied to generate a unified metric termed the Principal Component Averaged WQI (PCAWQI). Furthermore, to quantify the causal impact of the COVID-19 lockdown (March-May 2020) on river health, we employed a Regression Discontinuity Design (RDD) using Bayesian structural time-series modeling (CausalImpact). The composite PCAWQI successfully captured spatial and temporal pollution gradients, highlighting critical midstream deterioration. Additionally, site-specific improvements in water quality were observed during the lockdown, with some sites exhibiting significant gains likely due to industrial inactivity. However, heterogeneous responses underscored the influence of socio-cultural and hydrological factors. Thus, by integrating dimensionality reduction and causal inference techniques, we developed a robust and replicable framework for water quality assessment. This framework can be utilized for environmental monitoring and policy evaluation in heavily industrialized river basins.