Coupling WQI and multivariate statistical approaches for key parameter identification and human health risk assessment in the longevity region of Hechi City, China.

Abstract

This study addresses limitations in traditional water quality indices (WQIs) by developing a simplified yet accurate WQI model (WQI_min) that captures spatiotemporal dynamics and prioritizes health-risk drivers in ecologically sensitive regions. Monitored over 2021-2023 at 16 stations in Hechi City, China (a globally recognized longevity area), 17 water quality parameters were analyzed. Using multivariate statistics, WQI_min reduced parameters from 17 to 7 key drivers (P < 0.001) while maintaining 99.3% prediction accuracy. Results revealed excellent water quality (overall WQI = 95.10; station range: 93.22-96.44) meeting China's Class I standards. Heavy metal concentrations were in the following order: Zn > As > Cr > Cu > Se > Pb > Cd > Hg, with chromium (Cr) dominating health risks at 73.4% (adults) and 73.41% (children), followed by arsenic (As) at 26.47% (adults) and 26.46% (children). Despite compliance with surface water standards, trace contaminants like Cr and As posed disproportionate risks, highlighting the need for targeted monitoring in longevity regions. This innovative approach resolves conventional WQI aggregation issues, providing policymakers with a cost-effective tool to identify critical pollutants and optimize resource allocation for water protection.