Analysis of the Groundwater Quality Evolution and Pollution Source Identification over Multiple Periods in an Industrial Zone Based on the Hydrogeochemical-PMF Model

Groundwater quality degradation in industrial zones presents a critical environmental challenge, as diverse and compositionally complex pollution sources severely constrain precise source identification and the formulation of effective remediation strategies. This study systematically investigates groundwater quality evolution and quantitatively apportions pollution sources in a representative industrial zone in southwestern China, employing an integrated framework of hydrochemical graphical analysis, dual-dimensional hierarchical cluster analysis, and Positive Matrix Factorization (PMF) receptor modeling, based on 189 groundwater samples collected across three hydroperiods (2022–2025) from 94 monitoring wells. (1) The study reveals that overall groundwater quality was unsatisfactory, with Class IV and V waters collectively accounting for 75%, 95%, and 91% across the three campaigns; primary exceedance parameters included ammonia nitrogen, total hardness, Mn, and sulfate. (2) Hydrogeochemical analysis revealed stable HCO3-Ca type water at background monitoring points, slight contamination influence at diffusion points (occasional HCO3·SO4-Ca and Cl·SO4-Ca types), and pronounced hydrochemical diversification at internal points, evolving from Ca-Cl dominance to the coexistence of Ca-Cl·SO4, Ca-HCO3, and other mixed types. (3) The PMF model consistently resolved five pollution sources across all campaigns: agricultural non-point source pollution, geological background, domestic wastewater, industrial emissions, and natural hydrogeochemical evolution. Anthropogenic sources (agricultural, domestic, and industrial) collectively contributed approximately 63.8% of the total contamination load, with individual average contributions of 18.6%, 26.2%, and 19.0%, respectively, indicating that groundwater contamination in the zone is influenced not only by industrial inputs but also substantially by agricultural non-point sources and domestic wastewater. This study reveals a coupled natural–anthropogenic driving mechanism and establishes a replicable integrated framework for pollution source identification and zoned precision management in comparable industrial zone settings.