Abstract: Composite heavy metal contamination in groundwater within industrial areas is typically characterized by high concealment and mobility, posing substantial challenges for pollution identification and risk management. In this study, a typical industrial area in southeastern China was selected as the study area. Pearson correlation analysis, principal component analysis (PCA), and positive matrix factorization (PMF) were applied to systematically evaluate the indicative potential of conventional hydrochemical parameters for heavy metal contamination. The results indicate that nitrate (NO₃⁻) is significantly and positively correlated with Cd, Zn, Ni, and Pb (p < 0.01). PCA reveals that NO₃⁻ and these heavy metals exhibit high loadings on the same principal component, suggesting consistency in their sources and migration behaviors. PMF further identifies four major pollution factors, among which the NO₃⁻-dominated factor contributes the largest proportion to total heavy metal concentrations, demonstrating that nitrate sensitively reflects composite pollution processes driven by industrial inputs. Overall, nitrate exhibits strong potential as a rapid indicator of groundwater heavy metal contamination, providing valuable technical support for early warning and coordinated management of groundwater pollution risks in industrial areas.