Abstract: The composite contamination of heavy metals in groundwater in industrial areas is characterized by high toxicity, concealment, and strong mobility. Conventional monitoring is costly and difficult to achieve high-frequency coverage, posing severe challenges to pollution identification and risk management. We focused on 67 groundwater monitoring sites in a typical industrial area of southeastern China and, based on monitoring data of 20 indicators including hydrochemical parameters and heavy metals, performed Pearson correlation analysis to identify the covariation between conventional indicators and heavy metals. Principal component analysis (PCA) was used to elucidate the covariance structure of multiple indicators, and positive matrix factorization (PMF) was adopted to quantitatively identify exogenous industrial pollution factors and their contribution characteristics. Furthermore, we comprehensively elaborated on the coupling relationship between nitrate (\mathrmNO_3^- ) and composite heavy metal pollution processes as well as their response characteristics from multiple perspectives including statistical correlation, common source analysis and source contribution quantification. The results showed that \mathrmNO_3^- exhibited extremely significant positive correlations with Cd, Zn, and Ni (p<0.001), with correlation coefficients of 0.972, 0.967, and 0.895, respectively. PCA revealed that \mathrmNO_3^- and these metals had high loadings on the first principal component (34.3% of total variance), suggesting consistent variation patterns. PMF identified four pollution factors, among which the \mathrmNO_3^- -dominated industrial source contributed 75%, 81%, and 70% to Cd, Pb, and Ni, respectively. This contribution increased to 61% for Cd, Pb, Zn, and Ni after excluding Mn influenced by geogenic background, making it the dominant pollution source. The study shows that under hydrogeological conditions with shallow groundwater and weak denitrification, \mathrmNO_3^- can reflect the combined pollution process driven by exogenous industrial inputs, and can be used as a potential indicator for the rapid identification of heavy metal pollution in groundwater.