A new approach combining principal component factor analysis and K-means for identifying natural background levels of NO-N in shallow groundwater of the Huaihe River Basin.

Establishing natural background levels (NBLs) of nitrate‑nitrogen (NO-N) is crucial for groundwater resource management and pollution prevention. Traditional statistical methods for evaluating NO-N NBLs generally overlook the hydrogeochemical processes associated with NO-N pollution. We propose using a method that combines principal component factor analysis and K-means clustering (PCFA-KM) to identify NO-N anomalies in three typical areas of the Huaihe River Basin and evaluate the effectiveness of this method in comparison with the hydrochemical graphic method (Hydro) and the Gaussian mixture model (GMM). The results showed that PCFA-KM was the most robust and effective for identifying NO-N anomalies caused by human activities. This method not only considers the data's discreteness but also combines the influencing factors of NO-N pollution to identify anomalies, thus avoiding the influence of non-homogeneous hydrogeological conditions. Moreover, 70 % of the identified anomalies were explained by sampling survey data, geochemical ratios, and pollution percentage indices, confirming the method's effectiveness and reliability. The upper limits of NO-N NBLs obtained by PCFA-KM were 12.97 mg/L (CUs-I), 4.42 mg/L (CUs-V), and 5.57 mg/L (CUs-VI). This study provides a new approach for NO-N anomaly identification, which can guide future NO-N NBLs assessments and pollution prevention and control efforts.