A new approach combining principal component factor analysis and K-means for identifying natural background levels of NO3-N in shallow groundwater of the Huaihe River Basin

• Published in: The Science of the total environment 2024-12, Vol.957, p.177120, Article 177120
• Main Authors: Chen, Zhen, He, Jiangtao, He, Baonan, Chu, Yanjia, Xia, Qiwen
• Format: Article
• Language: English
• Subjects: environment; factor analysis; Groundwater; Huaihe River basin; humans; Hydrochemistry; hydrogeochemistry; Natural background levels; nitrate nitrogen; NO3-N; pollution; pollution control; resource management; surveys; watersheds
• ISSN: 0048-9697; 1879-1026; 1879-1026
• DOI: 10.1016/j.scitotenv.2024.177120

Establishing natural background levels (NBLs) of nitrate‑nitrogen (NO3-N) is crucial for groundwater resource management and pollution prevention. Traditional statistical methods for evaluating NO3-N NBLs generally overlook the hydrogeochemical processes associated with NO3-N pollution. We propose using a method that combines principal component factor analysis and K-means clustering (PCFA-KM) to identify NO3-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 NO3-N anomalies caused by human activities. This method not only considers the data's discreteness but also combines the influencing factors of NO3-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 NO3-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 NO3-N anomaly identification, which can guide future NO3-N NBLs assessments and pollution prevention and control efforts. [Display omitted] •A new PCFA-KM anomaly identification method set up for NO3-N NBLs assessment.•PCFA-KM combines the strengths of statistical and hydrogeochemical approaches.•PCFA-KM can effectively identify groundwater NO3-N anomalies.•Fertilizer and municipal sewage are the genesis of 70 % of NO3-N anomalies.