Seasonal hydrogeochemical response mechanism and comprehensive water quality assessment of groundwater in coastal industrial parks from the perspective of spatiotemporal interweaving

Coastal industrial parks are areas where chemical production activities are concentrated and play an extremely important role in economic development. The properties of chemical raw materials and products determine that chemical industrial parks are more dangerous than other types, and preventing groundwater pollution in chemical industrial parks has become an important task in environmental protection work. This study used self-organizing maps, entropy weight water quality index method, and K-means clustering method to analyze groundwater chemical data during the wet and dry seasons. Selecting 16 hydrochemical indicators, a study was conducted on 36 sampling points each during the wet and dry seasons. Based on SOM and K-means, the groundwater components were divided into four clusters, and the chemical characteristics of the four clusters during different periods were analyzed. The water quality difference between the wet and dry seasons was quantified through water quality evaluation. The results showed that the groundwater quality in the study area was sensitive to seasonal changes, and overall, the water quality was worse during the dry season. Comparing the characteristics of the four clusters, it was found that Cluster IV was the most severely invaded by seawater and had the poorest water quality. The spatial concentration in the lower right corner of the research area further proves that the SOM clustering analysis method can efficiently classify and achieve the effect of small intra-cluster differences and significant inter cluster differences, providing a scientific basis for the zoning management and protection of groundwater resources.