<p>Coastal industrial parks are areas where chemical production activities are concentrated and play an extremely important role in economic development. The hazardous nature of chemical raw materials and products, coupled with seawater intrusion as a major regional pollution source, makes chemical industrial parks face significantly higher environmental risks than other areas. In this study, self-organizing maps, entropy weight water quality index method, and K-means clustering method were adopted to analyze groundwater chemical data during the wet and dry seasons. pH, SO<sub>4</sub><sup>2−</sup>, Cl<sup>−</sup>, NO<sub>3</sub><sup>−</sup>, NO<sub>2</sub><sup>−</sup>, F<sup>−</sup>, I<sup>−</sup>, NH<sub>4</sub><sup>+</sup>, Na<sup>+</sup>, Fe, Mn, Zn, Pb, Al<sup>3+</sup>, Cu<sup>2+</sup> and TPH were selected to analyze 36 sampling points in the dry season and the wet season respectively. The groundwater components were divided into four clusters through SOM and K-means, and the water chemical characteristics in different periods were analyzed. The differences in water quality during the wet and dry seasons were quantitatively analyzed. Generally, the water quality is poorer during the dry season, indicating that the study area is responsive to seasonal hydrological changes. Cluster IV, located at the lower right of the research area, has the poorest water quality due to severe seawater intrusion. SOM achieves efficient classification with small intra-cluster differences and large inter-cluster differences, providing a scientific basis for the zonal management and protection of groundwater resources.</p>

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Hydrogeochemical response to seasonal variations and water quality assessment of groundwater in a coastal industrial park

  • Siwei Li,
  • Ruoliang Yang,
  • Yanshu Lu,
  • Boyang Sun,
  • Peng Zhang,
  • Chuqiao Wang,
  • Zhifei Ma Requester

摘要

Coastal industrial parks are areas where chemical production activities are concentrated and play an extremely important role in economic development. The hazardous nature of chemical raw materials and products, coupled with seawater intrusion as a major regional pollution source, makes chemical industrial parks face significantly higher environmental risks than other areas. In this study, self-organizing maps, entropy weight water quality index method, and K-means clustering method were adopted to analyze groundwater chemical data during the wet and dry seasons. pH, SO42−, Cl, NO3, NO2, F, I, NH4+, Na+, Fe, Mn, Zn, Pb, Al3+, Cu2+ and TPH were selected to analyze 36 sampling points in the dry season and the wet season respectively. The groundwater components were divided into four clusters through SOM and K-means, and the water chemical characteristics in different periods were analyzed. The differences in water quality during the wet and dry seasons were quantitatively analyzed. Generally, the water quality is poorer during the dry season, indicating that the study area is responsive to seasonal hydrological changes. Cluster IV, located at the lower right of the research area, has the poorest water quality due to severe seawater intrusion. SOM achieves efficient classification with small intra-cluster differences and large inter-cluster differences, providing a scientific basis for the zonal management and protection of groundwater resources.