🆕 Spatio-Temporal Analysis of Surface Water Quality: A California Case Study
Abstract
Surface water quality has a direct impact on public health, ecosystems, and agriculture, in addition to being an important indicator of the overall health of the environment. This study offers a comprehensive assessment of these patterns by leveraging around 70 years of data in California, taking into account climate zones and geographical types. We analyzed surface water quality indicators, including pH, dissolved oxygen, specific conductance, and water temperature, based on field results from 5,080 water quality stations in California Water Quality Data (CWQD). Machine learning (ML) models were developed to establish relationships between spatial and temporal variables, climate zones, geographical types, and water quality indicators. Applying these models to spatially interpolate the four water quality indicators over California, the research results indicate an uneven distribution of water quality indicators in California, suggesting the presence of potential pollution zones, seawater erosion, and effects of climate change.
