The nearshore water body, particularly in estuarine area, is an important interface for transporting land-based contaminants into the coastal ocean. This estuarine area is a very productive ecosystem due to its abundant organic matters, nutrients, and diverse biota. Estuarine water quality can be significantly affected by the tidal cycles due to the discharge of land-based pollutants from waterways to coastal area during ebb tides. A semi-continuous water quality monitoring system was installed in Yunlin Offshore Industrial Park (YOIP), the largest industrial park in Taiwan, since 2006 to provide real-time water quality information such as pH, ORP, water depth, dissolved oxygen, temperature, turbidity, conductivity, and chlorophyll. To interpret the large quantities of high-frequency data generated by this system, information theory was applied for data processing sand extraction of useful information for further coastal water quality management. Shannon entropy and Fisher information were calculated in this study to explore their applicability for signaling possible coastal pollution events in the YOIP. Results showed that Shannon entropy is a better indication than the raw monitoring data, especially for turbidity and salinity. When Shannon entropy was higher, multiple instable turbidity readings were observed. We conclude that these information contents may be a useful new tool for exploratory data analysis to signify some episodes of water quality degradation.