1517 / 2024-09-27 21:20:51
Establishment of Coastal Water Quality Monitoring for Early Warning for the Eastern Inner-Gulf of Thailand
Monitoring system,Water quality,Coastal Water,Real-time,Early warning
Session 67 - Advancing Ocean Sustainability: The Role of Early Career Ocean Professionals in Capacity Building, Ocean Literacy and Collaborative Leadership
Abstract Accepted
Coastal water quality degradation has severely impacted ecosystem services and socioeconomic activities in many populated regions, including the eastern upper Gulf of Thailand (eUGOT). This shallow water body, located at the innermost part of the Gulf, receives substantial freshwater, nutrient, and pollutant inputs from four major rivers draining urban, agricultural, and industrial areas in central Thailand. The eUGOT now experiences severe water quality issues, including high nutrient loads, marine debris, frequent harmful algal blooms, red tides, hypoxia, and anoxia, resulting in significant declines in fisheries productivity and tourism. To mitigate these impacts, the Department of Marine and Coastal Resources (DMCR), under the Ministry of Natural Resources and Environment, Thailand, is deploying a monitoring system with seven stations: four land-based, two island-based, and one offshore buoy at a depth of 20 meters. All stations will feature autonomous sensors with anti-biofouling technology to measure water temperature, salinity, turbidity, chlorophyll-a, dissolved oxygen, and pH. Two stations will also measure meteorological parameters. Land stations will include automated systems for water sampling, filtration, and macro-nutrient and total coliform analysis, aimed at enhancing the robustness of commercially available technologies for long-term sustainability in Thailand. An autonomous auto-profiling multi-parameter CTD will be installed at the offshore buoy to monitor deeper water quality. Collected data will be transmitted to a central server at DMCR for validation and analysis, and will be made publicly available. The system is designed for machine learning-based trend predictions, with early warnings delivered via web and mobile applications.