環境資源報告成果查詢系統

113年即時線上水質感測技術開發(3/4)計畫

中文摘要 為推動高效水質物聯網應用並延續環境部在環境水質物聯網的相關成果,本團隊以既有研發為基礎,持續投入研發、強化品管並擴大應用,執行多元場域驗證與商業化探討。本年度三大執行重點之計畫成果包括:(一)優化及整合水質感測器:(1)升級4台小型化光學感測模組並完成1個月場域測試,可自主運作2至3週,顯著降低維護需求;(2)於台中市大里溪河段進行3個月自清功能感測器測試,可延長維護週期兩倍,有效降低人力成本;(3)分析物聯網技術瓶頸與發展趨勢,提出應用於環境部業務的方向;(4)完成小型化物聯網重金屬感測設備研發,提出設計與成本效益分析。(二)多元運用感測元件,精進水質模式推估與污水廠節能:(1)應用LDC曲線及LQ公式分析南崁溪水質,中上游以點源污染為主,其中東門溪與大檜溪橋降雨時非點源污染增加顯著,符合水質須削減97%以上污染量;(2)應用時段與邏輯控制對污水廠鼓風機操作進行節能研究,分別節省了18%和32%的電能,相當於每年減少了12~23噸的二氧化碳排放;(3)於水庫以及主要河川試行無人機水質感測與標準方法採樣,除感測結果相近外,成本效益分析顯示,無人機採樣具備持續研發潛力;(4)近10年的水質長期監測數據結合污染整治大事件,顯示趨勢統計可有效連結水質改善與整治措施的影響。在6個測站中,WQI和水溫多呈常態分布,雨量無適合分布型態;流量與環境變數、雨量的關係較弱,需優化模型。相關性分析顯示,氨氮與總磷為東港溪主要污染因子,與畜牧廢水及生活污水排放密切相關。RPI多數測站呈下降趨勢,WQI上升,顯示10年間水質逐漸好轉,特別是生化需氧量和大腸桿菌群的改善,與畜牧廢水整治推動相關。(三)透過4次協辦會議與展覽、4篇論文發表及1項美國專利申請,持續優化技術與分析應用,擴大計畫影響。團隊定期追蹤進度,穩定數據品質,推動中央與地方合作,將水聯網融入生活,提升水質透明化與環境品質,讓民眾有感。
中文關鍵字 水質物聯網、無人機水質感測、污水廠節能

基本資訊

專案計畫編號 經費年度 113 計畫經費 10670 千元
專案開始日期 2024/03/21 專案結束日期 2024/12/31 專案主持人 朱振華
主辦單位 環境部監測資訊司 承辦人 陳香宇 執行單位 財團法人工業技術研究院

成果下載

類型 檔名 檔案大小 說明
期末報告 113年即時線上水質感測技術開發計畫_成果報告_定稿(本文+附件)_20241224.pdf 45MB

The Project of Developing Real-time Online Water Quality Sensing Technology in FY113

英文摘要 For enhancing and continuing applications of water IoT project with the Ministry of Environment (MOENV), the project team reviewed previous R&D accomplishments and focused on enhancing quality control, expanding the application, conducting diverse field verifications, and exploring commercialization opportunities this year. The main purpose of this project is concentrated on three areas: (1) Optimization and Integration of Water Quality Sensors: Upgraded four compact optical sensing modules, achieving a 1-month field test with 2-3 weeks of autonomous operation, significantly reducing maintenance needs; Conducted a 3-month trial of self-cleaning sensors in Taichung's Dali River, extending maintenance intervals by twofold and reducing labor costs; Analyzed IoT bottlenecks and development trends, proposing applications tailored to MOENV needs; Developed miniaturized IoT heavy metal sensing equipment with design and cost-effectiveness assessments. (2) Diverse Sensor Applications and Efficiency Improvements: Applied LDC and LQ models to analyze Nankan River water quality, identifying point source pollution upstream and significant non-point source increases during rainfall, requiring over 97% pollutant reduction; Conducted energy-saving studies on wastewater plant blower operations using time-based and logic-based controls, reducing energy consumption by 18% and 32%, equivalent to 12-23 tons of annual CO₂ reduction; Trialed drone-based water quality sensing and sampling in reservoirs and rivers, demonstrating comparable results to standard methods and potential for further development; Long-term monitoring data over 10 years showed significant links between pollution control events and water quality improvements, with WQI rising and RPI declining at most sites. Key factors included ammonia nitrogen and total phosphorus from agricultural and domestic wastewater sources. (3) Promotion and Collaboration: Through four collaborative meetings and exhibitions, four published papers, and one U.S. patent application, the team continues to optimize technology and expand project impacts. By tracking progress, ensuring data quality, and fostering cooperation between central and local governments, the project integrates IoT into daily life, enhancing water quality transparency and improving environmental outcomes for public benefit.
英文關鍵字 Water quality IoT, Drone-Based Water Quality Sensing, Energy Saving in Wastewater Treatment Plants