近年來，氣候變遷及極端氣候現象日益明顯，使水體優養化程度升高，導致水中因藍綠菌所引起的藻華頻率與強度增加，水體中臭味事件也日益頻繁出現。2-methylisoborneol (2-MIB) 和Geosmin (GSM) 是台灣及世界水源中最常見的藍綠菌臭味物質，具土霉味、低閾值特性，常影響民眾飲水感覺、進而對於民生用水安全產生疑慮；然而其在水庫及渠道等水體中生成及傳輸機制仍需進一步探討。
本研究主要探討藻毯在水中對2-MIB及GSM濃度的貢獻，量化藻毯的2-MIB及GSM生成通量，並以質量平衡模型模擬分析，探討2-MIB及GSM在渠道中的濃度變化。研究使用具產2-MIB及GSM的大崗山淨水場及葫蘆埤渠道藍綠菌藻毯進行通量實驗，顯微鏡分析顯示，藻毯中富含Pseudanabaena、Oscillatoria、Potamolineam與Lyngbya等藻，DNA及化學分析顯示藻毯具產生2-MIB及GSM能力。藻毯通量實驗顯示2-MIB和GSM兩臭味物質濃度會隨培養時間產生變化，2-MIB的臭味產生通量，最大值約為600 ng/m2/h，平均值約為200 ng/m2/h；GSM臭味產生通量，最大值約為28,000 ng/m2/h，而平均值約為200 ng/m2/h。
本研究以一維平流延散方程式，結合Crank-Nicolson數值法，並應用實驗室求得之藻毯臭味產生通量及2-MIB一階生物降解係數、現場量測之水理參數及渠道性質、文獻參考之臭味一階生物降解係數、理論計算之延散係數及揮發參數，以及李(2026)藻毯辨識技術獲取懸浮藻毯水面之面積比例，模擬三爺宮溪水體5公里段的2-MIB和GSM空間變化，並與實際場址分析數據比對。研究結果顯示，其中2-MIB於兩次採樣之NRMSE約為8-15%；GSM則因採樣日期及通量情境不同而有所差異，NRMSE約為9.77-47.3%。以簡易模式而言，其擬合情形大致上皆相當好。
研究中並建立懸浮與底棲藻毯產臭能力累積機率圖數據資料庫，可提供藻毯辨識系統之資料庫應用。透過本研究，除可量化藻毯在2-MIB與GSM污染事件中的角色，亦可作為影像辨識與水源預警系統的基礎，對水源管理與提升應變效率具有實質應用價值。

Cyanobacterial blooms have become increasingly frequent due to eutrophication and climate change, resulting in recurrent taste-and-odor events in drinking water sources. Among the odor-causing compounds, 2-methylisoborneol (2-MIB) and geosmin (GSM) are the most frequently detected compounds worldwide. However, the contribution of cyanobacterial mats to odor production and the transport behavior of these compounds in open channels remain poorly understood. This study quantified the odor emission fluxes from cyanobacterial mats through laboratory cultivation experiments and incorporated the experimental results into a one-dimensional advection–dispersion–reaction model to simulate odor transport in Sanyegong Creek, Taiwan. Microscopic observation, gas chromatography-mass spectrometry (GC/MS) analysis, and quantitative polymerase chain reaction (qPCR) were employed to characterize cyanobacterial species, odor compounds, and odor synthesis genes. Representative odor emission fluxes were determined to be approximately 200 ng/m2/h for both 2-MIB and GSM, whereas maximum fluxes reached approximately 600 ng/m2/h for 2-MIB and 28,000 ng/m2/h for GSM. Model validation demonstrated satisfactory agreement between simulated and observed concentrations, with normalized root mean square errors (NRMSE) generally between 8% and 15% for 2-MIB. In addition, probability distribution databases of odor content in suspended and benthic cyanobacterial mats were established and integrated with an AI-based image recognition system to estimate potential odor release under different risk scenarios. The proposed framework provides a practical tool for predicting odor events and supporting water source management.

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