隨著氣候變遷與水體優養化問題日益嚴重，飲用水源中藍綠菌藻華的頻率與強度持續上升，導致產生如2-methylisoborneol (2-MIB)等臭味物質的風險大增。2-MIB為常見藻類代謝產物，具強烈土霉味，即使在極低濃度下亦會引起感官問題，對飲水接受度與公共信任造成影響。近年台灣多起臭味事件中皆偵測到2-MIB，尤其在灌溉渠道與水庫中，但其生成與傳輸機制仍未被完整掌握。
本研究旨在分析供水渠道中藻毯對2-MIB濃度的貢獻，整合現地調查、實驗分析與數值模擬，利用質量平衡模型以探討2-MIB在渠道中的遷移行為與來源結構。研究場址選定高雄復興渠與桃園石門大圳兩地，並於高雄路竹淨水場採集藻毯樣本進行2-MIB與其合成基因(mibC)的萃取與分析。實驗結果顯示，藻毯中發現具2-MIB生產潛力的藍綠菌屬如Oscillatoria、Lyngbya與Pseudanabaena。機率分布分析顯示，在中位數情況下，每平方公分藻毯約含有2–55 ng 2-MIB，mibC基因量則為2×10⁴至10⁶ copies/cm²。
模擬部分，應用一維平流延散方程式並結合Crank–Nicolson數值法，模擬不同生成與降解參數下2-MIB濃度的空間變化，並與實測數據比對。在復興渠中，2-MIB濃度分布平穩，推測主要來自上游輸入，無顯著本地生成；而在石門大圳中則於9–12公里處形成污染熱點，模擬結果亦支持該段底泥與藻毯累積為可能來源。透過本研究，除可量化藻毯在2-MIB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。

This study conducted a comprehensive investigation into the sources of the odor compound 2-methylisoborneol (2-MIB) in water supply channels, with a particular focus on the contribution of algal mats. Through a combination of literature review, field sampling, laboratory analysis, and numerical modeling, both 2-MIB concentrations and the presence of its biosynthesis gene (mibC) were systematically analyzed. All collected algal mat samples tested positive for 2-MIB and mibC, with odor-producing cyanobacteria such as Oscillatoria, Lyngbya, and Pseudanabaena identified. Probability distribution analysis revealed that algal mats contained 2–55 ng/cm² of 2-MIB and 10⁴–10⁶ copies/cm² of mibC. When algal mat coverage exceeded 7.9% of the channel bed area, there was a 70% probability that 2-MIB concentrations in the water would exceed 10 ng/L, indicating a high risk of odor pollution.
A one-dimensional advection–dispersion model, coupled with the Crank–Nicolson numerical method, was employed to simulate 2-MIB transport and transformation in two case studies: the Fuxing Canal and the Shimen Main Canal. In the Fuxing Canal, the estimated generation and degradation rates (13.6 ng/m³·s and 56.4 day⁻¹, respectively) reduced the root mean square error (RMSE) to 0.47 ng/L. For the Shimen Canal, based on August 2022 data from Taiwan Water Corporation, RMSE was reduced from 62.2 to 20.5 ng/L and from 27.5 to 10.9 ng/L after parameter calibration, successfully identifying pollution hotspots and reductions following dredging activities. Through the simplified advection model, although the RMSE value cannot be reduced, it can provide more stable estimates of the average generation and loss rates, as well as the final equilibrium concentration.
This study established a quantitative relationship between algal mats and 2-MIB concentrations and demonstrated that combining numerical modeling with field data can effectively identify pollution hotspots and temporal trends. With the future integration of image recognition technology, this approach has the potential to support real-time early warning systems, enhancing the management of odor risks in drinking water sources.

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