藻霉味物質二甲基冰片2-methylisoborneol (2‑MIB)為用水水源中常出現的臭味物質，一般人對該物質的嗅覺閾值低(約2至20 ng/L)，且傳統淨水程序對2‑MIB去除效果不佳，常會成為民眾抱怨的原因。目前雖已經有許多關於2-MIB的監測與處理研究。然而，對於2-MIB的模式模擬研究卻很少，尤其缺乏對於渠道是水源的模擬及應用。
本研究收集文獻、並回顧2-MIB在河道中的生成與消失機制，進而以一維平流延散方程式為主軸，建立出2-MIB在渠道中的質量平衡模型，並應用Crank-Nicolson法來建立一維平流延散方程式的數值解。研究中在台灣南部三處渠道採集底泥與水樣，以調查渠道中2-MIB臭味事件及渠道中的藻類組成；並透過調整模擬參數之輸入，使用參數敏感性分析來評估敏感度較大的參數，從而確定對2-MIB濃度流布影響大的因素。最後並使用2018年南韓北漢江臭味事件案例分析來評估此模型之應用性。
本研究建立了連續式輸入與脈衝式/階梯式輸入兩種污染物輸入形式。參數敏感性分析結果顯示，在這兩種輸入模式中，流速是對模型輸出結果影響最大的參數，因此在獲取流速參數時必須謹慎，避免因誤差導致模型產生較大偏差。在南韓漢江的案例分析中，與Kang等人(2023)所使用的2D流體動力模型EFDC-NIER相比，本研究能以較簡單模式，獲得與該研究相當的模擬準確度。

2-Methylisoborneol (2-MIB) is a common taste and odor (T&O) compound in water sources, known for its distinctive earthy-musty odor. It has a low odor threshold, ranging from approximately 2 to 20 ng/L, making it easily detectable even at low concentrations. Traditional water treatment processes often struggle to remove 2-MIB effectively, leading to frequent complaints, particularly during cyanobacterial blooms. While there has been extensive research on monitoring and treating 2-MIB, studies on modeling its behavior are relatively scarce. Existing models primarily focus on reservoirs, leaving a gap in understanding the dynamics of 2-MIB in channels.
This study aims to address this gap by reviewing the mechanisms of 2-MIB generation and degradation in rivers and developing a mass balance model for 2-MIB in channels using a one-dimensional advection-dispersion equation. The Crank-Nicolson method was employed to numerically solve this equation. Fieldwork was conducted at three channels in southern Taiwan, where sediment and water samples were collected to investigate 2-MIB-related odor events and cyanobacterial composition. A parameter sensitivity analysis was also performed by adjusting model input parameters to identify the factors most significantly affecting 2-MIB concentration distributions. Additionally, the model’s applicability was assessed using a case study of an odor event in the North Han River, South Korea, in 2018.
The study considered two input scenarios: continuous and pulse/step inputs. Sensitivity analysis revealed that flow velocity was the most influential parameter in both scenarios, necessitating careful acquisition of this parameter to minimize model deviation. In the case study of the Han River in South Korea, the model developed in this study achieved accuracy comparable to the 2D hydrodynamic model EFDC-NIER used by Kang et al. (2023), while employing a more simplified approach.

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