感潮河川水質動態模式之研究

Dynamic Water Quality Modeling of a Tidal River

陳建宏(Chien-Hung Chen)

指導教授 ： 林正芳

國立臺灣大學/工學院/土木工程學研究所/博士(2013年)

https://doi.org/10.6342/NTU.2013.01028

摘要

以往在面對台灣地區山坡陡峭、河川湍急以及污染量分布廣(尤其以來自不同位置的生活污水或事業廢水，以及所帶來較高負荷的生化需氧量、氨氮)等狀況下，仍然使用目前現有及成熟的模式來加以應用，致使本研究應找出在台灣地區的模式應用上，其所面臨的技術挑戰或水質管理問題，並透過不同模式應用手法加以克服。在本文中，淡水河WASP模型框架主要在強化及建立水體內具有空間變化之污染物反應係數，並透過現場實際直接參數測定，一方面達到去參數化，另一方面則可以建立具本土化參數及適用於台灣地區之水質模式。
本研究利用淡水河與中港溪等兩個水質模擬結果進行分析，發現到BOD/DO模式在台灣應用上的技術挑戰問題。以中港溪模式來看，點源污染的BOD負荷無論使用CBODu或CBOD5作為輸入條件，整體來看，其模擬結果與觀測值差別不大。反觀淡水河卻是另一種情況。首先，CBODu是用來表示不同程度的效力和最終有機碳排放強度，而不是利用CBOD5來替代。因此在進行模擬時，應先行轉換至CBODu濃度，再輸入模式模擬，然後再將結果轉換為CBOD5或 BOD5，而後再進行因水質管理目的需求之數據分析。
本研究為取得適宜之空間分布參數值，在新北市水利局的支持下，分析了淡水河系重大主支流水質水量資料，並建立水質參數測定程序，且於主流上布設了16站水質參數調查測站，透過長達20天的實驗及多次的實驗流程修正，取得各河段適宜之水質參數值，此對未來河川水質模式應用上將有良好的依循參考。
為解決底泥需氧量調查不易及近年來政府重視底泥品質改善下，本研究基於原開發基礎上，新增下部底泥網格，使原本1維水質模式調整為2維水質模式，利用底泥沉降特性，由模式自動計算底泥需氧量。經由分析比對，該調整後模式可達到原定目標。最後本研究透過主流及支流排水24小時連續水質水量調查資料，將各側入流修正為動態入流曲線，並結合潮汐周期水位變化，以動態擬2維網格，連續模擬河川水體生化需氧量(BOD5)、氨氮(NH4+)、溶氧(DO)、懸浮固體(SS)等水質項目在潮汐影響下之變化狀況，所獲致之結果可提供河川水質管理單位未來實施污染管制時之參考。

關鍵字

動態水質模擬 ； 空間化參數 ； 袪氧係數 ； 底泥需氧量 ； WASP模式

並列摘要

In the past, readily available off-the-shelf water quality models have been adopted for use in Taiwan, where many rivers, particularly upstream portions with steep slopes and rapid water velocity, are known to receive significant BOD and ammonia loads from domestic and industrial wastewaters. These loads are characterized by excessive strength (high concentrations and wastewater flows) – often with great spatial intensity. As a result, a different modeling approach must be adopted to address technical challenges associated with modeling for water quality management in Taiwan. In this paper, the WASP modeling framework was configured for the Danshui River with the enhancement of spatially variable kinetic coefficients in the water column. Field work was conducted to directly measure these kinetic coefficients to minimize model parameterization and to reach the goal of developing a modeling platform for use in Taiwan.
The results of two water quality modeling studies are analyzed to demonstrate the technical challenges of river BOD/DO modeling in Taiwan. In the BOD/DO modeling analysis of the Chungkang River, the point source BOD loads to the river are potent enough that using CBODu or CBOD5 as the surrogate shows little difference in the modeling outcome for the Chungkang River. In general, model results of CBOD5 match the ambient data closely along the river. Modeling the Danshui River, however, is a different story. First, CBODu, instead of CBOD5, must be used to characterize the varying degrees of potency and ultimate strength of organic carbon discharges and thereby as a surrogate of BOD loads along the river. The model calculated CBODu concentrations in the river are then converted to CBOD5 for comparison with the BOD5 data for water quality management purpose.
A subsequent study supported by the New Taipei City, Hydraulic Bureau was then carried out to conduct long-term (20-day) BOD tests at 16 sampling station throughout the Danshui River watershed. CBOD deoxygenation and ammonia nitrification rates were derived from the lab results to yield spatially variable kinetic rates.
The Danshui River model was then enhanced to incorporate the sediment system to track the fate and transport of contaminants by expanding the 1-D configuration to 2-D configuration. Key processes include settling of suspended solids and sediment-water transport of dissolved oxygen. As a result, sediment oxygen demand is no longer assigned but automatically computed by the enhanced model. The enhanced model is also incorporated with real-time river flow and pollutant loads over a 24-hr period, along with tidal period at the downstream boundary to perform full 2-D dynamic simulations of CBOD, ammonia, and DO, suspended solids. Results from this modeling framework are expected to be used to develop pollutant control strategies for water quality management.

並列關鍵字

dynamic water quality modeling ； spatial variables ； de-oxygenation coefficient ； sediment oxygen demand (SOD) ； WASP model

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