A Study of Ecotechnology on River Pollution Control Using a Dynamic Programming Technique

• Main Authors: Ling-Hsuan Wang, 王羚宣
• Other Authors: Shyang-Lai Kung
• Format: Others
• Language: zh-TW
• Published: 2009
• Online Access: http://ndltd.ncl.edu.tw/handle/73388661537024265608

碩士 === 逢甲大學 === 環境工程與科學所 === 97 === The industrial development in the past decades has resulted in serious water pollution in Taiwan. The pollution has directly affected the living environment of the general public. The Central Government and local governments of Taiwan are continuously promoting improving and maintaining water quality in rivers, lakes, and reservoirs. With limited resources and budget, it is imperative to identify Best Management Practices (BMPs) in pollution control strategies for multi-objective rivers, lakes, and reservoirs that include water quality, water supply, recreation, and landscape. The objective of this research is to develop a dynamic programming model on water pollution controls for rivers. The research uses a dynamic programming technique to identify best strategies for reducing water pollution. These control strategies include several eco-technologies (e.g., grassed waterway, infiltration trench, and constructed wetland) for river basins. The study uses Nangang River Basin (a major tributary of Wu River) that includes three tributaries (Dongpu River, Nangang River, and Taomikeng River) to demonstrate the feasibility of the dynamic programming model. Results shows that the Nangang River Basin requires a combination of eco-technologies to help these rivers comply with the stringent water quality standard (Category A standards—almost no pollution). In addition, the expected increase in pollution in the future does not impose a threat to the water quality in Nangang River Basin. Comparing with previous studies using multi-objective gray linear programming and probabilistic models, the advantage of dynamic programming is that the method does not require the objective function and constraints be linear. On the other hand, the dynamic programming technique can be more complicated and cumbersome in arriving at optimal solutions. The dynamic programming model can be applied to other river basins in Taiwan and can identify optimal strategies of BMPs for rivers at different locations and with different water pollutants. Analysis results of this research can provide invaluable information and bases for amendments in future water quality standards.