Modeling Methods for Identifying Critical Source Areas of Bacteria: Recent Developments and Future Perspectives

作     者：Tong, Yangbin Deng, Zhiqiang 

作者机构：Louisiana State Univ Dept Civil & Environm Engn Baton Rouge LA 70803 USA 

出 版 物：《WATER ENVIRONMENT RESEARCH》 (水环境研究)

年 卷 期：2013年第85卷第3期

页      面：259-269页

核心收录：

学科分类：0710[理学-生物学] 0830[工学-环境科学与工程（可授工学、理学、农学学位）] 08[工学] 0815[工学-水利工程] 

基　　金：National Aeronautics and Space Administration [NNX09AR62G] Louisiana Space Consortium 

主　　题：bacterial source area tracking optimization algorithm inverse modeling watersheds 

摘      要：Identification of critical source areas of bacteria in a watershed is essential to environmental management and restoration. As a result of the nonpoint and distributed nature of bacterial pollution in watersheds, it is often difficult to identify specific source areas of bacteria for remediation because bacteria collected from different sampling sites might display similar fingerprints. Over the past decade, extensive efforts have been made to identify microbial pollution sources, especially in watersheds. The primary objective of this study was to identify effective methods that can be applied to tracking critical source areas of bacteria in a watershed by a review of recent developments in several modeling methods. Comparisons of the models and their applications revealed that comprehensive watershed-scale source area tracking primarily involves two steps-geographical tracking and mathematical tracking. In terms of geographical tracking, bacterial source locations must be identified to prepare structural best management practices or low impact development for site treatments. For mathematical tracking, the quantity (strength) or release history of bacterial sources must be computed to develop total maximum daily loads (TMDLs) for bacterial load reduction and water quality restoration. Mathematically, source tracking is essentially an inverse modeling issue under uncertainty, requiring inverse modeling combined with a geostatistical method or an optimization algorithm. Consequently, combining biological methods, mathematical models, and sensor technologies (including remote sensing and in-situ sensing) provides an effective approach to identifying critical source locations of bacteria at the watershed-scale.