Virginia Institute of Marine Science, College of William and Mary, 1208 Greate Road, P.O. Box 1346, Gloucester Point, VA 23062, USA.
Water Res. 2010 Jan;44(1):77-84. doi: 10.1016/j.watres.2009.09.002. Epub 2009 Sep 6.
Nonpoint source load estimation is an essential part of the development of the bacterial total maximum daily load (TMDL) mandated by the Clean Water Act. However, the currently widely used watershed-receiving water modeling approach is usually associated with a high level of uncertainty and requires long-term observational data and intensive training effort. The load duration curve (LDC) method recommended by the EPA provides a simpler way to estimate bacteria loading. This method, however, does not take into consideration the specific fate and transport mechanisms of the pollutant and cannot address the uncertainty. In this study, a Bayesian statistical approach is applied to the Escherichia coli TMDL development of a stream on the Eastern Shore of Virginia to inversely estimate watershed bacteria loads from the in-stream monitoring data. The mechanism of bacteria transport is incorporated. The effects of temperature, bottom slope, and flow on allowable and existing load calculations are discussed. The uncertainties associated with load estimation are also fully described. Our method combines the merits of LDC, mechanistic modeling, and Bayesian statistics, while overcoming some of the shortcomings associated with these methods. It is a cost-effective tool for bacteria TMDL development and can be modified and applied to multi-segment streams as well.
非点源负荷估算是非点源总最大日负荷（TMDL）制定的《清洁水法》的重要组成部分。然而，目前广泛使用的流域-受纳水体建模方法通常具有较高的不确定性，需要长期的观测数据和密集的培训工作。美国环保署推荐的负荷持续曲线（LDC）方法提供了一种更简单的估算细菌负荷的方法。然而，这种方法没有考虑污染物的特定归宿和迁移机制，也无法解决不确定性问题。在这项研究中，贝叶斯统计方法被应用于弗吉尼亚州东海岸一条溪流的大肠杆菌 TMDL 开发中，从河流监测数据中反演估算流域细菌负荷。该方法纳入了细菌传输的机制。讨论了温度、底部坡度和流量对允许负荷和现有负荷计算的影响。还充分描述了与负荷估算相关的不确定性。我们的方法结合了 LDC、机理建模和贝叶斯统计学的优点，同时克服了这些方法的一些缺点。它是一种具有成本效益的细菌 TMDL 开发工具，也可以修改并应用于多段河流。
