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基于反向概率法的河网污染点源位置及排放时间识别

Location and release time identification of pollution point source in river networks based on the Backward Probability Method.

作者信息

Ghane Alireza, Mazaheri Mehdi, Mohammad Vali Samani Jamal

机构信息

Department of Water Structures, Tarbiat Modares University, Tehran, Iran.

出版信息

J Environ Manage. 2016 Sep 15;180:164-71. doi: 10.1016/j.jenvman.2016.05.015. Epub 2016 Jun 1.

Abstract

The pollution of rivers due to accidental spills is a major threat to environment and human health. To protect river systems from accidental spills, it is essential to introduce a reliable tool for identification process. Backward Probability Method (BPM) is one of the most recommended tools that is able to introduce information related to the prior location and the release time of the pollution. This method was originally developed and employed in groundwater pollution source identification problems. One of the objectives of this study is to apply this method in identifying the pollution source location and release time in surface waters, mainly in rivers. To accomplish this task, a numerical model is developed based on the adjoint analysis. Then the developed model is verified using analytical solution and some real data. The second objective of this study is to extend the method to pollution source identification in river networks. In this regard, a hypothetical test case is considered. In the later simulations, all of the suspected points are identified, using only one backward simulation. The results demonstrated that all suspected points, determined by the BPM could be a possible pollution source. The proposed approach is accurate and computationally efficient and does not need any simplification in river geometry and flow. Due to this simplicity, it is highly recommended for practical purposes.

摘要

因意外泄漏导致的河流污染是对环境和人类健康的重大威胁。为保护河流系统免受意外泄漏影响,引入一种用于识别过程的可靠工具至关重要。反向概率法(BPM)是最值得推荐的工具之一,它能够提供与污染的先前位置和释放时间相关的信息。该方法最初是为地下水污染源识别问题而开发和应用的。本研究的目标之一是将此方法应用于识别地表水(主要是河流)中的污染源位置和释放时间。为完成此任务,基于伴随分析开发了一个数值模型。然后使用解析解和一些实际数据对所开发的模型进行验证。本研究的第二个目标是将该方法扩展到河网中的污染源识别。在这方面,考虑了一个假设测试案例。在后续模拟中,仅通过一次反向模拟就识别出了所有可疑点。结果表明,由BPM确定的所有可疑点都可能是污染源。所提出的方法准确且计算效率高,不需要对河流几何形状和水流进行任何简化。由于这种简单性,强烈推荐将其用于实际目的。

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