14th National Conference on HYDRAULIC, 11-13 Nov. 2015 University of Sistan and Baluchestan, Zahedan, Iran
Pollution Point Source Identification in Rivers Based on the Backward Probability Method, Case Study River Severn
Alireza Ghane1, Mehdi Mazaheri2, Jamal Mohammad Vali Samani3 1-MSc. Student of Water Structures, University of Tarbiat Modares 2- Assistant Prof., Department of Water Structures, University of Tarbiat Modares 3- Prof., Department of Water Structures, University of Tarbiat Modares
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Abstract Pollution source identification in rivers is a secure issue. Backward Probability Method (BPM) is one of the tools that gives useful information about the prior position and the release time of pollution. It was originally developed in groundwater, so the main goal of this study is the application of BPM to identify the source location and the release time of the pollutant in surface water. In order to apply the model, A numerical code was developed based on adjoint analysis. We try to use this method in a real case. So this model is applied for Severn River. The results show that this model is able to trace the pollution source in a river with natural situation accurately. It doesn't need any simplification in domain. Since the simulation is done once, this method effectively reduces computational cost and gives a better choice to decrease the damages. It just needs a limited observation data. Keywords: Pollution Source Identification, Backward Probability Method, Adjoint Model, Accidental Spills
1. Introduction The pollution of rivers due to accidental spills represents a major threat to ecology and to various uses of water (Leibundgut et al., 2009). To protect river system from accidental spills, we need accurate and efficient tools to identify several parameters such as the pollution source, release time and pollution mass. Monitoring is one of the most important activities in the surface water quality control. Water quality changes are detected when river is monitoring. Immediately after detection, the pollutant source location must identify. In order to determine the source location or the release time, it needs to use backward models for both location and time. Furthermore, finding the past events based on a few observations are uncertain. Backward Probability Method (BPM) is one of the backward models that able to identify the location and the release time of the pollutant. Pollution source identification is faced to two parameters. First parameter is the location of pollution source and the second one is the release time. Accordingly, the identification problems illustrate with two kinds of probability concept. Backward Location Probability and Backward Travel Time Probability are two different way to identify the location and the release time of the pollutant source. Backward Location Probability determines the prior location of the pollutant, and Backward Travel Time Probability gives the information about release time based on assumption that the source location is known. The probability of Location and Travel Time calculate with backward equation. The governing equations of BPM are similar to forward method. The basic differences between the backward and forward equation are reverse flow field and modification boundary condition. Backward model includes a new source term that it approximates numerically. BPM was used in groundwater hydrology at first. Wilson and Liu (1994) introduced it to identify the prior location of pollution that detected in well. They utilized a creative method to derive a backward model from a forward model, and then used the backward model to determine the spatial and temporal probability. Wilson and Liu (1997) applied BPM at Borden site. The prediction of the model was very reliable. Neupaure and Wilson (1999, 2001) showed the Backward Location Probability and Travel Time Probability are adjoint states of concentration. They represented a formal approach that has called adjoint state method to demonstrate the governing equation, boundary condition and initial condition of 1D (1999) and 2D (2001) domain. In 2002, they developed it to non-uniform and transient flow in porous domain. The method is based on single observation of pollution, after that Neupaure and Wilson (2005) developed it to multiple observation
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