Identification of groundwater pollution sources based on a modified plume comparison method
Identification of groundwater pollution sources based on a modified plume comparison method
复制标题
基于改进羽流比较法的地下水污染源识别
DOI：
10.2166/ws.2016.122
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发表时间：
2017-02
期刊：
影响因子：
--
通讯作者：
Xiao Chuanning
中科院分区：
文献类型：
--
作者：
Gu Wenlong;Lu Wenxi;Zhao Ying;Ouyang Qi;Xiao Chuanning
确定地下水污染源是设计和实施修复策略的首要步骤。这里介绍的工作是为了制定一种有效的策略来解决未知的多污染物源问题，并且可以确定地下水污染物源的数量、位置和规模并选择最佳采样位置。首先采用蒙特卡罗方法获得地下水流统计特征和输运模型。然后利用线性卡尔曼滤波器和改进的比较方法来更新浓度值和源权重的估计，这代表了估计的复合羽流与每个单独羽流之间的相似性。此外，采用优化方法来确定污染程度和最佳采样位置。重复所有这些步骤直到权重稳定并收敛。使用综合示例来测试该策略并进行进一步的不确定性分析。评估表明，该策略在浓度测量值误差控制在10%以内、时间误差控制在6%以内的情况下，有效解决了未知的多源问题。
The identification of groundwater contaminant sources is a primary step in designing and implementing a remediation strategy. The work presented here was undertaken to develop an efficient strategy that addresses the unknown multiple contaminant sources problem, and that could identify the number, location, and magnitude of the groundwater contaminant sources and select optimal sampling locations. A Monte Carlo approach was used first to obtain the statistical characteristics of groundwater flow and transport model. Then the linear Kalman filter and a modified comparison method were utilized to update the estimation of concentration values and source weights, which represent the similarity between the estimated composite plume and each individual plume. Moreover, an optimization method was employed to identify the magnitude of contamination and the optimal sampling location. All of these steps were repeated until the weights stabilized and converged. A synthetic example was used to test the strategy and a further uncertainty analysis was conducted. The evaluation demonstrated that the strategy effectively addresses unknown multiple-sources problems, under the condition that the error of concentration measurement value was controlled to less than 10%, and the time error was controlled to less than 6%.
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DOI：
10.1109/9780470544334.ch9
发表时间：
2001
期刊：
Comput. Electron. Agric.
影响因子：
--
作者：
T. Başar
通讯作者：
T. Başar
影响因子：
5.4
作者：
M. Snodgrass;P. Kitanidis
通讯作者：
M. Snodgrass;P. Kitanidis
DOI：
10.1061/(asce)0733-9496(2004)130:6(506
发表时间：
2004-10
影响因子：
3.1
作者：
R. Singh;B. Datta;Ashu Jain
通讯作者：
R. Singh;B. Datta;Ashu Jain
影响因子：
5.4
作者：
T. Skaggs;Z. Kabala
通讯作者：
T. Skaggs;Z. Kabala
影响因子：
2.4
作者：
M. Jha;B. Datta
通讯作者：
M. Jha;B. Datta