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-source 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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