Abstract
Prevention and remediation strategies for groundwater pollution can be successfully carried out if the location, concentration, and release history of contaminants can be accurately identified. This, however, presents a challenge due to complex groundwater systems. To address this issue, a simulation-optimization (S/O) model by integrating MODFLOW and MT3DMS into a shuffled complex evolution (SCE-UA) optimization algorithm was proposed; this coupled model can identify the unknown groundwater pollution source characteristics. Moreover, the Grids Traversal algorithm was used for automatically searching all possible combinations of pollution source location. The performance of the proposed S/O model was tested by three hypothetical scenarios with various combinations of mixed situations (i.e., single and multiple pollution source locations, known and unknown pollution source locations, steady-state flow and transient flow). The field measurement errors was additionally considered and analyzed. Our results showed that this proposed S/O model performed reasonably well. The identified locations and concentrations of contaminants fairly matched with the imposed inputs with average normalized deviations less than 1% after sufficient generations. We further assessed the impact of generation number on the performance of the S/O model. The performance could be significantly improved by increasing generation number, which yet resulted in a heavy computational burden. Furthermore, the proposed S/O model performed more efficiently and robustly than the traditionally used artificial neural network (ANN)-based model. This is due to the internal linkage of numerical simulation in the S/O model that promotes the data exchange from external files to programming variables. This new model allows for solving the source-identification problems considering complex conditions, and thus for providing a platform for groundwater pollution prevention and management.
Highlights
Groundwater is a precious fresh water supply in North China [1,2]
To produce reasonable and efficient identifications for unknown pollution source characteristics To produce reasonable and efficient identifications for unknown pollution source characteristics based on observation data, our proposed S/O model was built by integrating the Grids Traversal based on observation data, our proposed S/O model was built by integrating the Grids Traversal and and Shuffled Complex Evolution algorithm (SCE-UA)
A SCE-UA-based simulation-optimization (S/O) model and a Grids Traversal algorithm were introduced to address the inverse problem of identifying groundwater pollution sources. This proposed S/O model is applicable for scenarios where there is little information about the starting release time, locations, and concentrations
Summary
Groundwater is a precious fresh water supply in North China [1,2]. in the past decades, groundwater has been exposed to man-made pollution due to population growth, unplanned and uncontrolled industrialization, and irrigation activities [3]. Prevention, remediation, and management strategies are necessary to ensure the sustainable utilization and development of groundwater This presents a challenge because the accurate identification of pollution source characteristics remains largely unresolved. The optimized monitoring network method needs numerous sample data, which would cost lots of manpower and computational resources This method can only identify the potential direction of the pollution sources rather than their accurate locations and concentrations. Another example is the traditional least squares regression and linear programming method, which sometimes reaches a local optimal solution instead of a global optimal solution; this approach often leads to an inaccurate identification of contamination.
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