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Identification of groundwater pollution intensity and hydraulic conductivity field
The pollution source parameters and hydraulic conductivity field are the most important parameters of groundwater numerical models when making groundwater pollution remediation plans. However, previous studies focused mainly on the identification of single type parameters. The groundwater pollutant transport model (MT3DMS) and data assimilation method (iterative local updating ensemble smoother, ILUES) are used to form a solution framework for groundwater pollution source identification, and Karhunen-Loève expansion technique is used to realize parameter dimension reduction of the hydraulic conductivity field. The joint inversion of groundwater pollution source intensity and hydraulic conductivity field are also realized by assimilating hydraulic heads and concentration data. The results show that (1) the ILUES algorithm can accurately identify pollution source parameters and permeability coefficient field, and it is of high universality. (2) Accurate characterization of spatial heterogeneity of the coefficient of permeability is the key to predict pollutant migration path and inversion of pollution intensity. (3) The ILUES algorithm parameters affect the inversion results. By considering the computational efficiency and accuracy, the optimal sample set size (Ne=4000) and the optimal parameter combination of ILUES algorithm (α=0.4, b=4) can be obtained. However, in practical engineering cases, the empirical combination (α=0.1, b=1) is more recommendable if the requirement for accuracy is not too high. The results of this study have strong practical significance for regional groundwater resources investigation, evaluation and management, and can provide technical support for later groundwater pollution prediction and optimization of groundwater monitoring well networks.
Identification of groundwater pollution intensity and hydraulic conductivity field
The pollution source parameters and hydraulic conductivity field are the most important parameters of groundwater numerical models when making groundwater pollution remediation plans. However, previous studies focused mainly on the identification of single type parameters. The groundwater pollutant transport model (MT3DMS) and data assimilation method (iterative local updating ensemble smoother, ILUES) are used to form a solution framework for groundwater pollution source identification, and Karhunen-Loève expansion technique is used to realize parameter dimension reduction of the hydraulic conductivity field. The joint inversion of groundwater pollution source intensity and hydraulic conductivity field are also realized by assimilating hydraulic heads and concentration data. The results show that (1) the ILUES algorithm can accurately identify pollution source parameters and permeability coefficient field, and it is of high universality. (2) Accurate characterization of spatial heterogeneity of the coefficient of permeability is the key to predict pollutant migration path and inversion of pollution intensity. (3) The ILUES algorithm parameters affect the inversion results. By considering the computational efficiency and accuracy, the optimal sample set size (Ne=4000) and the optimal parameter combination of ILUES algorithm (α=0.4, b=4) can be obtained. However, in practical engineering cases, the empirical combination (α=0.1, b=1) is more recommendable if the requirement for accuracy is not too high. The results of this study have strong practical significance for regional groundwater resources investigation, evaluation and management, and can provide technical support for later groundwater pollution prediction and optimization of groundwater monitoring well networks.
Identification of groundwater pollution intensity and hydraulic conductivity field
Yanhao WU (author) / Simin JIANG (author) / Zijun WU (author)
2023
Article (Journal)
Electronic Resource
Unknown
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