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...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Hydrogeology & Engineering Geology
2023-07-01
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| Series: | Shuiwen dizhi gongcheng dizhi |
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| Online Access: | https://www.swdzgcdz.com/en/article/doi/10.16030/j.cnki.issn.1000-3665.202208042 |
| _version_ | 1797780774174326784 |
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| author | Yanhao WU Simin JIANG Zijun WU |
| author_facet | Yanhao WU Simin JIANG Zijun WU |
| author_sort | Yanhao WU |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T23:48:37Z |
| format | Article |
| id | doaj.art-a5222328351a456e8b753a7a284cb90d |
| institution | Directory Open Access Journal |
| issn | 1000-3665 |
| language | zho |
| last_indexed | 2024-03-12T23:48:37Z |
| publishDate | 2023-07-01 |
| publisher | Editorial Office of Hydrogeology & Engineering Geology |
| record_format | Article |
| series | Shuiwen dizhi gongcheng dizhi |
| spelling | doaj.art-a5222328351a456e8b753a7a284cb90d2023-07-14T03:11:20ZzhoEditorial Office of Hydrogeology & Engineering GeologyShuiwen dizhi gongcheng dizhi1000-36652023-07-0150419320310.16030/j.cnki.issn.1000-3665.202208042202208042Identification of groundwater pollution intensity and hydraulic conductivity fieldYanhao WU0Simin JIANG1Zijun WU2Department of Hydraulic Engineering, School of Civil Engineering, Tongji University, Shanghai 200092, ChinaDepartment of Hydraulic Engineering, School of Civil Engineering, Tongji University, Shanghai 200092, ChinaState Key Laboratory of Marine Geology, (Tongji University), Shanghai 200092, ChinaThe 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.https://www.swdzgcdz.com/en/article/doi/10.16030/j.cnki.issn.1000-3665.202208042groundwater pollutionparameter inversiondata assimilationensemble smoother |
| spellingShingle | Yanhao WU Simin JIANG Zijun WU Identification of groundwater pollution intensity and hydraulic conductivity field Shuiwen dizhi gongcheng dizhi groundwater pollution parameter inversion data assimilation ensemble smoother |
| title | Identification of groundwater pollution intensity and hydraulic conductivity field |
| title_full | Identification of groundwater pollution intensity and hydraulic conductivity field |
| title_fullStr | Identification of groundwater pollution intensity and hydraulic conductivity field |
| title_full_unstemmed | Identification of groundwater pollution intensity and hydraulic conductivity field |
| title_short | Identification of groundwater pollution intensity and hydraulic conductivity field |
| title_sort | identification of groundwater pollution intensity and hydraulic conductivity field |
| topic | groundwater pollution parameter inversion data assimilation ensemble smoother |
| url | https://www.swdzgcdz.com/en/article/doi/10.16030/j.cnki.issn.1000-3665.202208042 |
| work_keys_str_mv | AT yanhaowu identificationofgroundwaterpollutionintensityandhydraulicconductivityfield AT siminjiang identificationofgroundwaterpollutionintensityandhydraulicconductivityfield AT zijunwu identificationofgroundwaterpollutionintensityandhydraulicconductivityfield |