Abstract
The location and release history of groundwater contaminant sources (GCSs) are usually unknown after groundwater contamination is detected, thereby greatly hindering the design of contamination remediation schemes and contamination risk assessments. Many previous studies have used prior information such as the observed contaminant concentrations (OCC) to obtain information of GCSs, and various methods have been proposed for identifying GCSs, including simulation optimization (S/O) and ensemble Kalman filter (EnKF) methods. For the first time, the present study compared the suitability of the S/O and EnKF methods for GCSs identification based on two case studies by specifically considering the calculation time and effectiveness of GCS identification. The results showed that EnKF could reduce the calculation time required by more than 62% compared with S/O. However, the time saved did not compensate for the poor accuracy of the GCSs identification results. When the simulated contaminant concentrations (SCC) were used for GCSs identification, the MRE of the identification results with the S/O and EnKF methods were 2.79% and 5.09% in case one, respectively, and were 4.75% and 6.72% in case two. When the OCC were used for GCSs identification, the MRE of the identification results with the S/O and EnKF methods were 27.77% and 110.74% in case one, respectively, and 27.53% and 60.61% in case two. The identification results obtained using the EnKF method were not credible and the superior performance of the S/O method was obvious, thereby indicating that the EnKF method is much less suitable for actual GCSs identification compared with the S/O method.
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Data and code will be available online following journal acceptance at https://github.com/lijiuhui666/Comparative-analysis-of-groundwater-contaminant-sources-identification-based-on-simulation-optimizat/upload.
Change history
12 December 2022
A Correction to this paper has been published: https://doi.org/10.1007/s11356-022-24691-1
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Acknowledgements
Special gratitude is extended to the journal editors for their efforts in evaluating this study. The valuable comments provided by the anonymous reviewers are also gratefully acknowledged. In addition, special gratitude is given to the International Science Editing (http://www.internationalscienceediting.com) for editing this manuscript.
Funding
This work was supported by the National Key R&D Program of China (Grant Nos. 2019YFC0409101) and the National Nature Science Foundation of China (Grant Nos. 41972252).
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All authors contributed to the study conception and design. Conceptualization, methodology, software, writing–original draft, validation, and formal analysis were performed by Jiuhui Li. Conceptualization, writing–review & editing, supervision, and project administration were performed by Zhengfang Wu. Methodology, software, validation, and writing – review & editing were performed by Hongshi He. Software, validation, and writing – review & editing were performed by Wenxi Lu. All authors read and approved the final manuscript.
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Li, J., Wu, Z., He, H. et al. Comparative analysis of groundwater contaminant sources identification based on simulation optimization and ensemble Kalman filter. Environ Sci Pollut Res 29, 90081–90097 (2022). https://doi.org/10.1007/s11356-022-21974-5
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DOI: https://doi.org/10.1007/s11356-022-21974-5