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A least squares method for identification of unknown groundwater pollution source.

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Title: A least squares method for identification of unknown groundwater pollution source.
Authors: Zhukun He1,2, Rui Zuo1,2 zr@bnu.edu.cn, Dan Zhang3, Pengcheng Ni1,2, Kexue Han1,2, Zhenkun Xue1,2, Jinsheng Wang1,2, Donghui Xu1,2
Source: Hydrology Research. Apr2021, Vol. 52 Issue 2, p450-460. 11p.
Subjects: Groundwater pollution, Least squares, Pollution prevention, Pollution remediation, Analytical solutions, Fuel tanks
Abstract: The identification of unknown groundwater pollution sources is one of the most important premises in groundwater pollution prevention and remediation. In this paper, an exploratory application of a least squares method to identify the unknown groundwater pollution source is conducted. Supported by a small amount of observation data and the analytical solutions of the pollutant transport model, the initial concentration, the leakage location and the pollutant mass are identified by using the least squares method under a sand tank experiment and a gas station area. In the sand tank experiment, it is found that the fitting errors of three cross-sections are within 6%. In the gas station area, it is found that the results are nearly consistent with the site investigation information. The results indicate that the least squares method has considerable application values in the identification of groundwater pollution sources. [ABSTRACT FROM AUTHOR]
Copyright of Hydrology Research is the property of IWA Publishing and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 149914971
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Group: Ti
  Data: A least squares method for identification of unknown groundwater pollution source.
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  Data: <searchLink fieldCode="AR" term="%22Zhukun+He%22">Zhukun He</searchLink><relatesTo>1,2</relatesTo><br /><searchLink fieldCode="AR" term="%22Rui+Zuo%22">Rui Zuo</searchLink><relatesTo>1,2</relatesTo><i> zr@bnu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Dan+Zhang%22">Dan Zhang</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Pengcheng+Ni%22">Pengcheng Ni</searchLink><relatesTo>1,2</relatesTo><br /><searchLink fieldCode="AR" term="%22Kexue+Han%22">Kexue Han</searchLink><relatesTo>1,2</relatesTo><br /><searchLink fieldCode="AR" term="%22Zhenkun+Xue%22">Zhenkun Xue</searchLink><relatesTo>1,2</relatesTo><br /><searchLink fieldCode="AR" term="%22Jinsheng+Wang%22">Jinsheng Wang</searchLink><relatesTo>1,2</relatesTo><br /><searchLink fieldCode="AR" term="%22Donghui+Xu%22">Donghui Xu</searchLink><relatesTo>1,2</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Hydrology+Research%22">Hydrology Research</searchLink>. Apr2021, Vol. 52 Issue 2, p450-460. 11p.
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  Data: <searchLink fieldCode="DE" term="%22Groundwater+pollution%22">Groundwater pollution</searchLink><br /><searchLink fieldCode="DE" term="%22Least+squares%22">Least squares</searchLink><br /><searchLink fieldCode="DE" term="%22Pollution+prevention%22">Pollution prevention</searchLink><br /><searchLink fieldCode="DE" term="%22Pollution+remediation%22">Pollution remediation</searchLink><br /><searchLink fieldCode="DE" term="%22Analytical+solutions%22">Analytical solutions</searchLink><br /><searchLink fieldCode="DE" term="%22Fuel+tanks%22">Fuel tanks</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The identification of unknown groundwater pollution sources is one of the most important premises in groundwater pollution prevention and remediation. In this paper, an exploratory application of a least squares method to identify the unknown groundwater pollution source is conducted. Supported by a small amount of observation data and the analytical solutions of the pollutant transport model, the initial concentration, the leakage location and the pollutant mass are identified by using the least squares method under a sand tank experiment and a gas station area. In the sand tank experiment, it is found that the fitting errors of three cross-sections are within 6%. In the gas station area, it is found that the results are nearly consistent with the site investigation information. The results indicate that the least squares method has considerable application values in the identification of groundwater pollution sources. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Hydrology Research is the property of IWA Publishing and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.2166/nh.2021.088
    Languages:
      – Code: eng
        Text: English
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        PageCount: 11
        StartPage: 450
    Subjects:
      – SubjectFull: Groundwater pollution
        Type: general
      – SubjectFull: Least squares
        Type: general
      – SubjectFull: Pollution prevention
        Type: general
      – SubjectFull: Pollution remediation
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      – SubjectFull: Analytical solutions
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      – SubjectFull: Fuel tanks
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              M: 04
              Text: Apr2021
              Type: published
              Y: 2021
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