Geophysics-supported contaminant mass discharge quantification downgradient of a landfill and a former pharmaceutical factory

Nicola Balbarini; Vinni Rønde; Pradip Kumar Maurya; Gianluca Fiandaca; Ingelise Møller Balling; Knud Erik Klint; Anders Vest Christiansen; Phillip John Binning; Poul Løgstrup Bjerg

doi:10.1029/2017WR021855

Geophysics-supported contaminant mass discharge quantification downgradient of a landfill and a former pharmaceutical factory

Nicola Balbarini, Vinni Rønde, Pradip Kumar Maurya, Gianluca Fiandaca, Ingelise Møller Balling, Knud Erik Klint, Anders Vest Christiansen, Phillip John Binning, Poul Løgstrup Bjerg

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avis › Tidsskriftartikel › Forskning › peer review

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Abstract

Contaminant mass discharge is a commonly applied tool to evaluate the environmental impact of contaminated sites on water resources. At large contaminated sites with heterogeneous sources, such as landfills, the number of wells available is often not sufficient, leading to a high uncertainty of mass discharge estimates. In this study, we tackle the uncertainty of the contaminant mass discharge due to low sampling densities by interpolating limited water-sample data with the support of surface direct current resistivity and induced polarization geophysical data. The method relies on finding a conceptual link between the bulk conductivity imaged from geophysics and the contaminant concentrations. We investigate the link between (1) imaged bulk and electrical water conductivity, (2) water conductivity and conservative ionic species, (3) water conductivity and redox-sensitive species, (4) water conductivity and semipersistent organic species, and (5) water conductivity and biodegradable organic compounds. The method successfully identify similarities between the distribution of the bulk conductivity and chloride and pharmaceutical compounds in a landfill leachate plume and between the bulk conductivity data and benzene and chlorinated ethenes for a contaminant plume from a former pharmaceutical factory. Contaminant concentrations were interpolated through regression kriging, using geophysical data as the dependent variable. The distribution of concentration determined with the novel method showed a lower mean relative estimation error than the traditional method of kriging only contaminant concentration data. At large sites, the method can improve contaminant mass discharge estimates, especially if surface geophysical measurements are integrated in the site investigation at an early stage.

Originalsprog	Engelsk
Tidsskrift	Water Resources Research
Vol/bind	54
Nummer	8
Sider (fra-til)	5436-5456
Antal sider	21
ISSN	0043-1397
DOI	https://doi.org/10.1029/2017WR021855
Status	Udgivet - aug. 2018

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10.1029/2017WR021855Licens: CC BY-NC-ND

Balbarini_et_al-2018-Water_Resources_ResearchForlagets udgivne version, 4,21 MBLicens: CC BY-NC-ND

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title = "Geophysics-supported contaminant mass discharge quantification downgradient of a landfill and a former pharmaceutical factory",

abstract = "Contaminant mass discharge is a commonly applied tool to evaluate the environmental impact of contaminated sites on water resources. At large contaminated sites with heterogeneous sources, such as landfills, the number of wells available is often not sufficient, leading to a high uncertainty of mass discharge estimates. In this study, we tackle the uncertainty of the contaminant mass discharge due to low sampling densities by interpolating limited water-sample data with the support of surface direct current resistivity and induced polarization geophysical data. The method relies on finding a conceptual link between the bulk conductivity imaged from geophysics and the contaminant concentrations. We investigate the link between (1) imaged bulk and electrical water conductivity, (2) water conductivity and conservative ionic species, (3) water conductivity and redox-sensitive species, (4) water conductivity and semipersistent organic species, and (5) water conductivity and biodegradable organic compounds. The method successfully identify similarities between the distribution of the bulk conductivity and chloride and pharmaceutical compounds in a landfill leachate plume and between the bulk conductivity data and benzene and chlorinated ethenes for a contaminant plume from a former pharmaceutical factory. Contaminant concentrations were interpolated through regression kriging, using geophysical data as the dependent variable. The distribution of concentration determined with the novel method showed a lower mean relative estimation error than the traditional method of kriging only contaminant concentration data. At large sites, the method can improve contaminant mass discharge estimates, especially if surface geophysical measurements are integrated in the site investigation at an early stage.",

keywords = "contaminant mass discharge, direct current resistivity, groundwater contamination, induced polarization, risk assessment, uncertainty",

author = "Nicola Balbarini and Vinni R{\o}nde and Maurya, {Pradip Kumar} and Gianluca Fiandaca and Balling, {Ingelise M{\o}ller} and Klint, {Knud Erik} and Christiansen, {Anders Vest} and {John Binning}, Phillip and {L{\o}gstrup Bjerg}, Poul",

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month = aug,

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Geophysics-supported contaminant mass discharge quantification downgradient of a landfill and a former pharmaceutical factory. / Balbarini, Nicola; Rønde, Vinni; Maurya, Pradip Kumar et al.
I: Water Resources Research, Bind 54, Nr. 8, 08.2018, s. 5436-5456.

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avis › Tidsskriftartikel › Forskning › peer review

TY - JOUR

T1 - Geophysics-supported contaminant mass discharge quantification downgradient of a landfill and a former pharmaceutical factory

AU - Balbarini, Nicola

AU - Rønde, Vinni

AU - Maurya, Pradip Kumar

AU - Fiandaca, Gianluca

AU - Balling, Ingelise Møller

AU - Klint, Knud Erik

AU - Christiansen, Anders Vest

AU - John Binning, Phillip

AU - Løgstrup Bjerg, Poul

PY - 2018/8

Y1 - 2018/8

N2 - Contaminant mass discharge is a commonly applied tool to evaluate the environmental impact of contaminated sites on water resources. At large contaminated sites with heterogeneous sources, such as landfills, the number of wells available is often not sufficient, leading to a high uncertainty of mass discharge estimates. In this study, we tackle the uncertainty of the contaminant mass discharge due to low sampling densities by interpolating limited water-sample data with the support of surface direct current resistivity and induced polarization geophysical data. The method relies on finding a conceptual link between the bulk conductivity imaged from geophysics and the contaminant concentrations. We investigate the link between (1) imaged bulk and electrical water conductivity, (2) water conductivity and conservative ionic species, (3) water conductivity and redox-sensitive species, (4) water conductivity and semipersistent organic species, and (5) water conductivity and biodegradable organic compounds. The method successfully identify similarities between the distribution of the bulk conductivity and chloride and pharmaceutical compounds in a landfill leachate plume and between the bulk conductivity data and benzene and chlorinated ethenes for a contaminant plume from a former pharmaceutical factory. Contaminant concentrations were interpolated through regression kriging, using geophysical data as the dependent variable. The distribution of concentration determined with the novel method showed a lower mean relative estimation error than the traditional method of kriging only contaminant concentration data. At large sites, the method can improve contaminant mass discharge estimates, especially if surface geophysical measurements are integrated in the site investigation at an early stage.

AB - Contaminant mass discharge is a commonly applied tool to evaluate the environmental impact of contaminated sites on water resources. At large contaminated sites with heterogeneous sources, such as landfills, the number of wells available is often not sufficient, leading to a high uncertainty of mass discharge estimates. In this study, we tackle the uncertainty of the contaminant mass discharge due to low sampling densities by interpolating limited water-sample data with the support of surface direct current resistivity and induced polarization geophysical data. The method relies on finding a conceptual link between the bulk conductivity imaged from geophysics and the contaminant concentrations. We investigate the link between (1) imaged bulk and electrical water conductivity, (2) water conductivity and conservative ionic species, (3) water conductivity and redox-sensitive species, (4) water conductivity and semipersistent organic species, and (5) water conductivity and biodegradable organic compounds. The method successfully identify similarities between the distribution of the bulk conductivity and chloride and pharmaceutical compounds in a landfill leachate plume and between the bulk conductivity data and benzene and chlorinated ethenes for a contaminant plume from a former pharmaceutical factory. Contaminant concentrations were interpolated through regression kriging, using geophysical data as the dependent variable. The distribution of concentration determined with the novel method showed a lower mean relative estimation error than the traditional method of kriging only contaminant concentration data. At large sites, the method can improve contaminant mass discharge estimates, especially if surface geophysical measurements are integrated in the site investigation at an early stage.

KW - contaminant mass discharge

KW - direct current resistivity

KW - groundwater contamination

KW - induced polarization

KW - risk assessment

KW - uncertainty

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U2 - 10.1029/2017WR021855

DO - 10.1029/2017WR021855

M3 - Journal article

SN - 0043-1397

VL - 54

SP - 5436

EP - 5456

JO - Water Resources Research

JF - Water Resources Research

IS - 8

ER -

Geophysics-supported contaminant mass discharge quantification downgradient of a landfill and a former pharmaceutical factory

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