Using positive matrix factorization to investigate microbial dehalogenation of chlorinated benzenes in groundwater at a historically contaminated site

Staci L. Capozzi, Lisa Rodenburg, Valdis Krumins, Donna Fennell, E. Erin Mack

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Chlorinated benzenes are common groundwater contaminants in the United States, so demonstrating whether they undergo degradation in the subsurface is important in determining the best remedy for this contamination. The purpose of this work was to use a new data mining approach to investigate chlorinated benzene degradation pathways in the subsurface. Positive Matrix Factorization (PMF) was used to analyze long-term measurements of chlorinated benzene concentrations in groundwater at a contaminated site in New Jersey. A dataset containing 597 groundwater samples and 5 chlorinated benzenes and benzene collected from 144 wells over 20 years was investigated using PMF2 software. Despite the heterogeneity of this dataset, PMF analysis revealed patterns indicative of microbial dechlorination in the groundwater and provided insight about where dechlorination is occurring, to what extent, and under which geochemical conditions. PMF resolved a factor indicative of a source of 1,2,4-trichlorobenzene and 1,2-dichlorobenzene and two factors representing stages of dechlorination, one more advanced than the other. The PMF results indicated that virtually all of the 1,2-dichlorobenzene at the site arises from its use onsite, not from the dechlorination of trichlorobenzenes. Factors were further interpreted using ancillary data such as geochemical indicators and field parameters also measured in the samples. Analysis suggested that the partial and advanced dechlorination signals occur under different subsurface physical conditions. The results provided field validation of the current understanding of anaerobic dechlorination of chlorinated benzenes in the subsurface developed from laboratory studies. PMF is thereby shown to be a useful tool for investigating chlorinated benzene dechlorination.

Original languageEnglish (US)
Pages (from-to)515-523
Number of pages9
JournalChemosphere
Volume211
DOIs
StatePublished - Nov 1 2018

Fingerprint

Dehalogenation
Dechlorination
Groundwater
dechlorination
Benzene
Factorization
benzene
matrix
groundwater
Degradation
degradation
Data Mining
data mining
Data mining
Contamination
Software
Impurities
software
well
pollutant

All Science Journal Classification (ASJC) codes

  • Environmental Engineering
  • Environmental Chemistry
  • Chemistry(all)
  • Pollution
  • Health, Toxicology and Mutagenesis

Keywords

  • Chlorinated benzenes
  • Data mining
  • Groundwater
  • Positive matrix factorization

Cite this

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title = "Using positive matrix factorization to investigate microbial dehalogenation of chlorinated benzenes in groundwater at a historically contaminated site",
abstract = "Chlorinated benzenes are common groundwater contaminants in the United States, so demonstrating whether they undergo degradation in the subsurface is important in determining the best remedy for this contamination. The purpose of this work was to use a new data mining approach to investigate chlorinated benzene degradation pathways in the subsurface. Positive Matrix Factorization (PMF) was used to analyze long-term measurements of chlorinated benzene concentrations in groundwater at a contaminated site in New Jersey. A dataset containing 597 groundwater samples and 5 chlorinated benzenes and benzene collected from 144 wells over 20 years was investigated using PMF2 software. Despite the heterogeneity of this dataset, PMF analysis revealed patterns indicative of microbial dechlorination in the groundwater and provided insight about where dechlorination is occurring, to what extent, and under which geochemical conditions. PMF resolved a factor indicative of a source of 1,2,4-trichlorobenzene and 1,2-dichlorobenzene and two factors representing stages of dechlorination, one more advanced than the other. The PMF results indicated that virtually all of the 1,2-dichlorobenzene at the site arises from its use onsite, not from the dechlorination of trichlorobenzenes. Factors were further interpreted using ancillary data such as geochemical indicators and field parameters also measured in the samples. Analysis suggested that the partial and advanced dechlorination signals occur under different subsurface physical conditions. The results provided field validation of the current understanding of anaerobic dechlorination of chlorinated benzenes in the subsurface developed from laboratory studies. PMF is thereby shown to be a useful tool for investigating chlorinated benzene dechlorination.",
keywords = "Chlorinated benzenes, Data mining, Groundwater, Positive matrix factorization",
author = "Capozzi, {Staci L.} and Lisa Rodenburg and Valdis Krumins and Donna Fennell and Mack, {E. Erin}",
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T1 - Using positive matrix factorization to investigate microbial dehalogenation of chlorinated benzenes in groundwater at a historically contaminated site

AU - Capozzi, Staci L.

AU - Rodenburg, Lisa

AU - Krumins, Valdis

AU - Fennell, Donna

AU - Mack, E. Erin

PY - 2018/11/1

Y1 - 2018/11/1

N2 - Chlorinated benzenes are common groundwater contaminants in the United States, so demonstrating whether they undergo degradation in the subsurface is important in determining the best remedy for this contamination. The purpose of this work was to use a new data mining approach to investigate chlorinated benzene degradation pathways in the subsurface. Positive Matrix Factorization (PMF) was used to analyze long-term measurements of chlorinated benzene concentrations in groundwater at a contaminated site in New Jersey. A dataset containing 597 groundwater samples and 5 chlorinated benzenes and benzene collected from 144 wells over 20 years was investigated using PMF2 software. Despite the heterogeneity of this dataset, PMF analysis revealed patterns indicative of microbial dechlorination in the groundwater and provided insight about where dechlorination is occurring, to what extent, and under which geochemical conditions. PMF resolved a factor indicative of a source of 1,2,4-trichlorobenzene and 1,2-dichlorobenzene and two factors representing stages of dechlorination, one more advanced than the other. The PMF results indicated that virtually all of the 1,2-dichlorobenzene at the site arises from its use onsite, not from the dechlorination of trichlorobenzenes. Factors were further interpreted using ancillary data such as geochemical indicators and field parameters also measured in the samples. Analysis suggested that the partial and advanced dechlorination signals occur under different subsurface physical conditions. The results provided field validation of the current understanding of anaerobic dechlorination of chlorinated benzenes in the subsurface developed from laboratory studies. PMF is thereby shown to be a useful tool for investigating chlorinated benzene dechlorination.

AB - Chlorinated benzenes are common groundwater contaminants in the United States, so demonstrating whether they undergo degradation in the subsurface is important in determining the best remedy for this contamination. The purpose of this work was to use a new data mining approach to investigate chlorinated benzene degradation pathways in the subsurface. Positive Matrix Factorization (PMF) was used to analyze long-term measurements of chlorinated benzene concentrations in groundwater at a contaminated site in New Jersey. A dataset containing 597 groundwater samples and 5 chlorinated benzenes and benzene collected from 144 wells over 20 years was investigated using PMF2 software. Despite the heterogeneity of this dataset, PMF analysis revealed patterns indicative of microbial dechlorination in the groundwater and provided insight about where dechlorination is occurring, to what extent, and under which geochemical conditions. PMF resolved a factor indicative of a source of 1,2,4-trichlorobenzene and 1,2-dichlorobenzene and two factors representing stages of dechlorination, one more advanced than the other. The PMF results indicated that virtually all of the 1,2-dichlorobenzene at the site arises from its use onsite, not from the dechlorination of trichlorobenzenes. Factors were further interpreted using ancillary data such as geochemical indicators and field parameters also measured in the samples. Analysis suggested that the partial and advanced dechlorination signals occur under different subsurface physical conditions. The results provided field validation of the current understanding of anaerobic dechlorination of chlorinated benzenes in the subsurface developed from laboratory studies. PMF is thereby shown to be a useful tool for investigating chlorinated benzene dechlorination.

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