Discriminant analysis as a decision-making tool for geochemically fingerprinting sources of groundwater salinity

Nathaniel P. Chien, Laura K. Lautz

Research output: Research - peer-reviewArticle

Abstract

Concern over contamination of groundwater resources in areas impacted by anthropogenic activities has led to an increasing number of baseline groundwater quality surveys intended to provide context for interpreting water quality data. Flexible screening tools that can parse through these large, regional datasets to identify spatial or temporal changes in water quality are becoming more important to groundwater scientists. One such tool, developed from previous work by the authors, makes use of linear discriminant analysis (LDA) to identify the most probable source of chloride salinity in groundwater samples based on their geochemical fingerprints. Here, we applied the model to a dataset of shallow groundwater with known sources of contamination compiled from two studies of groundwater quality in Illinois: Panno et al. (2005) and Hwang et al. (2015). By predicting the source of salinity in groundwater samples for which the sources of contamination are known, we validated model prediction-accuracy. Results show high classification accuracy for groundwater samples impacted by basin brines (e.g. deep saline groundwater) and road salt (> 80%), with diminishing success for those impacted by organic sources of chloride, such as septic effluent and animal waste. Posterior probabilities, a statistic inherent to LDA, provide a proxy for prediction confidence that enables the model to be used for assessment and accountability measures, such as identifying parties responsible for contamination. LDA is complementary to fingerprinting using halogen ratios (e.g. Cl/Br) because it implicitly relies on halogen ratios for classification decisions while providing a clearer, more quantitative classification of contamination sources. Our model is ideal for regional assessment or initial screening of salinity sources in groundwater because it makes use of commonly measured solute concentrations in publicly available water quality databases.

LanguageEnglish (US)
Pages379-387
Number of pages9
JournalScience of the Total Environment
Volume618
DOIs
StatePublished - Mar 15 2018

Fingerprint

discriminant analysis
decision making
salinity
groundwater
Discriminant analysis
Groundwater
Decision making
contamination
Contamination
water quality
Water quality
halogen
chloride
prediction
screening
Halogens
Chlorides
Screening
accountability
groundwater resource

Keywords

  • Basin brines
  • Deicers
  • Geochemical fingerprinting
  • Linear discriminant analysis
  • Road salt
  • Salinity

ASJC Scopus subject areas

  • Environmental Engineering
  • Environmental Chemistry
  • Waste Management and Disposal
  • Pollution

Cite this

Discriminant analysis as a decision-making tool for geochemically fingerprinting sources of groundwater salinity. / Chien, Nathaniel P.; Lautz, Laura K.

In: Science of the Total Environment, Vol. 618, 15.03.2018, p. 379-387.

Research output: Research - peer-reviewArticle

@article{ee1e729d642e4e76a86d84175ff76f80,
title = "Discriminant analysis as a decision-making tool for geochemically fingerprinting sources of groundwater salinity",
abstract = "Concern over contamination of groundwater resources in areas impacted by anthropogenic activities has led to an increasing number of baseline groundwater quality surveys intended to provide context for interpreting water quality data. Flexible screening tools that can parse through these large, regional datasets to identify spatial or temporal changes in water quality are becoming more important to groundwater scientists. One such tool, developed from previous work by the authors, makes use of linear discriminant analysis (LDA) to identify the most probable source of chloride salinity in groundwater samples based on their geochemical fingerprints. Here, we applied the model to a dataset of shallow groundwater with known sources of contamination compiled from two studies of groundwater quality in Illinois: Panno et al. (2005) and Hwang et al. (2015). By predicting the source of salinity in groundwater samples for which the sources of contamination are known, we validated model prediction-accuracy. Results show high classification accuracy for groundwater samples impacted by basin brines (e.g. deep saline groundwater) and road salt (> 80%), with diminishing success for those impacted by organic sources of chloride, such as septic effluent and animal waste. Posterior probabilities, a statistic inherent to LDA, provide a proxy for prediction confidence that enables the model to be used for assessment and accountability measures, such as identifying parties responsible for contamination. LDA is complementary to fingerprinting using halogen ratios (e.g. Cl/Br) because it implicitly relies on halogen ratios for classification decisions while providing a clearer, more quantitative classification of contamination sources. Our model is ideal for regional assessment or initial screening of salinity sources in groundwater because it makes use of commonly measured solute concentrations in publicly available water quality databases.",
keywords = "Basin brines, Deicers, Geochemical fingerprinting, Linear discriminant analysis, Road salt, Salinity",
author = "Chien, {Nathaniel P.} and Lautz, {Laura K.}",
year = "2018",
month = "3",
doi = "10.1016/j.scitotenv.2017.11.019",
volume = "618",
pages = "379--387",
journal = "Science of the Total Environment",
issn = "0048-9697",
publisher = "Elsevier",

}

TY - JOUR

T1 - Discriminant analysis as a decision-making tool for geochemically fingerprinting sources of groundwater salinity

AU - Chien,Nathaniel P.

AU - Lautz,Laura K.

PY - 2018/3/15

Y1 - 2018/3/15

N2 - Concern over contamination of groundwater resources in areas impacted by anthropogenic activities has led to an increasing number of baseline groundwater quality surveys intended to provide context for interpreting water quality data. Flexible screening tools that can parse through these large, regional datasets to identify spatial or temporal changes in water quality are becoming more important to groundwater scientists. One such tool, developed from previous work by the authors, makes use of linear discriminant analysis (LDA) to identify the most probable source of chloride salinity in groundwater samples based on their geochemical fingerprints. Here, we applied the model to a dataset of shallow groundwater with known sources of contamination compiled from two studies of groundwater quality in Illinois: Panno et al. (2005) and Hwang et al. (2015). By predicting the source of salinity in groundwater samples for which the sources of contamination are known, we validated model prediction-accuracy. Results show high classification accuracy for groundwater samples impacted by basin brines (e.g. deep saline groundwater) and road salt (> 80%), with diminishing success for those impacted by organic sources of chloride, such as septic effluent and animal waste. Posterior probabilities, a statistic inherent to LDA, provide a proxy for prediction confidence that enables the model to be used for assessment and accountability measures, such as identifying parties responsible for contamination. LDA is complementary to fingerprinting using halogen ratios (e.g. Cl/Br) because it implicitly relies on halogen ratios for classification decisions while providing a clearer, more quantitative classification of contamination sources. Our model is ideal for regional assessment or initial screening of salinity sources in groundwater because it makes use of commonly measured solute concentrations in publicly available water quality databases.

AB - Concern over contamination of groundwater resources in areas impacted by anthropogenic activities has led to an increasing number of baseline groundwater quality surveys intended to provide context for interpreting water quality data. Flexible screening tools that can parse through these large, regional datasets to identify spatial or temporal changes in water quality are becoming more important to groundwater scientists. One such tool, developed from previous work by the authors, makes use of linear discriminant analysis (LDA) to identify the most probable source of chloride salinity in groundwater samples based on their geochemical fingerprints. Here, we applied the model to a dataset of shallow groundwater with known sources of contamination compiled from two studies of groundwater quality in Illinois: Panno et al. (2005) and Hwang et al. (2015). By predicting the source of salinity in groundwater samples for which the sources of contamination are known, we validated model prediction-accuracy. Results show high classification accuracy for groundwater samples impacted by basin brines (e.g. deep saline groundwater) and road salt (> 80%), with diminishing success for those impacted by organic sources of chloride, such as septic effluent and animal waste. Posterior probabilities, a statistic inherent to LDA, provide a proxy for prediction confidence that enables the model to be used for assessment and accountability measures, such as identifying parties responsible for contamination. LDA is complementary to fingerprinting using halogen ratios (e.g. Cl/Br) because it implicitly relies on halogen ratios for classification decisions while providing a clearer, more quantitative classification of contamination sources. Our model is ideal for regional assessment or initial screening of salinity sources in groundwater because it makes use of commonly measured solute concentrations in publicly available water quality databases.

KW - Basin brines

KW - Deicers

KW - Geochemical fingerprinting

KW - Linear discriminant analysis

KW - Road salt

KW - Salinity

UR - http://www.scopus.com/inward/record.url?scp=85033394978&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85033394978&partnerID=8YFLogxK

U2 - 10.1016/j.scitotenv.2017.11.019

DO - 10.1016/j.scitotenv.2017.11.019

M3 - Article

VL - 618

SP - 379

EP - 387

JO - Science of the Total Environment

T2 - Science of the Total Environment

JF - Science of the Total Environment

SN - 0048-9697

ER -