BayesiaLab
Characterizing Background Metal Concentration in Soils from Southeastern U.s. Cities

Characterizing Background Metal Concentration in Soils from Southeastern U.S. Cities

Presented at the 9th Annual BayesiaLab Conference on October 14, 2021.

Abstract

Understanding the background metal concentrations of soils is important for setting remedial goals at polluted sites. To better understand urban background concentrations for contaminated site remediation and risk assessment, personnel from Region 4 at the U.S. Environmental Protection Agency led a collection and analysis effort for urban soils in five states of the southeastern U.S. Each of the cities within these states had 50 samples collected from randomly chosen grid cells with additional qualifying criteria for within-grid cell sampling. Seven cities in these five states were included in the current Bayesian network analysis (Gainesville, FL; Lexington, KY; Louisville, KY; Raleigh, NC; Winston-Salem, NC; Columbia, SC; and Memphis, TN). Chemical concentration data frequently contain analyzed values that are considered non-detected data. These data are often assumed to have a potential concentration that ranges from 0 to the method detection limit of the analysis. Preliminary work examined the influence of substitution for case file usage on discretization thresholds for these non-detected data. The final metals chosen for analysis and other urban site measurement data were condensed into a single case file with each case representing one sampling site with columns for concentrations of metals, coordinates, land use, nearby emission sources, city, and state information for each sampling site. Data clustering with expectation-maximization was used to create a new factor variable with cluster states based on the metals data from all cities. Relationships between the identified metals concentration clusters and nodes from the case file that were excluded from the clustering analysis (cities, nearby emission sources, and land use) were also examined. These analyses explored the relationship of different sampling site characteristics with the metals clusters through sensitivity analyses and probability distribution changes. Data clustering analysis can be useful for interpreting and exploring background metals concentration sampling data for urban regions.

EPA Disclaimer: The views expressed in this presentation are those of the authors and do not necessarily represent the views or policies of the U.S. Environmental Protection Agency.

Authors

John F. Carriger
U.S. Environmental Protection Agency, Office of Research and Development, Center for Environmental Solutions and Emergency Response, Land Remediation Technology Division, Environmental Decision Analytics Branch, Cincinnati, OH

Robert G. Ford
U.S. Environmental Protection Agency, Office of Research and Development, Center for Environmental Solutions and Emergency Response, Land Remediation Technology Division, Contaminated Sites and Sediments Branch, Cincinnati, OH

Tim Frederick
Sydney Chan
U.S. Environmental Protection Agency, Region 4, Superfund and Emergency Management Division, Resource and Scientific Integrity Branch, Scientific Support Section, Atlanta, GA

Yuen-Chang Fung
Tetra Tech, Inc.

About the Presenter

John Carriger is a researcher with the U.S. Environmental Protection Agency’s Office of Research and Development. John received his Ph.D. in Marine Science from the College of William & Mary in 2009. His research interests are developing and applying causal modeling, decision analysis, and risk assessment tools to diverse environmental problems. John lives and works in Cincinnati, OH, USA.


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