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Characterizing Background Metal Concentrations in Soils from Southeastern U.S. Cities with Bayesian Networks
Citation:
Carriger, John F, Robert G Ford, T. Frederick, S. Chan, AND Y. Fung. Characterizing Background Metal Concentrations in Soils from Southeastern U.S. Cities with Bayesian Networks. 2021 Bayesialab Conference, NA, N/A, October 11 - 15, 2021.
Impact/Purpose:
This presentation focuses on ongoing research on the application of Bayesian networks to an urban background metals concentration dataset for the southeast U.S. The presentation will discuss the methods applied and the insights gained as well as the challenges faced in using Bayesian networks for this application area. The conference attendees are practitioners from diverse fields and may have insights that can benefit the analysis. Moreover, this area is important for environmental assessments and the application is new to understanding urban background concentrations of metals.
Description:
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.