Application of discriminant analysis with clustered data to determine anthropogenic metals contamination.
Environmental site assessments involve, among other things, characterization of the nature and extent of contamination. In general, environmental assessors are interested in empirical methodologies that can be applied to a broad range of environmental media (e.g., soils, sediments, etc.) and situations. To date, no unified guidance has been adopted, and site investigations usually involve a tiered process with multiple analyses. We propose a multivariate analysis methodology utilizing discriminant analysis with clustered chemical concentrations as a novel application to environmental site assessments that determine, in relative order of magnitude, contaminated chemicals. Finite mixture models are presented as a means to assess latent chemical clusters with some basis in statistical inference. The methodology is illustrated with a typical localized data set containing total metal and metalloid (i.e., chemical) concentrations, extracted from bulk soil collected from reference and site-related locations, obtained from a former military installation in the southeast United States. The illustration is particularly applicable because site-related soils inherently possessed higher background chemical levels than reference soils, which biased conventional analyses. However, contrasting chemical compositions were inferred within site-related samples illustrating the versatility of the proposed methodology. Using these results along with known information regarding the history of contamination at the site, a qualitative and quantitative assessment of contaminated chemicals was made. Results are intended for illustration purposes only and are discussed within the context of environmental site assessment.
Anderson
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RH
National Center for Environmental Assessment, Office of Research and Development, United States Environmental Protection Agency, Cincinnati, OH 45268, United States. anderson.hunter@epa.gov
Farrar
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DB
Thoms
S R
SR
D016000:Cluster Analysis; D016002:Discriminant Analysis; D004784:Environmental Monitoring / Q000379:methods / Q000706:statistics & numerical data; D005843:Geography; D019015:Geologic Sediments / Q000032:analysis* / Q000737:chemistry; D008670:Metals / Q000032:analysis*; D015999:Multivariate Analysis; D018570:Risk Assessment; D012989:Soil Pollutants / Q000032:analysis* / Q000737:chemistry; D015144:Southeastern United States
D008670:Metals; D012989:Soil Pollutants