Development of Pattern Recognition Techniques for the Evaluation of Toxicant Impacts to Multispecies Systems.

Abstract

The program has evaluated the toxicity of two complex mixtures, the water soluble fractions (WSF) of the commercial turbine fuel Jet-A and the military fuel JP-4 using single species toxicity tests as well as the Standard Aquatic Microcosm (SAM). The WSF were not particularly toxic to the algal species tested although toxicity was observed when Daphnia magna was used as the test organism. The SAM experiments have been completed using concentrations of 0.0, 1, 5 and 15 percent WSF. Among the more interesting effects were the shifts in time of population peaks and some other variables compared to controls. Regression analysis of control to treatment groups often demonstrated only weak correlations. Multivariate nonmetric clustering (NMC) analysis, however, also demonstrated a marked separation between the 4 treatment groups for the Jet-A experiment. NMC proved to be the most powerful multivariate method of those examined for distinguishing the control and other treatment groups. An additional research effort is focused on applying multivariate methods and other mathematical techniques into the process of ecological risk assessment. Application of multivariate methods coupled with new ways of distinguishing uncertainty have the potential for revolutionizing the risk assessment process. Jet fuel, Non-metric clustering, Microcosms, Risk assessment.

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Document Details

Document Type
Technical Report
Publication Date
Jun 22, 1992
Accession Number
ADA253251

Entities

People

  • Robin A. Matthews
  • Wayne G. Landis

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Air Force
  • Algae
  • Artificial Intelligence
  • Chemistry
  • Clustering
  • Complex Mixtures
  • Computer Science
  • Data Analysis
  • Data Sets
  • Databases
  • Ecosystems
  • Ecotoxicology
  • Environment
  • Fuels
  • Information Science
  • Standards
  • Toxicity

Fields of Study

  • Environmental science

Readers

  • Aquatic Ecology
  • Petroleum Engineering
  • Regression Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference