A NEW DOSE MODEL FOR ASSESSMENT OF HEALTH RISK DUE TO CONTAMINANTS IN AIR

Making quantitative assessments of risks associated with human exposure to toxic contaminants in the environment is a pressing problem. This paper demonstrates the capability of a new computational technique involving the use of fuzzy logic and neural networks to produce realistic risk assessments. The systematic analysis of human exposure involves the use of measurements and models, the results of which are often used in policy decisions. However, the interpretation and measurement of these models often involve substantial uncertainty and expense. The approach tested here uses a new model incorporating sophisticated artificial intelligence algorithms. Exposure assessment often requires that a number of factors be evaluated. These factors are then incorporated into a system that can "learn" the relevant relationships based on a known data set. Results can then be applied to new data sets without the need for extensive measurements. In this analysis, an example is developed for human health risk through inhalation exposure to benzene from vehicular emissions in Auckland and Christchurch, New Zealand. Risk factors considered were inhaled contaminant concentration (ICC), age, body weight, and activity patterns. Three major variables affecting the ICC were emissions, meteorology, and site factors. Results are presented.

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  • Corporate Authors:

    Air & Waste Management Association

    One Gateway Center, 3rd Floor, 420 Fort Duquesne Boulevard
    Pittsburgh, PA  United States  15222
  • Authors:
    • Rajkumar, T
    • Guesgen, H W
    • ROBINSON, S
    • Fisher, G W
  • Publication Date: 2000-1

Language

  • English

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Filing Info

  • Accession Number: 00798708
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Sep 12 2000 12:00AM