A hybrid risk assessment model using artificial intelligence techniques

Abstract

The purpose of this thesis is to study and reduce the uncertainty involved in risk assessment procedures. Uncertainty regarding gaps in scientific theory required for predictions is dealt with using a new hybrid model which is also extended to include information on dose/response characteristics resulting in a full risk assessment methodology. The problem of making quantitative assessments of the risks associated with human exposure to toxic contaminants in the environment is a pressing one. Extensive measurement programmes can be very expensive. The high complexity and inherent heterogeneity of exposure analysis is still a major challenge. The approach tested here to this challenge is to use a new model incorporating sophisticated artificial intelligence algorithms. Exposure assessment often requires that a number of factors be evaluated, including exposure concentrations, intake rates, exposure times and frequencies. These factors are incorporated into a system which can 'learn' the relevant relationships based on a known data set. The results can then be applied to new data sets and thus be applied widely without the need for extensive measurements. In this thesis an example is developed for human health risk through inhalation and dermal absorption to benzene from vehicular emissions in the cities of Auckland and Christchurch, New Zealand. Risk factors considered were inhaled contaminant concentration, dermal absorbed concentration, age. body weight and activity patterns of humans. There are three major variables affecting the absorbed concentration: emissions (mainly from motor vehicles), meteorological (wind speed, temperature and atmospheric stability) and site factors (hilly, flat etc). The complex relationships are established using neural network and fuzzy logic. The relationships between the variables are dynamic and non-linear. This hybrid model proved highly successful in the application described in the thesis. Further research should be focussed in determining the human risk due to mixtures of chemicals and for special target groups.