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(21-24) Fuzzy logic and environmental risk assessment. Ghomshei, Mory*,1, Meech, John1, Keen, Particia, 1 University of British Columbia, Centre for Environmental Research in Metals, Minerals and Materials (CERM3). UBC/MMPE, 6350 Stor, Vancouver, BC, Canada ABSTRACT- Fuzzy logic and environmental risk assessment Mory Ghomshei, John Meech and Patricia Keen University of British Columbia, Centre for Environmental Research in Metals, Minerals and Materials (CERM3). UBC/MMPE, 6350 Stores Road, Vancouver, B.C. V6T 1Z4 In environmental risk assessment regulators, health experts, epidemiologists, engineers, regulators, ecologists, politicians and general public, perceive the environmental risk in different ways. Considering that perceptions create a realities, the task of a realistic environmental risk assessment should take into consideration, not only the phenomenological truth but the perception-based realities. While conventional mathematics is often unable to handle perceptions, fuzzy logic provides an excellent tool to quantify perception-based realities and to conduct appropriate arithmetic to arrive at a net reality. In Fuzzy arithmetic, uncertainty in the input information propagates linearly (i.e. neither magnified nor dampened) into the net output. In this approach each input reality (perception-based or phenomenological) is evaluated by a fuzzy set which is simply the distribution function of the degree of belief in a concept (such as contamination) over a range of variations of another (often more quantitative) concept (such as concentration of a contaminant in a particular medium). This paper provides a range of fuzzy sets for environmental risk assessment. Concepts such as contamination, socio-economical impact, health effect are defined in the form of fuzzy sets. An example of heavy metal contamination from an orphan mine-site in British Columbia (Canada) is given to demonstrate the efficiency of the fuzzy approach in handling complicated environmental risk issues. Key words: environmental risk assessment, fuzzy logic, mineral industry, heavy metal contamination |
