ISSN:
1432-1009
Keywords:
Environmental risk
;
Assessment method
;
Fuzzy set
;
Green Bay
;
Great Lakes
Source:
Springer Online Journal Archives 1860-2000
Topics:
Energy, Environment Protection, Nuclear Power Engineering
Notes:
Abstract The Science Advisory Board of the US Environmental Protection Agency has recommended that risk reduction strategies become the centerpiece of environmental protection. The goal in developing such strategies is to identify opportunities for greatest reduction of ecological risks. This is a perspective that is significantly more comprehensive than the traditional focus on human health risks arising from environmental degradation. The identification of ecological risks upon which environmental protection efforts should be focused requires an ecological risk assessment methodology that is based on anthropogenic stressors affecting an ecosystem and a set of impaired use criteria. A methodology based on this concept is developed and discussed in this article. The methodology requires that risk values be assigned to each ecosystem stressor-impaired use pair that reflect the degree to which the given stressor contributes to ecosystem risk as measured by the given impaired use criterion. Once these data are available, mathematical analyses based on concepts from fuzzy set theory are performed to obtain a ranking of ecosystem stressors. The methodology has been tested by applying it to a case study involving Green Bay of Lake Michigan. A workshop was held in which 11 persons with extensive knowledge of the Green Bay ecosystem determined risk values through a group-consensus process. The analytical portion of the methodology was then used to rank the ecosystem risks (stressors) from several perspectives, including prevention management and remediation management. The overall conclusion of the workshop participants was that the fuzzy set decision model is a useful and effective methodology for differentiating environmental risk.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1007/BF02393770
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