Ecological Risk Assessment,Environmental Effects Monitoring and Radionuclides

摘要

The fundamentals of Ecological Risk Assessment (ERA) have been outlined by Suter et al. (1993) and prescribed in various federal, provincial and state guidance documents (e.g., CCME, 1996; U.S. EPA, 1998; MOE, 1996; Oregon DEQ, 1998). While the various guidance documents differ somewhat in their details, some common essential features of ERA are shared, as follows: rn 1. quantitative estimation of the potential for ecological effects; 2. consideration of specific contaminants and/or other stressors; rn 3. a focus on population-relevant endpoints of effect; 4. risk quotients (RQ) indicate potential for effects, or likelihood of effect is estimated using probabilistic methods; and rn 5. weight of evidence considers environmental effects monitoring, as well as computed estimates of effect potential. rnThe weight of evidence considerations take us into the realm of Environmental Effects Monitoring (EEM). Formal EEM programs have been implemented in Canada over the past decade, first in the pulp and paper sector, and most recently in the mining sector (Canada Gazette, 1992, 2002; AQUAMIN, 1996; Environment Canada, 1997, 1998). The philosophy behind EEM is that, to evaluate our success in environmental protection, we must: rn 1. go beyond effluent chemical characterization; rn 2. focus on measuring biological effects; 3. shift our effort into the receiving environment; rn 4. develop an understanding of the local ecosystem; and rn 5. design monitoring studies based on this understanding, i.e., based on risk models. rnThe ERA and EEM concepts are closely related (Figure 1). In ERA, we develop a model of the local ecosystem that enables prediction of where and how particular stressors (e.g., chemicals, radionuclides, thermal increments, entrainment/impingement) might produce adverse effects on natural biota. In EEM, we monitor local populations to look for evidence of such effects. If we see effects where we’ve predicted them, we gain confidence in the ERA model. If we see no effects where we expected to see effects, we may wish to reconsider the risk model.