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Probability Surveys, Conditional Probability, and Ecological Risk Assessment
Citation:
PAUL, J. F. AND W. R. MUNNS, JR. Probability Surveys, Conditional Probability, and Ecological Risk Assessment. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY. Society of Environmental Toxicology and Chemistry, Pensacola, FL, 30(6):1488-1495, (2011).
Impact/Purpose:
Ecological risk assessment is a process to estimate the likelihood of adverse ecological effect (degradation) given exposure to varying levels of environmental stressors. In this definition, likelihood is equivalent to the probability of an event or, in the case of ecological risk, the probability that adverse effects (degradation) will result from exposure. Risk is estimated through the overlay of information describing exposure intensity in time and space with the expected responses of ecological systems to that exposure. Typically, the expected responses are modeled using laboratory and/or field data. Several methods have been used to estimate risk, including calculation of hazard (risk) quotients, fault tree analysis, and simulation modeling. Although the intention of ecological risk assessment is to estimate the probability of adverse effects that are occurring or will occur in the future, some of these methods do not do so in terms of a probability of occurrence, either in space or in time. The purpose of this article is to show that data from probability-based environmental resource monitoring programs, such as the U.S. Environmental Protection Agency’s (U.S. EPA) Environmental Monitoring and Assessment Program, and conditional probability analysis can serve as a basis for estimating ecological risk probabilistically over broad geographic areas. Under certain conditions (including appropriate stratification of the sampled population, sufficient density of samples, and sufficient range of exposure levels paired with concurrent response values), this empirical approach provides estimates of risk using extant field-derived monitoring data. The monitoring data were used to prescribe the exposure field and to model the exposure–response relationship. We illustrate this approach by estimating risks to benthic communities from low dissolved oxygen (DO) in freshwater streams of the mid-Atlantic region and in estuaries of the Virginian Biogeographical Province of the United States. In both cases, the estimates of risk are consistent with the U.S. EPA’s ambient water quality criteria for DO. Conditional probability analysis using probability-based surveillance extends the range of risk assessment tools available to inform decisions concerning management of stressors in the environment.
Description:
We show that probability-based environmental resource monitoring programs, such as the U.S. Environmental Protection Agency’s (U.S. EPA) Environmental Monitoring and Assessment Program, and conditional probability analysis can serve as a basis for estimating ecological risk over broad geographic areas. Under certain conditions (including appropriate stratification of the sampled population, sufficient density of samples, and sufficient range of exposure levels paired with concurrent response values), this empirical approach provides estimates of risk using extant field-derived monitoring data. The monitoring data were used to prescribe the exposure field and to model the exposure–response relationship. We illustrate this approach by estimating risks to benthic communities from low dissolved oxygen (DO) in freshwater streams of the mid-Atlantic region and in estuaries of the Virginian Biogeographical Province of the United States. In both cases, the estimates of risk are consistent with the U.S. EPA’s ambient water quality criteria for DO.