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Ecological Risk Assessment Step 7

Sections of Step 7 Baseline ERA (BERA) Risk Characterization:

Risk Characterization

Risk Characterization is the seventh step of the Superfund Ecological Risk Assessment process and includes two major components: risk estimation and risk description. Risk characterization should be well-balanced, clear, reasonable, consistent, easy to follow and understand, with all assumptions, uncertainties, and professional judgments clearly identified. 

Risk Estimation

Risk estimation: combining exposure profiles with exposure-effects information and summarizing the associated uncertainties.

Data interpretation methods should be presented in the risk characterization documentation. For example, if the triad approach (i.e., toxicity test, benthic invertebrate survey, and sediment chemistry) was used to evaluate contaminated sediments, the risk estimation section should describe how the three types of studies are integrated to draw conclusions about risk.

In addition to developing point estimates of exposure concentrations, it might be possible to develop a distribution of exposure levels based on the potential variability in various exposure parameters. This is called a dose-response curve, where likely responses (i.e., harmful effects) can be predicted from specific levels of contamination. Probabilities of exceeding a threshold for adverse effects might then be estimated.

Risk Description

Risk description: provides information important for interpreting the risk results and identifies a level for harmful effects on the assessment endpoints.

A key to risk description for Superfund sites is the documentation of environmental contamination levels that bound the threshold for adverse effects on the assessment endpoints. In other words, what are the NOAELs and LOAELs for specific animals and plants that are affected at the site under investigation. The risk description can also provide information to help the risk manager judge the likelihood and ecological significance of the estimated risks. It is important to document the contaminant concentrations in each environmental medium (soil, sediment, water) that bound the threshold for estimated harmful ecological effects, because there is uncertainty inherent in the data and models used. The lower bound of the threshold would be based on consistent conservative assumptions and NOAEL toxicity values. The upper bound would be based on observed impacts or predictions that ecological impacts could occur. The upper bound would be developed using consistent assumptions, site-specific data, LOAEL toxicity values, or an impact evaluation. 

In addition to identifying one or more thresholds for effects, the risk assessment team might develop estimates of the probability that exposure levels would exceed the ecotoxicity thresholds given the distribution of values likely for various exposure parameters.

The risk assessor should also put the estimates in context with a description of the extent, magnitude, and potential ecological significance of those estimates. Additional ecological risk descriptors are as follows:

Uncertainty Analysis

There are several sources of uncertainties associated with ecological risk estimates. One is the initial selection of Contaminants of Potential Ecological Concern (COPECs) based on the sampling data and available toxicity information. Other sources of uncertainty include estimates of toxicity to ecological receptors at the site based on limited data from the laboratory, other ecosystem, or over a limited period of time from the site; the exposure assessment as a result of uncertainty in chemical monitoring data and models; and estimates of risk when simultaneous exposures to multiple COPECs occur. These sources fall into three basic categories of uncertainties:

  1. Conceptual model uncertainties;
  2. Natural variation and parameter errors;
  3. Modeling error.

Conceptual model uncertainties (CSM):

The initial description of the ecological problems at a site, likely exposure pathways, COPECs, and exposed ecological components required a degree of professional judgment and assumptions, which results in uncertainty in the CSM.

Natural variation uncertainties:

Ecosystems include highly variable abiotic (e.g., weather, soils) and biotic (e.g., population density) components. Only a fraction of the instances can be sampled and known, thus leaving an uncertainty concerning the true distribution of values.

Modeling uncertainty:

There is uncertainty over how well a model approximates true relationships among site-specific environmental conditions and plants and animals. Models tend to be relatively simple and normally only partially validated with field tests.

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