Services
Ecological Risk Assessment
For ecological risk assessment contact:
Representative Projects
- Industrial Site Ecological Risk Assessment
- Freshwater Pond Stage II Ecological Risk Assessment
- Gasoline Station Stage II Ecological Risk Assessment
Description
Since before the 1970s, a number of federal, state and local environmental regulations have mandated the assessment of ecological impacts, which may result from development or other uses of land and environmental resources. The science of ecological risk assessment and the regulations directing its application have developed to address the increased public concern that the environment can be readily damaged by human activity.
At OTO, we don’t believe there is a viable cook-book approach to ecological risk assessment. Each assessment should describe the rationale behind the assessment by explaining how the assessment’s overall design answers the environmental questions posed, and it needs to be supported by the level of technical rigor necessary to make compelling and credible scientific arguments.
An ecological risk assessor uses quantitative methods to identify the effects of potentially harmful contaminants on terrestrial, wetland, and aquatic biota. The means through which the identification of potential risk occurs is referred to as an Ecological Risk Assessment (ERA). The ERA incorporates chemical/toxicity data, toxicological profiles, potential contaminant migration pathways, and an evaluation of biota/habitats at and within the vicinity of the site. Sophisticated “measures of effect” studies are also conducted as part of the ERA. These collective lines of evidence will either identify risk to ecological receptors related to the onsite contaminants or not.
Our risk assessments range from Massachusetts Contingency Plan (MCP) Stage I screening assessments to more complex MCP Stage II and US Environmental Protection Agency (USEPA) baseline ecological risk assessments. A hallmark of our approach is the use of rigorous biostatistical techniques to help us differentiate between real effects and false positives created by the use of impaired or inadequate data sets.
