An approach for addressing hard-to-detect hot spots

Area of Science:

• Environmental Science
• Radiological Health
• Risk Assessment

Background:

• Standard Multi-Agency Radiation Survey and Site Investigation Manual (MARSSIM) methods may miss localized contamination.
• Hard-to-detect nuclides and chemical contaminants are often identified only through volumetric sampling.
• Contamination is frequently highly localized, posing challenges for uniform sampling strategies.

Purpose of the Study:

• To introduce a Bayesian approach for identifying hard-to-detect nuclides and chemical hot spots.
• To enhance environmental characterization and site cleanup under the Comprehensive Environmental Response, Compensation, and Liability Act.
• To optimize sampling strategies for accurate risk assessment.

Main Methods:

• Utilizing available data to predict the probability of unacceptable hot spots.
• Integrating Bayesian analysis with systematic random sampling and radiation scanning.
• Employing the data quality objectives process for targeted sampling campaigns.

Main Results:

• The Bayesian approach provides a probabilistic framework for hot spot identification.
• Coupling Bayesian methods with graded sampling can optimize characterization efforts.
• This approach enhances the likelihood of detecting localized contamination.

Conclusions:

• The proposed Bayesian method offers a more effective strategy for addressing challenging contamination scenarios.
• Optimized sampling campaigns are crucial for identifying risk-relevant hot spots.
• This approach improves the accuracy of environmental site investigations and remediation.