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An improved model for prediction of resuspension
Reed M Maxwell1, Lynn R Anspaugh
1Department of Geology and Geological Engineering, Colorado School of Mines, Golden, CO 80401, USA. rmaxwell@mines.edu
Health Physics
|November 4, 2011
Summary
This study presents a comprehensive dataset of radionuclide resuspension factors, developing improved models to predict the airborne dispersal of materials deposited on the ground.
Area of Science:
- Environmental Science
- Radiochemistry
- Atmospheric Science
Background:
- Radionuclide resuspension is a critical process in environmental contamination.
- Existing models for predicting resuspension have limitations in accuracy and applicability.
- A comprehensive historical dataset is needed to improve predictive capabilities.
Purpose of the Study:
- To compile a comprehensive historical dataset of radionuclide resuspension factors.
- To derive and validate empirical models for predicting radionuclide resuspension.
- To assess the uncertainty associated with model predictions.
Main Methods:
- Compilation of over 300 historical data points spanning six orders of magnitude in time.
- Application of data-fitting techniques to derive various empirical models.
- Statistical evaluation of model performance, including power law and modified Anspaugh models.
Main Results:
- A robust dataset of radionuclide resuspension factors was established.
- Two models, a power law and a modified Anspaugh model, were identified as suitable.
- The modified Anspaugh model was favored for its early-time data fit and analytical tractability.
Conclusions:
- The developed empirical models provide improved predictions for radionuclide resuspension.
- The modified Anspaugh model offers a practical tool for assessing environmental contamination risks.
- Accurate resuspension factor data is crucial for environmental remediation and safety assessments.
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