An improved model for prediction of resuspension

Reed M. Maxwell, Lynn R. Anspaugh

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

Abstract

A complete, historical dataset is presented of radionuclide resuspension-factors. These data span six orders of magnitude in time (ranging from 0.1 to 73,000 d), encompass more than 300 individual values, and combine observations from events on three continents. These data were then used to derive improved, empirical models that can be used to predict resuspension of trace materials after their deposit on the ground. Data-fitting techniques were used to derive models of various types and an estimate of uncertainty in model prediction. Two models were found to be suitable: a power law and the modified Anspaugh et al. model, which is a double exponential. Though statistically the power-law model provides the best metrics of fit, the modified Anspaugh model is deemed the more appropriate due to its better fit to data at early times and its ease of implementation in terms of closed analytical integrals.

Original languageEnglish (US)
Pages (from-to)722-730
Number of pages9
JournalHealth Physics
Volume101
Issue number6
DOIs
StatePublished - Dec 2011
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Epidemiology
  • Radiology Nuclear Medicine and imaging
  • Health, Toxicology and Mutagenesis

Keywords

  • accidents, nuclear
  • air sampling
  • Chernobyl
  • contamination, environmental

Fingerprint Dive into the research topics of 'An improved model for prediction of resuspension'. Together they form a unique fingerprint.

Cite this