UNCERTAINTY AND SENSITIVITY ANALYSIS OF CHRONIC EXPOSURE RESULTS WITHTHE MACCS REACTOR ACCIDENT CONSEQUENCE MODEL

Uncertainty and sensitivity analysis techniques based on Latin hypercu be sampling, partial correlation analysis and stepwise regression anal ysis are used in an investigation with the MACCS model of the chronic exposure pathways associated with a severe accident at a nuclear power station. The primary purpose of this study is to provide guidance on the variables to be considered in future review work to reduce the unc ertainty in the important variables used in the calculation of reactor accident consequences. The effects of 75 imprecisely known input vari ables on the following reactor accident consequences are studied: crop growing-season dose, crop long-term dose, water ingestion dose, milk growing-season dose, long-term groundshine dose, long-term inhalation dose, total food pathways dose, total ingestion pathways dose, total l ong-term pathways dose, total latent cancer fatalities, area-dependent . cost, crop disposal cost, milk disposal cost, population-dependent c ost, total economic cost, condemnation area, condemnation population, crop disposal area and milk disposal area. When the predicted variable s are considered collectively, the following input variables were foun d to be the dominant contributors to uncertainty: dry deposition veloc ity, transfer of cesium from animal feed to milk, transfer of cesium f rom animal feed to meat, ground concentration of Cs-134 at which the d isposal of milk products will be initiated, transfer of Sr-90 from soi l to legumes, maximum allowable ground concentration of Sr-90 for prod uction of crops, fraction of cesium entering surface water that is con sumed in drinking water, groundshine shielding factor, scale factor de fining resuspension, dose reduction associated with decontamination, a nd ground concentration of I-131 at which disposal of crops will be in itiated due to accidents that occur during the growing season. Reducin g the uncertainty in the preceding variables was found to substantiall y reduce the uncertainty in the predicted variables under consideratio n. For total number of latent cancer fatalities, the dominant variable was dry deposition velocity, with small effects indicated for a large number of additional variables.