Record Display for the EPA National Library Catalog


Main Title Determining Uncertainty in Physical Parameter Measurements by Monte Carlo Simulation.
Author Coy, D. W. ; Kew, G. A. ; Mullins, M. E. ; Piserchia, P. V. ;
CORP Author Research Triangle Inst., Research Triangle Park, NC.;Environmental Protection Agency, Washington, DC. Office of Health and Environmental Assessment.
Year Published 1986
Report Number EPA-68-03-3149; EPA/600/D-86/052;
Stock Number PB86-164902
Additional Subjects Monte Carlo method ; Mathematical prediction ; Stochastic processes ; Simulation ; Errors ; Environmental transport ; Uncertainty
Library Call Number Additional Info Location Last
NTIS  PB86-164902 Some EPA libraries have a fiche copy filed under the call number shown. 07/26/2022
Collation 30p
A statistical approach, often called Monte Carlo Simulation, has been used to examine propagation of error with measurement of several parameters important in predicting environmental transport of chemicals. These parameters are vapor pressure, water solubility, octanol-water partition coefficient, and 'volatilization from water' (based on the ratio of laboratory-measured volatilization rate constant to oxygen reaeration rate constant for a specific system). Column chromatographic and high pressure liquid chromatographic (HPLC) methods tend to under-predict aqueous solubility and vapor pressure and overpredict octanol-water partition coefficient. Measurement error proves not to be normally distributed, with differing bias for each parameter. For 'volatilization from water', determination of the ratio of rate constants for compounds whose Henry's Law constant equals or exceeds 1,000 torr/mole/liter typically report 95% confidence limits equal to 5 to 10 percent of the ratio. Analysis of a regression approach often used to determine the ratio suggests underestimation of both the ratio and its variance. Monte Carlo Simulation did not confirm underestimation of the ratio but suggests variances may be under-estimated by a factor of 2.3. Using the statistical approach in other cases might allow an investigator to choose levels of a parameter (e.g. a drinking water standard) knowing the uncertainty associated with the choice, or the converse.