Abstract:
Modelers of contaminant fate and transport in surface waters typically rely on literature values when selecting parameter values for mechanistic models. While the expert judgement with which these selections are made is valuable, the information contained in contaminant concentration measurements should not be ignored. In this full-scale Bayesian analysis of PCB contamination in the upper Hudson River, these two sources of information are combined using Bayes Theorem. A simulation model for the fate and transport of the PCBs in the upper Hudson River forms the basis of the likelihood function while the prior density is developed from literature values.
The method provides estimates for the anaerobic biodegradation halflife, aerobic biodegradation plus volatilization half-life, contaminated sediment depth, and resuspension velocity of 4400 days, 3.2 days, 0.32 meters, and 0.02 m/year, respectively. These are significantly different than values obtained with more traditional methods, and are shown to produce better predictions than those methods when used in a cross-validation study.
tr37.pdf