A bayesian approach to parameter estimation for a crayfish (Procambarus spp): Bioaccumulation model

Abstract

Bioaccumulation models are used to describe chemical uptake and clearances by organisms. Averaged input parameter values are traditionally used and yield point estimates of model outputs. Hence, the uncertainty and variability of model predictions are ignored. Probabilistic modeling approaches, such as Monte Carlo simulation and the Bayesian method, have been recommended by the U.S. Environmental Protection Agency to provide a quantitative description of the degree of uncertainty and/or variability in risk estimates in ecological hazards and human health effects. In this study, a Bayesian analysis was conducted to account for the combined uncertainty and variability of model parameters in a crayfish bioaccumulation model. After a 5-d exposure in the LaBranche Wetlands (LA, USA), crayfish were analyzed for polycyclic aromatic hydrocarbon concentrations and lipid fractions. The posterior distribution of model parameters were derived from the joint posterior parameter distributions using a Markov chain Monte Carlo approach and the experimental data. The results were then used to predict the distribution of chrysene concentration versus time in the crayfish to compare the predicted ranges at the different study sites.

Bioaccumulation kinetics of organic xenobiotic pollutants in the freshwater invertebrateGammarus pulexmodeled with prediction intervals

“…Finally, we demonstrate how to derive toxicokinetic parameters using a Monte Carlo Markov Chain method for parameter estimation, which yields the distribution of and correlations between parameter values and facilitates subsequent Monte Carlo simulations to generate prediction intervals around simulations of internal concentrations [35] under fluctuating and pulsed exposure conditions.…”

Parameter uncertainty in modeling bioaccumulation factors of fish

“…For example, the fitting of parameters can be done by weighted least squares so that the estimates match measurements [8]. More sophisticated approaches, such as Markov chain Monte Carlo simulations, Bayesian estimates, or linear inverse modeling, have also been applied to parameterize bioaccumulation models [9][10][11]. These approaches combine parameterization with uncertainty assessment.…”

Bayesian latent variable regression via Gibbs sampling: methodology and practical aspects

“…Park et al [13] used a Bayesian approach to handle the uncertainty in multivariate receptor model to estimate the major pollution sources and their composite profiles and contributions. Lin et al [14] developed and applied a Bayesian approach to estimate parameters of a crayfish bioaccumulation model. Chen et al [15] applied a sequential Bayesian method to estimate state values in nonlinear dynamic systems.…”

“…Many of the previously mentioned Bayesian chemometric efforts [13][14][15]17,18] have used sampling-based approaches. These methods can efficiently execute Bayesian estimation, even for high dimensional problems.…”