A stochastic regression approach to analyzing thermodynamic uncertainty in chemical speciation modeling.

Chemical speciation modeling is a vital tool for assessing the bioavailability of inorganic species, yet significant uncertainties in thermodynamic parameters and model form limit its potential for decision-making. In this paper we present a novel method for the quantification of thermodynamic parameter uncertainty and ionic strength correction model uncertainty using Bayesian Markov Chain Monte Carlo (MCMC) estimation methods. These methods allow for the inclusion of correlation modeling, which has not been present in previous work. The MCMC simulations are used to model a natural river water to determine the uncertainty in the calculated environmental speciation of ethylenediamenetetraacetate, a chelating agent that has attracted considerable environmental interest. The results indicate that incorporating correlation among related thermodynamic parameters into the uncertainty model is necessary to correctly quantify the overall system uncertainty. This result indicates the superiority of MCMC estimation methods overtraditional Monte Carlo methods when available data are used to estimate parameter uncertainty in systems with closely related model parameters.