Using Information on Uncertainty to Improve Environmental Fate Modeling: A Case Study on DDT
Present and future concentrations of DDT in the environment are calculated with the global multi-media model CliMoChem. Monte Carlo simulations are used to assess the importance of uncertainties in substance property data, emission rates, and environmental parameters for model results. Uncertainties in the model results, expressed as 95% confidence intervals of DDT concentrations in various environmental media, in different geographical locations, and at different points in time are typically between one and two orders of magnitude. An analysis of rank correlations between model inputs and predicted DDT concentrations indicates thatemission estimates and degradation rate constants, in particular in the atmosphere, are the most influential model inputs. For DDT levels in the Arctic, temperature dependencies of substance properties are also influential parameters. A Bayesian Monte Carlo approach is used to update uncertain model inputs based on measurements of DDT in the field. The updating procedure suggests a lower value for half-life in air and a reduced range of uncertainty for KOW of DDT. As could be expected, the Bayesian updating yields model results that are closer to observations, and model uncertainties have decreased. The combined sensitivity analysis and Bayesian Monte Carlo approach provide new insight into important processes that govern the global fate and persistence of DDT in the environment.