Application of Bayesian Monte Carlo analysis to a Lagrangian photochemical air quality model

Abstract

Uncertainties in ozone concentrations predicted with a Lagrangian photochemical air quality model have been estimated using Bayesian Monte Carlo (BMC) analysis. Bayesian Monte Carlo analysis provides a means of combining subjective “prior” uncertainty estimates developed by standard Monte Carlo techniques with information about the agreement between model outputs and observations. The resulting “posterior” uncertainty estimates reflect both the model's performance and subjective judgments about uncertainties in model parameters and inputs. To demonstrate the approach, BMC analysis was applied to a model of ozone concentrations along two-day trajectories ending on 28 August 1987 at Azusa and Riverside, CA. Refined estimates of uncertainties in base case O 3 concentrations were calculated, along with estimates of uncertainties in the response to 25% reductions in motor vehicle emissions of nitrogen oxides and volatile organic compounds. For the cases studied, the model results were in reasonable agreement with spatially interpolated observations. Bayesian updating reduced the estimated uncertainty in predicted peak O 3 concentrations from 35 to 20% at Azusa and from 24 to 18% at Riverside.