Iterative Inverse Modeling and Direct Sensitivity Analysis of a Photochemical Air Quality Model

A four-dimensional data assimilation approach that combines emission-based modeling and inverse modeling techniques has been developed to provide a method to help identify emissions inventory improvements. The method is based on linking formal direct sensitivity analysis of three-dimensional air quality models with an inverse modeling technique such that observational data of multiple species can be used to suggest improvements in emission strengths, patterns, and compositions of various source categories simultaneously. Information regarding the characteristics of the data and the emissions is incorporated into the model by means of weighting factors. A penalty function allows determining if the method is fully observation-driven, emission-driven, or mixed. The assimilation of the data requires an iterative process since the sensitivity coefficients change as emissions are adjusted, though results suggest that only three or four iterations are necessary. The method has been applied to the Atlanta Metro Area and tested with pseudo-observations. Perturbed emissions were adjusted close to their known original value in these test scenarios. When noisy pseudo-observations were used, the final simulated concentration errors were of the order of the noise applied to the pseudo-observations.