Improved Estimation of Background Ozone and Emission Impacts Using Chemical Transport Modeling and Data Fusion

Abstract

As ozone air quality standards are tightened, the portion of the standard taken up by background ozone (BGO) increases. BGO is the ozone that would be observed in the absence of anthropogenic emissions. BGO can be a major, sometimes dominant, contribution to overall ozone. BGO originates from noncontrollable sources (e.g., wildfires, stratosphere-troposphere exchange, non-domestic pollution) and can vary significantly by region, elevation, and season, leading to high uncertainty in BGO contributions. In this work, US BGO is first quantified using a chemical transport model, specifically the Community Multiscale Air Quality (CMAQ) model, with US anthropogenic emissions set to zero. A method of adjusting for model bias in the estimation of BGO is developed that fuses model results with observations. This method uses observational and modelled data to develop functions of space, time, and meteorology that relate CMAQ-simulated base case (using estimated emissions) and CMAQ-modelled US BGO (no US anthropogenic emissions) to the observations. Separate adjustment factors are developed for locally formed and background ozone. This allows for calculating both an adjusted US BGO and the amount of ozone formed from anthropogenic emissions that better align with observations and elucidation of the key influences and sources of bias for these two sources of ozone. The effects of boundary conditions on BGO estimates and model bias is also examined. Application of this adjustment factor method improves agreement between BGO estimated with two different sets of boundary conditions from r = 0.58 for the original modelled values to r = 0.85 for the adjusted values.