Dynamic evaluation of regional air quality model’s response to emission reductions in the presence of uncertain emission inventories

A method is presented and applied for evaluating an air quality model’s changes in pollutant concentrations stemming from changes in emissions while explicitly accounting for the uncertainties in the base emission inventory. Specifically, the Community Multiscale Air Quality (CMAQ) model is evaluated for its ability to simulate the change in ozone (O3) levels in response to significant reductions in nitric oxide (NOx = NO + NO2) emissions from the NOx State Implementation Plan (SIP) Call and vehicle fleet turnover between the years of 2002 and 2005. The dynamic model evaluation (i.e., the evaluation of a model’s ability to predict changes in pollutant levels given changes in emissions) differs from previous approaches by explicitly accounting for known uncertainties in the NOx emissions inventories. Uncertainty in three sectors of NOx emissions is considered – area sources, mobile sources, and point sources – and is propagated using sensitivity coefficients calculated by the decoupled direct method in three dimensions (DDM-3D). The change in O3 levels between 2002 and 2005 is estimated based on differences in the empirical distributions of the modeled and observed data during the two years. Results indicate that the CMAQ model is able to reproduce the observed change in daily maximum 8-hr average O3 levels at more than two-thirds of Air Quality System (AQS) monitoring locations when a relatively moderate amount of uncertainty (50%) is assumed in area and mobile emissions of NOx together with a low amount of uncertainty (3%) in the utility sector (elevated point sources) emissions. The impact of other sources of uncertainty in the model is also briefly explored.

► We evaluate CMAQ modeled change in surface O3 between the summers of 2002 and 2005. ► We consider uncertainties in area, mobile and point NOx emission inventories. ► Assuming moderate amount of uncertainty, the model predicts observed changes well. ► Other sources of the discrepancy in the modeled and observed signals are explored. ► Dynamic evaluation methodology is flexible for extension to other uncertainties.