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Evaluation of CMAQ Estimated NOx from 2002 to 2016 (2019 CMAS Conference)
Citation:
Foley, K., H. Simon, C. Toro, K. Baker, Keith Appel, B. Henderson, A. Eyth, AND D. Luecken. Evaluation of CMAQ Estimated NOx from 2002 to 2016 (2019 CMAS Conference). 2019 CMAS Conference, Chapel Hill, NC, October 21 - 23, 2019.
Impact/Purpose:
Several external groups have produced analyses suggesting that the NOx emissions from EPA’s mobile emissions model MOVES are too high by a factor of 2. Such conclusions have the potential to put into question the credibility of EPA’s emissions inventory and air quality modeling tools, as well as the benefits of additional NOx reductions. This presentation will summarize evaluation of CMAQ model output across 15 years to explore how emissions, model formulation and meteorological drivers contribute to high NOx bias.
Description:
In recent years, many published studies comparing ambient NOX and NOY concentrations to modeled or inventory values found a high bias on the order of 1.4 - 2 times observed levels. Some researchers proposed reducing mobile-source NOx emissions by 30-70% in their modeling applications. Many of these applications were based on evaluation of summer 2011 modeling to leverage the latest 2011 National Emissions Inventory and various summer field campaigns such as the 2011 DISCOVER-AQ Baltimore. Here, model estimates of NOX from 2002 through 2016 from the Community Multiscale Air Quality (CMAQ) model were compared to routine surface network measurements to identify differences in NOX bias across years, seasons, time of day, and regions of the country. Evaluation against NOX observations across the U.S. show that the high summertime bias has significantly decreased across this period with decreasing ambient NOX levels and improvements in the emissions inventories and the CMAQ system over time. Wintertime NOX is found to be underestimated in many regions of the country in this set of simulations. Aircraft measurements taken as part of the 2011 DISCOVER-AQ Baltimore field campaign were compared to model estimates to further explore how emissions, model formulation (e.g., chemistry), and measurement uncertainty contribute to predictive skill. In-depth analysis using 2011 field measurements showed that the model bias in NOX and NOz components was sensitive to choices about pairing of model and measured values with disparate spatial and temporal resolution and to the chemical mechanism used. Estimates of daytime NOY normalized mean bias in the boundary layer aloft could vary from 76% to 28% depending solely on choice of model chemistry and measurement method.
