Why do models overestimate surface ozone in the Southeast United States?

Abstract

Ozone pollution in the Southeast US involves complex chemistry driven by emissions of anthropogenic nitrogen oxide radicals (NO ≡ NO + NO) and biogenic isoprene. Model estimates of surface ozone concentrations tend to be biased high in the region and this is of concern for designing effective emission control strategies to meet air quality standards. We use detailed chemical observations from the SEACRS aircraft campaign in August and September 2013, interpreted with the GEOS-Chem chemical transport model at 0.25°×0.3125° horizontal resolution, to better understand the factors controlling surface ozone in the Southeast US. We find that the National Emission Inventory (NEI) for NO from the US Environmental Protection Agency (EPA) is too high. This finding is based on SEACRS observations of NO and its oxidation products, surface network observations of nitrate wet deposition fluxes, and OMI satellite observations of tropospheric NO columns. Our results indicate that NEI NO emissions from mobile and industrial sources must be reduced by 30-60%, dependent on the assumption of the contribution by soil NO emissions. Upper tropospheric NO from lightning makes a large contribution to satellite observations of tropospheric NO that must be accounted for when using these data to estimate surface NO emissions. We find that only half of isoprene oxidation proceeds by the high-NO pathway to produce ozone; this fraction is only moderately sensitive to changes in NO emissions because isoprene and NO emissions are spatially segregated. GEOS-Chem with reduced NO emissions provides an unbiased simulation of ozone observations from the aircraft, and reproduces the observed ozone production efficiency in the boundary layer as derived from a regression of ozone and NO oxidation products. However, the model is still biased high by 8±13 ppb relative to observed surface ozone in the Southeast US. Ozonesondes launched during midday hours show a 7 ppb ozone decrease from 1.5 km to the surface that GEOS-Chem does not capture. This bias may reflect a combination of excessive vertical mixing and net ozone production in the model boundary layer.

Tropospheric Ozone Assessment Report: Assessment of global-scale model performance for global and regional ozone distributions, variability, and trends

“…For the U.S, the positive bias ranges from 5-15 nmol mol -1 over the eastern U.S. but exceeds 15 nmol mol -1 over North American coastal regions compared with observations. The mean seasonal cycle is generally reproduced with correlation coefficients for monthly mean values greater than 0.6 (Figure 6d), although the multi-model mean tends to peak later in the year over the eastern U.S., consistent with previous model evaluations ( Murazaki and Hess 2006;Fiore et al, 2009;Reidmiller et al, 2009;Lamarque et al, 2012;Naik et al, 2013;BrownSteiner et al, 2015;Strode et al, 2015;Travis et al, 2016). In Europe, the annual multi-model mean performs better over northern Europe (biases range from -2 to +10 nmol mol .…”

Impacts of Different Characterizations of Large-Scale Background on Simulated Regional-Scale Ozone Over the Continental United States

“…Significant positive ozone biases at CASTNET sites in the SE were also reported for GEOSChem for summer 2013 by Travis et al (2016) who attributed a large portion of the bias to overestimated anthropogenic NOx emissions. In the current study, the annual total anthropogenic NOx emissions are shared across all regional and large-scale simulations since the HTAP2 global inventory (Janssens-Maenhout et al, 2015) incorporated the AQMEII2 regional inventory (Pouliot et al, 2015) over North America, although differences may exist in terms of temporally and vertically allocating these 20 emissions for a specific model.…”

“…Zhang et al (2014) and Travis et al (2016) note that the standard GEOS-Chem treatment of lightning NOx yields 10 for midlatitudes may be too high and can lead to positive ozone biases at the surface.…”

The Dawn of Geostationary Air Quality Monitoring: Case Studies From Seoul and Los Angeles

“…The spatial (<10 km) and temporal (hourly) requirements for air quality measurements have largely been determined by the desire to resolve the processes affecting the emissions, lifetime and transport of tropospheric NO 2 (e.g., Beirle et al, 2011;Valin et al, 2011bValin et al, , 2013de Foy et al, 2015) because of its fundamental role in the formation of tropospheric O 3 and particulate matter. There have been a variety of approaches for validating NO 2 products retrieved from LEO platforms (e.g., Bucsela et al, 2008Bucsela et al, , 2013Irie et al, 2008;Boersma et al, 2009;Lamsal et al, 2010;Russell et al, 2011;Travis et al, 2016). These works have identified and addressed gaps in the understanding of NO 2 retrievals, including methods for subtracting stratospheric NO 2 column contributions, a priori vertical profile deviations between urban and rural settings, and surface reflectance variations (e.g., Zhou et al, 2010;Russell et al, 2011).…”

“…These data have been useful for understanding global (e.g., Martin et al, 2003;Jaegl et al, 2005), regional (e.g., Duncan et al, 2016;Travis et al, 2016) and local air quality (e.g., Zhu et al, 2017) over daily (e.g., Valin et al, 2014;de Foy et al, 2016), seasonal (e.g., Russell et al, 2010), interannual, and decadal time periods (van der et al, 2008;De Smedt et al, 2015). However, the relatively coarse spatial resolutions and single daily observation times have substantially limited these applications, particularly within the air quality management community which needs to be able to distinguish temporal profiles of emissions from different source sectors and identify specific physical processes to justify regulatory decisions.…”