Abstract
Ozone pollution in the Southeast US involves complex chemistry driven by emissions of anthropogenic nitrogen oxide radicals (NO(x) ≡ NO + NO(2)) 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 SEAC(4)RS 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(x) from the US Environmental Protection Agency (EPA) is too high. This finding is based on SEAC(4)RS observations of NO(x) and its oxidation products, surface network observations of nitrate wet deposition fluxes, and OMI satellite observations of tropospheric NO(2) columns. Our results indicate that NEI NO(x) emissions from mobile and industrial sources must be reduced by 30-60%, dependent on the assumption of the contribution by soil NO(x) emissions. Upper tropospheric NO(2) from lightning makes a large contribution to satellite observations of tropospheric NO(2) that must be accounted for when using these data to estimate surface NO(x) emissions. We find that only half of isoprene oxidation proceeds by the high-NO(x) pathway to produce ozone; this fraction is only moderately sensitive to changes in NO(x) emissions because isoprene and NO(x) emissions are spatially segregated. GEOS-Chem with reduced NO(x) 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(x) 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.