Towards an Improved Understanding of Ozone Air Pollution in the United States

Abstract

Ground-level ozone pollution is a serious public health and environmental concern in the US and globally. Tropospheric ozone is produced by photochemical oxidation of volatile organic compounds (VOCs) and carbon monoxide in the presence of nitrogen oxide radicals (NOx ≡ NO+NO2). These precursors have both anthropogenic and natural sources. My thesis focuses on improving our current knowledge of ozone sources and sinks in the US to inform policy decisions at the local and national level. Model estimates of surface ozone concentrations tend to be biased high in the Southeast US and this is of concern for designing effective emission control strategies to meet air quality standards. Ozone pollution in this region involves complex chemistry driven by emissions of anthropogenic NOx and biogenic isoprene. We use detailed chemical observations from the SEAC4RS aircraft campaign in August and September 2013, interpreted with the GEOS-Chem chemical transport model at 0.25° x 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 NOx from the US Environmental Protection Agency is too high. This finding is based on SEAC4RS observations of NOx and its oxidation products, surface network observations of nitrate wet deposition fluxes, and satellite observations of tropospheric NO2 columns. Our results indicate that NEI NOx emissions from mobile and industrial sources must be reduced by 30-60%, dependent on the assumption of the contribution by soil NOx emissions. Upper tropospheric NO2 from lightning makes a large contribution to satellite observations of tropospheric NO2 that must be accounted for when using these data to estimate surface NOx emissions. We find that only half of isoprene oxidation proceeds by the high-NOx pathway to produce ozone; this fraction is only moderately sensitive to changes in NOx emissions because isoprene and NOx emissions are spatially segregated. Geos-Chem with reduced NOx 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 NOx oxidation products. However, the model is still biased high by 6 +/- 14 ppb relative to observed surface ozone in the Southeast US. We refine our analysis to focus on surface observations just during the SEAC4RS campaign from the CASTNET network. Maximum daily 8-h average (MDA8) ozone is still biased high in the model (averaging 48 +/- 9 ppb) compared to CASTNET observations (40 +/- 9 ppb). The low tail in the observations (MDA8 ozone < 25 ppb) is associated with rain and is not captured by the model. Model bias decreases by 3 ppb when accounting for the sub grid vertical gradient between the lowest model level (centered 60 m above ground) and the measurement altitude (10 m). The model underestimates low cloud cover but this is insufficient to explain the remaining surface ozone bias because the response of model ozone to cloud cover is weaker than observed. Midday ozonesondes at Huntsville, Alabama show mean decreases in ozone from 1 km to the surface of 4 ppb under clear-sky and 7 ppb under low cloud, whereas the model decreases only 1 ppb under both conditions. By contrast, potential temperature below 1 km is well-mixed in both the observations and the model. The observations thus imply a strong asymmetry between top-down and bottom-up mixing that is missing from GEOS-Chem and appears to be insufficiently represented in current air quality models. A sensitivity simulation reducing top-down eddy diffusion and suppressing non-local vertical transport of ozone can reproduce the observed ozone gradients in the mixed layer. Additional suppression of vertical transport is needed in cloud-topped boundary layers.