ALBERT

Description: Satellite observations of formaldehyde (HCHO) columns provide top-down constraints on emissions of highly reactive volatile organic compounds (HRVOCs). This approach has been used previously to constrain emissions of isoprene from vegetation, but application to US anthropogenic emissions has been stymied by lack of a discernable HCHO signal. Here we show that oversampling of HCHO data from the Ozone Monitoring Instrument (OMI) for 2005 - 2008 enables quantitative detection of urban and industrial plumes in eastern Texas including Houston, Port Arthur, and Dallas-Fort Worth. By spatially integrating the individual urban-industrial HCHO plumes observed by OMI we can constrain the corresponding HCHO-weighted HRVOC emissions. Application to the Houston plume indicates a HCHO source of 260 plus or minus 110 kmol h-1 and implies a factor of 5.5 plus or minus 2.4 underestimate of anthropogenic HRVOC emissions in the US Environmental Protection Agency inventory. With this approach we are able to monitor the trend in HRVOC emissions over the US, in particular from the oil-gas industry, over the past decade.

Description: Formaldehyde (HCHO) column data from satellites are widely used as a proxy for emissions of volatile organic compounds (VOCs), but validation of the data has been extremely limited. Here we use highly accurate HCHO aircraft observations from the NASA SEAC4RS (Studies of Emissions, Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys) campaign over the southeast US in August-September 2013 to validate and intercompare six retrievals of HCHO columns from four different satellite instruments (OMI (Ozone Monitoring Instrument), GOME (Global Ozone Monitoring Experiment) 2A, GOME (Global Ozone Monitoring Experiment) 2B and OMPS (Ozone Mapping and Profiler Suite)) and three different research groups. The GEOS (Goddard Earth Observing System)-Chem chemical transport model is used as a common intercomparison platform. All retrievals feature a HCHO maximum over Arkansas and Louisiana, consistent with the aircraft observations and reflecting high emissions of biogenic isoprene. The retrievals are also interconsistent in their spatial variability over the southeast US (r equals 0.4 to 0.8 on a 0.5 degree by 0.5 degree grid) and in their day-to-day variability (r equals 0.5 to 0.8). However, all retrievals are biased low in the mean by 20 to 51 percent, which would lead to corresponding bias in estimates of isoprene emissions from the satellite data. The smallest bias is for OMI-BIRA (Ozone Monitoring Instrument - Belgian Institute for Space Aeronomy), which has high corrected slant columns relative to the other retrievals and low scattering weights in its air mass factor (AMF) calculation. OMI-BIRA has systematic error in its assumed vertical HCHO shape profiles for the AMF calculation, and correcting this would eliminate its bias relative to the SEAC (sup 4) RS data. Our results support the use of satellite HCHO data as a quantitative proxy for isoprene emission after correction of the low mean bias. There is no evident pattern in the bias, suggesting that a uniform correction factor may be applied to the data until better understanding is achieved.

Description: Ozone pollution in the Southeast US involves complex chemistry driven by emissions of anthropogenic nitrogen oxide radicals (NO(x) triple bond NO + NO2) 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(exp 4)RS aircraft campaign in August and September 2013, interpreted with the GEOS-Chem chemical transport model at 0.25 deg x 0.3125 deg 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(exp 4)RS observations of NO(x) and its oxidation products, surface network observations of nitrate wet deposition fluxes, and OMI satellite observations of tropospheric NO2 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 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 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 6 plus or minus 14 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.