Person:

Chance, Kelly

We use tropospheric NO2 columns from the Global Ozone Monitoring Experiment (GOME) satellite instrument to derive top-down constraints on emissions of nitrogen oxides (NOx ≡ NO + NO2), and combine these with a priori information from a bottom-up emission inventory (with error weighting) to achieve an optimized a posteriori estimate of the global distribution of surface NOx emissions. Our GOME NO2 retrieval improves on previous work by accounting for scattering and absorption of radiation by aerosols; the effect on the air mass factor (AMF) ranges from +10 to −40% depending on the region. Our AMF also includes local information on relative vertical profiles (shape factors) of NO2 from a global 3-D chemical transport model (GEOS-CHEM); assumption of a globally uniform shape factor, as in most previous retrievals, would introduce regional biases of up to 40% over industrial regions and a factor of 2 over remote regions. We derive a top-down NOx emission inventory from the GOME data by using the local GEOS-CHEM relationship between NO2 columns and NOx emissions. The resulting NOx emissions for industrial regions are aseasonal, despite large seasonal variation in NO2 columns, providing confidence in the method. Top-down errors in monthly NOx emissions are comparable with bottom-up errors over source regions. Annual global a posteriori errors are half of a priori errors. Our global a posteriori estimate for annual land surface NOx emissions (37.7 Tg N yr−1) agrees closely with the GEIA-based a priori (36.4) and with the EDGAR 3.0 bottom-up inventory (36.6), but there are significant regional differences. A posteriori NOx emissions are higher by 50–100% in the Po Valley, Tehran, and Riyadh urban areas, and by 25–35% in Japan and South Africa. Biomass burning emissions from India, central Africa, and Brazil are lower by up to 50%; soil NOx emissions are appreciably higher in the western United States, the Sahel, and southern Europe.

We present a methodology for deriving emissions of volatile organic compounds (VOC) using space-based column observations of formaldehyde (HCHO) and apply it to data from the Global Ozone Monitoring Experiment (GOME) satellite instrument over North America during July 1996. The HCHO column is related to local VOC emissions, with a spatial smearing that increases with the VOC lifetime. Isoprene is the dominant HCHO precursor over North America in summer, and its lifetime (≃1 hour) is sufficiently short that the smearing can be neglected. We use the Goddard Earth Observing System global 3-D model of tropospheric chemistry (GEOS-CHEM) to derive the relationship between isoprene emissions and HCHO columns over North America and use these relationships to convert the GOME HCHO columns to isoprene emissions. We also use the GEOS-CHEM model as an intermediary to validate the GOME HCHO column measurements by comparison with in situ observations. The GEOS-CHEM model including the Global Emissions Inventory Activity (GEIA) isoprene emission inventory provides a good simulation of both the GOME data (r2 = 0.69, n = 756, bias = +11%) and the in situ summertime HCHO measurements over North America (r2 = 0.47, n = 10, bias = −3%). The GOME observations show high values over regions of known high isoprene emissions and a day-to-day variability that is consistent with the temperature dependence of isoprene emission. Isoprene emissions inferred from the GOME data are 20% less than GEIA on average over North America and twice those from the U.S. EPA Biogenic Emissions Inventory System (BEIS2) inventory. The GOME isoprene inventory when implemented in the GEOS-CHEM model provides a better simulation of the HCHO in situ measurements than either GEIA or BEIS2 (r2 = 0.71, n = 10, bias = −10%).