Global inventory of nitrogen oxide emissions constrained by space‐based observations of NO₂ columns

Abstract

, and combine these with a priori information from a bottomup emission inventory (with error weighting) to achieve an optimized a posteriori estimate of the global distribution of surface NO x emissions. Our GOME NO 2 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 À40% depending on the region. Our AMF also includes local information on relative vertical profiles (shape factors) of NO 2 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 NO x emission inventory from the GOME data by using the local GEOS-CHEM relationship between NO 2 columns and NO x emissions. The resulting NO x emissions for industrial regions are aseasonal, despite large seasonal variation in NO 2 columns, providing confidence in the method. Top-down errors in monthly NO x 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 NO x emissions (37.7 Tg N yr À1 ) agrees closely with the GEIAbased a priori (36.4) and with the EDGAR 3.0 bottom-up inventory (36.6), but there are significant regional differences. A posteriori NO x 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 NO x emissions are appreciably higher in the western United States, the Sahel, and southern Europe.

Systematic analysis of tropospheric NO<sub>2</sub> long-range transport events detected in GOME-2 satellite data

“…NO 2 and glyoxal (CHOCHO) from large bush fires such as the Black Saturday fires are routinely observed in satellite data (Wittrock et al, 2006;Vrekoussis et al, 2009) and GOME-2 data show elevated NO 2 vertical column densities over southwestern Australia on 25 and 26 April 2008 -on the order of 3 × 10 15 molecules cm −2 , a factor 2 higher than background levels. It is likely, that our data product underestimates these values due to a lowered air-mass factor in the presence of black carbon aerosol in bush fire smoke plumes (Martin et al, 2003;Leitão et al, 2010;Giles et al, 2012). An origin in bush fires would also explain the necessary lifting of the NO 2 from the planetary boundary layer into the free troposphere in the absence of a frontal system (see Labonne et al, 2007).…”

“…The dominant sources are anthropogenic emissions from combustion processes in transportation, power plants, industry and agricultural biomass burning, as well as natural sources such as lightning emissions, natural biomass burning and microbial soil emissions. Martin et al (2003) report a total yearly NO x emission rate of 43 TgN a −1 in 1996-1997, with anthropogenic sources contributing roughly half of the emissions.…”

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

“…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.…”

Intercomparison of SCIAMACHY and OMI tropospheric NO2 columns: Observing the diurnal evolution of chemistry and emissions from space

“…Data sets of tropospheric NO 2 columns retrieved from the Global Ozone Monitoring Experiment (GOME), the Scanning Imaging Absorption Chartography (SCIAMACHY) and the Ozone Monitoring Experiment (OMI) now span more than 10 years (1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006) and have been used for trend studies van der A et al, 2006a]. GOME and SCIAMACHY tropospheric NO 2 data has been used to estimate surface emissions of NO x [Martin et al, 2003;Jaeglé et al, 2004Jaeglé et al, , 2005Müller and Stavrakou, 2005;Bertram et al, 2005;Toenges-Schüller et al, 2006;Konovalov et al, 2006;Martin et al, 2006;Kim et al, 2006] and lightning NO x production [Boersma et al, 2005;Beirle et al, 2006;Martin et al, 2007].…”