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BackgroundEpidemiologic and health impact studies of fine particulate matter with diameter < 2.5 μm (PM2.5) are limited by the lack of monitoring data, especially in developing countries. Satellite observations offer valuable global information about PM2.5 concentrations.ObjectiveIn this study, we developed a technique for estimating surface PM2.5 concentrations from satellite observations.MethodsWe mapped global ground-level PM2.5 concentrations using total column aerosol optical depth (AOD) from the MODIS (Moderate Resolution Imaging Spectroradiometer) and MISR (Multiangle Imaging Spectroradiometer) satellite instruments and coincident aerosol vertical profiles from the GEOS-Chem global chemical transport model.ResultsWe determined that global estimates of long-term average (1 January 2001 to 31 December 2006) PM2.5 concentrations at approximately 10 km × 10 km resolution indicate a global population-weighted geometric mean PM2.5 concentration of 20 μg/m3. The World Health Organization Air Quality PM2.5 Interim Target-1 (35 μg/m3 annual average) is exceeded over central and eastern Asia for 38% and for 50% of the population, respectively. Annual mean PM2.5 concentrations exceed 80 μg/m3 over eastern China. Our evaluation of the satellite-derived estimate with ground-based in situ measurements indicates significant spatial agreement with North American measurements (r = 0.77; slope = 1.07; n = 1057) and with noncoincident measurements elsewhere (r = 0.83; slope = 0.86; n = 244). The 1 SD of uncertainty in the satellite-derived PM2.5 is 25%, which is inferred from the AOD retrieval and from aerosol vertical profile errors and sampling. The global population-weighted mean uncertainty is 6.7 μg/m3.ConclusionsSatellite-derived total-column AOD, when combined with a chemical transport model, provides estimates of global long-term average PM2.5 concentrations.

We present a methodology for estimating the seasonal and interannual variation of biomass burning designed for use in global chemical transport models. The average seasonal variation is estimated from 4 years of fire‐count data from the Along Track Scanning Radiometer (ATSR) and 1–2 years of similar data from the Advanced Very High Resolution Radiometer (AVHRR) World Fire Atlases. We use the Total Ozone Mapping Spectrometer (TOMS) Aerosol Index (AI) data product as a surrogate to estimate interannual variability in biomass burning for six regions: Southeast Asia, Indonesia and Malaysia, Brazil, Central America and Mexico, Canada and Alaska, and Asiatic Russia. The AI data set is available from 1979 to the present with an interruption in satellite observations from mid‐1993 to mid‐1996; this data gap is filled where possible with estimates of area burned from the literature for different regions. Between August 1996 and July 2000, the ATSR fire‐counts are used to provide specific locations of emissions and a record of interannual variability throughout the world. We use our methodology to estimate mean seasonal and interannual variations for emissions of carbon monoxide from biomass burning, and we find that no trend is apparent in these emissions over the last two decades, but that there is significant interannual variability.

, 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.

Exposure to ambient air pollution is a major risk factor for global disease. Assessment of the impacts of air pollution on population health and evaluation of trends relative to other major risk factors requires regularly updated, accurate, spatially resolved exposure estimates. We combined satellite-based estimates, chemical transport model simulations, and ground measurements from 79 different countries to produce global estimates of annual average fine particle (PM2.5) and ozone concentrations at 0.1° × 0.1° spatial resolution for five-year intervals from 1990 to 2010 and the year 2013. These estimates were applied to assess population-weighted mean concentrations for 1990-2013 for each of 188 countries. In 2013, 87% of the world's population lived in areas exceeding the World Health Organization Air Quality Guideline of 10 μg/m(3) PM2.5 (annual average). Between 1990 and 2013, global population-weighted PM2.5 increased by 20.4% driven by trends in South Asia, Southeast Asia, and China. Decreases in population-weighted mean concentrations of PM2.5 were evident in most high income countries. Population-weighted mean concentrations of ozone increased globally by 8.9% from 1990-2013 with increases in most countries-except for modest decreases in North America, parts of Europe, and several countries in Southeast Asia.

[1] 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 (r 2 = 0.69, n = 756, bias = +11%) and the in situ summertime HCHO measurements over North America (r 2 = 0.47, n = 10, bias = À3%). 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 (r 2 = 0.71, n = 10, bias = À10%).

[1] We evaluate the sensitivity of tropospheric OH, O 3 , and O 3 precursors to photochemical effects of aerosols not usually included in global models: (1) aerosol scattering and absorption of ultraviolet radiation and (2) reactive uptake of HO 2 , NO 2 , and NO 3 . Our approach is to couple a global 3-D model of tropospheric chemistry (GEOS-CHEM) with aerosol fields from a global 3-D aerosol model (GOCART). Reactive uptake by aerosols is computed using reaction probabilities from a recent review (g HO2 = 0.2, g NO2 = 10À4 , g NO3 = 10 À3 ). Aerosols decrease the O 3 ! O( 1 D) photolysis frequency by 5-20% at the surface throughout the Northern Hemisphere (largely due to mineral dust) and by a factor of 2 in biomass burning regions (largely due to black carbon). Aerosol uptake of HO 2 accounts for 10-40% of total HO x radical ( OH + peroxy) loss in the boundary layer over polluted continental regions (largely due to sulfate and organic carbon) and for more than 70% over tropical biomass burning regions (largely due to organic carbon). Uptake of NO 2 and NO 3 accounts for 10-20% of total HNO 3 production over biomass burning regions and less elsewhere. Annual mean OH concentrations decrease by 9% globally and by 5-35% in the boundary layer over the Northern Hemisphere. Simulated CO increases by 5-15 ppbv in the remote Northern Hemisphere, improving agreement with observations. Simulated boundary layer O 3 decreases by 15-45 ppbv over India during the biomass burning season in March and by 5-9 ppbv over northern Europe in August, again improving comparison with observations. We find that particulate matter controls would increase surface O 3 over Europe and other industrial regions.

[1] We present a retrieval of tropospheric nitrogen dioxide (NO 2 ) columns from the Global Ozone Monitoring Experiment (GOME) satellite instrument that improves in several ways over previous retrievals, especially in the accounting of Rayleigh and cloud scattering. Slant columns, which are directly fitted without low-pass filtering or spectral smoothing, are corrected for an artificial offset likely induced by spectral structure on the diffuser plate of the GOME instrument. The stratospheric column is determined from NO 2 columns over the remote Pacific Ocean to minimize contamination from tropospheric NO 2 . The air mass factor (AMF) used to convert slant columns to vertical columns is calculated from the integral of the relative vertical NO 2 distribution from a global 3-D model of tropospheric chemistry driven by assimilated meteorological data (Global Earth Observing System (GEOS)-CHEM), weighted by altitude-dependent scattering weights computed with a radiative transfer model (Linearized Discrete Ordinate Radiative Transfer), using local surface albedos determined from GOME observations at NO 2 wavelengths. The AMF calculation accounts for cloud scattering using cloud fraction, cloud top pressure, and cloud optical thickness from a cloud retrieval algorithm (GOME Cloud Retrieval Algorithm). Over continental regions with high surface emissions, clouds decrease the AMF by 20-30% relative to clear sky. GOME is almost twice as sensitive to tropospheric NO 2 columns over ocean than over land. Comparison of the retrieved tropospheric NO 2 columns for July 1996 with GEOS-CHEM values tests both the retrieval and the nitrogen oxide radical (NO x ) emissions inventories used in GEOS-CHEM. Retrieved tropospheric NO 2 columns over the United States, where NO x emissions are particularly well known, are within 18% of GEOS-CHEM columns and are strongly spatially correlated (r = 0.78, n = 288, p < 0.005). Retrieved columns show more NO 2 than GEOS-CHEM columns over the Transvaal region of South Africa and industrial regions of the northeast United States and Europe. They are lower over Houston, India, eastern Asia, and the biomass burning region of central Africa, possibly because of biases from absorbing aerosols. Citation: Martin, R. V., et al., An improved retrieval of tropospheric nitrogen dioxide from GOME,