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Atmospheric Environment 42 (2008) 7823–7843

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Atmospheric Environment

journal homepage: www.elsevier .com/locate/atmosenv

Review

Satellite remote sensing of surface air quality

Randall V. Martin a,b,*

a Department of Physics and Atmospheric Science, Dalhousie University, Halifax, NS, Canada B3H 3J5b Harvard-Smithsonian Center for Astrophysics, Cambridge, MA, USA

a r t i c l e i n f o

Article history:Received 14 December 2007Received in revised form 26 June 2008Accepted 2 July 2008

Keywords:Remote sensingAir qualitySatellitePollutionEmissions

* Corresponding author. Department of PhysicScience, Dalhousie University, Halifax, NS, Canada B494 3915; fax: þ1 902 494 5191.

E-mail address: [email protected]

1352-2310/$ – see front matter � 2008 Elsevier Ltddoi:10.1016/j.atmosenv.2008.07.018

a b s t r a c t

Satellite remote sensing of air quality has evolved dramatically over the last decade. Globalobservations are now available for a wide range of species including aerosols, troposphericO3, tropospheric NO2, CO, HCHO, and SO2. Capabilities for satellite remote sensing of thesespecies in the boundary layer are reviewed for current instruments, along with physicalprocesses affecting their accuracy and precision. Applications of satellite observations arediscussed for case studies of specific events, for estimates of surface concentrations, and toimprove emission inventories of trace gases and aerosols. Aerosol remote sensing at visiblewavelengths exhibits high sensitivity to boundary layer concentrations. Although atmo-spheric scattering and surface emission of thermal radiation generally reduce instrumentsensitivity to trace gases near the surface, a strong boundary layer signal in NO2 arises fromits large boundary layer concentrations relative to the free troposphere. Recommendationsare presented including (1) additional dedicated validation activities, especially fortropospheric NO2 and HCHO; (2) improved characterization of geophysical fields thataffect remote sensing of trace gases and aerosols; (3) continued development ofcomprehensive assimilation and inversion capabilities to relate satellite observations toemissions and surface concentrations; (4) development of satellite instruments andalgorithms to achieve higher spatial resolution to resolve urban scales, facilitate validation,and reduce cloud contamination that increases remote sensing error; and (5) support forthe next generate of satellite instrumentation designed for air quality applications.

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1. Introduction

Common pollutants in surface (i.e. ground-level) airinclude aerosols, ozone (O3), nitrogen dioxide (NO2),carbon monoxide (CO), and sulfur dioxide (SO2). O3 isproduced in the troposphere by photochemical oxidation ofvolatile organic compounds (VOCs) and CO in the presenceof nitrogen oxide radicals (NOx h NOþNO2). Aerosols canenter the atmosphere as primary particles, or can beproduced chemically in the atmosphere from a variety ofcompounds including organics as well as oxidation prod-ucts of NOx and SO2. NO2, SO2, and CO are themselves also

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toxic and regulated by environmental protection agencies.Satellite remote sensing is reducing uncertainty in thespatial distribution of these deleterious species and theprocesses affecting them.

The formation of O3 and aerosols depends in a compli-cated manner upon the sources of their precursors. Airquality management is impeded by uncertainty in tradi-tional ‘‘bottom-up’’ emission inventories based on appli-cation of emission factors to activity rates. This reviewincludes satellite remote sensing of O3 and aerosolprecursors that provide constraints on emission invento-ries through a ‘‘top-down’’ approach based on inversemodeling of observations.

Remote sensing refers to the use of electromagneticradiation to acquire information without being in physicalcontact with the object, which in this case is the atmo-sphere. This review focuses on satellite remote sensing of

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Table 1Selected list of acronyms

ADEOS Advanced Earth Observing SatelliteAIRS Atmospheric Infrared SounderAPS Aerosol Polarimetry SensorAVHRR Advanced Very High-Resolution RadiometerCALIOP Cloud-Aerosol LIdar with Orthogonal PolarizationCALIPSO Cloud-Aerosol Lidar and Infrared Pathfinder

Satellite ObservationERS-2 European Remote Sensing Satellite-2GLAS Geoscience Laser Altimeter SystemGOES Geostationary Operational Environmental SatellitesGOME Global Ozone Monitoring ExperimentIASI Infrared Atmospheric Sounding InterferometerICESat Ice, Cloud and land Elevation SatelliteIGOS Integrated Global Observation StrategyIGACO Integrated Global Atmospheric Chemistry ObservationsIMG Interferometric Monitor for Greenhouse GasesMAPS Mapping of Atmospheric Pollution from SpaceMERIS MEdium Resolution Imaging SpectrometerMIPAS Michelson Interferometer for Passive

Atmospheric SoundingMISR Multiangle Imaging SpectroRadiometerMLS Microwave Limb SounderMODIS Moderate Resolution Imaging SpectroradiometerMOPITT Measurements Of Pollution In The TroposphereOMI Ozone Monitoring InstrumentPARASOL Polarization & Anisotropy of Reflectances for

Atmospheric Sciences coupled withObservations from a Lidar

SCIAMACHY SCanning Imaging Absorption SpectroMeterfor Atmospheric CHartographY

TES Tropospheric Emission SpectrometerTOMS Total Ozone Mapping Spectrometer

R.V. Martin / Atmospheric Environment 42 (2008) 7823–78437824

the composition of the boundary layer over land, which isof direct relevance for surface air quality due to rapidvertical mixing during the day. Long-range transport in thefree troposphere of trace gases and aerosols is also an issueof growing concern (Keating and Zhuber, 2007). However,discussion of such observations would detract from thefocus of this article. Recent reviews with more emphasis onobservations in the free troposphere and over ocean areavailable elsewhere (King et al., 1999; IGAC, 2007; Fishmanet al., 2008). A recent workshop, entitled Air QualityRemote Sensing from Space: Defining an OptimumObserving Strategy, was a milestone in assessing air qualityapplications from space (Edwards, 2006). Recommenda-tions from the workshop are available at http://www.acd.ucar.edu/Events/Meetings/Air_Quality_Remote_Sensing/Reports/AQRSinputDS.pdf.

Satellite remote sensing of trace gases and aerosols forair quality applications has a rich history. Lyons and Husar(1976) presented an image from the GOES satelliteshowing a large area of haze covering the Midwest UnitedStates. Todd et al. (1979) applied land use informationfrom the Landsat satellite complemented with ground-based monitors to determine population exposure to airpollution. Fraser et al. (1984) used GOES observations toconduct the first retrieval of aerosol optical depth overland and applied it to examine a haze event over theeastern United States. Fishman et al. (1987) used O3

columns retrieved from the TOMS satellite instrument toexamine a surface O3 episode over the eastern UnitedStates. This review focuses on current satellite instru-ments specifically designed for tropospheric trace gasesand aerosols.

Table 1 contains a list of satellite acronyms. Section 2describes geophysical considerations affecting theretrieval of surface air quality. Section 3 gives an over-view of the major satellite instruments being applied toremote sensing of surface air quality. Section 4 discussesretrieved species and their application to surface airquality. Section 5 presents recommendations for futuredevelopment.

2. Geophysical and remote sensing considerations

2.1. Orbits and viewing geometry

The primary satellites that yield information on lowertropospheric trace constituents fly in near-polar, Sun-synchronous, low Earth orbits. A common orbit altitude is705 km, yielding 100 min per orbit and approximately 14complete orbits per day. The non-uniformity of the Earth’sgravitational field is exploited to induce orbital precessionthat compensates for the Earth’s revolution about the Sun.The satellite crosses the equator twice an orbit, once in thesouthward or descending direction, and once in thenorthward or ascending direction. At low and mid lati-tudes, the resultant observations occur at a near-constantlocal time. At high latitudes such orbits have a relativelyhigh sampling frequency.

The point directly beneath the satellite is called thenadir. Here the nadir-viewing geometry refers to alldownward looking observations, as in common practice. In

contrast with the limb geometry, the nadir-viewinggeometry observes the entire atmospheric column and isthe only geometry that probes the boundary layer. Near-global coverage for a Sun-synchronous orbit is achieved inmore than 12 h for daytime and nighttime measurementsof thermal emission, and more than 24 h for daytimemeasurements of solar backscatter. The actual time toachieve near-global coverage exceeds a week for someinstruments as determined by swath width, cloud cover,and orbital geometry.

2.2. Retrievals

Satellite retrievals of the lower atmosphere fall broadlyinto three categories. The majority of instruments employpassive techniques, observing either solar backscatter(<4 mm) or thermal infrared emission (4–50 mm). Veryrecently, active instruments have been deployed onsatellites which transmit energy downward and measurethe backscatter. Active measurements are discussed inSection 3.7.

It is worth emphasizing that remote sensing instru-ments do not directly measure atmospheric composition.Rather, a retrieval is conducted by calculating the atmo-spheric composition that best reproduces the observedradiation. Such retrievals often require external informa-tion on geophysical fields as discussed below. The devel-opment of a variety of algorithms to extract physicalparameters by accounting for atmospheric radiativetransfer has been integral to the success of modern remotesensing.

http://www.acd.ucar.edu/Events/Meetings/Air_Quality_Remote_Sensing/Reports/AQRSinputDS.pdf



300 350 400 450 500−4

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Fig. 1. Optical thickness of air quality relevant trace gases at ultraviolet andvisible wavelengths for nominal atmospheric concentrations (1�1016 mol-ecules cm�2 for HCHO and SO2, 5�1015 molecules cm�2 for NO2, and 300Dobson Units for O3. A Dobson Unit¼ 2.69�1016 molecules cm�2). Adaptedfrom Chance (2006).

R.V. Martin / Atmospheric Environment 42 (2008) 7823–7843 7825

2.2.1. Trace gas remote sensingTrace gas remote sensing includes both solar back-

scatter and thermal infrared emission. Trace gas remotesensing using solar backscatter takes advantage of attenu-ation in the intensity of radiation traversing a medium. Thisattenuation is commonly expressed as Beer’s Law:

Il ¼ Il;0 e�slUs (1)

where Il is the backscattered intensity observed by a satelliteinstrument at a specific wavelength l, Il,0 is the back-scattered intensity that would be observed in the absence ofabsorption, sl is the absorption cross-section of the trace gas,and Us is the trace gas abundance over the atmospheric pathlength which is commonly referred to as the slant column.

Trace gas retrievals using solar backscatter exploitspectral variation in s to infer U as recently reviewed byPlatt (1994) and Chance (2006). Retrievals include a spec-tral fit to determine atmospheric abundance over theradiation path, and a radiative transfer calculation todetermine the path of radiation through the atmosphere.Specific issues involved in the spectral fit include the choiceof spectral region (fitting window), selection of solar andmolecular reference spectra, wavelength calibration, aswell as accounting for instrument characteristics andinelastic scattering by air molecules.

The radiative transfer calculation is of particularimportance at ultraviolet and short visible wavelengths. Atthese wavelengths the land surface reflectivity is typicallyless than 5% (Herman and Celarier, 1997; Koelemeijer et al.,2003), and molecular scattering is a major contributor tobackscattered radiation. The instrument sensitivity to tracegases in the lower troposphere increases with increasingreflectivity. Scattering by clouds enhances the instrumentsensitivity to the trace gas above the cloud and decreasesthe instrument sensitivity to the trace gas below the cloud(Newchurch et al., 2001; Martin et al., 2002; Millet et al.,2006). Aerosols can similarly either enhance or decreasethe instrument sensitivity depending on their single scat-tering albedo and vertical distribution, with the largesteffect arising from absorbing aerosols (Torres and Bhartia,1999; Martin et al., 2003; Fu et al., 2007).

Optically thin cases (slUs< 1) enable separation of thesolar backscatter retrieval into an independent spectral fitand air mass factor (AMF) calculation. An AMF is the ratio ofUs to the vertical column Uv and depends on the atmo-spheric path as determined by geometry, species verticalprofile, and radiative transfer properties of the atmosphere.

AMF ¼ Us

Uv(2)

The AMF calculation decouples the vertical dependenceof the sensitivity to the trace gas from the shape of the tracegas vertical profile (Palmer et al., 2001). A radiative transfermodel is used to calculate the sensitivity of backscatteredradiation at the top of the atmosphere to the verticallyresolved trace gas concentration in the atmosphere. Thelocal shape of the trace gas vertical profile is generallycalculated with an atmospheric chemistry model.

For optically thick (slUs> 1) conditions that exist atshort ultraviolet wavelengths, Eq. (2) is insufficient. The

spectral fit and radiative transfer calculation are conductedsimultaneously. In the case of O3, vertical profile informa-tion can be inferred from observations over a broad rangeof ultraviolet wavelengths that exploit the dependence ofphoton penetration into the atmosphere, with some addi-tional information from temperature-sensitive spectralvariation (Bhartia et al., 1996; Chance et al., 1997). However,atmospheric scattering reduces sensitivity to the boundarylayer. Given the limited vertical resolution of the retrievedprofiles, external information is needed about the shape ofthe ozone profile to separate boundary layer O3 from freetropospheric O3. Smoothing error, which is the error fromunresolved vertical structure in the trace gas, typicallydominates the retrieval error.

Fig. 1 shows the log of the optical thickness sl of severalgases affecting air quality for nominal atmosphericconcentrations, where

sl ¼ slUv (3)

All trace gases are optically thin at wavelengths longerthan 320 nm. O3 is the dominant absorber at wavelengthsshorter than 350 nm. The SO2 spectrum (Bogumil et al.,2003) exhibits similar spectral structure, but its sl is threeorders of magnitude lower than that for O3, resulting ina more challenging retrieval. The HCHO spectrum (Cantrellet al., 1990) exhibits pronounced spectral structure with sl

that approaches that of O3 near 350 nm. The NO2 spectrum(Vandaele et al., 2002) is the dominant trace gas absorberover much of 350–450 nm and exhibits distinct spectralstructure between 425 and 450 nm where retrievals arecommonly conducted.

Retrievals of trace gases in the thermal infrared usespectral variation in absorbed and emitted radiation toinfer trace gas abundance. The upwelling thermal intensityat the top of the atmosphere Il(0) is the sum of contribu-tions from the surface Il[T(ps)] and the atmosphere Il[T(p)],attenuated following (1) by sl(p), the optical thicknessbetween pressure p and the top of the atmosphere


Ilð0Þ ¼ Il½TðpsÞ�e�slðpsÞ þZ 0

ps

Il½TðpÞ�ve�slðpÞ

vpdp (4)

where T is the temperature and ps is the surface pressure.Angular dependence has been omitted for clarity.

The vertical distribution of a trace gas can be obtained byexploiting the pressure dependence of the trace gas spectralemission lines. Trace gas profiles derived from thermalinfrared observations typically have little sensitivity near thesurface because infrared instruments depend on thermalcontrast, although boundary layer sensitivity is possibleunder conditions with high contrast between the skintemperature and the air temperature, and with enhancedboundary layer concentrations. Smoothing error usuallydominates the retrieval error (Deeter et al., 2003; Bowmanet al., 2006). Interference from aerosols is typically weak.

Remote sensing of trace gas profiles using solar back-scatter and/or thermal emission is usually conductedwithin an optimal estimation framework (Rodgers, 2000).A trace gas profile is calculated through nonlinear mini-mization of observed and modeled radiances, subject toa smoothness constraint on the retrieved profile. Assumingthe estimated vertical profile bx is spectrally linear withrespect to the true profile x, then these two profiles arerelated by

bx ¼ xa þ Aðx � xaÞ þ 3 (5)

where 3 is the retrieval error, A is the averaging kernelmatrix, and xa is the a priori or mean state of the trace gas. Adescribes the vertically resolved sensitivity of the esti-mated profile to variations in the true profile. A can be usedto provide a measure of the vertical resolution of the esti-mate. A is a function of the sensitivities of the top ofatmosphere spectral radiances to species concentrations atdifferent altitudes, the signal-to-noise of the measure-ments, and a priori constraints used in the retrievals.

2.2.2. Aerosol remote sensingAerosol remote sensing using solar backscatter relies on

aerosol-induced changes in reflectance. The measured totalreflectance R at a specific wavelength, for dark scenes andsmall aerosol optical thickness, can be approximated asa function of the atmospheric reflectance due to molecularscattering Rm, the atmospheric reflectance due to aerosolscattering Ra, and the surface reflectance Rs

RðQÞzRmðQÞ þ RaðQÞ þ RsðQÞ (6)

where the scattering angle Q depends on the viewing andsolar geometry. The implicit dependence on wavelength isomitted for clarity. Rm(Q) is readily calculated. Thus theaccuracy with which Ra(Q) can be determined is directlyrelated to the accuracy of Rs(Q).

Ra(Q) is a function of the aerosol optical thickness sa,which is a measure of light extinction by particulate matterin the atmospheric column, of the aerosol single scatteringalbedo 6, which represents the fraction of incident radia-tion that is scattered, and of the aerosol phase functionP(Q) which represents the angular distribution of scatteredradiation. For small sa

RaðQÞfsa6PðQÞ (7)

Thus in addition to sa, measurements of R(Q) at differentwavelengths and angles over land contain information onaerosol optical properties such as the aerosol size distri-bution (Tanre et al., 1996; Martonchik et al., 1998), aerosolshape (e.g. Kahn et al., 1997; Kalashnikova and Kahn, 2006),and aerosol composition (Torres et al., 1998; Kahn et al.,2001; Kaufman et al., 2005a). External information on therelative vertical aerosol profile is needed due to atmo-spheric scattering, although this is primarily for absorbingspecies at short visible and ultraviolet wavelengths. Alsoprimarily at short wavelengths, aerosol absorption due tosoot, dust, and organic carbon can decrease or even reversethe effects of aerosol scattering. Non-absorbing aerosols aregenerally not observable over bright surfaces such as snow,ice, and clouds. However, absorbing aerosols are readilyobserved over such surfaces by exploiting the wavelengthdependence of aerosol absorption, which is particularlystrong in the ultraviolet and blue wavelengths (Hermanet al., 1997a).

Major challenges in aerosol remote sensing are toexclude cloudy scenes which affect R, and to determine Rs.High spatial resolution is necessary for aerosol remotesensing to minimize cloud contamination. One approach toestimate Rs at visible wavelengths is based on empiricalrelationships with Rs at infrared wavelengths (Kaufmanet al., 1997b). This approach generally performs well overdark surfaces, but can lead to high bias (Ichoku et al., 2002;Matsui et al., 2004), especially over coastal and arid regions(Abdou et al., 2005). Another approach to estimate surfacereflectivity is to assume the angular shape of the surfaceradiance is nearly independent of wavelength, and use thespatial variation of surface brightness in the angularsignature to separate surface from atmospheric contribu-tions (Martonchik, 1997; Diner et al., 2005). A thirdapproach is to take advantage of the larger sensitivity ofpolarized radiation to aerosol scattering than to surfacereflection (Herman et al., 1997b). A fourth approach usesultraviolet observations to minimize the surface signal(Herman et al., 1997a; Torres et al., 1998). The ultravioletapproach is sensitive to aerosol absorption, is more sensi-tive to free tropospheric aerosol than to boundary layeraerosol, and works as well over land as water (Torres et al.,2005, 2007). A fifth approach is to use the near-minimumreflectivity over a period of time (Knapp et al., 2005).

2.3. Vertical sensitivity

The ability of nadir-viewing satellite instruments todetect trace gases and aerosols in the atmosphere dependson the surface reflectivity or emissivity, clouds, the viewinggeometry, and the retrieval wavelength. Detection of tracegases additionally depends on its vertical profile and, forsolar backscatter, on aerosols. The instrument sensitivity totrace gases in the middle and upper troposphere is typicallynear 100%. However, atmospheric scattering and emissionresult in part of the measured intensity not passing throughthe boundary layer, and therefore decrease the instrumentsensitivity to trace gases and ultraviolet-absorbing aerosolsnear the ground. The decrease in sensitivity in the boundary

0 0.5 10

2

4

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12

Shape Factor (km−1)

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NO2SO2HCHOExtinctionCOO3

Fig. 2. Normalized vertical profiles averaged over the United States andsouthern Canada (30�N–50�N, 125�W–70�W) for 2004 as calculated witha global chemical transport model (GEOS-Chem). Trace gases profiles are thenumber density divided by the tropospheric column. Normalized aerosolextinction is calculated by dividing by aerosol optical thickness. All profilesintegrate to unity over the troposphere.


layer is particularly pronounced at ultraviolet wavelengthswhere there is strong molecular scattering (Klenk et al.,1982), or in the thermal infrared due to reduced thermalcontrast between the atmosphere and surface (Pan et al.,1995).

Following is a summary of the calculated nominalsensitivity of a nadir-viewing instrument to trace gases andaerosols in the boundary layer over land. The sensitivity is15–30% at ultraviolet wavelengths used for retrieval of O3,SO2, and HCHO (Palmer et al., 2001; Valks et al., 2003;Krotkov et al., 2008). The sensitivity increases to 40–60% atvisible wavelengths used for retrieval of NO2 as a result ofdecreased molecular scattering (Martin et al., 2002; Lad-statter-Weißenmayer et al., 2003; Boersma et al., 2004). Itcan approach 100% for cloud-free and dust-free scenes inthe near-infrared where CO can be retrieved (Buchwitzet al., 2007). However, the sensitivity decreases in thethermal infrared to less than 20% due to decreased thermalcontrast (Worden et al., 2007b). The actual sensitivity var-ies with surface conditions. The sensitivity to trace gases inthe boundary layer increases with thermal contrast forinfrared measurements and with surface albedo for solarbackscatter measurements of trace gases. The sensitivity toaerosols in the boundary layer is generally high except forabsorbing aerosols at ultraviolet and blue wavelengths.

A useful parameter which describes the number ofindependent pieces of information available in theretrieved vertical profile is the Degrees of Freedom forSignal (DFS) (Rodgers, 2000). The DFS depend heavily uponinstrument characteristics and atmospheric physics.Typical DFS for CO and O3 profiles are less than 1 at thepoles, and 1–2 in the tropics (Deeter et al., 2004; Wordenet al., 2004; Liu et al., 2005b; Luo et al., 2007).

It is worth emphasizing that satellite remote sensingdoes not explicitly resolve ground-level concentrations.Rather an integrated column amount is inferred over finitevertical thickness that usually exceeds a few kilometers forpassive remote sensing. Relating satellite-derived quanti-ties to actual ground-level concentrations requires infor-mation on the vertical structure of the atmosphere. Achemical transport model is one source of information onmixing within and concentrations above the boundarylayer. The relationship of satellite-derived quantities toground-level concentrations is more straightforward in latemorning and afternoon when the lower mixed layer iswell-developed.

2.4. Species vertical variation

The spatial distribution of trace gases and aerosols hasimplications for their retrieval. Two issues of concern arethe stratospheric column and the vertical profile in thetroposphere. High variability in the stratospheric O3

column inhibits discrimination of the tropospheric O3

column at middle and high latitudes (Schoeberl et al.,2007). In contrast, relatively weak zonal variability in thestratospheric NO2 column facilitates separation of thestratospheric and tropospheric columns (Martin et al.,2002; Boersma et al., 2004).

Fig. 2 shows normalized annual mean vertical profiles oftrace gases and aerosol extinction over the United States

and southern Canada as calculated with a global chemicaltransport model (GEOS-Chem; Bey et al., 2001), v7-03-03(http://www.as.harvard.edu/chemistry/trop/geos/).Normalizing each species by its column abundance facili-tates comparison of the relative vertical profile. Tropo-spheric NO2 and SO2 concentrations are enhanced stronglyin the boundary layer due to strong surface sources, shortlifetimes, and the increase in the NO/NO2 ratio with alti-tude that is driven by the temperature dependence of theNOþO3 reaction. As a result, column observations of NO2

contain large contributions from the boundary layer. In situaircraft measurements indicate that the boundary layercontains more than two-thirds of the tropospheric NO2 andSO2 column over polluted regions (Martin et al., 2004b;Taubman et al., 2006). HCHO columns and sa are alsostrongly affected by boundary layer enhancements in bothHCHO concentration and aerosol extinction. In contrast,weak vertical variation in the number density of O3 and COindicates that vertical profile information in both species isoften necessary to extract a boundary layer signal.Boundary layer O3 constitutes only a small fraction (typi-cally <2%) of the total O3 column.

3. Satellite overview

The first applications of satellite remote sensing ofaerosols used the AVHRR, Landsat, and GOES instrumentsto observe desert particles over ocean (Fraser, 1976; Carlsonand Wendling, 1977; Mekler et al., 1977), and later volcanicsulfate (Stowe et al., 1992). All three instruments weredesigned primarily for monitoring of surface and meteo-rological fields, but also obtain information about aerosolsprimarily over water.

Satellite remote sensing of tropospheric trace gasesbegan in 1978 with the launch of the TOMS instrumentonboard the Nimbus 7 satellite. The TOMS instruments wereaimed at determining global knowledge of stratospheric O3.

http://www.as.harvard.edu/chemistry/trop/geos

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However, it was later recognized that these instruments alsoyield information about volcanic SO2 (Krueger, 1983),tropospheric O3 (Fishman et al., 1990), and ultraviolet-absorbing aerosols (Herman et al., 1997a). The last TOMSinstrument (Earth Probe) was deactivated in 2007, but OMIon the EOS Aura satellite is the successor to the TOMS series.

In an early endeavor to measure tropospheric chemicalcomposition from space, the MAPS instrument wasdeployed onboard the Space Shuttle in 1981 (Reichle et al.,1986), 1984 (Reichle et al., 1990), and 1994 (Reichle et al.,1999), making the first successful measurements of carbonmonoxide in the middle and upper troposphere.

Table 2 contains a summary of the capabilities of themajor satellite instruments designed for remote sensing ofaerosols and chemically reactive trace gases in the lowertroposphere. Only instruments currently in orbit areincluded. Each is discussed below. All are in Sun-synchronous, polar orbits. Solar backscatter measurementsare used for NO2, HCHO, and sa. Both solar backscatter andthermal infrared measurements are used for O3, CO, andSO2. Global coverage in the absence of clouds is achieved ona timescale of days for most instruments. A trend of rele-vance for remote sensing of surface air quality is increasingspatial resolution. For example, the nadir resolutionincreases over that of GOME-1 by a factor of 7 for SCIA-MACHY, and 40 for OMI.

3.1. ERS-2 (GOME-1)

Satellite remote sensing of the lower troposphereeffectively began in 1995 with the launch of the GOME-1instrument (Burrows et al., 1999) aboard the ERS-2 satellite.GOME-1 is a nadir-viewing grating spectrometer thatmeasures solar backscatter with broad spectral coverage(230–790 nm) and moderate resolution (0.2–0.4 nm). Thespatial resolution of a ground scene is variable but is typi-cally 40� 320 km2. GOME-1 made global measurementsfrom July 1995 to June 2003, when the tape recorder onERS-2 failed. Since then GOME measurements have beenobtained using the ERS-2 direct broadcast and a network ofreceivers, with a global coverage of about 40%.

Of particular interest here are the retrievals of tropo-spheric NO2, HCHO, SO2, and tropospheric O3. Khokharet al. (2005) retrieved global SO2 slant columns from GOMEwith sufficient accuracy to study volcanic plumes andmajor pollution sources.

Global HCHO vertical columns have been retrieved bya few groups (Chance et al., 2000; Ladstatter-Weißenmayeret al., 2003). Typical values over land range from less than5�1014 to more than 3�1016 molecules cm�2. The overallretrieval uncertainty is 5�1014 molecules cm�2 from thespectral fit (Chance et al., 2000), and a 30% uncertaintyfrom the AMF calculation (Millet et al., 2006) that increasesin the presence of biomass burning aerosol (Fu et al., 2007).Validation of GOME observations with in situ measure-ments is challenging due to its large spatial footprint.However, the limited validation of HCHO retrievals withaircraft measurements over the Mediterranean (Ladstatter-Weißenmayer et al., 2003) and the southeast United States(Martin et al., 2004b) is consistent with the expecteduncertainty.


Tropospheric NO2 columns have been retrieved fromGOME by several groups (Martin et al., 2002; Richter andBurrows, 2002; Beirle et al., 2003; Boersma et al., 2004). Allretrievals involve a spectral fit, determination of thestratospheric NO2 column using observations over remoteregions, oceans, and an AMF calculation to convert slantcolumns into vertical columns. Retrieved values range fromless than 1�1015 to more than 1�1016 molecules cm�2.The typical uncertainty is 0.5–1�1015 molecules cm�2

from the spectral fit and subtraction of the stratosphericNO2 column, and 20–50% from the AMF (Martin et al.,2002; Boersma et al., 2004). The limited validation oftropospheric NO2 columns with aircraft measurementsover central Europe (Heland et al., 2002), the Mediterra-nean (Ladstatter-Weißenmayer et al., 2003), and thesoutheast United States (Martin et al., 2004b) is consistentwith that error estimate. Intercomparison of three differentNO2 retrievals (van Noije et al., 2006) indicates a systematiccomponent to the error that is most pronounced overindustrial regions in winter. Evaluation of the trend inretrieved tropospheric NO2 columns with independentobservations over East Asia indicates a small drift of�7�1013 molecules cm�2 in the retrieved columns over1996–2002 (Irie et al., 2005).

Munro et al. (1998) first retrieved tropospheric O3 fromGOME-1. Liu et al. (2005b) developed a more computa-tionally efficient algorithm to retrieve global troposphericO3 columns through optimal estimation, yielding 0.5–1.5 DFS. Typical values are 20–55 Dobson Units. Validationof the retrieved O3 columns with in situ profiles fromozonesondes (Liu et al., 2005b) and the MOZAIC aircraftprogram (Liu et al., 2006b) yields typical biases within 5Dobson Units.

3.2. Terra (MOPITT, MODIS, MISR)

The launch of NASA’s Terra satellite in December 1999significantly expanded scientific perspective about thescale of tropospheric pollution. The MOPITT instrumentonboard Terra is a nadir-viewing gas correlation radiometeroperating in the 4.7 mm band of carbon monoxide (Drum-mond and Mand, 1996). The MOPITT pixel is 22� 22 km2 atnadir with a 29 pixel wide swath. Carbon monoxide isretrieved from MOPITT (Deeter et al., 2003) with a targetaccuracy and precision of 10% (Edwards et al., 2003). Thetypical DFS values are 0.5–2 (Deeter et al., 2004). MOPITTretrievals have been extensively validated (Emmons et al.,2004; Emmons et al., 2007) and are within the targetaccuracy and precision over much of the planet.

The MISR (Diner et al., 1998) and MODIS (Barnes et al.,1998) instruments provide unprecedented informationabout aerosol abundance and properties at high spatialresolution. The MISR instrument includes nine fixed push-broom cameras pointed at angles varying from þ70�,through nadir, to �70�. Each camera has four line-arraycharge-coupled devices (CCDs) covering spectral bandscentered at 446, 558, 672, and 867 nm, and having spectralwidths of 20–40 nm, giving a total of 36 channels. MISR’shighest spatial sampling is 275 m at all angles. The stan-dard acquisition mode provides full resolution data in allfour nadir-viewing channels and the red-band channels at

the other eight angles; the remaining 24 channels arereported at 1.1 km. MISR retrievals over land (Martonchiket al., 2002; Diner et al., 2005) use the spatial variation ofsurface brightness to separate surface and atmosphericcontributions, and self-consistently retrieve both. Astrength of MISR is the ability to retrieve sa even over brightsurfaces such as deserts (Martonchik et al., 2004). Theretrieval provides information on aerosol size, single scat-tering albedo, and sphericity (Kahn et al., 1997, 2001;Kalashnikova and Kahn, 2006; Chen et al., 2008). Stereo-derived elevations are also retrieved for distinct aerosolplumes (Moroney et al., 2002; Kahn et al., 2007). MISR sa

retrievals have been validated with ground-basedmeasurements from AERONET (Liu et al., 2004b; Abdouet al., 2005; Kahn et al., 2005; Jiang et al., 2007), yieldinga typical accuracy over land of better than 0.05� 20%.

The MODIS instrument has 36 channels with varyingspatial resolution of 250, 500 and 1000 m, depending onthe channel. The channels span the spectral range from 410to 14,200 nm, and bandwidth varies from channel tochannel. Aerosol retrievals over land from MODIS aredescribed originally by Kaufman et al. (1997a). Two inde-pendent retrievals are conducted at 470 and 660 nm, andsubsequently interpolated to 550 nm. The surface reflec-tances for the channels at 470 and 660 nm are estimatedfrom measurements at 2.1 mm using empirical relation-ships. Geographically and seasonally varying multimodalaerosol models are assumed. Validation of the retrieved sa

with AERONET yields a typical accuracy of 0.05�15% (Chuet al., 2002; Ichoku et al., 2005; Remer et al., 2005) withhigher errors over deserts and coastal regions (Abdou et al.,2005). Hsu et al. (2004) developed the ‘‘Deep Blue’’ algo-rithm for aerosol retrievals over deserts using short wave-lengths where surface reflectivity is low. Levy et al. (2007)describe a second-generation operational algorithm thatuses revised geophysical parameters and include the2.1 mm measurements in the aerosol retrieval.

3.3. ENVISAT (SCIAMACHY)

SCIAMACHY measures backscattered solar radiationupwelling from the atmosphere, alternately in nadir andlimb viewing geometry. Eight channels, comprised ofgrating optics and a linear diode array detector, measurethe spectrum over 214–1750 nm at resolution of 0.2–1.4 nm, and two spectral bands around 2.0 and 2.3 mm,having a spectral resolution of 0.2 nm. The typical spatialresolution of SCIAMACHY is 30� 60 km2.

Retrieval algorithms of tropospheric trace gases andtheir expected uncertainty for SCIAMACHY are similar tothose for GOME-1. Wittrock et al. (2006) retrieved HCHOand glyoxal columns from SCIAMACHY. Lee et al. (2008)developed a weighting function algorithm for SO2

retrievals from SCIAMACHY. A few groups have retrievedtropospheric NO2 columns from SCIAMACHY (Richter et al.,2005; Martin et al., 2006; van der A et al., 2006). Validationof the retrieved tropospheric NO2 columns with aircraftremote sensing measurements over central Europe (Heueet al., 2005), and with aircraft in situ measurements overeastern North America and the North Atlantic (Martin et al.,


2006), yields mean agreement within the sum of6�1014 molecules cm�2 and 20% of the retrieved column.

Retrieval of CO from SCIAMACHY is challenging due tocalibration issues and weak signals (Gloudemans et al.,2005). CO has been retrieved from SCIAMACHY with threeindependent retrievals (Buchwitz et al., 2005; Frankenberget al., 2005; Gloudemans et al., 2006). Validation of thethree retrievals with ground-based FTIR measurementsindicates a typical bias of 5–20% and an overall scatter thatis 2.3 times the target precision of 10% (Dils et al., 2006).

3.4. Aqua (MODIS, AIRS)

Aqua contains a MODIS instrument (described in Section3.2) and AIRS. AIRS is a cross-track scanning grating spec-trometer that covers the 3.7–16 mm spectral range with 2378channels and a 13.5 km nadir field-of-view (Aumann et al.,2003). CO retrievals are conducted at 4.7 mm with a spatialresolution of 45 km at nadir, yielding a profile with0.5–1.5 DFS (McMillan et al., 2005). AIRS CO agrees withMOPITT CO over land to within 10–15 ppbv with nosystematic bias, but AIRS CO exhibits a positive bias of15–20 ppbv relative to MOPITT CO over ocean (Warner et al.,2007). Volcanic SO2 also is being retrieved (Carn et al., 2005;Prata and Bernardo, 2007).

3.5. Aura (OMI, TES)

OMI is a nadir-viewing, imaging spectrometer that usestwo-dimensional CCD detectors to measure the solarradiation backscattered by the Earth’s atmosphere andsurface over 270–500 nm with a spectral resolution of0.5 nm (Levelt et al., 2006). The OMI spatial resolution13� 24 km2 at nadir with coarser resolution at largerviewing angles. Retrieval algorithms of tropospheric tracegases and their expected uncertainty for OMI are similar tothose for GOME-1 and SCIAMACHY. In addition, sa for bothabsorption and extinction is being retrieved from OMI(Torres et al., 2007). The retrieval algorithms for HCHOcolumns are described by Chance (2002) and for SO2

columns by Krotkov et al. (2006). Tropospheric O3 columnsfrom OMI are retrieved with a residual technique that useslimb observations from MLS (Ziemke et al., 2006) anda forward trajectory model (Schoeberl et al., 2007). Vali-dation of the trajectory based estimate with ozonesondesin the northern hemisphere extratropics indicates anoverall seasonal bias of 3–5 Dobson Units, with a standarddeviation of 9–13 Dobson Units (Schoeberl et al., 2007).Tropospheric NO2 columns are retrieved from OMI by twogroups (Bucsela et al., 2006; Boersma et al., 2007). Valida-tion of the retrieved NO2 columns with ground-basedremote sensing measurements (Celarier et al., 2008; Weniget al., 2008), with aircraft measurements over NorthAmerica and the Pacific Ocean (Boersma et al., 2008;Bucsela et al., 2008), and with ground-based in situ NO2

measurements (Lamsal et al., 2008) yields mean agreementto within 30%.

TES is a Fourier transform infrared emission spectrom-eter with high spectral resolution (0.1 cm�1) and coverageover a wide spectral range (650–3050 cm�1) (Beer et al.,2001). The TES nadir footprint is 5� 8 km2 and has 71

observations per orbit (spaced approximately 175 kmapart). Tropospheric O3 and CO are retrieved with anoptimal estimation method (Bowman et al., 2006; Cloughet al., 2006). The TES CO retrievals (Rinsland et al., 2006)typically have 0.5–1.5 DFS (Luo et al., 2007). In cloud-freeconditions the vertical resolution of the O3 estimate isabout 6 km, with sensitivity to both the lower and uppertroposphere, but reduced sensitivity in the boundary layer(Bowman et al., 2002; Worden et al., 2004). The TES O3

retrievals typically have 1–2 DFS in the troposphere (Wor-den et al., 2004; Bowman et al., 2006). Validation of theretrieved O3 with ozonesondes confirms the detection oflarge-scale features in O3 profiles, with a high bias in theupper troposphere (Worden et al., 2007a). A retrieval oftropospheric O3 that combines information from TES andOMI has been proposed that would enable discriminationof the boundary layer from the free troposphere (Wordenet al., 2007b).

3.6. PARASOL

The PARASOL instrument (Lier and Bach, 2008) is largelybased on POLDER. PARASOL consists of a digital camera witha CCD detector array, wide-field telecentric optics anda rotating filter wheel enabling measurements in nine spec-tral channels from blue (0.443 mm) through to near-infrared(1.020 mm) and in several polarization directions. Polarizationmeasurements are performed at 0.490, 0.670 and 0.865 mm.The bandwidth is between 20 and 40 nm depending on thespectral band. The instrument can view ground targets fromdifferent angles,�51� along track and �43� across track. Theaerosol algorithm over land, based on Deuze et al. (2001),retrieves sa of the fine mode by using the polarized radiancewhich is primarily sensitive to small particles.

3.7. CALIPSO (CALIOP)

CALIOP is a lidar, LIght Detection And Ranging, other-wise known as a laser radar. CALIOP is a nadir-pointinginstrument which provides information on the verticaldistribution of aerosols and clouds as well as their opticaland physical properties (Winker et al., 2003). CALIOP isbuilt around a diode-pumped Nd:YAG laser producinglinearly polarized pulses of light at 1064 and 532 nm. Theatmospheric return is collected by a 1-m telescope, whichfeeds a three-channel receiver measuring the back-scattered intensity at 1064 nm and the two orthogonalcomponents of the 532 nm return (parallel and perpen-dicular to the polarization plane of the transmitted beam).Cloud and aerosol layers are discriminated using themagnitude and spectral variation of the lidar backscatter(Liu et al., 2004c). Aerosol layers with sa of 0.01 can bedetected with sufficient averaging (McGill et al., 2007).Aerosol extinction profiles are computed with a verticalresolution of 120–360 m from an extinction-to-backscatterratio, or lidar ratio.

3.8. MetOP (GOME-2, IASI)

MetOP is the first operational meteorological platformto have instrumentation dedicated to making tropospheric


trace gas measurements: GOME-2 and IASI. GOME-2 isa nadir-scanning double spectrometer covering the 240–790 nm wavelength range with a spectral resolution of0.26–0.51 nm. GOME-2 observes all the species of GOME-1at a typical resolution of 40� 80 km2. Retrievals of tropo-spheric trace gases from GOME-2 are similar to those fromGOME-1, SCIAMACHY, and OMI.

The IASI instrument consists of a Fourier transformspectrometer associated with an imaging system, designedto measure the infrared spectrum emitted by the Earth inthe thermal infrared using a nadir geometry. The instru-ment is providing spectra of high radiometric quality at0.5 cm�1 resolution (apodised), from 645 to 2760 cm�1. TheIASI instrument field-of-view is sampled by a matrix of2� 2 circular pixels of 12 km diameter each. Measurementsare taken every 50 km at nadir with broad horizontalcoverage. Turquety et al. (2004) describe the operationalretrievals of tropospheric O3 and CO which employa nonlinear artificial neural network. A research retrieval ofHNO3 columns is described by Wespes et al. (2007).

3.9. Other platforms

The IMG instrument was launched as part of the ADEOSsatellite in August 1996. IMG was the first high-resolutionnadir infrared tropospheric sounder allowing for simulta-neous retrieval of several trace gases (Kobayashi et al.,1999). The CO (Turquety et al., 2004; Barret et al., 2005) andHNO3 (Wespes et al., 2007) retrievals are of particularinterest for surface air quality. The ADEOS satellite ceasedto collect and transmit data in June 1997 due to a powerfailure in its solar panel.

A few other satellite instruments are being used foraerosol retrievals. The GLAS lidar onboard ICESat (Schutzet al., 2005) was designed to measure ice sheet elevation,but is being applied to retrieve aerosol profiles (Spinhirneet al., 2005). Both the MERIS instrument onboard ENVISAT,and the Sea-Wifs onboard SeaStar are both designedprimarily for ocean color. However, sa can be retrieved overland from these sensors using observations at blue wave-lengths where most surfaces are only weakly reflective(Santer et al., 1999; von Hoyningen-Huene et al., 2003).

Observations from the latest generation of GOESimagers (Menzel and Purdom, 1994) onboard geosta-tionary satellites have been applied to retrieve sa ata resolution of 4� 4 km2 and a temporal frequency of30 min (Knapp et al., 2002; Knapp et al., 2005). Validationof the retrieved sa with AERONET observations over NorthAmerica indicates that daily variability generally is wellcaptured over the northeastern United States and Canada,but exhibits biases and poor correlations in other regions(Prados et al., 2007).

The ultraviolet aerosol index (UVAI) (Herman et al.,1997a; Torres et al., 1998), first developed for TOMS,provides a measure of aerosol that absorbs at ultravioletwavelengths, which is primarily soot, organic carbon, andmineral dust. The UVAI has subsequently been measuredfrom GOME-1 (De Graaf et al., 2005), SCIAMACHY (De Graafand Stammes, 2005), OMI (Torres et al., 2007), and GOME-2.The UVAI therefore provides a useful tracer of biomassburning aerosol (Duncan et al., 2003). However, most of the

UVAI signal is from the free troposphere due to strongmolecular scattering at ultraviolet wavelengths.

Landsat and AVHRR were designed to monitor surfaceproperties at high spatial resolution. Under someconditions they can be applied to retrieve sa, with highuncertainty (e.g. Tanre et al., 1988; Holben et al., 1992).

4. Applications

Three major applications of retrieved trace gases andaerosols are analyses and forecasts of events that affect airquality, inference of surface air quality itself, and estimatesof emissions.

4.1. Analyses and forecasts of events that affect air quality

Satellite retrievals add synoptic and geospatial contextto ground-based air quality measurements. Such context isapplied for qualitative and quantitative analyses of eventsthat affect air quality. Most applications have been of long-lived tracers such as sa and CO where transport patterns areapparent. Falke et al. (2001) examined four case studies oftransport of mineral dust and biomass burning smoke toNorth America. Hutchison (2003) presented a case study inwhich MODIS satellite data show pollution transport thatled to 150 health advisories in Texas. Engle-Cox et al.(2004a) assessed the integrated use of ground-based andsatellite data for air quality monitoring and providedrecommendations on their application to air qualityresearch. Engle-Cox et al. (2005) applied MODIS sa obser-vations and back trajectory calculations to understand theorigin of events in five U.S. cities. Liu et al. (2006a) foundgradients of 50–100% in MOPITT CO columns that arecoherent at the synoptic scale and driven by frontal activity.A synthesis of satellite and in situ measurements withphotochemical modeling and Lagrangian trajectory anal-yses provides an estimate of regional influences and distantsources on Houston and Dallas air quality during TexAQS2006 (Cowling et al., 2007). Lee et al. (2007) used SCIA-MACHY SO2 columns and ground-based observations tolink SO2 transport from Chinese sources to increasedground-level SO2 in Korea.

Analysis of satellite and in situ observations withnumerical models also is providing growing evidence ofintercontinental transport events that affect surface airquality. Pfister et al. (2005, 2006) used a global model andin situ measurements to interpret MOPITT CO observationsduring July 2004 and concluded that North Americanwildfires increased ground-level O3 in eastern NorthAmerica and Europe. Guerova et al. (2006) interpretedGOME NO2 and MOPITT CO with a global model to identifylong-range transport events from North America and theircontribution to surface O3 concentrations in Europe. Morriset al. (2006) used a combination of several satellite obser-vations to track a biomass burning plume from Alaska andwestern Canada to conclude that the forest fires exacer-bated surface O3 in Houston. Transpacific transport of Asiananthropogenic aerosols as observed with MODIS sa hasbeen associated with increases in springtime surfaceaerosol concentrations in the northwest United States(Heald et al., 2006) and southwest Canada (van Donkelaar


et al., 2008). Transpacific transport of Asian O3 and CO asobserved by TES, AIRS, and MOPITT has been associatedwith increased ground-level O3 in western North America(Zhang et al., 2008).

Fig. 3 shows an illustrative example of MODIS obser-vations for September 10, 2002 as presented by Al-Saadiet al. (2005). The top panel contains visual imagery thatindicates a large haze region over the eastern United States.The bottom panel shows sa and cloud optical depth

Fig. 3. Observations on September 10, 2002 from the MODIS instrumentonboard the Terra satellite. The top panel contains visual imagery thatillustrates familiar weather patterns. The bottom panel contains aerosoloptical thickness (AOD) and cloud optical thickness (COT) retrieved from theMODIS observations. Figure from Al-Saadi et al. (2005).

retrieved from the MODIS observations. These approachesare being extended to air quality prediction. Hutchisonet al. (2004) applied MODIS sa and trajectory forecasts fora case study of air quality prediction in Texas. Al-Saadi et al.(2005) developed the first near-real-time application ofsatellite data for air quality as part of the IDEA (Infusingsatellite Data into Environmental Applications) partnershipbetween NASA, EPA, and NOAA. Aerosol data and forecastproducts are available at http://idea.ssec.wisc.edu/.

4.2. Inference of surface air quality

Most estimates of air quality from satellite observationshave focused on ground-level aerosol mass concentration.However, information on ground-level NO2, O3, and COconcentration is becoming available, in part due to theincreasing spatial resolution afforded by more recentinstrumentation (Table 2).

4.2.1. Particulate matterA primary application of satellite observations has

been the inference of ground-level particulate matter(PM) concentrations from retrieved sa. This research areahas a long history, beginning with sa from GOES over theeastern United States (Fraser et al., 1984). In recent years,improvements to the quality of retrieved sa are enablingmore quantitative estimates of PM. Chu et al. (2003)demonstrated the close relationship between MODIS sa

and PM10 (diameter< 10 mm) measurements in northernItaly. Wang and Christopher (2003) found an evenstronger relationship (r> 0.9) in Alabama between themonthly mean sa and PM2.5 (diameter< 2.5 mm). Therelationship between sa and PM2.5 varies considerablywith region, generally being higher in the eastern UnitedStates than either Europe (Koelemeijer et al., 2006) or thewestern United States (Engle-Cox et al., 2004b; Al-Saadiet al., 2005) where there is more dust aloft. Empiricalrelationships have been developed between satelliteretrievals of sa and surface PM2.5 for the southeast UnitedStates using both MODIS (Wang and Christopher, 2003)and MISR (Liu et al., 2005a). Gupta et al. (2006) comparedMODIS sa and PM2.5 at 26 global urban areas and foundthat the relationship was highest for cloud-free condi-tions, low boundary layer heights, low relative humidity,and enhanced sa. Pelletier et al. (2007) found that mete-orological variables improved the relationship between sa

and PM10.Liu et al. (2004a) developed a simple, yet effective,

approach for estimating surface PM2.5 by applying localscaling factors from a global chemical transport model(GEOS-Chem) to aerosol optical thickness from MISR.Advantages of such an approach are that the chemicaltransport model can account for the variety of factors (e.g.aerosol vertical profile, aerosol size distribution, aerosoloptical properties) that can affect the relationship betweensa and PM2.5. van Donkelaar et al. (2006) compared PM2.5

inferred from MODIS, MISR, and GEOS-Chem with surfacePM2.5 measurements in Canada and the United States, andconcluded that the relative vertical profile of aerosolextinction is the most important factor affecting the spatialrelationship between sa and surface PM2.5. Engel-Cox et al.

http://idea.ssec.wisc.edu


(2006) applied aerosol profile information from an upwardpointing lidar to better understand variability in thecorrelation between sa and surface PM2.5. Global retrievalsof aerosol profiles from CALIPSO should provide informa-tion to better relate sa with surface PM2.5.

The relationship between sa and surface PM is strongestduring summer and fall. However, high PM concentrationscan occur in winter when subpixel snow and ice contributeto errors in sa. Additional attention is needed to retrievals ofsa over frozen surfaces (Chin et al., 2004).

Liu et al. (2007a) developed a method to estimate thecomposition and size distribution of near-surface PM2.5

using the fractional column sa for different aerosol typesretrieved from MISR. Comparison with surface observa-tions reveals that their approach shows substantialimprovements over the previous approach of using total sa

as a predictor (Liu et al., 2007b). The APS to be launchedonboard the Glory satellite in 2009 is expected to furtherdistinguish between different aerosol species by exploitingpolarization and multiangle information (Mishchenkoet al., 2007).

4.2.2. NO2

Fig. 4 shows a 3-year average of tropospheric NO2

columns retrieved from OMI. Pronounced enhancementsare evident over major urban and industrial regions. Thehigh degree of spatial heterogeneity provides empiricalevidence that the dominant signals are from the boundarylayer. For example, in situ aircraft measurements of NO2

indicate that 75% of the NO2 column is in the lowest 1500 mover Houston (Martin et al., 2004b).

Tropospheric NO2 columns are closely related to surfaceNO2 concentrations. GOME observations of troposphericNO2 columns have been applied to reveal a significantcorrelation with in situ NO2 measurements (Petritoli et al.,2004; Ordonez et al., 2006). Lamsal et al. (2008) estimatedsurface NO2 concentrations by applying local scaling factorsfrom the GEOS-Chem model to OMI tropospheric NO2

columns, and demonstrated significant agreement withground-level in situ measurements. Additional ground-levelmeasurements specific to NO2 would aid validation.

OMI Tropospheric NO2 Column (1015 molec cm−2)

10 2 3 4 5 6 7 8 9

Fig. 4. Tropospheric NO2 columns from the OMI standard product (Bucselaet al., 2006) over October 2004–September 2007 inclusive.

A weekly pattern in GOME tropospheric NO2 columnsover urban regions has been observed with significantreductions on weekends (Beirle et al., 2003). Additionalanalysis is needed on how day-to-day variation in thediurnal variability of NO2 concentrations could affect theobserved weekly pattern (e.g. Harley et al., 2005).

4.2.3. O3

Retrieval of boundary layer O3 from satellite remotesensing remains a daunting task. Current O3 retrievalsexhibit low sensitivity to the boundary layer due tomolecular scattering in the ultraviolet and surface emissionin the thermal infrared (Section 2). Furthermore, boundarylayer O3 is a small fraction of the total column. However,tropospheric O3 retrievals provide constraints on boundaryconditions in air quality models. Fiore et al. (2002) foundthat background O3 produced outside of the North Amer-ican boundary layer contributes substantially (15–35 ppbv)to afternoon O3 concentrations in U.S. surface air duringsummer. Regional simulations of surface O3 concentrationsare sensitive to O3 boundary conditions (Tang et al., 2007),with implications for O3 control strategies (Winner et al.,1995). Assimilation will play an important role in relatingO3 retrievals to surface concentrations.

Information about O3 also can be inferred fromretrievals of HCHO and NO2. The design of control strategiesfor surface O3 has been impeded by limited observations ofO3–NOx–VOC sensitivity (Sillman, 1999). Martin et al.(2004a) demonstrated the capability for satellite remotesensing of O3–NOx–VOC sensitivity using the ratio of HCHOcolumns to tropospheric NO2 columns; diagnoses usingGOME observations were consistent with current under-standing of surface O3 chemistry. Further analysis is neededat higher spatial resolution to assess urban scale features.

4.2.4. COSatellite retrievals of CO exhibit strong signals from the

free troposphere due to broad averaging kernels of currentinstruments and reduced thermal contrast near the surface.Nonetheless, enhanced signals in CO columns over citiesare apparent in long-term averages for SCIAMACHY(Buchwitz et al., 2007) and MOPITT (Clerbaux et al., 2008).Furthermore, ground-level CO concentrations in regionalair quality models are sensitive to boundary conditions(Tang et al., 2007), which can be constrained by satelliteobservations. CO retrievals feature more lower tropo-spheric information in regions with strong thermalcontrast such as arid environments (Deeter et al., 2007).

4.3. Estimates of surface emissions

Inverse modeling is a formal approach to infer pollutantsource strength from observations of atmosphericconcentrations as reviewed by Kasibhatla et al. (2000) andJacob (2006). A chemical transport model is often used tocalculate the emissions of various pollutants that wouldreproduce the observations. This ‘‘top-down’’ informationis being used to evaluate and improve ‘‘bottom-up’’ emis-sion inventories. Satellites provide a major source ofobservations used for inverse modeling of emissions


(Palmer, in press). The primary retrievals of interest aretropospheric NO2, HCHO, CO, and more recently sa.

4.3.1. NOx

Tropospheric NO2 columns are closely related to surfaceNOx emissions due to the short NOx lifetime and anincreasing NO/NO2 ratio with altitude. Leue et al. (2001)initially applied GOME retrievals of tropospheric NO2

columns, together with an assumed constant global NOx

lifetime, to derive a global NOx emissions inventory by massbalance. Martin et al. (2003) improved on this approach byusing coincident local information from the GEOS-Chemmodel on the NOx lifetime and the NO2/NOx ratio, and bycombining top-down constraint with a bottom-up a prioriinventory to produce an optimal a posteriori inventory.Jaegle et al. (2005) developed a method for global parti-tioning of satellite-derived NOx sources into contributionsfrom fossil fuel combustion, biomass burning, and soilemissions. Subsequent improvements to NOx inversionsinclude developing an adjoint model for inference ofemissions (Muller and Stavrakou, 2005), applying regionalmodels at higher resolution (Kim et al., 2006; Konovalovet al., 2006; Napelenok et al., 2008), better accounting forfree tropospheric NO2 in the inversion (Wang et al., 2007b),and inversions at high temporal resolution (Wang et al.,2007a). Toenges-Schuller et al. (2006) demonstrated theclose relationship between tropospheric NO2 columns andanthropogenic NOx emissions. Boersma et al. (2008)compared satellite observations in the late morning(SCIAMACHY) and the early afternoon (OMI) to inferinformation on the diurnal variation in NOx emissions fromdifferent sources.

The long-term record of tropospheric NO2 columns isalso being applied to infer trends in NOx emissions.Significant increases have been found since 1995 over EastAsia, in contrast with reductions in Europe and parts of theUnited States (Richter et al., 2005; van der A et al., 2006,2008). Zhang et al. (2007) compared bottom-up estimatesof NOx emissions over 1995–2004 with trends in NO2

columns to find consistent summertime trends, but a largediscrepancy in wintertime. Additional analysis of thediscrepancy is needed. A thorough understanding of thediurnal variation in NO2 concentrations will be needed tounderstand trends across instruments with differentobservation times (Boersma et al., 2008).

4.3.2. VOCsFig. 5 shows seasonal variation in OMI HCHO columns

over the United States. HCHO columns are closely related tosurface VOC emissions since HCHO is a high-yieldintermediate product from the oxidation of reactive non-methane VOCs, and since reactive VOCs have a shortlifetime. The seasonal and spatial variation in HCHOcolumns over North America is broadly consistent with thatof isoprene emission (Abbot et al., 2003). Palmer et al.(2003) first developed a method for deriving emissions ofVOCs using GOME retrievals of HCHO over North America,by applying an inversion based on the relationship betweenthe HCHO column and the sum of VOC emissions scaled bytheir HCHO yields. Palmer et al. (2006) used 6 years ofGOME HCHO data to investigate the seasonal and

interannual variation in isoprene emissions from NorthAmerica. Fu et al. (2007) applied GOME satellite measure-ments of HCHO columns over East and South Asia toimprove regional emission estimates of reactive non-methane volatile organic compounds, and found the needfor a 25% increase in anthropogenic VOC emissions anda fivefold increase in biomass burning VOC emissions inorder to be consistent with the satellite observations. Milletet al. (2008) compared the OMI-derived emissions toa bottom-up isoprene emission inventory (MEGAN), anduse the results to optimize the MEGAN emission factors forbroadleaf trees, the main isoprene source. Simultaneousretrievals of HCHO and glyoxal should offer additionalconstraints on VOC sources (Wittrock et al., 2006).

4.3.3. COStudies of CO emissions deduced from satellite obser-

vations have concentrated on the continental or regionalscale, due to the long lifetime of CO. Such inversions havetypically been conducted with global chemical transportmodels to better understand fossil fuel, biofuel, andbiomass burning sources (Arellano et al., 2004; Heald et al.,2004; Petron et al., 2004; Pfister et al., 2005). Inversemodeling of MOPITT CO retrievals consistently reportedthat emissions from fossil fuel and biofuel use in Asia arelarger than estimated from bottom-up approaches. Asa result the Asian bottom-up inventory was reexaminedand updated (Streets et al., 2006). These inverse modelingstudies also provided important constraints on themagnitude and variability of forest fires. Recent develop-ments to CO inversion algorithms include adjoint models toinfer emissions at higher spatial resolution (Muller andStavrakou, 2005; Kopacz et al., in press), and multi-instrument inversions to quantitatively interpret informa-tion from both TES and MOPITT (Jones et al., 2007).

4.3.4. Aerosol sourcesRetrievals of sa are being used to examine aerosol

sources. An hourly smoke emission inventory (Reid et al.,2004) has been inferred from an automated fire productbased on GOES sa observations (Prins et al., 1998). Duboviket al. (2007) found and quantified the major aerosol sourcesduring a 1-week period in August by applying inversemodeling techniques to MODIS sa with the GOCART model.van Donkelaar et al. (2008) interpreted the trend over2000–2006 in MODIS and MISR sa over China to estimatethe annual growth rate in sulfur emissions.

The height at which aerosol sources are injected into theatmosphere is a major factor in determining its transportcharacteristics. Aerosol plume height inferred from MISR isbeing applied to determine injection heights of smokeplumes from wildfires (Mazzoni et al., 2007) and a range ofaerosol plume physical characteristics (Kahn et al., 2007).

Nascent retrievals of SO2 from GOME and OMI overindustrial regions demonstrate promise for constrainingaerosol sources (e.g. Eisinger and Burrows, 1998; Carn et al.,2007; Krotkov et al., 2008; Lee et al., 2008). Satellite remotesensing of SO2 typically occurs at short wavelengths whereinterference from O3 and strong molecular scatteringreduce sensitivity to the boundary layer. Additional SO2

retrieval development is needed.

Fig. 5. HCHO columns from the OMI satellite instrument for June–September 2006. The monthly average is taken over all observations with cloud fraction lessthan 20% (Data courtesy of Thomas Kurosu).


Satellite observations of glyoxal contain information onthe source of secondary organic aerosol from the irrevers-ible uptake of dicarbonyls (Fu et al., in press). Furtheranalysis of the glyoxal observations could quantify thissource.

5. Recommendations

Several studies have identified the need for and dis-cussed options to improve the capability for satelliteremote sensing of air quality in the boundary layer (IGOS–IGACO, 2004; Edwards, 2006; National Research Council,2007). Below are recommendations that build on thosestudies and on the discussion in the previous sections.

5.1. Satellite design for the boundary layer

As described in Section 2.3, different wavelength regionsare sensitive to different vertical regions of the atmosphere.This variation in sensitivity can be exploited to betterdiscriminate the boundary layer from the free troposphere.Two species with potential for such an approach are CO andO3. A satellite instrument that simultaneously observes COin the 4.7 mm thermal region (most sensitive to the freetroposphere), and in the 2.4 mm backscatter region

(sensitive to the entire troposphere) would offer consider-able opportunity to separate CO concentrations in theboundary layer from those in the free troposphere (Panet al., 1995). A related technique has been proposed toretrieve tropospheric O3 profiles with increased ability todiscriminate O3 in the boundary layer (Worden et al.,2007b). A space-based lidar may yield O3 profiles in thelower troposphere at coarse spatial resolution (NationalResearch Council, 2007). Additional development andapplication of these techniques remain needed.

Clouds within a satellite pixel are a primary obstacle toremote sensing of the boundary layer as discussed inSection 2.2. Krijger et al. (2007) used the MODIS cloud maskat 1�1 km2 to examine the effect of sensor resolution onthe number of cloud-free observations from space. Theyfound that the fraction of global cloud-free observationsdecreases nearly linearly with the logarithm of the pixelsize from a value of 30% for a pixel of 1 km2, to 15% fora pixel of 100 km2 (e.g. as reported from the MODIS sa

retrieval), to less than 10% for pixels greater than 1000 km2

(e.g. SCIAMACHY). Future instrument design for air qualityapplications should minimize pixel size to reduce retrievalerror due to clouds.

An active area of health effects assessment is focusingon pollution gradients within cities (e.g. Jerrett et al., 2005).


The current spatial resolution of satellite observations isinsufficient to resolve intra-urban scales. Higher spatialresolution measurements will need to be associated withdevelopments to instrument design and retrieval algo-rithms. Loughner et al. (2007) present an approach todetermine the appropriate spatial resolution for individualspecies.

A satellite overpass time near noon offers severaladvantages over other times. The minimum solar zenithangle at this time increases the sensitivity of solar back-scatter to the boundary layer. A large thermal contrastbetween the surface and lower troposphere is favorable forinfrared remote sensing (Deeter et al., 2007). A well-developed lower mixed layer simplifies the relationshipbetween the lower troposphere and surface concentra-tions. Observations later in the day are inhibited byincreased cloud cover.

5.2. Validation of satellite retrievals

Validation of satellite retrievals of the lower tropo-sphere is challenging. Surface and aircraft measurementsavailable for validation are often at different spatial andtemporal scales than the satellite measurements, leading toambiguous comparisons. Several independent retrievalalgorithms exist for tropospheric trace gases and aerosols.While the development of multiple algorithms has fosteredrapid improvement of retrievals from these instruments,the existence of multiple products provides a measure ofretrieval uncertainty. The current validation database isinsufficient to resolve differences between retrievals, suchas the factor of 2 discrepancy in tropospheric NO2 columnsover industrial regions in winter noted by van Noije et al.(2006). Additional in situ measurements are neededparticularly for tropospheric NO2 and HCHO over a varietyof surface types, for a range of pollution levels, and over allseasons. Such measurements should span an entire satellitepixel and include profiles from the surface to tropopause.Higher spatial resolution satellite observations wouldreduce the challenges of their validation.

5.3. Retrieval development

Satellite retrieval algorithms depend on external infor-mation on geophysical fields as discussed in Section 2.Complete characterization of surface reflectivity isa primary challenge in aerosol retrievals (Section 2.2.2);improved estimates continue to translate into improvedretrievals (Levy et al., 2007; Drury et al., in press).Accounting for subpixel snow and ice remains particularlychallenging (Chin et al., 2004). Surface reflectivity isa major source of uncertainty in solar backscatter retrievalsof trace gases (Martin et al., 2002; Boersma et al., 2004);a higher resolution database with angular dependence isneeded. Accurate surface emissivity fields are also impor-tant for thermal infrared retrievals (Ho et al., 2005).

Cloudy-pixels adjacent to clear-sky scenes introducebias in aerosol retrievals (Wen et al., 2007; Marshak et al.,2008; Tian et al., 2008); 3-D radiation transfer calculationsmay be necessary for retrievals at higher spatial resolution.Residual thin cirrus and subpixel clouds continue to

contribute to bias in aerosol retrievals (Kaufman et al.,2005b; Zhang et al., 2005). Ambiguity in the detection ofclouds over snow and ice leads to rejection of clear-skyscenes that would be useful for trace gas retrievals usingsolar backscatter.

Aerosol and trace gas retrievals depend on externalinformation about the species itself (Section 2). Localinformation on the species profile (Palmer et al., 2001) andon aerosol properties (Wang and Martin, 2007) can reduceretrieval bias. Development of numerical models thataccurately represent in situ measurements remains animportant source of this profile information.

Scattering and absorption by aerosols are a significantsource of uncertainty in trace gas solar backscatterretrievals (Torres and Bhartia, 1999; Martin et al., 2003; Fuet al., 2007). A complicating factor is that aerosols can affectretrieved cloud properties retrieved from solar backscattertrace gas instruments (Boersma et al., 2004). Trace gasretrievals need cloud fields that have been corrected forthese aerosol effects.

Sections 3 and 4 included a few species, in addition tothose in Table 2, which would be valuable for air qualitywith further development. These include HNO3 (Wespeset al., 2007), glyoxal (Wittrock et al., 2006), as well asaerosol composition and size information (Liu et al., 2007a;Mishchenko et al., 2007; Chen et al., 2008; Torres et al.,2007). Beer et al. (2008) demonstrated the first satelliteobservation of lower tropospheric methanol and ammonia;the latter offers encouraging prospects to better under-stand aerosol processes.

5.4. Model development

The satellite data sets discussed here contain a wealth ofinformation that is only beginning to be exploited. The dataexist at higher spatial and temporal resolution than havebeen used for many applications. Concurrent modeldevelopment is needed to simultaneously take into accountthe global scale of the observations and the high spatialresolution of air quality processes. For example, regionalmodels have historically focused on the lower troposphere.Accurate representation of the free troposphere isa prerequisite for comparison with a tropospheric column(e.g. Napelenok et al., 2008). Further development of freetropospheric processes such as lightning NOx emissions isneeded for air quality models (e.g. Kaynak et al., 2008).

Interpretation of satellite observations of aerosol opticalproperties would benefit from additional development ofthe physical processes of aerosols. For example, the simu-lated relationship between aerosol mass and sa oftenremains crudely parameterized (Kinne et al., 2003). Prog-nostic representations of aerosol size distribution andmixing state are needed.

The sophistication of numerical inversions to applysatellite observations for emission estimates has increasedmarkedly over the last decade. Additional developmentand application of computationally efficient algorithms(such as an adjoint) are still needed to calculate modelsensitivities to multiple parameters (Sandu et al., 2005).Consistent assumptions are necessary in both the satelliteretrievals and the models used to interpret those


observations (e.g. Drury et al., in press). Concurrent devel-opment of a comprehensive assimilation capability wouldyield improved air quality assessment and forecast fromthe satellite observations (Fisher and Lary, 1995; Elbern andSchmidt, 2001; Lamarque and Gille, 2003). The spatial andtemporal specificity of the a priori information is of highimportance for boundary layer retrievals. The assimilatedfields could also be applied to provide observationalconstraints on trace gas and aerosol concentrations (Matsuiet al., 2004; Pierce et al., 2007), and ultimately improve thea priori information used in satellite retrievals themselves.

5.5. Support for the next generation

We are currently at a peak in satellite instrumentationfor remote sensing of tropospheric trace gases and aerosols.Many current instruments have already exceeded theirdesign lifetime. Few new satellites have been commis-sioned. All of the satellites featured in Section 3 are in Sun-synchronous orbits. Multiple satellites in a variety of orbitsare needed to infer diurnal variation at high temporalresolution on a global scale. The National Research Council(2007) made recommendations on missions for satelliteremote sensing of air quality. These recommendationsinclude development of a geostationary mission to providecontinuous observations, to improve air quality forecasts,monitor pollutant emissions, and to understand pollutanttransport. High priority should be given to supporting thenext generation of satellite observations.

Acknowledgements

Kevin Bowman, James Drummond, Ralph Kahn, DavenHenze, Daniel Jacob, Gray O’Byrne, and two anonymousreviewers provided helpful comments that improved thismanuscript. This work was supported by the NaturalSciences and Engineering Research Council of Canada, andby the National Aeronautics and Space Administration.

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