Some Applications of Satellite Remote Sensing for Air Quality: Implications for a

Geostationary Constellation

Randall Martin, Dalhousie and Harvard-Smithsonian

Chulkyu Lee, Aaron van Donkelaar, Lok Lamsal, Dalhousie University

National Institute of Meteorological Research (Korea)

Nick Krotkov, Ralph Kahn, Rob Levy, NASA

Andreas Richter, University of Bremen

Some Air Quality Applications of Satellite ObservationsSome Air Quality Applications of Satellite Observations

Key pollutants: PM2.5, O3, NO2

(AQHI)

Top-down Constraints on EmissionsTop-down Constraints on Emissions

(to improve AQ and climate

simulations)

Smog Alert, Toronto

Estimating Surface ConcentrationsEstimating Surface Concentrations

(large regions w/o ground-based obs)

Long-Range Transport of PollutionLong-Range Transport of Pollution

Encouraging Consistency of Simulated and Measured ProfilesEncouraging Consistency of Simulated and Measured Profiles

Martin et al., JGR, 2004

Texas AQSTexas AQS In Situ

GEOS-Chem

Lee et al., JGR, 2009

SO2

NO2

Optical depth above altitude z Total column optical depth

Model (GC)CALIPSO (CAL)

Alti

tude

[km

]

van Donkelaar et al., EHP, 2010

Aerosol Extinction

General Approach to Estimate Surface ConcentrationGeneral Approach to Estimate Surface Concentration

Daily Tropospheric Column

S → Surface Concentration

Ω → Tropospheric column

In Situ

GEOS-Chem

Coincident Model Profile

OM

MO S S

Promising Ground-Level NOPromising Ground-Level NO2 2 Inferred From OMI for 2005: Inferred From OMI for 2005: Need Higher Temporal and Spatial ResolutionNeed Higher Temporal and Spatial Resolution

Temporal Correlation with In Situ Over 2005

Lamsal et al., JGR, 2008

Spatial Correlation of Annual Mean vs In Situ for North America = 0.78

×In situ—— OMI

Evaluation with measurements outside Canada/US

Global Climatology (2001-2006) of PMGlobal Climatology (2001-2006) of PM2.5 2.5 from MODIS & MISR AOD:from MODIS & MISR AOD:Need Higher Temporal and Spatial ResolutionNeed Higher Temporal and Spatial Resolution

Number sites Correlation Slope Bias (ug/m3)Including Europe 244 0.83 0.86 1.2Excluding Europe 84 0.83 0.91 -2.6

van Donkelaar et al., EHP, 2010

Evaluation for US/Canada

r=0.77 slope=1.07 n=1057

• 80% of world population exceeds WHO guideline of 10 μg/m3

• 30% of eastern Asia exposed to >50 μg/m3 in annual mean

• 0.61±0.20 years life lost per 10 μg/m3 [Pope et al., 2009]

• Estimate decreased life expectancy due to PM2.5 exposure

Data Valuable to Assess Global PMData Valuable to Assess Global PM2.5 2.5 Exposure: Exposure: Constellation Required for Global High ResolutionConstellation Required for Global High Resolution

van Donkelaar et al., EHP, 2010 PM2.5 Exposure [μg/m3]

WHO GuidelineAQG IT-3 IT-2 IT-1

100

90

80

70

60

50

40

30

20

10

0

Pop

ulat

ion

[%]

5 10 15 25 35 50 100

Insight into Aerosol Source/Type with Precursor ObservationsInsight into Aerosol Source/Type with Precursor Observations

Lee et al., JGR, 2009

Satellite SO2 data corrected with local air mass factor improves agreement versus aircraft observations (INTEX-A and B)

Orig: slope = 1.6, r=0.71 New: slope = 0.95, r=0.92

Improved SO2 Vertical Columns for 2006

Orig: slope = 1.3, r=0.78 New: slope = 1.1, r=0.89

OMI SCIAMACHY

Global Sulfur Emissions Over Land for 2006Global Sulfur Emissions Over Land for 2006Volcanic SOVolcanic SO22 Columns (>10 DU) Excluded From Inversion Columns (>10 DU) Excluded From Inversion

47.0 Tg S/yr

54.6 Tg S/yr

r = 0.77 vs bottom-up

SO2 Emissions (1011 molecules cm-2 s-1) Chulkyu Lee

Top-Down (OMI)

Bottom-Up in GEOS-Chem (EDGAR2000, NEI99, EMEP2005, Streets2006) Scaled to 2006

52.1 Tg S/yrTop-Down (SCIAMACHY)

r = 0.78 vs bottom-up

Geostationary Constellation Valuable to Connect Geostationary Constellation Valuable to Connect Long-Range Transport EventsLong-Range Transport Events

Aaron van Donkelaar

Challenge: Large Inter-retrieval DifferencesChallenge: Large Inter-retrieval DifferencesNeed for Inter-instrument Calibration and Common RetrievalsNeed for Inter-instrument Calibration and Common Retrievals

0.1 2 4 6 8 10 Tropospheric NO2 Column

(1015 molecules cm-2)

SO2 Slant Columns 2006 OMI NO2 DJF 2005

Lamsal et al., JGR, 2010

AOD 2001-2006

0 0.1 0.2 0.3τ [unitless]

SP

DP

MODIS

MISR

Lee et al., JGR, 2009 van Donkelaar et al., EHP, 2010

SCIAMACHY

OMI

ChallengesChallenges•Intercalibration of geostationary instruments & retrievals•High spatial resolution obs (urban scales, cloud-free, validation) •Resolve current inter-retrieval differences•New algorithms (i.e. tropospheric residual for geostationary)•Boundary-layer ozone (clever retrievals, precursor emissions, assimilation)•Continue develop simulation of vertical profile•Comprehensive assimilation capability

Encouraging Prospects for Satellite Remote Encouraging Prospects for Satellite Remote Sensing of Air QualitySensing of Air Quality

Attributes of Geostationary ConstellationAttributes of Geostationary Constellation•Resolves diurnal processes in global-scale analyses

(emissions, long-range transport, air quality)