Insight from the A-Train into Global Air Quality

Randall Martin, Dalhousie and Harvard-Smithsonian

Aaron van Donkelaar, Lok Lamsal, Akhila Padmanabhan, Dalhousie University

Chulkyu Lee, Dalhousie National Institute of Meteorological Research, Korea

with contributions from

Rob Levy, Ralph Kahn, Nick Krotkov, NASA

Mark Parrington, Dylan Jones, University of Toronto

OMI NO2 Team

A-Train Symposium, New Orleans

October 26, 2010

Two Applications of Satellite Observations for Air QualityTwo Applications of Satellite Observations for Air Quality

Key pollutants: PM2.5, O3, NO2

(AQHI)

Top-down Constraints on EmissionsTop-down Constraints on Emissions

(to improve AQ simulations)

Smog Alert

Estimating Surface ConcentrationsEstimating Surface Concentrations

(large regions w/o ground-based obs)

PM2.5 : fine aerosol

Column Observations of Aerosol and NOColumn Observations of Aerosol and NO2 2 Strongly Influenced Strongly Influenced

by Boundary Layer Concentrationsby Boundary Layer Concentrations

S(z) = shape factor C(z) = concentration Ω = columnNO2

Aerosol Extinction

O3

Martin, AE, 2008

0.30 0.36 0.43 0.52 0.62 2.2 4.7

Aerosol O3 NO2

0.75 9.6

Normalized GEOS-Chem Normalized GEOS-Chem Summer Mean Profiles Summer Mean Profiles over North Americaover North America

Strong Rayleigh Scattering

( )( )

C zS z

Weak Thermal Contrast

Vertical Profile Affects Boundary-Layer Information in Satellite ObsVertical Profile Affects Boundary-Layer Information in Satellite Obs

O3

Wavelength (μm)

Temporal Correlation of AOD vs In Situ PMTemporal Correlation of AOD vs In Situ PM2.52.5

Correlation over Aug-Oct 2010

Combined AOD Combined AOD from MODIS (and MISR) for 2004-2008from MODIS (and MISR) for 2004-2008Rejected Retrievals over Land Types with Monthly Error vs AERONET >0.1 or 20%Rejected Retrievals over Land Types with Monthly Error vs AERONET >0.1 or 20%

van Donkelaar et al., EHP, 2010

Spatial Correlation (r) of AOD vs in situ PM2.5 for North AmericaMODIS: r = 0.39MISR: r = 0.39Simple Average: r = 0.44 Combined: r = 0.61

Chemical Transport Model (GEOS-Chem) Chemical Transport Model (GEOS-Chem) Simulation of Aerosol Optical Depth Simulation of Aerosol Optical Depth

Aaron van Donkelaar

Ground-level “dry” PMGround-level “dry” PM2.52.5 = = ηη · AOD AOD

η affected by vertical structure, aerosol properties, relative humidity

Obtain η from aerosol-oxidant model (GEOS-Chem) sampled coincidently with satellite obs

GEOS-Chem Simulation of η for 2004-2008

van Donkelaar et al., EHP, 2010

τ(z)/τ(z=0)

Alti

tud

e [k

m]

Evaluate GEOS-Chem Evaluate GEOS-Chem Vertical Profile with Vertical Profile with

CALIPSO ObservationsCALIPSO Observations

• Coincidently sample model and CALIPSO extinction profiles

– Jun-Dec 2006

• Compare % within boundary layer

Model (GC)CALIPSO (CAL)

Optical depth above altitude zTotal column optical depth

van Donkelaar et al., EHP, 2010

Evaluation with measurements outside Canada/US

Global Climatology (2004-2008) of PMGlobal Climatology (2004-2008) of PM2.5 2.5 from MODIS (& MISR) AODfrom MODIS (& MISR) AOD

and GEOS-Chem AOD/PM and GEOS-Chem AOD/PM2.52.5 Relationship Relationship

Number sites Correlation Slope Offset (ug/m3)

Including Europe 297 0.75 0.89 0.52

Excluding Europe 107 0.76 0.96 -2.8

van Donkelaar et al., EHP, 2010

Evaluation for US/Canada

r=0.77 slope=1.07 n=1057

Better than in situ vs model (GEOS-Chem): r=0.52-0.62, slope = 0.63 – 0.71

US EPA standard

van Donkelaar et al., EHP, 2010

van Donkelaar et al., EHP, 2010

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

• 35% of East Asia exposed to >50 μg/m3 in annual mean

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

• Estimate decreased life expectancy due to PM2.5 exposure

PM2.5 Exposure [μg/m3]

Data Valuable to Assess Data Valuable to Assess Health Effects of PMHealth Effects of PM2.52.5

van Donkelaar et al., EHP, 2010

100

90

80

70

60

50

40

30

20

10

0

AQG IT-3 IT-2 IT-1

Pop

ulat

ion

[%]

5 10 15 25 35 50 100

WHO Guideline & Interim Targets

USA Today: Hundreds Dead from Heat, Smog, USA Today: Hundreds Dead from Heat, Smog, Wildfires in MoscowWildfires in Moscow

9 Aug 2010: “Deaths in Moscow have doubled to an average of 700 people a day as the Russian capital is engulfed by poisonous smog from wildfires and a sweltering heat wave, a top health official said Monday.”

MODIS/Aqua: 7 Aug 2010

PMPM2.52.5 Estimate from MODIS AOD and GEOS-Chem AOD/PM Estimate from MODIS AOD and GEOS-Chem AOD/PM2.52.5

Aaron van Donkelaar

Near Moscow

In Situ

MODIS-based

Evaluation Near Moscow

Regional Mean MODIS-based Estimate

Before Fires During Fires

Impact of TES Assimilation on Surface Ozone (Aug. 2006)

The model underestimates surface ozone in the west

[Parrington et al., GRL, 2009]

TES-based estimates of background O3 are 20-40 ppb

GEOS-Chem before assimilation GEOS-Chem after assimilation

Surface O3 difference (assim - no assim) AQS and NAPS surface O3 data

Background O3 at the surface before assim. Background O3 at the surface after assim.

Without assimilation the model underestimates background ozone by as much as 9 ppb (in western North America)

OMI Tropospheric NOOMI Tropospheric NO22 Column Proxy for Column Proxy for

Surface ConcentrationSurface Concentration

NO/NO2

with altitude

OMI Standard Product: October 2004 – September 2007 Inclusive

General Approach to Estimate Surface ConcentrationGeneral Approach to Estimate Surface Concentration

Daily OMI Tropospheric Column

S → Surface Concentration

Ω → Tropospheric column

In Situ

GEOS-Chem

Coincident Model (GEOS-Chem)

Profile

OM

MO S

S

Ground-Level NOGround-Level NO2 2 Inferred From OMI for 2005 Inferred From OMI for 2005

Temporal Correlation with In Situ Over 2005

Lamsal et al., JGR, 2008

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

×In situ—— OMI

Application of Satellite Observations for Timely Updates Application of Satellite Observations for Timely Updates to NOto NOxx Emission Inventories Emission Inventories

Use GEOS-Chem to Calculate Local Sensitivity of Use GEOS-Chem to Calculate Local Sensitivity of Changes in Trace Gas Column to Changes in EmissionsChanges in Trace Gas Column to Changes in Emissions

E

Walker et al., ACP, 2010

Local sensitivity of column changes to emissions changes

Fractional Change

in Emissions

Fractional Change in

Trace Gas Column

Lamsal et al., GRL, in prep

Apply to regions where anthropogenic emissions dominate (>50%)

Forecast Inventory for 2009 Based on Bottom-up for 2006 Forecast Inventory for 2009 Based on Bottom-up for 2006 and OMI NOand OMI NO22 for 2006-2009 for 2006-2009

Lamsal et al., GRL, in prep

9% increase in global emissions

21% increase in Asian emissions

OMI SOOMI SO2 2 Column Retrievals Reflect Anthropogenic EmissionsColumn Retrievals Reflect Anthropogenic Emissions

Lee et al., JGR, 2009

OMI Improved SO2 Vertical Columns for 2006

Agreement with Aircraft Observations (INTEX-B): slope = 0.95, r=0.92

Use OMI SOUse OMI SO22 Columns to Map SO Columns to Map SO22 Emissions Emissions

Apply GEOS-Chem for the InversionApply GEOS-Chem for the Inversion

Combustion, Smelters, Volcanoes

Emission

SO2SO4

2-

~day

OH, cloud

Tropospheric SO2 column ~ ESO2 Over Land

Phytoplankton

DMSday

Deposition

Global Sulfur Emissions Over Land for 2006Global Sulfur Emissions Over Land for 2006

49.9 Tg S/yr

54.6 Tg S/yr

r = 0.77 vs bottom-up

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

Lee et al., JGR, submitted

Top-Down (OMI)

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

Volcanic SO2 Columns (>10 DU) Excluded From Inversion

Top-Down Minus Bottom-up

ChallengesChallenges•Develop retrievals to increase boundary-layer information

•Continue to develop simulation to relate retrieved and desired quantity

•Develop comprehensive assimilation capability

(i.e. CALIPSO vertical profiles and OMI SO2 to inform AOD/PM2.5 relationship)

A-Train Has Provided Unprecedented Insight Into A-Train Has Provided Unprecedented Insight Into Global Air QualityGlobal Air Quality

Chemical Transport Model Plays a Critical Role in

Relating Retrieved and Desired Quantity

NASA, NSERC, Health Canada, Environment Canada

AcknowledgementsAcknowledgements