Recent Developments in Satellite Remote Sensing of Air Quality Parameters
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Recent Developments in Satellite Remote Sensing of Air Quality Parameters Randall Martin, Dalhousie and Harvard-Smithsonian Chulkyu Lee, Aaron van Donkelaar, Lok Lamsal, Dalhousie University Nick Krotkov, Ralph Kahn, Rob Levy, Ed Celarier, Eric Bucsela, NASA Folkert Boersma, Ruud Dirksen, KNMI Andreas Richter, University of Bremen
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Recent Developments in Satellite Remote Sensing of Air Quality Parameters. Randall Martin, Dalhousie and Harvard-Smithsonian Chulkyu Lee, Aaron van Donkelaar, Lok Lamsal, Dalhousie University Nick Krotkov, Ralph Kahn, Rob Levy, Ed Celarier, Eric Bucsela, NASA - PowerPoint PPT Presentation
Transcript of Recent Developments in Satellite Remote Sensing of Air Quality Parameters
Recent Developments in Satellite Remote Sensing of Air Quality Parameters
Randall Martin, Dalhousie and Harvard-Smithsonian
Chulkyu Lee, Aaron van Donkelaar, Lok Lamsal, Dalhousie University
Nick Krotkov, Ralph Kahn, Rob Levy, Ed Celarier, Eric Bucsela, NASA
Folkert Boersma, Ruud Dirksen, KNMI
Andreas Richter, University of Bremen
OutlineOutline
• NO2 (NOx lifetime): Lok Lamsal
• AOD PM2.5: Aaron van Donkelaar
• SO2 (evaluation and emissions): Chulkyu Lee
Air Quality ParametersAir Quality Parameters
• Emissions, Trends, Long-range transport, Surface concentrations, O3-NOx-VOC sensitivity, Lifetime
• Synergy from multiple species
NONOxx Lifetime Drives the Seasonal Variation in Tropospheric Lifetime Drives the Seasonal Variation in Tropospheric
NONO2 2 Over Eastern USOver Eastern US
τGC
τOMI
τGC
OMI NO2 (DP_GC)
Lamsal et al., JGR, submitted
Daily Average
OMI time
OMI time
GEOS-Chem Simulation
Gas phase (NO2+OH)
Heterogeneous (N2O5 Hydrolysis)
22
1xNO
eff
NO
NO E
OMIGEOS-Chem
NOx Lifetime (τ)
Tro
po
sph
eric
NO
2 (1
015
m
ole
cule
s cm
-2)
Bottom-up
Weak (<10%) Seasonal Variation in NOx Emissions or NO2/NOx
PMPM2.52.5 and SO and SO22 Emissions Emissions
Evaluation with measurements outside Canada/US
Global Climatology (2001-2006) of PMGlobal Climatology (2001-2006) of PM2.5 2.5 from MODIS & MISR AODfrom MODIS & MISR AOD and and
GEOS-Chem AOD/PMGEOS-Chem AOD/PM2.52.5 Relationship Relationship
Number sites Correlation Slope Bias (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, in prep
Evaluation for US/Canada
r=0.78 slope=1.02 n=1073
Insight into Aerosol Source/Type with Precursor ObservationsInsight into Aerosol Source/Type with Precursor Observations
Lee et al., JGR, in press
Operational OMI PBL SO2 data corrected with local air mass factor improves agreement of OMI SO2 versus aircraft observations (INTEX-B)
Orig: slope = 1.6, r=0.71 New: slope = 0.95, r=0.92
OMI Improved SO2 Vertical Columns for 2006
Anthropogenic Sources Dominate Annual Mean SO2 ColumnVolcanic SO2 Emissions 10% of Anthropogenic Source
Total SO2 Column
Anthropogenic SO2 Column
Fraction from Anthropogenic
Chulkyu Lee
GEOS-Chem Simulations for 2006
Use OMI and SCIAMACHY SOUse OMI and SCIAMACHY SO22 Columns to Map SO Columns to Map SO22 Emissions Emissions
Combustion, Smelters, Volcanoes
Emission
SO2SO4
2-
~day
OH, cloud
Tropospheric SO2 column ~ ESO2 Over Land
Phytoplankton
DMSday
Deposition
2
2
( )
( )SO
tSO
OMIE
GEOS Chem
Top-Down Emissions
Global Anthropogenic Sulfur Emissions Over Land for 2006Global Anthropogenic 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
SO2 Emissions (1011 molecules cm-2 s-1)
Chulkyu LeeCloud Radiance Fraction < 0.2
Top-Down (OMI)
Bottom-Up in GEOS-Chem (EDGAR2000, NEI99, EMEP2005, Streets2006) Scaled to 2006
Anthropogenic Emissions Differences (2006) Show Some ConsistencyAnthropogenic Emissions Differences (2006) Show Some Consistency
Top-down (OMI) – Bottom-up (GC) -7.6 Tg S/yr
Top-down (SCIAMACHY)– Bottom-up (GC) -2.6 Tg S/yr
Chulkyu Lee
ΔSO2 Emissions (1011 molecules cm-2 s-1)
Top-Down Minus Bottom-Up Emissions
Cloud Radiance Fraction < 0.2
Indirect Validation of OMI and SCIAMACHY SOIndirect Validation of OMI and SCIAMACHY SO2 2 with Surface Measurementswith Surface MeasurementsInfer Surface SOInfer Surface SO22 from OMI and SCIAMACHY Using GEOS-Chem SO from OMI and SCIAMACHY Using GEOS-Chem SO22 Profiles Profiles
Chulkyu Lee
Year 2006
Cloud Radiance Fraction < 0.2
slope=0.79 r=0.81
slope=0.91 r=0.86
GEOS-Chem: r=0.83, slope=0.81(at OMI) and 0.84(at SCIA)
In Situ (at OMI) In Situ (at SCIA)
ChallengesChallenges
- Better understanding of differences between OMI and SCIAMACHY
- Reduce uncertainty in simulated SO2 lifetime
- Develop adjoint-based inversion
OMI NO2 Columns Provide Information into NOx Loss Processes
Encouraging Prospects for Applying SOEncouraging Prospects for Applying SO22 Observations Observations
to Constrain Anthropogenic Emissionsto Constrain Anthropogenic Emissions
Wintertime Observations Reflect Heterogeneous Processes
