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Insight into Global Air Quality using Satellite Remote Sensing and Modeling. Randall Martin with contributions from Aaron van Donkelaar , Shailesh Kharol , Matthew Cooper, Sajeev Philip, Colin Lee Lok Lamsal (Dalhousie NASA ), Chulkyu Lee (Dalhousie KMA) - PowerPoint PPT Presentation
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Insight into Global Air Quality using Satellite Remote Sensing and Modeling
Randall Martin
with contributions from
Aaron van Donkelaar, Shailesh Kharol, Matthew Cooper, Sajeev Philip, Colin Lee
Lok Lamsal (Dalhousie NASA), Chulkyu Lee (Dalhousie KMA)
Siwen Wang (Tsinghua & Dalhousie), Qiang Zhang (Tsinghua)
10 Oct 2012
Two Major Applications of Satellite Observations of Atmospheric Composition
Top-down Constraints on Emissions
Estimating Pollution Concentrations (large regions w/o ground-based obs)
(to improve air quality and climate simulations)
Column Observations of Aerosol and NO2 Strongly Influenced by 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 Model Summer Mean Profiles over North America
Strong Rayleigh Scattering
( )( ) C zS z
Weak Thermal Contrast
Vertical Profile Affects Boundary-Layer Information in Satellite Obs
O3
Wavelength (μm)
WHO Global Burden of Disease Report for 2000Impaired by Insufficient Global Observations of
Fine Particulate Matter (PM2.5)
• Estimated air pollution in urban areas causes ~1 million excess deaths (~1%)
• Could not estimate effects outside of urban areas for 2000 report
• Could this be improved with satellite remote sensing?
Cohen et al., 2005
~ 1 year increase of life expectancy for decreasing long-term exposure of PM2.5 by 10 ug/m3
Aerosol Optical Depth (AOD) for 2001-2006Rejected Retrievals for Land Types with Monthly Error vs AERONET >0.1 or 20%
MODISr = 0.39
(vs. in-situ PM2.5)
MISRr = 0.39
(vs. in-situ PM2.5)
CombinedMODIS/MISR
r = 0.61 (vs. in-situ PM2.5)
0.3
0.25
0.2
0.15
0.1
0.05
0
AO
D [u
nitle
ss]
van Donkelaar et al., EHP, 2010
Chemical Transport Model (GEOS-Chem) Simulation of Aerosol Optical Depth
Use GEOS-Chem to Calculate AOD/PM2.5 Ratio for Each Obs
Aaron van Donkelaar
τa(z)/τa(z=0)
Alti
tude
[km
]
Evaluate GEOS-Chem Vertical Profile with
CALIOP Observations
• Coincidently sample model and CALIOP extinction profiles– Jun-Dec 2006
• Compare % within boundary layer
Model (GC)CALIOP (CAL)
Optical depth above altitude zTotal column optical depth
SatelliteDerived
In-situ
Sat
ellit
e-D
eriv
ed [μ
g/m
3]
In-situ PM2.5 [μg/m3]
Ann
ual M
ean
PM
2.5 [
μg/m
3 ] (2
001-
2006
)
r
MODIS AOD 0.39
MISR AOD 0.39
Combined AOD 0.61
Combined PM2.5 0.77
van Donkelaar et al., EHP, 2010
Significant Agreement of Satellite-Derived PM2.5 with Coincident In situ Measurements
Evaluation with measurements outside Canada/US
Global Climatology (2001-2006) of PM2.5
Better than in situ vs model (GEOS-Chem): r=0.52-0.62, slope = 0.63 – 0.71
Number sites Correlation Slope Bias (ug/m3)Including Europe 244 0.83 0.86 1.15Excluding Europe 84 0.83 0.91 -2.5
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
PM2.5 Exposure [μg/m3]
Long-term Exposure to Outdoor Ambient PM2.5
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
Forthcoming Global Burden of Disease (WHO) Study
PM2.5 Causal Role in ~70 Million Disability Adjusted Life Years (~3%)~3 Million Excess Deaths (~5%)
Wildfires near Moscow in Summer 2010
MODIS/Aqua: 7 Aug 2010
Spatial and Temporal Variation in Satellite-Based PM2.5 during Moscow 2010 Fires
van Donkelaar et al., AE, 2011
Satellite-based Estimates of PM2.5 in Moscow
Before Fires During Fires
van Donkelaar et al., AE, 2011
MODIS-based
In Situ PM2.5
In Situ from PM10
r =0.92, slope=1.06
Daily Satellite-based Estimates for North America>30M People Live in Regions w/o Local Air Quality Info
GOCI and GEMS Provide Opportunities for Asia
van Donkelaar et al., ES&T, in press
In Situ Satellite-derived
SPARTAN: An Emerging Global Network to Evaluate and Improve Satellite-Based Estimates of PM2.5
Measures PM2.5 Mass & Composition at AERONET sites
PM2.5 Sampling Station fromVanderlei Martins (UMBC)
AOD from CIMEL Sunphotometer (AERONET)
General Approach to Estimate Surface NO2 Concentration
NO2 Column
S → Surface Concentration
Ω → Tropospheric column
In Situ
GEOS-Chem
Model Profile
OM
MO S S
NO2: Indicator of Population Exposure to Combustion SourcesOMI-Derived NO2
Lamsal et al., ES&T, in prep
• Mortality not well explained by PM2.5 and O3 alone
• Mortality can be more strongly associated with NO2 than either PM2.5 or O3 (e.g. Stieb et al. 2007)
• NO2 is an indicator of exposure to combustion sources that increase airmass toxicity (Brook et al., 2007)
Multiple Pollutants Affect Air Quality
A Satellite-Based Multipollutant Index from PM2.5 & NO2
𝑀𝑃𝐼=𝑃𝑀 2.5
𝐴𝑄𝐺𝑃𝑀 2.5 [1+𝑁𝑂2
𝐴𝑄𝐺𝑁𝑂 2 ]Cooper et al., ES&T, 2012
PM2.5
NO2
MPI0 4 8 12
Multipollutant Index
Satellite-Based Multipollutant Index (Unitless)
ShanghaiBeijing
DelhiKarachi
SeoulCairoLima
TehranLos Angeles
BerlinMoscowNairobi 0 1 2 5 7 9 11 13 15
AQG = WHO Air Quality GuidelinePM2.5 (ug/m3)
MPI (unitless)
Emission Inventories are Notoriously Difficult to Determineand Take Years to Compile
Top-Down Constraints on NOx & SOx Emissions
SCIAMACHY Tropospheric NO2 (1015 molec cm-2) NOx emissions (1011 atoms N cm-2 s-1)
Lee et al., 2011
2004-2005
Inverse Modeling
SOx emissions (1011 atoms N cm-2 s-1)OMI SO2 (1016 molec cm-2)
2006
Martin et al., 2006
49.9 Tg S yr-1
Global Anthropogenic Sulfur Emissions Over Land for 2006Volcanic SO2 Columns (>1x1017 molec cm-2) Excluded From Inversion
49.9 Tg S/yr
54.6 Tg S/yr
r = 0.77
SO2 Emissions (1011 molecules cm-2 s-1)Cloud Radiance Fraction < 0.2
Top-Down (OMI)
Bottom-Up in GEOS-Chem (EDGAR2000, NEI2005, EMEP2005, Streets2006) Scaled to 2006
Lee et al., JGR, 2011
Anthropogenic Emissions Differences (2006)
-2.2 Tg S/yr
-4.7 Tg S/yr
Cloud Radiance Fraction < 0.2, SZA < 50o Lee et al., JGR, 2011
OMI Observations of Changes in NOx Emissions from Chinese Power Plants
Ratio of OMI NO2 Columns for 2007 / 2005Open Circles Indicate New Power Plants
Wang et al., ACP, 2012
Lamsal et al., GRL, 2011
Application of Satellite Observations for Timely Updates to NOx Emission Inventories
Use GEOS-Chem to Calculate Local Sensitivity of Changes in Trace Gas Column to Changes in Emissions
Forecast Inventory for 2009 Based on Bottom-up for 2006 and Monthly OMI NO2 for 2006-2009
9% increase in global emissions
19% increase in Asian emissions
6% decrease in North American emissions
PM2.5 Nearly as Sensitive to NOx as to SO2 and NH3
Kharol et al., GRL, in prep
GEOS-Chem Calculation of Annual PM2.5 Response to 10% Change in Emissions
ΔNOx Emissions ΔSO2 Emissions ΔNH3 Emissions
Increasing NOx Emissions Increases NO3-, NH4
+, and SO42-
Increasing SO2 Emissions Decreases NO3-
Kharol et al., GRL, in prep
Preliminary GEOS-Chem Adjoint Calculation of the Change in Annual Mortality to Local Changes in Emissions
Effects of PM2.5 on Mortality from WHO Global Burden of Disease Project
Lee et al., EHP, in prep
ΔM = Change in Global Mortality
Emerging Applications of Satellite Remote Sensing of Atmospheric Composition
Acknowledgements: NSERC, Environment Canada, Health Canada, NASA
Chemical Transport Model Plays a Valuable Role in
Relating Retrieved and Desired Quantity
Challenges • Continue to develop retrieval capability • Evaluate and improve simulation to relate retrieved and desired quantity
(e.g. AOD/PM2.5, NO2 / NOx emissions)
• Estimates of Ground-level Air Pollutants • Top-down Constraints on Emission Inventories