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Global Climatology of Fine Particulate Matter Concentrations Estimated from Remote-Sensed Aerosol Optical Depth. Aaron van Donkelaar 1 , Randall Martin 1,2 , Ralph Kahn 3 and Robert Levy 3 International Aerosol Modeling Algorithms December 9-11, 2009 - PowerPoint PPT Presentation
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Global Climatology of Fine Particulate Matter Concentrations Estimated from Remote-Sensed
Aerosol Optical Depth
Aaron van Donkelaar1, Randall Martin1,2, Ralph Kahn3 and Robert Levy3
International Aerosol Modeling AlgorithmsDecember 9-11, 2009
1Dalhousie University 2Harvard-Smithsonian 3NASA Goddard
We relate satellite-based measurements of aerosol optical depth to PM2.5 using a global chemical transport model
Approach
Estimated PM2.5 = η· τ Combined MODIS/MISRAerosol Optical Depth
GEOS-Chem
Following Liu et al., 2004:
• vertical structure• aerosol type• meteorological effects• meteorology• diurnal effects
η
MODIS and MISR τ
MODIS τ• 1-2 days for global coverage• Requires assumptions about
surface reflectivity
MISR τ• 6-9 days for global coverage• Simultaneous surface
reflectance and aerosol retrieval
Mean τ 2001-2006 at 0.1º x 0.1º
0 0.1 0.2 0.3τ [unitless]
MIS
RM
OD
IS
r = 0.40vs. in-situ PM2.5
r = 0.54vs. in-situ PM2.5
Submitted to Environmental Health Perspectives
Agreement varies with surface type
9 surface types, defined by monthly mean surface albedo ratios,evaluation against AERONET AOD
MODIS
MISR
Jul
y
Combining MODIS and MISR improves agreement
MODISr = 0.40
(vs. in-situ PM2.5)
MISRr = 0.54
(vs. in-situ PM2.5)
CombinedMODIS/MISR
r = 0.63 (vs. in-situ PM2.5)
0.3
0.25
0.2
0.15
0.1
0.05
0
τ [u
nitle
ss]
Global CTMs can directly relate PM2.5 to τ
• Detailed aerosol-oxidant model
• 2º x 2.5º• 54 tracers, 100’s reactions• Assimilated meteorology
• Year-specific emissions• Dust, sea salt, sulfate-
ammonium-nitrate system, organic carbon, black carbon, SOA
GEOS-Chem
η[ug/m]
Significant agreement with coincident ground measurements over NA
SatelliteDerived
In-situ
Sat
ellit
e-D
eriv
ed
[μg/
m3]
In-situ PM2.5 [μg/m3]
Ann
ual M
ean
PM
2.5 [
μg/
m3]
(200
1-20
06)
r
MODIS τ 0.40
MISR τ 0.54
Combined τ 0.63
Combined PM2.5 0.78
• Annual mean measurements– Outside Canada/US– 244 sites (84 non-EU)
• r = 0.83 (0.91)• slope = 0.86 (0.84)• bias = 1.15 (-2.52) μg/m3
Method is globally applicable
Coincident PM2.5 error has two sources
Satellite• Error limited to 0.1 + 20%
by AERONET filter• Implication for satellite
PM2.5 determined by η
Estimated PM2.5 = η· τ
Model• Affected by aerosol optical
properties, concentrations, vertical profile, relative humidity
• Most sensitive to vertical profile [van Donkelaar et al., 2006]
τ(z)/τsurface
Alti
tud
e [k
m]
CALIPSO allows profile evaluation
• Coincidently sample model and CALIPSO extinction profiles– Jun-Dec 2006
• Compare % within boundary layer
Model (GC)CALIPSO (CAL)
Optical Depth from TOAOptical Depth at surface
Profile, τ and sampling define error
• Vary satellite-derived PM2.5 by
profile and τ biases– One-sigma uncertainty of ±25%
• Agrees with NA ground measurements
– Global population-weight mean uncertainty of 6.7 μg/m3
Sat
ellit
e-D
eriv
ed
PM
2.5
[μg/
m3]
In-situ PM2.5 [μg/m3]
PM
2.5 B
ias
Est
imat
e [%
]
Satellite-Derived PM2.5
[μg/m3]
0 2 4 6 8 10 12 14 16 18 -3 -2 -1 0 2 4 6 8 10
Mean Annual Change[μg/m3 / year]
-3 -2 -1 0 2 4 6 8 10
Mean Annual Change[μg/m3 / year]
0 5 10 15 20 25 35
Satellite-Derived PM2.5
[μg/m3]
-3 -2 -1 0 2 4 6 8 10
Mean Annual Change[μg/m3 / year]
0 5 10 15 45 70 100
Satellite-Derived PM2.5
[μg/m3]
Significant global deviations from model
2º x 2.5ºr = 0.75 (0.76)slope = 0.59 (0.65)bias = 4.36 (0.85) μg/m3
2º x 2.5ºr = 0.63 (0.71)slope = 0.51 (0.56)bias = 8.51 (2.75) μg/m3
0.1º x 0.1ºr = 0.83 (0.84)slope = 0.86 (0.91)bias = 1.15 (-2.52) μg/m3
• Satellite-PM2.5 + population map → exposure
• 80% of world population exceeds WHO guideline of 10 μg/m3
• 49% of eastern Asia exceeds 35 μg/m3
WHO Guideline
PM2.5 Exposure [μg/m3]
Pop
ulat
ion
[%]
High global PM2.5 exposure
5 10 15 25 35 50 100
100
90
80
70
60
50
40
30
20
10
0
AQG IT-3 IT-2 IT-1