Global Fine Particulate Matter Concentrations and
Trends Inferred from Satellite Observations,
Modeling, and Ground-Based Measurements
Randall Martin
with contributions from
Aaron van Donkelaar, Brian Boys, Matthew Cooper, Colin Lee, Ryan MacDonell,
Graydon Snider, Crystal Weagle, Mark Gibson
Michael Brauer (UBC), Aaron Cohen (HEI), Daven Henze (CU Boulder), Christina Hsu
(NASA), Yang Liu (Emory), Zifeng Lu (Argonne), Vanderlei Martins (AirPhoton),
David Streets (Argonne), Siwen Wang (Tsinghua), Qiang Zhang (Tsinghua)
EGU
30 April 2014
Vast Regions Have Insufficient Measurements for Fine
Particulate Matter (PM2.5) Exposure Assessment
Locations of Publicly Accessible Long-Term PM2.5 Monitoring Sites
Emerging
Network
Previous Global Burden of Disease Project for the Year 2000
Impaired by Insufficient Global Observations of PM2.5
General Approach to Estimate Daily PM2.5 Concentration
Coincident Model
(GEOS-Chem) Profile
Altitude
Daily Satellite(MODIS, MISR, SeaWifs)
Column of AOD
Concentration
PM 2.5,sat
 PM 2.5,model 
 AODsat 

A
O
D
model 

Accounts for
• relation of “dry” PM2.5 with ambient extinction
• relation of aerosol during satellite-observation vs continuous
Climatology (2001-2006) of MODIS- and MISR-Derived PM2.5
Evaluation in
North America:
r=0.77
slope = 1.07
N=1057
EHP Paper of the Year
Outside Canada/US
N = 244 (84 non-EU)
r = 0.83 (0.83)
Slope = 0.86 (0.91)
Bias = 1.15 (-2.64) μg/m3
van Donkelaar et al., EHP, 2010
Used in Global Burden of Disease Study 2010
PM2.5 Causal Role in
70 Million Disability Adjusted Life Years (~3%)
>3 Million Excess Deaths (~5%)
Three-fold increase in
premature mortality rate
over previous GBD study for
2000
Lim et al., Lancet, 2012
Similar Conclusions Reached by WHO in 2014
Significant Association of Long-term PM2.5 Exposure with
Cardiovascular Mortality at Low PM2.5
Benefits from Large
Statistical Power
Crouse et al., EHP, 2012
Enhanced Algorithm to Infer PM2.5 from MODIS
• Optimal Estimation AOD
• CALIOP-adjusted AOD/PM2.5
MODIS
Imaging Spectroradiometer
Optimal Estimation allows:
• Error-constrained AOD solution
• Consistent optical properties
• Local reflectance information
CALIOP
Space-borne LIDAR
Optimal Estimation constrains AOD retrieval by error:
a priori
AOD
a posteriori
AOD
2
 AOD  AODa 
J(AOD) 
a priori error
Observed TOA
reflectance
σ a2
2


 dρ a 
AOD
 ρ  


dAOD
a 



2
σ ε observational
error
van Donkelaar et al., JGR, 2013
Optimal Estimation (OE) Can Improve Global AOD Retrieval
2005
Eastern
North
America
slope=1.47
r=0.65
slope=0.81
r=0.75
slope=0.56
r=0.53
slope=1.25
r=0.85
slope=0.94
r=0.87
slope=0.71
r=0.71
correlation
Simulated
OE
Operational
Simulated
OE
Operational
Region
slope
Western
North
America
Number of
Observations
Optimal Estimation
AOD (Unitless)
Operational
E NA
1.3 0.9 0.7 0.9 0.9 0.7
W NA
1.5 0.8 0.6 0.7 0.8 0.5
EU
1.2 1.1 1.4 0.8 0.8 0.6
India
1.2 0.7 0.7 0.8 0.8 0.5
E Asia
1.1 0.8 0.7 0.9 0.8 0.7
Africa
0.9 0.8 1.0 0.9 0.9 0.8
Operational = NASA MODIS Collection 5
Best agreement
van Donkelaar et al., JGR, 2013
Use CALIOP Observations (2006-2011) to Correct
Seasonal Bias in Simulated Aerosol Extinction
East China
η = PM2.5 / AOD
Eastern US
van Donkelaar et al., JGR, 2013
Satellite-Derived PM2.5 Trends Inferred from SeaWifs &
MISR AOD and GEOS-Chem AOD/PM2.5
MISR
2000 -2012
SeaWiFS
1998 -2010
PM2.5 Trend [μg m-3 yr-1]
Boys et al., submitted
PM2.5 (μg m-3)
PM2.5 (μg m-3)
Combine SeaWifs & MISR to Calculate 15-Year PM2.5
East Asia
Eastern North America
Timeseries (1998-2012)
0.1
0.05
0.01
-2
-1
0
1
PM2.5 Trend [µg m-3 yr-1]
2
South Asia
PM2.5 (μg m-3)
P- value
Middle East
Boys et al., submitted
Consistent Trends in Satellite-Derived and In Situ PM2.5
Eastern US
PM2.5 Anomaly (ug m-3)
Satellite-Derived
In Situ
1999-2012
SeaWifs & MISR
In Situ
In Situ (1999-2012)
0.37 ± 0.06 μg m-3 yr-1
Satellite-Derived (1999-2012)
0.36 ± 0.13 μg m-3 yr-1
Boys et al., submitted
Interpret Satellite-derived PM2.5 Trends with GEOS-Chem
Eastern North America
PM2.5 [ug/m3]
SeaWifs & MISR -0.39±0.10 μg m-3 yr-1
GEOS-Chem Secondary Inorganic -0.4 μg m-3 yr-1
PM2.5 [ug/m3]
South Asia
SeaWifs & MISR 0.93±0.22 μg m-3 yr-1
Middle East
SeaWifs & MISR 0.81±0.21 μg m-3 yr-1
GEOS-Chem Mineral Dust 0.7 μg m-3 yr-1
East Asia
SeaWifs & MISR 0.79±0.27 μg m-3 yr-1
GEOS-Chem Secondary Inorganic 0.7 μg m-3 yr-1
GEOS-Chem Secondary Inorganic 0.8 μg m-3 yr-1
GEOS-Chem Organic 0.2 μg m-3 yr-1
Year
GEOS-Chem Organic 0.04 μg m-3 yr-1
Year
Boys et al., submitted
Changes in Long-term Population-Weighted Ambient PM2.5
Clean Areas are Improving; High PM2.5 Areas are Degrading
WHO Guideline & Interim Targets
van Donkelaar et al., submitted
Changes in Long-term Population-Weighted Ambient PM2.5
Clean Areas are Improving; High PM2.5 Areas are Degrading
WHO Guideline & Interim Targets
1998 (51%)
2012 (70%)
Exceedance of
WHO IT1
increases from
51% to 70%
WHO
IT1
1998
Exceedance of
WHO AQG
drops from
62% to 19%
2012
WHO
AQG
van Donkelaar et al., submitted
SPARTAN: An Emerging Global Network to Evaluate and
Enhance Satellite-Based Estimates of PM2.5
Measures PM2.5 Mass & Composition at Sites Measuring AOD
Testing
Deployed
Committed
Prospective
Semi-Autonomous
PM2.5 & PM10
Impaction
Sampling Station
(AirPhoton)
Ions & metals
3-λ Nephelometer
AOD from CIMEL
Sunphotometer
(e.g. AERONET)
www.spartan-network.org
Snider et al., in prep
Nonlinear Relation Between PM2.5 and Sources
Which Local Sources Should be Reduced to
Decrease Mortality from PM2.5?
Primary
PM2.5
Chemistry
Precursors
Nitrogen Oxides (NOx)
Sulfur Dioxide (SO2)
Ammonia (NH3)
Adjoint Model: Calculate Sensitivity of Global
Premature Mortality to Local Emissions
GEOS-Chem
Emissions
Chemistry &
Transport
GEOS-Chem Adjoint
Chemistry &
∂
Transport
∂Emissions
Concentrations
Health
Impact
Function
Global
Mortality
∂
∂Concentrations
Colin Lee
Sensitivity of Global Premature Mortality to SO2 Emissions
ΔMortalityglobal / 10% ΔEmissions
PM2.5 subgrid variability resolved using satellite AOD
Exposure-response function from Global Burden of Disease Project
Lee et al., in prep
Sensitivity of Global Premature Mortality to:
SO2
Emissions
NH3
Emissions
ΔMortalityglobal / 10% ΔEmissions
Lee et al., in prep
Insight into Global PM2.5 through Satellite Remote Sensing
Modeling, and Ground-based Instruments
• Particulate matter is major risk factor for global premature mortality
• Regions with high PM2.5 have increasing concentrations
• Regions with low PM2.5 have decreasing concentrations
• Asian PM2.5 increasing by 1-2 ug/m3/yr
• SPARTAN and CALIOP evaluate AOD/PM2.5 simulation
• Adjoint allows efficient calculation of sensitivity of premature
mortality to emissions changes
• Controls in South Asia on SO2 much more effective than on NH3
Acknowledgements:
NSERC, Health Canada,
Environment Canada