1. with contributions (alphabetical) from
Advances in Studies of Air Quality and Health Informed with Satellite Remote Sensing
Randall Martin
with contributions (alphabetical) from
Brian Boys, Jeff Geddes, Mark Gibson, Colin Lee, Caroline Nowlan (now SAO), Sajeev Philip, Graydon Snider, Crystal Weagle, Aaron van Donkelaar, Junwei Xu
Michael Brauer (UBC), Myungie Choi (Yonsei), Aaron Cohen (HEI), Daven Henze (CU Boulder), Christina Hsu (NASA), Jhoon Kim (Yonsei), Yang Liu (Emory), Zifeng Lu (Argonne), Vanderlei Martins (AirPhoton), David Streets (Argonne), Siwen Wang (Tsinghua), Qiang Zhang (Tsinghua), SPARTAN Team
AGU
19 December 2014

2. Insufficient In Situ Measurements for Exposure Assessment General Approach to Estimate Daily PM2.5 Concentration
Daily Satellite(MODIS, MISR, SeaWifs, GOCI)
GOME, SCIAMACHY, OMI, GOME-2)
Column of AOD or NO2
Coincident Model
(GEOS-Chem) Profile
Altitude
Concentration
Accounts for
relation of “dry” PM2.5 with ambient extinction
relation of aerosol during satellite-observation vs continuous

3. Climatology (2001-2006) of MODIS- and MISR-Derived PM2.5
Global Burden of Disease 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 Global Burden of Disease study for 2000
Lim et al., Lancet, 2012
Similar Conclusions Reached by WHO in 2014
Evaluation in
North America:
r=0.77
slope = 1.07
N=1057
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
EHP Paper of the Year
van Donkelaar et al., EHP, 2010

4. Combine SeaWifs & MISR to Calculate 15-Year PM2
Combine SeaWifs & MISR to Calculate 15-Year PM2.5 Timeseries ( )
East Asia
Eastern North America
PM2.5 (μg m-3)
PM2.5 (μg m-3)
Middle East
South Asia
0.25
-0.25 1 -1
1.5
-1.5 2 -2
0.01
0.05
0.1
P- value
PM2.5 Trend [µg m-3 yr-1]
-0.5
0.5
PM2.5 (μg m-3)
Boys et al., ES&T, 2014

5. Consistent Trends in Satellite-Derived and In Situ PM2.5
Eastern US
PM2.5 Anomaly (ug m-3)
In Situ
SeaWifs & MISR
In Situ
In Situ ( )
0.37 ± 0.06 μg m-3 yr-1
Satellite-Derived ( )
0.36 ± 0.13 μg m-3 yr-1
Satellite-Derived
Boys et al., ES&T, 2014

6. Interpret Satellite-derived PM2.5 Trends with GEOS-Chem
Eastern North America
Middle East
SeaWifs & MISR ±0.10 μg m-3 yr-1
SeaWifs & MISR 0.81±0.21 μg m-3 yr-1
PM2.5 [ug/m3]
GEOS-Chem Mineral Dust 0.7 μg m-3 yr-1
GEOS-Chem Secondary Inorganic -0.4 μg m-3 yr-1
South Asia
East Asia
Year
Year
SeaWifs & MISR 0.93±0.22 μg m-3 yr-1
SeaWifs & MISR 0.79±0.27 μg m-3 yr-1
PM2.5 [ug/m3]
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
GEOS-Chem Organic 0.04 μg m-3 yr-1
Boys et al., ES&T, 2014

7. WHO Guideline & Interim Targets
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%)
1998
Exceedance of WHO AQG drops from 62% to 19%
2012 (70%)
Exceedance of WHO IT1 increases from 51% to 70%
2012
WHO AQG
WHO IT1
Exceedance of WHO AQG drops from 62% to 19%
van Donkelaar et al., EHP, 2014

8. Population Weighted PM2
Population Weighted PM2.5 Composition Use GEOS-Chem to Partition Satellite AOD into PM2.5 Composition
Particulate Organic Matter (OM)
Secondary Inorganic Aerosols (SIA)
11 ug/m ug/m3
11 ug/m3
r=0.94, slope=0.89
r=0.70, slope=0.44
Mineral Dust
Satellite-Model outperforms pure model. Examples:
Slope GEOS-Chem SIA = 0.65 (vs 0.93 with sat)
GEOS-Chem OM: r = 0.61 (vs r=0.70 with sat)
Philip et al., ES&T, 2014

9. GOCI Offers High Temporal and Spatial Resolution for Inference of PM2
In Situ PM2.5 better represented by GOCI-derived PM2.5 (slope = 0.91) than by GEOS-Chem (slope =0.53)
Also better than MODIS (r=0.8, slope=1.3)
Junwei Xu et al., in prep
AOD from Jhoon Kim

10. 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)
Snider et al., AMTD, 2014

11. PM2.5/AOD Driven by Scattering Vertical Profile & Mass Scattering Efficiency
bsp = nephelometer measurements of aerosol scatter
overpass = satellite overpass times (taken as 10am – 2pm)
Snider et al., AMTD, 2014
PM2.5 / AOD (μg / m3)

12. Nonlinear Relation Between PM2
Nonlinear Relation Between PM2.5 and Sources Which Local Sources Should be Reduced to Decrease Premature Mortality from PM2.5?
PM2.5
Primary
Chemistry
Precursors
Nitrogen Oxides (NOx)
Sulfur Dioxide (SO2)
Ammonia (NH3)
Adjoint Model: Calculate Sensitivity of Global Premature Mortality to Local Emissions 3.
Global
Premature Mortality ∂
∂Emissions
Chemistry & Transport ∂
∂Concentrations
Colin Lee

13. Sensitivity of Global Premature Mortality to:
SO2
Emissions
NH3
Emissions 7.
ΔMortalityglobal / 10% ΔEmissions
Lee et al., in prep

14. For Surface NO2 Also Consistent Trends ( ) in In Situ and Satellite-Derived (GOME, SCIAMACHY, GOME-2)
Relative NO2 (unitless)
Year
Geddes et al., submitted

15. Surface NO2 Trends over from GOME, SCIAMACHY, GOME-2 Similarities with PM2.5 Trends where Driven by Secondary Inorganics Differences Elsewhere
NO2(ppbv)
NO2(ppbv)
NO2(ppbv)
Geddes et al., submitted

16. Dry Deposition of NO2 and SO2 Inferred from OMI (2005-2008) and GEOS-Chem Deposition Velocity
Nowlan et al., GBC, 2014

17. Insight into Global PM2.5 & NO2 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 evaluates AOD/PM2.5 simulation
Controls in South Asia on SO2 much more effective than on NH3
Dramatic trends in NO2 worldwide indicate changing air quality mix
NO2 and SO2 offer constraints on dry deposition
Acknowledgements: NSERC, Health Canada, Environment Canada