1. Progress in Application of Satellite Remote Sensing for Global Air Quality Assessment
Randall Martin (Dalhousie and Harvard-Smithsonian), with contributions from
Aaron van Donkelaar1, Graydon Snider1, Crystal L. Weagle2, Sajeev Philip1, Robyn Latimer1, Kalaivani K. Murdymootoo1, Amanda Ring1, Yvonne Ritchie1, Emily Stone1, Ainsley Walsh1, Clement Akoshile3, Nguyen Xuan Anh4, Rajasekhar Balasubramanian5, Jeff Brook6, Fatimah D. Qonitan7, Jinlu Dong8, Derek Griffith9, Kebin He8, Brent N. Holben10, Christina Hsu10, Ralph Kahn10, Nofel Lagrosas11, Puji Lestari7, Robert Levy10, Alexei Lyapustin10, Zongwei Ma12, Amit Misra13, Leslie K. Norford14, Eduardo J. Quel15, Abdus Salam16, Andrew Sayer10, Bret Schichtel17, Lior Segev18, Sachchida Tripathi13, Chien Wang19, Chao Yu20, Qiang Zhang8, Yuxuan Zhang8
Michael Brauer21, Aaron Cohen22, Mark D. Gibson23, Yang Liu20, J. Vanderlei Martins24, Yinon Rudich18
1Department of Physics and Atmospheric Science, Dalhousie University, Halifax, Nova Scotia, Canada  2Department of Chemistry, Dalhousie University, Halifax, Nova Scotia, Canada 3Department of Physics, University of Ilorin, Ilorin, Nigeria 4Institute of Geophysics, Vietnam Academy of Science and Technology, Hanoi, Vietnam 5Department of Civil and Environmental Engineering, National University of Singapore, Singapore 6Department of Public Health Sciences, University of Toronto, Toronto, Ontario, Canada 7Faculty of Civil and Environmental Engineering, ITB, JL. Ganesha No.10, Bandung, Indonesia 8Center for Earth System Science, Tsinghua University, Beijing, China 9Council for Scientific and Industrial Research (CSIR), Pretoria, South Africa 10Earth Science Division, NASA Goddard Space Flight Center, Greenbelt, MD, USA 11Manila Observatory, Ateneo de Manila University, Quezon City, Philippines 12School of Environment, Nanjing University, Nanjing, China 13Center for Environmental Science and Engineering, Indian Institute of Technology Kanpur, Kanpur, India 14Department of Architecture, Massachusetts Institute of Technology, Cambridge, MA, USA 15UNIDEF (CITEDEF-CONICET) Juan B. de la Salle 4397 – B1603ALO Villa Martelli, Buenos Aires, Argentina 16Department of Chemistry, University of Dhaka, Dhaka, Bangladesh 17Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO, USA 18Department of Earth and Planetary Sciences, Weizmann Institute of Science, Rehovot, Israel 19Center for Global Change Science, Massachusetts Institute of Technology, Cambridge, MA, USA 20Rollins School of Public Health, Emory University, 1518 Clifton Road NE, Atlanta, GA, USA 21School of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada 22Health Effects Institute, 101 Federal Street Suite 500, Boston, MA, USA 23Department of Process Engineering and Applied Science, Dalhousie University, Halifax, Nova Scotia, Canada 24Department of Physics and Joint Center for Earth Systems Technology, University of Maryland, Baltimore County, Baltimore, MA, USA
WHO Meeting, March 2017

2. Long-Term ( ) Aerosol Optical Depth (AOD) Use AERONET AOD to Assess Relative Accuracy & Combine
AERONET
18 km
10 km
Deep Blue
10 km
1 km
Deep Blue
Dark Target
14 km
10 km
van Donkelaar et al., ES&T, 2016

3. Apply Chemical Transport Model (GEOS-Chem) to Calculate AOD/PM2
Apply Chemical Transport Model (GEOS-Chem) to Calculate AOD/PM2.5 for Each Observation
Aaron van Donkelaar

4. Satellite-Derived PM2.5 for 2010 Promising Consistency with In Situ, & Room for Improvement
Evaluation in
North America:
r2 = 0.66
slope = 0.96
n=974
Satellite-derived (ug/m3)
Gridded at 1km resolution
Error likely driven by modeled relation between AOD and PM2.5
van Donkelaar et al., ES&T, 2016

5. 15-Year Timeseries (1998-2012) of Satellite-Derived PM2.5
Modeled AOD to PM2.5 Relation Essential for Accurate Trend
East Asia
Eastern North America
PM2.5 (μg m-3)
PM2.5 (μg m-3)
Evaluation over North America
In situ 0.37 ± 0.06 μg m-3 yr-1
Satellite-derived 0.36 ± 0.13 μg m-3 yr-1
If constant PM2.5 / AOD: 0.22 ± 0.09 μg m-3 yr-1
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

6. Few Collocated Measurements of PM2.5 & AOD
Within 3 km
Data for 2012

7. 3-λ nephelometer (AirPhoton) AOD from Sunphotometer (e.g. AERONET)
Surface Particulate Matter Network (SPARTAN): Measures PM2.5 Mass & Composition at Sites Measuring AOD
3-λ nephelometer (AirPhoton)
Scatter
AOD from Sunphotometer (e.g. AERONET)
Semi-autonomous PM2.5 & PM10 Impaction Sampling Station (AirPhoton)
Mass (35% RH)
Black Carbon
Ions Metals
(IC) (ICP-MS)
Surface/Column Diurnal Mass Scattering Efficiency
bsp = nephelometer measurements of aerosol scatter
overpass = satellite overpass time
Snider, Weagle, et al., AMT, 2015
Organizing Committee: M. Brauer, A. Cohen, M. Gibson, Y. Liu, V Martins, Y. Rudich, R. Martin

8. Globally consistent PM2.5 mass and composition network
SPARTAN: Grass-roots Network Measuring PM2.5 Mass & Composition at Sites Measuring AOD
Testing
Deployed
Committed
An IGAC Activity
Globally consistent PM2.5 mass and composition network
Fires
Agriculture
Sea Spray
Mineral Dust
Coal, Traffic
SO42-, Pb, As, V, Zn, BC
SPARTAN Measurements K
NH4+
Na+
Al, Fe, Mg, Ti
Organizing Committee: M. Brauer, A. Cohen, M. Gibson, Y. Liu, V. Martins, Y. Rudich, R. Martin

9. Initial SPARTAN PM2.5 Mass and Composition (>1000 filters)
Low PM2.5
High PM, ANO3
High Dust Fraction
High BC
Smoke
High BC
Data publicly available at spartan-network.org
Middle contains PM2.5 Mass in ug/m3
Snider et al., ACP, 2016

10. Some Emerging Satellite Datasets
Ongoing global aerosol monitoring (e.g. VIIRS on NPP)
Improved aerosol algorithms (e.g. MAIAC with global 1km AOD)
Finer resolution trace gas information (e.g. TROPOMI for NO2), 2017 launch
Diurnal information (e.g. geostationary constellation; launch in few years)
Additional information on aerosol composition (e.g. MAIA 2020 launch)
Key Challenge for All These Datasets: Relate Columnar Satellite Observation to Ground-level Mass

11. Satellites offer high quality global columnar observations that contains information about PM2.5 Ground-based collocated measurements of AOD, PM2.5 mass, PM2.5 scatter, and PM2.5 composition key to improving global satellite-based estimates of PM2.5
These data serve as an improved input of for subsequent PM2.5 estimates statistically-fused with ground-based monitors (e.g. Shaddick et al. 2017)