0. Crystal L. Weagle1, with contributions from
Insight into the Global Distribution and Chemical Composition of PM2.5 from the SPARTAN Global Aerosol Network
Crystal L. Weagle1, with contributions from
Randall V. Martin1,2,3, Graydon Snider2, Sajeev Philip4, Aaron van Donkelaar1, Clement Akoshile5, Nguyen Xuan Anh6, Rajasekhar Balasubramanian7, Sarah K. Guttikunda8, Daven K. Henze9, Brent N. Holben10, Ralph Kahn10, Christoph Keller11, Zbigniew Klimont11, Bret Schichtel13, Lior Segev14, Chandra Venkataraman15, Chien Wang16, Qiang Zhang17, Michael Brauer18, Aaron Cohen19, Mark D. Gibson20, Yang Liu21, J. Vanderlei Martins22, Yinon Rudich14
1Department of Chemistry, Dalhousie University, Halifax, Nova Scotia, Canada
2Department of Physics and Atmospheric Science, Dalhousie University, Halifax, Nova Scotia, Canada
3Harvard-Smithsonian Center for Astrophysics, Cambridge, MA, USA
4NASA Ames Research Center, Moffett Field, California, United States of America 5Department of Physics, University of Ilorin, Ilorin, Nigeria 6Institute of Geophysics, Vietnam Academy of Science and Technology, Hanoi, Vietnam 7Department of Civil and Environmental Engineering, National University of Singapore, Singapore 8Division of Atmospheric Sciences, Desert Research Institute, Reno, United States of America 9Department of Mechanical Engineering, University of Colorado at Boulder, Boulder, Colorado, United States of America
10Earth Science Division, NASA Goddard Space Flight Center, Greenbelt, MD, USA
11Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, MD, USA
12International Institute for Applied Systems Analysis, Laxenburg, Austria
13Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO, USA
14Department of Earth and Planetary Sciences, Weizmann Institute of Science, Rehovot, Israel
15Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, India
16Center for Global Change Science, Massachusetts Institute of Technology, Cambridge, MA, USA
17Center for Earth System Science, Tsinghua University, Beijing, China
18School of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada
19Health Effects Institute, 101 Federal Street Suite 500, Boston, MA, USA
20Department of Process Engineering and Applied Science, Dalhousie University, Halifax, Nova Scotia, Canada
21Rollins School of Public Health, Emory University, 1518 Clifton Road NE, Atlanta, GA, USA
22Department of Physics and Joint Center for Earth Systems Technology, UMBC, Baltimore, MA, USA
Thank you.
Before I begin I’d like to thank every one on this list as well as many other SPARTAN site operators for their contributions to this work.

1. Long-Term Exposure to Fine Particulate Matter (PM2
Long-Term Exposure to Fine Particulate Matter (PM2.5) Affects Health and Longevity
PM2.5 leading environmentally-related risk factor for premature mortality with 4 million attributable deaths annually (Global Burden of Disease, 2015)
Annual global welfare costs projected to rise from US$ 3 trillion in 2015 to US$ trillion in 2060 (OECD, 2016)
Fine particulate matter, is a known robust indicator of increased mortality and morbidity. The Global Burden of disease estimates that over 4 million premature deaths are attributable to PM2.5 each year. In addition to these health effects, the costs associated with exposure are estimated to be about 3% of of global GDP and are projected to grow over the coming decades.
Global chemical transport models, such as GEOS-Chem are powerful tools for offering insight into the global distribution of PM2.5, however there is a lack of a globally consistent monitoring network for evaluation.

2. Initial Composition Measurements from the Surface Particulate Matter Network (SPARTAN)
Growing network of PM2.5 mass, composition, and scatter, collocated with AERONET AOD
SPARTAN
EPA
Trace metals
To address this need, we’ve developed the Surface Particulate Matter Network to measure ground-based PM2.5 collocated with AERONET AOD measurements.. All analysis is completed using standardized methods, including weighing in a clean room environment following US EPA protocol. Here I’ve shown the locations of SPARTAN monitors as well as some examples of excellent agreement with independent measurements for mass, composition, and trace metals.
We use GEOS-Chem to interpret initial SPARTAN measurements in densely populated regions relevant for health

3. Anthropogenic, Fugitive, Combustion, and Industrial Dust (AFCID) improves agreement with measurements
GC v11-01
SPARTAN measurements identify GEOS-Chem low dust bias
Elevated measured Zn:Al ratios at SPARTAN sites imply anthropogenic dust source (G. Snider, C.L. Weagle, et al., ACP, 2016)
AFCID inventory from Qiang Zhang, Sarah Guttikunda, and Chandra Venkataraman
Inclusion of AFCID in GEOS-Chem:
Increases correlation from to 0.66 vs. SPARTAN dust
Reduces model low bias from 17% to 7%
Increases global population- weighted PM2.5 by 2.9 µg m-3
One of the first things we discovered with SPARTAN \ was a lack of anthropogenic dust representation in urban areas revealed by elevated Zn:Al ratios.
Work by Sajeev Philip shows that including an Anthropogenic, Fugitive, Combustion, and Industrial Dust source increases agreement across SPARTAN sites.
In addition, including AFCID reduces the low bias in PM2.5 mass by ~ 10% and increases population-weighted PM2.5 by ~ 3 ug/m3.
This shows the utility of SPARTAN measurements for assessing PM2.5 bias in the model
S. Philip, R.V. Martin, G. Snider, C. L. Weagle, A. van Donkelaar, M. Brauer, D. K. Henze, Z. Klimont, C. Venkataraman, S. Guttikunda, and Q. Zhang, ERL, 44018, 2017

4. Model speciation shows promising consistency
Slope = 0.80 ± 0.10; r = 0.90
Slope = 1.07 ± 0.16; r = 0.85
Slope = 2.03 ± 0.46; r = 0.63
Slope = 0.67 ± 0.15; r = 0.66
Here I show annual mean concentrations of major chemical components as simulated by GEOS-Chem with overlaid circles showing SPARTAN measurements.
The simulation exhibits skill in reproducing the concentration and spatial variation of major chemical components measured at SPARTAN sites.
Correlations for all species are generally above 0.6, with the exception of BC, which is known to have highly heterogeneous sources.
High correlation is found for SO4 and NH4, signifying the ability to capture global variation of this major component but a slope below unity for SO4 suggests an underestimate.
The high bias in nitrate implied by the regression is driven by overestimation in East Asia.
The low slope for organics implies an underestimate in this primary component.
Dust simulation really benefited from Davens dust inventory, however a high bias remains.
Slope = 0.48 ± 0.12; r = 0.49
Slope = 1.53 ± 0.32; r = 0.70
*Includes AFCID emissions
GC v11-01

5. Evidence of positive bias in dust near source regions
Satellite based PM GEOS Chem PM 2.5
Rehovot
Ilorin -
*PM2.5 Scalar =
Total PM2.5:
RMSE = 15.1 µg m-3
r = 0.75
RMSE = 12.8 µg m-3
r = 0.92
*PM2.5 Scalar
The plot on the upper left shows the ratio of satellite-based to GC PM2.5. We see high spatial variation, however a ratio below 1 in dust source regions suggest an overestimate in simulated PM2.5.
Comparison of measured and simulated composition at 2 arid sampling sites, located in Rehovot Israel and Ilorin Nigeria show that this overestimation is predominately from a positive bias in natural dust near source regions.
This implies a need to further reduce dust over this region.
Scaling of GEOS-Chem PM2.5 to satellite-based estimates shows a significant increase in ability to capture the spatial variation of PM2.5 mass.
GC dust vs. SPARTAN dust
All sites:
Slope = 1.53 ± 0.32
r = 0.70
Without Arid sites:
Slope = 1.12 ± 0.17
r = 0.87

6. Measured speciation supports source apportionment
We use the PM2.5 scalar along with GEOS-Chem to estimate the contribution of 7 source categories.
Here I show the source apportionment at a sampling site located in Hanoi, Vietnam.
**Describe figure**
Here we see that the measured speciation results generally support the source apportionment, showing that we can use these results to understand sources at SPARTAN sites and elsewhere.

7. Contribution of source categories to PM2.5
Here we investigate the contribution of source categories to global population weighted PM2.5.
Natural sources is the highest contributing sector at ~ 25% of the total driven by mineral dust and a diffuse background.
Residential energy use contributes to 20% of total PM2.5 due to the widespread use of domestic cooking and heating using solid fuels in highly populated regions such as south and southeast asia as well as over many areas in Africa
Industry and Power generation combine to contribute ~30% of the total and the contribution is most pronounced over industrialized and emerging economies.
Transport, agriculture, and biomass burning combine to make up the remaining 20% of the total PW PM2.5
*PW = population-weighted

8. Summary
Satellite-based PM2.5 offers additional constraint on simulated PM2.5 mass and composition
Simulated PM2.5 mass shows a decrease in RMSE and increase in correlation across SPARTAN sites
Increased ability to capture spatial diversity of PM2.5
Inclusion of AFCID increases agreement with SPARTAN and other global PM measurements
Measured PM2.5 chemical composition supports GEOS-Chem source apportionment
Emerging measurements from SPARTAN offer new insight into GEOS-Chem simulation of PM2.5 composition
Furthermore, GEOS-Chem offers a powerful constraint for global source attribution when constrained by ground-based and satellite-based PM2.5
Thank you!

9. Sulfate neutralization at SPARTAN Sites

10. Evidence of OM low bias Total GC PM2.5 vs. SPARTAN: Default:
Satellite PM GEOS−Chem PM 2.5
Total GC PM2.5 vs. SPARTAN:
Default:
RMSE = 15.1 µg m-3
r = 0.75
Satellite-based PM2.5 Scalar: RMSE = 12.8 µg m-3
r = 0.92
Comparison at all SPARTAN sites vs. residual mass:
Default GEOS-Chem:
Slope = 0.67 ± 0.15
r = 0.66
With satellite-based PM2.5 Scalar:
Slope = 0.98 ± 0.12
r = 0.90