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Discussion papers | Copyright
https://doi.org/10.5194/acp-2018-152
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 27 Mar 2018

Research article | 27 Mar 2018

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This discussion paper is a preprint. A revision of this manuscript was accepted for the journal Atmospheric Chemistry and Physics (ACP) and is expected to appear here in due course.

Constraining Chemical Transport PM2.5 Modeling Using Surface Monitor Measurements and Satellite Retrievals: Application over the San Joaquin Valley

Mariel D. Friberg1,2, Ralph A. Kahn1, James A. Limbacher1,3, K. Wyat Appel4, and James A. Mulholland2 Mariel D. Friberg et al.
  • 1NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
  • 2School of Civil & Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
  • 3Science Systems and Applications Inc., Lanham, MD 20706, USA
  • 4US EPA, Research Triangle Park, NC 27711, USA

Abstract. Advances in satellite retrieval of aerosol type can improve the accuracy of near-surface air quality characterization, by providing broad regional context. In addition to aerosol optical depth, qualitative constraints on aerosol size, shape, and single-scattering albedo provided by multi-angle instruments, such as the Multi-angle Imaging SpectroRadiometer (MISR) on the NASA Earth Observing System’s Terra satellite, can provide frequent, spatially extensive, instantaneous constraints on chemical transport models (CTMs), which can be especially useful in areas away from ground monitors and progressively downwind of emission sources. CTMs (e.g. the Community Multi-scale Air Quality Modeling System) complement such data by providing complete spatial and temporal coverage, offering additional physical constraints (e.g., conservation of aerosol mass, meteorological consistency) independent of observations, and aid in identifying relationships between observed species concentrations and emission sources. Incorporating satellite aerosol information in the development of PM2.5 concentration metrics can lead to a decrease in metric uncertainties and errors.

This work focuses on the degree to which regional-scale satellite and CTM data can be combined to improve surface estimates of PM2.5, its major chemical component species estimates, and related estimates of uncertainty. Aerosol airmass types over populated regions of Southern California are characterized using satellite data acquired during the 2013 San Joaquin field deployment of the NASA DISCOVER-AQ project. Using the MISR Research Aerosol retrieval algorithm (RA), we investigate and evaluate the optimal application of incorporating 275m horizontal-resolution aerosol airmass-type maps and total-column aerosol optical depth into a 2km resolution, regional-scale CTM output, to obtain constrained fields of surface PM2.5. Contemporaneous surface observations are used to evaluate the results. The impact of incorporating MISR aerosol data on the ability to characterize air quality progressively downwind of large single sources is discussed. The spatiotemporal R2 values for the model, constrained by both satellite and surface-monitor measurements based on 10% withholding, are 0.79 for PM2.5, 0.88 for NO3, 0.78 for SO42−, 1.00 for NH4+, 0.73 for OC, and 0.31 for EC. Regional cross-validation temporal and spatiotemporal R2 results for the satellite-based PM2.5 improve by 30% and 13%, respectively, in comparison to CTM simulations, and provide finer spatial resolution. SO42− cross-validation values showed the largest spatial and spatiotemporal R2 improvement with a 43% increase. Assessing this technique in a well-instrumented region opens the possibility of using the satellite data to apply the technique globally.

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Mariel D. Friberg et al.
Mariel D. Friberg et al.
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Short summary
Advances in satellite retrieval of aerosol type can improve ambient air quality concentration estimates, by providing regional context where surface monitors are scarce or absent. This work focuses on the degree to which regional-scale satellite and model data can be combined to improve surface estimates of fine particles and its major speciated components. The physically based method applies satellite-derived column observations directly to total and speciated surface particle concentrations.
Advances in satellite retrieval of aerosol type can improve ambient air quality concentration...
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