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Volume 18, issue 17
Atmos. Chem. Phys., 18, 12891–12913, 2018
https://doi.org/10.5194/acp-18-12891-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Atmos. Chem. Phys., 18, 12891–12913, 2018
https://doi.org/10.5194/acp-18-12891-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 07 Sep 2018

Research article | 07 Sep 2018

Constraining chemical transport PM2.5 modeling outputs 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 and decreasing metric uncertainties and errors. The frequent, spatially extensive and radiometrically consistent instantaneous constraints can be especially useful in areas away from ground monitors and progressively downwind of emission sources. We present a physical approach to constraining regional-scale estimates of PM2.5, its major chemical component species estimates, and related uncertainty estimates of chemical transport model (CTM; e.g., the Community Multi-scale Air Quality Model) outputs. This approach uses ground-based monitors where available, combined with aerosol optical depth and qualitative constraints on aerosol size, shape, and light-absorption properties from the Multi-angle Imaging SpectroRadiometer (MISR) on the NASA Earth Observing System's Terra satellite. The CTM complements these data by providing complete spatial and temporal coverage. Unlike widely used approaches that train statistical regression models, the technique developed here leverages CTM physical constraints such as the conservation of aerosol mass and meteorological consistency, independent of observations. The CTM also aids in identifying relationships between observed species concentrations and emission sources.

Aerosol air mass types over populated regions of central California are characterized using satellite data acquired during the 2013 San Joaquin field deployment of the NASA Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) project. We investigate the optimal application of incorporating 275 m horizontal-resolution aerosol air-mass-type maps and total-column aerosol optical depth from the MISR Research Aerosol retrieval algorithm (RA) into regional-scale CTM output. The impact on surface PM2.5 fields progressively downwind of large single sources is evaluated using contemporaneous surface observations. Spatiotemporal R2 and RMSE values for the model, constrained by both satellite and surface monitor measurements based on 10-fold cross-validation, are 0.79 and 0.33 for PM2.5, 0.88 and 0.65 for NO3, 0.78 and 0.23 for SO42−, 1.00 and 1.01 for NH4+, 0.73 and 0.23 for OC, and 0.31 and 0.65 for EC, respectively. Regional cross-validation temporal and spatiotemporal R2 results for the satellite-based PM2.5 improve by 30 % and 13 %, respectively, in comparison to unconstrained 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 physical technique in a well-instrumented region opens the possibility of applying it globally, especially over areas where surface air quality measurements are scarce or entirely absent.

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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 their 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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