Constraining Chemical Transport Model Simulations of PM2.5 Using Surface Aerosol Airmass Type Maps

Advancements in the interpretation of aerosol type and aerosol microphysical property data products from satellite retrievals can improve the accuracy of near-surface-condition diagnostics by providing broad regional context. In addition to aerosol optical depth (AOD), qualitative constraints on aerosol size, shape, and single-scattering albedo (SSA) provided by multi-angle instruments, such as the NASA Earth Observing System’s Multi-angle Imaging SpectroRadiometer (MISR), can provide some spatial constraints on chemical transport models (CTM), especially useful in areas away from ground monitors and progressively downwind of emission sources. CTMs (e.g. Community Multiscale Air Quality Modeling System) complement such data by providing uniform spatial and temporal coverage, offering additional physical constraints independent of observations, and identifying relationships to emission sources. Incorporating satellite aerosol information in the development of PM_2.5concentration metrics can lead to a decrease in metric uncertainties and errors.

Following the work of Patadia et al. (2013) over Mexico City, aerosol air-mass types over populated regions of Southern California were characterized using satellite data, taking advantage of suborbital data and high resolution CTM results acquired from the 2013 San Joaquin deployment and possibly other deployments of the NASA DISCOVER-AQ project, to validate the results [Friberg et al., 2017]. Using the MISR Research Aerosol Retrieval algorithm (RA), we investigated and evaluated the optimal application of incorporating 275 m horizontal resolution aerosol airmass maps and total column aerosol optical depth into a 2 km resolution, regional-scale CTM, to obtain constrained fields of surface PM_2.5 expanding upon previously developed fusion methods [Friberg et al., 2016; 2017]. In this presentation we will summarize our recent work, including the degree to which regional-scale satellite and CTM data can be combined to improve surface PM_2.5 estimates and related estimates of uncertainty, and the impacts of this work on the ability to characterize aerosol air masses, and monitor air quality, progressively downwind of large single sources.

References

Friberg, M.D., R.A. Kahn, et al., 2017. Surface aerosol airmass type mapping over the San Joaquin airshed basin using space-based multi-angle imaging and chemical transport modeling. Atmosph. Chem. Phys. (in preparation)

Friberg, M.D., R.A. Kahn, H.A. Holmes, H.H. Chang, M.J. Strickland, S.E. Sarnat, P.E. Tolbert, A.G. Russell, and J.A. Mulholland, 2017. Evaluation of Spatiotemporally-resolved Daily Ambient Air Pollution Metric Estimates and Uncertainties for Five-cities Developed Using a Fusion Method. Atm. Env. 158, 36-50.

Friberg, M.D., Zhai, X., Holmes, H.A., Chang, H.H., Strickland, M.J., Sarnat, S.E., Tolbert, P.E., Russell, A.G., Mulholland, J.A., 2016. Method for fusing observational data and chemical transport model simulations to estimate spatiotemporally resolved ambient air pollution. Environ. Sci. Technol. 50 (7), 3695e3705.

Patadia, F., Kahn, R. A., Limbacher, J. A., Burton, S. P., Ferrare, R. A., Hostetler, C. A., Hair, J. W. (2013). Aerosol airmass type mapping over the Urban Mexico City region from space-based multi-angle imaging. Atmospheric Chemistry and Physics, 13(18), 9525-9541.