Bethany Sutherland
Ph.D. Candidate - Atmospheric Science
Bio
Under the advisement of Dr. Nicholas Meskhidze, my doctoral research can be categorized into two main projects. One project focused on air quality and another on the radiative effect of aerosols. The common theme between these projects is the desire to test whether information about the aerosol type derived from remote High Spectral Resolution Lidar (HSRL) retrievals can be leveraged to improve our understanding of the role of aerosols in our atmosphere. I have several publications on a novel remote-sensing based methodology for estimating PM2.5 concentration and speciation from NASA HSRL retrievals; Sutherland et al. 2023 (https://doi.org/10.1016/j.atmosenv.2023.119719) and Meskhidze et al. 2021 (https://doi.org/10.1016/j.atmosenv.2021.118250). My other project involved modifying the GEOS-Chem model to estimate the amount of uncertainty that would be introduced in estimates of the direct radiative effect of aerosols if HSRL aerosol type-specific values for single scattering albedo and asymmetry parameter were used in the radiative transfer calculations.
Previously I have earned a M.S. in Applied Mathematics from the University of Washington (UW) and a B.S. in Physics with an atmospheric physics option from New Mexico Institute of Mining and Technology (NMT). My work has been supported by NASA FINESST and the North Carolina Space Grant.
Publications
- Wind-driven emissions of coarse-mode particles in an urban environment , ATMOSPHERIC CHEMISTRY AND PHYSICS (2024)
- Application of DIAL/HSRL and CATCH algorithm-based methodologies for surface PM2.5 concentrations during the KORUS-AQ campaign , ATMOSPHERIC ENVIRONMENT (2023)
Grants
Particulate matter is one of the six criteria air pollutants that the United States Environmental Protection Agency (EPA) has established national ambient air quality standards (NAAQS) for under the Clean Air Act. Particles with aerodynamic diameter less than 2.5 ??m (PM2.5) have been found to have the most serious adverse effect on human health and the environment. While the importance of measuring PM2.5 has been clearly demonstrated, doing so remotely remains challenging. We have developed and validated a methodology, HSRL-CH, for using High Spectral Resolution Lidar (HSRL) retrieved information about aerosol types (Burton et al., 2012) and extinction to inform model independent estimates of surface PM2.5 with information about likely chemical speciation for North American aerosols (Meskhidze et al., 2021). The HSRL-CH method uses type information and HSRL retrieved extinction to derive a CMAQ model-independent estimate of PM2.5 concentration and chemical composition. This algorithm works in combination with information about aerosol chemical composition derived from CATCH (Creating Aerosol Types from Chemistry) (Dawson et al., 2017). Our preliminary analysis shows the method is not working as well on retrievals from the KORUS_AQ campaign in Korea as it did in the eastern US. This could be due to two main factors: (1) particularly low mixed layer heights encountered during KORUS-AQ and (2) differences in the chemical composition and optical properties for Asian aerosols versus eastern American. Through the work proposed here, we intend to develop a framework similar to that of Meskhidze et al., (2021) for remotely estimating PM2.5 in east Asia using HSRL retrievals. This will include incorporating a threshold for discarding data based on MLH heights derived from HSRL retrievals following the method of Scarino et al. (2014), retraining the CATCH algorithm for east Asian aerosols similar to Dawson et al. (2017), and completing a validation of the method by comparing estimated PM2.5 concentrations and chemical speciation with data from National Institute of Environmental Research (NIER) ground sites. The successful completion of this project will mean that a new methodology has been developed and validated which can be used to estimate PM2.5 concentration and chemical speciation in both North America and Asia with region specific chemical speciation once a HSRL is orbiting as part of the NASA Earth System Observatory (ESO) Atmosphere Observing System (AOS).