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Particulate Matter from Lidars in Space (PMLS)

Project Lead: Travis Toth

Particulate matter with diameters smaller than 2.5 ┬Ám (PM2.5) is a major contributor to air pollution worldwide and negatively impacts human health. The Particulate Matter from Lidars in Space (PMLS) Project focuses on the development of robust PM2.5 retrievals from global lidar measurements for use in air quality research and applications. While a limitation of space-based lidars is that their repeat cycle at any location is on the order of weeks, the strength of lidar instruments is the vertical component of information they provide (e.g., curtains of extrinsic aerosol optical properties and subsequent PM2.5 estimates). These lidar curtains — validated with surface measurements — provide a means of improving and validating air quality models. The lidar profiles can also be a critical tool for use in model assimilation to obtain estimates of surface PM2.5 concentrations. The lidar curtains could also improve and validate passive remote sensing techniques involving the development of PM2.5 proxies at the surface. For example, a well-documented technique in the literature is to use column-integrated aerosol optical depth (AOD) from passive satellite sensors for this task. Lidar measurements can be leveraged to scale the AOD to the surface to improve the PM2.5 proxies. The synergistic use of active and passive sensors for PM2.5 derivation algorithm development is likely for the future Atmosphere Observing System (AOS) satellite mission (e.g., lidar + polarimeter). Furthermore, the lidar curtains may be able to demonstrate the fidelity of currently modeled boundary layer heights (BLHs) and used for new algorithms for model-based BLHs.

Additional benefits of PM2.5 retrievals from spaceborne lidars include helping to approximate PM2.5 concentration levels for those areas lacking in situ ground station coverage, providing nighttime characterization of air quality, and assessing spatial and temporal variations of PM2.5 pollution on both regional and global scales. For the PMLS Project, we seek international mass scattering/absorption coefficient and aerosol hygroscopic property datasets for various aerosol species in order to develop more robust PM2.5 retrievals from lidar measurements. We also seek international ground-based in situ PM2.5 datasets in order to validate the lidar-derived PM2.5 concentration estimates.