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Satellite-Assisted Particulate Matter (SAPM)

Project Lead: Travis Toth

Particulate matter with aerodynamic diameters smaller than 2.5 ┬Ám (PM2.5) is a major contributor to air pollution worldwide and negatively impacts human health. For the past few decades, researchers have developed a variety of approaches to estimate surface PM2.5 from satellite observations, due to a lack of ground station in situ PM2.5 coverage in many regions globally. A well-documented technique is the use of column-integrated aerosol optical depth (AOD) retrievals from passive sensors to obtain surface PM2.5 proxies. Active sensors, like lidars, provide aerosol vertical distribution, and thus can be used to scale column AOD to the surface to improve the proxies. In addition to this synergistic passive/active sensor approach, near-surface aerosol extinction retrievals from lidar measurements can also be used independently to derive PM2.5 concentrations. Furthermore, researchers have used aerosol models to convert satellite AOD to surface PM2.5, and data assimilation methods can be employed to improve forecast PM2.5 model simulations. Benefits of space-based/model-assisted PM2.5 estimates 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 this Models, In situ, and Remote sensing of Aerosols (MIRA) Topic, we aim to provide intercomparisons of various methods and techniques for retrieving surface PM2.5 assisted by satellite remote sensors, global aerosol models, and in situ aerosol measurements. These comprehensive PM2.5 estimates can be useful for current and future efforts in air quality research, modeling, forecasting, and applications. We seek international in situ datasets (e.g., mass scattering/absorption coefficients and aerosol hygroscopic properties) for various aerosol species to develop more robust PM2.5 estimates. We also seek international ground-based in situ PM2.5 datasets to validate the PM2.5 concentration estimates.