Particles with aerodynamic diameter under 2.5 mm (PM
2.5) have been linked to a variety of adverse health effects. This has led to increased focus on enhancing remote sensing techniques for PM
2.5 estimation. However, remote monitoring of PM
2.5 remains challenging due to the way particles interact with light, which is influenced by aerosol properties such as size and composition as well as environmental parameters such as hygroscopicity that vary in both space and time. Traditionally, attempts to remotely derive PM
2.5 have often relied on derived relationships between surface concentration and column optical properties such as aerosol optical depth. Emerging lidar systems offers promising opportunities for remotely deriving PM
2.5, partly due to their ability to provide vertically resolved profiles of aerosol properties. Research has demonstrated that NASA Langley's High Spectral Resolution Lidar (HSRL) system retrievals can categorize aerosols into specific types (such as urban, smoke, fresh smoke, maritime, and dusty mix) based on retrieved depolarization, lidar ratio, backscatter color ratio, and spectral depolarization ratio (Burton et al. 2012). To leverage this capability, probable aerosol type-specific chemical compositions have been derived using GEOS-Chem and the Creating Aerosol Types from CHemistry (CATCH) algorithm (Dawson et al. 2017). HSRL-CH is a novel method for estimating surface PM
2.5 concentration and likely speciation which has been developed within our research group. This method utilizes HSRL retrievals from the DISCOVER-AQ Baltimore-Washington D.C. (Meskhidze et al. 2021) and KORUS-AQ (Sutherland et al. 2023) campaigns. HSRL-CH accounts for varying aerosol hygroscopicity by combining GEOS-Chem/CATCH derived type-specific compositions with HSRL retrievals of aerosol loading and type.
In this study we applied the HSRL-CH methodology to data from five additional NASA campaigns: DISCOVER-AQ California, Texas, and Colorado, SEAC4RS, and ACEPOL. To validate the HSRL-CH derived PM2.5 concentrations and compositions, we used hourly PM2.5 measurements from 23 U.S. EPA Air Quality System (AQS) sites and daily averaged PM2.5 chemical compositions from 10 AQS sites. Across the five campaigns, the HSRL-CH method showed a Mean Absolute Error (MAE) of 10.2 µg/m³. However, excluding DISCOVER-AQ California, each individual campaign's MAE was below 7.5 µg/m³. These new results provide further insight into the suitability of GEOS-Chem/CATCH derived aerosol compositions for estimating PM2.5 from remote HSRL retrievals of aerosol type. This presentation will address the impact of HSRL system limitations (such as the inability to retrieve aerosol extinction within ~300 m of the surface) on HSRL-CH derived PM2.5 concentrations. The expanded dataset offers more clarity on the conditions best suited for accurate PM2.5 estimation using HSRL-CH. These findings suggest that the HSRL-CH method could be valuable for remote air quality monitoring in the future, particularly when a spaceborne NASA HSRL-like observing system becomes available. The results also highlight areas for continued improvement in the interim.
References:
Burton et al. (2012): doi:10.5194/amt-5-73-2012
Dawson et al. (2017): doi:10.1002/2017JD026913
Meskhidze et al. (2021): doi:10.1016/j.atmosenv.2021.118250
Sutherland et al. (2023): doi:10.1016/j.atmosenv.2023.119719
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