Assessment of high spectral resolution lidar-derived PM2.5 concentration from SEAC4RS, ACEPOL, and three DISCOVER-AQ campaigns

Abstract

PM_2.5 (particulate matter with an aerodynamic diameter of less than 2.5 μm) exposure at elevated levels has been associated with adverse health outcomes. However, the high spatiotemporal variability of aerosols poses challenges in monitoring PM_2.5 using ground-based measurement networks. Previously, we developed a new method (referred to as HSRL-CH) to estimate surface PM_2.5 concentration and chemical composition using High Spectral Resolution Lidar (HSRL)-retrieved extinction and derived aerosol types. In this study, we evaluate HSRL-CH performance across the United States using HSRL retrievals from five campaigns: DISCOVER-AQ California, SEAC⁴RS, DISCOVER-AQ Texas, DISCOVER-AQ Colorado, and ACEPOL. We assess the remotely derived PM_2.5 estimates against measurements from the EPA Air Quality System (AQS) and compare HSRL-CH-derived aerosol chemical compositions with AQS-measured compositions. Across all campaigns, HSRL-CH-derived PM_2.5 shows a mean absolute error (MAE) of 10.2 μg m⁻³. The DISCOVER-AQ California campaign had the highest MAE (14.8 μg m⁻³), while other campaigns had MAE ≤ 7.2 μg m⁻³. The lowest MAE occurs when dusty mix type aerosols dominate the retrieved aerosol optical depth, while the highest MAE is associated with smoke type aerosols. Different planetary boundary layer height estimates can lead to a 20% difference in MAE. We anticipate that the HSRL-CH method will provide reliable estimates of PM_2.5 concentration and chemical composition once satellite-based HSRL data acquisition becomes feasible.