Observational Constraints on the Aerosol Optical Depth–Surface PM2.5 Relationship during Alaskan Wildfire Seasons

Abstract

Wildfire is one of the main sources of PM_2.5 (particulate matter with aerodynamic diameter < 2.5 μm) in the Alaskan summer. The complexity in wildfire smokes, as well as limited coverage of ground measurements, poses a big challenge to estimate surface PM_2.5 during wildfire season in Alaska. Here we aim at proposing a quick and direct method to estimate surface PM_2.5 over Alaska, especially in places exposed to strong wildfire events with limited measurements. We compare the AOD–surface PM_2.5 conversion factor (η = PM_2.5/AOD; AOD, aerosol optical depth) from the chemical transport model GEOS-Chem (η_GC) and from observations (η_obs). We show that η_GC is biased high compared to η_obs under smoky conditions, largely because GEOS-Chem assigns the majority of AOD (67%) within the planetary boundary layer (PBL) when AOD > 1, inconsistent with satellite retrievals from CALIOP. The overestimation in η_GC can be to some extent improved by increasing the injection height of wildfire emissions. We constructed a piecewise function for η_obs across different AOD ranges based on VIIRS-SNPP AOD and PurpleAir surface PM_2.5 measurements over Alaska in the 2019 summer and then applied it on VIIRS AOD to derive daily surface PM_2.5 over continental Alaska in the 2021 and 2022 summers. The derived satellite PM_2.5 shows a good agreement with corrected PurpleAir PM_2.5 in Alaska during the 2021 and 2022 summers, suggesting that aerosol vertical distribution likely represents the largest uncertainty in converting AOD to surface PM_2.5 concentrations. This piecewise function, η′_obs, shows the capability of providing an observation-based, quick and direct estimation of daily surface PM_2.5 over the whole of Alaska during wildfires, without running a 3-D model in real time.