Tessa Clarizio

and 3 more

The Global Burden of Disease attributes millions of premature deaths to ambient air pollution each year, making it one of the largest environmental health risks faced by society. This mortality is largely due to exposure to fine particulate matter (PM2.5). In the United States, the Environmental Protection Agency estimated that 50.5 million people lived in counties with PM2.5 concentrations above the level of the National Ambient Air Quality Standards in 2020. PM2.5 levels can be derived from satellite aerosol optical depth (AOD) measurements providing comprehensive spatial and temporal coverage. However, the chemical composition of PM2.5 affects the mechanisms by which adverse health effects occur, and thus there is a pressing need for linking satellite data with high-resolution atmospheric modeling of PM2.5 composition. In order to better inform public health policy and decision-making, we aim to estimate near-real-time (NRT) surface PM2.5 composition informed by satellite AOD measurements and chemical transport modeling for the first time. Here, we demonstrate this framework for hindcast estimates in year 2021. NRT AOD is collected from multi-source remote sensing data including Moderate Resolution Imaging Spectroradiometer (MODIS; Aqua and Terra), the Visible Infrared Imaging Radiometer Suite (VIIRS; Dark Target and Deep Blue), and Multi-Angle Imaging SpectroRadiometer (MISR). The data obtained from these products are combined into daily, 10-km AOD estimates and used to scale simulated total PM2.5. GEOS-Chem (v13.1.2) nested regional simulations are run over North America with GEOS-Forward Processing (FP) assimilated meteorology at resolution 0.25° lat. x0.3125° lon. (approximately 20-30km) to simulate daily AOD and get an initial estimate of PM2.5 composition. This estimate is interpolated into the 10-km grid and multiplied with the satellite-adjusted total PM2.5 composition to produce concentrations of each PM2.5 species. This satellite-constrained chemical transport model framework estimates of PM2.5 will ultimately be evaluated against observations and compared to estimates using standard satellite products to inform future use of this framework to predict ambient air pollution health risks in true near-real-time.

Kaitlyn Confer

and 7 more

We evaluate the effects of rapidly changing Arctic sea ice conditions on sea salt aerosol (SSA) produced by oceanic wave-breaking and the sublimation of wind-lofted salty blowing snow on sea ice. We use the GEOS-Chem chemical transport model to assess the influence of changing extent of the open ocean, multi-year sea ice, first-year sea ice (FYI), and snow depths on SSA emissions for 1980-2017. We combine snow depths from the Lagrangian snow-evolution model (SnowModel-LG) together with an empirically-derived snow salinity function of snow depth to derive spatially and temporally varying snow surface salinity over Arctic FYI. We find that snow surface salinity on Arctic sea ice is increasing at a rate of ~30% decade-1 and SSA emissions are increasing at a rate of 7-9% decade-1 during the cold season (November – April). As a result, simulated SSA mass concentrations over the Arctic increased by 8-12% decade-1 in the cold season for 1980-2017. Blowing snow SSA accounts for more than 75% of this increase. During the warm season (May – October), sea ice loss results in a 12-14% decade-1 increase in SSA emissions due to increasing open ocean emissions. Observations of SSA mass concentrations at Alert, Canada display positive trends during the cold season (10-12% decade-1), consistent with our pan-Arctic simulations. During fall, Alert observations show a negative trend (-18% decade-1), due to locally decreasing wind speeds and thus lower open ocean emissions. These significant changes in SSA concentrations could potentially affect past and future bromine explosions and Arctic climate feedbacks.