A document by Olivia Sablan. Click on the document to view its contents.

Familiarity with the use of face coverings to reduce the risk of respiratory disease has increased during the coronavirus pandemic; however, recommendations for their use outside of the pandemic remains limited. Here, we develop a modeling framework to quantify the potential health benefits of wearing a face covering or respirator to mitigate exposure to severe air pollution. This framework accounts for the wide range of available face coverings and respirators, fit factors and efficacy, air pollution characteristics, and exposure-response data. Our modeling shows that N95 respirators offer robust protection against different sources of air pollution, reducing exposure by more than a factor of 14 when worn with a leak rate of 5%. Synthetic-fiber masks offer less protection with a strong dependence on aerosol size distribution (protection factors ranging from 4.4 to 2.2.), while natural-fiber and surgical masks offer reductions in exposure of 1.9 and 1.7, respectively. To assess the ability of face coverings to provide population-level health benefits to wildfire smoke, we perform a case study for the 2012 Washington state fire season. Our models suggest that although natural-fiber masks offer minor reductions in respiratory hospitalizations attributable to smoke (2-11%) due to limited filtration efficiency, N95 respirators and to a lesser extent surgical and synthetic-fiber masks may lead to notable reductions in smoke-attributable hospitalizations (22-39%, 9-24%, and 7-18%, respectively). The filtration efficiency, bypass rate, compliance rate (fraction of time and population wearing the device) are the key factors governing exposure reduction potential and health benefits during severe air pollution events.

Ambient fine particulate matter (PM2.5) concentrations in India frequently exceed 100 μg/m3 during fall and winter pollution episodes. We use the GEOS-Chem chemical transport model with the TwO-Moment Aerosol Sectional microphysics scheme with 15 size bins (TOMAS15) to assess PM2.5 composition and impacts on radiation and cloud condensation nuclei (CCN) during pollution episodes as compared to the seasonal (October-December) average. We conduct high resolution (0.25 degree x0.3125 degree) nested-domain simulations over India for short-duration, high-PM2.5 episodes in fall 2015 and 2017. The simulations capture the magnitude and spatial patterns of pollution episodes measured by surface monitors (r2PM2.5=0.69) although aerosol optical depth is underestimated. During the episodes, near-surface organic matter (OM), black carbon (BC), and secondary inorganic aerosol concentrations increase from seasonal averages by up to 36, 7, and 7 µg/m3, respectively. Episodic aerosol increases enhance cooling by lowering the top-of-atmosphere clear-sky direct radiative effect (DRETOA) during the 2015 episode (-6 W/m2), with a smaller impact during the 2017 episode (-1 W/m2). Differences in DRETOA reflect larger increases in scattering aerosols in the column during the 2015 episode (+17 mg/m2) than in 2017 (+13 mg/m2), while absorbing aerosol column enhancements are smaller (+3 mg/m2) in both years. Changes in shortwave radiation at the surface (SWsfc) are spatially similar to DRETOA and mostly negative during both episodes. CCN enhancements during these episodes occur across the western Indo-Gangetic Plain, coincident with higher PM2.5 concentrations. Changes in DRETOA, SWsfc, and CCN during high-PM2.5 episodes may have implications for crops, the hydrologic cycle, and surface temperature.

GEOS-Chem TOMAS (GCT) simulations of AERONET-inversion products during 2015 were compared with AERONET-inversion products from the multi-year climatology of Aboel-Fetouh et al. (2020) (AeF) and for year 2015 acquired over 5 stations in the North American and European Arctic. The GCT simulations of particle size distributions (PSD) did not capture a spring to summer radius increase of the fine mode (FM) peak observed by AeF but did capture AeF’s springtime coarse mode (CM) peak (small-sized CM peak with a radius ~ 1.3 µm) and a weak late summer / fall increase in the amplitude of that peak. The lack of a spring to summer FM radius increase was likely due to the large GCT cell size (4° x 5°) and associated difficulties in the modelling of coagulation-induced smoke particle size. Conversely, the GCT simulation of the small-sized CM peak indicated a successful capture of the springtime influx of Asian dust. The fall increase of that GCT peak was associated with an increase of a larger (4 -7 µm) PSD mode that AeF suggested was due to local dust. GCT captured the seasonal (climatological-scale) FM AOD trend, the decreasing CM AOD trend, and the increasing trend of the FM fraction. The GCT CM AOD also showed a fall increase that was coherent with the increase of the simulated small-sized CM peak and with a lesser rate of decrease of the AeF CM AOD. Large GCT deviations from the AERONET retrievals were attributed to an extreme July, 2015 forest fire event.