High efficacy of layered controls for reducing exposure to airborne pathogens.

To optimize strategies for curbing the transmission of airborne pathogens, the efficacy of three key controls-face masks, ventilation, and physical distancing-must be well understood. In this study, we used the Quadrature-based model of Respiratory Aerosol and Droplets to quantify the reduction in exposure to airborne pathogens from various combinations of controls. For each combination of controls, we simulated thousands of scenarios that represent the tremendous variability in factors governing airborne transmission and the efficacy of mitigation strategies. While the efficacy of any individual control was highly variable among scenarios, combining universal mask-wearing with distancing of 1 m or more reduced the median exposure by more than 99% relative to a close, unmasked conversation, with further reductions if ventilation is also enhanced. The large reductions in exposure to airborne pathogens translated to large reductions in the risk of initial infection in a new host. These findings suggest that layering controls is highly effective for reducing transmission of airborne pathogens and will be critical for curbing outbreaks of novel viruses in the future.

Simulating near-field enhancement in transmission of airborne viruses with a quadrature-based model.

Some infectious diseases, such as influenza, tuberculosis, and SARS-CoV-2, may be transmitted when virus-laden particles expelled from an infectious person are inhaled by someone else, which is known as the airborne transmission route. These virus-laden particles are more concentrated in the expiratory jet of an infectious person than elsewhere in a well-mixed room, but this near-field enhancement in virion exposure has not been well quantified. Transmission of airborne viruses depends on factors that are inherently variable and, in many cases, poorly constrained, and quantifying this uncertainty requires large ensembles of model simulations that span the variability in input parameters. However, models that are well-suited to simulate the near-field evolution of respiratory particles are also computationally expensive, which limits the exploration of parametric uncertainty. In order to perform many simulations that span the wide variability in factors governing airborne transmission, we developed the Quadrature-based model of Respiratory Aerosol and Droplets (QuaRAD). QuaRAD is an efficient framework for simulating the evolution of virus-laden particles after they are expelled from an infectious person, their deposition to the nasal cavity of a susceptible person, and the subsequent risk of initial infection. We simulated 10 000 scenarios to quantify the risk of initial infection by a particular virus, SARS-CoV-2. The predicted risk of infection was highly variable among scenarios and, in each scenario, was strongly enhanced near the infectious individual. In more than 50% of scenarios, the physical distancing needed to avoid near-field enhancements in airborne transmission was beyond the recommended safe distance of two meters (six feet) if the infectious person is not wearing a mask, though this distance defining the near-field extent was also highly variable among scenarios; the variability in the near-field extent is explained predominantly by variability in expiration velocity. Our findings suggest that maintaining at least two meters of distance from an infectious person greatly reduces exposure to airborne virions; protections against airborne transmission, such as N95 respirators, should be available when distancing is not possible.

Disentangling the Microphysical Effects of Fire Particles on Convective Clouds Through A Case Study.

Aerosol emissions from forest fires may impact cloud droplet activation through an increase in particle number concentrations ("the number effect") and also through a decrease in the hygroscopicity  κ of the entire aerosol population ("the hygroscopicity effect") when fully internal mixing is assumed in models. This study investigated these effects of fire particles on the properties of simulated deep convective clouds (DCCs), using cloud-resolving simulations with the Weather Research and Forecasting model coupled with Chemistry for a case study in a partly idealized setting. We found that the magnitude of the hygroscopicity effect was in some cases strong enough to entirely offset the number/size effect, in terms of its influence on modeled droplet and ice crystal concentrations. More specifically, in the case studied here, the droplet number concentration was reduced by about 37% or more due solely to the hygroscopicity effect. In the atmosphere, by contrast, fire particles likely have a much weaker impact on the hygroscopicity of the pre-existing background aerosol, as such a strong impact would occur only if the fire particles mixed immediately and uniformly with the background. We also show that the differences in the number of activated droplets eventually led to differences in the optical thickness of the clouds aloft, though this finding is limited to only a few hours of the initial development stage of the DCCs. These results suggest that accurately and rigorously representing aerosol mixing and  κ in models is an important step toward accurately simulating aerosol-cloud interactions under the influence of fires.

Radiative absorption enhancements by black carbon controlled by particle-to-particle heterogeneity in composition.

Black carbon (BC) absorbs solar radiation, leading to a strong but uncertain warming effect on climate. A key challenge in modeling and quantifying BC's radiative effect on climate is predicting enhancements in light absorption that result from internal mixing between BC and other aerosol components. Modeling and laboratory studies show that BC, when mixed with other aerosol components, absorbs more strongly than pure, uncoated BC; however, some ambient observations suggest more variable and weaker absorption enhancement. We show that the lower-than-expected enhancements in ambient measurements result from a combination of two factors. First, the often used spherical, concentric core-shell approximation generally overestimates the absorption by BC. Second, and more importantly, inadequate consideration of heterogeneity in particle-to-particle composition engenders substantial overestimation in absorption by the total particle population, with greater heterogeneity associated with larger model-measurement differences. We show that accounting for these two effects-variability in per-particle composition and deviations from the core-shell approximation-reconciles absorption enhancement predictions with laboratory and field observations and resolves the apparent discrepancy. Furthermore, our consistent model framework provides a path forward for improving predictions of BC's radiative effect on climate.

Black carbon absorption at the global scale is affected by particle-scale diversity in composition.

Atmospheric black carbon (BC) exerts a strong, but uncertain, warming effect on the climate. BC that is coated with non-absorbing material absorbs more strongly than the same amount of BC in an uncoated particle, but the magnitude of this absorption enhancement (Eabs) is not well constrained. Modelling studies and laboratory measurements have found stronger absorption enhancement than has been observed in the atmosphere. Here, using a particle-resolved aerosol model to simulate diverse BC populations, we show that absorption is overestimated by as much as a factor of two if diversity is neglected and population-averaged composition is assumed across all BC-containing particles. If, instead, composition diversity is resolved, we find Eabs=1-1.5 at low relative humidity, consistent with ambient observations. This study offers not only an explanation for the discrepancy between modelled and observed absorption enhancement, but also demonstrates how particle-scale simulations can be used to develop relationships for global-scale models.