The lack of quantitative risk assessment of airborne transmission of COVID-19 under practical settings leads to large uncertainties and inconsistencies in our preventive measures. Combining in situ measurements and computational fluid dynamics simulations, we quantify the exhaled particles from normal respiratory behaviors and their transport under elevator, small classroom, and supermarket settings to evaluate the risk of inhaling potentially virus-containing particles. Our results show that the design of ventilation is critical for reducing the risk of particle encounters. Inappropriate design can significantly limit the efficiency of particle removal, create local hot spots with orders of magnitude higher risks, and enhance particle deposition causing surface contamination. Additionally, our measurements reveal the presence of a substantial fraction of faceted particles from normal breathing and its strong correlation with breathing depth.

Particle size measurement based on digital holography with conventional algorithms are usually time-consuming and susceptible to noises associated with hologram quality and particle complexity, limiting its usage in a broad range of engineering applications and fundamental research. We propose a learning-based hologram processing method to cope with the aforementioned issues. The proposed approach uses a modified U-net architecture with three input channels and two output channels, and specially-designed loss functions. The proposed method has been assessed using synthetic, manually-labeled experimental, and water tunnel bubbly flow data containing particles of different shapes. The results demonstrate that our approach can achieve better performance in comparison to the state-of-the-art non-machine-learning methods in terms of particle extraction rate and positioning accuracy with significantly improved processing speed. Our learning-based approach can be extended to other types of image-based particle size measurements.

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