Opto-electrical real-time bioaerosol characterization

Bioaerosol play an important role in human health both directly and indirectly. The direct eBect results from a subset of these aerosol particles that are pathogenic. Their indirect comes from their role in atmospheric processes. Bioaerosol particles constitute ~5% of all particles in the environment and these particles are typically larger than 1µm, making them an important constituent of our PM2.5 mass loading. Most interestingly, bioaerosol are speculated to play an important role in aerosol-cloud formation process, particularly as ice nuclei (IN). As quantitative knowledge of IN in the atmospheric is critical for accurate modeling of aerosol-cloud-precipitation process, the ability to characterize atmospheric bioaerosol in-situ is critical from a climate and, hence, human health perspective. The overall objective of the Phase 1 project is to demonstrate that a small compact sensor based on optical-sensing and electrical-mobility measurements can provide a means to identify and classify bioaerosol in the atmosphere in real-time. This Phase 1 project will build on our development of an advanced aerosol classification sensor called OPERA (opto-electrical real-time aerosol sensor) for NASA. While the OPERA is being built to classify particles such as dust vs smoke, its design allows for extension to bioaerosol sensing and classification using a combination of specific light sources and deep learning techniques. In Phase I, we will demonstrate in lab studies that the OPERA-B can successfully sample biological particles as large as 30 µm while also selectively identify these particles in the presence of other ambient particles. The advancement of this proposal to Phase II and beyond will enable honing the design of the sensor to precisely classify a wide range of bioaerosol particles in real-time and in realworld challenging environments including under conditions typical of a moving platform such as an aircraft. Additionally, with Phase II funding and beyond, we will reduce the cost of the sensor to enable its use for ventilation control, which will have tremendous implication in optimizing energy use in buildings while improving safety of indoor air.