Laurel Thompson, Utah Valley University
Soldiers and non-combatants are at risk of exposure to dangerous aerosols (airborne particles or droplets) in the form of biological agents such as bacteria, toxins, or viruses. The current method for assessing health risk in the field is a moist swath which turns dark upon contact with a biological aerosol. Optical methods are more sensitive to the physical properties of aerosols, and many systems have been developed for optically measuring particle properties. However, they are generally limited to bio-aerosol detection at a single point in space where the system directly samples an aerosol from within the aerosol cloud. The desired solution is a system that can employ remote sensing to measure aerosol properties from a distance. Standoff detection methods allow a much larger area to be measured at once, providing a more general or big-picture view of the aerosols in a given area. There are several ways that standoff optical scattering data can be analyzed for determining aerosol properties. Light scattered by aerosols of known size and composition can be modeled exceptionally well with Mie scattering theory, but the reverse problem determining aerosol properties from the light signals using inverse Mie theory is difficult to solve because a unique set of aerosol properties must be found to correspond to the optical spectra. This is challenging since different combinations of aerosol properties can result in similar spectra. The size distribution, however, has a large effect on the optical signal and may therefore be used to differentiate aerosols. Specifically, biological aerosols have a more narrow size distribution than mineral-based dusts due to genetic limitations. Their refractive indices will also contribute to distinct optical spectra. The hypothesis for this project was that these factors would be sufficient to classify aerosols for risk assessment. Three analysis methods were used to test this hypothesis: Mie inversion with matrix solutions, empirical curve fitting with polynomial functions, and principal component analysis (PCA). Particles suspended in methanol were used as the model aerosol system. A range of particle sizes and compositions were illuminated by a balanced deuterium/halogen light source and spectral measurements from 200-1100 nm were taken. Optical data over a 200-1300 nm range were also collected from a variety of bio-aerosols using an open path remote sensing system at a 30-meter standoff distance.