Abstract
Increasing knowledge about the health impacts of particulate matter (PM) air pollutants resulted in the widespread development and use of compact low-cost particulate matter sensors that have enabled spatially dense and high temporal resolution measurements. These PM sensors are advantageous due to their small size, lightweight design, affordability, and ability to connect to the internet for real-time data analysis. Most of these sensors operate on the principle of light scattering and require calibration based on the environment in which they are deployed. While there is a proliferation of such sensors, the performance over a wide range of particle properties needs to be firmly established. To the best of my knowledge, the current market (2025) has no commercial portable particulate matter sensor that can provide particle composition properties. The chemical, biological composition of a particle directly influences its refractive index. The refractive index of the particles plays an important role in establishing the inversion algorithms for accurate determination of PM concentration levels. In addition, not being able to determine the composition limits our understanding of how particulate matter affects the health and safety of humans in different occupational environments on a real-time scale. This dissertation focuses on the real-time measurement of particulate matter exposure and characterization of particles in occupational environments such as healthcare, orchestra, firefighting environments by utilizing a network of calibrated low-cost PM sensors complemented by research grade reference aerosol instruments for particle characterization. Furthermore, a novel multi-wavelength multi-angle optical sensor prototype using light scattering theory was developed to determine the refractive index of particles in addition to size and number concentration.