Bravo-Frank, Nicholas2025-03-212025-03-212023https://hdl.handle.net/11299/270522University of Minnesota M.S. thesis. 2023. Major: Electrical/Computer Engineering. Advisor: Jiarong Hong. 1 computer file (PDF); iv, 23 pages.This study introduces a holographic air-quality monitor (HAM) for real-time detection, sizing, and classification of airborne particulate matter. Integrated with Arduino and additional sensors, the HAM operates in a 10-500 μm size range at 26 liters per minute (LPM). Performance assessments involved comparisons with brightfield microscopy and an integrated reference PM sensor, accurately sizing 20μm silver-coated glass balls and 100μm PMMA clear microspheres. Practical demonstrations in a living room and kitchen setting further confirmed the HAM's sensitivity to varied particulate matter. However, challenges in handling transparent particles and high particle concentrations highlight future improvement areas, including optimizing machine learning models and enhancing segmentation techniques. This innovative approach promises significant contributions to air quality monitoring by offering precise and comprehensive data for large and irregular shaped particulate matter.enThesis or Dissertation