12a Holographic Air-quality Monitor (HAM) for Comprehensive Aerosol Characterization

Airborne particles such as pollen, dust, and fungal spores, collectively known as coarse mode aerosols, significantly impact our climate and public health. These particles influence weather patterns, affect cloud formation, and can trigger respiratory issues like allergies and asthma. However, current technologies struggle to accurately detect and identify these larger, irregularly shaped particles, limiting our ability to understand their effects and respond effectively. To address this challenge, we propose developing the Holographic Aerosol Monitor, an innovative instrument that combines advanced imaging technology called digital holography with artificial intelligence. Existing particle detectors are designed for particles smaller than 10 micrometers and cannot provide detailed information about particle shape and type and they offer only simple and often inaccurate estimates of concentration and size. In contrast, our system captures detailed, three-dimensional images of particles as they pass through a laser beam, allowing us to observe the actual shape and size of each particle, even if it is irregular like pollen or dust. By instantly analyzing these images with deep learning methods, our system provides real-time information about the types and quantities of particles in the air. By integrating deep learning with digital holographic imaging, our system achieves real-time, highthroughput three-dimensional microscopic imaging of airborne particles. This represents a significant leap forward compared to existing aerosol diagnostic methods, offering unprecedented, detailed information about particles that impact our climate and public health. Specifically, with immediate and precise data, our system will enhance climate models by improving our understanding of how airborne particles interact with clouds and weather systems, leading to better weather predictions and climate projections. Furthermore, by accurately monitoring particles that affect air quality and health, such as pollen and dust, our system can help public health agencies issue timely warnings and develop strategies to protect people, especially those with respiratory conditions. Environmental regulators can use this technology to monitor pollution sources more effectively, and industries can improve their processes to reduce harmful emissions. In Phase I, we will build and test a prototype of our system. First, we will validate its performance in the laboratory using controlled experiments that simulate real-world conditions. Then, we will deploy our system at a national forest to collect actual field data. This data will help us refine our deep learning models to ensure accurate, real-time detection and classification of different types of airborne particles. Finally, we will demonstrate our system's ability to operate autonomously in the field, providing continuous monitoring without the need for constant human supervision. Beyond Phase I, our system has significant commercial potential. Researchers at government agencies, universities, and private companies can use our system for advanced environmental monitoring and climate research. Public health agencies and environmental regulators can utilize our system to improve air quality assessments and protect public health. The widespread adoption of digital holographic technology will contribute to better environmental stewardship and health outcomes for communities.