UCLA researchers have developed a novel method to monitor air quality using mobile microscopy and machine learning.
Air quality is an increasing concern in the industrialized world. Particulate matter (PM) is a mixture of solid and liquid particles in air and forms a significant form of air pollution. PM comes in a range of sizes which can cause serious health problems by entering the lings and bloodstream. Some PM has even been linked to be carcinogenic. Monitoring PM air quality as a function of space and time is critical for understanding the effects of industrial activities, studying atmospheric models, and providing regulatory and advisory guidelines for transportation, residents, and industries. There is a need for a low-cost, accurate, easy to use, mobile method to sample and analyze particulate matter in the field. Current solutions, such as conventional microscope-based screening of aerosols, cannot be conducted in the field and are cumbersome, heavy, expensive, and require specialized skills to operate.
Field-portable cost-effective platform for high-throughput quantification of particulate matter (PM) using computational lens-free microscopy and machine-learning
The invention was demonstrated on 2/1/2015
Air monitoring, particulate matter, machine learning, mobile, air quality, mobile microscopy