,Air quality monitoring using mobile microscopy and machine learning

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Rapid,accurate and high-throughput sizing and quantification of particulate matter (PM) in air is crucial for monitoring and improving air quality.In fact,particles in air with a diameter of ≤ 2.5 μm have been classified as carcinogenic by the World Health Organization.Here we present a field-portable cost-effective platform for high-throughput quantification of particulate matter using computational lens-free microscopy and machine-leaming.This platform,termed c-Air,is also integrated with a smartphone application for device control and display of results.This mobile device rapidly screens 6.5 L of air in 30 s and generates microscopic images of the aerosols in air.It provides statistics of the particle size and density distribution with a sizing accuracy of ~ 93%.We tested this mobile platform by measuring the air quality at different indoor and outdoor environments and measurement times,and compared our results to those of an Environmental Protection Agency-approved device based on beta-attenuation monitoring,which showed strong correlation to c-Air measurements.Furthermore,we used c-Air to map the air quality around Los Angeles Inteational Airport (LAX) over 24 h to confirm that the impact of LAX on increased PM concentration was present even at >7km away from the airport,especially along the direction of landing flights.With its machineleaing-based computational microscopy interface,c-Air can be adaptively tailored to detect specific particles in air,for example,various types of pollen and mold and provide a cost-effective mobile solution for highly accurate and distributed sensing of air quality.
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