Air quality monitoring using mobile microscopy and machine learning

• Published in: Light, science & applications Vol. 6; no. 9; p. e17046
• Main Authors: Wu, Yi-Chen, Shiledar, Ashutosh, Li, Yi-Cheng, Wong, Jeffrey, Feng, Steve, Chen, Xuan, Chen, Christine, Jin, Kevin, Janamian, Saba, Yang, Zhe, Ballard, Zachary Scott, Göröcs, Zoltán, Feizi, Alborz, Ozcan, Aydogan
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01.09.2017; Springer Nature B.V; Nature Publishing Group
• Subjects: 639/624/1075; 639/624/1107/328; 639/624/1107/510; Air quality; Applied and Technical Physics; Atomic; Classical and Continuum Physics; Computer applications; Lasers; Learning algorithms; Microscopy; Molecular; Optical and Plasma Physics; Optical Devices; Optics; Original; original-article; Outdoor air quality; Particulate matter; Photonics; Physics; Physics and Astronomy; particulate matter; holography; machine learning; air-quality monitoring
• ISSN: 2047-7538; 2095-5545; 2047-7538
• DOI: 10.1038/lsa.2017.46

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-learning. 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 International Airport (LAX) over 24 h to confirm that the impact of LAX on increased PM concentration was present even at >7 km away from the airport, especially along the direction of landing flights. With its machine-learning-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. Air-quality monitoring: lens-free microscopy system Accurate on-site air-quality monitoring can be performed using lens-free microscopy on a chip coupled with machine learning. To monitor and enhance air quality, it is vital to realize rapid, accurate and high-throughput sizing of airborne particles. A portable system built by Aydogan Ozcan and co-workers from the University of California, Los Angeles, generates statistics of particle size and density from microscopic images of particulate matter in air. A sticky coverslip captures airborne particles and then light from three LEDs (red, green and blue) creates holograms of the particle distribution, captured on a CMOS image sensor and processed. The system can screen 6.5 litres of air in about 30 s and has a particle sizing accuracy of about 93%. Results obtained using this technology achieved a strong correlation with those acquired using conventional particle-sizing devices.