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dc.contributor.authorWu, Yi-Chen
dc.contributor.authorShiledar, Ashutosh
dc.contributor.authorLi, Yi-Cheng
dc.contributor.authorWong, Jeffrey
dc.contributor.authorFeng, Steve
dc.contributor.authorChen, Xuan
dc.contributor.authorChen, Christine
dc.contributor.authorJin, Kevin
dc.contributor.authorJanamian, Saba
dc.contributor.authorYang, Zhe
dc.contributor.authorBallard, Zachary Scott
dc.contributor.authorGöröcs, Zoltán
dc.contributor.authorFeizi, Alborz
dc.contributor.authorOzcan, Aydogan
dc.date.accessioned2017-10-04T14:59:15Z
dc.date.available2017-10-04T14:59:15Z
dc.date.issued2017-03-15
dc.identifier.citationWu Y-C, Shiledar A, Li Y-C, Wong J, Feng S, et al. (2017) Air quality monitoring using mobile microscopy and machine learning. Light: Science & Applications 6: e17046. Available: http://dx.doi.org/10.1038/lsa.2017.46.
dc.identifier.issn2047-7538
dc.identifier.doi10.1038/lsa.2017.46
dc.identifier.urihttp://hdl.handle.net/10754/625780
dc.description.abstractRapid, 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.
dc.description.sponsorshipThe Ozcan Research Group at UCLA gratefully acknowledges the support of the Presidential Early Career Award for Scientists and Engineers (PECASE), the Army Research Office (ARO; W911NF-13-1-0419 and W911NF-13-1-0197), the ARO Life Sciences Division, the National Science Foundation (NSF) CBET Division Biophotonics Program, the NSF Emerging Frontiers in Research and Innovation (EFRI) Award, the NSF EAGER Award, NSF INSPIRE Award, NSF Partnerships for Innovation: Building Innovation Capacity (PFI:BIC) Program, Office of Naval Research (ONR), the National Institutes of Health (NIH), the Howard Hughes Medical Institute (HHMI), Vodafone Americas Foundation, the Mary Kay Foundation, Steven & Alexandra Cohen Foundation and KAUST. This work is based upon research performed in a laboratory renovated by the National Science Foundation under Grant No. 0963183, which is an award funded under the American Recovery and Reinvestment Act of 2009 (ARRA). The authors also acknowledge the support of South Coast Air Quality Management District (AQMD) for their assistance in the experiment at Reseda Air Quality Monitoring Station, and Aerobiology Laboratory Association, Inc. in Huntington Beach, CA for providing experiment materials. YW also acknowledges Dr Euan McLeod and Dr Jingshan Zhong for helpful discussions. We also acknowledge Dr Yair Rivenson, Mr Calvin Brown, Mr Yibo Zhang, Mr Hongda Wang and Mr Shuowen Shen for their help with some of the c-Air measurements.
dc.publisherSpringer Nature
dc.subjectair-quality monitoring
dc.subjectholography
dc.subjectmachine learning
dc.subjectparticulate matter
dc.titleAir quality monitoring using mobile microscopy and machine learning
dc.typeArticle
dc.identifier.journalLight: Science & Applications
dc.contributor.institutionCalifornia NanoSystems Institute (CNSI), University of California, Los Angeles, CA 90095, USA
dc.contributor.institutionBioengineering Department, University of California, Los Angeles, CA 90095, USA
dc.contributor.institutionElectrical Engineering Department, University of California, Los Angeles, CA 90095, USA
dc.contributor.institutionComputer Science Department, University of California, Los Angeles, CA 90095, USA
dc.contributor.institutionDavid Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
dc.date.published-online2017-03-15
dc.date.published-print2017-09


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