Air quality monitoring using mobile microscopy and machine learning

Yi-Chen Wu; Ashutosh Shiledar; Yi-Cheng Li; Jeffrey Wong; Steve Feng; Xuan Chen; Christine Chen; Kevin Jin; Saba Janamian; Zhe Yang; Zachary Scott Ballard; Zoltán Göröcs; Alborz Feizi; Aydogan Ozcan

doi:10.1038/lsa.2017.46

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Air quality monitoring using mobile microscopy and machine learning

Original Articles

- Air quality monitoring using mobile microscopy and machine learning
- Light: Science & Applications Vol. 6, Issue 3, (2017)
- Affiliations：
  
  1.Electrical Engineering Department, University of California, Los Angeles, CA 90095, USA
  2.Bioengineering Department, University of California, Los Angeles, CA 90095, USA
  3.California NanoSystems Institute (CNSI), University of California, Los Angeles, CA 90095, USA
  4.Computer Science Department, University of California, Los Angeles, CA 90095, USA
  5.David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
- Author bio：
  
  A Ozcan, Email: ozcan@ucla.edu, Web: http://innovate.ee.ucla.edu/
- Funds：
- DOI：10.1038/lsa.2017.46
  CLC：
- Published：2017-09，
  
  Published Online：15 March 2017，
  
  Received：11 December 2016，
  
  Revised：10 March 2017，
  
  Accepted：11 March 2017
- Accepted：
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Wu, Y. C. et al. Air quality monitoring using mobile microscopy and machine learning. Light: Science & Applications, 6, e17046 (2017).
DOI：

Wu, Y. C. et al. Air quality monitoring using mobile microscopy and machine learning. Light: Science & Applications, 6, e17046 (2017). DOI： 10.1038/lsa.2017.46.

摘要

Abstract

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.

关键词

Keywords

air-quality monitoringholographymachine learningparticulate matter

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