Air quality monitoring using mobile microscopy and machine learning.

Journal: Light, science & applications
Published Date: Sep 8, 2017

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.

Authors

  • Yi-Chen Wu
    Electrical Engineering Department, University of California, Los Angeles, CA 90095, USA.
  • Ashutosh Shiledar
    Electrical Engineering Department, University of California, Los Angeles, CA 90095, USA.
  • Yi-Cheng Li
    Electrical Engineering Department, University of California, Los Angeles, CA 90095, USA.
  • Jeffrey Wong
    Computer Science Department, University of California, Los Angeles, CA 90095, USA.
  • Steve Feng
    Electrical Engineering Department, University of California, Los Angeles, CA 90095, USA.
  • Xuan Chen
    Electrical Engineering Department, University of California, Los Angeles, CA 90095, USA.
  • Christine Chen
    Electrical Engineering Department, University of California, Los Angeles, CA 90095, USA.
  • Kevin Jin
    Electrical Engineering Department, University of California, Los Angeles, CA 90095, USA.
  • Saba Janamian
    Electrical Engineering Department, University of California, Los Angeles, CA 90095, USA.
  • Zhe Yang
    Department of Pathology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China.
  • Zachary Scott Ballard
    Electrical Engineering Department, University of California, Los Angeles, CA 90095, USA.
  • Zoltán Göröcs
    Electrical Engineering Department, University of California, Los Angeles, CA 90095, USA.
  • Alborz Feizi
    Electrical Engineering Department, University of California, Los Angeles, CA 90095, USA.
  • Aydogan Ozcan
    Electrical and Computer Engineering Department, University of California, Los Angeles, California 90095, USA.

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