Air quality monitoring using mobile microscopy and machine learning

• 来源期刊: 光：科学与应用（英文版）
• 关键词: air-quality monitoring holography machine learning particulate matter
• 年，卷（期）: 2017,（3）
• 页码范围: 214-226
• 页数: 13页
• 语种: 英文
• DOI: 10.1038/Isa.2017.46

摘要

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 International 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 machinelearning-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.