Evidence map›Paper›PMID 30167294›Full record

ArticleLight, science & applications2017

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 and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Light, science & applications, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

32 citing papers in PubMed.

  1. Article
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  4. Preparing Allergists to Practice in 2050 Using Artificial Intelligence.The journal of allergy and clinical immunology. In practice · 2025
    Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Yi-Chen WuElectrical Engineering Department, University of California, Los Angeles, CA 90095, USA.
Ashutosh ShiledarElectrical Engineering Department, University of California, Los Angeles, CA 90095, USA.
Yi-Cheng LiElectrical Engineering Department, University of California, Los Angeles, CA 90095, USA.
Jeffrey WongComputer Science Department, University of California, Los Angeles, CA 90095, USA.
Steve FengElectrical Engineering Department, University of California, Los Angeles, CA 90095, USA.
Xuan ChenElectrical Engineering Department, University of California, Los Angeles, CA 90095, USA.
Christine ChenElectrical Engineering Department, University of California, Los Angeles, CA 90095, USA.
Kevin JinElectrical Engineering Department, University of California, Los Angeles, CA 90095, USA.
Saba JanamianElectrical Engineering Department, University of California, Los Angeles, CA 90095, USA.
Zhe YangElectrical Engineering Department, University of California, Los Angeles, CA 90095, USA.
Zachary Scott BallardElectrical Engineering Department, University of California, Los Angeles, CA 90095, USA.
Zoltán GöröcsElectrical Engineering Department, University of California, Los Angeles, CA 90095, USA.
Alborz FeiziElectrical Engineering Department, University of California, Los Angeles, CA 90095, USA.
Aydogan OzcanElectrical Engineering Department, University of California, Los Angeles, CA 90095, USA.ORCID 0000-0002-0717-683X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

air-quality monitoringholographymachine learningparticulate matter

Identifiers

PMID30167294
PMCPMC6062327

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.