Literature DB >> 28038970

Can commercial low-cost sensor platforms contribute to air quality monitoring and exposure estimates?

Nuria Castell1, Franck R Dauge2, Philipp Schneider2, Matthias Vogt2, Uri Lerner3, Barak Fishbain3, David Broday3, Alena Bartonova2.   

Abstract

The emergence of low-cost, user-friendly and very compact air pollution platforms enable observations at high spatial resolution in near-real-time and provide new opportunities to simultaneously enhance existing monitoring systems, as well as engage citizens in active environmental monitoring. This provides a whole new set of capabilities in the assessment of human exposure to air pollution. However, the data generated by these platforms are often of questionable quality. We have conducted an exhaustive evaluation of 24 identical units of a commercial low-cost sensor platform against CEN (European Standardization Organization) reference analyzers, evaluating their measurement capability over time and a range of environmental conditions. Our results show that their performance varies spatially and temporally, as it depends on the atmospheric composition and the meteorological conditions. Our results show that the performance varies from unit to unit, which makes it necessary to examine the data quality of each node before its use. In general, guidance is lacking on how to test such sensor nodes and ensure adequate performance prior to marketing these platforms. We have implemented and tested diverse metrics in order to assess if the sensor can be employed for applications that require high accuracy (i.e., to meet the Data Quality Objectives defined in air quality legislation, epidemiological studies) or lower accuracy (i.e., to represent the pollution level on a coarse scale, for purposes such as awareness raising). Data quality is a pertinent concern, especially in citizen science applications, where citizens are collecting and interpreting the data. In general, while low-cost platforms present low accuracy for regulatory or health purposes they can provide relative and aggregated information about the observed air quality.
Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Entities:  

Keywords:  Air pollution monitoring; Exposure estimates; Low-cost sensor nodes; Performance evaluation

Mesh:

Substances:

Year:  2016        PMID: 28038970     DOI: 10.1016/j.envint.2016.12.007

Source DB:  PubMed          Journal:  Environ Int        ISSN: 0160-4120            Impact factor:   9.621


  82 in total

Review 1.  A Review of Low-Cost Particulate Matter Sensors from the Developers' Perspectives.

Authors:  Brigida Alfano; Luigi Barretta; Antonio Del Giudice; Saverio De Vito; Girolamo Di Francia; Elena Esposito; Fabrizio Formisano; Ettore Massera; Maria Lucia Miglietta; Tiziana Polichetti
Journal:  Sensors (Basel)       Date:  2020-11-29       Impact factor: 3.576

2.  Mapping urban air quality using mobile sampling with low-cost sensors and machine learning in Seoul, South Korea.

Authors:  Chris C Lim; Ho Kim; M J Ruzmyn Vilcassim; George D Thurston; Terry Gordon; Lung-Chi Chen; Kiyoung Lee; Michael Heimbinder; Sun-Young Kim
Journal:  Environ Int       Date:  2019-07-27       Impact factor: 9.621

3.  Opportunities and Challenges for Filling the Air Quality Data Gap in Low- and Middle-Income Countries.

Authors:  Robert W Pinder; Jacqueline M Klopp; Gary Kleiman; Gayle S W Hagler; Yewande Awe; Sara Terry
Journal:  Atmos Environ (1994)       Date:  2019-06-18       Impact factor: 4.798

4.  Advantages and challenges of the implementation of a low-cost particulate matter monitoring system as a decision-making tool.

Authors:  Víctor Caquilpán P; Gabriel Aros G; Sebastián Elgueta A; Rodrigo Díaz S; Gonzalo Sepúlveda K; Carlos Sierralta J
Journal:  Environ Monit Assess       Date:  2019-10-24       Impact factor: 2.513

5.  Statistical field calibration of a low-cost PM2.5 monitoring network in Baltimore.

Authors:  Abhirup Datta; Arkajyoti Saha; Misti Levy Zamora; Colby Buehler; Lei Hao; Fulizi Xiong; Drew R Gentner; Kirsten Koehler
Journal:  Atmos Environ (1994)       Date:  2020-07-22       Impact factor: 4.798

Review 6.  Applications of low-cost sensing technologies for air quality monitoring and exposure assessment: How far have they gone?

Authors:  Lidia Morawska; Phong K Thai; Xiaoting Liu; Akwasi Asumadu-Sakyi; Godwin Ayoko; Alena Bartonova; Andrea Bedini; Fahe Chai; Bryce Christensen; Matthew Dunbabin; Jian Gao; Gayle S W Hagler; Rohan Jayaratne; Prashant Kumar; Alexis K H Lau; Peter K K Louie; Mandana Mazaheri; Zhi Ning; Nunzio Motta; Ben Mullins; Md Mahmudur Rahman; Zoran Ristovski; Mahnaz Shafiei; Dian Tjondronegoro; Dane Westerdahl; Ron Williams
Journal:  Environ Int       Date:  2018-04-26       Impact factor: 9.621

7.  Contribution of low-cost sensor measurements to the prediction of PM2.5 levels: A case study in Imperial County, California, USA.

Authors:  Jianzhao Bi; Jennifer Stowell; Edmund Y W Seto; Paul B English; Mohammad Z Al-Hamdan; Patrick L Kinney; Frank R Freedman; Yang Liu
Journal:  Environ Res       Date:  2019-10-10       Impact factor: 6.498

8.  Performance of Four Consumer-grade Air Pollution Measurement Devices in Different Residences.

Authors:  Sydonia Manibusan; Gediminas Mainelis
Journal:  Aerosol Air Qual Res       Date:  2020-02       Impact factor: 3.063

9.  Development of an in-home, real-time air pollutant sensor platform and implications for community use.

Authors:  Sara E Gillooly; Yulun Zhou; Jose Vallarino; MyDzung T Chu; Drew R Michanowicz; Jonathan I Levy; Gary Adamkiewicz
Journal:  Environ Pollut       Date:  2018-10-15       Impact factor: 8.071

10.  Using Gas-Phase Air Quality Sensors to Disentangle Potential Sources in a Los Angeles Neighborhood.

Authors:  Ashley Collier-Oxandale; Nicole Wong; Sandy Navarro; Jill Johnston; Michael Hannigan
Journal:  Atmos Environ (1994)       Date:  2020-05-06       Impact factor: 4.798

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