Can commercial low-cost sensor platforms contribute to air quality monitoring and exposure estimates?
Nuria Castell,Franck R. Dauge,Philipp Schneider,Matthias Vogt,Uri Lerner,Barak Fishbain,David M. Broday,Alena Bartonova +7 more
TLDR
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 shows that their performance varies spatially and temporally.About:
This article is published in Environment International.The article was published on 2017-02-01 and is currently open access. It has received 607 citations till now. The article focuses on the topics: Data quality & Environmental exposure.read more
Citations
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Enhanced Ambient Sensing Environment—A New Method for Calibrating Low-Cost Gas Sensors
Hugo Savill Russell,Louise Bøge Frederickson,Szymon Kwiatkowski,Ana Paula Mendes Emygdio,Prashant Kumar,Johan A. Schmidt,Ole Hertel,Matthew S. Johnson +7 more
TL;DR: The Enhanced Ambient Sensing Environment (EASE) method described in this article is a hybrid of the two, combining the advantages of a laboratory calibration with the increased accuracy of a field calibration.
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Applications of artificial intelligence in the field of air pollution: A bibliometric analysis
Qiangqiang Guo,Mengjuan Ren,Shouyuan Wu,Yajia Sun,Jianjian Wang,Qi Wang,Yan Ma,Xuping Song,Yaolong Chen +8 more
TL;DR: Wang et al. as mentioned in this paper explored the latest trends in the application of AI in the field of air pollution and found that the majority of the researchers concentrated on air pollutant concentration prediction, particularly the combined use of AI and environmental science methods.
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Tackling Data Quality When Using Low-Cost Air Quality Sensors in Citizen Science Projects
TL;DR: In this article, the authors present a perspective paper to increase awareness of data quality issues and provide strategies to minimize labor intensity and expenses while maintaining adequate QA/QC for robust applications of low-cost air quality sensors in citizen science projects.
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Machine learning techniques to improve the field performance of low-cost air quality sensors
TL;DR: In this article , the authors investigated field baseline and interference correction using random forest regression methods for low-cost sensing of NO2, PM10 (particulate matter) and PM2.5.
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Comparative Study on the Use of Some Low-Cost Optical Particulate Sensors for Rapid Assessment of Local Air Quality Changes
TL;DR: In this article , two types of pocket-size LiDAR sensors were tested at urban, suburban and background sites in Budapest, Hungary, to monitor PM1, PM2.5, PM10, and microclimatic parameters at high resolutions (1 s to 5 min).
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The rise of low-cost sensing for managing air pollution in cities.
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