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

Environment International
Chris C LimSun-Young Kim

Abstract

Recent studies have demonstrated that mobile sampling can improve the spatial granularity of land use regression (LUR) models. Mobile sampling campaigns deploying low-cost (<$300) air quality sensors could potentially offer an inexpensive and practical approach to measure and model air pollution concentration levels. In this study, we developed LUR models for street-level fine particulate matter (PM2.5) concentration levels in Seoul, South Korea. 169 h of data were collected from an approximately three week long campaign across five routes by ten volunteers sharing seven AirBeams, a low-cost ($250 per unit), smartphone-based particle counter, while geospatial data were extracted from OpenStreetMap, an open-source and crowd-generated geographical dataset. We applied and compared three statistical approaches in constructing the LUR models - linear regression (LR), random forest (RF), and stacked ensemble (SE) combining multiple machine learning algorithms - which resulted in cross-validation R2 values of 0.63, 0.73, and 0.80, respectively, and identification of several pollution 'hotspots.' The high R2 values suggest that study designs employing mobile sampling in conjunction with multiple low-cost air quality monitors could be a...Continue Reading

Citations

Dec 28, 2019·International Journal of Environmental Research and Public Health·Jill E JohnstonWendy Gutschow
Feb 5, 2021·Environmental Science & Technology·Meng Qi, Steve Hankey
Feb 24, 2021·Environmental Pollution·Aslı BozdağÖznur Begüm Gökçek
Mar 25, 2021·Environmental Science & Technology·Bu ZhaoMing Xu
Jun 2, 2021·Environmental Monitoring and Assessment·Kiwon Yeom
Jun 15, 2021·The Science of the Total Environment·Rana Alaa AbbassAhmed El-Gendy
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Jul 25, 2021·Sensors·Yacine Mohamed IdirPatrice Chatellier
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Aug 21, 2021·Environmental Monitoring and Assessment·Hoda TalaatHossam Abdelgawad
Sep 18, 2021·Environmental Science and Pollution Research International·Abdulwaheed Tella, Abdul-Lateef Balogun

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