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Mapping urban air quality using mobile sampling with low-cost sensors and machine learning in Seoul, South Korea
Cited 105 time in
Web of Science
Cited 121 time in Scopus
- Authors
- Issue Date
- 2019-10
- Publisher
- Pergamon Press Ltd.
- Citation
- Environment International, Vol.131, p. 105022
- 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 R-2 values of 0.63, 0.73, and 0.80, respectively, and identification of several pollution 'hotspots.' The high R-2 values suggest that study designs employing mobile sampling in conjunction with multiple low-cost air quality monitors could be applied to characterize urban street-level air quality with high spatial resolution, and that machine learning models could further improve model performance. Given this study design's cost-effectiveness and ease of implementation, similar approaches may be especially suitable for citizen science and community-based endeavors, or in regions bereft of air quality data and preexisting air monitoring networks, such as developing countries.
- ISSN
- 0160-4120
- Language
- ENG
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