Mapping urban air quality using mobile sampling with low-cost sensors and machine learning in Seoul, South Korea. (October 2019)

• Record Type: Journal Article
• Authors: Lim, Chris C.; Kim, Ho; Vilcassim, M.J. Ruzmyn; Thurston, George D.; Gordon, Terry; Chen, Lung-Chi; Lee, Kiyoung; Heimbinder, Michael; Kim, Sun-Young
• Is Part Of: Environment international. Volume 131(2019)
• Publication Date: 2019-10
• Subjects: Environmental protection -- Periodicals; Environmental health -- Periodicals; Environmental monitoring -- Periodicals; Environmental Monitoring -- Periodicals; Environnement -- Protection -- Périodiques; Hygiène du milieu -- Périodiques; Environnement -- Surveillance -- Périodiques; Environmental health; Environmental monitoring; Environmental protection; Periodicals; 333.705
• DOI: 10.1016/j.envint.2019.105022
• Languages: English
• ISSNs: 0160-4120

Abstract

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. Highlights: Mobile sampling with low-cost air quality sensors could offer a cost-effective approach to characterize urban air quality. A sampling campaign deploying multiple AirBeams across five routes was conducted during a three week period in Seoul. Land use regression (LUR) models were constructed using the collected data and the OpenStreetMap (OSM) geospatial data. Three approaches – linear regression, random forest, and stacked ensemble – were employed to construct the LUR models.