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

Recent studies have demonstrated that mobile sampling can improve the spatial granularity of land use regression (LUR) models. Mobile sampling campaigns deploying low-cost (

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Bibliographische Detailangaben
Veröffentlicht in:	Environment international 2019-10, Vol.131, p.105022-105022, Article 105022
Hauptverfasser:	Lim, Chris C., Kim, Ho, Vilcassim, M.J. Ruzmyn, Thurston, George D., Gordon, Terry, Chen, Lung-Chi, Lee, Kiyoung, Heimbinder, Michael, Kim, Sun-Young
Format:	Artikel
Sprache:	eng
Schlagworte:	air Air Pollutants - analysis air pollution Air Pollution - analysis air quality algorithms artificial intelligence cost effectiveness data collection developing countries Environmental Monitoring - economics Environmental Monitoring - methods experimental design Humans land use Linear Models Machine Learning Mobile Applications model validation monitoring Particulate Matter - analysis particulates regression analysis Republic of Korea Seoul South Korea spatial data Urban Population
Online-Zugang:	Volltext
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Beschreibung
Zusammenfassung:	Recent studies have demonstrated that mobile sampling can improve the spatial granularity of land use regression (LUR) models. Mobile sampling campaigns deploying low-cost (
ISSN:	0160-4120 1873-6750 1873-6750
DOI:	10.1016/j.envint.2019.105022