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Mapping urban air quality using mobile sampling with low-cost sensors and machine learning in Seoul, South Korea
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lim, Chris C. | - |
dc.contributor.author | Kim, Ho | - |
dc.contributor.author | Vilcassim, M. J. Ruzmyn | - |
dc.contributor.author | Thurston, George D. | - |
dc.contributor.author | Gordon, Terry | - |
dc.contributor.author | Chen, Lung-Chi | - |
dc.contributor.author | Lee, Kiyoung | - |
dc.contributor.author | Heimbinder, Michael | - |
dc.contributor.author | Kim, Sun-Young | - |
dc.creator | 김호 | - |
dc.date.accessioned | 2020-01-23T07:33:00Z | - |
dc.date.available | 2020-04-05T07:33:00Z | - |
dc.date.created | 2020-01-22 | - |
dc.date.created | 2020-01-22 | - |
dc.date.issued | 2019-10 | - |
dc.identifier.citation | Environment International, Vol.131, p. 105022 | - |
dc.identifier.issn | 0160-4120 | - |
dc.identifier.uri | https://hdl.handle.net/10371/163833 | - |
dc.description.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. | - |
dc.language | 영어 | - |
dc.language.iso | ENG | en |
dc.publisher | Pergamon Press Ltd. | - |
dc.title | Mapping urban air quality using mobile sampling with low-cost sensors and machine learning in Seoul, South Korea | - |
dc.type | Article | - |
dc.identifier.doi | 10.1016/j.envint.2019.105022 | - |
dc.citation.journaltitle | Environment International | - |
dc.identifier.wosid | 000493550200074 | - |
dc.identifier.scopusid | 2-s2.0-85069833094 | - |
dc.description.srnd | OAIID:RECH_ACHV_DSTSH_NO:T201914180 | - |
dc.description.srnd | RECH_ACHV_FG:RR00200001 | - |
dc.description.srnd | ADJUST_YN: | - |
dc.description.srnd | EMP_ID:A001370 | - |
dc.description.srnd | CITE_RATE:7.943 | - |
dc.description.srnd | DEPT_NM:보건학과 | - |
dc.description.srnd | EMAIL:hokim@snu.ac.kr | - |
dc.description.srnd | SCOPUS_YN:Y | - |
dc.citation.startpage | 105022 | - |
dc.citation.volume | 131 | - |
dc.description.isOpenAccess | Y | - |
dc.contributor.affiliatedAuthor | Kim, Ho | - |
dc.contributor.affiliatedAuthor | Lee, Kiyoung | - |
dc.identifier.srnd | T201914180 | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.subject.keywordPlus | USE REGRESSION-MODELS | - |
dc.subject.keywordPlus | SPATIAL VARIABILITY | - |
dc.subject.keywordPlus | BLACK CARBON | - |
dc.subject.keywordPlus | POLLUTION | - |
dc.subject.keywordPlus | EXPOSURE | - |
dc.subject.keywordPlus | AMBIENT | - |
dc.subject.keywordPlus | NETWORK | - |
dc.subject.keywordPlus | PARTICLES | - |
dc.subject.keywordPlus | PM2.5 | - |
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