Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/91300
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dc.contributorDepartment of Land Surveying and Geo-Informatics-
dc.creatorTong, C-
dc.creatorShi, Z-
dc.creatorShi, W-
dc.creatorZhao, P-
dc.creatorZhang, A-
dc.date.accessioned2021-11-02T08:22:09Z-
dc.date.available2021-11-02T08:22:09Z-
dc.identifier.issn1939-1404-
dc.identifier.urihttp://hdl.handle.net/10397/91300-
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.rightsThis work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/en_US
dc.rightsThe following publication C. Tong, Z. Shi, W. Shi, P. Zhao and A. Zhang, "Mapping Microscale PM2.5 Distribution on Walkable Roads in a High-Density City," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 6855-6870, 2021, doi: 10.1109/JSTARS.2021.3075442 is available at https://doi.org/10.1109/JSTARS.2021.3075442en_US
dc.subjectAir pollutionen_US
dc.subjectAtmospheric modelingen_US
dc.subjectInstrumentsen_US
dc.subjectLegged locomotionen_US
dc.subjectMonitoringen_US
dc.subjectPollution measurementen_US
dc.subjectPollution measurementen_US
dc.subjectRoadsen_US
dc.subjectUrban areasen_US
dc.subjectUrban areasen_US
dc.titleMapping microscale PM2.5 distribution on walkable roads in a high-density cityen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage6855-
dc.identifier.epage6870-
dc.identifier.volume14-
dc.identifier.doi10.1109/JSTARS.2021.3075442-
dcterms.abstractMonitoring pollution of PM2.5 on walkable roads is important for resident health in high-density cities. Due to the spatiotemporal resolution limitations of Aerosol Optical Depth (AOD) observation, fixed-point monitoring, or traditional mobile measurement instruments, the microscale PM2.5 distribution in the walking environment cannot be fully estimated at the fine scale. In this study, by the integration of mobile measurement data, OpenStreetMap (OSM) data, Landsat images, and other multi-source data in land-use regression (LUR) models, a novel framework is proposed to estimate and map PM2.5 distribution in a typical microscale walkable environment of the high-density city Hong Kong. First, the PM2.5 data on the typical walking paths were collected by the handheld mobile measuring instruments, to be selected as the dependent variables. Second, Geographic prediction factors calculated by Google Street View, OpenStreetMap (OSM) data, Landsat images, and other multi-source data were further selected as independent variables. Then, these dependent and independent variables were put into the LUR models to estimate the PM2.5 concentration on sidewalks, footbridges, and footpaths in the microscale walkable environment. The proposed models showed high performance relative to those in similar studies (adj R2, 0.593 to 0.615 [sidewalks]; 0.641 to 0.682 [footpaths]; 0.783 to 0.797 [footbridges]). This study is beneficial for mapping PM2.5 concentration in the microscale walking environment and the identification of hot spots of air pollution, thereby helping people avoid the PM2.5 hotspots and indicating a healthier walking path.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIEEE journal of selected topics in applied earth observations and remote sensing, 2021, v. 14, p. 6855-6870-
dcterms.isPartOfIEEE journal of selected topics in applied earth observations and remote sensing-
dcterms.issued2021-
dc.identifier.scopus2-s2.0-85105111994-
dc.identifier.eissn2151-1535-
dc.description.validate202110 bcvc-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_Scopus/WOSen_US
dc.description.pubStatusPublisheden_US
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