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Article: Assessing Spatial Variability Of Extreme Hot Weather Conditions In Hong Kong: A Land Use Regression Approach

TitleAssessing Spatial Variability Of Extreme Hot Weather Conditions In Hong Kong: A Land Use Regression Approach
Authors
KeywordsExtreme hot weather events
Hong Kong
Land surface morphology
Land use regression
Spatial mapping
Issue Date2019
PublisherAcademic Press. The Journal's web site is located at http://www.elsevier.com/locate/envres
Citation
Environmental Research, 2019, v. 171, p. 403-415 How to Cite?
AbstractThe number of extreme hot weather events have considerably increased in Hong Kong in the recent decades. The complex urban context of Hong Kong leads to a significant intra-urban spatial variability in climate. Under such circumstance, a spatial understanding of extreme hot weather condition is urgently needed for heat risk prevention and public health actions. In this study, the extreme hot weather events of Hong Kong were quantified and measured using two indicators – very hot day hours (VHDHs) and hot night hours (HNHs) which were counted based on the summertime hourly-resolved air temperature data from a total of 40 weather stations (WSs) from 2011 to 2015. Using the VHDHs and HNHs at the locations of the 40 WSs as the outcome variables, land use regression (LUR) models are developed to achieve a spatial understanding of the extreme hot weather conditions in Hong Kong. Land surface morphology was quantified as the predictor variables in LUR modelling. A total of 167 predictor variables were considered in the model development process based on a stepwise multiple linear regression (MLR). The performance of resultant LUR models was evaluated via cross validation. VHDHs and HNHs were mapped at the community level for Hong Kong. The mapping results illustrate a significant spatial variation in the extreme hot weather conditions of Hong Kong in both the daytime and nighttime, which indicates that the spatial variation of land use configurations must be considered in the risk assessment and corresponding public health management associated with the extreme hot weather.
Persistent Identifierhttp://hdl.handle.net/10722/274781
ISSN
2017 Impact Factor: 4.732
2015 SCImago Journal Rankings: 1.452
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorShi, Y-
dc.contributor.authorRen, C-
dc.contributor.authorCai, M-
dc.contributor.authorLau, K-
dc.contributor.authorLee, T-
dc.contributor.authorWong, W-
dc.date.accessioned2019-09-10T02:28:34Z-
dc.date.available2019-09-10T02:28:34Z-
dc.date.issued2019-
dc.identifier.citationEnvironmental Research, 2019, v. 171, p. 403-415-
dc.identifier.issn0013-9351-
dc.identifier.urihttp://hdl.handle.net/10722/274781-
dc.description.abstractThe number of extreme hot weather events have considerably increased in Hong Kong in the recent decades. The complex urban context of Hong Kong leads to a significant intra-urban spatial variability in climate. Under such circumstance, a spatial understanding of extreme hot weather condition is urgently needed for heat risk prevention and public health actions. In this study, the extreme hot weather events of Hong Kong were quantified and measured using two indicators – very hot day hours (VHDHs) and hot night hours (HNHs) which were counted based on the summertime hourly-resolved air temperature data from a total of 40 weather stations (WSs) from 2011 to 2015. Using the VHDHs and HNHs at the locations of the 40 WSs as the outcome variables, land use regression (LUR) models are developed to achieve a spatial understanding of the extreme hot weather conditions in Hong Kong. Land surface morphology was quantified as the predictor variables in LUR modelling. A total of 167 predictor variables were considered in the model development process based on a stepwise multiple linear regression (MLR). The performance of resultant LUR models was evaluated via cross validation. VHDHs and HNHs were mapped at the community level for Hong Kong. The mapping results illustrate a significant spatial variation in the extreme hot weather conditions of Hong Kong in both the daytime and nighttime, which indicates that the spatial variation of land use configurations must be considered in the risk assessment and corresponding public health management associated with the extreme hot weather.-
dc.languageeng-
dc.publisherAcademic Press. The Journal's web site is located at http://www.elsevier.com/locate/envres-
dc.relation.ispartofEnvironmental Research-
dc.subjectExtreme hot weather events-
dc.subjectHong Kong-
dc.subjectLand surface morphology-
dc.subjectLand use regression-
dc.subjectSpatial mapping-
dc.titleAssessing Spatial Variability Of Extreme Hot Weather Conditions In Hong Kong: A Land Use Regression Approach-
dc.typeArticle-
dc.identifier.emailRen, C: renchao@hku.hk-
dc.identifier.authorityRen, C=rp02447-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.envres.2019.01.041-
dc.identifier.pmid30716517-
dc.identifier.scopuseid_2-s2.0-85060866534-
dc.identifier.hkuros302542-
dc.identifier.volume171-
dc.identifier.spage403-
dc.identifier.epage415-
dc.identifier.isiWOS:000460081300044-
dc.publisher.placeUnited States-

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