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• W2765735078 abstract "Urban heat island (UHI) effect significantly raises the health burden and building energy consumption in the high-density urban environment of Hong Kong. A better understanding of the spatiotemporal pattern of UHI is essential to health risk assessments and energy consumption management but challenging in a high-density environment due to the sparsely distributed meteorological stations and the highly diverse urban features. In this study, we modelled the spatiotemporal pattern of UHI effect using the land use regression (LUR) approach in geographic information system with meteorological records of the recent 4years (2013-2016), sounding data and geographic predictors in Hong Kong. A total of 224 predictor variables were calculated and involved in model development. As a result, a total of 10 models were developed (daytime and nighttime, four seasons and annual average). As expected, meteorological records (CLD, Spd, MSLP) and sounding indices (KINX, CAPV and SHOW) are temporally correlated with UHI at high significance levels. On the top of the resultant LUR models, the influential spatial predictors of UHI with regression coefficients and their critical buffer width were also identified for the high-density urban scenario of Hong Kong. The study results indicate that the spatial pattern of UHI is largely determined by the LU/LC (RES1500, FVC500) and urban geomorphometry (h¯, BVD, λ¯F, Ψsky and z0) in a high-density built environment, especially during nighttime. The resultant models could be adopted to enrich the current urban design guideline and help with the UHI mitigation." @default.
• W2765735078 created "2017-11-10" @default.
• W2765735078 creator A5046192263 @default.
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• W2765735078 date "2018-03-01" @default.
• W2765735078 modified "2023-09-28" @default.
• W2765735078 title "Modelling the fine-scale spatiotemporal pattern of urban heat island effect using land use regression approach in a megacity" @default.
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• W2765735078 doi "https://doi.org/10.1016/j.scitotenv.2017.08.252" @default.
• W2765735078 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/29096959" @default.
• W2765735078 hasPublicationYear "2018" @default.
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