Qingming Zhan

Prof. Qingming Zhan

School of Urban Design, Wuhan University, Wuhan 430072, China
orcid

Research Focus & Metrics

Academic Career Length

16Years (2011-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 August 2021 in Sustainable Cities and Society
Verified
Journal Article
Closed access
Huimin LiuHuimin Liu
Huimin Liu
Bo HuangBo Huang
Bo Huang
Qingming ZhanQingming Zhan
Qingming Zhan
SGSihang Gao
Sihang Gao
RLRongrong Li
Rongrong Li
ZFZhiyu Fan
Zhiyu Fan


1 June 2019 in Science of The Total Environment
Verified
Journal Article
Closed access
YWYasha Wang
Yasha Wang
Qingming ZhanQingming Zhan
Qingming Zhan
Wanlu OUYANGWanlu OUYANG
Wanlu OUYANG

23 February 2021 in Building and Environment
Verified
Journal Article
Closed access
ZCzhi cai
zhi cai
YTYan Tang
Yan Tang
Qingming ZhanQingming Zhan
Qingming Zhan

SGSihang Gao
Sihang Gao
Qingming ZhanQingming Zhan
Qingming Zhan
CYChen Yang
Chen Yang
Huimin LiuHuimin Liu
Huimin Liu
Local warming induced by rapid urbanization has been threatening residents’ health, raising significant concerns among urban planners. Local climate zone (LCZ), a widely accepted approach to reclassify the urban area, which is helpful to propose planning strategies for mitigating local warming, has been well documented in recent years. Based on the LCZ framework, many scholars have carried out diversified extensions in urban zoning research in recent years, in which urban functional zone (UFZ) is a typical perspective because it directly takes into account the impacts of human activities. UFZs, widely used in urban planning and management, were chosen as the basic unit of this study to explore the spatial heterogeneity in the relationship between landscape composition, urban morphology, urban functions, and land surface temperature (LST). Global regression including ordinary least square regression (OLS) and random forest regression (RF) were used to model the landscape-LST correlations to screen indicators to participate in following spatial regression. The spatial regression including semi-parametric geographically weighted regression (SGWR) and multiscale geographically weighted regression (MGWR) were applied to investigate the spatial heterogeneity in landscape-LST among different types of UFZ and within each UFZ. Urban two-dimensional (2D) morphology indicators including building density (BD); three-dimensional (3D) morphology indicators including building height (BH), building volume density (BVD), and sky view factor (SVF); and other indicators including albedo and normalized difference vegetation index (NDVI) and impervious surface fraction (ISF) were used as potential landscape drivers for LST. The results show significant spatial heterogeneity in the Landscape-LST relationship across UFZs, but the spatial heterogeneity is not obvious within specific UFZs. The significant impact of urban morphology on LST was observed in six types of UFZs representing urban built up areas including Residential (R), Urban village (UV), Administration and Public Services (APS), Commercial and Business Facilities (CBF), Industrial and Manufacturing (IM), and Logistics and Warehouse (LW). Specifically, a significant correlation between urban 3D morphology indicators and LST in CBF was discovered. Based on the results, we propose different planning strategies to settle the local warming problems for each UFZ. In general, this research reveals UFZs to be an appropriate operational scale for analyzing LST on an urban scale.

22 September 2017 in Sustainability
Verified
Journal Article
Open access
PDF file
YWYasha Wang
Yasha Wang
Qingming ZhanQingming Zhan
Qingming Zhan
Wanlu OUYANGWanlu OUYANG
Wanlu OUYANG
Facing urban warming, mitigation and adaptation strategies are not efficient enough to tackle excessive urban heat, especially at the local scale. The local climate zone (LCZ) classification scheme is employed to examine the diversity and complexity of the climate response within a city. This study suggests that zonal practice could be an efficient way to bridge the knowledge gap between climate research and urban planning. Urban surfaces classified by LCZ are designated as urban climate landscapes, which extends the LCZ concept to urban planning applications. Selecting Wuhan as a case study, we attempt to explore the climatic effect of landscape patterns. Thermal effects are compared across the urban climate landscapes, and the relationships between patch metrics and land surface temperature (LST) are quantified. Results indicate that climate landscape layout is a considerable factor impacting local urban climate. For Wuhan, 500 m is an optimal scale for exploring landscape pattern-temperature relationships. Temperature contrast between surrounding landscape patches has a major influence on LST. Generally, fragmental landscape patches contribute to heat release. For most climate landscape types, patch metrics also have a significant effect on thermal response. When three metrics are included as predictive variables, 53.3% of the heating intensity variation can be explained for the Large Lowrise landscape, while 57.4% of the cooling intensity variation can be explained for the Water landscape. Therefore, this article claims that land-based layout optimization strategy at local scale, which conforms to planning manner, should be taken into account in terms of heat management.