Excessive urban temperature exerts a substantially negative impact on urban sustainability. Three-dimensional (3D) landscapes have a great impact on urban thermal environments, while their heat conditions and driving factors still remain unclear. This study mapped urban 3D neighborhoods and their associated SUHI (surface urban heat island) intensities in summer daytime across 57 Chinese cities, and then explored their relationships, driving factors as well as implications. Nine categories of urban 3D neighborhoods existed in Chinese cities and the 3D neighborhood of High Density & Medium Rise (HDMR) contributed the largest share of urban areas. The distribution of 3D neighborhoods varied among cities due to their distinct natural and economic traits. The average SUHI intensity can amount to 4.27 °C across all Chinese 3D neighborhoods. High Density & Low Rise (HDLR) and HDMR presented higher SUHI intensities than other 3D neighborhoods in China. Urban green space (UGI) and building height (BH) had great influences on SUHI intensities. The relative contribution of UGI decreased with the increase of building density and building height, but BH presented the opposite trend. The interaction of urban 3D landscapes and function zones led to highly complicated urban thermal environments, with higher SUHI intensities in industrial zones. Besides, the SUHI intensities of 3D neighborhoods presented great diurnal and seasonal variations, with higher SUHI intensities in HDHR and HDMR at nighttime in winter and summer. What's more, urban residents may suffer unequal heat risk inside cities due to the deviations of SUHI intensities among different 3D neighborhoods. It could be a highly effective way to mitigate SUHI effects in cities by increasing urban greening and improving urban ventilation.
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http://dx.doi.org/10.1016/j.scitotenv.2022.157662 | DOI Listing |
Environ Monit Assess
March 2025
RoboPI Laboratory, Dept. of ECE, University of Florida, Gainesville, FL, USA.
Existing technologies for distributed light-field mapping and light pollution monitoring (LPM) rely on either remote satellite imagery or manual light surveying with single-point sensors such as SQMs (sky quality meters). These modalities offer low-resolution data that are not informative for dense light-field mapping, pollutant identification, or sustainable policy implementation. In this work, we propose LightViz-an interactive software interface to survey, simulate, and visualize light pollution maps in real time.
View Article and Find Full Text PDFScientificWorldJournal
February 2025
Natural Resource Management, College of Agriculture and Environmental Sciences, Bahir Dar University, Bahir Dar, Amhara, Ethiopia.
The rise in urban temperature has several impacts on the urban population. It manifests in water consumption, quality, and availability; energy consumption; greenhouse gas emissions; ecological disturbances; and human health. Studies have been conducted on the severity of the impact of surface urban heat island intensity (SUHII) on these variables at different scales in different parts of the world.
View Article and Find Full Text PDFJ Environ Manage
February 2025
Sun Yat-sen University, School of Geography and Planning, GuangZhou, 510275, China. Electronic address:
Sci Total Environ
December 2024
School of Ecology, Hainan University, Hainan 570228, China.
The vast majority of urban heat island (UHI) studies are now derived from surface temperatures, substituting for the original air temperature-based definition. The disparities in hourly surface-canopy UHI effects (SUHI, CUHI) and the contrasting mechanisms are currently poorly understood. Here, we use high-resolution hourly LST and air temperature data from 2064 urban clusters in China to estimate SUHI and CUHI intensities and their driving mechanisms during the summer and winter of 2022.
View Article and Find Full Text PDFSci Total Environ
November 2024
Sichuan Academy of Ecology and Environmental Sciences, Chengdu 610041, China.
Surface urban heat island (SUHI) intensity generally determined by satellite-derived clear-sky land surface temperature (LST) has ignored the impacts of cloud coverage and cannot reflect the real SUHI intensity. Only a few studies focus on the effects of this issue based on short-time LST datasets, which could contain non-negligible uncertainties to summarize reliable rules. To investigate the influence, the SUHI intensity (SUHII) clear-sky bias (CSB), which is defined as the SUHII difference between clear-sky and all-weather conditions, was investigated in 35 cities in China, based on clear-sky and all-weather LST datasets from 2003 to 2022.
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