Blue-Green space seasonal influence on land surface temperatures across different urban functional zones: Integrating Random Forest and geographically weighted regression

Journal of Environmental Management, Год журнала: 2025, Номер 374, С. 123975 - 123975

Опубликована: Янв. 16, 2025

Язык: Английский

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Sensing-based park cooling performance observation and assessment: A review

Building and Environment, Год журнала: 2023, Номер 245, С. 110915 - 110915

Опубликована: Окт. 10, 2023

Язык: Английский

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A comprehensive framework of cooling effect-accessibility-urban development to assessing and planning park cooling services

Sustainable Cities and Society, Год журнала: 2023, Номер 98, С. 104817 - 104817

Опубликована: Июль 22, 2023

Язык: Английский

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Quantification and mapping of the cooling effect of urban parks on the temperate monsoon climate zone

Sustainable Cities and Society, Год журнала: 2023, Номер 101, С. 105111 - 105111

Опубликована: Дек. 14, 2023

Язык: Английский

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Evaluating the impact of landscape configuration, patterns and composition on land surface temperature: an urban heat island study in the Megacity Lahore, Pakistan

Environmental Monitoring and Assessment, Год журнала: 2024, Номер 196(7)

Опубликована: Июнь 18, 2024

Язык: Английский

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Exploring the influence of local urban heat features on park cooling effects: insights from Chinese cities

Building and Environment, Год журнала: 2024, Номер 262, С. 111782 - 111782

Опубликована: Авг. 1, 2024

Язык: Английский

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Economic value of the hot-day cooling provided by urban green and blue space

Urban forestry & urban greening, Год журнала: 2024, Номер 93, С. 128212 - 128212

Опубликована: Янв. 23, 2024

Increasing high temperatures due to climate change are exacerbated by urban heat island effects, resulting in a range of human health and economic impacts. The green blue infrastructure (GBI) cities that underpins nature-based solutions (NBS) can help alleviate hot-day temperatures. In this study we bring together multiple data sources evaluate the cooling benefit provided GBI terms avoided losses labour productivity, for eleven City Regions Great Britain, over ten-year period. We defined extent include (woodland, grassland parks, gardens) (rivers canals, lakes ponds) features within cities, derived aggregate factors areas each Region, applying additional buffer zones around larger features. collated gridded meteorology assess number hot-days exceeding 28 °C Wet Bulb Globe Temperature Region period 2008-2017, applied response functions loss worker productivity ten sectors. For (aggregated adjacent >200m2), gardens make up biggest component (26% extent) closely followed parks (24%), with woodland at 6%. factor ranged from 0.64 – 0.89 across Regions. was greatest London, its greater exposure hot days, contribution economy than other hottest year 2015, London £13.97 m. varied considerably one next, depending on meteorology, will increase under change.

Язык: Английский

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Revealing the driving factors of urban wetland park cooling effects using Random Forest regression and SHAP algorithm

Sustainable Cities and Society, Год журнала: 2025, Номер unknown, С. 106151 - 106151

Опубликована: Янв. 1, 2025

Язык: Английский

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Spatiotemporal patterns and influencing factors of remotely sensed regional heat islands from 2001 to 2020 in Zhengzhou Metropolitan area

Ecological Indicators, Год журнала: 2023, Номер 155, С. 111026 - 111026

Опубликована: Окт. 9, 2023

Developing urban agglomerations and establishing metropolitan areas have led to heat islands breaking through the original single scale form regional (RHIs) with a large influencing range in recent decades. However, spatial temporal patterns of their evolution are still poorly understood, which limit decisionmakers make more informed decisions improve environment. We selected Zhengzhou Metropolitan Area as case study MOD11A2 surface temperature products from 2001 2020 describe intensity RHIs by calculating relative temperature. Then multi-scale spatiotemporal characteristics RHI were identified help Sen + MK trend analysis, contribution index analysis standard deviation ellipse main driving factors internal relations discussed correlation Random Forest (RF) regression analysis. found that (1) increased 2970 km2 5776 2020, growth rate about 0.46% per year. The contributions each county-level region differ significantly. positive include City, Kaifeng Zhongmu County, Xinzheng while negative Lankao Fengqiu Huixian City. (2) There two aggregated within area. Xinxiang City Jiaozuo formed complete, cross-city, large-area 2005, accounting for 63% total Zhengzhou, Kaifeng, Xuchang Cities developed an all-round, cross-regional, rapid manner 2010 78% (3) Different land cover types varying effects on RHIs. cooling effect forests water is better than grasslands. Human social positively affect RHI, vegetation strongly inhibits meteorological less influence. methods results this play significant role analyzing construction status, optimizing layouts, improving thermal environments Area. Insights into environment research practice also provided other agglomerations.

Язык: Английский

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Diurnal contrast of urban park cooling effects in a “Furnace city” using multi-source geospatial data and optimal parameters-based geographical detector model

Sustainable Cities and Society, Год журнала: 2024, Номер 114, С. 105765 - 105765

Опубликована: Авг. 24, 2024

Язык: Английский

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