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, Journal Year: 2025, Volume and Issue: 374, P. 123975 - 123975

Published: Jan. 16, 2025

Language: Английский

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

Building and Environment, Journal Year: 2023, Volume and Issue: 245, P. 110915 - 110915

Published: Oct. 10, 2023

Language: 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, Journal Year: 2023, Volume and Issue: 98, P. 104817 - 104817

Published: July 22, 2023

Language: 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, Journal Year: 2023, Volume and Issue: 101, P. 105111 - 105111

Published: Dec. 14, 2023

Language: 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, Journal Year: 2024, Volume and Issue: 196(7)

Published: June 18, 2024

Language: Английский

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

Building and Environment, Journal Year: 2024, Volume and Issue: 262, P. 111782 - 111782

Published: Aug. 1, 2024

Language: Английский

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

Urban forestry & urban greening, Journal Year: 2024, Volume and Issue: 93, P. 128212 - 128212

Published: Jan. 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.

Language: 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, Journal Year: 2025, Volume and Issue: unknown, P. 106151 - 106151

Published: Jan. 1, 2025

Language: 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, Journal Year: 2023, Volume and Issue: 155, P. 111026 - 111026

Published: Oct. 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.

Language: 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, Journal Year: 2024, Volume and Issue: 114, P. 105765 - 105765

Published: Aug. 24, 2024

Language: Английский

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