Construction of a cold island network for the urban heat island effect mitigation

The Science of The Total Environment, Год журнала: 2024, Номер 915, С. 169950 - 169950

Опубликована: Янв. 9, 2024

Язык: Английский

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Examining water bodies' cooling effect in urban parks with buffer analysis and random forest regression

Urban Climate, Год журнала: 2025, Номер 59, С. 102301 - 102301

Опубликована: Янв. 30, 2025

Язык: Английский

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Quantifying threshold and scale response of urban air and surface temperature to surrounding landscapes under extreme heat

Building and Environment, Год журнала: 2023, Номер 247, С. 111029 - 111029

Опубликована: Ноя. 16, 2023

Язык: Английский

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Assessing the effects of urban green spaces metrics and spatial structure on LST and carbon sinks in Harbin, a cold region city in China

Sustainable Cities and Society, Год журнала: 2024, Номер 113, С. 105659 - 105659

Опубликована: Июль 14, 2024

Язык: Английский

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An investigation on the impact of blue and green spatial pattern alterations on the urban thermal environment: A case study of Shanghai

Ecological Indicators, Год журнала: 2023, Номер 158, С. 111244 - 111244

Опубликована: Ноя. 24, 2023

Consistent urbanization and global warming escalates the summer temperatures of urban, significantly impacting daily lives endangering well-being. It is difficult to balance urban construction increasing blue-green space. Hence, understanding impact changes in spatial patterns different spaces on thermal environment beneficial rational layout patterns. Drawing from case study Shanghai, by employing bivariate autocorrelation multiscale geographically weighted regression, interplay between distribution modifications land surface temperature grades scrutinized, thus unraveling underlying mechanisms their mutual influence. The findings reveal following: (1) transformation pattern exhibited substantial discrepancies northern southern sectors. (2) alteration Shanghai varies spatially characterized a decrease grade southwestern suburbs, an increase east, almost no change central region. (3) Furthermore, correlation extent manifested unevenness. (4) Finally, mechanism alterations city emanates primarily influence heat exchange areas. instability can provide implications for planners.

Язык: Английский

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Urban green spaces enhanced human thermal comfort through dual pathways of cooling and humidifying

Sustainable Cities and Society, Год журнала: 2024, Номер unknown, С. 106032 - 106032

Опубликована: Дек. 1, 2024

Язык: Английский

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Unveiling the nonlinear relationships and co-mitigation effects of green and blue space landscapes on PM2.5 exposure through explainable machine learning

Sustainable Cities and Society, Год журнала: 2025, Номер unknown, С. 106234 - 106234

Опубликована: Фев. 1, 2025

Язык: Английский

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A new method for evaluating the synergistic effect of urban water body and vegetation in the summer outdoor thermal environment

Journal of Cleaner Production, Год журнала: 2023, Номер 414, С. 137680 - 137680

Опубликована: Июнь 2, 2023

Язык: Английский

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A novel approach for quantifying the influence intensity of urban water and greenery resources on microclimate for efficient utilization

Sustainable Cities and Society, Год журнала: 2024, Номер 112, С. 105597 - 105597

Опубликована: Июнь 20, 2024

Climate changes have led to increasing global energy consumption, detrimental the sustainable development of society. Urban blue-green infrastructure (UBGI) can improve urban microclimate. However, influence intensity UBGI on microclimate has not been quantified deeply use efficiency water and greenery resources. To solve research deficiencies, this study numerically simulated for 44 scenarios with different resource configurations (various body areas coverages) in summer. Based simulations, developed novel mathematical models thermo-environment (BGTE) quantify UBGI. The results indicated that daytime synergies first increased then decreased time. significance time (t), area (Sw), tree coverage rate (TCR), shrub (SCR), grassland (GLCR) synergy was by artificial neural network: t (39.4%), Sw (22.6%), TCR (22.0%), SCR (13.2%), GLCR (2.8%). make overall effect relatively efficient, should be less than 10000 m2, greater 65%, close 15%. This provides practical ideas efficient

Язык: Английский

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Empowering urban climate resilience and adaptation: Crowdsourcing weather citizen stations-enhanced temperature prediction

Sustainable Cities and Society, Год журнала: 2024, Номер 101, С. 105208 - 105208

Опубликована: Янв. 14, 2024

The growing impact of climate change, including extreme weather events, represents a significant challenge for humanity. With most the world's population living in urban areas, heat island effect and anthropogenic contribute to elevated city temperatures. This increase warming threatens human health demands deeper understanding thermal distribution environments. Collecting accessible widespread temperature data areas is essential address this challenge. study aims develop methodology anticipating environments, leveraging Citizen Weather Stations (CWS) as valuable crowdsourcing sources. ultimate goal create predictive model that estimates temperatures based on government meteorological station forecasts, improving planning, regulating temperature-based routes, preventing issues vulnerable populations, enhancing livability. divided into three fundamental stages: acquisition through CWS with citizen collaboration, development evaluation optimal forecast models stations (SWS) data, its exploitation terms utility applicability. encompasses collection filtering ensure usefulness implement reliable models. resulting tool facilitates informed decision-making precise seasonal event planning effectively addressing challenges extrapolation contributing more effective adaptation mitigation strategies change heatwaves. results obtained probe feasibility using predict which has been demonstrated accurately. achievement, proven be source context. Also, process described applied case effective, discarding approximately 34.87% data. achieved by detecting eliminating anomalies, considering availability, adhering specific quality criteria. Finally, developed prediction ability optimally estimate temperatures, utilizing provided (SWS). performance indicators support claim. For linear regression model, Mean Squared Error (MSE) 2.177 an R-squared (R2) 0.960 are obtained, while neural network, MSE 1.284 R2 0.976 achieved.

Язык: Английский

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