Urban Climate最新文献_第8页

Greening policies have led to an overturning in the trend of humidity changes across Chinese cities

IF 6 2区工程技术

Urban Climate Pub Date : 2025-03-01 DOI: 10.1016/j.uclim.2025.102349

Beiqian Lei , Lei Li , Pak Wai Chan , Chunxiang Shi , Hongling Zeng , Shaojia Fan

{"title":"Greening policies have led to an overturning in the trend of humidity changes across Chinese cities","authors":"Beiqian Lei , Lei Li , Pak Wai Chan , Chunxiang Shi , Hongling Zeng , Shaojia Fan","doi":"10.1016/j.uclim.2025.102349","DOIUrl":"10.1016/j.uclim.2025.102349","url":null,"abstract":"<div><div>This study analyzed changes in relative humidity (RH) over the past 70 years across China's five major climate zones, encompassing 336 cities. The findings indicate that changes in the annual average RH levels in China are closely related to the stages of urban development. From 1951 to 1978, RH levels decreased in the middle and warm temperature zones (MTZ and WTZ, respectively), areas that experienced early urbanization driven by heavy industry. Similarly, the Qinghai–Tibet Plateau zone (QPZ) has also experienced a decline in RH levels, largely due to the deterioration of the local ecological environment. From 1978 to 2004, rapid urbanization following the implementation of the reform and opening-up policy led to further decline in RH levels across most climate zones, with the exception of the QPZ. However, after 2004, the RH levels increased in the tropical zone (TZ), subtropical zone (SZ), WTZ, and QPZ, with the SZ showing the largest increase at 28 % per year. This rise in RH levels after 2004 is attributed to the implementation of eco-environmental policies that promoted normalized difference vegetation index (NDVI) and environmental improvements, enhancing surface water content, evapotranspiration and RH levels. These findings and mechanisms have practical significance and provide broader implications for urban planning, management, and enhancing urban climate resilience.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"60 ","pages":"Article 102349"},"PeriodicalIF":6.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143534262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Spatial prediction of on-road air pollution using long-term mobile monitoring: Insights from Delhi 利用长期移动监测对路面空气污染进行空间预测：德里的启示

IF 6 2区工程技术

Urban Climate Pub Date : 2025-03-01 DOI: 10.1016/j.uclim.2025.102347

Vikram Singh, Amit Agarwal

{"title":"Spatial prediction of on-road air pollution using long-term mobile monitoring: Insights from Delhi","authors":"Vikram Singh, Amit Agarwal","doi":"10.1016/j.uclim.2025.102347","DOIUrl":"10.1016/j.uclim.2025.102347","url":null,"abstract":"<div><div>A rapid increase in the density of urban activities nudges for a dense air quality monitoring network. Mobile monitoring using low-cost air quality devices provides a valuable method for capturing spatiotemporal variations of pollutants in the absence of a dense air quality monitoring network. Few studies advocated using linear and nonlinear models, whereas others have utilized machine learning (ML) models for spatial prediction. However, the application in the existing studies is limited to a shorter period and smaller area. Additionally, an understanding of the selection of these models is absent. This study uses <span><math><msub><mi>PM</mi><mn>2.5</mn></msub></math></span> concentrations from 15 low-cost air quality devices deployed in buses in Delhi for over eight months to compare the performance of different model categories. <span><math><msub><mi>PM</mi><mn>2.5</mn></msub></math></span> data is aggregated at the midpoint of the 1110 road segments. Various predictor variables, which exhibit spatiotemporal variations, are used in the prediction models. Among the linear models, Backward Stepwise Regression achieved the highest R<sup>2</sup> (0.61) for the training dataset, and among ML models, Extreme Gradient Boosting exhibits the highest R<sup>2</sup> (0.98). Temperature, humidity, built-up area, building height, road length, and traffic signals are the main influencing predictor variables. ML models perform better among all model categories, whereas linear models have a smaller divergence between training and validation R<sup>2</sup>. Additionally, linear models have better prediction consistency than nonlinear and ML models. These results confirm the high performance of ML models and exhibit the potential for improving prediction accuracy by splitting the data into smaller time bins and including more road segments.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"60 ","pages":"Article 102347"},"PeriodicalIF":6.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143561861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An enhanced machine learning model for urban air quality forecasting under intense human activities

IF 6 2区工程技术

Urban Climate Pub Date : 2025-03-01 DOI: 10.1016/j.uclim.2025.102359

Yelin Wang , Feiyang Xia , Linlin Yao , Shunyu Zhao , Youjie Li , Yanpeng Cai

{"title":"An enhanced machine learning model for urban air quality forecasting under intense human activities","authors":"Yelin Wang , Feiyang Xia , Linlin Yao , Shunyu Zhao , Youjie Li , Yanpeng Cai","doi":"10.1016/j.uclim.2025.102359","DOIUrl":"10.1016/j.uclim.2025.102359","url":null,"abstract":"<div><div>With industrialization and urbanization, intense human activities are intensifying the complexity and dynamics of air quality variations, presenting significant challenges to prediction efforts. In this research, an enhanced machine learning model was proposed for forecasting urban air quality, based on integrating data de-noising, an optimized decomposition method, and error adaptive reduction into a hybrid framework. The Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Threshold (CEEMDANT) was developed by introducing statistical methods into the decomposition process, which enhanced the capability of extracting abrupt variations. At the same time, an error adaptive reduction strategy was designed to enhance the model's robustness and minimize forecasting risks. The model was demonstrated through a real-world case study of air quality prediction in four megacities of China, including Beijing, Shanghai, Guangzhou, and Shenzhen. The results indicated that CEEMDANT decreased the loss ratio of valid information by 16.01 %. Compared to traditional hybrid models, the error adaptive reduction strategy improved forecasting accuracy and stability by 9.96 % and 3.41 %, respectively. The proposed model provided precise benchmarks for residents to avoid health risks.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"60 ","pages":"Article 102359"},"PeriodicalIF":6.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143580192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Supply and demand analysis of urban thermal environments regulation services from an accessibility perspective: A coupled thermal risk and green space cooling assessment model

IF 6 2区工程技术

Urban Climate Pub Date : 2025-03-01 DOI: 10.1016/j.uclim.2025.102356

Zeqi Wang, Yikai Liu, Tianyu Wang, Shuyang Tang, Xuefei Wu

{"title":"Supply and demand analysis of urban thermal environments regulation services from an accessibility perspective: A coupled thermal risk and green space cooling assessment model","authors":"Zeqi Wang, Yikai Liu, Tianyu Wang, Shuyang Tang, Xuefei Wu","doi":"10.1016/j.uclim.2025.102356","DOIUrl":"10.1016/j.uclim.2025.102356","url":null,"abstract":"<div><div>Urban Green Spaces (UGS) play a pivotal role in regulating the urban thermal environment, but the balance between the supply and demand for hot and cold spaces is often mismatched, leaving high-risk areas with inadequate cooling services. Oreover, due to population mobility and the complexity of spatial structures, the radiative effects between hot and cold spaces are often insufficiently considered. Therefore, this study proposed an accessibility-based framework for thermal environment assessment, which integrates thermal risks with the cooling benefits of green spaces, offering a comprehensive evaluation of urban heat conditions. Taking the central urban area of Fuzhou as a case study, the results demonstrate that Fuzhou's thermal environment risks are significant and worsening, with areas at level 4 and level 5 risk constituting 36.42 % of the city. The exposure to thermal risks has also increased, with high-exposure risk areas in 2023 rising by 4.67 % compared to 2008. In addition, the cooling service capacity has decreased, but overall cooling service accessibility is largely unaffected. In terms of the interaction between heat exposure and cooling accessibility, there is a significant spatial development inequality within the central city. This study provides a scientific basis for improving the urban thermal environment by identifying these problem areas and proposing appropriate spatial development strategies.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"60 ","pages":"Article 102356"},"PeriodicalIF":6.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143548884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Parameterizing urban canopy radiation transfer using three-dimensional urban morphological parameters 利用三维城市形态参数为城市冠层辐射传输设置参数

IF 6 2区工程技术

Urban Climate Pub Date : 2025-03-01 DOI: 10.1016/j.uclim.2025.102363

Shuo-Jun Mei , Guanwen Chen , Kai Wang , Jian Hang

{"title":"Parameterizing urban canopy radiation transfer using three-dimensional urban morphological parameters","authors":"Shuo-Jun Mei , Guanwen Chen , Kai Wang , Jian Hang","doi":"10.1016/j.uclim.2025.102363","DOIUrl":"10.1016/j.uclim.2025.102363","url":null,"abstract":"<div><div>The radiative schemes in urban canopy models are typically based on the two-dimensional street canyon model, which cannot fully capture the three-dimensional geometries in East Asia cities. This study addresses this limitation by parameterizing radiative flux by three-dimensional urban morphology parameters: frontal area density (<em>λ</em><sub>f</sub>) and plan area density (<em>λ</em><sub>p</sub>). The parameterization is supported by simulation results from the newly developed building-resolving radiative model (PyUray), which utilizes the Monte Carlo ray tracing method to achieve high accuracy and leverages GPU processing to accelerate simulation. The PyUray model is fully validated by comparing the urban canyon albedo with field measurements from the SOMUCH project. To support the parameterization, the solar gain and longwave radiative heat loss at ground surfaces and urban canyon surfaces are evaluated at various <em>λ</em><sub>f</sub> (0.16 ≤ <em>λ</em><sub>f</sub> ≤ 2.49) and <em>λ</em><sub>f</sub> (0.16 ≤ <em>λ</em><sub>p</sub> ≤ 0.83) using PyUray. With regression models, this study formulates functions to estimate the radiative flux of urban surfaces using three-dimensional morphological parameters <em>λ</em><sub>f</sub> and <em>λ</em><sub>p</sub>, along with the three-dimensional solar direction. The simulation indicates that <em>λ</em><sub>f</sub> and <em>λ</em><sub>p</sub> have different impacts on the net radiative absorption of the urban canopy layer, with ground and street canyon surfaces exhibiting different responses to these morphological parameters.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"60 ","pages":"Article 102363"},"PeriodicalIF":6.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143561863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Impact of an urban street canyon's greening configurations on its traffic-related particulate matter

IF 6 2区工程技术

Urban Climate Pub Date : 2025-03-01 DOI: 10.1016/j.uclim.2025.102365

Zhen Liu , Zhaowen Qiu , Na Yan , Feihong Ren

{"title":"Impact of an urban street canyon's greening configurations on its traffic-related particulate matter","authors":"Zhen Liu , Zhaowen Qiu , Na Yan , Feihong Ren","doi":"10.1016/j.uclim.2025.102365","DOIUrl":"10.1016/j.uclim.2025.102365","url":null,"abstract":"<div><div>Roadside greening is gaining attention because it can mitigate street air pollution. To investigate the effects of greening configurations along bicycle lanes and sidewalks on exposure to traffic-related particulate matter (PM), we monitored in situ concentrations (PM10, PM2.5, PM1) in a shallow street canyon in Xi'an, China, and numerically simulated the people's exposure at common street greening scenarios with a microclimate model (ENVI-met). Field measurements showed that greenery in late winter had a higher PM removal effect than in late spring, but late spring's greenery had an overall negative effect on PM concentrations. Notably, greening is more effective at removing coarse particles (PM10) than fine particles (PM2.5, PM1). Simulation results revealed that when trees are planted at different locations on the roadway, the ranking of their PM-removal effect is sidewalk > bicycle lane > traffic lane; however, for planted shrubs, the ranking changed to bicycle lane > traffic lane > sidewalk. PM removal from simultaneous planting of greenery (shrubs, trees, tree-shrub mixes) on sidewalks and bicycle paths is not satisfactory. The greening configuration of “planting shrubs on bicycle lanes and trees on sidewalks” performed best at removing PM. These findings can provide a practical reference for optimizing street greening designs.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"60 ","pages":"Article 102365"},"PeriodicalIF":6.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A geospatial-based framework for rapid simulation of storm-induced flooding and comprehensive risk assessment leveraging GWDD and fuzzy-SWARA

IF 6 2区工程技术

Urban Climate Pub Date : 2025-03-01 DOI: 10.1016/j.uclim.2025.102373

Hai Sun , Xiaowei Zhang , Jun Wang , Zhimin Li , Qiwei Yu , Xuejing Ruan

{"title":"A geospatial-based framework for rapid simulation of storm-induced flooding and comprehensive risk assessment leveraging GWDD and fuzzy-SWARA","authors":"Hai Sun , Xiaowei Zhang , Jun Wang , Zhimin Li , Qiwei Yu , Xuejing Ruan","doi":"10.1016/j.uclim.2025.102373","DOIUrl":"10.1016/j.uclim.2025.102373","url":null,"abstract":"<div><div>With the increasing frequency of storm surges due to extreme weather, coastal protection and emergency strategies have become more critical than ever. A key element in evaluating these measures is the simulation of storm-induced flooding. As both optimization and emergency response require rapid simulations, achieving high computational efficiency is essential. In this study, a geospatial-based framework for rapid flood simulation, combined with a comprehensive risk assessment system, is introduced. The Geospatial Water Displacement and Distribution (GWDD) Model was developed, integrating GIS and Manning's equation to calculate water influx and redistribute it across affected areas. By combining spatial data with coastal vulnerability indices, Fuzzy Logic and SWARA were employed to identify flood-prone zones. Compared to traditional methods, this geospatial analysis approach simplifies physical models, increasing computational efficiency by 67.44 times while maintaining a calculation accuracy of 84.08 %, with minimal loss of precision. In addition, this paper combines multi-layered vulnerabilities such as political, economic, and environmental factors with disaster assessments, fully considering the role of vulnerability elements. It accurately identifies high-risk areas for storm surge flooding on the Leizhou Peninsula and proves the accuracy of these findings through comparison with news reports, optimizing coastal defense and emergency response strategies.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"60 ","pages":"Article 102373"},"PeriodicalIF":6.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143621105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Emission offsets by albedo manipulations strategies based on bright materials and greening in urban areas assessed by hyperspectral remote sensing

IF 6 2区工程技术

Urban Climate Pub Date : 2025-03-01 DOI: 10.1016/j.uclim.2025.102357

F. Carotenuto, L. Brilli , G. De Luca, M. Nardino, L. Cremonini, L. Genesio, J.L. Pancorbo, B. Gioli

{"title":"Emission offsets by albedo manipulations strategies based on bright materials and greening in urban areas assessed by hyperspectral remote sensing","authors":"F. Carotenuto, L. Brilli , G. De Luca, M. Nardino, L. Cremonini, L. Genesio, J.L. Pancorbo, B. Gioli","doi":"10.1016/j.uclim.2025.102357","DOIUrl":"10.1016/j.uclim.2025.102357","url":null,"abstract":"<div><div>Climate neutral cities require climate mitigation strategies such as albedo manipulation approaches. This could be a low-cost approach able to reach short term results while working on reducing emissions on the long term. Currently, only few studies were carried out to investigate the potentiality of albedo changes to effectively reduce radiative forcing (RF) at city scale. This study makes use of high-resolution hyperspectral remote sensing (RS) to simulate three scenarios of albedo manipulation in the city of Prato (Italy). The scenarios are “W” (white, where roofing materials are substituted by white painted concrete), “G” (green, simulating the albedo of green roofs) and “B” (bright, where the dark asphalt of the roads is substituted by brighter paving materials), also considering restrictions due to cultural heritage constraints. Albedo manipulation is then translated into equivalent CO<sub>2</sub> savings/emissions via a global warming potential (GWP) model and these CO<sub>2</sub> amounts are compared with the city's inventorial emissions. Results show that the W scenario can offset up to 10.3 % of the whole city's emissions. This study suggests that while RF-based interventions on urban materials are unable to totally compensate direct CO<sub>2eq</sub> emissions, they can still contribute especially when coupled with decarbonization and urban emission reduction strategies.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"60 ","pages":"Article 102357"},"PeriodicalIF":6.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143601275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

End-to-end deep learning for pollutant prediction using street view images

IF 6 2区工程技术

Urban Climate Pub Date : 2025-03-01 DOI: 10.1016/j.uclim.2025.102368

Lijie Wu , Xiansheng Liu , Xun Zhang , Rui Wang , Zhihao Guo

{"title":"End-to-end deep learning for pollutant prediction using street view images","authors":"Lijie Wu , Xiansheng Liu , Xun Zhang , Rui Wang , Zhihao Guo","doi":"10.1016/j.uclim.2025.102368","DOIUrl":"10.1016/j.uclim.2025.102368","url":null,"abstract":"<div><div>Urban outdoor air pollution poses significant threats to public health, underscoring the need for accurate pollutant concentration estimation. This study presents an innovative end-to-end pollutant prediction model (E2EPPM) that directly predicts pollutant levels from street-level imagery. To the best of our knowledge, the model is the first to achieve pollutant concentration estimation at the street scale solely based on street-view images collected through mobile monitoring. The framework comprises three stages: feature extraction from image sequences, temporal feature extraction across sequences, and pollutant concentration fitting based on the Kolmogorov-Arnold theorem. E2EPPM was evaluated using datasets and street-view images from Augsburg, Beijing, and Hotan, focusing on particulate matter (PM<sub>1.0</sub>, PM<sub>2.5</sub>, PM<sub>10</sub>, particle number concentration (PNC), lung-deposited surface area (LDSA), ultraviolet PM (UVPM), black carbon (BC), and brown carbon (BrC)) and four gaseous pollutants (CO, NH<sub>3</sub>, SO<sub>2</sub>, O<sub>3</sub>). The model achieved over 90 % explanatory power for all pollutants. Ablation experiments demonstrated its effectiveness, while SHAP analysis identified vegetation, buildings, sky, and vehicles as the most significant contributors to pollutant levels. E2EPPM offers a robust approach for urban air quality assessment, providing actionable insights for urban planning and pollution exposure evaluation.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"60 ","pages":"Article 102368"},"PeriodicalIF":6.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143609412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mitigating urban heat island effect: Optimization metrics for road pavement based on entransy dissipation

IF 6 2区工程技术

Urban Climate Pub Date : 2025-03-01 DOI: 10.1016/j.uclim.2025.102355

Yu Zhao , Xiaoming Liu , Xihe Zhang , Yi Huang

{"title":"Mitigating urban heat island effect: Optimization metrics for road pavement based on entransy dissipation","authors":"Yu Zhao , Xiaoming Liu , Xihe Zhang , Yi Huang","doi":"10.1016/j.uclim.2025.102355","DOIUrl":"10.1016/j.uclim.2025.102355","url":null,"abstract":"<div><div>Mitigating urban heat island (UHI) effect caused by high temperature of urban road, optimizing heat transfer design is essential for urban sustainability. Current design methods rely on experimental designs, which often depend on single metrics such as temperature and heat dissipation, neglecting their varying impacts on heat transfer performance. In this study, through the application of topology optimization technology, the pavements were optimized by temperature and heat dissipation, respectively. Based on the entransy dissipation theory, the variations in convection, surface radiation and heat conduction performance of optimized pavements were analyzed. The relationship between entransy dissipation, temperature and heat dissipation was discussed, and the optimization metrics of entransy dissipation were determined. The results indicated that pavement structure differed when temperature and heat dissipation were used as metrics. The heat conduction entransy dissipation of the upper layer (G<sub>condup</sub>) of the pavement designed with temperature decreased, while the heat conduction entransy dissipation of the middle layer (G<sub>condmid</sub>) and lower layer (G<sub>condlow</sub>) increased. Conversely, the pavement designed with heat dissipation exhibited the opposite trend. A strong correlation was found between the temperature, heat dissipation and the entransy dissipation, with surface radiation entransy dissipation (G<sub>radia</sub>) and G<sub>condup</sub> having the most significant influence. Consequently, these were selected as the design metrics. The optimized pavement designed with entransy dissipation exhibited good cooling and heat dissipation performance.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"60 ","pages":"Article 102355"},"PeriodicalIF":6.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143549262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0