Sustainable Cities and Society最新文献_第10页

Isolating urban form impacts on spatiotemporal distribution of surface meteorology in coastal cities during extreme heat events

IF 10.5 1区工程技术

Sustainable Cities and Society Pub Date : 2025-02-26 DOI: 10.1016/j.scs.2025.106242

Dun Zhu , Ryozo Ooka

{"title":"Isolating urban form impacts on spatiotemporal distribution of surface meteorology in coastal cities during extreme heat events","authors":"Dun Zhu , Ryozo Ooka","doi":"10.1016/j.scs.2025.106242","DOIUrl":"10.1016/j.scs.2025.106242","url":null,"abstract":"<div><div>Coastal cities in tropical and temperate zones are prone to heat waves, and the interplay between moist air and urban heat islands exacerbates the risk of heatstroke. However, the complexities introduced by land–sea temperature and humidity gradients pose challenges in quantifying the influence of urban forms on thermal environments. Thus, we conducted simulations using the Weather Research and Forecasting model for 49 high-temperature days across 2018–2021 in Tokyo—a developed coastal city. Control experiments were designed to eliminate the land–sea gradients and isolate the independent effects of urban form on the spatial fluctuation of pedestrian-level air temperature (ΔTa_u) and specific humidity (ΔSH_u). The findings reveal that the correlation between urban form and ΔTa_u is stronger than that with ΔSH_u. A positive linear relationship exists with building density at midday (<em>R</em><sup>2</sup> = 0.72) and building surface area at night (<em>R</em><sup>2</sup> = 0.78). In contrast, ΔSH_u exhibits a negative linear relationship at midday (<em>R</em><sup>2</sup> = 0.60) and a weak correlation with urban form at night (<em>R</em><sup>2</sup> < 0.15). Notably, ΔTa_u and ΔSH_u displayed clear diurnal variations, with the most significant spatial dispersion observed at midday (−1.3 to 2.2 K and −0.8 to 0.4 g/kg, respectively). Conversely, as the spatial patterns of ΔTa_u and ΔSH_u exhibited minimal fluctuations across varying high-temperature days, their historical averages effectively represented their general distribution. Furthermore, a preliminary method is proposed, utilizing a simple meteorological model along with empirical ΔTa_u and ΔSH_u for the rapid prediction of thermal environments during future heat events.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"123 ","pages":"Article 106242"},"PeriodicalIF":10.5,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143594121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Impacts of land surface temperature and ambient factors on near-surface air temperature estimation: A multisource evaluation using SHAP analysis

IF 10.5 1区工程技术

Sustainable Cities and Society Pub Date : 2025-02-26 DOI: 10.1016/j.scs.2025.106257

Songyang Li , Man Sing Wong , Rui Zhu , Guoqiang Shi , Jinxin Yang

{"title":"Impacts of land surface temperature and ambient factors on near-surface air temperature estimation: A multisource evaluation using SHAP analysis","authors":"Songyang Li , Man Sing Wong , Rui Zhu , Guoqiang Shi , Jinxin Yang","doi":"10.1016/j.scs.2025.106257","DOIUrl":"10.1016/j.scs.2025.106257","url":null,"abstract":"<div><div>Near-surface air temperature (Ta) is a vital indicator depicting urban thermal environments and sustainability. Machine learning (ML) models have been increasingly adopted for Ta estimation. However, there is still an urgent need to investigate how daytime and nighttime Ta are impacted by multisource ambient physical and anthropogenic factors across various environments. To this end, geospatial datasets incorporating MODIS-derived land surface temperature and 29 ancillary factors were employed to estimate Ta from 292 stations in China using ML modeling (training: 2017–2020). The optimal LightGBM-based models outperformed and obtained testing RMSEs of 3.03 °C (daytime) and 2.64 °C (nighttime) in 2021. Distinct spatiotemporal patterns in stations’ Ta prediction were observed, with coastal areas showing better daytime estimates and northern mid-temperate regions exhibiting lower nighttime accuracy. Comprehensive and individual models-based SHapley Additive exPlanations (SHAP) interpretation highlights the importance of incorporating macroscale meteorological backgrounds and terrain-related variables for Ta estimation improvement, as well as the critical impact of local urban morphology and anthropogenic indicators. This study has the potential to offer suggestions on ambient factors for improving Ta modeling and future urban heat island-related planning within specific regional and local climatical contexts.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"122 ","pages":"Article 106257"},"PeriodicalIF":10.5,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143580385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Charging station localization and sizing determination considering smart charging strategies based on NSGA-III and MOPSO

IF 10.5 1区工程技术

Sustainable Cities and Society Pub Date : 2025-02-26 DOI: 10.1016/j.scs.2025.106233

Jiale Li, Yuxuan Zhang, Xuefei Wang

{"title":"Charging station localization and sizing determination considering smart charging strategies based on NSGA-III and MOPSO","authors":"Jiale Li, Yuxuan Zhang, Xuefei Wang","doi":"10.1016/j.scs.2025.106233","DOIUrl":"10.1016/j.scs.2025.106233","url":null,"abstract":"<div><div>The ownership of electric vehicles (EVs) has experienced a significant increase in recent years all over the world. However, the unmanaged and uncontrolled connection of a large number of EVs to the grid poses significant threats to grid stability and may result in heightened carbon emissions. This study introduces a smart charging scheduling method that concurrently takes into account charging costs, grid stability, and carbon emissions for EV users. This method is solved using the Non-dominated Sorting Genetic Algorithm III (NSGA-III). Based on the derived solution, three charging strategies were compared with data from four different countries with different energy structure. Both the total distance cost and total construction cost were considered to determine four options for the localization and sizing of EV CSs. The findings indicate that, in temporal terms, the optimal case for each strategy reduces charging costs, grid peak-valley difference, and carbon emissions by 6.66 %, 42.39 %, and 3.38 %, respectively. In spatial terms, the study elucidates the impact of various charging strategies on the localization and sizing of CSs. This study demonstrates the potential of an innovative method for long-term CS localization and sizing determination to provide direct guidance to management department.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"122 ","pages":"Article 106233"},"PeriodicalIF":10.5,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143580389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Research on the cool island effect of green spaces in megacity cores: A case study of the main urban area of Xi'an, China

IF 10.5 1区工程技术

Sustainable Cities and Society Pub Date : 2025-02-26 DOI: 10.1016/j.scs.2025.106255

Kaili Zhang , Qiqi Liu , Bin Fang , Zhicheng Zhang , Tan Liu , Jianxun Yuan

{"title":"Research on the cool island effect of green spaces in megacity cores: A case study of the main urban area of Xi'an, China","authors":"Kaili Zhang , Qiqi Liu , Bin Fang , Zhicheng Zhang , Tan Liu , Jianxun Yuan","doi":"10.1016/j.scs.2025.106255","DOIUrl":"10.1016/j.scs.2025.106255","url":null,"abstract":"<div><div>Optimizing urban green spaces (UGS) cool island effects is crucial for urban climate resilience. In the main urban region of Xi'an, the UGS cool island effect was thoroughly investigated in this study, which covered pattern detection, characteristic analysis, scale consideration, mechanism discovery, and layout optimization. Among the primary research instruments were the Generalized Additive Model (GAM), the urban cooling model, and optimal parameter geographic identification technology. According to the study, within a 200-meter radius, UGS can considerably lower land surface temperature (LST). The cool island effect is mostly caused by wetlands and woods, and it is most noticeable around parks, water systems, and lakes in the Baqiao District. When exploring the factors influencing HMI, we found that two-dimensional UGS landscape indicators dominate, followed closely by socio-economic factors, with three-dimensional building indicators ranking third. Notably, the interactions between different pairs of factors were all more pronounced than the effects of individual factors. The Sky View Factor (SVF), a crucial three-dimensional indicator, has a significant impact that cannot be disregarded. Complex nonlinear interactions between these major components and HMI are evident, and certain elements may have threshold effects. Consideration of multi-factor interactions and geographical variations is necessary for efficient UGS layout optimization.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"122 ","pages":"Article 106255"},"PeriodicalIF":10.5,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143528956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Unraveling the effects of extreme heat conditions on urban heat environment: Insights from local climate zones and integrated temperature data

IF 10.5 1区工程技术

Sustainable Cities and Society Pub Date : 2025-02-25 DOI: 10.1016/j.scs.2025.106254

Bin Wang , Meiling Gao , Yumin Li , Zhenhong Li , Zhenjiang Liu , Xuesong Zhang , Ying Wen

{"title":"Unraveling the effects of extreme heat conditions on urban heat environment: Insights from local climate zones and integrated temperature data","authors":"Bin Wang , Meiling Gao , Yumin Li , Zhenhong Li , Zhenjiang Liu , Xuesong Zhang , Ying Wen","doi":"10.1016/j.scs.2025.106254","DOIUrl":"10.1016/j.scs.2025.106254","url":null,"abstract":"<div><div>Rapid urbanization and increasing human activities pose significant challenges to urban climates, particularly the urban heat island (UHI) effect, with UHI intensity (UHII) exacerbated by more frequent extreme heat events. Local climate zone (LCZ) provides insights into urban thermal environments but lacks high-accuracy LCZ maps and studies on the extreme heat impacts in non-metropolitan cities. Additionally, gaps exist in understanding how extreme daytime and nighttime heat conditions affect urban heat when integrating seamless near-surface air temperature (NSAT) and land surface temperature (LST) data. To address these gaps, we propose a high-accuracy LCZ mapping framework for the Guanzhong Plain urban agglomeration (GPUA) in China. By combining the LCZ map with 1-km gridded NSAT and LST data derived from machine learning methods, we comprehensively analyze extreme heat effects on surface UHII (SUHII) and canopy UHII (CUHII) at the LCZ scale, considering daytime and nighttime conditions. We also discuss the impacts of changes in radiation fluxes and wind speed associated with extreme heat on UHII. Our findings reveal that: (a) The proposed framework provides an LCZ map over GPUA with an accuracy of 0.84. The maximum RMSE of daytime and nighttime NSAT are 1.73 °C and 1.93 °C, while the maximum RMSE of daytime and nighttime LST are 1.95 °C and 4.20 °C. (b) Extreme heat amplifies NSAT and LST disparities among LCZs, intensifying CUHII and SUHII more during the daytime than at nighttime, although nighttime extreme heat can lower CUHII and SUHII in certain built LCZs. (c) Higher daytime UHII under extreme heat correlates with increased differences in downward longwave radiation between built LCZs and LCZ D. These insights aid in mitigating urban heat risks and guide policymakers and urban planners.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"122 ","pages":"Article 106254"},"PeriodicalIF":10.5,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143548620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Quantifying the combined and individual impacts of climate and human activity on the urban green space carbon sink capacity in Beijing

IF 10.5 1区工程技术

Sustainable Cities and Society Pub Date : 2025-02-25 DOI: 10.1016/j.scs.2025.106253

Kai Zhou, Xi Zheng, Shoubang Huang, Hao Li, Hao Yin

{"title":"Quantifying the combined and individual impacts of climate and human activity on the urban green space carbon sink capacity in Beijing","authors":"Kai Zhou, Xi Zheng, Shoubang Huang, Hao Li, Hao Yin","doi":"10.1016/j.scs.2025.106253","DOIUrl":"10.1016/j.scs.2025.106253","url":null,"abstract":"<div><div>Urban green space plays a crucial role in mitigating climate change through enhancing the carbon sink and ecosystem services in urban areas. Understanding how vegetation responds to both climate and human activity in urban areas is essential for effective green space planning. Despite the existence of large-scale studies examining the effects of climate change and human activity on green space, the specific mechanisms driving the Urban Green Space Carbon Sink Capacity (UGCSC) across different urban functional zones remain unclear. The present study used boosted regression trees and structural equation models to investigate the spatiotemporal dynamics of the UGCSC in Beijing from 2000 to 2020 and to assess the relative contributions of climatic factors and human activity to the UGCSC. The findings indicate that the UGCSC increased by 74.2 % of the study area, with 50.7 % of the change driven primarily by human activity, 24.6 % by climate change, and 24.8 % by their combined effects. Key drivers such as elevation, slope, temperature, and Landscape Shape Index showed varying effects across different functional zones. Climatic factors exhibited significant spatial heterogeneity, with temperature being the most influential, contributing 47.2 % to the UGCSC in central urban areas. Conversely, human activity had a dual impact: it reduced UGCSC in densely urbanized zones due to socioeconomic pressures, while landscape connectivity and green space coverage enhanced UGCSC in development and ecological zones. These insights provide a scientific basis for promoting nature-based solutions and guiding sustainable urban planning with the goal of moving toward carbon neutrality.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"122 ","pages":"Article 106253"},"PeriodicalIF":10.5,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143548908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Application of hybrid machine learning algorithms for life cycle carbon prediction and optimization of buildings: A case study in China

IF 10.5 1区工程技术

Sustainable Cities and Society Pub Date : 2025-02-25 DOI: 10.1016/j.scs.2025.106248

Hongyu Chen , Jingyi Wang , Qiping Geoffrey Shen , Bin Chen , Jiarui Dong , Zongbao Feng , Yang Liu

{"title":"Application of hybrid machine learning algorithms for life cycle carbon prediction and optimization of buildings: A case study in China","authors":"Hongyu Chen , Jingyi Wang , Qiping Geoffrey Shen , Bin Chen , Jiarui Dong , Zongbao Feng , Yang Liu","doi":"10.1016/j.scs.2025.106248","DOIUrl":"10.1016/j.scs.2025.106248","url":null,"abstract":"<div><div>Buildings are a significant source of carbon emissions (CEs). In this work, the life cycle carbon emissions of buildings (LCCEBs) are dynamically calculated, spatiotemporal dynamic evolution laws are analyzed at the macro level, and the LCCEBs and driving factors are predicted and analyzed by integrating geographically and temporally weighted regression (GTWR) with machine learning algorithms. The results of a case study in China show the following. (1) The level of CEs in China has great spatiotemporal and geographical variation. The fitting accuracy of the GTWR prediction model can reach more than 0.75. (2) The accuracy of natural gradient boosting (NGBoost) is higher than the regression fitting accuracy of the GTWR model, especially with larger datasets. (3) The main driving factors obtained from the analysis of LCCEB driving factors using the NGBoost algorithm and SHapley additive explanation (SHAP) are CE per capita at the construction phase (ECP), construction area per capita (EAP), and carbon intensity of operation (OCI). The influence degrees and variation patterns of each factor are clarified, thereby proposing targeted measures for controlling carbon emissions in buildings. The theoretical knowledge of mining spatiotemporal patterns and driving factors of building CEs is enriched, and guidance for formulating policies and measures is provided.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"122 ","pages":"Article 106248"},"PeriodicalIF":10.5,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143548617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Willingness to pay and health benefits of reducing PM2.5 and O3 in China's Jing-Jin-Ji region

IF 10.5 1区工程技术

Sustainable Cities and Society Pub Date : 2025-02-25 DOI: 10.1016/j.scs.2025.106251

Dandan Liu , Hecheng Man , Minghui Xie , Xueying Li , Qi Qiao

{"title":"Willingness to pay and health benefits of reducing PM2.5 and O3 in China's Jing-Jin-Ji region","authors":"Dandan Liu , Hecheng Man , Minghui Xie , Xueying Li , Qi Qiao","doi":"10.1016/j.scs.2025.106251","DOIUrl":"10.1016/j.scs.2025.106251","url":null,"abstract":"<div><div>Quantifying the health benefits of air quality improvement is critical to increase residents' attention to and participation in air pollution control. A health benefit evaluation model for reducing PM2.5 and O<sub>3</sub> by the contingent valuation method (CVM) based on the multiple bounded discrete choice (MBDC) elicitation technique is proposed in this study. This study focuses on the Jing-Jin-Ji region, the willingness to pay (WTP) for reducing PM2.5 and O<sub>3</sub> is obtained via the CVM based on MBDC elicitation technology under two scenarios. A logistic regression model is used to explore influence factor of WTP. Then, the health benefit for reducing PM2.5 and O<sub>3</sub> is estimated by statistical life values and disability-adjusted life years. The WTP was 2916.12-3426.00 yuan/person-year, which was mainly affected by influence degree of air pollution, pollution status, knowledge of the impact on air pollution. The health benefit of reducing PM2.5 and O<sub>3</sub> was 8.46 × 10<sup>5</sup>–4.18 × 10<sup>7</sup> yuan/year. This study provides a new approach into quantifying health benefits for improving air quality and provides a reference for the formulation of market-oriented incentive mechanisms.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"122 ","pages":"Article 106251"},"PeriodicalIF":10.5,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143511815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Revealing key factors of heat-related illnesses using geospatial explainable AI model: A case study in Texas, USA

IF 10.5 1区工程技术

Sustainable Cities and Society Pub Date : 2025-02-25 DOI: 10.1016/j.scs.2025.106243

Ehsan Foroutan , Tao Hu , Ziqi Li

{"title":"Revealing key factors of heat-related illnesses using geospatial explainable AI model: A case study in Texas, USA","authors":"Ehsan Foroutan , Tao Hu , Ziqi Li","doi":"10.1016/j.scs.2025.106243","DOIUrl":"10.1016/j.scs.2025.106243","url":null,"abstract":"<div><div>The increasing frequency of extreme weather has led to a notable rise in heat-related health issues. Machine learning algorithms have shown promise in modeling and predicting such outcomes. However, previous studies often neglect spatial components, overlooking the importance of spatial heterogeneity in assessing regional differences in environmental impacts. This study addresses these gaps by employing the geospatial explainable AI (GeoXAI) framework to enhance the spatial interpretability of complex models. The main objective of this study is to understand how geographic location influences factors associated with heat-related emergency department visits (EDVs) across urban and rural areas in Texas. We first leverage automated machine learning (AutoML) to optimize model selection. Then, we employ the GeoShapley approach to analyze the spatial variability of factors contributing to heat-related EDVs. Key findings revealed significant spatial variability and distinct feature importance across urban and rural areas. Socioeconomic and demographic factors were more strongly associated with vulnerability to heat-related health incidents compared to environmental and meteorological variables. Additionally, infrastructure elements, such as transportation systems, were associated with an increased risk of heat in urban areas. These findings highlight the necessity of incorporating geospatial analysis into heat vulnerability assessments to inform targeted public health interventions. By recognizing spatial variability in risk factors, policymakers can implement location-specific strategies to reduce heat-related health burdens, particularly in vulnerable urban communities.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"122 ","pages":"Article 106243"},"PeriodicalIF":10.5,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143511816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Effects of tree characteristics and arcade design on the traffic pollutant dispersion inside the asymmetric street canyon

IF 10.5 1区工程技术

Sustainable Cities and Society Pub Date : 2025-02-24 DOI: 10.1016/j.scs.2025.106244

Yang Luo, Zuohong Yin, Qianfeng Liang, Cheng Yao, Chenlong Bao, Yiping Wu, Yuandong Huang

{"title":"Effects of tree characteristics and arcade design on the traffic pollutant dispersion inside the asymmetric street canyon","authors":"Yang Luo, Zuohong Yin, Qianfeng Liang, Cheng Yao, Chenlong Bao, Yiping Wu, Yuandong Huang","doi":"10.1016/j.scs.2025.106244","DOIUrl":"10.1016/j.scs.2025.106244","url":null,"abstract":"<div><div>Arcade design and tree planting both provide shading for pedestrians and improve the microenvironment of street canyons in urban areas, but their combined effect on air quality remains underexplored. To address this gap, wind tunnel experiments and numerical simulations were conducted in this study to analyze airflow and pollutant dispersion in a step-up street canyon, focusing on variables such as tree canopy shapes, pressure loss coefficients, and planting gaps. The results demonstrate that selecting tree with low-pressure loss coefficients and increasing canopy spacing significantly improve ventilation efficiency. Notably, triangular canopies outperform rectangular canopies in promoting airflow and pollutant dispersion, a trend observed in both step-up and step-down canyons with similar building height differentials. For step-up canyons, air exchange rates (ACH) are lowest in configurations without arcades, with leeward-side arcades outperforming windward-side arcades. Leeward-side arcades reduce pedestrian-level pollutant concentrations by 6.1% compared to non-arcade canyons. This study concludes that combining leeward-side arcades with triangular canopies is the most effective strategy for enhancing ventilation and reducing pollutant concentrations in step-up street canyons. This approach is particularly beneficial in hot, sun-exposed regions, providing both shading and improved air quality. The findings offer actionable recommendations for optimizing street canyon environments through integrated design strategies.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"122 ","pages":"Article 106244"},"PeriodicalIF":10.5,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143519058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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