Sustainable Cities and Society最新文献_第2页

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 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

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

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.

{"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 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

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

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.

{"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 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

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

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.

{"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 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

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

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 O₃ 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 O₃ 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 O₃ 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 O₃ was 8.46 × 10⁵–4.18 × 10⁷ 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.

{"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 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Sustainable Cities and Society

Pub Date : 2025-02-25 DOI: 10.1016/j.scs.2025.106243

Ehsan Foroutan , Tao Hu , Ziqi Li

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.

{"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 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

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

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.

{"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

Geospatial clustering as a method to reduce the computational load in urban building energy simulation

IF 10.5 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Sustainable Cities and Society

Pub Date : 2025-02-24 DOI: 10.1016/j.scs.2025.106247

Mohamad Hasan Khajedehi, Enrico Prataviera, Sara Bordignon, Angelo Zarrella, Michele De Carli

Since the recent birth of physics-based urban building energy modeling (UBEM), researchers have started tackling the issues characterizing this research field, mainly linked to the lack of extensive and standardized building information datasets and the necessity of simplifying the modeling process. Concerning the latter, geospatial clustering approaches seem to be plausible methods to reduce the computational load in urban simulation, and this work aims to test their suitability and performance.

For this purpose, a case study of almost 3800 buildings in Padova, Italy, is analyzed. The tendency analysis is first used to quantify the underlying clusters that could be present. The study of this metric reveals the organic morphology and the heterogeneity of building stock in European cities like Padova. Additionally, several clustering algorithms are applied to the location, use, envelope, and geometry variables to simulate building clusters and quantify the increase in geometric and heating/cooling demand uncertainty.

Results show that, for this case study, building clusters are characterized by lower volumes than when considering single buildings, which is also reflected in a lower heating and cooling demand prediction. Nonetheless, these errors are found to be in an acceptable range (less than 6%) for UBEM applications.

{"title":"Geospatial clustering as a method to reduce the computational load in urban building energy simulation","authors":"Mohamad Hasan Khajedehi, Enrico Prataviera, Sara Bordignon, Angelo Zarrella, Michele De Carli","doi":"10.1016/j.scs.2025.106247","DOIUrl":"10.1016/j.scs.2025.106247","url":null,"abstract":"<div><div>Since the recent birth of physics-based urban building energy modeling (UBEM), researchers have started tackling the issues characterizing this research field, mainly linked to the lack of extensive and standardized building information datasets and the necessity of simplifying the modeling process. Concerning the latter, geospatial clustering approaches seem to be plausible methods to reduce the computational load in urban simulation, and this work aims to test their suitability and performance.</div><div>For this purpose, a case study of almost 3800 buildings in Padova, Italy, is analyzed. The tendency analysis is first used to quantify the underlying clusters that could be present. The study of this metric reveals the organic morphology and the heterogeneity of building stock in European cities like Padova. Additionally, several clustering algorithms are applied to the location, use, envelope, and geometry variables to simulate building clusters and quantify the increase in geometric and heating/cooling demand uncertainty.</div><div>Results show that, for this case study, building clusters are characterized by lower volumes than when considering single buildings, which is also reflected in a lower heating and cooling demand prediction. Nonetheless, these errors are found to be in an acceptable range (less than 6%) for UBEM applications.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"122 ","pages":"Article 106247"},"PeriodicalIF":10.5,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143548616","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

Combining the WRF model and LCZ scheme to assess spatiotemporal variations of thermal comfort in Shenzhen's built-up areas

IF 10.5 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Sustainable Cities and Society

Pub Date : 2025-02-24 DOI: 10.1016/j.scs.2025.106252

Jiacheng Huang , Zhengdong Huang , Wen Liu

Applying the local climate zone (LCZ) scheme is effective for guiding the urban morphology to enhance outdoor thermal comfort. Previous studies have extensively explored thermal comfort in built-up areas and their inter-LCZ variations by applying temperature attributes. However, the combined effects of other factors (humidity and wind speed) have received little attention, and intra-LCZ thermal comfort variations are not fully understood. This study aimed to assess spatiotemporal variations in thermal comfort across built-up LCZs based on multiple meteorological factors. We incorporated the Weather Research and Forecasting model with the LCZ scheme and calculated the net effective temperature using simulated air temperature, relative humidity, and wind speed. Inter-LCZ and intra-LCZ thermal comfort variations were analyzed using spatial autocorrelation and statistical methods. The study was conducted during both dry and wet seasons in the subtropical city of Shenzhen, China. The results revealed that 1) the southwestern area experienced the poorest thermal comfort during the wet season owing to high temperatures and low wind speeds; 2) significant inter-LCZ thermal comfort differences existed within the same season, with higher development intensity correlating to poorer thermal comfort; and 3) intra-LCZ thermal comfort varied across spatial locations and fluctuated with the season and time of day.

{"title":"Combining the WRF model and LCZ scheme to assess spatiotemporal variations of thermal comfort in Shenzhen's built-up areas","authors":"Jiacheng Huang , Zhengdong Huang , Wen Liu","doi":"10.1016/j.scs.2025.106252","DOIUrl":"10.1016/j.scs.2025.106252","url":null,"abstract":"<div><div>Applying the local climate zone (LCZ) scheme is effective for guiding the urban morphology to enhance outdoor thermal comfort. Previous studies have extensively explored thermal comfort in built-up areas and their inter-LCZ variations by applying temperature attributes. However, the combined effects of other factors (humidity and wind speed) have received little attention, and intra-LCZ thermal comfort variations are not fully understood. This study aimed to assess spatiotemporal variations in thermal comfort across built-up LCZs based on multiple meteorological factors. We incorporated the Weather Research and Forecasting model with the LCZ scheme and calculated the net effective temperature using simulated air temperature, relative humidity, and wind speed. Inter-LCZ and intra-LCZ thermal comfort variations were analyzed using spatial autocorrelation and statistical methods. The study was conducted during both dry and wet seasons in the subtropical city of Shenzhen, China. The results revealed that 1) the southwestern area experienced the poorest thermal comfort during the wet season owing to high temperatures and low wind speeds; 2) significant inter-LCZ thermal comfort differences existed within the same season, with higher development intensity correlating to poorer thermal comfort; and 3) intra-LCZ thermal comfort varied across spatial locations and fluctuated with the season and time of day.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"122 ","pages":"Article 106252"},"PeriodicalIF":10.5,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143548619","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

Hydrodynamic model-based flood risk of coastal urban road network induced by storm surge during typhoon

IF 10.5 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Sustainable Cities and Society

Pub Date : 2025-02-24 DOI: 10.1016/j.scs.2025.106250

Yan Li , Sige Peng , Jingmin Xu , Tao Xu , Junliang Gao

The occurrence of storm surges during typhoons results in the exacerbation of flooding incidents in coastal cities, with road networks vulnerable to inundation facing an intensified risk. This study presents a framework for assessing the flood risk of urban road networks resulting from the storm surge caused by Typhoon Mangkhut in Macau. Tidal changes in the Pearl River Estuary were simulated using a storm surge model integrated with a cyclone wind field. A high-resolution, small-scale urban hydrodynamic model, accounting for buildings and drainage systems, was further developed. Based on the flood characteristics within the model grid and the stability of people and vehicles, the threat posed by the typhoon-induced storm surge on urban roads was estimated. The results indicate that the maximum storm surge in the Pearl River Estuary during Typhoon Mangkhut exceeded 4.0 m, with approximately 25 % of roads experiencing flooding depth greater than 1.5 m. Most vehicles were at risk of instability, while fewer areas on the west coast of the Macau Peninsula presented a risk to human stability on flooded roads. The findings of this study contribute to the development of flood risk management strategies and emergency evacuation during typhoons.

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引用次数: 0

Quantifying the impact of built environment on traffic congestion: A nonlinear analysis and optimization strategy for sustainable urban planning

IF 10.5 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Sustainable Cities and Society

Pub Date : 2025-02-24 DOI: 10.1016/j.scs.2025.106249

Heng Ding, Zhengrui Zhao, Shiguang Wang, Yubin Zhang, Xiaoyan Zheng, Xiaoshan Lu

Traffic congestion is a critical issue that must be addressed for sustainable urban development, as it directly impacts residents’ quality of life and the economic vitality of cities. Understanding the mechanisms through which the built environment (BE) influences traffic performance is essential for optimizing the sustainable development of future cities. To this end, we first identified six categories of BE indicators, including road network design, traffic convenience, regional economic level, accessibility, population density, and land use mix. These indicators were then used to establish a comprehensive evaluation framework for characterizing the built environment. Subsequently, a composite traffic congestion status model was developed using clustering techniques, and a nonlinear impact model of composite traffic congestion status was constructed based on the Gradient Boosting Decision Tree (GBDT) method. Finally, we analyzed the nonlinear impact mechanism of built environment characteristics on traffic congestion using Hefei, China as a case study, and proposed regulatory optimization strategies. By strategically optimizing BE factors, traffic congestion within the study area was alleviated to varying degrees. The findings provide valuable insights for urban planners and policymakers to better understand the influence of the built environment on transportation performance, offering guidance for designing more efficient transportation systems and promoting sustainable urban development.

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引用次数: 0