Pub Date : 2026-01-27DOI: 10.1016/j.buildenv.2026.114285
Xia Chen , Ruiji Sun , Philipp Geyer , André Borrmann , Stefano Schiavon
Human-factor analysis typically employs correlation analysis and significance testing to identify relationships between variables. However, these descriptive (‘what-is’) methods, while effective for identifying associations, are often insufficient for answering causal (‘what-if’) questions. Their application in such contexts often overlooks confounding and colliding variables, potentially leading to bias and suboptimal or incorrect decisions. We advocate for explicitly distinguishing descriptive from interventional questions in human-factor analysis, and applying causal inference frameworks specifically to these problems to prevent methodological mismatches. This approach disentangles complex variable relationships and enables counterfactual reasoning. Using post-occupancy evaluation (POE) data from the Center for the Built Environment’s (CBE) Occupant Survey as a demonstration case, we show how causal discovery generates testable hypotheses about intervention hierarchies and directional relationships that traditional associational analysis cannot explore. The systematic distinction between causally associated and independent variables, combined with intervention prioritization capabilities, offers broad applicability to complex human-centric systems, for example, in building science or ergonomics, where understanding intervention effects is critical for optimization and decision-making.
{"title":"From ‘What-is’ to ‘What-if’ in human-factor analysis: A post-occupancy evaluation case","authors":"Xia Chen , Ruiji Sun , Philipp Geyer , André Borrmann , Stefano Schiavon","doi":"10.1016/j.buildenv.2026.114285","DOIUrl":"10.1016/j.buildenv.2026.114285","url":null,"abstract":"<div><div>Human-factor analysis typically employs correlation analysis and significance testing to identify relationships between variables. However, these descriptive (‘what-is’) methods, while effective for identifying associations, are often insufficient for answering causal (‘what-if’) questions. Their application in such contexts often overlooks confounding and colliding variables, potentially leading to bias and suboptimal or incorrect decisions. We advocate for explicitly distinguishing descriptive from interventional questions in human-factor analysis, and applying causal inference frameworks specifically to these problems to prevent methodological mismatches. This approach disentangles complex variable relationships and enables counterfactual reasoning. Using post-occupancy evaluation (POE) data from the Center for the Built Environment’s (CBE) Occupant Survey as a demonstration case, we show how causal discovery generates testable hypotheses about intervention hierarchies and directional relationships that traditional associational analysis cannot explore. The systematic distinction between causally associated and independent variables, combined with intervention prioritization capabilities, offers broad applicability to complex human-centric systems, for example, in building science or ergonomics, where understanding intervention effects is critical for optimization and decision-making.</div></div>","PeriodicalId":9273,"journal":{"name":"Building and Environment","volume":"292 ","pages":"Article 114285"},"PeriodicalIF":7.6,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146076676","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}
Pub Date : 2026-01-26DOI: 10.1016/j.buildenv.2026.114295
Bekir Huseyin Tekin
The accelerating impacts of climate change demand design frameworks that reduce environmental burdens while enhancing human well-being. Although biophilic design is increasingly recognised as a regenerative paradigm, its direct application in hot-arid regions remains limited and often unsuitably adapted from temperate contexts, resulting in “greenwashing” and ecological imbalance. As arid regions continue to expand, the need for climate-adaptive design in these contexts is increasingly urgent. Using an exploratory sequential mixed-methods design, this study synthesises a systematic review of six high-quality papers and in-depth interviews with five regional experts to develop a conceptual framework for biophilic design in desert environments. The findings show that temperate biophilia, largely aesthetic and visual, fails under arid conditions. The framework defines two interlinked dimensions of success: the human response (thermal comfort, well-being, and cultural resonance) and the ecological response (resource efficiency and habitat creation). It identifies passive design, water neutrality, native flora, and privacy-sensitive spatial configurations as prerequisites for restorative biophilic environments. Key strategies include hybrid ventilation, adaptive shading hierarchies, kinetic façades, reuse of traditional materials, and multi-scalar microclimates that mitigate urban heat. Acting as a complementary conceptual layer to regional sustainability standards (e.g., Al Sa’fat, GSAS, Mostadam, Estidama), the framework positions biophilic design as a climate-adaptive, ecologically grounded, and culturally embedded methodology. This work provides a conceptual foundation for policymakers and designers seeking integrated strategies that align climate mitigation with human adaptation in the rapidly expanding climatic zone.
气候变化的加速影响要求设计框架在减少环境负担的同时提高人类福祉。尽管亲生物设计越来越被认为是一种再生模式，但其在炎热干旱地区的直接应用仍然有限，而且往往不适合温带环境，导致“绿色清洗”和生态失衡。随着干旱地区的不断扩大，在这些情况下对气候适应性设计的需求日益迫切。本研究采用探索性顺序混合方法设计，综合了六篇高质量论文的系统综述，并与五位地区专家进行了深入访谈，以开发沙漠环境中亲生物设计的概念框架。研究结果表明，在干旱条件下，温带的亲生物性，主要是审美和视觉上的，失败了。该框架定义了成功的两个相互关联的维度：人类反应（热舒适、幸福感和文化共鸣）和生态反应（资源效率和栖息地创造）。它确定了被动设计、水中性、本地植物群和隐私敏感的空间配置，作为恢复亲生物环境的先决条件。关键策略包括混合通风、自适应遮阳层次、动态立面、传统材料的再利用以及缓解城市热量的多尺度微气候。作为区域可持续性标准（如Al Sa 'fat、GSAS、Mostadam、Estidama）的补充概念层，该框架将亲生物设计定位为一种气候适应、生态基础和文化嵌入的方法。这项工作为决策者和设计者寻求在迅速扩大的气候带中使气候减缓与人类适应相结合的综合战略提供了概念基础。
{"title":"From greenwashing to grounded practice: A context-specific biophilic design framework for hot arid climates","authors":"Bekir Huseyin Tekin","doi":"10.1016/j.buildenv.2026.114295","DOIUrl":"10.1016/j.buildenv.2026.114295","url":null,"abstract":"<div><div>The accelerating impacts of climate change demand design frameworks that reduce environmental burdens while enhancing human well-being. Although biophilic design is increasingly recognised as a regenerative paradigm, its direct application in hot-arid regions remains limited and often unsuitably adapted from temperate contexts, resulting in “greenwashing” and ecological imbalance. As arid regions continue to expand, the need for climate-adaptive design in these contexts is increasingly urgent. Using an exploratory sequential mixed-methods design, this study synthesises a systematic review of six high-quality papers and in-depth interviews with five regional experts to develop a conceptual framework for biophilic design in desert environments. The findings show that temperate biophilia, largely aesthetic and visual, fails under arid conditions. The framework defines two interlinked dimensions of success: the human response (thermal comfort, well-being, and cultural resonance) and the ecological response (resource efficiency and habitat creation). It identifies passive design, water neutrality, native flora, and privacy-sensitive spatial configurations as prerequisites for restorative biophilic environments. Key strategies include hybrid ventilation, adaptive shading hierarchies, kinetic façades, reuse of traditional materials, and multi-scalar microclimates that mitigate urban heat. Acting as a complementary conceptual layer to regional sustainability standards (e.g., Al Sa’fat, GSAS, Mostadam, Estidama), the framework positions biophilic design as a climate-adaptive, ecologically grounded, and culturally embedded methodology. This work provides a conceptual foundation for policymakers and designers seeking integrated strategies that align climate mitigation with human adaptation in the rapidly expanding climatic zone.</div></div>","PeriodicalId":9273,"journal":{"name":"Building and Environment","volume":"292 ","pages":"Article 114295"},"PeriodicalIF":7.6,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146049059","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}
Pub Date : 2026-01-26DOI: 10.1016/j.buildenv.2026.114296
Zheng Huang , Ruiqi Jiang , Qiuyan Yang , Yunpeng Shi , Mingdao Zhang , Qiang Liu
Optical see-through augmented reality (OST-AR) is increasingly used in both consumer and professional contexts where accurate color presentation is critical. However, the effects of indoor spatial lighting on color discrimination in OST-AR glasses remain underexplored. This study systematically investigates how correlated color temperature (CCT), illuminance, and spatial lighting distribution (i.e., wall lighting, ceiling lighting, and mixed lighting) affect color discrimination performance when using birdbath-type OST-AR glasses. A total of 64 participants were recruited to participate in three experiments, each targeting one of the three spatial lighting distributions. Each experiment was conducted in a controlled laboratory setting, employing a color discrimination task with four hues (i.e., red, yellow, green, blue). Discrimination thresholds were quantified using fitted ellipses based on the concept of MacAdam ellipses in the CIE 1976 u′v′ chromaticity diagram. Results show that yellow color exhibits the lowest color discrimination threshold and blue the highest across all lighting conditions. The color discrimination thresholds of all colors range from 0.004 to 0.008 u′v′ units. Both CCT and illuminance of wall lighting significantly affect discrimination performance, with higher CCT and lower illuminance improving color discrimination sensitivity. Wall lighting and ceiling lighting each demonstrate a significant main effect when tested independently, but wall lighting dominates over ceiling lighting under mixed lighting condition. This study demonstrates that spatial lighting distribution and lighting parameters (CCT, illuminance) are key factors regulating color discrimination in OST-AR glasses, providing a scientific basis for optimizing lighting environments for AR application as well as device design.
光学透明增强现实（OST-AR）越来越多地用于消费者和专业环境中，准确的颜色呈现至关重要。然而，室内空间照明对OST-AR眼镜辨色的影响仍未得到充分研究。本研究系统探讨了相关色温（CCT）、照度和空间光照分布（即墙面照明、天花板照明和混合照明）对鸟浴式OST-AR眼镜辨色性能的影响。总共招募了64名参与者参加三个实验，每个实验针对三种空间照明分布中的一种。每个实验都在受控的实验室环境中进行，采用四种色调（即红、黄、绿、蓝）的辨色任务。根据CIE 1976 u ‘ v ’色度图中MacAdam椭圆的概念，使用拟合椭圆对判别阈值进行量化。结果表明，在所有照明条件下，黄色表现出最低的颜色识别阈值，蓝色表现出最高的颜色识别阈值。所有颜色的辨色阈值范围为0.004 ~ 0.008 u’v’单位。墙体照明的CCT和照度均显著影响分辨性能，较高的CCT和较低的照度可提高辨色灵敏度。墙壁照明和天花板照明在单独测试时都显示出显著的主要影响，但在混合照明条件下，墙壁照明优于天花板照明。本研究表明，空间照明分布和照明参数（CCT、照度）是影响OST-AR眼镜辨色的关键因素，为优化AR应用的照明环境和器件设计提供了科学依据。
{"title":"Impact of spatial lighting on color discrimination in optical see-through augmented reality: The role of CCT and illuminance","authors":"Zheng Huang , Ruiqi Jiang , Qiuyan Yang , Yunpeng Shi , Mingdao Zhang , Qiang Liu","doi":"10.1016/j.buildenv.2026.114296","DOIUrl":"10.1016/j.buildenv.2026.114296","url":null,"abstract":"<div><div>Optical see-through augmented reality (OST-AR) is increasingly used in both consumer and professional contexts where accurate color presentation is critical. However, the effects of indoor spatial lighting on color discrimination in OST-AR glasses remain underexplored. This study systematically investigates how correlated color temperature (CCT), illuminance, and spatial lighting distribution (i.e., wall lighting, ceiling lighting, and mixed lighting) affect color discrimination performance when using birdbath-type OST-AR glasses. A total of 64 participants were recruited to participate in three experiments, each targeting one of the three spatial lighting distributions. Each experiment was conducted in a controlled laboratory setting, employing a color discrimination task with four hues (i.e., red, yellow, green, blue). Discrimination thresholds were quantified using fitted ellipses based on the concept of MacAdam ellipses in the CIE 1976 <em>u′v′</em> chromaticity diagram. Results show that yellow color exhibits the lowest color discrimination threshold and blue the highest across all lighting conditions. The color discrimination thresholds of all colors range from 0.004 to 0.008 <em>u′v′</em> units. Both CCT and illuminance of wall lighting significantly affect discrimination performance, with higher CCT and lower illuminance improving color discrimination sensitivity. Wall lighting and ceiling lighting each demonstrate a significant main effect when tested independently, but wall lighting dominates over ceiling lighting under mixed lighting condition. This study demonstrates that spatial lighting distribution and lighting parameters (CCT, illuminance) are key factors regulating color discrimination in OST-AR glasses, providing a scientific basis for optimizing lighting environments for AR application as well as device design.</div></div>","PeriodicalId":9273,"journal":{"name":"Building and Environment","volume":"292 ","pages":"Article 114296"},"PeriodicalIF":7.6,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146076656","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}
Pub Date : 2026-01-25DOI: 10.1016/j.buildenv.2026.114294
Yueping Luo , Victor Nee Shin Bong , Jibril Adewale Bamgbade , Morshed Alam , San Chuin Liew
As a significant source of global carbon emissions, the construction industry’s full life cycle carbon emission assessment is crucial for addressing climate change. Although a substantial body of relevant research has been accumulated in academia, significant differences still exist among countries and regions in terms of research depth, system coverage, and tool application. Therefore, systematically reviewing the status, trends, and challenges of carbon emissions in the building sector holds important significance for achieving carbon neutrality goals. This study analyzed 80 peer‑reviewed papers using quantitative and qualitative methods to compare China’s research progress with that of other countries from the perspectives of research focus, methodological approach, and software tools. The results indicate that life‑cycle assessment (LCA) has become the mainstream analytical method worldwide; however, methodological subjectivity in defining system boundaries continues to limit result comparability. Chinese studies tend to emphasize emissions in the construction phase, while international research concentrates on operational and maintenance stages, revealing different developmental priorities between emerging and mature construction markets. In terms of technical tools, China still relies heavily on domestic platforms such as Glodon and less on advanced simulation tools like SimaPro or EnergyPlus, suggesting opportunities for deeper integration with international standards and digital technologies. Overall, these patterns highlight a global convergence toward comprehensive, tool‑assisted life‑cycle carbon assessment, yet also expose the uneven pace of methodological and technological advancements across regions. By quantifying these gaps, this study provides empirical and strategic insights to support the green and low‑carbon transformation of the global construction industry.
{"title":"A comparative review of whole-life-cycle carbon emission assessment in the building sector: progress, challenges, and trends in China and globally","authors":"Yueping Luo , Victor Nee Shin Bong , Jibril Adewale Bamgbade , Morshed Alam , San Chuin Liew","doi":"10.1016/j.buildenv.2026.114294","DOIUrl":"10.1016/j.buildenv.2026.114294","url":null,"abstract":"<div><div>As a significant source of global carbon emissions, the construction industry’s full life cycle carbon emission assessment is crucial for addressing climate change. Although a substantial body of relevant research has been accumulated in academia, significant differences still exist among countries and regions in terms of research depth, system coverage, and tool application. Therefore, systematically reviewing the status, trends, and challenges of carbon emissions in the building sector holds important significance for achieving carbon neutrality goals. This study analyzed 80 peer‑reviewed papers using quantitative and qualitative methods to compare China’s research progress with that of other countries from the perspectives of research focus, methodological approach, and software tools. The results indicate that life‑cycle assessment (LCA) has become the mainstream analytical method worldwide; however, methodological subjectivity in defining system boundaries continues to limit result comparability. Chinese studies tend to emphasize emissions in the construction phase, while international research concentrates on operational and maintenance stages, revealing different developmental priorities between emerging and mature construction markets. In terms of technical tools, China still relies heavily on domestic platforms such as Glodon and less on advanced simulation tools like SimaPro or EnergyPlus, suggesting opportunities for deeper integration with international standards and digital technologies. Overall, these patterns highlight a global convergence toward comprehensive, tool‑assisted life‑cycle carbon assessment, yet also expose the uneven pace of methodological and technological advancements across regions. By quantifying these gaps, this study provides empirical and strategic insights to support the green and low‑carbon transformation of the global construction industry.</div></div>","PeriodicalId":9273,"journal":{"name":"Building and Environment","volume":"292 ","pages":"Article 114294"},"PeriodicalIF":7.6,"publicationDate":"2026-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146076658","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}
Pub Date : 2026-01-24DOI: 10.1016/j.buildenv.2026.114274
Jiongye Li , Yingwei Yan , Rudi Stouffs
The cooling capacity of Urban Green Spaces (UGS) has been widely recognized, yet how to distribute UGS to maximize their cooling effect remains a critical issue. Previous studies have revealed the unequal spatial distribution of UGS using diverse methods. However, existing evaluation approaches often fail to adequately account for sufficient temporal coverage and spatial distribution of Urban High-Temperature Hot Spots (UHS), resulting in less accurate and informative outcomes. This study proposes a novel framework to evaluate UGS distribution across multiple years in Miami-Dade County, Florida, thereby reducing the influence of occasional annual fluctuations while incorporating the spatial distribution of UHS. Applying this framework, we find that communities with high and very high socioeconomic status consistently enjoy high accessibility to UGS and low exposure to UHS. By contrast, although low and very low socioeconomic communities have experienced improvements in UGS accessibility and availability, they have also faced increasing exposure to UHS over time. Furthermore, the highest level of inequity in UGS distribution occurs in high and very high socioeconomic communities, due to some areas holding disproportionately high cooling resources while remaining largely shielded from UHS. Our findings also reveal that communities with higher proportions of white populations, higher incomes, and higher housing prices are associated with lower exposure to UHS and greater access to UGS. The proposed framework advances current research by providing an improved method for evaluating equity in UGS distribution and offers practical insights to guide urban planners in identifying inequitable communities.
城市绿地（Urban Green Spaces， UGS）的降温能力已得到广泛认可，但如何合理分配城市绿地以使其降温效果最大化仍是一个关键问题。以往的研究使用不同的方法揭示了UGS的空间分布不均匀。然而，现有的评价方法往往不能充分考虑城市高温热点（UHS）的时间覆盖范围和空间分布，导致结果的准确性和信息量不足。本研究提出了一个新的框架来评估佛罗里达州迈阿密-戴德县UGS的多年分布，从而减少了偶尔的年度波动的影响，同时纳入了UHS的空间分布。应用这一框架，我们发现社会经济地位高和非常高的社区始终享有高的UGS可及性和低的UHS暴露。相比之下，虽然低和非常低的社会经济社区在UGS的可及性和可用性方面有所改善，但随着时间的推移，他们也面临着越来越多的UHS暴露。此外，UGS分布的不平等程度最高发生在高和非常高的社会经济社区，因为一些地区拥有不成比例的高冷却资源，而在很大程度上仍然受到UHS的保护。我们的研究结果还表明，白人人口比例较高、收入较高、房价较高的社区，UHS的暴露率较低，UGS的使用率较高。该框架提供了一种评估UGS分配公平性的改进方法，从而推进了当前的研究，并为指导城市规划者识别不公平社区提供了实际见解。
{"title":"Reevaluating the equity of urban green space distribution: a proposed method integrating urban high-temperature hot spots and temporal dimensions","authors":"Jiongye Li , Yingwei Yan , Rudi Stouffs","doi":"10.1016/j.buildenv.2026.114274","DOIUrl":"10.1016/j.buildenv.2026.114274","url":null,"abstract":"<div><div>The cooling capacity of Urban Green Spaces (UGS) has been widely recognized, yet how to distribute UGS to maximize their cooling effect remains a critical issue. Previous studies have revealed the unequal spatial distribution of UGS using diverse methods. However, existing evaluation approaches often fail to adequately account for sufficient temporal coverage and spatial distribution of Urban High-Temperature Hot Spots (UHS), resulting in less accurate and informative outcomes. This study proposes a novel framework to evaluate UGS distribution across multiple years in Miami-Dade County, Florida, thereby reducing the influence of occasional annual fluctuations while incorporating the spatial distribution of UHS. Applying this framework, we find that communities with high and very high socioeconomic status consistently enjoy high accessibility to UGS and low exposure to UHS. By contrast, although low and very low socioeconomic communities have experienced improvements in UGS accessibility and availability, they have also faced increasing exposure to UHS over time. Furthermore, the highest level of inequity in UGS distribution occurs in high and very high socioeconomic communities, due to some areas holding disproportionately high cooling resources while remaining largely shielded from UHS. Our findings also reveal that communities with higher proportions of white populations, higher incomes, and higher housing prices are associated with lower exposure to UHS and greater access to UGS. The proposed framework advances current research by providing an improved method for evaluating equity in UGS distribution and offers practical insights to guide urban planners in identifying inequitable communities.</div></div>","PeriodicalId":9273,"journal":{"name":"Building and Environment","volume":"292 ","pages":"Article 114274"},"PeriodicalIF":7.6,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146076660","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}
Pub Date : 2026-01-24DOI: 10.1016/j.buildenv.2026.114287
Yinqiao Zhou , Wei Cao , Jiandong Zhou
Urban heat mitigation has become a key issue for high-density cities. Understanding the impact mechanisms and thresholds of urban land use patterns on the surface thermal environment is crucial for cities to formulate climate response planning and design strategies. Multi-source remote sensing data of Shanghai, China were used to retrieve the land surface temperature (LST) and the 2D/3D morphological indicators. The Random Forest-SHAP method was applied to explore the multi-scale and multi-seasonal impacts and threshold effects of urban morphology on surface thermal effect intensity (STEI). The results showed that: (1) Construction land was the major contributor to urban warming in Shanghai, whereas water bodies were the main cooling factor; vegetation contributed relatively little to STEI and exhibited seasonal variations. (2) The impacts of 2D and 3D morphological indicators on STEI exhibited significant scale effects. At a small scale (300 m), 3D morphological indicators were the most influential in summer (52.57%), whereas at a large scale (2400 m), 2D morphological indicators played the most important role in spring (90.64%). The average importance of 2D morphological indicators (73.01%) was greater than that of 3D morphological indicators (26.99%). (3) The leading morphological indicators influencing STEI varied across seasons. STEI was primarily influenced by the 2D composition index of water bodies in spring, by the 3D building index in summer, and by both the 2D and 3D building indices in autumn and winter. (4) Threshold effects were identified in the influences of 2D and 3D morphological indicators on STEI, with thresholds varying across scales and seasons. These findings reveal the nonlinear impact mechanisms of urban land morphology on STEI, which can provide a reference for urban planning theory and practice aimed at heat mitigation.
{"title":"The multi-scale and multi seasonal effects of 2D/3D morphology on urban thermal environment: Mechanism, importance and thresholds","authors":"Yinqiao Zhou , Wei Cao , Jiandong Zhou","doi":"10.1016/j.buildenv.2026.114287","DOIUrl":"10.1016/j.buildenv.2026.114287","url":null,"abstract":"<div><div>Urban heat mitigation has become a key issue for high-density cities. Understanding the impact mechanisms and thresholds of urban land use patterns on the surface thermal environment is crucial for cities to formulate climate response planning and design strategies. Multi-source remote sensing data of Shanghai, China were used to retrieve the land surface temperature (LST) and the 2D/3D morphological indicators. The Random Forest-SHAP method was applied to explore the multi-scale and multi-seasonal impacts and threshold effects of urban morphology on surface thermal effect intensity (STEI). The results showed that: (1) Construction land was the major contributor to urban warming in Shanghai, whereas water bodies were the main cooling factor; vegetation contributed relatively little to STEI and exhibited seasonal variations. (2) The impacts of 2D and 3D morphological indicators on STEI exhibited significant scale effects. At a small scale (300 m), 3D morphological indicators were the most influential in summer (52.57%), whereas at a large scale (2400 m), 2D morphological indicators played the most important role in spring (90.64%). The average importance of 2D morphological indicators (73.01%) was greater than that of 3D morphological indicators (26.99%). (3) The leading morphological indicators influencing STEI varied across seasons. STEI was primarily influenced by the 2D composition index of water bodies in spring, by the 3D building index in summer, and by both the 2D and 3D building indices in autumn and winter. (4) Threshold effects were identified in the influences of 2D and 3D morphological indicators on STEI, with thresholds varying across scales and seasons. These findings reveal the nonlinear impact mechanisms of urban land morphology on STEI, which can provide a reference for urban planning theory and practice aimed at heat mitigation.</div></div>","PeriodicalId":9273,"journal":{"name":"Building and Environment","volume":"292 ","pages":"Article 114287"},"PeriodicalIF":7.6,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146076657","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}
Pub Date : 2026-01-24DOI: 10.1016/j.buildenv.2026.114289
Siqi Huang , Mingxing Zhang , Cuiping Yan , Junzhe Fan , Shu Liu , Xingla Li
In traditional low-velocity supply systems, non-directional exhaust inflow reduces control performance. This study investigates a low-velocity jet-assisted curtain formed along the perimeter of an exhaust hood to enhance capture efficiency. The auxiliary jet formed along the hood edge effectively enhanced the stability of the capture flow and improved the pollutant control performance. Through experimental measurements and CFD simulations, the results show that capture efficiency is significantly improved only when the curtain outlet velocity reaches a critical value, with further increases having little effect on the centerline velocity. Airflow rate, jet outlet angle, outlet width, and hood equivalent diameter significantly influence both critical velocity and centerline velocity: airflow rate and hood diameter positively correlate, while outlet angle and width inversely affect critical velocity and system energy consumption. Box-Behnken response surface analysis confirms that hood average velocity, outlet angle, outlet width, and hood size significantly impact critical velocity, ranked as hood velocity > outlet angle > outlet width > hood size and he model exhibited a high goodness of fit (R²> 0.95). The jet-assisted configuration increased capture efficiency by 9.7%-33.41%, indicating a significant improvement in contaminant control. When the pollutant source intensity was high, effective control required increasing both the supply and exhaust flow rates. Optimal airflow for 48 common industrial sources was identified, offering practical guidance for efficient system operation.
{"title":"Flow dynamics and performance optimization of jet-assisted exhaust hoods under low-velocity ventilation","authors":"Siqi Huang , Mingxing Zhang , Cuiping Yan , Junzhe Fan , Shu Liu , Xingla Li","doi":"10.1016/j.buildenv.2026.114289","DOIUrl":"10.1016/j.buildenv.2026.114289","url":null,"abstract":"<div><div>In traditional low-velocity supply systems, non-directional exhaust inflow reduces control performance. This study investigates a low-velocity jet-assisted curtain formed along the perimeter of an exhaust hood to enhance capture efficiency. The auxiliary jet formed along the hood edge effectively enhanced the stability of the capture flow and improved the pollutant control performance. Through experimental measurements and CFD simulations, the results show that capture efficiency is significantly improved only when the curtain outlet velocity reaches a critical value, with further increases having little effect on the centerline velocity. Airflow rate, jet outlet angle, outlet width, and hood equivalent diameter significantly influence both critical velocity and centerline velocity: airflow rate and hood diameter positively correlate, while outlet angle and width inversely affect critical velocity and system energy consumption. Box-Behnken response surface analysis confirms that hood average velocity, outlet angle, outlet width, and hood size significantly impact critical velocity, ranked as hood velocity > outlet angle > outlet width > hood size and he model exhibited a high goodness of fit (R²> 0.95). The jet-assisted configuration increased capture efficiency by 9.7%-33.41%, indicating a significant improvement in contaminant control. When the pollutant source intensity was high, effective control required increasing both the supply and exhaust flow rates. Optimal airflow for 48 common industrial sources was identified, offering practical guidance for efficient system operation.</div></div>","PeriodicalId":9273,"journal":{"name":"Building and Environment","volume":"292 ","pages":"Article 114289"},"PeriodicalIF":7.6,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146076654","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}
Pub Date : 2026-01-23DOI: 10.1016/j.buildenv.2026.114286
Keying Zhang , Hui Cai , Lingzhi Li , Xin Zhang , Ying Wang , Zhoujing Wang
Air balancing is crucial for indoor comfort, and energy efficiency in ventilation systems. Distributed cooperative control (DCC) improves airflow stability in multi-zone environments by coordinating damper adjustments. Existing DCCs typically adjust terminal dampers first and then the supply fan, that is, the air source. This non-synchronous regulation yields nonlinear pressure changes during source adjustment, causing control deviations and slow convergence. To address these issues, this paper proposes a novel distributed cooperative control air balancing method with source-terminal synchronous regulation (STS-AB). An adjacency matrix is constructed to capture source-terminal connectivity, enabling their synchronous adjustment and essentially improving air balancing performance with fewer iterations. A dynamic deviation term and an angle factor are introduced to decouple the critical damper, achieving independent regulation. The STS-AB was validated in a ventilation system experimental platform. Compared to the 3-stage DCC-AB method, the STS-AB ensures the critical damper reaches full open. The iterations are reduced by up to 70 %. Flow accuracy is doubled, with all terminal deviations held within 5 %. Furthermore, the STS-AB responds effectively to sudden flow demand changes. Enhancing both the efficiency and accuracy of air balancing, this method supports energy conservation and healthier indoor environments.
{"title":"A novel distributed cooperative control air balancing method with source-terminal synchronous regulation for HVAC system","authors":"Keying Zhang , Hui Cai , Lingzhi Li , Xin Zhang , Ying Wang , Zhoujing Wang","doi":"10.1016/j.buildenv.2026.114286","DOIUrl":"10.1016/j.buildenv.2026.114286","url":null,"abstract":"<div><div>Air balancing is crucial for indoor comfort, and energy efficiency in ventilation systems. Distributed cooperative control (DCC) improves airflow stability in multi-zone environments by coordinating damper adjustments. Existing DCCs typically adjust terminal dampers first and then the supply fan, that is, the air source. This non-synchronous regulation yields nonlinear pressure changes during source adjustment, causing control deviations and slow convergence. To address these issues, this paper proposes a novel distributed cooperative control air balancing method with source-terminal synchronous regulation (STS-AB). An adjacency matrix is constructed to capture source-terminal connectivity, enabling their synchronous adjustment and essentially improving air balancing performance with fewer iterations. A dynamic deviation term and an angle factor are introduced to decouple the critical damper, achieving independent regulation. The STS-AB was validated in a ventilation system experimental platform. Compared to the 3-stage DCC-AB method, the STS-AB ensures the critical damper reaches full open. The iterations are reduced by up to 70 %. Flow accuracy is doubled, with all terminal deviations held within 5 %. Furthermore, the STS-AB responds effectively to sudden flow demand changes. Enhancing both the efficiency and accuracy of air balancing, this method supports energy conservation and healthier indoor environments.</div></div>","PeriodicalId":9273,"journal":{"name":"Building and Environment","volume":"292 ","pages":"Article 114286"},"PeriodicalIF":7.6,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146076655","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}
Pub Date : 2026-01-22DOI: 10.1016/j.buildenv.2026.114281
Xinting Gao , Mufeng Yuan , Yuren Yang , Shuyang Zhang , Jiazhi Ni , Weimin Zhuang , Yang Geng
Open-plan office shared spaces are essential to employee well-being, reflecting genuine behavioral responses to environmental quality. This study proposes a data-driven approach using multi-sensor spatiotemporal data fusion for automated spatial evaluation. In a representative open-plan technology office building, ubiquitous sensing collected approximately 210 thousand positioning records and 5.6 million light–thermal environment measurements, together with structured spatial design data, from 25 breakout areas over 21 days. The data revealed distinct spatial patterns of behavior and environment, allowing shared spaces to be classified into four stay-duration types. Based on a 10-min average stay interval, univariate regression identified key factors influencing occupancy. Random forest and interpretable models further confirmed that openness, distance to workstations, temperature, illumination, and area were the most influential variables affecting space utilization. Subjective comfort assessments validated the reliability of the sensor-based results, showing a consistency coefficient of 0.81. These findings establish a multi-dimensional framework for behavioral research in the built environment and provide practical guidance for architects, facility managers, employees, and corporate administrators.
{"title":"Decoding occupant behavior in shared spaces of open-plan offices: A multi-sensor data fusion and machine learning approach","authors":"Xinting Gao , Mufeng Yuan , Yuren Yang , Shuyang Zhang , Jiazhi Ni , Weimin Zhuang , Yang Geng","doi":"10.1016/j.buildenv.2026.114281","DOIUrl":"10.1016/j.buildenv.2026.114281","url":null,"abstract":"<div><div>Open-plan office shared spaces are essential to employee well-being, reflecting genuine behavioral responses to environmental quality. This study proposes a data-driven approach using multi-sensor spatiotemporal data fusion for automated spatial evaluation. In a representative open-plan technology office building, ubiquitous sensing collected approximately 210 thousand positioning records and 5.6 million light–thermal environment measurements, together with structured spatial design data, from 25 breakout areas over 21 days. The data revealed distinct spatial patterns of behavior and environment, allowing shared spaces to be classified into four stay-duration types. Based on a 10-min average stay interval, univariate regression identified key factors influencing occupancy. Random forest and interpretable models further confirmed that openness, distance to workstations, temperature, illumination, and area were the most influential variables affecting space utilization. Subjective comfort assessments validated the reliability of the sensor-based results, showing a consistency coefficient of 0.81. These findings establish a multi-dimensional framework for behavioral research in the built environment and provide practical guidance for architects, facility managers, employees, and corporate administrators.</div></div>","PeriodicalId":9273,"journal":{"name":"Building and Environment","volume":"291 ","pages":"Article 114281"},"PeriodicalIF":7.6,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146074790","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}
Urban nighttime illumination has been associated with circadian disruption in prior research, potentially reducing sleep quality and thereby affecting residents’ physical and mental health under certain conditions. Most studies on outdoor artificial light at night (ALAN) focus on photometric indicators, neglecting city-scale circadian-effective measures. This study maps pedestrian-scale nocturnal light environment using melanopic equivalent daylight illuminance (m-EDI). Field measurements were conducted at 652 sampling points across 14 representative sites, including commercial districts, green spaces, and roads. The predictors comprise SDGSAT-1 nighttime imagery, Sentinel-2 vegetation indices, 10 m-resolution building-height data (CNBH-10 m), and point-of-interest (POI) density. Multiple-scale regression and Extreme Gradient Boosting (XGBoost) models were constructed for buffer radii ranging from 5 to 80 m, and a mixed-scale XGBoost model was further introduced to capture cross-scale interactions. Model evaluation using out-of-fold and nested cross-validation indicates that the 20 m neighborhood scale yields the most stable and generalizable performance (R² = 0.73). SHapley Additive Explanations (SHAP) interpretation reveals that blue-band radiance and POI density are dominant positive contributors to m-EDI, whereas vegetation consistently lowers the predicted m-EDI levels, and building height shows context-dependent negative effects. The best-performing model produced a 10 m-resolution map of nighttime m-EDI across Nanjing, highlighting areas with elevated m-EDI along commercial corridors and transport hubs and lower levels in greener or residential areas. By investigating a circadian-effective indicator and explainable machine learning, this study provides an explainable and scalable approach for characterizing nocturnal light environment relevant to circadian-effective illumination, with potential implications for sustainable urban lighting management.
{"title":"Predicting pedestrian-level melanopic illuminance from multi-source urban data with explainable machine learning","authors":"Wenjin Hong, Zhe Kong, Peng Tang, Ziqi Fan, Rui Duan, Zixuan He, Xintong Li, Xinyi Wu","doi":"10.1016/j.buildenv.2026.114276","DOIUrl":"10.1016/j.buildenv.2026.114276","url":null,"abstract":"<div><div>Urban nighttime illumination has been associated with circadian disruption in prior research, potentially reducing sleep quality and thereby affecting residents’ physical and mental health under certain conditions. Most studies on outdoor artificial light at night (ALAN) focus on photometric indicators, neglecting city-scale circadian-effective measures. This study maps pedestrian-scale nocturnal light environment using melanopic equivalent daylight illuminance (m-EDI). Field measurements were conducted at 652 sampling points across 14 representative sites, including commercial districts, green spaces, and roads. The predictors comprise SDGSAT-1 nighttime imagery, Sentinel-2 vegetation indices, 10 m-resolution building-height data (CNBH-10 m), and point-of-interest (POI) density. Multiple-scale regression and Extreme Gradient Boosting (XGBoost) models were constructed for buffer radii ranging from 5 to 80 m, and a mixed-scale XGBoost model was further introduced to capture cross-scale interactions. Model evaluation using out-of-fold and nested cross-validation indicates that the 20 m neighborhood scale yields the most stable and generalizable performance (R² = 0.73). SHapley Additive Explanations (SHAP) interpretation reveals that blue-band radiance and POI density are dominant positive contributors to m-EDI, whereas vegetation consistently lowers the predicted m-EDI levels, and building height shows context-dependent negative effects. The best-performing model produced a 10 m-resolution map of nighttime m-EDI across Nanjing, highlighting areas with elevated m-EDI along commercial corridors and transport hubs and lower levels in greener or residential areas. By investigating a circadian-effective indicator and explainable machine learning, this study provides an explainable and scalable approach for characterizing nocturnal light environment relevant to circadian-effective illumination, with potential implications for sustainable urban lighting management.</div></div>","PeriodicalId":9273,"journal":{"name":"Building and Environment","volume":"291 ","pages":"Article 114276"},"PeriodicalIF":7.6,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146024795","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}
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