Building and Environment最新文献

{"title":"Dynamic Estimation of PM2.5 Penetration and Removal Rates Using Physics-Informed Neural Networks for Indoor Air Quality Management","authors":"Jihoon Kim ,&nbsp;Jiin Son ,&nbsp;Junemo Koo","doi":"10.1016/j.buildenv.2025.113038","DOIUrl":"10.1016/j.buildenv.2025.113038","url":null,"abstract":"<div><div>Quantifying indoor air pollutant dynamics is crucial for assessing exposure risks and optimizing ventilation strategies. This study advances previous research by developing a Physics-Informed Neural Network (PINN) model that dynamically estimates ventilation rate, penetration factor, and particulate removal rate in real time. The model integrates space operation factors (e.g., occupancy, window/door status, air purifier and air conditioner use) and meteorological variables (e.g., temperature, humidity, wind conditions, and outdoor PM₂.₅ levels) to predict indoor PM₂.₅ behavior without assuming static coefficients.</div><div>A key contribution of this study is the application of SHapley Additive exPlanations (SHAP) to quantitatively analyze the influence of each variable. The results indicate that outdoor humidity, window opening, and occupancy significantly impact the penetration factor, while air purifier operation, occupancy, and window opening play major roles in particulate removal. Notably, this study identifies a previously unreported effect: occupancy enhances removal rates due to particle inhalation, allowing for a direct estimation of personal exposure. Specifically, the mass flow rate of PM₂.₅ inhaled per occupant is approximately 10 times the indoor PM₂.₅ concentration (μg/hour). This approach refines traditional exposure assessments by quantifying PM₂.₅ uptake per person.</div><div>While the model is currently specific to a single measured space, it provides a practical tool for real-time air quality management. Future research will focus on expanding its applicability through long-term data collection across diverse environments and integrating reinforcement learning to optimize air quality control strategies. This study lays the groundwork for adaptive ventilation management, balancing air quality improvements with energy efficiency.</div></div>","PeriodicalId":9273,"journal":{"name":"Building and Environment","volume":"278 ","pages":"Article 113038"},"PeriodicalIF":7.1,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143850386","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}

{"title":"Fast fluid dynamics simulations of the drag effect of trees on airflow distributions","authors":"Ruibin Li ,&nbsp;Yi Zhao ,&nbsp;Liangzhu (Leon) Wang ,&nbsp;Jianlei Niu ,&nbsp;Xing Shi ,&nbsp;Naiping Gao","doi":"10.1016/j.buildenv.2025.113039","DOIUrl":"10.1016/j.buildenv.2025.113039","url":null,"abstract":"<div><div>Trees are widely recognized for their effectiveness in regulating urban microclimates through shading, absorption and reflection of solar radiation, and transpiration. However, their drag effect on airflow may influence this regulatory capacity. Incorporating tree source terms related to leaf area density (LAD) and drag coefficient (<em>C_d</em>) into governing equations provides a balance between computational accuracy and efficiency when studying the drag effect of trees on airflow. Nevertheless, conventional simulation methods typically require significant computational time, limiting their practicality. In this study, tree source terms are integrated into the Fast Fluid Dynamics (FFD) method, and the computational performance of three FFD methods (i.e., SLFFD, NIPC, and NSPF) is evaluated for quickly predicting the drag effect of trees on airflow. Results indicate negligible differences between the predictions of FFD methods and conventional numerical simulation methods such as the Pressure-Implicit with Splitting of Operators (PISO) method. At the single tree canopy scale, the computational speeds of NIPC and NSPF methods are about 1.77 and 1.96 times faster than the PISO method, respectively, while the SLFFD method is about 1.50 times faster. When using the maximum time step size and a first-order discretization scheme, the computational speed of the SLFFD method increases to 4.13 times that of the PISO method. In larger computational domains, the improvement in computational speed provided by the FFD methods becomes even more pronounced. In conclusion, the FFD methods coupled with tree source terms significantly improve computational efficiency for predicting the drag effect of trees on airflow.</div></div>","PeriodicalId":9273,"journal":{"name":"Building and Environment","volume":"278 ","pages":"Article 113039"},"PeriodicalIF":7.1,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143850387","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}

{"title":"Sensor fault diagnosis and calibration based on voting mechanism for online application using virtual in-situ calibration and time series prediction","authors":"Jiteng Li ,&nbsp;Jiaming Wang ,&nbsp;Peng Wang ,&nbsp;Sungmin Yoon ,&nbsp;Yu Li ,&nbsp;Yacine Rezgui ,&nbsp;Yuxin Li ,&nbsp;Tianyi Zhao","doi":"10.1016/j.buildenv.2025.113040","DOIUrl":"10.1016/j.buildenv.2025.113040","url":null,"abstract":"<div><div>Sensors are essential components in building energy control systems. Sensor fault can result in inappropriate control, thereby increasing energy consumption or discomfort. This study proposes a novel method that combines virtual in-situ calibration and time series prediction (VIC-TSP) to diagnose and calibrate sensor faults for online application to guarantee data accuracy. The method is applied to an actual heating, ventilation, and air conditioning system for the real-time comparison of residuals from measurement, calibration, and prediction values. Subsequently, sensor faults are diagnosed and calibrated via a voting mechanism. The results indicate the following: (1) Faults in the measurement values are identified by discrepancies between the residuals of the measurement and calibration predictions. After determining the measurement value faults, performing virtualization can decrease residuals by more than 73.61 %. (2) Calibration and prediction value faults indicate residuals that exceed predefined thresholds. A retraining interval of one week reduces the calibration and prediction residuals by more than 81.63 % and 78.82 %, respectively. (3) The VIC-TSP method can reduce pump energy consumption by 10 % and increase the adjustment frequency to the supply fan by 9.83 times per day.</div></div>","PeriodicalId":9273,"journal":{"name":"Building and Environment","volume":"278 ","pages":"Article 113040"},"PeriodicalIF":7.1,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143850385","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}

{"title":"Comprehensive in-situ assessment of glazing systems: thermal properties, comfort impacts, and machine learning-based predictive modelling","authors":"Saman Abolghasemi Moghaddam ,&nbsp;Michael Brett ,&nbsp;Manuel Gameiro da Silva ,&nbsp;Nuno Simões","doi":"10.1016/j.buildenv.2025.113027","DOIUrl":"10.1016/j.buildenv.2025.113027","url":null,"abstract":"<div><div><em>In-situ</em> methods have the potential to reliably evaluate building façade components, including glazing systems. These methods have the potential to assess key glazing properties such as thermal transmittance (U-value) and solar heat gain coefficient (g-value), as well as aspects like thermal comfort near the glazing and the impact of dynamic outdoor conditions on glazing performance. This study employs an extensive <em>in-situ</em> evaluation approach, utilizing an average-based strategy to determine the U-value and g-value of a double-glazed unit while analyzing the influence of solar radiation on occupants’ thermal comfort near glazing. Additionally, the study explores the potential of machine learning to predict variations in glazing surface temperatures across seasons, based on a relatively short measurement period. Results indicate that the standard deviations for the measured U-value and g-value across seasons range from approximately 8 % to 25 % and 2 % to 10 %, respectively. Solar radiation significantly affected thermal comfort near the glazing, increasing the Predicted Percentage Dissatisfied (PPD) up to threefold in summer, causing discomfort, while reducing it by half in winter, improving comfort. Although machine learning predictions correlated strongly with nighttime measurements, discrepancies emerged during the day due to the highly dynamic nature of solar radiation, making daytime predictions more challenging than nighttime ones. Nonetheless, general variation patterns were reasonably captured. The study concludes by proposing a comprehensive approach that integrates reliable laboratory methods with <em>in-situ</em> evaluations to more effectively test the reliability of the adopted method.</div></div>","PeriodicalId":9273,"journal":{"name":"Building and Environment","volume":"279 ","pages":"Article 113027"},"PeriodicalIF":7.1,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143864459","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}

{"title":"Statistical models incorporating mean and standard deviation to predict probability distributions of pedestrian-level wind speed in a realistic urban area","authors":"Wei Wang,&nbsp;Yezhan Li,&nbsp;Naoki Ikegaya","doi":"10.1016/j.buildenv.2025.113034","DOIUrl":"10.1016/j.buildenv.2025.113034","url":null,"abstract":"<div><div>Understanding the probabilistic characteristics of urban wind environments is crucial for pedestrian safety and comfort. Previous studies have used various distribution functions based on statistics to evaluate gusty winds; however, the prediction accuracy of gusts and plausibility of the various distribution functions have not been discussed. This paper models the probability density function (PDF) using four distribution functions: Gaussian, Lognormal, Weibull, and Gamma, with parameters determined by the method of moments based on only two statistics: mean and standard deviation. The large-eddy simulations (LES) results of a realistic urban case were used to assess their effectiveness in estimating PDFs and quantiles of wind speed. The key findings indicate that while all distributions accurately modeled the mean and standard deviation, none effectively captured skewness and kurtosis. The Gamma distribution provided the best global fit of PDFs, followed by the Weibull distribution. The Lognormal and Gaussian distributions performed less effectively, with the Gaussian distribution showing the largest errors due to its constrained, symmetric bell-shaped PDF, which struggles to capture the asymmetry in wind speed data. Although the Gamma distribution had the highest overall accuracy in modelling PDFs, other distributions occasionally provided more accurate estimates at specific locations. For wind speed quantiles, particularly extreme values with an exceedance probability of 1 % (i.e., <span><math><msub><mi>s</mi><mrow><mn>1</mn><mspace></mspace><mo>%</mo></mrow></msub></math></span>), the Weibull and Gamma distributions showed superior accuracy, while the Gaussian and Lognormal distributions had larger errors. This study is expected to provide valuable insights into modeling wind speed PDFs, serving as a foundation for further developments of statistical models.</div></div>","PeriodicalId":9273,"journal":{"name":"Building and Environment","volume":"279 ","pages":"Article 113034"},"PeriodicalIF":7.1,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143864463","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}

{"title":"Long-term characteristics of formaldehyde concentrations in four Chinese residences and estimation of annual average","authors":"Jin Ye ,&nbsp;Zhiyuan Wang ,&nbsp;Tao Yu ,&nbsp;Wenbin Zhuang ,&nbsp;Wenqing Lai ,&nbsp;Lei Tian ,&nbsp;Wenjun Leng ,&nbsp;Ying Song ,&nbsp;Shaodan Huang ,&nbsp;Yinping Zhang ,&nbsp;Cong Liu ,&nbsp;Bencheng Lin","doi":"10.1016/j.buildenv.2025.113024","DOIUrl":"10.1016/j.buildenv.2025.113024","url":null,"abstract":"<div><div>Long-term characteristics of concentrations of indoor formaldehyde is critical to evaluate health risk and burden of disease and better protect occupants’ health. However, long-term measurement of pollutants with a high time coverage degree (TCD, defined as sampling duration/sampling period) remains a challenge. To address this issue, we measured week-average formaldehyde concentrations by continuously weekly sampling utilizing a low flow-rate active sampler. This achieved a TCD of 100 %, which is significantly higher than those previously reported (0.05 %-0.7 %). Concentration profiles obtained in four Chinese residences for about one-year show that the highest concentration occurs in summer, followed by spring and autumn, and the lowest in winter. The ratios of summer to winter concentration varied from 1.4 to 3.5, which was linearly correlated with annual average concentration (Pearson correlation coefficient=0.81). Although compliance with standard threshold (0.08 mg/m³) in winter, severe exceedance (38 % to 77 %) was found in three residences in summer. This highlights necessity of worst-scenario prediction in attainment evaluation, instead of single-point test. A Monte Carlo analysis revealed that deviation of estimated annual average concentration decreased as TCD of a sampling strategy increased. Sampling for one week once a season had an estimation deviation less than 30 % in average. This work provides new insights to realize long-term assessment of exposure to indoor pollutants.</div></div>","PeriodicalId":9273,"journal":{"name":"Building and Environment","volume":"278 ","pages":"Article 113024"},"PeriodicalIF":7.1,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143835207","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}

{"title":"Advancing thermal comfort prediction: Improvement of the Berkeley local thermal sensation model and overall thermal sensation prediction by machine learning","authors":"Xingjun Hu ,&nbsp;Keyuan Shi ,&nbsp;Zirui Wang ,&nbsp;Jingyu Wang ,&nbsp;Yang Yang ,&nbsp;Peng Guo","doi":"10.1016/j.buildenv.2025.113036","DOIUrl":"10.1016/j.buildenv.2025.113036","url":null,"abstract":"<div><div>The in-vehicle driving environment is typically complex and dynamic. To enhance the comfort of both drivers and passengers, accurately predicting the thermal sensation within the vehicle is crucial. This lays the foundation for future research aimed at optimizing air conditioning control systems. The present study involved 108 male participants from China, who were studied through subjective thermal sensation assessments. The research explored the discrepancies between the thermal sensation ratings reported by participants and the predictions made by the Berkeley thermal sensation model. By incorporating data from human thermal neutrality tests, the skin temperature set point in the model was modified, and the coefficient representing human thermal sensitivity was refined using the Particle Swarm Optimization (PSO) algorithm. This led to the development of a model capable of accurately predicting the local thermal sensation of Chinese male participants. Furthermore, through machine learning techniques, the relationship between local and overall thermal sensation was established. Among the four machine learning algorithms evaluated, Support Vector Regression (SVR) demonstrated the highest effectiveness, achieving excellent accuracy.</div></div>","PeriodicalId":9273,"journal":{"name":"Building and Environment","volume":"278 ","pages":"Article 113036"},"PeriodicalIF":7.1,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143864725","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}

{"title":"Multifaceted Assessment of Fungal Diversity and Biodegradation Activity in Historical Library","authors":"Tereza Branysova ,&nbsp;Nicole Petru ,&nbsp;Marketa Baronova ,&nbsp;Hana Sykorova ,&nbsp;Hana Stiborova","doi":"10.1016/j.buildenv.2025.113025","DOIUrl":"10.1016/j.buildenv.2025.113025","url":null,"abstract":"<div><div>Historical books are an irreplaceable part of cultural heritage. Unfortunately, their constituent materials are highly susceptible to microbial colonization, especially by fungi, which commonly leads to deterioration through growth and metabolic activity. This process, characterized by changes in color and texture culminating in material decomposition, is responsible for incalculable losses. This study investigated fungal communities present in the National Library of the Czech Republic, analyzing both book-associated fungi (blocks and covers) and airborne fungi through complementary approaches: culture-dependent methods utilizing multiple media (MEA, PDA, SDA, YGC) and culture-independent DNA/RNA-based Illumina MiSeq sequencing. An integral part involved assessing the degradation potential of the most prevalent isolates to determine their threat to the books. The culture-dependent analysis revealed <em>Aspergillus, Cladosporium,</em> and <em>Penicillium</em> as predominant genera in both air and book samples. While no significant differences were observed between media types, each captured unique genera, making their combined use essential for comprehensive community analysis. The culture-independent sequencing identified additional abundant taxa, notably <em>Cryptococcus</em> and <em>Saccharomyces,</em> in both sample types. Despite the distinct properties of the examined cover materials (leather, textile, paper), no significant differences in fungal communities were observed. Further analysis revealed that while each environment maintained its unique populations, 20 genera were shared across all sample types. Enzymatic assays demonstrated widespread cellulolytic, amylolytic, and proteolytic capabilities, particularly among <em>Penicillium</em> and <em>Aspergillus</em> species, which also demonstrated remarkable adaptability to diverse nutrient conditions. Notably, culture-dependent methods identified a wider range of genera than molecular approaches, challenging common assumptions about detection capabilities.</div></div>","PeriodicalId":9273,"journal":{"name":"Building and Environment","volume":"278 ","pages":"Article 113025"},"PeriodicalIF":7.1,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848271","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}

{"title":"Integrating urban heat island analysis for sustainable urban planning: Insights from Reunion Island","authors":"A. Lefevre,&nbsp;B. Malet-Damour,&nbsp;F. Bénard,&nbsp;H. Boyer,&nbsp;G. Rivière","doi":"10.1016/j.buildenv.2025.112964","DOIUrl":"10.1016/j.buildenv.2025.112964","url":null,"abstract":"<div><div>This study investigates the Urban Heat Island (UHI) phenomenon in Reunion Island, a small tropical island with diverse climatic zones. By using Local Climate Zone (LCZ) classification combined with regional climate clustering, the research characterizes urban morphologies and their influence on UHI dynamics. The analysis reveals UHI intensity differences of up to 4.14 °C between urban and natural areas, primarily driven by urban density, with additional variations observed across the island’s distinct microclimates. These findings emphasize the need to consider regional climatic variability in UHI studies, especially in territories like Reunion with heterogeneous microclimates. The resulting framework provides valuable insights for urban planners, guiding densification strategies in areas with lower UHI potential to foster sustainable and climate-resilient urban development in tropical island contexts.</div></div>","PeriodicalId":9273,"journal":{"name":"Building and Environment","volume":"278 ","pages":"Article 112964"},"PeriodicalIF":7.1,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143843237","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}

{"title":"Development and performance evaluation of a hybrid louver window system using natural light and LED for energy-efficient vertical farming building","authors":"Youngsub An ,&nbsp;Kieu Ngoc Minh ,&nbsp;Hong-Jin Joo ,&nbsp;Kyoung-Ho Lee ,&nbsp;Wang-Je Lee ,&nbsp;Min-Hwi Kim ,&nbsp;Hyun-Hee Lee ,&nbsp;Seoyong Shin ,&nbsp;Sae-Byul Kang","doi":"10.1016/j.buildenv.2025.113020","DOIUrl":"10.1016/j.buildenv.2025.113020","url":null,"abstract":"<div><div>This study developed a hybrid louver window system (HLWS) that integrates window, natural light and LED technology to address the high electricity consumption and cooling energy due to heat generated by LED use in vertical farming buildings. The HLWS comprises transparent double glazing, a diffuser, and multiple horizontal slats within a louver air cavity, which effectively transmit natural light indoors and distribute it to achieve the required amount of light. The system dynamically adjusts the LED intensity based on real-time variations in natural light to maintain the target photosynthetic photon flux density. Vents installed at the upper and lower parts of the window system expel excess heat generated by the LED and solar radiation in tropical climates. The system's electricity consumption and cooling load, resulting from LED heat production, were evaluated using a full-scale mock-up facility. It was experimentally verified that the developed HLWS system not only reduces the electricity consumption of LED by increasing the use of natural light but also reduces the cooling load by reducing the operating time of LED in vertical farm buildings. The total energy consumption of vertical farm buildings with the HLWS was 31.6 % less than those without the HLWS.</div></div>","PeriodicalId":9273,"journal":{"name":"Building and Environment","volume":"279 ","pages":"Article 113020"},"PeriodicalIF":7.1,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143864462","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}