Particle characterization in commercial buildings: A cross-sectional study in 40 offices in Singapore.

There is a knowledge gap in understanding how existing office buildings are protecting occupants from exposure to particles from both indoor and outdoor sources. We report a cross-sectional study involving weekly measurements of size-resolved indoor and outdoor particle concentrations in forty commercial building offices in Singapore. The outdoor and indoor particles size distributions were single mode with daytime peak number concentrations at 36.5 nm and 48.7 nm. Outdoor concentrations were significantly greater than indoors for all particle diameters. Indoor particle concentrations were generally low due to: 1) relatively high indoor particle removal (IPR) rates; 2) low indoor source strengths; and 3) low indoor particle of outdoor proportion (IPOP). We found that the ventilation system type had a substantial effect on indoor particle levels, IPR and IPOP. Through linear mixed model analyses, we identified dependencies of IPR rates with the use of MERV13 filters in supply air and filter maintenance frequency, IPOP with the use of MERV13 filters in the fresh air and supply air ducts and low particle source strength with regular daily cleaning presumably due to dust reservoir removal. Lastly, the contribution of outdoor sources was mainly seen for ultrafine and fine particles but less pronounced for coarse particles. This study provided detailed understanding of particle exposure in building offices and their influencing factors, facilitating future research on health impact of particle exposures.

Fundamentals, Algorithms, and Technologies of Occupancy Detection for Smart Buildings Using IoT Sensors.

Smart buildings use advanced technologies to automate building functions. One important function is occupancy detection using Internet of Things (IoT) sensors for smart buildings. Occupancy information is useful information to reduce energy consumption by automating building functions such as lighting, heating, ventilation, and air conditioning systems. The information is useful to improve indoor air quality by ensuring that ventilation systems are used only when and where they are needed. Additionally, it is useful to enhance building security by detecting unusual or unexpected occupancy levels and triggering appropriate responses, such as alarms or alerts. Occupancy information is useful for many other applications, such as emergency response, plug load energy management, point-of-interest identification, etc. However, the accuracy of occupancy detection is limited by factors such as real-time occupancy data, sensor placement, privacy concerns, and the presence of pets or objects that can interfere with sensor reading. With the rapid development of IoT sensor technologies and the increasing need for smart building solutions, there is a growing interest in occupancy detection techniques. There is a need to provide a comprehensive survey of these technologies. Although there are some exciting survey papers, they all have limited scopes with different focuses. Therefore, this paper provides a comprehensive overview of the current state-of-the-art occupancy detection methods (including both traditional algorithms and machine learning algorithms) and devices with their advantages and limitations. It surveys and compares fundamental technologies (such as sensors, algorithms, etc.) for smart buildings. Furthermore, the survey provides insights and discussions, which can help researchers, practitioners, and stakeholders develop more effective occupancy detection solutions for smart buildings.

Synergies between digital construction technologies in smart buildings and smart city development to meet building users' expectations.

In smart buildings, digital construction technologies can support more efficient management of data and information related to building components. This paper aims to draw a robust linking mechanism between digital construction technologies that support smart buildings and smart city development to satisfy building users' expectations. Data was attained using a qualitative approach via secondary data from literature and primary data in the context of case study with building users. The study suggests the importance of recognising single/multi-purposed data to support better synergy between digital construction technologies and in smart buildings and smart city development to satisfy building users' expectations.

Green energy innovation initiatives for environmental sustainability: current state and future research directions.

Worldwide, all countries have been facing the crisis of climate change problem. They have been addressing this issue by focusing on implementing green energy innovation initiatives and promoting a sustainable future through environmental sustainability. In this research study, we focus on examining the role of green finance through green energy innovations, which are taking place in several sectors across different regions to promote environmental sustainability. The study has analysed 152 articles on this research domain through a systematic literature review to understand the present state of existing knowledge. The current study examines the Scopus-indexed research articles from the time period 2002 to 2023. Six emerging themes have been examined to understand their development and the potential impact of green initiatives for environmental sustainability. Various institutional theories have been explored to understand their association with the investigated research area. The paper has discussed multiple challenges that need to be addressed for the speedy implementation of green innovations. Finally, future research questions have been proposed based on the findings from the extant literature and the existing research gaps.

Critical success factors and collaborative governance mechanism for the transformation of existing residential buildings in urban renewal: From a social network perspective.

The renovation of urban residential buildings in the context of urban renewal presents social challenges due to the involvement of diverse stakeholders and complex interest relations. This study identifies 28 critical success factors (CSFs) and 9 stakeholders, drawing insights from literature and on-site research of 45 old residential renewal projects in Jiangsu Province, China. Employing social network analysis, the intricate interplay between CSFs and stakeholders is explored, emphasizing the imperative for collaborative governance and elucidating governance mechanism principles. Focusing on stakeholders' resource contributions to transformation projects, the study devises a collaborative governance mechanism based on the specific types of resources required to support each CSF. This approach ensures that CSFs receive the necessary resources, enhancing project success. The paper concludes by outlining nine governance mechanisms and their implementation paths, anchored in the relationships between 13 CSFs and their respective stakeholders.

Semantic Segmentation of Remote Sensing Images Depicting Environmental Hazards in High-Speed Rail Network Based on Large-Model Pre-Classification.

With the rapid development of China's railways, ensuring the safety of the operating environment of high-speed railways faces daunting challenges. In response to safety hazards posed by light and heavy floating objects during the operation of trains, we propose a dual-branch semantic segmentation network with the fusion of large models (SAMUnet). The encoder part of this network uses a dual-branch structure, in which the backbone branch uses a residual network for feature extraction and the large-model branch leverages the results of feature extraction generated by the segment anything model (SAM). Moreover, a decoding attention module is fused with the results of prediction of the SAM in the decoder part to enhance the performance of the network. We conducted experiments on the Inria Aerial Image Labeling (IAIL), Massachusetts, and high-speed railway hazards datasets to verify the effectiveness and applicability of the proposed SAMUnet network in comparison with commonly used semantic segmentation networks. The results demonstrated its superiority in terms of both the accuracies of segmentation and feature extraction. It was able to precisely extract hazards in the environment of high-speed railways to significantly improve the accuracy of semantic segmentation.

Home electricity data generator (HEDGE): An open-access tool for the generation of electric vehicle, residential demand, and PV generation profiles.

In this paper, we present the Home Electricity Data Generator (HEDGE), an open-access tool for the random generation of realistic residential energy data. HEDGE generates realistic daily profiles of residential PV generation, household electric loads, and electric vehicle consumption and at-home availability, based on real-life UK datasets. The lack of usable data is a major hurdle for research on residential distributed energy resources characterisation and coordination, especially when using data-driven methods such as machine learning-based forecasting and reinforcement learning-based control. We fill this gap with the open-access HEDGE tool which generates data sequences of energy data for several days in a way that is consistent for single homes, both in terms of profile magnitude and behavioural clusters.•From raw datasets, pre-processing steps are conducted, including filling in incomplete data sequences, and clustering profiles into behaviour clusters. Transitions between successive behaviour clusters and profiles magnitudes are characterised.•Generative adversarial networks (GANs) are then trained to generate realistic synthetic data representative of each behaviour groups consistent with real-life behavioural and physical patterns.•Using the characterisation of behaviour cluster and profile magnitude transitions, and the GAN-based profiles generator, a Markov chain mechanism can generate realistic energy data for successive days.

Passive Infrared Sensor-Based Occupancy Monitoring in Smart Buildings: A Review of Methodologies and Machine Learning Approaches.

Buildings are rapidly becoming more digitized, largely due to developments in the internet of things (IoT). This provides both opportunities and challenges. One of the central challenges in the process of digitizing buildings is the ability to monitor these buildings' status effectively. This monitoring is essential for services that rely on information about the presence and activities of individuals within different areas of these buildings. Occupancy information (including people counting, occupancy detection, location tracking, and activity detection) plays a vital role in the management of smart buildings. In this article, we primarily focus on the use of passive infrared (PIR) sensors for gathering occupancy information. PIR sensors are among the most widely used sensors for this purpose due to their consideration of privacy concerns, cost-effectiveness, and low processing complexity compared to other sensors. Despite numerous literature reviews in the field of occupancy information, there is currently no literature review dedicated to occupancy information derived specifically from PIR sensors. Therefore, this review analyzes articles that specifically explore the application of PIR sensors for obtaining occupancy information. It provides a comprehensive literature review of PIR sensor technology from 2015 to 2023, focusing on applications in people counting, activity detection, and localization (tracking and location). It consolidates findings from articles that have explored and enhanced the capabilities of PIR sensors in these interconnected domains. This review thoroughly examines the application of various techniques, machine learning algorithms, and configurations for PIR sensors in indoor building environments, emphasizing not only the data processing aspects but also their advantages, limitations, and efficacy in producing accurate occupancy information. These developments are crucial for improving building management systems in terms of energy efficiency, security, and user comfort, among other operational aspects. The article seeks to offer a thorough analysis of the present state and potential future advancements of PIR sensor technology in efficiently monitoring and understanding occupancy information by classifying and analyzing improvements in these domains.

Privacy preserved and decentralized thermal comfort prediction model for smart buildings using federated learning.

Thermal comfort is a crucial element of smart buildings that assists in improving, analyzing, and realizing intelligent structures. Energy consumption forecasts for such smart buildings are crucial owing to the intricate decision-making processes surrounding resource efficiency. Machine learning (ML) techniques are employed to estimate energy consumption. ML algorithms, however, require a large amount of data to be adequate. There may be privacy violations due to collecting this data. To tackle this problem, this study proposes a federated deep learning (FDL) architecture developed around a deep neural network (DNN) paradigm. The study employs the ASHRAE RP-884 standard dataset for experimentation and analysis, which is available to the general public. The data is normalized using the min-max normalization approach, and the Synthetic Minority Over-sampling Technique (SMOTE) is used to enhance the minority class's interpretation. The DNN model is trained separately on the dataset after obtaining modifications from two clients. Each client assesses the data greatly to reduce the over-fitting impact. The test result demonstrates the efficiency of the proposed FDL by reaching 82.40% accuracy while securing the data.

Building integrated diffusers' area ratio optimization.

This paper presents an investigation into the effect of area ratio parameter of diffusers on its energy output through power coefficient Cp. This parameter has effect both on diffusers' energy yield, besides diffuser's size for architectural integration prospects. A systematic increase in diffusers area ratio is adopted following standardized diffuser profile presented by NACA 1244 aerofoil. A series of area ratios were investigated (i.e., 1.25, 1.5, 1.75, 2, 2.5, 3 and 3.5). Area ratio of 1.5 (i.e., outlet/inlet, 0.75 m/0.50 m) exhibited the highest power coefficient Cp of 4.2, in addition to achieving highest resulting velocity of 25.8 m/s under incident velocity of 16m/s. Considerable wind separation inside inner walls of diffusers occurred from area ratio 1.75 onwards, which impacted resulting velocities. Simulations performed with ANSYS CFD Academic to standalone diffusers. A series of incident velocities employed from 1 to 16 m/s that resulted in velocity increase by 120-156% respectively.

Early energy performance analysis of smart buildings by consolidated artificial neural network paradigms.

The assessment of energy performance in smart buildings has emerged as a prominent area of research driven by the increasing energy consumption trends worldwide. Analyzing the attributes of buildings using optimized machine learning models has been a highly effective approach for estimating the cooling load (CL) and heating load (HL) of the buildings. In this study, an artificial neural network (ANN) is used as the basic predictor that undergoes optimization using five metaheuristic algorithms, namely coati optimization algorithm (COA), gazelle optimization algorithm (GOA), incomprehensible but intelligible-in-time logics (IbIL), osprey optimization algorithm (OOA), and sooty tern optimization algorithm (STOA) to predict the CL and HL of a residential building. The models are trained and tested via an Energy Efficiency dataset (downloaded from UCI Repository). A score-based ranking system is built upon three accuracy evaluators including mean absolute percentage error (MAPE), root mean square error (RMSE), and percentage-Pearson correlation coefficient (PPCC) to compare the prediction accuracy of the models. Referring to the results, all models demonstrated high accuracy (e.g., PPCCs >89%) for predicting both CL and HL. However, the calculated final scores of the models (43, 20, 39, 38, and 10 in HL prediction and 36, 20, 42, 42, and 10 in CL prediction for the STOA, OOA, IbIL, GOA, and COA, respectively) indicated that the GOA, IbIL, and STOA perform better than COA and OOA. Moreover, a comparison with various algorithms used in earlier literature showed that the GOA, IbIL, and STOA provide a more accurate solution. Therefore, the use of ANN optimized by these three algorithms is recommended for practical early forecast of energy performance in buildings and optimizing the design of energy systems.

Surviving indoor heat stress in United States: A comprehensive review exploring the impact of overheating on the thermal comfort, health, and social economic factors of occupants.

In the face of escalating global climate change and the increasing frequency of extreme heat events, the mitigation of building overheating has become an urgent priority. This comprehensive review converges insights from building science and public health domains to offer a thorough understanding of the multifaceted impacts of indoor overheating on occupants. The paper addresses a significant research gap by offering a holistic exploration of indoor overheating of residential buildings and its consequences, with a specific focus on the United States, an economically diverse nation that has been underrepresented in the literature. The review illuminates the effects of overheating on thermal comfort, health, and socio-economic aspects within the built environment. It emphasizes associated repercussions, including heightened cooling energy consumption, increased peak electricity demand, and elevated vulnerability, leading to exacerbated heat-related mortality and morbidity rates, especially among disadvantaged groups. The study concludes that vulnerabilities to these impacts are intricately tied to regional climatic conditions, highlighting the inadequacy of a one-size-fits-all approach. Tailored interventions for each climate zone are deemed necessary, considering the consistent occurrence of indoor temperatures surpassing outdoor levels, known as superheating, which poses distinct challenges. The research underscores the urgency of addressing indoor overheating as a critical facet of public health, acknowledging direct socioeconomic repercussions. It advocates for further research to inform comprehensive policies that safeguard public health across diverse indoor environments.

Legacy of last millennium timber use on plant cover in Central Europe: Insights from tree rings and pollen.

Throughout history, humans have relied on wood for constructions, tool production or as an energy source. How and to what extent these human activities have impacted plant abundance and composition over a long-term perspective is, however, not well known. To address this knowledge gap, we combined 44,239 precisely dated tree-ring samples from economically and ecologically important tree species (spruce, fir, pine, oak) from historical buildings, and pollen-based plant cover estimates using the REVEALS model from 169 records for a total of 34 1° × 1° grid cells for Central Europe. Building activity and REVEALS estimates were compared for the entire study region (4-15°E, 46-51°N), and for low (<500 m asl) and mid/high elevations (≥500 m asl) in 100-year time windows over the period 1150-1850. Spruce and oak were more widely used in wooden constructions, amounting to 35 % and 32 %, respectively, compared to pine and fir. Besides wood properties and species abundance, tree diameters of harvested individuals, being similar for all four species, were found to be the most crucial criterion for timber selection throughout the last millennium. Regarding land use changes, from the 1150-1250's onwards, forest cover generally decreased due to deforestation until 1850, especially at lower elevations, resulting in a more heterogeneous landscape. The period 1650-1750 marks a distinct change in the environmental history of Central Europe; increasing agriculture and intense forest management practices were introduced to meet the high demands of an increasing population and intensifying industrialization, causing a decrease in palynological diversity, especially at low elevations. Likely the characteristic vegetation structure and composition of contemporary landscapes originated from that period. We further show that land use has impacted vegetation composition and diversity at an increasing speed leading to a general homogenization of landscapes through time, highlighting the limited environmental benefits of even-aged plantation forestry.

Classification of thermal environment control indicators according to the thermal sensitivity of office occupants.

The control that have the greatest influence on comfortable in the office occupants are the heating, ventilation, and air conditioning (HVAC) system operation and the thermal environment. However, comfortable HVAC operation is difficult in the office space characterized by a recommended standard thermal environment or a centralized HVAC system. To consider the occupant's thermal comfort to the greatest possible extent, must establish a method to quantify the variables related to the occupant's thermal comfort. This study aims to group occupants in Thermal sensation vote (TSV) clusters and perform sensitivity analysis (SA) on the relationship between thermal environmental factors in an office building and each cluster's TSV to establish the typology of the control indicators for each cluster. A total of 10 field experiments were conducted in the same office. This field study was carried out 2022. The indoor thermal environmental parameters, the subjective evaluation of the thermal comfort of the resident and the operation pattern of the heating system were monitored at the same time. A total of 4,200 datasets related to indoor thermal environmental parameters and a total of 1,680 datasets related to occupants' thermal comfort were collected and analyzed. The results of this study show that people have different levels of adaptability and sensitivity to a given thermal environment. This study founded distinguishable similarities in their thermal sensation traits and grouped similar TSV values into five clusters that responded differently to the same thermal environment. Each cluster showed different TSV and Thermal comfort vote (TCV) patterns, which allowed us to classify the groups that had sensitive responses to the thermal environment and those that did not. This study was determined different control indicators and guidelines for the divided groups according to thermal sensitivity.

A methodology to simulate peak water demand for water supply systems using semi-direct methods: A case study for a residential building in Mexico.

Historically, approaches for determining peak water demand in buildings have been based on probabilistic methods. Extensive research has shown that these methods lack accuracy because of the human factor in the probability of use. Inaccuracy in the calculation of peak water demand is the main cause of oversized water supply systems in buildings. This has led to unfavorable effects such as: 1) increasing the building carbon footprint due to the use of more construction materials, and 2) engendering health hazards due to the stagnation of water causing microbial growing. This paper presents a step-by-step methodology that serves to calculate the peak water demand by simulating the use of plumbing fixtures based on data obtained from standardized flowrate. With the implementation of the methodology, the peak water demand estimated was 2.6 times lower in comparison to traditional methods. The main conclusion drawn from the research is the potential of the methodology to easily simulated peak water demand in residential buildings in the short term. Thus, it reveals a hotspot for peak water demand calculation and can serve as routes for future research.

Traffic noise distribution characteristics of high-rise buildings along ultra-wide cross section highway with multiple noise reduction measures.

Nowadays, ultra-wide cross section highway is a hotspot in construction and brings some unique noise distribution characteristics. In this work, we further investigate noise distribution characteristics of diverse building layouts along ultra-wide cross section highway in Guangdong Province with multiple noise mitigation measures. By the aid of vehicle noise emission model and noise mapping, the influence of high-rise building layouts and shielding in the urban planning on noise mitigation is also considered. Some key findings are summarized as follows: (1) Under the same distance, the noise level of non-frontage building facades is higher than frontage building facades. After taking noise reduction measures, the noise reduction effect of non-street-facing building facades, buildings facing the road, and buildings at a long distance to the road is greater than street-facing building facades, buildings sideways to the road, and buildings at a short distance; (2) the distribution trend of insertion loss (IL) of non-frontage buildings is influenced by the height of the frontage buildings. Specifically, the trend of insertion loss first increases and then decreases as the floor rises when the height of non-frontage buildings is higher than frontage buildings. Comparatively, the trend of insertion loss decreases as the floor rises when the height of non-frontage buildings is equal to frontage buildings; (3) when double noise reduction measures are implemented, the noise distribution trend in buildings is similar to that observed with individual noise reduction measure, where the difference between both is only 0.6 dB(A). Thanks to the high representativeness of the case area, this work can provide some design guidance for the urban planning and the selection of noise reduction measures along the ultra-wide cross section highway.

The practical implications of the EN 17037 minimum target daylight factor for building design and urban daylight in several European countries.

Sufficient daylight in the indoor environment of buildings is important not only for vision and well-being as daylight also has significant non-visual effects on the human organism. The provision of daylight in the interiors of buildings significantly affects the architectural and urban parameters of the building environment. Harmonized EN 17037 introduced a number of changes and ambiguities to the relatively established principles of incorporating daylight in buildings in several European countries; these were significant for both architects and other stakeholders. This paper compares the long-standing practice and historical context of daylight provision according to the criteria of national standards in selected European countries (Germany, Czech Republic, Slovak republic, Sweden) with the minimum target daylight factor according to the harmonized EN 17037. The consequences of the methodological differences and design criteria of daylight provision are presented in case studies of the assessment of the daylight in residential rooms and typical school classrooms. Daylight factor and lighting distribution are analyzed for different room scenarios, different window configurations and obstruction angles according to local standards in the mentioned European countries versus EN 17037. The paper also highlights the practical impact of the EN 17037 criteria on building design and the extent of façade obstruction.

Exploring the link between the increase in high-rise buildings and youth jumping suicide in Taiwan: A longitudinal study.

INTRODUCTION: From 2010 to 2021, suicide rates in 15-24 age group in Taiwan increased by 70%, with jumping being the most common method in 2021. We examined the link between the rise in youth suicides and the increase in high-rise buildings during this period. METHODS: Spearman's correlation coefficients and negative binomial mixed-effects models were employed to assess the association between the increase in high-rise buildings and jumping suicides over time. RESULTS: Spearman's correlation coefficients of high-rise buildings and jumping suicide rates in youth decreased from 0.692 (p < 0.001) in 2010 to 0.354 (p = 0.11) in 2021. Negative binomial mixed-effects models showed that although jumping suicide rates in youths increased over time, the increase in numbers of high-rise buildings was not related to rates of youth suicide by jumping. Conversely, in older age groups, the correlations were still prominent. CONCLUSION: Despite the rising trend in youth suicides by jumping over the past 11 years, our study refutes the intuitive notion that the increase in high-rise buildings contributes to this trend. It is imperative to identify and address other potential factors, such as academic stress and/or family disruptions, for effective prevention of youth suicide.

Effectively solve the obstacle in the old residential building energy-saving renovation from the perspective of a four-party evolutionary game.

Although the government highly focuses on old residential building energy-saving renovation (ORBESR), many hinders still exist and the efficiency of it is still low. This paper proposes a four-party evolutionary game model to study the impact of relative stakeholders' choices, involving developers, residents, neighborhood councils, and governments. Using this model, this paper studies what influences the conflicts between developers and residents take on the efficiency of ORBESR. In addition, what influence the residents, neighborhood councils, and developers' strategies will take on the ORBESR under the condition of evolutionary stability strategy. This paper finally concludes that governments could propose high penalties first to accelerate the stability of the system, then suitable subsidies to relieve the financial burden and to achieve high efficiency. The governments could provide a suitable plan for residents' investment to promote residents' participation. The neighborhood councils arouse the ways and facilities to help residents understand and participate in the ORBESR and try to solve the conflicts between developers and residents can improve the residents' participation and the developers' willingness to implement the ORBESR.

Development of a framework to forecast the urban residential building CO2 emission trend and reduction potential to 2060: A case study of Jiangxi province, China.

Investigating the CO2 abatement potential of urban residential building from systematic perspective is essential to reach the urban carbon neutrality target. However, previous studies on building CO2 emission trend forecasting were mainly focused on the building operational phase. In this study, a new framework that includes four building stages under a system dynamic model is developed to simulate urban residential building carbon emission changes and the related reduction potentials under three scenarios in Jiangxi Province up to 2060. Results showed that the overall process carbon emission dynamic had already peaked in 2014 under the three scenarios, with a peak value of 38.52 Mt. It then fell to 9.56 Mt in 2060 under the baseline (BAU) scenario. More importantly, seven carbon abatement measures were adopted during four building activities in this study, and the total carbon reduction was not the sum of the carbon reduction potential of the individual measures. Some carbon abatement strategies displayed synergistic effects such as low-carbon electrification where the combination of electrification and clean energy power generation was the largest contributor to reduced carbon emissions during building operation as a comprehensive carbon reduction measure. By contrast, extending a building's lifetime restrained the carbon abatement potential during the demolition stage, and it inhibited the carbon emission reduction by 24.84 Mt. These results highlight the significant need for effective policy interventions for clean production and the need to improve prefabricated building proportions, promote electrification, improve energy efficiency, strengthen recycling practices, and extend building lifetimes to promote decarbonization of urban residential building system development.