Search Results (157)

The integration of Mechanical Ventilation with Heat Recovery (MVHR) systems into heritage buildings poses a series of challenges, largely attributable to architectural constraints and conservation requirements. The present study offers an operational campaign of an MVHR system installed during the energy retrofit of a protected residential heritage dwelling in Vitoria-Gasteiz, Spain. Although environmental monitoring was carried out throughout the year, representative spring, autumn and winter days of continuous operation were analysed, as the occupants frequently avoided using the system due to noise perception. This limitation highlights the importance of considering acoustic comfort and user acceptance as critical factors in the long-term viability of MVHR in heritage contexts. The system was assessed under real-life conditions using continuous environmental monitoring, with a focus on indoor air quality (IAQ), thermal efficiency, airflow balance, and pressure losses. Despite the acceptable mean apparent thermal effectiveness (0.74) and total useful efficiency (0.96), the system’s performance was found to be constrained by significant flow imbalance (up to 106%) and elevated pressure drops, which were attributed to the legacy of the duct geometry. The results obtained demonstrate IAQ improved overall, with mean CO₂ concentrations below ~650 ppm across the analysed dataset; however, daily means occasionally exceeded 900–1000 ppm during high-occupancy periods and in the absence of spatially distributed demand control. These exceedances are consistent with the measured outdoor baseline (~400–450 ppm) and reflect the need for post-commissioning balancing and room-level sensing to sustain Category II performance in heritage dwellings. This study provides empirical evidence on the limitations and opportunities of MVHR deployment in historic retrofits, thus informing future guidelines for sustainable interventions in heritage contexts. Full article

The extraction of architectural structural line features can simplify the 3D spatial representation of built environments, reduce the storage and processing burden of large-scale point clouds, and provide essential geometric primitives for downstream modeling tasks. However, existing 3D line extraction methods suffer from incomplete and fragmented contours, with missing or misaligned intersections. To overcome these limitations, this study proposes a patch-level framework for 3D line extraction and structural optimization from building point clouds. The proposed method first partitions point clouds into planar patches and establishes local image planes for each patch, enabling a structured 2D representation of unstructured 3D data. Then, graph-cut segmentation is proposed to extract compact boundary contours, which are vectorized into closed lines and back-projected into 3D space to form the initial line segments. To improve geometric consistency, regularized geometric constraints, including adjacency, collinearity, and orthogonality constraints, are further designed to merge homogeneous segments, refine topology, and strengthen structural outlines. Finally, we evaluated the approach on three indoor building environments and four outdoor scenes, and experimental results show that it reduces noise and redundancy while significantly improving the completeness, closure, and alignment of 3D line features in various complex architectural structures. Full article

Severe sensitivity to various environmental chemicals affects an increasing number of people—a condition referred to as Multiple Chemical Sensitivity (MCS). The responses are both physical and psychological, where avoidance of chemical triggers can lead to social isolation, thereby increasing the level of disability. There is a need for user supportive environments where people with MCS can thrive, both indoors and outdoors. The study resulted in three principles for designing outdoor housing areas: (1) using spatial analysis to create a site layout that minimizes exposure to external and confounding triggers (e.g., noise, visual disturbances); (2) using zoning to clearly delineate private from semi-private areas; and (3) selecting vegetation and materials carefully to avoid triggers. The principles were developed in an iterative process based on existing research combined with a survey involving 58 MCS respondents. Full article

Accurate forecasting of energy consumption in buildings is essential for achieving energy efficiency and reducing carbon emissions. However, many existing models rely on limited input variables and overlook the complex influence of indoor environmental quality (IEQ). In this study, we assess the performance of hybrid machine learning ensembles for predicting hourly energy demand in a smart office environment using high-frequency IEQ sensor data. Environmental variables including carbon dioxide concentration (CO₂), particulate matter (PM_2.5), total volatile organic compounds (TVOCs), noise levels, humidity, and temperature were recorded over a four-month period. We evaluated two ensemble configurations combining support vector regression (SVR) with either Random Forest or LightGBM as base learners and Ridge regression as a meta-learner, alongside single-model baselines such as SVR and artificial neural networks (ANN). The SVR combined with Random Forest and Ridge regression demonstrated the highest predictive performance, achieving a mean absolute error (MAE) of 1.20, a mean absolute percentage error (MAPE) of 8.92%, and a coefficient of determination (R²) of 0.82. Feature importance analysis using SHAP values, together with non-parametric statistical testing, identified TVOCs, humidity, and PM_2.5 as the most influential predictors of energy use. These findings highlight the value of integrating high-resolution IEQ data into predictive frameworks and demonstrate that such data can significantly improve forecasting accuracy. This effect is attributed to the direct link between these IEQ variables and the activation of energy-intensive systems; fluctuations in humidity drive HVAC energy use for dehumidification, while elevated pollutant levels (TVOCs, PM_2.5) trigger increased ventilation to maintain indoor air quality, thus raising the total energy load. Full article

Ensuring a sustainable and healthy human environment in university dormitories is essential for students’ learning, living, and overall health and well-being. To address this need, we carried out a series of systematic field measurements of the noise levels at 30 dormitories in three representative major urban universities in a major provincial capital city in China and designed and implemented a comprehensive questionnaire and surveyed 1005 students about their perceptions of their acoustic environment. We proposed and applied a sustainability–health-oriented, multidimensional assessment framework to assess the acoustic environment of the dormitories and student responses to natural sound, technological sounds, and human-made sounds. Using the Structural Equation Modeling (SEM) approach combined with the field measurements and student surveys, we identified three categories and six factors on student health and well-being for assessing the acoustic environment of university dormitories. The field data indicated that noise levels at most of the measurement points exceeded the recommended or regulatory thresholds. Higher noise impacts were observed in early mornings and evenings, primarily due to traffic noise and indoor activities. Natural sounds (e.g., wind, birdsong, water flow) were highly valued by students for their positive effect on the students’ pleasantness and satisfaction. Conversely, human and technological sounds (traffic noise, construction noise, and indoor noise from student activities) were deemed highly disturbing. Gender differences were evident in the assessment of the acoustic environment, with male students generally reporting higher levels of the pleasantness and preference for natural sounds compared to female students. Educational backgrounds showed no significant influence on sound perceptions. The findings highlight the need for providing actionable guidelines for dormitory ecological design, such as integrating vertical greening in dormitory design, water features, and biodiversity planting to introduce natural soundscapes, in parallel with developing campus activity standards and lifestyle during noise-sensitive periods. The multidimensional assessment framework will drive a sustainable human–ecology–sound symbiosis in university dormitories, and the category and factor scales to be employed and actions to improve the level of student health and well-being, thus, providing a reference for both research and practice for sustainable cities and communities. Full article

This study assessed the baseline indoor environmental quality (IEQ) of secondary school classrooms in Tanzania by measuring temperature, relative humidity, noise, lighting, and indoor air quality. Objective measurements were conducted using calibrated sensors in 14 classrooms across five schools, with data collected during occupied school hours and additional noise measurements during unoccupied periods. All classrooms are naturally ventilated through operable windows and doors. The findings reveal a pattern of cumulative IEQ deficiencies: classroom temperatures frequently exceeded the recommended 20–24 °C range, reaching means as high as 30.4 °C, while relative humidity varied widely, with levels occasionally surpassing 65%. Noise levels consistently exceeded the World Health Organization (WHO)’s recommended 35 dBA threshold, with significant differences observed between occupied and unoccupied periods (p = 0.02). Light distribution was uneven, with significantly higher lux levels near windows than at classroom centers (p < 0.001), and artificial lighting was generally insufficient due to infrastructure limitations. Although CO₂ concentrations remained below the 1000 ppm threshold, indicating adequate ventilation, particulate matter levels were often elevated, with PM2.5 reaching up to 58.80 µg/m³ and PM10 up to 96.90 µg/m³, exceeding health-based guidelines. Together, these findings suggest that students are exposed to multiple environmental stressors that may impair health, comfort, and academic performance. This study provides a critical baseline for future research and context-specific interventions aimed at improving learning environments in Tanzanian schools and similar settings in East Africa. Full article

This study presents a comprehensive comparison of static and SLAM (Simultaneous Localization and Mapping) laser scanners of both new and old generation in a controlled indoor environment of a standard commercial building with long, linear corridors and recesses. The aim was to assess both global and local accuracy, as well as noise characteristics, of each scanner. Methods: A highly accurate static scanner was used to generate a reference point cloud. Five devices were evaluated: two static scanners (Leica RTC 360 and Trimble X7) and three SLAM scanners (GeoSLAM ZEB Horizon RT, Emesent Hovermap ST-X, and FARO Orbis). Accuracy analysis included systematic and random error assessment, axis-specific displacement evaluation, and profile-based local accuracy measurements. Additionally, noise was quantified before and after data smoothing. Static scanners yielded superior accuracies, with the Leica RTC 360 achieving the best performance (absolute accuracy of 1.2 mm). Among SLAM systems, the Emesent Hovermap ST-X and FARO Orbis—both newer-generation devices—demonstrated significant improvements over the older-generation GeoSLAM ZEB Horizon RT. After smoothing, the noise levels of these new-generation SLAM scanners (approx. 2.1–2.2 mm) approached those of static systems. The findings underline the ongoing technological progress in SLAM systems, with the new-generation SLAM scanners becoming increasingly viable alternatives to static scanners, especially when speed, ease of use, and reduced occlusions are prioritized. This makes them well-suited for rapid indoor mapping applications, provided that the slightly lower accuracy is acceptable for the intended use. Full article

To address the challenges of dynamic object interference and redundant information representation in map construction for indoor dynamic environments, this paper proposes an indoor dynamic environment mapping method based on semantic fusion and hierarchical filtering. First, prior dynamic object masks are obtained using the YOLOv8 model, and geometric constraints between prior static objects and dynamic regions are introduced to identify non-prior dynamic objects, thereby eliminating all dynamic features (both prior and non-prior). Second, an initial semantic point cloud map is constructed by integrating prior static features from a semantic segmentation network with pose estimates from an RGB-D camera. Dynamic noise is then removed using statistical outlier removal (SOR) filtering, while voxel filtering optimizes point cloud density, generating a compact yet texture-rich semantic dense point cloud map with minimal dynamic artifacts. Subsequently, a multi-resolution semantic octree map is built using a recursive spatial partitioning algorithm. Finally, point cloud poses are corrected via Transform Frame (TF) transformation, and a 2D traversability grid map is generated using passthrough filtering and grid projection. Experimental results demonstrate that the proposed method constructs multi-level semantic maps with rich information, clear structure, and high reliability in indoor dynamic scenarios. Additionally, the map file size is compressed by 50–80%, significantly enhancing the reliability of mobile robot navigation and the efficiency of path planning. Full article

Formaldehyde vapor (HCHO) is a harmful chemical substance and a potential air contaminant, with a permissible level in indoor spaces below 0.08 ppm (80 ppb). Thus, highly sensitive gas sensors for the continuous monitoring of HCHO are in demand. The electrical conductivity of semiconducting nanomaterials (e.g., single-walled carbon nanotubes (SWCNTs)) makes them sensitive to chemical substances adsorbed on their surfaces, and a variety of portable and highly sensitive chemiresistive gas sensors, including those capable of detecting HCHO, have been developed. However, when monitoring low levels of vapors (<1 ppm) found in ambient air, most chemiresistive sensors face practical issues, including false responses to interfering effects (e.g., fluctuations in room temperature and humidity), baseline drift, and the need to apply a purge gas. Here, we report an actuator-driven, purge-free chemiresistive gas sensor that is capable of reliably detecting 0.05 ppm of HCHO in the air. This sensor is composed of an HCHO→HCl converter (powdery hydroxylamine salt, HA), an HCl detector (a SWCNT-based chemiresistor), and an HCl blocker (a thin plastic plate). Upon exposure to HCHO, the HA emits HCl vapor, which diffuses onto the adjacent SWCNTs, increasing their electrical conductivity through p-doping. Meanwhile, inserting a plastic plate between HA and SWCNTs makes the conductivity of SWCNTs insensitive to HCHO. Thus, via periodic actuation (insertion and removal) of the plastic plate, HCHO can be detected reliably over a wide concentration range (0.05–15 ppm) with excellent selectivity over other volatile organic compounds. This actuator-driven system is beneficial because it does not require a purge gas for sensor recovery or baseline correction. Moreover, since the response to HCHO is synchronized with the actuation timing of the plate, even small (~0.8%) responses to 0.05 ppm of HCHO can be clearly separated from larger noise responses (>1%) caused by interfering effects and baseline drift. We believe that this work provides substantial insights into the practical implementation of nanomaterial-based chemiresistive gas sensors. Full article

This work presents an on-field noise analysis during the class breaks in Greek school units (a high school and a senior high school) based on the design and deployment of low-cost IoT sensor nodes and IoT platforms. The course breaks form 20% of a regular school day, during which intense mobility and high noise levels usually evolve. Indoor noise levels, along with environmental conditions, have been measured through a wireless network that comprises IoT nodes that integrate humidity, temperature, and acoustic level sensors. PM₁₀ and PM_2.5 values have also been acquired through data sensors located nearby the school complex. School buildings that have been recently renovated for minimizing their energy footprint and CO₂ emissions have been selected in comparison with similar works in academia. The data are collected, shipped, and stored into a time-series database in cloud facilities where an IoT platform has been developed for processing and analysis purposes. The findings show that low-cost sensors can efficiently monitor noise levels after proper adjustments. Additionally, the statistical evaluation of the received sensor measurements has indicated that ubiquitous high noise levels during the course breaks potentially affect teachers’ leisure time, despite the thermal isolation of the facilities. Within this context, we prove that the proposed IoT Sensor Network could form a tool to essentially monitor school infrastructures and thus to prompt for improvements regarding the building facilities. Several guides to further mitigate noise and achieve high-quality levels in learning institutes are also described. Full article

Current research on aged housing prioritizes community planning and environmental enhancement over older adults’ needs, creating a retrofit mismatch amid population aging. To investigate the relationship between indoor environmental quality and residential satisfaction among elderly occupants, this study examines 72 households in aged residential buildings, analyzing four environmental indicators (thermal, lighting, acoustic environments, and air quality). The environmental measurements reveal that 81.9% of thermal environment parameters fall below the ASHRAE-55 comfort range, with winter average temperatures reaching only 13.94 °C. Insufficient illumination exists in kitchen and bedroom areas. Lifestyle patterns including infrequent air conditioning use (87%) and window ventilation substituting range hoods (32%) may deteriorate thermal comfort and air quality. An ordered logistic regression analysis demonstrates significant correlations between all four environmental indicators and elderly satisfaction levels. Thermal comfort emerges as the priority focus for aging-adapted retrofitting. Air quality improvement shows particularly significant potential for enhancing residential satisfaction. Although prolonged window opening (73%) exacerbates low-temperature/high-humidity conditions and noise exposure, it still contributes positively to overall satisfaction. This research provides crucial insights for aligning aged residential retrofitting with home-based elderly care requirements, promoting housing development that better accommodates the lifestyle patterns of older populations, thereby improving quality of life for aging-in-place residents. Full article

Asymmetric double-suction centrifugal fans are commonly employed in home kitchens to remove cooking pollutants, and their performance is critical to maintaining a healthy indoor environment. However, inlet condition variations significantly influence the aerodynamic efficiency and noise levels. This study utilizes a combination of performance testing and a large eddy simulation to analyze the impact of different inlet conditions on the performance curve, impeller outlet pressure pulsation, unsteady flow structures, and sound quality of an asymmetric double-suction centrifugal fan. A non-uniform air distribution at the inlet is proposed to enhance the fan’s aerodynamic and noise characteristics. The findings reveal that when the inlet area is reduced to less than 70% of its fully open state, the aerodynamic performance declines with decreasing intake area. The amplitude of the superimposed blade-passing frequency is minimized when only the left inlet is open; the pressure coefficient’s fluctuation amplitude in the time domain reaches 0.4, with sharpness peaking at 3.1. In the optimized design, the maximum deviation in total pressure efficiency is limited to 1.96%, with loudness reduced by four sones and improved sharpness and roughness. These results provide valuable insights into the design and noise reduction of asymmetric double-suction squirrel-cage fans. Full article

Based on the resource conservation requirements of GB/T 50378-2019 “Green Building Evaluation Standard”, this study constructed a BIM–Ecotect collaborative analysis model and proposed a “four-dimensional integration” green performance optimization method. Taking a high-rise office building in Wuhan as an example, a LOD 300-level Revit building information model was established, and a multidisciplinary collaborative analysis was achieved through gbXML data interaction. The lighting simulation results show that the average natural lighting coefficient of the office area facing south is 2.4 (the standard 85%), while in the meeting room area, due to the optimized design of the curtain wall, the average natural lighting coefficient has increased to 2.6 (the standard 92%). In terms of energy-saving renovation, a three-dimensional collaborative design strategy was adopted. Through the optimization of the envelope structure, the cooling load of the air conditioning system was reduced by 25.3%, and the heat load was reduced by 23.6% (the u value of the exterior wall was reduced by 56.3%, the SHGC of the exterior windows was reduced by 42.9%, and the thermal resistance of the roof was increased by 150%). The ventilation optimization adopts the CFD flow field reverse design, adjusting the window opening rate of the exterior windows from 15% to 20% to form a turbulent diffusion effect. Therefore, the air change rate in the office area reached 2.5 times per hour, and the CO₂ concentration decreased by up to 27.1% at most. The innovative adoption of the “composite sound insulation curtain wall” technology in acoustic environment control has increased the indoor noise compliance rate by 27 percentage points (from 65% to 92%). The above research data indicate that digital collaborative design can achieve an overall energy-saving rate of over 20% for buildings, providing a replicable technical path for enhancing the performance of green buildings. Full article

The research aims to systematize the current scientific evidence on methodologies used to investigate the impact of the indoor built environment on well-being, focusing on indoor environmental quality (IEQ) variables such as thermal comfort, air quality, noise, and lighting. This systematic review adheres to the Joanna Briggs Institute framework and PRISMA guidelines to assess empirical studies that incorporate physiological measurements like heart rate, skin temperature, and brain activity, which are captured through various techniques in real-life contexts. The principal results reveal a significant interest in the relationship between the built environment and physiological as well as psychological states. For instance, thermal comfort was found to be the most commonly studied IEQ variable, affecting heart activity and skin temperature. The research also identifies the need for a shift towards using advanced technologies like Mobile Brain/Body Imaging (MoBI) for capturing real-time physiological data in natural settings. Major conclusions include the need for a multi-level, evidence-based approach that considers the dynamic interaction between the brain, body, and environment. This study advocates for the incorporation of multiple physiological signals to gain a comprehensive understanding of well-being in relation to the built environment. It also highlights gaps in current research, such as the absence of noise as a studied variable of IEQ and the need for standardized well-being assessment tools. By synthesizing these insights, the research aims to pave the way for future studies that can inform better design and policy decisions for indoor environments. Full article

The indoor climate quality in classrooms at the University of Coimbra, Portugal, was investigated as part of the 3SqAir project, supported by the Interreg SUDOE program. This research focused on two equipped classrooms with different ventilation systems: natural and mechanical ventilation. Both classrooms were continuously monitored for IEQ parameters: thermal comfort, indoor air quality, noise, and lighting during heating and cooling seasons. Air temperature, relative humidity, CO₂ concentration, particulate matter, nitrogen dioxide, volatile organic compounds, formaldehyde, sound pressure level, and illuminance were measured. Outdoor weather conditions were also recorded. The primary focus was on air temperature, CO₂ concentrations, and relative humidity, while air change rates (ACH) were estimated using the Tracer Gas Method. The results showed inadequate thermal conditions in both classrooms, particularly during the heating season. Most weekly mean CO₂ concentrations were within acceptable limits, while ACH were below standard recommendations in four CO₂ decay phases. Simulations of CO₂ decay revealed further air quality gaps in each room. Corrective measures within the 3SqAir project framework were suggested for approval and implementation while monitoring continues. This work represents the first phase in an evolving study towards developing sustainable strategies for improving indoor air quality in classrooms. Full article