Search Results (1,169)

As the importance of both indoor air quality (IAQ) and energy efficiency grows in residential buildings, the application of air filters in energy recovery ventilators has become essential. However, high-efficiency filters such as MERV 12 inevitably increase the pressure drop, adversely affecting the airflow, fan energy use, and heat exchange balance. This study quantitatively investigates how different levels of filter resistance—from clean conditions to 200% dust loading—affect system airflow, static pressure, exhaust air transfer, and power consumption. A standardized dust loading procedure was adopted to simulate long-term use conditions. The results show a 37% reduction in net supply airflow under heavily clogged filters, while the unit exhaust air transfer ratio increased from 7.2% to 17.7%, exceeding compliance limits. Surprisingly, electrical energy consumption decreased as the fan load dropped with the airflow. Despite an increase in the apparent heat exchange efficiency, this gain was driven by return air recirculation rather than true thermal effectiveness. These findings highlight the need for filter performance-based ERV certification and operational strategies that balance IAQ, energy use, and system compliance. Full article

Onboard the MetOp satellite series, Infrared Atmospheric Sounding Interferometer (IASI) is a Fourier Transform spectrometer based on the Michelson interferometer. IASI acquires interferograms, which are processed to provide high-resolution atmospheric emission spectra. These spectra enable the derivation of temperature and humidity profiles, among other parameters, with exceptional spectral resolution. In this study, we evaluate a novel, rapid retrieval approach in the interferogram domain, aiming for near-real-time (NRT) analysis of large spectral datasets anticipated from next-generation tropospheric sounders, such as MTG-IRS. The Partially Sampled Interferogram (PSI) method, applied to trace gas retrievals from IASI, has been sparsely explored. However, previous studies suggest its potential for high-accuracy retrievals of specific gases, including CO, CO₂, CH₄, and N₂O at the resolution of a single IASI footprint. This article presents the results of a study based on retrieval in the interferogram domain. Furthermore, the optical pathway differences sensitive to the parameters of interest are studied. Interferograms are generated using a fast Fourier transform on synthetic IASI spectra. Finally, the relationship to the total column of carbon monoxide is explored using three different algorithms—from the most intuitive to a complex neural network approach. These algorithms serve as a proof of concept for interferogram classification and rapid predictions of surface temperature, as well as the abundances of H₂O and CO. IASI spectra simulations were performed using the LATMOS Atmospheric Retrieval Algorithm (LARA), a robust and validated radiative transfer model based on least squares estimation. The climatological library TIGR was employed to generate IASI interferograms from LARA spectra. TIGR includes 2311 atmospheric scenarios, each characterized by temperature, water vapor, and ozone concentration profiles across a pressure grid from the surface to the top of the atmosphere. Our study focuses on CO, a critical trace gas for understanding air quality and climate forcing, which displays a characteristic absorption pattern in the 2050–2350 ${\mathrm{cm}}^{-1}$ wavenumber range. Additionally, the study explores the potential of correlating interferogram characteristics with surface temperature and H₂O content, aiming to enhance the accuracy of CO column retrievals. Starting with intuitive retrieval algorithms, we progressively increased complexity, culminating in a neural network-based algorithm. The results of the NN study demonstrate the feasibility of fast interferogram-domain retrievals, paving the way for operational applications. Full article

Maintaining air quality in operating rooms is critical for infection control and patient safety. Particulate matter, originating from surgical instruments, personnel, and external sources, is influenced by airflow patterns and ventilation efficiency. This study employs Computational Fluid Dynamics (CFD) simulations using Simcenter STAR-CCM+ 2410 to analyze airflow and particulate behavior in a surgical-grade operating room. A steady-state solver with the k–ε turbulence model was used to replicate airflow, while the Lagrangian multiphase method simulated particle trajectories (0.5 µm, 1 µm, and 5 µm). The simulation results demonstrated close agreement with the experimental data, with average errors of 17.3%, 17.7%, and 39.7% for 0.5 µm, 1 µm, and 5 µm particles, respectively. These error margins are considered acceptable given the device’s 10% measurement sensitivity and the observed experimental asymmetry—attributable to equipment placement—which resulted in variations of 17.2%, 18.0%, and 26.5% at corresponding symmetric points. Collectively, these findings support the validity of the simulation model in accurately predicting particulate transport and deposition within the operating room environment. Findings confirm that optimizing airflow can achieve ISO Class 7 cleanroom standards and highlight the potential for future studies incorporating dynamic elements, such as personnel movement and equipment placement, to further improve contamination control in critical environments. Full article

This study presents an approach based on machine learning (ML) techniques to analyze the relationship between emergency room (ER) admissions for cardiorespiratory diseases (CRDs) and environmental factors. The aim of this study is the development and verification of an interpretable machine learning framework applied to environmental and health data to assess the relationship between environmental factors and daily emergency room admissions for cardiorespiratory diseases. The model’s predictive accuracy was evaluated by comparing simulated values with observed historical data, thereby identifying the most influential environmental variables and critical exposure thresholds. This approach supports public health surveillance and healthcare resource management optimization. The health and environmental data, collected through meteorological sensors and air quality monitoring stations, cover eleven years (2013–2023), including meteorological conditions and atmospheric pollutants. Four ML models were compared, with XGBoost showing the best predictive performance (R² = 0.901; MAE = 0.047). A 10-fold cross-validation was applied to improve reliability. Global model interpretability was assessed using SHAP, which highlighted that high levels of carbon monoxide and relative humidity, low atmospheric pressure, and mild temperatures are associated with an increase in CRD cases. The local analysis was further refined using LIME, whose application—followed by experimental verification—allowed for the identification of the critical thresholds beyond which a significant increase in the risk of hospital admission (above the 95th percentile) was observed: CO > 0.84 mg/m³, P_atm ≤ 1006.81 hPa, Tavg ≤ 17.19 °C, and RH > 70.33%. The findings emphasize the potential of interpretable ML models as tools for both epidemiological analysis and prevention support, offering a valuable framework for integrating environmental surveillance with healthcare planning. Full article

Urban street canyons in high-density areas exacerbate PM_2.5 accumulation, posing significant public health risks. Through integrated empirical and computational methods—including empirical PM_2.5 and microclimate measurements, multivariate regression analysis, and high-resolution ENVI-met5.1 simulations—this study quantifies the threshold effects of pedestrian-oriented morphological indicators on PM_2.5 exposure in east–west-oriented residential streets. Key findings include the following: (1) the height-to-width ratio (H/W) negatively correlates with exposure, where H/W = 2.0 reduces the peak concentrations by 37–41% relative to H/W = 0.5 through enhanced vertical advection; (2) the Build-To-Line ratio (BTR) exhibits a positive correlation with exposure, with BTR = 63.2% mitigating exposure by 12–15% compared to BTR = 76.8% by reducing aerodynamic stagnation; (3) pollution exposure can be mitigated by enhancing airflow ventilation within street canyons through architectural facade design. These evidence-based morphological thresholds (H/W ≥ 1.5, BTR ≤ 70%) provide actionable strategies for reducing health risks in polluted urban corridors, supporting China to meet its national air quality improvement targets. Full article

This study is the first to investigate the performance of ultraviolet germicidal irradiation (UVGI) in underfloor air distribution (UFAD) systems. A simplified mathematical model is developed to predict airborne pathogen transport and inactivation by upper room UVGI in UFAD spaces. The proposed model is substantiated for the SARS-CoV-2 virus as a simulated pathogen through a comprehensive computational fluid dynamics methodology validated against published experimental data of upper room UVGI and UFAD flows. Simulations show an 11% decrease in viral concentration within the upper irradiated zone when a 15 W louvered germicidal lamp is utilized. Finally, a case study on Mycobacterium tuberculosis (M. tuberculosis) bacteria is carried out using the validated simplified model to optimize the use of return air and UVGI implementation, ensuring acceptable indoor air quality and enhanced energy efficiency. Results reveal that the UFAD-UVGI system may consume up to 13.6% less energy while keeping the occupants at acceptable levels of M. tuberculosis concentration and UV irradiance when operated with 26% return air and a UVGI output of 72 W. Full article

In this study, a combined approach joining the machine learning model Extreme Gradient Boosting (XGBoost) with Shapley Additive Explanation (SHAP) is adopted to simulate the temporal pattern of the air quality index (AQI) and subsequently explore the key factors affecting AQI variability. Based on the analysis of air pollutants and meteorological data acquired from two air quality monitoring stations in Rome (Italy), over the 2018–2022 period, the results demonstrate the effectiveness of the proposed methodological approach in elucidating the role of the main factors driving AQI evolution, and their interaction effects. Full article

Outdoor air pollution poses a major global health risk, yet monitoring remains insufficient, especially in regions with limited infrastructure. Solar-powered monitors could allow for increased coverage in regions lacking robust connectivity. However, reliable sample collection can be challenging with these systems due to extreme temperatures and insufficient solar energy. Proper planning can help overcome these challenges. Air Sampler Solar and Thermal Optimization for Reliable Monitoring (Air-STORM) is an open-source tool that uses meteorological and solar radiation data to identify temperature and solar charging risks for air pollution monitors based on the target deployment area. The model was validated experimentally, and its utility was demonstrated through illustrative case studies. Air-STORM simulations can be customized for specific locations, seasons, and monitor configurations. This capability enables the early detection of potential sampling risks and provides opportunities to optimize monitor design, proactively mitigate temperature and power failures, and increase the likelihood of successful sample collection. Ultimately, improving sampling success will help increase the availability of high-quality outdoor air pollution data necessary to reduce global air pollution exposure. Full article

To address the problems of the time consumption, labor intensiveness, easy contamination, uneven drying, and impact on the medicinal efficacy of Fritillaria ussuriensis in the traditional drying method, the hot-air-drying characteristics of Fritillaria ussuriensis were studied. The changes in the moisture ratio and drying rate of Fritillaria ussuriensis under different hot-air-drying conditions (45 °C, 55 °C, 65 °C) were compared and analyzed. Six common mathematical models were used to fit the moisture change law, and it was found that the cubic model was the most suitable for describing the drying characteristics of Fritillaria ussuriensis. The R² values after fitting under the three temperature conditions were all greater than 0.99, and the maximum was achieved at 45 °C. Based on the principle of hot-air drying, a drying device for Fritillaria ussuriensis with a processing capacity of 15 kg/h was designed. It adopted a thermal circulation structure of inner and outer drying ovens, with the heating chamber separated from the drying chamber. The structural parameters were optimized based on Fluent simulation analysis. After optimization, the temperature of each layer was stable at 338 K ± 2 K, and the pressure field and velocity field were evenly distributed. The drying process parameters of Fritillaria ussuriensis were optimized based on response surface analysis, and the optimal process parameters were obtained as follows: inlet temperature: 338 K (65 °C), inlet air velocity: 3 m/s, and drying time: 10 h. The simulation results showed that the predicted moisture content of Fritillaria ussuriensis under the optimal working conditions was 12.58%, the temperature difference of Fritillaria ussuriensis at different positions was within 0.8 °C, and the humidity deviation was about 1%. A prototype of the drying device was built, and the drying test of Fritillaria ussuriensis was carried out. It was found that the temperature and moisture content of Fritillaria ussuriensis were consistent with the simulation results and met the design requirements, verifying the rationality of the device structure and the reliability of the simulation model. This design can significantly improve the distribution of the internal flow field and temperature field of the drying device, improve the drying quality and production efficiency of Fritillaria ussuriensis, and provide a technical reference for the Chinese herbal medicine-drying industry. Full article

The Latin American and Caribbean (LAC) region faces persistent challenges of inequality, climate change vulnerability, and deteriorating air quality. The Aburrá Valley, where Medellín is located, is a narrow tropical valley with complex topography, strong thermal inversions, and unstable atmospheric conditions, all of which exacerbate the accumulation of pollutants. In Medellín, ${\mathrm{NO}}_2$ concentrations have remained nearly unchanged over the past eight years, consistently approaching critical thresholds, despite the implementation of air quality control strategies. These persistent high concentrations are closely linked to the variability of the atmospheric boundary layer (ABL) and are often intensified by prolonged dry periods. This study focuses on a representative street canyon in Medellín that has undergone recent urban interventions, including the construction of new public spaces and pedestrian areas, without explicitly considering their impact on ${\mathrm{NO}}_x$ dispersion. Using Computational Fluid Dynamics (CFD) simulations, this work evaluates the influence of urban morphology on ${\mathrm{NO}}_x$ accumulation. The results reveal that areas with high Aspect Ratios (AR > 0.65) and dense vegetation exhibit reduced wind speeds at the pedestrian level—up to 40% lower compared to open zones—and higher ${\mathrm{NO}}_2$ concentrations, with maximum simulated values exceeding 50 μg/m³. This study demonstrates that the design of pedestrian corridors in complex urban environments like Medellín can unintentionally create pollutant accumulation zones, underscoring the importance of integrating air quality considerations into urban planning. The findings provide actionable insights for policymakers, emphasizing the need for comprehensive modeling and field validation to ensure healthier urban spaces in cities affected by persistent air quality issues. Full article

Computational Fluid Dynamics (CFD) is regarded as an important tool for analyzing the flow field, thermal environment, and air quality around the built environment. However, for built environment applications, the high computational cost of CFD hinders large-scale scenario simulation and efficient design optimization. In the field of built environment research, surrogate modeling has become a key technology to connect the needs of high-fidelity CFD simulation and rapid prediction, whereas the low-dimensional nature of traditional surrogate models is unable to match the physical complexity and prediction needs of built flow fields. Therefore, combining machine learning (ML) with CFD to predict flow fields in built environments offers a promising way to increase simulation speed while maintaining reasonable accuracy. This review briefly reviews traditional surrogate models and focuses on ML-based surrogate models, especially the specific application of neural network architectures in rapidly predicting flow fields in the built environment. The review indicates that ML accelerates the three core aspects of CFD, namely mesh preprocessing, numerical solving, and post-processing visualization, in order to achieve efficient coupled CFD simulation. Although ML surrogate models still face challenges such as data availability, multi-physics field coupling, and generalization capability, the emergence of physical information-driven data enhancement techniques effectively alleviates the above problems. Meanwhile, the integration of traditional methods with ML can further enhance the comprehensive performance of surrogate models. Notably, the online ministry of trained ML models using transfer learning strategies deserves further research. These advances will provide an important basis for advancing efficient and accurate operational solutions in sustainable building design and operation. Full article

The production of thermal energy in buildings using internal greenhouses makes it possible to obtain substantial gains in energy consumption and, at the same time, contribute to improving occupants’ thermal comfort (TC) levels. This article proposes a study on the producing and transporting of renewable thermal energy in a circular auditorium equipped with an enveloping semi-circular greenhouse. The numerical study is based on software that simulates the building geometry and the building thermal response (BTR) numerical model and assesses the TC level and indoor air quality (IAQ) provided to occupants in spaces ventilated by the proposed system. The building considered in this study is a circular auditorium constructed from three semi-circular auditoriums supplied with internal semi-circular greenhouses. Each of the semi-circular auditoriums faces south, northeast, and northwest, respectively. The semi-circular auditoriums are occupied by 80 people each: the one facing south throughout the day, while the one facing northeast is only occupied in the morning, and the one facing northwest is only occupied in the afternoon. The south-facing semi-circular greenhouse is used by itself to heat all three semi-circular auditoriums. The other two semi-circular greenhouses are only used to heat the interior space of the greenhouse. It was considered that the building is located in a Mediterranean-type climate and subject to the typical characteristics of clear winter days. The results allow us to verify that the proposed heating system, in which the heat provided to the occupied spaces is generated only in the semi-circular greenhouse facing south, can guarantee acceptable TC conditions for the occupants throughout the occupancy cycle. Full article

This study investigated the fracture behavior of asphalt mixtures under indirect tensile loading by comparing the performance of homogenized and micromechanical finite element (FEMs) models based on the cohesive zone model (CZM). Five asphalt mixture types were tested experimentally, and both models were calibrated and validated using load–displacement curves from indirect tensile tests (IDTs). The micromechanical model, incorporating random aggregate generation and three-phase material definition, exhibited significantly higher predictive accuracy (R² = 0.86–0.98) than the homogenized model (R² = 0.66–0.77). The validated micromechanical model was further applied to quantify the impact of construction-related deficiencies—namely, increased air voids, non-continuous gradation, and aggregate segregation. The simulation results showed that higher void content (from 4% to 10%) reduced peak load by up to 35% and increased localized stress concentrations by up to 40%. Discontinuous gradation and uneven aggregate distribution also led to premature crack initiation and more complex fracture paths. These findings demonstrated the value of micromechanical modeling for evaluating sensitivity to mix design and compaction quality, providing a foundation for performance-based asphalt mixture optimization and durability improvement. Full article

This study provides a detailed dataset from two modern homes constructed inside an environmentally controlled chamber. These data are used to carefully calibrate a dynamic thermal simulation model of these homes. The calibrated models show good agreement with measurements taken under controlled conditions. The two case study homes, “The Future Home” and “eHome2”, were constructed within the University of Salford’s Energy House 2.0, and high-quality data were collected over eight days. The calibration process involved updating U-values, air permeability rates, and modelling refinements, such as roof ventilation, ground temperatures, and sub-floor void exchange rates, set as boundary conditions. Results demonstrated a high level of accuracy, with performance gaps in whole-house heat transfer coefficient reduced to 0.5% for “The Future Home” and 0.6% for “eHome2”, falling within aggregate heat loss test uncertainty ranges by a significant amount. The study highlights the improved accuracy of calibrated dynamic thermal simulation models, compared to results from the steady-state Standard Assessment Procedure model. By providing openly accessible calibrated models and a clearly defined methodology, this research presents valuable resources for future building performance modelling studies. The findings support the UK’s transition to dynamic modelling approaches proposed in the recently introduced Home Energy Model approach, contributing to improved prediction of energy efficiency and aligning with goals for zero carbon ready and sustainable housing development. Full article

Accurate prediction of drone motion within structured urban air corridors is essential for ensuring safe and efficient operations in Urban Air Mobility (UAM) systems. Although real-world Remote Identification (Remote ID) regulations require drones to broadcast critical flight information such as velocity, access to large-scale, high-quality broadcast data remains limited. To address this, this study leverages a Digital Twin (DT) framework to augment Remote ID spatio-temporal broadcasts, emulating the sensing environment of dense urban airspace. Using Remote ID data, we propose BiDGCNLLM, a hybrid prediction framework that integrates a Bidirectional Graph Convolutional Network (BiGCN) with Dynamic Edge Weighting and a reprogrammed Large Language Model (LLM, Qwen2.5–0.5B) to capture spatial dependencies and temporal patterns in drone speed trajectories. The model forecasts near-future speed variations in surrounding drones, supporting proactive conflict avoidance in constrained air corridors. Results from the AirSUMO co-simulation platform and a DT replica of the Cranfield University campus show that BiDGCNLLM outperforms state-of-the-art time series models in short-term velocity prediction. Compared to Transformer-LSTM, BiDGCNLLM marginally improves the $R^2$ by 11.59%. This study introduces the integration of LLMs into dynamic graph-based drone prediction. It shows the potential of Remote ID broadcasts to enable scalable, real-time airspace safety solutions in UAM. Full article