Search Results (60)

Urban parking around the world faces similar challenges of inadequate space, pollution, and carbon emissions. Although various smart parking technologies have been tested and implemented, they primarily aim to reduce the time spent searching for parking, without considering the impact on air quality. In this study, the air quality in three urban garages was investigated with portable instruments at the entrance and exit gates and inside the garages. Garage emissions measured include CO₂, PM_2.5, PM₁₀, NO₂, and total VOCs. The results suggested that the PM_2.5 levels in these garages tend to be higher than the ambient levels. The emissions also exhibit seasonal variations, with the highest concentrations occurring in the summer, which are 20.32 µg/m³ in Campus Green, 14.25 µg/m³ in CCM, and 15.23 µg/m³ in Washington Park garages, respectively. PM_2.5 measured from these garages is strongly correlated (with an R² of 0.64) with ambient levels. CO₂ emissions are higher than ambient levels but within the indoor air quality limit. This suggests that urban garages in Cincinnati tend to enrich ambient air concentrations, which can affect garage users and garage attendants. Portable sensors are capable of long-term emission monitoring and are compatible with other technologies in smart garage development. With portable air sensors becoming increasingly accessible and affordable, there is an opportunity to integrate these devices with smart garage management systems to enhance the sustainability of parking garages. Full article

We developed a microscale airflow modeling system with detailed building and terrain data to better understand the urban microclimate. Building shapes and heights, and terrain elevation data were integrated to construct a high-resolution urban surface geometry. The system, based on computational fluid dynamics using OpenFOAM, can resolve complex flow structures around built environments. Inflow boundary conditions were generated using logarithmic wind profiles derived from Automatic Weather System (AWS) observations under neutral stability. After validation with wind-tunnel data for a single block, the system was applied to airflow modeling around a university campus in Seoul using AWS data from four nearby stations. The results demonstrated that the system captured key flow characteristics such as channeling, wake, and recirculation induced by complex terrain and building configurations. In particular, easterly inflow cases with high-rise buildings on the leeward side of a mountain exhibited intensified wakes and internal recirculations, with elevated centers influenced by tall structures. This modeling framework, with further development, could support diverse urban applications for microclimate and air quality, facilitating urban resilience. 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

Air pollution poses a growing threat to public health and the environment, highlighting the need for continuous and precise urban air quality monitoring. The aim of this study was to implement and evaluate an autonomous air quality monitoring platform developed by the University of Ruse, “Angel Kanchev”, under Bulgaria’s National Recovery and Resilience Plan (project BG-RRP-2.013-0001), co-financed by the European Union through the NextGenerationEU initiative. The system, based on Libelium’s mobile sensor technology, was installed at a height of two meters on the university campus near Rodina Boulevard and operated continuously from 1 March 2024 to 30 March 2025. Every 15 min, it recorded concentrations of CO, CO₂, NO₂, SO₂, PM₁, PM_2.5, and PM₁₀, along with meteorological parameters (temperature, humidity, and pressure), transmitting the data via GSM to a cloud-based database. Analyses included a distributional assessment, Spearman rank correlations, Kruskal–Wallis tests with Dunn–Sidak post hoc comparisons, and k-means clustering to identify temporal and meteorological patterns in pollutant levels. The results indicate the high operational stability of the system and reveal characteristic pollution profiles associated with time of day, weather conditions, and seasonal variation. The findings confirm the value of combining calibrated IoT systems with advanced statistical methods to support data-driven air quality management and the development of predictive environmental models. Full article

Schools are vital infrastructures where students acquire essential skills and foster social values. Indoor air quality (IAQ) is of paramount importance in schools, given that students spend a considerable amount of time indoors. This study examines the influence of a natural green structure (NGS) on IAQ in an Eco-Campus classroom. The IAQ of a classroom with an NGS was compared to that of an adjacent classroom without an NGS. The thermal conditions were monitored, including air temperature (T) and relative humidity (RH), as well as indoor pollutants, including carbon dioxide (CO₂), volatile organic compounds (VOCs), and particulate matter (PM_2.5 and PM₁₀). The findings indicated a substantial improvement in indoor air quality in the classroom where the green structure was installed. This study lends support to the incorporation of biophilic solutions as sustainable approaches to fostering healthier learning environments, which in turn can lead to improvements in student performance and well-being. Full article

Urban campuses face critical environmental challenges due to high pedestrian density, traffic-induced air pollution, and thermal stress, especially in compact Mediterranean settings. These conditions can compromise the usability and livability of outdoor spaces. This study investigates whether greening and material-based interventions can offset a lower degree of traffic reduction in improving air quality and thermal comfort. The University Campus of Catania (Southern Italy) served as the case study. An integrated microscale simulation framework using ENVI-met was developed, calibrated, and validated with local traffic, meteorological data, and field measurements of PM₁₀ and PM_2.5. Three scenarios were tested: a baseline, Scenario 1 (50% traffic reduction with moderate greening), and Scenario 2 (30% traffic reduction with more extensive greening and material interventions). Results showed that Scenario 1 consistently outperformed Scenario 2 in all pedestrian hotspots. The highest reductions recorded in Scenario 1 were −0.150 μg/m³ for PM_2.5 (−11.5%), −0.069 μg/m³ for PM₁₀ (−5.9%), −2.16 °C for UTCI (−7.6%), and −2.52 °C for MRT (−4.5%). These findings confirm that traffic reduction is the dominant factor in achieving environmental improvements, although greening and innovative materials play a valuable complementary role. The study supports integrated planning strategies for climate-responsive and healthier university environments. Full article

Urbanization and the reduction of green spaces have significantly contributed to problems such as rising temperatures and declining air quality in urban areas. This study examines the impact of different types of green areas—broadleaved trees, coniferous trees, shrubs, and vines—on urban temperature regulation at the Recep Tayyip Erdoğan University Zihni Derin Campus. Surface temperature, humidity, ambient temperature, and wind speed measurements were collected using an infrared thermometer over a one-year period under various climatic conditions (August, October, January, and April) and at different times of the day (09:00 AM, 01:00 PM, and 05:00 PM). To quantitatively assess the cooling effect of each type of green area, a Response Surface Methodology (RSM) was applied, and a predictive formula was developed to estimate the cooling impact of various green areas under different environmental conditions. These formulated models enable the estimation of the temperature reduction provided by these four plant types based on different input parameters, achieving an accuracy of approximately 92% or higher without requiring direct measurements. The findings of this study provide a robust methodological framework and a practical tool for optimizing green space designs, mitigating urban heat island effects, and enhancing urban living comfort under various climatic conditions. Full article

Tropospheric ozone (O₃) and other pollutants significantly affect Chile’s Metropolitan Region, posing risks to human health. As a secondary pollutant and a major photochemical oxidant, O₃ formation is driven by anthropogenic volatile organic compounds (AVOCs) from the residential and transport sectors, the main sources of gaseous emissions. This study evaluated the AVOC capture capacity of leaf material from two tree species, Quillaja saponaria (native species) and Robinia pseudoacacia (exotic species), as potential urban biomonitors. Leaf samples were collected near nine SINCA official monitoring stations and the Antumapu University Campus, stored frozen, and analyzed by HS-SPME-GC/MSD for AVOC quantification. Photochemical reactivity and O₃ formation potential were assessed using equivalent propylene concentration (Prop-Equiv) and Ozone Formation Potential (OFP) methods. The results showed that both species captured atmospheric AVOCs, confirming their role as bioindicators. However, Q. saponaria adsorbed significantly higher AVOC concentrations and exhibited greater tropospheric O₃ formation potential than R. pseudoacacia. Given the AVOC adsorption capacity of both tree species, they could be used as biomonitors for styrene and also as a biomonitor for toluene in the case of Q. saponaria. This research highlights the importance of selecting tree capacity to improve urban air quality. Full article

Previous research has established that vegetation can significantly improve air quality. However, numerical simulations examining the purification effects of vegetation on air pollutants at the neighborhood scale remain limited, particularly regarding different neighborhood typologies. This study detailed the vegetation, buildings, and pollution emissions within neighborhoods by combining high-resolution imagery with field surveys. Then, a computational fluid dynamics model—validated through field monitoring—was used to design two scenarios to simulate and evaluate the air-purifying effects of vegetation in two typical Beijing neighborhoods. The simulation results were also well validated by the trial-and-error method compared with the computation of vegetation absorption coefficients. Findings indicated that in the Dashilar Traditional Hutong Community, vegetation contributed to reductions of 2.39% in PM_2.5 and 3.35% in CO, whereas in the east campus of Beijing University of Technology Pingleyuan, reductions were more substantial, reaching 10.07% for PM_2.5 and 8.21% for CO. The results also showed that the size and configuration of green patches directly influence PM_2.5 purification efficiency, with consolidated green areas outperforming scattered patches in particle absorption and deposition. Additionally, extensive vegetation near high-rise buildings may not yield the intended purification benefits. These findings provide a robust scientific basis for sustainable urban planning practices aimed at enhancing air quality. Full article

This research aims to demonstrate a machine learning (ML) algorithm-based indoor air quality (IAQ) monitoring and forecasting system for a public sector building use case. Such a system has the potential to automate existing heating/ventilation systems, therefore reducing energy consumption. One of Riga Technical University’s campus buildings, equipped with around 128 IAQ sensors, is used as a test bed to create a digital shadow including a comparison of five ML-based data prediction tools. We compare the IAQ data prediction loss using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) error metrics based on real sensor data. Gated Recurrent Unit (GRU) and Kolmogorov–Arnold Networks (KAN) prove to be the most accurate models regarding the prediction error. Also, GRU proved to be the most efficient model regarding the required computation time. Full article

Nowadays, enhancing campus environments through mitigations of air pollutions is an essential endeavor to support academic achievements, health, and safety of students and staffs in higher educational institutes. In laboratories, pollutants from welding, auto repairs, or chemical experiments can drastically degrade the air quality in the campus, endangering the respiratory and cognitive health of students and staffs. Besides, in universities in Indonesia, automobile emissions of harmful substances such as carbon monoxide (CO), nitrogen dioxide (NO₂), and hydrocarbon (HC) have been a serious problem for a long time. Almost everybody is using a motorbike or a car every day in daily life, while the number of students is continuously increasing. However, people in many campuses including managements do not be aware these problems, since air quality is not monitored. In this paper, we present a real-time air quality monitoring system utilizing Internet of Things (IoT) integrated sensors capable of detecting pollutants and measuring environmental conditions to visualize them. By transmitting data to the SEMAR IoT application server platform via an ESP32 microcontroller, this system provides instant alerts through a web application and Telegram notifications when pollutant levels exceed safe thresholds. For evaluations of the proposed system, we adopted three sensors to measure the levels of CO, NO₂, and HC and conducted experiments in three sites, namely, Mechatronics Laboratory, Power and Emission Laboratory, and Parking Lot, at the State Polytechnic of Malang, Indonesia. Then, the results reveal Good, Unhealthy, and Dangerous for them, respectively, among the five categories defined by the Indonesian government. The system highlighted its ability to monitor air quality fluctuations, trigger warnings of hazardous conditions, and inform the campus community. The correlation of the sensor levels can identify the relationship of each pollutant, which provides insight into the characteristics of pollutants in a particular scenario. Full article

The increasing global influence of FM in higher education institutions (HEIs) reported in the literature necessitates a comprehensive examination of the research landscape, with emphasis on how facility management (FM) plays a crucial role in enhancing the quality of teaching, learning, and research environments. This study provides an analysis of the research landscape of FM within HEIs. Using the PRISMA approach to analyze 428 documents from the Scopus™ database, this paper employs a combination of bibliometric analysis, systematic literature review, and meta-analysis to provide a comprehensive examination of FM research trends and key themes. This study reveals a significant increase in publications in the field of FM research over the past three decades, emphasizing its growing significance in fostering efficient and sustainable learning environments. The significant role of effective FM practices in enhancing student satisfaction, academic performance, and institutional reputation was emphasized. Indoor environmental quality (IEQ) (viz., thermal comfort, air quality, lighting, and acoustics) is crucial for the well-being and productivity of building occupants. The integration of FM with building information modeling (BIM), smart campus technologies, and sustainability initiatives has improved operational efficiency and environmental sustainability. This study underscores the importance of allocating resources for facility maintenance and professional services and implementing advanced technologies and sustainable practices in FM for HEIs to create a conducive academic environment. This study provides beneficial insights for researchers, policymakers, and practitioners aiming to increase FM in higher education. Full article

This study innovatively employs drones equipped with air quality sensors to collect three-dimensional air quality data in a campus microenvironment. Data are accurately corrected using a BP neural network, and a cubic model is constructed using three-dimensional interpolation. Combining photogrammetry technology, this study analyzes air quality patterns, finding significant differences from macro trends. Construction activities and large electronic experimental equipment significantly increase PM_2.5 levels in the air. In rainy weather, the respiration of vegetation is enhanced, leading to higher CO₂ concentrations, while water bodies exhibit higher temperatures in rainy weather due to their high specific heat capacity. This research not only provides a new perspective for microenvironment air quality monitoring but also offers a scientific basis for future air quality monitoring and management. Full article

This paper aimed to explore a suitable ventilation method at a lower cost of energy to pursue a high learning efficiency based on the characteristics of a Chinese student group and campus building. Firstly, the model was established by Modelica and a good agreement between the numerical simulation and the results by CONTAM 3.4.0.3 was obtained. Secondly, the effects of the fixed window-opening ratio method (FWM), switch control window-opening ratio method (SCM), and automatic control window-opening ratio method (ACM) on CO₂ concentration, indoor air temperature, and the heating capacity of air conditioning were investigated. The results showed that, when the FWM with 0% opening or 20% opening was adopted, the indoor CO₂ concentration (ICC) was higher than the limit value of the classroom air quality standard, which was 1000 ppm. When the fixed window-opening ratio was greater than 40%, the indoor air temperature could not be controlled at the set value of 18 °C, which presented bad indoor thermal comfort. Meanwhile, when the ACM was adopted, the duration to meet good indoor thermal comfort was 57.17% higher than that of the SCM. However, both of them could maintain the average ICC below the set value in the class. Lastly, the fresh air energy consumption under different ventilation methods was compared. When the design temperature was 13.5 °C, it could be revealed that the fresh air energy consumption under the ACM, SCM, and FWM with 40% opening was 46.58%, 48.38%, and 51.26% lower than those at 18 °C. In summary, it was recommended to set the design temperature of the classroom at 13.5 °C, and the ACM was suggested as a suitable ventilation method to provide fresh air for the classroom. Full article

This study focuses on predicting PM2.5 levels at the University of Petroșani by employing advanced machine-learning techniques to analyze a dataset that encapsulates a wide array of air pollutants and meteorological factors. Utilizing data from Internet of Things (IoT) sensors and established environmental monitoring stations, the research leverages Random Forest, Gradient Boosting Machines, and Support Vector Regression models to forecast air quality, emphasizing the complex interplay between various pollutants. The models demonstrate varying degrees of accuracy, with the Random Forest model achieving the highest predictive power, indicated by an R² score of 0.82764. Our findings highlight the significant impact of specific pollutants such as NO, NO₂, and CO on PM2.5 levels, suggesting targeted mitigation strategies could enhance local air quality. Additionally, the study explores the role of temporal dynamics in pollution trends, employing time-series analysis to further refine the predictive accuracy. This research contributes to the field of environmental science by providing a nuanced understanding of air-quality fluctuations in a university setting and offering a replicable model for similar environments seeking to reduce airborne pollutants and protect public health. Full article