Indoor air最新文献

This paper presents an Internet of Things (IoT)–enabled automated control framework for optimizing residential window ventilation systems. The system combines sensors, AI prediction, and automated motors to control ventilation based on environmental conditions and human behaviors. Unlike traditional fixed rule–based systems, our framework uses machine learning trained on 1.2 million data points to predict and automatically adjust ventilation beforehand. The modular automation system features message queuing telemetry transport (MQTT) and building automation and control network (BACnet) communication protocols for seamless integration with existing building management systems (BMSs), fail-safe mechanisms for operational reliability, and mobile override capabilities preserving user autonomy. A 1-month pilot study across 50 suburban Chinese households achieved an 18% reduction in heating, ventilation, and air-conditioning (HVAC) energy use. The framework is modular and scalable, working with current building systems. It includes technical specs, integration methods, and security protections. While behavioral insights inform control logic, the core innovation lies in the automation architecture. This study advances intelligent building systems by fusing human-centric design with robust automation engineering.

In 2020, COVID-19 caused a global pandemic, raising concerns regarding the possibility of another pandemic caused by a new or unknown virus in the future. Room ventilation is considered the most important factor to prevent disease transmission through droplets and aerosols. Acrylic partitions, as well as masks, have been used worldwide to prevent such transmissions. However, in a typical flat partition wall, aerosols expelled through a cough or sneeze diffuse back into the room after reaching the wall. To overcome this problem, we devised a partition wall equipped with aerosol diffusion prevention units above and below the wall. In this study, numerical analysis and flow visualization experiments were conducted using actual coughing and sneezing exhalation volumes to investigate the effectiveness of this partition. Particle image velocimetry analysis using video images obtained from flow visualization experiments and numerical analysis indicated that the exhaled air flowed into the device, thus reducing diffusion. In addition, low-flow suction fans were installed at the end of the aerosol diffusion prevention unit to draw the exhaled air into the unit, preventing it from diffusing, despite the small size of the partition. Thus, the proposed partition wall with the aerosol diffusion prevention units can serve as an effective barrier for limiting the transmission of infectious diseases.

Sampling viral aerosols in a small volume of liquid is crucial for effectively monitoring the aerosol transmission of viruses. However, the distribution of viral aerosols may fluctuate depending on the airflow. Therefore, sampling location in indoor spaces is crucial because the concentration of viral aerosols may fluctuate. In this study, the effects of sampling location on viral aerosol monitoring in indoor spaces were investigated. This study focused on seven negative-pressure isolation rooms of the same type for patients with SARS-CoV-2 infection where the source of the virus was present. Air sampling was conducted at two positions in each room: 30 cm below the ventilation air outlet (Sampling Position #1) and 80 cm above the floor (Sampling Position #2). The air samples were analyzed using polymerase chain reaction (PCR). The virus was detected at Sampling Position #1, but not at Sampling Position #2. To investigate the propagation of coronaviruses in the air, computational fluid dynamics (CFD) analysis was performed. A Eulerian–Lagrangian model was employed to examine the transport of cough droplets, accounting for their evaporation and dispersion. The CFD analysis revealed that the number of viral particles captured at Sampling Position #1 was about six times greater than that captured at Sampling Position #2. The results of the PCR and CFD analyses show that the proper selection of a sampling location is crucial for the successful monitoring of airborne viruses.

Bioaerosols, consisting of airborne particles carrying biological materials, represent a significant concern in healthcare settings and can reach high local concentrations during dental procedures, posing risks for both healthcare workers and patients. This study investigates the dynamics of bioaerosol deposition in the Maxillofacial Surgery and Dentistry Clinic at the Maggiore Hospital in Trieste (Italy), focusing on spatial and temporal variations during operational and nonoperational hours. Gravitational sampling was performed to assess bioaerosol quantity and distribution in the clinic using Petri dishes containing a growth medium, which were strategically placed within the dental units and in the adjoining office as a control site. Samples were collected over 10 days during operational hours, and colony counts were recorded postincubation. A subset of colonies underwent PCR amplification of the 16S rDNA gene for molecular taxonomic classification. Data were analyzed for spatial and temporal trends, and correlations were examined using a scatterplot matrix. Bioaerosol deposition rate was assessed both during routine dental procedures and the subsequent downtime using Petri dishes strategically placed in two dental units for a total of 4 days of sampling.

Results indicate that bioaerosol concentrations were highest near the patient, decreasing with distance in a proximity-dependent gradient. Colony counts were higher during operational hours, with more than 90% reduction in deposition rates postclinic operations. Unexpectedly, control samples from the adjoining office exhibited elevated colony counts, suggesting external factors influencing bioaerosol deposition. Taxonomic analysis revealed that all identified colonies belonged to the genus Staphylococcus, including opportunistic pathogens such as Staphylococcus epidermidis, Staphylococcus haemolyticus, and Staphylococcus saprophyticus. This study highlights the critical role of spatial dynamics, ventilation, and procedural activities in bioaerosol dispersion. By elucidating bioaerosol generation and deposition dynamics, these findings underscore the need for targeted interventions, such as enhanced air filtration and strategic clinic design, to mitigate bioaerosol exposure risks.

Air pollution has become a critical global concern due to rapid urbanization and industrialization, posing severe risks to environmental and public health. Effective indoor air quality monitoring systems (IAQMSs) are essential for accurately assessing pollutant levels, identifying sources, and implementing timely mitigation strategies. This paper presents a comprehensive review of recent advancements and challenges in IAQMSs, focusing on emerging techniques and technologies that enhance environmental and human health. The study explores the evolution of IAQ monitoring, emphasizing Internet of Things (IoT)–based solutions for real-time data acquisition and analysis. Advanced communication technologies such as Wi-Fi, Zigbee, and LoRa are evaluated for their efficiency and applicability in indoor environments. The review highlights key challenges, including sensor calibration, integration with renewable energy systems, and data reliability, and critically examines the suitability of low-cost sensors for consumer and large-scale applications, considering durability and performance under variable indoor conditions. Furthermore, the integration of sustainable energy solutions, such as photovoltaic solar panels and rechargeable batteries, is discussed for uninterrupted operation. The paper also investigates the role of artificial intelligence (AI) including machine learning and deep learning techniques in enhancing predictive capabilities, sensor stability, and operational efficiency. Covering literature published between 2019 and 2025, this review synthesizes current knowledge to inform the design, deployment, and future development of next-generation indoor air monitoring systems, offering actionable insights for researchers, policymakers, and public health practitioners.

Saudi Arabia′s Vision 2030 prioritises enhancing special education services for children with special needs, including autistic pupils who are particularly sensitive to their surrounding environment. Given that autistic pupils spend significant time in schools, Indoor Environmental Quality (IEQ) is critical for their well-being and learning outcomes yet remains underexplored. This study adopts a descriptive comparative design, using continuous monitoring and classroom activity observations to evaluate IEQ conditions in two autism schools in the Dammam region of Saudi Arabia during winter and summer. Measurements included air temperature, relative humidity, particulate matter (PM_2.5 and PM₁₀) concentrations, CO₂ levels, sound and lighting in classrooms. The IEQ parameters were measured using specific instruments installed at pupil level to accurately reflect their exposure. The findings reveal significant challenges in maintaining acceptable IEQ. PM_2.5 and PM₁₀ concentrations exceeded WHO guidelines, with PM_2.5 averaging 51 μg/m³ in School A and 30 μg/m³ in School B. PM₁₀ levels were even higher, peaking at 116 μg/m³ in School A and 101 μg/m³ in School B. These concentrations surpass those reported in mainstream schools in the same region, largely due to unique classroom activities (e.g., drawing, light physical activity) and cleaning practices (e.g., burning incense and use of sprays) prevalent in autism schools. Additionally, significant variations in lighting conditions highlight the need for adaptable systems to accommodate the sensory preferences and classroom activities of autistic pupils, which differ from mainstream students. These findings underscore the importance of addressing specific IEQ challenges in autism schools to improve pupil well-being and learning outcomes. This study advocates for the development of autism-friendly IEQ standards to guide future school design and operations.

The employment of deep convolutional neural networks (CNNs) signifies a substantial progression in the domain of image analysis. The application of this method is particularly suitable when the image set represents a spatial structure and predictive analysis can only be performed using Gaussian processes, which are computationally complex. The uncontrolled airflow of air into buildings, known as infiltration, poses a significant challenge in terms of characterisation and quantification. The irregular contours of gaps and cracks through the enclosure create a virtually endless variety of cases, making a generalizable scientific interpretation that can be applied to existing buildings very difficult. This circumstance is always clearly manifested by an irregular, three-dimensional incoming airflow. This study presents an innovative methodology for estimating airflow rates based on three-dimensional thermal patterns captured through infrared thermography. The experimental setup employs a 3D-printed matrix of spheres, facilitating the characterisation of the spatial temperature distribution within the airflow. The resulting thermal images are processed using a CNNs, which integrates the spatial information contained in the thermograms with a scalar input representing the inlet air temperature. The model′s performance was assessed under a range of conditions, including reduced image resolutions, varying experimental configurations (involving different flow apertures) and a comparison between full thermographic inputs and thermal difference-based features. The results indicate that the model can accurately infer airflow rates within the same aperture (medium absolute error [MAE] < 2%). While generalisation to new apertures presents a greater challenge, the experiments demonstrate that a sufficiently diverse training dataset can enhance the model′s predictive capacity for configurations not included in the training phase. These findings underscore the potential of deep learning as a nonintrusive and efficient tool for estimating airflow in systems where conventional measurement techniques are either difficult to apply or impractical.

Maintaining indoor air quality in densely built environments presents growing challenges due to rising energy demands. Vertical green living walls offer a promising, sustainable, and nature-based solution; however, their performance varies widely across different conditions, and their maintenance remains complex, posing barriers that limit their widespread adoption. We introduce VertINGreen, a first-of-its-kind web application that supports both the planning and real-time monitoring of indoor green wall systems. VertINGreen tools were developed using machine learning models trained on extensive environmental and remote sensing hyperspectral data. The planning tool is based on 1957 gas exchange measurements taken from six common indoor plant species. Data were used to model carbon assimilation and plant transpiration under varying indoor conditions. The resulting models achieved high predictive accuracy (R² > 0.94 for assimilation and > 0.66 for transpiration), enabling users to estimate carbon reduction and potential energy savings from decreased air exchange rates. The monitoring tool uses hyperspectral images and machine learning to map physiological activity across the wall and detect early signs of stress. Feature-selection methods allowed accurate predictions using as few as 10 spectral bands, making the system compatible with low-cost imaging hardware. The monitoring model successfully detected declines in plant performance weeks before visible symptoms appeared. By integrating accurate planning with early warning monitoring, VertINGreen provides a comprehensive framework for enhancing indoor environmental quality and reducing energy consumption. VertINGreen empowers architects, engineers, and building managers to design and maintain green wall systems with confidence and efficiency, translating scientific insight into practical tools for sustainable indoor environments.

Airborne cellulosic fibers (CFs) and microplastics (MPs) are emerging pollutants with potential environmental and health implications. This study presents an active sampling-based characterization of airborne CFs and MPs in Western Canada, focusing on a university campus in Kelowna. Sampling was conducted from September 2021 to October 2022, on three separate days each month, using a BioSampler operated at 12.5 L/min, across one outdoor site and three indoor locations (cafeteria, gym laundry, and manufacturing shop). Outdoor environments exhibited higher concentrations of both total particles (CFs and MPs combined, 31.4 ± 46.9 particles/m³) and MPs (5.67 ± 8.82 MPs/m³) compared to indoor air (13.7 ± 12.1 particles/m³ and 2.89 ± 4.72 MPs/m³). CFs dominated total particle counts, while MPs were predominantly fragments and fibers, suggesting differential sources and fragmentation processes. Polymer identification using μ-FTIR spectroscopy revealed that polyester and polyamide were most prevalent across all locations, likely reflecting contributions from synthetic textiles and clothing, which are known sources of airborne MPs. Smaller contributions from other polymer types suggest the presence of additional location-specific sources. Seasonal variations were also observed, with indoor MP concentrations peaking in summer, likely influenced by regional wildfires and the associated increase in indoor activities. Higher levels were additionally observed in winter at locations with increased fabric handling and material processing. These findings highlight the pervasive nature of airborne particles, even in smaller cities with localized sources. This study underscores the importance of targeted mitigation strategies and further research to understand the implications of chronic exposure to these pollutants on environmental and human health.