Indoor air最新文献

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.

Human movement across a doorway and associated door opening and closing motions is an important mechanism of containment failure in protective rooms. Detailed information regarding the 3D, time-dependent air flow field and aerosol concentration field induced by the motions is of pivotal importance for the development of effective intervention strategies. This study used boundary-conformal moving mesh techniques to simulate air and aerosol transport from a contaminated room into a pressure-equilibrium clean room. The simulations were conducted with different directions of manikin movement and door swinging in order to analyze their individual and combined effects on aerosol transport. The results showed that the net transport of air was dominated by the door swinging motion. The volume of air exchange caused by an opening door was around 47% of the volume displaced by the door as it swinged open, while the passage of a human-sized manikin across the doorway only added a few small fluctuations (< 10%) in the curve of air exchange rate. The net transport of aerosol was always associated with an outward motion, either an out-swinging door or an out-moving manikin from the contaminated room toward the clean room. An out-swinging door caused 44% of the aerosols in a volume equal to the displaced volume near the door to escape, with a further 28% added by an out-moving manikin. Comparatively, the amount of aerosol escape induced by an in-swinging door or in-moving making was very small. The study revealed that the vortex flows in the wake regions played a key role in aerosol transport, therefore proposing that destroying the wake flow regions of out-moving objects may be an effective method to mitigate containment failure induced by swinging doors and moving human occupants.

Indoor air quality (IAQ) in educational facilities is shaped by a dynamic interplay of microbial, chemical and physical factors, all of which influence health and cognitive performance. This study explored IAQ in university classrooms by combining microbiological, chemical and physical measurements to better understand microbial–chemical interactions in such environments. Samples (N = 33) were collected from 11 rooms of different sizes, including lecture halls, classrooms and computer labs. Bacteria and moulds were quantified using standard microbiological procedures, while CO₂, O₂, CH₄, temperature, relative humidity and pressure were monitored by portable analysers. MALDI-TOF MS was applied to identify airborne bacterial and fungal species, providing insight into microbial diversity and sources. The average CO₂ concentration was 906 ppm (range 509–1462 ppm). Although the overall mean was below the recommended 1000 ppm limit, more than half of the monitored rooms recorded CO₂ levels above this threshold. Mean bacterial and mould loads were 572 CFU/m³ (range 50–1376 CFU/m³) and 130 CFU/m³ (range 56–260 CFU/m³), respectively. Oxygen remained stable at 20.6 vol.%, while methane concentrations were negligible (mean 2.5 ppm). Relative humidity varied between 25% and 55%. Identified microorganisms were dominated by human-associated bacteria (Staphylococcus, Micrococcus) and environmental fungi (Cladosporium, Penicillium), with noticeable differences between occupied and unoccupied rooms. Correlation analysis showed significant positive associations between CO₂ and bacterial load (ρ = 0.56, p < 0.05), as well as relative humidity and bacterial abundance (ρ = 0.67, p < 0.05). Species richness was negatively correlated with occupancy (ρ = –0.77, p < 0.01), indicating microbial homogenisation in crowded conditions. Multiple regression analysis identified CO₂ and relative humidity as significant independent predictors of bacterial load (p < 0.05). These findings highlight the importance of integrating microbial and physico-chemical monitoring in IAQ assessments. CO₂ and relative humidity emerged as key controllable indicators, offering practical targets for improving air quality and limiting microbial contamination in educational environments.

Single-occupant faculty offices remain underexplored in indoor environmental quality research, despite extensive studies on classrooms and open-plan offices. This study provides case-specific exploratory evidence on how spatial orientation and acoustic conditions influence thermal satisfaction in a LEED-Gold academic building. Offices were classified as street-oriented, corridor-oriented, or void-oriented based on their exposure to outdoor streets, internal corridors, or the central atrium. Over a 40-day summer period, temperature, relative humidity, and airspeed were continuously monitored, and daily surveys captured occupants′ thermal and acoustic perceptions. Three offices representing the three orientations were instrumented, and 12 faculty members participated. Corridor-oriented offices showed the warmest and most humid conditions, with mean values of 29°C and 74.6% RH, exceeding ASHRAE 55-2020 thresholds. Void- and street-oriented offices maintained more moderate conditions. A significant association between acoustic satisfaction and thermal comfort (ρ > 0.60, p < 0.05) was observed, suggesting that sensory dimensions may reinforce one another. Because of the small sample size (n = 12), all findings should be interpreted as exploratory. Even so, the results provide empirical evidence that spatial microclimates and acoustic perception shape comfort in high-performance educational offices. Practical implications include refining HVAC zoning, improving acoustic control, and strengthening occupant-centered post-occupancy evaluation strategies.