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.

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.

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.

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/m 3 ) and MPs (5.67 ± 8.82 MPs/m 3 ) compared to indoor air (13.7 ± 12.1 particles/m 3 and 2.89 ± 4.72 MPs/m 3 ). 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.

Backgrounds Radon is the second leading cause of lung cancer, accounting for 3%–14% of cases worldwide. Aim To assess global and national trends in tracheal, bronchial, and lung (TBL) cancer attributable to residential radon from 1990 to 2021 and to project trends up to 2046. Methods The Global Burden of Disease (GBD) 2021 data were utilized to analyze TBL cancer burden by sex and age, focusing on the Top 20 granite and marble‐consuming countries. Age‐standardized rates, average annual percentage change (AAPC), and 95% uncertainty intervals (UIs) were calculated. Age–period–cohort (APC) analysis and Bayesian age–period–cohort (BAPC) modeling were applied for trend analysis and forecasting. Results In 2021, the global age‐standardized disability‐adjusted life years (ASDR) and mortality (ASMR) rates of TBL cancer attributable to residential radon were 30.47 and 1.34 per 100,000 individuals, respectively. From 1990 to 2021, ASDR and ASMR declined globally (AAPC ASDR : −1.19, 95% CI: −1.22, −1.16 and AAPC ASMR : −0.88, 95% CI: −0.91, −0.86). The burden remained higher among males and older adults. However, China and India exhibited increasing trends, particularly among females and the elderly. Projections suggested a continued global decline up to 2046. Conclusion Although there was a global decrease in burdens of residential radon–attributable TBL cancer, males and older populations remain disproportionately affected, underscoring the need for targeted public health interventions.

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 ( R 2 > 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.

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.

Mold is found in most indoor environments and is of great concern due to adverse health effects and infrastructure damage it can cause. One key aspect of this growing problem is early detection and localization of mold contamination so appropriate measures can be implemented. In this study, a combination of solid‐phase microextraction (SPME), semiquantitative gas chromatography–mass spectroscopy (GC‐MS) analysis, and principal component analysis (PCA) of 14 select microbial volatile organic compounds (MVOCs) was used to determine if volatile organic profiling could be used to differentiate between molds grown on various building materials. Briefly, SPME fibers with PDMS/DVB coatings were employed to collect and generate volatile organic profiles of target MVOCs emitted by Aspergillus versicolor and Penicillium chrysogenum when grown on two common building materials. The volatile compound extraction and identification method revealed that P. chrysogenum grown on gypsum and A. versicolor grown on pine produced unique MVOC profiles from one another, which indicated species and substrate differentiation could be made based on the volatile organic profiles. Additionally, the production of dimethyl disulfide (DMDS) and geosmin was found to be specific to P. chrysogenum and A. versicolor , respectively, and therefore could serve as potential biomarkers for screening for the presence of each species. This study suggests profiling select MVOCs is viable for detecting specific hazardous molds when the substrate is known and a streamlined workflow for indoor mold monitoring: initial broad‐spectrum GC‐MS screening for fungal presence → selected MVOC profiling for species identification → molecular verification of hazardous species.

Indoor environments can become contaminated with pathogen‐laden respiratory droplets exhaled by people, posing a risk of infection to others. To address this issue, indoor spaces are typically cleaned by ventilating—bringing in fresh outside air and expelling contaminated air. Mobile air purifiers can also help mitigate this risk and are widely used, particularly during emergent situations such as pandemics. However, a thorough assessment of how to measure the effective cleaning performance of air purifiers is still needed. Currently, the clean air delivery rate (CADR) is used as a standard metric to evaluate air purifiers. In this protocol, however, we identify steps that may erroneously influence the measured cleaning rate, potentially leading to falsely safe estimates. To address this, we propose a modified protocol to estimate the hygienic air delivery rate (HADR), which more accurately reflects the effective cleaning performance of air purifiers. We also suggest several considerations to enhance the HADR effectiveness of these devices. Additionally, we present case studies of real devices demonstrating that the HADR can be significantly lower than the volume flow rate of the air purifiers. We emphasize that the HADR—rather than the volume flow rate—should be used by both manufacturers and consumers to evaluate and compare devices, in order to prevent a false sense of safety.