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Air, Volume 3, Issue 4 (December 2025) – 5 articles

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28 pages, 1590 KB  
Review
Ventilation and Infection Control in Healthcare Facilities: A Post-COVID-19 Literature Synthesis
by Mohammad Saleh Nikoopayan Tak and Ehsan Mousavi
Air 2025, 3(4), 30; https://doi.org/10.3390/air3040030 - 4 Nov 2025
Abstract
The COVID-19 pandemic has reshaped the global understanding of airborne disease transmission, particularly in healthcare environments. This literature review examines how building ventilation and indoor air quality strategies have evolved in response to SARS-CoV-2, with a specific focus on healthcare settings. A systematic [...] Read more.
The COVID-19 pandemic has reshaped the global understanding of airborne disease transmission, particularly in healthcare environments. This literature review examines how building ventilation and indoor air quality strategies have evolved in response to SARS-CoV-2, with a specific focus on healthcare settings. A systematic review of 163 post-pandemic studies, alongside a selective review of pre-COVID-19 literature, was conducted to assess how scientific knowledge, practical recommendations, and HVAC-related interventions have changed. The review categorizes studies across detection methods, simulation models, observational analyses, and policy recommendations, drawing attention to novel findings and evidence-supported practices. While the body of research reaffirms the critical role of ventilation, many recommendations remain unevaluated through empirical methods. This study identifies the gaps in evidence and highlights the most impactful advances that can inform future design, maintenance, and operational protocols in healthcare facilities to mitigate airborne infection risks. Full article
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19 pages, 2402 KB  
Article
Toward Personalized Short-Term PM2.5 Forecasting Integrating a Low-Cost Wearable Device and an Attention-Based LSTM
by Christos Mountzouris, Grigorios Protopsaltis and John Gialelis
Air 2025, 3(4), 29; https://doi.org/10.3390/air3040029 - 1 Nov 2025
Viewed by 118
Abstract
Exposure to degraded indoor air quality (IAQ) conditions represents a major concern for health and well-being. PM2.5 is among the most prevalent indoor air pollutants and constitutes a key indicator in IAQ assessment. Conventional IAQ frameworks often neglect personalization, which in turn [...] Read more.
Exposure to degraded indoor air quality (IAQ) conditions represents a major concern for health and well-being. PM2.5 is among the most prevalent indoor air pollutants and constitutes a key indicator in IAQ assessment. Conventional IAQ frameworks often neglect personalization, which in turn compromises the reliability of exposure estimation and the interpretation of associated health implications. In response to this limitation, the present study introduces a human-centric framework that couples wearable sensing with deep learning, employing a low-cost wearable device to capture PM2.5 concentrations in the immediate human vicinity and an attention-based Long-Short Term Memory (LSTM) to deliver 5-min-ahead exposure predictions. During evaluation, the proposed framework demonstrated strong and consistent performance across both stable conditions and transient spikes in PM2.5, yielding a Mean Absolute Error (MAE) of 0.181 µg/m3. These findings highlighted the synergistic potential between wearable sensing and data-driven modeling in advancing personalized IAQ forecasting, informing proactive IAQ management strategies, and ultimately promoting healthier built environments. Full article
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12 pages, 980 KB  
Review
Innovation in Indoor Disinfection Technologies During COVID-19: A Comprehensive Patent and Market Analysis (2020–2025)
by Federica Paladini, Fabiana D’Urso, Francesco Broccolo and Mauro Pollini
Air 2025, 3(4), 28; https://doi.org/10.3390/air3040028 - 22 Oct 2025
Viewed by 265
Abstract
The COVID-19 pandemic catalyzed unprecedented innovation in indoor disinfection technologies, fundamentally transforming the patent landscape and commercial development in this sector. This comprehensive analysis examined patent filings from global databases and commercial market data spanning January 2020 to December 2025. Patent data were [...] Read more.
The COVID-19 pandemic catalyzed unprecedented innovation in indoor disinfection technologies, fundamentally transforming the patent landscape and commercial development in this sector. This comprehensive analysis examined patent filings from global databases and commercial market data spanning January 2020 to December 2025. Patent data were collected up to September 2022, while market data include both historical figures (2020–2023) and future projections (2024–2025) derived from industry research reports. A systematic review identified significant technological developments across five major categories: ultraviolet-C (UV-C) systems, ozone generators, photocatalytic oxidation systems, plasma disinfection technologies, and electromagnetic field applications. The analysis revealed that while patent activity surged dramatically during the pandemic period, commercial success rates varied significantly across technology categories. UV-C systems demonstrated the highest market penetration with established commercial viability, while emerging technologies such as electromagnetic disinfection faced substantial barriers to commercialization. Geographic analysis showed concentrated innovation in developed economies, with China leading in patent volume and South Korea achieving notable commercial success despite smaller patent portfolios. The study provides critical insights into the relationship between patent activity and commercial viability in emergency-driven innovation contexts. Full article
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15 pages, 1015 KB  
Article
Modelling the Presence of Smokers in Households for Future Policy and Advisory Applications
by David Moretón Pavón, Sandra Rodríguez-Sufuentes, Alicia Aguado, Rubèn González-Colom, Alba Gómez-López, Alexandra Kristian, Artur Badyda, Piotr Kepa, Leticia Pérez and Jose Fermoso
Air 2025, 3(4), 27; https://doi.org/10.3390/air3040027 - 7 Oct 2025
Viewed by 421
Abstract
Identifying tobacco smoke exposure in indoor environments is critical for public health, especially in vulnerable populations. In this study, we developed and validated a machine learning model to detect smoking households based on indoor air quality (IAQ) data collected using low-cost sensors. A [...] Read more.
Identifying tobacco smoke exposure in indoor environments is critical for public health, especially in vulnerable populations. In this study, we developed and validated a machine learning model to detect smoking households based on indoor air quality (IAQ) data collected using low-cost sensors. A dataset of 129 homes in Spain and Austria was analyzed, with variables including PM2.5, PM1, CO2, temperature, humidity, and total VOCs. The final model, based on the XGBoost algorithm, achieved near-perfect household-level classification (100% accuracy in the test set and AUC = 0.96 in external validation). Analysis of PM2.5 temporal profiles in representative households helped interpret model performance and highlighted cases where model predictions revealed inconsistencies in self-reported smoking status. These findings support the use of sensor-based approaches for behavioral inference and exposure assessment in residential settings. The proposed method could be extended to other indoor pollution sources and may contribute to risk communication, health-oriented interventions, and policy development, provided that ethical principles such as transparency and informed consent are upheld. Full article
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1 pages, 121 KB  
Correction
Correction: Leontjevaite et al. Air Pollution Effects on Mental Health Relationships: Scoping Review on Historically Used Methodologies to Analyze Adult Populations. Air 2024, 2, 258–291
by Kristina Leontjevaite, Aoife Donnelly and Tadhg Eoghan MacIntyre
Air 2025, 3(4), 26; https://doi.org/10.3390/air3040026 - 24 Sep 2025
Viewed by 240
Abstract
In the original publication [...] Full article
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