Air, Volume 3, Issue 4 (December 2025) – 8 articles

Fine particulate matter (PM_2.5) poses substantial urban health risks that vary across space, time, and population vulnerability. We integrate a spatio-temporal INLA–SPDE PM_2.5 field with an agent-based model (ABM) of 10,000 daily home–work commuters in Indianapolis’s Pleasant Run airshed (50 weeks; 250 m grid). The PM_2.5 surface fuses 23 corrected PurpleAir PA-II-SD sensors with meteorology, land use, road proximity, and MODIS AOD. Validation indicated strong agreement (leave-one-out R² = 0.79, RMSE = 3.5 μg/m³; EPA monitor comparison R² = 0.81, RMSE = 3.1 μg/m³). We model a spatial-equity counterfactual by assigning susceptibility independently of residence and workplace, isolating vulnerability from residential segregation. Under this design, annual PM_2.5 exposure was statistically indistinguishable across groups (16.22–16.29 μg/m³; max difference 0.07 μg/m³, <0.5%), yet VWDI differed by ~10× (High vs. Very Low). Route-level maps reveal recurrent micro-corridors (>20 μg/m³) near industrial zones and arterials that increase within-group variability without creating between-group exposure gaps. These findings quantify a policy-relevant “floor effect” in environmental justice: even with perfect spatial equity, substantial health disparities remain driven by susceptibility. Effective mitigation, therefore, requires dual strategies—place-based emissions and mobility interventions to reduce exposure for all, paired with vulnerability-targeted health supports (screening, access to care, indoor air quality) to address irreducible risk. The data and code framework provides a reproducible baseline against which real-world segregation and mobility constraints can be assessed in future, stratified scenarios. Full article

When it rains or snows over a city, water droplets capture airborne pollutants and transport them to the ground. Prolonged precipitation over the same area can remove a larger amount of pollution; however, rainfall systems vary in duration and tend to move rapidly across regions. Wet deposition sprinklers replicate this natural scavenging process. They can operate for extended periods as needed and can be installed at specific locations where pollution mitigation is most necessary. Despite encouraging experimental results and the widespread use of similar technologies in industrial sectors—such as mining, the construction industry, and waste management—very limited scientific research has focused on their application in urban environments. In particular, their use as an emergency measure during severe pollution episodes as a protective intervention for sensitive subjects, while awaiting the effects of long-term structural solutions, remain largely unexplored. In the present work, we systematically discuss the key elements required to design and implement a network of anti-smog cannons (ASC) to protect sensitive receptors from severe air pollution events in large cities. Based on this analysis, we established a generalized framework that can be applied to any urban context worldwide. We also examine the potential application of the proposed method to the city of Turin (≈850,000 inhabitants, north-western Italy), which is considered a representative case study for other cities in Western Europe. Our findings indicate that such a network is both technically feasible and economically sustainable for local government authorities. Full article

A large proportion of the existing building stock in northern Europe is facing energy renovation in the coming years. In this process, existing architecture in cold and temperate climates, originally designed for natural ventilation, is renovated, implementing mechanical ventilation with heat recovery, in the belief that mechanical ventilation performs better than natural ventilation. Yet, can natural ventilation outperform mechanical ventilation when comparing life cycle carbon emissions, cost, and indoor environmental parameters? This study compares two different ventilation strategies in a full-scale renovation of two identical Danish residential buildings: (1) natural ventilation with passive controlled NOTECH ventilation and two-layered high-transmittance windows vs. (2) mechanical ventilation with heat recovery and three-layered low energy windows. The study compares energy performance, life cycle carbon footprint, capital cost investments, payback period, and indoor environmental quality (IEQ). Under the observed conditions, the results show that natural ventilation outperforms mechanical ventilation when it comes to energy consumption for heating (MWh), global warming potential (t. CO₂-equivalent), and total costs, while mechanical ventilation has a slightly higher indoor environmental quality. The study shows that two-layered windows and natural ventilation, based on passive solar heating, can reduce the global warming potential and act as a viable alternative to three-layered windows and mechanical ventilation when renovating existing building stock. Full article

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

Exposure to degraded indoor air quality (IAQ) conditions represents a major concern for health and well-being. PM_2.5 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 PM_2.5 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 PM_2.5, 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

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

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 PM_2.5, 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 PM_2.5 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