Air

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.

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.

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.