PuSH - Publikationsserver des Helmholtz Zentrums München

Petrić, V.* ; Strbad, D.* ; Račić, N.* ; Hussein, T.* ; Kecorius, S. ; Mureddu, F.* ; Lovrić, M.*

Hybrid digital twin framework for real-time indoor air quality monitoring and filtration optimization.

Atmosphere 17:184 (2026)
Verlagsversion Forschungsdaten DOI
Open Access Gold
Creative Commons Lizenzvertrag
This study presents a hybrid digital twin system designed for real-time indoor air quality (IAQ) monitoring and filtration optimization within a residential environment. Using a network of low-cost sensors, physics-based simulations, and machine learning models, the system dynamically replicates the indoor environment to enable continuous assessment and optimization of key pollutants, including particulate matter, volatile organic compounds, and carbon dioxide. The system architecture integrates mass balance and decay models, computational fluid dynamics simulations, regression models, and neural network algorithms, all evaluated under both filtering and non-filtering conditions. A graphical user interface allows users to interact with the system, test air purifier placements, and visualize air quality dynamics in real time. The results demonstrate that, within this system, simpler models, such as linear regression, outperform more complex architectures under data-limited conditions, achieving test-set coefficients of determination ranging from 0.97 to 0.99 across multiple IAQ parameters. At the same time, the hybrid modelling approach enhances interpretability and robustness. Overall, this digital twin system contributes to smart building management by offering a scalable, interpretable, and cost-effective solution for proactive IAQ control and personalized decision-making.
Altmetric
Weitere Metriken?
[➜Einloggen]
Zusatzinfos bearbeiten [➜Einloggen]
Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Indoor Air Quality ; Interpretability ; Filtration (mathematics) ; Artificial Neural Network ; Hybrid System ; Air Quality Index ; Building Automation ; Graphical User Interface; Particulate Matter; Health; Pollution; Dynamics; Exposure
ISSN (print) / ISBN 2073-4433
e-ISSN 2073-4433
Zeitschrift Atmosphere
Quellenangaben Band: 17, Heft: 2, Seiten: , Artikelnummer: 184 Supplement: ,
Verlag MDPI
Verlagsort Mdpi Ag, Grosspeteranlage 5, Ch-4052 Basel, Switzerland
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Epidemiology (EPI)
Förderungen EU-Commission
Next Generation EU