Urban environments are increasingly exposed to environmental stressors such as electromagnetic fields (EMF) and air pollution, challenging public health and infrastructure sustainability. The widespread deployment of 5G networks, wire-less communication systems, as well as industrial emissions and heavy traffic has increased the need for advanced sensing and modeling techniques to monitor and mitigate potential risks due to pollution. In this study, we present an integrated urban sensing framework that combines low-cost sensor networks, data fusion methodologies, and machine learning models to predict and forecast environmental exposure to EMF and nitrogen dioxide (N02) in Ghent, Belgium. We evaluate the performance of different sensing technologies, data preprocessing techniques, and statistical and deep learning-based models for data imputation and forecasting; addressing the challenges of data calibration and sparsity. Our findings demonstrate the effectiveness and need for improving exposure assessment by providing valuable insights for urban planning, policy-making, and sustainable development.
Rodrigo-Bonet, E, Qin, X, Deprez, K, Christiaen, L, Martens, L, Celikkol, B, Fabius, J, Philips, W & Deligiannis, N 2025, An Integrated Sensing and AI Framework for EMF and NO 2 Exposure Assessment in Urban Environments: A Ghent Case Study. in 2025 25th International Conference on Digital Signal Processing (DSP). International Conference on Digital Signal Processing, DSP, IEEE, pp. 1-5, 25th International Conference on Digital Signal Processing (DSP), Pylos, Greece, 25/06/25. https://doi.org/10.1109/DSP65409.2025.11075059
Rodrigo-Bonet, E., Qin, X., Deprez, K., Christiaen, L., Martens, L., Celikkol, B., Fabius, J., Philips, W., & Deligiannis, N. (2025). An Integrated Sensing and AI Framework for EMF and NO 2 Exposure Assessment in Urban Environments: A Ghent Case Study. In 2025 25th International Conference on Digital Signal Processing (DSP) (pp. 1-5). (International Conference on Digital Signal Processing, DSP). IEEE. https://doi.org/10.1109/DSP65409.2025.11075059
@inproceedings{4ab907905cb74ea3907f243a38af4e8e,
title = "An Integrated Sensing and AI Framework for EMF and NO 2 Exposure Assessment in Urban Environments: A Ghent Case Study",
abstract = "Urban environments are increasingly exposed to environmental stressors such as electromagnetic fields (EMF) and air pollution, challenging public health and infrastructure sustainability. The widespread deployment of 5G networks, wire-less communication systems, as well as industrial emissions and heavy traffic has increased the need for advanced sensing and modeling techniques to monitor and mitigate potential risks due to pollution. In this study, we present an integrated urban sensing framework that combines low-cost sensor networks, data fusion methodologies, and machine learning models to predict and forecast environmental exposure to EMF and nitrogen dioxide (N02) in Ghent, Belgium. We evaluate the performance of different sensing technologies, data preprocessing techniques, and statistical and deep learning-based models for data imputation and forecasting; addressing the challenges of data calibration and sparsity. Our findings demonstrate the effectiveness and need for improving exposure assessment by providing valuable insights for urban planning, policy-making, and sustainable development.",
author = "Esther Rodrigo-Bonet and Xuening Qin and Kenneth Deprez and Lowie Christiaen and Luc Martens and Burcu Celikkol and Jasper Fabius and Wilfried Philips and Nikos Deligiannis",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 25th International Conference on Digital Signal Processing (DSP), DSP ; Conference date: 25-06-2025 Through 27-06-2025",
year = "2025",
month = jun,
day = "1",
doi = "10.1109/DSP65409.2025.11075059",
language = "English",
isbn = "9798331512149",
series = "International Conference on Digital Signal Processing, DSP",
publisher = "IEEE",
pages = "1--5",
booktitle = "2025 25th International Conference on Digital Signal Processing (DSP)",
url = "https://ieeexplore.ieee.org/xpl/conhome/11074775/proceeding",
}