Air quality monitoring in heterogeneous cities is challenging as a high resolution in both space and time is required to accurately assess population exposure. As regulatory monitoring networks are sparse due to high investment and maintenance costs, recent advances in sensor and IoT technologies have resulted in innovative sensing approaches like mobile sensing to increase the spatial monitoring resolution. An example of such an opportunistic mobile monitoring network is “Snuffelfiets”, a project where air quality data is collected from mobile sensors attached to bicycles in Utrecht (NL). The collected data results in a sparse spatiotemporal matrix of measurements which can be completed using data-driven techniques. This work reports on the potential of two machine learning approaches to infer the collected air quality measurements in both space and time; a deep learning model based on Variational Graph Autoencoders (AVGAE) and a Geographical Random Forest model (GRF). A temporal validation exercise is performed at two regulatory monitoring stations following the FAIRMODE modelling quality objectives protocol. This work demonstrates the potential of data-driven techniques for spatiotemporal air quality inference of sensor data as the considered models performed well in terms of accuracy and correlation. The model observed performance metrics approach current state-of-the-art physical models in terms of performance while needing much lower resources, computational power, infrastructure and processing time.
Hofman, J, Do Huu, T, Qin, X, Rodrigo Bonet, E, Niko-laou, M, Philips, W, Deligiannis, N & Panzica La Manna, V 2021, Spatiotemporal Air Quality Inference of Low-Cost Sensor Data; Application on a Cycling Monitoring Network. in A Del Bimbo, R Cucchiara, S Sclaroff, GM Farinella, T Mei, M Bertini, HJ Escalante & R Vezzani (eds), ICPR International Workshops and Challenges: Lecture Notes in Computer Science. vol. 12666, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12666 LNCS, Springer, pp. 139-147, Workshop on "Machine Learning Advances Environmental Science (MAES), Milan, Italy, 10/01/21. https://doi.org/10.1007/978-3-030-68780-9_14
Hofman, J., Do Huu, T., Qin, X., Rodrigo Bonet, E., Niko-laou, M., Philips, W., Deligiannis, N., & Panzica La Manna, V. (2021). Spatiotemporal Air Quality Inference of Low-Cost Sensor Data; Application on a Cycling Monitoring Network. In A. Del Bimbo, R. Cucchiara, S. Sclaroff, G. M. Farinella, T. Mei, M. Bertini, H. J. Escalante, & R. Vezzani (Eds.), ICPR International Workshops and Challenges: Lecture Notes in Computer Science (Vol. 12666, pp. 139-147). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12666 LNCS). Springer. https://doi.org/10.1007/978-3-030-68780-9_14
@inproceedings{472867a82fb74271aa82217a8ea4ddba,
title = "Spatiotemporal Air Quality Inference of Low-Cost Sensor Data; Application on a Cycling Monitoring Network",
abstract = "Air quality monitoring in heterogeneous cities is challenging as a high resolution in both space and time is required to accurately assess population exposure. As regulatory monitoring networks are sparse due to high investment and maintenance costs, recent advances in sensor and IoT technologies have resulted in innovative sensing approaches like mobile sensing to increase the spatial monitoring resolution. An example of such an opportunistic mobile monitoring network is “Snuffelfiets”, a project where air quality data is collected from mobile sensors attached to bicycles in Utrecht (NL). The collected data results in a sparse spatiotemporal matrix of measurements which can be completed using data-driven techniques. This work reports on the potential of two machine learning approaches to infer the collected air quality measurements in both space and time; a deep learning model based on Variational Graph Autoencoders (AVGAE) and a Geographical Random Forest model (GRF). A temporal validation exercise is performed at two regulatory monitoring stations following the FAIRMODE modelling quality objectives protocol. This work demonstrates the potential of data-driven techniques for spatiotemporal air quality inference of sensor data as the considered models performed well in terms of accuracy and correlation. The model observed performance metrics approach current state-of-the-art physical models in terms of performance while needing much lower resources, computational power, infrastructure and processing time.",
keywords = "IoT, air quality, mobile sensors, Machine learning, IoT, urban, air quality, mobile sensors, machine learning",
author = "Jelle Hofman and {Do Huu}, Tien and Xuening Qin and {Rodrigo Bonet}, Esther and Martha Niko-laou and Wilfried Philips and Nikolaos Deligiannis and {Panzica La Manna}, Valerio",
year = "2021",
month = feb,
day = "25",
doi = "10.1007/978-3-030-68780-9_14",
language = "English",
isbn = "978-3-030-68779-3",
volume = "12666",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "139--147",
editor = "{Del Bimbo}, Alberto and Rita Cucchiara and Stan Sclaroff and Farinella, {Giovanni Maria} and Tao Mei and Marco Bertini and Escalante, {Hugo Jair} and Roberto Vezzani",
booktitle = "ICPR International Workshops and Challenges",
note = "Workshop on {"}Machine Learning Advances Environmental Science (MAES) ; Conference date: 10-01-2021 Through 15-01-2021",
url = "https://sites.google.com/view/maes-icpr2020/",
}