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Combining mobile air quality sensor data and machine learning for more fine-grained air quality assessments in urban areas

Author
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Abstract
As conventional air quality monitoring networks are sparse, and recent advances in sensor and IoT technologies have revolutionized air quality monitoring applications for more-fine grained air quality mapping, more accurate personal exposure assessments, ... This presentation provides an overview of different mobile sensor testbeds deployed in Antwerp (BE), Utrecht (NL) and Oakland (US), used to train two different machine learning models; i.e. a Variational Graph Auto Encoder (AVGAE) and Geographical Random Forest (GRF) model with the aim of inferring the mobile data in both space and time. Moreover, we validated the prediction performance of the considered models at different regulatory station locations following the EU FAIRMODE protocol. Combining real-time air quality sensor data with data-driven modelling for fine-grained mapping of air quality in heterogeneous urban environments. The data-driven models show to perform while needing much lower resources, computational power, infrastructure and processing Time, when compared to the state-of-the-art physical models. Moreover, all Considered context information in this study is openly available and, therefore, scalable to any city worldwide.

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MLA
Hofman, Jelle, et al. “Combining Mobile Air Quality Sensor Data and Machine Learning for More Fine-Grained Air Quality Assessments in Urban Areas.” Meteorology and Climate : Modeling for Air Quality Conference, Abstracts, 2021.
APA
Hofman, J., Nikolaou, M., Tien, D. H., Qin, X., Esther, R. B., Philips, W., … Valerio, P. L. M. (2021). Combining mobile air quality sensor data and machine learning for more fine-grained air quality assessments in urban areas. Meteorology and Climate : Modeling for Air Quality Conference, Abstracts. Presented at the Meteorology and Climate : Modeling for Air Quality Conference, Online (UC Davis Conference Center).
Chicago author-date
Hofman, Jelle, Mania Nikolaou, Do Huu Tien, Xuening Qin, Rodrigo Bonet Esther, Wilfried Philips, Nikos Deligiannis, and Panzica La Manna Valerio. 2021. “Combining Mobile Air Quality Sensor Data and Machine Learning for More Fine-Grained Air Quality Assessments in Urban Areas.” In Meteorology and Climate : Modeling for Air Quality Conference, Abstracts.
Chicago author-date (all authors)
Hofman, Jelle, Mania Nikolaou, Do Huu Tien, Xuening Qin, Rodrigo Bonet Esther, Wilfried Philips, Nikos Deligiannis, and Panzica La Manna Valerio. 2021. “Combining Mobile Air Quality Sensor Data and Machine Learning for More Fine-Grained Air Quality Assessments in Urban Areas.” In Meteorology and Climate : Modeling for Air Quality Conference, Abstracts.
Vancouver
1.
Hofman J, Nikolaou M, Tien DH, Qin X, Esther RB, Philips W, et al. Combining mobile air quality sensor data and machine learning for more fine-grained air quality assessments in urban areas. In: Meteorology and Climate : Modeling for Air Quality Conference, Abstracts. 2021.
IEEE
[1]
J. Hofman et al., “Combining mobile air quality sensor data and machine learning for more fine-grained air quality assessments in urban areas,” in Meteorology and Climate : Modeling for Air Quality Conference, Abstracts, Online (UC Davis Conference Center), 2021.
@inproceedings{8737542,
  abstract     = {{As conventional air quality monitoring networks are sparse, and recent advances in sensor and IoT technologies have revolutionized air quality monitoring applications for more-fine grained air quality mapping, more accurate personal exposure assessments, ... This presentation provides an overview of different mobile sensor testbeds deployed in Antwerp (BE), Utrecht (NL) and Oakland (US), used to train two different machine learning models; i.e. a Variational Graph Auto Encoder (AVGAE) and Geographical Random Forest (GRF) model with the aim of inferring the mobile data in both space and time. Moreover, we validated the prediction performance of the considered models at different regulatory station locations following the EU FAIRMODE protocol. Combining real-time air quality sensor data with data-driven modelling for fine-grained mapping of air quality in heterogeneous urban environments. The data-driven models show to perform  while needing much lower resources, computational power, infrastructure and processing Time, when compared to the state-of-the-art physical models. Moreover, all Considered context information in this study is openly available and, therefore, scalable to any city worldwide.}},
  author       = {{Hofman, Jelle and Nikolaou, Mania and Tien, Do Huu and Qin, Xuening and Esther, Rodrigo Bonet and Philips, Wilfried and Deligiannis, Nikos and Valerio, Panzica La Manna}},
  booktitle    = {{Meteorology and Climate : Modeling for Air Quality Conference, Abstracts}},
  language     = {{eng}},
  location     = {{Online (UC Davis Conference Center)}},
  pages        = {{1}},
  title        = {{Combining mobile air quality sensor data and machine learning for more fine-grained air quality assessments in urban areas}},
  url          = {{https://macmaq.aqrc.ucdavis.edu/2021-program-content#Valerio_Panzica-La-Manna}},
  year         = {{2021}},
}