Qin, X., Platisa, L., Do Huu, T. , Tsiligianni, E. , Hofman, J., Panzica La Manna, V. , Deligiannis, N. , & Philips, W. (2020). Context-based analysis of urban air quality using an opportunistic mobile sensor network . In H. Santos, G. V. Pereira, M. Budde, S. F. Lopes, & P. Nikolic (Eds.), Science and Technologies for Smart Cities - 5th EAI International Summit, SmartCity360, Proceedings (pp. 285-300). (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST Vol. 323 LNICST). Springer International Publishing.

@inproceedings{fb3c49e335c740b38f29fdddeff340dd,
title = " Context-based analysis of urban air quality using an opportunistic mobile sensor network " ,
abstract = " Air pollution is becoming an important environmental issue and attracting increasing public attention, especially in urban environments where it is affected by a large variety of factors (like road type, urban architecture, land use and variety of emission sources) and changes very dynamically, both with time and space. In order to better understand the complexity of urban air pollution, hyperlocal air pollution monitoring is necessary, but the existing regulatory monitoring networks are typically sparse due to the high costs to cover a full city area at the necessary level of detail (spatial granularity). In this paper, we use the city of Antwerp in Belgium as a pilot to analyze the temporal and spatial distribution of four atmospheric pollutants (NO2, PM1, PM2.5 and PM10 ) at street level by using mobile air pollution monitoring. In particular, we explore how the atmospheric pollutant concentrationis affected by different context factors (e.g., road type, land use, source proximity). Our results demonstrate that these factors have an impact on the concentration distribution of the considered pollutants. For example, higher atmospheric NO 2 concentrations are observed on primary roads, compared to secondary roads, and some source locations such as traffic lights have shown to be hot spots of atmospheric NO 2 accumulation. These findings can be useful in order to formulate future local air quality measures and further improve current air quality models based on the observed impact of the considered context factors. " ,
keywords = " internet of things, Smart City " ,
author = " Xuening Qin and Ljiljana Platisa and {Do Huu}, Tien and Evangelia Tsiligianni and Jelle Hofman and {Panzica La Manna}, Valerio and Nikolaos Deligiannis and Wilfried Philips " ,
note = " Publisher Copyright: { extcopyright} ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2020. Copyright: Copyright 2020 Elsevier B.V., All rights reserved. EAI International Conference on Sensor Systems and Software - S-CUBE 2019, S-CUBE 2019 Conference date: 04-12-2019 Through 06-12-2019 " ,
year = " 2020 " ,
month = jul,
day = " 28 " ,
doi = " 10.1007/978-3-030-51005-3_24 " ,
language = " English " ,
isbn = " 9783030510046 " ,
series = " Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST " ,
publisher = " Springer International Publishing " ,
pages = " 285300 " ,
editor = " Henrique Santos and Pereira, {Gabriela Viale} and Matthias Budde and Lopes, {S{'e}rgio F.} and Predrag Nikolic " ,
booktitle = " Science and Technologies for Smart Cities - 5th EAI International Summit, SmartCity360, Proceedings " ,
url = " https://s-cubeconference.eai-conferences.org/2019/ " ,
}