Research Article
Context-Based Analysis of Urban Air Quality Using an Opportunistic Mobile Sensor Network
@INPROCEEDINGS{10.1007/978-3-030-51005-3_24, author={Xuening Qin and Ljiljana Platisa and Tien Huu Do and Evaggelia Tsiligianni and Jelle Hofman and Valerio Panzica La Manna and Nikos Deligiannis and Wilfried Philips}, title={Context-Based Analysis of Urban Air Quality Using an Opportunistic Mobile Sensor Network}, proceedings={Science and Technologies for Smart Cities. 5th EAI International Summit, SmartCity360, Braga, Portugal, December 4-6, 2019, Proceedings}, proceedings_a={SMARTCITY}, year={2020}, month={7}, keywords={Air pollution monitoring Smart city Internet of Things}, doi={10.1007/978-3-030-51005-3_24} }- Xuening Qin
Ljiljana Platisa
Tien Huu Do
Evaggelia Tsiligianni
Jelle Hofman
Valerio Panzica La Manna
Nikos Deligiannis
Wilfried Philips
Year: 2020
Context-Based Analysis of Urban Air Quality Using an Opportunistic Mobile Sensor Network
SMARTCITY
Springer
DOI: 10.1007/978-3-030-51005-3_24
Abstract
Air pollution is becoming an important environmental issue and attracting increasing public attention. In urban environments, air pollution changes very dynamically both with time and space and is affected by a large variety of factors such as road type, urban architecture, land use and variety of emission sources. 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 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 (NO(2), PM(1), PM({2.5})and PM({10})) at street level by using mobile air pollution monitoring. In particular, we explore how the atmospheric pollutant concentration is 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.
