Abstract
Air pollution is difficult to detect with human senses. It is to a large extent out of sight and out of sense, while causing a burden on our economy, our health and our environment. A relevant illustration of this is the exposure to air pollution during commutes. The air pollution commuters are exposed to remains to a considerable extent a hidden geography, with, for example, a lack of available reliable information regarding the on-the-road concentrations of several air pollutants. This research aims to unravel, to the best possible extent, spatio-temporal air pollution patterns (active) commuters are exposed to. Cyclists and pedestrians can be unaware that they commute in polluted air. They often travel close to motorised traffic, resulting in high exposure to several air pollutants, which have elevated levels on the road due to vehicular emissions. Significantly higher concentrations of particulate matter (<2.5 µm), black carbon and nitrogen dioxide were found on roads with high-traffic intensities than on roads with less traffic, cycling highways or separated cycle lanes. The amplitude of the concentration differences between routes depends on both temporal factors, such as the season, the day of the week, or the time of day, and spatial factors, such as the traffic’s density, the footpath or cycle lane’s location, the architectural makeup (e.g. street canyons) and the meteorological conditions. Using high-resolution air pollution models, it is possible to distinguish between routes of higher and lower air pollution concentrations, allowing active road users to choose an alternative route to lower their air pollution exposure. However, on-the-road concentrations displayed by the Belgian ATMO-Street model are often considerably underestimated, especially for routes with high levels of motorised traffic. In general, for air pollution models to distinguish between routes, a minimum spatial-model resolution of 10 m2 including street configuration effects (e.g. street canyons) is desired. For temporal resolution, static seasonal-hourly raster model data, calculated from a previous year’s hourly data, are sufficient to make a scientifically sound distinction between alternative routes regarding exposure to air pollution. Those tools are a great help in uncovering the spatio-temporal pollution patterns (active) commuters are exposed to and also provide relevant insights to reduce the health and economic burden of air pollution, which is unseen to a large extent and of which most people are not aware. Additional research using microscale measurement setups to further unravel gradients in air pollutant concentrations and further reveal reliable estimates of on-the-road concentrations of those pollutants is recommended.
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Notes
- 1.
It should be noted that models simulate near-the road concentrations and not on-the road concentrations, which are often—and especially in the case of a significant amount of motorised traffic on the road—far higher. This is examined in detail in the discussion section.
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Acknowledgements
Be-good project Some of the research conducted was triggered by the ongoing Interreg project Be-Good and further solutions (open-data endpoint, demo-application) developed based upon this research are developed within the Be-good project. We acknowledge the Be-good project (https://www.nweurope.eu/projects/project-search/begood/) for their contributions and thank the project and all project partners for providing the opportunity to convert the results of this scientific research in real-world solutions and applications.
Black Carbon Measurement Data Mobile BC data in Mechelen and Leuven were collected as part of airQmap projects of VITO (www.airQmap.com). We thank Martine van Poppel, Jan Peeters and Stijn Vranckx for sharing these data. We are grateful to the volunteers of the Citizen Observatory Meet Mee Mechelen for their assistance in the collection of the mobile BC data in Mechelen (https://mechelen.meetmee.be/kaart). This data collection in Mechelen was partly funded by the European Union’s Horizon 2020 project Ground Truth2.0, grand agreement No. 689744 and partly funded by the FLAMENCO project, a research project financially supported by Agentschap Innoveren & Ondernemen (VLAIO) contract number IWT-SBO 150044. We thank the volunteers of the citizens platform Straten Vol Leuven and the ngo Leuven 2030 for their assistance in the BC data collection in Leuven. We are also grateful to the Provincie Vlaams-Brabant for funding the measurement campaigns of 2016, 2017 and 2019 within the framework of Duurzame Klimaat Project.
CurieuzeNeuzen data We thank all 20.000 citizens scientists in CurieuzeNeuzen Vlaanderen for their enthusiasm and efforts, and all team members of University of Antwerp, De Standaard, the Flanders Environment Agency (VMM), VITO, HIVA KUleuven and Kariboo! for their contribution to planning, logistics and communication. We additionally acknowledge the citizen movement Ringland and Stad Antwerpen for their contribution to the CurieuzeNeuzen Antwerpen project, which acted as a pilot for CurieuzeNeuzen Vlaanderen.
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Vandeninden, B. et al. (2021). Uncovering Spatio-temporal Air Pollution Exposure Patterns During Commutes to Create an Open-Data Endpoint for Routing Purposes. In: Krevs, M. (eds) Hidden Geographies. Key Challenges in Geography. Springer, Cham. https://doi.org/10.1007/978-3-030-74590-5_6
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