Journal
ATMOSPHERIC ENVIRONMENT
Volume 141, Issue -, Pages 408-421Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.atmosenv.2016.06.063
Keywords
Opportunistic monitoring; Mobile measurements; Black carbon; Urban air quality; Spatial variation
Funding
- IDEA (Intelligent, Distributed Environmental Assessment) project
- IWT-Vlaanderen (Flemish Agency for Innovation by Science and Technology) [IWT-SBO 080054]
- VITO
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The goal of this paper is to explore the potential of opportunistic mobile monitoring to map the exposure to air pollution in the urban environment at a high spatial resolution. Opportunistic mobile monitoring makes use of existing mobile infrastructure or people's common daily routines to move measurement devices around. Opportunistic mobile,monitoring can also play a crucial role in participatory monitoring campaigns as a typical way to gather data. A case study to measure black carbon was set up in Antwerp, Belgium, with the collaboration of city employees (city wardens). The Antwerp city wardens are outdoors for a large part of the day on surveillance tours by bicycle or on foot, and gathered a total of 393 h of measurements. The data collection is unstructured both in space and time, leading to sampling bias. A temporal adjustment can only partly counteract this bias. Although a high spatial coverage was obtained, there is still a rather large uncertainty on the average concentration levels at a spatial resolution of 50 m due to a limited number of measurements and sampling bias. Despite of this uncertainty, large spatial patterns within the city are clearly captured. This study illustrates the potential of campaigns with unstructured opportunistic mobile monitoring, including participatory monitoring campaigns. The results demonstrate that such an approach can indeed be used to identify broad spatial trends over a wider area, enabling applications including hotspot identification, personal exposure studies, regression mapping, etc. But, they also emphasize the need for repeated measurements and careful processing and interpretation of the data. (C) 2016 Elsevier Ltd. All rights reserved.
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