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Title
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Using large-scale NO₂ data from citizen science for air-quality compliance and policy support
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Author
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Abstract
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| Citizen science projects that monitor air quality have recently drastically expanded in scale. Projects involving thousands of citizens generate spatially dense data sets using low-cost passive samplers for nitrogen dioxide (NO2), which complement data from the sparse reference network operated by environmental agencies. However, there is a critical bottleneck in using these citizen-derived data sets for air-quality policy. The monitoring effort typically lasts only a few weeks, while long-term air-quality guidelines are based on annual-averaged concentrations that are not affected by seasonal fluctuations in air quality. Here, we describe a statistical model approach to reliably transform passive sampler NO2 data from multiweek averages to annual-averaged values. The predictive model is trained with data from reference stations that are limited in number but provide full temporal coverage and is subsequently applied to the one-off data set recorded by the spatially extensive network of passive samplers. We verify the assumptions underlying the model procedure and demonstrate that model uncertainty complies with the EU-quality objectives for air-quality monitoring. Our approach allows a considerable cost optimization of passive sampler campaigns and removes a critical bottleneck for citizen-derived data to be used for compliance checking and air-quality policy use. |
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Language
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English
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Source (journal)
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Environmental science and technology / American Chemical Society. - Easton, Pa
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Publication
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Easton, Pa
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2020
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ISSN
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0013-936X
[print]
1520-5851
[online]
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DOI
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10.1021/ACS.EST.0C02436
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Volume/pages
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54
:18
(2020)
, p. 11070-11078
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ISI
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000572834700009
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Pubmed ID
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32822533
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Full text (Publisher's DOI)
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Full text (open access)
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Full text (publisher's version - intranet only)
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