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| Titel |
Mapping urban air quality in real-time: Applications of crowdsourced microsensor data |
| VerfasserIn |
Philipp Schneider, Nuria Castell, Matthias Vogt, William Lahoz, Alena Bartonova |
| Konferenz |
EGU General Assembly 2017
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| Medientyp |
Artikel
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| Sprache |
en
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 19 (2017) |
| Datensatznummer |
250140149
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| Publikation (Nr.) |
 EGU/EGU2017-3498.pdf |
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| Zusammenfassung |
| Recent advances in sensor technology have enabled the construction of small and low-cost
platforms for measuring various parameters related to air quality. These platforms are ideally
suited to be used within a crowdsourcing or citizen science framework. Due to
their small size and lower cost, such devices can be deployed throughout the urban
environment at much higher density than what is feasible with traditional air quality
monitoring stations equipped with reference instruments. A large network of such
low-cost sensors is thus capable of providing significantly more detail regarding
the spatial distribution of air pollutants in the environment. However, despite the
increased deployment density, such sensor networks continue to require additional
information for producing spatially exhaustive maps of air quality throughout the urban
environment.
We present here our recent work on mapping real-time urban air quality by combining
crowdsourced observations from the recent generation of low-cost air quality sensors with
time-invariant data from local-scale dispersion model. The approach is based on
geostatistical data fusion, which allows for combining observations with model
data in a mathematically objective way and therefore provides a means of adding
value to both the observations and the model. The observations are improved by
filling spatio-temporal gaps in the data and the model is improved by constraining it
with observations. The model further provides detailed spatial patterns in areas
where no observations are available. As such, data fusion of observations from
high-density low-cost sensor networks together with air quality models can contribute to
significantly improving urban-scale air quality mapping. We have implemented the
methodology to run in near-real time in several locations throughout Europe and focus here
primarily on results obtained for mapping nitrogen dioxide (NO2) in the city of Oslo,
Norway.
The results indicate that using a crowdsourced network of low-cost microsensors in
conjunction with model information is able to provide realistic high-resolution maps of urban
air quality. Comparisons with observations made at air quality monitoring stations equipped
with reference instruments show that the resulting maps are able to replicate the average true
NO2 measurements with a root mean squared error of 14.3 μg/m3 and an R2 value of 0.89. In
addition, we show that the resulting maps are able to replicate the typical bi-modal diurnal
cycle related to traffic emissions.
Detailed urban air quality maps such as those derived from data fusion techniques can then
further be used for providing personalized information about air quality to the citizens. We
present examples of how this kind of real-time data allows end users to find the currently least
polluted route through a city or to track their individual personal exposure to air pollutants
while moving through the urban environment. |
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