Ultrafine Particle Dataset Collected by the OpenSense Zurich Mobile Sensor Network

Ultrafine Particle Dataset Collected by the OpenSense Zurich Mobile Sensor Network

This dataset contains over 2 and a half years (04/2012-12/2014, >36 Mio samples) worth of ultra-fine particle (UFP) concentration measurements collected by a mobile senor network. The sensors are mounted on top of 10 streetcars in the city of Zurich, Switzerland.

Hardware:

• Ultrafine particle sensor: MiniDiSC (see also: Martin Fierz et al. Design, Calibration, and Field Performance of a Miniature Diffusion Size Classifier. Aerosol Science and Technology, Volume 45, 2011.)
• GPS receiver: u-blox EVK-6p
   

Sensor Data
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ufp_data*.csv column format:

1. Time of day: yyyy.mm.dd HH:MM
2. Latitude WGS84
3. Longitude WGS84
4. HDOP: horizontal dilution of precision, uncertainty of the GPS position
5. Tram ID
6. Number of particles [#/ccm]
7. Average particle diameter [nm]
8. LDSA: lung deposited surface area [um2 /cm3]

Data quality:
The data has been post-processed by performing a periodic null-offset calibration and  filtering samples during malfunction.

High-Resolution Maps
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The data has been used to create high-resolution ultrafine particle concentration maps. Four maps, which show the seasonal average particle concentration over seasonal periods, can be found in ufp_seasonal_maps_201204_201304.csv.

ufp_map*.csv column format:

1. Latitude WGS84
2. Longitude WGS84
3. Estimated number of particles [#/ccm]

Map quality

Please have a look at the papers in References 1. and 2. (Hasenfratz et al. 2014 and 2015) for a detailed evaluation of the maps.

References
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The dataset has been used and is described in more detail in the following publications:

1. David Hasenfratz et al. Pushing the Spatio-Temporal Resolution Limit of Urban Air Pollution Maps. IEEE International Conference on Pervasive Computing and Communications (PerCom). Budapest, Hungary, March 2014. Best Paper Award. 
2. David Hasenfratz et al. Deriving High-Resolution Urban Air Pollution Maps Using Mobile Sensor Nodes. Pervasive and Mobile Computing. Elsevier, 2015. 
3. David Hasenfratz et al. Demo Abstract: Health-Optimal Routing in Urban Areas. ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN). Seattle, USA, April 2015.
4. Michael Müller et al. Statistical modelling of particle number concentration in Zurich at high spatio-temporal resolution utilizing data from a mobile sensor network. Atmospheric Environment. Elsevier, 2016.

For further information, visit:  http://www.opensense.ethz.ch