Ozone and Carbon Monoxide Dataset Collected by the OpenSense Zurich Mobile Sensor Network

Ozone and Carbon Monoxide Dataset Collected by the OpenSense Zurich Mobile Sensor Network

This dataset contains ozone (O3) and carbon monoxide (CO) concentration measurements collected by the OpenSense (http://www.opensense.ethz.ch) mobile senor network over the course of 4.5 years (2012/02-2016/09). The sensors are mounted on top of 10 streetcars in the city of Zurich, Switzerland.

In particular, the dataset contains: 

Ozone (O3) data: 2012/02 - 2016/09 (19.9 Mio samples)

Carbonmonoxide (CO) data: 2014/03 - 2016/09 (49.7Mio samples)

Hardware:
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Ozone sensor: SGX (former e2V) MiCS-OZ-47 Ozone Sensing Head with Smart Transmitter PCB

Carbon monoxide sensor: Alphasense CO-B4

GPS receiver: u-blox EVK-6p

Data files format: 
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co_data_*: 

Time of day: yyyy.mm.dd HH:MM

Latitude WGS84

Longitude WGS84

HDOP: horizontal dilution of precision, uncertainty of the GPS position

Tram ID

WE_CHANNEL_SENSOR_1_MV: The voltage [in mV] at the working electrode of the electrochemical sensor (see Alphasense CO-B4 datasheet for more details)

o3_data_*: 

Time of day: yyyy.mm.dd HH:MM

Latitude WGS84

Longitude WGS84

HDOP: horizontal dilution of precision, uncertainty of the GPS position

Tram ID

Ozone [ppb]: On-device calibrated (according to manufacturer) ozone measurement [in parts-per-billion] 

Temperature [in °C]

Relative Humidity [in %]

Data quality:
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The data has NOT been post-processed!
In order to achieve high data quality, the data needs to be cleaned (e.g. outlier filtering) and, most importantly, the sensors need to be individually calibrated.
Reference data can be obtained from www.ostluft.ch, the official air quality monitoring network in eastern Switzerland, which operates multiple monitoring stations in the city of Zurich.

Plot Coverage Map (MATLAB):
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The provided MATLAB script plot_data_coverage.m plots the locations of the collected samples onto the map of Zurich (map_zurich.png). 

References:
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The dataset (and related aspects) has partly been used and is described in more detail in the following publications:

Balz Maag et al. SCAN: Multi-Hop Calibration for Mobile Sensor Arrays. In Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Vol.1, No.2 (IMWUT), 2017.

Olga Saukh et al. Reducing Multi-Hop Calibration Errors in Mobile Sensor Networks. In IEEE/ACM International Conference on Information Processing in Sensor Networks (IPSN), 2015. Best Paper Award!

Olga Saukh et al. Route Selection for Mobile Sensor Nodes on Public Transport Networks. In Journal of Ambient Intelligence and Humanized Computing, 5(3), Springer, 2014.

Olga Saukh et al. On Rendezvous in Mobile Sensing Networks. In Proceedings of the 5th Workshop on Real-World Wireless Sensor Networks (RealWSN), 2013.

Jason Jingshi Li  et al. Sensing the Air we Breathe – The OpenSense Zurich Dataset. In Proceedings of the 26th International Conference on Artificial Intelligence (AAAI), 2012.

Olga Saukh et al. Route Selection for Mobile Sensors with Checkpointing Constraints. In Proceedings of the 8th International Workshop on Sensor Networks and Systems for Pervasive Computing (PerSeNS, in conjunction with IEEE PerCom), March 2012.

David Hasenfratz et al. On-the-fly Calibration of Low-Cost Gas Sensors. In Proceedings of the 9th European Conference on Wireless Sensor Networks (EWSN), 2012. 

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