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Pushing the spatio-temporal resolution limit of urban air pollution maps.

, , , , , and . PerCom, page 69-77. IEEE Computer Society, (2014)

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Enabling Large-Scale Urban Air Quality Monitoring with Mobile Sensor Nodes.. ETH Zurich, Zürich, Switzerland, (2015)base-search.net (ftethz:oai:www.research-collection.ethz.ch:20.500.11850/100110).Transactional Memory: How to perform load adaption in a simple and distributed manner., , and . HPCS, page 163-170. IEEE, (2010)Statistical modelling of particle number concentration in Zurich at high spatio-temporal resolution utilizing data from a mobile sensor network, , , , and . Atmospheric Environment, (2016)Deriving high-resolution urban air pollution maps using mobile sensor nodes, and . Pervasive and Mobile Computing, (2015)Selected Papers from the Twelfth Annual IEEE International Conference on Pervasive Computing and Communications (PerCom 2014).Deriving high-resolution urban air pollution maps using mobile sensor nodes, , , , , , , and . Pervasive and Mobile Computing, (2014)Sensing the Air We Breathe - The OpenSense Zurich Dataset., , , , and . AAAI, page 323-325. AAAI Press, (2012)Ultrafine Particle Dataset Collected by the OpenSense Zurich Mobile Sensor Network, , , , , , and . https://zenodo.org/record/3298842, (September 2018)Ozone and Carbon Monoxide Dataset Collected by the OpenSense Zurich Mobile Sensor Network, , , , , , and . https://zenodo.org/record/3355208, (July 2019)Deriving high-resolution urban air pollution maps using mobile sensor nodes, , , , , , , and . Pervasive and Mobile Computing, (2015)