Research data supporting "A novel spatiotemporal prediction approach to fill air pollution data gaps using sensor, machine learning and citizen science techniques"

1144 12 5909 2 31293 document 5909 UBIRA.zip application/zip bc77c41e75f5c7e9931fd9dd33611205 MD5 76871168 2024-07-02 12:12:03 http://edata.bham.ac.uk/1144/1/UBIRA.zip 1144 1 1 application/zip archive public cc_by_4

UBIRA.zip

data archive 206 disk0/00/00/11/44 2024-07-11 15:18:39 2024-07-11 15:43:19 2024-07-11 15:18:39 data_collection show Bousiotis Dimitrios d.bousiotis@bham.ac.uk 0000-0002-5853-0624 Pope Francis F.Pope@bham.ac.uk 0000-0001-6583-8347 Baruah Arunik arunik.baruah@iusspavia.it 0000-0003-1613-7864 DataCollector Ghaffarpasand Omid o.ghaffarpasand@bham.ac.uk Damayanti Seny SXD946@student.bham.ac.uk Department of Engineering ‘Enzo Ferrari’ University of Modena and Reggio Emilia Modena, 41125, Italy Research data supporting "A novel spatiotemporal prediction approach to fill air pollution data gaps using sensor, machine learning and citizen science techniques" 10col_life PM; telematics; meteorological data; mobile and point measurements; traffic data Datasets of measurements and additional data used in the machine learning analysis for PM2.5 data prediction in Selly Oak, Birmingham 2024-07-11 published University of Birmingham https://doi.org/10.25500/edata.bham.00001144 Dataset research-data@contacts.bham.ac.uk School of Geography, Earth and Environmental Sciences ec RI-URBANS/1010362450 yes yes yes TRUE Professor Pope Francis F.Pope@bham.ac.uk PM data collected using low-cost sensors Meteorological and PM data using reference grade instruments at the Birmingham Air quality Supersite Telematics data from online resources Selly Oak, Birmingham, United Kingdom 2023-04-01 2023-07-31