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

Bousiotis, Dimitrios and Pope, Francis and Baruah, Arunik (2024) Research data supporting "A novel spatiotemporal prediction approach to fill air pollution data gaps using sensor, machine learning and citizen science techniques".
Dataset Details
Data creator(s):
CreatorsEmailORCID
Bousiotis, Dimitriosd.bousiotis@bham.ac.ukorcid.org/0000-0002-5853-0624
Pope, FrancisF.Pope@bham.ac.ukorcid.org/0000-0001-6583-8347
Baruah, Arunikarunik.baruah@iusspavia.itorcid.org/0000-0003-1613-7864
Research Data Type: Dataset
DOI: https://doi.org/10.25500/edata.bham.00001144
Publisher: University of Birmingham
Funder: European Commission
Keywords: PM; telematics; meteorological data; mobile and point measurements; traffic data
Managing organisational unit: Colleges (2008 onwards) > College of Life & Environmental Sciences
UoB School, Department or Institute: School of Geography, Earth and Environmental Sciences
Date: 11 July 2024
Available Files
Data
UBIRA.zip
+ UBIRA.zip
Export
Statistics

Downloads

Downloads per month over past year

Administer Item Administer Item