Using spatiotemporal prediction models to quantify PM_2.5 exposure due to daily movement

<p dir="ltr">This journal contribution is published Open Access by the publisher. Follow the DOI link to retrieve a copy of the full text. </p><p dir="ltr">This work is part of the Center for Air, Climate, and Energy Solutions (CACES), which was supported by the U.S. Environmental Protection Agency (assistance agreement number RD83587301). This work was also partially funded by the U.S. EPA under assistance agreement RD83628601. It has not been formally reviewed by the U.S. EPA. The views expressed in this document are solely those of authors and do not necessarily reflect those of the Agency. The U.S. EPA does not endorse any products or commercial services mentioned in this publication. Funding for this study was also provided by the Heinz Endowment Fund (Grants E2375 and E3145), and the NSERC Discovery Grant Program (RGPIN-2018-04582). The authors also thank Prof. Michael Brauer for helpful conversations. This research was undertaken, in part, thanks to funding from the Canada Research Chairs Program. The authors would like to acknowledge that The University of British Columbia, where this work was conducted, is built on the traditional, ancestral and unceded territory of the Musqueam peoples. The sensor data used for this work was collected on the traditional, ancestral, and unceded territory of the Shawandasse Tula (Shawnee) and Osage peoples.</p>