<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Li L</submitter><funding>U.S. Environmental Protection Agency</funding><funding>NHLBI NIH HHS</funding><funding>National Institutes of Health</funding><pagination>106643</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC9384849</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>156</volume><pubmed_abstract>Particulate radioactivity, a characteristic of particulate matter, is primarily determined by the abundance of radionuclides that are bound to airborne particulates. Exposure to high levels of particulate radioactivity has been associated with negative health outcomes. However, there are currently no spatially and temporally resolved particulate radioactivity data for exposure assessment purposes. We estimated the monthly distributions of gross beta particulate radioactivity across the contiguous United States from 2001 to 2017 with a spatial resolution of 32 km, via a multi-stage ensemble-based model. Particulate radioactivity was measured at 129 RadNet monitors across the contiguous U.S. In stage one, we built 264 base learning models using six methods, then selected nine base models tha</pubmed_abstract><journal>Environment international</journal><pubmed_title>A spatiotemporal ensemble model to predict gross beta particulate radioactivity across the contiguous United States.</pubmed_title><pmcid>PMC9384849</pmcid><funding_grant_id>T32 HL098048</funding_grant_id><funding_grant_id>T32 HL 098048</funding_grant_id><funding_grant_id>RD-835872</funding_grant_id><pubmed_authors>Liu M</pubmed_authors><pubmed_authors>Blomberg AJ</pubmed_authors><pubmed_authors>Lawrence J</pubmed_authors><pubmed_authors>Requia WJ</pubmed_authors><pubmed_authors>Koutrakis P</pubmed_authors><pubmed_authors>Peralta AA</pubmed_authors><pubmed_authors>Li L</pubmed_authors><pubmed_authors>Wei Y</pubmed_authors></additional><is_claimable>false</is_claimable><name>A spatiotemporal ensemble model to predict gross beta particulate radioactivity across the contiguous United States.</name><description>Particulate radioactivity, a characteristic of particulate matter, is primarily determined by the abundance of radionuclides that are bound to airborne particulates. Exposure to high levels of particulate radioactivity has been associated with negative health outcomes. However, there are currently no spatially and temporally resolved particulate radioactivity data for exposure assessment purposes. We estimated the monthly distributions of gross beta particulate radioactivity across the contiguous United States from 2001 to 2017 with a spatial resolution of 32 km, via a multi-stage ensemble-based model. Particulate radioactivity was measured at 129 RadNet monitors across the contiguous U.S. In stage one, we built 264 base learning models using six methods, then selected nine base models tha</description><dates><release>2021-01-01T00:00:00Z</release><publication>2021 Nov</publication><modification>2025-04-19T04:20:02.558Z</modification><creation>2025-04-19T04:20:02.558Z</creation></dates><accession>S-EPMC9384849</accession><cross_references><pubmed>34020300</pubmed><doi>10.1016/j.envint.2021.106643</doi></cross_references></HashMap>