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We develop a two\u2010phase data\u2010driven framework that uses geostationary Aerosol Optical Depth (AOD) retrievals to predict hourly PM\n                    <jats:sub>2.5<\/jats:sub>\n                    at 0.01\u00b0\u00a0\u00d7\u00a00.01\u00b0 resolution and support exposure assessment across multiple spatial and temporal contexts. We use a U\u2010Net\u2010like partial convolutional neural network framework inspired by image inpainting to address missing Aerosol Optical Depth (AOD) data from the GOES\u2010R geostationary satellite. To fill large spatiotemporal gaps in the GOES\u2010R record, we implement an iterative gap\u2010filling approach that expands contextual information for successive predictions, maintaining strong agreement with collocated AERONET observations (\n                    <jats:italic>R<\/jats:italic>\n                    \u00a0=\u00a00.75) while increasing paired observations from 430,603 to 728,115. A random forest is used to predict surface PM\n                    <jats:sub>2.5<\/jats:sub>\n                    concentrations using our spatiotemporally contiguous gap\u2010filled AOD. The resulting PM\n                    <jats:sub>2.5<\/jats:sub>\n                    estimates over 2019\u20132023 in the top 50 most populous counties over the CONUS capture the magnitude and spatial gradient as measured by the EPA Air Quality System (AQS) and PurpleAir low\u2010cost sensor network (daily\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    \u00a0=\u00a00.85, mean bias\u00a0=\u00a0\u22120.11\u00a0\u03bcg\/m\n                    <jats:sup>3<\/jats:sup>\n                    ). We use these predictions to assess population exposure and estimate PM\n                    <jats:sub>2.5<\/jats:sub>\n                    \u2010attributable mortality, finding that Black, Hispanic, and Indigenous populations experienced 1.7%\u20135.5% higher PM\n                    <jats:sub>2.5<\/jats:sub>\n                    exposure than White populations across these counties. Additional wildfire case studies in less densely monitored rural regions demonstrate the framework's utility for characterizing short\u2010term exposure events despite substantial gaps in satellite observations. These findings highlight the value of gap\u2010filled geostationary AOD for high\u2010resolution exposure assessment.\n                  <\/jats:p>","DOI":"10.1029\/2026gh001884","type":"journal-article","created":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T13:33:13Z","timestamp":1791466393000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning Predictions of PM\n                    <sub>2.5<\/sub>\n                    and Their Applications to Exposure and Health Assessment in the CONUS"],"prefix":"10.1029","volume":"10","author":[{"given":"K.","family":"Fan","sequence":"first","affiliation":[{"name":"Department of Marine, Earth, and Atmospheric Sciences North Carolina State University  Raleigh NC USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-0847-4720","authenticated-orcid":false,"given":"A.","family":"Bloom","sequence":"additional","affiliation":[{"name":"Center for Geospatial Analytics North Carolina State University  Raleigh NC USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3766-9838","authenticated-orcid":false,"given":"Z.","family":"Qu","sequence":"additional","affiliation":[{"name":"Department of Marine, Earth, and Atmospheric Sciences North Carolina State University  Raleigh NC USA"},{"name":"Center for Geospatial Analytics North Carolina State University  Raleigh NC USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"13","published-online":{"date-parts":[[2026,10,8]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2017.04.038"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1161\/JAHA.120.016890"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1029\/2020GH000270"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.5194\/gmd\u201014\u20102867\u20102021"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1175\/AIES\u2010D\u201022\u20100055.1"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.5194\/amt\u201014\u20104617\u20102021"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1021\/acs.est.1c05578"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.5194\/gmd\u201015\u20103281\u20102022"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1088\/1748\u20109326\/ad0066"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2018.04.251"},{"key":"e_1_2_8_13_1","unstructured":"Climate and Economic Justice Screening Tool. 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