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We developed a multistage deep learning framework to generate daily PM2.5 concentrations at 100 m resolution across the contiguous United States (CONUS) from 2000 to 2024. The framework first reconstructed missing satellite aerosol optical depth (AOD) using a U-Net-based encoder\u2013decoder informed by reanalysis data, refined temporal dependencies using a bidirectional long short-term memory network, and downscaled reconstructed AOD to 100 m using terrain information. Daily PM2.5 was subsequently estimated using a multistream deep learning architecture integrating reconstructed AOD, meteorological, spatiotemporal, and geospatial predictors. Evaluation using strict site-level data partitioning yielded strong predictive performance (R2 = 0.82, RMSE = 2.85 \u03bcg\/m3, MAE = 1.84 \u03bcg\/m3), with high spatial (R2 = 0.94) and temporal (R2 = 0.78) performance. The framework generated spatially continuous daily PM2.5 surfaces across more than 766 million 100 m grid cells while capturing broad spatial gradients and fine-scale variability. These long-term, high-resolution estimates provide an exposure surface suitable for epidemiological, environmental justice, and air-pollution assessment applications.<\/jats:p>","DOI":"10.1021\/acs.est.6c05375","type":"journal-article","created":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T04:01:53Z","timestamp":1790913713000},"page":"28816-28828","source":"Crossref","is-referenced-by-count":0,"title":["A Multi-Stage Deep\nLearning Framework for Daily PM2.5 Estimation at 100 m\nResolution Across the Contiguous United\nStates, 2000\u20132024 (A Quarter Century of Data)"],"prefix":"10.1021","volume":"60","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0480-5768","authenticated-orcid":true,"given":"Ali","family":"Sheidaei","sequence":"first","affiliation":[{"name":"Icahn School of Medicine at Mount Sinai , , , ,","place":["New York, New York, United States, 10029"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kimiya","family":"Gohari","sequence":"additional","affiliation":[{"name":"Icahn School of Medicine at Mount Sinai , , , ,","place":["New York, New York, United States, 10029"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ryan","family":"Michael","sequence":"additional","affiliation":[{"name":"Icahn School of Medicine at Mount Sinai , , , ,","place":["New York, New York, United States, 10029"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jitendra Kumar","family":"Barupal","sequence":"additional","affiliation":[{"name":"Icahn School of Medicine at Mount Sinai , , , ,","place":["New York, New York, United States, 10029"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ajit","family":"Rajiva","sequence":"additional","affiliation":[{"name":"Center for Chronic Disease Control , ,","place":["New Delhi, India, 110075"]},{"name":"Ashoka University , , , ,","place":["Sonipat, Haryana, India, 131029"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-3384-2494","authenticated-orcid":true,"given":"Anat","family":"Heilper","sequence":"additional","affiliation":[{"name":"VAST Data, Inc. , , ,","place":["New York, New York, United States, 10018"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1875-8448","authenticated-orcid":true,"given":"Elena","family":"Colicino","sequence":"additional","affiliation":[{"name":"Icahn School of Medicine at Mount Sinai , , , ,","place":["New York, New York, United States, 10029"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maayan","family":"Yitshak-Sade","sequence":"additional","affiliation":[{"name":"Icahn School of Medicine at Mount Sinai , , , ,","place":["New York, New York, United States, 10029"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Itai","family":"Kloog","sequence":"additional","affiliation":[{"name":"Icahn School of Medicine at Mount Sinai , , , ,","place":["New York, New York, United States, 10029"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"316","published-online":{"date-parts":[[2026,10,1]]},"reference":[{"key":"2026101310175046700_cit1a","doi-asserted-by":"publisher","first-page":"113588","DOI":"10.1016\/j.ecoenv.2022.113588","article-title":"The global burden of disease attributable to ambient fine particulate\nmatter in 204 countries and territories, 1990\u20132019: A systematic\nanalysis of the Global Burden of Disease Study 2019","volume":"238","author":"Sang","year":"2022","journal-title":"Ecotoxicol. 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