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TZID:Europe/Stockholm
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DTSTART:19700308T020000
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BEGIN:VEVENT
DTSTAMP:20250822T115810Z
LOCATION:Room 6.0D13
DTSTART;TZID=Europe/Stockholm:20250617T150000
DTEND;TZID=Europe/Stockholm:20250617T153000
UID:submissions.pasc-conference.org_PASC25_sess107_msa239@linklings.com
SUMMARY:Enhanced Uncertainty Quantification in Air Pollution Models and Im
 pact on Epidemiological Risk
DESCRIPTION:Rima Habre (University of Southern California)\n\nAdvancements
  in remote sensing, geospatial data, physicochemical and source apportionm
 ent models, citizen science networks and machine learning have greatly imp
 roved our ability to predict air pollution at high spatiotemporal resoluti
 on and over large domains and time periods. Air pollution models with high
  predictive performance and low uncertainty are critical for estimating po
 pulation and individual level human exposures and their health risks, in r
 etrospective studies and for forecasting. This talk will present advances 
 in air pollution modeling that integrate multi-modal data and deep learnin
 g and estimate uncertainty in predictions which can then inform or be inte
 grated into epidemiological analyses. Current challenges and future needs 
 for advancing these models that are currently being pursued by our team fo
 r biopreparedness and health risk applications will also be discussed.\n\n
 Domain: Applied Social Sciences and Humanities, Life Sciences, Computation
 al Methods and Applied Mathematics\n\nSession Chair: Adam Spannaus (Oak Ri
 dge National Laboratory)\n\n
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