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  • Presentation | GH41B: Computational Methods and Tools for Air Quality Exposure Assessments and Solutions I Poster
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  • GH41B-0690: High Resolution Air Pollution Monitoring in Louisiana Through Satellite, Sensor, and Machine Learning Integration
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  • Board 0690‚ Hall EFG (Poster Hall)
    NOLA CC
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Author(s):
Amanda Murray, Duke University (First Author, Presenting Author)
Alexander Kolker, LUMCON
Kory Kirshenbaum, University of California Berkeley
D. Alex Hughes, University of California Berkeley


Air pollution is a major concern in Louisiana, especially near industrial facilities. Traditional monitoring stations have limited coverage, leaving gaps in understanding where and how much pollution people are exposed to. This study combines new satellite data from NASA’s TEMPO mission with ground sensors and supplemental data to map hourly nitrogen dioxide (NO₂) and ozone (O₃) pollution at a 1-kilometer resolution across the state. Using machine learning, we estimate ground-level pollution to better assess exposure risk and guide placement of new sensors to protect public health.



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