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  • Presentation | A33G: Data-Driven Methods for Quantifying Atmospheric Composition: Advances in Computation and Statistical Learning II Poster
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  • A33G-2234: Characterizing Drivers of Organic Aerosol Using Data-Driven Hourly Models in Three California Cities
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Author(s):
Duncan Quevedo, University of California Berkeley (First Author, Presenting Author)
Roya Bahreini, University of California Riverside
Don Collins, University of California Riverside
Cesunica Ivey, University of California Berkeley


Atmospheric aerosols are a major public health concern in parts of California that remain out of attainment of national ambient air quality standards. Reaching attainment requires improving our understanding of the chemical and meteorological influences that drive aerosol levels. Focusing on organic aerosol (OA) as a major component of particulate matter in urban centers in California, we build statistical models from measurements taken during field campaigns in the southern California cities of Bakersfield, Riverside, and Wilmington. Our models help identify what factors drive OA and how OA responds in these locations, improving our understanding of the formation regimes of OA which can help inform control strategies to reduce harmful human exposures.



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