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  • Presentation | GH41B: Computational Methods and Tools for Air Quality Exposure Assessments and Solutions I Poster
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  • GH41B-0694: Machine Learning-Based Bias Correction for Improving PM2.5 Prediction Performance Using the Community Multiscale Air Quality (CMAQ) Model
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  • Board 0694‚ Hall EFG (Poster Hall)
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Author(s):
Seong-il Lee, Myongji University (First Author, Presenting Author)
Hyo-Jong Song, Myongji University
Hye-Ryun Oh, Kyungpook National University
Youngchae Kwon, Myongji University, Dept. of Environmental Engineering and Energy


This study enhances CMAQ PM2.5 predictions through machine learning bias correction using Integrated Process Rate data. Deep neural networks achieved highest accuracy among linear regression, random forest, and DNN methods tested during critical pollution periods in South Korea.



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