Quantifying the Impact of Multiple Factors on Air Quality Model Simulation Biases Using Machine Learning

Quantifying the Impact of Multiple Factors on Air Quality Model Simulation Biases Using Machine Learning

Accurate air pollutant prediction is essential for addressing environmental and public health concerns. Air quality models like WRF-CMAQ provide simulations, but often show significant errors compared to observed concentrations. To identify the sources of these model biases, we applied the XGBoost m...

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Bibliographic Details
Main Authors: Chunying Fan, Ruilin Wang, Ge Song, Mengfan Teng, Maolin Zhang, Huangchuan Liu, Zhujun Li, Siwei Li, Jia Xing
Format: Article
Language:English
Published: MDPI AG 2024-11-01
Series:Atmosphere
Subjects:
air quality
simulation
bias
machine learning
prediction
Online Access:https://www.mdpi.com/2073-4433/15/11/1337
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