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Published May 8, 2023 | Version v1
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Diagnosing drivers of PM2.5 simulation biases from meteorology, chemical composition, and emission sources using an efficient machine learning method

Creators

  • 1. Fudan University, China

Description

This study used an efficient machine learning method (LightGBM) to systematically diagnose the drivers of PM2.5 simulations biases in terms of meteorology, chemical composition, and emission sources. The training dataset is provided in csv format.

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