Published December 7, 2023
| Version v2
Journal article
Open
Diagnosing drivers of PM2.5 simulation biases from meteorology, chemical composition, and emission sources using an efficient machine learning method
- 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.
Files
Files
(269.6 MB)
| Name | Size | Download all |
|---|---|---|
|
md5:4fda7a5c569e24e8e81ee6a19c189eb7
|
269.6 MB | Download |