<script type="text/javascript">
<!--
document.write('<div id="oa_widget"></div>');
document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=undefined&type=result"></script>');
-->
</script>

COPY SCRIPT

For further information contact us at helpdesk@openaire.eu

Diagnosing drivers of PM2.5 simulation biases from meteorology, chemical composition, and emission sources using an efficient machine learning method

Name: Diagnosing drivers of PM2.5 simulation biases from meteorology, chemical composition, and emission sources using an efficient machine learning method
Keywords: machine learning, lightGBM, , CMAQ source apportionment, PM2.5, bias diagnosis

descriptionPublicationkeyboard_double_arrow_right Article 08 May 2023 English Publisher:Zenodo

Authors: Shuai Wang; Mengyuan Zhang; Yueqi Gao; Peng Wang; Hongliang Zhang;

doi: 10.5281/zenodo.10283739 , 10.5281/zenodo.7907625 , 10.5281/zenodo.7907626

Diagnosing drivers of PM2.5 simulation biases from meteorology, chemical composition, and emission sources using an efficient machine learning method

- Summary
- Subjects
- Metrics

Abstract

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.

Related Organizations

Fudan University
China (People's Republic of)

Keywords

machine learning, lightGBM, , CMAQ source apportionment, PM2.5, bias diagnosis

Impact byBIP!

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	0
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Average
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Average
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Average

Usage byUsageCounts

visibility	views	51
download	downloads	15

51
views
15
downloads
Powered by

Found an issue? Give us feedback

visibility

download

Average

Green

Related to Research communities

UArctic