Forecasting Ultrafine Dust Concentrations in Seoul: A Machine Learning Approach

This study applied various machine learning techniques, including shrinkage methods, XGBoost, CSR, and random forest, to forecast ultrafine particulate matter (PM2.5) concentrations in Seoul, South Korea. The analysis incorporated key variables known to significantly influence PM2.5 levels, includin...

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Bibliographic Details

Published in:	Atmosphere
Main Authors:	Sophia Park, Myeong Jun Kim
Format:	Article
Language:	English
Published:	MDPI AG 2025-02-01
Subjects:	ultrafine dust concentration PM2.5 air quality machine learning forecasting random forest
Online Access:	https://www.mdpi.com/2073-4433/16/3/239