Comparison of Models for Missing Data Imputation in PM-2.5 Measurement Data

The accurate monitoring and analysis of PM-2.5 are critical for improving air quality and formulating public health policies. However, environmental data often contain missing values due to equipment failures, data collection errors, or extreme weather conditions, which can hinder reliable analysis...

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Bibliographic Details

Published in:	Atmosphere
Main Authors:	Ju-Yong Lee, Seung-Hee Han, Jin-Goo Kang, Chae-Yeon Lee, Jeong-Beom Lee, Hyeun-Soo Kim, Hui-Young Yun, Dae-Ryun Choi
Format:	Article
Language:	English
Published:	MDPI AG 2025-04-01
Subjects:	data completeness missing data imputation machine learning deep learning environmental data
Online Access:	https://www.mdpi.com/2073-4433/16/4/438