Is replacing missing values of PM2.5 constituents with estimates using machine learning better for source apportionment than exclusion or median replacement?-Reference-Cited by-同舟云学术

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Is replacing missing values of PM2.5 constituents with estimates using machine learning better for source apportionment than exclusion or median replacement?

Published:2024-08 Issue: Volume:354 Page:124165
ISSN:0269-7491
Container-title:Environmental Pollution
language:en
Short-container-title:Environmental Pollution

Author:

Kim Youngkwon,Yi Seung-Muk,Heo Jongbae,Kim Hwajin,Lee Woojoo^ORCID,Kim Ho,Hopke Philip K.^ORCID,Lee Young Su^ORCID,Shin Hye-Jung,Park Jungmin,Yoo Myungsoo,Jeon Kwonho,Park Jieun^ORCID

Funder

National Institute of Environmental Research

National Research Foundation of Korea

Ministry of Education

Korea Environmental Industry and Technology Institute

Ministry of Environment

Publisher

Elsevier BV

Reference88 articles.

1. Imputation of missing PM2.5 observations in a network of air quality monitoring stations by a new kNN method;Belachsen;Atmosphere,2022

2. Random forests;Breiman;Mach. Learn.,2001

3. Comparison of different missing-imputation methods for MAIAC (multiangle implementation of atmospheric correction) AOD in estimating daily PM2.5 levels;Chen;Remote Sens (Basel),2020

4. Extreme gradient boosting model to estimate PM2.5 concentrations with missing-filled satellite data in China;Chen;Atmos. Environ.,2019

5. Source apportionment of PM2.5 at the coastal area in Korea;Choi;Sci. Total Environ.,2013

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