Graph Machine Learning for Improved Imputation of Missing Tropospheric Ozone Data

Gaps in the measurement series of atmospheric pollutants can impede the reliable assessment of their impacts and trends. We propose a new method for missing data imputation of the air pollutant tropospheric ozone by using the graph machine learning algorithm "correct and smooth". This algo...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	Environmental science & technology 2023-11, Vol.57 (46), p.18246-18258
Hauptverfasser:	Betancourt, Clara, Li, Cathy W Y, Kleinert, Felix, Schultz, Martin G
Format:	Artikel
Sprache:	eng
Schlagworte:	Air pollution Algorithms Atmospheric models Emission measurements Interpolation Learning algorithms Machine learning Mathematical models Missing data Ozone Pollutants Statistical analysis Troposphere
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!