Graph Machine Learning for Improved Imputation of Missing Tropospheric Ozone Data.

ABSTRACT: 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 algorithm uses auxiliary data that characterize the measurement location and, in addition, ozone observations at neighboring sites to improve the imputations of simple statistical and machine learning models. We apply our method to data from 278 stations of the year 2011 of the German Environment Agency (Umweltbundesamt - UBA) monitoring network. The preliminary version of these data exhibits three gap patterns: shorter gaps in the range of hours, longer gaps of up to several months in length, and gaps occurr

SUBMITTER: Betancourt C 

PROVIDER: S-EPMC10666531 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature