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 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 occurring at multiple stations at once. For short gaps of up to 5 h, linear interpolation is most accurate. Longer gaps at single stations are most effectively imputed by a random forest in connection with the correct and smooth. For longer gaps at multiple stations, the correct and smooth algorithm improved the random forest despite a lack of data in the neighborhood of the missing values. We therefore suggest a hybrid of linear interpolation and graph machine learning for the imputation of tropospheric ozone time series.

Contamination of tea leaves by anthraquinone: The atmosphere as a possible source.

The detection of anthraquinone in tea leaves has raised concerns due to a potential health risk associated with this species. This led the European Union to impose a maximum residue limit (MRL) of 0.02 mg/kg for anthraquinone in dried tea leaves. As atmospheric contamination has been identified as one of the possible sources of anthraquinone residue, this study investigates the contamination resulting from the deposition of atmospheric anthraquinone using a global chemical transport model that accounts for the emission, atmospheric transport, chemical transformation, and deposition of anthraquinone on the surface. The largest contribution to the global atmospheric budget of anthraquinone is from residential combustion followed by the secondary formation from oxidation of anthracene. Simulations suggest that atmospheric anthraquinone deposition could be a substantial source of the anthraquinone found on tea leaves in several tea-producing regions, especially near highly industrialized and populated areas of southern and eastern Asia. The high level of anthraquinone deposition in these areas may result in residues in tea products exceeding the EU MRL. Additional contamination could also result from local tea production operations.