Articles | Volume 19, issue 16
https://doi.org/10.5194/amt-19-5387-2026
https://doi.org/10.5194/amt-19-5387-2026
Research article
 | 
19 Aug 2026
Research article |  | 19 Aug 2026

A machine learning method for estimating atmospheric trace gas concentration baselines

Kirstin Gerrand, Elena Fillola, Alistair J. Manning, Jgor Arduini, Paul B. Krummel, Chris R. Lunder, Jens Mühle, Simon O'Doherty, Sunyoung Park, Ronald G. Prinn, Stefan Reimann, Dickon Young, and Matthew Rigby

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Cited articles

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Short summary
To analyse long-term trends in atmospheric trace gas concentrations, it is important to identify data points minimally affected by local pollution sources or air masses carried from other latitudes or altitudes. Traditional methods for detecting these “baselines” are computationally expensive or lack a basis in physical principles. This paper introduces a machine-learning method that uses meteorological data and offers significantly lower computational costs compared to physics-based techniques.
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