A machine learning method for estimating atmospheric trace gas concentration baselines

Abstract. Estimates of trace gas baseline mole fractions in high-frequency atmospheric measurement records are crucial for analysing long-term changes in atmospheric composition. Baseline mole fractions are those that would be observed far from emission sources (and hence are representative of background conditions). Previous methods for inferring baseline mole fractions have used statistical or meteorological approaches, or, if available, co-measured tracer species thought only to be emitted from non-baseline wind sectors. Combinations of these techniques have also been employed in some applications. Statistical methods typically fit a baseline to the observations themselves, while meteorological methods use atmospheric models of varying complexity to categorise air mass origins. In this paper, we present a novel machine learning method for estimating trace gas baseline mole fractions, which benefits from the physical basis of model-based filtering without the need for running an expensive simulator. Our approach offers the accessibility and computational cost-effectiveness of statistical models, without the associated smoothing or difficulty in identifying rapid baseline variations. By training on historical Lagrangian particle dispersion model outputs, our model learns to predict baseline mole fractions directly from meteorological fields. This advancement opens new avenues for low-latency trace gas time series data analysis, reconstruction of historical baseline trends, and improved utilisation of tracer measurement air mass classification methods.

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Publication Details

Journal
Atmospheric measurement techniques
Published
2026-08-19
DOI
https://doi.org/10.5194/amt-19-5387-2026
Primary Topic
Air Quality Monitoring and Forecasting
Type
article
Field-Weighted Citation Impact
0.00

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article

A machine learning method for estimating atmospheric trace gas concentration baselines

Jens Mühle, Ronald G. Prinn, Jgor Arduini, Alistair J. Manning et al.
Atmospheric measurement techniques
Air Quality Monitoring and Forecasting
article

A machine learning method for estimating atmospheric trace gas concentration baselines

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

Abstract

Abstract. Estimates of trace gas baseline mole fractions in high-frequency atmospheric measurement records are crucial for analysing long-term changes in atmospheric composition. Baseline mole fractions are those that would be observed far from emission sources (and hence are representative of background conditions). Previous methods for inferring baseline mole fractions have used statistical or meteorological approaches, or, if available, co-measured tracer species thought only to be emitted from non-baseline wind sectors. Combinations of these techniques have also been employed in some applications. Statistical methods typically fit a baseline to the observations themselves, while meteorological methods use atmospheric models of varying complexity to categorise air mass origins. In this paper, we present a novel machine learning method for estimating trace gas baseline mole fractions, which benefits from the physical basis of model-based filtering without the need for running an expensive simulator. Our approach offers the accessibility and computational cost-effectiveness of statistical models, without the associated smoothing or difficulty in identifying rapid baseline variations. By training on historical Lagrangian particle dispersion model outputs, our model learns to predict baseline mole fractions directly from meteorological fields. This advancement opens new avenues for low-latency trace gas time series data analysis, reconstruction of historical baseline trends, and improved utilisation of tracer measurement air mass classification methods.

Atmospheric measurement techniquesVol. 19(16)
CSIRO Oceans and Atmosphere (AU), Met Office (GB), Statistics New Zealand (NZ), Scripps Institution of Oceanography (US), University of Urbino (IT), Kyungpook National University (KR), University of Bristol (GB), University of California San Diego (US), Kjeller Innovasjon (Norway) (NO), Institute for Sustainability (GB), Massachusetts Institute of Technology (US), Swiss Federal Laboratories for Materials Science and Technology (CH)
National Aeronautics and Space Administration, UK Research and Innovation, University of Bristol, Commonwealth Scientific and Industrial Research Organisation, Korea Meteorological Administration, Nuclear Safety and Security Commission, Miljødirektoratet, National Oceanic and Atmospheric Administration, Natural Environment Research Council, Bundesamt für Umwelt
Openalex Percentile: Top 100%
Air Quality Monitoring and Forecasting
DOIOpen Access PDFOpenAlex
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