Articles | Volume 22, issue 12
https://doi.org/10.5194/acp-22-8385-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-22-8385-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
29 Jun 2022
Research article |
| 29 Jun 2022
| 29 Jun 2022
A machine learning approach to quantify meteorological drivers of ozone pollution in China from 2015 to 2019
School of Environmental Sciences, University of East Anglia,
Norwich, NR47 TJ, UK
Grant L. Forster
School of Environmental Sciences, University of East Anglia,
Norwich, NR47 TJ, UK
National Centre for Atmospheric Science, University of East
Anglia, Norwich, NR47 TJ, UK
Peer Nowack
School of Environmental Sciences, University of East Anglia,
Norwich, NR47 TJ, UK
Grantham Institute – Climate Change and the Environment, Imperial College London, London SW7 2AZ, UK
Department of Physics, Imperial College London, London SW7 2AZ, UK
Data Science Institute, Imperial College London, London SW7 2AZ, UK
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Measurements of atmospheric O2 can help constrain the carbon cycle processes and quantify fossil fuel CO2 emissions; however, measurement of atmospheric O2 is very challenging, and existing analysers are complex systems to build and maintain. We have tested a new O2 analyser (Picarro Inc. G2207-i) in the laboratory and at Weybourne Atmospheric Observatory. We have found that the G2207-i does not perform as well as an existing O2 analyser from Sable Systems Inc.
Peter Bergamaschi, Arjo Segers, Dominik Brunner, Jean-Matthieu Haussaire, Stephan Henne, Michel Ramonet, Tim Arnold, Tobias Biermann, Huilin Chen, Sebastien Conil, Marc Delmotte, Grant Forster, Arnoud Frumau, Dagmar Kubistin, Xin Lan, Markus Leuenberger, Matthias Lindauer, Morgan Lopez, Giovanni Manca, Jennifer Müller-Williams, Simon O'Doherty, Bert Scheeren, Martin Steinbacher, Pamela Trisolino, Gabriela Vítková, and Camille Yver Kwok
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We present a novel high-resolution inverse modelling system, "FLEXVAR", and its application for the inverse modelling of European CH4 emissions in 2018. The new system combines a high spatial resolution of 7 km x 7 km with a variational data assimilation technique, which allows CH4 emissions to be optimized from individual model grid cells. The high resolution allows the observations to be better reproduced, while the derived emissions show overall good consistency with two existing models.
Robert Woodward-Massey, Roberto Sommariva, Lisa K. Whalley, Danny R. Cryer, Trevor Ingham, William J1 Bloss, Sam Cox, James D. Lee, Chris P. Reed, Leigh R. Crilley, Louisa J. Kramer, Brian J. Bandy, Grant L. Forster, Claire E. Reeves, Paul S. Monks, and Dwayne E. Heard
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2022-207, https://doi.org/10.5194/acp-2022-207, 2022
Preprint withdrawn
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We measured radicals (OH, HO2, RO2) and OH reactivity at a UK coastal site and compared our observations to the predictions of an MCMv3.3.1 box model. We find variable agreement between measured and modelled radical concentrations and OH reactivity, where the levels of agreement for individual species display strong dependences on NO concentrations. The most substantial disagreement is found for RO2 at high NO (> 1 ppbv), when RO2 levels are underpredicted by a factor of ~10–30.
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Short summary
We use machine learning to quantify the meteorological drivers behind surface ozone variations in China between 2015 and 2019. Our novel approaches show improved performance when compared to previous analysis methods. We highlight that nonlinearity in driver relationships and the impacts of large-scale meteorological phenomena are key to understanding ozone pollution. Moreover, we find that almost half of the observed ozone trend between 2015 and 2019 might have been driven by meteorology.
We use machine learning to quantify the meteorological drivers behind surface ozone variations...
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