Articles | Volume 24, issue 5
https://doi.org/10.5194/acp-24-3163-2024
© Author(s) 2024. 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-24-3163-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 Mar 2024
Research article |
| 13 Mar 2024
| 13 Mar 2024
A machine learning approach to downscale EMEP4UK: analysis of UK ozone variability and trends
Lily Gouldsbrough
CORRESPONDING AUTHOR
Lancaster Environment Centre, Lancaster University, Lancaster, LA1 4YQ, UK
now at: UK Centre for Ecology & Hydrology, Lancaster Environment Centre, Lancaster, LA1 4AP, UK
Lancaster Environment Centre, Lancaster University, Lancaster, LA1 4YQ, UK
Centre of Excellence in Environmental Data Science (CEEDS), Lancaster University, Lancaster, LA1 4YQ, UK
Emma Eastoe
Mathematics and Statistics, Lancaster University, Lancaster, LA1 4YR, UK
Paul J. Young
Lancaster Environment Centre, Lancaster University, Lancaster, LA1 4YQ, UK
JBA Risk Management Limited, Broughton Park, Skipton, BD23 3FD, UK
Massimo Vieno
UK Centre for Ecology & Hydrology, Bush Estate, Penicuik, Midlothian, EH26 0QB, UK
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Paul T. Griffiths, Laura J. Wilcox, Robert J. Allen, Vaishali Naik, Fiona M. O'Connor, Michael Prather, Alex Archibald, Florence Brown, Makoto Deushi, William Collins, Stephanie Fiedler, Naga Oshima, Lee T. Murray, Bjørn H. Samset, Chris Smith, Steven Turnock, Duncan Watson-Parris, and Paul J. Young
Atmos. Chem. Phys., 25, 8289–8328, https://doi.org/10.5194/acp-25-8289-2025, https://doi.org/10.5194/acp-25-8289-2025, 2025
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The Aerosol Chemistry Model Intercomparison Project (AerChemMIP) aimed to quantify the climate and air quality impacts of aerosols and chemically reactive gases. We review its contribution to AR6 (Sixth Assessment Report of the Intergovernmental Panel on Climate Change) and the wider understanding of the role of these species in climate and climate change. We identify challenges and provide recommendations to improve the utility and uptake of climate model data, detailed summary tables of CMIP6 models, experiments, and emergent diagnostics.
Alok K. Pandey, David S. Stevenson, Alcide Zhao, Richard J. Pope, Ryan Hossaini, Krishan Kumar, and Martyn P. Chipperfield
Atmos. Chem. Phys., 25, 4785–4802, https://doi.org/10.5194/acp-25-4785-2025, https://doi.org/10.5194/acp-25-4785-2025, 2025
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Nitrogen dioxide is an air pollutant largely controlled by human activity that affects ozone, methane, and aerosols. Satellite instruments can quantify column NO2 and, by carefully matching the time and location of measurements, enable evaluation of model simulations. NO2 over south and east Asia is assessed, showing that the model captures not only many features of the measurements, but also important differences that suggest model deficiencies in representing several aspects of the atmospheric chemistry of NO2.
Ryan Hossaini, David Sherry, Zihao Wang, Martyn P. Chipperfield, Wuhu Feng, David E. Oram, Karina E. Adcock, Stephen A. Montzka, Isobel J. Simpson, Andrea Mazzeo, Amber A. Leeson, Elliot Atlas, and Charles C.-K. Chou
Atmos. Chem. Phys., 24, 13457–13475, https://doi.org/10.5194/acp-24-13457-2024, https://doi.org/10.5194/acp-24-13457-2024, 2024
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DCE (1,2-dichloroethane) is an industrial chemical used to produce PVC (polyvinyl chloride). We analysed DCE production data to estimate global DCE emissions (2002–2020). The emissions were included in an atmospheric model and evaluated by comparing simulated DCE to DCE measurements in the troposphere. We show that DCE contributes ozone-depleting Cl to the stratosphere and that this has increased with increasing DCE emissions. DCE’s impact on stratospheric O3 is currently small but non-zero.
Willem E. van Caspel, David Simpson, Jan Eiof Jonson, Anna M. K. Benedictow, Yao Ge, Alcide di Sarra, Giandomenico Pace, Massimo Vieno, Hannah L. Walker, and Mathew R. Heal
Geosci. Model Dev., 16, 7433–7459, https://doi.org/10.5194/gmd-16-7433-2023, https://doi.org/10.5194/gmd-16-7433-2023, 2023
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Radiation coming from the sun is essential to atmospheric chemistry, driving the breakup, or photodissociation, of atmospheric molecules. This in turn affects the chemical composition and reactivity of the atmosphere. The representation of photodissociation effects is therefore essential in atmospheric chemistry modeling. One such model is the EMEP MSC-W model, for which a new way of calculating the photodissociation rates is tested and evaluated in this paper.
Gemma Purser, Mathew R. Heal, Edward J. Carnell, Stephen Bathgate, Julia Drewer, James I. L. Morison, and Massimo Vieno
Atmos. Chem. Phys., 23, 13713–13733, https://doi.org/10.5194/acp-23-13713-2023, https://doi.org/10.5194/acp-23-13713-2023, 2023
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Forest expansion is a ″net-zero“ pathway, but change in land cover alters air quality in many ways. This study combines tree planting suitability data with UK measured emissions of biogenic volatile organic compounds to simulate spatial and temporal changes in atmospheric composition for planting scenarios of four species. Decreases in fine particulate matter are relatively larger than increases in ozone, which may indicate a net benefit of tree planting on human health aspects of air quality.
Ewa M. Bednarz, Ryan Hossaini, and Martyn P. Chipperfield
Atmos. Chem. Phys., 23, 13701–13711, https://doi.org/10.5194/acp-23-13701-2023, https://doi.org/10.5194/acp-23-13701-2023, 2023
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We quantify, for the first time, the time-varying impact of uncontrolled emissions of chlorinated very short-lived substances (Cl-VSLSs) on stratospheric ozone using a state-of-the-art chemistry-climate model. We demonstrate that Cl-VSLSs already have a non-negligible impact on stratospheric ozone, including a local reduction of up to ~7 DU in Arctic ozone in the cold winter of 2019/20, and any so future growth in emissions will continue to offset some of the benefits of the Montreal Protocol.
Ewa M. Bednarz, Ryan Hossaini, N. Luke Abraham, and Martyn P. Chipperfield
Geosci. Model Dev., 16, 6187–6209, https://doi.org/10.5194/gmd-16-6187-2023, https://doi.org/10.5194/gmd-16-6187-2023, 2023
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Development and performance of the new DEST chemistry scheme of UM–UKCA is described. The scheme extends the standard StratTrop scheme by including important updates to the halogen chemistry, thus allowing process-oriented studies of stratospheric ozone depletion and recovery, including impacts from both controlled long-lived ozone-depleting substances and emerging issues around uncontrolled, very short-lived substances. It will thus aid studies in support of future ozone assessment reports.
Yao Ge, Massimo Vieno, David S. Stevenson, Peter Wind, and Mathew R. Heal
Atmos. Chem. Phys., 23, 6083–6112, https://doi.org/10.5194/acp-23-6083-2023, https://doi.org/10.5194/acp-23-6083-2023, 2023
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The sensitivity of fine particles and reactive N and S species to reductions in precursor emissions is investigated using the EMEP MSC-W (European Monitoring and Evaluation Programme Meteorological Synthesizing Centre – West) atmospheric chemistry transport model. This study reveals that the individual emissions reduction has multiple and geographically varying co-benefits and small disbenefits on different species, demonstrating the importance of prioritizing regional emissions controls.
Markus Jesswein, Rafael P. Fernandez, Lucas Berná, Alfonso Saiz-Lopez, Jens-Uwe Grooß, Ryan Hossaini, Eric C. Apel, Rebecca S. Hornbrook, Elliot L. Atlas, Donald R. Blake, Stephen Montzka, Timo Keber, Tanja Schuck, Thomas Wagenhäuser, and Andreas Engel
Atmos. Chem. Phys., 22, 15049–15070, https://doi.org/10.5194/acp-22-15049-2022, https://doi.org/10.5194/acp-22-15049-2022, 2022
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This study presents the global and seasonal distribution of the two major brominated short-lived substances CH2Br2 and CHBr3 in the upper troposphere and lower stratosphere based on observations from several aircraft campaigns. They show similar seasonality for both hemispheres, except in the respective hemispheric autumn lower stratosphere. A comparison with the TOMCAT and CAM-Chem models shows good agreement in the annual mean but larger differences in the seasonal consideration.
Ewa M. Bednarz, Ryan Hossaini, Martyn P. Chipperfield, N. Luke Abraham, and Peter Braesicke
Atmos. Chem. Phys., 22, 10657–10676, https://doi.org/10.5194/acp-22-10657-2022, https://doi.org/10.5194/acp-22-10657-2022, 2022
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Atmospheric impacts of chlorinated very short-lived substances (Cl-VSLS) over the first two decades of the 21st century are assessed using the UM-UKCA chemistry–climate model. Stratospheric input of Cl from Cl-VSLS is estimated at ~130 ppt in 2019. The use of model set-up with constrained meteorology significantly increases the abundance of Cl-VSLS in the lower stratosphere relative to the free-running set-up. The growth in Cl-VSLS emissions significantly impacted recent HCl and COCl2 trends.
Yao Ge, Massimo Vieno, David S. Stevenson, Peter Wind, and Mathew R. Heal
Atmos. Chem. Phys., 22, 8343–8368, https://doi.org/10.5194/acp-22-8343-2022, https://doi.org/10.5194/acp-22-8343-2022, 2022
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Reactive N and S gases and aerosols are critical determinants of air quality. We report a comprehensive analysis of the concentrations, wet and dry deposition, fluxes, and lifetimes of these species globally as well as for 10 world regions. We used the EMEP MSC-W model coupled with WRF meteorology and 2015 global emissions. Our work demonstrates the substantial regional variation in these quantities and the need for modelling to simulate atmospheric responses to precursor emissions.
Daniel Clarkson, Emma Eastoe, and Amber Leeson
The Cryosphere, 16, 1597–1607, https://doi.org/10.5194/tc-16-1597-2022, https://doi.org/10.5194/tc-16-1597-2022, 2022
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The Greenland ice sheet has seen large amounts of melt in recent years, and accurately modelling temperatures is vital to understand how much of the ice sheet is melting. We estimate the probability of melt from ice surface temperature data to identify which areas of the ice sheet have experienced melt and estimate temperature quantiles. Our results suggest that for large areas of the ice sheet, melt has become more likely over the past 2 decades and high temperatures are also becoming warmer.
Sandip S. Dhomse, Martyn P. Chipperfield, Wuhu Feng, Ryan Hossaini, Graham W. Mann, Michelle L. Santee, and Mark Weber
Atmos. Chem. Phys., 22, 903–916, https://doi.org/10.5194/acp-22-903-2022, https://doi.org/10.5194/acp-22-903-2022, 2022
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Solar flux variations associated with 11-year sunspot cycle is believed to exert important external climate forcing. As largest variations occur at shorter wavelengths such as ultra-violet part of the solar spectrum, associated changes in stratospheric ozone are thought to provide direct evidence for solar climate interaction. Until now, most of the studies reported double-peak structured solar cycle signal (SCS), but relatively new satellite data suggest only single-peak-structured SCS.
Paul D. Hamer, Virginie Marécal, Ryan Hossaini, Michel Pirre, Gisèle Krysztofiak, Franziska Ziska, Andreas Engel, Stephan Sala, Timo Keber, Harald Bönisch, Elliot Atlas, Kirstin Krüger, Martyn Chipperfield, Valery Catoire, Azizan A. Samah, Marcel Dorf, Phang Siew Moi, Hans Schlager, and Klaus Pfeilsticker
Atmos. Chem. Phys., 21, 16955–16984, https://doi.org/10.5194/acp-21-16955-2021, https://doi.org/10.5194/acp-21-16955-2021, 2021
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Bromoform is a stratospheric ozone-depleting gas released by seaweed and plankton transported to the stratosphere via convection in the tropics. We study the chemical interactions of bromoform and its derivatives within convective clouds using a cloud-scale model and observations. Our findings are that soluble bromine gases are efficiently washed out and removed within the convective clouds and that most bromine is transported vertically to the upper troposphere in the form of bromoform.
Yao Ge, Mathew R. Heal, David S. Stevenson, Peter Wind, and Massimo Vieno
Geosci. Model Dev., 14, 7021–7046, https://doi.org/10.5194/gmd-14-7021-2021, https://doi.org/10.5194/gmd-14-7021-2021, 2021
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This study reports the first evaluation of the global EMEP MSC-W ACTM driven by WRF meteorology, with a focus on surface concentrations and wet deposition of reactive N and S species. The model–measurement comparison is conducted both spatially and temporally, covering 10 monitoring networks worldwide. The statistics from the comprehensive evaluations presented in this study support the application of this model framework for global analysis of the budgets and fluxes of reactive N and SIA.
Paul T. Griffiths, Lee T. Murray, Guang Zeng, Youngsub Matthew Shin, N. Luke Abraham, Alexander T. Archibald, Makoto Deushi, Louisa K. Emmons, Ian E. Galbally, Birgit Hassler, Larry W. Horowitz, James Keeble, Jane Liu, Omid Moeini, Vaishali Naik, Fiona M. O'Connor, Naga Oshima, David Tarasick, Simone Tilmes, Steven T. Turnock, Oliver Wild, Paul J. Young, and Prodromos Zanis
Atmos. Chem. Phys., 21, 4187–4218, https://doi.org/10.5194/acp-21-4187-2021, https://doi.org/10.5194/acp-21-4187-2021, 2021
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We analyse the CMIP6 Historical and future simulations for tropospheric ozone, a species which is important for many aspects of atmospheric chemistry. We show that the current generation of models agrees well with observations, being particularly successful in capturing trends in surface ozone and its vertical distribution in the troposphere. We analyse the factors that control ozone and show that they evolve over the period of the CMIP6 experiments.
Matt Amos, Paul J. Young, J. Scott Hosking, Jean-François Lamarque, N. Luke Abraham, Hideharu Akiyoshi, Alexander T. Archibald, Slimane Bekki, Makoto Deushi, Patrick Jöckel, Douglas Kinnison, Ole Kirner, Markus Kunze, Marion Marchand, David A. Plummer, David Saint-Martin, Kengo Sudo, Simone Tilmes, and Yousuke Yamashita
Atmos. Chem. Phys., 20, 9961–9977, https://doi.org/10.5194/acp-20-9961-2020, https://doi.org/10.5194/acp-20-9961-2020, 2020
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We present an updated projection of Antarctic ozone hole recovery using an ensemble of chemistry–climate models. To do so, we employ a method, more advanced and skilful than the current multi-model mean standard, which is applicable to other ensemble analyses. It calculates the performance and similarity of the models, which we then use to weight the model. Calculating model similarity allows us to account for models which are constructed from similar components.
Sarah A. Strode, James S. Wang, Michael Manyin, Bryan Duncan, Ryan Hossaini, Christoph A. Keller, Sylvia E. Michel, and James W. C. White
Atmos. Chem. Phys., 20, 8405–8419, https://doi.org/10.5194/acp-20-8405-2020, https://doi.org/10.5194/acp-20-8405-2020, 2020
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The 13C : 12C isotopic ratio in methane (CH4) provides information about CH4 sources, but loss of CH4 by reaction with OH and chlorine (Cl) also affects this ratio. Tropospheric Cl provides a small and uncertain sink for CH4 but has a large effect on its isotopic ratio. We use the GEOS model with several different Cl fields to test the sensitivity of methane's isotopic composition to tropospheric Cl. Cl affects the global mean, hemispheric gradient, and seasonal cycle of the isotopic ratio.
Christopher P. O. Reyer, Ramiro Silveyra Gonzalez, Klara Dolos, Florian Hartig, Ylva Hauf, Matthias Noack, Petra Lasch-Born, Thomas Rötzer, Hans Pretzsch, Henning Meesenburg, Stefan Fleck, Markus Wagner, Andreas Bolte, Tanja G. M. Sanders, Pasi Kolari, Annikki Mäkelä, Timo Vesala, Ivan Mammarella, Jukka Pumpanen, Alessio Collalti, Carlo Trotta, Giorgio Matteucci, Ettore D'Andrea, Lenka Foltýnová, Jan Krejza, Andreas Ibrom, Kim Pilegaard, Denis Loustau, Jean-Marc Bonnefond, Paul Berbigier, Delphine Picart, Sébastien Lafont, Michael Dietze, David Cameron, Massimo Vieno, Hanqin Tian, Alicia Palacios-Orueta, Victor Cicuendez, Laura Recuero, Klaus Wiese, Matthias Büchner, Stefan Lange, Jan Volkholz, Hyungjun Kim, Joanna A. Horemans, Friedrich Bohn, Jörg Steinkamp, Alexander Chikalanov, Graham P. Weedon, Justin Sheffield, Flurin Babst, Iliusi Vega del Valle, Felicitas Suckow, Simon Martel, Mats Mahnken, Martin Gutsch, and Katja Frieler
Earth Syst. Sci. Data, 12, 1295–1320, https://doi.org/10.5194/essd-12-1295-2020, https://doi.org/10.5194/essd-12-1295-2020, 2020
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Process-based vegetation models are widely used to predict local and global ecosystem dynamics and climate change impacts. Due to their complexity, they require careful parameterization and evaluation to ensure that projections are accurate and reliable. The PROFOUND Database provides a wide range of empirical data to calibrate and evaluate vegetation models that simulate climate impacts at the forest stand scale to support systematic model intercomparisons and model development in Europe.
Timo Keber, Harald Bönisch, Carl Hartick, Marius Hauck, Fides Lefrancois, Florian Obersteiner, Akima Ringsdorf, Nils Schohl, Tanja Schuck, Ryan Hossaini, Phoebe Graf, Patrick Jöckel, and Andreas Engel
Atmos. Chem. Phys., 20, 4105–4132, https://doi.org/10.5194/acp-20-4105-2020, https://doi.org/10.5194/acp-20-4105-2020, 2020
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In this paper we summarize observations of short-lived halocarbons in the tropopause region. We show that, especially during winter, the levels of short-lived bromine gases at the extratropical tropopause are higher than at the tropical tropopause. We discuss the impact of the distributions on stratospheric bromine levels and compare our observations to two models with four different emission scenarios.
Carole Helfter, Neil Mullinger, Massimo Vieno, Simon O'Doherty, Michel Ramonet, Paul I. Palmer, and Eiko Nemitz
Atmos. Chem. Phys., 19, 3043–3063, https://doi.org/10.5194/acp-19-3043-2019, https://doi.org/10.5194/acp-19-3043-2019, 2019
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We present a novel approach to estimate the annual budgets of carbon dioxide (881.0 ± 128.5 Tg) and methane (2.55 ± 0.48 Tg) of the British Isles from shipborne measurements taken over a 3-year period (2015–2017). This study brings independent verification of the emission budgets estimated using alternative products and investigates the seasonality of these emissions, which is usually not possible.
Ksenia Aleksankina, Stefan Reis, Massimo Vieno, and Mathew R. Heal
Atmos. Chem. Phys., 19, 2881–2898, https://doi.org/10.5194/acp-19-2881-2019, https://doi.org/10.5194/acp-19-2881-2019, 2019
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Atmospheric chemistry transport models are widely used to underpin policies to mitigate the detrimental effects of air pollution on human health and ecosystems. Understanding the level of confidence in model predictions is thus vital. We present a comprehensive approach for uncertainty assessment and global variance-based sensitivity analysis to propagate uncertainty from model input data and identify the extent to which uncertainty in different emissions drives the model output uncertainty.
Joe McNorton, Chris Wilson, Manuel Gloor, Rob J. Parker, Hartmut Boesch, Wuhu Feng, Ryan Hossaini, and Martyn P. Chipperfield
Atmos. Chem. Phys., 18, 18149–18168, https://doi.org/10.5194/acp-18-18149-2018, https://doi.org/10.5194/acp-18-18149-2018, 2018
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Since 2007 atmospheric methane (CH4) has been unexpectedly increasing following a 6-year hiatus. We have used an atmospheric model to attribute regional sources and global sinks of CH4 using observations for the 2003–2015 period. Model results show the renewed growth is best explained by decreased atmospheric removal, decreased biomass burning emissions, and an increased energy sector (mainly from Africa–Middle East and Southern Asia–Oceania) and wetland emissions (mainly from northern Eurasia).
Christina Hood, Ian MacKenzie, Jenny Stocker, Kate Johnson, David Carruthers, Massimo Vieno, and Ruth Doherty
Atmos. Chem. Phys., 18, 11221–11245, https://doi.org/10.5194/acp-18-11221-2018, https://doi.org/10.5194/acp-18-11221-2018, 2018
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A coupled atmospheric dispersion modelling system has been developed, comprising a regional chemical transport model and a street-scale urban dispersion model. It was applied in London for 2012 and for all common regulated air quality pollutants, with evaluation against measurements. The modelling demonstrates the interaction between local and regional scales, which differs between pollutants. Real-world estimates of emissions have been used to adjust standard factors and improve model results.
Richard Hyde, Ryan Hossaini, and Amber A. Leeson
Geosci. Model Dev., 11, 2033–2048, https://doi.org/10.5194/gmd-11-2033-2018, https://doi.org/10.5194/gmd-11-2033-2018, 2018
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Clustering, the automated grouping of similar data, can provide powerful insight into large/complex data. We demonstrate the benefits of clustering applied to output from climate model inter-comparison initiatives. We focus on modelled tropospheric ozone from the ACCMIP project. Cluster-based subsampling of the model ensemble can (i) remove outlier data on a grid-cell basis, reducing model–observation bias and (ii) provide a useful framework in which to investigate and visualise model diversity.
Fernando Iglesias-Suarez, Douglas E. Kinnison, Alexandru Rap, Amanda C. Maycock, Oliver Wild, and Paul J. Young
Atmos. Chem. Phys., 18, 6121–6139, https://doi.org/10.5194/acp-18-6121-2018, https://doi.org/10.5194/acp-18-6121-2018, 2018
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This study explores future ozone radiative forcing (RF) and the relative contribution due to different drivers. Climate-induced ozone RF is largely the result of the interplay between lightning-produced ozone and enhanced ozone destruction in a warmer and wetter atmosphere. These results demonstrate the importance of stratospheric–tropospheric interactions and the stratosphere as a key region controlling a large fraction of the tropospheric ozone RF.
Riinu Ots, Mathew R. Heal, Dominique E. Young, Leah R. Williams, James D. Allan, Eiko Nemitz, Chiara Di Marco, Anais Detournay, Lu Xu, Nga L. Ng, Hugh Coe, Scott C. Herndon, Ian A. Mackenzie, David C. Green, Jeroen J. P. Kuenen, Stefan Reis, and Massimo Vieno
Atmos. Chem. Phys., 18, 4497–4518, https://doi.org/10.5194/acp-18-4497-2018, https://doi.org/10.5194/acp-18-4497-2018, 2018
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The main hypothesis of this paper is that people who live in large cities in the UK disobey the
smoke control lawas it has not been actively enforced for decades now. However, the use of wood in residential heating has increased, partly due to renewable energy targets, but also for discretionary (i.e. pleasant fireplaces) reasons. Our study is based mainly in London, but similar struggles with urban air quality due to residential wood and coal burning are seen in other major European cities.
Amber A. Leeson, Emma Eastoe, and Xavier Fettweis
The Cryosphere, 12, 1091–1102, https://doi.org/10.5194/tc-12-1091-2018, https://doi.org/10.5194/tc-12-1091-2018, 2018
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Future melting of the Greenland Ice Sheet is predicted using regional climate models (RCMs). Here, we assess the ability of the MAR RCM to reproduce observed extreme temperature events and the melt energy produced during these times at 14 locations. We find that MAR underestimates temperatures by >0.5 °C during extreme events, which leads to an underestimate in melt energy by up to 41 %. This is potentially an artefact of the data used to drive the MAR simulation and needs to be corrected for.
Andrea Móring, Massimo Vieno, Ruth M. Doherty, Celia Milford, Eiko Nemitz, Marsailidh M. Twigg, László Horváth, and Mark A. Sutton
Biogeosciences, 14, 4161–4193, https://doi.org/10.5194/bg-14-4161-2017, https://doi.org/10.5194/bg-14-4161-2017, 2017
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This study describes and evaluates a new ammonia (NH3) exchange model for grazed fields (GAG_field). GAG_field is able to simulate the main features of the observed NH3 fluxes. A sensitivity analysis for the non-meteorological model parameters showed that the sensitivity of the NH3 fluxes to a parameter varies among urine patches. Moreover, the fluxes modelled with a dynamic soil pH are similar if a constant pH 7.5 is used, suggesting a useful simplification for regional-scale model application.
Chun Lin, Mathew R. Heal, Massimo Vieno, Ian A. MacKenzie, Ben G. Armstrong, Barbara K. Butland, Ai Milojevic, Zaid Chalabi, Richard W. Atkinson, David S. Stevenson, Ruth M. Doherty, and Paul Wilkinson
Geosci. Model Dev., 10, 1767–1787, https://doi.org/10.5194/gmd-10-1767-2017, https://doi.org/10.5194/gmd-10-1767-2017, 2017
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We evaluated EMEP4UK-WRF v4.3 atmospheric chemistry transport simulations at 5 km horizontal resolution over the UK for use in air pollution epidemiology and health burden assessment. Model-measurement comparison focused on daily and annual means for NO2, O3, PM10, and PM2.5. Important statistics for evaluation of air-quality model output against policy (and hence health)-relevant standards – correlation, bias, and root mean square error – were evaluated by site type, year, month and day-of-week.
Jochen Stutz, Bodo Werner, Max Spolaor, Lisa Scalone, James Festa, Catalina Tsai, Ross Cheung, Santo F. Colosimo, Ugo Tricoli, Rasmus Raecke, Ryan Hossaini, Martyn P. Chipperfield, Wuhu Feng, Ru-Shan Gao, Eric J. Hintsa, James W. Elkins, Fred L. Moore, Bruce Daube, Jasna Pittman, Steven Wofsy, and Klaus Pfeilsticker
Atmos. Meas. Tech., 10, 1017–1042, https://doi.org/10.5194/amt-10-1017-2017, https://doi.org/10.5194/amt-10-1017-2017, 2017
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A new limb-scanning Differential Optical Absorption Spectroscopy (DOAS) instrument was developed for NASA’s Global Hawk unmanned aerial system during the Airborne Tropical TRopopause EXperiment to study trace gases in the tropical tropopause layer. A new technique that uses in situ and DOAS O3 observations together with radiative transfer calculations allows the retrieval of mixing ratios from the slant column densities of BrO and NO2 at high accuracies of 0.5 ppt and 15 ppt, respectively.
Bodo Werner, Jochen Stutz, Max Spolaor, Lisa Scalone, Rasmus Raecke, James Festa, Santo Fedele Colosimo, Ross Cheung, Catalina Tsai, Ryan Hossaini, Martyn P. Chipperfield, Giorgio S. Taverna, Wuhu Feng, James W. Elkins, David W. Fahey, Ru-Shan Gao, Erik J. Hintsa, Troy D. Thornberry, Free Lee Moore, Maria A. Navarro, Elliot Atlas, Bruce C. Daube, Jasna Pittman, Steve Wofsy, and Klaus Pfeilsticker
Atmos. Chem. Phys., 17, 1161–1186, https://doi.org/10.5194/acp-17-1161-2017, https://doi.org/10.5194/acp-17-1161-2017, 2017
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The paper reports on inorganic and organic bromine measured in the tropical tropopause layer (TTL) over the eastern Pacific in early 2013. Bryinorg is found to increase from a mean of 2.63 ± 1.04 ppt for θ in the range of 350–360 K to 5.11 ± 1.57 ppt for θ=390 ± 400 K, whereas in the subtropical lower stratosphere, it reaches 7.66 ± 2.95 ppt for θ in the range of 390–400 K. Within the TTL, total bromine is found to range from 20.3 ppt to 22.3 ppt.
Martyn P. Chipperfield, Qing Liang, Matthew Rigby, Ryan Hossaini, Stephen A. Montzka, Sandip Dhomse, Wuhu Feng, Ronald G. Prinn, Ray F. Weiss, Christina M. Harth, Peter K. Salameh, Jens Mühle, Simon O'Doherty, Dickon Young, Peter G. Simmonds, Paul B. Krummel, Paul J. Fraser, L. Paul Steele, James D. Happell, Robert C. Rhew, James Butler, Shari A. Yvon-Lewis, Bradley Hall, David Nance, Fred Moore, Ben R. Miller, James W. Elkins, Jeremy J. Harrison, Chris D. Boone, Elliot L. Atlas, and Emmanuel Mahieu
Atmos. Chem. Phys., 16, 15741–15754, https://doi.org/10.5194/acp-16-15741-2016, https://doi.org/10.5194/acp-16-15741-2016, 2016
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Carbon tetrachloride (CCl4) is a compound which, when released into the atmosphere, can cause depletion of the stratospheric ozone layer. Its emissions are controlled under the Montreal Protocol, and its atmospheric abundance is slowly decreasing. However, this decrease is not as fast as expected based on estimates of its emissions and its atmospheric lifetime. We have used an atmospheric model to look at the uncertainties in the CCl4 lifetime and to examine the impact on its atmospheric decay.
Mark R. Theobald, David Simpson, and Massimo Vieno
Geosci. Model Dev., 9, 4475–4489, https://doi.org/10.5194/gmd-9-4475-2016, https://doi.org/10.5194/gmd-9-4475-2016, 2016
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Impacts of air pollution at a continental scale, estimated using air quality models, can potentially be greatly under- or overestimated due to the low spatial resolution used (grid cells of 10–50 km). We present a method to estimate the spatial variations in air quality within a model grid cell by combining high-resolution emission data with estimates of short range dispersion. This simple but robust technique has the potential to improve estimates of air quality impacts at a continental scale.
Riinu Ots, Massimo Vieno, James D. Allan, Stefan Reis, Eiko Nemitz, Dominique E. Young, Hugh Coe, Chiara Di Marco, Anais Detournay, Ian A. Mackenzie, David C. Green, and Mathew R. Heal
Atmos. Chem. Phys., 16, 13773–13789, https://doi.org/10.5194/acp-16-13773-2016, https://doi.org/10.5194/acp-16-13773-2016, 2016
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Emissions of cooking organic aerosol (COA; from charbroiling, frying, etc.) are currently absent in European emissions inventories yet measurements have pointed to significant COA concentrations. In this study, emissions of COA were developed for the UK by model iteration against year-long measurements at two sites in London. Modelled COA dropped rapidly outside of major urban areas, suggesting that although a notable component in UK urban air, COA does not have a significant effect on rural PM.
Marsailidh M. Twigg, Evgenia Ilyinskaya, Sonya Beccaceci, David C. Green, Matthew R. Jones, Ben Langford, Sarah R. Leeson, Justin J. N. Lingard, Gloria M. Pereira, Heather Carter, Jan Poskitt, Andreas Richter, Stuart Ritchie, Ivan Simmons, Ron I. Smith, Y. Sim Tang, Netty Van Dijk, Keith Vincent, Eiko Nemitz, Massimo Vieno, and Christine F. Braban
Atmos. Chem. Phys., 16, 11415–11431, https://doi.org/10.5194/acp-16-11415-2016, https://doi.org/10.5194/acp-16-11415-2016, 2016
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This study integrates high and low resolution temporal measurements to assess the impact of the Holuhraun effusive eruption in 2014 across the UK. Measurements, modelling and satellite analysis provides details on the transport and chemistry of both gases and particulates during this unique event. The results of the study can be used verify existing atmospheric chemistry models of volcano plumes in order to carry improved risk assessments for future volcanic eruptions.
R. Hossaini, P. K. Patra, A. A. Leeson, G. Krysztofiak, N. L. Abraham, S. J. Andrews, A. T. Archibald, J. Aschmann, E. L. Atlas, D. A. Belikov, H. Bönisch, L. J. Carpenter, S. Dhomse, M. Dorf, A. Engel, W. Feng, S. Fuhlbrügge, P. T. Griffiths, N. R. P. Harris, R. Hommel, T. Keber, K. Krüger, S. T. Lennartz, S. Maksyutov, H. Mantle, G. P. Mills, B. Miller, S. A. Montzka, F. Moore, M. A. Navarro, D. E. Oram, K. Pfeilsticker, J. A. Pyle, B. Quack, A. D. Robinson, E. Saikawa, A. Saiz-Lopez, S. Sala, B.-M. Sinnhuber, S. Taguchi, S. Tegtmeier, R. T. Lidster, C. Wilson, and F. Ziska
Atmos. Chem. Phys., 16, 9163–9187, https://doi.org/10.5194/acp-16-9163-2016, https://doi.org/10.5194/acp-16-9163-2016, 2016
Riinu Ots, Dominique E. Young, Massimo Vieno, Lu Xu, Rachel E. Dunmore, James D. Allan, Hugh Coe, Leah R. Williams, Scott C. Herndon, Nga L. Ng, Jacqueline F. Hamilton, Robert Bergström, Chiara Di Marco, Eiko Nemitz, Ian A. Mackenzie, Jeroen J. P. Kuenen, David C. Green, Stefan Reis, and Mathew R. Heal
Atmos. Chem. Phys., 16, 6453–6473, https://doi.org/10.5194/acp-16-6453-2016, https://doi.org/10.5194/acp-16-6453-2016, 2016
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This study investigates the contribution of diesel vehicle emissions to organic aerosol formation and particulate matter concentrations in London. Comparisons of simulated pollutant concentrations with observations show good agreement and give confidence in the skill of the model applied. The contribution of diesel vehicle emissions, which are currently not included in official emissions inventories, is demonstrated to be substantial, indicating that more research on this topic is required.
Andrea Móring, Massimo Vieno, Ruth M. Doherty, Johannes Laubach, Arezoo Taghizadeh-Toosi, and Mark A. Sutton
Biogeosciences, 13, 1837–1861, https://doi.org/10.5194/bg-13-1837-2016, https://doi.org/10.5194/bg-13-1837-2016, 2016
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A process-based, weather-driven model for ammonia emission from a urine patch has been developed and its sensitivity to various factors assessed. The model can simulate the ammoniacal nitrogen content, pH and the water content of the soil under a urine patch.
The simulated variables were in a good agreement with the measurements. The sensitivity analysis highlighted the vital role of temperature in ammonia exchange. The model is potentially suitable for larger scale application.
M. Vieno, M. R. Heal, M. L. Williams, E. J. Carnell, E. Nemitz, J. R. Stedman, and S. Reis
Atmos. Chem. Phys., 16, 265–276, https://doi.org/10.5194/acp-16-265-2016, https://doi.org/10.5194/acp-16-265-2016, 2016
F. Iglesias-Suarez, P. J. Young, and O. Wild
Atmos. Chem. Phys., 16, 343–363, https://doi.org/10.5194/acp-16-343-2016, https://doi.org/10.5194/acp-16-343-2016, 2016
D. Fowler, C. E. Steadman, D. Stevenson, M. Coyle, R. M. Rees, U. M. Skiba, M. A. Sutton, J. N. Cape, A. J. Dore, M. Vieno, D. Simpson, S. Zaehle, B. D. Stocker, M. Rinaldi, M. C. Facchini, C. R. Flechard, E. Nemitz, M. Twigg, J. W. Erisman, K. Butterbach-Bahl, and J. N. Galloway
Atmos. Chem. Phys., 15, 13849–13893, https://doi.org/10.5194/acp-15-13849-2015, https://doi.org/10.5194/acp-15-13849-2015, 2015
S. T. Lennartz, G. Krysztofiak, C. A. Marandino, B.-M. Sinnhuber, S. Tegtmeier, F. Ziska, R. Hossaini, K. Krüger, S. A. Montzka, E. Atlas, D. E. Oram, T. Keber, H. Bönisch, and B. Quack
Atmos. Chem. Phys., 15, 11753–11772, https://doi.org/10.5194/acp-15-11753-2015, https://doi.org/10.5194/acp-15-11753-2015, 2015
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Marine-produced short-lived trace gases such as halocarbons and DMS significantly impact atmospheric chemistry. To assess this impact on ozone depletion and the radiative budget, it is critical that their marine emissions in atmospheric chemistry models are quantified as accurately as possible. We show that calculating emissions online with an interactive atmosphere improves the agreement with current observations and should be employed regularly in models where marine sources are important.
L. Kritten, A. Butz, M. P. Chipperfield, M. Dorf, S. Dhomse, R. Hossaini, H. Oelhaf, C. Prados-Roman, G. Wetzel, and K. Pfeilsticker
Atmos. Chem. Phys., 14, 9555–9566, https://doi.org/10.5194/acp-14-9555-2014, https://doi.org/10.5194/acp-14-9555-2014, 2014
E. von Schneidemesser, M. Vieno, and P. S. Monks
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acpd-14-1287-2014, https://doi.org/10.5194/acpd-14-1287-2014, 2014
Revised manuscript not accepted
F. M. O'Connor, C. E. Johnson, O. Morgenstern, N. L. Abraham, P. Braesicke, M. Dalvi, G. A. Folberth, M. G. Sanderson, P. J. Telford, A. Voulgarakis, P. J. Young, G. Zeng, W. J. Collins, and J. A. Pyle
Geosci. Model Dev., 7, 41–91, https://doi.org/10.5194/gmd-7-41-2014, https://doi.org/10.5194/gmd-7-41-2014, 2014
S. Tegtmeier, K. Krüger, B. Quack, E. Atlas, D. R. Blake, H. Boenisch, A. Engel, H. Hepach, R. Hossaini, M. A. Navarro, S. Raimund, S. Sala, Q. Shi, and F. Ziska
Atmos. Chem. Phys., 13, 11869–11886, https://doi.org/10.5194/acp-13-11869-2013, https://doi.org/10.5194/acp-13-11869-2013, 2013
B. Hassler, P. J. Young, R. W. Portmann, G. E. Bodeker, J. S. Daniel, K. H. Rosenlof, and S. Solomon
Atmos. Chem. Phys., 13, 5533–5550, https://doi.org/10.5194/acp-13-5533-2013, https://doi.org/10.5194/acp-13-5533-2013, 2013
V. Naik, A. Voulgarakis, A. M. Fiore, L. W. Horowitz, J.-F. Lamarque, M. Lin, M. J. Prather, P. J. Young, D. Bergmann, P. J. Cameron-Smith, I. Cionni, W. J. Collins, S. B. Dalsøren, R. Doherty, V. Eyring, G. Faluvegi, G. A. Folberth, B. Josse, Y. H. Lee, I. A. MacKenzie, T. Nagashima, T. P. C. van Noije, D. A. Plummer, M. Righi, S. T. Rumbold, R. Skeie, D. T. Shindell, D. S. Stevenson, S. Strode, K. Sudo, S. Szopa, and G. Zeng
Atmos. Chem. Phys., 13, 5277–5298, https://doi.org/10.5194/acp-13-5277-2013, https://doi.org/10.5194/acp-13-5277-2013, 2013
K. W. Bowman, D. T. Shindell, H. M. Worden, J.F. Lamarque, P. J. Young, D. S. Stevenson, Z. Qu, M. de la Torre, D. Bergmann, P. J. Cameron-Smith, W. J. Collins, R. Doherty, S. B. Dalsøren, G. Faluvegi, G. Folberth, L. W. Horowitz, B. M. Josse, Y. H. Lee, I. A. MacKenzie, G. Myhre, T. Nagashima, V. Naik, D. A. Plummer, S. T. Rumbold, R. B. Skeie, S. A. Strode, K. Sudo, S. Szopa, A. Voulgarakis, G. Zeng, S. S. Kulawik, A. M. Aghedo, and J. R. Worden
Atmos. Chem. Phys., 13, 4057–4072, https://doi.org/10.5194/acp-13-4057-2013, https://doi.org/10.5194/acp-13-4057-2013, 2013
D. T. Shindell, J.-F. Lamarque, M. Schulz, M. Flanner, C. Jiao, M. Chin, P. J. Young, Y. H. Lee, L. Rotstayn, N. Mahowald, G. Milly, G. Faluvegi, Y. Balkanski, W. J. Collins, A. J. Conley, S. Dalsoren, R. Easter, S. Ghan, L. Horowitz, X. Liu, G. Myhre, T. Nagashima, V. Naik, S. T. Rumbold, R. Skeie, K. Sudo, S. Szopa, T. Takemura, A. Voulgarakis, J.-H. Yoon, and F. Lo
Atmos. Chem. Phys., 13, 2939–2974, https://doi.org/10.5194/acp-13-2939-2013, https://doi.org/10.5194/acp-13-2939-2013, 2013
D. S. Stevenson, P. J. Young, V. Naik, J.-F. Lamarque, D. T. Shindell, A. Voulgarakis, R. B. Skeie, S. B. Dalsoren, G. Myhre, T. K. Berntsen, G. A. Folberth, S. T. Rumbold, W. J. Collins, I. A. MacKenzie, R. M. Doherty, G. Zeng, T. P. C. van Noije, A. Strunk, D. Bergmann, P. Cameron-Smith, D. A. Plummer, S. A. Strode, L. Horowitz, Y. H. Lee, S. Szopa, K. Sudo, T. Nagashima, B. Josse, I. Cionni, M. Righi, V. Eyring, A. Conley, K. W. Bowman, O. Wild, and A. Archibald
Atmos. Chem. Phys., 13, 3063–3085, https://doi.org/10.5194/acp-13-3063-2013, https://doi.org/10.5194/acp-13-3063-2013, 2013
A. Voulgarakis, V. Naik, J.-F. Lamarque, D. T. Shindell, P. J. Young, M. J. Prather, O. Wild, R. D. Field, D. Bergmann, P. Cameron-Smith, I. Cionni, W. J. Collins, S. B. Dalsøren, R. M. Doherty, V. Eyring, G. Faluvegi, G. A. Folberth, L. W. Horowitz, B. Josse, I. A. MacKenzie, T. Nagashima, D. A. Plummer, M. Righi, S. T. Rumbold, D. S. Stevenson, S. A. Strode, K. Sudo, S. Szopa, and G. Zeng
Atmos. Chem. Phys., 13, 2563–2587, https://doi.org/10.5194/acp-13-2563-2013, https://doi.org/10.5194/acp-13-2563-2013, 2013
P. J. Young, A. T. Archibald, K. W. Bowman, J.-F. Lamarque, V. Naik, D. S. Stevenson, S. Tilmes, A. Voulgarakis, O. Wild, D. Bergmann, P. Cameron-Smith, I. Cionni, W. J. Collins, S. B. Dalsøren, R. M. Doherty, V. Eyring, G. Faluvegi, L. W. Horowitz, B. Josse, Y. H. Lee, I. A. MacKenzie, T. Nagashima, D. A. Plummer, M. Righi, S. T. Rumbold, R. B. Skeie, D. T. Shindell, S. A. Strode, K. Sudo, S. Szopa, and G. Zeng
Atmos. Chem. Phys., 13, 2063–2090, https://doi.org/10.5194/acp-13-2063-2013, https://doi.org/10.5194/acp-13-2063-2013, 2013
G. E. Bodeker, B. Hassler, P. J. Young, and R. W. Portmann
Earth Syst. Sci. Data, 5, 31–43, https://doi.org/10.5194/essd-5-31-2013, https://doi.org/10.5194/essd-5-31-2013, 2013
J.-F. Lamarque, D. T. Shindell, B. Josse, P. J. Young, I. Cionni, V. Eyring, D. Bergmann, P. Cameron-Smith, W. J. Collins, R. Doherty, S. Dalsoren, G. Faluvegi, G. Folberth, S. J. Ghan, L. W. Horowitz, Y. H. Lee, I. A. MacKenzie, T. Nagashima, V. Naik, D. Plummer, M. Righi, S. T. Rumbold, M. Schulz, R. B. Skeie, D. S. Stevenson, S. Strode, K. Sudo, S. Szopa, A. Voulgarakis, and G. Zeng
Geosci. Model Dev., 6, 179–206, https://doi.org/10.5194/gmd-6-179-2013, https://doi.org/10.5194/gmd-6-179-2013, 2013
Related subject area
Subject: Gases | Research Activity: Machine Learning | Altitude Range: Troposphere | Science Focus: Chemistry (chemical composition and reactions)
High-resolution greenhouse gas flux inversions using a machine learning surrogate model for atmospheric transport
Implications of VOC Oxidation in Atmospheric Chemistry: Development of a Comprehensive AI Model for Predicting Reaction Rate Constants
Multi-Machine Learning Approaches to Modeling Small-Scale Source Attribution of Ozone Formation
Technical note: Towards atmospheric compound identification in chemical ionization mass spectrometry with pesticide standards and machine learning
Identifying Drivers of Surface Ozone Bias in Global Chemical Reanalysis with Explainable Machine Learning
Diagnosing ozone–NOx–VOC–aerosol sensitivity and uncovering causes of urban–nonurban discrepancies in Shandong, China, using transformer-based estimations
Automated detection and monitoring of methane super-emitters using satellite data
Spatiotemporal modeling of air pollutant concentrations in Germany using machine learning
Estimating nitrogen and sulfur deposition across China during 2005 to 2020 based on multiple statistical models
Technical note: Improving the European air quality forecast of the Copernicus Atmosphere Monitoring Service using machine learning techniques
Nikhil Dadheech, Tai-Long He, and Alexander J. Turner
Atmos. Chem. Phys., 25, 5159–5174, https://doi.org/10.5194/acp-25-5159-2025, https://doi.org/10.5194/acp-25-5159-2025, 2025
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We developed an efficient GHG (greenhouse gas) flux inversion framework using a machine-learning emulator (FootNet) as a surrogate for an atmospheric transport model, resulting in a 650 × speedup. Paradoxically, the flux inversion using the ML (machine-learning) model outperforms the full-physics model in our case study. We attribute this to the ML model mitigating transport errors in the GHG flux inversion.
Xin Zhang, Jiaqi Luo, Wenxiao Pan, Qiao Xue, Xian Liu, Jianjie Fu, Aiqian Zhang, and Guibin Jiang
EGUsphere, https://doi.org/10.5194/egusphere-2025-1241, https://doi.org/10.5194/egusphere-2025-1241, 2025
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VOCs drive atmospheric chemistry via oxidation, forming PM2.5/ozone precursors. This study introduces Vreact, a graph-based AI model predicting reaction rate constands (ki) for multiple oxidants simultaneously. It achieves high accuracy (MSE=0.281and R²=0.941 for log10ki ), overcoming single-oxidant model limits. A web tool enables rapid rate screening. Vreact advances pollutant formation insights and supports emission control strategies, aiding global air quality and public health efforts.
Zheng Xiao, Yifeng Lu, and Guangli Xiu
EGUsphere, https://doi.org/10.5194/egusphere-2025-160, https://doi.org/10.5194/egusphere-2025-160, 2025
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This study innovates air pollution tracking in industrial areas by merging AI with traditional methods. Analyzing three years of data from a Shanghai chemical park, we identified ozone pollution sources and seasonal variations, revealing that chemical solvents and fuel vapors are key contributors. Our method enables faster, accurate source identification, aiding better air quality decisions in rapidly developing regions.
Federica Bortolussi, Hilda Sandström, Fariba Partovi, Joona Mikkilä, Patrick Rinke, and Matti Rissanen
Atmos. Chem. Phys., 25, 685–704, https://doi.org/10.5194/acp-25-685-2025, https://doi.org/10.5194/acp-25-685-2025, 2025
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Chemical ionization mass spectrometry (CIMS) is widely used in atmospheric chemistry studies. We still have a limited understanding of the complex functioning of the instrument; therefore, we applied machine learning to provide insights from CIMS analyses. We were able to predict both detection and signal intensity with a fair error, and we found out the most important structural fragments for negative ionization schemes (NH and OH) and positive ones (nitrogen-containing groups).
Kazuyuki Miyazaki, Yuliya Marchetti, James Montgomery, Steven Lu, and Kevin Bowman
EGUsphere, https://doi.org/10.5194/egusphere-2024-3753, https://doi.org/10.5194/egusphere-2024-3753, 2025
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This study employs explainable machine learning to analyze the causes of significant biases in surface ozone estimates from chemical reanalysis. By analyzing global observations and chemical reanalysis outputs, key bias drivers such as meteorological conditions and precursor emissions were identified. This provides actionable insights to improve chemical transport models, observation systems, and emissions inventories, ultimately enhancing ozone reanalysis for better air pollution management.
Chenliang Tao, Yanbo Peng, Qingzhu Zhang, Yuqiang Zhang, Bing Gong, Qiao Wang, and Wenxing Wang
Atmos. Chem. Phys., 24, 4177–4192, https://doi.org/10.5194/acp-24-4177-2024, https://doi.org/10.5194/acp-24-4177-2024, 2024
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We developed a novel transformer framework to bridge the sparse surface monitoring for inferring ozone–NOx–VOC–aerosol sensitivity and their urban–nonurban discrepancies at a finer scale with implications for improving our understanding of ozone variations. The change in urban–rural disparities in ozone was dominated by PM2.5 from 2019 to 2020. An aerosol-inhibited regime on top of the two traditional NOx- and VOC-limited regimes was identified in Jiaodong Peninsula, Shandong, China.
Berend J. Schuit, Joannes D. Maasakkers, Pieter Bijl, Gourav Mahapatra, Anne-Wil van den Berg, Sudhanshu Pandey, Alba Lorente, Tobias Borsdorff, Sander Houweling, Daniel J. Varon, Jason McKeever, Dylan Jervis, Marianne Girard, Itziar Irakulis-Loitxate, Javier Gorroño, Luis Guanter, Daniel H. Cusworth, and Ilse Aben
Atmos. Chem. Phys., 23, 9071–9098, https://doi.org/10.5194/acp-23-9071-2023, https://doi.org/10.5194/acp-23-9071-2023, 2023
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Using two machine learning models, which were trained on TROPOMI methane satellite data, we detect 2974 methane plumes, so-called super-emitters, in 2021. We detect methane emissions globally related to urban areas or landfills, coal mining, and oil and gas production. Using our monitoring system, we identify 94 regions with frequent emissions. For 12 locations, we target high-resolution satellite instruments to enlarge and identify the exact infrastructure responsible for the emissions.
Vigneshkumar Balamurugan, Jia Chen, Adrian Wenzel, and Frank N. Keutsch
Atmos. Chem. Phys., 23, 10267–10285, https://doi.org/10.5194/acp-23-10267-2023, https://doi.org/10.5194/acp-23-10267-2023, 2023
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In this study, machine learning models are employed to model NO2 and O3 concentrations. We employed a wide range of sources of data, including meteorological and column satellite measurements, to model NO2 and O3 concentrations. The spatial and temporal variability, and their drivers, were investigated. Notably, the machine learning model established the relationship between NOx and O3. Despite the fact that metropolitan regions are NO2 hotspots, rural areas have high O3 concentrations.
Kaiyue Zhou, Wen Xu, Lin Zhang, Mingrui Ma, Xuejun Liu, and Yu Zhao
Atmos. Chem. Phys., 23, 8531–8551, https://doi.org/10.5194/acp-23-8531-2023, https://doi.org/10.5194/acp-23-8531-2023, 2023
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We developed a dataset of the long-term (2005–2020) variabilities of China’s nitrogen and sulfur deposition, with multiple statistical models that combine available observations and chemistry transport modeling. We demonstrated the strong impact of human activities and national pollution control actions on the spatiotemporal changes in deposition and indicated a relatively small benefit of emission abatement on deposition (and thereby ecological risk) for China compared to Europe and the USA.
Jean-Maxime Bertrand, Frédérik Meleux, Anthony Ung, Gaël Descombes, and Augustin Colette
Atmos. Chem. Phys., 23, 5317–5333, https://doi.org/10.5194/acp-23-5317-2023, https://doi.org/10.5194/acp-23-5317-2023, 2023
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Post-processing methods based on machine learning algorithms were applied to refine the forecasts of four key pollutants at monitoring sites across Europe. Performances show significant improvements compared to those of the deterministic model raw outputs. Taking advantage of the large modelling domain extension, an innovative
globalapproach is proposed to drastically reduce the period necessary to train the models and thus facilitate the implementation in an operational context.
Cited articles
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AQEG, Monks, P., Carruthers, D., Carslaw, D., Dore, C., Harrison, R., Heal, M., Jenkin, M., Lewis, A., Stedman, J., Tomlin, A., and Williams, M.: Mitigation of United Kingdom PM2.5 Concentrations, 2013.
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Bergstra, J., Yamins, D., and Cox, D. D.: Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures. Presented at the Proceedings of the 30 th International Conference on Machine Learning, JMLR: W&CP, Atlanta, Georgia, USA, p. 9, 2013.
Betancourt, C., Stomberg, T. T., Edrich, A.-K., Patnala, A., Schultz, M. G., Roscher, R., Kowalski, J., and Stadtler, S.: Global, high-resolution mapping of tropospheric ozone – explainable machine learning and impact of uncertainties, Geosci. Model Dev., 15, 4331–4354, https://doi.org/10.5194/gmd-15-4331-2022, 2022.
Bravo, M. A., Anthopolos, R., Bell, M. L., and Miranda, M. L.: Racial isolation and exposure to airborne particulate matter and ozone in understudied US populations: Environmental justice applications of downscaled numerical model output, Environ. Int., 92, 247–255, https://doi.org/10.1016/j.envint.2016.04.008, 2016.
Carnell, E., Vieno, M., Vardoulakis, S., Beck, R., Heaviside, C., Tomlinson, S., Dragosits, U., Heal, M., and Reis, S.: Modelling public health improvements as a result of air pollution control policies in the UK over four decades – 1970 to 2010, Environ. Res. Lett., 14, 074001, https://doi.org/10.1088/1748-9326/ab1542, 2019.
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Cohen, A.J., Brauer, M., Burnett, R., Anderson, H.R., Frostad, J., Estep, K., Balakrishnan, K., Brunekreef, B., Dandona, L., Dandona, R., Feigin, V., Freedman, G., Hubbell, B., Jobling, A., Kan, H., Knibbs, L., Liu, Y., Martin, R., Morawska, L., Pope, C. A., Shin, H., Straif, K., Shaddick, G., Thomas, M., van Dingenen, R., van Donkelaar, A., Vos, T., Murray, C. J. L., and Forouzanfar, M. H.: Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015, The Lancet, 389, 1907–1918, https://doi.org/10.1016/S0140-6736(17)30505-6, 2017.
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Short summary
High-resolution spatial fields of surface ozone are used to understand spikes in ozone concentration and predict their impact on public health. Such fields are routinely output from complex mathematical models for atmospheric conditions. These outputs are on a coarse spatial resolution and the highest concentrations tend to be biased. Using a novel data-driven machine learning methodology, we show how such output can be corrected to produce fields with both lower bias and higher resolution.
High-resolution spatial fields of surface ozone are used to understand spikes in ozone...
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