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.
Cited articles
Alkuwari, F. A., Guillas, S., and Wang, Y.: Statistical downscaling of an air quality model using Fitted Empirical Orthogonal Functions, Atmos. Environ., 81, 1–10, https://doi.org/10.1016/j.atmosenv.2013.08.031, 2013.
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.
AQEG: Ozone in the UK – Recent Trends and Future Projections, 2021.
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.
Chen, T. and Guestrin, C.: XGBoost: A Scalable Tree Boosting System, in: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Presented at the KDD '16: The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, San Francisco California USA, 785–794, https://doi.org/10.1145/2939672.2939785, 2016.
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.
COMEAP: COMEAP: quantification of mortality and hospital admissions associated with ground-level ozone, https://www.gov.uk/government/publications/comeap-quantification-of-mortality-and-hospital-admissions-associated-with-ground-level-ozone (last access: 1 October 2023), 2015.
Cooper, O. R., Parrish, D. D., Ziemke, J., Balashov, N. V., Cupeiro, M., Galbally, I. E., Gilge, S., Horowitz, L., Jensen, N. R., Lamarque, J.-F., Naik, V., Oltmans, S. J., Schwab, J., Shindell, D. T., Thompson, A. M., Thouret, V., Wang, Y., and Zbinden, R. M.: Global distribution and trends of tropospheric ozone: An observation-based review, Elem. Sci. Anthr., 2, 000029, https://doi.org/10.12952/journal.elementa.000029, 2014.
Defra: Air pollution in the UK 2021 – Compliance Assessment Summary, 2022.
Defra: Emissions of air pollutants in the UK – Nitrogen oxides (NOx), https://www.gov.uk/government/statistics/emissions-of-air-pollutants/emissions-of-air-pollutants-in-the-uk-nitrogen-oxides-NOx (last access: 1 August 2023), 2021a.
Defra: Ozone in the UK Recent Trends and Future Projections, 2021b.
Diaz, F. M. R., Khan, M. A. H., Shallcross, B. M. A., Shallcross, E. D. G., Vogt, U., and Shallcross, D. E.: Ozone Trends in the United Kingdom over the Last 30 Years, Atmosphere, 11, 534, https://doi.org/10.3390/atmos11050534, 2020.
Doherty, R. M., Heal, M. R., Wilkinson, P., Pattenden, S., Vieno, M., Armstrong, B., Atkinson, R., Chalabi, Z., Kovats, S., Milojevic, A., Stevenson, D. S.: Current and future climate- and air pollution-mediated impacts on human health, Environ. Health, 8, p. 8, https://doi.org/10.1186/1476-069X-8-S1-S8, 2009.
EIDC (Environmental Information Data Centre): Air pollution removed by vegetation in the UK, 2015, https://www.data.gov.uk/dataset/90afc84e-3354-4257-9aa8-ea106c557681/air-pollution-removed-by-vegetation-in-the-uk-2015 (last access: 1 August 2023), 2021.
Entwistle, J., Weston, K., Singles, R., and Burgess, R.: The magnitude and extent of elevated ozone concentrations around the coasts of the British Isles, Atmos. Environ., 31, https://doi.org/10.1016/S1352-2310(97)00022-8, 1997.
Finch, D. P. and Palmer, P. I.: Increasing ambient surface ozone levels over the UK accompanied by fewer extreme events, Atmos. Environ., 237, 117627, https://doi.org/10.1016/j.atmosenv.2020.117627, 2020.
Friedman, J. H.: Greedy Function Approximation: A Gradient Boosting Machine, Ann. Stat., 29, 1189–1232, 2001.
Gao, Y., Fu, J. S., Drake, J. B., Lamarque, J.-F., and Liu, Y.: The impact of emission and climate change on ozone in the United States under representative concentration pathways (RCPs), Atmos. Chem. Phys., 13, 9607–9621, https://doi.org/10.5194/acp-13-9607-2013, 2013.
Gauthier-Manuel, H., Mauny, F., Boilleaut, M., Ristori, M., Pujol, S., Vasbien, F., Parmentier, A.-L., and Bernard, N.: Improvement of downscaled ozone concentrations from the transnational scale to the kilometric scale: Need, interest and new insights, Environ. Res., 210, 112947, https://doi.org/10.1016/j.envres.2022.112947, 2022.
Gouldsbrough, L., Hossaini, R., Eastoe, E., and Young, P. J.: A temperature dependent extreme value analysis of UK surface ozone, 1980–2019, Atmos. Environ., 273, 118975, https://doi.org/10.1016/j.atmosenv.2022.118975, 2022.
Granier, C. and Brasseur, G. P.: The impact of road traffic on global tropospheric ozone, Geophys. Res. Lett., 30, p. 36, https://doi.org/10.1029/2002GL015972, 2003.
Grell, G. A., Peckham, S. E., Schmitz, R., McKeen, S. A., Frost, G., Skamarock, W. C., and Eder, B.: Fully coupled “online” chemistry within the WRF model, Atmos. Environ., 39, 6957–6975, https://doi.org/10.1016/j.atmosenv.2005.04.027, 2005.
Guillas, S., Bao, J., Choi, Y., and Wang, Y.: Statistical correction and downscaling of chemical transport model ozone forecasts over Atlanta, Atmos. Environ., 42, 1338–1348, https://doi.org/10.1016/j.atmosenv.2007.10.027, 2008.
Hooyberghs, J., Mensink, C., Dumont, G., and Fierens, F.: Spatial interpolation of ambient ozone concentrations from sparse monitoring points in Belgium, J. Environ. Monit., 8, 1129–1135, https://doi.org/10.1039/B612607N, 2006.
Hu, X., Zhang, J., Xue, W., Zhou, L., Che, Y.,and Han, T.: Estimation of the Near-Surface Ozone Concentration with Full Spatiotemporal Coverage across the Beijing-Tianjin-Hebei Region Based on Extreme Gradient Boosting Combined with a WRF-Chem Model, Atmosphere, 13, 632, https://doi.org/10.3390/atmos13040632, 2022.
Ji, M., Cohan, D. S., and Bell, M. L.: Meta-analysis of the association between short-term exposure to ambient ozone and respiratory hospital admissions, Environ. Res. Lett., 6, 024006, https://doi.org/10.1088/1748-9326/6/2/024006, 2011.
Kendon, M., McCarthy, M., Jevrejeva, S., Matthews, A., and Legg, T.: State of the UK climate 2018, Int. J. Climatol., 39, 1–55, https://doi.org/10.1002/joc.6213, 2019.
Lang, P.: New approaches to the statistical analysis of air quality network data: insights from application to national and regional UK networks (phd), University of York, 2020.
Lauwaet, D., Viaene, P., Brisson, E., van Noije, T., Strunk, A., Van Looy, S., Maiheu, B., Veldeman, N., Blyth, L., De Ridder, K., and Janssen, S.: Impact of nesting resolution jump on dynamical downscaling ozone concentrations over Belgium, Atmos. Environ., 67, 46–52, https://doi.org/10.1016/j.atmosenv.2012.10.034, 2013.
Lin, C., Heal, M. R., Vieno, M., MacKenzie, I. A., Armstrong, B. G., Butland, B. K., Milojevic, A., Chalabi, Z., Atkinson, R. W., Stevenson, D. S., Doherty, R. M., and Wilkinson, P.: Spatiotemporal evaluation of EMEP4UK-WRF v4.3 atmospheric chemistry transport simulations of health-related metrics for NO2, O3, PM10, and PM2.5 for 2001–2010, Geosci. Model Dev., 10, 1767–1787, https://doi.org/10.5194/gmd-10-1767-2017, 2017.
Liška, T.: Effect of workplace mobility on air pollution exposure and its inequality in the UK (PhD), University of Edinburgh, https://doi.org/10.7488/era/1654, 2021.
Liu, R., Ma, Z., Liu, Y., Shao, Y., Zhao, W., and Bi, J.: Spatiotemporal distributions of surface ozone levels in China from 2005 to 2017: A machine learning approach, Environ. Int., 142, 105823, https://doi.org/10.1016/j.envint.2020.105823, 2020.
Liu, Z., Doherty, R. M., Wild, O., O'Connor, F. M., and Turnock, S. T.: Correcting ozone biases in a global chemistry–climate model: implications for future ozone, Atmos. Chem. Phys., 22, 12543–12557, https://doi.org/10.5194/acp-22-12543-2022, 2022.
Lundberg, S. and Lee, S.-I.: A Unified Approach to Interpreting Model Predictions, ArXiv170507874, Cs Stat, 2017.
McCarthy, M., Armstrong, L., and Armstrong, N.: A new heatwave definition for the UK, Weather, 74, 11, https://doi.org/10.1002/wea.3629, 2019.
Milojevic, A., Niedzwiedz, C. L., Pearce, J., Milner, J., MacKenzie, I. A., Doherty, R. M., and Wilkinson, P.: Socioeconomic and urban-rural differentials in exposure to air pollution and mortality burden in England, Environ. Health, 16, 104, https://doi.org/10.1186/s12940-017-0314-5, 2017.
National Centers for Environmental Prediction: NCEP FNL Operational Model Global Tropospheric Analyses, continuing from July 1999, https://doi.org/10.5065/D6M043C6, 2000.
Nolte, C. G., Spero, T. L., Bowden, J. H., Sarofim, M. C., Martinich, J., and Mallard, M. S.: Regional temperature-ozone relationships across the U.S. under multiple climate and emissions scenarios, J. Air Waste Manag. Assoc., 71, 1251–1264, https://doi.org/10.1080/10962247.2021.1970048, 2021.
Nuvolone, D., Petri, D., and Voller, F.: The effects of ozone on human health, Environ. Sci. Pollut. Res., 25, 8074–8088, https://doi.org/10.1007/s11356-017-9239-3, 2018.
Office for National Statistics: NUTS Level 1 (January 2018) Names and Codes in the United Kingdom, https://www.data.gov.uk/dataset/00d0c48d-afab-405e-98eb-5083aaae5fe8/nuts-level-1-january-2018-names-and-codes-in-the-united-kingdom (last access: 1 June 2023), 2018.
Otero, N., Sillmann, J., Schnell, J. L., Rust, H. W., and Butler, T.: Synoptic and meteorological drivers of extreme ozone concentrations over Europe, Environ. Res. Lett., 11, 024005, https://doi.org/10.1088/1748-9326/11/2/024005, 2016.
Pope, R. J., Butt, E. W., Chipperfield, M. P., Doherty, R. M., Fenech, S., Schmidt, A., Arnold, S. R., and Savage, N. H.: The impact of synoptic weather on UK surface ozone and implications for premature mortality, Environ. Res. Lett., 11, 124004, https://doi.org/10.1088/1748-9326/11/12/124004, 2016.
Porter, W. C. and Heald, C. L.: The mechanisms and meteorological drivers of the summertime ozone–temperature relationship, Atmos. Chem. Phys., 19, 13367–13381, https://doi.org/10.5194/acp-19-13367-2019, 2019.
Mann, R. C.: A high resolution atmospheric emissions inventory for road vehicle sources and the factors influencing its accuracy and sensitivity, Trans. Ecol. Environ., 15, ISSN 1743-3541, 1997.
Reich, B. J., Chang, H. H., and Foley, K. M.: A spectral method for spatial downscaling, Biometrics, 70, 932–942, https://doi.org/10.1111/biom.12196, 2014.
Ren, X., Mi, Z., Cai, T., Nolte, C. G., and Georgopoulos, P. G.: Flexible Bayesian Ensemble Machine Learning Framework for Predicting Local Ozone Concentrations, Environ. Sci. Technol., 56, 3871–3883, https://doi.org/10.1021/acs.est.1c04076, 2022.
Romer, P. S., Duffey, K. C., Wooldridge, P. J., Edgerton, E., Baumann, K., Feiner, P. A., Miller, D. O., Brune, W. H., Koss, A. R., de Gouw, J. A., Misztal, P. K., Goldstein, A. H., and Cohen, R. C.: Effects of temperature-dependent NOx emissions on continental ozone production, Atmos. Chem. Phys., 18, 2601–2614, https://doi.org/10.5194/acp-18-2601-2018, 2018.
Simpson, D., Benedictow, A., Berge, H., Bergström, R., Emberson, L. D., Fagerli, H., Flechard, C. R., Hayman, G. D., Gauss, M., Jonson, J. E., Jenkin, M. E., Nyíri, A., Richter, C., Semeena, V. S., Tsyro, S., Tuovinen, J.-P., Valdebenito, Á., and Wind, P.: The EMEP MSC-W chemical transport model – technical description, Atmos. Chem. Phys., 12, 7825–7865, https://doi.org/10.5194/acp-12-7825-2012, 2012.
Skamarock, C., Klemp, B., Dudhia, J., Gill, O., Barker, D., Duda, G., Huang, X., Wang, W., and Powers, G.: A Description of the Advanced Research WRF Version 3, https://doi.org/10.5065/D68S4MVH, 2008.
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Liu, Z., Berner, J., Wang, W., Powers, J. G., Duda, M. G., Barker, D. M., and Huang, X.-Y.: A Description of the Advanced Research WRF Model Version 4, 2021.
SRUC, 2017: Climate change: evidence review in Agriculture, Forestry, Land Use, Waste, 2019.
Sun, H., Shin, Y. M., Xia, M., Ke, S., Wan, M., Yuan, L., Guo, Y., and Archibald, A. T.: Spatial Resolved Surface Ozone with Urban and Rural Differentiation during 1990–2019: A Space–Time Bayesian Neural Network Downscaler, Environ. Sci. Technol., 56, 7337–7349, https://doi.org/10.1021/acs.est.1c04797, 2022.
Sun, J., Fu, J. S., Huang, K., and Gao, Y.: Estimation of future PM2.5- and ozone-related mortality over the continental United States in a changing climate: An application of high-resolution dynamical downscaling technique, J. Air Waste Manag. Assoc., 65, 611–623, https://doi.org/10.1080/10962247.2015.1033068, 2015.
Sun, W., Hess, P., and Liu, C.: The impact of meteorological persistence on the distribution and extremes of ozone, Geophys. Res. Lett., 44, 1545–1553, https://doi.org/10.1002/2016GL071731, 2017.
Torgo, L., Branco, P., Ribeiro, R. P., and Pfahringer, B.: Resampling strategies for regression, Expert Syst., 32, 465–476, https://doi.org/10.1111/exsy.12081, 2015.
Trail, M., Tsimpidi, A. P., Liu, P., Tsigaridis, K., Hu, Y., Nenes, A., and Russell, A. G.: Downscaling a global climate model to simulate climate change over the US and the implication on regional and urban air quality, Geosci. Model Dev., 6, 1429–1445, https://doi.org/10.5194/gmd-6-1429-2013, 2013.
Vardoulakis, S. and Heaviside, C.: Health Effects of Climate Change in the UK, 242, 2012.
Vieno, M., Dore, A. J., Stevenson, D. S., Doherty, R., Heal, M. R., Reis, S., Hallsworth, S., Tarrason, L., Wind, P., Fowler, D., Simpson, D., and Sutton, M. A.: Modelling surface ozone during the 2003 heat-wave in the UK, Atmos. Chem. Phys., 10, 7963–7978, https://doi.org/10.5194/acp-10-7963-2010, 2010.
Vieno, M., Heal, M. R., Williams, M. L., Carnell, E. J., Nemitz, E., Stedman, J. R., and Reis, S.: The sensitivities of emissions reductions for the mitigation of UK PM2.5, Atmos. Chem. Phys., 16, 265–276, https://doi.org/10.5194/acp-16-265-2016, 2016.
Watson, G. L., Telesca, D., Reid, C. E., Pfister, G. G., and Jerrett, M.: Machine learning models accurately predict ozone exposure during wildfire events, Environ. Pollut., 254, 112792, https://doi.org/10.1016/j.envpol.2019.06.088, 2019.
Wong, D. W., Yuan, L., and Perlin, S. A.: Comparison of spatial interpolation methods for the estimation of air quality data, J. Expo. Sci. Environ. Epidemiol., 14, 404–415, https://doi.org/10.1038/sj.jea.7500338, 2004.
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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