A machine learning approach to downscale EMEP4UK: analysis of UK ozone variability and trends
<p>High-resolution modelling of surface ozone is an essential step in the quantification of the impacts on health and ecosystems from historic and future concentrations. It also provides a principled way in which to extend analysis beyond measurement locations. Often, such modelling uses relat...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2024-03-01
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| Series: | Atmospheric Chemistry and Physics |
| Online Access: | https://acp.copernicus.org/articles/24/3163/2024/acp-24-3163-2024.pdf |
| _version_ | 1797263254618112000 |
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| author | L. Gouldsbrough L. Gouldsbrough R. Hossaini R. Hossaini E. Eastoe P. J. Young P. J. Young M. Vieno |
| author_facet | L. Gouldsbrough L. Gouldsbrough R. Hossaini R. Hossaini E. Eastoe P. J. Young P. J. Young M. Vieno |
| author_sort | L. Gouldsbrough |
| collection | DOAJ |
| description | <p>High-resolution modelling of surface ozone is an essential step in the quantification of the impacts on health and ecosystems from historic and future concentrations. It also provides a principled way in which to extend analysis beyond measurement locations. Often, such modelling uses relatively coarse-resolution chemistry transport models (CTMs), which exhibit biases when compared to measurements. EMEP4UK is a CTM that is used extensively to inform UK air quality policy, including the effects on ozone from mitigation of its precursors. Our evaluation of EMEP4UK for the years 2001–2018 finds a high bias in reproducing daily maximum 8 h average ozone (MDA8), due in part to the coarse spatial resolution. We present a machine learning downscaling methodology to downscale EMEP4UK ozone output from a <span class="inline-formula">5×5</span> km to <span class="inline-formula">1×1</span> km resolution using a gradient-boosted tree. By addressing the high bias present in EMEP4UK, the downscaled surface better represents the measured data, with a 128 % improvement in <span class="inline-formula"><i>R</i><sup>2</sup></span> and 37 % reduction in RMSE. Our analysis of the downscaled surface shows a decreasing trend in annual and March–August mean MDA8 ozone for all regions of the UK between 2001–2018, differing from increasing measurement trends in some regions. We find the proportion of the UK which fails the government objective to have at most 10 exceedances of 100 <span class="inline-formula">µ</span>g m<span class="inline-formula"><sup>−3</sup></span> per annum is 27 % (2014–2018 average), compared to 99 % from the unadjusted EMEP4UK model. A statistically significant trend in this proportion of <span class="inline-formula">−2.19</span> % yr<span class="inline-formula"><sup>−1</sup></span> is found from the downscaled surface only, highlighting the importance of bias correction in the assessment of policy metrics. Finally, we use the downscaling approach to examine the sensitivity of UK surface ozone to reductions in UK terrestrial NO<span class="inline-formula"><sub><i>x</i></sub></span> (i.e. NO <span class="inline-formula">+</span> NO<span class="inline-formula"><sub>2</sub>)</span> emissions on a <span class="inline-formula">1×1</span> km surface. Moderate NO<span class="inline-formula"><sub><i>x</i></sub></span> emission reductions with respect to present day (20 % or 40 %) increase both average and high-level ozone concentrations in large portions of the UK, whereas larger NO<span class="inline-formula"><sub><i>x</i></sub></span> reductions (80 %) cause a similarly widespread decrease in high-level ozone. In all three scenarios, very urban areas (i.e. major cities) are the most affected by increasing concentrations of ozone, emphasizing the broader air quality challenges of NO<span class="inline-formula"><sub><i>x</i></sub></span> control.</p> |
| first_indexed | 2024-04-25T00:10:05Z |
| format | Article |
| id | doaj.art-5ac1c626a99c46a4b4fba935a4bb9280 |
| institution | Directory Open Access Journal |
| issn | 1680-7316 1680-7324 |
| language | English |
| last_indexed | 2024-04-25T00:10:05Z |
| publishDate | 2024-03-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Atmospheric Chemistry and Physics |
| spelling | doaj.art-5ac1c626a99c46a4b4fba935a4bb92802024-03-13T15:12:12ZengCopernicus PublicationsAtmospheric Chemistry and Physics1680-73161680-73242024-03-01243163319610.5194/acp-24-3163-2024A machine learning approach to downscale EMEP4UK: analysis of UK ozone variability and trendsL. Gouldsbrough0L. Gouldsbrough1R. Hossaini2R. Hossaini3E. Eastoe4P. J. Young5P. J. Young6M. Vieno7Lancaster Environment Centre, Lancaster University, Lancaster, LA1 4YQ, UKnow at: UK Centre for Ecology & Hydrology, Lancaster Environment Centre, Lancaster, LA1 4AP, UKLancaster Environment Centre, Lancaster University, Lancaster, LA1 4YQ, UKCentre of Excellence in Environmental Data Science (CEEDS), Lancaster University, Lancaster, LA1 4YQ, UKMathematics and Statistics, Lancaster University, Lancaster, LA1 4YR, UKLancaster Environment Centre, Lancaster University, Lancaster, LA1 4YQ, UKJBA Risk Management Limited, Broughton Park, Skipton, BD23 3FD, UKUK Centre for Ecology & Hydrology, Bush Estate, Penicuik, Midlothian, EH26 0QB, UK<p>High-resolution modelling of surface ozone is an essential step in the quantification of the impacts on health and ecosystems from historic and future concentrations. It also provides a principled way in which to extend analysis beyond measurement locations. Often, such modelling uses relatively coarse-resolution chemistry transport models (CTMs), which exhibit biases when compared to measurements. EMEP4UK is a CTM that is used extensively to inform UK air quality policy, including the effects on ozone from mitigation of its precursors. Our evaluation of EMEP4UK for the years 2001–2018 finds a high bias in reproducing daily maximum 8 h average ozone (MDA8), due in part to the coarse spatial resolution. We present a machine learning downscaling methodology to downscale EMEP4UK ozone output from a <span class="inline-formula">5×5</span> km to <span class="inline-formula">1×1</span> km resolution using a gradient-boosted tree. By addressing the high bias present in EMEP4UK, the downscaled surface better represents the measured data, with a 128 % improvement in <span class="inline-formula"><i>R</i><sup>2</sup></span> and 37 % reduction in RMSE. Our analysis of the downscaled surface shows a decreasing trend in annual and March–August mean MDA8 ozone for all regions of the UK between 2001–2018, differing from increasing measurement trends in some regions. We find the proportion of the UK which fails the government objective to have at most 10 exceedances of 100 <span class="inline-formula">µ</span>g m<span class="inline-formula"><sup>−3</sup></span> per annum is 27 % (2014–2018 average), compared to 99 % from the unadjusted EMEP4UK model. A statistically significant trend in this proportion of <span class="inline-formula">−2.19</span> % yr<span class="inline-formula"><sup>−1</sup></span> is found from the downscaled surface only, highlighting the importance of bias correction in the assessment of policy metrics. Finally, we use the downscaling approach to examine the sensitivity of UK surface ozone to reductions in UK terrestrial NO<span class="inline-formula"><sub><i>x</i></sub></span> (i.e. NO <span class="inline-formula">+</span> NO<span class="inline-formula"><sub>2</sub>)</span> emissions on a <span class="inline-formula">1×1</span> km surface. Moderate NO<span class="inline-formula"><sub><i>x</i></sub></span> emission reductions with respect to present day (20 % or 40 %) increase both average and high-level ozone concentrations in large portions of the UK, whereas larger NO<span class="inline-formula"><sub><i>x</i></sub></span> reductions (80 %) cause a similarly widespread decrease in high-level ozone. In all three scenarios, very urban areas (i.e. major cities) are the most affected by increasing concentrations of ozone, emphasizing the broader air quality challenges of NO<span class="inline-formula"><sub><i>x</i></sub></span> control.</p>https://acp.copernicus.org/articles/24/3163/2024/acp-24-3163-2024.pdf |
| spellingShingle | L. Gouldsbrough L. Gouldsbrough R. Hossaini R. Hossaini E. Eastoe P. J. Young P. J. Young M. Vieno A machine learning approach to downscale EMEP4UK: analysis of UK ozone variability and trends Atmospheric Chemistry and Physics |
| title | A machine learning approach to downscale EMEP4UK: analysis of UK ozone variability and trends |
| title_full | A machine learning approach to downscale EMEP4UK: analysis of UK ozone variability and trends |
| title_fullStr | A machine learning approach to downscale EMEP4UK: analysis of UK ozone variability and trends |
| title_full_unstemmed | A machine learning approach to downscale EMEP4UK: analysis of UK ozone variability and trends |
| title_short | A machine learning approach to downscale EMEP4UK: analysis of UK ozone variability and trends |
| title_sort | machine learning approach to downscale emep4uk analysis of uk ozone variability and trends |
| url | https://acp.copernicus.org/articles/24/3163/2024/acp-24-3163-2024.pdf |
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