16 May 2023
 | 16 May 2023

A machine learning approach to downscale EMEP4UK: analysis of UK ozone variability and trends

Lily Gouldsbrough, Ryan Hossaini, Emma Eastoe, Paul J. Young, and Massimo Vieno

Abstract. 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-hr 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 5 × 5 km to 1 × 1 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 R2 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 µg/m3 per annum is 27 % (2014–2018 average), compared to 99 % from the unadjusted EMEP4UK model. A statistically significant trend in this proportion of −2.19 %/year 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 NOx (i.e., NO + NO2) emissions on a 1 × 1 km surface. Moderate NOx 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 NOx reductions (80 %) cause a similarly wide-spread 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, emphasising the broader air quality challenges of NOx control.

Lily Gouldsbrough et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-632', Anonymous Referee #1, 31 May 2023
  • RC2: 'Comment on egusphere-2023-632', Anonymous Referee #2, 25 Jun 2023
  • AC1: 'Response to reviewer comments', Lily Gouldsbrough, 21 Sep 2023

Lily Gouldsbrough et al.

Lily Gouldsbrough et al.


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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.