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| DOI | 10.5194/acp-24-3163-2024 |
| A machine learning approach to downscale EMEP4UK: analysis of UK ozone variability and trends | |
| 发表日期 | 2024 |
| ISSN | 1680-7316 |
| EISSN | 1680-7324 |
| 起始页码 | 24 |
| 结束页码 | 5 |
| 卷号 | 24期号:5 |
| 英文摘要 | 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 5 x 5 km to 1 x 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 R 2 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 mu g m - 3 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 % yr - 1 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 x (i.e. NO + NO 2 ) emissions on a 1 x 1 km surface. Moderate NO x 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 x 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 x control. |
| 语种 | 英语 |
| WOS研究方向 | Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences |
| WOS类目 | Environmental Sciences ; Meteorology & Atmospheric Sciences |
| WOS记录号 | WOS:001190665200001 |
| 来源期刊 | ATMOSPHERIC CHEMISTRY AND PHYSICS
 |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/305263 |
| 作者单位 | Lancaster University; Lancaster University; Lancaster University; UK Centre for Ecology & Hydrology (UKCEH); UK Centre for Ecology & Hydrology (UKCEH); Lancaster University |
| 推荐引用方式 GB/T 7714 | . A machine learning approach to downscale EMEP4UK: analysis of UK ozone variability and trends[J],2024,24(5). |
| APA | (2024).A machine learning approach to downscale EMEP4UK: analysis of UK ozone variability and trends.ATMOSPHERIC CHEMISTRY AND PHYSICS,24(5). |
| MLA | "A machine learning approach to downscale EMEP4UK: analysis of UK ozone variability and trends".ATMOSPHERIC CHEMISTRY AND PHYSICS 24.5(2024). |
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