[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118881-en":3,"doc-seo-118881-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118881,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Statistical and machine learning modelling of UK surface ozone - Thesis","Atmospheric observations and numerical modelling jointly support understanding environmental impacts of human activity, including attribution of degraded air quality and assessment of climate change effects. Process-based chemistry transport models remain widely used, yet data science advances enable faster statistical and machine learning approaches that can better exploit fine temporal and spatial resolutions. This thesis develops and applies advanced techniques to model UK surface ozone and its drivers. Temperature-dependent extreme value analysis quantifies probability, magnitude, and frequency of extreme events, while machine learning downscales and bias-corrects EMEP4UK, improving policy-relevant assessments and revealing robust ozone-temperature relationships.","Statistical and machine learning modelling of UK surface ozone  \nLily Gouldsbrough, BSc  \nLancaster Environment Centre Lancaster University  \nA thesis submitted for the degree of Doctor of Philosophy  \nOctober, 2023  \nDedicated to my beloved husband and best friend, Nic. I am deeply grateful foryour never-ending support, love and encouragement.  \nStatistical and machine learning modelling of UK surface ozone  \nLily Gouldsbrough, BSc.  \nLancaster Environment Centre, Lancaster University A thesis submitted for the degree of Doctor of Philosophy. October, 2023 .  \nAbstract  \nIn addition to atmospheric observations, numerical models are crucial to understand the impacts of human activities on the environment, from attributing poor air quality to assessing climate change impacts. While process-based models, such as chemistry transport models (CTMs), are widely used, recent data science advances enable greater use of statistical and machine learning methods as alternatives to describe and predict atmospheric composition. State-of-the-art data science methods can be faster to run than CTMs and used at high temporal and spatial resolutions due to codebase efficiencies.  \nThis thesis focuses on modelling UK surface ozone and its drivers (high levels of which are detrimental to human and plant health) through the development and novel application of sophisticated statistical and machine learning techniques. Motivated by possible adverse effect of climate change on ozone concentrations, a temperature-dependent Extreme Value Analysis is used to explore the probability, magnitude, and frequency of extreme ozone events over recent decades. For 2010–2019, it is found that the 1-year return level of daily maximum 8-h mean (MDA8) ozone exceeds the ‘moderate’ health threshold (100 µg/m3 ) at >90% of sites, but that the probability of extreme ozone events has markedly decreased since the 1980s.  \nA machine learning methodology to downscale and bias correct a CTM (EMEP4UK) ozone surface was developed and evaluated. Compared to the unadjusted CTM, the downscaled surface exhibits a lower bias in reproducing MDA8 ozone allowing more robust assessments of important policy metrics. Analysis of  \nthe downscaled product (2014–2018) reveals on average 27% of the UK fails the government long-term objective for MDA8 ozone to not exceed 100 µg/m3 more than 10 times per year, compared to 99% in the unadjusted CTM. A classification-based machine learning analysis into high-level ozone drivers was also performed and shows a robust relationship between ozone and temperature. The method is demonstrated to offer remarkable promise as a tool with which to forecast the presence of high-level ozone. Despite a UK focus, the data-driven methods developed and applied here are applicable to modelling ozone in other regions of the world where measurements exist  \nAcknowledgements  \nI would like to express my profound gratitude to my wonderful supervisors Emma Eastoe, Ryan Hossaini and Paul Young for their invaluable guidance and support during the course of my PhD. Their expertise, patience, and mentorship have played a pivotal role in the completion of this thesis.  \nI extend my thanks to Lancaster Environment Centre for their continuous support and for creating a welcoming research environment. Their commitment to a culture of academic excellence has played a significant role in my growth as a researcher.  \nFinally, I want to acknowledge the support and encouragement from my family and friends, and in particular, my parents. Your unwavering belief in my abilities, especially during moments of self-doubt, has led me to here. I love you both dearly.  \nDeclaration  \nI declare that the work presented in this thesis is, to the best of my knowledge and belief, original and my own work. The material has not been submitted, either in whole or in part, for a degree at this, or any other university. This thesis does not exceed the maximum permitted word length of 80,000 words in","cbCaitLnNJ6ofe3O","https://ap.wps.com/l/cbCaitLnNJ6ofe3O","pdf",11202180,1,220,"English","en",105,"# Introduction\n## Surface level ozone\n## Challenges of modelling surface level ozone\n## Data science methods to model surface level ozone\n## Thesis contributions\n# A temperature dependent extreme value analysis of UK surface ozone, 1980–2019\n## Introduction\n## UK surface ozone and temperature data\n## Extreme Value Analysis model\n## Results and discussion","[{\"question\":\"Why are statistical and machine learning methods used instead of only chemistry transport models?\",\"answer\":\"Data science methods can run faster than chemistry transport models and support higher temporal and spatial resolutions, enabling more efficient description and prediction of atmospheric composition.\"},{\"question\":\"What does the temperature-dependent extreme value analysis contribute in this thesis?\",\"answer\":\"It estimates the probability, magnitude, and frequency of extreme ozone events over recent decades and shows changes in extreme-event likelihood over time.\"},{\"question\":\"How does the machine learning approach improve modelling of UK surface ozone?\",\"answer\":\"A downscaling and bias-correction method is developed for the EMEP4UK CTM ozone surface, reducing bias and leading to more robust assessments of policy-relevant metrics.\"}]","Statistical and machine learning modelling of UK surface ozone - 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