[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128470-en":3,"doc-seo-128470-105":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},128470,13056712833777,"Logic","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Assessing Uncertainty and Heterogeneity in Machine Learning-Based Spatiotemporal Ozone Prediction in the Beijing-Tianjin-Hebei Region in China","Accurate prediction of spatiotemporal ozone concentration supports early warning systems and air pollution regulation. The work addresses a gap in evaluating uncertainty and heterogeneity in machine learning spatiotemporal ozone prediction. Hourly and daily predictive performance is analyzed across the Beijing-Tianjin-Hebei region from 2013–2018 using ConvLSTM and DCGAN. Results indicate improved performance under multiple meteorological conditions, and ConvLSTM feasibly reproduces high-ozone distributions and captures spatiotemporal variation at 15 km × 15 km. Comparison with NAQPMS and monitoring observations further validates practical applicability.","Science of the Total Environment 881 (2023) 163146  \nContents lists available at ScienceDirect  \nScience of the Total Environment  \njournal [homepage:](homepage: www. elsevier. com/locate/scitoten v)[ www. elsevier. com/locate/scitoten v](homepage: www. elsevier. com/locate/scitoten v)  \nAssessing uncertainty and heterogeneity in machine learning-based spatiotemporal ozone prediction in Beijing-Tianjin-Hebei region in China  \nMeiling Cheng a, Fangxin Fang a,⁎, Ionel Michael Navon b, Jie Zheng c, Jiang Zhu d, Christopher Pain a  \na Applied Modelling and Computation Group, Department of Earth Science and Engineering, Imperial College London, SW7 2BP, UK b Department of Scientific Computing, Florida State University, Tallahassee, FL 32306-4120, USA  \nc Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China d International Center for Climate and Environment Sciences, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China  \nH I G H L I G H T S  \nKeywords:  \nOzone concentration prediction Machine learning  \nUncertainty  \nSpatiotemporal  \nGenerative adversarial network  \nG R A P H I C A L A B S T R A C T  \n\n| \u003Cbr> |\n| --- |\n| A B S T R A C T |\n\nAccurate prediction of spatiotemporal ozone concentration is of great significance to effectively establish advanced early warning systems and regulate air pollution control. However, the comprehensive assessment of uncertainty and heterogeneity in spatiotemporal ozone prediction remains unknown. Here, we systematically analyze the hourly and daily spatiotemporal predictive performances using convolutional long short term memory (ConvLSTM) and deep convolutional generative adversarial network (DCGAN) models over the Beijing-Tianjin-Hebei region in China from 2013 to 2018. In extensive scenarios, our results show that the machine learning-based (ML-based) models achieve better spatiotemporal ozone concentration prediction performance with multiple meteorological conditions. A further comparison to the air pollution model-Nested Air Quality Prediction Modelling System (NAQPMS) and monitoring observations, the ConvLSTM model demonstrates the practical feasibility of identifying high ozone concentration distribution and capturing spatiotemporal ozone variation patterns at a high spatial resolution (here 15 km × 15 km).  \n1. Introduction  \nAir pollution has detrimental effects on human health, ecological environment, and climate change (Kuerban et al., 2020). Ground-level ozone as a major secondary pollutant in China has been the subject of intense studies in recent years (Zhao et al., 2021; Maji et al., 2020; Ou et al.,  \n⁎ Corresponding author.  \nE-mail address: [f.fang@imperial.ac.uk](f.fang@imperial.ac.uk) (F. Fang).  \n2020; Wang et al., 2017b). Ozone is generated through chemical reactions of nitrogen oxides and volatile organic compounds in the presence of favorable meteorological conditions, e.g., intense solar radiation, low humidity, high temperature, and low winds (Wang et al., 2017a). Ozone concentration exhibits certain spatiotemporal changes and involves nonlinear, strong coupling and multivariate problems (Ezimand and Kakroodi, 2019; Su et al., 2020). Traditional air quality models (Han et al., 2018; Grell et al., 2005), which have the potential capability of representing the pollutant transformation, diffusion, and migration mechanisms through  \n[http://dx.doi.org/10.1016/j.scitotenv.2023.163146](http://dx.doi.org/10.1016/j.scitotenv.2023.163146)  \nReceived 17 December 2022; Received in revised form 3 March 2023; Accepted 25 March 2023  \nAvailable online 1 April 2023  \n0048-9697/© 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license ([http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/)).  \nthe governing equations, have been widely used in regional air quality prediction (Yafouz et al., 20","cbCainrQ8fxJX6w1","https://ap.wps.com/l/cbCainrQ8fxJX6w1","pdf",6784382,1,13,"English","en",105,"# Introduction\n## Background on ozone formation and air pollution impacts\n## Limitations of physical-informed air quality models\n## Emergence of machine learning for ozone prediction\n# Methods and experimental setup\n## ML models for spatiotemporal ozone prediction\n## Study region and time period\n## Meteorological conditions and performance scenarios\n# Results and evaluation\n## Hourly and daily predictive performance\n## Uncertainty and heterogeneity assessment\n## Comparison with NAQPMS and monitoring observations\n# Conclusion\n## Practical feasibility and spatial resolution considerations","[{\"question\":\"What challenge does the study focus on regarding ML-based ozone prediction?\",\"answer\":\"It focuses on systematically assessing uncertainty and heterogeneity in spatiotemporal ozone prediction, which had not been comprehensively evaluated before.\"},{\"question\":\"Which models are used for spatiotemporal ozone concentration prediction?\",\"answer\":\"The study uses convolutional long short-term memory (ConvLSTM) and a deep convolutional generative adversarial network (DCGAN) across the Beijing-Tianjin-Hebei region.\"},{\"question\":\"How does the ConvLSTM model perform compared with other references?\",\"answer\":\"ConvLSTM shows practical feasibility for identifying high ozone concentration patterns and capturing spatiotemporal variation at high spatial resolution (15 km × 15 km), and it is compared against NAQPMS and monitoring observations.\"}]","Assessing Uncertainty and Heterogeneity in Machine Learning-Based Spatiotemporal Ozone Prediction in the Beijing-Tianjin-Hebei Region in China | 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challenge does the study focus on regarding ML-based ozone prediction?","Question",{"text":76,"@type":77},"It focuses on systematically assessing uncertainty and heterogeneity in spatiotemporal ozone prediction, which had not been comprehensively evaluated before.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which models are used for spatiotemporal ozone concentration prediction?",{"text":81,"@type":77},"The study uses convolutional long short-term memory (ConvLSTM) and a deep convolutional generative adversarial network (DCGAN) across the Beijing-Tianjin-Hebei region.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the ConvLSTM model perform compared with other references?",{"text":85,"@type":77},"ConvLSTM shows practical feasibility for identifying high ozone concentration patterns and capturing spatiotemporal variation at high spatial resolution (15 km × 15 km), and it is compared against NAQPMS and monitoring 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