[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119892-en":3,"doc-seo-119892-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},119892,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Advanced Air Quality Management with Machine Learning - PhD Dissertation","Air pollution poses a major health risk across regional and global scales. Conventional air quality management relies on models that produce exposure-related indices, yet their estimation outputs can carry substantial bias, reducing the realism of results and limiting the effectiveness of emission control strategies. This dissertation addresses two key obstacles: biased modeled air pollutant concentrations and inaccurate exposure risk estimations, using machine learning to connect model outputs with observed reality and improve decision-relevant inference.","University of Tennessee, Knoxville  \nTRACE: Tennessee Research and Creative Exchange  \n\n| Doctoral Dissertations | Graduate School |\n| --- | --- |\n| 5-2023\u003Cbr>Advanced Air Quality Management with Machine Learning\u003Cbr>Cheng-Pin Kuo\u003Cbr>University of Tennessee, Knoxville, [ckuo6@vols.utk.edu](ckuo6@vols.utk.edu)\u003Cbr>Follow this and additional works at: [https://trace.tennessee.edu/utk_graddiss](https://trace.tennessee.edu/utk_graddiss)\u003Cbr> Part of the Environmental Engineering Commons, and the Environmental Public Health Commons |  |\n\nRecommended Citation  \nKuo, Cheng-Pin, \"Advanced Air Quality Management with Machine Learning. \" PhD diss., University of Tennessee, 2023.  \n[https://trace.tennessee.edu/utk_graddiss/8169](https://trace.tennessee.edu/utk_graddiss/8169)  \nThis Dissertation is brought to you for free and open access by the Graduate School at TRACE: Tennessee Research and Creative Exchange. It has been accepted for inclusion in Doctoral Dissertations by an authorized administrator of TRACE: Tennessee Research and Creative Exchange. For more information, please contact [trace@utk.edu](trace@utk.edu).  \nTo the Graduate Council:  \nI am submitting herewith a dissertation written by Cheng-Pin Kuo entitled \"Advanced Air Quality Management with Machine Learning.\" I have examined the final electronic copy of this dissertation for form and content and recommend that it be accepted in partial fulfillment of the requirements for the degree of Doctor of Philosophy, with a major in Environmental Engineering.  \nJoshua S. Fu, Major Professor  \nWe have read this dissertation and recommend its acceptance: Chris Cox, Shuai Li, Russell Zaretzki  \nAccepted for the Council: Dixie L. Thompson  \nVice Provost and Dean of the Graduate School  \n(Original signatures are on file with official student records.)  \nAdvanced Air Quality Management with Machine Learning  \nA Dissertation Presented for the Doctor of Philosophy Degree  \nThe University of Tennessee, Knoxville  \nCheng-pin Kuo May 2023  \nCopyright © 2023 by Cheng-pin Kuo All rights reserved.  \nDedication  \nI dedicate this thesis to my beloved family, my partner, and my home country, Taiwan.  \nAcknowledgments  \nFirst, I would like to thank my advisor, Dr. Joshua Fu, for his help and support in my [Ph.D. degree](Ph.D. degree) since 2018. His outstanding teaching skills and patience guided me to develop professional skills and conduct scientific research independently. His outreach network also provided me with numerous opportunities to meet and work with talented researchers from universities , research communities, and governmental agencies. Second, I would like to thank the members of my committee, Dr. Chris Cox, Dr. Shuai Li, and Dr. Russell Zaretzki for their valuable comments and suggestions on my work. I would also like to thank laboratory members, Dr. Xinyi Dong, Dr. Cheng-en Yang, Dr. Jiani Tan, Matthew Jonathan Tipton, and Rong-You Chien for their selfless help during my research works. Last but not least , I gratefully thank my beloved family for their endless support and encouragement these years.  \nAbstract  \nAir pollution has been a significant health risk factor at a regional and global scale. Although the present method can provide assessment indices like exposure risks or air pollutant concentrations for air quality management, the modeling estimations still remain non-negligible bias which could deviate from reality and limit the effectiveness of emission control strategies to reduce air pollution and derive health benefits. The current development in air quality management is still impeded by two major obstacles: (1) biased air quality concentrations from air quality models and (2) inaccurate exposure risk estimations  \nInspired by more available and overwhelming data, machine learning techniques provide promising opportunities to solve the above-mentioned obstacles and bridge the gap between model results and reality. This dissertation illustrates three machine learning appli","cbCaimeKL3d2GixB","https://ap.wps.com/l/cbCaimeKL3d2GixB","pdf",7845947,1,222,"English","en",105,"# Chapter 1. Introduction\n## 1.1. Machine learning in atmospheric science\n## 1.2. Motivation of the dissertation\n## 1.3. Aim 1: Heterogeneous exposure risks and burden of the diseases (BD)\n## 1.4. Aim 2: Bias correction and quantification for numerical models\n## 1.5. Aim 3: Examining nonlinear pollutant responses to local emissions\n## 1.6. Study region","[{\"question\":\"What limitations in current air quality management motivate the dissertation?\",\"answer\":\"Model-based assessments can introduce non-negligible bias in pollutant concentrations and inaccurate exposure risk estimations, which may diverge from reality and weaken emission control effectiveness.\"},{\"question\":\"How does the dissertation use machine learning to improve air quality management?\",\"answer\":\"It presents three applications: characterizing heterogeneous exposure risk across urbanization levels, correcting modeled pollutant concentrations and quantifying source bias, and analyzing nonlinear pollutant responses to local emissions.\"},{\"question\":\"Why is Taiwan used as the case study?\",\"answer\":\"Taiwan provides well-established hospital data, an emission inventory, and an air quality monitoring network, enabling validation and assessment of the proposed methods.\"}]","Advanced Air Quality Management with Machine Learning - PhD 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