[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125914-en":3,"doc-seo-125914-105":31,"detail-sidebar-cat-0-en-105":93},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},125914,2336474466712,"Maeve","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Low-cost Optical Detection of Pollen Bioaerosols with Machine Learning for Human Health","Pollen and associated subpollen particles drive up to 40% of allergy cases, and their impacts are intensifying with changing lifestyles, environments, and climate. Pollen also influences cloud processes, altering cloud albedo, lifetime, and precipitation. Yet characterization of airborne pollen and bioaerosols remains constrained, limiting risk assessment for public health. Conventional monitoring relies largely on manual samplers with major limits in labour, time, and cost, while automated approaches remain too expensive for dense high-resolution networks. This thesis develops and evaluates low-cost monitoring using optical particle counters and machine learning, combined with physical-property studies using an acoustic levitator, macroscope, and computer vision.","Low-cost Optical Detection of Pollen Bioaerosols with Machine Learning for  \nHuman Health  \nBy  \nSophie A. Mills  \nA thesis submitted to the University of Birmingham for the degree of  \nDOCTOR OF PHILOSOPHY  \nSchool of Geography, Earth and Environmental Sciences College of Life and Environmental Sciences  \nUniversity of Birmingham July 2023  \nUniversity of Birmingham Research Archive  \ne-theses repository  \nThis unpublished thesis/dissertation is copyright of the author and/or third parties. The intellectual property rights of the author or third parties in respect of this work are as defined by The Copyright Designs and Patents Act 1988 or as modified by any successor legislation.  \nAny use made of information contained in this thesis/dissertation must be in accordance with that legislation and must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the permission of the copyright holder.  \n“道行之而成。 ”  \n[A path is made by walking on it.]  \n- Zhuangzi (Qiwulun 11)  \n“知，不知，上矣； 不知， 知，病也。”  \n[To know yet to think that one does not know is best; Not to know yet to think that one knows leads to difficulty.]  \n- Laozi (Dao De Jing, Chapter 71)  \nAbstract  \nPollen and associated subpollen particles are responsible for up to 40% of populations suffering from allergies and the situation in many countries is becoming more severe with changing lifestyles, environment, and climate. Meanwhile, pollen can interact with cloud processes, affecting cloud albedo, lifetime, and precipitation patterns. However, our means to characterise airborne pollen, and bioaerosols in general, are severely limited, making it difficult to answer many important questions and assess risk to public health. Conventional pollen monitoring instruments are generally manual samplers with crucial limitations of labour, time, and cost. There are recent advancements towards developing automated pollen monitoring instruments, however, these are expensive and not economically viable to populate large monitoring networks with high spatial resolution.  \nThe objective of this work is to address the limitations of conventional methods by investigating alternative methods that can provide useful information on pollen bioaerosols for fundamental understanding and public health. This thesis presents and evaluates novel, low-cost methods for monitoring airborne pollen, using optical particle counters (OPCs) and machine learning, and investigating physical properties of pollen under varying atmospheric conditions, using an acoustic levitator, macroscope and computer vision techniques. The superior ability of supervised machine learning models to distinguish pollen trends from OPC data is demonstrated, as well as their potential to provide useful, high spatiotemporal resolution data in unique locations and for public health. This work provides comprehensive detail on how to train, interpret and employ such models for purpose, including scrutinising how the models learn to distinguish between different pollen types. Collectively, these studies demonstrate the potential for these low-lost techniques to provide novel information on pollen bioaerosol characteristics that was previously inaccessible. This novel information could be vital for our comprehension of the bioaerosol component of atmospheric aerosols, climate models, pollen forecasts, and public health advice and warnings.  \nAcknowledgements  \nFirstly, I would like to sincerely thank my supervisors, Francis Pope and Rob MacKenzie, for the supervision and support they have given me these last few years. It has been along journey through my PhD, not to mention COVID as well, and I am sincerely grateful for their supportive and encouraging presence during this time. It has provided a healthy balance to my own self-doubts and I’m sure they will have also observed a great deal of positive change in me through the course of my PhD. Their patience and openness have allowed me to follow my","cbCainm2jbj1nXSn","https://ap.wps.com/l/cbCainm2jbj1nXSn","pdf",11089684,6,1,296,"English","en",105,"# Abstract\n# Acknowledgements\n## Supervisors and support\n## Funding and travel support\n## Colleagues and community","[{\"question\":\"Why is monitoring pollen bioaerosols important for human health?\",\"answer\":\"Pollen and subpollen particles contribute to a large share of allergy cases and their impact is increasing with environmental and lifestyle changes.\"},{\"question\":\"What limitations affect conventional pollen monitoring methods?\",\"answer\":\"Conventional approaches often depend on manual samplers, which are limited by labour, time, and cost, making high-resolution dense monitoring difficult.\"},{\"question\":\"How does this thesis address those limitations?\",\"answer\":\"It investigates low-cost monitoring methods using optical particle counters with machine learning, and evaluates physical-property measurements under varying atmospheric conditions using multiple experimental and computer-vision approaches.\"}]","Low-cost Optical Detection of Pollen Bioaerosols with Machine Learning for Human Health | 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