[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121173-en":3,"doc-seo-121173-105":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},121173,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Enhancing Air Pollution Control with Machine Learning in the Automation Field","Machine learning integrated with real-time data collection enables data-driven optimization of air pollution control strategies across multiple settings. Predictive models support timely, targeted interventions that reduce key pollutants including PM2.5, PM10, SO2, NOx, VOCs, and ammonia. Urban deployments show notable air quality improvements from proactive actions guided by high-accuracy forecasts. Industrial contexts report measurable sulfur dioxide emission reductions, coastal applications manage VOCs effectively, and rural optimization of agricultural practices lowers particulate matter and NH3 emissions. Results validate machine learning’s role in advancing adaptive air quality management to better protect public health and the environment.","RESEARCH ARTICLE  \nENHANCING AIR POLLUTION CONTROL WITH MACHINE LEARNING IN THE AUTOMATION FIELD  \n1Nourin Nishat ,2 Md Maruf Rahman ,3Mahrima Akter Mim, 4A S M  \nShoaib   \n1Graduate Researcher, Master of Science in Management Information Systems, College of Business, Lamar University, Texas,  \nUSA  \nemail: [nishatnitu203@gmail.com](nishatnitu203@gmail.com)  \n2Graduate Researcher, Master of Science in Business Analytics, Department of Marketing & Business Analytics, Texas A&M  \nUniversity Commerce, Texas, USA  \n[email: mrahman20@leomail.tamuc.edu](email: mrahman20@leomail.tamuc.edu)  \n3Bachelor of Science in Computer Information System, Queensborough Community College, Queens, New York, USA  \n[email: mahrimamim17@gmail.com](email: mahrimamim17@gmail.com)  \n4Graduate Researcher, Master of Science in Department of Electrical Engineering, Lamar University, Texas, USA  \nemail: [a.s.m.shoaib@gmail.com](a.s.m.shoaib@gmail.com)  \nABSTRACT  \nThe integration of machine learning with real-time data collection offers a transformative approach to optimizing pollution control strategies. This study explores the application of these advanced technologies in various environments, including urban, industrial, coastal, and rural areas. Using predictive machine learning models, significant reductions in pollutants such as PM2.5, SO2, NOx, VOCs, PM10, and NH3 were achieved through targeted and timely interventions. In urban areas, air quality improved notably due to proactive measures informed by high-accuracy predictions. Industrial areas saw a 20% reduction in sulfur dioxide emissions, while coastal areas effectively managed volatile organic compounds. In rural areas, optimizing agricultural practices led to substantial decreases in particulate matter and ammonia emissions. These findings validate the efficacy of machine learning in enhancing pollution control efforts, highlighting its potential to revolutionize air quality management. This study underscores the importance of continued investment in advanced, data-driven approaches to address the growing challenge of air pollution, advocating for more sophisticated, adaptive, and effective strategies to protect public health and the environment.  \nKeywords: Machine Learning, Real-Time Data Collection, Air Pollution Control, Predictive Modeling, Urban Air Quality, Industrial Emissions  \nSubmitted: April 22, 2024  \nAccepted: June 07, 2024  \nPublished: June 11, 2024  \n10.69593/ajbais.v4i2.68  \nGraduate Researcher, Master of Science in Management Information Systems, College of Business, Lamar University, Texas, USA.  \n*Corresponding Author:  \ne-mail: [nishatnitu203@gmail.com](nishatnitu203@gmail.com)  \n1 Introduction  \nAir pollution is a pressing global issue that significantly impacts human health, ecosystems, and the climate. According to the Stafoggia et al. (2019) , exposure to ambient air pollution is associated with a range of adverse health outcomes, including respiratory and cardiovascular diseases, and it is responsible for millions of premature deaths annually. The detrimental effects of air pollution extend beyond human health, affecting wildlife, damaging forests, and contributing to climate change through the emission of greenhouse gases. Studies have shown that pollutants such as particulate matter (PM2.5 and PM10), nitrogen oxides (NOx), sulfur dioxide (SO2), carbon monoxide (CO), and volatile organic compounds (VOCs) can cause serious health issues when inhaled. Fine particulate matter, in particular, can penetrate deep into the lungs and enter the bloodstream, leading to chronic respiratory conditions, heart attacks, and strokes (Tepanosyan et al., 2020) .  \nTraditional pollution control methods, such as filtration, chemical treatment, and regulatory measures, have been employed to mitigate the effects of air pollution. However, these methods often struggle to keep pace with the dynamic and complex nature of pollutant sources and atmospheric conditions. For instance, Yu and Lin (","cbCaivoY7CZEks80","https://ap.wps.com/l/cbCaivoY7CZEks80","pdf",639573,1,14,"English","en",105,"# Introduction\n## Air pollution impacts on health and environment\n## Limits of traditional pollution control methods\n## Role of automation and real-time monitoring\n## Machine learning for prediction and optimization\n## Deep learning for improved forecasting","[{\"question\":\"How does real-time data collection improve air pollution control using machine learning?\",\"answer\":\"It enables continuous monitoring and faster, automated responses, allowing predictive models to support timely, targeted interventions that improve pollutant management.\"},{\"question\":\"Which pollutants are reported as being reduced by the proposed machine learning approach?\",\"answer\":\"The study reports reductions in PM2.5, SO2, NOx, VOCs, PM10, and NH3 through targeted interventions informed by predictive modeling.\"},{\"question\":\"How do the outcomes differ across urban, industrial, coastal, and rural environments?\",\"answer\":\"Urban areas show improved air quality from proactive measures, industrial areas achieve about a 20% sulfur dioxide reduction, coastal regions manage VOCs effectively, and rural optimizations of agricultural practices reduce particulate matter and ammonia emissions.\"}]","Enhancing Air Pollution Control with Machine Learning in the Automation Field | 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does real-time data collection improve air pollution control using machine learning?","Question",{"text":76,"@type":77},"It enables continuous monitoring and faster, automated responses, allowing predictive models to support timely, targeted interventions that improve pollutant management.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which pollutants are reported as being reduced by the proposed machine learning approach?",{"text":81,"@type":77},"The study reports reductions in PM2.5, SO2, NOx, VOCs, PM10, and NH3 through targeted interventions informed by predictive modeling.",{"name":83,"@type":74,"acceptedAnswer":84},"How do the outcomes differ across urban, industrial, coastal, and rural environments?",{"text":85,"@type":77},"Urban areas show improved air quality from proactive measures, industrial areas achieve about a 20% sulfur dioxide reduction, coastal regions manage VOCs effectively, and rural optimizations of agricultural practices reduce particulate matter and 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