[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121234-en":3,"doc-seo-121234-105":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},121234,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Application of Machine Learning Methods to the Prediction of NO2 Concentration in the Air Environment","Air quality significantly affects public health, and nitrogen dioxide (NO2) is a major pollutant linked to respiratory and cardiovascular diseases. This study develops a machine learning model to predict hourly NO2 concentrations in Ternopil, Ukraine, using meteorological and time-related variables. The model is trained on a large dataset and validated with measurements from the Ecocity monitoring station, where NO2 can exceed legal limits. Neural network results show high predictive accuracy, with test errors of 3.9% and 1.4%, supporting improved air-quality monitoring and forecasting for resource-limited urban areas.","Application of machine learning methods to the prediction of NO2 concentration in the air environment  \nIryna Didych1,∗,†, Andrii Mykytyshyn1,∗,†, Andrii Stanko1,†, Mykola Mytnyk 1,†  \n1 Ternopil Ivan Puluj National Technical University, Ruska 56, 46001 Ternopil, Ukraine  \nAbstract  \nAir quality significantly impacts public health, with nitrogen dioxide (NO2) being a key pollutant linked to respiratory and cardiovascular diseases. In this study, we developed a machine learning model to accurately predict hourly NO2 concentrations in Ternopil, Ukraine, using readily available meteorological and temporal data. The model was trained on a large dataset and tested using data from the Ecocity monitoring station, known for recording NO2 levels exceeding legal limits. By employing neural networks, the model demonstrated high accuracy in predicting NO2 concentrations, with the error of 3.9% and 1.4%, respectively, in the test samples. Our findings underscore the potential of machine learning techniques to enhance air quality monitoring and forecasting, particularly in urban areas with limited resources. This approach offers a valuable tool for real-time pollution management and public health protection.  \nKeywords  \nAir quality, nitrogen dioxide, prediction, machine learning  \n1. Introduction  \nAir quality is a complex, multifactorial set of chemical, physical, and biological characteristics of air, and at the same time a very relevant topic because of its connection to human health. Numerous studies have demonstrated the link between cardiovascular and lung diseases and long-term exposure to pollutants, in particular nitrogen dioxide (NO 2) and particulate matter (PM2.5 and PM10). According to the European Environment Agency [1], in 2018, about 55,000 premature deaths in the EU could be attributed to exposure to NO 2. The results of several clinical and epidemiological studies show that there is at least moderate evidence that adverse health effects occur even with short-term exposure to pollutants, such as exposure below established limits [2] .  \nIncreasing concentrations of pollutants in the atmosphere have changed its properties, making it a harmful environment for humans and other living organisms [3] . Pollutants include  \n⋆ITTAP’2024: 4th International Workshop on Information Technologies: Theoretical and Applied Problems, October 23- 25, 2024, Ternopil, Ukraine, Opole, Poland  \n1∗ Corresponding author.  \n† These authors contributed equally.  \n [iryna.didych@tntu.edu.ua](iryna.didych@tntu.edu.ua) (I. Didych); [mikitishin@gmail.com](mikitishin@gmail.com) (A. Mykytyshyn); [stanko.andrjj@gmail.com](stanko.andrjj@gmail.com) (A. Stanko) ; mytnyk@networkacad.net (M. Mytnyk) .  \n 0000-0003-2846-6040 (I. Didych); 0000-0002-2999-3232 (A. Mykytyshyn); 0000-0002-5526-2599 (A. Stanko); 0000- 0003-3743-6310 (M. Mytnyk)  \n © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0) .  \nCEUR ~~  ~~[Workshop](Workshop ceur-ws.org)[ ceur-ws.org](Workshop ceur-ws.org)[ ](Workshop ceur-ws.org)[Proceedings](Proceedings ISSN 1613-0073)[ ISSN 1613-0073](Proceedings ISSN 1613-0073)   \na variety of gases, droplets, and particles that degrade air quality; therefore, their exposure to humans is believed to lead to serious health problems, especially in urban areas where pollution levels are high [4]. Air pollutants are chemical, physical (e.g., particulate matter), or biological agents that alter the natural characteristics of the atmosphere. Particulate matter, cited as an example of air pollutants, is the main factor that negatively affects human health due to its high toxicity. Air pollution results in the presence of certain gases in the atmosphere in concentrations that exceed the standard and can be seriously harmful to human health. Examples of such gases are nitrogen oxides, sulfur oxides, carbon monoxide, photochemical oxidants (e.g. ozone), lead, as well as various heav","cbCaikTOnDxBHwgs","https://ap.wps.com/l/cbCaikTOnDxBHwgs","pdf",696378,1,9,"English","en",105,"# Abstract\n# Introduction\n## Health impact of air pollutants\n## Air quality metrics and monitoring needs\n# Purpose of the study\n# Background on NO2 and exposure risks","[{\"question\":\"What does the study predict and in which location?\",\"answer\":\"The study predicts hourly NO2 concentrations in Ternopil, Ukraine, using meteorological and temporal data.\"},{\"question\":\"What data source is used to test the model?\",\"answer\":\"The model is tested using data from the Ecocity monitoring station, which records NO2 levels that can exceed legal limits.\"},{\"question\":\"How accurate is the machine learning approach?\",\"answer\":\"Neural network predictions achieve high accuracy, with errors of 3.9% and 1.4% in the test samples.\"}]","Application of Machine Learning Methods to the Prediction of NO2 Concentration in the Air Environment | PDF",1785734472,23,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"application-of-machine-learning-methods-to-the-prediction-of-no2-concentration-in-the-air-environment","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/application-of-machine-learning-methods-to-the-prediction-of-no2-concentration-in-the-air-environment/121234/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What does the study predict and in which location?","Question",{"text":75,"@type":76},"The study predicts hourly NO2 concentrations in Ternopil, Ukraine, using meteorological and temporal data.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data source is used to test the model?",{"text":80,"@type":76},"The model is tested using data from the Ecocity monitoring station, which records NO2 levels that can exceed legal limits.",{"name":82,"@type":73,"acceptedAnswer":83},"How accurate is the machine learning approach?",{"text":84,"@type":76},"Neural network predictions achieve high accuracy, with errors of 3.9% and 1.4% in the test samples.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]