[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126562-en":3,"doc-seo-126562-105":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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":11,"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},126562,13056712833777,"Logic","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Application of Machine Learning methods to correct the readings of low-cost air pollution sensors","Study analyzes machine learning approaches for correcting readings from low-cost air pollution sensors measuring PM2.5 against reference station data. Experiments use paired instruments—a CityAir low-cost sensor and an E-BAM reference dust analyzer—deployed in Krasnoyarsk, Russia, with observational data from 2019-01-01 to 2022-12-31. Statistical comparisons evaluate parametric models (Linear, Ridge, Lasso, Support Vector Machine, Elastic Net, regressions of feature distance) and nonparametric methods (Nearest Neighbor, Decision Tree, Random Forest) to quantify the relationship between sensor readings and improve accuracy for long-term monitoring.","Application of Machine Learning methods to correct the readings of low-cost air pollution sensors  \nViktoriya Petrakova1*  \n1Institute Of Computational Modeling, 50/44, Akademgorodok, 660036, Krasnoyarsk, Russia  \nAbstract. The study is devoted to the analysis of the application of machine learning methods for correcting the readings of inexpensive sensors that record the concentration of suspended particles PM2.5 in the surface layer of the atmosphere, relative to readings of reference stations. The analysis was carried out on the example of coupled sensors (an inexpensive CityAir sensor and a reference E-BAM) located in Krasnoyarsk (Russia) based on observational data from January 1, 2019 to December 31, 2022. Statistical analysis of the data and comparison of parametric (Linear, Ridge, Lasso, Support vectors machine, Elastic net regressions) and nonparametric (regressions of Nearest Neighbor, Decision Tree and Random Forest) methods for establishing the relationship between two samples was carried  \nout.  \n1 Introduction  \nAir pollution is not only a serious threat to human health (since pollutants are able to adsorb toxic and carcinogenic substances on their surface and, due to their microsize, penetrate into the human circulatory system, increasing the risk of bronchopulmonary, cardiovascular and oncological diseases), but also affect on the attractiveness of the region for life and the general well-being of its population. The most well-known air pollutant is suspended particulate matter PM2.5, which sources in the Earth's atmosphere are both natural (sandstorms, volcanic eruptions) and anthropogenic processes, such as the extraction, processing and combustion of natural raw materials, as well as agricultural and forest fires. Thus, the collection and analysis of data on PM2.5 concentrations are the global area, which has received much attention in countries such as England, China, South and the United States [1-6] . It should be noted that in 2021 and in 2023 Russia (Krasnoyarsk) twice topped the rating of major cities in the world with the highest level of air pollution according to the IQAir service that monitors air quality in real time ([https://www.iqair.com/ru/world-air](https://www.iqair.com/ru/world-air)quality-ranking) .  \nAt present, inexpensive (less than $2500) optical sensors can be used to measure the concentration of suspended particles. Their low cost and compact size facilitate wider deployment and collection of real-time pollution data and can therefore be useful in largescale pollution monitoring and spatial mapping. However, the optical principle of particle  \n* [Corresponding author: vika-svetlakova@yandex.ru](Corresponding author: vika-svetlakova@yandex.ru)  \n© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 ([http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/)).  \ndetection used in inexpensive sensors is based on a number of assumptions that lead to inaccuracies [7,8] . Assumptions lead to errors in counting the number of particles and their size, on which the detected concentration of suspended particles depends. In their turn, manufacturers of the reference type optical particle counters spend a lot of time and effort developing mass conversion factors to make their devices as accurate as possible [9].Thus, data collected by the low-cost sensor networks is needed to develop local environmental improvement measures, but the data obtained in this way can be doubtful due to serious limitations in the accuracy and reliability of these low-cost sensors, which leads to the necessity for their correction.  \nKrasnoyarsk is one of the few cities in Russia where atmospheric air quality is monitored with network of stationary observation post. First, the Ministry of Ecology and Rational Nature Management of the Krasnoyarsk Territory maintains the Regional Departmental Inf","cbCaioxuoVsvyPBw","https://ap.wps.com/l/cbCaioxuoVsvyPBw","pdf",4658545,4,1,"English","en",105,"# Introduction\n# Methods","[{\"question\":\"What problem does the study address for low-cost air pollution sensors?\",\"answer\":\"Low-cost optical sensors rely on assumptions that introduce inaccuracies in particle counting and sizing. This makes their PM2.5 concentration readings unreliable without correction against reference measurements.\"},{\"question\":\"Which sensors and location are used for the analysis?\",\"answer\":\"The study uses coupled sensors in Krasnoyarsk, Russia: a CityAir low-cost sensor and an E-BAM reference dust analyzer. Data come from 2019-01-01 to 2022-12-31.\"},{\"question\":\"Which machine learning models are compared to correct sensor readings?\",\"answer\":\"The study compares parametric regressions (Linear, Ridge, Lasso, Support Vector Machine, Elastic Net) and nonparametric models including Nearest Neighbor, Decision Tree, and Random Forest to model the relationship between sensor readings and reference data.\"}]","Application of Machine Learning methods to correct the readings of low-cost air pollution sensors | PDF",1785933340,20,{"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-correct-the-readings-of-low-cost-air-pollution-sensors","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":21},"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":20},"https://docshare.wps.com/document/application-of-machine-learning-methods-to-correct-the-readings-of-low-cost-air-pollution-sensors/126562/",{"url":52,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-27","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the study address for low-cost air pollution sensors?","Question",{"text":75,"@type":76},"Low-cost optical sensors rely on assumptions that introduce inaccuracies in particle counting and sizing. This makes their PM2.5 concentration readings unreliable without correction against reference measurements.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which sensors and location are used for the analysis?",{"text":80,"@type":76},"The study uses coupled sensors in Krasnoyarsk, Russia: a CityAir low-cost sensor and an E-BAM reference dust analyzer. Data come from 2019-01-01 to 2022-12-31.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning models are compared to correct sensor readings?",{"text":84,"@type":76},"The study compares parametric regressions (Linear, Ridge, Lasso, Support Vector Machine, Elastic Net) and nonparametric models including Nearest Neighbor, Decision Tree, and Random Forest to model the relationship between sensor readings and reference data.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":58,"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":21,"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":20,"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":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":29,"slug":126},9,"Religion & Spirituality","religion-spirituality",{"id":29,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":29,"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"]