Seasonal Variations of Air Quality Measurements of Aba Metropolis and Suburbs Using MATLAB and ANN

Seasonal Variations of Air Quality Measurements of Aba Metropolis and Suburbs Using MATLAB and ANN | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Seasonal Variations of Air Quality Measurements of Aba Metropolis and Suburbs Using MATLAB and ANN B. M. Adiele, U. U. Egereonu, C. O. Alisa, U. L. Onu, S. K. Egereonu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6801553/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Air pollution is a major life-threatening problem in industrialized and commercially vibrant cities like Aba metropolis and its suburbs in Abia State Nigeria. The study of selected air pollutants in these areas were performed using Matrix Laboratory (MATLAB) and Artificial Neural Networks (ANN) pollution models. Primary data was collected by conducting sampling analysis on air samples during dry and rainy seasons from 2024 and 2025. MATLAB and ANN pollution models were generated by integrating measurements and spatial databases using polynomial expressions. The MATLAB 7th degree linear regression polynomial described the relationship between dependent and independent variables for the pollutants. The correlation methods verified that most MATLAB models could accurately predict or forecast concentration levels. Also the Artificial Neural Network demonstrated tracking of the actual plots on MATLAB. The analysis of variance (ANOVA) was also deployed which showed p < 0.05 for carbon monoxide (CO), nitrogen dioxide (NO 2 ), sulphur dioxide (SO 2 ), and total particulate matter (TPM), indicating that, there was a significant impact by the seasons on the concentrations of all gaseous pollutants under study(i.e. seasonal variations of concentration was highly affected by the two seasons). ANN was able to track all gaseous pollutants represented by MATLAB successfully above 50%. Air Quality Pollutants MATLAB ANN ANOVA Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6801553","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":465758903,"identity":"ed18920f-df0b-4c49-b874-0b406d9a7730","order_by":0,"name":"B. M. Adiele","email":"","orcid":"","institution":"Rhema University","correspondingAuthor":false,"prefix":"","firstName":"B.","middleName":"M.","lastName":"Adiele","suffix":""},{"id":465758905,"identity":"033382e7-a157-450a-b415-e172a99d5e23","order_by":1,"name":"U. U. 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The study of selected air pollutants in these areas were performed using Matrix Laboratory (MATLAB) and Artificial Neural Networks (ANN) pollution models. Primary data was collected by conducting sampling analysis on air samples during dry and rainy seasons from 2024 and 2025. MATLAB and ANN pollution models were generated by integrating measurements and spatial databases using polynomial expressions. The MATLAB 7th degree linear regression polynomial described the relationship between dependent and independent variables for the pollutants. The correlation methods verified that most MATLAB models could accurately predict or forecast concentration levels. Also the Artificial Neural Network demonstrated tracking of the actual plots on MATLAB. The analysis of variance (ANOVA) was also deployed which showed p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for carbon monoxide (CO), nitrogen dioxide (NO\u003csub\u003e2\u003c/sub\u003e), sulphur dioxide (SO\u003csub\u003e2\u003c/sub\u003e), and total particulate matter (TPM), indicating that, there was a significant impact by the seasons on the concentrations of all gaseous pollutants under study(i.e. seasonal variations of concentration was highly affected by the two seasons). 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