Investigation of Atmospheric Particulates in Two Raingauged Stations, Aba Andumuahia Meteropolis, Nigeria Using Artificial Neural Network and Fuzzy Logic

Investigation of Atmospheric Particulates in Two Raingauged Stations, Aba Andumuahia Meteropolis, Nigeria Using Artificial Neural Network and Fuzzy Logic | 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 Investigation of Atmospheric Particulates in Two Raingauged Stations, Aba Andumuahia Meteropolis, Nigeria Using Artificial Neural Network and Fuzzy Logic U. L. Onu, U. U. Egereonu, A. I. Otuonye, S. K. Egereonu, A. O. Emeagubor This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6771190/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 This study conducts a thorough and multifaceted analysis of atmospheric particulate matter within the urban conglomerates of Aba and Umuahia, two prominent metropolitan areas in Abia State, Nigeria, both undergoing significant industrial and economic growth. Leveraging on advanced artificial neural network (ANN) and fuzzy logic framework, rainwater samples were meticulously collected from strategically located rain gauge stations, positioned at an optimal elevation of three meters over a carefully designed ten-week sampling period. These rainwater samples were employed to accurately quantify particulate matter concentrations, enabling the assessment of spatial and temporal variations, along with the broader atmospheric deposition dynamics. Results revealed considerable disparities in particulate concentrations, with Aba displaying significantly higher levels than Umuahia, likely attributable to heightened anthropogenic sources such as industrial emissions, vehicular exhaust, and urban activities. The mean particulate concentrations were also computed for both locations, yielding deeper insights into regional atmospheric chemistry. Furthermore, graphical analysis demonstrated an inverse relationship between rainfall frequency and particulate loading, corroborating the hypothesis of precipitation-induced atmospheric cleansing. The effectiveness of ANN based and fuzzy logic environmental models are further validated, underscoring their critical role in forecasting pollutant dispersion and facilitating sustainable urban air quality management policies. Atmospheric Particulates ANN Fuzzy Logic Precipitation Urban Emissions 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-6771190","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":464401517,"identity":"4a834321-3ef9-4f8f-b19e-3bf09fa7197a","order_by":0,"name":"U. L. 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