PM10 pollutant concentration forecasting through statistical and intelligent methods applied to Celaya City, México.

PM10 pollutant concentration forecasting through statistical and intelligent methods applied to Celaya City, México. | 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 PM10 pollutant concentration forecasting through statistical and intelligent methods applied to Celaya City, México. Amanda E. Violante Gavira, Felipe Trujillo-Romero This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4632026/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 In recent decades, the World Health Organization and the scientific community have provided convincing evidence of the harmful effects on health due to exposure to atmospheric particles with a diameter of fewer than 10 microns or PM10. For this reason, this organization has alerted governments to measure, control, and evaluate air quality in real-time and in the medium term. For this reason, this work predicts PM10 particles using time series and a multilayer perceptron system to estimate PM10 concentrations over time. Four-time series models were analyzed, and four different multilayer perceptron architectures were used to determine the best prediction and know the pollutant's in short-term and medium-term future behavior. The results show that the neural system provides a better forecast in the short term with an error of 7.68% compared to the best time series method, the TIE, whose error was 27.20%. While in the medium term, the error was 31.10% for the TIE and 9.16% for the neural network. Neural networks Particle tracking Pollution Predictive models Regression analysis Time series analysis 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-4632026","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":336915198,"identity":"718b7387-0062-4303-8339-0217e3b6d48d","order_by":0,"name":"Amanda E. 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