Enhancing daily PM2.5 air quality forecasts in Dakar, Senegal, through the integration of data from the automatic measuring station into a server using data assimilation

Enhancing daily PM2.5 air quality forecasts in Dakar, Senegal, through the integration of data from the automatic measuring station into a server using data assimilation | 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 Enhancing daily PM2.5 air quality forecasts in Dakar, Senegal, through the integration of data from the automatic measuring station into a server using data assimilation ahmed gueye, Mamadou Simina Drame, Serigne abdou aziz Niang, Mame Diarra Toure, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3938043/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract The objective of this work is to predict daily PM2.5 air quality in Dakar, Senegal using data from an automated measurement station integrated into a server using a data assimilation model. Initially, a 3-year data set was used to identify and validate an appropriate ARIMA data assimilation model. The data was split into an 80% training set and a 20% test set. The Augmented Dickey-Fuller (ADF) test was used to check the normality of the data series. Subsequently, we used the AutoArima method to determine the optimal model to represent the time series. Preliminary results show that a model with order (2,1,1) accurately represents the series. Additional analysis using model fit tests showed that the (3, 0, 1) model was most effective in representing and predicting the data. The statistical validation performance of this model demonstrates its capability to forecast PM2.5 concentrations for up to 72 hours (3 days), achieving correlation coefficients exceeding 80%. However, after three days, the predictions returned to background levels. In the final stage of the study, data from automatic stations were integrated into a server hosting the assimilation model to improve daily PM2.5 forecasts for Dakar. An interactive platform was developed to visualize measurements and forecasts over two days. The results show that by integrating the data with the assimilation model, predictions are significantly improved. PM2.5 Arima Model Data Assimilation Air Pollution Forecast Full Text Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 21 Feb, 2024 Reviewers invited by journal 20 Feb, 2024 Editor invited by journal 19 Feb, 2024 Editor assigned by journal 16 Feb, 2024 First submitted to journal 06 Feb, 2024 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. 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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-3938043","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":273995684,"identity":"8ed48c1f-7541-4e8a-816b-e68d59c50f2f","order_by":0,"name":"ahmed 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