EcoSense: A Revolution in Urban Air Quality Forecasting for Smart Cities

EcoSense: A Revolution in Urban Air Quality Forecasting for Smart Cities | 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 Short Report EcoSense: A Revolution in Urban Air Quality Forecasting for Smart Cities Kalyan Chatterjee, Machakanti Navya Thara, Mandadi Sriya Reddy, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4342593/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Feb, 2025 Read the published version in BMC Research Notes → Version 1 posted 3 You are reading this latest preprint version Abstract The Smart City (SC) framework, renowned for enhancing lives and bolstering public safety, encounters challenges due to its reliance on IoT devices, contributing to electronic waste and resource consumption. Integrating weather research and a weather-smart grid into the SC framework becomes crucial to address these issues and safeguard the environment and residents’ well-being. This study proposes a novel approach, EcoSense: A Revolution in Urban Air Quality Forecasting for Smart Cities , which incorporates Bi-directional Stacked LSTM with a Weather-Smart Grid (BlaSt). BlaSt utilizes temporal aggregation and lagged features to capture temporal dependencies and trends in air quality data, considering air pollutants and meteorological factors for future air pollutant concentration prediction. The model is designed for 1-hour prediction intervals and outperforms traditional methods by leveraging feature attribute values, enhancing accuracy, and reducing computational complexity. Experimental results demonstrate the improved accuracy and computational efficiency of the BlaSt model compared to conventional models. Additionally, it effectively handles extensive air quality data and exhibits promising predictive capabilities for future data. Air quality Air Pollutant Concentrations (APCs) Internet of Things (IoT) Meteorological Factors (MFs) Smart City (SC) Spatiotemporal Weather Smart Grid (WSG) Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 11 Feb, 2025 Read the published version in BMC Research Notes → Version 1 posted Editor assigned by journal 03 May, 2024 Submission checks completed at journal 03 May, 2024 First submitted to journal 29 Apr, 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-4342593","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":300054936,"identity":"b80e80c7-d441-45a2-9621-c962195dc713","order_by":0,"name":"Kalyan Chatterjee","email":"","orcid":"","institution":"Nalla Malla Reddy Engineering College","correspondingAuthor":false,"prefix":"","firstName":"Kalyan","middleName":"","lastName":"Chatterjee","suffix":""},{"id":300054937,"identity":"89fb7010-460d-49af-bcea-a7a9ca103542","order_by":1,"name":"Machakanti Navya Thara","email":"","orcid":"","institution":"Nalla Malla Reddy Engineering College","correspondingAuthor":false,"prefix":"","firstName":"Machakanti","middleName":"Navya","lastName":"Thara","suffix":""},{"id":300054938,"identity":"5b77cab7-f71e-49c8-8230-a5527c9b7665","order_by":2,"name":"Mandadi Sriya Reddy","email":"","orcid":"","institution":"Nalla Malla Reddy Engineering College","correspondingAuthor":false,"prefix":"","firstName":"Mandadi","middleName":"Sriya","lastName":"Reddy","suffix":""},{"id":300054939,"identity":"d90a912d-c1d4-48d7-8927-a6d3b9971923","order_by":3,"name":"N. 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