03475737-55e5-4c93-9ceb-5563c8eb1644 Andrea Vianello Eurac Research - Center for Sensing Solutions Researcher Viale Druso, 1 / Drususallee 1, eurac research Bolzano Autonomous Province of Bolzano 39100 Italy supportcss@eurac.edu http://www.eurac.edu/en/research/mountains/remsen/Pages/default.aspx WWW:LINK-1.0-http--link remote sensing eurac research 2026-05-07T08:33:21.232Z ISO 19139 1.0 Air Quality Utilizing Indoor AI Sensors (AQUINAS) - Postprocessing data AQUINAS 2026-03-27T00:00:00+01:00 2026-03-27T00:00:00+01:00 2025 publisher https://www.eurac.edu/en 10.48784/03475737-55e5-4c93-9ceb-5563c8eb1644 Mendicino, R., & Hussain, S. F. (2026). Air Quality Utilizing Indoor AI Sensors (AQUINAS) - Postprocessing data (Version 2025) [Data set]. Center for Sensing Solutions. https://doi.org/10.48784/03475737-55E5-4C93-9CEB-5563C8EB1644 This research provides knowledge on the performance and limitations of low-cost environmental sensors in real usage conditions. The EQ-OX platform will be used and the collected data will be compared to those of the reference station. The main purpose of this research is to compare these data and try to correct them through the use of machine learning algorithms. Project focused on the evaluation and correction of low-cost indoor and outdoor air quality sensors using machine learning, with comparison against reference stations. Roberto Mendicino Eurac Research - Center for Sensing Solutions roberto.mendicino@eurac.edu Simone Tritini Eurac Research - Center for Sensing Solutions simone.tritini@eurac.edu Roberto Mendicino Eurac Research - Center for Sensing Solutions roberto.mendicino@eurac.edu https://edp-portal.eurac.edu/geonetwork/srv/api/records/03475737-55e5-4c93-9ceb-5563c8eb1644/attachments/aquinas_postprocessing_data.png Gitlab repository GEMET - INSPIRE themes, version 1.0 2018-05-23T10:25:56 geonetwork.thesaurus.external.theme.httpinspireeceuropaeutheme-theme air quality low-cost sensors temperature humidity machine learning ai edge computing machine learning models Attribution 4.0 International (CC BY 4.0): You are free to share and adapt under the following conditions: attribution and no additional restriction. (https://creativecommons.org/licenses/by/4.0/deed.en) 2024-02-13 2025-07-11 23.5935 23.8622 37.878 38.0895 ML models, software Center for Sensing Solutions Viale Druso 1 Bolzano South-Tyrol 39100 Italy supportcss@eurac.edu https://gitlab.inf.unibz.it/css-public/aquinas WWW:LINK-1.0-http--link ML postprocessing software Gitlab repository for the software created to perform data analysis and the output models. https://doi.org/10.48784/1ec25e9f-ab2f-4535-8474-63d3a6a7d52e WWW:LINK-1.0-http--link Air Quality Utilizing Indoor AI Sensors (AQUINAS) - Input data https://doi.org/10.48784/03475737-55e5-4c93-9ceb-5563c8eb1644 DOI Digital Object Identifier (DOI) The machine learning models were developed using the Project Aquinas software (Hussain, 2025), implemented in Python and TensorFlow. The software is available at https://doi.org/10.48784/CQ7A-QE47. The metadata of input data is here: https://doi.org/10.48784/1ec25e9f-ab2f-4535-8474-63d3a6a7d52e