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Original Research
Received: 23 Jul 2025, Accepted: 09 Apr 2026,
 


AI-Based NO2 Concentration Prediction for Sustainable Environment Monitoring

Hassan Jari.

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    • Abstract
    Nitrogen dioxide (NO₂) is a very critical atmospheric pollutant, which has greatly influenced environmental sustainability and public health. The measurements collected for NO₂ concentrations are valuable to improve air quality management and mitigation strategies. We propose an AI-oriented sustainable environment management system that predicts NO₂ in urban and industrial areas by implementing machine learning (ML) techniques. The model is developed to forecast the NO₂ concentrations based on the recorded past data on air quality and meteorological parameters (temperature, humidity, and wind speed) traffic flow data. For time-series forecasting and feature analysis advanced ML algorithms such as LSTM, Random Forest, and XGboost have been used. The results of this study indicate that these models are highly accurate by employing actual datasets in their evaluation. Further, the use of ML to predict provides early intervention for policymakers, enforces data-based environmental policies, has an overall favorable impact in decreasing pollution. Thus, the present study enhances the understanding of smart city applicability, air quality sentinel, as well as sustainable environmental management by applying AI for enhanced pollution detection.

    Key words: NO₂ Prediction, Machine Learning, Air Quality Monitoring, Environmental, Sustainability, Time-Series Forecasting


    		 
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    How to Cite this Article
    Pubmed Style

    Hassan Jari. AI-Based NO2 Concentration Prediction for Sustainable Environment Monitoring. Journal of Engineering and Applied Sciences. 2026; 13(Recent Trends in Computational Modelling of Thermo-Fluid Systems and Nanofluid Applications): 1-13. doi:10.5455/jeas.2025060101

    Web Style

    Hassan Jari. AI-Based NO2 Concentration Prediction for Sustainable Environment Monitoring. https://jecasmu.org/?mno=272818 [Access: June 27, 2026]. doi:10.5455/jeas.2025060101

    AMA (American Medical Association) Style

    Hassan Jari. AI-Based NO2 Concentration Prediction for Sustainable Environment Monitoring. Journal of Engineering and Applied Sciences. 2026; 13(Recent Trends in Computational Modelling of Thermo-Fluid Systems and Nanofluid Applications): 1-13. doi:10.5455/jeas.2025060101


    Vancouver/ICMJE Style

    Hassan Jari. AI-Based NO2 Concentration Prediction for Sustainable Environment Monitoring. Journal of Engineering and Applied Sciences. (2026), [cited June 27, 2026]; 13(Recent Trends in Computational Modelling of Thermo-Fluid Systems and Nanofluid Applications): 1-13. doi:10.5455/jeas.2025060101


    Harvard Style

    Hassan Jari (2026) AI-Based NO2 Concentration Prediction for Sustainable Environment Monitoring. Journal of Engineering and Applied Sciences, 13 (Recent Trends in Computational Modelling of Thermo-Fluid Systems and Nanofluid Applications), 1-13. doi:10.5455/jeas.2025060101


    Turabian Style

    Hassan Jari. 2026. AI-Based NO2 Concentration Prediction for Sustainable Environment Monitoring. Journal of Engineering and Applied Sciences, 13 (Recent Trends in Computational Modelling of Thermo-Fluid Systems and Nanofluid Applications), 1-13. doi:10.5455/jeas.2025060101


    Chicago Style

    Hassan Jari. "AI-Based NO2 Concentration Prediction for Sustainable Environment Monitoring." Journal of Engineering and Applied Sciences 13 (2026), 1-13. doi:10.5455/jeas.2025060101


    MLA (The Modern Language Association) Style

    Hassan Jari. "AI-Based NO2 Concentration Prediction for Sustainable Environment Monitoring." Journal of Engineering and Applied Sciences 13.Recent Trends in Computational Modelling of Thermo-Fluid Systems and Nanofluid Applications (2026), 1-13. Print. doi:10.5455/jeas.2025060101


    APA (American Psychological Association) Style

    Hassan Jari (2026) AI-Based NO2 Concentration Prediction for Sustainable Environment Monitoring. Journal of Engineering and Applied Sciences, 13 (Recent Trends in Computational Modelling of Thermo-Fluid Systems and Nanofluid Applications), 1-13. doi:10.5455/jeas.2025060101


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    • ISSN: 1658-6638 (print)
    • ISSN: 1658-7073 (electronic)