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@article{178466,
        author = {Ayush Anand and Abhinav Chauhan and Junaid Ahmad},
        title = {Technological Innovations in Air Quality Monitoring: Advancements, Challenges, and Future Prospects in the Air Quality Index System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {3528-3533},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178466},
        abstract = {This article describes the ways that new technologies improved air quality monitoring and AQI systems through innovations such as low-cost sensors, IoT, satellite remote sensing, and AI. It addresses data reliability, standardization, and accessibility problems. Data from 2014 to 2019 for six pollutants (PM10, PM2.5, NO2, SO2, CO, and O3) from publicly available sources were examined. AQI prediction made use of four models: Random Forest (RF), Gradient Boosting (GB), Lasso Regression (LASSO), and a Stacked Regressor. AQI prediction employed K-Nearest Neighbors, Support Vector Machines (SVM), Decision Tree (DT), Multilayer Perceptron (MLP), RF, and a Stacked Classifier. Model performances were evaluated in terms of R², RMSE, MAE, Accuracy, MCC, and F1 score. The research emphasizes the value of an effective, robust AQI prediction system in enabling proactive environmental and public health measures. It also emphasizes increasing air pollution in Indian cities, its effects on health, and increased public awareness. Lastly, the paper suggests an AQI estimation model based on Convolutional Neural Networks (CNN) and enhanced Long Short-Term Memory (ILSTM)},
        keywords = {Air pollution monitoring, Low-cost sensors, RNN, LSTM, Real-time monitoring, XG -BOOST.},
        month = {May},
        }