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@article{195449,
        author = {A. Rajashekar Reddy and G. Swetha and N. V. N. Satwika and J. Sivani and Ch. Siri Harshini},
        title = {SMOGSENSE : PREDICTIVE ANALYTICS FOR AIR QUALITY INDEX},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {1269-1277},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=195449},
        abstract = {SmogSense is a comprehensive data-driven project designed to evaluate, inter- pret, and predict air quality con- ditions across multiple urban regions. The study integrates extensive datasets comprising Air Quality Index (AQI), carbon dioxide (CO2) concentration, meteorological factors, and city- specific demographic indicators to uncover hidden pollution patterns and long-term environmental trends. Using advanced data analytics techniques, the project conducts multi-dimensional visual exploration to highlight correlations between air quality deterioration and key urban elements such as rapid population expansion, industrial emissions, and abrupt socio-economic disturbances, including nationwide lockdowns. In addition to descriptive analysis, SmogSense employs robust machine learning models to generate accurate AQI predictions for future time intervals. These models are rigorously validated to ensure reliability and practical applicability in real-world scenarios. The outcome of this predictive modeling provides early warnings, supports pollution-control planning, and enables stakeholders to take proactive measures. The project’s results offer actionable insights for government bodies, environmental researchers, urban planners, and smart-city developers by identifying pollution hotspots, assessing temporal variations, and evaluating the effectiveness of existing mitigation policies. Ultimately, SmogSense contributes toward sustainable environmental management by supporting evidence-based decision-making in area such as traffic optimization, emission reduction strategies, green infrastructure development, and public health protection. Through these insights, the initiative aims to promote cleaner air, healthier communities, and long-term ecological resilience.},
        keywords = {Air Quality Index (AQI), Predictive Analytics, Ma- chine Learning Models, Data Analytics, Time Series Prediction, Data Visualization, Environmental Data Mining, Feature Correlation Analysis, Meteorological Data Integration, Urban Pollution Modeling, CO2 Concentration Analysis, Predictive Modeling.},
        month = {April},
        }