Optimized and Explainable Air Quality Index Classification System

Air pollution has become a major environmental and public health concern due to rapid urbanization, industrial growth, and increasing vehicular emissions. High concentrations of pollutants such as PM2.5, PM10, NO₂, SO₂, CO, and O₃ can significantly impact human health and environmental sustainability. Accurate monitoring and prediction of air quality are therefore essential for effective environmental management and public safety. This paper presents AirAware, a machine learning–based system designed to predict and monitor Air Quality Index (AQI) levels using historical air pollution data and real-time environmental information. The system utilizes the XGBoost algorithm to analyze pollutant parameters and generate accurate AQI predictions and classifications. Data preprocessing techniques such as cleaning, normalization, and SMOTE-based class balancing are applied to improve model performance and ensure reliable predictions across different AQI categories. In addition, the system integrates real-time air pollution data through the OpenWeather API, enabling continuous monitoring of current environmental conditions. The predicted AQI values and pollution trends are displayed through a web-based dashboard, allowing users to visualize air quality patterns and compare real-time data with machine learning predictions. By combining machine learning techniques with real-time data integration, the proposed system provides an effective solution for air quality prediction, monitoring, and environmental awareness.

Air Quality Index, XGBoost, Explainable AI, SHAP, SMOTE, Machine Learning.

1. S. Tırınk, “Machine Learning-Based Forecasting of Air Quality Index under Long-Term Environmental Patterns: A Comparative Approach with XGBoost, LightGBM, and SVM,” PLOS One, vol. 20, no. 10, 2025. [Google Scholar] [Crossref]

2. S. Singh, M. Kumar, V. Sengar, A. Kumar, K. Abhishek, and B. M. A. Shafeeq, “Ensemble Learning for Air Quality Index Prediction: Integrating Gradient Boosting, XGBoost, and Stacking with SHAP-Based Interpretability,” Scientific Reports, 2026. [Google Scholar] [Crossref]

3. G. Ravindiran, G. Hayder, K. Kanagarathinam, A. Alagumalai, and C. Sonne, “Air Quality Prediction by Machine Learning Models: A Predictive Study on the Indian Coastal City of Visakhapatnam,” Chemosphere, vol. 338, 2023. [Google Scholar] [Crossref]

4. S. Kasim, S. Malek, S. Cheen, P. N. Fatin, K. X. Ning, H. Hamidi, W. A. Wan Ahmad, K. S. Ibrahim, K. Negishi, M. N. Sulaiman, and A. Fong, “Machine Learning-Based Prediction of Mortality Risk from Air Pollution-Induced Acute Coronary Syndrome in the Western Pacific Region,” Scientific Reports, 2026. [Google Scholar] [Crossref]

5. P. Patel, S. Patel, K. Shah, K. Desai, S. Patel, and M. Shah, “A Systematic Study on PM2.5 and PM10 Concentration Prediction in Air Pollution Using Machine Learning and Deep Learning Model,” Environmental Chemistry and Ecotoxicology, vol. 7, 2025. [Google Scholar] [Crossref]

6. T. M. T. Lei, S. C. W. Ng, and S. W. I. Siu, “Application of ANN, XGBoost, and Other Machine Learning Methods to Forecast Air Quality in Macau,” Sustainability, vol. 15, 2023. [Google Scholar] [Crossref]

7. F. P. Arifianti and A. Salam, “XGBoost and Random Forest Optimization Using SMOTE to Classify Air Quality,” Advance Sustainable Science, Engineering and Technology (ASSET), vol. 6, no. 1, 2024. [Google Scholar] [Crossref]

8. S. Chakraborty, B. Misra, and N. Dey, “Explainable Artificial Intelligence (XAI) for Air Quality Assessment,” in Design Studies and Intelligence Engineering, IOS Press, 2024. [Google Scholar] [Crossref]

9. M. W. Jalali, B. Saidi, H. Farahmand, M. A. R. Panah, and E. N. Saruhan, “Scalable AI Driven Air Quality Forecasting and Classification for Public Health Applications,” Discover Atmosphere, vol. 3, 2025. [Google Scholar] [Crossref]

10. Q. Liu, B. Cui, and Z. Liu, “Air Quality Class Prediction Using Machine Learning Methods Based on Monitoring Data and Secondary Modeling,” Atmosphere, vol. 15, 2024. [Google Scholar] [Crossref]

11. J. David Sukeerthi Kumar, P. Rizwana, M. Pushpanjali, Y. Suma Mallika, P. Akshaya Sree, and T. Meghana, “Air Quality Index Forecasting via Genetic Algorithm-Based Improved Extreme Learning Machine,” International Journal of Progressive Research in Engineering Management and Science (IJPREMS), vol. 4, no. 4, pp. 1784–1790, 2024. [Google Scholar] [Crossref]

12. S. Pavani Muthyala, A. Anju Shaik, V. Reddy Peram, and N. Sai Priyanka, “Air Quality Index Forecasting via Genetic Algorithm-Based Improved Extreme Learning Machine,” Journal of Nonlinear Analysis and Optimization, vol. 15, no. 1, 2024. [Google Scholar] [Crossref]

13. S. Kumar Natarajan, P. Shanmurthy, D. Arockiam, B. Balusamy, and S. Selvarajan, “Optimized Machine Learning Model for Air Quality Index Prediction in Major Cities in India,” Scientific Reports, vol. 14, 2024. [Google Scholar] [Crossref]

14. M. Karmoude, B. Munhungewarwa, I. Chiraira, R. McKenzie, J. Kong, B. Smith, G. Ayana, N. Njara, T. Mathaha, M. Kumar, and B. Mellado, “Machine Learning for Air Quality Prediction and Data Analysis: Review on Recent Advancements, Challenges, and Outlooks,” Science of the Total Environment, vol. 1002, 2025. [Google Scholar] [Crossref]

15. C. Liu, G. Pan, D. Song, and H. Wei, “Air Quality Index Forecasting via Genetic Algorithm-Based Improved Extreme Learning Machine,” IEEE Access, vol. 11, 2023. [Google Scholar] [Crossref]

16. C. Liu, G. Pan, D. Song, and H. Wei, “Air Quality Index Forecasting via Genetic Algorithm-Based Improved Extreme Learning Machine,” IEEE Access, 2023. [Google Scholar] [Crossref]

17. S. Kumar Natarajan, P. Shanmurthy, D. Arockiam, B. Balusamy, and S. Selvarajan, “Optimized Machine Learning Model for Air Quality Index Prediction in Major Cities in India,” Scientific Reports, vol. 14, 2024. [Google Scholar] [Crossref]

• Enhancing Formation Control of Multi Agent Systems Using Ann Based Technique
• Improving Sliding Mode Control with Chattering Reduction using Fuzzy Based Technique
• Cooking Quality, Fasting Blood Glucose, Glycemic Index and Load of High–Fiber Noodles Made from Wheat, Tiger Nut Residue and Cassava Flour Blends
• Matrix Rhythm Therapy Versus Interferential Therapy Combined with Lumbar Stabilization Exercises in Chronic Non-Specific Low Back Pain: A Randomized Comparative Trial
• Formulation and Sensory Evaluation of Functional Cake Prepared from Sweet Potato Powder