EcoCast : Predictive Modeling of Urban Air Quality Indices Using Spatiotemporal

Abstract

Air pollution has emerged as a critical environmental challenge affecting public health and ecosystem sustainability worldwide. The Air Quality Index serves as a standardized metric for communicating air pollution levels to the general public. This research presents a comprehensive web-based application for predicting AQI values using machine learning techniques, specifically employing Linear Regression algorithms. The system accepts seven key pollutant parameters including ammonia, particulate matter (PM2.5 and PM10), ozone, nitrogen dioxide, sulfur dioxide, and carbon monoxide as input features. The model was trained on historical air quality data and achieved promising prediction accuracy. The implementation utilizes Python Flask framework for backend processing, scikit-learn for machine learning operations, and responsive HTML/CSS/JavaScript for frontend development. The application provides real-time AQI predictions categorized into six quality levels ranging from Good to Severe, enabling users to make informed decisions regarding outdoor activities and health precautions. Performance evaluation demonstrates the model\'s capability to accurately predict AQI values with minimal error margins. This system offers a practical tool for environmental monitoring agencies, healthcare professionals, and general public to assess air quality conditions effectively.

Introduction

Air quality has become a major global public health concern due to rapid urbanization, industrial emissions, and vehicular pollution. Poor air quality contributes to millions of premature deaths annually, making accurate monitoring and prediction essential. The Air Quality Index (AQI) provides a standardized way to communicate pollution levels, but traditional monitoring stations only offer historical data without predictive abilities.

To address this gap, the study develops a machine-learning-based web application capable of predicting AQI in real time. Using seven pollutants—NH?, PM2.5, PM10, O?, NO?, SO?, and CO—the system employs linear regression, a simple yet effective machine learning algorithm, to model the relationship between pollutant concentrations and AQI values. The trained model is deployed using a Flask-based backend and an interactive, responsive HTML/CSS/JavaScript frontend, enabling users to input pollutant levels and instantly receive AQI predictions along with categorized health advisories.

The literature review shows that machine learning, including regression, RNNs, and LSTM models, significantly enhances air quality prediction. Linear regression remains highly effective for short-term forecasting due to its transparency and low computational cost.

The system architecture includes data preprocessing, model training with a 70:30 train-test split, model serialization using pickle, and REST-based interaction between frontend and backend. The predicted AQI values are classified into six standard categories: Good, Satisfactory, Moderately Polluted, Poor, Very Poor, and Severe.

Evaluation results indicate strong model performance, with the regression model accurately capturing pollutant–AQI relationships and providing reliable category predictions. The web application demonstrates high responsiveness, cross-browser compatibility, and positive user feedback for usability and clarity.

While advanced models may offer slightly higher accuracy, linear regression strikes an optimal balance between simplicity, interpretability, and efficiency for real-time deployment. Future improvements could include historical trend visualization and location-based prediction features.

Conclusion

This research successfully developed and implemented a comprehensive web-based system for Air Quality Index prediction using machine learning techniques. The linear regression model demonstrates reliable performance in forecasting AQI values based on seven key pollutant parameters, providing accurate predictions suitable for real-world applications. The integration of Flask framework for backend processing and responsive web technologies for frontend presentation creates a user-friendly platform accessible to diverse stakeholders including environmental agencies, healthcare professionals, and general public. The system addresses the critical need for accessible air quality prediction tools by offering real-time AQI forecasts with categorical classifications aligned with international standards. Performance evaluation confirms the model\'s capability to accurately predict air quality conditions across various pollution levels, enabling users to make informed decisions regarding outdoor activities and health precautions. The modular architecture facilitates future enhancements and integration with additional data sources or predictive algorithms. Future research directions include incorporating temporal dependencies through time series models, expanding the feature set to include meteorological parameters, implementing ensemble learning approaches for improved accuracy, and developing mobile applications for enhanced accessibility. The integration of real-time data feeds from monitoring stations would enable continuous prediction updates, transforming the system into a comprehensive air quality forecasting platform. Additionally, explainable AI techniques could be incorporated to provide users with insights into which pollutants contribute most significantly to predicted AQI values. The successful implementation of this system demonstrates the practical applicability of machine learning technologies in environmental monitoring and public health protection. By combining rigorous scientific methodologies with accessible technology platforms, this research contributes to the broader objective of creating healthier, more sustainable urban environments through data-driven decision-making and proactive environmental management.

References

[1] A. Kumar, P. Goyal, and A. K. Gupta, \"Air quality forecasting using advanced machine learning techniques: A review,\" Atmospheric Environment, vol. 235, pp. 117-129, 2020. [2] S. Zhang, B. Guo, A. Dong, J. He, Z. Xu, and S. X. Chen, \"Cautionary tales on air-quality improvement in Beijing,\" Proceedings of the Royal Society A, vol. 473, no. 2205, pp. 20170457, 2017. [3] J. Ma, J. C. Cheng, C. Jiang, W. Chen, and R. Zhang, \"Real-time prediction of air quality index based on LSTM neural network,\" in Proc. International Conference on Smart Infrastructure and Construction, Cambridge, UK, 2019, pp. 255-262. [4] R. Patel, N. Shah, and M. Desai, \"Cloud-based environmental monitoring system with machine learning integration,\" IEEE Access, vol. 8, pp. 145623-145635, 2020. [5] L. Breiman, \"Random forests,\" Machine Learning, vol. 45, no. 1, pp. 5-32, 2001. [6] World Health Organization, \"WHO global air quality guidelines: Particulate matter (PM2.5 and PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide,\" Geneva, Switzerland, 2021. [7] T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed. New York, NY: Springer, 2009. [8] M. Grinberg, Flask Web Development: Developing Web Applications with Python, 2nd ed. Sebastopol, CA: O\'Reilly Media, 2018. [9] F. Pedregosa et al., \"Scikit-learn: Machine learning in Python,\" Journal of Machine Learning Research, vol. 12, pp. 2825-2830, 2011. [10] European Environment Agency, \"Air quality in Europe — 2020 report,\" EEA Report No. 09/2020, Copenhagen, Denmark, 2020. [11] S. Rao, \"Development of web-based air quality monitoring and forecasting system using machine learning,\" M.S. thesis, Dept. Computer Science, Stanford University, Stanford, CA, 2019. [12] Central Pollution Control Board, \"National Air Quality Index,\" Ministry of Environment, Forest and Climate Change, Government of India, New Delhi, India, Tech. Rep. AQI-2014, 2014.

Copyright

Copyright © 2025 Nagaveni ., Shilpa Joshi . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.