Big Data–Driven Urban Air Quality Forecasting and Pollution Source Attribution

Abstract

Urban air pollution is one of the most alarming issues in the context of environmental and health concerns. The rapid growth of the urban population and industrialization are factors that have significantly contributed to the deterioration of air quality in urban areas. The adverse effects of high concentrations of air pollutants such as particulate matter (PM2.5 and PM10), nitrogen dioxide (NO?), sulfur dioxide (SO?), carbon monoxide (CO), and ozone (O?) have been related to different types of health hazards and even death. With the advent of modern technology in the context of environmental monitoring, massive amounts of data related to air quality have been generated using IoT devices, meteorological data sources, and public data sources. The presence of such data presents an opportunity to apply Big Data analytics and machine learning algorithms to precisely predict and analyze air quality. The limitation of traditional statistical methods in dealing with complex relationships and large amounts of data makes it essential to apply advanced computational methods. This paper presents a comprehensive Big Data-based framework for air quality prediction and pollution source identification in the context of air quality management. The proposed system uses air quality information and meteorological information, and machine learning algorithms such as Random Forest, XGBoost, and Long Short-Term Memory are used for air quality prediction in the context of Air Quality Index.

Introduction

Air pollution is a major global issue caused by urbanization, industrialization, and increased vehicle use. Harmful pollutants like PM2.5, nitrogen oxides, and ozone pose serious risks to human health and the environment. Traditional air quality monitoring methods, based on statistical models, struggle to handle complex, non-linear relationships and large-scale data, and they often fail to identify pollution sources.

To address these challenges, the study proposes a Big Data-driven air quality prediction and source identification system. The system uses a multi-layer architecture including data collection (from IoT sensors and monitoring stations), data processing (using technologies like Hadoop and Spark), machine learning models (Random Forest, XGBoost), and deep learning (LSTM) for accurate forecasting. It also includes a pollution source attribution layer using techniques like correlation and clustering to identify major pollution sources such as traffic and industry.

The methodology involves data collection, preprocessing, feature engineering, model development, prediction, and evaluation. Compared to traditional models, machine learning and deep learning approaches provide higher accuracy and better handling of complex data.

Results show that XGBoost and LSTM models achieve high prediction accuracy, while Random Forest effectively handles non-linear and missing data. The system also enables real-time monitoring and visualization through dashboards and alerts.

Overall, the proposed system improves air quality prediction, identifies pollution sources, and supports better environmental decision-making, making it a scalable and effective solution for smart city applications.

Conclusion

This paper proposed a comprehensive Big Data framework for air quality forecasting in an urban environment. The proposed framework integrated various machine learning models such as Random Forest, XGBoost, and LSTM with Big Data technologies for accurate air quality forecasting. The proposed system has the advantage of overcoming the limitations of the conventional methods of air quality forecasting. This is because the proposed system efficiently handles the complex relationships between the environmental factors. Therefore, the proposed system can be utilized to accurately forecast the air quality in the urban environment. The proposed system can be utilized to accurately predict the air quality in the urban environment with the aid of the LSTM model. The proposed system can be utilized to derive valuable insights about the air quality with the aid of the XGBoost model. The proposed system can be utilized to attribute the pollution sources. This would enhance the overall utility of the proposed system. The proposed framework would be utilized to efficiently solve the air quality management problem in the urban environment. This would enhance the concept of sustainability.

References

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Copyright

Copyright © 2026 Kalaivani Sri R, Akshayaa M, Arthi T, Gayathri V, Kanchana S. 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.