Spatiotemporal Analysis of Urban Air Quality in Visakhapatnam, Andhra Pradesh:  Integrating Machine Learning Models for AQI Forecasting and Pollutant Attribution

P. Srivyshnavi, Sreenivasulu T ,P Shankaraiah , M Darshan Teja

Spatiotemporal Analysis of Urban Air Quality in Visakhapatnam, Andhra Pradesh: Integrating Machine Learning Models for AQI Forecasting and Pollutant Attribution

Authors

P. Srivyshnavi, Sreenivasulu T ,P Shankaraiah , M Darshan Teja

Keywords:

Air Quality Index; PM2.5; PM10; Visakhapatnam; Random Forest; XGBoost; LSTM; Spatiotemporal Analysis

Abstract

Urban air quality deterioration in rapidly industrialising coastal cities poses a substantial public health challenge, particularly in megapolitan growth corridors of South Asia. This study undertakes a comprehensive spatiotemporal analysis of air quality in Visakhapatnam (Vizag), Andhra Pradesh a city experiencing simultaneous industrial expansion, port-driven commercial activity, and population growth.

References

Balakrishnan, K., Dey, S., Gupta, T., Dhaliwal, R. S., Brauer, M., Cohen, A. J., & Lim, S. S. (2019). The impact of air pollution on deaths, disease burden, and life expectancy across the states of India: The Global Burden of Disease Study 2017. The Lancet Planetary Health, 3(1), e26–e39.

Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.

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Published

2026-06-05

How to Cite

P. Srivyshnavi, Sreenivasulu T ,P Shankaraiah , M Darshan Teja. (2026). Spatiotemporal Analysis of Urban Air Quality in Visakhapatnam, Andhra Pradesh: Integrating Machine Learning Models for AQI Forecasting and Pollutant Attribution . Journal of Computational Analysis and Applications (JoCAAA), 34(12), 1212–1221. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/5531