Inland Ozone Production Due to Nitrogen Dioxide Transport Downwind of a Coastal Urban Area: A Neural Network Assessment

 The rapid advancement of Information Technology is transforming research in atmospheric and environmental sciences, with Artificial Intelligence and Machine Learning (AI/ML) offering novel tools to explore complex environmental systems. AI/ML techniques have demonstrated significant potential in atmospheric research and pollutant dynamics [6]. Machine learning’s capability to capture non-linear relationships among environmental variables has been validated in prior studies [5]. 

This study leverages a feed-forward neural network (FFNN) to investigate nitrogen dioxide (NO₂) transport from a coastal urban environment in Central Italy to an inland rural area, leading to increased ozone (O₃) production downwind. Such transport phenomena underscore the need to address both direct and transported emissions, as observed in urban-rural gradients worldwide [4,6]. 

By integrating observational data and meteorological parameters, including wind speed and direction alongside NO_x and O₃ levels, the FFNN model effectively predicted O₃ concentrations at the inland site.  Results showed consistently higher O₃ levels at the rural site compared to the urban area, reflecting significant O₃ production during transport. The model exhibited a high correlation (R = 0.82) between observed and predicted O₃ concentrations, underscoring AI’s value in enhancing air pollution dynamics understanding. These findings align with broader research demonstrating AI’s role in refining air quality predictions and improving source attribution [1,2]. 

This study highlights the effectiveness of AI techniques in environmental research, particularly in elucidating interactions between transportation emissions and secondary pollutants like O₃. The results stress the importance of regional air quality modeling and advanced computational approaches in supporting environmental policy and decision-making. AI-driven insights can inform more effective mitigation strategies, enhance air quality forecasting, and assist policymakers in addressing public health concerns related to air pollution [1,2]. Recent reviews emphasize the necessity of integrating AI into air quality management frameworks [7]. 

Additionally, this research underscores the potential of hybrid AI methods and physics-informed machine learning to further improve atmospheric models and source attribution accuracy. Such innovations are critical for advancing air quality modeling and developing targeted strategies to mitigate environmental and public health impacts [7]. 

[1] World Health Organization. (2021). Air pollution and health.

[2] Lelieveld, J. et al. (2019). The contribution of outdoor air pollution sources to premature mortality on a global scale. Nature, 525(7569), 367-371.

[3] Heal, M. R., et al.  (2013). Particles, air quality, policy, and health. Chemical Society Reviews, 41(19), 6606-6630.

[4] Crutzen, P. J. (2006). The role of NO and NO2 in the chemistry of the troposphere and stratosphere. Annual Review of Earth and Planetary Sciences, 7, 443-472.

[5] Jacob, D. J. (1999). Introduction to Atmospheric Chemistry. Princeton University Press.

[6] Monks, P. S., et al. (2015). Tropospheric ozone and its precursors from the urban to the global scale. Atmospheric Chemistry and Physics, 15(15), 8889-8973.

[7] Kumar, P., et al. (2018). Ambient volatile organic compounds in urban environments: Techniques for sampling, analysis, and implications for air quality. Progress in Environmental Science and Technology, 2(1), 3-13.