Seasonal Patterns and Forecasting of CO and Ozone Using Singular Spectrum Analysis in a Tropical Urban Environment

Authors

  • Amaury de Souza

    Institute of Physics, University of Mato Grosso do Sul, Campo Grande 79070-900, Brazil
  • Raquel Soares Casaes Nunes

    Saude-Decania Science Center, Federal University of Rio de Janeiro, Rio de Janeiro 21941-901, Brazil
  • José Francisco de Oliveira Junior

    Institute of Atmospheric Sciences (ICAT), University of Alagoas, Maceió 57072-900, Brazil
  • Ivana Pobocikova

    Department of Applied Mathematics, Faculty of Mechanical Engineering, University of Zilina, 010 26 Zilina, Slovakia
  • Sianny Vanessa da Silva Freitas

    Institute of Tropical Diseases, Federal University of Pará, Belém 66075-900, Brazil
  • Kelvy Rosalvo Alencar Cardoso

    Institute of Atmospheric Sciences (ICAT), University of Alagoas, Maceió 57072-900, Brazil

DOI:

https://doi.org/10.30564/jasr.v9i1.12273
Received: 1 November 2025 | Revised:17 December 2025 | Accepted: 25 December 2025 | Published Online: 2 January 2026

Abstract

Singular Spectrum Analysis (SSA) was applied to daily time series of carbon monoxide (CO) and ozone (O₃) observed between 2000 and 2018 in Campo Grande, MS, Brazil, to identify seasonal patterns, long-term variability, and evaluating the predictive capacity of the technique. The methodology involved the decomposition of the series into structural components and subsequent prediction using the Linear Recurrence Formula (LRF). The analysis revealed strong and persistent annual seasonality for both pollutants, particularly for CO, whose maximum concentrations occur between August and October, coinciding with the dry season and intensified biomass-burning activity. SSA proved effective in extracting low-frequency components, including trend and seasonal cycles, providing a clear representation of the dominant temporal structure of both pollutants. Forecasting results indicated that SSA-LRF successfully reproduced the main seasonal behavior of O₃, while daily prediction skill remained limited, as reflected by negative R² values during the validation period. For CO, the highly irregular and episodic nature of fire-related peaks resulted in larger forecast errors and reduced predictive skill. These results highlight that univariate SSA is more suitable for reconstructing and predicting low-frequency pollutant dynamics than short-term daily variability. The findings demonstrate that SSA is a robust exploratory and decomposition tool for air-quality time series in tropical environments, particularly for identifying seasonal and structural patterns. For operational forecasting of pollutants with strong volatility, such as CO, hybrid approaches combining SSA with statistical or machine-learning models are recommended to improve predictive performance.

Keywords:

Singular Spectrum Analysis; Air Pollution; CO; O₃; Seasonal Forecast

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How to Cite

Souza, A. de, Nunes, R. S. C., Oliveira Junior, J. F. de, Pobocikova, I., Freitas, S. V. da S., & Cardoso, K. R. A. (2026). Seasonal Patterns and Forecasting of CO and Ozone Using Singular Spectrum Analysis in a Tropical Urban Environment. Journal of Atmospheric Science Research, 9(1), 1–15. https://doi.org/10.30564/jasr.v9i1.12273

Issue

Vol. 9 , Iss. 1 (January 2026): In Progress

Article Type

ARTICLE

License

Copyright © 2026 Amaury de Souza, Raquel Soares Casaes Nunes, José Francisco de Oliveira Junior, Ivana Pobocikova, Sianny Vanessa da Silva Freitas, Kelvy Rosalvo Alencar Cardoso

Creative Commons License

This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.

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