https://journals.bilpubgroup.com/index.php/jasr/issue/feed 2026-01-31T00:00:00+08:00 Managing Editor: Lesley Lu jasr@bilpubgroup.com Open Journal Systems <p>ISSN: 2630-5119(Online)</p> <p>Email: jasr@bilpubgroup.com</p> <p>Follow the journal: <a style="display: inline-block;" href="https://twitter.com/jasr_editorial" target="_blank" rel="noopener"><img style="position: relative; top: 5px; left: 5px;" src="https://journals.bilpubgroup.com/public/site/Twitter _logo.jpg" alt="" /></a></p> https://journals.bilpubgroup.com/index.php/jasr/article/view/12273 2025-12-17T14:33:41+08:00 Amaury de Souza amaury.souza@ufms.br Raquel Soares Casaes Nunes quelcasaes@micro.ufrj.br José Francisco de Oliveira Junior jose.junior@icat.ufal.br Ivana Pobocikova ivana.pobocikova@fstroj.uniza.sk Sianny Vanessa da Silva Freitas siannyfreitas2@gmail.com Kelvy Rosalvo Alencar Cardoso cardoso@icat.ufal.br

<p>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.</p>

2026-01-02T00:00:00+08:00 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