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<p>Understanding the temporal dynamics of sulfur dioxide (SO<sub>₂</sub>) in tropical urban environments is essential for evaluating air quality trends and supporting environmental management policies. This study investigates daily SO<sub>₂</sub> variability in Campo Grande, Brazil, from 2003 to 2022, integrating meteorological information into a multivariate autoregressive state space (MARSS) model. SO<sub>₂</sub> data were obtained from SISAM/INPE (based on the CAMS global reanalysis), and meteorological covariates (precipitation, air temperature, relative humidity, and wind speed) were extracted from the MERRA-2 reanalysis. The MARSS framework was used to (i) characterize long-term and seasonal behavior, (ii) quantify the contribution of meteorological drivers to SO<sub>₂</sub> variability, and (iii) identify pollution episodes not explained by atmospheric factors. SO<sub>₂</sub> concentrations showed a decreasing long-term trend, with most values remaining below 10&#xa0;μg&#xa0;m⁻<sup>3</sup> and higher peaks occurring during the dry season (June–September). Including meteorological covariates improved model performance by ~ 41% (<i>R</i><sup>2</sup>: 0.41–0.57). Temperature was positively associated with SO<sub>₂</sub>, whereas precipitation, humidity, and wind speed contributed to dilution and removal processes. Residual analyses highlighted extreme events mainly in the dry season, likely linked to atmospheric stability and episodic anthropogenic emissions such as biomass burning and local combustion sources. Overall, the results indicate that MARSS models provide a robust and interpretable approach for diagnosing SO<sub>₂</sub> variability and separating meteorological control from emission-driven anomalies in tropical urban regions, supporting meteorology-informed mitigation and air quality management strategies.</p>

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Environmental drivers of sulfur dioxide variability in a tropical urban region: a state space modeling approach for Campo Grande, Brazil

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月30日

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  • Amaury de Souza,
  • José Francisco de Oliveira Júnior,
  • Fernando Lucambio Pérez,
  • Kelvy Rosalvo Alencar Cardoso

摘要

Understanding the temporal dynamics of sulfur dioxide (SO₂) in tropical urban environments is essential for evaluating air quality trends and supporting environmental management policies. This study investigates daily SO₂ variability in Campo Grande, Brazil, from 2003 to 2022, integrating meteorological information into a multivariate autoregressive state space (MARSS) model. SO₂ data were obtained from SISAM/INPE (based on the CAMS global reanalysis), and meteorological covariates (precipitation, air temperature, relative humidity, and wind speed) were extracted from the MERRA-2 reanalysis. The MARSS framework was used to (i) characterize long-term and seasonal behavior, (ii) quantify the contribution of meteorological drivers to SO₂ variability, and (iii) identify pollution episodes not explained by atmospheric factors. SO₂ concentrations showed a decreasing long-term trend, with most values remaining below 10 μg m⁻3 and higher peaks occurring during the dry season (June–September). Including meteorological covariates improved model performance by ~ 41% (R2: 0.41–0.57). Temperature was positively associated with SO₂, whereas precipitation, humidity, and wind speed contributed to dilution and removal processes. Residual analyses highlighted extreme events mainly in the dry season, likely linked to atmospheric stability and episodic anthropogenic emissions such as biomass burning and local combustion sources. Overall, the results indicate that MARSS models provide a robust and interpretable approach for diagnosing SO₂ variability and separating meteorological control from emission-driven anomalies in tropical urban regions, supporting meteorology-informed mitigation and air quality management strategies.