Air quality surveillance through time-series forecasting: a comparative SARIMA analysis of NO ₂ and SO ₂ in Baltimore and New York

Urban air pollutants exhibit distinct temporal dynamics that reflect differences in emission sources, atmospheric processes, and regulatory influences. This study introduces forecast tractability - the extent to which a pollutant's temporal behavior is captured by standard linear seasonal time-series models - as a diagnostic for distinguishing pollutants by source structure. Monthly nitrogen dioxide (NO₂) and sulfur dioxide (SO₂) concentrations in Baltimore and New York (2018-2024) were analyzed under a unified seasonal SARIMA framework, with 24-month forecasts validated against January-September 2025 observations. NO₂ showed a strong, consistent seasonal pattern in both cities, with recurring winter peaks and summer troughs. SO₂ exhibited moderate seasonality with residual variability not fully captured by linear seasonal specifications; residual dependence was particularly evident for Baltimore SO₂, and no structural regime changes were detected for either pollutant. Findings demonstrate that forecast tractability differs systematically: NO₂, dominated by continuous mobile sources, fits well under standard SARIMA specifications, whereas SO₂, particularly in cities with discrete legacy industrial sources, shows wider forecast intervals and a different residual structure under the fixed specification. These differences inform pollutant-specific monitoring and modeling: predictable pollutants suit forecast-driven regulatory planning, while less predictable ones warrant denser sampling or nonlinear approaches.