Abstract

Carbon monoxide (CO) remains an important urban air quality and traffic-exposure tracer despite rarely exceeding regulatory limits in modern European cities. This study presents an interpretable machine learning framework for short-term CO forecasting at two operational horizons—next hour and next-day maximum—applied identically to four automated monitoring stations spanning central-urban, urban, urban-background, and suburban typologies in the Greater Athens Area (2021–2024). Using only univariate CO history and calendar-derived features, four learners (support vector regression, random forest, gradient boosting, and a multilayer perceptron) were benchmarked against a naïve baseline under a chronological train/validation/test split. At the next-hour horizon, all learners outperformed naïve at every station, with gradient boosting achieving the best or joint-best skill (test R2=0.81–0.90); Wilcoxon signed-rank tests confirmed that these small but consistent margins were statistically significant. The next-day-maximum task proved substantially harder (R2=0.44–0.59), with the neural and random forest models overtaking gradient boosting and SVR losing competitiveness. A SHAP (Shapley Additive Explanations) analysis of the next-hour gradient-boosting model showed that the most recent hourly lag dominates the forecast, with an effect nearly an order of magnitude over any other predictor, with hour-of-day encoding and short-lag rolling statistics contributing secondary, sign-consistent effects—providing a transparent, mechanistic account of model behavior rather than a black-box skill score. Unlike ozone, a secondary pollutant whose predictability degrades toward the trafficked urban core, CO concentration and forecastability increase together at the traffic-dominated site, indicating that primary-pollutant forecasts are most reliable precisely where exposure is greatest. These findings support a horizon-specific, interpretable forecasting strategy for operational deployment on real-time monitoring networks.

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