Interpretable PM2.5 Forecasting for Urban Air Quality: A Comparative Study of Operational Time-Series Models

Abstract

Accurate short-term air-quality forecasting is essential for public health protection and urban management, yet many recent forecasting frameworks rely on complex, data-intensive, and computationally demanding models. This study investigates whether lightweight and interpretable forecasting approaches can provide competitive performance for hourly PM2.5 prediction in Beijing, China. Using multi-year pollutant and meteorological time-series data, we developed a leakage-aware forecasting workflow that combined chronological data partitioning, preprocessing, feature selection, and exogenous-driver modeling under the Perfect Prognosis setting. Three forecasting families were evaluated: SARIMAX, Facebook Prophet, and NeuralProphet. To assess practical deployment behavior, the models were tested under two adaptive regimes: weekly walk-forward refitting and frozen forecasting with online residual correction. Results showed clear differences in both predictive accuracy and computational efficiency. Under walk-forward refitting, Facebook Prophet achieved the strongest completed performance, with an MAE of $37.61$ and an RMSE of $50.10$, while also requiring substantially less execution time than NeuralProphet. In the frozen-model regime, online residual correction improved Facebook Prophet and SARIMAX, with corrected SARIMAX yielding the lowest overall error (MAE $32.50$; RMSE $46.85$). NeuralProphet remained less accurate and less stable across both regimes, and residual correction did not improve its forecasts. Notably, corrected Facebook Prophet reached nearly the same error as its walk-forward counterpart while reducing runtime from $15$ min $21.91$ sec to $46.60$ sec. These findings show that lightweight additive forecasting strategies can remain highly competitive for urban air-quality prediction, offering a practical balance between accuracy, interpretability, ...

Figures (9)

• Figure 1: Framework of the proposed forecasting workflow. The workflow shows data preparation, chronological train–test partitioning, the forecasting setup, benchmarking of three forecasting model families, comparison of two operational regimes, and performance evaluation for air quality forecasting.
• Figure 2: Correlation heatmap of candidate variables. The heatmap shows strong linear associations between PM2.5 and several gaseous precursors, as well as between PM2.5 and PM10.
• Figure 3: Mutual information scores for candidate predictors with respect to PM2.5. The figure highlights the strongest nonlinear dependencies used to guide feature selection.
• Figure 4: Representative weekly forecasts for NeuralProphet under weekly walk-forward refitting. The plot shows the best weekly forecast in the upper panel and the worst weekly forecast in the lower panel across the rolling evaluation. The x-axis denotes hour of week (0--168), and the y-axis denotes PM2.5 concentration.
• Figure 5: Representative weekly forecasts for Facebook Prophet under weekly walk-forward refitting. The plot shows the best weekly forecast in the upper panel and the worst weekly forecast in the lower panel across the rolling evaluation. The x-axis denotes hour of week (0--168), and the y-axis denotes PM2.5 concentration.