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

Moazzam Umer Gondal; Hamad ul Qudous; Asma Ahmad Farhan; Sultan Alamri

cs.LGMar 26, 2026

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

Authors: Moazzam Umer Gondal, Hamad ul Qudous, Asma Ahmad Farhan, Sultan Alamri

Organizations: School of Computing, National University of Computer & Emerging Sciences (FAST), Lahore 54000, Pakistan · College of Computing and Informatics, Saudi Electronic University, Riyadh, 11673, Saudi Arabia

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 & tables

#	Task/Target	Data/Region	Method and key highlights
[ 7 ]	IoT AQM + AI (review)	PRISMA 2016–2024	Systematic review; taxonomy (imputation, calibration, anomalies, AQI, short-term forecasting); stresses data quality, scalability, and deployment gaps.
[ 4 ]	PM 2.5 forecasting (spatio-temporal hybrid)	Delhi, India (2018–2023)	BiGRU–1DCNN hybrid integrates recurrent and convolutional layers for spatio-temporal learning across 28 CPCB stations; achieves low RMSE/MAE and strong seasonal trend capture.
[ 19 ]	AQI forecast; statistical vs NN	Beijing, China	ARIMA + ANN ; linear vs nonlinear contrast; hybridization aids robustness; seasonality handling highlighted.
[ 21 ]	Review of AI techniques for air-pollution forecasting	Global survey (2000–2020)	Comprehensive review of AI/ML methods including SVM, ensembles, and DL; highlights evolution from statistical to hybrid models.
[ 27 ]	Univariate short-horizon forecast (pollutants/AQI)	City station series	SARIMA vs Prophet ; additive seasonality and log-transform improve short-horizon accuracy; interpretable baseline for ML/DL.
[ 1 ]	PM 2.5 /PM 10 comparison	UAE monitoring sites	DT/RF/SVR vs CNN/LSTM vs Prophet ; ML competitive across 1–7 day horizons; notes climate-specific transfer.

Table 1: Condensed summary of representative literature.

Fig 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.

Fig 2: Correlation heatmap of candidate variables. The heatmap shows strong linear associations between PM 2.5 and several gaseous precursors, as well as between PM 2.5 and PM 10 .

Fig 3: Mutual information scores for candidate predictors with respect to PM 2.5 . The figure highlights the strongest nonlinear dependencies used to guide feature selection.

Model	MAE	RMSE	Execution time	Remark
NeuralProphet	140.55	228.87	88 min 35.19 s	Full 23-week evaluation completed
Facebook Prophet	37.61	50.10	15 min 21.91 s	Full 23-week evaluation completed
SARIMAX	39.61	56.32	$>$ 100 min	Cumulative results available only up to week 21 because of memory-related termination

Table 2: Overall performance under weekly walk-forward refitting. For SARIMAX, values are cumulative results obtained up to week 21 because the full run could not be completed in the available environment.

Fig 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 PM 2.5 concentration.

Fig 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 PM 2.5 concentration.

Fig 6: Representative weekly forecasts for SARIMAX 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 PM 2.5 concentration.

Model	Base MAE	Corrected MAE	Base RMSE	Corrected RMSE	Execution time
NeuralProphet	145.75	162.14	239.95	247.21	7 min 30.68 s
Facebook Prophet	45.61	37.67	60.39	50.08	0 min 46.60 s
SARIMAX	47.14	32.50	65.45	46.85	12 min 58.94 s

Table 3: Overall performance under frozen forecasting with online residual correction. Base metrics refer to the frozen model without correction, and corrected metrics refer to the same model after online residual correction. Execution time denotes the observed end-to-end runtime for the full regime.

Fig 7: Representative weekly forecasts for NeuralProphet under frozen forecasting with online residual correction. 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 PM 2.5 concentration.

Fig 8: Representative weekly forecasts for Facebook Prophet under frozen forecasting with online residual correction. 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 PM 2.5 concentration.

Fig 9: Representative weekly forecasts for SARIMAX under frozen forecasting with online residual correction. 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 PM 2.5 concentration.

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Air quality prediction estimates future pollutant levels and maps local exposure to support health warnings, planning, and pollution control.

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Global probabilistic forecasting of air pollution

Ammar Kheder, Michael Boy, Zhi-song Liu

Department of Computational Engineering, LUT University, Lahti, Finland · Institute for Atmospheric and Earth System Research, University of Helsinki, Helsinki, Finland · Atmospheric Modelling Centre Lahti (AMC-Lahti), Lahti, Finland

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Fine particulate matter (PM2.5) refers to particulate matter with an aerodynamic diameter of 2.5 micrometre or less. It comes from human activities, such as traffic and industry, and from wildfires, desert dust and gases emitted by vegetation. Short-term exposure to it accounts for about a million deaths a year. Operational forecasts run on grids tens of kilometres wide, which hide the pollution breathed by nearly two hundred million people a day. Machine learning has transformed weather forecasting, but data-driven forecasts of air pollution still blur extremes and lose skill as the air travels. Here we introduce Foehn, a generative model that learns from global kilometre-scale estimates of PM2.5 and computes where the air comes from. Over a year of retrospective forecasts, it outperforms the operational forecast at three stations in four and forecasts at the scale of this hidden exposure. Its ensembles are calibrated, and their mean detects local pollution extremes four days ahead as well as the operational forecast does one day ahead. Such forecasts could warn the millions whom coarse maps miss, once kilometre-scale data are available in real time.

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