Abstract

Disentangling the relative roles of emissions and atmospheric processes in controlling air-pollutant concentrations remains a central challenge in air-quality management. Although machine-learning (ML) models can accurately predict pollutant concentrations, the temporal evolution of the importance of individual drivers is often difficult to interpret. Here, we introduce a framework for reconstructing time-resolved feature importance (FI) in ML air-quality models. Hourly NO2 and PM2.5 concentrations were simulated across clusters of observations, defined along concentration trajectories in a state–space spanned by source intensity and meteorological variables. Within each cluster, predictor importance is quantified and mapped back onto the corresponding time points, yielding continuous FI time series for all predictors. The framework is demonstrated using observations from the nationwide air-quality network in Israel, together with traffic-related source indicators and meteorological parameters. The dominant drivers differ markedly between the two pollutants: NO2 variability is primarily associated with local emissions, mechanical transport, and turbulent mixing, whereas PM2.5 variability reflects predictors that are related to nucleation, coagulation, hygroscopic growth, long-range transport, and chemical transformation. The feature importance exhibits pronounced seasonal, regional, and diurnal variability, including modulation around traffic rush hours. These results demonstrate the value of time-resolved interpretability for diagnosing drivers of air-pollutant variability and improving the representation of processes in statistical air-quality models.

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