ArticleScientific reports2025
Unravelling the importance of spatial and temporal resolutions in modeling urban air pollution using a machine learning approach.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Urban air pollution poses a major threat to public health and environmental sustainability. This study proposes a structured machine learning (ML)-based framework to examine how temporal and spatial resolution choices affect the accuracy of urban air pollution modeling. The research is conducted in two distinct phases. In the temporal phase, the impact of incorporating pollutant autocorrelation into ML models (Multilayer Perceptron (MLP) and Random Forest (RF)) is analyzed by comparing results with and without temporal lag features derived through autoregressive (AR) modeling. In the spatial phase, emission inventory data are aggregated at three spatial resolutions (500 m, 750 m, and 1000 m) to evaluate their effect on model performance in predicting PM and NOx concentrations. Results from the temporal modeling phase indicate that including lag features significantly improves PM predictions: RMSE for PM₁₀ is reduced by 25.9% (from 92.56 µg/m
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