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Abstract Urban air quality and temperature are closely linked through coupled physical and chemical processes. However, most existing evidence relies on correlation-based associations lacking directionality, or process-based models whose causal pathways depend on model structure and parameterizations. Here we apply a nonlinear causal inference method to quantify directed coupling between near-surface air temperature and three major air pollutants (PM 2.5 , ozone, and NO 2 ) across 481 U.S. urban areas using 14 years of daily data. We identify distinct diurnal and seasonal regimes of temperature–pollution coupling not captured by linear correlation. Temperature–PM 2.5 and temperature–O 3 coupling strengthens in summer, whereas temperature–NO 2 coupling intensifies in winter. Ozone shows the most consistent causal structure among all pollutants, with temperature dominant in roughly 80% of urban areas in both seasons. PM 2.5 exhibits balanced and spatially heterogeneous coupling, while NO 2 shifts from mixed behavior in summer to pronounced temperature dominance in winter. Across pollutants, linear correlations frequently overestimate coupling strength, especially for winter NO 2 . As the first continental-scale causal assessment of urban temperature–pollution interactions in the U.S., this study offers a data-driven complement to process-based modeling. The identified pollutant-specific sensitivities and their regional, diurnal, and seasonal variability provide new insight for understanding and managing urban heat stress and air quality.
Published in: Environmental Research Letters
Volume 21, Issue 6, pp. 064029-064029