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Air pollution is a major threat to public health. However, two issues have not been adequately addressed in most conventional Land Use Regression models for air pollution prediction: 1). A combination of urban forest involvement and urban form representation; 2). Scale sensitivity analysis of model variables. Here, we apply lacunarity to investigate the spatial sensitivity of predictors, incorporate 2-D and 3-D urban form to comprehensively characterize the urban environment, and examine the tree diversity impacts on air pollution distribution using unique NO 2 datasets collected through opportunistic mobile monitoring in the Bronx, New York, and Oakland, California. We find that lacunarity-optimized models could reduce the computation burden by extracting the upper limits of the spatial heterogeneity of predictors while keeping the model accuracy simultaneously. Furthermore, there are synthetic effects between the urban form and tree diversity on NO 2 distribution, and such effect directions could be non-monotonic. Finally, although the increase in tree diversity could facilitate the reduction of regional NO 2 concentration, it is essential to seek a balance between tree diversity and tree dominance to effectively improve air quality on the city scale. The findings are useful for environmental scientists striving for better air quality and urban planners caring for the well-being of cities. [Display omitted] • Examined the interaction between the urban forest, physical environment, and air quality with mobile measurements. • Characterized the urban environment through both 2D and 3D urban morphological measurements. • Quantified spatial heterogeneity of regression predictors using lacunarity analysis. • The synthetic effects of the urban form and tree diversity on NO 2 distribution could be non-monotonic. • Seek a balance between tree diversity and tree dominance is essential to effectively improve air quality on the city scale. [ABSTRACT FROM AUTHOR]