Incorporating tree diversity for a better understanding of urban form-air quality relationships through mobile monitoring

Abstract Context: Air pollution is a major threat to landscape ecology and public health. The conventional LUR (Land Use Regression) method has been developed mostly based on 2-D urban form and emission source information. Besides, the effects of tree diversity on air quality have not been adequately addressed. Objectives: This study explores the integration of 2-D and 3-D urban form metrics and examines the impact of tree diversity in urban form-air quality relationships. Methods: We introduce these predictors into the LUR tools using unique NO2 datasets collected through opportunistic mobile monitoring in the Bronx, New York, and Oakland, California, and further apply lacunarity to investigate the spatial scale sensitivity for regression predictors. Results: The lacunarity-optimized model helps to reduce the computation burden by finding the upper limit of the spatial heterogeneity of predictors while keeping the model accuracy in both Bronx (R2≈0.50) and Oakland (R2≈0.79). Furthermore, although deciduous trees are surrounded by the highest NO2 concentrations (9.73 ppb and 3.61 ppb in the Bronx and Oakland, respectively), the increase in tree diversity could facilitate the reduction of NO2 concentration. However, according to the non-monotonic and marginal effects of tree diversity on NO2 concentration, a higher level of tree diversity is not always better. Conclusions: It is reasonable to seek a balance between the diversity and dominance of tree species to effectively improve air quality on the city scale. The findings are useful for both environmental scientists striving for better air quality and urban planners that care for the well-being of cities.