Understanding how local land use and land cover (LULC) shapes intra-urban concentrations of atmospheric pollutants—and thus human health—is a key component in designing healthier cities. Here, NO₂ is modeled based on spatially dense summer and winter NO₂ observations in Portland-Hillsboro-Vancouver (USA), and the spatial variation of NO₂ with LULC investigated using random forest, an ensemble data learning technique. The NO₂ random forest model, together with BenMAP, is further used to develop a better understanding of the relationship among LULC, ambient NO₂ and respiratory health. The impact of land use modifications on ambient NO₂, and consequently on respiratory health, is also investigated using a sensitivity analysis. We find that NO₂ associated with roadways and tree-canopied areas may be affecting annual incidence rates of asthma exacerbation in 4–12 year olds by +3000 per 100,000 and −1400 per 100,000, respectively. Our model shows that increasing local tree canopy by 5% may reduce local incidences rates of asthma exacerbation by 6%, indicating that targeted local tree-planting efforts may have a substantial impact on reducing city-wide incidence of respiratory distress. Our findings demonstrate the utility of random forest modeling in evaluating LULC modifications for enhanced respiratory health.

Reducing exposure to degraded air quality is essential for building healthy cities. Although air quality and population vary at fine spatial scales, current regulatory and public health frameworks assess human exposures using county- or city-scales. We build on a spatial analysis technique, dasymetric mapping, for allocating urban populations that, together with emerging fine-scale measurements of air pollution, addresses three objectives: (1) evaluate the role of spatial scale in estimating exposure; (2) identify urban communities that are disproportionately burdened by poor air quality; and (3) estimate reduction in mobile sources of pollutants due to local tree-planting efforts using nitrogen dioxide. Our results show a maximum value of 197% difference between cadastrally-informed dasymetric system (CIDS) and standard estimations of population exposure to degraded air quality for small spatial extent analyses, and a lack of substantial difference for large spatial extent analyses. These results provide the foundation for improving policies for managing air quality, and targeting mitigation efforts to address challenges of environmental justice.