Global Land Use Regression Model for Nitrogen Dioxide Air Pollution.

Nitrogen dioxide is a common air pollutant with growing evidence of health impacts independent of other common pollutants such as ozone and particulate matter. However, the worldwide distribution of NO exposure and associated impacts on health is still largely uncertain. To advance global exposure estimates we created a global nitrogen dioxide (NO) land use regression model for 2011 using annual measurements from 5,220 air monitors in 58 countries. The model captured 54% of global NO variation, with a mean absolute error of 3.7 ppb. Regional performance varied from R = 0.42 (Africa) to 0.67 (South America). Repeated 10% cross-validation using bootstrap sampling (n = 10,000) demonstrated a robust performance with respect to air monitor sampling in North America, Europe, and Asia (adjusted R within 2%) but not for Africa and Oceania (adjusted R within 11%) where NO monitoring data are sparse. The final model included 10 variables that captured both between and within-city spatial gradients in NO concentrations. Variable contributions differed between continental regions, but major roads within 100 m and satellite-derived NO were consistently the strongest predictors. The resulting model can be used for global risk assessments and health studies, particularly in countries without existing NO monitoring data or models.