Assessing NO Concentration and Model Uncertainty with High Spatiotemporal Resolution across the Contiguous United States Using Ensemble Model Averaging.

NO is a combustion byproduct that has been associated with multiple adverse health outcomes. To assess NO levels with high accuracy, we propose the use of an ensemble model to integrate multiple machine learning algorithms, including neural network, random forest, and gradient boosting, with a variety of predictor variables, including chemical transport models. This NO model covers the entire contiguous U.S. with daily predictions on 1-km-level grid cells from 2000 to 2016. The ensemble produced a cross-validated R of 0.788 overall, a spatial R of 0.844, and a temporal R of 0.729. The relationship between daily monitored and predicted NO is almost linear. We also estimated the associated monthly uncertainty level for the predictions and address-specific NO levels. This NO estimation has a very high spatiotemporal resolution and allows the examination of the health effects of NO in unmonitored areas. We found the highest NO levels along highways and in cities. We also observed that nationwide NO levels declined in early years and stagnated after 2007, in contrast to the trend at monitoring sites in urban areas, where the decline continued. Our research indicates that the integration of different predictor variables and fitting algorithms can achieve an improved air pollution modeling framework.