NO2 is a combustion byproduct that has been associated with multiple adverse health outcomes. To assess NO2 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 NO2 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 R2 of 0.788 overall, a spatial R2 of 0.844, and a temporal R2 of 0.729. The relationship between daily monitored and predicted NO2 is almost linear. We also estimated the associated monthly uncertainty level for the predictions and address-specific NO2 levels. This NO2 estimation has a very high spatiotemporal resolution and allows the examination of the health effects of NO2 in unmonitored areas. We found the highest NO2 levels along highways and in cities. We also observed that nationwide NO2 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.

中文翻译：

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使用Ensemble Model Averaging评估全美国范围内高时空分辨率的NO2浓度和模型不确定性。

NO2是一种燃烧副产物，与多种不良健康后果相关。为了高精度地评估NO2的水平，我们建议使用集成模型将多种机器学习算法（包括神经网络，随机森林和梯度提升）与各种预测变量（包括化学迁移模型）集成在一起。该NO2模型涵盖了从2000年到2016年每天对1公里级别的网格单元进行连续预报的整个美国。该集合产生的交叉验证的R2总体为0.788，空间R2为0.844，时间R2为0.729。每日监测的NO2与预测的NO2之间的关系几乎是线性的。我们还估计了与预测相关的每月不确定性水平以及特定于地址的NO2水平。此NO2估算具有非常高的时空分辨率，并可以检查NO2在不受监视的区域对健康的影响。我们发现高速公路和城市地区的NO2含量最高。我们还观察到，全国范围内的二氧化氮水平在早期有所下降，并在2007年之后停滞不前，这与城市地区监测点呈下降趋势的趋势形成了鲜明对比。我们的研究表明，将不同的预测变量和拟合算法进行集成可以实现改进的空气污染建模框架。