Assessing NO2 Concentration and Model Uncertainty with High Spatiotemporal Resolution across the Contiguous United States Using Ensemble Model Averaging
Assessing NO2 Concentration and Model Uncertainty with High Spatiotemporal Resolution across the Contiguous United States Using Ensemble Model Averaging
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DOI：
10.1021/acs.est.9b03358
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发表时间：
2020-02-04
影响因子：
11.4
通讯作者：
Schwartz, Joel
中科院分区：
文献类型：
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作者：
Di, Qian;Amini, Heresh;Schwartz, Joel
二氧化氮是一种与多种不良健康结果相关的燃烧副产物。为了高精度地评估NO2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、随机森林和梯度增强）与各种预测变量（包括化学运输模型）集成在一起。该NO2模型覆盖了整个美国，并在2000年至2016年期间对1km级别的网格单元进行了每日预测。总体交叉验证R-2为0.788，空间R-2为0.844，时间R-2为0.729。每日监测值与预测值之间的关系几乎是线性的。我们还估计了预测和特定地址NO2水平的相关月度不确定性水平。这种二氧化氮估算具有非常高的时空分辨率，可以在未监测的地区检查二氧化氮对健康的影响。我们发现，高速公路和城市的二氧化氮水平最高。我们还观察到，全国范围内的二氧化氮水平在早期下降，并在2007年之后停滞不前，与城市地区监测点的趋势相反，城市地区的二氧化氮水平继续下降。我们的研究表明，不同预测变量和拟合算法的集成可以实现改进的空气污染建模框架。
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 1km-level grid cells from 2000 to 2016. The ensemble produced a cross-validated R-2 of 0.788 overall, a spatial R-2 of 0.844, and a temporal R-2 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.