Daily and Annual NO2 Concentrations for the Contiguous United States, 1-km Grids, Version 1.10 (2000-2016)

The Daily and Annual NO2 Concentrations for the Contiguous United States, 1-km Grids, Version 1.10 (2000-2016) data set contains daily predictions of Nitrogen Dioxide (NO2) concentrations at a high resolution (1-km grid cells) for the years 2000 to 2016. An ensemble modeling framework was used to assess NO2 levels with high accuracy, which combined estimates from three machine learning models (neural network, random forest, and gradient boosting), with a generalized additive model. Predictor variables included NO2 column concentrations from satellites, land-use variables, meteorological variables, predictions from two chemical transport models, GEOS-Chem and the U.S. Environmental Protection Agency (EPA) CommUnity Multiscale Air Quality Modeling System (CMAQ), along with other ancillary variables. The annual predictions were calculated by averaging the daily predictions for each year in each grid cell. The ensemble produced a cross-validated R-squared value of 0.79 overall, a spatial R-squared value of 0.84, and a temporal R-squared value of 0.73. In version 1.10, the completeness of daily NO2 predictions have been enhanced by employing linear interpolation to impute missing values. Specifically, for days with small spatial patches of missing data with less than 100 grid cells, inverse distance weighting interpolation was used to fill the missing grid cells. Other missing daily NO2 predictions were interpolated from the nearest days with available data. Annual predictions were updated by averaging the imputed daily predictions for each year in each grid cell. These daily and annual NO2 predictions allow public health researchers to respectively estimate the short- and long-term effects of NO2 exposures on human health, supporting the U.S. EPA for the revision of the National Ambient Air Quality Standards for daily average and annual average concentrations of NO2. The data are available in RDS and GeoTIFF formats for statistical research and geospatial analysis.

该《美国本土每日及年度二氧化氮（NO2）浓度数据集》版本1.10（2000-2016）包含了对2000至2016年期间每日氮氧化物（NO2）浓度的高分辨率（1公里网格单元）预测。本数据集采用了集成建模框架以高精度评估NO2水平，该框架结合了三种机器学习模型（神经网络、随机森林和梯度提升）的估计结果以及广义加性模型。预测变量包括来自卫星的NO2柱浓度、土地利用变量、气象变量，以及两个化学传输模型GEOS-Chem和美国环境保护署（EPA）的CommUnity多尺度空气质量建模系统（CMAQ）的预测，以及其他辅助变量。年度预测通过计算每年每个网格单元每日预测的平均值得出。集成模型产生了0.79的整体交叉验证R平方值、0.84的空间R平方值和0.73的时间R平方值。在版本1.10中，通过线性插值方法对缺失值进行插补，提高了每日NO2预测的完整性。具体而言，对于小于100个网格单元的微小空间缺失数据区域，使用了倒数距离加权插值方法来填补缺失的网格单元。其他缺失的每日NO2预测则通过插补最近可用数据的日子来补充。年度预测通过平均每个网格单元每年插补后的每日预测值进行更新。这些每日及年度NO2预测数据允许公共卫生研究人员分别估算NO2暴露对人类健康的短期和长期影响，为美国EPA修订NO2的每日平均和年度平均浓度国家环境空气质量标准提供支持。数据以RDS和GeoTIFF格式提供，供统计研究和地理空间分析使用。