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.
To provide daily and annual Nitrogen Dioxide (NO2) concentration data in the U.S. at a resolution of 1-km (about 30 arc-seconds) for public health research to respectively estimate short- and long-term effects on human health, and for other related research.

《美国本土逐日及逐年二氧化氮（NO2）浓度数据集（1公里网格，版本1.10，2000-2016年）》包含2000至2016年间高分辨率（1公里网格单元）的二氧化氮逐日浓度预测值。该研究采用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、梯度提升树）与广义加性模型的预测结果。预测变量涵盖卫星反演的NO2柱浓度、土地利用变量、气象变量、两种化学传输模型（GEOS-Chem以及美国环境保护署（U.S. Environmental Protection Agency, EPA）的社区多尺度空气质量建模系统（Community Multiscale Air Quality Modeling System, CMAQ））的预测结果，以及其他辅助变量。
逐年预测值通过对每个网格单元内各年份的逐日预测值取平均计算得到。该集成模型的整体交叉验证决定系数（R²）为0.79，空间决定系数为0.84，时间决定系数为0.73。
在版本1.10中，通过线性插值填补缺失值以提升逐日NO2预测的完整性。具体而言，对于缺失数据空间斑块小于100个网格单元的情况，采用反距离加权插值填补缺失网格单元；其余缺失的逐日NO2预测值则通过邻近有效数据日期进行插值补全。
逐年预测值通过对每个网格单元内各年份的补全后逐日预测值取平均完成更新。
这些逐日和逐年NO2预测值可帮助公共卫生研究者分别评估NO2暴露对人类健康的短期及长期影响，同时可为美国环境保护署修订《国家环境空气质量标准》中NO2日平均和年平均浓度限值提供支撑。该数据集以RDS和GeoTIFF格式提供，适用于统计研究与地理空间分析。
本数据集旨在为公共卫生研究者提供美国本土1公里分辨率（约30角秒）的逐日及逐年二氧化氮浓度数据，以分别评估NO2暴露对人类健康的短期与长期影响，并服务于其他相关研究。