Daily and Annual PM2.5 Concentrations for the Contiguous United States, 1-km Grids, v1 (2000 - 2016)

The Daily and Annual PM2.5 Concentrations for the Contiguous United States, 1-km Grids, v1 (2000 - 2016) data set includes predictions of PM2.5 concentrations in grid cells at a resolution of 1 km for the years 2000 to 2016. A generalized additive model was used that accounted for geographic difference to ensemble daily predictions of three machine learning models: neural network, random forest, and gradient boosting. The three machine learners incorporated multiple predictors, including satellite data, meteorological variables, land-use variables, elevation, chemical transport model predictions, several reanalysis data sets, as well as other predictors. The annual predictions were calculated by averaging the daily predictions for each year in each grid cell. The ensembled model demonstrated better predictive performance than the individual machine learners with 10-fold cross-validated R-squared values of 0.86 for daily predictions and 0.89 for annual predictions.

《美国大陆每日及年度PM2.5浓度数据集v1（2000-2016）》，该数据集包含了从2000年至2016年，以1公里网格分辨率预测的PM2.5浓度数据。该数据集采用了一种广义加性模型，该模型考虑了地理差异，并集成了三种机器学习模型的每日预测结果：神经网络、随机森林和梯度提升。三种机器学习模型结合了多种预测因子，包括卫星数据、气象变量、土地利用变量、海拔高度、化学传输模型预测结果、多个再分析数据集以及其他预测因子。年度预测是通过计算每个网格单元格每年每日预测的平均值得出的。集成模型在预测性能上优于单一机器学习模型，其10折交叉验证的R平方值分别为每日预测0.86和年度预测0.89。