Daily 8-Hour Maximum and Annual O3 Concentrations for the Contiguous United States, 1-km Grids, v1 (2000 - 2016)

The Daily 8-Hour Maximum and Annual O3 Concentrations for the Contiguous United States, 1-km Grids, v1 (2000 - 2016) data set contains estimates of ozone concentrations at a high resolution in space (1 km x 1 km grid cells) and time (daily) for the years 2000 to 2016. These predictions incorporated various predictor variables such as Ozone (O3) ground measurements from the U.S. Environmental Protection Agency (EPA) Air Quality System (AQS) monitoring data, land-use variables, meteorological variables, chemical transport models and remote sensing data, along with other data sources. After imputing missing data with machine learning algorithms, a geographically weighted ensemble model was applied that combined estimates from three types of machine learners (neural network, random forest, and gradient boosting). The annual predictions were computed by averaging the daily 8-hour maximum predictions in each year for each grid cell. The results demonstrate high overall model performance with a cross-validated R-squared value against daily observations of 0.90 and 0.86 for annual averages.

该数据集《美国大陆每日8小时最大臭氧浓度及年度臭氧浓度，1公里网格，版本1（2000-2016）》包含了在空间分辨率（1公里x1公里网格单元）和时间分辨率（每日）方面，对2000年至2016年臭氧浓度的估计。这些预测综合了多种预测变量，包括来自美国环境保护署（EPA）空气质量系统（AQS）监测数据的臭氧（O3）地面测量值、土地利用变量、气象变量、化学传输模型和遥感数据，以及其他数据源。通过机器学习算法填补缺失数据后，应用了一种地理加权集成模型，该模型结合了三种类型机器学习者的估计（神经网络、随机森林和梯度提升）。通过计算每年每个网格单元每日8小时最大预测值的平均值，得出了年度预测。结果表明，模型整体性能优异，针对每日观测值的交叉验证R平方值达到0.90和0.86，对于年度平均值也达到了0.86。