Random forest model based fine scale spatiotemporal O trends in the Beijing-Tianjin-Hebei region in China, 2010 to 2017.

Ambient ozone (O) concentrations have shown an upward trend in China and its health hazards have also been recognized in recent years. High-resolution exposure data based on statistical models are needed. Our study aimed to build high-performance random forest (RF) models based on training data from 2013 to 2017 in the Beijing-Tianjin-Hebei (BTH) region in China at a 0.01 ° × 0.01 ° resolution, and estimated daily maximum 8h average O (O-8hmax) concentration, daily average O (O-mean) concentration, and daily maximum 1h O (O-1hmax) concentration from 2010 to 2017. Model features included meteorological variables, chemical transport model output variables, geographic variables, and population data. The test-R of sample-based O-8hmax, O-mean and O-1hmax models were all greater than 0.80, while the R of site-based and date-based model were 0.68-0.87. From 2010 to 2017, O-8hmax, O-mean, and O-1hmax concentrations in the BTH region increased by 4.18 μg/m, 0.11 μg/m, and 4.71 μg/m, especially in more developed regions. Due to the influence of weather conditions, which showed high contribution to the model, the long-term spatial distribution of O concentrations indicated a similar pattern as altitude, where high concentration levels were distributed in regions with higher altitude.