Full-coverage 1 km daily ambient PM_2.5 and O₃ concentrations of China in 2005–2017 based on a multi-variable random forest model

Abstract

The health risks of fine particulate matter (PM2.5) and ambient ozone (O3) have been widely recognized in recent years. An accurate estimate of PM2.5 and O3 exposures is important for supporting health risk analysis and environmental policy-making. The aim of our study was to construct random forest models with high-performance and estimate daily average PM2.5 concentration and O3 daily maximum of 8 h average concentration (O3-8 hmax) of China in 2005–2017 at a spatial resolution of 1 km × 1 km. The model variables included meteorological variables, satellite data, chemical transport model output, geographic variables and socioeconomic variables. Random forest model based on 10-fold cross-validation was established, and spatial and temporal validations were performed to evaluate the model performance. According to our sample-based division method, the daily, monthly and yearly estimations of PM2.5 from test datasets gave average model-fitting R2 values of 0.85, 0.88 and 0.90, respectively; these R2 values were 0.77, 0.77 and 0.69 for O3-8 hmax, respectively. The meteorological variables and their lagged values can significantly affect both PM2.5 and O3-8 hmax estimations. During 2005–2017, PM2.5 concentration exhibited an overall downward trend, while ambient O3 concentration experienced an upward trend. Whilst the spatial patterns of PM2.5 and O3-8 hmax barely changed between 2005 and 2017, the temporal trend had spatial characteristics. The dataset is accessible to the public at https://doi.org/10.5281/zenodo.4009308 (Ma et al., 2021a), and the shared dataset of Chinese Environmental Public Health Tracking (CEPHT, 2022) is available at https://cepht.niehs.cn:8282/developSDS3.html.