%0 Dataset
%T Full coverage dataset of 1-kilometer daily environmental PM2.5 and O3 concentrations in China based on multivariate random forest model (2005-2017)
%J National Cryosphere Desert Data Center
%I National Cryosphere Desert Data Center(www.ncdc.ac.cn)
%U http://www.ncdc.ac.cn/portal/metadata/f8eb2dc3-7336-48b9-aae1-98bd27fad833
%W NCDC
%R 10.5281/zenodo.4009308
%A None
%K Random forest;PM2.5;O3 concentration
%X In recent years, the health risks of fine particulate matter (PM2.5) and environmental ozone (O3) have been widely recognized. Accurate estimation of PM2.5 and O3exposure is crucial for supporting health risk analysis and environmental policy formulation. The purpose of this dataset study is to construct a high-performance random forest model with a distance of 1 km ×  Estimating the daily average concentration of PM2.5 and the maximum 8-hour average concentration of ozone in China from 2005 to 2017 with a spatial resolution of 1 km (O3-8 hmax). Model variables include meteorological variables, satellite data, chemical transfer model outputs, geographic variables, and socio-economic variables. A random forest model based on 10 fold cross validation was established and spatiotemporal validation was conducted to evaluate model performance. According to our sample based partitioning method, the average model fitting R2values of daily, monthly, and annual estimates of PM2.5 in the test dataset are 0.85, 0.88, and 0.90, respectively; The R2values of O3-8 hmax were 0.77, 0.77, and 0.69, respectively. Meteorological variables and their hysteresis values will significantly affect the estimated values of PM2.5 and O3-8 hmax. During the period from 2005 to 2017, the concentration of PM2.5 showed an overall downward trend, while the concentration of environmental O3showed an upward trend. From 2005 to 2017, the spatial patterns of PM2.5 and O3-8 hmax remained almost unchanged, but the temporal trend exhibited spatial characteristics.