Fig. 2: Spatial distributions and boxplots of PM2.5 trends from different methods. | npj Climate and Atmospheric Science

Fig. 2: Spatial distributions and boxplots of PM2.5 trends from different methods.

From: An intercomparison of weather normalization of PM2.5 concentration using traditional statistical methods, machine learning, and chemistry transport models

Fig. 2: Spatial distributions and boxplots of PM2.5 trends from different methods.

a–c Spatial distributions in the average values of trends for PM2.5 observation (\({{\rm{PM}}}_{2.5}^{{\rm{OBS}}}\)), emission-related PM2.5 (\({{\rm{PM}}}_{2.5}^{{\rm{EMI}}}\)), and meteorology-related PM2.5 (\({{\rm{PM}}}_{2.5}^{{\rm{MET}}}\)) derived from six methods. d–f Boxplots of \({{\rm{PM}}}_{2.5}^{{\rm{OBS}}}\), \({{\rm{PM}}}_{2.5}^{{\rm{EMI}}}\), and \({{\rm{PM}}}_{2.5}^{{\rm{MET}}}\) trends for each method. The meteorological conditions resampling strategy for the RF and XGB was from Grange et al.36. The color and size of dots in the top panels are mapped to the mean values and standard deviations of trends calculated from six methods. The marks in the bottom panels represent the differences between the two paired methods and the NS., and * mean the differences are not significant and significant at 0.05 levels.

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