Temporal and spatial characteristics of turbulent transfer and diffusion coefficient of PM_2.5

The temporal and spatial characteristics of turbulent transfer and diffusion coefficient of PM_2.5 (K_C) were investigated by determining the deviation, turbulent flux and form of universal function of PM_2.5 mass concentrations. Turbulence and sounding observations from December 8-25, 2019, of three sites, Tuonan, Baoding, and Renqiu stations in the North China Plain were selected. Mean PM_2.5 mass fluxes during the intensive observational period of three stations were negative. The spatial distribution of PM_2.5 mass flux of three stations showed no obvious tendency. Then, the fact that PM_2.5 mass concentrations satisfied the Monin-Obukhov similarity were reconfirmed by examining the relationship between the normalized standard deviation of PM_2.5 mass concentrations and stability factor ζ. Thus, the universal functions in the three stations were achieved. The time series and profiles of K_C in the three stations were also shown. There was a good inverse correlation between K_C and PM_2.5 mass concentration which suggested that the influence of turbulent diffusion is remarkably important during observational time even the emission, deposition, secondary transformation can all affect the change of mass concentrations of PM_2.5 in the ABL. Changes in K_C obviously presented diurnal characteristics. The comparisons of K_C and K_M and K_H suggested that the strength of turbulent PM_2.5 mass flux exchange could be weaker or stronger than the strength of turbulent momentum and heat flux exchange at different stations. The magnitude relationship between K_C and K_H could not be completely determined, so there were limitations in using K_H to replace directly K_C in the existing numerical weather or climate models. Finally, the spatial distribution of K_C at the three stations presented almost symmetrical characteristics from east to west (Tuonan to Renqiu); that is, Baoding always had the lowest K_C, and Tuonan and Renqiu stations had higher K_Cs. The spatial distribution of K_C at the three stations corresponded well with that of PM_2.5 mass concentrations, and Baoding always had the largest PM_2.5 mass concentrations.