Quantification of PM2.5 Concentrations Reduction during Rainfall Due to below-Cloud Scavenging Based on Observations and Simulations.

The wet scavenging process is the removal of aerosols by wet convection and controls the amount of aerosols and deposition in the atmosphere. It can be classified into in-cloud scavenging associated with cloud nucleation (rainout) and below-cloud scavenging due to collisions with precipitation particles (washout). The wet scavenging process is one of the most uncertain factors in chemical transport modelling and is not quantitatively understood (Rasch et al., 2000). Therefore, the aim of this study is to quantify the amount of wet scavenging from both sides using observational data analysis and meteorology - chemistry simulation analysis.

In this study, we used Offline SCALE-Chem (Nakata et al., 2021), an offline coupled meteorology model (Nishizawa et al., 2015; Sato et al., 2015) and chemical transport model (Kajino et al., 2019; Kajino et al., 2021). The initial and boundary conditions were obtained from MANL and the calculation period was from 00UTC on Oct. 1^st to 00UTC on Nov. 1^st, 2019. To compare simulation results with observation results, rainfall data (AMeDAS) and PM_2.5 concentration data (the Ministry of the Environment's Air Pollutant Observation System) were used at 10 sites in Japan.

The composited analysis showed that the PM_2.5 concentrations after 1 hour of the onset of rainfall decreased by 9.76% in observed values and 10.88% in simulated values compared to that before 1 hour of the onset of rainfall. There was also an increase in wet deposition with a decrease in PM_2.5 concentrations. The analysis of the contribution of the washout process to the change in the average of PM_2.5 concentrations revealed that the amount of change in PM_2.5 concentrations was -1.498 μg/m³ (-27.98%), quantitatively indicating that the washout process contributed to the reduction of PM_2.5 concentrations. The present results suggest that the wet scavenging processes are effective in reducing PM_2.5 concentration.