Application of turbulent diffusion term of aerosols in mesoscale model

The presence of unfavorable meteorological conditions triggers pollution, and then further weakens turbulence, especially in the stable boundary layer (SBL), which is a frequent situation in heavy pollution episodes in China. The inapplicability of the classical Monin-Obukhov similarity theory (MOST) and the uncertainty of the planetary boundary layer height can lead to large deviation of turbulent diffusion in the SBL in numerical simulations. However, in current mesoscale models, no term has been used to accurately describe the turbulent diffusion of aerosols. Therefore, we use the Mixing-Length theory to obtain the turbulent diffusion term of aerosols based on high-resolution observational data, and, for the first time, embed this term into a mesoscale model, which makes the turbulent diffusion process of aerosols more truly depicted. Results from a two-way coupled atmospheric-chemistry mesoscale model demonstrate that the turbulent diffusion term of aerosols can improve the problem of overestimated PM2.5 concentration in Eastern China.Funding provided by: NSFC Project*Crossref Funder Registry ID: Award Number: U19A2044Funding provided by: National Key Project of MOST*Crossref Funder Registry ID: Award Number: 2016YFC0203306Funding provided by: Atmospheric Pollution Control of the Prime Minister Fund*Crossref Funder Registry ID: Award Number: DQGG0104Funding provided by: Key Projects of Fundamental Scientific Research Fund of CAMS*Crossref Funder Registry ID: Award Number: 2017Z001Funding provided by: NSFC ProjectCrossref Funder Registry ID: Award Number: U19A2044Funding provided by: National Key Project of MOSTCrossref Funder Registry ID: Award Number: 2016YFC0203306Funding provided by: Atmospheric Pollution Control of the Prime Minister FundCrossref Funder Registry ID: Award Number: DQGG0104Funding provided by: Key Projects of Fundamental Scientific Research Fund of CAMSCrossref Funder Registry ID: Award Number: 2017Z001Hourly averaged concentrations of surface PM2.5 can be found on the official website of the China National Environmental Monitoring Center. The PM2.5 concentration data comes from 308 stations of 74 cities in Eastern China. The observational turbulence data used in this study is obtained from the Pingyuan County Meteorological Bureau (37.15°N, 116.47°E), Shandong Province, from 27 December 2018 to 8 January 2019. Datasets of the horizontal wind speed, potential temperature and moisture are obtained with a frequency of 10 Hz by an integrated three-dimensional sonic anemometer-thermometer (IRGASON, Campbell Scientific, USA) and CO2/H2O open-path gas analyzer (LI7500, LI-COR, USA). The turbulence data was divided into 30-min segments and then processed with several procedures