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Application of turbulent diffusion term of aerosols in mesoscale model

Citation

Jia, Wenxing (2021), Application of turbulent diffusion term of aerosols in mesoscale model, Dryad, Dataset, https://doi.org/10.5061/dryad.98sf7m0hz

Abstract

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.

Methods

Hourly 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.

Funding

NSFC Project, Award: U19A2044

National Key Project of MOST, Award: 2016YFC0203306

Atmospheric Pollution Control of the Prime Minister Fund, Award: DQGG0104

Key Projects of Fundamental Scientific Research Fund of CAMS, Award: 2017Z001

NSFC Project, Award: U19A2044

National Key Project of MOST, Award: 2016YFC0203306

Atmospheric Pollution Control of the Prime Minister Fund, Award: DQGG0104

Key Projects of Fundamental Scientific Research Fund of CAMS, Award: 2017Z001