Atmosphere (Oct 2022)

A Numerically Sensitive Study of Two Continuous Heavy-Pollution Episodes in the Southern Sichuan Basin of China

  • Li Chen,
  • Chunhong Zhou,
  • Lei Zhang,
  • Shigong Wang

DOI
https://doi.org/10.3390/atmos13111771
Journal volume & issue
Vol. 13, no. 11
p. 1771

Abstract

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To explore the causes of pollution formation and changes in the complex topography of the Sichuan Basin, China, and improve the comprehensive simulation capability of pollution models, we use two online coupling models, WRF/Chem and WRF/CUACE, to simulate two heavy pollution episode that successively occurred in the southern part of Sichuan Province from 15 December 2016 to 11 January 2017 in this paper. Additionally, two sets of meteorological physics parameterization schemes MET1 and MET2 are designed, and four groups of experiments are carried out. The results suggest that the two models are good at simulating the static weather parameters such as temperature, low speed of winds and boundary layer height. The four groups of tests can accurately simulate the beginning, maintenance and turning point of the two pollution episodes’ life cycles. CUACE shows better performance in terms of higher correlation coefficients and lower errors in most of the particles and particulate components evaluations. It also performs better in the competitive mechanism of sulfate and nitrate against ammonium in the thermodynamic equilibrium mechanism. In addition, the evaluation of PM2.5 and the component simulation show that CUACE is more capable of simulating the mechanisms of heavy pollutions in southern Sichuan. Meanwhile, MET2 scheme is more appropriate for the simulation than MET1 dose. Based on the simulated concentrations of components and their precursors, the models overestimate the conversion of NO2 to nitrate but underestimate the conversion of SO2 to sulfate, which is the essential cause of the general overestimation of nitrate. Therefore, reducing the overestimation of nitrate is one major target for future model improvement.

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