4.7 Article

Improving the representation of HONO chemistry in CMAQ and examining its impact on haze over China

期刊

ATMOSPHERIC CHEMISTRY AND PHYSICS
卷 21, 期 20, 页码 15809-15826

出版社

COPERNICUS GESELLSCHAFT MBH
DOI: 10.5194/acp-21-15809-2021

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资金

  1. National Natural Science Foundation of China [41907190, 51861135102, 41877304]
  2. Youth Innovation Promotion Association of the Chinese Academy of Sciences [2018060]
  3. Toyota Motor Corporation
  4. Toyota Central Research and Development Laboratories Inc.

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The study found a significant underestimation of HONO concentrations in Beijing using the CMAQ model, which was rectified by implementing six additional heterogeneous reactions, leading to improved model predictions and closer agreement with observed data.
We compare Community Multiscale Air Quality (CMAQ) model predictions with measured nitrous acid (HONO) concentrations in Beijing, China, for December 2015. The model with the existing HONO chemistry in CMAQ severely underestimates the observed HONO concentrations with a normalized mean bias of 97 %. We revise the HONO chemistry in the model by implementing six additional heterogeneous reactions in the model: the reaction of nitrogen dioxide (NO2) on ground surfaces, the reaction of NO2 on aerosol surfaces, the reaction of NO2 on soot surfaces, the photolysis of aerosol nitrate, the nitric acid displacement reaction, and the hydrochloric acid displacement reaction. The model with the revised chemistry substantially increases HONO predictions and improves the comparison with observed data with a normalized mean bias of 5 %. The photolysis of HONO enhances daytime hydroxyl radical by almost a factor of 2. The enhanced hydroxyl radical concentrations compare favorably with observed data and produce additional sulfate via the reaction with sulfur dioxide, aerosol nitrate via the reaction with nitrogen dioxide, and ganic compounds. The additional sulfate stemming from revised HONO chemistry improves the comparison with observed concentration; however, it does not close the gap between model prediction and the observation during polluted days.

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