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ORIGINAL RESEARCH article

Front. Environ. Sci.
Sec. Environmental Informatics and Remote Sensing
Volume 12 - 2024 | doi: 10.3389/fenvs.2024.1382233

Comparative analysis of multi-directional long-distance pollution transport effects on heavily polluted weather in the Liaoning region Provisionally Accepted

Hang Su1  Kejin Ren2 Jinhu Wang2* Zhongyan Lu1 Jinglong Lu1  Dongdong Wang3 Yuhao Wang2
  • 1Meteorological Observatory of Liaoning Province, China
  • 2Nanjing University of Information Science and Technology, China
  • 3Institute of Atmospheric Environment, China Meteorological Administration, China

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In recent years, although the air quality in the Liaoning region has improved, heavy pollution weather is still caused by external pollution transportation. In order to study the influence of multi-directional long-distance pollution on the heavily polluted weather in Liaoning, this paper analyzes three types of transport channel (northerly, northwesterly, and southwesterly transport corridors) by combining ground observations, airborne observations, AQI and the concentration data of atmospheric pollutants such as PM2.5, PM10 and so on. The results show that both northerly and southwesterly winds caused widespread fog and haze in the Liaoning region. The PM2.5 concentration reached the second-highest value on record (1479μg/m3) during the pollution period caused by northwesterly wind transport. Wind and sand transported from the northwest mainly caused dusty weather in Liaoning, with PM10 concentration reaching up to 1200μg /m3 during the pollution period. Northeasterly and northwesterly wind transport mainly led to the accumulation of pollutants in high places to high pollution values. Convergence of winds due to southwesterly wind transport led to prolonged accumulation of pollutants in the Liaoning region, resulting in severe pollution. Long-distance pollution transport is now one of the main sources of heavy pollution in the Liaoning region.

Keywords: Long-distance pollution transport, Wind convergence, Hysplit trajectory model, PSCF method, CWT method

Received: 05 Feb 2024; Accepted: 10 Apr 2024.

Copyright: © 2024 Su, Ren, Wang, Lu, Lu, Wang and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Jinhu Wang, Nanjing University of Information Science and Technology, Nanjing, China