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ORIGINAL RESEARCH article

Front. Environ. Sci.
Sec. Environmental Informatics and Remote Sensing
Volume 12 - 2024 | doi: 10.3389/fenvs.2024.1534113
This article is part of the Research Topic Air Quality: Observation, Remote Sensing, and Model Development - Volume II View all 6 articles

Comparison of simulating visibility using XGBoost and IMPROVE method: a case study in East China

Provisionally accepted
Xin Zhang Xin Zhang 1*Yue Wang Yue Wang 2Zibo Zhuang Zibo Zhuang 3*Yuxi Liu Yuxi Liu 4*Chengduo Yuan Chengduo Yuan 5*Lei Su Lei Su 2*Jingyuan Shao Jingyuan Shao 1*Pak Wai Chan Pak Wai Chan 6*
  • 1 Flight Academy, Civil Aviation University of China, Tianjin, China
  • 2 Department of Safety Science and Engineering, Civil Aviation University of China, Tianjin, China
  • 3 Aviation Meteorological Research Institute, Civil Aviation University of China, Tianjin, China
  • 4 State Environmental Protection Key Laboratory of Environmental Pollution and Greenhouse Gases Co-control, Chinese Academy for Environmental Planning, Beijing, Beijing Municipality, China
  • 5 Department of Flight Area Management, Harbin International Airport Co., Ltd, Harbin, China
  • 6 Hong Kong Observatory, Hong Kong, Hong Kong, SAR China

The final, formatted version of the article will be published soon.

    Abstract：The prediction accuracy of atmospheric visibility significantly impacts daily life. However, there is a relative scarcity of research on post-processing methods for visibility obtained from the WRF-Chem atmospheric chemistry model results. In order to explore a more accurate method for visibility calculation, we conducted a study on the meteorological conditions in the East China region during a heavy pollution period from October 1 to 23 in the year of 2022. The meteorological data were processed using both the XGBoost (XGB) model and the IMPROVE to calculate visibility. The results indicate that XGB outperforms the IMPROVE in various aspects. The visibility improved from a correlation of 0.56 to 0.71 with the use of XGB. And in comparison with the IMPROVE equation, XGB demonstrated a statistically significant reduction in RMSE by 1.96 km. Even in regions where the IMPROVE performs poorly, XGB demonstrates superior performance. In regions where the correlation simulated by the IMPROVE equation is less than 0.2 ( Anqing and Nanyang), XGB still performs well, achieving correlations of 0.69 (Anqing) and 0.68 (Nanyang). Throughout the entire study period, the average visibility results obtained by XGB deviate by only 0.07 km from the observed values. These findings underscore the importance of incorporating the XGBoost model into WRF-Chem visibility simulations, as it significantly improves the accuracy of visibility predictions.

    Keywords: ：visibility simulation, XGBoost, IMPROVE EQUATION, WRF-Chem model, machine learning, Atmospheric Chemistry

    Received: 26 Nov 2024; Accepted: 30 Dec 2024.

    Copyright: © 2024 Zhang, Wang, Zhuang, Liu, Yuan, Su, Shao and Chan. 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:
    Xin Zhang, Flight Academy, Civil Aviation University of China, Tianjin, China
    Zibo Zhuang, Aviation Meteorological Research Institute, Civil Aviation University of China, Tianjin, China
    Yuxi Liu, State Environmental Protection Key Laboratory of Environmental Pollution and Greenhouse Gases Co-control, Chinese Academy for Environmental Planning, Beijing, Beijing Municipality, China
    Chengduo Yuan, Department of Flight Area Management, Harbin International Airport Co., Ltd, Harbin, China
    Lei Su, Department of Safety Science and Engineering, Civil Aviation University of China, Tianjin, China
    Jingyuan Shao, Flight Academy, Civil Aviation University of China, Tianjin, China
    Pak Wai Chan, Hong Kong Observatory, Hong Kong, Hong Kong, SAR China

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