Coordinated control of PM2.5 and O3 compound pollution in Jincheng City based on the WRF-CMAQ model

被引:0
|
作者
Li, Chen [1 ]
Zhang, Zhi-Juan [1 ]
Chen, Xi [1 ,2 ]
Ye, Cui-Ping [1 ]
机构
[1] College of Environment and Ecology, Taiyuan University of Technology, Jinzhong,030600, China
[2] Shanxi Key Laboratory of Compound Air Pollutions Identification and Control, Jinzhong,030600, China
关键词
Co-ordinated control - Collaborative control - Compound pollution - Coordinated control - EKMA curve - NO x - O3 - PM 2.5 - Reduction ratios - WRF-CMAQ;
D O I
暂无
中图分类号
学科分类号
摘要
This study used the WRF-CMAQ model to simulate and conduct source apportionment for a case of compound pollution in Jinzhong City. By designing 49 different scenarios of VOCs and NOx emission reductions and combining them with EKMA curves to evaluate the scientific reduction ratios of their precursors. The results revealed that industrial and traffic sources are the main contributors to VOCs and NOx in Jincheng City. O3 pollution is mainly influenced by NOx levels, whereas PM2.5 pollution is primarily controlled by VOCs. Considering non-extreme reduction scenarios, for O3 pollution control alone, the optimal VOCs/NOx reduction ratio is 1:2; for PM2.5 pollution control alone, the optimal reduction ratio is 2:1. When considering the coordinated control of both PM2.5 and O3 pollution, the best precursor reduction ratio of VOCs to NOx is 2:1. © 2024 China Environmental Science. All rights reserved.
引用
收藏
页码:6569 / 6577
相关论文
共 50 条
  • [11] 基于WRF–CMAQ的晋城市PM2.5与O3复合污染协同控制
    李晨
    张芝娟
    陈曦
    叶翠平
    中国环境科学, 2024, 44 (12) : 6569 - 6577
  • [12] Coordinated control of PM2.5 and O3 : Investigating the physical and chemical processes underlying regional complex air pollution
    Ding, Aijun
    Zhang, Meigen
    Xue, Likun
    ATMOSPHERIC AND OCEANIC SCIENCE LETTERS, 2024, 17 (06)
  • [13] Synergistic PM 2.5 and O3 control to address the emerging global PM2.5-O3 compound pollution challenges
    He, Chao
    Liu, Jianhua
    Zhou, Yiqi
    Zhou, Jingwei
    Zhang, Lu
    Wang, Yifei
    Liu, Lu
    Peng, Sha
    ECO-ENVIRONMENT & HEALTH, 2024, 3 (03): : 325 - 337
  • [14] Assessing satellite AOD based and WRF/CMAQ output PM2.5 estimators
    Cordero, Lina
    Wu, Yonghua
    Gross, Barry M.
    Moshary, Fred
    SENSING TECHNOLOGIES FOR GLOBAL HEALTH, MILITARY MEDICINE, AND ENVIRONMENTAL MONITORING III, 2013, 8723
  • [15] Understanding the physical mechanisms of PM2.5 formation in Seoul, Korea: assessing the role of aerosol direct effects using the WRF-CMAQ model
    Yoo, Jung-Woo
    Park, Soon-Young
    Jeon, Wonbae
    Jung, Jia
    Park, Jaehyeong
    Mun, Jeonghyeok
    Kim, Dongjin
    Lee, Soon-Hwan
    AIR QUALITY ATMOSPHERE AND HEALTH, 2024, 17 (08): : 1701 - 1714
  • [16] 基于WRF-CMAQ模型的辽宁中部城市群PM2.5化学组分特征
    秦思达
    王帆
    王堃
    郎咸明
    吴萱
    夏广峰
    王莹
    李梅
    环境科学研究, 2021, 34 (06) : 1277 - 1286
  • [17] Coordinated control of PM2.5 and O3 is urgently needed in China after implementation of the "Air pollution prevention and control action plan"
    Zhao, Hui
    Chen, Kaiyu
    Liu, Zhen
    Zhang, Yuxin
    Shao, Tian
    Zhang, Hongliang
    CHEMOSPHERE, 2021, 270
  • [18] Fatal PM2.5 and O3
    Chung, Min Suk
    JOURNAL OF KOREAN MEDICAL SCIENCE, 2019, 34 (48)
  • [19] Process analysis of PM2.5 pollution events in a coastal city of China using CMAQ
    Qiang Zhang
    Di Xue
    Xiaohuan Liu
    Xiang Gong
    Huiwang Gao
    Journal of Environmental Sciences, 2019, 79 (05) : 225 - 238
  • [20] Evaluation of real-time PM2.5 forecasts with the WRF-CMAQ modeling system and weather-pattern-dependent bias-adjusted PM2.5 forecasts in Taiwan
    Cheng, Fang-Yi
    Feng, Chih-Yung
    Yang, Zhih-Min
    Hsu, Chia-Hua
    Chan, Ka-Wa
    Lee, Chia-Ying
    Chang, Shuenn-Chin
    ATMOSPHERIC ENVIRONMENT, 2021, 244