Impact of modified turbulent diffusion of PM2.5 aerosol in WRF-Chem simulations in eastern China

被引:9
|
作者
Jia, Wenxing [1 ,2 ]
Zhang, Xiaoye [1 ,3 ]
机构
[1] Chinese Acad Meteorol Sci, Key Lab Atmospher Chem CMA, Beijing 100081, Peoples R China
[2] Nanjing Univ Informat Sci & Technol, Key Lab Aerosol Cloud Precipitat, China Meteorol Adm, Nanjing 210044, Peoples R China
[3] Chinese Acad Sci, Ctr Excellence Reg Atmospher Environm, IUE, Xiamen 361021, Peoples R China
关键词
PARTICLE DRY DEPOSITION; BEIJING-TIANJIN-HEBEI; BOUNDARY-LAYER; RADIATION FEEDBACK; NORTHERN CHINA; SEVERE HAZE; MODEL; SCHEME; MECHANISMS; POLLUTION;
D O I
10.5194/acp-21-16827-2021
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Correct description of the boundary layer mixing process of particle is an important prerequisite for understanding the formation mechanism of pollutants, especially during heavy pollution episodes. Turbulent vertical mixing determines the distribution of momentum, heat, water vapor and pollutants within the planetary boundary layer (PBL). However, what is questionable is that the turbulent mixing process of particles is usually denoted by turbulent diffusion of heat in the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). With mixing-length theory, the turbulent diffusion relationship of particle is established, embedded into the WRF-Chem and verified based on long-term simulations from 2013 to 2017. The new turbulent diffusion coefficient is used to represent the turbulent mixing process of pollutants separately, without deteriorating the simulation results of meteorological parameters. The new turbulent diffusion improves the simulation of pollutant concentration to varying degrees, and the simulated results of PM2.5 concentration are improved by 8.3% (2013), 17% (2014), 11% (2015) and 11.7% (2017) in eastern China, respectively. Furthermore, the pollutant concentration is expected to increase due to the reduction of turbulent diffusion in mountainous areas, but the pollutant concentration did not change as expected. Therefore, under the influence of complex topography, the turbulent diffusion process is insensitive to the simulation of the pollutant concentration. For mountainous areas, the evolution of pollutants is more susceptible to advection transport because of the simulation of obvious wind speed gradient and pollutant concentration gradient. In addition to the PM2.5 concentration, the concentration of CO as a primary pollutant has also been improved, which shows that the turbulent diffusion process is extremely critical for variation of the various aerosol pollutants. Additional joint research on other processes (e.g., dry deposition, chemical and emission processes) may be necessary to promote the development of the model in the future.
引用
收藏
页码:16827 / 16841
页数:15
相关论文
共 50 条
  • [1] Investigating impact of emission inventories on PM2.5 simulations over North China Plain by WRF-Chem
    Ma, Xiaoyan
    Sha, Tong
    Wang, Jianying
    Jia, Hailing
    Tian, Rong
    ATMOSPHERIC ENVIRONMENT, 2018, 195 : 125 - 140
  • [2] Regional PM2.5 Estimation in Beijing Based on WRF-Chem Model
    Zhang, Yichen
    4TH INTERNATIONAL CONFERENCE ON ENVIRONMENTAL ENGINEERING AND SUSTAINABLE DEVELOPMENT (CEESD 2019), 2020, 435
  • [3] Assimilating Fengyun-4A observations to improve WRF-Chem PM2.5 predictions in China
    Hong, Jia
    Mao, Feiyue
    Gong, Wei
    Gan, Yuan
    Zang, Lin
    Quan, Jihong
    Chen, Jiangping
    ATMOSPHERIC RESEARCH, 2022, 265
  • [4] Simulations of Summertime Ozone and PM2.5 Pollution in Fenwei Plain (FWP) Using the WRF-Chem Model
    Wang, Yuxi
    Cao, Le
    Zhang, Tong
    Kong, Haijiang
    ATMOSPHERE, 2023, 14 (02)
  • [5] Assessment of Health Impact of PM2.5 Exposure by Using WRF-Chem Model in Kathmandu Valley, Nepal
    Tuladhar, Avalokita
    Manandhar, Palistha
    Shrestha, Kundan Lal
    FRONTIERS IN SUSTAINABLE CITIES, 2021, 3
  • [6] Improvement of inorganic aerosol component in PM2.5 by constraining aqueous-phase formation of sulfate in cloud with satellite retrievals: WRF-Chem simulations
    Sha, Tong
    Ma, Xiaoyan
    Wang, Jun
    Tian, Rong
    Zhao, Jianqi
    Cao, Fang
    Zhang, Yan-Lin
    SCIENCE OF THE TOTAL ENVIRONMENT, 2022, 804
  • [7] Evaluating the sensitivity of fine particulate matter (PM2.5) simulations to chemical mechanism in WRF-Chem over Delhi
    Jat, Rajmal
    Jena, Chinmay
    Yadav, Prafull P.
    Govardhan, Gaurav
    Kalita, Gayatry
    Debnath, Sreyashi
    Gunwani, Preeti
    Acharja, Prodip
    Pawar, Pooja V.
    Sharma, Pratul
    Kulkarni, Santosh H.
    Kulkarni, Akshay
    Kaginalkar, Akshara
    Chate, Dilip M.
    Kumar, Rajesh
    Soni, Vijay Kumar
    Ghude, Sachin D.
    ATMOSPHERIC ENVIRONMENT, 2024, 323
  • [8] Evaluation of WRF-Chem simulations on vertical profiles of PM2.5 with UAV observations during a haze pollution event
    Liu, Cheng
    Huang, Jianping
    Hu, Xiao-Ming
    Hu, Cheng
    Wang, Yongwei
    Fang, Xiaozhen
    Luo, Li
    Xiao, Hong-Wei
    Xiao, Hua-Yun
    ATMOSPHERIC ENVIRONMENT, 2021, 252
  • [9] A hybrid method for PM2.5 source apportionment through WRF-Chem simulations and an assessment of emission-reduction measures in western China
    Yang, Junhua
    Kang, Shichang
    Ji, Zhenming
    Chen, Xintong
    Yang, Sixiao
    Lee, Shao-Yi
    de Foy, Benjamin
    Chen, Deliang
    ATMOSPHERIC RESEARCH, 2020, 236
  • [10] WRF-Chem模式降水对上海PM2.5预报的影响
    周广强
    高伟
    谷怡萱
    瞿元昊
    环境科学学报, 2017, 37 (12) : 4476 - 4482