Effects of nocturnal boundary layer subsidence and long-distance transports on PM2.5 vertical profiles in the Yangtze River Delta of China measured by PM sensor on unmanned aerial vehicle and PM Lidar☆

被引:0
|
作者
Chen, Lang [1 ,3 ]
Xu, Haonan [2 ,5 ]
Huang, Riyang [2 ,5 ]
Pang, Xiaobing [2 ,5 ]
Wang, Baozhen [4 ]
Wu, Zhentao [2 ,5 ]
Yu, Shaocai [3 ,6 ]
机构
[1] Zhejiang Gongshang Univ, Collaborat Innovat Ctr Stat Data Engn Technol & Ap, Sch Stat & Math, Hangzhou 310018, Peoples R China
[2] Zhejiang Univ Technol, Coll Environm, Hangzhou 310000, Peoples R China
[3] Zhejiang Gongshang Univ, Sch Environm Sci & Engn, Zhejiang Prov Key Lab Solid Waste Treatment & Recy, Hangzhou 310018, Peoples R China
[4] Yangtze Normal Univ, Green Intelligence Environm Sch, Chongqing 408100, Peoples R China
[5] Zhejiang Univ Technol, Shaoxing Res Inst, Shaoxing 312077, Peoples R China
[6] Zhejiang Gongshang Univ, Sch Stat & Math, Hangzhou 310018, Peoples R China
基金
中国国家自然科学基金;
关键词
PM2.5 vertical profiles; Unmanned aerial vehicles; PM Lidar; Nocturnal boundary layer; Long-distance transports; POLLUTION; LIDAR; HAZE;
D O I
10.1016/j.envpol.2025.125935
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Atmospheric boundary layer structures and long-distance transports significantly affect fine particulate matter (PM2.5) vertical profiles. In this study, the PM2.5 vertical profiles in the Yangtze River Delta (YRD) of China were measured by PM sensor on unmanned aerial vehicle (UAV) and PM Lidar in 2022 (April, June, October) and 2023 (February). The results showed that the PM2.5 vertical profiles appeared obvious stratification on the top of nocturnal boundary layer (NBL). The NBL subsidence stabilized the boundary layer structure and inhibited the vertical diffusion of PM2.5, increasing the ground PM2.5 concentrations. However, when there was an active turbulent motion during the NBL subsidence, the PM2.5 from the surface might be transported upward. The PM2.5 vertical mean concentrations (0-500 m) in the YRD decreased by 58-95% within 3 h, which might be caused by the rapid shift of long-distance transport sources from the North China Plain to the Yellow Sea with higher wind speeds according to backward trajectory. When the ambient PM2.5 concentrations were high (>20 mu g m(-3)) and the weather was clear, the PM Lidar could also observe the diurnal variations of PM2.5 vertical profiles (200-500 m) like the PM sensor on UAV. However, there were differences in the PM2.5 vertical concentrations, and the differences of PM2.5 vertical mean concentrations (200-500 m) measured by the two methods in different seasons were 2.2-13.6 mu g m(-3). When the PM2.5 concentrations were lower than 17 mu g m(-3), the measurement performance of PM Lidar was significantly lower than those of the PM sensor on UAV.
引用
收藏
页数:13
相关论文
共 16 条
  • [1] Influence of coastal planetary boundary layer on PM2.5 with unmanned aerial vehicle observation
    Han, Suqin
    Tang, Yingxiao
    Lu, Miaomiao
    Yang, Xu
    Shi, Jing
    Cai, Ziying
    Ding, Jing
    ATMOSPHERIC RESEARCH, 2023, 294
  • [2] Identification of the atmospheric boundary layer structure through vertical distribution of PM2.5 obtained by unmanned aerial vehicle measurements
    Jiang, Yu-hang
    Li, Bai
    He, Hong-di
    Li, Xiao-bing
    Wang, Dong-sheng
    Peng, Zhong-ren
    ATMOSPHERIC ENVIRONMENT, 2022, 278
  • [3] Effects of land use and landscape pattern on PM2.5 in Yangtze River Delta, China
    Lu, Debin
    Mao, Wanliu
    Yang, Dongyang
    Zhao, Jianan
    Xu, Jianhua
    ATMOSPHERIC POLLUTION RESEARCH, 2018, 9 (04) : 705 - 713
  • [4] PM2.5 vertical variation during a fog episode in a rural area of the Yangtze River Delta, China
    Zhu, Jun
    Zhu, Bin
    Huang, Yong
    An, Junlin
    Xu, Jiaping
    SCIENCE OF THE TOTAL ENVIRONMENT, 2019, 685 : 555 - 563
  • [5] Vertical profiles of O3, NO2 and PM in a major fine chemical industry park in the Yangtze River Delta of China detected by a sensor package on an unmanned aerial vehicle
    Chen, Lang
    Pang, Xiaobing
    Li, Jingjing
    Xing, Bo
    An, Taicheng
    Yuan, Kaibin
    Dai, Shang
    Wu, Zhentao
    Wang, Shuaiqi
    Wang, Qiang
    Mao, Yiping
    Chen, Jianmeng
    SCIENCE OF THE TOTAL ENVIRONMENT, 2022, 845
  • [6] Exploring the spatial effects and influencing factors of PM2.5 concentration in the Yangtze River Delta Urban Agglomerations of China
    He, Yong
    Lin, Kunrong
    Liao, Nuo
    Chen, Zhihao
    Rao, Jiwen
    ATMOSPHERIC ENVIRONMENT, 2022, 268
  • [7] Vertical Profiles of PM2.5 and O3 Measured Using an Unmanned Aerial Vehicle (UAV) and Their Relationships with Synoptic- and Local-Scale Air Movements
    Hwang, Hyemin
    Lee, Ju Eun
    Shin, Seung A.
    You, Chae Rim
    Shin, Su Hyun
    Park, Jong-Sung
    Lee, Jae Young
    REMOTE SENSING, 2024, 16 (09)
  • [8] Characterizing vertical distribution patterns of PM2.5 in low troposphere of Shanghai city, China: Implications from the perspective of unmanned aerial vehicle observations
    Song, Rui-feng
    Wang, Dong-sheng
    Li, Xiao-bing
    Li, Bai
    Peng, Zhong-ren
    He, Hong-di
    ATMOSPHERIC ENVIRONMENT, 2021, 265 (265)
  • [9] Surveillance of long-term environmental elements and PM2.5 health risk assessment in Yangtze River Delta, China, from 2016 to 2020
    Wu, Keqin
    Meng, Yuanhua
    Gong, Yan
    Zhang, Xuhui
    Wu, Linlin
    Ding, Xinliang
    Chen, Xiaofeng
    ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2022, 29 (54) : 81993 - 82005
  • [10] Surveillance of long-term environmental elements and PM2.5 health risk assessment in Yangtze River Delta, China, from 2016 to 2020
    Keqin Wu
    Yuanhua Meng
    Yan Gong
    Xuhui Zhang
    Linlin Wu
    Xinliang Ding
    Xiaofeng Chen
    Environmental Science and Pollution Research, 2022, 29 : 81993 - 82005