Three-dimensional analysis of ozone and PM2.5 distributions obtained by observations of tethered balloon and unmanned aerial vehicle in Shanghai, China

  • Xiao-Bing Li
  • Dongfang Wang
  • Qing-Chang Lu
  • Zhong-Ren Peng
  • Qingyan Fu
  • Xiao-Ming Hu
  • Juntao Huo
  • Guangli Xiu
  • Bai Li
  • Chao Li
  • Dong-Sheng Wang
  • Hanyu Wang
Original Paper


A lightweight unmanned aerial vehicle (UAV) and a tethered balloon platform were jointly used to investigate three-dimensional distributions of ozone and PM2.5 concentrations within the lower troposphere (1000 m) at a localized coastal area in Shanghai, China. Eight tethered balloon soundings and three UAV flights were conducted on May 25, 2016. Generalized additive models (GAMs) were used to quantitatively describe the relationships between air pollutants and other obtained parameters. Field observations showed that large variations were captured both in the vertical and horizontal distributions of ozone and PM2.5 concentrations. Significant stratified layers of ozone and PM2.5 concentrations as well as wind directions were observed throughout the day. Estimated bulk Richardson numbers indicate that the vertical mixing of air masses within the lower troposphere were heavily suppressed throughout the day, leading to much higher concentrations of ozone and PM2.5 in the planetary boundary layer (PBL). The NO and NO2 concentrations in the experimental field were much lower than that in the urban area of Shanghai and demonstrated totally different vertical distribution patterns from that of ozone and PM2.5. This indicates that aged air masses of different sources were transported to the experimental field at different heights. Results derived from the GAMs showed that the aggregate impact of the selected variables for the vertical variations can explain 94.3% of the variance in ozone and 94.5% in PM2.5. Air temperature, relative humidity and atmospheric pressure had the strongest effects on the variations of ozone and PM2.5. As for the horizontal variations, the GAMs can explain 56.3% of the variance in ozone and 57.6% in PM2.5. The strongest effect on ozone was related to air temperature, while PM2.5 was related to relative humidity. The output of GAMs also implied that fine aerosol particles were in the stage of growth in the experimental field, which is different from ozone (aged air parcels of ozone). Geographical parameters influenced the horizontal variations of ozone and PM2.5 concentrations by changing underlying surface types. The differences of thermodynamic properties between land and sea resulted in quick changes of PBL height, air temperature and dew point over the coastal area, which was linked to the extent of vertical mixing at different locations. The results of GAMs can be used to analyze the sources and formation mechanisms of ozone and PM2.5 pollutions at a localized area.


UAS Air pollution Generalized additive model Lower tropospheric ozone Fine aerosol particles 



This study was Funded by the National Key R&D Program of China (No. 2016YFC0200500), the Shanghai Environmental Protection Bureau (No. 2014-8) and the National Planning Office of Philosophy and Social Science (No. 16ZDA048). We express our sincere appreciation to the Second Surveying and Mapping Institute of Zhejiang Province for their help in flying the UAV. We are very grateful for the help from No. 38 institute of China Electronics Technology Group Company in operating the tethered balloon platform.

Supplementary material

477_2018_1524_MOESM1_ESM.docx (7.9 mb)
Supplementary material 1 (DOCX 8107 kb)


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Xiao-Bing Li
    • 1
  • Dongfang Wang
    • 2
  • Qing-Chang Lu
    • 1
  • Zhong-Ren Peng
    • 1
    • 3
    • 4
  • Qingyan Fu
    • 2
  • Xiao-Ming Hu
    • 5
  • Juntao Huo
    • 2
  • Guangli Xiu
    • 6
  • Bai Li
    • 1
  • Chao Li
    • 1
  • Dong-Sheng Wang
    • 1
  • Hanyu Wang
    • 1
  1. 1.State Key Laboratory of Ocean Engineering, School of Naval Architecture, Ocean and Civil EngineeringShanghai Jiao Tong UniversityShanghaiChina
  2. 2.Shanghai Environmental Monitoring CenterShanghaiChina
  3. 3.China Institute for Urban GovernanceShanghai Jiao Tong UniversityShanghaiChina
  4. 4.Department of Urban and Regional PlanningUniversity of FloridaGainesvilleUSA
  5. 5.Center for Analysis and Prediction of Storms, and School of MeteorologyUniversity of OklahomaNormanUSA
  6. 6.Research Center of Risk Assessment and Control of Hazardous Chemical MaterialsEast China University of Science and TechnologyShanghaiChina

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