Using unmanned aerial vehicle to investigate the vertical distribution of fine particulate matter

  • D. Wang
  • Z. WangEmail author
  • Z.-R. Peng
  • D. Wang
Original Paper


The vertical distribution of fine particulate matter (PM2.5) is a vital link in understanding the transport and evolution of haze. However, existing ground stations cannot provide sufficient vertical observations of PM2.5, especially at fine scales regarding space and time. This study deployed a six-rotor unmanned aerial vehicle (UAV) equipped with portable monitors to observe the vertical distributions of PM2.5 and meteorological parameters within 1000 m lower troposphere. By comparing with ground-based monitoring station and tethered balloon platform for PM2.5 measurements, the UAV was improved and then used to perform a field observation experiment in the Qingpu district of Shanghai, China. The UAV-based observations showed a decreasing vertical profile of PM2.5 in the experimental day, with a decrease of more than 50% at 0–1000 m height. PM2.5 had a vertical pattern that declined rapidly after 700 m in the afternoon, but the morning PM2.5 had a rapid decline from 200 to 500 m compared with other height intervals in this period. A temperature inversion at a lower height in the morning blocked newly formed PM2.5 at ground to disperse upward, and PM2.5 above the temperature inversion was composed of residuals in last night. The temperature inversion gradually climbed up in the afternoon, which was beneficial to the dispersion of near-ground PM2.5. The difference of relative humidity above and below 700 m height implies different geographical origins that were well identified and explained by a cluster analysis. This study generally highlights the significance of using a lightweight UAV to understand air pollution and governance environments in the urban area.


PM2.5 Vertical distribution Cluster analysis Unmanned aerial vehicle 



This work was partially supported by the National Key R&D Program of China (No. 2016YFC0200500), the National Natural Science Foundation of China (No. 41701552), and the Science and Technology Project of Guangzhou, China (No. 201803030032). The authors thank the NOAA Air Resources Laboratory (ARL) for providing the HYSPLIT transport and dispersion model and the READY Web site ( used in this study. The authors also declare no conflict of interest.


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Copyright information

© Islamic Azad University (IAU) 2019

Authors and Affiliations

  1. 1.Center for Intelligent Transportation Systems and Unmanned Aerial Systems Applications, State Key Laboratory of Ocean Engineering, School of Naval Architecture, Ocean and Civil EngineeringShanghai Jiao Tong UniversityShanghaiChina
  2. 2.College of Transportation and Civil EngineeringFujian Agriculture and Forestry UniversityFuzhouChina
  3. 3.China Institute for Urban GovernanceShanghai Jiao Tong UniversityShanghaiChina
  4. 4.International Center for Adaptation Planning and Design (iAdapt), School of Landscape Architecture and Planning, College of Design, Construction, and PlanningUniversity of FloridaGainesvilleUSA
  5. 5.Shanghai Environmental Monitoring CenterShanghaiChina

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