Wen-Hsi Cheng This email address is being protected from spambots. You need JavaScript enabled to view it.1, Ching-Ho Lin2, Chung-Shin Yuan This email address is being protected from spambots. You need JavaScript enabled to view it.3 

1 Department of Occupational Safety and Hygiene, Fooyin University, Kaohsiung 831301, Taiwan
2 Department of Environmental Engineering and Science, Fooyin University, Kaohsiung 831301, Taiwan
3 Institute of Environmental Engineering, National Sun Yat-sen University, Kaohsiung 804201, Taiwan

Received: July 15, 2023
Revised: August 19, 2023
Accepted: August 28, 2023

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.4209/aaqr.230169  

Cite this article:

Cheng, W.H., Lin, C.H., Yuan, C.S., (2023). VOC Sampler on a Drone Assisting in Tracing the Potential Sources by a Dispersion Model - Case Study of Industrial Emissions. Aerosol Air Qual. Res. 23, 230169. https://doi.org/10.4209/aaqr.230169


  • A drone was equipped with a micro sampler for extracting VOCs.
  • VOCs were identified by a GC-MS in the laboratory.
  • A backward trajectory model was used to track the paths of VOCs.
  • Two petrochemical plants were tracked as the emission sources.
  • The micro sampler on a drone is proven as a cost-effective device.


Volatile organic compound (VOC) related air pollution cause public concern and pose adverse effects on human health in the communities in most developed and developing countries. Our recent studies have applied a quadrotor drone (Mavic Pro, DJI) equipped with a micro needle trap sampler (NTS), and it could fast arrive at the polluted locations for immediately sampling and further tracking the suspended VOC sources. Notably, a remote-controlled telescoping sampling device was also equipped on the drone in order to extend the NTS outside the disturbed downward wind zone, which was resulted from the rotating propellers. Two plants which manufacture petrochemical products at an industrial complex in Kaohsiung City, southern Taiwan, were applied as the targets for VOCs sampling and further qualitative and quantitative analysis in the laboratory. Aromatic hydrocarbons, including toluene of 433 ppb, ethylbenzene and xylenes of 100–200 ppb and phenol of 111 ppb were identified. Additionally, an air mass backward trajectory model, FYTRAJ, was used to track the paths of VOC emitted from the potential sources and transported in the ambient air. According to the analyzed constituents of VOCs and the raw material data of the suspected plant, which was combined with the backward trajectory tracking simulation of VOC plumes, the NTS carried by a drone has been proven as a cost-effective air pollution monitoring apparatus for locating the VOC emission sources.

Keywords: Drone, Micro sampler, Backward trajectory simulation, Volatile organic compounds, Source tracking

Share this article with your colleagues 


Subscribe to our Newsletter 

Aerosol and Air Quality Research has published over 2,000 peer-reviewed articles. Enter your email address to receive latest updates and research articles to your inbox every second week.

77st percentile
Powered by
   SCImago Journal & Country Rank

2022 Impact Factor: 4.0
5-Year Impact Factor: 3.4

The Future Environment and Role of Multiple Air Pollutants

Aerosol and Air Quality Research partners with Publons

CLOCKSS system has permission to ingest, preserve, and serve this Archival Unit
CLOCKSS system has permission to ingest, preserve, and serve this Archival Unit

Aerosol and Air Quality Research (AAQR) is an independently-run non-profit journal that promotes submissions of high-quality research and strives to be one of the leading aerosol and air quality open-access journals in the world. We use cookies on this website to personalize content to improve your user experience and analyze our traffic. By using this site you agree to its use of cookies.