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ABSTRACT
An unmanned aerial vehicle equipped with an isokinetic sampling device and a portable aerosol particle size spectrometer was used to detect atmospheric particles at different altitudes in the suburbs of Tianjin. In January 2017, four flight tests under different air conditions were conducted, and the Severe polluted and Excellent levels were classified according to the air quality index of the State-controlled site. Changes in mass concentration with altitude, and the vertical distribution of mass concentration and number concentration spectrum were analyzed. Results showed that under the Severe polluted condition, the concentration of particulate matter (PM) decreased significantly within 200 m, and became stable above 400 m. Two peaks at 260 m and 400 m were observed – they were attributed to local emissions and transportation of nearby areas. When the mass concentration significantly increased, the greatest contributor was particles with a size of 15–30 μm. As altitude increased, PM with the size of less than 1 μm accounted for the largest proportion. Regarding number concentration, the main particle size was less than 0.35 μm. Under the Excellent condition, particles less than 0.35 μm in size were the most important components in mass and number concentrations. Correlation analysis of meteorological factors and backward trajectory indicated that pollution was caused by particle concentration transport in several provinces and the particle accumulation due to temperature inversion.Implications: Study on the vertical structure of air pollutants was very important for understanding the occurrence and development of regional heavy pollution. Unmanned aerial vehicles (UAVs) was an efficient method for its easy operation, good stability, high flexibility and safety, and was introduced to observe the vertical distribution characteristics of particle concentration under different weather conditions. This study provided the number concentration spectra and the mass concentration spectra at different height, and would give some support for the precise control of air pollution.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
The data that support the findings of this study are available from the corresponding author, upon reasonable request.
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Notes on contributors
Shanshan Li
Shanshan Li is a professor in Green Development & Intelligent EP Tech Center, Beijing Municipal Research Institute of Eco-Environmental Protection, Beijing, PR China.
Min Xing
Min Xing is an associate professor in Green Development & Intelligent EP Tech Center, Beijing Municipal Research Institute of Eco-Environmental Protection, Beijing, PR China.
Lei Jiang
Lei Jiang is a professor in Mentougou District Ecology and Environment Bureau of Beijing Municipality, Beijing, PR China.
Peng Chen
Peng Chen is an engineer in Green Development & Intelligent EP Tech Center, Beijing Municipal Research Institute of Eco-Environmental Protection, Beijing, PR China.
Feng Ding
Feng Ding is a senior engineer in Beijing Shangyun Environment Co. Ltd, Beijing, PR China.
Wen Yang
Wen Yang is a professor in State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, PR China.