A study of vertical distribution patterns of PM2.5 concentrations based on ambient monitoring with unmanned aerial vehicles: A case in Hangzhou, China. (December 2015)

• Record Type: Journal Article
• Authors: Peng, Zhong-Ren; Wang, Dongsheng; Wang, Zhanyong; Gao, Ya; Lu, Sijia
• Is Part Of: Atmospheric environment. Volume 123:Part B(2015)
• Page Start: 357
• Page End: 369
• Publication Date: 2015-12
• Subjects: Fine particulate matter -- Vertical distribution -- Meteorological parameters -- Unmanned aerial vehicle; Air -- Pollution -- Periodicals; Air -- Pollution -- Meteorological aspects -- Periodicals; 551.51
• DOI: 10.1016/j.atmosenv.2015.10.074
• Languages: English
• ISSNs: 1352-2310

Abstract

Abstract: Measurements of the vertical distribution of air pollutant concentrations can provide essential information for accurate estimates of the dispersion mechanism of local pollutants between boundary layer and troposphere. This paper reports unique measurements using an unmanned aerial vehicle (UAV) with mobile sensors to collect three-dimensional fine particulate matter (PM2.5 ) mass concentration data on sixteen flights within 1000 m altitude from August, 2014 to December, 2014 in Hangzhou, China. The study demonstrates the feasibility of UAV with mobile monitoring devices as an effective and flexible means to collect three-dimensional air pollutant concentration data, particularly for monitoring the vertical profile of air pollutants. The experimental results show that in general, the PM2.5 concentrations decrease as height increases, with an exception when the air temperature inversion layer appears, and the decrease rate of PM2.5 concentrations is larger in the morning than in the afternoon flights. This is a result of the accumulated pollutant emission of human activities during the day and the varied meteorological conditions. At the same horizontal layer, there are fluctuations in PM2.5 concentrations during different time periods of the day. The vertical fluctuations of PM2.5 concentrations become nearly uniform in two afternoon flights, which is directly related with the extent of atmospheric mixture. Seen from the multiple regression models, the distribution of relative PM2.5 concentrations between vertical and ground observations is well characterized and the regression coefficients of four measured factors (i.e., air temperature, relative humidity, air pressure and height) effectively explain their impacts on the vertical distribution patterns. Air temperature and relative humidity are the most influential factors that affect the vertical distribution of PM2.5 concentrations. Highlights: Three-dimensional observations of PM2.5 mass are conducted using unmanned aerial vehicle with mobile sensors. Vertical profile of PM2.5 mass concentrations is characterized with four time periods of four days. Relationship between PM2.5 mass and meteorological parameters is discussed at 300 m–1000 m altitude.