the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Three-dimensional distribution of fine particulate matter concentrations and synchronous meteorological data measured by an unmanned aerial vehicle (UAV) in Yangtze River Delta, China
Abstract. Three-dimensional distribution of fine particulate matter (PM2.5) and meteorological factors are of great importance to clarify the formation mechanism of haze pollution and to help forecast atmospheric pollution under different meteorological conditions. The objective of this study was to measure PM2.5 concentrations and meteorological data at 300–1000 m altitude using an unmanned aerial vehicle (UAV) equipped with mobile instruments. The study was conducted in a 4 × 4 km2 space in Lin'an, Yangtze River Delta (YRD), China. The UAV was operated repeatedly for four times in one day along the designed route spirally from the ground to 1000 m altitude with a total of 8 layers and a 100 m interval between two adjacent layers for five days from 21th August 2014 to 2nd February 2015. PM2.5, air temperature, relative humidity, dew point temperature and air pressure were measured during the data collection. The study results indicated that the PM2.5 concentrations decreased with altitude at 300–1000 m and the variations of PM2.5 with altitude in morning flights were much bigger than in afternoon flights. Besides, the PM2.5 concentration levels in morning flights were generally lower than in afternoon flights. PM2.5 concentrations were positively correlated with dew point temperature and pressure, but positively correlated with relative humidity only on pollution days in autumn or winter. The vertical gradient of PM2.5 concentrations was small in pollution days compared with on clean days. These findings provide the key theoretical foundation for PM2.5 pollution forecast and environmental management.
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RC1: 'Referee comments on “Three-dimensional distribution of fine particulate matter concentrations and synchronous meteorological data measured by an unmanned aerial vehicle (UAV) in Yangtze River Delta, China” by S.-J. Lu et al.', Anonymous Referee #1, 29 Mar 2016
- AC2: 'Response to Reviewer #1', Si-Jia Lu, 01 Jun 2016
- RC2: 'Review', Anonymous Referee #2, 13 Apr 2016
- AC1: 'Response to the referees' comments and suggestions', Si-Jia Lu, 01 Jun 2016
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RC1: 'Referee comments on “Three-dimensional distribution of fine particulate matter concentrations and synchronous meteorological data measured by an unmanned aerial vehicle (UAV) in Yangtze River Delta, China” by S.-J. Lu et al.', Anonymous Referee #1, 29 Mar 2016
- AC2: 'Response to Reviewer #1', Si-Jia Lu, 01 Jun 2016
- RC2: 'Review', Anonymous Referee #2, 13 Apr 2016
- AC1: 'Response to the referees' comments and suggestions', Si-Jia Lu, 01 Jun 2016
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Cited
6 citations as recorded by crossref.
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- A new efficiency calibration methodology for different atmospheric filter geometries by using coaxial Ge detectors A. Barba-Lobo & J. Bolívar 10.1007/s11869-023-01336-x
- Quadcoptor Monitoring M. Ganesan et al. 10.32628/CSEIT1952158
- Risk Assessment for People Exposed to PM2.5 and Constituents at Different Vertical Heights in an Urban Area of Taiwan H. Chen et al. 10.3390/atmos11111145
- Adaptive Deep Learning-Based Air Quality Prediction Model Using the Most Relevant Spatial-Temporal Relations P. Soh et al. 10.1109/ACCESS.2018.2849820