本研究利用搭载多污染物传感器的旋翼无人机,于2023年10月12~18日每日上午(9:00)、下午(15:00)、晚上(21:00)在广东省惠州市大亚湾区开展多项参数垂直观测.观测结果显示,随着高度的上升,温度总体呈现逐渐下降趋势,部分时段在350m高度出现逆温现象;相对湿度总体呈现上升趋势;NO2浓度总体呈现逐渐上升趋势,部分时段在350m左右高度有较为显著的浓度抬升现象;SO2浓度总体呈现逐渐下降趋势,但是部分时段在350m左右高度也有显著浓度抬升现象.这表明350m高度可能发生了NO2和SO2污染传输过程. CO浓度在地面相对较高,并随高度上升而迅速降低, 200m高度以上浓度差异很小,这表明高空CO浓度主要受近地面排放扩散影响,没有明显的污染传输过程. PM10和PM2.5浓度较低,在各高度的差异较小,且总体呈现“上午高、下午晚上低”的现象,这可能是夜间残留层累积或排放的颗粒物对第2d上午产生了影响.利用后向轨迹模拟分析气团来源,发现0m、300m和500m高度的气团均经过大亚湾石化园区,而且300m和500m高度气团在12~14日来源于广东省东北部县市,在16~18日则来源于广东及福建沿海区域,因此大亚湾区石化园区排放和上风向县市均对大亚湾区高空污染物存在一定贡献.利用WRF~CAMx模型模拟分析区域高空NO2和SO2分布,发现大亚湾区上风向高空存在污染物浓度高值区,表明该区域可能存在高空污染物传输带.通过无人机观测得到的垂直廓线特征,有助于理解大气污染物在高空的传输规律,为区域空气质量影响研究提供了支撑.
Abstract
This study utilized a rotor UAV equipped with multi-pollutant sensors to conduct vertical observations of multiple parameters in the Daya Bay District of Huizhou City, Guangdong Province, from October 12 to 18, 2023, during the morning (9:00), afternoon (15:00), and evening (21:00) each day. The results showed that temperature generally decreased with increasing altitude, with temperature inversion observed at 350 meters during certain periods. Relative humidity exhibited an overall increasing trend with height. NO2 concentrations typically increased with altitude, with significant peaks around 350 meters at specific times. SO2 concentrations exhibited a general decreasing trend, but significant elevations were also observed around 350meters at certain times, suggesting potential NO2 and SO2 pollution transport processes at this altitude. CO concentrations were relatively high at ground level and decreased rapidly with altitude, showing minimal variation above 200meters, indicating that high-altitude CO concentrations were primarily influenced by near-surface emissions and diffusion, with no significant pollution transport processes. PM10 and PM2.5 concentrations were generally low, with slight variations across altitudes. They tended to be higher in the morning and lower in the afternoon and evening, likely due to residual layer accumulation or overnight particle emissions. Backward trajectory analysis revealed that air masses at 0, 300, and 500meters all passed over the Daya Bay Petrochemical Industrial Park. Air masses at 300meters and 500meters originated from northeastern counties and cities of Guangdong Province from October 12 to 14, and from coastal areas of Guangdong and Fujian Provinces from October 16 to 18, indicating that emissions from the Daya Bay Petrochemical Industrial Park and upwind regions contributed to high-altitude pollutant levels in the Daya Bay area. WRF-CAMx model simulations further identified high-concentration zones of NO2 and SO2 upwind of Daya Bay, confirming the existence of high-altitude pollutant transport pathways. The vertical profile characteristics obtained from UAV observations provide valuable insights into the transport patterns of atmospheric pollutants at high altitudes, offering support for regional air quality impact studies.
关键词
无人机观测 /
垂直分布特征 /
大气污染物 /
来源分析
Key words
UAV observation /
vertical distribution characteristics /
atmospheric pollutants /
source analysis
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] Jin X, Cheng S, Zheng X, et al. Characteristics of cloud and aerosol derived from lidar observations during Winter in Lhasa, Tibetan Plateau [J]. Remote Sensing, 2024,16(12):2074.
[2] Sun K, Dai G, Wu S, et al. Effect of wind speed on marine aerosol optical properties over remote oceans with use of spaceborne lidar observations [J]. Atmospheric Chemistry and Physics, 2024,24(7): 4389-4409.
[3] Hauser D, Abdalla S, Ardhuin F, et al. Satellite remote sensing of surface winds, waves, and currents where are we now [J]. Surveys in Geophysics, 2023,44(5):1357-1446.
[4] Zhao S, Liu M, Tao M, et al. The role of satellite remote sensing in mitigating and adapting to global climate change [J]. Science of the Total Environment, 2023,904:166820.
[5] Johnson M R, Conrad B M, Tyner D R. Creating measurement-based oil and gas sector methane inventories using source-resolved aerial surveys [J]. Communications Earth & Environment, 2023,4(1):139.
[6] Yang X, Ji D, Li J, et al. Impacts of springtime biomass burning in Southeast Asia on atmospheric carbonaceous components over the Beibu Gulf in China: Insights from aircraft observations [J]. The Science of the total environment, 2023,857:159232.
[7] Fonseca J, Carreiro F, Silva V, et al. μVOC-A lightweight environmental data and air samples acquisition system to install in captive balloons [J]. International Symposium on Industrial Electronics, 2003,1:584-588.
[8] Yokoyama O. Measurements of wind fluctuations by a vane mounted on the captive balloon cable [J]. Journal of the Meteorological Society of Japan. Ser. II, 1968,47(3):159-166.
[9] Yin C, Xu J, Gao W, et al. Characteristics of fine particle matter at the top of Shanghai Tower [J]. Atmospheric Chemistry and Physics, 2023,23(2):1329-1343.
[10] Andreae M O, Acevedo O C, Araùjo A, et al. The amazon tall tower observatory (ATTO) overview of pilot measurements on ecosystem ecology, meteorology, trace gases, and aerosols [J]. Atmospheric Chemistry and Physics, 2015,15(18):10723-10776.
[11] Chen L, Pang X, Li J, et al. Vertical profiles of O3, NO2 and PM in a major fine chemical industry park in the Yangtze River Delta of China detected by a sensor package on an unmanned aerial vehicle [J]. Science of The Total Environment, 2022,845:157113.
[12] Song R, Wang D, Li X, et al. Characterizing vertical distribution patterns of PM2.5 in low troposphere of Shanghai city, China: Implications from the perspective of unmanned aerial vehicle observations [J]. Atmospheric Environment, 2021,265:118724.
[13] Villa T F, Jayaratne E R, Gonzalez L F, et al. Determination of the vertical profile of particle number concentration adjacent to a motorway using an unmanned aerial vehicle [J]. Environmental Pollution, 2017,230:134-142.
[14] Nca J J, Pawnuk M, Bezyk Y, et al. Drone-assisted monitoring of atmospheric pollution—A comprehensive review [J]. Sustainability, 2022,14(18):11516.
[15] 郭伟,朱凌云,李雁宇,等.基于无人机观测的不同污染天气O3垂直分布特征 [J].中国环境科学, 2024,44(12):6578-6589. Guo W, Zhu L Y, Li Y Y, et al. Vertical distribution characteristics of O3under diverse polluted weather based on unmanned aerial vehicle observations [J]. China Environmental Science, 2024,44(12):6578- 6589.
[16] Li C, Liu M, Hu Y, et al. Investigating the vertical distribution patterns of urban air pollution based on unmanned aerial vehicle gradient monitoring [J]. Sustainable Cities and Society, 2022,86:104144.
[17] Olivares I, Langner J, Soto C, et al. Vertical distribution of PM2.5 in Santiago de Chile studied with an unmanned aerial vehicle and dispersion modelling [J]. Atmospheric Environment, 2023,310: 119947.
[18] Zhao Q, He Q, Zhang X, et al. Unmanned aerial vehicle observations of the vertical distribution of particulate matter and sulfur dioxide at a regional background site in northwest China [J]. Atmospheric Pollution Research, 2023,14(12):101919.
[19] Wang Z, Cao R, Li B, et al. Characterizing nighttime vertical profiles of atmospheric particulate matter and ozone in a megacity of South China using unmanned aerial vehicle measurements [J]. Environmental Research, 2023,236:116854.
[20] Wu C, Liu B, Wu D, et al. Vertical profiling of black carbon and ozone using a multicopter unmanned aerial vehicle (UAV) in urban Shenzhen of South China [J]. Science of the Total Environment, 2021,801: 149689.
[21] 刘若岚,刘端阳,袁淑杰,等.基于旋翼无人机观测的雾天和霾天VOCs垂直分布特征研究 [J].大气科学学报, 2023,46(5):655-666. Liu R L, Liu D L, Yuan S J, et al. Vertical characteristics of VOCs during fog and haze events in the lower troposphere over eastern China: insights from multi-rotor UAV observations [J]. Transactions of Atmospheric Sciences, 2023,46(5):655-666.
[22] 赵竹君,何清,陆忠奇,等.基于无人机观测的塔克拉玛干沙漠反应性气体特征及来源分析 [J].环境科学研究, 2023,36(9):1665-1675. Zhao Z J, He Q, Lu Z Q, et al. Characterization of reactive gas and source analysis in Taklimakan Desert based on unmanned aerial vehicle observation [J]. Research of Environmental Sciences, 2023, 36(9):1665-1675.
[23] Peng Z, Wang D, Wang Z, et al. A study of vertical distribution patterns of PM2.5 concentrations based on ambient monitoring with unmanned aerial vehicles: A case in Hangzhou, China [J]. Atmospheric Environment, 2015,123:357-369.
[24] 沈奥,周树道,王敏,等.旋翼无人机大气探测设备布局仿真优化设计 [J].计算机测量与控制, 2018,26(2):165-169. Shen A, Zhou S D, Wang M, et al. Simulation and optimization design of atmospheric detection equipment layout based on UAV [J]. Computer Measurement & Control, 2018,26(2):165-169.
[25] 陆忠奇,赵竹君,何清.库尔勒市大气颗粒物浓度特征及来源 [J].中国沙漠, 2022,42(6):74-84. Lu Z Q, Zhao Z J, He Q.Concentrations characteristics and sources of particulate matter in Korla, Xinjiang, China [J]. Journal of Desert Research, 2022,42(6):74-84.
[26] Wang Q, Tong Z, Wang R, et al. Backward trajectory and multifractal analysis of air pollution in Zhengzhou region of China [J]. Mathematical Problems in Engineering, 2022,2022(1):2226565.
[27] Lee S, Ashbaugh L. The impact of trajectory starting heights on the MURA trajectory source apportionment (TSA) method [J]. Atmospheric Environment, 2007,41(33):7022-7036.
[28] Karaca F, Anil I, Alagha O. Long-range potential source contributions of episodic aerosol events to PM10profile of a megacity [J]. Atmospheric Environment, 2009,43(36):5713-5722.
[29] Thongsame W, Henze D K, Kumar R, et al. Evaluation of WRF-Chem PM2.5 simulations in Thailand with different anthropogenic and biomass-burning emissions [J]. Atmospheric Environment: X, 2024,23: 100282.
[30] 孟昭阳,丁国安,于海青,等.北京北部城区SO2和NO2浓度垂直分布特点初探 [J].应用气象学报, 2002,13(U01):109-112. Meng Z Y, Ding G A, Yu H Q, et al. The preliminary research on the concentration vertical distribution of SO2 and NO2 in the northern part of Beijing [J]. Quarterly Journal of Applied Meteorology, 2002,13 (U01):109-112.
[31] 王碧璇,程勇,于广河,等.基于无人机的深圳近地层大气VOCs垂直分布监测 [J].中国环境科学, 2024,44(6):3021-3029. Wang B X, Cheng Y, Yu G H, et al. Vertical distribution monitoring of near-surface atmospheric VOCs in Shenzhen based on UAV-borne [J]. China Environmental Science, 2024,44(6):3021-3029.
基金
国家重点研发计划项目(2023YFC3706105);大亚湾区空气质量精细化管理项目(GDHR-2022-013)
PDF(4667 KB)
京公网安备 11010802030352号 
