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In order to meet the technical requirements for miniaturization, multi-angle, multi-altitude, and fast simultaneous acquisition of atmospheric pollutants, this study develops an integrated, lightweight, and cost-effective airborne differential optical absorption spectroscopy (DOAS) system. This system is designed in order to be used on a rotorcraft unmanned aerial vehicle (UAV) platform for monitoring atmospheric pollutants. The compositions of the hexacopter UAV platform and the airborne DOAS system are detailed in this work. The system includes a multi axis differential optical absorption spectroscopy (MAX-DOAS) spectral acquisition system, a control system, and a flight environment monitoring system. Commands are sent from a computer via serial communication to drive a gimbal, controlling the azimuth angle and elevation angle of the telescope, with a camera recording the light obstruction. The sunlight scattered by the atmosphere is collected by the telescope and transmitted via fiber optics to the spectrometer, which then transmits the data to the control computer. Additionally, the system captures data of altitude, temperature, humidity, and GPS location during flight, and filters out spectral data obtained under abnormal flight conditions. Stability studies indicate that the mean angular deviations for yaw, roll, and pitch are 0.07°, –0.13°, and –0.12° respectively, which meet the requirements for monitoring stability. Comparative experiments with a commercial ground-based DOAS system show that the correlation coefficients between the monitoring data of both systems are both greater than 0.92, confirming the reliability of the airborne system. In field flight experiments, the airborne DOAS system conducts observations at altitudes of 30 m, 60 m, and 90 m, with the elevation angle set at 0° and the azimuth angle measured every 30° from 0° to 360°. The system successfully obtains the concentration distributions of NO2, SO2, and HCHO at different azimuth angles and altitudes. The results indicate that the concentrations of these three gases decrease with altitude increasing, with higher concentrations observed in the southeast direction, indicating the presence of pollution sources in that direction. Further analysis with considering altitude changes indicates that the rate of decrease in NO2 concentration and SO2 concentration slow down with altitude increasing, while the rate of decrease in HCHO remains relatively constant. These findings indicate that this system effectively meets the technical requirements for simultaneous, rapid, multi-angle, and multi-altitude detection of atmospheric pollutants, providing essential support for the detailed monitoring of complex urban micro-environments.
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Keywords:
- airborne detection system /
- rotary-wing unmanned aerial vehicle /
- two-dimensional differential optical absorption /
- multi-dimensional
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图 5 机载系统与商用地基MAX-DOAS观测的NO2差分斜柱浓度时间序列对比图 (a)仰角为1°; (b)仰角为2°; (c)仰角为5°; (d)仰角为6°; (e)仰角为8°; (f)仰角为15°
Figure 5. Comparison of NO2 differential slant column density time series observed by airborne system and commercial ground-based MAX-DOAS: (a) Elevation angle of 1°; (b) elevation angle of 2°; (c) elevation angle of 5°; (d) elevation angle of 6°; (e) elevation angle of 8°; (f) elevation angle of 15°.
图 6 机载系统与商用地基MAX-DOAS观测的NO2差分斜柱浓度的对比相关图 (a)仰角为1°; (b)仰角为2°; (c)仰角为5°; (d)仰角为6°; (e)仰角为8°; (f)仰角为15°
Figure 6. Comparison correlation plot of NO2 differential slant column density observed by airborne system and commercial ground-based MAX-DOAS: (a) Elevation angle of 1°; (b) elevation angle of 2°; (c) elevation angle of 5°; (d) elevation angle of 6°; (e) elevation angle of 8°; (f) elevation angle of 15°.
图 9 NO2, SO2, HCHO光谱拟合效果实例 (a) NO2光谱拟合; (b) NO2斜柱浓度拟合残差; (c) SO2光谱拟合; (d) SO2斜柱浓度拟合残差; (e) HCHO光谱拟合; (f) HCHO斜柱浓度拟合残差
Figure 9. Examples of spectral fitting results for NO2, SO2 and HCHO: (a) NO2 spectral fitting; (b) fitting residuals of NO2 slant column density; (c) SO2 spectral fitting; (d) fitting residuals of SO2 slant column density; (e) HCHO spectral fitting; (f) fitting residuals of HCHO slant column density.
表 1 具体实验拟合参数
Table 1. Specific parameters of experimental fitting.
Parameter NO2 SO2 HCHO Fitting wavelength/nm 337—370 309—323 324—342 Polynomial degree 5 5 5 Intensity offset Constant Constant Constant NO2 220 K, 294 K[23] 294 K[23] 294 K[23] SO2 — 293 K[24] — HCHO 297 K[25] — 297 K[25] O3 223 K, 243 K[26] 223 K, 243 K[26] 223 K, 243 K[26] O4 293 K[27] — 293 K[27] Bro 223 K[28] — — Ring Calculated with FRS Calculated with FRS Calculated with FRS -
[1] Su W J, Liu C, Chan K L, Hu Q H, Liu H, Ji X G, Zhu Y Z, Liu T, Zhang C X, Chen Y J, Liu J G 2020 Atmos. Meas. Tech. 13 6271
Google Scholar
[2] Wu S S, Huang B, Wang J H, He L J, Wang Z Y, Yan Z, Lao X Q, Zhang F, Liu R Y, Du Z H 2021 Environ. Pollut. 273 116456
Google Scholar
[3] 徐晋, 谢品华, 司福祺, 李昂, 刘文清 2012 61 282
Google Scholar
Xu J, Xie P H, Si F Q, Li A, Liu W Q 2012 Acta Phys. Sin. 61 282
Google Scholar
[4] 梁帅西, 秦敏, 段俊, 方武, 李昂, 徐晋, 卢雪, 唐科, 谢品华, 刘建国 2017 66 090704
Google Scholar
Liang S X, Qin M, Duan J, Fang W, Li A, Xu J, Lu X, Tang K, Xie P H, Liu J G 2017 Acta Phys. Sin. 66 090704
Google Scholar
[5] Zhang H K, Huang B, Zhang M, Cao K, Yu L 2015 Int. J. Remote Sens. 36 4411
Google Scholar
[6] Liu M X, Liu X N, Wu L, Zou X Y, Jiang T, Zhao B Y 2018 Remote Sens. 10 772
Google Scholar
[7] Zhou B, Zhang S B, Xue R B, Li J Y, Wang S S 2023 J. Environ. Sci. 123 3
Google Scholar
[8] Pang X B, Chen L, Shi K L, Wu F, Chen J M, Fang S X, Wang J L, Xu M 2021 Sci. Total Environ. 764 142828
Google Scholar
[9] Wu C, Liu B, Wu D, Yang H L, Mao X, Tan J, Liang Y, Sun J Y, Xia R, Sun J R, He G W, Li M, Deng T, Zhou Z, Li Y J 2021 Sci. Total Environ. 801 149689
Google Scholar
[10] Li X M, Xie P H, Li A, Xu J, Ren H M, Ren B, Li Y Y, Li J 2021 J. Environ. Sci. 107 1
Google Scholar
[11] Arroyo P, Gómez-Suárez J, Herrero J L, Lozano J 2022 Sens. Actuators B Chem. 364 131815
Google Scholar
[12] Platt U, Stutz J, Platt U, Stutz J 2008 Differential Absorption Spectroscopy (Berlin Heidelberg: Springer) pp135–174
[13] Liu C, Xing C Z, Hu Q H, Wang S S, Zhao S H, Gao M 2022 Earth Sci. Rev. 226 103958
Google Scholar
[14] Chen X, Chen Y P, Chen Y X, Fang Y X, Yu J X, Sun Y 2023 IEEE International Geoscience and Remote Sensing Symposium United States of America, July 16–21, 2023 p3866
[15] Xing C Z, Liu C, Li Q H, Wang S S, Tan W, Zou T L, Wang Z, Lu C 2024 Sci. Total Environ. 915 169159
Google Scholar
[16] Li L, Lu C, Chan P W, Zhang X, Yang H L, Lan Z J, Zhang W H, Liu Y W, Pan L, Zhang L 2020 Atmos. Environ. 220 117083
Google Scholar
[17] Mo Z W, Huang S, Yuan B, Pei C L, Song Q C, Qi J P, Wang M, Wang B L, Wang C, Shao M 2022 Environ. Pollut. 292 118454
Google Scholar
[18] Chen L, Pang X B, Li J J, Xing B, An T C, Yuan K B, Dai S, Wu Z T, Wang S Q, Wang Q, Mao Y P, Chen J M 2022 Sci. Total Environ. 845 157113
Google Scholar
[19] Zheng Z L, Wang H C, Chen X R, Wang J, Li X, Lu K D, Yu G H, Huang X F, Fan S J 2024 Atmos. Environ. 321 120361
Google Scholar
[20] Hedworth H, Page J, Sohl J, Saad T 2022 Drones 6 253
Google Scholar
[21] 刘进, 司福祺, 周海金, 赵敏杰, 窦科, 王煜, 刘文清 2015 64 34217
Google Scholar
Liu J, Si F Q, Zhou H J, Zhao M J, Dou K, Wang Y, Liu W Q 2015 Acta Phys. Sin. 64 34217
Google Scholar
[22] Mou F S, Luo J, Zhang Q J, Zhou C, Wang S, Ye F, Li S W, Sun Y W 2023 Atmosphere 14 739
Google Scholar
[23] Vandaele A C, Hermans C, Simon P C, Carleer M, Colin R, Fally S, Mérienne M F, Jenouvrier A, Coquart B 1998 J. Quant. Spectrosc. Radiat. Transf. 59 171
Google Scholar
[24] Bogumil K, Orphal J, Homann T, Voigt S, Spietz P, Fleischmann O C, Vogel A, Hartmann M, Kromminga H, Bovensmann H, Frerick J, Burrows J P 2003 J. Photoch. Photobio. A 157 167
Google Scholar
[25] Meller R, Moortgat G K 2000 J. Geophys. Res 105 7089
Google Scholar
[26] Serdyuchenko A, Gorshelev V, Weber M, Chehade W, Burrows J P 2014 Atmos. Meas. Tech. 7 625
Google Scholar
[27] Thalman R, Volkamer R 2013 Phys. Chem. Chem. Phys. 15 15371
Google Scholar
[28] Fleischmann O C, Hartmann M, Burrows J P, Orphal J 2004 J. Photochem. Photobiol. A Chem. 168 117
Google Scholar
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