Research and application of differential optical absorption two-dimensional detection system for rotorcraft unmanned aerial vehicle

Ye Fan; Li Su-Wen; Mou Fu-Sheng; Wang Song; Wang Zhi-Duo; Tang Yu-Jie; Luo Jing

doi:10.7498/aps.73.20240909

Abstract

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.

Keywords:

airborne detection system /
rotary-wing unmanned aerial vehicle /
two-dimensional differential optical absorption /
multi-dimensional

Authors and contacts

Anhui Province Key Laboratory of Pollutant Sensitive Materials and Environmental Remediation, Huaibei Normal University, Huaibei 235000, China

Corresponding author: Li Su-Wen, swli@chnu.edu.cn

Funds: Project supported by the National Natural Science Foundation of China (Grant No. 41875040), the Innovation Team Project of Anhui Educational Committee, China (Grant No. 2023AH010043), the Natural Science Foundation of Anhui Province, China (Grant No. 2208085QF215), and the Natural Science Research Project of Anhui Educational Committee, China (Grant No. 2023AH050338).

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References

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Cited By

图 1 旋翼无人机平台实物图

Figure 1. Photograph of the rotorcraft unmanned aerial vehicle platform.

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图 2 机载差分光学吸收二维系统结构示意图

Figure 2. Schematic diagram of the airborne differential optical absorption 2D system.

DownLoad: Full-Size Img PowerPoint

图 3 旋翼无人机载差分光学吸收二维探测系统实物图

Figure 3. Photograph of the differential optical absorption 2D detection System mounted on a rotorcraft unmanned aerial vehicle.

DownLoad: Full-Size Img PowerPoint

图 4 机载系统3种飞行姿态的角度偏差图　(a) 500 ms采样数据; (b)固定间隔采样数据

Figure 4. Angle deviation diagram of the three flight attitudes of the airborne system: (a) 500 ms sampled data; (b) data sampled at fixed intervals.

DownLoad: Full-Size Img PowerPoint

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°.

DownLoad: Full-Size Img PowerPoint

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°.

DownLoad: Full-Size Img PowerPoint

图 7 无人机外场实验　(a)测量地点; (b)实景图

Figure 7. UAV field experiment: (a) Measurement site; (b) real-world image.

DownLoad: Full-Size Img PowerPoint

图 8 无人机测量方式图

Figure 8. Diagram of UAV measurement method.

DownLoad: Full-Size Img PowerPoint

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.

DownLoad: Full-Size Img PowerPoint

图 10 水平观测下不同高度的NO₂浓度分布图　(a)高度为30 m; (b)高度为60 m; (c)高度为90 m

Figure 10. Distribution of NO₂ concentration at different heights under horizontal observation: (a) Height of 30 m; (b) height of 60 m; (c) height of 90 m

DownLoad: Full-Size Img PowerPoint

图 12 水平观测下不同高度的HCHO浓度分布图　(a)高度为30 m; (b)高度为60 m; (c)高度为90 m

Figure 12. Distribution of HCHO concentration at different heights under horizontal observation: (a) Height of 30 m; (b) height of 60 m; (c) height of 90 m.

DownLoad: Full-Size Img PowerPoint

图 13 观测期间0°方位角下不同高度的NO₂, SO₂, HCHO浓度变化图　(a) NO₂; (b) SO₂; (c) HCHO

Figure 13. Variation of NO₂, SO₂, HCHO concentrations at different heights under 0° azimuth during the observation period: (a) NO₂; (b) SO₂; (c) HCHO.

DownLoad: Full-Size Img PowerPoint

图 11 水平观测下不同高度的SO₂浓度分布图　(a)高度为30 m; (b)高度为60 m; (c)高度为90 m

Figure 11. Distribution of SO₂ concentration at different heights under horizontal observation: (a) Height of 30 m; (b) height of 60 m; (c) height of 90 m.

DownLoad: Full-Size Img PowerPoint

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

DownLoad: CSV

[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 物理学报 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 物理学报 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 物理学报 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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