Ozone origin identification device and method

Abstract

The present application relates to spectral technology, and specifically provides a kind of walking formula ozone cause recognition device and method, and the recognition device includes first light source, resonant cavity and detector, the wavelength of the first light source covers the absorption spectrum of nitrogen dioxide;Unmanned aerial vehicle carries measuring unit, measuring unit includes: air passes through first detection channel and enters detection module;Second detection channel is provided with the first chamber and the second chamber of intercommunication, first pump is connected with the first chamber;Third detection channel is provided with the third chamber and the fourth chamber of intercommunication, second pump is connected with the third chamber;Gas supply module includes gas source and valve, nitrogen monoxide provided by gas source is sent after the second chamber and fourth chamber of the valve;Detection module includes second light source, gas chamber and detector;Processing module processes the output signal of detector and detector, to identify ozone cause. The present application has the advantages of high precision.

Description

Technical Field

[0001] This invention relates to spectral technology, and in particular to a mobile ozone formation identification device and method. Background Technology

[0002] Ozone pollution is caused by both local photochemical generation and external transport. Accurately distinguishing between the two is the key to targeted governance, and OPR (net ozone generation rate) monitoring is the core method.

[0003] Current mobile OPR systems mostly use CAPS technology to measure NO2, which is susceptible to interference from NO2 in vehicle exhaust, leading to OPR calculation errors. Furthermore, instruments used in mobile monitoring are significantly affected by environmental factors such as vibration and temperature; existing systems do not effectively correct for this type of interference, making it difficult to guarantee monitoring accuracy. In addition, existing devices cannot simultaneously measure ozone and NO2 using a single optical unit, requiring multiple separate optical units, resulting in high equipment load and power consumption, making them unsuitable for the lightweight mobile monitoring requirements of UAVs. Summary of the Invention

[0004] To address the shortcomings of the existing technical solutions, the present invention provides a mobile ozone formation identification device and method.

[0005] The objective of this invention is achieved through the following technical solution: A mobile ozone formation identification device includes a first light source, a resonant cavity, and a detector. The emission wavelength of the first light source covers the absorption spectrum of nitrogen dioxide. The identification device also includes a drone and a measurement unit, wherein the drone carries the measurement unit, and the measurement unit includes: Multiple detection channels are provided. Air enters the detection module through the first detection channel. The second detection channel is equipped with a first chamber and a second chamber that are connected. A first pump is connected to the first chamber, and the second chamber is connected to the detection module. A light-shielding module is provided on the first chamber. The third detection channel is equipped with a third chamber and a fourth chamber that are connected. A second pump is connected to the third chamber, which allows ultraviolet light to pass through. The fourth chamber is connected to the resonant cavity. A gas supply module, comprising a gas source and a valve, wherein nitrogen monoxide supplied by the gas source is sent to the second chamber and the fourth chamber after passing through the valve; The detection module includes a second light source, a gas chamber, and a detector, wherein the emission wavelength of the second light source and the operating band of the detector cover the absorption spectra of ozone and nitrogen dioxide. The processing module processes the output signals of the detector and the sensor to identify the cause of ozone formation.

[0006] Another object of the present invention is to provide an identification method based on the identification device of the present invention, which is achieved through the following technical solution: The identification method of the identification device includes a measurement stage and a calibration stage, wherein the measurement stage is as follows: The drone carries the measurement unit to the testing point; Air enters the detection module directly through the first detection channel, and the detection module outputs an ozone absorption signal; Air enters the first chamber of the second detection channel, then enters the second chamber for reaction, where all the ozone is converted into nitrogen dioxide, and finally enters the detection module, which outputs the first absorption signal of nitrogen dioxide. Air enters the third chamber of the third detection channel and is converted into ozone. It then enters the fourth chamber and reacts, where all the ozone is converted into nitrogen dioxide. Finally, it enters the resonant cavity and outputs the second absorption signal of nitrogen dioxide. The processing module identifies the cause of ozone formation based on the ozone absorption signal, the first absorption signal, and the second absorption signal.

[0007] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. By employing multi-path, multi-optical detection technology, ozone and NO2 can be detected simultaneously in situ within seconds, providing accurate data from the same source without temporal or spatial differences. Simultaneously, it reduces the size, weight, and power consumption of the equipment, making it suitable for mobile monitoring applications.

[0008] 2. The differential algorithm of dual CEAS can deduct gas interference in real time; at the same time, the dual-mode signal drift automatic correction function eliminates the detection deviation caused by the underway operation and improves the detection accuracy.

[0009] 3. By using spatiotemporal differences in ozone and OPR data from the same source, accurate identification of local ozone generation and external transport can be achieved. Attached Figure Description

[0010] The disclosure of this invention will become more readily understood with reference to the accompanying drawings. It will be readily understood by those skilled in the art that these drawings are merely illustrative of the technical solutions of this invention and are not intended to limit the scope of protection of this invention. In the drawings: Figure 1 This is a schematic diagram of a mobile ozone formation identification device according to the present invention. Detailed Implementation

[0011] Figure 1The following description illustrates optional embodiments of the invention to teach those skilled in the art how to implement and reproduce the invention. Some conventional aspects have been simplified or omitted to teach the technical solutions of the invention. Those skilled in the art should understand that variations or substitutions derived from these embodiments will be within the scope of the invention. Those skilled in the art should understand that the following features can be combined in various ways to form multiple variations of the invention. Therefore, the invention is not limited to the optional embodiments described below, but is defined only by the claims and their equivalents.

[0012] Example 1

[0013] This embodiment provides a mobile ozone formation identification device, such as... Figure 1 As shown, the identification device includes: A first light source, a resonant cavity, and a detector, wherein the emission wavelength of the first light source covers the absorption spectrum of nitrogen dioxide.

[0014] The drone carries a measurement unit. The measurement unit includes: Multiple detection channels are provided. Air enters the detection module through the first detection channel. The second detection channel is connected to a first chamber and a second chamber. A first pump is connected to the first chamber, and the second chamber is connected to the detection module. A light-shielding module is provided on the first chamber to prevent the gas in the first chamber from converting into ozone. The third detection channel is connected to a third chamber and a fourth chamber. The second pump is connected to the third chamber, which allows ultraviolet light to pass through, causing the gas in the third chamber to convert into ozone. The fourth chamber is connected to the resonant cavity.

[0015] The gas supply module includes a gas source and a valve. Nitrogen monoxide supplied by the gas source is sent to the second and fourth chambers after passing through the valve, so that all the ozone in the second and fourth chambers is converted into nitrogen dioxide.

[0016] The detection module includes a second light source, a gas chamber, and a detector. The emission wavelength of the second light source and the operating band of the detector cover the absorption spectra of ozone and nitrogen dioxide. The processing module processes the output signals of the detector and the sensor to identify the cause of ozone formation.

[0017] To accurately determine the cause of ozone formation, the processing module includes a first submodule and a second submodule.

[0018] The first submodule uses differential technology to process the signals corresponding to nitrogen dioxide output by the detector and the detector respectively to obtain a first ozone change rate; and uses spectral technology to process the signal corresponding to ozone output by the detector to obtain a second ozone change rate.

[0019] The second submodule obtains the difference between the first ozone change rate and the second ozone change rate, and obtains the ozone formation based on the difference.

[0020] If the difference is positive, ozone is output; if the difference is negative, ozone is input.

[0021] To improve the accuracy of ozone detection, the identification device also includes a controller. The controller controls the valve; when the valve is closed, the first submodule uses differential technology to process the signals corresponding to nitrogen dioxide output by the detector and the sensor, respectively, to obtain and store the difference signal. The first ozone change rate is obtained by subtracting the detector's output signal and the difference signal from the detector's output signal.

[0022] To obtain emitted light corresponding to the absorption lines of ozone and nitrogen dioxide, respectively, the second light source includes: The first sub-light source emits wavelengths that cover the absorption spectrum of ozone in the ultraviolet band; The second sub-light source emits wavelengths that cover the absorption spectrum of nitrogen dioxide in the visible light band; The beam combiner is used to combine the emitted light from the first sub-light source and the second sub-light source.

[0023] The four-way valve allows air to enter the multiple detection channels respectively.

[0024] The mobile ozone generation identification method of this embodiment includes a measurement phase and a calibration phase, wherein the measurement phase is as follows: The drone carries the measurement unit to the testing point; Air enters the detection module directly through the first detection channel, and the detection module outputs an ozone absorption signal; Air enters the first chamber of the second detection channel, then enters the second chamber for reaction, where all the ozone is converted into nitrogen dioxide, and finally enters the detection module, which outputs the first absorption signal of nitrogen dioxide. Air enters the third chamber of the third detection channel and is converted into ozone. It then enters the fourth chamber and reacts, where all the ozone is converted into nitrogen dioxide. Finally, it enters the resonant cavity and outputs the second absorption signal of nitrogen dioxide. The processing module identifies the cause of ozone formation based on the ozone absorption signal, the first absorption signal, and the second absorption signal.

[0025] To accurately determine the cause of ozone formation, the processing module uses differential processing to process the first and second absorption signals to obtain the first ozone change rate; and uses spectral processing to process the ozone absorption signal to obtain the second ozone change rate. The processing module obtains the difference between the first ozone change rate and the second ozone change rate, and obtains the ozone formation based on the difference. If the difference is positive, ozone is output; if the difference is negative, ozone is input.

[0026] To improve ozone detection accuracy, during the calibration phase, the controller shuts off the reaction gas supply to the second and fourth chambers. The processing module uses differential technology to process the first and second absorption signals, obtains the difference signal, and stores it. During the measurement phase, the first absorption signal and the difference signal are subtracted from the second absorption signal, and the first ozone change rate is obtained after analysis.

[0027] In order to obtain emitted light corresponding to the absorption spectral lines of ozone and nitrogen dioxide respectively, the emitted wavelength of the first sub-light source covers the absorption spectral lines of ozone in the ultraviolet band; the emitted wavelength of the second sub-light source covers the absorption spectral lines of nitrogen dioxide in the visible light band. The beam combiner combines the emitted light from the first sub-source light source and the second sub-source light source, and the combined light enters the resonant cavity.

[0028] Example 2

[0029] An application example of the identification device and method according to Embodiment 1 of the present invention.

[0030] In this application example, the need to identify the causes of ozone pollution in multiple scenarios, including urban areas and industrial park perimeters, is integrated. A drone equipped with a measurement unit is used to accurately identify both local ozone generation and external transport in urban areas and industrial park perimeters. The measurement unit has been lightweighted and adapted to meet the drone's payload, power supply, and mobile monitoring requirements.

[0031] The first and third chambers use the same glass tubes of identical dimensions. The first chamber is shielded from sunlight to prevent the air from converting into ozone. The third chamber allows sunlight to enter, facilitating the conversion of air into ozone.

[0032] The second and fourth chambers are identical. The gas source provides excess nitric oxide to convert all the ozone in the second and fourth chambers into nitrogen dioxide.

[0033] The controller is used to control the opening and closing of the valve, so that when the valve is open, the identification device is in the measurement phase, and when the valve is closed, the identification device is in the calibration phase.

[0034] Mass flow meters are installed on the second and third detection channels respectively, so that the air flow in the two detection channels can be controlled when the pump is drawing samples.

[0035] In the detection module, the first sub-light source emits wavelengths covering 254nm ultraviolet light (corresponding to the ozone absorption spectrum), and the second sub-light source emits wavelengths covering 405nm (corresponding to the nitrogen dioxide absorption spectrum). The beam combiner uses a beam-combining dichroic mirror to reflect 254nm ultraviolet light and transmit 405nm visible light. The gas chamber uses an optical resonant cavity, and the reflector has a reflectivity of 99.995% for both ultraviolet and visible light. The detector includes an ultraviolet photomultiplier tube and a second silicon photodiode. Light transmitted through the resonant cavity is split by a beam-splitting dichroic mirror into transmitted ultraviolet light and reflected visible light. The ultraviolet light is received by the ultraviolet photomultiplier tube, and the visible light is received by the second silicon photodiode.

[0036] In the processing module, the first submodule uses differential technology to process the signals corresponding to nitrogen dioxide output by the detector and the detector, respectively, to obtain a first ozone change rate; and uses spectral technology to process the signal corresponding to ozone output by the detector to obtain a second ozone change rate. The second submodule obtains the difference between the first and second ozone change rates, and determines the cause of ozone formation based on the difference.

[0037] If the difference is positive, ozone is output; if the difference is negative, ozone is input.

[0038] In this embodiment, the gas flow rate in the second and third detection channels is 2 L / min, ensuring that the gas residence time in both the first and third chambers is 30 s. A 100 ppm NO miniature standard gas cylinder is used as the gas source, and the mass flow meter precisely controls the standard gas flow rate to 6 sccm.

[0039] The mobile ozone generation identification method of this embodiment includes a measurement phase and a calibration phase, wherein the measurement phase is as follows: Before takeoff, the A1 drone completes a full-process calibration of the equipment, checks the sealing of each module connection and the stability of the power supply, and presets the drone's walking route according to the monitoring scenario. The drone then takes off autonomously along the preset route and carries out walking monitoring.

[0040] At the monitoring point, the pump is started to collect atmospheric samples. The outside air is collected through the four-way connector and divided into three channels of the same source gas. These channels are directly introduced into the detection module, and also enter the first and third chambers respectively, and remain for 30 seconds. The first chamber prevents the air from converting into ozone. The third chamber is where ozone is converted.

[0041] In the second and fourth chambers, ozone reacts with excess NO standard gas to generate NO2, which then enters the gas chamber and the resonant cavity, respectively.

[0042] The 254nm ultraviolet light emitted by the ultraviolet light-emitting diode and the 405nm visible light emitted by the visible light distribution feedback laser are collimated by a collimating lens group and then precisely combined into a composite light by a beam-combining dichroic mirror. This composite light is then incident into the gas chamber to enhance the optical signal, and then split by a beam-splitting dichroic mirror. The light is then transmitted to the ultraviolet photomultiplier tube and the second silicon photodiode, respectively, to achieve second-level synchronous in-situ detection of ozone and NO2.

[0043] The 405nm visible light emitted by the visible light distribution feedback laser (first light source) is collimated by a collimating lens group and then incident into the resonant cavity to enhance the optical signal. The emitted light is received by the first silicon photodiode (detector) to achieve second-level in-situ detection of NO2.

[0044] The first submodule uses differential technology to process the signals corresponding to nitrogen dioxide output by the detector and the detector respectively to obtain a first ozone change rate; and uses spectral technology to process the signal corresponding to ozone output by the detector to obtain a second ozone change rate.

[0045] Using the second submodule, the difference between the first ozone change rate and the second ozone change rate, and the difference signal (obtained and stored in the correction phase) is obtained, and the cause of ozone formation is obtained based on the difference.

[0046] If the difference is positive, ozone is output; if the difference is negative, ozone is input.

[0047] During mobile monitoring, if a sudden increase in NO2 signal is encountered due to vehicle exhaust, fugitive emissions from industrial parks, etc., the dual CEAS will simultaneously capture the signal change and deduct external NO2 fluctuations by performing difference calculations on the detection data of the first and second chambers.

[0048] The correction phase is as follows: Every 30 minutes of flight, the controller closes the valve and switches to the automatic signal drift correction mode. The processing module uses differential technology to process the signals corresponding to nitrogen dioxide output by the detector and the detector, respectively, to obtain the difference signal. This difference signal includes all systematic errors caused by CEAS drift and environmental disturbances. The original difference signal is replaced by this difference signal for the next measurement.

[0049] example: The detector (the second sub-module of the processing module) was used to monitor the ambient ozone concentration in real time using spectral technology. The measured ozone concentration was 12 ppb at 9:00 and 16.3 ppb at 9:30. Based on the concentration and time interval before and after these times, the rate of change of the measured ozone concentration during this period was calculated to be 8.6 ppb / h.

[0050] During the measurement period from 9:00 to 9:30, the system uses differential detection technology (the first submodule of the processing module) to collect the response signals from the detector and the detector output, corresponding to the characteristic absorption wavelengths of nitrogen dioxide. The difference in ozone concentration, ΔNO2, representing the local photochemical reaction, is obtained through subtraction. In this period, ΔNO2 is 2 ppb. Dividing ΔNO2 by the corresponding residence time of 30 minutes yields the local photochemical ozone generation rate (OPR) of 4 ppb / h.

[0051] To eliminate the effects of instrument drift and system errors, the controller switches the valve to enter the automatic signal drift correction mode. The first submodule of the processing module again uses differential technology to process the corresponding absorption signals of the two nitrogen dioxide signals, obtaining a drift correction difference of 0.2 ppb. After being converted into a rate, it is superimposed on the original OPR to obtain the actual photochemical generation rate after drift correction, which is 4.4 ppb / h.

[0052] The difference between the measured ozone concentration change rate and the corrected actual OPR is calculated as: 4.4 ppb / h – 8.6 ppb / h = -4.2 ppb / h. This negative difference indicates that the measured ozone rise rate is higher than the local photochemical reaction generation rate. The difference represents the external ozone input rate, indicating a significant ozone regional transport or external input contribution during this period.

Claims

1. A mobile ozone formation identification device, comprising a first light source, a resonant cavity, and a detector, wherein the emission wavelength of the first light source covers the absorption spectrum of nitrogen dioxide; characterized in that, The identification device further includes a drone and a measurement unit, wherein the drone carries the measurement unit, and the measurement unit includes: Multiple detection channels are provided. Air enters the detection module through the first detection channel. The second detection channel is equipped with a first chamber and a second chamber that are connected. A first pump is connected to the first chamber, and the second chamber is connected to the detection module. A light-shielding module is provided on the first chamber. The third detection channel is equipped with a third chamber and a fourth chamber that are connected. A second pump is connected to the third chamber, which allows ultraviolet light to pass through. The fourth chamber is connected to the resonant cavity. A gas supply module, comprising a gas source and a valve, wherein nitrogen monoxide supplied by the gas source is sent to the second chamber and the fourth chamber after passing through the valve; The detection module includes a second light source, a gas chamber, and a detector, wherein the emission wavelength of the second light source and the operating band of the detector cover the absorption spectra of ozone and nitrogen dioxide. The processing module processes the output signals of the detector and the sensor to identify the cause of ozone formation.

2. The identification device according to claim 1, characterized in that, The processing module includes: The first submodule uses differential technology to process the signals corresponding to nitrogen dioxide output by the detector and the detector respectively to obtain a first ozone change rate; and uses spectral technology to process the signal corresponding to ozone output by the detector to obtain a second ozone change rate. The second submodule obtains the difference between the first ozone change rate and the second ozone change rate, and obtains the ozone formation based on the difference.

3. The identification device according to claim 2, characterized in that, If the difference is positive, ozone is output; if the difference is negative, ozone is input.

4. The identification device according to claim 1, characterized in that, The identification device further includes a controller for controlling the valve. When the valve is closed, the first submodule uses differential technology to process the signals corresponding to nitrogen dioxide output by the detector and the detector, respectively, to obtain a difference signal, which is then stored. The first ozone change rate is obtained by subtracting the detector's output signal and the difference signal from the detector's output signal.

5. The identification device according to claim 1, characterized in that, The second light source includes: The first sub-light source emits wavelengths that cover the absorption spectrum of ozone in the ultraviolet band; The second sub-light source emits wavelengths that cover the absorption spectrum of nitrogen dioxide in the visible light band; A beam combiner is used to combine the emitted light from the first sub-light source and the second sub-light source.

6. The identification device according to claim 1, characterized in that, The identification device further includes: The four-way valve allows air to enter the multiple detection channels respectively.

7. The identification method of the identification device according to claim 1, comprising a measurement stage and a calibration stage, characterized in that, The measurement phase is as follows: The drone carries the measurement unit to the testing point; Air enters the detection module directly through the first detection channel, and the detection module outputs an ozone absorption signal; Air enters the first chamber of the second detection channel, then enters the second chamber for reaction, where all ozone is converted into nitrogen dioxide, and finally enters the detection module, which outputs the first absorption signal of nitrogen dioxide. Air enters the third chamber of the third detection channel and is converted into ozone. It then enters the fourth chamber and reacts, where all the ozone is converted into nitrogen dioxide. Finally, it enters the resonant cavity and outputs the second absorption signal of nitrogen dioxide. The processing module identifies the cause of ozone formation based on the ozone absorption signal, the first absorption signal, and the second absorption signal.

8. The identification method according to claim 1, characterized in that, The processing module uses differential technology to process the first absorption signal and the second absorption signal to obtain the first ozone change rate; and uses spectral technology to process the ozone absorption signal to obtain the second ozone change rate. The processing module obtains the difference between the first ozone change rate and the second ozone change rate, and obtains the ozone formation based on the difference. If the difference is positive, ozone is output; if the difference is negative, ozone is input.

9. The identification method according to claim 8, characterized in that, During the calibration phase, the controller shuts off the reaction gas supply to the second and fourth chambers. The processing module uses differential technology to process the first and second absorption signals, obtains the difference signal, and stores it. During the measurement phase, the first absorption signal and the difference signal are subtracted from the second absorption signal, and the first ozone change rate is obtained after analysis.

10. The identification method according to claim 7, characterized in that, The emission wavelength of the first sub-light source covers the absorption spectrum of ozone in the ultraviolet band; the emission wavelength of the second sub-light source covers the absorption spectrum of nitrogen dioxide in the visible light band. The beam combiner combines the emitted light from the first sub-source light source and the second sub-source light source, and the combined light enters the resonant cavity.

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