A high-carbon alkane flux measurement system based on high-frequency mass spectrometry technology

Abstract

The application discloses a high-carbon alkane flux measuring system based on high-frequency mass spectrometry technology, and relates to the field of atmospheric environment monitoring.The system comprises a meteorological data monitoring module, which is used for collecting meteorological data in an environment atmosphere; a high-carbon alkane monitoring module, which adopts proton transfer reaction time-of-flight mass spectrometry technology and is used for collecting the measured concentration of high-carbon alkane species in the environment atmosphere; and a flux data processing module, which is used for determining the turbulent flux of high-carbon alkane species by adopting the vorticity covariance method according to the meteorological data and the measured concentration of high-carbon alkane species.The application realizes accurate and efficient determination of the turbulent flux of high-carbon alkane.

Description

Technical Field

[0001] This application relates to the field of atmospheric environmental monitoring, and in particular to a high-carbon alkane flux measurement system based on high-frequency mass spectrometry technology. Background Technology

[0002] High-carbon alkanes are alkanes with more than 12 carbon atoms. Based on their volatility, they can be classified as semi-volatile / moderately volatile organic compounds (S / IVOCs). Due to their relatively low volatility, high-carbon alkanes are prone to undergoing gas-solid partitioning after being oxidized by free radicals in the ambient atmosphere, generating secondary organic aerosols (SOA). SOA has significant impacts on atmospheric chemistry, atmospheric oxidation capacity, radiation balance, and human health.

[0003] The eddy covariance method is a flux measurement technique in the field of micrometeorology for quantifying mass exchange within the atmospheric boundary layer. The associated flux measurement system is typically an integrated system composed of multiple types of instruments, including a high-frequency concentration measurement system for the target species and a high-frequency measurement system for micrometeorological parameters. This method acquires high-frequency measurement data and calculates the covariance of vertical wind speed and mass concentration fluctuations every half hour, thereby estimating the turbulent-dominated mass flux within the source region extending from the measurement point to a radius of tens to several kilometers. Compared to other flux measurement techniques, the eddy covariance method can measure data with higher temporal resolution and larger spatial scale, and has become one of the most accurate methods for quantifying regional mass fluxes. The flux results of high-carbon alkane species obtained using this technique can provide technical support for accurately quantifying the emission patterns and characteristics of regional high-carbon alkane species. Since turbulent transport in the ambient atmosphere is a superposition of contributions from turbulent eddies of various frequencies, it is necessary to conduct high-frequency measurements of vertical wind speed and the concentration of the measured substance to fully cover the contributions of turbulent transport from high to low frequencies, thereby accurately obtaining the flux of the measured substance. When applying the eddy covariance method, the instrument's measurement frequency is generally required to be 5Hz-10Hz.

[0004] High-frequency measurement techniques for micrometeorological parameters such as three-dimensional wind speed and temperature, as well as components such as greenhouse gases, are now very mature. Numerous studies have successfully measured the fluxes of multiple greenhouse gases, including carbon dioxide and methane, using the eddy covariance method, and have successfully conducted observations and related research on emission patterns and characteristics in different ecosystems. However, there are currently no precedents for using the eddy covariance method to measure the fluxes of high-carbon alkane species.

[0005] Current measurement techniques for high-carbon alkanes, such as gas chromatography-mass spectrometry, require a measurement cycle of 0.5 to 1 hour, which is far from meeting the 5 Hz to 10 Hz measurement frequency required for eddy covariance observation. Therefore, there is currently no flux observation technique that can combine high-carbon alkane measurement with the eddy covariance method. Summary of the Invention

[0006] The purpose of this application is to provide a high-carbon alkane flux measurement system based on high-frequency mass spectrometry technology, which can realize the accurate and efficient determination of high-carbon alkane turbulent flux.

[0007] To achieve the above objectives, this application provides a high-carbon alkane flux measurement system based on high-frequency mass spectrometry, comprising: The meteorological data monitoring module is used to collect meteorological data in the ambient atmosphere; The high-carbon alkane monitoring module is used to collect and measure the concentration of high-carbon alkane species in the ambient atmosphere using proton transfer reaction time-of-flight mass spectrometry. The flux data processing module is used to determine the turbulent flux of the high-carbon alkane species based on the meteorological data and the measured concentration of the high-carbon alkane species using the eddy covariance method.

[0008] In one embodiment, the system further includes a flux measurement platform; the high-carbon alkane monitoring module and the meteorological data monitoring module are both located on the flux measurement platform; the height of the flux measurement platform is 3 to 5 times that of the urban underlying canopy and is located in the constant flux layer above the canopy.

[0009] In one embodiment, the meteorological data monitoring module includes: a three-dimensional ultrasonic anemometer for collecting wind speed, sound speed, and wind direction in the ambient atmosphere to obtain meteorological data; a sampling pipeline for transmitting the ambient atmosphere to the high-carbon alkane monitoring module; and a data acquisition device for recording the meteorological data measured by the three-dimensional ultrasonic anemometer.

[0010] In one embodiment, the high-carbon alkane monitoring module includes: an auxiliary sampling pump, a catalytic device, a measuring instrument, and a calibration device; the auxiliary sampling pump is used to stabilize the ambient air flow rate in the sampling pipeline and simultaneously transmit the ambient air for subsequent measurement; the catalytic device is used to catalyze volatile organic compounds in the ambient air into carbon dioxide and water, generating a background sample for the background signal measurement of the measuring instrument, and correcting and reducing systematic errors from the measuring instrument itself by cyclically measuring the atmospheric sample and the background sample; the measuring instrument is used to measure the measurement signal of high-carbon alkane species in the atmospheric sample using proton transfer reaction time-of-flight mass spectrometry with nitrosyl cations as the parent ion, to obtain the measured concentration of high-carbon alkane species; the calibration device is used to convert the measured signal of high-carbon alkane species measured by the measuring instrument into a measured concentration.

[0011] In one embodiment, the calibration device includes a cylinder of standard synthetic air, a cylinder of high-carbon alkane standard gas, and a mass flow controller; the mass flow controller is used to mix the cylinder of standard synthetic air and the cylinder of high-carbon alkane standard gas to generate a high-carbon alkane sample of known concentration, and to obtain the response factor of the high-carbon alkane species through calibration experiments, so as to convert the high-carbon alkane species measurement signal measured by the measuring instrument into a measurement concentration.

[0012] In one embodiment, the flux data processing module includes: a data preprocessing submodule, used to perform quality control on the meteorological data and the measured concentration of the high-carbon alkane species respectively, to obtain preprocessed measured concentrations of the high-carbon alkane species and preprocessed meteorological data; a correction submodule, used to perform coordinate axis transformation and time delay determination on the preprocessed measured concentrations of the high-carbon alkane species and the preprocessed meteorological data to correct tilt errors and time delay errors, to obtain vertical wind speed data and high-carbon alkane species concentration, and to correct the measurement error caused by the response time of the high-carbon alkane monitoring module to the measurement of high-carbon alkane, to obtain corrected high-carbon alkane species concentration; a decomposition submodule, used to perform Reynolds decomposition on the corrected high-carbon alkane species concentration and vertical wind speed data, to obtain high-carbon alkane concentration fluctuations and vertical wind speed fluctuations; and a flux calculation submodule, used to calculate the covariance between the high-carbon alkane concentration fluctuations and the vertical wind speed fluctuations using the eddy covariance method, to obtain the turbulent flux of the high-carbon alkane species.

[0013] In one embodiment, the process of coordinate axis transformation of the preprocessed high-carbon alkane species measurement concentration and preprocessed meteorological data by the correction submodule is as follows: the observation coordinate system is transformed into the mean streamline coordinate system by rotating around the z-axis and x-axis using a two-stage rotation method, so as to perform coordinate axis transformation on the preprocessed high-carbon alkane species measurement concentration and preprocessed meteorological data; the x-axis of the mean streamline coordinate system is consistent with the horizontal wind speed direction, and the z-axis is perpendicular to the ground.

[0014] In one embodiment, the process by which the correction submodule determines the time lag between the preprocessed high-carbon alkane species measurement concentration and the preprocessed meteorological data is as follows: cross-correlation analysis is performed on the preprocessed high-carbon alkane species measurement concentration and the preprocessed meteorological data to obtain the cumulative covariance equation, and the cumulative covariance equation is used as the standard for time lag determination.

[0015] In one embodiment, the correction submodule uses the following formula to correct the measurement error caused by the response time of the high-carbon alkane monitoring module to the measurement of high-carbon alkane: ;in, The corrected concentration of high-carbon alkane species, For high concentrations of alkane species, t For time, t i The relative time from the start of the measurement. To measure the response time of high-carbon alkane species, To fit the parameters, the response time of high-carbon alkanes was obtained through relevant pipeline experiments. And obtained through the following fitting: .

[0016] In one embodiment, the flux data processing module is further used to perform stationarity tests and turbulence development tests on the turbulent flux of high-carbon alkane species, obtain relative unsteady-state parameters and complete turbulence characteristic parameters, and classify the turbulent flux of the high-carbon alkane species according to the relative unsteady-state parameters and the complete turbulence characteristic parameters.

[0017] According to the specific embodiments provided in this application, this application has the following technical effects: Using proton transfer reaction time-of-flight mass spectrometry (PTF-MS) to monitor the concentration of high-carbon alkane species offers faster response and higher detection sensitivity compared to traditional detection methods. It enables real-time, high-frequency acquisition of high-carbon alkane in the atmosphere and further eliminates data bias caused by sample pretreatment and delayed detection based on a correction algorithm. Combined with environmental meteorological parameters obtained from the meteorological data monitoring module, the turbulent flux is directly calculated using the eddy covariance method based on the flux data processing module, eliminating the need for complex indirect conversions and improving the accuracy and reliability of flux results. The overall system has a high degree of integration, simultaneously completing environmental data acquisition, target concentration detection, and flux calculation, achieving in-situ, continuous observation of high-carbon alkane flux. This provides precise data support for research on the emission characteristics and transport patterns of atmospheric high-carbon alkane, effectively compensating for the shortcomings of traditional methods in terms of detection frequency and accuracy. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a schematic diagram of a high-carbon alkane flux measurement system based on high-frequency mass spectrometry technology provided in an embodiment of this application.

[0020] Figure 2 This is a schematic diagram of the processing flow of the throughput data processing module in one embodiment of this application. Detailed Implementation

[0021] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0022] The purpose of this application is to design a high-carbon alkane flux measurement system that combines high-frequency mass spectrometry with the eddy covariance method, filling the technological gap for the application of eddy covariance technology to conduct flux observation of high-carbon alkane and quantify the emissions of high-carbon alkane in typical regions.

[0023] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0024] First, some technical terms involved in the embodiments of this application will be introduced.

[0025] Air pollutants include primary and secondary pollutants. Primary pollutants refer to pollutants that are directly emitted into the atmosphere from pollution sources; secondary pollutants refer to new pollutants with different physical and chemical properties from primary pollutants that have undergone changes under the influence of physical, chemical, or biological factors, or reacted with other substances in the environment. These are also known as secondary pollutants.

[0026] Semi- / moderately volatile organic compounds (S / IVOCs) are an important class of organic compounds whose volatility lies between that of gaseous volatile organic compounds and particulate non-volatile organic compounds. They can undergo gas-solid partitioning in both the gas and particulate phases. Based on their structure, they can be classified into aliphatic and alicyclic compounds, aromatic compounds, and heterocyclic compounds.

[0027] Secondary organic aerosols (SOA) are products of atmospheric photochemical processes and a major component of fine particulate matter in urban and suburban atmospheres. Volatile organic compounds (VOCs) are oxidized in the atmosphere, and the resulting non-volatile organic compounds are partitioned in the gas and solid phases to form organic aerosols, which are called secondary organic aerosols.

[0028] High-carbon alkanes: Alkanes are organic compounds composed only of carbon and hydrogen elements. Based on their molecular skeletons, they can be divided into chain hydrocarbons and cyclic hydrocarbons. High-carbon alkanes are those with more than 12 carbon atoms; according to their volatility, they can be classified as semi-volatile / moderately volatile organic compounds.

[0029] Flux measurement: Flux is a physics concept referring to the amount of momentum, heat, and matter transported per unit area of ​​an interface per unit time. Flux measurement is a measurement technique that quantitatively describes the material cycle and energy exchange processes between the atmosphere, vegetation, and soil.

[0030] Eddy covariance technique: Eddy covariance technique refers to the estimation of the covariance between vertical wind speed and material or energy fluctuations using micrometeorological principles. It is an important technique that can directly measure the energy and material flux between the underlying canopy and the atmosphere, and is widely used in multi-scale material cycling and budget balance research.

[0031] Urban canopy: The underlying surface refers to the Earth's surface that is in direct contact with the lower atmosphere. With the Earth's land and water surfaces as its lower boundary, it is called the atmospheric canopy. It includes topography, geology, soil, and vegetation, and is one of the important factors influencing climate. In the near-surface layer of a city, the average height from the ground to the top of buildings is called the urban canopy. Within this layer, the thermal properties of urban underlying surface building materials and the spatial distribution of buildings exhibit non-uniformity. Its underlying surface structure and human activities directly affect the thermal and dynamic processes of the urban atmosphere.

[0032] Tilt error: Tilt error refers to a systematic error in flux measurement caused by the vertical axis of a three-dimensional ultrasonic anemometer failing to align with the local vertical surface, resulting in cross-contamination of the components of flux dispersion. Momentum flux is particularly sensitive to tilt error; under moderately unstable conditions, each 1° tilt typically leads to an error greater than 10%, while under self-convection conditions, the error can be as high as 100%.

[0033] Time lag error: Time lag error refers to the lag in time scale between wind speed data and species data caused by spatial differences between the sampling port of the three-dimensional ultrasonic anemometer and the sampling port of the species being measured, delays in pipeline transmission, and time differences in signal recording by the sampling instrument itself. Time lag error can introduce significant errors into flux calculations, therefore it is necessary to accurately quantify and correct for time lag.

[0034] In one exemplary embodiment, such as Figure 1 As shown, a high-carbon alkane flux measurement system based on high-frequency mass spectrometry is provided, including: a flux measurement platform 10, a meteorological data monitoring module 20, a high-carbon alkane monitoring module 30, and a flux data processing module. The functions and structures of each component are described below.

[0035] (a) Flux Measurement Platform 10.

[0036] The flux measurement platform 10 is located in a representative ecosystem at a site that meets the micrometeorological requirements for flux measurement. The platform 10 is constructed at the required flux measurement height to accommodate instruments, gas cylinders, and support equipment. Generally, the flux measurement platform 10 is specifically designed to meet flux measurement needs, typically requiring a height higher than the urban underlying canopy and situated above a well-mixed constant flux layer, generally 3-5 times the canopy height. Simultaneously, the flux measurement platform 10 itself, its supporting structure, and all instruments need to be sufficiently robust to withstand the anticipated harsh environment during operation. Specifically, the high-carbon alkane monitoring module 30 and the meteorological data monitoring module 20 are both mounted on the flux measurement platform 10.

[0037] (ii) Meteorological data monitoring module 20.

[0038] The meteorological data monitoring module 20 is used to collect meteorological data in the ambient atmosphere.

[0039] The meteorological data monitoring module 20 includes: a three-dimensional ultrasonic anemometer 201, a sampling pipeline, a data acquisition device 204, and a power control box 206.

[0040] The 3D ultrasonic anemometer 201 is used to collect wind speed, sound speed, and wind direction in the ambient atmosphere to obtain meteorological data. Specifically, the 3D ultrasonic anemometer 201 can measure at a frequency of up to 10Hz, meeting the requirements for flux measurement. Its basic principle is to measure the transmission time difference of ultrasonic pulses between a pair of separated but known-distance sensors (such as the GILL series, CAST3B, LI-560, and other 3D ultrasonic anemometers), and combine this with wind speed and sound speed to derive high-frequency meteorological data including wind direction and wind speed.

[0041] The sampling pipeline is used to transmit ambient air to the high-carbon alkane monitoring module 30. The sampling pipeline consists of a first sampling tube 202 and a second sampling tube 203, which are connected via an adapter 301. The outer diameter of the first sampling tube 202 is 1 / 4 inch, and the outer diameter of the second sampling tube 203 is 1 / 8 inch. Both the first sampling tube 202 and the second sampling tube 203 are made of perfluoroalkoxy (PFA). The lengths of the first sampling tube 202 and the second sampling tube 203 are flexibly determined according to actual requirements. The sampling pipeline can be fixed using a tripod 205.

[0042] The data acquisition device 204 is used to record meteorological data measured by the three-dimensional ultrasonic anemometer. Specifically, the data acquisition device 204 can operate well under harsh environmental conditions such as low temperature and high humidity, and can simultaneously process data from multiple instruments with different output signals and data transmitted through the RS232 serial interface in real time.

[0043] (III) High-carbon alkane monitoring module 30.

[0044] The high-carbon alkane monitoring module 30 is used to collect and measure the concentration of high-carbon alkane species in the ambient atmosphere using proton transfer reaction time-of-flight mass spectrometry.

[0045] The high-carbon alkane monitoring module 30 includes: an auxiliary sampling pump 302, a catalytic device 303, a measuring instrument 304, and a calibration device. The auxiliary sampling pump 302 is installed on the sampling pipeline and connected to the catalytic device 303 through the sampling pipeline. The measuring instrument 304 is connected to the catalytic device 303 and the calibration device.

[0046] The auxiliary sampling pump 302 is used to stabilize the ambient air flow rate in the sampling pipeline and simultaneously transmit the ambient air for subsequent measurements. The purpose of the auxiliary sampling pump 302 is to ensure that the flow rate in the sample inlet pipeline is as high as possible, thereby reducing the impact of pipeline absorption on the measurement and maintaining a stable measurement at 10 Hz.

[0047] The catalytic device 303 is used to catalyze volatile organic compounds (VOCs) in the ambient atmosphere into carbon dioxide and water, generating a background sample that can be used for background signal measurement by the measuring instrument. By cyclically measuring the atmospheric sample and the background sample, systematic errors from the measuring instrument itself can be corrected and reduced. Specifically, the purpose of the catalytic device 303 is to measure the background signal of the measuring instrument 304 during actual sampling, reducing systematic errors from the instrument itself. The catalytic device 303 contains a specific catalyst that can catalyze the conversion of VOCs in the ambient atmosphere into carbon dioxide and water, generating a background sample, thereby removing VOCs from the atmospheric sample. The measuring instrument 304 cyclically measures the atmospheric sample and the background sample converted by the catalytic device 303. The catalytic device 303 has a built-in switching valve that can switch between the atmospheric sample and the background sample according to actual requirements.

[0048] The measuring instrument 304 is used for proton transfer reaction time-of-flight mass spectrometry (PTR-TOF-MS) with nitrosyl cations as the parent ion to measure the measurement signal of high-carbon alkane species in the atmospheric sample, and to obtain the measured concentration of high-carbon alkane species. Specifically, the measuring instrument 304 uses nitrosyl cations (NO... + PTR-TOF-MS, using the parent ion as a precursor ion, can achieve high-frequency measurement of high-carbon alkanes in the ambient atmosphere at 10 Hz.

[0049] The calibration device is used to convert the measurement signal of high-carbon alkane species measured by the measuring instrument 304 into a measurement concentration.

[0050] Specifically, the calibration device includes a cylinder-mounted standard synthetic air 305 (O2+N2≥99.99%), a cylinder-mounted high-carbon alkane standard gas 306, and a mass flow controller. The mass flow controller is used to mix the cylinder-mounted standard synthetic air 305 and the cylinder-mounted high-carbon alkane standard gas 306 to generate a high-carbon alkane sample of known concentration. The response factor of the high-carbon alkane species is obtained through calibration experiments to convert the high-carbon alkane species measurement signal measured by the measuring instrument 304 into a measured concentration. The mass flow controller includes a first mass flow controller 307 and a second mass flow controller 308, corresponding to the cylinder-mounted standard synthetic air 305 and the cylinder-mounted high-carbon alkane standard gas 306, respectively.

[0051] Among them, the high-carbon alkane standard gas 306 in steel cylinders contains C8~C 15 n-alkanes and C 10 ~C 14Cycloalkanes. The first mass flow controller 307 sets the flow rate of standard synthesis air to 1L, and the second mass flow controller 308 sets the flow rate of high-carbon alkane standard gas to 0sccm~10sccm for mixing and dynamic dilution to a high-carbon alkane sample with a concentration range of (0ppb~10ppb). A calibration experiment is carried out to obtain the response factor of the high-carbon alkane species, which serves as the quantitative basis for actual sampling analysis.

[0052] The high-carbon alkane flux data obtained in this application are calculated based on high-carbon alkane concentration data, and the mass spectrometer used can directly measure the signal intensity (cps) of high-carbon alkane in the ambient atmosphere. Therefore, it is necessary to periodically measure high-carbon alkane standard gases of known concentrations directly using a mass spectrometer to conduct calibration experiments to obtain the response factor of high-carbon alkane, thereby further obtaining the concentration (ppb) of high-carbon alkane in the ambient atmosphere.

[0053] (iv) Throughput data processing module.

[0054] The flux data processing module is used to determine the turbulent flux of high-carbon alkane species based on the meteorological data and the measured concentration of the high-carbon alkane species, using the eddy covariance method. The flux data processing module is executed by a computer device, specifically either a terminal or a server, or both.

[0055] This application adopts the above-mentioned NO. + PTR-TOF-MS technology and three-dimensional ultrasonic anemometers can stably and efficiently acquire high-frequency high-carbon alkane species and meteorological data. After preprocessing, data correction and quality assurance, and flux calculation of the acquired high-frequency high-carbon alkane and meteorological data, the turbulent flux of high-carbon alkane species in the measurement area can be obtained.

[0056] The throughput data processing module includes: a data preprocessing submodule, a correction submodule, a decomposition submodule, and a throughput calculation submodule.

[0057] The data preprocessing submodule is used to perform quality control on the meteorological data and the measured concentration of the high-carbon alkane species, respectively, to obtain the preprocessed measured concentration of the high-carbon alkane species and the preprocessed meteorological data.

[0058] In a specific application example, for measuring the concentration of high-carbon alkane species, a proton transfer reaction-time-of-flight mass spectrometry data post-processing system is used to perform data quality control and quality assurance operations on the raw data, such as removing sampling outliers, removing background, and quantifying concentrations, to obtain high-frequency concentration data of high-carbon alkane species.

[0059] For meteorological data, the raw signal output by the 3D ultrasonic anemometer 201 needs to be converted into micrometeorological variables according to the formula preset by the instrument to obtain high-frequency vertical wind speed data. If the observation time is long, a calibration is required after the observation to ensure that the state of the 3D ultrasonic anemometer 201 has not changed significantly. During the observation period, the 3D ultrasonic anemometer 201 automatically generates data quality diagnostic information and marks abnormal data. When the marked data within a time interval (30 minutes) exceeds the defined percentage of marked or missing data (set to 50%), the flux within that time interval is considered unreliable and will be discarded. After quality control, the raw data will be converted into a data format and input into the flux processing software for further processing.

[0060] The correction submodule is used to perform coordinate axis transformation and time delay determination on the preprocessed high-carbon alkane species measurement concentration and preprocessed meteorological data to correct tilt error and time delay error, obtain vertical wind speed data and high-carbon alkane species concentration, and correct the measurement error caused by the response time of the high-carbon alkane monitoring module 30 to the high-carbon alkane measurement, to obtain the corrected high-carbon alkane species concentration.

[0061] In a specific application example, the basic condition for applying the eddy covariance technique is that the vertical mean wind speed at the measurement location is zero, and the anemometer should be kept vertical with its horizontal tilt relative to the ground controlled within 0.1°. Therefore, this application uses a quadratic rotation method to rotate around the z-axis and x-axis, generating deflection and pitch angles, and transforming the observation coordinate system into a mean streamline coordinate system to perform coordinate axis transformation on the preprocessed high-carbon alkane species measurement concentration and the preprocessed meteorological data. The x-axis of the mean streamline coordinate system is aligned with the horizontal mean wind speed direction, and the z-axis is perpendicular to the ground, ultimately resulting in a vertical wind speed of zero.

[0062] In a specific application example, the correction submodule performs time lag determination on the preprocessed high-carbon alkane species measurement concentration and the preprocessed meteorological data as follows: Cross-correlation analysis is performed on the preprocessed high-carbon alkane species measurement concentration and the preprocessed meteorological data to obtain the cumulative covariance equation. This cumulative covariance equation is then used as the standard for time lag determination. Specifically, a single covariance equation is constructed based on measurement results every 30 minutes, and the cumulative covariance equation is obtained by superimposing the single covariance equations for the entire measurement period, which serves as the standard for time lag determination.

[0063] In a specific application example, as semi-volatile organic compounds, high-carbon alkanes are prone to time delays during sampling, which weaken high-frequency changes. Therefore, the correction submodule described in this application uses the following formula to correct the measurement error caused by the response time of the high-carbon alkanes monitoring module 30 to the measurement of high-carbon alkanes, so as to eliminate the changes in high-frequency signals during sampling: ; in, The corrected concentration of high-carbon alkane species, For high concentrations of alkane species, t For time, t i The relative time from the start of the measurement. To measure the response time (s) of high-carbon alkane species. To fit the parameters, the response time of high-carbon alkanes was obtained through relevant pipeline experiments. And obtained through the following fitting .

[0064] The decomposition submodule is used to perform Reynolds decomposition on the corrected high-carbon alkane species concentration and vertical wind speed data to obtain the high-carbon alkane concentration fluctuation C′ and vertical wind speed fluctuation. w ′: ; .in, To begin with measurement i The concentration pulsations of high-carbon alkane species obtained at each moment. To begin with measurement i The wind speed pulse obtained at that moment. This represents the average concentration of high-carbon alkane species over a period of time. This represents the average vertical wind speed over a period of time.

[0065] The flux calculation submodule is used to calculate the covariance between the high-carbon alkane concentration fluctuations and the vertical wind speed fluctuations using the eddy covariance method, thereby obtaining the turbulent flux of the high-carbon alkane species. : .

[0066] The flux results obtained through the above process need to be graded through corresponding signal probability range tests, meteorological probability range tests, and statistical tests to ultimately select valid data. The signal probability range test and meteorological probability range test have already been completed in data preprocessing; this section mainly introduces the statistical tests. The statistical tests primarily include stationarity tests and turbulence development degree tests.

[0067] In this application, the flux data processing module is also used to perform stationarity tests and turbulence development tests on the turbulent flux of high-carbon alkane species, obtain relative unsteady-state parameters and complete turbulence characteristic parameters, and classify the turbulent flux of the high-carbon alkane species according to the relative unsteady-state parameters and the complete turbulence characteristic parameters.

[0068] The stationarity test is mainly used to test whether the requirements for eddy covariance measurement are met. It is determined by the changes in meteorological data at different times of the day, changes in weather patterns, significant mesoscale variability, or changes in the measurement point relative to the measurement time.

[0069] Specifically, the covariance within short intervals is calculated using the following formula: ; ; in, To calculate the first interval j Vertical wind speed at each measurement data point To calculate the first interval j Horizontal wind speed at each measurement data point J To calculate the sequence number of the measurement data points within the interval, the value range is determined according to the divided intervals; here, the value range is 1-3000. N N represents the number of data points measured within a short interval (N=3000 for a 10Hz measurement frequency and a 5-minute interval). M To divide the measurement time series into intervals, each 30-minute measurement is typically divided into 6 short intervals, each short interval being 5 minutes. The covariance within short intervals. Let be the covariance of the overall wind speed signal measured by the three-dimensional ultrasonic anemometer. Let be the covariance of the wind speed in the horizontal direction. i For time sequence number, To begin with measurement i The covariance at each moment.

[0070] The covariance for the entire time period is calculated using the following formula. : ; Based on the covariance within short intervals and the covariance over the entire time period, the relative unsteady-state parameters are calculated using the following formula: ; in, This is the relative unsteady-state parameter used for the wind component.

[0071] The degree of turbulence development is assessed using flux-variance similarity, where variance is the standard deviation of the wind components. Specifically, the standard deviation of the wind components is calculated using the following formula: ; in, The standard deviation of the wind component, The standard deviation of vertical wind speed. The standard deviation of the horizontal or longitudinal wind speed components. The standard deviation of the lateral wind component. Horizontal or longitudinal wind speed components, It is a component of the lateral wind. w Vertical wind speed, For frictional wind speed, It is the Obukhov length. For observation altitude, Zero planar displacement, and It is a constant value. Different parameters... and according to The selection is based on the range, as shown in Table 1 below.

[0072] Table 1 and Selection criteria

[0073] The standard deviation of horizontal wind speed under neutral conditions is further calculated using the following formula. : ; in, The horizontal wind speed under neutral conditions. for The standard deviation.

[0074] Finally, the complete turbulence characteristic parameters are calculated using the following formula. : .

[0075] The two statistical tests mentioned above can be used to classify the quality of throughput data, and the classification criteria are shown in Table 2.

[0076] Table 2. Data quality classification based on steady-state tests and complete turbulence characteristics.

[0077] The highest parameter from both tests is selected to obtain the total parameter (e.g., 2 for steady-state test and 6 for complete turbulence test, resulting in a total label of 6). Based on the total parameter, data at levels 1-6 are selected as acceptable flux results for further analysis, while data at levels 7-10 are discarded. High-carbon alkane flux results meeting the requirements can be selected based on the label classification.

[0078] like Figure 2 As shown, the complete processing flow of high-carbon alkane flux data consists of three core stages, with the specific steps as follows: (1) Flux data acquisition and preprocessing: for NO + The PTR-ToF-MS raw data was used to quantify and correct the concentration of high-carbon alkanes, resulting in high-frequency high-carbon alkanes concentration data. The raw data from the three-dimensional anemometer underwent signal conversion and outlier removal to obtain high-frequency meteorological parameter data. Both types of data were then input into the flux calculation program to prepare for subsequent calculations.

[0079] (2) Flux calculation: The coordinate rotation, time delay determination, high carbon alkane response time correction and covariance calculation are completed in sequence to obtain the high carbon alkane flux results.

[0080] (3) Flux results classification and screening: Quality control inspection: The flux results are tested for data stationarity and turbulence development to evaluate data quality. Based on the inspection results, a high-carbon alkane flux dataset with quality labels is generated.

[0081] The following example demonstrates flux measurement in a typical region by installing the flux measurement system on a flux measurement tower that meets the measurement requirements. Utilizing NO + PTR-ToF-MS and a 3D ultrasonic anemometer acquired 10Hz high-frequency micrometeorological raw data and 10Hz high-frequency high-carbon alkane raw data. The raw data were then preprocessed to obtain high-carbon alkane concentration data and micrometeorological variables. Relevant programs were then used to mark and / or eliminate peaks and abrupt changes in the raw data caused by electronic noise. The high-frequency high-carbon alkane concentration data and meteorological data were then converted to a unified format for input into the flux calculation program. Tilt errors, time lag errors, and high-carbon alkane species response time errors were corrected during flux calculation. Finally, Reynolds decomposition was performed on the high-frequency high-carbon alkane and micrometeorological data to obtain high-carbon alkane concentration fluctuations (C′) and vertical wind speed fluctuations. w The turbulent flux of high-carbon alkanes in the measurement area was calculated using the eddy covariance method. Statistical tests were performed on the calculated flux results, including stationarity tests and turbulence development tests. Based on the test results, the flux data were quality-labeled, and data with quality labels of 1-6 were selected as acceptable flux results for further analysis.

[0082] Current techniques for measuring high-carbon alkanes typically involve sampling and analysis times measured in hours or days. Even the most advanced instruments require 0.5-1 hour for measurement cycles, which cannot meet the 5Hz-10Hz or higher measurement frequency requirements for flux calculations. Therefore, this application will [relevant document / requirement]. +This high-frequency mass spectrometry technique, PTR-ToF-MS, for measuring high-carbon alkane emissions, is combined with the eddy covariance method. By acquiring high-frequency concentration data and high-frequency vertical wind speed data of high-carbon alkane emissions within a region, concentration fluctuations and vertical wind speed fluctuations are calculated. This achieves 10Hz flux measurement of high-carbon alkane species while minimizing pipeline interference, thus meeting the frequency requirements of the eddy covariance method for concentration measurement. This fills a gap in existing high-carbon alkane flux measurement technologies and helps to accurately quantify the emissions of high-carbon alkane species in typical regions, thereby improving our understanding of the environmental effects and secondary pollution contributions of these species. This application has the potential for widespread application in the verification and updating of atmospheric pollutant emission inventories, as well as in the generation and environmental impact of secondary atmospheric pollutants, demonstrating significant commercial prospects.

[0083] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0084] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0085] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A high-carbon alkane flux measurement system based on high-frequency mass spectrometry, characterized in that, The system includes: The meteorological data monitoring module is used to collect meteorological data in the ambient atmosphere; The high-carbon alkane monitoring module is used to collect and measure the concentration of high-carbon alkane species in the ambient atmosphere using proton transfer reaction time-of-flight mass spectrometry. The flux data processing module is used to determine the turbulent flux of the high-carbon alkane species based on the meteorological data and the measured concentration of the high-carbon alkane species using the eddy covariance method. The throughput data processing module includes: The data preprocessing submodule is used to perform quality control on the meteorological data and the measured concentration of the high-carbon alkane species, respectively, to obtain the preprocessed measured concentration of the high-carbon alkane species and the preprocessed meteorological data. The correction submodule is used to perform coordinate axis transformation and time delay determination on the preprocessed high-carbon alkane species measurement concentration and preprocessed meteorological data to correct tilt error and time delay error, obtain vertical wind speed data and high-carbon alkane species concentration, and correct the measurement error caused by the response time of the high-carbon alkane monitoring module to the high-carbon alkane measurement, to obtain the corrected high-carbon alkane species concentration. The process of coordinate axis transformation of the preprocessed high-carbon alkane species measurement concentration and preprocessed meteorological data by the correction submodule is as follows: a two-stage rotation method is used to rotate around the z-axis and x-axis to transform the observation coordinate system into the mean streamline coordinate system, so as to perform coordinate axis transformation on the preprocessed high-carbon alkane species measurement concentration and preprocessed meteorological data; the x-axis of the mean streamline coordinate system is consistent with the horizontal wind speed direction, and the z-axis is perpendicular to the ground; The process of determining the time lag of the preprocessed high-carbon alkane species measurement concentration and the preprocessed meteorological data by the correction submodule is as follows: cross-correlation analysis is performed on the preprocessed high-carbon alkane species measurement concentration and the preprocessed meteorological data to obtain the cumulative covariance equation, and the cumulative covariance equation is used as the standard for time lag determination. The correction submodule uses the following formula to correct the measurement error caused by the response time of the high-carbon alkane monitoring module to the measurement of high-carbon alkane: ;in, The corrected concentration of high-carbon alkane species, For high concentrations of alkane species, t For time, t i The relative time from the start of the measurement. To measure the response time of high-carbon alkane species, To fit the parameters, the response time of high-carbon alkanes was obtained through relevant pipeline experiments. And obtained through the following fitting: ; The flux data processing module is also used to perform stationarity tests and turbulence development tests on the turbulent flux of high-carbon alkane species, obtain relative unsteady-state parameters and complete turbulence characteristic parameters, and classify the turbulent flux of the high-carbon alkane species according to the relative unsteady-state parameters and the complete turbulence characteristic parameters. The stationarity test is used to verify whether the requirements for eddy covariance measurement are met. It is determined by variations in meteorological data at different times of day, changes in weather patterns, significant mesoscale variability, or variations in measurement points relative to measurement time. The covariance over short intervals is calculated using the following formula: ; ;in, To calculate the first interval j Vertical wind speed at each measurement data point To calculate the first interval j Horizontal wind speed at each measurement data point J To calculate the sequence number of the measurement data points within the interval, N The number of data points measured within a short interval. M The intervals are divided based on the measurement time series. The covariance within short intervals. Let be the covariance of the overall wind speed signal measured by the three-dimensional ultrasonic anemometer. Let be the covariance of the wind speed in the horizontal direction. i For time sequence number, To begin with measurement i Covariance at each moment; The covariance for the entire time period is calculated using the following formula. : ; Based on the covariance within short intervals and the covariance over the entire time period, the relative unsteady-state parameters are calculated using the following formula: ;in, For the relative unsteady-state parameters used in wind components; The degree of turbulence development is assessed using flux-variance similarity, where variance is the standard deviation of the wind component. The standard deviation of the wind component is calculated using the following formula: ;in, The standard deviation of the wind component, The standard deviation of vertical wind speed. The standard deviation of the horizontal or longitudinal wind speed components. The standard deviation of the lateral wind component. Horizontal or longitudinal wind speed components, It is a component of the lateral wind. w Vertical wind speed, For frictional wind speed, It is the Obukhov length. For observation altitude, Zero planar displacement, and The value is constant; the standard deviation of horizontal wind speed under neutral conditions is calculated using the following formula. : ;in, The horizontal wind speed under neutral conditions. for The standard deviation; the complete turbulence characteristic parameters are calculated using the following formula. : ; The decomposition submodule is used to perform Reynolds decomposition on the corrected high-carbon alkane species concentration and vertical wind speed data to obtain high-carbon alkane concentration fluctuations and vertical wind speed fluctuations. The flux calculation submodule is used to calculate the covariance between the high-carbon alkane concentration fluctuation and the vertical wind speed fluctuation using the eddy covariance method, so as to obtain the turbulent flux of the high-carbon alkane species.

2. The high-carbon alkane flux measurement system based on high-frequency mass spectrometry technology according to claim 1, characterized in that, The system also includes a flux measurement platform; the high-carbon alkane monitoring module and the meteorological data monitoring module are both located on the flux measurement platform; the height of the flux measurement platform is 3 to 5 times that of the urban underlying canopy and is located in the constant flux layer above the canopy.

3. The high-carbon alkane flux measurement system based on high-frequency mass spectrometry technology according to claim 1, characterized in that, The meteorological data monitoring module includes: A three-dimensional ultrasonic anemometer is used to collect wind speed, sound speed and wind direction in the ambient atmosphere to obtain meteorological data. The sampling pipeline is used to transmit ambient air to the high-carbon alkane monitoring module; A data acquisition device is used to record the meteorological data measured by the three-dimensional ultrasonic anemometer.

4. The high-carbon alkane flux measurement system based on high-frequency mass spectrometry technology according to claim 3, characterized in that, The high-carbon alkane monitoring module includes: an auxiliary sampling pump, a catalytic device, a measuring instrument, and a calibration device; the auxiliary sampling pump is installed on the sampling pipeline and connected to the catalytic device through the sampling pipeline; the measuring instrument is connected to the catalytic device and the calibration device. The auxiliary sampling pump is used to stabilize the ambient air flow rate in the sampling pipeline, and at the same time, the ambient air is transmitted for subsequent measurements. The catalytic device is used to catalyze volatile organic compounds in the ambient atmosphere into carbon dioxide and water, generating a background sample for the background signal measurement of the measuring instrument. The system error from the measuring instrument itself is corrected and reduced by cyclically measuring the atmospheric sample and the background sample. The measuring instrument is used to measure the measurement signal of high-carbon alkane species in the atmospheric sample using proton transfer reaction time-of-flight mass spectrometry with nitrosyl cations as the parent ion, and to obtain the measured concentration of high-carbon alkane species. The calibration device is used to convert the measurement signal of high-carbon alkane species measured by the measuring instrument into a measurement concentration.

5. The high-carbon alkane flux measurement system based on high-frequency mass spectrometry technology according to claim 4, characterized in that, The calibration device includes standard synthetic air in a gas cylinder, standard high-carbon alkane gas in a gas cylinder, and a mass flow controller. The mass flow controller is used to mix the standard synthetic air from the cylinder with the standard high-carbon alkane gas from the cylinder to generate a high-carbon alkane sample of known concentration, and to obtain the response factor of the high-carbon alkane species through calibration experiments, so as to convert the high-carbon alkane species measurement signal measured by the measuring instrument into a measured concentration.

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