Method and device for constructing volatile organic compound emission list

Abstract

The invention discloses a method and device for constructing a volatile organic compound emission list, and the method comprises the steps: obtaining observation data based on a preset observation point, and obtaining the concentration data of volatile organic compounds in a boundary layer based on the observation data; constructing a box model, and calculating the discharge flux in the boundary layer based on the box model and the concentration data; matrix decomposition is carried out on the concentration data and the emission flux based on an orthogonal matrix factor model, and a target factor contribution matrix and a target factor spectrum matrix are obtained; and determining an emission source and a contribution ratio thereof based on the target factor contribution matrix and the target factor spectrum matrix, and constructing a volatile organic compound emission list based on the emission flux, the emission source and the contribution ratio thereof. According to the method, the observation data is obtained based on the preset observation point, the concentration data of the VOCs in the boundary layer is obtained, the emission flux in the boundary layer is evaluated through the box model and the orthogonal matrix factor model, the reliability and accuracy of the evaluation result are ensured, and then the volatile organic compound emission list is accurately constructed.

Description

Technical Field

[0001] The present invention relates to the field of environmental science, and particularly to a method and device for constructing a volatile organic compound emission inventory. Background Art

[0002] The emission inventory of volatile organic compounds (VOCs) is an important tool for studying the causes of atmospheric VOCs pollution and formulating pollution control measures. The compilation of existing emission inventories is mainly based on the "bottom-up" method, that is, by collecting emission factors and statistical activity levels to estimate the VOCs emissions in each link. At present, this method has combined local emission factors and source component spectra to construct an emission inventory with multi-scale and high spatio-temporal resolution, which has played a key role in providing support for pollution source tracing and control.

[0003] The existing research methods for volatile organic compound emission inventories are based on the "top-down" method of technologies such as ground observation, tall tower observation, aircraft aerial survey, and satellite remote sensing to verify and evaluate the emission inventory. However, the "bottom-up" emission inventory has high testing costs, and generally has problems such as incomplete identification of pollution sources, missing emission factors, and incomplete statistical data, resulting in significant uncertainties in aspects such as component spectrum allocation, spatial and temporal allocation, etc.; at the same time, most of the current emission inventory compilations have not fully considered the temporal variation characteristics of VOCs emissions and the emission contributions of source types of different VOCs components, resulting in low accuracy of the emission inventory. Summary of the Invention

[0004] The present invention provides a method and device for constructing a volatile organic compound emission inventory to improve the component accuracy of the volatile organic compound emission inventory.

[0005] To solve the above technical problems, an embodiment of the present invention provides a method for constructing a volatile organic compound emission inventory, including:

[0006] Obtaining observation data based on a preset observation point, and obtaining concentration data of volatile organic compounds in the boundary layer based on the observation data;

[0007] Constructing a box model, and calculating the emission flux in the boundary layer based on the box model and the concentration data;

[0008] Performing matrix decomposition on the concentration data and the emission flux based on an orthogonal matrix factorization model to obtain a target factor contribution matrix and a target factor spectrum matrix;

[0009] Determining the emission sources and their contribution ratios based on the target factor contribution matrix and the target factor spectrum matrix, and constructing a volatile organic compound emission inventory based on the emission flux, the emission sources, and their contribution ratios.

[0010] The present invention obtains observation data based on preset observation points, acquires concentration data of volatile organic compounds in the boundary layer, and through multi-level and multi-dimensional data acquisition and analysis, can accurately identify and quantify various emission sources and their contribution ratios. At the same time, by combining the box model and the orthogonal matrix factorization model to evaluate the emission flux in the boundary layer, the reliability and accuracy of the evaluation results are ensured, and thus an accurate volatile organic compound emission inventory can be constructed, providing data support for subsequent pollution control and treatment measures.

[0011] Further, the observation points include ground grid observation points and high tower observation points. The obtaining of observation data based on the preset observation points and the obtaining of concentration data of volatile organic compounds in the boundary layer based on the observation data include:

[0012] Obtain the surface emission flux based on the ground grid observation points;

[0013] Obtain the boundary layer height and the emission flux at the top of the boundary layer based on the high tower observation points;

[0014] Calculate the vertical concentration gradient of the volatile organic compounds based on the surface emission flux, the boundary layer height, and the emission flux at the top of the boundary layer;

[0015] Obtain the concentration data of volatile organic compounds in the boundary layer based on the vertical concentration gradient and the vertical profile.

[0016] By combining ground grid observation and high tower observation, the present invention can obtain observation data with different heights and spatial distributions, comprehensively reflect the concentration characteristics of volatile organic compounds in the boundary layer, and combine the surface emission flux, the boundary layer height, and the emission flux at the top, so as to accurately calculate the vertical concentration gradient of volatile organic compounds, and then combine the vertical concentration gradient and the profile to obtain the concentration data of volatile organic compounds in the boundary layer.

[0017] Further, the concentration data includes the average concentration of volatile organic compounds in the boundary layer. The construction of the box model and the calculation of the emission flux in the boundary layer based on the box model and the concentration data include:

[0018] Construct a box model based on the mass balance method and optimize the box model based on the wind speed profile and the concentration profile;

[0019] Calculate the surface emission flux of volatile organic compounds based on the optimized box model and the concentration data.

[0020] The present invention constructs a box model by the mass balance method, optimizes it in combination with the wind speed profile and the concentration profile, improves the accuracy and applicability of the model, and based on the optimized box model and the concentration data, can accurately calculate the surface emission flux of volatile organic compounds, providing reliable data for source analysis.

[0021] Further, the matrix factorization of the concentration data and the emission flux based on the orthogonal matrix factorization model to obtain the target factor contribution matrix and the target factor spectrum matrix includes:

[0022] Obtain a concentration matrix based on the concentration data, and obtain a flux matrix based on the emission flux;

[0023] Perform matrix factorization on the concentration matrix and the flux matrix based on the orthogonal matrix factorization model to obtain an initial factor contribution matrix, an initial factor spectrum matrix, and a residual matrix;

[0024] Optimize the initial factor contribution matrix and the initial factor spectrum matrix based on the residual matrix to obtain the target factor contribution matrix and the target factor spectrum matrix.

[0025] By separately obtaining the concentration matrix and the flux matrix, the present invention provides a comprehensive data basis for subsequent matrix factorization, and improves the accuracy and reliability of the factor contribution matrix and the factor spectrum matrix by combining the optimization of the initial matrix and the residual matrix.

[0026] Further, determining the emission sources and their contribution ratios based on the target factor contribution matrix and the target factor spectrum matrix, and constructing a volatile organic compound emission inventory based on the emission flux, the emission sources, and their contribution ratios includes:

[0027] Determine the emission source factors based on the emission flux;

[0028] Obtain the characteristic components of all emission source factors based on the target factor spectrum matrix;

[0029] Obtain the contribution ratio of the emission source based on the target factor contribution matrix;

[0030] Construct a volatile organic compound emission inventory based on the emission flux, the emission sources, and their contribution ratios.

[0031] The present invention analyzes the emission flux data, identifies the main emission source factors, uses the target factor spectrum matrix to extract the characteristic components of each emission source factor to help identify the characteristics of different sources, and then calculates the contribution ratio of each emission source to the total emission through the target factor contribution matrix to quantify the influence degree of each source. Finally, a detailed VOCs emission inventory is constructed by integrating the emission flux, the emission sources, and their contribution ratios, providing a basis for environmental management and policy making.

[0032] In a second aspect, the present invention provides a device for constructing a volatile organic compound emission inventory, including: a concentration calculation module, a flux calculation module, a matrix factorization module, and an inventory construction module;

[0033] The concentration calculation module is used to obtain observation data based on preset observation points, and obtain the concentration data of volatile organic compounds in the boundary layer based on the observation data;

[0034] The flux calculation module is used to construct a box model and calculate the emission flux in the boundary layer based on the box model and the concentration data;

[0035] The matrix decomposition module is used to perform matrix decomposition on the concentration data and the emission flux based on the orthogonal matrix factorization model to obtain a target factor contribution matrix and a target factor spectrum matrix;

[0036] The inventory construction module is used to determine emission sources and their contribution ratios based on the target factor contribution matrix and the target factor spectrum matrix, and construct a volatile organic compound emission inventory based on the emission flux, emission sources and their contribution ratios.

[0037] Further, the observation points include ground grid observation points and high tower observation points, and the concentration calculation module is used to:

[0038] Obtain the surface emission flux based on the ground grid observation points;

[0039] Obtain the boundary layer height and the emission flux at the top of the boundary layer based on the high tower observation points;

[0040] Calculate the vertical concentration gradient of the volatile organic compounds based on the surface emission flux, the boundary layer height and the emission flux at the top of the boundary layer;

[0041] Obtain the concentration data of volatile organic compounds in the boundary layer based on the vertical concentration gradient and the vertical profile.

[0042] Further, the concentration data includes the average concentration of volatile organic compounds in the boundary layer; the flux calculation module is used to:

[0043] Construct a box model based on the mass balance method and optimize the box model based on the wind speed profile and the concentration profile;

[0044] Calculate the surface emission flux of volatile organic compounds based on the optimized box model and the concentration data.

[0045] Further, the matrix decomposition module is used to:

[0046] Obtain a concentration matrix based on the concentration data and obtain a flux matrix based on the emission flux;

[0047] Perform matrix decomposition on the concentration matrix and the flux matrix based on the orthogonal matrix factorization model to obtain an initial factor contribution matrix, an initial factor spectrum matrix and a residual matrix;

[0048] Optimize the initial factor contribution matrix and the initial factor spectrum matrix based on the residual matrix to obtain a target factor contribution matrix and a target factor spectrum matrix.

[0049] Further, the inventory construction module is configured to:

[0050] Determine emission source factors based on the emission fluxes;

[0051] Obtain characteristic components of all emission source factors based on the target factor spectrum matrix;

[0052] Obtain the contribution ratio of the emission source based on the target factor contribution matrix;

[0053] Construct a volatile organic compound emission inventory based on the emission fluxes, emission sources, and their contribution ratios. Description of the Drawings

[0054] Figure 1 It is a schematic flow chart of a method for constructing a volatile organic compound emission inventory provided in an embodiment of the present invention;

[0055] Figure 2 It is a fitted vertical profile diagram of VOCs component concentrations provided in an embodiment of the present invention;

[0056] Figure 3 It is a comparison schematic diagram of ground observation data and boundary layer average data obtained based on the profile provided in an embodiment of the present invention;

[0057] Figure 4 It is a structural schematic diagram of a box model provided in an embodiment of the present invention;

[0058] Figure 5 It is a schematic diagram of the daily variation characteristics of fluxes and concentrations provided in an embodiment of the present invention;

[0059] Figure 6 It is a PMF analysis factor contribution matrix and factor spectrum matrix based on concentrations and fluxes provided in an embodiment of the present invention;

[0060] Figure 7 It is a PMF analysis source contribution schematic diagram based on concentrations and fluxes provided in an embodiment of the present invention;

[0061] Figure 8 It is a structural schematic diagram of a device for constructing a volatile organic compound emission inventory provided in an embodiment of the present invention;

[0062] Figure 9 It is a schematic diagram of box model estimated emission fluxes and inventory verification provided in an embodiment of the present invention;

[0063] Figure 10 A schematic diagram of PMF parsing source contribution and inventory verification provided by an embodiment of the present invention;

[0064] Figure 11 A schematic diagram of the spatial distribution of box model estimated emission fluxes provided by an embodiment of the present invention;

[0065] Figure 12 A schematic diagram of PMF parsing source spatial distribution and inventory verification provided by an embodiment of the present invention. Detailed implementation manners

[0066] The following further describes in detail the specific implementation manners of the present invention in conjunction with the drawings and embodiments. The following embodiments are used to illustrate the present invention, but are not used to limit the scope of the present invention.

[0067] The terms "first" and "second" etc. in the specification, claims and drawings of the present application are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes steps or units not listed, or optionally further includes other steps or units inherent to these processes, methods, products or devices.

[0068] Referring to "embodiment" herein means that a specific feature, structure or characteristic described in conjunction with the embodiment may be included in at least one embodiment of the present application. The phrase appears at various positions in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art explicitly and implicitly understand that the embodiments described herein may be combined with other embodiments.

[0069] Embodiment 1

[0070] Refer to Figure 1 , Figure 1 A flowchart of a method for constructing a volatile organic compound emission inventory provided by an embodiment of the present invention. An embodiment of the present invention provides a method for constructing a volatile organic compound emission inventory, including steps 101 to 104, specifically as follows:

[0071] Step 101: Obtain observation data based on a preset observation point, and obtain concentration data of volatile organic compounds in the boundary layer based on the observation data;

[0072] In this embodiment, the observation points include ground grid observation points and high tower observation points. The obtaining of observation data based on a preset observation point and the obtaining of concentration data of volatile organic compounds in the boundary layer based on the observation data include:

[0073] Obtain the surface emission flux based on the ground grid observation points;

[0074] Obtain the boundary layer height and the emission flux at the top of the boundary layer based on the tall tower observation points;

[0075] Calculate the vertical concentration gradient of the volatile organic compounds based on the surface emission flux, the boundary layer height and the emission flux at the top of the boundary layer;

[0076] Obtain the concentration data of the volatile organic compounds in the boundary layer based on the vertical concentration gradient and the vertical profile.

[0077] In this embodiment, obtain the parameter data related to the emission flux based on the grid ground observation points. The parameter data related to the emission flux includes the component concentration of VOCs (volatile organic compounds), the concentration of oxidants (OH radicals), and the wind speed.

[0078] In this embodiment, the background concentration can take the minimum observed concentration or the 5% quantile value according to the data volume. The background concentration refers to the concentration of pollutants from natural sources or long-range transport that are not directly affected by human activities in a specific area. These background concentrations are usually caused by pollutants from long-range transport or natural sources (such as plant emissions).

[0079] In this embodiment, when the minimum observed concentration is used as the background concentration, the lowest concentration value that appears in the monitoring data over a period of time is assumed to represent the background concentration.

[0080] In this embodiment, when the 5% quantile value is used as the background concentration, all the observed data are sorted in ascending order of concentration, and the maximum value within the range of the lowest 5% of the data is taken as the estimated value of the background concentration.

[0081] In this embodiment, set up observation equipment at the tall tower observation points to measure data such as the component concentration of VOCs, the concentration of oxidants, the wind speed, and the boundary layer height at different heights. Thus, obtain the boundary layer height and the emission flux at the top of the boundary layer.

[0082] In this embodiment, since the tall tower observation points are located in a relatively stable convective boundary layer (PBL), in this environment, the concentration gradient can be estimated by measuring the concentration of VOCs (volatile organic compounds) at different heights. For example, if the concentrations c1 and c2 are measured at heights z1 and z2 respectively, then the vertical concentration gradient is specifically:

[0083]

[0084] In this embodiment, for multiple height measurement points, the concentration gradient between different heights can be calculated in a similar manner, so as to obtain the change rate of concentration with height according to the concentration gradient between different heights.

[0085] In this embodiment, based on the vertical concentration gradient, the vertical distribution of concentration can be further deduced. Based on the Boundary layer gradient (BLG) technique, the relationship between the vertical concentration gradient of pollutants in the boundary layer and the surface emission flux and the entrainment flux at the top of the boundary layer can be expressed as:

[0086]

[0087] where c is the concentration at the vertical height z, h is the boundary layer height, is the surface emission flux, is the entrainment flux at the top of the boundary layer, g b and g t are dimensionless bottom-up gradient and top-down function, w * is the convective velocity scale, which can be expressed as:

[0088]

[0089] where the exponent α can be determined by comparing the model predicted value with the actually measured concentration gradient through the Generalized Reduced Gradient (GRG) non-linear method and minimizing the Root Mean Square (RMS) error between the two for parameter fitting; g is the gravitational constant, is the sensible heat flux, and T is the temperature. The parameters that cannot be actually measured in the study can be obtained using a database, such as the European Centre for Medium-Range Weather Forecasts database.

[0090] In this embodiment, based on these parameters, the concentration values at different heights (the concentration distribution within the boundary layer) are fitted, that is, the vertical distribution profile of the concentration is obtained. Figure 2A vertical profile diagram of the concentration of VOCs components provided by an embodiment of the present invention. In this embodiment, based on the observation data at 14:00 on different heights (3m, 118m, 488m) of the Canton Tower during the observation period from November 3 to 11, 2018, combined with the boundary layer gradient technology, the vertical profile of the concentration of VOCs components is fitted. Among them, the boundary layer height h is about 1161m (ranging from 849m to 1490m during the measurement period), the convective velocity scale w* is 1 - 2m / s, and the exponent α is optimized according to the root mean square error (RMS) of the fitting data, about 0.5 - 0.8. The unmeasured data is obtained based on the European Medium-Range Weather Forecast Database. Figure 2 a is the vertical profile diagram of the concentration of propane, Figure 2 b is the vertical profile diagram of the concentration of benzene, Figure 2 c is the vertical profile diagram of the concentration of toluene, Figure 2 d is the vertical profile diagram of the concentration of ethylene, Figure 2 e is the vertical profile diagram of the concentration of isoprene. From the fitting results, the vertical concentration gradient increases with the increase in chemical reaction activity. Among them, the vertical concentration gradient of isoprene is the largest. Substitute the ground observation concentration into the vertical profile to calculate the average concentration c of VOCs in the boundary layer m 。

[0091] In this embodiment, by measuring the concentration values at various heights and calculating the vertical concentration gradient, the atmospheric stability and pollution diffusion conditions can be quickly understood. By measuring the concentration values at different heights and calculating the vertical concentration gradient, the atmospheric stability and pollution diffusion state can be further evaluated.

[0092] In this embodiment, through the combination of ground grid observation and tall tower observation, the observation data at different heights and spatial distributions can be obtained, comprehensively reflecting the concentration characteristics of volatile organic compounds in the boundary layer. Combined with the surface emission flux, boundary layer height, and top emission flux, the vertical concentration gradient of volatile organic compounds can be accurately calculated. Then, combined with the vertical concentration gradient and profile, the concentration data of volatile organic compounds in the boundary layer can be obtained.

[0093] Figure 3 A comparison schematic diagram of the ground observation data and the boundary layer average data obtained based on the profile provided by an embodiment of the present invention. In this embodiment, based on the ground observation data in the urban area of Guangzhou from September to November 2018 and Figure 2 the average concentration c in the boundary layer is calculated according to the vertical profile obtained in m。The background concentration is the lowest concentration value among all sampling points in each sampling. The chemical reaction time τ is estimated from the chemical reaction rate constant kOH and the OH concentration (ignoring the reactions with O3 and NO3). The OH concentration and its profile are based on the simulation results of the CMAQ (Community Multiscale Air Quality) model in previous studies. The wind speed is from the observational data, and the wind speed profile uses the parameterization scheme for typical urban areas. During the observation period of this embodiment, the daily variation range of the total VOC concentration is from 20 ppb to 35 ppb. The concentration starts to decrease at 8:00 (GMT+8), reaches the lowest value at 15:00, and then levels off at 20:00. The boundary layer height and the OH concentration start to increase rapidly at 8:00, reflecting the development of the mixed layer and the enhancement of photochemical reactions respectively. The boundary layer height reaches the maximum value (about 1150 meters) at 14:00 and remains at about 200 meters at night. The OH concentration reaches the maximum value (4.68×10 6 molecule cm -3 ) at 12:00. The wind speed is relatively high (about 3.6 m / s) during the period from 8:00 to 14:00 and remains at about 2.6 m / s at night. Considering the significant variations in the ventilation conditions (such as boundary layer growth and wind speed changes) and oxidation capacity (such as OH concentration) within the atmospheric boundary layer, when evaluating the emission intensity based on the measured concentrations, it is necessary to consider the effects of both physical diffusion and chemical reactions simultaneously. Through vertical profile correction, the average concentration (c m ) in the boundary layer is lower than the surface concentration, especially during the day, due to the higher boundary layer height and stronger vertical gradient, with a difference from the surface concentration of about 2.7–5.8 ppb. The average OH concentration ([OH] m ) in the boundary layer is higher because the OH concentration increases with height. The difference from the ground OH concentration reaches the maximum value at 14:00. The average wind speed (u m ) in the boundary layer reaches 7.3 m / s at noon, which is twice the surface wind speed, and is about 4.0 m / s at night, about 2 m / s higher than the surface wind speed.

[0094] Step 102: Construct a box model and calculate the emission flux within the boundary layer based on the box model and the concentration data;

[0095] In this embodiment, the concentration data includes the average concentration of volatile organic compounds within the boundary layer; the construction of the box model and the calculation of the emission flux within the boundary layer based on the box model and the concentration data include:

[0096] Construct a box model based on the mass balance method and optimize the box model based on the wind speed profile and the concentration profile;

[0097] Calculate the surface emission flux of volatile organic compounds based on the optimized box model and the concentration data.

[0098] In this embodiment, a box model is constructed based on the mass balance method, that is, the boundary layer mass conservation technique (MB). Specifically, based on the pollutant concentration in the atmospheric boundary layer (i.e., the imaginary box) in a specific area, the pollutant emissions are deduced by considering processes such as diffusion transport and chemical consumption.

[0099] In this embodiment, in the mixed boundary layer, the mass conservation formula for pollutants is:

[0100]

[0101] where c is the average pollutant concentration, U is the horizontal average wind speed, t is the time, x is the horizontal distance, F h and F s are the entrainment flux of pollutants at the top of the boundary layer and the surface emission flux respectively, and S is the chemical consumption or deposition.

[0102] Please refer to Figure 4 , Figure 4 , which is a schematic structural diagram of a box model provided by an embodiment of the present invention.

[0103] In this embodiment, it is assumed that in a specific box with a cuboid shape (length L × width W) in the area to be studied (where the length scale is determined according to the reaction rate of VOCs), the wind speed and wind direction are parallel to L, and the VOCs are mixed evenly and reach an equilibrium state There is no vertical exchange of pollutants at the top of the boundary layer, and the vertical flux transport can be ignored (F h = 0), and the dry and wet depositions are small, that is, the surface emission flux is:

[0104]

[0105] where c m is the average concentration of VOCs in the boundary layer, which can be obtained from the vertical concentration profile; the background concentration entering the box is c0; the chemical consumption time is τ, and the chemical consumption of VOCs includes reactions with OH, O3, and NO3, so τ = 1 / (k OH ×[OH] + k O3 ×[O3] + k NO3 ×[NO3]). Then, the emission flux of VOCs component i at the surface considering the vertical gradients of the boundary layer VOCs concentration and wind speed can be expressed as:

[0106]

[0107] where F VOCi is the emission flux of VOCs component i at the surface, H is the height of the top of the boundary layer, c i(h) represents the concentration of component i at height h, b i (h) represents the background concentration of component i at height h, u(h) represents the wind speed at height h, L represents the box length, τ i (h) represents the chemical consumption time of component i at height h.

[0108] In this embodiment, a box model is constructed by the mass balance method and optimized by combining the wind speed profile and the concentration profile, which improves the accuracy and applicability of the model. Based on the optimized box model and concentration data, the surface emission flux of volatile organic compounds can be accurately calculated, providing reliable data for source apportionment.

[0109] Figure 5 This is a schematic diagram of the diurnal variation characteristics of flux and concentration provided by an embodiment of the present invention. Figure 5 a, 5b are respectively schematic diagrams of the diurnal variation characteristics of the concentration and flux of total VOCs (divided by alkanes, alkenes, aromatic hydrocarbons, alkynes, etc.), Figure 5 c, 5d, 5e, 5f are respectively schematic diagrams of the diurnal variation characteristics of the concentration and flux of isoprene, toluene, propane, and acetylene. In this embodiment, due to chemical loss and enhanced ventilation caused by the increase in the boundary layer height, the total VOC concentration significantly decreases from 11:00 to 13:00. However, the estimated emission flux is higher during this period than at night. The emission flux continuously increases in the morning, reaches a peak from 11:00 to 13:00, and then decreases. The higher emission flux during the day can be attributed to the enhancement of human daily activities and industrial activities. These characteristics are also reflected in the characteristic anthropogenic VOC components, such as toluene (a characteristic component of solvents), propane (a characteristic component of fuel combustion and evaporation), and acetylene (a characteristic component of fuel combustion). For isoprene, its emission flux shows a "single-peak" diurnal variation pattern and is highly sensitive to solar radiation during the day, indicating that its main source is biogenic emissions.

[0110] Step 103: Perform matrix decomposition on the concentration data and emission flux based on the orthogonal matrix factorization model to obtain a target factor contribution matrix and a target factor spectral matrix;

[0111] In this embodiment, the performing matrix decomposition on the concentration data and emission flux based on the orthogonal matrix factorization model to obtain a target factor contribution matrix and a target factor spectral matrix includes:

[0112] Obtain a concentration matrix based on the concentration data and obtain a flux matrix based on the emission flux;

[0113] Perform matrix decomposition on the concentration matrix and the flux matrix based on the orthogonal matrix factorization model to obtain an initial factor contribution matrix, an initial factor spectral matrix, and a residual matrix;

[0114] Optimize the initial factor contribution matrix and the initial factor spectrum matrix based on the residual matrix to obtain a target factor contribution matrix and a target factor spectrum matrix.

[0115] In this embodiment, source apportionment is performed by applying the positive matrix factorization (PMF) model based on concentration data and emission flux data respectively.

[0116] In this embodiment, the concentration data is converted into a concentration matrix, and the concentration matrix is analyzed based on the positive matrix factorization model and decomposed into an initial source factor matrix and its initial contribution ratio matrix. Specifically:

[0117] X (n×m) =G (n×p) ×F (p×m) +E (n×m) (9)

[0118] Where X is the component concentration matrix with a size of n×m, n is the number of samples, and m is the number of component types. G is the factor contribution matrix with a size of n×p, and p is the number of factors (pollution sources) to be resolved. F is the factor spectrum matrix with a size of p×m, representing the characteristics of each source factor in each component. E is the residual matrix, representing the error between the data matrix X and GF obtained by decomposition. PMF calculates the factor contribution matrix G and the factor spectrum matrix F by minimizing the residual matrix E.

[0119] In this embodiment, the flux data is converted into a flux matrix, and the flux matrix is analyzed based on the positive matrix factorization model and decomposed into an initial source factor matrix and its initial contribution ratio matrix. Specifically:

[0120] X (n×m) =G (n×p) ×F (p×m) +E (n×m) (10)

[0121] Where X is the flux matrix with a size of n×m (n is the number of samples, and m is the number of component types). G is the factor contribution matrix with a size of n×p, and p is the number of factors (pollution sources) to be resolved. F is the factor spectrum matrix with a size of p×m, representing the characteristics of each source factor in each component. E is the residual matrix, representing the error between the data matrix X and GF obtained by decomposition. PMF calculates the factor contribution matrix G and the factor spectrum matrix F by minimizing the residual matrix E.

[0122] In this embodiment, source analysis is performed through the flux matrix and the concentration matrix respectively to obtain the factor contribution matrix and the factor spectrum matrix, and the factor contribution matrix and the factor spectrum matrix obtained by decomposing the two types of data are fused to obtain the target factor contribution matrix and the target factor spectrum matrix.

[0123] In this embodiment, the factor contribution matrix (G) represents the contribution degree of each source factor in each sample. The factor spectrum matrix (F) is the characteristic spectrum of each source factor on each pollutant.

[0124] Figure 6 This is a factor contribution matrix and a factor spectrum matrix for PMF analysis based on concentration and flux provided by an embodiment of the present invention. In this embodiment, using the ERA PMF 5.0 model, 29 kinds of atmospheric indicators that are easy to detect and often regarded as tracing sources are selected to conduct PMF source analysis based on concentration (CON-PMF) and flux (FLU-PMF) data respectively, and six emission sources are identified: solvent use ( Figure 6 a), biogenic emissions ( Figure 6 b), fuel combustion ( Figure 6 c), vehicle emissions ( Figure 6 d), oil and gas volatilization ( Figure 6 e) and industrial emissions ( Figure 6 f), and their factor contribution matrix (bar chart, corresponding to the left axis) and factor spectrum matrix (scatter plot, corresponding to the right axis) are obtained.

[0125] The present invention provides a comprehensive data basis for subsequent matrix decomposition by separately obtaining the concentration matrix and the flux matrix, and improves the accuracy and reliability of the factor contribution matrix and the factor spectrum matrix by combining the optimization of the initial matrix and the residual matrix.

[0126] Step 104: Determine the emission sources and their contribution ratios based on the target factor contribution matrix and the target factor spectrum matrix, and construct a volatile organic compound emission inventory based on the emission flux, emission sources and their contribution ratios.

[0127] In this embodiment, the determining the emission sources and their contribution ratios based on the target factor contribution matrix and the target factor spectrum matrix, and constructing a volatile organic compound emission inventory based on the emission flux, emission sources and their contribution ratios includes:

[0128] Determine the emission source factors based on the emission flux;

[0129] Obtain the characteristic components of all emission source factors based on the target factor spectrum matrix;

[0130] Obtain the contribution ratio of the emission source based on the target factor contribution matrix;

[0131] Construct a volatile organic compound emission inventory based on the emission flux, emission sources and their contribution ratios.

[0132] In this embodiment, by analyzing the source spectrum matrix F, the characteristics of each source factor can be identified, and qualitative identification of the source can be carried out in combination with the main identification components of various source categories. For example, if the identification components of a certain factor spectrum account for a relatively high proportion compared with other factor spectra, it can be identified as the corresponding source category. The contribution ratio of each source factor can be obtained through the G matrix, indicating the contribution of each source factor to the component concentration (or flux).

[0133] In this embodiment, atmospheric dilution diffusion and chemical reactions will affect the concentration distribution of pollutants, resulting in errors in the calculation of the contribution of some sources. If these reaction effects are ignored during source apportionment, the contribution of some sources may be overestimated or underestimated. Since the flux has already considered processes such as chemical consumption, by comparing the source apportionment results based on concentration data and flux data (i.e., the source composition and contribution ratio obtained by analyzing the factor contribution matrix G and the factor spectrum matrix F), sources whose concentration changes caused by chemical reactions may be overestimated or underestimated can be identified.

[0134] Figure 7 FIG. is a schematic diagram of the source contribution of PMF analysis based on concentration and flux provided by an embodiment of the present invention. Among them, Figure 7 a is a schematic diagram of the source contribution of PMF analysis based on concentration (CON-PMF), Figure 7 b is a schematic diagram of the source contribution of PMF analysis based on flux (FLU-PMF). The source contributions analyzed based on concentration and flux both show that vehicle exhaust is the main emission source, accounting for 34.8% and 32.3% of the total emissions respectively. The next is oil and gas volatilization, accounting for 29.7% and 28.9% respectively. The main differences between the two methods are reflected in the contributions of biogenic emissions and solvent use. The contributions of biogenic emissions and solvent use in the source apportionment based on flux are 6.8% and 16.8% respectively, higher than 2.4% and 13.4% in the source apportionment based on concentration; while the contributions of fuel combustion and industrial processes are lower in the source apportionment based on flux (11.3% and 3.7% respectively), lower than 14.4% and 5.3% in the source apportionment based on concentration. The increase in the contribution of solvent use in the source apportionment based on flux is consistent with the high proportion of its related reactive VOC components, while the source apportionment based on concentration may underestimate the contribution of this source due to the neglect of chemical losses. In addition, the decrease in the contribution of fuel combustion in the source apportionment based on flux is more reasonable because the contribution of fossil fuel combustion in urban areas has decreased significantly. At the same time, the contribution of biogenic emissions in the source apportionment based on flux (6.8%) is higher than that in the source apportionment based on concentration (2.4%), which may be due to the contribution of urban greening and street trees.

[0135] In this embodiment, by estimating the VOCs emission flux, the emission flux distribution characteristics of VOCs components are obtained. Specifically, the emission flux can be divided according to different chemical categories of VOCs (such as alkanes, alkenes, aromatic hydrocarbons, etc.), so as to reveal the proportion of the emission flux composition of each type of VOCs.

[0136] In this embodiment, the PMF model is used to analyze the sources of VOCs based on concentration and flux data, which can determine the source composition of VOCs, that is, identify the main emission sources and their corresponding contribution ratios. The PMF model analyzes the concentration (or flux) data and decomposes it into a matrix of source factors and a matrix of their contribution ratios, so as to identify the characteristics of each source factor.

[0137] In this embodiment, the construction of the volatile organic compound emission inventory also includes time-scale analysis and spatial distribution analysis.

[0138] In this embodiment, the time-scale analysis includes concentration observations and flux estimations at different time scales, which can reveal the variation laws of VOCs and their components at different time scales such as within a day, monthly, and seasonal. Since the diurnal variation characteristics can reflect the emission differences of VOCs between day and night, the monthly variation characteristics can reveal the emission changes brought by seasonal fluctuations, and the seasonal variation characteristics are helpful to analyze the emission patterns of VOCs in different seasons, so as to further reveal the emission dynamics related to factors such as climate, temperature, and light.

[0139] In this embodiment, the spatial distribution analysis includes continuous concentration observations and emission flux estimations by setting up multiple monitoring points in different geographical regions, so as to obtain the emission intensity, source distribution, and spatial differences of VOCs in different regions. For example, the urban area is affected by traffic activities and has a higher emission flux. Thus, the spatial distribution characteristics of VOCs emissions are revealed, and the main emission sources are identified.

[0140] In this embodiment, based on the VOCs observation data and their emission flux estimations at different time and spatial scales, the spatial distribution characteristics and time variation laws of VOCs and their sources can be comprehensively revealed, and a VOCs emission inventory can be constructed.

[0141] In this embodiment, through the estimation of the VOCs emission flux and the source analysis of the PMF model, the emission characteristics of VOCs can be comprehensively understood, including the emission flux composition of VOCs of different chemical categories, the main emission sources and their contribution ratios.

[0142] In this embodiment, through observations at different time scales and geographical regions, the variation laws of VOCs and their components at different time scales such as within a day, monthly, and seasonal can be revealed, as well as the emission intensity, source distribution, and spatial differences of VOCs in different regions.

[0143] In this embodiment, first, the data of ground grid observation points are used, combined with the background concentration (the minimum observed concentration or the 5% quantile value can be taken), to calculate the surface emission flux. And through the data of high tower observation points, the boundary layer height and the emission flux at the top of the boundary layer are obtained. The VOCs concentration is measured at different heights, and the change rate of the concentration with height, that is, the vertical concentration gradient, is calculated. Subsequently, based on the boundary layer gradient technology, combined with the surface emission flux, the boundary layer height and the top emission flux, the vertical concentration distribution profile of VOCs is deduced. Then, based on the mass balance method, a box model is constructed, and combined with the wind speed profile and the concentration profile, the model is optimized. Using the optimized box model and concentration data, the emission flux within the boundary layer is calculated. Furthermore, the PMF model is applied to the concentration data and the emission flux data respectively for matrix decomposition to obtain the target factor contribution matrix and the target factor spectrum matrix. By analyzing the factor spectrum matrix, the characteristics of each source factor are identified, and combined with the main identification components, the source is qualitatively identified. Through the factor contribution matrix, the contribution ratio of each emission source is determined. Finally, based on the emission flux, the emission sources and their contribution ratios, a VOCs emission inventory is constructed. By combining ground grid observation and high tower observation, the VOCs concentration data at different heights and spatial distributions are obtained, comprehensively reflecting the concentration characteristics of VOCs within the boundary layer. And through the application of the box model and the PMF model, the surface emission flux of VOCs is accurately calculated, the main emission sources and their contribution ratios are identified. At the same time, through the observation at different time scales and geographical regions, the variation laws of VOCs and their components at different time scales such as daily, monthly, and seasonal are revealed, as well as the emission intensity, source distribution and spatial differences of VOCs in different regions, so as to provide a detailed VOCs emission inventory for environmental management.

[0144] Please refer to Figure 8 , Figure 8 FIG. is a schematic structural diagram of a device for constructing a volatile organic compound emission inventory provided by an embodiment of the present invention, including: a concentration calculation module 401, a flux calculation module 402, a matrix decomposition module 403, and an inventory construction module 404;

[0145] The concentration calculation module is used to obtain observation data based on preset observation points, and obtain the concentration data of volatile organic compounds within the boundary layer based on the observation data;

[0146] The flux calculation module is used to construct a box model and calculate the emission flux within the boundary layer based on the box model and the concentration data;

[0147] The matrix decomposition module is used to perform matrix decomposition on the concentration data and the emission flux based on the orthogonal matrix factor model to obtain a target factor contribution matrix and a target factor spectrum matrix;

[0148] The inventory construction module is used to determine emission sources and their contribution ratios based on the target factor contribution matrix and the target factor spectrum matrix, and construct a volatile organic compound emission inventory based on the emission flux, emission sources and their contribution ratios.

[0149] In this embodiment, the observation points include ground grid observation points and high tower observation points. The concentration calculation module is used to:

[0150] Obtain the surface emission flux based on the ground grid observation points;

[0151] Obtain the boundary layer height and the emission flux at the top of the boundary layer based on the high tower observation points;

[0152] Calculate the vertical concentration gradient of the volatile organic compounds based on the surface emission flux, the boundary layer height and the emission flux at the top of the boundary layer;

[0153] Obtain the concentration data of the volatile organic compounds in the boundary layer based on the vertical concentration gradient and the vertical profile.

[0154] In this embodiment, the concentration data includes the average concentration of the volatile organic compounds in the boundary layer. The flux calculation module is used to:

[0155] Construct a box model based on the mass balance method and optimize the box model based on the wind speed profile and the concentration profile;

[0156] Calculate the surface emission flux of the volatile organic compounds based on the optimized box model and the concentration data.

[0157] In this embodiment, the matrix decomposition module is used to:

[0158] Obtain a concentration matrix based on the concentration data and obtain a flux matrix based on the emission flux;

[0159] Perform matrix decomposition on the concentration matrix and the flux matrix based on the orthogonal matrix factor model to obtain an initial factor contribution matrix, an initial factor spectrum matrix and a residual matrix;

[0160] Optimize the initial factor contribution matrix and the initial factor spectrum matrix based on the residual matrix to obtain a target factor contribution matrix and a target factor spectrum matrix.

[0161] In this embodiment, the inventory construction module is used to:

[0162] Determine emission source factors based on the emission flux;

[0163] Obtain the characteristic components of all emission source factors based on the target factor spectrum matrix;

[0164] Obtain the contribution ratio of the emission source based on the target factor contribution matrix;

[0165] Construct a volatile organic compound emission inventory based on the emission flux, emission source, and their contribution ratios.

[0166] Embodiment 2

[0167] In this embodiment, the annual emissions of VOCs components in 2008 and 2009 are estimated using the gridded observational data of the Pearl River Delta region (the study area is 200 km × 200 km, which is divided into grid cells of 20 km × 20 km). The mixed layer height data is from the Global Data Assimilation System (GDAS) of the National Centers for Environmental Prediction (NCEP) of the United States, which is 441–538 m at night and 513–1174 m during the day. The background concentration is the lowest concentration value of all sampling points in each sampling. The wind speed is 2–4 m / s according to the sampling records. The OH concentration uses the OH observational values in the Comprehensive Air Quality Regional Experiment Program of the Pearl River Delta. During the day, it is 2 - 5×106 molecule cm-3, and the chemical reaction with VOCs is ignored at night. Using the ERA PMF 5.0 model, 30 indicators that are easy to detect in the atmosphere and are often regarded as tracers are selected for source apportionment. Refer to Figures 9 - 12 , and comparative verification is carried out from the perspectives of emissions, source contributions, and spatial distributions with the VOC / CO ratio method and the "bottom-up" emission inventories based on emission factors (including ZhengEI in 2006, Zheng EI in 2010, MEIC in 2008, and MEIC in 2010).

[0168] Refer to Figure 9 , in terms of emissions, the CO emissions estimated by the box model are 4.1×10 3 Gg, compared with the estimated values of the emission inventory (3.8×10 3 –4.9×10 3Gg), verifying the reliability of the estimation method. The estimated annual toluene emissions were 167.8 ± 100.5 Gg, which were close to the estimated values by the VOC / CO ratio method (176.6 ± 132.6 Gg), but significantly higher than those of Zheng EI in 2006 (103.1 Gg) and Zheng EI in 2010 (70.7 Gg), and were closer to those of MEIC in 2008 (160.2 Gg) and MEIC in 2010 (193.7 Gg). The estimated emissions of aromatic hydrocarbons such as benzene, m / p-xylene, and o-xylene were relatively consistent among different methods. The estimated values of C3-C5 alkanes (such as propane, isobutane, n-butane, etc.) (27.9 Gg–49.2 Gg) were slightly higher than those of the VOC / CO ratio method (25.1 Gg–40.6 Gg), but significantly higher than those of the "bottom-up" emission inventory (3.9 Gg–37.5 Gg). These alkanes are the main components of fuel evaporation, and their emissions may be underestimated in the "bottom-up" inventory. The estimated ethylene and propylene emissions were 47.6 ± 27.6 Gg and 19.2 ± 10.7 Gg respectively, which were close to those of the "bottom-up" emission inventory, but significantly higher than those of the VOC / CO ratio method (23.2 ± 13.7 Gg and 8.3 ± 4.8 Gg). The estimated values of trans-2-butene and cis-2-butene were 2–4 times higher than those of the VOC / CO ratio method and the "bottom-up" emission inventory, mainly because the VOC / CO ratio method did not consider the chemical loss caused by the high reaction rate of these olefins with OH radicals.

[0169] Refer to Figure 10 , the contribution of the emission sources of the key VOCs components resolved by PMF was compared with the results of the existing emission inventories (Zheng EI in 2010 and MEIC in 2008). The 10 key species selected had relatively high emissions and represented different VOC categories. Figure 10a is the comparison with the Zheng EI list in 2010. For propane and n-butane, the PMF results indicate that gasoline vehicle emissions and fuel volatilization are the main sources, while industrial emissions are the main sources in the Zheng EI in 2010. For 1-butene, the PMF results show that fixed fuel combustion is the main source, while gasoline vehicle emissions are the main sources in the Zheng EI in 2010. Since gasoline vehicle emissions include engine fuel combustion, 1-butene in the PMF may partially originate from combustion sources. For aromatic hydrocarbons (such as toluene and m / p-xylene), solvent use is the main source of toluene and m / p-xylene in the Zheng EI in 2010, while the PMF results show that industrial emissions are also an important source of toluene. For benzene, the PMF results show that gasoline vehicle emissions are the main source of benzene, while solvent use and industrial emissions are the main sources in the Zheng EI in 2010. Since benzene is strictly controlled in solvents and industrial manufacturing, its emissions may need to be further verified in the emission inventory. Figure 10 b is the comparison with the MEIC in 2008. For propane, ethane, acetylene and benzene, the PMF results show that transportation is the main source, while industrial emissions are the main sources in the MEIC in 2008. These compounds have relatively high contents in the vehicle emission profiles, and the differences may be due to the underestimation of the contribution of transportation in the MEIC.

[0170] Figure 11 This is the emission spatial distribution of total VOCs and three key components (propane, ethylene and toluene) estimated by the box model of the present invention. The total VOCs emissions are allocated to 84 grids according to the PMF model results, and it is assumed that the spatial distribution of VOCs emissions is similar to their environmental concentration distribution. As Figure 11 shown in a, there are two hotspots in the Pearl River Delta region for the total VOCs emissions. Hotspot 1 is located in Zhongshan and Zhuhai in the southwestern part of the Pearl River Delta, mainly due to the scattered small manufacturing workshops in the Zhongshan area and various emissions from Gaolan Port in Zhuhai. Hotspot 2 is located at the junction of Guangzhou, Dongguan and Shenzhen near the Pearl River Estuary, mainly due to the emissions from ships and containers at large ports such as Nansha Port, Huangpu Port and Humen Port, as well as the emissions from industrial factories such as printing, shoemaking and electronic painting in Dongguan and Shenzhen. In addition, as a highly urbanized and industrialized city, Foshan also has a small emission hotspot, mainly due to vehicle and industrial emissions. The emission spatial distributions of propane and ethylene are similar, and the hotspots are concentrated in the southwestern part of the Pearl River Delta ( Figure 11 b, c). In addition to Zhongshan and Zhuhai, Jiangmen also makes a significant contribution to the emissions of propane and ethylene, which may be due to the liquefied petroleum gas used for cooking and its leakage losses in this area. The emission hotspots of toluene are concentrated in Foshan, Dongguan, Shenzhen and Zhongshan ( Figure 11 d). These cities are the most industrialized areas in the Pearl River Delta, and the relatively high toluene emissions in Zhuhai may be due to ship painting and ship emissions.

[0171] Figure 12 Gasoline vehicle emissions for PMF analysis ( Figure 12 a), Solvent use ( Figure 12 c), and Fuel volatilization ( Figure 12 e) spatial distribution, Figure 12 b, d, f are the comparisons with the "bottom-up" emission inventory of Zheng EI in 2010 respectively. The PMF results show that gasoline vehicle emissions are mainly distributed in the central region of the Pearl River Delta, corresponding to major cities such as Guangzhou, Foshan, Zhongshan, and Dongguan, which is basically consistent with the distribution of the "bottom-up" emission inventory in terms of emission space. The PMF results show that the significant emission areas of solvent use are concentrated near Zhongshan, Dongguan, and Shenzhen, which is consistent with the distribution of industrial factories in these areas. Compared with the "bottom-up" emission inventory, the estimated solvent use emissions are lower in the urban areas of Guangzhou and Shenzhen, but higher in the northeast and southwest. The PMF results show that the fuel volatilization emissions are higher in the port areas near Foshan and the Pearl River Estuary, which may be due to vehicle and ship emissions. While the emissions of fuel evaporation in the "bottom-up" emission inventory are generally lower.

[0172] In an embodiment of the present invention, a terminal device is further provided, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the above method for constructing a volatile organic compound emission inventory is implemented.

[0173] In an embodiment of the present invention, a computer-readable storage medium is further provided. The computer-readable storage medium includes a stored computer program, wherein when the computer program runs, it controls the device where the computer-readable storage medium is located to execute the above method for constructing a volatile organic compound emission inventory.

[0174] Exemplarily, the computer program can be divided into one or more modules. One or more modules are stored in the memory and executed by the processor to complete the present invention. One or more modules can be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program in the terminal device.

[0175] The terminal device can be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The terminal device may include, but is not limited to, a processor, a memory, and a display. Those skilled in the art can understand that the above components are only examples of the terminal device and do not constitute a limitation to the terminal device. It may include more or fewer components than those described, or combine certain components, or different components. For example, the terminal device may further include input / output devices, network access devices, a bus, etc.

[0176] The so-called processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The processor is the control center of the terminal device, and connects all parts of the entire terminal device through various interfaces and lines.

[0177] The memory can be used to store computer programs and / or modules. By running or executing the computer programs and / or modules stored in the memory, and calling the data stored in the memory, the processor realizes various functions of the terminal device. The memory may mainly include a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function (such as a voice playback function, a text conversion function, etc.); the data storage area can store data created according to the use of the mobile phone (such as audio data, text message data, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0178] Among them, when the module for constructing the volatile organic compound emission inventory is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above-described embodiment methods of the present invention, it can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-described various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0179] The specific embodiments described above have further elaborated on the purpose, technical solutions, and beneficial effects of the present invention. It should be understood that the above description is only for the specific embodiments of the present invention and is not used to limit the protection scope of the present invention. In particular, it is pointed out that for those skilled in the art, any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for constructing a volatile organic compound emission inventory, characterized in that Including: Obtaining observation data based on preset observation points, and obtaining concentration data of volatile organic compounds in the boundary layer based on the observation data; Constructing a box model, and calculating the emission flux in the boundary layer based on the box model and the concentration data; Performing matrix decomposition on the concentration data and the emission flux based on an orthogonal matrix factorization model to obtain a target factor contribution matrix and a target factor spectrum matrix; Determining emission sources and their contribution ratios based on the target factor contribution matrix and the target factor spectrum matrix, and constructing an emission inventory of volatile organic compounds based on the emission flux, emission sources and their contribution ratios.

2. The method for constructing a volatile organic compound emission inventory according to claim 1, wherein The observation points include ground grid observation points and high tower observation points. The obtaining of observation data based on preset observation points and the obtaining of concentration data of volatile organic compounds in the boundary layer based on the observation data include: Obtaining surface emission flux based on the ground grid observation points; Obtaining the boundary layer height and the emission flux at the top of the boundary layer based on the high tower observation points; Calculating the vertical concentration gradient of the volatile organic compounds based on the surface emission flux, the boundary layer height and the emission flux at the top of the boundary layer; Obtaining the concentration data of volatile organic compounds in the boundary layer based on the vertical concentration gradient and the vertical profile.

3. The method for constructing a volatile organic compound emission inventory according to claim 2, wherein The concentration data includes the average concentration of volatile organic compounds in the boundary layer. The constructing of a box model and calculating the emission flux in the boundary layer based on the box model and the concentration data include: Constructing a box model based on the mass balance method, and optimizing the box model based on the wind speed profile and the concentration profile; Calculating the surface emission flux of volatile organic compounds based on the optimized box model and the concentration data.

4. The method for constructing a volatile organic compound emission inventory according to claim 3, wherein The performing of matrix decomposition on the concentration data and the emission flux based on an orthogonal matrix factorization model to obtain a target factor contribution matrix and a target factor spectrum matrix includes: Obtaining a concentration matrix based on the concentration data, and obtaining a flux matrix based on the emission flux; Performing matrix decomposition on the concentration matrix and the flux matrix based on the orthogonal matrix factorization model to obtain an initial factor contribution matrix, an initial factor spectrum matrix and a residual matrix; Optimizing the initial factor contribution matrix and the initial factor spectrum matrix based on the residual matrix to obtain a target factor contribution matrix and a target factor spectrum matrix.

5. The method for constructing a volatile organic compound emission inventory according to claim 4, wherein The determining of emission sources and their contribution ratios based on the target factor contribution matrix and the target factor spectrum matrix, and the constructing of an emission inventory of volatile organic compounds based on the emission flux, emission sources and their contribution ratios includes: Determining emission source factors based on the emission flux; Obtaining the characteristic components of all emission source factors based on the target factor spectrum matrix; Obtaining the contribution ratio of the emission source based on the target factor contribution matrix; Constructing an emission inventory of volatile organic compounds based on the emission flux, emission sources and their contribution ratios.

6. An apparatus for constructing a volatile organic compound emission inventory, characterized in that Including: A concentration calculation module, a flux calculation module, a matrix decomposition module and an inventory construction module; The concentration calculation module is used to obtain observation data based on preset observation points, and obtain concentration data of volatile organic compounds in the boundary layer based on the observation data; The flux calculation module is used to construct a box model and calculate the emission flux within the boundary layer based on the box model and the concentration data; The matrix decomposition module is used to perform matrix decomposition on the concentration data and the emission flux based on the orthogonal matrix factorization model to obtain a target factor contribution matrix and a target factor spectrum matrix; The inventory construction module is used to determine emission sources and their contribution ratios based on the target factor contribution matrix and the target factor spectrum matrix, and construct a volatile organic compound emission inventory based on the emission flux, emission sources, and their contribution ratios.

7. The device for constructing a volatile organic compound emission inventory according to claim 6, wherein The observation points include ground grid observation points and tall tower observation points. The concentration calculation module is used to: Obtain the surface emission flux based on the ground grid observation points; Obtain the boundary layer height and the emission flux at the top of the boundary layer based on the tall tower observation points; Calculate the vertical concentration gradient of the volatile organic compounds based on the surface emission flux, the boundary layer height, and the emission flux at the top of the boundary layer; Obtain the concentration data of the volatile organic compounds within the boundary layer based on the vertical concentration gradient and the vertical profile.

8. The device for constructing a volatile organic compound emission inventory according to claim 7, characterized in that, The concentration data includes the average concentration of the volatile organic compounds within the boundary layer. The flux calculation module is used to: Construct a box model based on the mass balance method and optimize the box model based on the wind speed profile and the concentration profile; Calculate the surface emission flux of the volatile organic compounds based on the optimized box model and the concentration data.

9. The device for constructing a volatile organic compound emission inventory according to claim 8, wherein, The matrix decomposition module is used to: Obtain a concentration matrix based on the concentration data and obtain a flux matrix based on the emission flux; Perform matrix decomposition on the concentration matrix and the flux matrix based on the orthogonal matrix factorization model to obtain an initial factor contribution matrix, an initial factor spectrum matrix, and a residual matrix; Optimize the initial factor contribution matrix and the initial factor spectrum matrix based on the residual matrix to obtain a target factor contribution matrix and a target factor spectrum matrix.

10. The device for constructing a volatile organic compound emission inventory according to claim 9, characterized in that, The inventory construction module is used to: Determine emission source factors based on the emission flux; Obtain the characteristic components of all emission source factors based on the target factor spectrum matrix; Obtain the contribution ratio of the emission sources based on the target factor contribution matrix; Construct a volatile organic compound emission inventory based on the emission flux, emission sources, and their contribution ratios.

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