VOCs pollution source tracing analysis method for industrial clusters based on cruise monitoring

Abstract

The VOCs pollution source tracing analysis method of industrial clusters based on cruise monitoring, S1. Record the parameters required for inversion simulation of the emission level of VOCs in industrial clusters; S2. Coordinate transformation of longitude and latitude; S3. Calculation of the relative position of cruise monitoring points and VOCs emission sources; S4. Calculation of emission coefficients of organized sources; S5. Calculation of emission coefficients of unorganized sources; S6. Inversion of emission intensity; S7. Tracing the source of VOCs pollution emissions; S8. Verification of simulation effect; S9. Simulation of spatial distribution characteristics of VOCs pollution in industrial clusters. The present invention uses the cruise monitoring results of VOCs in industrial clusters to invert the background concentration of low industrial clusters and the emission levels of each organized and unorganized source. On this basis, according to the background concentration and source strength information obtained by inversion, the distribution characteristics of VOCs pollutants in the park are re-simulated and calculated, which solves the difficulties in tracing the source of VOCs pollution emissions in industrial clusters and realizes intelligent management and monitoring of the environment.

Description

Technical Field

[0001] The present invention relates to the technical field of pollutant diffusion simulation and source tracing, and in particular to a VOCs pollution source tracing analysis method for industrial clusters based on cruise monitoring. Background Art

[0002] Volatile organic compounds (VOCs) are PM 2.5 The common precursor of O3 pollution is the PM2.5 pollution in the Beijing-Tianjin-Hebei region. 2.5 It is an important starting point for the coordinated prevention and control of VOCs pollution. Finding out the emission intensity, regional distribution and composition characteristics of VOCs pollution is of great significance for carrying out targeted PM 2.5 , O3 pollution coordinated prevention and control is of great significance. my country's full-process control of VOCs pollutants in the petrochemical, chemical and other industries is relatively systematic and complete, and these industries generally involve a large number of overhead source emissions. However, for VOCs emissions in low-rise industrial clusters dominated by small enterprises, they are subject to factors such as the large number of emitting enterprises, small enterprise scale, low pollution collection efficiency, serious unorganized emission problems, inefficient treatment facilities, and intermittent pollution emissions. Environmental management work still has unclear base numbers and difficulty in quantifying emission levels in real time, which is an important obstacle to the implementation of VOCs pollution prevention and control strategies at the park and enterprise levels.

[0003] In recent years, with the gradual maturity of VOCs cruise monitoring technology, the cruise monitoring method has become an important means to grasp the fine distribution of VOCs pollution in low-rise industrial clusters and has been widely used in the environmental management process. At this stage, through cruise monitoring, the monitoring results of pollution distribution around the perimeter and internal roads of industrial clusters can be obtained. However, the tracing of sources from roads within the park to polluting enterprises and the calculation of emission levels still need to rely on on-site investigation and monitoring by environmental management personnel, which has become an important factor affecting the level of intelligent and refined environmental management, thus making the control and management of atmospheric pollutant emissions in my country's industrial parks fall into the dilemma of difficult tracing and supervision. Summary of the invention

[0004] In order to solve the above problems, the present invention provides an industrial cluster VOCs pollution source tracing analysis method based on cruise monitoring.

[0005] The technical solution adopted by the present invention is:

[0006] The VOCs pollution source tracing analysis method for industrial clusters based on cruise monitoring includes the following steps:

[0007] S1. Record the parameters required for inversion simulation of VOCs emission levels in industrial clusters, including observation date, meteorological conditions, cruise monitoring data, organized source parameters and unorganized source parameters. The input parameters of the algorithm for real-time VOCs emissions from low-rise industrial clusters are shown in Table 1.

[0008] Table 1

[0009]

[0010] S2. Coordinate transformation of longitude and latitude: Use WGS-84 coordinate system to coordinate the longitude and latitude of the cruise monitoring point, the exhaust chimney, and the center of the unorganized source. Before simulation analysis, the longitude and latitude of the cruise monitoring point, the exhaust chimney, and the center of the unorganized source are converted into plane rectangular coordinate system coordinates using Gauss projection coordinate forward calculation formula;

[0011] S3. Calculation of the relative position between the cruise monitoring point and the VOCs emission source:

[0012] (1) The emission source and the cruise monitoring point are moved horizontally, and the emission source M j Translate to the Z axis (0,0,c j ), in the spatial rectangular coordinate system, the navigation monitoring point is A i (x i ,y i ,z i ) by vector Move to another point B ij (x ij ,y ij ,z i ), then:

[0013]

[0014] (2) Calculation of the angle between the observation point and the emission source: Starting from the positive direction of the Y axis, turn clockwise to The angle of rotation is θ ij (0≤θ ij <2π), then:

[0015] ①y ij ≠0:

[0016] If y ij >0 and x ij =0, then θ ij =0;

[0017] If y ij <0 and x ij =0, then θ ij =π;

[0018] If x ij >0, then:

[0019] If x ij <0, then:

[0020] ②y ij =0:

[0021] If x ij =0, the cruise monitoring point and the emission source cannot coincide, and the data is wrong;

[0022] If x ij >0, then

[0023] If x ij <0, then

[0024] (3) Determine whether the cruise monitoring point is located downwind of the emission source, and convert the wind direction ω into radians (W): The unit of ω is °, the north wind is 0°, the east wind is 90°, let β ij =|θ ij -W|,

[0025] like or Indicates navigation monitoring point A i Located at emission source Q j Downwind, it is necessary to simulate the emission source Q j The diffusion of A i The impact of VOCs concentration at each point;

[0026] like Indicates navigation monitoring point A i Located at emission source Q j In the upwind or vertical direction, there is no need to consider the emission source Q j The diffusion of A i The impact of VOCs concentration at each point;

[0027] (4) Calculate B ij Angle to wind direction:

[0028] like or To B ij (x ij ,y ij , z i ) to rotate the coordinates, and the rotated coordinates are B 1ij (x 1ij ,y 1ij , z i ),in: Realize the navigation monitoring point A i For emission source M j The coordinate transformation of B 1ij (x 1ij ,y 1ij, z i ) as the coordinates of the cruise monitoring points for subsequent diffusion simulation;

[0029] S4. Calculation of emission coefficients of organized sources: Establish the source strength (Q j ) and VOCs concentration at each cruise monitoring point (C i ), according to the steady-state diffusion model, the two satisfy the following relationship: ij is the emission coefficient, C i =α ij Q j ; If the navigation point is located downwind of the exhaust pipe, α ij It can be expressed as:

[0030]

[0031] Where: u is the average wind speed at a height of 10m; σ yij , σ zij is the diffusion coefficient, which is affected by the atmospheric stability. The atmospheric stability is calculated by the Pasquill classification method. The calculation method of the diffusion coefficient is shown in Table 4;

[0032] Table 4 Calculation method of diffusion coefficient

[0033]

[0034] S5. Calculation of emission coefficient of unorganized sources: The diffusion of unorganized sources is simulated by virtual point source method, and the polluting enterprises are divided into several squares according to the distribution of pollution sources, with a width of B. j Each square is regarded as an unorganized source unit, and the unorganized source is equivalent to the upwind direction and the distance from it is x 0ij The width of the smoke plume generated by the virtual point source at the center line of the unorganized source is equal to the width of the square of the unorganized source, such as Figure 5 As shown, x 0ij It can be calculated based on the atmospheric stability. The distance x between the virtual point source and the center of the surface source unit is 0ij As shown in Table 5, the diffusion coefficient σ is calculated according to the atmospheric stability. yij , σ zij The calculation method of the diffusion coefficient is shown in Table 6. The concentration of the unorganized source diffusion is converted into the diffusion of the virtual point source downwind area. Referring to the group source diffusion simulation formula, the unorganized source emission coefficient α is calculated. ij ,

[0035]

[0036] Table 5 Distance x between the virtual point source and the center of the surface source unit 0ij

[0037]

[0038]

[0039] Table 6 Calculation method of diffusion coefficient

[0040]

[0041] S6. Emission intensity inversion: The total number of organized and unorganized sources within the simulation scope of the industrial park is p, and the emission intensities are Q1, Q2, …, Q p , the number of cruise monitoring track points is n, n>p, and the VOCs concentrations at each point are C1, C2, …, C n , let the VOCs concentration diffused from single emission source j to cruise monitoring point i be Cij, thus, Where Q0 is the background concentration, is the fitting value of the concentration at each point. The above formula can be expressed as a vector or matrix: The emission coefficient α matrix represents the relationship between the background concentration, the emission rate of organized and unorganized sources and the concentration of VOCs at the cruise monitoring point; VOCs concentration fitting value at the cruise monitoring point and VOCs emission rate column vector of each emission source Q are:

[0042]

[0043] Q=(Q0,Q1,Q2,…,Q p ) T

[0044] The deviation between the fitted value and the observed value of VOCs concentration at each cruise monitoring point is defined as bias:

[0045]

[0046] Based on the Monte Carlo method, a new column vector Q is continuously generated randomly, and the corresponding column vector is calculated. The bias is gradually approached to the minimum value, and finally the optimal fitting result of the VOCs emission rate column vector Q, including the emission intensity of each VOCs organized and unorganized source, and the VOCs background concentration of the industrial cluster, is obtained, which is recorded as Q fin ;

[0047] S7.VOCs pollution emission source tracing: After obtaining the emission rate fitting results Q of each VOCs emission source fin Afterwards, statistics are collected on the emission rates of organized sources, unorganized sources and total VOCs of each enterprise to achieve the traceability of high-value emission enterprises;

[0048] S8. Verification of simulation effect: through fitting results Q fin The VOCs concentration simulation results of each cruise monitoring point are recalculated according to the following formula, and the monitoring results of VOCs concentration at each cruise monitoring point are compared with the fitting results, which is indirectly used as the basis for examining the simulation effect of the model.

[0049] S9. Simulation of spatial distribution characteristics of VOCs pollution in industrial clusters: Select simulation points at fixed intervals in a grid around the simulation area, calculate the longitude and latitude of each simulation point and the VOCs concentration, so as to understand the spatial distribution of VOCs pollution in the simulation area; calculate the coordinates of each simulation point in the XYZ coordinate system, calculate the relative position of each simulation point and the VOCs emission source, and the emission coefficient α of the organized source and the unorganized source ij , establish the emission coefficient α matrix between each simulation point and the VOCs emission source and background concentration, and calculate the VOCs concentration at each simulation point according to the following formula

[0050] When the coordinates are transformed in the step S2, it is assumed that there are p VOCs emission sources in the simulation area, and the VOCs emission sources are organized sources or unorganized sources, which are Q1, Q2, ..., Q p , let the coordinates of the jth emission source be M j (a j ,b j ,c j ), where 1≤j≤p;

[0051] VOCs cruise monitoring is carried out in the area around the emission source, and a total of n cruise monitoring points are obtained, namely A1, A2, ..., An, and the corresponding VOCs cruise monitoring concentrations at each point are C1, C2, ..., Cn. Let the coordinates of the i-th cruise monitoring point be A i (x i ,y i ,z i ), where 1≤i≤n, we get a j 、b j 、x i ,y i For organized source points, the corresponding c j The value is the height of the exhaust chimney; for the unorganized source point, the corresponding c j The value is set to 3m; for each navigation monitoring point, z i It is the sampler height of the cruise monitoring equipment.

[0052] The plane rectangular coordinate system in the step S2 is a three-dimensional rectangular coordinate system XYZ, whose origin is O, the XOY plane is a horizontal plane, the positive directions of the Y axis and the X axis are respectively pointing to the due north and due east, the positive direction of the Z axis is perpendicular to the horizontal plane and points to the sky, and the unit lengths of the X, Y, and Z axes are all 1m.

[0053] In the above-mentioned step S4, the atmospheric stability is calculated by the Pasquill classification method, where H is the effective height of the leakage source, i.e., the sum of the exhaust stack height and the plume lift height, where the plume lift height adopts the Holland formula by default, and the diffusion coefficient σ yij , σ zij The size of is related to the atmospheric turbulence structure, height above the ground, ground roughness, leakage duration, sampling time interval, wind speed and distance from the leakage source. The Pasquill method is used for classification calculation in this algorithm.

[0054] In the Pasquill method, as the stability of meteorological conditions increases, the atmospheric stability can be divided into six categories: A, B, C, D, E, and F. Among them, categories A, B, and C indicate unstable meteorological conditions, categories E and F indicate stable meteorological conditions, and category D indicates neutral meteorological conditions, that is, the stability of meteorological conditions is between stable and unstable. The Pasquill atmospheric stability classification is shown in Table 2, and the sunshine intensity values ​​are shown in Table 3;

[0055] Table 2 Pasquill atmospheric stability classification

[0056]

[0057]

[0058] Table 3 Sunshine intensity values ​​(α is the sun altitude angle)

[0059]

[0060] Beneficial effects of the present invention: The present invention uses the VOCs cruise monitoring results of industrial clusters, the distribution information of enterprises in the park, exhaust pipe parameters, and meteorological data as input information. Through a round of cruise monitoring inside the park, the emission levels of organized and unorganized sources of each enterprise in the park are obtained by inversion. The algorithm is based on the Gaussian diffusion model and the virtual point source method to invert the background concentration of low-rise industrial clusters and the emission levels of each organized and unorganized source. On this basis, according to the background concentration and source strength information obtained by inversion, the distribution characteristics of VOCs pollutants in the park are re-simulated and calculated, which solves the difficulty of tracing the source of VOCs pollution emissions in industrial clusters and realizes intelligent management and monitoring of the environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 This is a schematic diagram of the industrial cluster VOCs emission inversion algorithm of the present invention.

[0062] Figure 2 It is a schematic diagram of the coordinate transformation of the group source and the cruise monitoring point of the present invention.

[0063] Figure 3 It is a schematic diagram of the angle of the observation point relative to the emission source of the present invention.

[0064] Figure 4 It is a schematic diagram of the rotation of the coordinates of the cruise monitoring points of the present invention.

[0065] Figure 5 It is a schematic diagram of virtual point source simulation of the present invention. DETAILED DESCRIPTION

[0066] The VOCs pollution source tracing analysis method for industrial clusters based on cruise monitoring includes the following steps:

[0067] S1. Record the parameters required for inversion simulation of VOCs emission levels in industrial clusters, including observation date, meteorological conditions, cruise monitoring data, organized source parameters and unorganized source parameters. The input parameters of the algorithm for real-time VOCs emissions from low-rise industrial clusters are shown in Table 1.

[0068] Table 1

[0069]

[0070] S2. Coordinate transformation of longitude and latitude: The WGS-84 coordinate system is used to coordinate the longitude and latitude of the cruise monitoring point, the exhaust pipe, and the center of the unorganized source. The plane rectangular coordinate system is a three-dimensional rectangular coordinate system XYZ, with its origin O, and the XOY plane as the horizontal plane. The positive directions of the Y axis and X axis are respectively pointing to the due north and due east, and the positive direction of the Z axis is perpendicular to the horizontal plane and points to the sky. The unit lengths of the X, Y, and Z axes are all 1m.

[0071] Assume that there are p VOCs emission sources in the simulation area, and the VOCs emission sources are organized sources or unorganized sources, namely Q1, Q2, ..., Q p , let the coordinates of the jth emission source be M j (a j ,b j ,c j ), where 1≤j≤p;

[0072] VOCs cruise monitoring is carried out in the area around the emission source, and a total of n cruise monitoring points are obtained, namely A1, A2, ..., An, and the corresponding VOCs cruise monitoring concentrations at each point are C1, C2, ..., Cn. Let the coordinates of the i-th cruise monitoring point be A i (x i ,y i ,z i), where 1≤i≤n,

[0073] Before the simulation analysis, the latitude and longitude of the cruise monitoring point, the longitude and longitude of the exhaust pipe, and the longitude and longitude of the center point of the unorganized source are converted into plane rectangular coordinate system coordinates through the Gauss projection coordinate forward calculation formula; j 、b j 、x i ,y i For organized source points, the corresponding c j The value is the height of the exhaust chimney; for the unorganized source point, the corresponding c j The value is set to 3m; for each navigation monitoring point, z i The sampler height for the cruise monitoring equipment;

[0074] S3. Calculation of the relative position between the cruise monitoring point and the VOCs emission source:

[0075] (1) The emission source and the cruise monitoring point are moved horizontally, and the emission source M j Translate to the Z axis (0,0,c j ), in the spatial rectangular coordinate system, the navigation monitoring point is A i (x i ,y i ,z i ) by vector Move to another point B ij (x ij ,y ij , z i ), then: like Figure 2 As shown;

[0076] (2) Calculation of the angle between the observation point and the emission source: Starting from the positive direction of the Y axis, turn clockwise to The angle of rotation is θ ij (0≤θ ij <2π), then:

[0077] ①y ij ≠0:

[0078] If y ij >0 and x ij =0, then θ ij =0;

[0079] If y ij <0 and x ij =0, then θ ij =π;

[0080] If x ij >0, then:

[0081] If x ij <0, then:

[0082] ②y ij =0:

[0083] If x ij =0, the cruise monitoring point and the emission source cannot coincide, and the data is wrong;

[0084] If x ij >0, then

[0085] If x ij <0, then

[0086] (3) Determine whether the cruise monitoring point is located downwind of the emission source, and convert the wind direction ω into radians (W): The unit of ω is °, the north wind is 0°, the east wind is 90°, let β ij =|θ ij -W|,

[0087] like or Indicates navigation monitoring point A i Located at emission source Q j Downwind, it is necessary to simulate the emission source Q j The diffusion of A i The impact of VOCs concentration at each point;

[0088] like Indicates navigation monitoring point A i Located at emission source Q j In the upwind or vertical direction, there is no need to consider the emission source Q j The diffusion of A i The impact of VOCs concentration at each point;

[0089] (4) Calculate B ij Angle to wind direction:

[0090] like or To B ij (x ij ,y ij , z i ) to rotate the coordinates, and the rotated coordinates are B 1ij (x 1ij ,y 1ij , z i ),in: Realize the navigation monitoring point A i For emission source M jThe coordinate transformation of B 1ij (x 1ij ,y 1ij , z i ) as the coordinates of the cruise monitoring points for subsequent diffusion simulation;

[0091] S4. Calculation of emission coefficients of organized sources: Establish the source strength (Q j ) and VOCs concentration at each cruise monitoring point (C i ), according to the steady-state diffusion model, the two satisfy the following relationship: ij is the emission coefficient, C i =α ij Q j ; If the navigation point is located downwind of the exhaust pipe, α ij It can be expressed as:

[0092]

[0093] Where: u is the average wind speed at a height of 10m; σ yij , σ zij is the diffusion coefficient, which is affected by the atmospheric stability. The atmospheric stability is calculated by the Pasquill classification method. The calculation method of the diffusion coefficient is shown in Table 4;

[0094] Atmospheric stability is calculated using the Pasquill classification method. H is the effective height of the leak source, which is the sum of the exhaust stack height and the plume lift height. The plume lift height uses the Holland formula by default, and the diffusion coefficient σ yij , σ zij The size of is related to the atmospheric turbulence structure, height above the ground, ground roughness, leakage duration, sampling time interval, wind speed and distance from the leakage source. The Pasquill method is used for classification calculation in this algorithm.

[0095] In the Pasquill method, as the stability of meteorological conditions increases, the atmospheric stability can be divided into six categories: A, B, C, D, E, and F. Among them, categories A, B, and C indicate unstable meteorological conditions, categories E and F indicate stable meteorological conditions, and category D indicates neutral meteorological conditions, that is, the stability of meteorological conditions is between stable and unstable. The Pasquill atmospheric stability classification is shown in Table 2, and the sunshine intensity values ​​are shown in Table 3;

[0096] Table 2 Pasquill atmospheric stability classification

[0097]

[0098] Table 3 Sunshine intensity values ​​(α is the sun altitude angle)

[0099]

[0100]

[0101] Table 4 Calculation method of diffusion coefficient

[0102]

[0103] S5. Calculation of emission coefficient of unorganized sources: The diffusion of unorganized sources is simulated by virtual point source method, and the polluting enterprises are divided into several squares according to the distribution of pollution sources, with a width of B. j Each square is regarded as an unorganized source unit, and the unorganized source is equivalent to the upwind direction and the distance from it is x 0ij The width of the smoke plume generated by the virtual point source at the center line of the unorganized source is equal to the width of the square of the unorganized source, such as Figure 5 As shown, x 0ij It can be calculated based on the atmospheric stability. The distance x between the virtual point source and the center of the surface source unit is 0ij As shown in Table 5, the diffusion coefficient σ is calculated according to the atmospheric stability. yij , σ zij The calculation method of the diffusion coefficient is shown in Table 6. The concentration of the unorganized source diffusion is converted into the diffusion of the virtual point source downwind area. Referring to the group source diffusion simulation formula, the unorganized source emission coefficient α is calculated. ij ,

[0104]

[0105] Table 5 Distance x between virtual point source and surface source unit center 0ij

[0106]

[0107] Table 6 Calculation method of diffusion coefficient

[0108]

[0109]

[0110] S6. Emission intensity inversion: The total number of organized and unorganized sources within the simulation scope of the industrial park is p, and the emission intensities are Q1, Q2, …, Q p , the number of cruise monitoring track points is n, n>p, and the VOCs concentrations at each point are C1, C2, …, C n , let the VOCs concentration diffused from single emission source j to cruise monitoring point i be Cij, thus, Where Q0 is the background concentration, is the fitting value of the concentration at each point. The above formula can be expressed as a vector or matrix: The emission coefficient α matrix represents the relationship between the background concentration, the emission rate of organized and unorganized sources and the concentration of VOCs at the cruise monitoring point; VOCs concentration fitting value at the cruise monitoring point and VOCs emission rate column vector of each emission source Q are:

[0111]

[0112] Q=(Q0,Q1,Q2,…,Q p ) T

[0113] The deviation between the fitted value and the observed value of VOCs concentration at each cruise monitoring point is defined as bias:

[0114]

[0115] Based on the Monte Carlo method, a new column vector Q is continuously generated randomly, and the corresponding column vector is calculated. The bias is gradually approached to the minimum value, and finally the optimal fitting result of the VOCs emission rate column vector Q, including the emission intensity of each VOCs organized and unorganized source, and the VOCs background concentration of the industrial cluster, is obtained, which is recorded as Q fin ;

[0116] S7.VOCs pollution emission source tracing: After obtaining the emission rate fitting results Q of each VOCs emission source fin Afterwards, statistics are collected on the emission rates of organized sources, unorganized sources and total VOCs of each enterprise to achieve the traceability of high-value emission enterprises;

[0117] S8. Verification of simulation effect: through fitting results Q fin The VOCs concentration simulation results of each cruise monitoring point are recalculated according to the following formula, and the monitoring results of VOCs concentration at each cruise monitoring point are compared with the fitting results, which is indirectly used as the basis for examining the simulation effect of the model.

[0118] S9. Simulation of spatial distribution characteristics of VOCs pollution in industrial clusters: Select simulation points at fixed intervals in a grid around the simulation area, calculate the longitude and latitude of each simulation point and the VOCs concentration, so as to understand the spatial distribution of VOCs pollution in the simulation area; calculate the coordinates of each simulation point in the XYZ coordinate system, calculate the relative position of each simulation point and the VOCs emission source, and the emission coefficient α of the organized source and the unorganized source ij , establish the emission coefficient α matrix between each simulation point and the VOCs emission source and background concentration, and calculate the VOCs concentration at each simulation point according to the following formula

[0119] Example 1

[0120] This study selected Hangu Modern Industrial Park as a demonstration park for the real-time VOCs emission algorithm of low-rise industrial clusters. The industrial enterprises in the park are mainly small enterprises, and there are many enterprises involved in VOCs emission, involving pharmaceutical manufacturing, coating and ink manufacturing, polymer material manufacturing, metal product manufacturing, food manufacturing and other industries. VOCs emissions are mainly low-rise sources. This study is aimed at tracing the source of VOCs pollution emissions in industrial clusters. The algorithm performs a single cruise monitoring of low-rise industrial clusters and inverts the emission levels of organized and unorganized sources in the cluster. The algorithm assumes that during the cruise monitoring, the local meteorological conditions remain basically stable and the pollutants maintain steady-state diffusion. In view of this, the duration of cruise monitoring in low-rise industrial clusters should be shortened as much as possible, and the monitoring of repeated sections should be reduced to reduce the impact of changes in local meteorological fields on the inversion results. At the same time, the cruise monitoring route planning should include all roads in the industrial cluster as comprehensively as possible to avoid missing high-value pollution areas.

[0121] Obtaining basic data

[0122] Acquisition of VOCs cruise monitoring data: In order to ensure the accuracy of the monitoring results, the PTR-MS equipment is single-point calibrated before daily cruise monitoring. During the monitoring process, the PTR-MS equipment obtains a set of VOCs monitoring data approximately every 4 seconds. The vehicle speed is maintained at about 20-30 km / h, that is, a set of VOCs monitoring data can be obtained approximately every 22.2-33.3 meters. The distance between two adjacent roads in the selected low-rise industrial cluster is about 330-390 meters, that is, about 10-15 sets of monitoring results can be obtained in each section. The VOCs concentration data obtained by cruise monitoring can effectively reflect the spatial distribution of pollutants in the industrial cluster.

[0123] Through cruise monitoring, the VOCs concentration distribution of the roads within the demonstration low-rise industrial cluster was obtained. The concentration range of VOCs pollution on the roads within the cluster was about 35.3-197.9μg / m 3 The average VOCs pollution value is about 71.1μg / m 3 The high-value areas mainly appear on the west side (Huangshan Road) and north side (Bibo Street, Qixia Street, and Songshan Road) of the navigation route. Enterprises in the northern part of the industrial cluster are concentrated, and the overall pollution level is higher than that in the southern part.

[0124] Meteorological data: Meteorological data is obtained through the meteorological instrument on the cruise monitoring vehicle. Before each cruise monitoring of the low-rise industrial cluster, the monitoring vehicle conducts fixed-point monitoring within the cluster for 10 minutes to obtain the required wind direction, wind speed, ambient temperature and other parameters.

[0125] Exhaust chimney parameters, unorganized source parameters and other enterprise information: Exhaust chimney height, diameter, gas cloud release speed, smoke temperature and other parameters are obtained through the routine monitoring report of VOCs exhaust chimneys of the surveyed enterprises, and the longitude and latitude of the exhaust chimney, the longitude and latitude of the center point of the enterprise's unorganized source, the width of the unorganized source and other information are obtained through satellite maps. In the demonstration cluster, there are 63 enterprises involved in VOCs emissions, of which 55 organized source-related data are obtained from 22 enterprises, and all organized and unorganized source input parameters are established for them; for the remaining 41 enterprises involved in VOCs emissions, only unorganized source input parameters are set for them, and finally a total of 139 unorganized source information of 63 enterprises is set in the demonstration cluster.

[0126] Inversion and deviation analysis of VOCs emission levels in low-rise industrial clusters:

[0127] Inversion of VOCs emission levels in low-rise industrial clusters: Based on the real-time quantification algorithm for VOCs emissions developed in this project, combined with meteorological conditions and cruise monitoring results, the organized and unorganized emission intensity and background concentration of VOCs in low-rise industrial clusters were inverted. The inversion result of the background concentration in the park was 30.4μg / m 3 The average value of the inversion results of the concentration at each cruise monitoring point is 71.3 μg / m 3 It can be seen that the organized and unorganized source emissions of enterprises in the study area contribute about 57.4% to the VOCs concentration in the industrial cluster. The VOCs emission rates (including organized and unorganized emissions) of 63 enterprises obtained by algorithm inversion show that the enterprises with large VOCs emissions in the demonstration industrial cluster mainly involve multiple industries such as coating manufacturing, packaging printing, chemical industry, and food manufacturing. The distribution of enterprises with high emission rates obtained by inversion is basically consistent with the high-value areas found by cruise monitoring.

[0128] In the demonstration cluster, the organized emission rates of 22 enterprises and the unorganized emission intensities of 63 enterprises were obtained through inversion. The total emission intensity of the industrial cluster obtained through inversion was 48.4kg / h. The emission intensity of each organized source was lower than or equivalent to the allowed emission rate of VOCs (refer to Tianjin's "Volatile Organic Compound Emission Control Standards for Industrial Enterprises" (DB12 / 524-2020)). For the 22 enterprises that obtained all the exhaust pipe information, the total emission rates of organized and unorganized sources were 7.8kg / h and 10.2kg / h respectively, and the unorganized emission intensity was slightly higher than the organized emission.

[0129] Analysis of fitting results of VOCs emission levels from unorganized sources: Quantification of unorganized emission intensity is an important challenge in understanding VOCs emission levels. In order to examine the simulation of unorganized source emissions by the algorithm, this work set up three simulation scenarios and compared the fitting results. The three simulation scenarios all included the exhaust pipe information of 22 enterprises, and the number of enterprises with unorganized source parameters were set as follows: ①63, ②43, ③22, and ④0.

[0130] From the fitting results we can see that:

[0131] a As the unorganized sources are gradually eliminated, the simulation bias increases from 13.9 μg / m 3 Gradually increased to 17.6 μg / m 3 , with a maximum increase of 26.7% in the demonstration area;

[0132] b. The total VOCs emission intensity in the study area is less affected by the lack of unorganized sources. In the simulation scenario containing the unorganized source parameters of all enterprises, the simulation result of the total VOCs emission is 48.4 kg / h. With the reduction of the unorganized source parameters, the simulation result of the total VOCs emission is 48.0-53.6 kg / h, and the total emission level changes within about 10.7%;

[0133] As the unorganized sources are gradually eliminated, the fitting results of the emission intensity of organized sources increase significantly, because the emission rates of organized sources of some enterprises are significantly higher. This result shows that due to the lack of unorganized sources, the algorithm needs to convert the impact of unorganized sources on air quality into the impact of organized source emissions, resulting in a significant increase in the fitting results of organized sources. Due to the difference in the diffusion of pollutants emitted by organized and unorganized sources, this conversion will inevitably lead to an increase in the bias of the simulation results.

[0134] The simulation results show that the algorithm can effectively simulate the emission level of unorganized sources. With the introduction of unorganized sources in the program, the deviation level of the simulation results is effectively reduced. The fitting results of low-rise industrial clusters under three different scenarios are shown in Table 7.

[0135] Table 7 demonstrates the fitting results of low-rise industrial clusters under three different scenarios

[0136]

[0137] Simulation of spatial distribution characteristics of VOCs pollution in low-rise industrial clusters: Cruise monitoring can only obtain the VOCs distribution of roads within the industrial cluster. In order to obtain more detailed VOCs pollution distribution characteristics in the study area, this project re-fitted the spatial distribution characteristics of VOCs at each point near the ground in the demonstration cluster based on the organized and unorganized emission intensities obtained by inversion. According to the results, it can be found that areas with high pollutant concentrations mainly appear in the Zidong Street-Caiyun Street area in the northern part of the park.

[0138] The embodiments of the present invention are described in detail above, but the contents described are only preferred embodiments of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. The VOCs pollution source tracing analysis method of industrial clusters based on cruise monitoring is characterized by: Here are the steps: S1. Record the parameters required for inversion simulation of VOCs emission levels in industrial clusters, including observation date, meteorological conditions, cruise monitoring data, organized source parameters and unorganized source parameters; S2. Coordinate transformation of longitude and latitude: Use WGS-84 coordinate system to coordinate the longitude and latitude of the cruise monitoring point, the exhaust chimney, and the center of the unorganized source. Before simulation analysis, the longitude and latitude of the cruise monitoring point, the exhaust chimney, and the center of the unorganized source are converted into plane rectangular coordinate system coordinates using Gauss projection coordinate forward calculation formula; S3. Calculation of the relative position between the cruise monitoring point and the VOCs emission source: (1) The emission source and the cruise monitoring point are moved horizontally, and the emission source M j Translate to the Z axis (0,0,c j ), in the spatial rectangular coordinate system, the navigation monitoring point is A i (x i ,y i ,z i ) by vector Move to another point B ij (x ij ,y ij ,z i ), then: (2) Calculation of the angle between the observation point and the emission source: Starting from the positive direction of the Y axis, turn clockwise to The angle of rotation is θ ij (0≤θ ij <2π), then: ①y ij ≠0: If y ij >0 and x ij =0, then θ ij =0; If y ij <0 and x ij =0, then θ ij =π; If x ij >0, then: If x ij <0, then: ②y ij =0: If x ij =0, the cruise monitoring point and the emission source cannot coincide, and the data is wrong; If x ij >0, then If x ij <0, then (3) Determine whether the cruise monitoring point is located downwind of the emission source, and convert the wind direction ω into radians (W): The unit of ω is °, the north wind is 0°, the east wind is 90°, let β ij =|θ ij -W|, like or Indicates navigation monitoring point A i Located at emission source Q j Downwind, it is necessary to simulate the emission source Q j The diffusion of A i The impact of VOCs concentration at each point; like Indicates navigation monitoring point A i Located at emission source Q j In the upwind or vertical direction, there is no need to consider the emission source Q j The diffusion of A i The impact of VOCs concentration at each point; (4) Calculate B ij Angle to wind direction: like or To B ij (x ij ,y ij ,z i ) to rotate the coordinates, and the rotated coordinates are B 1ij (x 1ij ,y 1ij ,z i ),in: Realize the navigation monitoring point A i For emission source M j The coordinate transformation of B 1ij (x 1ij ,y 1ij ,z i ) as the coordinates of the cruise monitoring points for subsequent diffusion simulation; S4. Calculation of emission coefficients of organized sources: Establish the source strength (Q j ) and VOCs concentration at each cruise monitoring point (C i ), according to the steady-state diffusion model, the two satisfy the following relationship: ij is the emission coefficient, C i =α ij Q j ; If the navigation point is located downwind of the exhaust pipe, α ij It can be expressed as: Where: u is the average wind speed at a height of 10m; σ yij , σ zij is the diffusion coefficient, which is affected by the atmospheric stability, which is calculated by the Pasquill classification method; S5. Calculation of emission coefficient of unorganized sources: The diffusion of unorganized sources is simulated by virtual point source method, and the polluting enterprises are divided into several squares according to the distribution of pollution sources, with a width of B. j Each square is regarded as an unorganized source unit, and the unorganized source is equivalent to the upwind direction and the distance from it is x 0ij The width of the smoke plume generated by the virtual point source at the center line of the unorganized source is equal to the width of the square of the unorganized source. 0ij It can be calculated based on the atmospheric stability and the diffusion coefficient σ yij , σ zij Calculate, thereby converting the concentration of the unorganized source diffusion into the diffusion of the virtual point source in the downwind area, and referring to the group source diffusion simulation formula to calculate the unorganized source emission coefficient α ij , S6. Emission intensity inversion: The total number of organized and unorganized sources within the simulation scope of the industrial park is p, and the emission intensities are Q1, Q2, …, Q p , the number of cruise monitoring track points is n, n>p, and the VOCs concentrations at each point are C1, C2, …, C n , let the VOCs concentration diffused from a single emission source j to the cruise monitoring point i be Cij, The emission coefficient α matrix represents the relationship between the background concentration, the emission rate of organized and unorganized sources and the concentration of VOCs at the cruise monitoring point; VOCs concentration fitting value at the cruise monitoring point and VOCs emission rate column vector of each emission source Q are: Q=(Q0,Q1,Q2,…,Q p ) T The deviation between the fitted value and the observed value of VOCs concentration at each cruise monitoring point is defined as bias: Based on the Monte Carlo method, a new column vector Q is continuously generated randomly, and the corresponding column vector is calculated. The bias is gradually approached to the minimum value, and finally the optimal fitting result of the VOCs emission rate column vector Q, including the emission intensity of each VOCs organized and unorganized source, and the VOCs background concentration of the industrial cluster, is obtained, which is recorded as Q fin ; S7.VOCs pollution emission source tracing: After obtaining the emission rate fitting results Q of each VOCs emission source fin Afterwards, statistics are collected on the emission rates of organized sources, unorganized sources and total VOCs of each enterprise to achieve the traceability of high-value emission enterprises; S8. Verification of simulation effect: through fitting results Q fin The VOCs concentration simulation results of each cruise monitoring point are recalculated according to the following formula, and the monitoring results of VOCs concentration at each cruise monitoring point are compared with the fitting results, which is indirectly used as the basis for examining the simulation effect of the model. S9. Simulation of spatial distribution characteristics of VOCs pollution in industrial clusters: Select simulation points at fixed intervals in a grid around the simulation area, calculate the longitude and latitude of each simulation point and the VOCs concentration, so as to understand the spatial distribution of VOCs pollution in the simulation area; calculate the coordinates of each simulation point in the XYZ coordinate system, calculate the relative position of each simulation point and the VOCs emission source, and the emission coefficient α of the organized source and the unorganized source ij , establish the emission coefficient α matrix between each simulation point and the VOCs emission source and background concentration, and calculate the VOCs concentration at each simulation point according to the following formula 2. The method for tracing the source of VOCs pollution in industrial clusters based on cruise monitoring according to claim 1 is characterized in that: When the coordinates are transformed in the step S2, it is assumed that there are p VOCs emission sources in the simulation area, and the VOCs emission sources are organized sources or unorganized sources, which are Q1, Q2, ..., Q p , let the coordinates of the jth emission source be M j (a j ,b j ,c j ), where 1≤j≤p; VOCs cruise monitoring is carried out in the area around the emission source, and a total of n cruise monitoring points are obtained, namely A1, A2, ..., An, and the corresponding VOCs cruise monitoring concentrations at each point are C1, C2, ..., Cn. Let the coordinates of the i-th cruise monitoring point be A i (x i ,y i ,z i ), where 1≤i≤n, we get a j 、b j 、x i ,y i For organized source points, the corresponding c j The value is the height of the exhaust chimney; for the unorganized source point, the corresponding c j The value is set to 3m; for each navigation monitoring point, z i It is the sampler height of the cruise monitoring equipment.

3. The method for tracing the source of VOCs pollution in industrial clusters based on cruise monitoring according to claim 1 is characterized in that: The plane rectangular coordinate system in the step S2 is a three-dimensional rectangular coordinate system XYZ, whose origin is O, the XOY plane is a horizontal plane, the positive directions of the Y axis and the X axis are respectively pointing to the due north and due east, the positive direction of the Z axis is perpendicular to the horizontal plane and points to the sky, and the unit lengths of the X, Y, and Z axes are all 1m.

4. The method for tracing the source of VOCs pollution in industrial clusters based on cruise monitoring according to claim 1 is characterized in that: In the above-mentioned step S4, the atmospheric stability is calculated by the Pasquill classification method, where H is the effective height of the leakage source, i.e., the sum of the exhaust stack height and the plume lift height, where the plume lift height adopts the Holland formula by default, and the diffusion coefficient σ yij , σ zij The size of is related to the atmospheric turbulence structure, height above the ground, ground roughness, leakage duration, sampling time interval, wind speed and distance from the leakage source. The Pasquill method is used for classification calculation in this algorithm.

5. The method for tracing the source of VOCs pollution in industrial clusters based on cruise monitoring according to claim 4 is characterized in that: In the Pasquill method, as the stability of meteorological conditions increases, the atmospheric stability can be divided into six categories: A, B, C, D, E, and F. Among them, categories A, B, and C indicate unstable meteorological conditions, categories E and F indicate stable meteorological conditions, and category D indicates neutral meteorological conditions, that is, the stability of meteorological conditions is between stable and unstable.

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