A quantitative assessment method for the impact of meteorological conditions on atmospheric particulate matter pollution

Abstract

This invention discloses a quantitative assessment method for the impact of meteorological conditions on atmospheric particulate matter pollution. Through modeling, historical data on wind speed, humidity, temperature, and mixing layer height can be used to scientifically predict the future trend of pollutant concentrations. This invention's quantitative assessment method for the impact of meteorological conditions on atmospheric particulate matter pollution constructs a quantitative assessment model of the impact of meteorological conditions on particulate matter pollution through the analysis of experimental data. This model can accurately assess the degree of influence of meteorological conditions, providing effective assistance for the control of atmospheric particulate matter pollution.

Description

Technical Field

[0001] The invention relates to the field of environmental protection and management, and in particular to a quantitative evaluation method for the impact of meteorological conditions on atmospheric particulate matter pollution. Background Art

[0002] Under the background of relatively stable atmospheric particulate matter emission sources, the change of particulate matter pollution concentration mainly depends on meteorological conditions. It can be seen that in order to control atmospheric pollution, it is necessary not only to control the source of pollutant emissions, but also to have a comprehensive understanding of meteorological conditions. According to survey and research data, the concentration of particulate matter varies greatly under different meteorological conditions for the same pollutant emission source at the same location, which can reach dozens of times, which fully proves that meteorological conditions have a vital impact on pollutant concentration. There are many research results on the impact of meteorological conditions on atmospheric pollution, but there are still many unresolved issues, especially the impact of meteorological conditions on particulate matter. Because there are many aspects involved, it is difficult to determine the degree and value of the impact. This paper proposes a quantitative evaluation model for the impact of meteorological conditions on atmospheric particulate matter pollution. Based on the quantitative evaluation value of the model, targeted governance of atmospheric particulate matter pollution is carried out, which has positive significance for providing a better atmospheric environment for people's production and life. Summary of the invention

[0003] The purpose of the present invention is to provide a quantitative evaluation method which is novel and unique in structure, easy to use, and can accurately reflect the impact of meteorological conditions on atmospheric particulate matter pollution; the specific technical scheme is:

[0004] A quantitative evaluation method for the impact of meteorological conditions on atmospheric particulate matter pollution comprises the following steps:

[0005] 1) Set data sampling points, which are evenly distributed in the monitoring area; continuously measure wind speed, humidity, temperature, and mixing layer height;

[0006] 2) Integrate the collected data into a data set; the expression is:

[0007]

[0008] η represents the mean vector of the data set Z; n represents the total number of data in the set Z; Z i It represents the i-th sample data;

[0009] 3) Centralize the processing set Z, that is, subtract each sample data from the mean vector η calculated in step 2), and the expression is:

[0010]

[0011] It represents the data set after centering processing;

[0012] 4) Based on the output result of step 3) construct its corresponding covariance matrix, and the expression is:

[0013]

[0014] μ represents the covariance matrix of the data set ; T represents the matrix transpose symbol;

[0015] 5) Perform eigenvalue decomposition on the output result μ of step 4), and solve the eigenvalue equation of the covariance matrix |μ - λI| = 0. Obtain the eigenvalues of the covariance matrix μ, denoted as λ i , and arrange the eigenvalues λ i in descending order;

[0016] 6) Calculate the contribution rate of each component according to the eigenvalues λ i , and its calculation formula is:

[0017]

[0018] υ i represents the contribution rate value of the i-th component; p represents the total number of eigenvalues;

[0019] 7) According to the calculation result of step 5), set the cumulative contribution rate threshold υ * ≥85%, and according to the size of the cumulative contribution rate, take the first m (m < p) meteorological conditions to determine them as the principal components, that is, meteorological elements;

[0020]

[0021] 8) Obtain the maximum fitting curve of the mixing layer height and the atmospheric particulate matter pollution concentration data, and the fitting curve equation is:

[0022] y = 169.15e -0.001x (6)

[0023] where the Rs of wind speed, humidity, temperature, and mixing layer height 2 are taken as 0.23, 0.15, 0.35, and 0.20 respectively

[0024] 9) Construct a quantitative evaluation model for the impact of meteorological elements on atmospheric particulate matter pollution, and the expression is:

[0025]

[0026] C and C0 represent the current and initial particulate matter pollution concentrations respectively; under the same wind direction, after multiple pre-evaluations, the following parameters are obtained: χ2 and β represent auxiliary calculation parameters; ω1, ω2, ω3 and ω4 represent the numerical value of the influence of meteorological elements; X1, X2, X3 and X4 represent wind speed, humidity, temperature and mixing layer height; ε represents the error compensation term; in order to obtain parameters for subsequent evaluation.

[0027] Furthermore, the data collection points are arranged at a density of 1 data sampling point per square kilometer.

[0028] Furthermore, the number of pre-evaluations in step 9) is no less than 4 times.

[0029] The quantitative assessment method of the impact of meteorological conditions on atmospheric particulate pollution of the present invention constructs a quantitative assessment model of the impact of meteorology on particulate pollution through the analysis of experimental data; the model can accurately assess the numerical value of the impact of meteorological conditions, and can provide effective assistance for the control of atmospheric particulate pollution. A relatively accurate initial pollutant concentration can be obtained through inversion, which is convenient for quantitatively determining the pollution responsibility. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 This is the spectrum of atmospheric particulate matter pollution sources;

[0031] Figure 2 An example diagram for the classification of particulate matter pollution stages;

[0032] Figure 3 This is a schematic diagram of the relationship between wind speed and atmospheric particulate pollution concentration;

[0033] Figure 4 This is a schematic diagram of the relationship between humidity and atmospheric particulate pollution concentration;

[0034] Figure 5 This is the relationship between temperature and atmospheric particulate pollution concentration;

[0035] Figure 6 This is the relationship between the height of the mixing layer and the concentration of atmospheric particulate matter pollution;

[0036] Figure 7 This is an overview of the experimental area;

[0037] Figure 8 It is a schematic diagram of the relationship between unknown parameters and model accuracy;

[0038] Fig. 9 The experimental results are shown Figure 1 ;

[0039] Fig.10 The experimental results are shown Figure 2 . DETAILED DESCRIPTION

[0040] The present invention is described more fully below using embodiments. The present invention can be embodied in many different forms and should not be construed as being limited to the exemplary embodiments described herein.

[0041] For ease of description, spatially relative terms such as "upper," "lower," "left," and "right" may be used herein to describe the relationship of one element or feature shown in the figures relative to another element or feature. It should be understood that, in addition to the orientation shown in the figures, the spatial terms are intended to include different orientations of the device in use or operation. For example, if the device in the figure is inverted, elements described as being "under" other elements or features will be positioned "above" the other elements or features. Therefore, the exemplary term "under" can include both upper and lower orientations. The device can be positioned in other ways (rotated 90 degrees or in other orientations), and the spatially relative descriptions used here can be interpreted accordingly.

[0042] 1. Research on the construction of quantitative assessment model of meteorological impact on particulate matter pollution

[0043] 1.1 Analysis of the sources of atmospheric particulate matter pollution

[0044] In order to reasonably and effectively control the atmospheric particulate matter pollution and accurately analyze the degree and value of the impact of meteorological conditions on particulate matter pollution, the source analysis method is used to deeply analyze the source of atmospheric particulate matter pollution. The requirements for sampling points, sampling environment, sampling height and sampling frequency in the collection of atmospheric particulate matter shall be implemented in accordance with the atmospheric part of the "Technical Specifications for Environmental Monitoring". The "Technical Specifications for Environmental Monitoring" is published by China Environment Press. From the perspective of the practicality and current effectiveness of environmental monitoring methods, combined with the main areas of environmental monitoring in my country, it compiles the current effective and commonly used environmental testing method standards and monitoring technical specifications in my country. The source analysis of atmospheric particulate matter adopts the orthogonal matrix factor analysis method (PMF). The PMF model uses observational data to analyze the source of pollutants. It does not require the prior knowledge of the source component spectrum, which has great advantages in pollution source analysis. The atmospheric particulate matter pollution source spectrum obtained by collection and analysis is as follows: Figure 1 shown.

[0045] The sources of atmospheric particulate matter pollution mainly come from industrial emissions, garbage burning, household fuel, traffic emissions, soil dust and its secondary particulate matter, but the above-mentioned pollution sources are not all, such as rural straw burning, winter heating fuel burning, etc. In order to facilitate research, they are collectively referred to as other pollution sources.

[0046] The above process completes the analysis of the sources of atmospheric particulate matter pollution and provides some spectra of particulate matter pollution sources, providing a certain theoretical basis for the subsequent selection of meteorological elements.

[0047] 1.2 Selection of meteorological elements

[0048] Based on the above-analyzed sources of atmospheric particulate matter pollution and combined with the characteristics of the particulate matter pollution stage, the main influencing factors of meteorological conditions are selected, which are referred to as meteorological elements for short, in order to make adequate preparations for the subsequent research on the impact of meteorological conditions on atmospheric particulate matter pollution.

[0049] According to the concentration of particulate matter pollution, the pollution stages are reasonably divided, as follows: Figure 2 shown.

[0050] like Figure 2 As shown in the figure, the particulate matter pollution stage is divided into four stages, namely, the pollution persistence stage, the pollution dissipation stage, the pollution formation stage and the pollution-free stage.

[0051] During the phase change of particulate matter pollution, the factor that has the greatest impact on the change of pollution concentration is meteorological conditions, which mainly include wind speed, humidity, temperature, mixing layer height, PG stability, precipitation, cloud liquid water content, etc. If the impact of all meteorological conditions on particulate matter pollution is analyzed, the complexity of the research will increase. Therefore, this study uses the principal component analysis method to determine the meteorological factors with greater influence, which is convenient for subsequent research.

[0052] The specific steps for determining meteorological elements based on the principal component analysis method are as follows:

[0053] Step 1: Set the meteorological conditions - wind speed, humidity, temperature, mixing layer height, PG stability, precipitation and cloud liquid water content as 7 variables, expressed as X i ={X1,X2,X3,X4,X5,X6,X7}, set the atmospheric particulate pollution concentration as the objective function, expressed as Y;

[0054] Step 2: Obtain the meteorological condition variable Xi data set in step 1 and integrate it into a data set, denoted as Z, then Calculate the mean vector of the data set Z, the expression is:

[0055]

[0056] In formula (1), η represents the mean vector of the data set Z; n represents the total number of data in the set Z; Z i It represents the i-th sample data.

[0057] Step 3: Centralize the processing set Z, that is, subtract each sample data from the mean vector η calculated in step 2. The expression is:

[0058]

[0059] In formula (2), It represents the data set after centering processing.

[0060] Step 4: Based on the output result of Step 3 construct its corresponding covariance matrix, and the expression is:

[0061]

[0062] In formula (3), μ represents the covariance matrix of the data set ; T represents the matrix transpose symbol.

[0063] Step 5: Perform eigen-decomposition processing on the output result μ of Step 4, and solve the characteristic equation of the covariance matrix |μ - λI| = 0. Obtain the eigenvalues of the covariance matrix μ, denoted as λ i , and arrange the eigenvalues λ i in descending order;

[0064] Step 6: Calculate the contribution rate of each component according to the eigenvalues λ i , and its calculation formula is:

[0065]

[0066] In formula (4), υ i represents the contribution rate value of the i-th component; p represents the total number of eigenvalues.

[0067] Step 7: According to the calculation result of Step 5, set the cumulative contribution rate threshold υ * ≥85%, according to the magnitude of the cumulative contribution rate.

[0068] In formula (5), take the first m (m < p) meteorological conditions to determine them as the principal components, that is, meteorological elements.

[0069]

[0070] Operating the sample data according to the above steps, four meteorological elements are obtained, namely wind speed, humidity, temperature and mixing layer height, which can reflect the influence of meteorological conditions on particulate matter pollution to the greatest extent while reducing the research difficulty.

[0071] 1.3 Research on the relationship between meteorological elements and atmospheric particulate matter pollution concentration

[0072] Based on the above-selected meteorological elements - wind speed, humidity, temperature and mixing layer height, explore the relationship between the meteorological elements in the research area and the atmospheric particulate matter pollution concentration, as follows:

[0073] 1.3.1 Analysis of the relationship between wind speed and atmospheric particulate matter pollution concentration

[0074] Collect the wind speed and atmospheric particulate pollution concentration data in the study area, draw the corresponding scatter plot on the two-dimensional coordinate system, and perform curve fitting on it to determine the maximum fitting curve equation, such as Figure 3 shown.

[0075] like Figure 3 As shown in the figure, as the wind speed gradually increases, the concentration of atmospheric particulate pollution shows a trend of gradually decreasing. According to existing literature research, wind speed has a certain dilution capacity for particulate pollution. When the wind speed is high, the atmospheric transport capacity is strong, the particulate matter diffuses with the wind, and the pollution concentration decreases accordingly; conversely, when the wind speed is low, the atmospheric transport capacity is weak, the particulate matter forms an accumulation phenomenon, and the pollution concentration increases accordingly.

[0076] 1.3.2 Analysis of the relationship between humidity and atmospheric particulate matter pollution concentration

[0077] According to the steps in Section 1.3.1, the maximum fitting curve of humidity and atmospheric particulate pollution concentration data is obtained, as follows: Figure 4 shown.

[0078] like Figure 4 As shown in the figure, with the gradual increase of humidity, the concentration of atmospheric particulate pollution shows a trend of first rising and then falling. When the humidity increases from 30% to 75%, the particulate matter will absorb water vapor and swell to form small fog-like particles, but due to the insufficient water vapor content in the atmosphere, the particulate matter cannot settle, causing the pollution concentration to rise; when the humidity exceeds 75%, the water vapor content in the atmosphere increases rapidly, and precipitation is likely to occur. At this time, the particulate matter will be adsorbed on the surface of the water vapor and then settle on the ground, thereby reducing the concentration of particulate pollution.

[0079] 1.3.3 Analysis of the relationship between temperature and atmospheric particulate matter pollution concentration

[0080] According to the steps in Section 1.3.1, the maximum fitting curve of temperature and atmospheric particulate pollution concentration data is obtained, as follows: Figure 5 shown.

[0081] like Figure 5 As shown in Figure 1, with the increase of temperature, the concentration of atmospheric particulate pollution shows a trend of gradually decreasing. When the temperature value is large, the thermal motion performance of the particles is stimulated, and the collision between the particles is strengthened, which reduces the number of particles in the atmosphere, thereby reducing the concentration of particulate pollution.

[0082] 1.3.4 Analysis of the relationship between mixing layer height and atmospheric particulate matter pollution concentration

[0083] According to the steps in Section 1.3.1, the maximum fitting curve of the mixing layer height and atmospheric particulate pollution concentration data is obtained. The fitting curve equation is:

[0084] y=169.15e-0.001x (6)

[0085] R 2 =0.20

[0086] like Figure 6 As shown in the figure, with the increase of the mixing layer height, the concentration of atmospheric particulate matter pollution shows a gradually decreasing trend.

[0087] The above process completed the relationship between meteorological elements and particulate matter pollution concentration, and obtained the correlation coefficients between meteorological elements and pollution concentration, which were 0.23, 0.15, 0.35 and 0.20 respectively, which provided assistance for the calculation of subsequent quantitative evaluation values ​​of the impact of meteorological elements.

[0088] 1.4 Numerical calculation of quantitative assessment of meteorological factors’ impact

[0089] Based on the correlation coefficient obtained above and combined with the characteristics of particulate matter pollution, a quantitative assessment model of the impact of meteorological factors on atmospheric particulate matter pollution is constructed, which is expressed as follows:

[0090]

[0091] In formula (7), C and C0 represent the current and initial particle pollution concentrations respectively; 2 β represents auxiliary calculation parameters, which are strongly correlated with wind direction. Relatively fixed parameters can be obtained after multiple evaluations. ω1, ω2, ω3 and ω4 represent weight coefficients, which are the numerical values ​​of the influence of meteorological elements. Weight coefficients under the same wind direction can be obtained after multiple evaluations. X1, X2, X3 and X4 represent wind speed, humidity, temperature and mixing layer height. ε represents the error compensation term, which is strongly correlated with wind direction. Relatively fixed parameters can be obtained after multiple evaluations.

[0092] Formula (7) can be used for short-term quantitative assessment of the impact of atmospheric particulate pollution: record the particle pollution concentration C0 at the current time t0; substitute the actual values ​​of the particle pollution concentration and wind speed, humidity, temperature and mixing layer height recorded at the previous five times t0-1, t0-2, t0-3, t0-4, t0-5 (hourly) into formula (7) to form an equation group, and obtain the numerical value of the impact degree of meteorological factors (ω1, ω2, ω3, ω4, ε) by solving; substitute the predicted values ​​of wind speed, humidity, temperature and mixing layer height at any future time t into the equation group to realize the quantitative assessment of the impact of meteorological conditions on atmospheric particulate pollution, which can provide certain help for the control of atmospheric particulate pollution.

[0093] like Fig. 9 , Fig.10As shown, the short-term quantitative assessment (0-12) of the impact of atmospheric particulate matter pollution by formula (7) is highly accurate, and the impact value of atmospheric particulate matter pollution is almost consistent with the actual value, with an error of about 0.005. However, due to the uncertainty of wind speed, humidity, temperature and mixing layer height, quantitative assessment for more than 12 hours is of guiding significance.

[0094] 2 Experiments and results analysis

[0095] 2.1 Experimental area selection

[0096] In order to verify the application performance of the constructed model, an appropriate experimental area is selected, the location of the pollution source is set, and the required data collection equipment is configured to facilitate the experiment. Figure 7 shown.

[0097] like Figure 7 As shown in the figure, there are three particulate pollution sources in the selected experimental area (3*3 km), the number of data collection devices is 10, and the density is 1 data sampling point per square kilometer, which can cover the entire area after uniform distribution. In order to ensure the accuracy of the experimental conclusion, the average value of the 10 collection devices is used as the experimental data.

[0098] The above process completes the selection of the experimental area. According to the "Measurement of particulate matter in exhaust gas from stationary pollution sources and sampling methods for gaseous pollutants", "Smoke from stationary pollution sources (SO2, NO X , particulate matter) emissions continuous monitoring technical specifications, stationary pollution source flue gas (SO2, NO X The application equipment was configured accordingly in accordance with relevant standards and specifications such as "Technical Requirements and Test Methods for Continuous Monitoring Systems for Air Pollutants and Particulate Matter" and "Technical Guidelines for Monitoring Unorganized Emissions of Atmospheric Pollutants". In order to comply with the principle of universal applicability, the locations of pollution sources were randomly set to facilitate the smooth progress of the experiment.

[0099] 2.2 Experimental parameter settings

[0100] There are unknown parameters in the quantitative evaluation model. 2 and β, which determine the accuracy of the quantitative evaluation results of the constructed model, so the parameter χ needs to be adjusted before the experiment. 2 The optimal value of β is determined.

[0101] The relationship between unknown parameters and model accuracy is obtained through testing. Figure 8 shown.

[0102] like Figure 8 As shown, when the parameter χ 2 When the value is 0.6 and the value of β is 2.3, the accuracy of the quantitative evaluation model reaches the maximum value, which is about 94%.2 The best values ​​of β are 0.6 and 2.3.

[0103] The above process completes the determination of the optimal values ​​of the relevant parameters for building the model, providing support for the subsequent analysis of experimental results.

[0104] Based on the experimental area selected above and the experimental parameters determined, a quantitative evaluation experiment on the impact of meteorological conditions on atmospheric particulate pollution was conducted. Through the experiment, the numerical value of the impact of meteorological elements - wind speed, humidity, temperature and mixing layer height on atmospheric particulate pollution was obtained.

[0105] like Fig. 9 , Fig.10 As shown in the figure, the impact of meteorological conditions such as wind speed, humidity, temperature and mixing layer height on atmospheric particulate matter pollution obtained by applying the constructed model is almost consistent with the actual value, with an error of about 0.005, which meets the needs of quantitative impact assessment and fully proves that the constructed model has good quantitative impact assessment performance.

[0106] Air pollution has become a global environmental problem. How to control and improve it is the key to sustainable development. Atmospheric particulate pollution not only reduces environmental visibility, but is also absorbed by the human body, causing respiratory diseases. Meteorological conditions are the key factors affecting the changes in atmospheric pollution concentrations. Therefore, a quantitative evaluation model for the impact of meteorological conditions on atmospheric particulate pollution is proposed. The experimental results show that the model is constructed by the embodiment.

[0107] The above examples are only used to illustrate the present invention. In addition, there are many different implementations. These implementations are all conceivable by those skilled in the art after understanding the concept of the present invention. Therefore, they are not listed here one by one.

Claims

1. A quantitative assessment method for the impact of meteorological conditions on atmospheric particulate matter pollution, characterized in that: The following steps are involved: 1) Set data sampling points, which are evenly distributed in the monitoring area; continuously measure wind speed, humidity, temperature, and mixing layer height; 2) Integrate the collected data into a data set; the expression is: η represents the mean vector of the data set Z; n represents the total number of data in the set Z; Z i It represents the i-th sample data; 3) Centralize the processing set Z, that is, subtract each sample data from the mean vector η calculated in step 2), and the expression is: It represents the data set after centralized processing; 4) Output the result in step 3) Based on it, construct the corresponding covariance matrix, the expression is: μ represents the data set The covariance matrix of ; T represents the matrix transpose symbol; 5) Perform eigendecomposition on the output result μ of step 4) to solve the characteristic equation of the covariance matrix |μ-λI|=0, and obtain the eigenvalue of the covariance matrix μ, denoted as λ i , according to the descending order of eigenvalue λ i To arrange; 6) According to the eigenvalue λ i Calculate the contribution rate of each component, the calculation formula is: υ i It represents the contribution rate of the i-th component; p represents the total number of eigenvalues; 7) According to the calculation results of step 5), set the cumulative contribution rate threshold υ * ≥85%, according to the size of the cumulative contribution rate, take the first m meteorological conditions to determine the main component, that is, the meteorological element; among them, m <p; 8) Obtain the maximum fitting curve of the mixing layer height and atmospheric particulate pollution concentration data. The fitting curve equation is: y=169.15e -0.001x (6) Among them, wind speed, humidity, temperature, and mixing layer height R 2 Take 0.23, 0.15, 0.35 and 0.20 respectively 9) Construct a quantitative assessment model of the impact of meteorological factors on atmospheric particulate matter pollution, expressed as: C and C0 represent the current and initial particle pollution concentrations respectively. Under the same wind direction, multiple pre-evaluations were performed to obtain the following parameters: 2 and β represent auxiliary calculation parameters; ω1, ω2, ω3 and ω4 represent the numerical value of the influence of meteorological elements; X1, X2, X3 and X4 represent wind speed, humidity, temperature and mixing layer height; ε represents the error compensation term; in order to obtain parameters for subsequent evaluation.

2. The quantitative assessment method for the impact of meteorological conditions on atmospheric particulate matter pollution according to claim 1, characterized in that: The data collection points are arranged at a density of 1 data sampling point per square kilometer.

3. The quantitative assessment method for the impact of meteorological conditions on atmospheric particulate matter pollution according to claim 1, characterized in that: The number of pre-evaluations in step 9) is no less than 4 times.

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