A method and system for determining dust-retaining forest protection density

Abstract

The present invention relates to the field of environmental engineering technology, and in particular to a method and system for determining the dust retention forest protection density, comprising the following steps: S1: selecting a number of rectangular sample plots that can represent the overall vegetation density distribution of the dust retention forest; S2: arranging a plurality of sampling points along the diagonal direction in each sample plot; S3: collecting air samples at the sampling points in each sample plot, and detecting the concentrations of total suspended particulate matter TSP, PM10 and PM2.5 in the air samples; S4: establishing a relationship model between the dust retention forest protection density and air quality; S5: calculating the optimal dust retention forest vegetation density value in the target area according to the established relationship model; S6: dynamically adjusting the optimal dust retention forest vegetation density value. The present invention, by adopting multiple linear regression analysis and dynamic adjustment strategies, can accurately adjust the vegetation density of the dust retention forest according to real-time environmental data, ensure the best air purification effect under various climatic conditions, thereby effectively improving the environmental quality and the management efficiency of the dust retention forest.

Description

Technical Field

[0001] The invention relates to the technical field of environmental engineering, and in particular to a method and system for determining the protection density of a dust-retaining forest. Background Art

[0002] Dust-retention forests, as an effective ecological engineering tool, absorb and block particulate matter in the air through vegetation cover, and play an important role in improving air quality. The protective effect of dust-retention forests is closely related to their vegetation density, that is, vegetation density directly affects their ability to absorb and block air particles. Traditionally, the vegetation density design of dust-retention forests often relies on empirical judgment or general forestry planning standards, and lacks precise adjustments for specific environmental conditions. In addition, existing methods fail to fully consider the dynamic effects of regional climate conditions and seasonal changes on the vegetation density of dust-retention forests, which leads to fluctuations in the protective effect of dust-retention forests under different environmental conditions, making it difficult to maintain the optimal state.

[0003] In view of these deficiencies in the prior art, the present invention aims to solve the accuracy and adaptability problems in the design of dust-retaining forest protection density. Existing design methods often ignore the complex interaction between vegetation density and air quality and its dynamic adjustment requirements as the environment changes.

[0004] Therefore, it is necessary to develop a method and system that can scientifically determine the vegetation density of dust-retaining forests and dynamically adjust the vegetation density according to real-time environmental data, so as to achieve the optimal air purification effect in different seasons and different climatic conditions. Summary of the invention

[0005] Based on the above objectives, the present invention provides a method and system for determining the dust retention forest protection density.

[0006] A method for determining the dust retention forest protection density comprises the following steps:

[0007] S1: In the target area, according to the vegetation density gradient of the dust retention forest, select a number of rectangular sample plots that can represent the overall vegetation density distribution of the dust retention forest. The density of the sample plots presents a continuous gradient change between different sample plots, and the size of the sample plot is determined according to statistical requirements;

[0008] S2: Multiple sampling points are arranged along the diagonal direction in each plot;

[0009] S3: Collect air samples at the sampling points in each sample plot, detect the concentrations of total suspended particulate matter TSP, PM10 and PM2.5 in the air samples, and calculate the average concentration of each particle at each sampling point in the same sample plot to obtain the mean concentration of each particle in the sample plot;

[0010] S4: Using the mean concentration of each particle in the sample plot as the independent variable and the vegetation density of the dust-retaining forest as the dependent variable, a multivariate linear regression analysis method was used to establish a relationship model between the dust-retaining forest protection density and air quality;

[0011] S5: Based on the established relationship model and in combination with the air pollutant concentration limits specified in the ambient air quality standard GB3095-2012, calculate the optimal dust-trapping forest vegetation density value when the target area reaches the first or second level air quality standard;

[0012] S6: According to the seasonal changes, climate conditions and vegetation growth conditions of the dust retention forest area, the optimal dust retention forest vegetation density value is dynamically adjusted, and the air particulate matter concentration is periodically re-measured, and the relationship model is corrected using the detection results.

[0013] Optionally, the S1 specifically includes:

[0014] S11: In the target area, based on the pre-collected vegetation density data of the dust retention forest, a method combining geographic information system technology and remote sensing technology is used to conduct data analysis to clarify the distribution of vegetation density of the dust retention forest in different areas. Specifically, the vegetation density in the dust retention forest area is firstly gridded, and the vegetation density value of each grid unit is calculated based on the area of ​​each grid unit through the density estimation formula, thereby forming a density gradient map of the dust retention forest;

[0015] S12: Based on the dust retention forest density gradient map formed in step S11, representative areas of different density intervals are selected according to the gradient variation range of the dust retention forest vegetation density, and the density of the dust retention forest vegetation is divided into several density levels based on the density interval, including 100-300 plants / hectare in low-density areas, 300-500 plants / hectare in medium-density areas, and 500-800 plants / hectare in high-density areas;

[0016] S13: Delineate rectangular plots within the selected area. The size of each plot is determined according to the sample size calculation formula in statistics to ensure that the plot area is large enough to contain a sufficient number of vegetation and that the uniformity of vegetation density within the plot is ensured;

[0017] S14: The locations of sample plots are selected within each selected area by random sampling to ensure that the selected sample plots can represent the continuous gradient changes in the overall vegetation density distribution of the dust-trapping forest.

[0018] Optionally, the S2 specifically includes:

[0019] S21: determine the rectangular boundary of the sample plot, and use the four vertices of the sample plot to connect to form two diagonal lines, measure the length L of each diagonal line, and mark the starting point and end point of the diagonal line;

[0020] S22: Determine the number of sampling points on each diagonal line based on the uniformity of vegetation density within the sample plot and the required sample size;

[0021] S23: Along each diagonal direction, arrange the sampling points according to the principle of equidistant distance to ensure that the distance between each sampling point is The sampling points are distributed to cover the entire area of ​​the plot, and the spacing between the sampling points is The calculation formula is: ,in, is the distance between each sampling point, is the length of the diagonal, is the number of sampling points. According to the calculation results, the sampling points are arranged one by one on the diagonal line.

[0022] S24: Record the specific location coordinates of each sampling point and conduct on-site inspection after deployment to ensure that all sampling points are located in representative areas of actual vegetation density.

[0023] Optionally, the S3 specifically includes:

[0024] S31: At each sampling point, an air sampler is used to collect air samples. Specifically, the sampling port of the air sampler is set at a height of 1.5 meters from the ground. A predetermined volume of air sample is collected at each sampling point. The sampling time is According to the required sample volume and sampling flow rate Sure;

[0025] S32: After pre-processing the collected air samples, the concentrations of total suspended particulate matter TSP, PM10 and PM2.5 in the air samples are measured using a particle monitoring device; specifically, the air samples are passed through a glass fiber filter membrane or a quartz fiber filter membrane, and the mass concentration of the particles in the samples is detected using the gravity method. ;

[0026] S33: For all sampling points in each sample plot, the concentration data of TSP, PM10 and PM2.5 of each sampling point are calculated respectively, and then the average particle concentration value of the sample plot is calculated by arithmetic mean method. The calculation formula of the average concentration value is: ,in, is the average particle concentration of the sample site, in micrograms per cubic meter. is the particle concentration value of the i-th sampling point, in micrograms per cubic meter, is the total number of sampling points in the sample plot.

[0027] Optionally, the S4 specifically includes:

[0028] S41: The average particle concentration value of the sample site is selected as the independent variable, including the concentration values ​​of three types of particles: total suspended particulate matter TSP, PM10 and PM2.5, which are recorded as ;

[0029] S42: The vegetation density of the dust-trapping forest is taken as the dependent variable, denoted as , and based on the average particle concentration values ​​of each plot and the corresponding vegetation density data, a multivariate linear regression model was established, and the expression is:

[0030] ,in, is a constant term, is the regression coefficient corresponding to the independent variable, is the error term;

[0031] S43: The regression coefficients were calculated by the least squares method. and Estimate to minimize the error term The sum of squares;

[0032] S44: Perform statistical tests on the obtained regression model. Specifically, the overall significance of the model is tested through the F test, the F value is calculated and compared with the critical value to determine whether the model is significant; and the significance of each regression coefficient is tested through the t test, the t value is calculated and compared with the critical value to determine whether the influence of each variable on the dependent variable is significant.

[0033] Optionally, the S44 specifically includes:

[0034] S441: Perform an F test on the overall significance of the regression model, including:

[0035] S4411: Calculate the regression sum of squares and residual sum of squares of the regression model. The calculation formula for the regression sum of squares is: ,in, is the predicted value of the model, is the mean value of the dependent variable, is the sample size, is the regression sum of squares; the formula for calculating the residual sum of squares is: in, is the actual observed value, is the regression sum of squares;

[0036] S4412: Calculate the F value of the regression model. The formula is: ,in, is the number of independent variables, is the sample size;

[0037] S4413: Find the critical value under the corresponding degrees of freedom according to the preset significance level. If the calculated value If the value is greater than the critical value, the regression model is considered to be significant as a whole;

[0038] S442: Perform a t-test on the significance of each regression coefficient, including:

[0039] S4421: Calculate the standard error of each regression coefficient , the formula is:

[0040] ,in, For the The regression coefficients of the independent variables, is the inverse matrix of the product of the transpose matrix of the independent variable matrix and the independent variable matrix. diagonal elements of elements;

[0041] S4422: Calculate the value of each regression coefficient Value, the formula is: in, is the estimated value of the regression coefficient of the jth independent variable;

[0042] S4423: Find the critical value under the corresponding degrees of freedom according to the preset significance level. If the calculated value Value greater than critical If the independent variable has a significant impact on the dependent variable,

[0043] Optionally, the S5 specifically includes:

[0044] S51: Determine the air quality standards for the target area. According to the ambient air quality standard GB3095-2012, specify the pollutant concentration limits for the primary and secondary air quality standards, especially the concentration limits for total suspended particulate matter TSP, PM10 and PM2.5. The specific primary standard is TSP≤120μg / m³, PM10≤50μg / m³, PM2.5≤35μg / m³; the secondary standard is TSP≤300μg / m³, PM10≤150μg / m³, PM2.5≤75μg / m³;

[0045] S52: Substitute the air pollutant concentration limit values ​​of the target area into the established relationship model between the dust-retaining forest protection density and air quality, and use the model to calculate the optimal dust-retaining forest vegetation density values ​​under the primary air quality standards and the secondary air quality standards.

[0046] Optionally, the S52 specifically includes:

[0047] S521: Substitute the pollutant concentration limits under the primary and secondary air quality standards into the established multiple linear regression relationship model, with the vegetation density of the dust-retaining forest as the dependent variable and the concentrations of TSP, PM10 and PM2.5 as the independent variables;

[0048] S522: Calculate the optimal dust-trapping forest vegetation density value Y1 under the first-level air quality standard and the optimal dust-trapping forest vegetation density value Y2 under the second-level air quality standard by solving equations;

[0049] S523: Record and compare the Y1 and Y2 values ​​to determine the optimal dust-trapping forest vegetation density required under different air quality standards.

[0050] Optionally, the S6 specifically includes:

[0051] S61: Collect data on seasonal changes, climate conditions and vegetation growth in the area where the dust-trapping forest is located, including temperature fluctuations in different seasons, changes in precipitation, air humidity and vegetation growth cycle;

[0052] S62: Based on the data obtained in S61, the optimal dust retention forest vegetation density value is corrected by applying a dynamic adjustment formula. The correction formula is: in, is the vegetation density value after dynamic adjustment. is the original optimal vegetation density value, is the temperature change of the current season, is the reference temperature, is the humidity change in the current season, is the base humidity, is the change in vegetation growth conditions, It is the benchmark vegetation growth status index;

[0053] S63: Periodically re-measure the concentration of air particulate matter in the area where the dust retention forest is located and record the measurement results. The measurement cycle is once every quarter to capture the impact of seasonal changes on the purification effect of the dust retention forest;

[0054] S64: Substitute the latest particulate matter concentration data obtained in S63 into the established relationship model, and modify the relationship model to ensure that the relationship model can accurately reflect the latest relationship between air quality and vegetation density.

[0055] A system for determining the dust retention forest protection density, used to implement the above-mentioned method for determining the dust retention forest protection density, includes the following modules:

[0056] Data collection module: used to collect environmental data and air samples from various plots within the dust retention forest area, including concentration data of total suspended particulate matter TSP, PM10, PM2.5, as well as climatic conditions of vegetation density, temperature, humidity and precipitation, and data on vegetation growth status;

[0057] Data processing module: connected to the data acquisition module, used to pre-process the collected environmental data and air sample data, including data cleaning, outlier removal and interpolation completion;

[0058] Model building module: connected to the data processing module, using the processed environmental data and air sample data, adopting the multivariate linear regression analysis method, to establish the relationship model between the dust retention forest protection density and air quality;

[0059] Density calculation module: connected to the model building module, used to calculate the optimal dust-trapping forest vegetation density value under different air quality standards based on the established relationship model and the ambient air quality standard GB3095-2012;

[0060] Density adjustment module: connected to the density calculation module, used to dynamically adjust the optimal vegetation density value according to seasonal changes, climate conditions and vegetation growth conditions in the area where the dust retention forest is located;

[0061] Density verification module: connects the density adjustment module and the data acquisition module to verify whether the adjusted vegetation density value meets the expected air purification effect. By regularly re-measuring the concentration of air particles and feeding back the new data to the model building module, the dust retention forest protection density is cyclically corrected and optimized.

[0062] Beneficial effects of the present invention:

[0063] The present invention, by adopting a multivariate linear regression analysis method and a dynamic adjustment strategy, can accurately calculate and adjust the vegetation density of the dust retention forest, thereby optimizing the protective effect of the dust retention forest. This method is based on detailed environmental data analysis to ensure that the dust retention forest can achieve the best air purification effect under different air quality standards, thereby effectively improving air quality and having a positive impact on human health and the ecosystem.

[0064] The present invention ensures the adaptability and sustainability of the protection strategy by periodically re-measuring the concentration of air particles and adjusting the vegetation density. This not only makes the dust retention forest continuously effective under different environmental conditions, but also improves the scientificity and systematicness of dust retention forest management, providing solid technical support for the long-term maintenance of dust retention forests and environmental protection strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0066] Figure 1 A schematic diagram of a method for determining the dust retention forest protection density according to an embodiment of the present invention;

[0067] Figure 2 A schematic diagram of a system for determining the dust retention forest protection density according to an embodiment of the present invention. DETAILED DESCRIPTION

[0068] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0069] It should be noted that the references to "one embodiment", "embodiment", "exemplary embodiments", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0070] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0071] like Figure 1 As shown, a method for determining the dust retention forest protection density comprises the following steps:

[0072] S1: In the target area, according to the vegetation density gradient of the dust retention forest, select a number of rectangular plots that can represent the overall vegetation density distribution of the dust retention forest. The density of the plots shows a continuous gradient change between different plots, and the size of the plots is determined according to statistical requirements;

[0073] S2: Multiple sampling points are arranged along the diagonal direction in each sample plot to ensure the accuracy of the measurement results;

[0074] S3: Collect air samples at the sampling points in each sample plot, detect the concentrations of total suspended particulate matter TSP, PM10 and PM2.5 in the air samples, and calculate the average concentration of each particle at each sampling point in the same sample plot to obtain the mean concentration of each particle in the sample plot;

[0075] S4: Using the mean concentration of each particle in the sample plot as the independent variable and the vegetation density of the dust-retaining forest as the dependent variable, a multivariate linear regression analysis method was used to establish a relationship model between the dust-retaining forest protection density and air quality;

[0076] S5: Based on the established relationship model and in combination with the air pollutant concentration limits specified in the ambient air quality standard GB3095-2012, calculate the optimal dust-trapping forest vegetation density value when the target area reaches the first or second level air quality standard;

[0077] S6: According to the seasonal changes, climate conditions and vegetation growth conditions of the dust retention forest area, the optimal dust retention forest vegetation density value is dynamically adjusted, and the air particulate matter concentration is periodically re-measured. The test results are used to revise the relationship model to ensure that the dust retention forest can achieve the best air purification effect at different times.

[0078] S1 specifically includes:

[0079] S11: In the target area, based on the pre-collected vegetation density data of the dust retention forest, a method combining geographic information system (GIS) technology and remote sensing technology is used to conduct data analysis to clarify the distribution of vegetation density of the dust retention forest in different areas. Specifically, the vegetation density in the dust retention forest area is first gridded, and the area of ​​each grid unit (for example, 1 hectare) is used as the basis. The vegetation density value of each grid unit is calculated using the density estimation formula, thereby forming a density gradient map of the dust retention forest. The density calculation formula is:

[0080] ,in, is the vegetation density, in plants / hectare, is the amount of vegetation in the sample plot, and A is the area of ​​the sample plot;

[0081] S12: Based on the dust retention forest density gradient map formed in step S11, representative areas of different density intervals are selected according to the gradient variation range of the dust retention forest vegetation density. Specifically, the dust retention forest vegetation density is divided into several density levels based on the density interval, including 100-300 plants / hectare in low-density areas, 300-500 plants / hectare in medium-density areas, and 500-800 plants / hectare in high-density areas. Factors such as topography, soil type, and climatic conditions are considered to ensure that the selected sample plots can continuously transition between different density levels, so as to ensure the representativeness of the selected areas and the continuity between the sample plots;

[0082] S13: Rectangular plots are drawn within the selected area. The size of each plot is determined according to the sample size calculation formula in statistics to ensure that the plot area is large enough to contain a sufficient number of vegetation and that the uniformity of vegetation density within the plot is ensured to provide sufficient statistical power. The specific calculation formula is:

[0083] Where z is the z value corresponding to the confidence level (e.g. The confidence level corresponds to a z-value of 1.96 , is the estimated vegetation density ratio, is the allowable error;

[0084] S14: The locations of sample plots are selected in each selected area by random sampling method to ensure that the selected sample plots can represent the continuous gradient changes of the overall vegetation density distribution of the dust-trapping forest, so as to ensure the scientificity and rationality of the sample plot distribution.

[0085] The random sampling method adopts a hierarchical random sampling strategy, and the specific steps include:

[0086] S141: First, the target area is divided into several layers, each layer represents a specific vegetation density level. This division ensures that the vegetation density within each layer is relatively uniform, which is convenient for subsequent random sampling;

[0087] S142: In each layer, the number of sample plots to be selected is determined according to the proportion of the layer area in the total area. This process allocates sample plots by layer proportion to ensure that areas of different density levels are reasonably represented in the sample plot selection, avoids the sample plots being concentrated in a certain density area, and thus ensures the comprehensiveness of the sample plot distribution;

[0088] S143: After determining the number of sample plots in each stratum, the random sampling method is used to randomly select the location of the sample plots within the stratum. The specific method is to number each possible sample plot location in the stratum and randomly select the sample plot location corresponding to the number through a random number generator. This randomness ensures the objectivity of the sampling process and avoids human bias;

[0089] S144: To ensure the representativeness and scientificity of the sample plots, the selected sample plot locations are verified to ensure that the selected locations accurately reflect the vegetation density characteristics within the layer. If it is found that some locations do not meet the expected requirements, they can be adjusted by random sampling again until they meet the requirements;

[0090] Through the detailed implementation of the above steps, it can be ensured that the selected sample plots are not only representative, but also can accurately reflect the distribution of dust retention forests under different vegetation density gradients, and reasonably determine the size of the sample plots according to statistical requirements, thereby providing a scientific and reliable data basis for the subsequent determination of the dust retention forest protection density, and significantly improving the ecological protection effect of the dust retention forest.

[0091] S2 specifically includes:

[0092] S21: Determine the rectangular boundary of the sample plot, and use the four vertices of the sample plot to connect to form two diagonal lines, and measure the length L of each diagonal line, and mark the starting point and end point of the diagonal line. The length L of the diagonal line is the straight line distance connecting the two vertices, ensuring that these diagonal lines are the basis for the layout of sampling points;

[0093] S22: Determine the number of sampling points on each diagonal line based on the uniformity of vegetation density within the sample plot and the required sample size;

[0094] S23: Along each diagonal direction, arrange the sampling points according to the principle of equidistant distance to ensure that the distance between each sampling point is The sampling points should cover the entire area of ​​the plot, and the spacing between sampling points should be equal. The calculation formula is: ,in, is the distance between each sampling point, is the length of the diagonal, is the number of sampling points. According to the calculation results, the sampling points are arranged one by one on the diagonal line.

[0095] S24: Record the specific location coordinates of each sampling point, and conduct on-site inspections after the deployment is completed to ensure that all sampling points are located in the representative area of ​​the actual vegetation density. If any abnormal or non-compliant sampling point locations are found, they will be re-deployed or adjusted to ensure the accuracy and consistency of data collection. Through the above steps, the uniform distribution and scientific deployment of sampling points in the sample plot are ensured, which effectively avoids deviations in the sampling process, ensures the representativeness and accuracy of the data, and provides reliable data support for the scientific determination of the dust-retention forest protection density.

[0096] S3 specifically includes:

[0097] S31: At each sampling point, an air sampler is used to collect air samples. Specifically, the sampling port of the air sampler is set at a height of 1.5 meters from the ground. A predetermined volume of air sample is collected at each sampling point. The sampling time is According to the required sample volume and sampling flow rate OK, the calculation formula is: ,in, is the sampling volume in cubic meters, is the sampling flow rate in cubic meters per minute, is the sampling time in minutes;

[0098] S32: After pre-processing the collected air samples, the concentrations of total suspended particulate matter TSP, PM10 and PM2.5 in the air samples are measured using a particle monitoring device; specifically, the air samples are passed through a glass fiber filter membrane or a quartz fiber filter membrane, and the mass concentration of the particles in the samples is detected using the gravity method. , the formula is: ,in, is the concentration of particulate matter in micrograms per cubic meter, is the mass of the filter membrane after sampling, in micrograms, is the mass of the filter membrane before sampling, in micrograms, is the sampling volume in cubic meters;

[0099] S33: For all sampling points in each sample plot, the concentration data of TSP, PM10 and PM2.5 of each sampling point are calculated respectively, and then the average particle concentration value of the sample plot is calculated by arithmetic mean method. The calculation formula of the average concentration value is: ,in, is the average particle concentration of the sample site, in micrograms per cubic meter. is the particle concentration value of the i-th sampling point, in micrograms per cubic meter, is the total number of sampling points in the sample plot; finally, the average particle concentration value of each sample plot is recorded, and subsequent analysis and data processing are carried out to ensure that the data obtained accurately reflects the air quality conditions in the sample plot; through the above steps, it is disclosed in detail how to collect air samples at the sampling points in each sample plot, detect the particle concentration, and calculate the average particle concentration value of the sample plot. All parameters have been defined to ensure the accuracy and scientificity of the measurement data, thereby providing a reliable basis for the scientific determination of the dust retention forest protection density.

[0100] S4 specifically includes:

[0101] S41: The average particle concentration value of the sample site is selected as the independent variable, including the concentration values ​​of three types of particles: total suspended particulate matter TSP, PM10 and PM2.5, which are recorded as ;

[0102] S42: The vegetation density of the dust-trapping forest is taken as the dependent variable, denoted as , and based on the average particle concentration values ​​of each plot and the corresponding vegetation density data, a multivariate linear regression model was established, and the expression is:

[0103] ,in, is a constant term, is the regression coefficient corresponding to the independent variable, is the error term;

[0104] S43: The regression coefficients were calculated by the least squares method. and Estimate to minimize the error term The specific steps include:

[0105] S431: Calculate the independent variable matrix The transposed matrix of and The product of

[0106] S432: Calculate the inverse matrix of the product matrix ;

[0107] S433: Combine the above results with the independent variable matrix The transposed matrix of and the dependent variable vector Multiply them together to get the estimated value of the regression coefficient , the expression is: .

[0108] S44: Perform statistical tests on the obtained regression model. Specifically, the overall significance of the model is tested through the F test, the F value is calculated and compared with the critical value to determine whether the model is significant; and the significance of each regression coefficient is tested through the t test, the t value is calculated and compared with the critical value to determine whether the influence of each variable on the dependent variable is significant; through the above steps, it is disclosed in detail how to use the multivariate linear regression analysis method to establish a relationship model between the dust retention forest protection density and air quality based on the average particulate matter concentration value of the sample site and the vegetation density data of the dust retention forest, thereby providing reliable technical support for the reasonable determination of the dust retention forest protection density.

[0109] S44 specifically includes:

[0110] S441: Perform an F test on the overall significance of the regression model, including:

[0111] S4411: Calculate the regression sum of squares (SSR) and residual sum of squares (SSE) of the regression model. The calculation formula for the regression sum of squares is: ,in, is the predicted value of the model, is the mean value of the dependent variable, is the sample size, is the regression sum of squares; the formula for calculating the residual sum of squares is: in, is the actual observed value, is the regression sum of squares;

[0112] S4412: Calculate the F value of the regression model. The formula is: ,in, is the number of independent variables, is the sample size;

[0113] S4413: Find the critical value at the corresponding degrees of freedom according to the preset significance level (usually 0.05). If the calculated value If the value is greater than the critical value, the regression model is considered to be significant as a whole;

[0114] S442: Perform a t-test on the significance of each regression coefficient, including:

[0115] S4421: Calculate the standard error of each regression coefficient , the formula is:

[0116] ,in, For the The regression coefficients of the independent variables, is the inverse matrix of the product of the transpose matrix of the independent variable matrix and the independent variable matrix. diagonal elements of elements;

[0117] S4422: Calculate the value of each regression coefficient Value, the formula is: in, is the estimated value of the regression coefficient of the jth independent variable;

[0118] S4423: Find the critical value under the corresponding degrees of freedom according to the preset significance level (usually 0.05). If the calculated value Value greater than critical If the independent variable has a significant impact on the dependent variable,

[0119] S5 specifically includes:

[0120] S51: Determine the air quality standards for the target area. According to the ambient air quality standard GB3095-2012, specify the pollutant concentration limits for the primary and secondary air quality standards, especially the concentration limits for total suspended particulate matter TSP, PM10 and PM2.5. The specific primary standard is TSP≤120μg / m³, PM10≤50μg / m³, PM2.5≤35μg / m³; the secondary standard is TSP≤300μg / m³, PM10≤150μg / m³, PM2.5≤75μg / m³;

[0121] S52: Substitute the air pollutant concentration limit values ​​in the target area into the established relationship model between the dust retention forest protection density and air quality. The model is used to calculate the optimal dust retention forest vegetation density values ​​under the primary air quality standard and the secondary air quality standard. These calculation results provide specific vegetation density recommendations under different air quality targets to ensure that dust retention forests achieve the best air purification effect. Through the above steps, the optimal dust retention forest vegetation density value in the target area is calculated. This process ensures that the protection function of dust retention forests can be effectively realized and provides practical technical support for the scientific management and design of dust retention forests.

[0122] S52 specifically includes:

[0123] S521: Substitute the pollutant concentration limits under the primary and secondary air quality standards into the established multiple linear regression relationship model, with the vegetation density of the dust-retaining forest as the dependent variable and the concentrations of TSP, PM10 and PM2.5 as the independent variables. The model equations after substitution are used to calculate the vegetation density values ​​required under different air quality standards;

[0124] S522: Calculate the optimal dust-retaining forest vegetation density value Y1 under the first-level air quality standard and the optimal dust-retaining forest vegetation density value Y2 under the second-level air quality standard by solving equations. Specifically, substitute the TSP, PM10, and PM2.5 concentration values ​​of the first-level standard into the model to solve for the Y1 value; similarly, substitute the TSP, PM10, and PM2.5 concentration values ​​of the second-level standard into the model to solve for the Y2 value;

[0125] S523: Record and compare the Y1 and Y2 values, clarify the optimal dust retention forest vegetation density required under different air quality standards, ensure that these density values ​​can meet the purification requirements of the corresponding air quality standards, and provide a basis for the actual planning of dust retention forests; through the above steps, it is disclosed in detail how to use the relational model to calculate the optimal dust retention forest vegetation density value under the first and second level air quality standards. This process clarifies the vegetation density requirements under different air quality goals, ensures that dust retention forests can effectively play their protective functions, and provides accurate data support for the scientific design and management of dust retention forests.

[0126] S6 specifically includes:

[0127] S61: Collect data on seasonal changes, climate conditions (such as temperature, humidity, and precipitation) and vegetation growth in the areas where the dust retention forests are located. Specific data include temperature fluctuations in different seasons, changes in precipitation, air humidity, and vegetation growth cycles (such as peak growing season and dormancy period) to determine the impact of these factors on the protective function of the dust retention forests;

[0128] S62: Based on the data obtained in S61, the optimal dust retention forest vegetation density value is corrected by applying a dynamic adjustment formula. The correction formula is: in, is the vegetation density value after dynamic adjustment. is the original optimal vegetation density value, is the temperature change of the current season, is the reference temperature, is the humidity change in the current season, is the base humidity, is the change in vegetation growth conditions, is the benchmark vegetation growth status index;

[0129] S63: Periodically re-measure the concentration of air particulate matter (including TSP, PM10 and PM2.5) in the area where the dust retention forest is located and record the measurement results. The measurement cycle is once every quarter to capture the impact of seasonal changes on the purification effect of the dust retention forest;

[0130] S64: Substitute the latest particulate matter concentration data obtained in S63 into the established relationship model and revise the relationship model. The specific revision process includes re-estimating the model parameters to ensure that the relationship model can accurately reflect the latest relationship between air quality and vegetation density. The revised model will be used for dynamic adjustment of vegetation density in the next cycle. This process ensures that the dust retention forest always maintains the best protection function under different environmental conditions, and provides a scientific and reliable method for the long-term management and optimization of the dust retention forest.

[0131] like Figure 2 As shown, a system for determining the dust retention forest protection density is used to implement the above-mentioned method for determining the dust retention forest protection density, including the following modules:

[0132] Data collection module: used to collect environmental data and air samples from various plots within the dust retention forest area, including concentration data of total suspended particulate matter TSP, PM10, PM2.5, as well as climatic conditions of vegetation density, temperature, humidity and precipitation, and data on vegetation growth status;

[0133] Data processing module: connected to the data acquisition module, used to pre-process the collected environmental data and air sample data, including data cleaning, outlier removal and interpolation completion;

[0134] Model building module: connected to the data processing module, using the processed environmental data and air sample data, adopting the multivariate linear regression analysis method, to establish the relationship model between the dust retention forest protection density and air quality;

[0135] Density calculation module: connected to the model building module, used to calculate the optimal dust-trapping forest vegetation density value under different air quality standards based on the established relationship model and the ambient air quality standard GB3095-2012. This module provides specific vegetation density recommendations based on the pollutant concentration limits of the primary and secondary air quality standards, and passes the calculation results to the density adjustment module;

[0136] Density adjustment module: connected to the density calculation module, used to dynamically adjust the optimal vegetation density value according to the seasonal changes, climate conditions and vegetation growth conditions of the dust retention forest area, to ensure that the dust retention forest can maintain the best protection effect under different seasons and climate conditions;

[0137] Density verification module: connects the density adjustment module and the data acquisition module to verify whether the adjusted vegetation density value meets the expected air purification effect. By regularly re-measuring the concentration of air particles and feeding back the new data to the model building module, the dust retention forest protection density is cyclically corrected and optimized.

[0138] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0139] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for determining the dust retention forest protection density, characterized in that: The following steps are involved: S1: In the target area, according to the vegetation density gradient of the dust retention forest, select a number of rectangular sample plots that can represent the overall vegetation density distribution of the dust retention forest. The density of the sample plots presents a continuous gradient change between different sample plots, and the size of the sample plot is determined according to statistical requirements; S2: Multiple sampling points are arranged along the diagonal direction in each plot; S3: Collect air samples at the sampling points in each sample plot, detect the concentrations of total suspended particulate matter TSP, PM10 and PM2.5 in the air samples, and calculate the average concentration of each particle at each sampling point in the same sample plot to obtain the mean concentration of each particle in the sample plot; S4: Using the mean concentration of each particle in the sample plot as the independent variable and the vegetation density of the dust-retaining forest as the dependent variable, a multivariate linear regression analysis method was used to establish a relationship model between the dust-retaining forest protection density and air quality; S5: Based on the established relationship model and in combination with the air pollutant concentration limits specified in the ambient air quality standard GB3095-2012, calculate the optimal dust-trapping forest vegetation density value when the target area reaches the first or second level air quality standard; S6: According to the seasonal changes, climate conditions and vegetation growth conditions of the dust retention forest area, the optimal dust retention forest vegetation density value is dynamically adjusted, and the air particulate matter concentration is periodically re-measured, and the relationship model is corrected using the detection results.

2. A method for determining the dust retention forest protection density according to claim 1, characterized in that: The S1 specifically includes: S11: In the target area, based on the pre-collected vegetation density data of the dust retention forest, a method combining geographic information system technology and remote sensing technology is used to conduct data analysis to clarify the distribution of vegetation density of the dust retention forest in different areas. Specifically, the vegetation density in the dust retention forest area is firstly gridded, and the vegetation density value of each grid unit is calculated based on the area of ​​each grid unit through the density estimation formula, thereby forming a density gradient map of the dust retention forest; S12: Based on the dust retention forest density gradient map formed in step S11, representative areas of different density intervals are selected according to the gradient variation range of the dust retention forest vegetation density, and the density of the dust retention forest vegetation is divided into several density levels based on the density interval, including 100-300 plants / hectare in low-density areas, 300-500 plants / hectare in medium-density areas, and 500-800 plants / hectare in high-density areas; S13: Delineate rectangular plots within the selected area. The size of each plot is determined according to the sample size calculation formula in statistics to ensure that the plot area is large enough to contain a sufficient number of vegetation and that the uniformity of vegetation density within the plot is ensured; S14: The locations of sample plots are selected within each selected area by random sampling to ensure that the selected sample plots can represent the continuous gradient changes in the overall vegetation density distribution of the dust-trapping forest.

3. The method for determining the dust retention forest protection density according to claim 1, characterized in that: The S2 specifically includes: S21: determine the rectangular boundary of the sample plot, and use the four vertices of the sample plot to connect to form two diagonal lines, measure the length L of each diagonal line, and mark the starting point and end point of the diagonal line; S22: Determine the number of sampling points on each diagonal line based on the uniformity of vegetation density within the sample plot and the required sample size; S23: Along each diagonal direction, arrange the sampling points according to the principle of equidistant distance to ensure that the distance between each sampling point is The sampling points are distributed to cover the entire area of ​​the plot, and the spacing between the sampling points is The calculation formula is: ,in, is the distance between each sampling point, is the length of the diagonal, is the number of sampling points. According to the calculation results, the sampling points are arranged one by one on the diagonal line. S24: Record the specific location coordinates of each sampling point and conduct on-site inspection after deployment to ensure that all sampling points are located in representative areas of actual vegetation density.

4. The method for determining the dust retention forest protection density according to claim 1, characterized in that: The S3 specifically includes: S31: At each sampling point, an air sampler is used to collect air samples. Specifically, the sampling port of the air sampler is set at a height of 1.5 meters from the ground. A predetermined volume of air sample is collected at each sampling point. The sampling time is According to the required sample volume and sampling flow rate Sure; S32: After pre-processing the collected air samples, the concentrations of total suspended particulate matter TSP, PM10 and PM2.5 in the air samples are measured using a particle monitoring device; specifically, the air samples are passed through a glass fiber filter membrane or a quartz fiber filter membrane, and the mass concentration of the particles in the samples is detected using the gravity method. ; S33: For all sampling points in each sample plot, the concentration data of TSP, PM10 and PM2.5 of each sampling point are calculated respectively, and then the average particle concentration value of the sample plot is calculated by arithmetic mean method. The calculation formula of the average concentration value is: ,in, is the average particle concentration of the sample site, in micrograms per cubic meter. is the particle concentration value of the i-th sampling point, in micrograms per cubic meter, is the total number of sampling points in the sample plot.

5. The method for determining the dust retention forest protection density according to claim 1, characterized in that: The S4 specifically includes: S41: The average particle concentration value of the sample site is selected as the independent variable, including the concentration values ​​of three types of particles: total suspended particulate matter TSP, PM10 and PM2.5, which are recorded as ; S42: The vegetation density of the dust-trapping forest is taken as the dependent variable, denoted as , and based on the average particle concentration values ​​of each plot and the corresponding vegetation density data, a multivariate linear regression model was established, and the expression is: ,in, is a constant term, is the regression coefficient corresponding to the independent variable, is the error term; S43: The regression coefficients were calculated by the least squares method. and Estimate to minimize the error term The sum of squares; S44: Perform statistical tests on the obtained regression model. Specifically, the overall significance of the model is tested through the F test, the F value is calculated and compared with the critical value to determine whether the model is significant; and the significance of each regression coefficient is tested through the t test, the t value is calculated and compared with the critical value to determine whether the influence of each variable on the dependent variable is significant.

6. A method for determining the dust retention forest protection density according to claim 5, characterized in that: The S44 specifically includes: S441: Perform an F test on the overall significance of the regression model, including: S4411: Calculate the regression sum of squares and residual sum of squares of the regression model. The calculation formula for the regression sum of squares is: ,in, is the predicted value of the model, is the mean value of the dependent variable, is the sample size, is the regression sum of squares; the formula for calculating the residual sum of squares is: in, is the actual observed value, is the regression sum of squares; S4412: Calculate the F value of the regression model. The formula is: ,in, is the number of independent variables, is the sample size; S4413: Find the critical value under the corresponding degrees of freedom according to the preset significance level. If the calculated value If the value is greater than the critical value, the regression model is considered to be significant as a whole; S442: Perform a t-test on the significance of each regression coefficient, including: S4421: Calculate the standard error of each regression coefficient , the formula is: ,in, For the The regression coefficients of the independent variables, is the inverse matrix of the product of the transpose matrix of the independent variable matrix and the independent variable matrix. diagonal elements of elements; S4422: Calculate the value of each regression coefficient Value, the formula is: in, is the estimated value of the regression coefficient of the jth independent variable; S4423: Find the critical value under the corresponding degrees of freedom according to the preset significance level. If the calculated value Value greater than critical If the independent variable has a significant impact on the dependent variable, 7. The method for determining the dust retention forest protection density according to claim 1, characterized in that: The S5 specifically includes: S51: Determine the air quality standards for the target area. According to the ambient air quality standard GB3095-2012, specify the pollutant concentration limits for the primary and secondary air quality standards, especially the concentration limits for total suspended particulate matter TSP, PM10 and PM2.

5. The specific primary standard is TSP≤120μg / m³, PM10≤50μg / m³, PM2.5≤35μg / m³; the secondary standard is TSP≤300μg / m³, PM10≤150μg / m³, PM2.5≤75μg / m³; S52: Substitute the air pollutant concentration limit values ​​of the target area into the established relationship model between the dust-retaining forest protection density and air quality, and use the model to calculate the optimal dust-retaining forest vegetation density values ​​under the primary air quality standards and the secondary air quality standards.

8. The method for determining the dust retention forest protection density according to claim 7, characterized in that: The S52 specifically includes: S521: Substitute the pollutant concentration limits under the primary and secondary air quality standards into the established multiple linear regression relationship model, with the vegetation density of the dust-retaining forest as the dependent variable and the concentrations of TSP, PM10 and PM2.5 as the independent variables; S522: Calculate the optimal dust-trapping forest vegetation density value Y1 under the first-level air quality standard and the optimal dust-trapping forest vegetation density value Y2 under the second-level air quality standard by solving equations; S523: Record and compare the Y1 and Y2 values ​​to determine the optimal dust-trapping forest vegetation density required under different air quality standards.

9. The method for determining the dust retention forest protection density according to claim 1, characterized in that: The S6 specifically includes: S61: Collect data on seasonal changes, climate conditions and vegetation growth in the area where the dust-trapping forest is located, including temperature fluctuations in different seasons, changes in precipitation, air humidity and vegetation growth cycle; S62: Based on the data obtained in S61, the optimal dust retention forest vegetation density value is corrected by applying a dynamic adjustment formula. The correction formula is: in, is the vegetation density value after dynamic adjustment. is the original optimal vegetation density value, is the temperature change of the current season, is the reference temperature, is the humidity change in the current season, is the base humidity, is the change in vegetation growth conditions, It is the benchmark vegetation growth status index; S63: Periodically re-measure the concentration of air particulate matter in the area where the dust retention forest is located and record the measurement results. The measurement cycle is once every quarter to capture the impact of seasonal changes on the purification effect of the dust retention forest; S64: Substitute the latest particulate matter concentration data obtained in S63 into the established relationship model, and modify the relationship model to ensure that the relationship model can accurately reflect the latest relationship between air quality and vegetation density.

10. A system for determining the protection density of a dust retention forest, used to implement a method for determining the protection density of a dust retention forest as claimed in any one of claims 1 to 9, characterized in that: Includes the following modules: Data collection module: used to collect environmental data and air samples from various plots within the dust retention forest area, including concentration data of total suspended particulate matter TSP, PM10, PM2.5, as well as climatic conditions of vegetation density, temperature, humidity and precipitation, and data on vegetation growth status; Data processing module: connected to the data acquisition module, used to pre-process the collected environmental data and air sample data, including data cleaning, outlier removal and interpolation completion; Model building module: connected to the data processing module, using the processed environmental data and air sample data, adopting the multivariate linear regression analysis method, to establish the relationship model between the dust retention forest protection density and air quality; Density calculation module: connected to the model building module, used to calculate the optimal dust-trapping forest vegetation density value under different air quality standards based on the established relationship model and the ambient air quality standard GB3095-2012; Density adjustment module: connected to the density calculation module, used to dynamically adjust the optimal vegetation density value according to seasonal changes, climate conditions and vegetation growth conditions in the area where the dust retention forest is located; Density verification module: connects the density adjustment module and the data acquisition module to verify whether the adjusted vegetation density value meets the expected air purification effect. By regularly re-measuring the concentration of air particles and feeding back the new data to the model building module, the dust retention forest protection density is cyclically corrected and optimized.

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