Basin non-point source pollution control measure configuration method based on multivariate data

Abstract

The invention discloses a watershed non-point source pollution control measure configuration method based on multivariate data. The method comprises the following steps: identifying and classifying land utilization types; dividing non-point source pollution control units; determining the load capacity of each pollutant in the non-point source pollution control unit; establishing a non-point source pollution reduction engineering measure database; according to the construction condition set of each engineering measure, determining the maximum construction space area of the non-point source pollution control measure; and taking the suitable construction space area of the non-point source pollution control measure as a decision variable, combining with the corresponding unit construction investment, constructing an objective function, and searching a non-point source pollution control measure configuration scheme with the optimal construction benefit under a given constraint condition. According to the method, multi-source data are utilized, the influence of complex underlying face-to-face non-point source pollution and the construction benefits of treatment measures are comprehensively considered, and the scientificity, accuracy and efficiency of planning and design of a watershed scale non-point source pollution treatment scheme are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of watershed non-point source pollution control, and particularly to a method for configuring non-point source pollution control measures in a watershed based on multi-source data. Background Art

[0002] In recent years, with the remarkable achievements in point source pollution control, the attention paid to non-point source pollution control has been increasing. Different from fixed pollution sources (point source pollution), non-point source pollution has the characteristics of wide distribution, large total amount, randomness, etc., and it is difficult to achieve the goal of non-point source pollution control with a single control measure. From the perspective of the influencing mechanism, the ground pollutants accumulated on different underlying surfaces are washed into the receiving water body by rainfall runoff. Therefore, the non-point source pollution control should take the watershed as the control unit.

[0003] In the prior art, the following technical problems exist in the practice of watershed environmental governance:

[0004] First, the lack or timeliness lag of environmental and social data affects the accuracy and scientificity of the governance plan;

[0005] Second, the watershed control unit usually includes multiple types of underlying surfaces, and the pollution sources of different underlying surface types show spatial heterogeneity, and different control measures need to be taken. However, the current non-point source pollution control research usually targets a single underlying surface environment such as urban non-point source or agricultural non-point source, lacking a systematic governance path for the entire underlying surface environment at the watershed scale. Summary of the Invention

[0006] Aiming at the deficiencies of the prior art, the present invention proposes a method for configuring non-point source pollution control measures in a watershed based on multi-source data, and proposes a comprehensive, systematic and combined set of non-point source pollution measures at the watershed scale for different underlying surface characteristics, and selects the optimal governance plan.

[0007] To achieve the above object, a method for configuring non-point source pollution control measures in a watershed based on multi-source data designed by the present invention is characterized in that it includes the following steps:

[0008] S1) Identify and classify the land use types in the study area;

[0009] S2) According to the terrain, land use type, and production activity characteristics, divide the study area into mountain area, farmland area, and urban area non-point source pollution control units; then divide the mountain area non-point source pollution control unit according to natural water collection, divide the farmland area non-point source pollution control unit according to rainwater pipe network drainage, and divide the urban area non-point source pollution control unit according to agricultural drainage; finally, superimpose and splice the divided mountain area, farmland area, and urban area non-point source pollution control units.

[0010] S3) Determine the pollutant loads of each pollutant in the mountainous areas, farmland areas, and urban area non-point source pollution control units within the study area under the baseline scenario;

[0011] S4) Establish a database of non-point source pollution reduction engineering measures;

[0012] The database includes three levels. The first level is the treatment path layer, including source control, process blockage, and end strengthening; the second level is the basic measure layer, including specific engineering measures for controlling various non-point source pollution sources under each treatment path; the third level is the engineering attribute layer, including the construction conditions, unit pollutant reduction amount, and unit construction investment corresponding to each engineering measure;

[0013] S5) Determine the maximum construction space area of non-point source pollution control measures according to the set of construction conditions of each engineering measure;

[0014] S6) Use the suitable construction space area of non-point source pollution control measures as the decision variable, combine with the corresponding unit construction investment, construct an objective function, and find the optimal configuration plan of non-point source pollution control measures with the best construction benefits under the given constraints.

[0015] Furthermore, in S1), the specific steps for identifying and classifying land use types are as follows:

[0016] S11) Collect remote sensing data for the study area;

[0017] S12) Preprocess the remote sensing data;

[0018] S13) For the preprocessed remote sensing image, perform true color synthesis of remote sensing bands;

[0019] S14) Combine the true color spectral information synthesized from multiple spectral bands with the panchromatic band, perform panchromatic image sharpening, and improve the image resolution;

[0020] S15) For the multi-spectral band synthesized image sharpened by the panchromatic image, use the visual method to delimit one or more land classification training samples of the same type, and assign the same classification number to the same land classification training sample;

[0021] S16) Apply a classification algorithm to classify the remote sensing image assigned with classification numbers into land use types;

[0022] S17) Export the grid data of land use types.

[0023] Even further, in S2), the specific steps for dividing the mountainous area non-point source pollution control unit according to natural water catchments are as follows:

[0024] S21) Perform depression filling on the elevation raster data based on GIS hydrological analysis to remove the depression points existing in the data;

[0025] S22) Perform flow direction analysis based on the elevation raster data after depression filling;

[0026] S23) Perform flow analysis based on the flow direction analysis raster, create a raster of the cumulative flow of each pixel, and identify the concentrated flow areas;

[0027] S24) Apply the watershed tool to divide the natural catchment areas of mountains using a flow threshold.

[0028] Furthermore, in S2), during the process of overlaying and mosaicking the non-point source pollution control units in the mountain area, farmland area, and urban area after zoning, it is necessary to correct the boundary conflicts of different zones according to the actual investigation situation.

[0029] Further, in S3), for the non-point source pollution control unit in the urban area, by establishing a land use type-pollution load model, determine the pollutant load amounts of each pollutant in the non-point source pollution control unit in the urban area;

[0030] The land use type-pollution load model is expressed by the following formula

[0031]

[0032] In the formula,

[0033] L 1i represents the load amount of pollutant i in the urban area,

[0034] E 1yi represents the pollutant load amount per unit area of pollutant i in the y-th land use type,

[0035] P represents the number of types of land types as pollution sources within the research scope,

[0036] S y represents the area of the y-th land use type.

[0037] Furthermore, in S3), under the baseline scenario, the pollutant load amounts in the control unit are calculated by the following formula

[0038] L i =L 1i +L 2i +L 3i

[0039] In the formula,

[0040] L i represents the load amount of the i-th pollutant in the non-point source pollution control unit,

[0041] L 1i represents the load of pollutant i in the urban area,

[0042] L 2i represents the load of the i-th pollutant generated by agricultural planting,

[0043] L 3i represents the load of the i-th pollutant generated by aquaculture.

[0044] Furthermore, in S5), the maximum construction space area of the non-point source pollution control measures is calculated by the following formula

[0045] A nmj_max = J nmj1 ∩ J nmj2 ∩ J nmj3 ∩... ∩ J nmjt

[0046] In the formula,

[0047] A nmj—max represents the maximum construction space area of the non-point source pollution control measures,

[0048] n represents the path layer number, n = A for source control, n = B for process interruption, n = C for end treatment,

[0049] m is the basic measure layer number,

[0050] j is the specific project measure number,

[0051] t is the construction condition number,

[0052] J represents the construction condition.

[0053] Even further, in S6), the objective function is calculated by the following formula

[0054] minf(A nmj ) = ∑A nmj × M nmj

[0055] In the formula,

[0056] f represents the objective function,

[0057] A nmj represents the suitable construction space area of the j-th project measure in the m-th basic measure layer of path n,

[0058] M nmj represents the unit construction cost corresponding to the suitable construction space area.

[0059] Further, in S6), the constraint conditions include that the suitable construction space area of the non-point source pollution control measures is less than or equal to the maximum construction space area of the non-point source pollution control measures, and the total reduction amount control of each pollutant.

[0060] Further, in S6), the total reduction amount control of each pollutant is calculated by the following formula

[0061] ∑A nmj ×E nmj-TN ≥L TN ×P TN

[0062] ∑A nmj ×E nmj-TP ≥L TP ×P TR

[0063] ∑A nmj ×E nmj-NH3N ≥L NH3N ×P NH3N

[0064] In the formula,

[0065] A nmj represents the suitable construction space area of the j-th engineering measure in the m-th basic measure layer of path n,

[0066] E nmj-TN 、E nmj-TP 、E nmj-NH3N respectively represent the unit pollutant removal amounts of total nitrogen, total phosphorus, and ammonia nitrogen of the pollutant corresponding to the j-th engineering measure in the m-th basic measure layer of path n,

[0067] L TN 、L TP 、L NH3N respectively represent the benchmark pollutant emissions of total nitrogen, total phosphorus, and ammonia nitrogen in the research scope,

[0068] P TN 、P TP 、P NH3N respectively represent the target reduction rates of total nitrogen, total phosphorus, and ammonia nitrogen in the research scope.

[0069] The advantages of the present invention are as follows:

[0070] 1. The present invention establishes a database of engineering measures for non-point source pollution reduction based on the pollutant loads in each non-point source pollution control unit within the research area. Using the multi-source data in the database, it determines the maximum construction space area of non-point source pollution control measures. Then, with the appropriate construction space area of non-point source pollution control measures as the decision variable and combined with the corresponding unit construction investment, it constructs an objective function and, under the given constraints, searches for the optimal configuration plan of non-point source pollution control measures with the best construction benefits.

[0071] 2. The present invention fully considers the mechanisms of non-point source pollution generation in different built environments within a small watershed and proposes different non-point source calculation methods for mountainous areas, farmland areas, and urban areas.

[0072] 3. Based on engineering examples and experience, the present invention proposes an ecological engineering measure database for non-point source pollution control applicable to different built environments, including attributes such as engineering application conditions, non-point source pollution control efficiency, and construction investment. With the maximization of comprehensive benefits as the goal, it establishes a method for configuring non-point source pollution control measures in a small watershed.

[0073] The method for configuring non-point source pollution control measures in a watershed based on multi-source data of the present invention uses multi-source data, comprehensively considers the impact of complex underlying surfaces on non-point source pollution and the recommended benefits of treatment measures, and improves the scientificity, accuracy, and efficiency of the planning and design of non-point source pollution treatment plans at the watershed scale. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 is a flowchart of the present invention;

[0075] Figure 2 is a flowchart of land use type identification and classification in the present invention;

[0076] Figure 3 is the non-point source pollution control unit division process in the present invention;

[0077] Figure 4 is a schematic structural diagram of the non-point source pollution reduction engineering measure database in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0078] The following further describes the present invention in detail with reference to the accompanying drawings and specific embodiments.

[0079] A method for configuring non-point source pollution control measures in a watershed based on multi-source data according to the present invention includes the following steps:

[0080] S1) Identify and classify the land use types within the research area.

[0081] The semi-automatic classification tool (SAC) based on the QGIS platform identifies land use types according to satellite remote sensing images. SAC is an image processing technology that identifies spectral features in remote sensing images and applies different classification algorithms for land use type classification.

[0082] Specifically, the specific steps for identifying and classifying land use types are as follows:

[0083] S11) Collect remote sensing data for the study area.

[0084] Satellite remote sensing images that SAC can identify include: Landsat (resolution 30m), Sentinel-2 (resolution 10m), ASTER (resolution 15m), MODIS (resolution 250 - 500m). The data type can be any type recognizable by SAC. In this embodiment, Landsat-8 remote sensing data is used.

[0085] S12) Preprocess the remote sensing data.

[0086] Use ENVI software to perform radiometric calibration and atmospheric correction on the remote sensing data. Radiometric calibration is to eliminate the errors of the satellite sensor itself and determine the accurate radiation value at the sensor entrance. Atmospheric correction is to eliminate the errors caused by atmospheric scattering, absorption, and reflection.

[0087] S13) For the preprocessed remote sensing image, perform true color synthesis of remote sensing bands.

[0088] Use the preprocessed remote sensing image to perform true color synthesis of remote sensing bands. The true color synthesis uses three characteristic bands: red, green, and blue. In this embodiment, the bands for Landsat-8 true color synthesis are Band 4, Band 3, and Band 2.

[0089] S14) Combine the true color spectral information synthesized from multiple spectral bands with the panchromatic band for panchromatic image sharpening to improve the image resolution.

[0090] In this embodiment, the resolution of the Landsat-8 true color synthesized image is 30m. After synthesis with the panchromatic band (panchromatic, Band 8), the resolution of the true color synthesized image is increased to 15m.

[0091] In the SAC tool, the panchromatic image sharpening adopts the Brovey transformation, and the transformation formula is:

[0092] M span =MS×Pan / I

[0093] In the formula,

[0094] M span is the multispectral band composite image after panchromatic image sharpening,

[0095] MS is the true color image of the multispectral band composite,

[0096] I is the intensity coefficient, which is a function related to the multispectral bands,

[0097] In this embodiment: I = (0.42 × blue band + 0.98 × green band + 0.6 × red band) / 2.

[0098] S15) For the multispectral band composite image after panchromatic image sharpening, use the visual method to delimit one or more land classification training samples of the same type, and assign the same classification number to the same land classification training sample.

[0099] Form vector polygons from one or more land classification training samples of the same type, and assign the same classification number to the samples of the same land type. In this embodiment, high-density residential areas (classification number 1), medium-density residential areas (classification number 2), low-density residential areas (classification number 3), roads and squares (classification number 4), bare land (classification number 5), urban green spaces (classification number 6), farmland (classification number 7), natural forests (classification number 8), economic forests (classification number 9), natural shrub-grasslands (classification number 10), river water systems (classification number 11), lake pond water systems (classification number 12), and aquaculture ponds (classification number 13) and other 13 land use types are delimited.

[0100] S16) Apply a classification algorithm to classify the land use types of the remotely sensed image after assigning classification numbers.

[0101] The classification algorithm realizes the land type classification of the target image by comparing the spectral characteristics of the unit pixels of each type of training sample with the spectral characteristics of the target remotely sensed image. The classification algorithms adopted in SAC include the minimum distance method, the maximum likelihood method, the spectral angle mapping method, etc. Among them, the most widely used is the maximum likelihood method.

[0102] Applying the maximum likelihood method requires that each training area has enough pixels to perform covariance matrix calculations. The calculation formula of the maximum likelihood method for land type classification is:

[0103]

[0104] In the formula,

[0105] g k (x) is the discriminant function of a single pixel,

[0106] C kis the land use type k (classification number k),

[0107] x is the characteristic spectral vector of the target image pixel,

[0108] p(C k ) is the probability that the land use type of the target image is C k ,

[0109] |∑k| is the row and column of the covariance matrix of the data in class C k ,

[0110] is the inverse function of the covariance matrix,

[0111] y k is the characteristic spectral vector of the land use type K,

[0112]

[0113] Derive the grid data of the land use type from S17).

[0114] After the above steps, the land use type raster map of the study area will be obtained, and the raster pixel values are the classification numbers of different land types. Use the SAC tool to export the land use type raster as the basis for subsequent calculations.

[0115] S2) According to the terrain, land use type, and characteristics of production activities, divide the study area into mountainous areas, farmland areas, and urban non-point source pollution control units; then divide the mountainous area non-point source pollution control units according to natural water collection, divide the farmland area non-point source pollution control units according to rainwater pipe network drainage, and divide the urban non-point source pollution control units according to agricultural drainage; finally, overlay and merge the divided mountainous areas, farmland areas, and urban non-point source pollution control units.

[0116] The mountainous area non-point source pollution control unit is bounded by the natural water collection area, and the GIS hydrological analysis module is used to conduct a water collection analysis on the elevation raster data. The elevation data source is the measured topographic map data or the DEM digital elevation model data. In this embodiment, the GDEM V2 30m resolution digital elevation data is used.

[0117] Specifically, the specific steps for dividing the mountainous area non-point source pollution control unit according to natural water collection are as follows:

[0118] S21) Based on GIS hydrological analysis, conduct a depression filling process on the elevation raster data to remove the depression points existing in the data;

[0119] S22) Based on the elevation raster data after the depression filling process, conduct a flow direction analysis;

[0120] S23) Based on the flow direction analysis grid, perform flow analysis, create a grid of the cumulative flow of each pixel, and identify concentrated flow areas, including rivers and catchment lines;

[0121] S24) Apply the watershed tool and use the flow threshold to divide the natural catchment areas of the mountains. The selection of the flow threshold determines the area of the catchment area. In this embodiment, the selected flow threshold is 3000.

[0122] Specifically, the urban non-point source pollution control unit is bounded by the stormwater pipe network drainage area, and the data source is measured data, and the general data format is the dwg format.

[0123] Specifically, the agricultural non-point source pollution control unit is bounded by the agricultural drainage area, and the data source is measured data, and the general data format is the dwg format.

[0124] Preferably, during the process of superimposing and merging the mountain area, farmland area, and urban non-point source pollution control units after zoning, it is necessary to correct the boundary conflicts of different zones according to the actual investigation situation. In particular, manually visually correct the boundary conflicts of different construction areas according to the actual investigation situation.

[0125] S3) Determine the pollutant load of each pollutant in the mountain area, farmland area, and urban non-point source pollution control units within the study area under the baseline scenario.

[0126] In particular, natural forest land, natural shrub grassland, and ponds without aquaculture functions are not regarded as non-point source pollution sources, and the non-point source pollution generated by them is not considered in this method.

[0127] For the land use types in the urban area, including high-density residential areas, medium-density residential areas, low-density residential areas, roads and squares, bare land, and urban green spaces. Establish a land use type-pollution load model based on the actual investigation of the study area to determine the pollutant load of each pollutant in the urban non-point source pollution control unit. For areas lacking data, the recommended values in the water quality management of the Storm Water Management Model (SWMM) can be referred to, as shown in Table 1.

[0128] Table 1 Pollutant load per unit area of land use type, unit: kg / ha·year

[0129]

[0130] The land use type-pollution load model is represented by the following formula

[0131]

[0132] In the formula,

[0133] L 1i represents the load of pollutant i in the urban area,

[0134] E 1yi represents the pollutant load per unit area of pollutant i in the y-th land use type.

[0135] P represents the number of land type categories as pollution sources within the study area.

[0136] S y represents the area of the y-th land use type.

[0137] In this embodiment, P = 6, and the land type categories as non-point source pollution sources are high-density residential areas, medium-density residential areas, low-density residential areas, roads and squares, bare land and permeable parking lots, and urban green spaces.

[0138] In S3), the non-point source pollution load estimation in the farmland area includes farmland and economic forests, and the main pollutant indicators are total nitrogen, ammonia nitrogen, and total phosphorus.

[0139] Specifically, the pollutant load of each pollutant entering the soil through fertilizers under various planting patterns is calculated by the following formula. The fertilizers include chemical fertilizers (nitrogen fertilizers, phosphorus fertilizers, and compound fertilizers), organic fertilizers, and straw returning to the field.

[0140] Q 1kj =(A kj f 1k +B j f 2k +C j f 3kj +D j f 4kj )×S j

[0141] In the formula,

[0142] Q 1kj represents the amount of nitrogen and phosphorus entering the field under the j-th planting pattern, in kg / ha / year. k = 1, 2, respectively representing nitrogen and phosphorus. When i is total nitrogen and ammonia nitrogen, k = 1; when i is total phosphorus, k = 2.

[0143] A kj 、B j 、C j are the amounts of nitrogen and phosphorus fertilizers, compound fertilizers, and organic fertilizers applied per unit area on average under the j-th planting pattern, in kg / ha / year. This data is obtained through on-site visits and investigations.

[0144] D j is the amount of straw returned to the field per unit area on average under the j-th planting pattern, in kg / ha / year.

[0145] f 1k 、f 2k are the conversion coefficients of nitrogen and phosphorus in nitrogen and phosphorus fertilizers and compound fertilizers.

[0146] f 3kj and f 4kj are the nitrogen and phosphorus contents in the organic fertilizers and returned straw applied under the j-th planting pattern.

[0147] S j is the planting area of the j-th planting pattern, in hectares.

[0148] Specifically, the pollutant load generated by agricultural planting is calculated by the following formula

[0149]

[0150] In the formula,

[0151] L 2i represents the nitrogen and phosphorus loss in surface runoff during agricultural planting, in tons / year. i is total nitrogen, ammonia nitrogen, or total phosphorus.

[0152] Q 1kj represents the nitrogen and phosphorus input into the field under the j-th planting pattern in the control unit, in kg / ha / year. k = 1, 2, representing nitrogen and phosphorus respectively. When i is total nitrogen and ammonia nitrogen, k = 1; when i is total phosphorus, k = 2.

[0153] m is the number of agricultural planting patterns in the control unit.

[0154] E 2ij represents the loss coefficient of total nitrogen, ammonia nitrogen, or total phosphorus under the j-th planting pattern.

[0155] Specifically, the pollutant load generated by aquaculture ponds is calculated by the following formula

[0156]

[0157] In the formula,

[0158] L 3i represents the i-th pollution load generated by aquaculture.

[0159] Q 3jt and I 3jt represent the output and input of the t-th aquaculture species under the j-th aquaculture pattern, in tons / year.

[0160] E 3ijt represents the freshwater aquaculture pollution discharge coefficient of the t-th aquaculture species under the j-th aquaculture pattern, in g / kg.

[0161] m is the number of freshwater aquaculture patterns.

[0162] n is the number of freshwater aquaculture species.

[0163] Therefore, under the baseline scenario, the pollutant load in the control unit is calculated by the following formula

[0164] L i = L 1i + L 2i + L 3i

[0165] In the formula,

[0166] L i represents the load of the i-th pollutant in the non-point source pollution control unit,

[0167] L 1i represents the load of pollutant i in the urban area,

[0168] L 2i represents the load of the i-th pollutant generated by agricultural planting,

[0169] L 3i represents the load of the i-th pollutant generated by aquaculture.

[0170] S4) Establish a database of engineering measures for non-point source pollution reduction;

[0171] The said database includes three levels. The first level is the treatment path layer, including source control, process interruption, and end strengthening; the second level is the basic measure layer, including specific engineering measures for controlling various non-point source pollution sources under each treatment path; the third level is the engineering attribute layer, including the construction conditions, unit pollutant reduction amount, and unit construction cost corresponding to each engineering measure.

[0172] The treatment path layer defines the treatment path of non-point source pollution in the small watershed from a systematic level. In this embodiment, according to the current technical regulations and engineering practice experience, a database of non-point source pollution reduction measures for the small watershed is established, as shown in Table 2 specifically.

[0173] Table 2 Database of Non-point Source Pollution Reduction Measures for Small Watersheds

[0174]

[0175]

[0176] S5) Determine the maximum construction space area of non-point source pollution control measures according to the set of construction conditions of each engineering measure.

[0177] Specifically, apply the GIS spatial analysis tool. According to the set of construction conditions J nmjt = {J nmj1 .J nmj2 , J nmj3 ... J nmjt}, determine the maximum construction space area of non-point source pollution control measures. The maximum construction space area of non-point source pollution control measures is calculated by the following formula

[0178] A nmj_max = J nmj1 ∩ J nmj2 ∩ J nmj3 ∩... ∩ J nmjt

[0179] In the formula,

[0180] A nmj—max represents the maximum construction space area of the non - point source pollution control measures,

[0181] n represents the path layer number, where n = A is source control, n = B is process blockage, and n = C is end treatment,

[0182] m is the basic measure layer number,

[0183] j is the specific engineering measure number,

[0184] t is the construction condition number,

[0185] J represents the construction condition.

[0186] S6) Taking the suitable construction space area of the non - point source pollution control measures as the decision variable, combined with the corresponding unit construction investment, construct the objective function, and under the given constraint conditions, find the non - point source pollution control measure configuration plan with the optimal construction benefit.

[0187] Specifically, the Monte Carlo method is used to find the non - point source pollution control measure configuration plan with the optimal construction benefit.

[0188] In S6), the objective function is calculated by the following formula

[0189] min f(A nmj ) = ∑A nmj × M nmj

[0190] In the formula,

[0191] f represents the objective function,

[0192] A nmj represents the suitable construction space area of the j - th engineering measure in the m - th basic measure layer of path n,

[0193] M nmj represents the unit construction investment corresponding to the suitable construction space area.

[0194] The constraint conditions include that the suitable construction space area of the non - point source pollution control measures is less than or equal to the maximum construction space area of the non - point source pollution control measures, and the total reduction amount control of each pollutant.

[0195] Specifically, the constraint conditions are:

[0196] The suitable construction space area of the non-point source pollution control measures is less than or equal to the maximum construction space area of the non-point source pollution control measures, which is expressed by the following formula

[0197] A nmj ≤A nmj_max

[0198] In the formula,

[0199] A nmj represents the suitable construction space area of the jth engineering measure in the mth basic measure layer of path n,

[0200] A nmj—max represents the maximum construction space area of the non-point source pollution control measures.

[0201] The total reduction amount control of each pollutant is calculated by the following formula

[0202] ∑A nmj ×E nmj-TN ≥L TN ×P TN

[0203] ∑A nmj ×E nmj-TP ≥L TP ×P TR

[0204] ∑A nmj ×E nmj-NH3N ≥L NH3N ×P NH3N

[0205] In the formula,

[0206] A nmj represents the suitable construction space area of the jth engineering measure in the mth basic measure layer of path n,

[0207] E nmj-TN 、E nmj-TP 、E nmj-NH3N respectively represent the unit pollutant removal amounts of total nitrogen (TN), total phosphorus (TP), and ammonia nitrogen (NH 3 N) corresponding to the jth engineering measure in the mth basic measure layer of path n,

[0208] L TN 、L TP 、L NH3N respectively represent the benchmark pollutant emission amounts of total nitrogen (TN), total phosphorus (TP), and ammonia nitrogen (NH 3 N) within the research scope,

[0209] P TN 、PTP , P NH3N respectively represent the target reduction rates (%) of total nitrogen (TN), total phosphorus (TP), and ammonia nitrogen (NH 3 N) within the research scope.

[0210] The method for configuring watershed non-point source pollution control measures based on multi-source data of the present invention utilizes multi-source data, comprehensively considers the impact of complex underlying surfaces on non-point source pollution and the construction benefits of treatment measures, and improves the scientificity, accuracy, and efficiency of the planning and design of non-point source pollution treatment plans at the watershed scale.

[0211] The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited by the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications made without departing from the spirit and principle of the present invention shall be equivalent replacement methods and are all included in the protection scope of the present invention.

Claims

1. A method for configuring watershed non-point source pollution control measures based on multivariate data, characterized in that: The steps include: S1) Identify and classify land use types in the study area; S2) According to the terrain, land use type, and production activity characteristics, the study area is divided into mountainous area, farmland area, and urban area non-point source pollution control units; the mountainous area non-point source pollution control unit is then divided according to natural water catchment, the farmland area non-point source pollution control unit is divided according to rainwater pipe network drainage, and the urban area non-point source pollution control unit is divided according to agricultural drainage; finally, the divided mountainous area, farmland area, and urban area non-point source pollution control units are superimposed and spliced; S3) Determine the pollutant loads in the non-point source pollution control units in the mountainous areas, farmland areas, and urban areas within the study area under the baseline scenario; S4) Establish a database of engineering measures to reduce non-point source pollution; The database includes three levels, of which the first level is the governance path layer, including source control, process blocking and terminal reinforcement; The second level is the basic measures layer, including specific engineering measures for controlling various types of non-point source pollution under various governance paths; the third level is the engineering attribute layer, including the construction conditions, unit pollutant reduction and unit construction investment corresponding to each engineering measure; S5) Determine the maximum construction space area of ​​non-point source pollution control measures based on the construction conditions of each engineering measure; S6) Taking the suitable construction space area of ​​non-point source pollution control measures as the decision variable and combining it with the corresponding unit construction investment, the objective function is constructed to find the non-point source pollution control measures configuration plan with the best construction efficiency under given constraints.

2. The method for configuring watershed non-point source pollution control measures based on multivariate data according to claim 1 is characterized in that: In S1), the specific steps for identifying and classifying land use types are as follows: S11) Collect remote sensing data in the study area; S12) preprocessing the remote sensing data; S13) performing true color synthesis of remote sensing bands on the pre-processed remote sensing image; S14) combining the true color spectrum information synthesized by the multi-spectral bands with the panchromatic band to perform panchromatic image sharpening to improve the image resolution; S15) using a visual method to delineate one or more land classification training samples of the same type from the multispectral band synthetic image after sharpening the panchromatic image, and assigning the same classification number to the same land classification training samples; S16) applying a classification algorithm to classify the land use types of the remote sensing images assigned with classification numbers; S17) Exporting grid data of land use types.

3. The method for configuring watershed non-point source pollution control measures based on multivariate data according to claim 2 is characterized in that: In S2), the specific steps for dividing the non-point source pollution control units in mountainous areas according to natural water catchment are as follows: S21) performing depression filling processing on the elevation grid data based on GIS hydrological analysis to remove the depression points existing in the data; S22) performing flow direction analysis based on the elevation grid data after the depression filling process; S23) performing flow analysis based on the flow direction analysis grid, creating a grid of accumulated flow in each pixel, and identifying concentrated flow areas; S24) Apply the watershed tool and use flow thresholds to divide the natural water catchment areas of the mountain.

4. The method for configuring watershed non-point source pollution control measures based on multivariate data according to claim 3 is characterized in that: In S2), when superimposing and merging the non-point source pollution control units in the mountainous area, farmland area, and urban area after division, the boundary conflicts of different divisions need to be corrected according to the actual investigation situation.

5. The method for configuring watershed non-point source pollution control measures based on multivariate data according to claim 1 is characterized in that: In S3), for the urban area non-point source pollution control unit, by establishing a land use type-pollution load model, the load of each pollutant in the urban area non-point source pollution control unit is determined; The land use type-pollution load model is expressed by the following formula: In the formula, L 1i represents the load of pollutant i in urban areas, E 1yi represents the pollutant load per unit area of ​​pollutant i in the yth land use type, P represents the number of land types that are pollution sources within the study area. S y Represents the area of ​​the yth land use type.

6. The method for configuring watershed non-point source pollution control measures based on multivariate data according to claim 5 is characterized in that: S3), under the baseline scenario, the pollutant load in the control unit is calculated by the following formula L i =L 1i +L 2i +L 3i In the formula, L i represents the i-th pollutant load in the non-point source pollution control unit, L 1i represents the load of pollutant i in urban areas, L 2i represents the i-th pollutant load generated by agricultural planting, L 3i represents the i-th pollutant load generated by aquaculture.

7. The method for configuring watershed non-point source pollution control measures based on multivariate data according to claim 1, characterized in that: S5), the maximum construction space area of ​​non-point source pollution control measures is calculated by the following formula HAS nmj_max =J nmj1 ∩J nmj2 ∩J nmj3 ∩...∩J nmjt In the formula, A nmj—max represents the maximum construction space area of ​​non-point source pollution control measures, n represents the path layer number, n=A is source control, n=B is process blocking, n=C is terminal management, m is the basic measure layer number, j is the specific engineering measure number, t is the construction condition number, J represents construction conditions.

8. The method for configuring watershed non-point source pollution control measures based on multivariate data according to claim 7 is characterized in that: S6), the objective function is calculated by the following formula: minf(A nmj )=ΣA nmj ×M nmj In the formula, f represents the objective function, A nmj represents the suitable construction space area of ​​the jth engineering measure of the mth basic measure layer of path n, M nmj It indicates the unit construction investment corresponding to the area of ​​suitable construction space.

9. The method for configuring watershed non-point source pollution control measures based on multivariate data according to claim 8, characterized in that: S6), the constraints include that the suitable construction space area of ​​non-point source pollution control measures is less than or equal to the maximum construction space area of ​​non-point source pollution control measures, and the total amount of pollutant reduction is controlled.

10. The method for configuring watershed non-point source pollution control measures based on multivariate data according to claim 9, characterized in that: In S6), the total amount of each pollutant reduction is calculated by the following formula ∑A nmj ×E nmj-TN ≥L TN ×P TN ∑A nmj ×E nmj-TP ≥L TP ×P TP ∑A nmj ×E nmj-nH3N ≥L NH3N ×P NH3N In the formula, A nmj represents the suitable construction space area of ​​the jth engineering measure of the mth basic measure layer of path n, E nmj-TN 、E nmj-TP 、E nmj-NH3N They represent the unit pollutant removal amount of total nitrogen, total phosphorus and ammonia nitrogen corresponding to the jth engineering measure of the mth basic measure layer of path n, respectively. L TN , L TP , L NH3N They represent the baseline pollutant emissions of total nitrogen, total phosphorus, and ammonia nitrogen within the research scope, respectively. P TN , P TP , P NH3N They respectively represent the target reduction rates of the pollutants total nitrogen, total phosphorus and ammonia nitrogen within the study scope.

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