A method for identifying key areas of ecological restoration based on non-point source pollution distribution

Abstract

The present invention belongs to the field of ecological restoration and environmental investigation, and particularly relates to a method for identifying key areas of ecological restoration based on the distribution of non-point source pollution. First, according to the improved output coefficient model, combined with rainfall data, DEM data, land use data, pollutant emission coefficients, etc., the spatial distribution of non-point source pollution in the target area is calculated; then, according to the river vector data, the transmission distance of non-point source pollution from different spatial grids to the river is calculated; finally, after standardizing the calculation results of the spatial distribution of non-point source pollution and the transmission distance, the key areas of ecological restoration can be determined according to the calculated ecological restoration importance index. The method of the present invention can identify in advance the areas that are more severely polluted by non-point source pollution and are more likely to pollute rivers, improving the accuracy and convenience of selecting ecological restoration areas.

Description

Technical Field

[0001] The present invention belongs to the field of ecological restoration and environmental investigation, and particularly relates to a method for identifying key areas of ecological restoration based on the distribution of non-point source pollution. Background Art

[0002] With the gradual control of point source pollution, non-point source pollution is considered to be the main threat to the current aquatic ecosystem and drinking water safety. Non-point source pollution has characteristics such as randomness, dispersion, and hysteresis, and ecological restoration has become one of the main means to control non-point source pollution. Some scholars have conducted some research on non-point source pollution management. To ensure the effectiveness of the best solution, Ding et al. used non-point source pollution simulation of control units to determine key source areas (Ding Y et al., Non-pointsource pollutionsimulation and best management practices analysis basedoncontrolunits in Northern China[J]. International Journal of

[0003] Environmental Research and Public Health,2020,17(3):868). When designing the implementation plan of large-scale non-point source pollution control measures, Lee et al. further identified the spatial distribution of the loss risk of non-point source pollution sources at the watershed scale (Lee G et al., Framework to prioritizewatersheds for diffusepollution management inthe Republic ofKorea: application of multi-criteriaanalysis using the Delphi method[J]. Natural Hazards and Earth SystemSciences,2019,19(12):2767-2779). However, non-point source pollution is an uncertain process. When determining the pollution reduction target, the previous studies did not distinguish the land use patterns and the driving factors of non-point source pollution emissions, and at the same time, there was a lack of corresponding selection methods for the areas that need to focus on ecological restoration during the non-point source pollution transmission process, resulting in a difference between the proposed policy solutions and the actual development situation.

[0004] With the growth of the population and the increasing demand for non-food crops such as food crops, biofuels, and animal-based foods, the global consumption of nitrogen and phosphorus is steadily increasing, leading to the overuse of nitrogen and phosphorus in global industrial and agricultural systems. Excessive input of pollutants such as nitrogen and phosphorus nutrients and COD will cause ecological and environmental problems such as rising water body nutrient concentrations and algal blooms, ultimately resulting in serious consequences such as the loss of water body functions, a sharp reduction in biodiversity, and changes in the structure and function of ecosystems. Therefore, providing a method and technical means for identifying key areas of ecological restoration based on non-point source pollution distribution and reducing the interference of non-point source pollution on water bodies through ecological restoration methods is of great significance for improving the water quality of rivers and lakes, alleviating lake eutrophication, and cyanobacteria blooms. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for identifying key areas of ecological restoration based on non-point source pollution distribution, so as to identify in advance the key areas that need to carry out ecological restoration and provide support for controlling non-point source pollution and carrying out ecological restoration work in the later stage.

[0006] In order to achieve the above purpose, the present invention adopts the following technical solutions:

[0007] A method for identifying key areas of ecological restoration based on non-point source pollution distribution, comprising the following steps:

[0008] (1) Divide the target area into different spatial grids according to administrative divisions, land use patterns, and pollution load source data. Based on the improved non-point source pollution output coefficient model, calculate the load amounts of various non-point source pollutants in different spatial grids at high-precision spatial resolution, and sum them to obtain the total intensity of grid non-point source pollutant load PM i in the target area; i ;

[0009] (2) According to the vector data of the rivers around different spatial grids in the target area, calculate the transmission distance from the grid to the river in the target area through the Euclidean distance calculation method i ; SL i ;

[0010] (3) Standardize PM i and SL i respectively to obtain the grid non-point source pollution load intensity index Q i and the pollutant transmission distance index L i from the grid to the river. Calculate the importance index E i of grid ecological restoration in the target area according to E i =Q i ×L i ; i Calculate the importance index E i of grid ecological restoration in the target areai , determine the key areas for ecological restoration according to the magnitude of E i , the higher the E, the more urgent the need for restoration in the area. i The higher it is, the more the area needs to be restored.

[0011] Furthermore, the improved non-point source pollution output coefficient model described in step (1) is expressed as follows:

[0012] ;

[0013] In the formula: L is the non-point source pollution load; E s is the output coefficient of the s-type pollution source, kg / (km 2 •a) or kg / (head•a) or kg / (person•a), where a represents the year; A m is the area of the m-th land use type or the number of the m-th livestock or the rural population, km 2 or head (only) or person; α is the precipitation driving factor; β is the terrain driving factor; γ is the land use driving factor. Specifically:

[0014] α is the precipitation driving factor, expressed as follows:

[0015] ;

[0016] Among them, r is the annual precipitation of the target area; is the average annual precipitation of the target area over the years; R j is the annual precipitation of the basic spatial unit j of the target area; the precipitation data is obtained from rainfall data; the rainfall data is obtained from the meteorological bureau of the area where the target area is located or downloaded from the China Meteorological Data Network;

[0017] β is the terrain driving factor, expressed as follows:

[0018] ;

[0019] Among them, θ j is the slope of the basic spatial unit j of the target area, is the average slope of the basic measurement unit of the typical area, d is a constant; the slope data is obtained from open-source digital elevation data;

[0020] The basic spatial unit refers to a 100-square-meter grid or a 1-square-kilometer grid, determined according to the size of the target area;

[0021] γ is the land use driving factor; the land use driving factor is used to describe the impact of different land use patterns on non-point source pollution load. Different land use types such as forest land, grassland, cultivated land, and construction land directly affect the formation of surface runoff during rainfall, and at the same time will have a certain interception effect on non-point source pollution, which is obtained from land use open-source data combined with relevant literature;

[0022] The steps for obtaining the output coefficient values are to calculate and determine the output coefficient through literature review and relevant census data. The sources of non-point source pollution in the target area are attributed to three categories: land use, livestock and poultry breeding, and domestic life. Among them, land use includes cultivated land, forest land, grassland, and construction land, livestock and poultry breeding includes pigs, cows, sheep, and poultry, and domestic life includes urban and rural areas.

[0023] Further, the non-point source pollutants in step (1) refer to total phosphorus, total nitrogen, ammonia nitrogen, or COD.

[0024] Further, the target area in step (1) i The total intensity of grid non-point source pollutant load PM i The calculation formula is as follows:

[0025] ;

[0026] In the formula: PM i is i the total intensity of grid pollutant load; S it is i the load intensity of the t th pollutant in the grid, t = 1, 2, 3, 4, corresponding to total phosphorus, total nitrogen, ammonia nitrogen, and COD in sequence.

[0027] Further, the calculation formula of the Euclidean distance in step (2) is as follows:

[0028] ;

[0029] Among them, x and y are n two points in the x k and y k are the coordinates of points x and y in the k th dimension respectively.

[0030] Further, the i grid non-point source pollution load intensity index Q i is calculated as: , PM iwithin the target area i total intensity of grid surface source pollutant load;

[0031] the said i pollutant transmission distance index L from the grid to the river i The calculation formula is: , SL i is the target area i transmission distance from the grid to the river.

[0032] Furthermore, the ecological restoration importance index E described in step (3) i has a numerical range between 0 and 1. Among them, E i ≥0.8 belongs to the extremely high pollution level, indicating that the target area is an extremely urgent area for ecological restoration; 0.6 ≤ E i <0.8 belongs to the high pollution level, indicating that the target area is an urgent area for ecological restoration; 0.4 ≤ E i <0.6 belongs to the medium pollution level, indicating that the target area is a relatively urgent area for ecological restoration; 0.2 ≤ E i <0.4 belongs to the low pollution level, indicating that the target area is a generally urgent area for ecological restoration; 0 ≤ E i <0.2 belongs to the extremely low pollution level, indicating that the target area does not need ecological restoration temporarily.

[0033] The beneficial effects of the present invention are as follows:

[0034] The present invention creatively proposes a method system for non-point source pollution load identification - transmission distance calculation - ecological restoration key area identification. First, according to the output coefficient model, combined with rainfall data, DEM data, land use data, pollutant emission coefficients, etc., calculate the spatial distribution of non-point source pollution in the region; then, calculate the transmission distance of non-point source pollution from different spatial grids to the river according to the river vector data; finally, after standardizing the calculation results of the non-point source pollution spatial distribution and transmission distance, the key areas for ecological restoration can be determined according to the calculated ecological restoration importance index.

[0035] The method of the present invention can identify in advance the areas that are more severely affected by non-point source pollution and are more likely to pollute rivers, so that the ecological restoration project is more targeted, effectively reducing the impact of non-point source pollution, and is of great significance for protecting the water quality of rivers and reducing the risk of cyanobacteria blooms in lakes and reservoirs.

[0036] Meanwhile, this method system can improve the accuracy and convenience of selecting ecological restoration areas. It only needs to use existing data such as precipitation, digital elevation, and land use for prediction, overcoming the disadvantages of traditional on-site surveys, such as low efficiency, high cost, subjective bias, difficulty in dynamic monitoring, and spatial limitations. The method of the present invention provides technical support for implementing digital and refined water ecological protection and ecological restoration governance strategies at different spatio-temporal scales, and will significantly improve the final effect of ecological restoration projects. Description of the Drawings

[0037] Figure 1 It is a spatial distribution map of precipitation driving factors in City A.

[0038] Figure 2 It is a spatial distribution map of topographic driving factors in City A.

[0039] Figure 3 It is a spatial distribution map of output coefficients of non-point source pollution from different sources in City A.

[0040] Figure 4 It is a spatial distribution map of the COD load into the river in City A.

[0041] Figure 5 It is a spatial distribution map of the ammonia nitrogen load into the river in City A.

[0042] Figure 6 It is a spatial distribution map of the total nitrogen load into the river in City A.

[0043] Figure 7 It is a spatial distribution map of the total phosphorus load into the river in City A.

[0044] Figure 8 It is a spatial distribution map of the non-point source pollutant transmission distance index in City A.

[0045] Figure 9 It is a spatial distribution map of the importance index of ecological restoration areas in City A. Detailed Implementation Modes

[0046] The technical solutions of the present invention will be further described below in conjunction with the drawings and embodiments. Embodiment

[0047] In this embodiment, City A located in the middle and upper reaches of the Han River is taken as an example for illustration.

[0048] 1. Description of the Target Area

[0049] City A has a well-developed water system with a dense river network. In addition to the Han River running through the whole territory from west to east, there are about 2,000 rivers of various sizes. Reservoir B within the territory of City A is the water source of the Middle Route Project of the South-to-North Water Diversion in China, a national first-class water source protection area, and an important wetland protection area, which is of great significance to the water ecological environment of the Han River Basin. As Reservoir B has changed from a backup water source to the main water source, its water quality will directly affect the drinking water safety of residents in more than 20 cities along the line in 4 provinces (municipalities) of M, N, O, and P around City A. Therefore, it is of great significance to identify in advance the key areas for ecological restoration affected by non-point source pollution in City A.

[0050] 2. Data Sources

[0051] The DEM data used in this study has a resolution of 30 m and comes from the ASTER GDEM dataset on the China Geospatial Data Cloud website. The land use data comes from the remote sensing monitoring data of the current land use situation in China by the Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences. The meteorological data comes from the China Meteorological Assimilation Driving Datasets (CMADs) of the China Institute of Water Resources and Hydropower Research. The pollution load data mainly comes from relevant results such as the environmental statistics report of City A and the pollution source census. The river vector data is automatically generated from the DEM data using GIS software (it can also be obtained through local relevant departments or extracted through software such as Baidu Maps and Water Resource Atlas).

[0052] 3. Research Methods

[0053] The following method is used in this embodiment to identify the key areas for ecological restoration in City A, which specifically includes the following steps:

[0054] (1) Divide City A into different spatial grids according to administrative divisions, land use patterns, and pollution load source data. Based on the improved non-point source pollution output coefficient model, calculate the load amounts of various non-point source pollutants in different spatial grids at a high-precision spatial resolution, and sum them up to obtain i The total intensity PM of non-point source pollutant load in the grid i ;

[0055] Specifically, the improved non-point source pollution output coefficient model is expressed as follows:

[0056] ;

[0057] In the formula: L is the non-point source pollution load amount; E s is the output coefficient of the s-type pollution source, kg / (km 2 •a) or kg / (head•a) or kg / (person•a), where a represents year; A m is the area of the m th land use type or the number of the mth livestock or the rural population, km2 either the head (only) or a person; α is the precipitation driving factor; β is the terrain driving factor; γ is the land use driving factor.

[0058] α is the precipitation driving factor, which is expressed as follows:

[0059] ;

[0060] where r is the annual precipitation of the target area; is the multi-year average precipitation of the target area; R j is the basic spatial unit of the target area j of the annual precipitation. As Figure 1 shown is the spatial distribution status of the precipitation driving factor calculated by the formula in this embodiment.

[0061] β is the terrain driving factor, which is expressed as follows:

[0062] ;

[0063] where θ j is the slope of the basic spatial unit of the target area j ; is the average slope of the basic measurement unit of the typical area; d is a constant; the slope data is obtained from open source digital elevation data; the basic spatial unit refers to a 100-square-meter grid; as Figure 2 shown is the spatial distribution status of the terrain driving factor calculated by the formula in this embodiment.

[0064] γ is the land use driving factor; the land use driving factor is used to describe the impact of different land use patterns on the non-point source pollution load. Different land use types such as forest land, grassland, cultivated land, and construction land directly affect the formation of surface runoff during rainfall, and at the same time will have a certain interception effect on non-point source pollution. In this embodiment, the values of γ corresponding to different land use patterns are shown in Table 1:

[0065] Table 1

[0066] .

[0067] The steps for obtaining the output coefficient values are to calculate the output coefficient by referring to literature and relevant census data, and classify the sources of non-point source pollution in the target area into three categories: land use, livestock and poultry breeding, and residents' living. Among them, land use includes cultivated land, forest land, grassland, and construction land, livestock and poultry breeding includes pigs, cows, sheep, and poultry, and residents' living includes urban and rural areas. In this embodiment, the spatial distribution status of the output coefficients of non-point source pollution from different sources is as Figure 3as shown

[0068] After calculating the load amounts of total phosphorus, total nitrogen, ammonia nitrogen, and COD in different spatial grids at high-precision spatial resolution respectively according to the improved non-point source pollution output coefficient model described above, calculate the i total intensity PM of non-point source pollutant load in the grid i as follows:

[0069] ;

[0070] In the formula: PM i is i the total intensity of pollutant load in the grid; S it is i the load intensity of the t th pollutant in the grid, t = 1, 2, 3, 4, corresponding to total phosphorus, total nitrogen, ammonia nitrogen, and COD in sequence.

[0071] For example, Figure 4 is the spatial distribution map of the COD load into the river in City A; Figure 5 is the spatial distribution map of the ammonia nitrogen load into the river in City A; Figure 6 is the spatial distribution map of the total nitrogen load into the river in City A; Figure 7 is the spatial distribution map of the total phosphorus load into the river in City A.

[0072] (2) According to the vector data of the rivers around different spatial grids in the area of City A, calculate the transmission distance i from the grid to the river in the area of City A through the Euclidean distance calculation method SL i ;

[0073] The calculation formula of the Euclidean distance is as follows:

[0074] ;

[0075] Among them, x and y are two points in the n-dimensional space, x k and y k are the coordinates of points x and y in the k th dimension respectively.

[0076] (3) First, standardize the i total intensity PM of non-point source pollutant load in the grid in the area of City A i and i the transmission distance SL i from the grid to the river according to the following formula:

[0077] Positive indicator: ;

[0078] Negative indicator: ;

[0079] For example Figure 8 is the spatial distribution map of the non-point source pollutant transmission distance index within the area of City A. Among them, the higher the pollutant transmission distance index, the redder the color, indicating that the non-point source pollution is closer to the river and the transmission distance is shorter.

[0080] Then, calculate the importance index E i of grid ecological restoration within the area of City A according to the following formula: i :

[0081] E i =Q i ×L i ; The higher E i , the more urgent the restoration of the area.

[0082] For example Figure 9 shows the spatial distribution map of the importance index of the ecological restoration area in City A. Among them, the value range of E i is between 0 and 1. The higher the value of E i , the redder the color of the area, indicating that the area needs more ecological restoration. Specifically, when E i ≥0.8, it belongs to the extremely high pollution level (red), indicating that the target area is an extremely urgent area for ecological restoration; when 0.6≤E i <0.8, it belongs to the high pollution level (orange), indicating that the target area is an urgent area for ecological restoration; when 0.4≤E i <0.6, it belongs to the medium pollution level (yellow), indicating that the target area is a relatively urgent area for ecological restoration; when 0.2≤E i <0.4, it belongs to the low pollution level (yellowish green), indicating that the target area is a generally urgent area for ecological restoration; when 0≤E i <0.2, it belongs to the extremely low pollution level (green), indicating that the target area does not need ecological restoration temporarily.

[0083] The above embodiments are only used to illustrate the technical solutions of the present invention, not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for identifying key areas of ecological restoration based on the distribution of non-point source pollution, characterized in that Including the following steps: (1) Divide the target area into different spatial grids according to administrative divisions, land use patterns, and pollution load source data. Based on the improved non-point source pollution output coefficient model, calculate the load amounts of various non-point source pollutants in different spatial grids at a high-precision spatial resolution, and sum them up to obtain the i total intensity PM of non-point source pollutant load in the grid i ; The improved non-point source pollution output coefficient model is expressed as follows: ； In the formula: L is the non-point source pollution load; E s is the output coefficient of the s-type pollution source, with the unit of kg / (km 2 •a) or kg / (head•a) or kg / (person•a), where a represents year; A m is the area of the m-th land use type or the number of the m-th livestock or the rural population, km 2 or head (only) or person; α is the precipitation driving factor; β is the terrain driving factor; γ is the land use driving factor; (2)Based on the vector data of the rivers around different spatial grids in the target area, calculate the transmission distance from the target area i grid to the river SL i ; (3) PM i and SL i Standardization is performed to obtain the target area i Grid surface source pollution load intensity index Q i and i Pollutant transmission distance index L from grid to river i , press E i =Q i ×L i Calculate target area i Grid ecological restoration importance index E i , according to E i The value determines the key area of ecological restoration, E i The higher the number, the more the area needs repair; Among them, the i mesh surface source pollution load intensity index Q i is calculated by the formula: , PM i is the total intensity of the grid surface source pollutant load within the target area i ; The said i Pollutant transport distance index from grid to river L i The calculation formula is as follows: , SL i is the transmission distance from the grid to the river within the target area i .

2. The method for identifying key areas for ecological restoration based on non-point source pollution distribution according to claim 1, wherein α described in step (1) is expressed as follows: ； Among them, r is the annual precipitation in the target area; is the multi-year average precipitation in the target area; R j is the basic spatial unit in the target area j of the annual precipitation; the precipitation data is obtained from rainfall data; the rainfall data is obtained through the meteorological bureau in the area where the target area is located or downloaded from the China Meteorological Data Network.

3. The ecological restoration key area identification method based on non-point source pollution distribution according to claim 1, characterized in that β described in step (1) is expressed as follows: ； Among them, θ j is the basic spatial unit of the target area j is the slope, is the average slope of the basic measurement unit of the typical area, d is a constant; the slope data is obtained from open source digital elevation data; The basic spatial unit refers to a 100-square-meter grid or a 1-square-kilometer grid, which is determined according to the size of the target area.

4. The ecological restoration key area identification method based on non-point source pollution distribution according to claim 1, characterized in that γ described in step (1) is used to describe the impact of different land use patterns on the non-point source pollution load, and it is obtained from the open-source land use data combined with relevant literature.

5. The method for identifying key areas for ecological restoration based on non-point source pollution distribution according to claim 1, wherein The non-point source pollutants described in step (1) refer to total phosphorus, total nitrogen, ammonia nitrogen or COD.

6. The method for identifying key areas for ecological restoration based on non-point source pollution distribution according to claim 1, wherein The target area described in step (1) i Total intensity of grid surface source pollutant load PM i The calculation formula is as follows: ； where: PM i is the target area i the total intensity of grid pollutant load; S it is i the load intensity of the t nth pollutant in the grid, t n = 1, 2, 3, 4, corresponding to total phosphorus, total nitrogen, ammonia nitrogen, and COD in sequence.

7. The method for identifying key areas for ecological restoration based on non-point source pollution distribution according to claim 1, characterized in that The calculation formula of the Euclidean distance described in step (2) is as follows: ； Among them, x and y are n two points in the x k and y k are respectively the x and y coordinates in the k dimension.

8. The method for identifying key areas for ecological restoration based on non-point source pollution distribution according to claim 1, wherein The ecological restoration importance index E described in step (3) i has a numerical range between 0 and 1. Among them, E i ≥0.8 belongs to an extremely high pollution level, indicating that the target area is an extremely urgent area for ecological restoration; 0.6 ≤ E i <0.8 belongs to a high pollution level, indicating that the target area is an urgent area for ecological restoration; 0.4 ≤ E i <0.6 belongs to a medium pollution level, indicating that the target area is a relatively urgent area for ecological restoration; 0.2 ≤ E i <0.4 belongs to a low pollution level, indicating that the target area is an area with general urgency for ecological restoration; 0 ≤ E i <0.2 belongs to an extremely low pollution level, indicating that the target area does not need ecological restoration for the time being.

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