Ecological restoration key area identification method based on non-point source pollution distribution

Abstract

The invention belongs to the field of ecological restoration and environment investigation, and particularly relates to an ecological restoration key area identification method based on non-point source pollution distribution. The method comprises the following steps: firstly, according to an improved output coefficient model, calculating a non-point source pollution spatial distribution condition in a target area in combination with rainfall data, DEM data, land utilization data, a pollutant emission coefficient and the like; then, calculating transmission distances from different space grid non-point source pollution to the river according to the river vector data; and finally, after standardization processing is carried out on the calculation result of the non-point source pollution space distribution and the transmission distance, an ecological restoration key area can be determined according to the calculated ecological restoration importance index. According to the method, early-stage identification can be carried out on areas which are seriously polluted by non-point sources and are easier to pollute rivers, and the accuracy and convenience of ecological restoration area selection are improved.

Description

Technical Field

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

[0002] With the gradual control of point source pollution, non-point source pollution is considered to be the main threat to aquatic ecosystems and drinking water safety. Non-point source pollution has the characteristics of randomness, dispersion, and hysteresis. 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. In order to ensure the effectiveness of the best solution, Ding et al. used non-point source pollution simulation of control units to determine the key source areas (Ding Y et al., Non-point source pollution simulation and best management practices analysis based on control units in Northern China [J]. International Journal of Environmental Research and Public Health, 2020, 17(3): 868). When designing an implementation plan for large-scale non-point source pollution control measures, Lee et al. further identified the spatial distribution of non-point source pollution source loss risks at the watershed scale (Lee G et al., Framework to prioritize watersheds for diffuse pollution management in the Republic of Korea: application of multi-criteria analysis using the Delphi method [J]. Natural Hazards and Earth System Sciences, 2019, 19(12): 2767-2779). However, non-point source pollution is an uncertain process. When determining pollution reduction targets, previous studies did not distinguish between land use patterns and the driving factors of non-point source pollution emissions. At the same time, there is a lack of corresponding selection methods for the areas where ecological restoration needs to be carried out during the transmission of non-point source pollution, which leads to differences between the proposed policy options and actual development situations.

[0003] With the growth of population and the increasing demand for non-food crops such as food crops, biofuels and animal food, the global consumption of nitrogen and phosphorus is steadily increasing, leading to the overuse of nitrogen and phosphorus in the global industrial and agricultural systems. Excessive input of nitrogen, phosphorus nutrients and pollutants such as COD will cause ecological and environmental problems such as increased nutrient concentrations in water bodies and algal blooms, ultimately leading to serious consequences such as loss of water body functions, sharp decline 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 the distribution of non-point source pollution and reducing the interference of non-point source pollution on water bodies through ecological restoration methods is of great significance to improving the water quality of rivers and lakes and alleviating lake eutrophication and blue algae blooms. Summary of the invention

[0004] The purpose of the present invention is to provide a method for identifying key areas of ecological restoration based on the distribution of non-point source pollution, so as to identify key areas that need ecological restoration in advance and provide support for controlling non-point source pollution and carrying out ecological restoration work in the later stage.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions: A method for identifying key ecological restoration areas based on non-point source pollution distribution includes the following steps: (1) The target area is divided 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, the load of various non-point source pollutants in different spatial grids at high-precision spatial resolution is calculated and summed to obtain the target area. i Total intensity of grid surface source pollutant load PM i ; (2) Based on the vector data of rivers around different spatial grids in the target area, the Euclidean distance calculation method is used to calculate the distance between the target area and the surrounding rivers. i Transmission distance from grid to river SL i ; (3) PM i and SL i Standardize to get 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 Size determines the key areas for ecological restoration, E iThe higher the value, the more repair is needed in the area.

[0006] Furthermore, the improved non-point source pollution output coefficient model in step (1) is expressed as follows: ; Where: L is the non-point source pollution load; E s is the output coefficient of type s pollution source, kg / (km 2 •a) or kg / (head•a) or kg / (person•a), a represents year; A m is the area of ​​the mth land use type or the number of the mth livestock or the number of rural population, km 2 or head (piece) or person; α is the precipitation driving factor; β is the terrain driving factor; γ is the land use driving factor. Specific: α is the precipitation driving factor, expressed as follows: ; in, r is the annual precipitation in the target area; is the multi-year average precipitation in the target area; R j The basic spatial unit of the target area j The precipitation data of the year is obtained from the rainfall data; the rainfall data is obtained through the meteorological bureau in the target area or downloaded from the China Meteorological Data Network; β is the topographic driving factor, expressed as follows: ; in, θ j The basic spatial unit of the target area j The slope is the average slope of the basic measurement unit in a typical area, d is a constant; the slope data is obtained from the digital elevation open source data; The basic spatial unit refers to a grid of 100 square meters or a grid of 1 square kilometer, which is determined according to the size of the target area; 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 have a certain interception effect on non-point source pollution. It is obtained from land use open source data combined with relevant literature; The output coefficient value determination step determines the output coefficient by consulting literature and relevant census data, and attributes the sources of non-point source pollution in the target area to three categories: land use, livestock and poultry breeding, and residents' lives. Land use includes cultivated land, forest land, grassland, and construction land, livestock and poultry breeding includes pigs, cattle, sheep, and poultry, and residents' lives include towns and rural areas.

[0007] Furthermore, the non-point source pollutants in step (1) refer to total phosphorus, total nitrogen, ammonia nitrogen or COD.

[0008] Furthermore, the target area in step (1) i Total intensity of grid surface source pollutant load PM i The calculation formula is as follows: ; Where: PM i for i Total intensity of grid pollutant load; S it for i Grid t The load intensity of pollutants, t =1, 2, 3, 4, corresponding to total phosphorus, total nitrogen, ammonia nitrogen, and COD respectively.

[0009] Furthermore, the calculation formula of the Euclidean distance in step (2) is as follows: ; in, x and y yes n Two points in dimensional space, x k and k They are points x and y In the k Dimensional coordinates.

[0010] Furthermore, step (3) i Grid surface source pollution load intensity index Q i The calculation formula is: , PM i In the target area i Total intensity of grid area source pollutant load; Said i Pollutant transmission distance index L from grid to river i The calculation formula is: , SL i Target area i Transmission distance from grid to river.

[0011] Furthermore, the ecological restoration importance index E in step (3) i The value range is between 0 and 1, where E i ≥0.8 is an extremely high pollution level, indicating that the target area is an extremely urgent area for ecological restoration; 0.6≤E i <0.8 is a high pollution level, indicating that the target area is an urgent ecological restoration area; 0.4≤E i <0.6 is a medium pollution level, indicating that ecological restoration in the target area is more urgent; 0.2≤E i <0.4 is a low pollution level, indicating that the target area is a generally urgent area for ecological restoration; 0≤E i <0.2 is an extremely low pollution level, indicating that the target area does not require ecological restoration for the time being.

[0012] The beneficial effects of the present invention are: 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, the spatial distribution of non-point source pollution in the region is calculated in combination with rainfall data, DEM data, land use data, pollutant emission coefficients, etc.; then, the transmission distance of non-point source pollution from different spatial grids to the river is calculated according to river vector data; finally, after standardizing the spatial distribution of non-point source pollution and the calculation results of the transmission distance, the key area of ​​ecological restoration can be determined according to the calculated ecological restoration importance index.

[0013] The method of the present invention can identify in advance the areas that are more seriously affected by non-point source pollution and more easily polluted rivers, thereby making the ecological restoration project more targeted and effectively reducing the impact of non-point source pollution. It is of great significance to protecting river water quality and reducing the risk of blue algae blooms in lakes and reservoirs.

[0014] At the same time, this method system can improve the accuracy and convenience of ecological restoration area selection, and only needs to use existing precipitation, digital elevation, land use and other data to make predictions, overcoming the shortcomings of traditional field 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 the implementation of digital and refined water ecological protection and ecological restoration management strategies at different time and space scales, which will significantly improve the final effect of ecological restoration projects. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is the spatial distribution map of precipitation driving factors in City A.

[0016] Figure 2 This is the spatial distribution map of terrain driving factors in City A.

[0017] Figure 3This is the spatial distribution map of the output coefficients of non-point source pollution from different sources in City A.

[0018] Figure 4 This is the spatial distribution map of COD inflow into the river in City A.

[0019] Figure 5 This is the spatial distribution map of ammonia nitrogen load into the river in City A.

[0020] Figure 6 This is the spatial distribution map of total nitrogen load into the river in City A.

[0021] Figure 7 This is the spatial distribution map of total phosphorus inflow into the river in City A.

[0022] Figure 8 This is the spatial distribution map of the transmission distance index of surface source pollutants in City A.

[0023] Fig. 9 This is the spatial distribution map of the ecological restoration area importance index in City A. DETAILED DESCRIPTION

[0024] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments. Example

[0025] This embodiment is described by taking City A located in the middle and upper reaches of the Han River as an example of the target area.

[0026] 1. Target area description City A has a well-developed water system and a dense network of rivers. In addition to the Han River, which runs across the city from west to east, there are about 2,000 rivers of all sizes. The B Reservoir in City A is the water source of the middle route of the South-to-North Water Diversion Project, a national first-level water source protection area, and an important wetland protection area. It is of great significance to the water ecological environment of the Han River Basin. As the B Reservoir changes from a backup water source to a main water source, its water quality will directly affect the drinking water safety of more than 20 cities along the four provinces (cities) of M, N, O, and P around City A. Therefore, it is of great significance to identify the key ecological restoration areas in City A affected by non-point source pollution in advance.

[0027] 2. Data Sources The DEM data used in this study has a resolution of 30 m and comes from the ASTERGDEM dataset of the China Geospatial Data Cloud website. The land use data comes from the China Land Use Status Remote Sensing Monitoring Data of the Resource and Environmental Science Data Center of the Chinese Academy of Sciences. The meteorological data comes from the China Atmospheric Assimilation Driven Dataset (CMADs) of the China Institute of Water Resources and Hydropower Research. The pollution load data mainly comes from the environmental statistical report of City A, the pollution source census and other related results. The river vector data is automatically generated by GIS software using DEM data (it can also be obtained from relevant local departments or extracted through Baidu Maps, Shuijingzhu and other software).

[0028] 3. Research Methods This embodiment uses the following method to identify the key ecological restoration areas in City A, which specifically includes the following steps: (1) City A is divided 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, the loads of various non-point source pollutants in different spatial grids with high-precision spatial resolution are calculated and summed to obtain i Total intensity of grid surface source pollutant load PM i ; Specifically, the improved non-point source pollution output coefficient model is expressed as follows: ; Where: L is the non-point source pollution load; E s is the output coefficient of type s pollution source, kg / (km 2 •a) or kg / (head•a) or kg / (person•a), a represents year; A m For the m Area of ​​land use type m or number of livestock of type m or number of rural population, km 2 Or head (piece) or person; α is the precipitation driving factor; β is the terrain driving factor; γ is the land use driving factor.

[0029] α is the precipitation driving factor, expressed as follows: ; in, r is the annual precipitation in the target area; is the multi-year average precipitation in the target area; R j The basic spatial unit of the target area j The amount of precipitation in the year. Figure 1 The figure shows the spatial distribution of precipitation driving factors calculated by the formula in this embodiment.

[0030] β is the topographic driving factor, expressed as follows: ; in, θ j The basic spatial unit of the target area j The slope is the average slope of the basic measurement unit in a typical area, d is a constant; the slope data is obtained from the digital elevation open source data; the basic spatial unit refers to a grid of 100 square meters; Figure 2 The figure shows the spatial distribution of the terrain driving factors calculated by the formula in this embodiment.

[0031] 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 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: Table 1 .

[0032] The output coefficient value determination step determines the output coefficient by consulting the literature method and relevant census data, and attributes the sources of non-point source pollution in the target area to three categories: land use, livestock and poultry breeding, and residents' lives. Land use includes cultivated land, forest land, grassland, and construction land. Livestock and poultry breeding includes pigs, cattle, sheep, and poultry. Residents' lives include towns and rural areas. In this embodiment, the spatial distribution of the output coefficients of non-point source pollution from different sources is as follows: Figure 3 shown.

[0033] According to the above improved non-point source pollution output coefficient model, the total phosphorus, total nitrogen, ammonia nitrogen and COD loads in different spatial grids under high-precision spatial resolution are calculated respectively, and then the following formula is used to calculate the total phosphorus, total nitrogen, ammonia nitrogen and COD loads in different spatial grids under high-precision spatial resolution. i Total intensity of grid surface source pollutant load PM i Perform the calculation: ; Where: PM i for i Total intensity of grid pollutant load; S it for i Grid t The load intensity of pollutants, t =1, 2, 3, 4, corresponding to total phosphorus, total nitrogen, ammonia nitrogen, and COD respectively.

[0034] like Figure 4 This is the spatial distribution map of COD inflow into the river in City A; Figure 5 This is the spatial distribution map of ammonia nitrogen load into the river in City A; Figure 6 This is the spatial distribution map of total nitrogen inflow into the river in City A; Figure 7 This is the spatial distribution map of total phosphorus inflow into the river in City A.

[0035] (2) Based on the vector data of rivers around different spatial grids in City A, the Euclidean distance calculation method is used to calculate the distance between rivers in City A and the surrounding rivers. i Transmission distance from grid to river SL i ; The calculation formula of the Euclidean distance is as follows: ; in, x and y are two points in n-dimensional space, x k and k They are points x and y in the k Dimensional coordinates.

[0036] (3) First, according to the following formula, i Total intensity of grid surface source pollutant load PM i and i Transmission distance from grid to river SL i To standardize: Positive indicators: ; Negative indicators: ; like Figure 8 This is the spatial distribution map of the non-point source pollutant transmission distance index in City A. The higher the pollutant transmission distance index, the redder the color, indicating that the closer the non-point source pollution is to the river, the shorter the transmission distance.

[0037] Then, the following formula is used to calculate the i Grid ecological restoration importance index E i Perform the calculation: E i =Q i ×L i ; E i The higher the value, the more repair is needed in the area.

[0038] like Fig. 9 The figure shows the spatial distribution map of the importance index of ecological restoration area in City A, where E i The value range is between 0 and 1. i The higher the value, the redder the area, indicating that the area needs more ecological restoration. i≥0.8 is extremely high pollution level (red), indicating that the target area is an extremely urgent area for ecological restoration; 0.6≤E i <0.8 is a high pollution level (orange), indicating that the target area is an urgent ecological restoration area; 0.4≤E i <0.6 is a medium pollution level (yellow), indicating that ecological restoration in the target area is more urgent; 0.2≤E i <0.4 is a low pollution level (yellow-green), indicating that the target area is a generally urgent area for ecological restoration; 0≤E i <0.2 is an extremely low pollution level (green), indicating that the target area does not require ecological restoration for the time being.

[0039] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions 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 non-point source pollution distribution, characterized in that: The following steps are involved: (1) The target area is divided 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, the load of various non-point source pollutants in different spatial grids at high-precision spatial resolution is calculated and summed to obtain the target area. i Total intensity of grid surface source pollutant load PM i ; (2) Based on the vector data of rivers around different spatial grids in the target area, the target area is calculated by the Euclidean distance calculation method. i Transmission distance from grid to river SL i ; (3) PM i and SL i Standardize to get 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 value, the more repair is needed in the area.

2. The method for identifying key ecological restoration areas based on non-point source pollution distribution according to claim 1 is characterized in that: The improved non-point source pollution output coefficient model in step (1) is expressed as follows: ； Where: L is the non-point source pollution load; E s is the output coefficient of the s-type pollution source, in kg / (km 2 •a) or kg / (head•a) or kg / (person•a), a represents year; A m is the area of ​​the mth land use type or the number of the mth livestock or the number of rural population, km 2 Or head (piece) or person; α is the precipitation driving factor; β is the terrain driving factor; γ is the land use driving factor.

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

4. The method for identifying key ecological restoration areas based on non-point source pollution distribution according to claim 2 is characterized in that: The β is expressed as follows: ； in, θ j The basic spatial unit of the target area j The slope is the average slope of the basic measurement unit in a typical area, d is a constant; the slope data is obtained from the digital elevation open source data; The basic spatial unit refers to a grid of 100 square meters, or a grid of 1 square kilometer, which is determined according to the size of the target area.

5. The method for identifying key ecological restoration areas based on non-point source pollution distribution according to claim 2 is characterized in that: The γ is used to describe the impact of different land use patterns on non-point source pollution load, which is obtained from open source land use data combined with relevant literature.

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

7. The method for identifying key ecological restoration areas based on non-point source pollution distribution according to claim 1 is characterized in that: The target area in step (1) i Total intensity of grid surface source pollutant load PM i The calculation formula is as follows: ； Where: PM i Target area i Total intensity of grid pollutant load; S it for i Grid t The load intensity of pollutants, t =1, 2, 3, 4, corresponding to total phosphorus, total nitrogen, ammonia nitrogen, and COD respectively.

8. The method for identifying key ecological restoration areas based on non-point source pollution distribution according to claim 1 is characterized in that: The calculation formula of the Euclidean distance in step (2) is as follows: ； in, x and y yes n Two points in dimensional space, x k and k They are points x and y in the k Dimensional coordinates.

9. The method for identifying key ecological restoration areas based on non-point source pollution distribution according to claim 1, characterized in that: Step (3) i Grid surface source pollution load intensity index Q i The calculation formula is: , PM i In the target area i Total intensity of grid area source pollutant load; Said i Pollutant transport distance index from grid to river L i The calculation formula is: , SL i In the target area i Transmission distance from grid to river.

10. The method for identifying key ecological restoration areas based on non-point source pollution distribution according to claim 1, characterized in that: The ecological restoration importance index E in step (3) i The value range is between 0 and 1, where E i ≥0.8 is an extremely high pollution level, indicating that the target area is an extremely urgent area for ecological restoration; 0.6≤E i <0.8 is a high pollution level, indicating that the target area is an urgent ecological restoration area; 0.4≤E i <0.6 is a medium pollution level, indicating that ecological restoration in the target area is more urgent; 0.2≤E i <0.4 is a low pollution level, indicating that the target area is a generally urgent area for ecological restoration; 0≤E i <0.2 is an extremely low pollution level, indicating that the target area does not require ecological restoration for the time being.

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