Agricultural non-point source pollution risk assessment method considering pollutant transportation process

Abstract

The invention discloses an agricultural non-point source pollution risk assessment method considering a pollutant transportation process, and the method comprises the steps: selecting a pollution input process, a pollution conversion process and a pollution reduction process as first-stage evaluation indexes, and determining second-stage evaluation indexes; obtaining the weight of each evaluation index by adopting a Delphi method and an entropy weight method; constructing an agricultural non-point source pollution risk measurement model, wherein I = q1I1 + q2I2 + q3I3; i is a risk measure comprehensive score; q1 is a pollution input process evaluation factor weight; i1 is a pollution input process evaluation factor score; q2 is the evaluation factor weight of the pollution conversion process; i2 is a pollution conversion process evaluation factor score; q3 is the evaluation factor weight of the pollution abatement process; i3 is the evaluation factor score of the pollution abatement process; and according to the agricultural non-point source pollution risk measurement model, calculating to obtain a risk measurement comprehensive score, and dividing risk grades by adopting a total quantity Quantile grading method. The method can effectively improve the precision of agricultural non-point source risk assessment, and is more in line with objective actual conditions.

Description

Technical Field

[0001] The present invention belongs to the technical field of environmental pollution assessment, relates to agricultural non-point source pollution, and particularly relates to a method for assessing the risk of agricultural non-point source pollution considering the pollutant transport process. Background Art

[0002] Agricultural non-point source pollution mainly refers to the phenomenon that during the process of agricultural production activities, due to various pollutants moving slowly in the soil circle at low concentrations and over a large range or diffusing from the soil circle to the water circle, the ecological systems such as soil, aquifer, lakes, rivers, shorelines, and the atmosphere are polluted. It has the characteristics of large randomness in the formation process, many influencing factors, wide distribution range, long latent period, and great harm.

[0003] The sources of agricultural non-point source pollution mainly include chemical fertilizer pollution, pesticide pollution, livestock and poultry manure pollution, plastic agricultural film pollution, domestic sewage and industrial "three wastes" pollution, and straw burning pollution. Due to the complexity of the sources of non-point source pollution, the fuzziness of the mechanism, and the potentiality of formation, it is difficult to study and control non-point source pollution.

[0004] In the process of controlling agricultural non-point source pollution, it is first necessary to determine the scope of agricultural non-point source pollution and the pollution risk level, so as to obtain the maximum pollution prevention and control benefits with limited resources and targeted control measures. Therefore, the research on the assessment of agricultural non-point source pollution risk is particularly important.

[0005] Traditional agricultural non-point source pollution risk assessment is mainly based on the output coefficient of land use types or the pollutant discharge coefficients of planting, breeding, and rural residents' lives. However, in fact, there are pollution transformation processes and pollution reduction processes, so traditional agricultural non-point source pollution risk assessment does not consider the pollution transformation process and pollution reduction process, resulting in low accuracy of agricultural non-point source pollution risk assessment and not conforming to the objective actual situation. Summary of the Invention

[0006] Aiming at the above deficiencies of the existing technology, the purpose of the present invention is to provide a method for assessing the risk of agricultural non-point source pollution considering the pollutant transport process, which can effectively improve the accuracy of agricultural non-point source risk assessment and is more in line with the objective actual situation.

[0007] The technical solution of the present invention is realized as follows:

[0008] A method for assessing the risk of agricultural non-point source pollution considering the pollutant transport process specifically includes the following steps:

[0009] Step 1: Select the pollution input process, pollution transformation process, and pollution reduction process as the first-level evaluation indicators. Among them, the pollution input process includes the following five second-level evaluation indicators: chemical fertilizer application emission intensity indicator, pesticide use emission intensity indicator, livestock and poultry breeding emission intensity indicator, aquaculture emission intensity indicator, and residential life emission intensity indicator; the pollution transformation process includes the following five second-level evaluation indicators: rainfall erosion indicator, slope length and slope indicator, sloping cultivated land indicator, terrain wetness indicator, and distance to water area indicator; the pollution reduction process includes the following two second-level evaluation indicators: forest and grass retention indicator and water body accommodation indicator;

[0010] Step 2: Use the Delphi method and entropy weight method to obtain the weights of each evaluation indicator. The evaluation indicators include first-level evaluation indicators and second-level evaluation indicators;

[0011] Step 3: Construct an agricultural non-point source pollution risk measurement model, and its model is as follows:

[0012] I = q1I1 + q2I2 + q3I3 (1)

[0013] Where: I is the comprehensive score of risk measurement; q1 is the weight of the pollution input process; I1 is the score of the pollution input process; q2 is the weight of the pollution transformation process; I2 is the score of the pollution transformation process; q3 is the weight of the pollution reduction process; I3 is the score of the pollution reduction process;

[0014] Step 4: According to the agricultural non-point source pollution risk measurement model, calculate the comprehensive score of risk measurement, and use the total amount Quantile grading method to divide the risk level.

[0015] Furthermore, the weight q of each evaluation indicator j is calculated according to formula (2):

[0016] q j =(D j +S j )×0.5 (2)

[0017] Where: D j is the weight of the evaluation indicator obtained by the Delphi method; S j is the weight of the evaluation indicator obtained by the entropy weight method.

[0018] Furthermore, the steps for obtaining the weight of the evaluation indicator by the Delphi method include:

[0019] (1) Solicit opinions on agricultural non-point source pollution risk measurement from multiple experts, and obtain the importance of each evaluation indicator for each expert. The importance of the indicator includes very important, important, general, and unnecessary;

[0020] (2) Calculate the weight a of each evaluation indicator for each expertj :

[0021]

[0022] where: z j is the score assigned by a certain expert to the j-th evaluation index. Generally, the score is assigned according to the importance of the evaluation index by the expert. If it is very important, the score is 100 points; if it is important, the score is 80 points; if it is general, the score is 60 points; if it is not necessary, the score is 40 points. m is the number of each evaluation index. For the first-level evaluation index, m = 3; for the second-level evaluation index of the pollution input process and the pollution transfer process, m = 5; for the second-level evaluation index of the pollution reduction process, m = 2.

[0023] (3) Obtain the weight D of each evaluation index according to the weight of each evaluation index given by each expert j :

[0024]

[0025] where: r is the participating expert, and n is the number of participating experts.

[0026] Furthermore, the method for obtaining the weight of the evaluation index by the entropy weight method is as follows:

[0027] (1) Calculate the proportion PP of the k-th sample under the j-th index in this index kj :[[]]

[0028]

[0029] where: k is the sample, k = 1,..., f, and f is the number of samples; x kj is the value of the k-th sample under the j-th index, which is specifically obtained through measurement;

[0030] (2) Calculate the entropy value e of the j-th evaluation index j: :[[]]

[0031]

[0032] (3) Calculate the weight S of each evaluation index r :[[]]

[0033]

[0034] Furthermore, the score I1 of the pollution input process is calculated according to formula (8):

[0035] I1 = q 11 I 11 + q 12 I 12 + q 13 I 13 + q14 I 14 +q 15 I 15 (8)

[0036] Where: q 11 is the weight of the fertilizer application emission intensity index; I 11 is the score of the fertilizer application emission intensity index; q 12 is the weight of the pesticide use emission intensity index; I 12 is the score of the pesticide use emission intensity index; q 13 is the weight of the livestock and poultry breeding emission intensity index; I 13 is the score of the livestock and poultry breeding emission intensity index; q 14 is the weight of the aquaculture emission intensity index; I 14 is the score of the aquaculture emission intensity index; q 15 is the weight of the residential life emission intensity index; I 15 is the score of the residential life emission intensity index; among which the weights of each index are calculated by formula (2).

[0037] Furthermore, the pollution conversion process score I2 is calculated according to formula (9):

[0038] I2 = q 21 I 21 +q 22 I 22 +q 23 I 23 +q 24 I 24 +q 25 I 25 (9)

[0039] Where: q 21 is the weight of the rainfall erosion index; I 21 is the score of the rainfall erosion index; q 22 is the weight of the slope length and slope index; I 22 is the score of the slope length and slope index; q 23 is the weight of the sloping cultivated land index; I 23 is the score of the sloping cultivated land index; q 24 is the weight of the sloping cultivated land index; I 24 is the score of the terrain wetness index; q 25 is the weight of the distance to water area index; I 25 is the score of the distance to water area index; among which the weights of each index are calculated by formula (2).

[0040] Furthermore, the pollution reduction process score I3 is calculated according to formula (10):

[0041] I3 = q 31 I31 +q 32 I 32 (10)

[0042] Where: q 31 is the weight of the forest and grass retention index; I 31 is the score of the forest and grass retention index; q 32 is the weight of the water body accommodation index; I 32 is the score of the water body accommodation index; where the weights of each index are calculated by formula (2).

[0043] Furthermore, in step 4, if I ∈ [0, 1], it is a risk-free situation; if I ∈ (1, 2], it is a low-risk situation; if I ∈ (2, 3], it is a medium-risk situation; if I ∈ (3, 4], it is a high-risk situation; if I ∈ (4, 5], it is an extremely high-risk situation.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] 1. The present invention conducts an agricultural non-point source pollution risk assessment from three dimensions of the pollution input process, pollution transformation process, and pollution reduction process, that is, from the whole process of "source-process-end", effectively improving the accuracy of the agricultural non-point source pollution risk assessment and being more in line with the objective actual situation.

[0046] 2. The present invention determines the selected indicators and their weights by combining qualitative and quantitative methods (i.e., the Delphi method and the entropy weight method), effectively improving the accuracy of the weights, thereby being conducive to improving the accuracy of the agricultural non-point source pollution risk assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 - The flowchart of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0048] The present invention will be further described in detail below with reference to the drawings and specific embodiments.

[0049] An agricultural non-point source pollution risk assessment method considering the pollutant transport process, the flowchart is as Figure 1 shown, and specifically includes the following steps:

[0050] Step 1: Select the pollution input process, pollution transformation process, and pollution reduction process as the primary evaluation indicators.

[0051] Here, the pollution input process is selected as one of the first-level evaluation indicators, considering from the perspective of pollution source input. Specifically, the fertilizer application intensity index, pesticide application and emission intensity index, livestock and poultry breeding emission intensity index are selected. At the same time, in combination with the actual situation of current agricultural non-point source pollution, the aquaculture emission intensity index, which is usually overlooked, and the residential life emission intensity index reflecting the degree of human interference are added as the five secondary evaluation indicators of the pollution input process.

[0052] Based on the fact that the natural environment has a certain impact on the transport of pollutants, the pollution transformation process is selected as one of the first-level evaluation indicators. Specifically, the rainfall erosion index, slope length and slope index, sloping cultivated land index, terrain wetness index, and distance to water area index are selected as the five secondary evaluation indicators of the pollution transformation process.

[0053] Based on the consideration from the perspective of terminal interception and consumption, the pollution reduction process is selected as one of the first-level evaluation indicators. Specifically, the forest and grass retention index reflecting the interception ability of forest and grass land for pollutants and the water body accommodation index reflecting the water body accommodation ability are selected as the two secondary evaluation indicators of the pollution reduction process.

[0054] Step 2: Use the Delphi method and entropy weight method to obtain the weights of each first-level evaluation indicator and secondary evaluation indicator.

[0055] Step 2.1: Obtain the weights of each indicator using the Delphi method

[0056] To ensure the scientificity of the subjective weighting method, the essential meaning of the weight is the impact degree of a certain evaluation indicator on the system evaluation result and the degree of people's attention to this indicator. The former reflects the objectivity of the weight, and the latter reflects the subjectivity of the weight. Therefore, the classical Delphi method is selected, and through expert investigation and statistical analysis, the weights of each indicator are obtained.

[0057] (1) Questionnaire design

[0058] The questionnaire survey scientifically designs the questionnaire according to the actual situation of the research area, and solicits opinions on the risk measurement of agricultural non-point source pollution from many experts in domestic agricultural and rural departments, ecological environment departments, natural resources departments, universities and research institutes. The evaluation sample table of risk measurement indicators is shown in Table 1.

[0059] Table 1 Evaluation sample table of risk measurement indicators

[0060]

[0061] (2) Weight calculation using the Delphi method

[0062] The Delphi method belongs to the subjective weighting method. Through expert investigation and statistical analysis, the weights of each evaluation indicator are obtained.

[0063] The Delphi method is used to conduct a questionnaire survey on the selected factors and their weights, and the selection frequencies and weights of different importance levels of each evaluation index are obtained using a formula.

[0064] ① Calculate the weight of each evaluation index for each expert.

[0065]

[0066] In the formula: a j is the weight of a certain expert for the j-th evaluation index (including the first-level evaluation index and the second-level evaluation index) in the questionnaire survey; z j is the score given by an expert for the j-th evaluation index. Generally, according to the importance of the evaluation index by the expert, a score of 100 is given for very important, 80 for important, 60 for general, and 40 for not necessary; m is the number of each evaluation index. For the first-level evaluation index, m = 3. For the second-level evaluation index of the pollution input process and the pollution transfer process, m = 5. For the second-level evaluation index of the pollution reduction process, m = 2.

[0067] ② Use the following formula to calculate the proportion of each evaluation index and the degree of difference in the scores given by experts.

[0068]

[0069] In the formula, D j is the weight of each evaluation index obtained from the scores given by experts; r is the participating expert, and n is the number of participating experts.

[0070] In a specific embodiment, the weight results of each evaluation index obtained by the Delphi method are shown in Table 2.

[0071] Table 2. Weight Results of Each Evaluation Index by Delphi Method

[0072]

[0073] Step 2.2: Obtain the weights of each index using the entropy weight method

[0074] The entropy weight method belongs to an objective weight assignment method, which can effectively avoid the deviation in the assignment of weights of evaluation indexes caused by subjective human factors. The specific calculation steps are as follows:

[0075] ① Calculate the proportion PP of the k-th sample under the j-th index in this index kj .

[0076]

[0077] In the formula: k is the sample, k = 1,..., f, where f is the number of samples; j is the risk evaluation index, j = 1,..., m; x kjIt is the value of the k-th sample under the j-th index.

[0078] Generally, a risk assessment area to be evaluated is divided into several small areas, each small area corresponding to a sample. Correspondingly, x kj is the value of the j-th evaluation index of the k-th sample, which is specifically obtained through measurement.

[0079] ② Calculate the entropy value e of the j-th evaluation index j .

[0080]

[0081] ③ Calculate the weight S of each evaluation index r .

[0082]

[0083] In a specific embodiment, the results of the weights of each evaluation index obtained by the entropy weight method are shown in Table 3.

[0084] Table 3. Results of the weights of each evaluation index by the Delphi method

[0085]

[0086] Step 2.3: Determination of the weights of each evaluation index

[0087] The average value of the weight results of the Delphi method and the entropy weight method is the weight of each evaluation index.

[0088] q j =(D j +S j )×0.5 (6)

[0089] According to Table 2 and Table 3, the weights of each evaluation index are obtained, as shown in Table 4 specifically.

[0090] Table 4. Weights of each evaluation index

[0091]

[0092] Step 3: Construct an agricultural non-point source pollution risk measurement model.

[0093] It is constructed by using the method of superposing the weights of multiple evaluation indexes.

[0094] Pollution input process:

[0095] I1 = q 11 I 11 +q 12 I 12 +q 13 I 13 +q14 I 14 +q 15 I 15 (7)

[0096] Where: I1 is the score of the first-level evaluation index for the pollution input process; I 11 is the score of the fertilizer application emission intensity index, representing the emission intensity of chemical fertilizers such as nitrogen fertilizer and phosphate fertilizer per unit cultivated land area in the area to be evaluated; I 12 is the score of the pesticide use emission intensity index, representing the input intensity of pesticides per unit cultivated land area; I 13 is the score of the livestock and poultry breeding emission intensity index, representing the emission intensity of livestock and poultry breeding pollutants per unit cultivated land area; I 14 is the score of the aquaculture emission intensity index, representing the emission intensity of aquaculture pollutants, calculated by using pond kernel density and aquaculture output; I 15 is the score of the residential life emission intensity index, reflecting the impact of human activities on the region, and the residential life emission intensity is obtained by using kernel density analysis based on the data layer of non-central urban areas.

[0097] Pollution transformation process:

[0098] I2 = q 21 I 21 +q 22 I 22 +q 23 I 23 +q 24 I 24 +q 25 I 25 (8)

[0099] Where: I2 is the score of the first-level evaluation index for the pollution transformation process; I 21 is the score of the rainfall erosion index, representing the ability to cause soil erosion by rainfall, calculated based on basic elements such as rainfall, longitude, latitude, altitude, slope, and aspect, calculated using the daily rainfall erosivity model to obtain the monthly rainfall erosivity, and then the annual rainfall erosivity is measured based on the monthly rainfall erosivity; I 22 is the score of the slope length and slope index, representing the soil and water loss ability caused by large-scale terrain undulation in the region, reflecting the amount of soil erosion under different slope lengths and slopes, and obtaining the slope length and slope of the study area by measuring the terrain undulation; I 23 is the score of the sloping cultivated land index, representing the soil and water loss ability caused by micro-scale terrain drive. The slope size of the sloping cultivated land can reflect the ease of pollutants flowing out of the cultivated land. According to the grading evaluation of the slope in the ecological function area, the sloping cultivated land factor is assigned according to the cultivated land slope; I 24It is the score of the terrain wetness index, which represents the impact of the regional terrain on the runoff direction and accumulation, and helps to identify rainfall runoff patterns, potential areas with increased soil moisture content, and waterlogging areas; I 25 It is the score of the distance to water index, which represents the distance from a certain spatial point to the nearest water area, and can reflect the distance resistance that pollutants need to overcome to enter the receiving body. The distance to water is obtained by using the Euclidean distance analysis of GIS. The Euclidean distance refers to the true distance between two points or the natural length of a vector in an m-dimensional space.

[0100] Pollution reduction process:

[0101] I3 = q 31 I 31 +q 32 I 32 (9)

[0102] In the formula: I3 is the score of the first-level evaluation index of the pollution reduction process; I 31 It is the score of the forest and grass retention index, which represents the pollutant absorption capacity of forest land and grassland. The forest and grass retention results are obtained by kernel density analysis of forest land and grassland data; I 32 It is the score of the water body accommodation index, which represents the purification capacity of pollutants in the incoming water body. The water body accommodation value is obtained by multiplying the water network density by the kernel density of the water body distribution. The water network density is calculated from the water area and the total water resources.

[0103] Comprehensive risk measure score I:

[0104] I = q1I1 + q2I2 + q3I3 (10)

[0105] According to Table 4 and Table 5 and through formula (10), the comprehensive risk measure score I can be obtained.

[0106] Step 4: According to the agricultural non-point source pollution risk measure model, calculate the comprehensive risk measure score, and use the total Quantile classification method to divide the risk levels. Specifically, use the total Quantile classification method to divide it into five levels: no risk, low risk, medium risk, high risk, and extremely high risk according to 20%, 40%, 60%, and 80% of the total index cumulative value, and assign values of 1, 2, 3, 4, and 5, that is, if I ∈ [0, 1], it is no risk; if I ∈ (1, 2], it is low risk; if I ∈ (2, 3], it is medium risk; if I ∈ (3, 4], it is high risk; if I ∈ (4, 5], it is extremely high risk.

[0107] Finally, it should be noted that the above embodiments of the present invention are only examples for explaining the present invention, rather than limitations on the implementation manners of the present invention. For those of ordinary skill in the art, other different forms of changes and modifications can be made on the basis of the above description. It is impossible to list all the implementation manners here. Any obvious changes or modifications derived from the technical solutions of the present invention still fall within the protection scope of the present invention.

Claims

1. An agricultural non-point source pollution risk assessment method considering the pollutant transport process, characterized in that, Specifically, it includes the following steps: Step 1: Select the pollution input process, pollution transformation process, and pollution reduction process as the first-level evaluation indicators. Among them, the pollution input process includes the following five second-level evaluation indicators: chemical fertilizer application emission intensity indicator, pesticide use emission intensity indicator, livestock and poultry breeding emission intensity indicator, aquaculture emission intensity indicator, and residential life emission intensity indicator; the pollution transformation process includes the following five second-level evaluation indicators: rainfall erosion indicator, slope length and slope indicator, sloping cultivated land indicator, topographic wetness indicator, and distance to water area indicator; the pollution reduction process includes the following two second-level evaluation indicators: forest and grass retention indicator and water body accommodation indicator; Step 2: Use the Delphi method and entropy weight method to obtain the weights of each evaluation indicator, and the evaluation indicators include first-level evaluation indicators and second-level evaluation indicators; Step 3: Construct an agricultural non-point source pollution risk measurement model, and its model is as follows: I = q1I1 + q2I2 + q3I3 (1) In the formula: I is the comprehensive score of risk measurement; q1 is the weight of the pollution input process; I1 is the score of the pollution input process; q2 is the weight of the pollution transformation process; I2 is the score of the pollution transformation process; q3 is the weight of the pollution reduction process; I3 is the score of the pollution reduction process; Step 4: According to the agricultural non-point source pollution risk measurement model, calculate the comprehensive score of risk measurement, and use the total amount Quantile grading method to divide the risk level.

2. The agricultural non-point source pollution risk assessment method considering the pollutant transport process according to claim 1, characterized in that, The weight q of each evaluation index j Calculated according to formula (2): q j = (D j + S j ) × 0.5 (2) Where: D j is the weight of evaluation index obtained by Delphi method; S j is the weight of evaluation index obtained by entropy weight method.

3. The agricultural non-point source pollution risk assessment method considering the pollutant transport process according to claim 2, wherein The steps for obtaining the weights of evaluation indicators by the Delphi method include: (1) Solicit opinions on agricultural non-point source pollution risk measurement from multiple experts, and obtain the importance of each evaluation indicator for each expert. The importance of the indicator includes very important, important, general, and unnecessary; (2) Calculate the weight \(a\) of each expert for each evaluation index j : where: z j is the score assigned by an expert to the j-th evaluation index. Generally, the score is assigned according to the importance of the evaluation index by the expert. If it is very important, the score is 100 points; if it is important, the score is 80 points; if it is general, the score is 60 points; if it is not necessary, the score is 40. m is the number of each evaluation index. For the first-level evaluation index, m = 3; for the second-level evaluation index of the pollution input process and the pollution transfer process, m = 5; for the second-level evaluation index of the pollution reduction process, m = 2. (3) Obtain the weight D of each evaluation index according to the weight of each evaluation index by each expert j : In the formula: r is the participating expert, and n is the number of participating experts.

4. The agricultural non-point source pollution risk assessment method considering the pollutant transport process according to claim 2, wherein, The method for obtaining the weights of evaluation indicators by the entropy weight method is as follows: (1) Calculate the proportion PP of the k-th sample under the j-th indicator in this indicator kj : where: k is the sample, k = 1, …, f, and f is the number of samples; x kj is the value of the k-th sample under the j-th index, which is specifically obtained through measurement; (2) Calculate the entropy value e of the j-th evaluation index j: : (3) Calculate the weight S of each evaluation index r :

5. The agricultural non-point source pollution risk assessment method considering the pollutant transport process according to claim 2, characterized in that, The score I1 of the pollution input process is calculated according to formula (8): I1 = q 11 I 11 +q 12 I 12 +q 13 I 13 +q 14 I 14 +q 15 I 15 (8) Where: q 11 is the weight of the fertilizer application emission intensity index; I 11 is the score of the fertilizer application emission intensity index; q 12 is the weight of the pesticide use emission intensity index; I 12 is the score of the pesticide use emission intensity index; q 13 is the weight of the livestock and poultry breeding emission intensity index; I 13 is the score of the livestock and poultry breeding emission intensity index; q 14 is the weight of the aquaculture emission intensity index; I 14 is the score of the aquaculture emission intensity index; q 15 is the weight of the residential life emission intensity index; I 15 is the score of the residential life emission intensity index; among which, the weights of each index are calculated through formula (2).

6. The agricultural non-point source pollution risk assessment method considering the pollutant transport process according to claim 2, characterized in that The score I2 of the pollution transformation process is calculated according to formula (9): I2 = q 21 I 21 +q 22 I 22 +q 23 I 23 +q 24 I 24 +q 25 I 25 (9) Where: q 21 is the weight of the rainfall erosion index; I 21 is the score of the rainfall erosion index; q 22 is the weight of the slope length and slope gradient index; I 22 is the score of the slope length and slope gradient index; q 23 is the weight of the sloping cultivated land index; I 23 is the score of the sloping cultivated land index; q 24 is the weight of the sloping cultivated land index; I 24 is the score of the terrain wetness index; q 25 is the weight of the distance to water area index; I 25 is the score of the distance to water area index; among which the weights of each index are calculated by formula (2).

7. The agricultural non-point source pollution risk assessment method considering the pollutant transport process according to claim 6, characterized in that The score I3 of the pollution reduction process is calculated according to formula (10): I3 = q 31 I 31 +q 32 I 32 (10) Where: q 31 is the weight of the forest and grass retention index; I 31 is the score of the forest and grass retention index; q 32 is the weight of the water body accommodation index; I 32 is the score of the water body accommodation index; among which the weights of each index are calculated through formula (2).

8. The agricultural non-point source pollution risk assessment method considering the pollutant transport process according to claim 1, wherein In Step 4, if I ∈ [0, 1], it is risk-free; if I ∈ (1, 2], it is low-risk; if I ∈ (2, 3], it is medium-risk; if I ∈ (3, 4], it is high-risk; if I ∈ (4, 5], it is extremely high-risk.

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