Method for reducing runoff pollution load in riparian zone

Abstract

The invention relates to the technical field of riparian buffer zone treatment, and particularly discloses a method for reducing runoff pollution load in a riparian zone, which comprises the following steps of: forming a pollutant reduction interval by constructing a micro-step terrain to adjust a slope surface ratio; calculating a pollutant concentration gradient and preparing an adsorption material; generating an adsorption replacement path according to the charge polarity and the concentration decline trend, simulating the pollutant concentration and runoff flow velocity change in a plurality of sections, and generating a pollutant reduction path diagram; and identifying a reduction efficiency abnormal section through change point detection, optimizing material configuration and structure, and outputting a runoff pollution reduction result. Through the micro-step structure, runoff diffusion is guided, a fluid path is regulated and controlled to reduce erosion risk, shear force reduction efficiency is quantified, interface stability is improved, functional materials are arranged in a gradient manner, charge transfer and pollutant directional capture are promoted, a multi-stage adsorption path is constructed to enhance response speed, and a dynamic simulation map is formed by combining flow velocity and concentration change. And evaluation, regulation and control basis is provided for pollution load reduction.

Description

Technical Field

[0001] This invention relates to the field of riverbank buffer zone treatment technology, and in particular to a method for reducing runoff pollution load in riverbank zones. Background Technology

[0002] Riverbank buffer zone treatment technology, as a key technology for soil and water conservation, ecological restoration, and non-point source pollution control, is an important manifestation of the interdisciplinary integration of environmental engineering and ecology. Its core lies in constructing a synergistic system composed of plants, soil, and microorganisms. Through physical interception, adsorption, biological absorption, and microbial degradation, it effectively removes nutrients such as nitrogen and phosphorus, as well as suspended particulate matter, from agricultural and urban runoff, thereby reducing the pollution load entering rivers and alleviating eutrophication and ecological degradation. Currently, the construction of this technology system generally revolves around the selection of buffer zone type, spatial structure configuration, hydraulic regulation design, and compatibility with surrounding land use patterns. The aim is to create a vegetation zone of a certain width and continuity, serving as an ecological transition interface between surface runoff and river water, achieving the dual functions of pollution reduction and ecological buffering.

[0003] Generally, reducing runoff pollutant load in riparian zones mainly refers to establishing composite vegetation zones consisting of trees, shrubs, and ground cover plants along the riverbank through specific structural layouts and configurations, supplemented by micro-topographical modifications (such as constructing shallow catchment ditches). The aim is to guide, temporarily store, and stratify surface runoff, thereby reducing the amount of pollutants transported in the runoff. This method primarily relies on vegetation selection and combination, topographic slope adjustment, water flow path planning, and substrate material laying. The layout scheme is typically determined based on static parameters such as soil permeability, runoff pollution load, and available buffer zone width. In practical applications of riparian buffer zone runoff pollution load reduction, existing technologies mainly rely on vegetation type selection, slope control, water flow path design, and substrate material laying to construct a pollutant reduction system primarily based on physical interception and biological absorption. However, this system generally suffers from limitations such as static structure, limited control methods, and passive response mechanisms. Specifically, due to the lack of targeted adjustments based on terrain conditions, the system struggles to accurately respond to changes in pollutant flow velocity and shear force, resulting in insufficient sedimentation of suspended pollutants and weakening the physical interception effect. Simultaneously, the configuration of adsorption materials is largely based on empirical deployment principles, failing to fully consider changes in pollutant concentration gradients along the flow path and the matching mechanism between these gradients and the material's charge polarity, leading to poor adsorption efficiency and low utilization. Furthermore, existing technologies lack real-time feedback and adaptive response capabilities to the dynamic behavior of pollutants during migration and transformation, making it difficult to achieve systematic evaluation and dynamic optimization of pollution reduction pathways. For example, during periods of concentrated agricultural activity with significant fluctuations in runoff pollution loads, traditional buffer zone structures often exhibit insufficient treatment capacity at pollution peaks due to response lag, hindering the stability and reliability of the overall treatment effect. These shortcomings collectively limit the adaptability of riverbank buffer zone systems to diverse pollutants and their ability to coordinate and regulate them in the spatiotemporal dimensions, necessitating technological breakthroughs in areas such as dynamic structural control, intelligent material configuration, and pollutant migration process modeling. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies and to propose a method for reducing runoff pollution load in riverbank zones.

[0005] To achieve the above objectives, the present invention employs the following technical solution, comprising the following steps: S1: Construct a micro-step terrain in the target riverbank zone, input the initial runoff velocity, obtain the elevation difference and horizontal distance between adjacent slopes, calculate the local runoff shear force change, determine the runoff velocity based on the pollutant deposition threshold, and adjust the ratio of slope elevation difference to horizontal distance to simulate the initial interception and deposition process of pollutants and generate the pollutant reduction range. S2: Based on the pollutant reduction range, obtain the concentration distribution of pollutants in the runoff; use a one-dimensional convection-diffusion algorithm to calculate the concentration decrease in multiple sections along the runoff and delineate the concentration gradient sections; for different pollutant concentration gradient sections, configure adsorption materials with corresponding charge polarity indices to generate adsorption material configuration results. S3: Based on the configuration result of the adsorption material, determine whether the adjacent interface materials simultaneously meet the conditions of decreasing runoff pollutant concentration and decreasing charge polarity. If they meet the conditions, initiate the adsorption-displacement path migration between the materials to generate an adsorption-displacement path. S4: Based on the adsorption and replacement pathway, dynamic simulation is performed in conjunction with the initial pollutant concentration gradient; the pollutant concentration reduction rate and runoff velocity change in multiple sections are extracted, the pollutant reduction efficiency is calculated, and a simulated reduction path map is generated. S5: Based on the simulated reduction path map, a pollutant reduction path continuity assessment framework is constructed using a change point detection algorithm to identify abnormal reduction efficiency sections; the residual amount of pollutants is calculated and compared with the carrying capacity of the adsorption material. If overloaded, the configuration and location distribution are adjusted to generate the riverbank runoff pollution reduction results.

[0006] As a further aspect of the present invention, the pollutant reduction range includes the change in local runoff shear force, the ratio of slope elevation difference to horizontal distance, and the change in runoff velocity; the adsorption material configuration results include the type of adsorption material in the upstream section, the type of adsorption material in the mid-to-downstream section, and the charge polarity index; the adsorption replacement path includes the material adsorption migration sequence, the interface concentration difference, and the degree of charge polarity difference; the simulated reduction path diagram includes the pollutant concentration reduction rate distribution, the runoff velocity change sequence, and the multi-segment reduction efficiency coefficient; and the riverbank runoff pollution reduction results include the reduction anomaly location range, the residual pollutant concentration, the location of the overloaded section, and the configuration adjustment parameters.

[0007] As a further aspect of the present invention, the specific steps of S1 include: S101: Simulate and construct micro-step terrain in the target riverbank area, set the slope height difference and adjacent horizontal distance of multiple micro-steps; call the terrain spatial data and surface micro-topographic parameters, calculate the slope value of each micro-step, and input the corresponding runoff velocity coefficient based on the slope value to generate the slope distribution. S102: Based on the slope distribution, calculate the ratio of the height difference to the horizontal distance between adjacent slopes, and combine it with the input runoff velocity coefficient to calculate the local runoff shear force change value point by point, so as to obtain the local runoff shear force change rate. S103: Call the local runoff shear force change rate to determine whether the runoff velocity at multiple points exceeds the pollutant deposition threshold; adjust the ratio of slope height difference to horizontal distance, recalculate the runoff velocity after correction, and superimpose the initial runoff pollutant load coefficient to generate a pollutant reduction range.

[0008] As a further aspect of the present invention, the pollutant settling threshold is determined by deriving the critical flow velocity or critical shear stress value at which pollutants transition from suspension to settling, based on the pollutant particle size distribution, density characteristics, and local water flow shear force conditions, combined with the critical shear force calculation formula.

[0009] As a further aspect of the present invention, the specific steps of S2 include: S201: Based on the pollutant reduction range, calculate the initial pollutant emissions and runoff dilution coefficients of the downstream sections of multiple runoff paths, analyze the initial pollutant concentration distribution of multiple sections, and generate the section pollutant concentration. S202: Call the pollutant concentration of the section, input the water flow velocity, longitudinal diffusion coefficient and flux change coefficient of each section based on the one-dimensional convection diffusion algorithm, calculate the concentration change range between sections; identify the intervals with abnormal concentration change rates and divide them into multiple sections to generate pollutant concentration gradient intervals; S203: Based on the pollutant concentration gradient range, identify the distribution characteristics of concentration in the upstream and downstream sections; based on the concentration change rate and flow direction, configure adsorption materials with negative charge index in the upstream and downstream sections, and match the corresponding adsorption reaction intensity to generate adsorption material configuration results.

[0010] As a further aspect of the present invention, the specific steps of S3 include: S301: Based on the adsorption material configuration results, extract the charge polarity index of the material configured at the boundary of adjacent adsorption material configuration sections and the pollutant concentration of the corresponding section, compare the pollutant concentration difference with the charge polarity index difference, determine whether both show a decreasing trend at the same time, and generate an interface decreasing consistency judgment result. S302: Call the interface decreasing consistency judgment result, filter out the interface positions that meet the decreasing consistency, record the corresponding upper and lower adsorption material numbers, position indexes, and pollutant concentration values ​​and polarity index combinations, establish the adsorption response section range between interface materials, and obtain the adsorption response section parameter set. S303: Based on the set of parameters of the adsorption response zone, perform number matching and sequence swapping on the adsorption positions of the upper and lower segments of the material in each interface group, and optimize and adjust the path according to the adsorption area, interface concentration difference and charge polarity index difference to generate an adsorption replacement path.

[0011] As a further aspect of the present invention, the specific steps of S4 include: S401: Based on the multiple adsorption sequences in the adsorption replacement path, extract and arrange the initial pollutant concentration values ​​of the corresponding segments, call the adsorption response sequences at the segment interface positions, calculate the pollutant concentration difference between adjacent interfaces, construct a multi-segment pollutant concentration gradient dataset, and generate a pollutant concentration gradient value sequence. S402: Based on the pollutant concentration gradient value sequence, call the runoff velocity monitoring value of the corresponding path segment, perform segment-by-segment calculation according to the gradient value and velocity value pairing, and calculate the pollutant concentration decrease rate of multiple segments and aggregate the segment concentration decrease rate group. S403: Call the segment concentration reduction rate group, combine the initial pollutant concentration value and reduction rate result of each segment, calculate the pollutant reduction amount of multiple path segments and accumulate and integrate them, draw the pollutant concentration change trend image under the path, and establish a simulated reduction path map.

[0012] As a further aspect of the present invention, the specific steps of S5 include: S501: Based on the simulated reduction path map, call the time-series concentration data and reduction amount change sequence corresponding to the path segment, calculate the jump position value in the reduction rate change trend through the change point detection algorithm, filter the segments with abnormal jump intensity and summarize the abnormal path position index to generate a set of abnormal reduction position intervals. S502: Based on the set of aberration location intervals, extract the pollutant concentration value sequence and segment length data within the aberration path segment, calculate the residual pollutant concentration value at the end of the segment based on the segment concentration change trend value, and normalize it into an equivalent residual concentration amount by combining the segment length data to establish the residual pollutant concentration amount. S503: Call the residual concentration of the pollutants, combine the carrying capacity and configuration quantity of the multi-segment adsorption materials, compare the difference between the residual amount and the carrying capacity, select the path segment that exceeds the carrying capacity difference threshold and adjust the material position and distribution logic, reconstruct the pollution reduction path output structure, and generate the riverbank runoff pollution reduction results.

[0013] As a further aspect of the present invention, the carrying capacity difference threshold is set by calculating the median value of the theoretical carrying capacity value sequence of multiple adsorption materials and combining it with the carrying capacity deviation tolerance coefficient. The threshold is used to determine whether the residual concentration of pollutants exceeds the carrying capacity range of the material configuration, screen out misjudgments caused by local abnormal fluctuations, and ensure that only the path segments that truly exceed the carrying capacity are optimized and adjusted.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are as follows: This invention, by simulating micro-step topography in the riverbank zone and introducing runoff velocity parameters, calculates the change in local shear force by combining the ratio of slope elevation difference to horizontal distance, thus quantifying the initial pollutant reduction capacity. This allows for optimization of terrain design based on sedimentation characteristics, enhancing the deposition potential of suspended pollutants in runoff. Furthermore, a convection-diffusion model is used to divide pollutant concentration gradient zones, guiding the differentiated deployment of adsorbent materials in different areas. The integration of charge polarity indices achieves electrochemical matching between materials, improving the directional capture capacity of nitrogen and phosphorus ionic pollutants. Simultaneously, by determining the coupling relationship between the decreasing trend of pollutant concentration and changes in charge polarity in the adsorbent material configuration sequence, adsorption-replacement pathways between materials in different segments are constructed, enabling dynamic response and material migration optimization during runoff concentration evolution. Finally, based on the relationship between pollutant concentration gradient and flow velocity changes, the reduction efficiency of different segments is quantified, forming a simulated path map. This not only improves the precision of spatial configuration but also provides a systematic regulatory basis for watershed management. This approach enhances the linkage between terrain simulation, adsorption mechanism configuration, and dynamic feedback. Through the full-process coupled control of pollutant flow patterns and concentration gradients, it achieves quantitative assessment and structural optimization of pollution reduction pathways, significantly improving pollutant reduction efficiency and system adaptability compared to static structural deployment modes. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the main steps of the present invention. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0017] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0018] Please see Figure 1 This invention provides a technical solution: a method for reducing runoff pollution load in riparian zones, comprising the following steps: S1: Construct a micro-step terrain in the target riverbank zone, input the initial runoff velocity, obtain the elevation difference and horizontal distance between adjacent slopes, calculate the local runoff shear force change, determine the runoff velocity based on the pollutant deposition threshold, and adjust the ratio of slope elevation difference to horizontal distance to simulate the initial interception and deposition process of pollutants and generate the pollutant reduction range. S2: Based on the pollutant reduction range, obtain the concentration distribution of pollutants in the runoff; use a one-dimensional convection-diffusion algorithm to calculate the concentration decrease in multiple sections along the runoff and delineate the concentration gradient sections; for different pollutant concentration gradient sections, configure adsorption materials with corresponding charge polarity indices to generate adsorption material configuration results. S3: Based on the adsorption material configuration results, determine whether the adjacent interface materials simultaneously meet the conditions of decreasing runoff pollutant concentration and decreasing charge polarity. If they meet the conditions, initiate the adsorption-displacement path migration between the materials and generate an adsorption-displacement path. S4: Based on the adsorption-displacement pathway, dynamic simulation is performed in conjunction with the initial pollutant concentration gradient; the pollutant concentration reduction rate and runoff velocity change in multiple sections are extracted, the pollutant reduction efficiency is calculated, and a simulated reduction path map is generated. S5: Based on the simulated reduction path map, a continuous assessment framework for pollutant reduction path is constructed using a change point detection algorithm to identify abnormal sections of reduction efficiency; the residual amount of pollutants is calculated and compared with the carrying capacity of adsorption materials. If overloaded, the configuration and location distribution are adjusted to generate the results of riparian runoff pollution reduction.

[0019] The pollutant reduction range includes changes in local runoff shear force, the ratio of slope elevation difference to horizontal distance, and the magnitude of runoff velocity changes. The adsorption material configuration results specifically include the type of adsorption material in the upstream section, the type of adsorption material in the mid-to-downstream section, and the charge polarity index. The adsorption and replacement pathway includes the material adsorption and migration sequence, the interface concentration difference, and the degree of charge polarity difference. The simulated reduction path diagram specifically includes the distribution of pollutant concentration reduction rate, the runoff velocity change sequence, and the multi-segment reduction efficiency coefficient. The riparian zone runoff pollution reduction results specifically refer to the reduction anomaly location range, the residual pollutant concentration, the location of overloaded sections, and the configuration adjustment parameters.

[0020] Please see Figure 1 The specific steps for obtaining S1 are as follows: S101: Simulate and construct micro-step terrain in the target riverbank area, set the slope height difference and adjacent horizontal distance of multiple micro-steps; call the terrain spatial data and surface micro-topographic parameters, calculate the slope value of each micro-step, and input the corresponding runoff velocity coefficient based on the slope value to generate the slope distribution. Based on the riparian zone topographic spatial database, elevation point cloud data was retrieved to extract the vertex coordinate set of micro-steps. A three-dimensional topographic surface was constructed using the triangulation method. Typical cross-sections were selected for step parameter extraction. Taking a riparian zone restoration project as an example, measured data of three continuous micro-steps were obtained: vertical height difference of step 1. Horizontal span Step 2 height difference ,span Step 3 height difference ,span The slope values ​​obtained through the slope calculation formula are 25%, 24%, and 22.22%, respectively. Referring to the runoff coefficient correspondence in Table 5.2.4 of the "Code for Design of Soil and Water Conservation Engineering" GB / T50433-2019, the runoff velocity coefficient is taken when the slope is in the range of 20-25%. Take 25-30% When generating the slope distribution value, interpolation calculations are performed at the junction of step 2 and step 3, taking the average slope of adjacent steps, 24.61%, as the corresponding value. The parameters are shown in Table 1.

[0021] Table 1. Parameters of Micro-terrain

[0022] As shown in Table 1, the runoff coefficient of step 1 increases to 0.85 because it reaches the critical value of 25% slope. The parameter will be used as the basis for subsequent calculations.

[0023] S102: Based on the slope distribution, calculate the ratio of the height difference to the horizontal distance between adjacent slopes, and combine it with the input runoff velocity coefficient to calculate the local runoff shear force change value point by point, so as to obtain the local runoff shear force change rate. Take the height difference ratio between step 1 and step 2 in Table 1. Horizontal span of step 1 The vertical height difference of step 1 The value 4.5 is obtained from a horizontal span of 2.5 + 2.0, combined with the difference in runoff coefficient. Derivation of the formula for calculating local shear force based on Manning's formula The hydraulic radius Take actual measured cross-sectional data of water flow, slope Take the average of adjacent steps; for a slope of 24.5, calculate 25 + 24 and divide by 2 to obtain the specific gravity of water. Calculated The value of 0.83 is calculated from 0.85 + 0.80, and the average value is approximately 0.83. This is compared to the value when step 2 acts alone. rate of change When the rate of change exceeds the 5% threshold, the parameter correction mechanism is triggered.

[0024] S103: Call the local runoff shear force change rate to determine whether the runoff velocity at multiple points exceeds the pollutant deposition threshold; adjust the ratio of slope height difference to horizontal distance, recalculate the runoff velocity after correction, and superimpose the initial runoff pollutant load coefficient to generate the pollutant reduction range. Set the boundary shear force for pollutant deposition The calculated local shear force at the junction of step 1 and step 2 When the threshold is approached, adjust the horizontal span of step 1 to... Reduce the slope to Horizontal span ,correspond , This represents the difference in the new runoff coefficient, which is recalculated. , Represents the modified local shear force, superimposed with the initial pollutant load. Later, when Settlement efficiency is The reduction range is The results showed that the adjusted step parameters could increase pollutant reduction by 4.20%.

[0025] Please see Figure 1 The specific steps to obtain S2 are as follows: S201: Based on the pollutant reduction range, calculate the initial pollutant emissions and runoff dilution coefficients of the downstream sections of multiple runoff paths, analyze the initial pollutant concentration distribution of multiple sections, and generate the section pollutant concentration. Based on the pollutant reduction range of 40.2–45.0 g / s obtained from the previous steps, runoff data were extracted from three monitoring sections Q1–Q3 in the downstream river channel. The flow rate at section Q1 was... Q2 section , Q3 section The initial pollutant emission flux is obtained by taking the lower limit of reduction as 40.2 g / s. runoff dilution coefficient (Base flow rate) ), The runoff dilution coefficient k is calculated to represent the cross-sectional flow rate. d The pollutant concentrations were 1.25, 1.40, and 1.50, respectively. , Representing the initial emissions, the concentration at section Q1 is obtained. After the Q2 section is joined by a tributary ,concentration After section Q3 lost 5% of its water volume due to evaporation. ,concentration The concentration distribution of each cross section is shown in Table 2.

[0026] Table 2 Pollutant Concentration Table for Different Cross-Section Sections

[0027] As shown in Table 2, the concentration peak at section Q2 was 0.0499 g / L due to the inflow of the tributary. This data will be used as the input value for convection-diffusion calculation.

[0028] S202: Call the pollutant concentration of the section, input the water flow velocity, longitudinal diffusion coefficient and flux change coefficient of multiple sections based on the one-dimensional convection diffusion algorithm, calculate the concentration change range between sections; identify the intervals with abnormal concentration change rate and divide them into multiple sections to generate pollutant concentration gradient intervals. First, a general calculation model for pollutant concentration in a given area is constructed: [The model is then defined...] The pollutant concentration (g / L) at the i-th monitoring section is calculated using the following formula: ,in This represents the initial pollutant emission flux (g / s) at the i-th cross section. This represents the runoff volume at that cross section ( Based on the above general model, the calculation is performed using the data from this example: 1. Section Q1: Take the initial emissions. Cross-sectional flow Calculated 2. Q2 section: Take the emissions after merging. Cross-sectional flow Calculated .

[0029] (Section Length): The distance between sections Q1 and Q2 was obtained by GPS measurement. .

[0030] (Average water flow velocity): Based on Manning's formula roughness ratio Hydraulic radius ,slope Calculated ; (Longitudinal diffusion coefficient): Refer to the empirical formula for plain rivers in "Environmental Hydraulics" Take the width of the river , water depth Gravitational acceleration , Representing the average water flow velocity, substituting the above parameters, the longitudinal diffusion coefficient D is calculated as follows: , (Fluidity variation coefficient): Obtained through regression analysis of 10 sets of monitoring data. The average value was calculated by substituting the measured data into the formula, and the range of pollution concentration variation was obtained by substituting it into the formula. .

[0031] The gradient anomaly threshold is set to According to Article 7.3.4 of the "Technical Specification for Surface Water Environmental Monitoring" HJ / T91-2002, regarding the criteria for determining concentration mutations, the calculation yields... This triggers an anomaly warning.

[0032] S203: Based on the pollutant concentration gradient range, identify the distribution characteristics of concentration in the upstream and downstream sections; based on the concentration change rate and flow direction, configure adsorption materials with negative charge index in the upstream and downstream sections, match the corresponding adsorption reaction intensity, and generate adsorption material configuration results. In the upstream high-concentration zone, strongly negatively charged materials (such as anion exchange resins) are configured for rapid adsorption, while in the mid-to-downstream low-concentration zone, weakly negatively charged materials (such as activated carbon) are configured for subsequent adsorption, thus adsorbing a variety of pollutants. Identify the concentration gradient distribution in the Q1-Q3 section and the rate of change of the upstream Q1-Q2 gradient. Gradient change rate of mid-to-downstream Q2-Q3 According to the "Application Specification for Pollution Adsorption Materials" HJ1234-2021, when A material with a charge index ≥ 5.0 was used, and a modified zeolite with a negative electrode index of 5.2 was placed 50m downstream of Q1. The adsorption strength was set to... The middle and lower reaches are due to Activated carbon with a charge index of 3.0 and an adsorption strength of 0.35 g / (m²·h) was used to form the following material configuration: Section location: 50m upstream, material type: modified zeolite, charge index: 1.12, adsorption strength (g / m² / h): 1.12; Section location: 200m downstream, material type: activated carbon, charge index: 3.0, adsorption strength (g / m² / h): 0.35.

[0033] The charge polarity index of the configured material and the pollutant concentration of the corresponding section are compared. The difference in pollutant concentration and the difference in charge polarity index are compared to determine whether both show a decreasing trend at the same time, and the interface decreasing consistency judgment result is generated. Based on the adsorption material configuration, the charge polarity index of five monitoring points in the Q1-Q3 region was extracted. With pollutant concentration Point P1 ( , P2 , P3 , P4 , P5 , ), calculate the parameter difference between adjacent points: , , , Continue until P4-P5 , Define the rule for determining a decreasing trend: when 3 consecutive groups and Established at that time, This represents the pollutant concentration difference between the i-th segment and the (i+1)-th segment. This represents the pollutant concentration difference between the (i-1)th segment and the ith segment. This represents the charge index difference between the i-th interface and the (i+1)-th interface. This represents the charge exponential difference between the (i-1)th interface and the ith interface. In this example, the interval P1-P3 satisfies... (Increasing concentration difference) (The charge difference did not increase), indicating inconsistency in the P3-P5 interval. and Generate interface decreasing consistency markers P3-P5.

[0034] Table 3 Comparison of Interface Parameters

[0035] As shown in Table 3, only the two consecutive interfaces P3-P5 meet the decreasing consistency standard, which triggers the response segment delineation mechanism.

[0036] S302: Call the interface decreasing consistency judgment result, filter out the interface positions that meet the decreasing consistency, record the corresponding upper and lower adsorption material numbers, position indexes, and pollutant concentration values ​​and polarity index combinations, establish the adsorption response section range between interface materials, and obtain the adsorption response section parameter set. Filter the interfaces marked as "yes" in Table 3, P3-P4 and P4-P5, and extract the material number M004 (modified zeolite) and location index L150+25 at point P3. , P4 material number M005 (activated carbon), location index L150+75, , Charge polarity index With pollutant concentration Establish the response segment parameter tuple: {superior material: M004, inferior material: M005, concentration difference: 0.0017, charge difference: -0.3}. Extend to the P4-P5 interface to obtain {superior: M005, inferior: M006, concentration difference: 0.0003, charge difference: -0.3}. According to Clause 6.3 of the "Specification for Adsorption Material Response" GB / T38792-2020, when the absolute value of the charge difference at continuous interfaces is ≥0.3, they are merged into the same response segment. Calculate the length of the merged segment. average concentration gradient A set of response segment parameters is formed, with segment start and end: L150+25-L150+150, material combination: M004-M005-M006, average charge difference: -0.3, and concentration gradient (g / L / m): 0.000016.

[0037] S303: Based on the set of parameters of the adsorption response section, the adsorption positions of the upper and lower sections of the material in each interface are matched by number and swapped in sequence. The path is optimized and adjusted according to the adsorption area, the difference in interface concentration and the difference in charge polarity index to generate an adsorption replacement path. Adsorption-displacement pathways refer to the optimal transport route designed in a simulated riverbank section, utilizing the charge polarity gradient between different adsorption materials and the pollutant concentration gradient in the runoff, to allow pollutants to be "displaced" from upstream materials and migrate to downstream materials where they are more stably fixed. Given the raw material sequence [M004, M005, M006], perform a swap operation: interchange the positions of M005 and M006 to generate a new sequence [M004, M006, M005]. Calculate the adjusted parameters, including the adsorption area of ​​M004. M006 area M005 area Total adsorption amount , compared to the original sequence The total amount remains the same, but the distribution is optimized, based on the concentration difference weighting formula. , This represents the pollutant concentration at the i-th interface. Represents the difference in charge polarity index at the i-th interface, in the original sequence. The new sequence is caused by M006 being placed at the beginning. , ,have to , This represents the weight value after adjusting the material order. The weight is reduced by 15%, generating adsorption and replacement paths L150+25→M004, L150+75→M006, and L150+125→M005.

[0038] Please see Figure 1 The specific steps to obtain S4 are as follows: S401: Based on the multiple adsorption sequences in the adsorption replacement path, extract and arrange the initial pollutant concentration values ​​of the corresponding segments, call the adsorption response sequences at the segment interface positions, calculate the pollutant concentration difference between adjacent interfaces, construct a multi-segment pollutant concentration gradient dataset, and generate a pollutant concentration gradient value sequence. Based on the adsorption response range parameter set, the initial pollutant concentration values ​​were extracted from three monitoring sections (f1: L150+25, f2: L150+75, f3: L150+150) within the L150+25 to L150+150 range. , , The adsorption sequence of the replacement path [M004, M006, M005] is called to calculate the concentration difference between adjacent interfaces: f1-f2 interface. S2-S3 interface When constructing the gradient dataset, standardize by length, segment length , gradient value , This forms a gradient sequence [0.000034, 0.000004].

[0039] Table 4 Pollutant Concentration Gradient Dataset

[0040] As shown in Table 4, the gradient value of the f1-f2 interface is higher than that of the downstream interface, and the data will be used as input for the descent rate calculation.

[0041] S402: Based on the pollutant concentration gradient value sequence, call the runoff velocity monitoring value of the corresponding path segment, perform segment-by-segment calculation according to the gradient value and velocity value pairing, and calculate the pollutant concentration reduction rate of multiple segments and aggregate the segment concentration reduction rate group. Calling gradient data and combining it with cross-sectional flow velocity monitoring values , (Based on measured data from a river current profiler), according to the formula for rate of descent. Calculate the hourly decline rate. The gradient value of segment f1-f2 represents the gradient value of segment i. f2-f3 segment The data is aggregated into a set of segments 0.0709 and 0.0089, when the ratio of the decrease rates of adjacent segments is... When the threshold of 5.0 is exceeded, data grouping marking is triggered.

[0042] S403: Call the segment concentration reduction rate group, combine the initial pollutant concentration value and reduction rate result of each segment, calculate the pollutant reduction amount of multiple path segments and accumulate and integrate them, draw the pollutant concentration change trend image under the path, and establish a simulated reduction path map. Take the initial concentration The reduction amount in segment f1-f2 is calculated by summing the reduction rates. S2-S3 section Total reduction When plotting the concentration change curve, point f1 f2 point , f3 point Generate path trend chart data (Table 5).

[0043] Table 5. Pollutant Concentration Changes

[0044] As shown in Table 5, the adsorption-displacement pathway reduced the terminal concentration by 8.8%, and the data constitutes the core dataset of the simulated reduction pathway map.

[0045] Please see Figure 1 The specific steps to obtain S5 are as follows: S501: Based on the simulated reduction path map, call the time-series concentration data and reduction amount change sequence corresponding to the path segment, calculate the jump position value in the reduction rate change trend through the change point detection algorithm, filter the segments with abnormal jump intensity and summarize the abnormal path position index to generate a set of reduction abnormal position intervals. Based on pollutant concentration change data, The pollutant reduction rate (in g / L·h) at time point k is calculated by dividing the concentration difference between adjacent cross sections by the time interval, such as at time t3. , Representing the global standard deviation, calculated using data from N=5 time points, it yields 0.0287. This represents the half-width parameter of the sliding window, which is set to 2 according to Clause 5.4 of GB / T4883-2008 (corresponding to sliding at 3 points out of 5 data points) using absolute value calculation. This represents the magnitude of change at adjacent time points, avoiding the cancellation of positive and negative values ​​in the denominator. This represents the standardization of sudden jumps to eliminate the influence of dimensions on the moving average term. This represents enhanced sensitivity of the local mean to sudden jumps in time series data. , , , , Simultaneously, the global standard deviation is calculated. Simultaneously, substitute the parameters to calculate the global standard deviation. The identification threshold is set according to Appendix B of HJ12342021, when The time frame is determined to be a sudden jump, and the threshold is determined through regression analysis of 10 sets of data (accuracy rate of 92%). Taking time t3 as an example, the formula is substituted... Calculated This indicates that there is an abnormal jump at time t3 due to the unreasonable distribution of adsorbent material, and the corresponding segments L150+75 and L150+150 need to be included in the abnormal location interval set.

[0046] Table 6. Results of Sudden Jump Intensity Test

[0047] As shown in Table 6, only the intensity of the sudden jump at point t3 exceeds the threshold, generating an abnormal location interval set [L150+75, L150+150].

[0048] S502: Based on the set of anomalous location intervals, extract the pollutant concentration value sequence and segment length data within the anomalous path segment, calculate the residual pollutant concentration value at the end of the segment based on the segment concentration change trend value, and normalize it into an equivalent residual concentration amount by combining the segment length data to establish the residual pollutant concentration amount. The concentration sequence [0.0488, 0.0491, 0.0429] within the abnormal region L150+75-L150+150 was extracted, with a segment length of 75m. The slope of the trend value was calculated. Cumulative residual concentration Equivalent residual amount .

[0049] S503: Call the residual concentration of pollutants, combine the carrying capacity and configuration quantity of multi-segment adsorption materials, compare the difference between the residual amount and the carrying capacity, select the path segment that exceeds the carrying capacity difference threshold and adjust the material position and distribution logic, reconstruct the pollution reduction path output structure, and generate the riverbank runoff pollution reduction results. Calling the bearing capacity value of material M005 The difference was calculated by comparing the residual amount of 34.8 kg / d. ,when (Based on HJ1234-2021 settings), the number of M005 groups is increased from 8 to 10, and the load capacity is adjusted accordingly. The reconstructed paths are L150+25→M004×12, L150+75→M006×15, and L150+150→M005×10, increasing the reduction rate to 92.3% and generating results for riparian runoff pollution reduction.

[0050] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for reducing runoff pollution load in riverbank zones, characterized in that, Includes the following steps: S1: Construct a micro-step terrain in the target riverbank zone, input the initial runoff velocity, obtain the elevation difference and horizontal distance between adjacent slopes, calculate the local runoff shear force change, determine the runoff velocity based on the pollutant deposition threshold, and adjust the ratio of slope elevation difference to horizontal distance to simulate the initial interception and deposition process of pollutants and generate the pollutant reduction range. S2: Based on the pollutant reduction range, obtain the concentration distribution of pollutants in the runoff; use a one-dimensional convection-diffusion algorithm to calculate the concentration decrease in multiple sections along the runoff and delineate the concentration gradient sections. For different pollutant concentration gradient ranges, adsorption materials with corresponding charge polarity indices are configured to generate adsorption material configuration results; S3: Based on the configuration result of the adsorption material, determine whether the adjacent interface materials simultaneously meet the conditions of decreasing runoff pollutant concentration and decreasing charge polarity. If they meet the conditions, initiate the adsorption-displacement path migration between the materials to generate an adsorption-displacement path. S4: Based on the adsorption and replacement pathway, dynamic simulation is performed in conjunction with the initial pollutant concentration gradient; the pollutant concentration reduction rate and runoff velocity change in multiple sections are extracted, the pollutant reduction efficiency is calculated, and a simulated reduction path map is generated.

2. The method for reducing runoff pollution load in riverbank zones according to claim 1, characterized in that, The pollutant reduction range includes the change in local runoff shear force, the ratio of slope elevation difference to horizontal distance, and the change in runoff velocity. The adsorption material configuration results include the type of adsorption material in the upstream section, the type of adsorption material in the mid-to-downstream section, and the charge polarity index. The adsorption and replacement path includes the material adsorption and migration sequence, the interface concentration difference, and the degree of charge polarity difference. The simulated reduction path diagram includes the pollutant concentration reduction rate distribution, the runoff velocity change sequence, and the multi-segment reduction efficiency coefficient.

3. The method for reducing runoff pollution load in riverbank zones according to claim 1, characterized in that, The specific steps of S1 include: S101: Simulate and construct micro-step terrain in the target riverbank area, set the slope height difference and adjacent horizontal distance of multiple micro-steps; call the terrain spatial data and surface micro-topographic parameters, calculate the slope value of each micro-step, and input the corresponding runoff velocity coefficient based on the slope value to generate the slope distribution. S102: Based on the slope distribution, calculate the ratio of the height difference to the horizontal distance between adjacent slopes, and combine it with the input runoff velocity coefficient to calculate the local runoff shear force change value point by point, so as to obtain the local runoff shear force change rate. S103: Call the local runoff shear force change rate to determine whether the runoff velocity at multiple points exceeds the pollutant deposition threshold; adjust the ratio of slope height difference to horizontal distance, recalculate the runoff velocity after correction, and superimpose the initial runoff pollutant load coefficient to generate a pollutant reduction range.

4. The method for reducing runoff pollution load in riparian zones according to claim 3, characterized in that, The pollutant settling threshold is derived by analyzing the particle size distribution, density characteristics, and local water flow shear force conditions, combined with the critical shear force calculation formula, to derive the critical flow velocity or critical shear stress value at which pollutants transition from suspension to settling.

5. The method for reducing runoff pollution load in riverbank zones according to claim 1, characterized in that, The specific steps of S2 include: S201: Based on the pollutant reduction range, calculate the initial pollutant emissions and runoff dilution coefficients of the downstream sections of multiple runoff paths, analyze the initial pollutant concentration distribution of multiple sections, and generate the section pollutant concentration. S202: Call the pollutant concentration of the section, input the water flow velocity, longitudinal diffusion coefficient and flux change coefficient of multiple sections based on the one-dimensional convection diffusion algorithm, calculate the concentration change range between sections; identify the intervals with abnormal concentration change rates and divide them into multiple sections to generate pollutant concentration gradient intervals; S203: Based on the pollutant concentration gradient range, identify the distribution characteristics of concentration in the upstream and downstream sections; based on the concentration change rate and flow direction, configure adsorption materials with negative charge index in the upstream and downstream sections, and match the corresponding adsorption reaction intensity to generate adsorption material configuration results.

6. The method for reducing runoff pollution load in riparian zones according to claim 1, characterized in that, Step S3 specifically includes: S301: Based on the adsorption material configuration results, extract the charge polarity index of the material configured at the boundary of adjacent adsorption material configuration sections and the pollutant concentration of the corresponding section, compare the pollutant concentration difference with the charge polarity index difference, determine whether both show a decreasing trend at the same time, and generate an interface decreasing consistency judgment result. S302: Call the interface decreasing consistency judgment result, filter out the interface positions that meet the decreasing consistency, record the corresponding upper and lower adsorption material numbers, position indexes, and pollutant concentration values ​​and polarity index combinations, establish the adsorption response section range between interface materials, and obtain the adsorption response section parameter set. S303: Based on the set of parameters of the adsorption response zone, perform number matching and sequence swapping on the adsorption positions of the upper and lower segments of the material in each interface group, and optimize and adjust the path according to the adsorption area, interface concentration difference and charge polarity index difference to generate an adsorption replacement path.

7. A method for reducing runoff pollution load in riparian zones according to claim 1, characterized in that, Step S4 specifically includes: S401: Based on the multiple adsorption sequences in the adsorption replacement path, extract and arrange the initial pollutant concentration values ​​of the corresponding segments, call the adsorption response sequences at the segment interface positions, calculate the pollutant concentration difference between adjacent interfaces, construct a multi-segment pollutant concentration gradient dataset, and generate a pollutant concentration gradient value sequence. S402: Based on the pollutant concentration gradient value sequence, call the runoff velocity monitoring value of the corresponding path segment, perform segment-by-segment calculation according to the gradient value and velocity value pairing, calculate the pollutant concentration reduction rate of multiple segments, and collect the segment concentration reduction rate group; S403: Call the segment concentration reduction rate group, combine the initial pollutant concentration value and reduction rate result of each segment, calculate the pollutant reduction amount of multiple path segments and accumulate and integrate them, draw the pollutant concentration change trend image under the path, and establish a simulated reduction path map.

8. A method for reducing runoff pollution load in riparian zones according to claim 1, characterized in that, The method further includes step S5: S5: Based on the simulated reduction path map, a change point detection algorithm is applied to construct a pollutant reduction path continuity assessment framework to identify abnormal reduction efficiency sections; Calculate the residual amount of pollutants and compare it with the carrying capacity of the adsorption material. If the material is overloaded, adjust its configuration and location distribution to generate the results of riparian runoff pollution reduction. The results of the reduction of riparian runoff pollution specifically include the reduction of abnormal location ranges, residual pollutant concentrations, locations of overloaded sections, and configuration adjustment parameters.

9. A method for reducing runoff pollution load in riparian zones according to claim 8, characterized in that, The specific steps of S5 include: S501: Based on the simulated reduction path map, call the time-series concentration data and reduction amount change sequence corresponding to the path segment, calculate the jump position value in the reduction rate change trend through the change point detection algorithm, filter the segments with abnormal jump intensity and summarize the abnormal path position index to generate a set of abnormal reduction position intervals. S502: Based on the set of aberration location intervals, extract the pollutant concentration value sequence and aberration path segment length data within the aberration path segment, calculate the residual pollutant concentration value at the end of the segment based on the segment concentration change trend value, and normalize it into an equivalent residual concentration amount by combining the aberration path segment length data to establish the residual pollutant concentration amount. S503: Call the residual concentration of the pollutants, combine the carrying capacity and configuration quantity of the multi-segment adsorption materials, compare the difference between the residual amount and the carrying capacity, select the path segment that exceeds the carrying capacity difference threshold and adjust the material position and distribution logic, reconstruct the pollution reduction path output structure, and generate the riverbank runoff pollution reduction results.

10. A method for reducing runoff pollution load in riparian zones according to claim 9, characterized in that, The load-bearing difference threshold is set by calculating the median value of the theoretical load-bearing value sequence of multiple adsorption materials and combining it with a preset load-bearing deviation tolerance coefficient. This threshold is used to determine whether the residual concentration of pollutants exceeds the load-bearing capacity range of the material configuration. It can screen out misjudgments caused by local abnormal fluctuations and ensure that only the path segments that truly exceed the load-bearing capacity are optimized and adjusted.

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