River pollution in-situ remediation system construction method

Abstract

The invention discloses a river pollution in-situ remediation system construction method, which comprises the following steps: S1, constructing a hydrodynamic force and pollutant migration coupling model, and simulating pollutant distribution; 2, constructing a biological collaborative purification quantitative model, screening efficient aquatic plants and benthonic animals, and establishing a purification kinetic equation; 3, combining the biological collaborative purification quantitative model output and biological parameters, and optimizing a plant planting area position; and 4, configuring a dynamic three-dimensional ecological niche, and constructing a bioremediation system. According to the method, the pollution remediation efficiency is remarkably improved through a synergistic effect, an eco-friendly in-situ remediation system is constructed, interception or desilting is not needed, biocenosis is directly constructed in a water body, damage to a river channel structure is reduced, and secondary pollution caused by physical and chemical methods is avoided. By simulating a natural river ecological system, a suitable habitat is provided for organisms, so that the recovery of an ecological chain is accelerated, and the self-cleaning capacity of a water body is maintained for a long time.

Description

Technical Field

[0001] This invention relates to the field of ecological restoration, and in particular to a method for constructing an in-situ river pollution remediation system based on the coupling effect of aquatic plants and benthic animals. Background Technology

[0002] With the continuous improvement of living standards, people's demand for improving their living environment is becoming increasingly urgent. Rivers, as an important component of the residential environment, are one of the main targets for improving the urban and rural ecological environment. Problems such as insufficient ecological flow, water ecosystem imbalance, and increased non-point source pollution in urban and rural areas not only affect residents' production and lifestyles but also, to some extent, hinder urban modernization. Currently, the situation regarding urban river water ecosystem restoration remains severe, with problems such as declining biodiversity, habitat fragmentation, and ecological function degradation, leading to an imbalance in the aquatic ecosystem and a decline in the water body's self-purification capacity, far from reaching the 85% target.

[0003] Currently, physical, chemical, and biological ecological methods are the main means of river management. Physical methods such as artificial aeration, water diversion and regulation, mechanical algae control, construction of hydraulic structures, and riverbank filtration can effectively improve water quality, but they require a large amount of labor and capital. Chemical methods, mainly including flocculation, oxidation, and sedimentation, have the advantages of rapid effectiveness and quick reaction, and can deal with sudden water pollution. However, the addition of chemical agents is not only costly, but may also lead to secondary pollution, posing a potential threat to aquatic organisms and human health. Although physical and chemical methods can achieve good remediation effects, their high cost and short-term limitations are difficult to avoid in engineering applications. Compared with these methods, biological methods are more economical, environmentally friendly, and have stronger self-sustaining capabilities, thus becoming the main means of river restoration. Among the many biological treatment methods, aquatic ecological restoration technology has received widespread attention. This technology is a water body ecological purification method based on ecological balance. It utilizes suitable aquatic animals, plants, microorganisms, etc., to form a stable river and lake aquatic ecosystem, thereby gradually achieving the purification of river and lake water quality. However, current single-mode bioremediation still faces two major bottlenecks: first, the unclear biological synergy mechanism limits pollutant removal efficiency; second, the lack of spatially adaptable design in ecological restoration systems makes it difficult to cope with dynamic hydrological conditions. Therefore, developing an in-situ remediation system that quantifies biological coupling effects and possesses both spatial optimization and dynamic regulation capabilities has become an urgent need for watershed management. Summary of the Invention

[0004] To achieve these objectives and other advantages according to the present invention, a method for constructing an in-situ river pollution remediation system is provided to address the two major problems of existing single bioremediation technologies mentioned in the background art. Specifically, the remediation system is constructed through four core steps:

[0005] (1) Coupled modeling of hydrodynamics and pollutant migration (S1): Establish a high-precision river model to simulate pollutant distribution;

[0006] (2) Construction of a quantitative model for biosynergistic purification (S2): Screening efficient aquatic plants and benthic animals and establishing purification kinetic equations;

[0007] (3) Optimization of planting area division (S3): Optimize the planting location of plants by combining model output and biological parameters;

[0008] (4) Three-dimensional ecological niche dynamic configuration and system construction (S4): Design biological spatial layout and implant feedback regulation module to finally form a technical system of 'simulation-quantification-zoning-regulation'.

[0009] Step S1: Coupled Modeling of Hydrodynamics and Pollutant Migration

[0010] This step aims to construct a high-precision coupled hydrodynamic and pollutant migration model for the target river section, quantify pollutant migration patterns, and provide environmental baseline data support for biological system configuration. Through the hydrodynamic-pollutant coupled model, pollution hotspots (such as...) are identified. Figure 3 (As shown in the high TN concentration area), this addresses the core issue of "where repair is needed," laying the foundation for subsequent biological system configuration.

[0011] S11 uses an irregular triangular network (TIN) to divide the river channel into grids with a total of ≥20,000 grids, and the shoreline boundary grids are refined to a resolution of ≤5m. Based on satellite elevation data (resolution 50m×50m), underwater topographic data is mapped to grid nodes using a weighted inverse distance interpolation method to generate a three-dimensional topographic model of the river channel. A two-dimensional hydrodynamic model is established based on the Navier-Stokes equations, with input parameters including: riverbed roughness coefficient (0.025-0.035), wind stress field, and dynamic threshold of dry and wet boundaries (water depth ≤0.01m is defined as a dry element).

[0012] S12 constructs pollutant migration equations:

[0013] Establish a nitrogen kinetic model and define the following state variable equations:

[0014]

[0015] Among them, S BOD F represents the ammonia nitrogen release rate due to BOD degradation. plant F bact R represents the uptake rate of plants and bacteria, respectively. nitr The rate of bacterial nitrification.

[0016] Step S2: Construction of a quantitative model for bio-synergistic purification

[0017] Based on the characteristics of the polluted area output by S1, this step quantifies the synergistic purification efficiency of aquatic plants and benthic animals through controlled laboratory experiments, establishes a multi-pathway dynamic model of "plant absorption - animal filter feeding - microbial decomposition", solves the key issues of "what to use for remediation" and "how to quantify synergy", and establishes a key dynamic model.

[0018] S21 Plant Screening and Quantification

[0019] Emergent plants mainly include: Thalia dealbata, reeds, cattails, irises, loosestrife, canna lilies, water onions, water celery, calamus, yellow iris, variegated reed, alligator weed, and arrowhead.

[0020] The main floating-leaved plants are water lilies and duckweed.

[0021] Submerged plants mainly include: Potamogeton malaianus, Potamogeton crispus, Ceratophyllum demersum, Myriophyllum spicatum, Hydrilla verticillata, and Vallisneria natans.

[0022] Purification efficiency equation:

[0023] k p Species-specific absorption rate constant

[0024] C: Pollutant concentration

[0025] B: Plant biomass density (kg / m³) 3 )

[0026] K m Michaelis constant

[0027] I max Maximum instantaneous absorption rate per unit biomass

[0028] S22 Plant Absorption Kinetics Model

[0029] Due to the species-specific absorption rate constant k mentioned in step S21 p The maximum instantaneous absorption rate I in the plant absorption kinetics equation max There is a direct correlation, i.e., k p =I max Therefore, this step establishes a target plant absorption kinetic model to calculate the purification efficiency equation.

[0030] Among them, I max : Maximum instantaneous absorption rate per unit biomass; V: Water volume (L)

[0031] The net benefit model of plants can be expressed as:

[0032] Screening and quantification of the S23 benthic fauna

[0033] Species: *Odontodon dorsiflorus*, *Triangular sail mussel*

[0034] Animal net benefit equation:

[0035] k: Filtering coefficient

[0036] n: Nonlinear response index of pollutant concentration C

[0037] TSS: Total suspended solids concentration

[0038] γ: TSS inhibition coefficient

[0039] T opt At 25℃, β = 0.02℃ -1 γ = 0.005 L / mg

[0040] F = k·C n ·e -0.02(T-25) (1-0.05 TSS)

[0041] S24 Synergistic Effect Quantification Method for Pollutant Multipath Removal Rate: Total pollutant removal rate (η) of the system total ) is plant absorption (η) plant Animal filter feed (η) animal ), microbial decomposition (η) microbe The superposition of contributions from multiple paths, such as η. plant It can be calculated through the established purification efficiency equation; η animal η can be estimated using the established net efficiency kinetic equation and removal rate data. microbe It can be obtained using the following formula;

[0042]

[0043] in:

[0044] HRT: Hydraulic residence time; ξDO: DO correction factor (0-1).

[0045] ξT=θ (T-20) (θ: 1.02~1.08).

[0046] Step S3: Optimize the division of planting areas

[0047] Based on S1 and S2, this step constructs a two-factor evaluation system of "hydrodynamic stability-pollution exposure intensity" to generate biospatial configuration and solve the problems of "where to remediate" and "remediation intensity".

[0048] Construction of S31 Multi-factor Coupled Evaluation System

[0049] Based on the pollutant concentration field data output from step 1, a planting suitability evaluation system including the following quantitative indicators is established:

[0050] a. Hydrodynamic Stability Index (HSSI):

[0051]

[0052] Where m is the number of grid cells, V i For the flow rate of the grid cells, For the average flow velocity, σ v The standard deviation of the flow rate, The water level gradient is represented by α, where α is the attenuation coefficient.

[0053] b. Pollutant Exposure Intensity Threshold (PEI): Based on experimental data and extended water quality standards, a composite exposure intensity threshold function is constructed:

[0054] Multi-pollutant tolerance threshold (C crit )

[0055] Pollutant concentration exposure threshold function

[0056]

[0057] Among them, C k Concentration of the kth pollutant

[0058] C crit,k : Sensitivity threshold of organisms to target pollutants

[0059] β k : Nonlinear response coefficient of pollutants, where pollutant NH4 + The response coefficient is set to 3, the response coefficient for pollutant COD is set to 2, and the response coefficient for other pollutants is set to 1.5.

[0060] w k Weighting factor, Σ w-k =1, according to the pollution contribution, S32 uses a fuzzy comprehensive evaluation algorithm to fuzzify the HSSI and PEI indicators using a trapezoidal membership function; the weight vector W = [0.3, 0.7] is determined based on the analytic hierarchy process (AHP); and the suitability score is calculated using the weighted average method for comprehensive evaluation. in This represents the rules for fuzzy multiplication operations. μ represents the bounded sum operation rules. k Let μ1 be the fuzzy membership degree value of the Kth evaluation index, where μ1 is the membership degree of HSSI and μ2 is the membership degree of PEI.

[0061] S33 Planting Area Output

[0062] Generate a contour map of planting suitability, and extract areas with suitability S≥0.7 as priority planting areas; output the coordinate parameter set {GPS}. i (x,y)}, each coordinate point corresponds to a priority planting location, and each coordinate point is associated with dynamic configuration parameters, including the type of plant to be planted at that point, the planting density, and the ratio of benthic animals to plant biomass, thus forming a precise spatial configuration scheme.

[0063] Step S4: Three-dimensional niche configuration and system construction

[0064] Based on the S3 spatial configuration, this step achieves a three-dimensional coordinated configuration of "emergent-floating-sinking-benthic" through layered spacing calculation and multi-parameter feedback control, forming an adaptive in-situ remediation system and solving the problem of "how to operate effectively in the long term".

[0065] S41, based on the planting areas divided in S3 and the biological combinations and parameters screened in S2, performs three-dimensional spatial optimization and dynamic control. For the generated optimal planting location map of aquatic plants, a dynamic three-dimensional ecological niche is configured to ensure its spatial coupling configuration meets the following requirements.

[0066] 1. Spacing D of emergent plants emer

[0067]

[0068] in:

[0069] Q: Flow rate (m) 3 / s)

[0070] ΔC: Purification gradient (mg / L)

[0071] C0: Background pollutant concentration (mg / L)

[0072] k p : Species-specific absorption rate constant (by I) max Conversion, kg -1 ·d-1)

[0073] B: Plant biomass density (kg / m³) 3 )

[0074] Constraint: When C0 < ΔC, take D. emer = 0.5m (minimum dense planting spacing)

[0075] 2. Spacing D of floating-leaved plants float ,

[0076] in:

[0077] H: Average water depth (m)

[0078] v drift =0.021·LAI 0.6 (LAI: Leaf Area Index)

[0079] Constraints: D float ≥0.8 × crown diameter (to prevent leaf overlap)

[0080] 3. Spacing D of submerged plants sub ,

[0081] in:

[0082] Q: Flow rate (m) 3 / s)

[0083] ΔC: Purification gradient (mg / L)

[0084] C0: Background pollutant concentration (mg / L)

[0085] k p : Species-specific absorption rate constant (by I) max Conversion, kg -1 ·d-1)

[0086] 4. Distance between benthic animals D benthos

[0087]

[0088] Parameter description:

[0089] N: Benthic animal density (ind. / m²) 2 )

[0090] A eff Effective area of ​​a single body (m²) 2 / ind.), for clams, the range is 0.05–0.1m. 2

[0091] B plant Associated plant biomass (kg / m³) 2 )

[0092] The S42 multi-parameter feedback control module is equipped with the following dynamic control units to respond to environmental changes and maintain optimal purification efficiency:

[0093] Hydraulic control unit: Flow velocity is controlled via adjustable weir gates; response formula:

[0094]

[0095] Where t is time (h), simulating the diurnal flow fluctuation;

[0096] Light regulation unit: Adjusts the underwater light intensity (PAR) in the submerged plant area, maintaining it at 300-600 μmol / m². 2 The range is [number] / s, meeting its optical compensation point requirements.

[0097] Dissolved oxygen compensation unit: When dissolved oxygen (DO) is lower than a set threshold, it is compensated by a microporous aeration system to ensure the needs of microbial nitrification and biological metabolism.

[0098] The present invention has at least the following beneficial effects:

[0099] (1) This invention significantly improves the efficiency of pollution remediation through synergistic effects. Aquatic plants absorb nutrients such as nitrogen and phosphorus through their roots and release oxygen to improve the aquatic environment; while benthic animals promote the decomposition of organic matter and the fixation of heavy metals in the sediment through feeding and burrowing activities. The combination of these two forms a virtuous cycle of "plant purification - animal regulation", which significantly improves the removal efficiency of pollutants. In addition, the disturbance effect of benthic animals can also enhance the activity of rhizosphere microorganisms and accelerate the degradation process of pollutants, thereby avoiding the efficiency limitations that may be encountered by single technologies (such as using only phytoremediation).

[0100] (2) This invention can construct an eco-friendly in-situ remediation system that does not require interception or dredging, but directly builds biological communities in the water body to reduce damage to the river structure and avoid secondary pollution that may be caused by physical and chemical methods (such as chemical dosing and mechanical cleaning). By simulating the natural river ecosystem, it provides suitable habitats for organisms such as fish and insects, thereby accelerating the restoration of the food chain and maintaining the self-purification capacity of the water body in the long term.

[0101] (3) The coupling system of the present invention can effectively buffer environmental fluctuations, such as changes in flow rate and pollution load, and exhibits higher stability compared to single bioremediation technologies. In addition, the coupling system has the characteristics of low energy consumption and sustainability: it does not require continuous input of chemical agents or use of high-energy-consuming equipment, and its operation and maintenance costs are significantly reduced compared with traditional engineering methods.

[0102] (4) This invention enables synergistic treatment of multiple pollutants: plants specifically target soluble nutrients (such as nitrogen and phosphorus), while benthic animals focus on particulate organic matter and heavy metals; combined with the degradation capabilities of microorganisms, it effectively covers a wider range of pollutant types. Other advantages, objectives, and features of this invention will be partly apparent from the following description and partly understood by those skilled in the art through study and practice of this invention. Attached Figure Description

[0103] Figure 1 System construction flowchart;

[0104] Figure 2 Target river location in a specific embodiment;

[0105] Figure 3 In a specific embodiment, the river TN migration distribution is shown.

[0106] Figure 4 The removal efficiency of TN by various aquatic plants;

[0107] Figure 5 The efficiency of TN removal by aquatic plants and benthic animals. Detailed Implementation

[0108] The present invention will now be described in further detail with reference to the accompanying drawings, so that those skilled in the art can implement it based on the description.

[0109] Reference Figure 1 This embodiment provides a method for constructing an in-situ river pollution remediation system, which includes the following steps:

[0110] Step S1: Coupled Modeling of Hydrodynamics and Pollutant Migration

[0111] This step aims to establish a high-precision coupled model of hydrodynamics and pollutant migration in the target river section, providing a foundation for subsequent biological system configuration.

[0112] S11 Mesh Generation and Terrain Modeling: The river channel is meshed using a triangular irregular network (TIN), with a total of ≥20,000 grids. The shoreline boundary grids are refined to a resolution ≤5m. Based on satellite elevation data (50m×50m resolution), underwater topographic data is mapped to grid nodes using a weighted inverse distance interpolation method to generate a three-dimensional topographic model of the river channel. A two-dimensional hydrodynamic model is established based on the Navier-Stokes equations, with input parameters including: riverbed roughness coefficient (0.025-0.035), wind stress field, and dynamic threshold for wet and dry boundaries (water depth ≤0.01m is defined as a dry element).

[0113] Construction of S12 pollutant migration equations:

[0114] Establish a nitrogen kinetic model and define the following state variable equations:

[0115]

[0116] Among them, S BOD F represents the ammonia nitrogen release rate due to BOD degradation. plant F bact R represents the uptake rate of plants and bacteria, respectively. nitr The rate of bacterial nitrification;

[0117] like Figure 2As shown, the target river has a drainage area of ​​approximately 54 square kilometers, a total length of approximately 17.9 kilometers, and a width in its hinterland ranging from 99 meters to 484 meters. The simulated section of the river is approximately 4000 meters long, with a width ranging from 150 meters to 480 meters, and a total area of ​​approximately 821,300 square meters (excluding the north bank). Its geographical coordinates are between 120°24′33″ and 120°43′ east longitude and 36°03′10″ and 36°20′23″ north latitude. The target river is a typical northern seasonal river, with rainfall concentrated between June and September, and a rapid rise and fall in flow during the flood season. During the dry season, the river lacks a significant ecological base flow. The river flows from east to west; the lowest elevation is 8.21 meters, and the highest elevation is 123.3 meters; the elevation difference between the upstream and downstream sections is approximately 110 meters; the riverbed gradient is 0.67%.

[0118] The river section was divided into 21,374 triangular grids, with the original data being satellite data at a resolution of 50m × 50m. To improve the accuracy of the simulation, 13 sampling points were selected in the simulation section: C1, C2 South, C2 North, C3 South, C3 North, C4 South, C4 North, C5 South, C5 North, C6 South, C6 North, C7 South, and C7 North. Measured values ​​of flow velocity, flow rate, water level, and total nitrogen were recorded. After continuous calibration and adjustment, the relative error between the simulated and measured water quality values ​​was controlled within 20%, indicating that the model effectively simulates pollutant concentrations. The final output results are as follows: Figure 3 As shown, areas with higher total nitrogen concentrations are mainly distributed in the upper and lower reaches of the river section.

[0119] Step S2: Construction of a quantitative model for bio-synergistic purification

[0120] This step involves laboratory water purification experiments to quantify the pollutant removal efficiency of aquatic plants and benthic animals and their interactions, and to establish a key kinetic model.

[0121] S21 Plant Screening and Quantification:

[0122] Test species:

[0123] Emergent plants: Thalia dealbata, reed, cattail, iris, loosestrife, canna, water onion, water celery, calamus, yellow iris, variegated reed, alligator weed, arrowhead;

[0124] Floating-leaved plants: water lilies, duckweed;

[0125] Submerged plants: Potamogeton malaianus, Potamogeton crispus, Ceratophyllum demersum, Myriophyllum spicatum, Hydrilla verticillata, Vallisneria natans.

[0126] Experimental Method: Selected plants were planted in uniformly sized boxes (15L water volume). A 4cm thick (approximately 5kg) layer of deionized water-washed quartz sand was laid at the bottom of the boxes to stabilize the roots. The plants were pretreated with Hogrange nutrient solution for the first week, and then pruned after the seedlings had recovered to ensure uniform growth.

[0127] Test water: Prepared according to the pollutant concentration of the target river, as shown in the table below:

[0128]

[0129] Purification efficiency equation:

[0130] k p Species-specific absorption rate constant

[0131] C: Pollutant concentration

[0132] B: Plant biomass density (kg / m³) 3 )

[0133] K m Michaelis constant

[0134] I max Maximum instantaneous absorption rate per unit biomass

[0135] The selected aquatic plants and their pollutant purification capabilities are shown in the table below:

[0136]

[0137]

[0138] S22 Plant Absorption Kinetics Model

[0139] Based on the above data, seven aquatic plants were selected for the experiment, including three emergent plants: loosestrife, Siberian iris, and yellow iris; submerged plants: eelgrass, pondweed, and Malayan pondweed; and floating-leaved plants: water lily.

[0140] Further experiments were conducted on the absorption kinetics of seven aquatic plants. Modified Hoogland's nutrient solution was used as the base solution, and absorption solutions were prepared using NH4Cl, KNO3, KH2PO4, glucose, and sodium acetate, respectively. The concentrations of TN, TP, and COD were 2 mg / L, 0.4 mg / L, and 40 mg / L, respectively. The aquatic plants were acclimatized with tap water, and plants in good growth condition were selected. After starvation treatment, they were transferred to glass beakers containing 500 ml of absorption solution. The experimental setup was a light incubator with the following parameters: 75% humidity, 4000 lx light intensity, and (25 ± 1) °C temperature.

[0141] Samples were taken at 0, 0.5, 1, 2, 3, 4, 6, 8, 10, 12, 14, and 24 hours, with 2 mL samples taken each time. At the end of the experiment, plant samples were promptly removed, roots were cut off, and the surface moisture of the roots was fully absorbed with absorbent cotton. The wet weight of the roots and the whole plant was measured, followed by drying and measurement of the dry weight. The absorption curve equations for TN uptake by the seven aquatic plants and I... max The following table shows:

[0142]

[0143]

[0144] The absorption curve equation describes the trend of pollutant absorption over time. Maximum instantaneous absorption rate I max k in the purification efficiency equation p There is a direct correlation, i.e., k p =I max ·V

[0145] Among them, I max : Maximum instantaneous absorption rate per unit biomass; V: Water volume (L)

[0146] The net efficiency model can be expressed as:

[0147] To further screen for the optimal plant combination, based on I max Based on absorption kinetic parameters and overall purification potential, among the three emergent plants, *Iris siberiana* was selected for subsequent system construction due to its optimal overall potential. Among the three submerged plants, *Vallisneria natans* was selected for subsequent system construction due to its greatest TN absorption potential. While the pollutant removal potential of the floating-leaved plant *Nymphaea rubra* was not significant, it was still included in the subsequent system construction to select the optimal plant combination, considering the rationality of the plant's vertical spatial arrangement.

[0148] Screening and quantification of the S23 benthic fauna

[0149] Based on the results of experiment S22, aquatic plants with good growth, similar stem thickness, root length, and weight were selected, pruned to similar heights, and introduced into a rectangular transparent acrylic water tank (approximately 35×25×23cm). 3 In the control group R1, approximately 5 kg of experimental sediment and 15 L of experimental water were used. In the experimental group R2, in addition to aquatic plants, benthic animals of similar size and active life (dentate mussels or triangular sail mussels) were introduced, and other conditions were kept the same as in R1.

[0150] Holes were made at both ends of the water tank for water inlet and outlet to simulate a dynamic river, with a hydraulic residence time of 2 days. Natural light was used during the experiment, and the temperature was (25±1)℃. Samples were taken daily for the first 3 days, and then every other day thereafter, to compare the changes in pollutant concentrations in the two groups of experiments.

[0151] Animal net efficacy kinetic equation:

[0152] k: Filtering coefficient

[0153] n: Nonlinear response index of pollutant concentration C

[0154] TSS: Total suspended solids concentration

[0155] γ: TSS inhibition coefficient

[0156] T opt =25℃, β=0.02℃ -1 γ = 0.005 L / mg (Total Suspended Solids (TSS) inhibition coefficient)

[0157] That is: F = k·C n ·e -0.02(T-25) ·(1-0.005·TSS)

[0158]

[0159] S24 Synergistic Effect Quantification Method

[0160] Total pollutant removal rate (η) of the system total ) is plant absorption (η) plant Animal filter feed (η) animal ), microbial decomposition (η) microbe The superposition of contributions from multiple paths: η total =η plant +η animal +η microbe Wherein, η plant The purification efficiency equation established in S22 can be used to calculate η. animal The net efficiency kinetic equation and removal rate data established in S23 can be used to estimate η; microbe for Where: HRT: hydraulic residence time; ξDO: DO correction factor (0~1),

[0161] ξ T =θ (T-20) (θ: 1.02~1.08).

[0162] Step S3: Optimize the division of planting areas

[0163] Based on the S1 model and S2 quantitative parameters, a multi-factor evaluation system was constructed to optimize and determine the planting area for aquatic plants.

[0164] Construction of S31 Multi-Factor Coupled Evaluation System: Establishing a planting suitability evaluation system that includes the following core quantitative indicators.

[0165] 1. Hydrodynamic Stability Index (HSSI):

[0166]

[0167] Where m is the number of grid cells, V i For the flow rate of the grid cells, For the average flow velocity, σ v The standard deviation of the flow rate. The water level gradient is represented by α, where α is the attenuation coefficient.

[0168] 2. Pollution Exposure Intensity Index (PEI)

[0169] Further, based on toxicological experiments and water quality standards, a composite exposure intensity threshold was constructed.

[0170] function:

[0171]

[0172] Among them, C k Concentration of the kth pollutant

[0173] C crit,k : Sensitivity threshold of organisms to target pollutants

[0174] β k : Nonlinear response coefficient of pollutants, where NH4 + Take 3, COD 2, and other pollutants 1.5.

[0175] w k Weighting factor, Σ w-k =1, S32 comprehensive evaluation method based on pollution contribution:

[0176] The fuzzy comprehensive evaluation algorithm was used to fuzzify the HSSI and PEI indicators using a trapezoidal membership function; the weight vector W = [0.3, 0.7] was determined based on the analytic hierarchy process (AHP); and the suitability score was calculated using the weighted average method for comprehensive evaluation. Among them This represents the rules for fuzzy multiplication operations. μ represents the bounded sum operation rules. k Let μ1 be the fuzzy membership degree value of the Kth evaluation index, where μ1 is the membership degree of HSSI and μ2 is the membership degree of PEI.

[0177] S33 Planting Area Output

[0178] Generate a contour map of planting suitability, and extract areas with suitability S≥0.7 as priority planting areas; output the coordinate parameter set {GPS}. i (x,y)}, each coordinate point corresponds to a priority planting location, and each coordinate point is associated with dynamic configuration parameters, including the type of plant to be planted at that point, the planting density, and the ratio of benthic animals to plant biomass, thus forming a precise spatial configuration scheme.

[0179] Step S4: Dynamic Configuration and System Construction of Three-Dimensional Niches

[0180] Based on the planting areas divided by S3 and the biological combinations and parameters screened by S2, three-dimensional spatial optimization and dynamic regulation are carried out.

[0181] S41 Three-dimensional ecological niche spatial configuration:

[0182] Based on the planting areas divided by S3 and the biological combinations and parameters selected by S2, three-dimensional spatial optimization and dynamic control are performed. For the generated optimal planting location map of aquatic plants, dynamic three-dimensional ecological niches are configured to ensure that their spatial coupling configuration meets the following requirements.

[0183] 1. Spacing D of emergent plants emer

[0184]

[0185] in:

[0186] Q: Flow rate (m) 3 / s)

[0187] ΔC: Purification gradient (mg / L)

[0188] C0: Background pollutant concentration (mg / L)

[0189] k p : Species-specific absorption rate constant (by I) max Conversion, kg -1 ·d-1)

[0190] B: Plant biomass density (kg / m³) 3 )

[0191] Constraint: When C0 < ΔC, take D. emer = 0.5m (minimum dense planting spacing)

[0192] 2. Spacing D of floating-leaved plants float ,

[0193] in:

[0194] H: Average water depth (m)

[0195] v drift =0.021·LAI 0.6 (LAI: Leaf Area Index)

[0196] Constraints: D float ≥0.8 × crown diameter (to prevent leaf overlap)

[0197] 5. Spacing D of submerged plants sub ,

[0198] in:

[0199] Q: Flow rate (m) 3 / s)

[0200] ΔC: Purification gradient (mg / L)

[0201] C0: Background pollutant concentration (mg / L)

[0202] k p : Species-specific absorption rate constant (by I) max Conversion, kg -1 ·d-1)

[0203] 6. Benthic animal spacing D benthos

[0204]

[0205] Parameter description:

[0206] N: Benthic animal density (ind. / m²) 2 )

[0207] A eff Effective area of ​​a single body (m²) 2 / ind.), for clams, the range is 0.05–0.1m. 2

[0208] B plant Associated plant biomass (kg / m³) 2 )

[0209] S42 Multi-Parameter Feedback Control Module:

[0210] The system is equipped with the following dynamic control units to respond to environmental changes and maintain optimal purification efficiency: Hydraulic control unit: controls the flow velocity through adjustable weir gates, response formula:

[0211]

[0212] Where t is time (h), simulating the diurnal flow fluctuation;

[0213] Light regulation unit: Adjusts the underwater light intensity (PAR) in the submerged plant area, maintaining it at 300-600 μmol / m². 2 The range is [number] / s, meeting its optical compensation point requirements.

[0214] Dissolved oxygen compensation unit: When dissolved oxygen (DO) is lower than a set threshold, it is compensated by a microporous aeration system to ensure the needs of microbial nitrification and biological metabolism.

[0215] Comparative Example 1

[0216] Based on steps S1 and S2 of the embodiment, the effects of different aquatic plant combinations on TN removal are as follows: Figure 5 As shown, the combination of multiple plants has a significant advantage over single-plant groups. The Siberian iris + Vallisneria natans + water lily group showed the best removal effect, with a removal rate of 96.66%. The Siberian iris + Vallisneria natans group also showed a significant effect, with a removal rate of 94.75%. In contrast, the TN removal rate in the single aquatic plant treatment groups was only 57.61% to 60.75%. This is because the combination of multiple types of plants makes more efficient use of the water space. Other treatment groups, in descending order, were iris + water lily, water lily + Vallisneria natans, iris, Vallisneria natans, and water lily, with removal rates of 92.38%, 90.47%, 59.52%, and 57.1%, respectively. Furthermore, due to the self-purification capacity of the water body, the TN removal rate of the control group reached 28.09% on day 23.

[0217] Comparative Example 2

[0218] Based on steps S1, S2, and S3 of the embodiment, a plant combination of Siberian iris, Vallisneria natans, and water lily with good removal effect was selected. Aquatic plants with good growth, thick stems, long roots, and similar weight were chosen, pruned to similar height, and introduced into a rectangular transparent acrylic water tank (approximately 35×25×23cm). 3 In group R1, which served as the control group, approximately 5 kg of experimental sediment and 11 L of experimental water were used. In experimental group R2, in addition to the aforementioned aquatic plants, a similarly sized and actively active benthic animal, *Gnaphalium affine*, was introduced to create a dynamic three-dimensional ecological niche. Other conditions remained consistent with R1. Figure 5 As shown, the TN removal rate of group R2 (93.03%) with the addition of *Odontocercus dorsalis* was significantly higher than that of group R1 (70.05%), indicating that configuring a dynamic three-dimensional ecological niche can promote its TN removal effect.

[0219] As described above, this invention significantly improves the efficiency of pollution remediation through synergistic effects. Aquatic plants absorb nutrients such as nitrogen and phosphorus through their roots and release oxygen to improve the aquatic environment; while benthic animals promote the decomposition of organic matter and the fixation of heavy metals in the sediment through feeding and burrowing activities. The combination of these two forms a virtuous cycle of "plant purification - animal regulation," significantly improving the removal efficiency of pollutants. Furthermore, the disturbance effect of benthic animals can enhance the activity of rhizosphere microorganisms, accelerating the degradation process of pollutants, thus avoiding the efficiency limitations that may be encountered with single technologies (such as phytoremediation alone). This invention can construct an eco-friendly in-situ remediation system that does not require interception or dredging, directly building biological communities in the water body to reduce damage to the river structure and avoid secondary pollution that may be caused by physical and chemical methods (such as chemical application and mechanical cleaning). By simulating a natural river ecosystem, it provides suitable habitats for organisms such as fish and insects, thereby accelerating the restoration of the food chain and maintaining the self-purification capacity of the water body in the long term.

[0220] Meanwhile, the coupling system of this invention can effectively buffer environmental fluctuations, such as changes in flow rate and pollution load, exhibiting higher stability compared to single bioremediation technologies. Furthermore, the coupling system is characterized by low energy consumption and sustainability: it eliminates the need for continuous chemical inputs or high-energy-consuming equipment, significantly reducing operation and maintenance costs compared to traditional engineering methods. This invention enables the synergistic treatment of multiple pollutants: plants specifically target dissolved nutrients (such as nitrogen and phosphorus), while benthic animals focus on particulate organic matter and heavy metals. Combined with the degradation capabilities of microorganisms, it achieves integrated "water-sludge-biological" three-in-one treatment, effectively covering a wider range of pollutant types.

[0221] Although the embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the specification and embodiments. They can be applied to various fields suitable for the present invention. For those skilled in the art, other modifications can be easily made. Therefore, without departing from the general concept defined by the claims and their equivalents, the present invention is not limited to the specific details.

Claims

1. A method for constructing an in-situ river pollution remediation system, characterized in that, Includes the following steps: Step S1: Construct a coupled model of hydrodynamics and pollutant migration to simulate pollutant distribution; Step S2: Construct a biological synergistic purification quantitative model, screen for highly efficient aquatic plants and benthic animals, and establish purification kinetic equations; Step S3: Optimize plant planting locations by combining the output of the biological synergistic purification quantitative model with biological parameters; Step S4: Configure the three-dimensional ecological niche and construct the bioremediation system.

2. The method for constructing an in-situ river pollution remediation system as described in claim 1, characterized in that, Step S1 includes: Step S11: The river channel is divided into grids using an irregular triangular network. Based on satellite elevation data, underwater topographic data is mapped to grid nodes using a weighted inverse distance interpolation method to generate a three-dimensional topographic model of the river channel. A two-dimensional hydrodynamic model is established based on the Navier-Stokes equations. Step S12: Construct pollutant migration equations; Establish a nitrogen kinetic model and define the following state variable equations: Among them, S BOD F represents the ammonia nitrogen release rate due to BOD degradation. plant F bact R represents the uptake rate of plants and bacteria, respectively. nitr The denominator represents the bacterial nitrification rate.

3. The method for constructing an in-situ river pollution remediation system as described in claim 2, characterized in that, Step S2 includes: Step S21: Plant screening and quantification. Emergent plants, floating-leaved plants, and submerged plants were screened separately, and purification efficiency equations were constructed: Where, k p C: Species-specific uptake rate constant; B: Pollutant concentration; C: Plant biomass density (kg / m³) 3 ), K m Michaelis constant, I max : Maximum instantaneous absorption rate per unit biomass; Step S22: Construct a plant absorption kinetics model; The species-specific absorption rate constant k p The maximum instantaneous absorption rate I per unit biomass in the plant absorption kinetics equation max There is a direct correlation, i.e., k p =I max ·V, the plant net benefit model is transformed into: Where V is the volume of water (L); Step S23: Screening and quantification of benthic fauna; Screening benthic animals and constructing an animal net benefit equation: Where, k: filter coefficient, n: nonlinear response exponent of pollutant concentration C, TSS: total suspended solids concentration, γ: TSS inhibition coefficient, T opt At 25℃, β = 0.02℃ -1 γ = 0.005 L / mg; F=k·C n .e -0.02(T-25) ·(1-0.005·TSS)。 4. The method for constructing an in-situ river pollution remediation system as described in claim 3, characterized in that, Step S2 further includes: Step S24: Synergistic effect quantification method for pollutant removal rate via multiple pathways; Total pollutant removal rate η of the system total It is the plant that absorbs η plant Animal filter feeders animal Microbial decomposition η microbe The superposition of multi-path contributions, where η plant It can be calculated through the established purification efficiency equation; η animal η can be estimated using the established net efficiency kinetic equation and removal rate data. microbe It can be obtained through the following formula: Where HRT is the hydraulic residence time, and ξDO is the DO correction factor (0-1).

5. The method for constructing an in-situ river pollution remediation system as described in claim 4, characterized in that, Step S3 includes: Step S31: Construct a multi-factor coupled evaluation system; Based on the pollutant concentration field data output in step S1, a planting suitability evaluation system including the following quantitative indicators is established: a. Hydrodynamic Stability Index (HSSI): Where m is the number of grid cells, V i For the flow rate of the grid cells, For the average flow velocity, σ v The standard deviation of the flow rate, The water level gradient is represented by α, where α is the attenuation coefficient. b. Pollutant Exposure Intensity Threshold (PEI): Based on experiments and extensions to water quality standards, a pollutant concentration exposure threshold function is constructed. Among them, C k : Concentration of the kth pollutant, C crit,k : The sensitivity threshold of an organism to a target pollutant, β k : Nonlinear response coefficient of pollutants, where pollutant NH4 + The response coefficient for pollutant 'w' is set to 3, the response coefficient for pollutant COD is set to 2, and the response coefficient for other pollutants is set to 1.

5. k Weighting factor, Σ w-k =1, allocated according to pollution contribution; Step S32: The HSSI and PEI indicators are fuzzified using a trapezoidal membership function based on a fuzzy comprehensive evaluation algorithm; the weight vector W = [0.3, 0.7] is determined based on the analytic hierarchy process; and the suitability score is calculated using a weighted average method for comprehensive evaluation. in This represents the rules for fuzzy multiplication operations. μ represents the bounded sum operation rules. k Let μ1 be the fuzzy membership degree value of the Kth evaluation index, where μ1 is the membership degree of HSSI and μ2 is the membership degree of PEI. Step S33: Output planting location; Generate a contour map of planting suitability, and extract areas with suitability S≥0.7 as priority planting areas; output the coordinate parameter set {GPS}. i (x,y)}, each coordinate point corresponds to a priority planting location, and each coordinate point is associated with dynamic configuration parameters, including the type of plant to be planted at that point, the planting density, and the ratio of benthic animals to plant biomass, thus forming a precise spatial configuration scheme.

6. The method for constructing an in-situ river pollution remediation system as described in claim 5, characterized in that, Step S4 includes: Step S41: Based on the planting areas divided in Step S3 and the biological combinations and parameters screened in Step S2, perform three-dimensional spatial optimization configuration and dynamic control. Step S42: Multi-parameter feedback control module. The system is equipped with a dynamic control unit to respond to environmental changes and maintain optimal purification efficiency.

7. The method for constructing an in-situ river pollution remediation system as described in claim 6, characterized in that, In step S41, for the generated optimal planting location map of aquatic plants, a dynamic three-dimensional ecological niche is configured to ensure that its spatial coupling configuration meets the following requirements: a. Spacing between emergent plants D emer Where, Q: flow rate (m³) 3 / s), ΔC: purification gradient (mg / L), C0: background pollutant concentration (mg / L), k p : Species-specific absorption rate constant (by I) max Conversion, kg -1 ·d-1), B: Plant biomass density (kg / m³) 3 Constraint: When C0 < ΔC, take D. emer =0.5m (minimum dense planting spacing); b. Spacing between floating-leaved plants D float Where H: average water depth (m), v drift =0.021·LAI 0.6 (LAI: Leaf Area Index), Constraint: D float ≥0.8 × crown diameter (to prevent leaf overlap); c. Spacing between aquatic plants D sub Where Q: flow rate (m 3 / s), ΔC: purification gradient (mg / L), C0: background pollutant concentration (mg / L); d. Distance between benthic animals D benthos Wherein, N: Benthic animal density (ind. / m³) 2 A eff Effective area of ​​a single body (m²) 2 / ind.), for clams, the range is 0.05–0.1m. 2 B plant Associated plant biomass (kg / m³) 2 ).

8. The method for constructing an in-situ river pollution remediation system as described in claim 7, characterized in that, In step S42, the system is equipped with a dynamic control unit including: a. Hydraulic control unit: Flow velocity is controlled via adjustable weir gates; response formula: Where t is time (h), simulating the diurnal flow fluctuation; b. Light control unit: Adjusts the underwater light intensity (PAR) in the submerged plant area, maintaining it at 300-600 μmol / m². 2 The range of / s meets its optical compensation point requirements; Dissolved oxygen compensation unit: When dissolved oxygen (DO) is lower than a set threshold, it is compensated by a microporous aeration system to ensure the needs of microbial nitrification and biological metabolism.

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