Prediction method for the effect of urban river ecological restoration on microbial-mediated ammonia nitrogen transformation

Abstract

The present invention discloses a prediction method for the effect of urban river ecological restoration on microbial-mediated ammonia nitrogen conversion, comprising the following steps: establishing a piecewise fitting function of ammonia nitrogen removal coefficient and microbial functional gene abundance under different dissolved oxygen concentrations and submerged plant biomass based on a microcosm simulation experiment; quantifying the contribution rate of microbial action in the ammonia nitrogen removal process; applying the fitting function to the ammonia nitrogen removal system in the traditional water quality model, and constructing a model of ammonia nitrogen conversion process mediated by microorganisms in urban rivers in combination with the microbial contribution rate; using the determination coefficient and Nash efficiency coefficient to evaluate the effect of the model on simulating the temporal and spatial distribution of ammonia nitrogen concentration; setting different artificial aeration and submerged plant restoration scenarios, and quantifying the effect of typical water ecological restoration measures on the microbial-mediated ammonia nitrogen removal process. The present invention has important guiding significance for the stable compliance of urban river water quality and the optimal layout of water ecological restoration measures.

Description

Technical Field

[0001] The present invention relates to the field of water ecological restoration, and in particular to a method for predicting the impact of urban river ecological restoration on microbial-mediated ammonia nitrogen conversion. Background Art

[0002] Urban rivers have multiple functions such as flood control and drainage, climate regulation, landscape beautification and ecological protection. In recent years, with the continuous improvement of urbanization, rapid development of regional socio-economic development and sharp increase in urban population, a large amount of nitrogen has entered the water body through atmospheric deposition, non-point source erosion, rainfall runoff and other means, making urban rivers a hot spot for nitrogen pollution. Excessive NH3-N in water bodies can cause eutrophication, reduced dissolved oxygen and decreased aquatic biodiversity, and has become the primary pollutant affecting the water quality of urban rivers. Microorganisms play a vital role in the removal of NH3-N in urban rivers. Compared with biological absorption and deposition, which only temporarily fix NH3-N and eventually return it to the river ecosystem due to the apoptosis of plants and animals and the slow release of sediments, the microbial-mediated nitrification-denitrification process can release NH3-N into the atmosphere in the form of gas, thereby permanently removing nitrogen from the water body. This process is the most important denitrification process in river ecosystems, accounting for 60-90% of the total denitrification.

[0003] Existing typical water ecological restoration measures such as artificial aeration and submerged plant restoration will cause changes in the composition and function of the microbial community in urban rivers, thereby affecting the NH3-N concentration in the water body. For example, aeration and reoxygenation will make aerobic microorganisms the absolute dominant species in the water body, thereby promoting the nitrification-denitrification process; the leaves and roots of submerged plants can provide niches and nutrients for microorganisms, while increasing the heterogeneity of the water microenvironment and improving the richness of the exogenous dominant probiotic community.

[0004] The long-term monitoring and evaluation of the effects of water ecological restoration measures is costly and difficult to cover the entire river section. The hydrodynamic-water quality model can predict the NH3-N removal rate of any river section at any time under the action of a single measure or a combination of measures. Common models currently include WASP, MIKE11, QUAL2K, etc. However, traditional models often regard the microbial NH3-N removal process as a constant about the absorption amount, underestimate the important contribution of microbial action, and ignore the dynamic changes of NH3-N removal rate under different external environmental conditions. This will lead to inaccurate estimation of NH3-N concentration in urban river restoration projects. Therefore, it is necessary to construct a model of NH3-N conversion process mediated by microorganisms in urban rivers, quantify the impact of typical water ecological restoration measures on the microbial mediated NH3-N removal process, and have important guiding significance for the stable compliance of urban river water quality and the optimal layout of ecological restoration measures. Summary of the invention

[0005] In view of the deficiencies of the above-mentioned prior art, the purpose of the present invention is to provide a quantitative prediction method for the effect of urban river ecological restoration on microbial mediated ammonia nitrogen conversion, which more scientifically and effectively overcomes the problem that the traditional hydrodynamic-water quality model regards the microbial NH3-N removal process as a constant about the absorption amount, underestimates the important contribution of microbial action, ignores the dynamic changes of NH3-N removal rate under different external environmental conditions, and leads to inaccurate estimation of NH3-N concentration in urban river restoration projects. By independently constructing a model of NH3-N conversion process mediated by urban river microorganisms, the quantification of the effect of typical water ecological restoration measures on the microbial mediated NH3-N removal process is achieved. The specific technical scheme is as follows:

[0006] The prediction method of the impact of urban river ecological restoration on microbial-mediated ammonia nitrogen transformation includes the following steps:

[0007] S1. Based on the microcosm simulation experiment, a piecewise fitting function of the ammonia nitrogen removal coefficient k and the abundance of microbial functional genes under different dissolved oxygen concentrations and submerged plant biomass was established;

[0008] S2. Quantify the contribution rate α of microbial action in the removal of ammonia nitrogen by calculating the comprehensive degradation and biodegradation coefficients;

[0009] S3, applying the fitting function to determine the ammonia nitrogen removal coefficient k in the traditional water quality model, and combining the microbial contribution rate α to construct a model of ammonia nitrogen transformation process mediated by urban river microorganisms;

[0010] S4. Use the coefficient of determination and Nash efficiency coefficient to evaluate the effectiveness of the improved water quality model and the traditional water quality model in simulating the spatial and temporal distribution of ammonia nitrogen concentration;

[0011] S5. Set up different scenarios such as artificial aeration and submerged plant restoration to quantify the impact of typical water ecological restoration measures on the microbial-mediated ammonia nitrogen removal process.

[0012] Furthermore, the step S1 includes the following steps:

[0013] S1.1. An indoor microcosm culture system was established with a 50L plastic barrel with an open top (pre-washed with 10% HCl). Considering the actual water depth of the urban river and the limitations of the experimental device, a 0.2m thick sediment was laid on the bottom of the plastic barrel with a volume of about 10L, and then a 0.6m deep water sample was added with a volume of about 30L. The mud-water ratio was 1:3. The lower half of the device was shading with black opaque stickers to simulate the actual situation of the river. The water samples and sediments used in the simulation experiment were collected in situ from urban rivers. The overlying water of the river was collected using a water sampler and collected and mixed with a polyethylene barrel. The surface sediments were collected using a grab bucket mud sampler (XDB0201, Pusen, China). Foreign objects such as branches and stones were removed and mixed evenly for the experiment. The room temperature was kept constant (25±1℃) during the experiment to simulate the effects of artificial aeration and submerged plants.

[0014] A microporous aerator was installed 0.1m above the mud-water interface, and connected to a gas flowmeter, a pressure gauge and an air compressor by a ventilation pipe. Argon (Ar) was first introduced into the reactor to reduce the DO content, and then the aeration flow was adjusted to control the DO concentration gradient of the experimental system water body. Local dominant species were selected as experimental submerged plants. Before planting, the benthic animals, attached algae and fish eggs attached to the plants were washed with tap water to remove them. Healthy plants were selected for cultivation experiments, and the biomass of individual plants was kept consistent as much as possible during planting.

[0015] S1.2. Adjust the DO concentration of the water body and the coverage rate of submerged plants respectively to simulate the dynamic changes of NH3-N concentration and microbial community under different conditions. After standing for two days for the system to stabilize, collect an initial sample before the experiment, and then collect water quality and sediment samples for 7 consecutive days, with 3 parallel samples for each sample. Use a siphon device to extract water samples from different water depths in the upper, middle and lower layers and mix them. 500mL of them are used for water quality physical and chemical parameter analysis, and 1L is filtered through a 0.22μm pore size filter membrane and stored in a -80℃ refrigerator together with the sediment sample for microbial sequencing. The water samples used for microbial sequencing are filtered and poured back into the device to avoid excessive water loss.

[0016] S1.3. Taking the abundance of nitrogen conversion functional genes as the independent variable and the NH3-N removal rate as the dependent variable, the "optimal" regression equation was established by applying the stepwise regression analysis method to quantify the effects of different functional genes on the NH3-N conversion rate. Specifically, each variable was introduced into the regression equation in turn, and the explanatory variables were introduced through the F test (significance test), and the insignificant variables were eliminated; the explanatory variables that have been selected may lose their significance and importance due to the introduction of new explanatory variables, and a t test was performed to consider whether to eliminate them from the regression equation to ensure that before each new variable is introduced, the regression equation only contains variables that have a significant effect on the dependent variable; this process was repeated until there was no need to select significant new variables in the regression equation, nor to eliminate insignificant variables.

[0017] Furthermore, the step S2 includes the following steps:

[0018] The river environment was simulated in a 5L reactor, and the mud-water ratio was controlled to be 1:3. 50mL of saturated ZnCl2 solution was added to one group to inhibit biological effects, and the other group was not treated. Before the experiment, 500mL of water sample was taken to determine the initial value of NH3-N concentration. After 24 hours, water sample was taken to determine the NH3-N concentration again. The measurement results were taken as the average of 3 parallel samples. The pollutant degradation process is similar to the first-order kinetic relationship. Due to the lack of fluidity of urban river water bodies, the effect of flow rate on the degradation coefficient is ignored in the calculation process. The specific formula is as follows:

[0019] C t =C0×e kt (1)

[0020] Where C0 is the initial concentration of NH3-N, mg / L; k is the degradation coefficient, d -1 ; t is time, d.

[0021] Taking the biodegradation coefficient as a constant times the comprehensive degradation coefficient, the kinetic equation of NH3-N degradation is established, and the formula is as follows:

[0022]

[0023] Among them, K b represents the biodegradation coefficient, K c represents the comprehensive degradation coefficient, α represents the microbial contribution rate, C0 represents the initial concentration of NH3-N, C b It represents the concentration of NH3-N after degradation by microorganisms only, C c Represents the concentration of NH3-N after comprehensive degradation.

[0024] Furthermore, step S3 includes the following steps:

[0025] Based on the results of the microcosm simulation experiment, the NH3-N removal rate is expressed as a piecewise function including the microbial contribution rate α and the abundance of microbial functional genes, and the equation is coupled with the hydrodynamic model MIKE 21. The specific formula is as follows:

[0026]

[0027] In the formula, k m is the NH3-N removal coefficient under different DO concentrations and submerged plant coverage rates; G i is the abundance of the ith microbial functional gene at 25°C; μ max,25 is the maximum growth rate of the microorganism at 25°C, θ is the temperature coefficient of microbial growth, °C -1 ; K NH3 It is the half-saturation constant of NH3-N absorption by microorganisms.

[0028] Furthermore, the step S4 includes the following steps:

[0029] Using the coefficient of determination (R 2 ) and Nash efficiency coefficient (NS) were used to evaluate the effects of the improved water quality model and the traditional water quality model in simulating the temporal and spatial distribution of ammonia nitrogen concentration.

[0030]

[0031] Where X m,i is the ith measured value; X m is the average of all measured values; X s,i is the ith simulation value; X s is the average of all simulated values; n is the number of measured values. 2 The closer it is to 1, the closer the simulated value is to the measured value, and the better the model effect. The range of NS is -∞ to 1, where NS>0.5 indicates that the model effect is acceptable.

[0032] Furthermore, the step S5 includes the following steps:

[0033] Based on field surveys and remote sensing images, the spatial location and operation rules of artificial aeration and submerged plant restoration measures in the study area were clarified, and these measures were generalized through the ECO lab module. Artificial aeration measures were generalized in the form of connected sources (point source pairs), and point source concentrations were set in the Source module of ECO Lab to add additional dissolved oxygen to the water body. The setting of this source term was based on the aeration volume and efficiency, reflecting the oxygen enhancement effect of the facility. Submerged plant restoration measures can be generalized by adjusting the dfsu file of the spatial distribution characteristics of submerged plants and adjusting the plant absorption capacity parameters in the ECO Lab module to achieve submerged plants in the study area.

[0034] According to one aspect of the present invention, there is provided a storage medium storing instructions, and when a computer reads the instructions, the computer executes any of the above-mentioned methods for predicting the impact of urban river ecological restoration on microbial-mediated ammonia nitrogen conversion.

[0035] According to another aspect of the present invention, there is provided an electronic device, comprising a processor and the above-mentioned storage medium, wherein the processor executes instructions in the storage medium.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] 1. The present invention expresses the NH3-N removal rate as a piecewise function including the microbial contribution rate α and the abundance of microbial functional genes, and couples the equation with the hydrodynamic model MIKE 21, thereby solving the problem that the traditional model ignores the dynamic expression of complex processes such as nitrification-denitrification mediated by microorganisms under different external environmental conditions.

[0038] 2. This invention uses multi-scenario simulation technology to reveal how the NH3-N removal process in urban rivers is affected by different water ecological restoration measures under the microbial-mediated mechanism, and then constructs a detailed impact mechanism model. This breakthrough not only effectively overcomes the inherent limitations of traditional long-term monitoring methods in evaluating the effects of water ecological restoration, such as high cost and difficulty in river network coverage, but also provides a precise "key" for the field of water environment management and protection, helping to achieve more efficient and economical river ecological restoration and water quality purification goals. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0040] Figure 1 is a schematic diagram of a flow chart of an embodiment of the present invention;

[0041] FIG. 2 is a schematic diagram of a microcosm simulation experimental device according to an embodiment of the present invention, wherein FIG. 2( a ) is a diagram of an artificial aeration simulation experimental device, and FIG. 2( b ) is a diagram of a submerged plant restoration simulation experimental device;

[0042] FIG3 is a schematic diagram of the NH3-N concentration simulation effect of the improved model in an embodiment of the present invention, wherein FIG3(a) is a time scale simulation effect diagram of the NH3-N concentration, and FIG3(b) is a spatial scale simulation effect diagram of the NH3-N concentration;

[0043] Figure 4 is a schematic diagram of the change in ammonia nitrogen concentration in an urban river under different artificial aeration and submerged plant restoration scenarios in an embodiment of the present invention, wherein Figure 4(a) is a comparison diagram of the current situation and scenarios 1, 2, and 3, Figure 4(b) is a comparison diagram of the current situation and scenarios 4, 5, and 6, and Figure 4(c) is a comparison diagram of scenarios 7, 8, 9, and 10. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0045] like Figure 1 As shown, the quantitative prediction method of the effect of urban river ecological restoration on microbial-mediated ammonia nitrogen conversion disclosed in the embodiment of the present invention mainly includes the following steps:

[0046] 1. Based on the microcosm simulation experiment, a piecewise fitting function of the ammonia nitrogen removal coefficient k and the abundance of microbial functional genes under different dissolved oxygen concentrations and submerged plant biomass was established. This step specifically includes:

[0047] As shown in Figure 2, this study takes the northwest moat of Gulou District, Nanjing as an example. An indoor microcosm culture system was established with a 50L plastic barrel with an open top (pre-washed with 10% HCl) as the main body. Considering the actual water depth of urban rivers and the limitations of the experimental device, a 0.2m thick bottom mud with a volume of about 10L was laid on the bottom of the plastic barrel, and then a 0.6m deep water sample was added with a volume of about 30L. The mud-water ratio was 1:3, and the lower half of the device was shielded with black opaque stickers to simulate the actual situation of the river. The water samples and bottom mud used in the simulation experiment were collected in situ from urban rivers. The overlying water of the river was collected using a water sampler and collected and mixed with a polyethylene barrel. The surface sediments were collected using a grab bucket mud sampler (XDB0201, Pusen, China), and foreign objects such as branches and stones were removed and mixed evenly for the experiment. The experimental process maintained a constant room temperature (25±1℃), simulating the effects of artificial aeration and submerged plants.

[0048] A microporous aerator is installed 0.1m above the mud-water interface, and is connected to a gas flow meter, a pressure gauge and an air compressor through a ventilation pipe. Argon (Ar) is first introduced into the reactor to reduce the DO content, and then the aeration flow rate is adjusted to control the DO concentration gradient of the experimental system water.

[0049] The local dominant species, Ceratophyllum demersum, was selected as the submerged plant for the experiment. As the preferred plant for bioremediation, Ceratophyllum demersum has the functions of purifying water quality, inhibiting the malignant proliferation of algae, preventing and controlling eutrophication of water bodies, and stabilizing the water ecological environment. Before planting, the plants were washed with tap water to remove benthic animals, algae and fish eggs attached to the plants, and healthy plants were selected for cultivation experiments. The biomass of individual plants was kept consistent as much as possible during planting.

[0050] The DO concentration of the water body and the coverage rate of submerged plants were adjusted respectively to simulate the dynamic changes of NH3-N concentration and microbial community under different conditions. The specific experimental groups are shown in Table 1. After standing for two days to stabilize the system, an initial sample was collected before the experiment began, and then water quality and sediment samples were collected for 7 consecutive days, with 3 parallel samples collected for each sample. Water samples from different water depths in the upper, middle and lower layers were extracted and mixed by a siphon device, of which 500mL was used for water quality physical and chemical parameter analysis, and 1L was filtered through a 0.22μm pore size filter membrane and stored in a -80℃ refrigerator together with the sediment sample for microbial sequencing. The water samples used for microbial sequencing were filtered and poured back into the device to avoid excessive water loss.

[0051] Table 1 Grouping of microcosm simulation experiments

[0052]

[0053]

[0054] The piecewise function of NH3-N removal coefficient k and functional gene abundance (amoA_AOA, amoA_AOB, nirS, nosZ and hzo) under different DO concentrations and submerged plant coverage was established by stepwise regression model. The abundance values ​​of the five functional genes in water and sediment samples were introduced into the regression equation as independent variables, and all insignificant variables were eliminated. A total of five groups of multivariate linear regression equations were constructed, and the R of each group of equations was 0. 2 All were higher than 0.8. The results showed that changing the DO concentration in the water body and whether to plant submerged plants would affect the functional genes involved in NH3-N conversion, and the functional genes that played a major role under different conditions were different. NH3-N conversion is jointly determined by nitrification and anaerobic ammonium oxidation. The amoA and hzo genes play a major role in NH3-N oxidation and determine the removal rate of NH3-N. In addition, functional genes directly related to the NO3-N reduction process, such as nosZ and hzo, will also affect the NH3-N conversion rate.

[0055] Table 2 Segmented fitting function of ammonia nitrogen removal coefficient k and microbial functional gene abundance

[0056]

[0057] 2. Through the calculation of comprehensive degradation and biodegradation coefficients, the contribution rate α of microbial action in the removal of ammonia nitrogen is quantified.

[0058] In this step, the results of indoor experimental calculations show that the comprehensive degradation coefficient of NH3-N is 0.020-0.187, the biodegradation coefficient is 0.020-0.084, and the biodegradation coefficient is less than the comprehensive degradation coefficient, which is consistent with the theory that biodegradation is a component of comprehensive degradation. The biodegradation coefficient is used as the horizontal axis and the comprehensive degradation coefficient is used as the vertical axis to fit the regression equation. The NH3-N biodegradation coefficient is linearly positively correlated with the comprehensive degradation coefficient, R 2 =0.91, indicating that as the biodegradation coefficient increases, its comprehensive degradation coefficient also increases synchronously, and microorganisms play a stable and continuous role in pollutant degradation. The ratio of the biodegradation coefficient of NH3-N to the comprehensive degradation coefficient, that is, the contribution rate of microorganisms in the NH3-N removal process, is 53.7%, and the biological contribution rate is dimensionless, which can effectively reduce the interference of other factors in characterizing the law of NH3-N conversion in rivers.

[0059] 3. The fitting function is applied to the determination of the ammonia nitrogen removal coefficient k in the traditional water quality model, and the ammonia nitrogen transformation process model mediated by microorganisms in urban rivers is constructed in combination with the microbial contribution rate α.

[0060] In this step, the riverbed terrain is interpolated based on the measured data for the part of the model that lacks section data. After sorting, it is uniformly input as a section file. A total of 43 sections are set, and the riverbed elevation ranges from 4.2 to 6.9 m. Using an unstructured triangular grid, the number of grids in the final constructed terrain file is 11250, the number of nodes is 6651, and the model time step is 1 day. The flow and water quality concentration long series data of the Xiaotaoyuan Pumping Station are used at the inboundary, and the water level and water quality concentration long series data of the Jinchuan River Pumping Station are used at the outboundary. The water level of 4.69m at the Jinchuan River Pumping Station on July 1, 2022 is set as the initial water level, and the water quality monitoring results in July are set as the initial concentration. Among the parameters, the dry water depth is 0.005m, the submerged water depth is 0.05m, the wet water depth is 0.1m, the riverbed roughness is set to 32, and other parameter settings are shown in Table 3.

[0061] Table 3 Main parameter values ​​of ammonia nitrogen conversion process model

[0062]

[0063]

[0064] The model parameters were modified using the template built into ECO Lab, the piecewise function of the NH3-N removal coefficient k and the abundance of functional genes was edited, and the equation was coupled with the hydrodynamic model MIKE 21. The specific formula is as follows:

[0065]

[0066] 4. Use the coefficient of determination (R 2 ) and Nash efficiency coefficient (NS) were used to evaluate the effects of the improved water quality model and the traditional water quality model in simulating the temporal and spatial distribution of ammonia nitrogen concentration.

[0067] As shown in Figure 3, in this step, the improved water quality model is compared with the traditional water quality model in simulating the temporal and spatial distribution of ammonia nitrogen concentration. The results show that the simulation results of NH3-N concentration by the improved model are basically reliable. 2 =0.76, NS = 0.70, the accuracy is higher than before improvement (R 2 =0.66, NS =0.70); spatially, R 2 =0.80, NS =0.64, the accuracy is higher than before improvement (R 2 =-0.08, NS =-0.78).

[0068] 5. Set up different scenarios such as artificial aeration and submerged plant restoration to quantify the impact of typical water ecological restoration measures on the microbial-mediated ammonia nitrogen removal process.

[0069] In this step, based on the constructed microbial-mediated NH3-N conversion process model, the NH3-N removal rate of the river section (Lulong Bridge-Jianning West Road section) after different water ecological restoration measures were continuously operated for 2 months in summer (July to August) was simulated to evaluate the effectiveness of the water ecological restoration measures.

[0070] As shown in Figure 4, the current average concentration of NH3-N in the study section is 1.11 mg / L. When using scenario 1 (additional oxygenation 6 mg / L), scenario 2 (additional oxygenation 7 mg / L) and scenario 3 (additional oxygenation 8 mg / L), the average concentration of NH3-N in the study section is about 0.8 mg / L, and the average concentration of NH3-N has dropped by about 30%. It is speculated that after the additional oxygenation reaches 6 mg / L (Class II water standard), the NH3-N removal rate will no longer increase if the additional oxygenation continues to increase.

[0071] In the study section, the average NH3-N concentration in scenario 4 (1 / 3 coverage of submerged plants) was 0.75 mg / L, the average NH3-N concentration in scenario 5 (2 / 3 coverage of submerged plants) was 0.74 mg / L, and the average NH3-N concentration in scenario 6 (3 / 3 coverage of submerged plants) was 0.72 mg / L, which were 32.4%, 33.3% and 37.0% lower than the current average NH3-N concentration, respectively. Planting submerged plants can effectively reduce the NH3-N concentration in the water body, and the higher the coverage of submerged plants, the better the NH3-N removal effect in the study section.

[0072] In the study section, the average concentrations of NH3-N in scenarios 7 (additional oxygenation 6 mg / L + submerged plant coverage 1 / 3), 8 (additional oxygenation 6 mg / L + submerged plant coverage 3 / 3), 9 (additional oxygenation 8 mg / L + submerged plant coverage 1 / 3) and 10 (additional oxygenation 8 mg / L + submerged plant coverage 3 / 3) were 0.71 mg / L, 0.74 mg / L, 0.72 mg / L and 0.75 mg / L, respectively. That is, when the two measures were combined, the ammonia nitrogen removal rate in the study section was basically the same as when submerged plants with corresponding coverage were planted alone, but significantly higher than the artificial aeration measure with corresponding oxygenation amount alone.

[0073] Technologies not described in detail in the present invention are existing technologies, such as river hydrodynamics-water quality models, microbial sequencing methods, etc., and will not be described in detail.

[0074] The examples described in the present invention are merely descriptions of the preferred implementation modes of the present invention, and are not intended to limit the concept and scope of the present invention. Without departing from the design concept of the present invention, various modifications and improvements made to the technical solutions of the present invention by engineers and technicians in this field should all fall within the protection scope of the present invention.

Claims

1. A method for predicting the impact of urban river ecological restoration on microbial-mediated ammonia nitrogen transformation, characterized in that: The steps include: S1, establish piecewise fitting function; Based on the microcosm simulation experiment, a piecewise fitting function of the ammonia nitrogen removal coefficient k and the abundance of microbial functional genes under different dissolved oxygen concentrations and submerged plant biomass was established; S2, quantify the microbial contribution rate α; By calculating the comprehensive degradation and biodegradation coefficients, the microbial contribution rate α of microorganisms in the ammonia nitrogen removal process was quantified; S3. Construct a model of ammonia nitrogen transformation mediated by microorganisms in urban rivers; The piecewise fitting function is applied to the ammonia nitrogen removal coefficient k in the traditional water quality model, and the ammonia nitrogen transformation process model mediated by urban river microorganisms is constructed in combination with the microbial contribution rate α, thereby obtaining an improved water quality model. S4. Evaluate the effectiveness of the improved water quality model and the traditional water quality model in simulating the spatial and temporal distribution of ammonia nitrogen concentration; use the coefficient of determination and Nash efficiency coefficient for evaluation; S5. Quantify the effects of typical water ecological restoration measures on microbial-mediated ammonia nitrogen removal processes; Wherein, step S2 specifically includes: Taking the biodegradation coefficient as a constant times the comprehensive degradation coefficient, the kinetic equation of NH3-N degradation is established. The specific formula is: Among them, K b represents the biodegradation coefficient, K c represents the comprehensive degradation coefficient, α represents the microbial contribution rate, C0 represents the initial concentration of NH3-N, C b It represents the concentration of NH3-N after degradation by microorganisms only, C c It indicates the concentration of NH3-N after comprehensive degradation; Step S3 specifically includes: The regression equation is coupled with the hydrodynamic model MIKE 21, and the specific formula is as follows: Among them, k m represents the NH3-N removal coefficient under different DO concentrations and submerged plant coverage conditions; G i represents the abundance of the i-th microbial functional gene at 25°C; μ max,25 represents the maximum growth rate of microorganisms at 25°C, and θ represents the temperature coefficient of microbial growth; represents the half-saturation constant of microbial absorption of NH3-N, NH3 represents the concentration of NH3-N, θ (T-25) represents the temperature coefficient of the temperature T compared to the room temperature 25°C, k1, k2 and k3 represent the NH3-N removal process functions under different DO concentrations and submerged plant coverage conditions, DO1 and DO2 represent the threshold values ​​of DO concentration, Coverage represents the submerged plant coverage, and C1 and C2 represent the threshold values ​​of submerged plant coverage.

2. The method according to claim 1, characterized in that Step S1 specifically includes the following steps: S1.

1. Establish an indoor microcosm cultivation system. The specific steps are as follows: lay bottom mud on the bottom of the plastic barrel, add water samples, use black opaque stickers to shade the lower part of the device, maintain a constant room temperature during the experiment, and simulate the effects of artificial aeration and submerged plants. Install a microporous aerator above the mud-water interface, connected to the gas flow meter, pressure gauge and air compressor by a ventilation pipe; S1.

2. Collect data from the indoor microcosm culture system. The specific steps are as follows: After the system is left to stand for two days to stabilize, collect an initial sample before the experiment begins, and then collect water quality and sediment samples for 7 consecutive days. Collect 3 parallel samples for each sample, and use a siphon device to extract water samples from different water depths in the upper, middle and lower layers, mix them, and perform water quality physical and chemical parameter analysis and microbial sequencing; S1.3, taking the abundance of nitrogen transformation functional genes as the independent variable and the NH3-N removal rate as the dependent variable, the regression equation was established by using the stepwise regression analysis method; Based on the results of the microcosm simulation experiment, the NH3-N removal rate was expressed as a piecewise function including the microbial contribution rate α and the abundance of microbial functional genes.

3. The method according to claim 1, characterized in that: In step S4, the determination coefficient R 2 The specific formula of Nash efficiency coefficient NS is: Among them, R 2 and NS represent the coefficient of determination and Nash efficiency coefficient respectively, X m,i represents the ith measured value, X m represents the average value of all measured values, X s,i represents the ith simulation value, X s Represents the average of all simulated values.

4. The method according to claim 1, characterized in that Step S5 specifically includes the following steps: Setting up different scenarios of artificial aeration and submerged plant restoration; Based on field investigation and remote sensing images, the spatial location and operation rules of artificial aeration and submerged plant restoration measures in the study area were clarified and generalized through the ECO lab module; Among them, artificial aeration measures are generalized in the form of connected sources, and point source concentrations are set in the Source module of ECO Lab to add additional dissolved oxygen to the water body; The submerged plant restoration measures were implemented by adjusting the dfsu file of the spatial distribution characteristics of submerged plants and adjusting the parameters of plant absorption capacity in the ECO Lab module to achieve the generalization of submerged plants in the study area.

5. A storage medium, characterized in that: The storage medium stores instructions, and when a computer reads the instructions, the computer executes the method for predicting the impact of urban river ecological restoration on microbial-mediated ammonia nitrogen conversion as described in any one of claims 1 to 4.

6. An electronic device, characterized in that: The invention comprises a processor and the storage medium as claimed in claim 5, wherein the processor executes instructions in the storage medium.

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