Source-plant-network-river integration-based overflow pollution risk simulation evaluation method

Abstract

The invention discloses a source-plant-network-river integration-based overflow pollution risk simulation evaluation method. The method comprises the steps of obtaining a source-plant-network-river integration hydrological, hydrodynamic and water quality model; simulating water quality of a plurality of river network sections, calculating the risk degree, and calculating the inflow load capacity corresponding to a plurality of source positions; collecting the population density, the water area ecological asset value and the water resource asset value close to the land area, and calculating the vulnerability corresponding to each section; calculating a river network section risk evaluation index; calculating a source position risk evaluation index; model-driven remote monitoring is adopted, the water quality of multiple river network sections is simulated, the flow-in load capacity conditions of multiple source positions corresponding to the river network sections are traced, the water environment conditions in the'source-plant-network-river 'basin can be known remotely in real time in a large range, the field monitoring construction and installation cost and the operation and maintenance cost are reduced, and the monitoring efficiency is improved. And dynamic monitoring of the whole watershed is realized.

Description

Technical Field

[0001] This invention belongs to the field of water environment pollution control technology, specifically involving an overflow pollution risk simulation and evaluation method based on the integrated "source-plant-network-river" model. Background Technology

[0002] Overflow pollution risk simulation assessment, by quantitatively analyzing overflow volume and pollutant migration paths, is a key means to improve the resilience and sustainability of urban drainage systems.

[0003] Traditional assessment methods have significant limitations: ① They focus solely on pipe networks or river segments, lacking a coupled analysis of the entire chain from "source to wastewater treatment plant to pipe network to river," resulting in insufficient accuracy in pollution source tracing; ② Conventional hydrological models struggle to dynamically simulate the migration and diffusion of pollutants during rainfall events, particularly lacking characterization of the synergistic response mechanisms of water quality parameters (COD, ammonia nitrogen, total phosphorus); ③ Existing risk assessments often focus on pollutant concentrations themselves, neglecting the quantitative correlation with social vulnerability factors such as population density and ecological assets. Therefore, there is an urgent need for an integrated risk assessment method that combines hydrodynamic simulation, multi-source pollution load allocation, and social vulnerability assessment to achieve precise prevention and control from pollution sources to environmental impacts. Summary of the Invention

[0004] This invention aims to provide a method for simulating and evaluating the risk of overflow pollution by combining socioeconomic vulnerability factors such as population density and ecological assets, tracing the source of pollution responsibility, and achieving real-time monitoring of the entire watershed.

[0005] The technical solution of this invention is as follows:

[0006] A method for simulating and assessing overflow pollution risk based on an integrated "source-plant-network-river" model includes the following steps:

[0007] S1. Obtain an integrated hydrological, hydrodynamic, and water quality model of the "source-plant-network-river".

[0008] Hydrological models are mathematical tools used to simulate precipitation, evaporation, infiltration, surface runoff, and groundwater recharge during the water cycle. Their core objective is to quantify the spatiotemporal distribution of water in a watershed or urban area. Examples include, but are not limited to, SWMM, HSPF, and HEC-HMS. Hydrodynamic models focus on simulating the movement of water bodies. By solving mass conservation equations (continuity equations) and momentum conservation equations (such as the Navier-Stokes equations or the Saint-Venant equations), they characterize the spatiotemporal changes in hydraulic parameters such as velocity, water level, and flow rate in water bodies like rivers, pipe networks, and lakes. Examples include, but are not limited to, the SWMM pipe network module, MIKE11, and EFDC. Water quality models quantitatively describe the migration, transformation, and degradation processes of pollutants in water bodies. Their core components include convection-diffusion equations, biochemical reaction kinetic equations, and ecological process coupling. Models take hydrodynamic parameters (flow velocity, water depth) and pollution source data (emission concentration, load) as inputs, and output the spatiotemporal distribution and ecological effects of pollutants (such as COD, ammonia nitrogen, and heavy metals). Examples include, but are not limited to, WASP, QUAL2K, and EFDC. Integrated hydrological, hydrodynamic, and water quality models can simultaneously simulate hydrological, hydrodynamic, and water quality processes, such as, but not limited to, SWMM and Infoworks ICM; they can also be composed of coupled multiple models, such as, but not limited to, a coupling of HSPF and EFDC.

[0009] The integrated hydrological, hydrodynamic, and water quality model of "source-plant-network-river" can simulate the natural and social water cycle changes, including but not limited to runoff generation and confluence on the underlying surface after rainfall, sewage generated by drainage users' activities, stormwater and sewage pipe network transport and interception ratios, effluent quality regulated by sewage treatment plants, and the water environment status of natural water bodies. By inputting historical rainfall, hydrological, and water quality data into the model for simulation, and after calibration and verification to reach usability standards, a complete model is obtained for use in step S2. Historical rainfall, hydrological, and water quality data are transmitted back via the Internet of Things (IoT) from on-site rainfall, hydrological, and water quality monitoring equipment.

[0010] S2. Input the historical rainfall time series into the integrated hydrological, hydrodynamic and water quality model of "source-plant-network-river" to simulate the water quality of multiple river network sections and calculate the risk level, and calculate the load flowing into the corresponding multiple source locations;

[0011] S3. For multiple river network cross sections, data on adjacent land population density, aquatic ecological asset value, and water resource asset value are collected respectively, and the vulnerability of each cross section is calculated:

[0012]

[0013] Among them, V k This represents the vulnerability of the k-th river network section. This represents the normalized population density of the adjacent landmass. This represents the normalized value of aquatic ecological assets. β1, β2, and β3 represent the normalized value of water resources assets, and β1 + β2 + β3 = 1.

[0014] S4. Calculate the risk assessment indicators for river network sections:

[0015] R k =H k ×V k

[0016] Among them, R k H represents the risk assessment index for the k-th river network section. k The hazard level of the k-th river network cross-section;

[0017] S5. Calculate the source location risk assessment indicators:

[0018]

[0019]

[0020] Among them, T kl This represents the normalized total load flowing from the l-th source location into the k-th river network section, expressed in dimensionless units. This represents the normalized permanganate loading. This represents the normalized ammonia nitrogen load. S represents the normalized total phosphorus load, where γ1, γ2, and γ3 represent preset weighting coefficients, and γ1 + γ2 + γ3 = 1; l This represents the risk assessment indicator for the l-th source location.

[0021] Calculating the hazard level of a river network cross-section can be used to analyze the degree of potential pollution risk at that cross-section. Calculating the vulnerability level of a river network cross-section can be used to analyze the economic impact of pollution at that cross-section. By analyzing the inflow load from multiple river network cross-sections corresponding to multiple source locations, the pollution load flowing into a particular river network cross-section from a specific source location can be determined, indicating the importance of the source location to the occurrence of water pollution at that cross-section and the magnitude of its impact.

[0022] Furthermore, the hazard level of the corresponding river network section is calculated using the following formula:

[0023]

[0024] Among them, H k Let k be the hazard level of the k-th river network cross section. This represents the normalized average concentration of pollutants from permanganate event rainfall. This represents the normalized average concentration of pollutants from ammonia nitrogen event rainfall. The normalized average concentration of total phosphorus event-related pollutants in rainfall is represented by α1, α2, and α3, which represent preset weighting coefficients, and α1 + α2 + α3 = 1.

[0025] Furthermore, the formula for calculating the average concentration of pollutants from event-driven rainfall in normalized water quality is as follows:

[0026]

[0027] in, Let represent the normalized average concentration of pollutants from event rainfall for the i-th water quality in the k-th river network section, with its maximum and minimum values ​​being 1 and 0, respectively. These represent the maximum and minimum values ​​of the i-th water quality in the k-th river network section, respectively.

[0028] Furthermore, the formula for calculating the average concentration of event-related rainfall pollutants for the i-th water quality in the k-th river network section is as follows:

[0029]

[0030] in, Let be the average concentration of pollutants from event rainfall for the i-th water quality at the k-th river network cross-section; It represents the number of time series data points for the i-th type of water quality at the k-th river network cross-section.

[0031] Furthermore, the load flowing in from multiple source locations is calculated using the following formula:

[0032]

[0033] in, q represents the load of the j-th type of water flowing from the l-th source location into the k-th river network section; kl This refers to the time series data of water volume flowing from the l-th source location into the k-th river network cross-section. This represents the time series data of the j-th water quality at the l-th source location.

[0034] Furthermore, the normalized water quality load is obtained by normalizing the load flowing in from multiple source locations, as shown in the following formula.

[0035]

[0036] in, This represents the normalized load of the j-th water quality flowing into the k-th river network section from the l-th source location, with its maximum and minimum values ​​being 1 and 0, respectively. These represent the maximum and minimum values ​​of the j-th water quality in the k-th river network section, respectively.

[0037] Preferably, the water quality described in this invention includes, but is not limited to, permanganate, ammonia nitrogen, and total phosphorus content.

[0038] Preferably, the historical rainfall time series mentioned in step S2 refers to a rainfall history consisting of a set of time series produced by methods including but not limited to obtaining the design rainfall from the local rainstorm manual, statistically analyzing historical extreme rainfall data, selecting rainfall data from a certain historical event, and analyzing or simulating future rainfall data.

[0039] Compared with the prior art, the present invention has the following advantages:

[0040] This invention employs model-driven remote monitoring to simulate the water quality of multiple river network sections, trace the load flowing into the river network sections from multiple source locations, and provide real-time, remote, and large-scale understanding of the water environment within the "source-plant-network-river" basin. This reduces the construction, installation, operation, and maintenance costs of on-site monitoring and enables dynamic monitoring of the entire basin.

[0041] This invention makes targeted decisions based on the hazard level, vulnerability and pollution load of multiple river network sections. With limited resources, it focuses on areas with a high risk of water pollution, enabling effective and rapid response to problems and early prevention of water pollution. Attached Figure Description

[0042] Figure 1 This is a schematic flowchart of the method of the present invention;

[0043] Figure 2 This is a flowchart illustrating the method for calculating the hazard level of river network sections and the load at the source location according to the present invention.

[0044] Figure 3 To obtain calibration and validation results for the model;

[0045] Figure 4 Risk assessment map of river network section;

[0046] Figure 5 This is a risk assessment map of the source location. Detailed Implementation

[0047] A method for simulating and assessing overflow pollution risks based on an integrated "source-plant-network-river" framework, with the overall implementation process as follows: Figure 1 This includes the following steps S1 to S5:

[0048] S1. Obtain an integrated hydrological, hydrodynamic, and water quality model of the "source-plant-network-river".

[0049] Taking a large urban river network basin as an example, this step specifically includes:

[0050] 1. Collect historical rainfall data, pipeline network data, river network data, catchment area division, underlying surface runoff generation and confluence, pump station data, and water quality data for the study area.

[0051] 2. Model selection: This embodiment uses a coupled architecture of Infoworks and EFDC models. Infoworks completes the hydrological, hydrodynamic, and water quality "source-plant-network" model for land areas; EFDC completes the hydrodynamic and water quality river network model for water areas.

[0052] 3. Calibration and Validation: This embodiment uses rainfall event data from June 29 to calibrate the model. The calibration process is as follows: First, calibrate the Infoworks land-based hydrological, hydrodynamic, and water quality "source-plant-network" model. The optimized parameters of the Infoworks model include water quantity, soil erosion, suspended solids, and water quality (permanganate, ammonia nitrogen, and total phosphorus).

[0053] The hydrodynamic and water quality river network model of the EFDC water area was recalibrated. The optimized parameters for the EFDC model included water quantity, suspended solids, and water quality (permanganate, ammonia nitrogen, and total phosphorus). Finally, rainfall data from September 11th was used for validation. The simulation results for permanganate, ammonia nitrogen, and total phosphorus are as follows: Figure 3 As shown.

[0054] Depend on Figure 3 It can be seen that the simulation results are within one order of magnitude (error less than 10 times the error), thus obtaining an integrated hydrological, hydrodynamic, and water quality model of "source-plant-network-river" that can simulate the real situation.

[0055] S2. Input historical rainfall time series into the integrated hydrological, hydrodynamic, and water quality model of "source-plant-network-river" to simulate the water quality of multiple river network sections and calculate the hazard level, as well as the load flowing in from the corresponding multiple source locations. The establishment steps are as follows: Figure 2 The specific steps are as follows: S21 to S24.

[0056] S21. Input the historical rainfall time series into the integrated hydrological, hydrodynamic, and water quality model of "source-plant-network-river".

[0057] Water quality at multiple river network sections includes total phosphorus, ammonia nitrogen, and permanganate.

[0058] Water quality time series data from multiple river network sections are presented in the following format:

[0059]

[0060] in, This represents the time series data of the i-th type of water quality at the k-th river network cross-section, with the unit being the mass of pollutant per unit volume; k represents the number of river network cross-sections; and i represents the type of water quality at the k-th river network cross-section.

[0061] Among them, water quality i includes, but is not limited to, three types of water quality: permanganate (COD), ammonia nitrogen (NH), and total phosphorus (TP); For the i-th river network cross-section at time t, the water quality data is of the i-th type. (Water quality time series data) For use in step S22.

[0062] Time series data on water quantity and quality from multiple source locations are presented in the following format:

[0063] q=(q 1 ,q 2 ,…,q t )

[0064]

[0065] Where q represents the water volume time series data; q t Here is the water volume data at time t; This is the time series data of the j-th water quality at the l-th source location; j represents the water quality type at the source location; the j-th water quality includes, but is not limited to, three water qualities: permanganate (COD), ammonia nitrogen (NH), and total phosphorus (TP). The water quantity time series data and the water quality time series data are used in step S24.

[0066] S22. Analyze the water quality status of multiple river network sections.

[0067] The water quality time series data of multiple river network sections obtained in step S21 are analyzed. The analysis methods include, but are not limited to, statistical calculations such as average concentration of pollutants from event-induced rainfall and worst-case concentration of pollutants from event-induced rainfall; and evaluation standards such as surface water environmental quality standards, urban black and odorous water body identification standards, trophic status assessment, and Carlson index. This invention uses the average concentration of pollutants from event-induced rainfall as an example for illustration as follows:

[0068]

[0069] in, Let be the average concentration of pollutants from an event rainfall event at the k-th river network section for the i-th water quality, expressed as the mass of pollutants per unit volume. This represents the number of time-series data points for the i-th water quality at the k-th river network cross-section. The average concentration of pollutants during rainfall events. For use in step S23.

[0070] S23. Calculate the hazard level of multiple river network sections.

[0071] The average concentration of pollutants from event-related rainfall can reflect the impact of water pollution on the corresponding river network cross-section. For example, lower chlorophyll concentration, greater transparency depth, lower water temperature, lower total phosphorus concentration, and lower total nitrogen concentration are all beneficial to the environment. For the aforementioned river network cross-sections, the corresponding hazard level is calculated based on the average concentration of pollutants from the event-related rainfall, using the following formula.

[0072]

[0073] Among them, H k The hazard level of the k-th river network cross-section is expressed in dimensionless units. This represents the normalized average concentration of pollutants from permanganate event rainfall. This represents the normalized average concentration of pollutants from ammonia nitrogen event rainfall. The normalized average total phosphorus event rainfall pollutant concentration is represented by α1, α2, and α3, which are preset weighting coefficients, and α1 + α2 + α3 = 1. The hazard level H obtained through this step is... k For use in step S4. The normalized average concentration of pollutants from event-induced rainfall is obtained by normalizing the average concentration of pollutants from event-induced rainfall obtained in step S22, as shown in the following formula.

[0074]

[0075] in, Let represent the normalized average concentration of pollutants from event rainfall for the i-th water quality in the k-th river network section, with its maximum and minimum values ​​being 1 and 0, respectively. These represent the maximum and minimum values ​​of the i-th type of water quality obtained in step S24 for the undifferentiated river network cross section.

[0076] S24. Analyze the load flowing into multiple river network sections corresponding to multiple source locations.

[0077] The water quantity and water quality time series data obtained from multiple source locations are analyzed using step S21. The analysis method and formula are as follows:

[0078]

[0079] in, q represents the load of the j-th type of water quality flowing from the l-th source location into the k-th river network section, expressed in terms of the mass of pollutant; kl This refers to the time series data of water volume flowing from the l-th source location into the k-th river network cross-section. This represents the time series data for the j-th water quality at the l-th source location. The load L flowing into multiple river network sections corresponding to multiple source locations obtained through this step represents the inflow load L at these multiple source locations. kl For use in step S5.

[0080] S3. For multiple river network sections, the population density of adjacent land, the value of aquatic ecological assets, and the value of water resource assets are collected respectively, and the corresponding vulnerability is calculated.

[0081] Higher population density indicates a greater impact on people's lives when water pollution occurs. Higher asset value indicates more severe economic losses after water pollution. For the aforementioned river network sections, the vulnerability is calculated based on the population density of the adjacent land area, the value of the aquatic ecological assets, and the value of the water resource assets, using the following formula:

[0082]

[0083] Among them, V k This represents the vulnerability of the k-th river network section, expressed in dimensionless units. This represents the normalized population density of the adjacent landmass. This represents the normalized value of aquatic ecological assets. Let β1, β2, and β3 represent the normalized value of water resource assets, and let β1 + β2 + β3 = 1. The vulnerability V obtained through this step... k For use in step S4.

[0084] S4. Calculate the risk assessment indicators for river network sections.

[0085] Based on the degree of hazard and vulnerability, the corresponding risk assessment indicators for multiple river network sections are calculated using the following formula:

[0086] R k =H k ×V k

[0087] Among them, R k This represents the risk assessment index for the k-th river network section. The risk assessment index for the river network section obtained in this step is used in step S5, and the result is as follows: Figure 4 As shown.

[0088] The river network cross-section risk assessment index R can be based on risk sequence and conventional river network pollution decision-making logic, and can quickly identify and determine the severity of the river network pollution risk factors from the river network cross-section. This allows for enhanced preparation for pollution control in terms of time and space, enabling early and accurate response to pollution disasters before they occur, and timely control responses to potential river network pollution to ensure that no major economic losses occur.

[0089] 1. High population density and severe pollution indicate high risk and significant harm to people;

[0090] 2. Low population density and severe pollution mean relatively low risk, because no one lives there, so pollution only has an indirect impact.

[0091] 3. High population density and low pollution mean relatively low risk, because people live there, so even though there is not much pollution, some attention still needs to be paid;

[0092] 4. Low population density and low pollution mean low risk, because there is no pollution and no need for treatment.

[0093] S5. Calculate the risk assessment indicators for the source location.

[0094] The load inflow from multiple source locations obtained in step S24 and the river network cross-section risk assessment index obtained in step S4 are used to calculate the corresponding risk assessment index for multiple source locations according to the following formula:

[0095]

[0096]

[0097] Among them, T kl This represents the normalized total load flowing from the l-th source location into the k-th river network section, expressed in dimensionless units. This represents the normalized permanganate loading. This represents the normalized ammonia nitrogen load. S represents the normalized total phosphorus load, where γ1, γ2, and γ3 represent preset weighting coefficients, and γ1 + γ2 + γ3 = 1. l This represents the risk assessment indicator for the l-th source location. The normalized water quality load is calculated by normalizing the loads flowing in from multiple source locations obtained in step S24, as shown in the following formula.

[0098]

[0099] in, This represents the normalized load of the j-th water quality flowing into the k-th river network section from the l-th source location, with its maximum and minimum values ​​being 1 and 0, respectively. These respectively represent the results obtained in step S24 The maximum and minimum values ​​of the j-th water quality type, regardless of the source location and river network section.

[0100] A source location risk assessment map is drawn based on the calculation results of the source location risk assessment indicators, such as... Figure 5As shown, based on risk sequence and conventional source pollution decision-making logic, the severity of the source pollution risk factors can be quickly identified and determined from the source location. This allows for enhanced governance preparations in terms of both time and space when pollution occurs, enabling early and accurate responses to pollution disasters before they strike, and timely governance responses to potential source pollution to ensure no major economic losses occur.

Claims

1. A method for simulating and assessing overflow pollution risk based on an integrated "source-plant-network-river" model, characterized in that, Includes the following steps: S1. Obtain an integrated hydrological, hydrodynamic, and water quality model of the "source-plant-network-river" system; S2. Input the historical rainfall time series into the integrated hydrological, hydrodynamic and water quality model of "source-plant-network-river" to simulate the water quality of multiple river network sections and calculate the risk level, and calculate the load flowing in from multiple source locations. S3. For multiple river network cross sections, data on adjacent land population density, aquatic ecological asset value, and water resource asset value are collected respectively, and the vulnerability of each cross section is calculated: Among them, V k This represents the vulnerability of the k-th river network section. This represents the normalized population density of the adjacent landmass. This represents the normalized value of aquatic ecological assets. β1, β2, and β3 represent the normalized value of water resources assets, and β1 + β2 + β3 = 1. S4. Calculate the risk assessment indicators for river network sections: R k =H k ×V k Among them, R k H represents the risk assessment index for the k-th river network section. k The hazard level of the k-th river network cross-section; S5. Calculate the source location risk assessment indicators: Among them, T kl This represents the normalized total load flowing from the l-th source location into the k-th river network section, expressed in dimensionless units. This represents the normalized permanganate loading. This represents the normalized ammonia nitrogen load. S represents the normalized total phosphorus load, where γ1, γ2, and γ3 represent preset weighting coefficients, and γ1 + γ2 + γ3 = 1; l This represents the risk assessment indicator for the l-th source location.

2. The overflow pollution risk simulation and assessment method based on the integrated "source-plant-network-river" system as described in claim 1, characterized in that, The hazard level of the corresponding river network section is calculated using the following formula: Among them, H k Let k be the hazard level of the k-th river network cross section. This represents the normalized average concentration of pollutants from permanganate event rainfall. This represents the normalized average concentration of pollutants from ammonia nitrogen event rainfall. The normalized average concentration of total phosphorus event-related pollutants in rainfall is represented by α1, α2, and α3, which represent preset weighting coefficients, and α1 + α2 + α3 = 1.

3. The overflow pollution risk simulation and assessment method based on the integrated "source-plant-network-river" system as described in claim 2, characterized in that, The formula for calculating the average concentration of pollutants from event-driven rainfall in normalized water quality is shown below: in, Let represent the normalized average concentration of pollutants from event rainfall for the i-th water quality in the k-th river network section, with its maximum and minimum values ​​being 1 and 0, respectively. These represent the maximum and minimum values ​​of the i-th water quality in the k-th river network section, respectively.

4. The overflow pollution risk simulation and assessment method based on the integrated "source-plant-network-river" system as described in claim 3, characterized in that, The formula for calculating the average concentration of pollutants from event-driven rainfall in the k-th river network cross-section for the i-th water quality is as follows: in, Let be the average concentration of pollutants from event rainfall for the i-th water quality at the k-th river network cross-section; It represents the number of time series data points for the i-th type of water quality at the k-th river network cross-section.

5. The overflow pollution risk simulation and assessment method based on the integrated "source-plant-network-river" system as described in claim 2, characterized in that, The formula for calculating the load flowing in from multiple source locations is: in, q represents the load of the j-th type of water flowing from the l-th source location into the k-th river network section; kl This refers to the time series data of water volume flowing from the l-th source location into the k-th river network cross-section. This represents the time series data of the j-th water quality at the l-th source location.

6. The overflow pollution risk simulation and assessment method based on the integrated "source-plant-network-river" system as described in claim 5, characterized in that, The normalized water quality load is calculated by normalizing the load flowing in from multiple source locations, as shown in the following formula. in, This represents the normalized load of the j-th water quality flowing into the k-th river network section from the l-th source location, with its maximum and minimum values ​​being 1 and 0, respectively. These represent the maximum and minimum values ​​of the j-th water quality in the k-th river network section, respectively.

7. The overflow pollution risk simulation and assessment method based on the integrated "source-plant-network-river" system as described in claim 6, characterized in that, Water quality includes, but is not limited to, permanganate, ammonia nitrogen, and total phosphorus content.

8. The overflow pollution risk simulation and assessment method based on the integrated "source-plant-network-river" system according to claim 2, characterized in that, The historical rainfall time series mentioned in step S2 refers to a rainfall history consisting of a set of time series produced by methods including but not limited to obtaining the design rainfall from the local rainstorm manual, statistically analyzing historical extreme rainfall data, selecting rainfall data from a certain historical event, and analyzing or simulating future rainfall data.

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