A Water Environment Risk Assessment Method and System Based on Multi-Source Information and Numerical Simulation

Abstract

This invention provides a water environment risk assessment method and system based on multi-source information and numerical simulation, applicable to environmental risk management in key water areas such as reservoir sections of water conservancy and hydropower projects. The method includes: constructing a multi-level, multi-dimensional risk assessment index system; collecting multi-source data; classifying the various index factors of the reservoir pollution source risk assessment index system according to the magnitude of risk generated; performing dynamic index simulation based on a mechanistic model; using the fuzzy membership function method to perform dimensionless processing on the original index factors; establishing an optimal combination weighting model based on the analytic hierarchy process, entropy weight method, and coefficient of variation method, and optimizing each index factor through combined weights; calculating the comprehensive risk index and level classification of each pollution source, generating a risk list containing the risk index and level of each pollution source. This invention combines the assessment results with a geographic information system to achieve a visual display of the risk space, enabling intuitive identification of high-risk clusters.

Description

Technical Field

[0001] This invention relates to the field of water environment risk assessment and management technology, and more specifically, to a water environment risk assessment method and system based on multi-source information and numerical simulation. Background Technology

[0002] With the continuous development of cascade hydropower in river basins, natural rivers are dissected by cascade reservoirs, forming unique reservoir water environments: water flow velocity is significantly reduced, water depth is increased, reoxygenation capacity is decreased, and pollutant dispersion capacity is weakened. Within reservoir areas, point source pollution discharge outlets such as sewage treatment plants, industrial parks, and industrial enterprises are often distributed. Changes in the hydrological conditions of river sections within reservoir areas lead to a reduction in the self-purification capacity of pollutants. Once excessive discharges or sudden pollution incidents occur, pollutants are prone to remain and accumulate within the reservoir area, forming large-scale pollution belts that pose a serious threat to the water quality of the reservoir area and downstream sensitive protected targets (such as drinking water sources, national monitoring sections, and ecological protection zones). Accurately assessing the environmental risks of pollution sources in reservoir areas is a prerequisite for formulating targeted control measures and preventing water environment risks. Existing risk assessment methods typically have the following shortcomings: 1. Static and One-Sided Assessment Indicators: Traditional methods (such as the Potential Pollution Index) often focus on the static attributes of the pollution source itself, such as pollutant emission concentration, emission volume, and distance from the protected target. These methods fail to fully consider the migration, diffusion, and transformation processes of pollutants after they enter the water body under specific reservoir hydrodynamic conditions (such as flow velocity, water depth, and water temperature stratification), ignoring the crucial fact that "the same emission volume has drastically different environmental effects under different hydrological conditions." For example, in reservoir bay areas where the flow velocity is almost stagnant, even low emission concentrations can form large-scale pollution belts with extremely high environmental risks.

[0003] 2. Single weighting method for indicators, resulting in subjective or unstable results: Existing methods mostly employ a single weighting method. If a subjective weighting method (such as the Analytic Hierarchy Process, AHP) is used alone, the results rely too heavily on expert experience and are prone to introducing subjective bias. If an objective weighting method (such as the entropy weighting method) is used alone, it depends entirely on the dispersion of the sample data, which may ignore the physical meaning and importance of the indicators themselves, and is sensitive to data fluctuations, resulting in unstable weight allocation and poor interpretability.

[0004] 3. Lack of consideration for the dynamic nature of reservoir hydrology: Cascade reservoirs are affected by the operation and scheduling of upstream power stations, resulting in drastic periodic or non-periodic fluctuations in water levels and discharge rates. These dynamic hydrological conditions significantly impact the spread and velocity of pollutants. Most existing assessment methods do not incorporate these dynamic hydrological situations (such as the worst-case scenario) into the risk assessment framework, leading to assessment results that fail to reflect the true worst-case scenario and limiting their applicability in risk warning and emergency management.

[0005] Therefore, there is an urgent need for a method that can overcome the above-mentioned shortcomings, integrate the static emission characteristics and dynamic diffusion process of pollutants, and adopt a scientific, reasonable and reliable pollution source risk assessment method to achieve more accurate and reliable quantitative assessment and hierarchical management of water environment risks in reservoir areas. Summary of the Invention

[0006] This invention addresses the technical problems existing in the prior art by providing a water environment risk assessment method and system based on multi-source information and numerical simulation, thereby systematically solving the three core defects of the prior art: static indicators, one-sided weighting, and neglect of the dynamic nature of reservoir hydrology.

[0007] According to a first aspect of the present invention, a water environment risk assessment method based on multi-source information and numerical simulation is provided, comprising: Construct a multi-level, multi-dimensional risk assessment indicator system; Multi-source data collection was conducted, and the various indicator factors of the pollution source risk assessment indicator system in the reservoir area were classified into levels according to the magnitude of the risk, and dynamic indicator simulation based on mechanism model was carried out. The original index factors are dimensionless by using the fuzzy membership function method. An optimal combination weighting model is established based on the analytic hierarchy process, entropy weighting method and coefficient of variation method, and the factors of each index are optimized by combining weights. Calculate the comprehensive risk index and level classification for each pollution source, generate a risk list containing the risk index and level of each pollution source, combine the assessment results with the geographic information system to achieve a visual display of risk space and intuitively identify high-risk cluster areas.

[0008] Based on the above technical solution, the present invention can also be improved as follows.

[0009] Optionally, the multi-level, multi-dimensional risk assessment indicator system includes a target layer, a criterion layer, and an indicator factor layer; the target layer includes a comprehensive environmental risk index for point source pollution in the reservoir area; the criterion layer includes scale factors, location factors, emission intensity factors, pollution zone characteristic factors, and hydrological characteristic factors; the indicator factor layer includes: average daily treatment capacity, distance from national monitoring sections, COD emission concentration, ammonia nitrogen emission concentration, pollution zone length, pollution zone width, average flow velocity at the discharge outlet section, and average water depth above the discharge outlet section.

[0010] Optionally, the step of classifying the various indicator factors of the reservoir area pollution source risk assessment indicator system into levels according to the magnitude of the risk, and conducting dynamic indicator simulation based on a mechanism model, includes: A hydrodynamic-water quality coupled model was constructed, and characteristic operating conditions were set. Each pollution source was generalized as a constant point source, and the emission concentration was input for simulation. The length of the pollution zone, the width of the pollution zone, the average flow velocity of the discharge outlet section, and the average water depth were extracted as dynamic indicators. From the simulation results, the maximum length and maximum width of the pollution zone formed by each pollution source under the characteristic operating conditions, as well as the average flow velocity and average water depth of the discharge outlet section, were extracted.

[0011] Optionally, the characteristic operating condition includes the most unfavorable operating condition, corresponding to the minimum ecological flow discharged from the upstream cascade hydropower station or the flow corresponding to the minimum operating water level maintained during the reservoir impoundment period.

[0012] Optionally, the step of using the fuzzy membership function method to perform dimensionless processing on the original index factors includes: Assuming that the index factors can form continuous broken lines within the range of their attribute values ​​and quantified values, and that the inflection points of these broken lines are the standard reference values ​​of the index factors in the table, a continuous broken line function for each index factor is established. Then, the quantified value of each index factor is calculated using the transformation function of the fuzzy membership function method.

[0013] Optionally, the weight calculation formula for the optimal combination weighting model is expressed as follows:

[0014] In the formula, Indicates the number of indicators to be evaluated; For preference coefficients, ; , , These represent the calculations of the first, second, and third steps using the analytic hierarchy process (AHP), entropy weight method, and coefficient of variation method, respectively. The weight of each indicator; Indicates the first The optimal combination of weights for each indicator.

[0015] Optionally, a linear weighted comprehensive model can be used to calculate the comprehensive risk index for each pollution source, expressed by the formula:

[0016] in, Let i be the risk index of the i-th pollution source. The optimal combination weight for the j-th indicator is... Let be the standardized value of the j-th indicator for the i-th pollution source.

[0017] Optionally, the risk level classification adopts the natural breakpoint method or the equal interval method to divide the risk index into multiple levels, namely high risk, relatively high risk, general risk, relatively low risk and low risk.

[0018] Optionally, the visualization display achieves visualization of the spatial distribution of risk by marking the risk level of each pollution source with different colors or dot symbols on a GIS map.

[0019] According to a second aspect of the present invention, a water environment risk assessment system based on multi-source information and numerical simulation is provided, comprising: The indicator construction module is used to build a multi-level, multi-dimensional risk assessment indicator system; The data acquisition and simulation module is used to collect multi-source data and perform dynamic indicator simulation. The indicator processing module is used to perform dimensionless processing on indicator factors. The weight optimization module is used to establish the optimal combination weighting model and calculate the indicator weights; The risk assessment and visualization module is used to calculate the comprehensive risk index, classify risk levels, and combine it with a geographic information system to achieve visual display.

[0020] The technical effects and advantages of this invention are as follows: This invention provides a method and system for water environment risk assessment based on multi-source information and numerical simulation, aiming to... This approach extends the assessment dimensions from the "emission end" to the "environmental impact end." Through coupled numerical simulations, the actual diffusion range (pollution zone) of pollutants under specific hydrodynamic conditions is used as a direct basis for risk assessment. A stable and balanced indicator weighting system is constructed: integrating expert experience with objective data patterns to overcome the subjective arbitrariness of single weighting or excessive sensitivity to data anomalies. Embedded reservoir scheduling scenario analysis: by setting typical and most unfavorable hydrological conditions, the assessment results dynamically reflect the real impact of cascade hydropower station operation on pollution risks, providing quantitative support for risk warning and scheduling decisions. Attached Figure Description

[0021] Figure 1 A flowchart of a water environment risk assessment method based on multi-source information and numerical simulation provided in an embodiment of the present invention; Figure 2 A schematic diagram of the multi-level, multi-dimensional risk assessment indicator system architecture constructed for embodiments of the present invention; Figure 3 This is a schematic diagram of the indicator factor conversion function provided in the embodiment of the present invention. In the figure, a is a schematic diagram of the conversion function of the average daily processing capacity, and b is a schematic diagram of the conversion function of the distance to the national control section. Figure 4 A schematic diagram illustrating the weight allocation of various indicators for risk assessment of pollution sources in hydropower station reservoir areas, provided for embodiments of the present invention. Figure 5 A bar chart of various pollution source indexes provided in this embodiment of the invention; Figure 6A comparison chart of pollution source risk assessment results for hydropower station reservoir areas provided by this invention. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] Understandably, given the deficiencies in the background technology, this invention proposes a water environment risk assessment method based on multi-source information and numerical simulation, such as... Figure 1 As shown, it includes the following steps: Step S1: Construct a multi-level, multi-dimensional risk assessment indicator system; like Figure 2 As shown, the multi-level, multi-dimensional risk assessment indicator system of this invention is divided into: Target layer: Comprehensive environmental risk index of point source pollution in the reservoir area; The criteria layer comprises five core elements, covering the entire process from pollution source attributes to environmental response: scale element (A), which characterizes the potential pollution load of the pollution source; location element (B), which characterizes the locational sensitivity of the pollution source; emission intensity element (C), which characterizes the actual pollution level of the wastewater discharged by the pollution source; pollution zone characteristic element (D), a core dynamic indicator, which characterizes the actual diffusion range of pollutants after entering the water body under specific hydrodynamic conditions in the reservoir area, directly reflecting the intuitive scale of environmental impact; and hydrological characteristic element (E), a core dynamic indicator, which characterizes the key local hydrodynamic conditions that affect the diffusion and mixing intensity of pollutants.

[0024] Indicator factor layer: As shown in Table 1, there are a total of 8 specific quantifiable indicators, namely: average daily treatment capacity, distance from national control section, COD emission concentration, ammonia nitrogen emission concentration, pollution zone length, pollution zone width, average flow velocity at the discharge outlet section, and average water depth at the discharge outlet section.

[0025] Table 1. Set of indicators for pollution source risk

[0026] Step S2: Collect multi-source data, classify the various indicator factors of the reservoir area pollution source risk assessment indicator system according to the magnitude of the risk, and conduct dynamic indicator simulation based on the mechanism model; Multi-source data acquisition includes collecting static attribute data such as the design / actual treatment scale, precise geographic coordinates, and COD and ammonia nitrogen emission concentrations of all point sources to be evaluated in the reservoir area.

[0027] The process of classifying the various indicator factors of the reservoir area pollution source risk assessment indicator system according to the magnitude of the risk, and conducting dynamic indicator simulation based on a mechanism model, includes: First, a hydrodynamic-water quality coupled model, such as MIKE21, EFDC, or Delft3D, is used to construct a computational grid based on a high-precision digital elevation model (DEM) of the reservoir area. The upstream boundary uses a flow process curve, and the downstream boundary uses a water level-flow relationship curve. Key parameters (such as the Manning roughness coefficient) are calibrated and validated using historical hydrological observation data of the reservoir area to ensure that the simulation errors of flow velocity and water depth are within acceptable ranges (e.g., average relative error <15%).

[0028] Secondly, the transport and degradation of pollutants (represented by COD and ammonia nitrogen) are simulated using convection-diffusion equations. The attenuation coefficient (K) is determined based on the reservoir's water environment conditions. The lateral diffusion coefficient (Ey), a key parameter determining the width of the pollution zone, is dynamically estimated using the Taylor formula, applicable to wide and shallow water bodies. (1) In the formula, and Average water depth and average width of the river channel cross-section, in meters; The acceleration due to gravity is m / s². 2 ; This refers to the hydraulic gradient of the river.

[0029] To comprehensively assess the risks, four representative operating conditions were simulated: Operating Condition 1: Typical Day of High Water (Upstream inflow is the multi-year average flow during the high water season).

[0030] Operating Condition 2: Typical Day of Normal Water Flow (Multi-Year Average Flow).

[0031] Operating Condition 3: Typical dry day (multi-year average dry season flow).

[0032] Operating Scenario 4: Most Unfavorable Dispersion Scenario (This is a key scenario for risk assessment. It typically corresponds to the minimum ecological flow discharged from upstream cascade hydropower stations or the flow corresponding to the minimum operating water level maintained during reservoir impoundment. Under this scenario, the overall flow velocity in the reservoir area is the slowest, the pollutant dispersion capacity is the weakest, and the greatest potential risk is most exposed.) Then, each pollution source is generalized as a constant point source in the model, its emission concentration is input, and simulations of the above four operating conditions are run respectively.

[0033] Finally, from the simulation results, the maximum length (D1) and maximum width (D2) of the pollution zone formed by each pollution source under each operating condition (usually defined as the area where the pollutant concentration exceeds the Class III water standard value of the "Surface Water Environmental Quality Standard") are extracted. Simultaneously, the time-averaged flow velocity (E1) and water depth (E2) of the model grid cell where each discharge outlet is located are extracted. The values ​​used for the final risk assessment prioritize the simulation results under the "most unfavorable operating condition" to reflect the principle of "preventing the worst-case scenario."

[0034] Step S3: Use the fuzzy membership function method to perform dimensionless processing on the original index factors; It should be noted that during the dimensionless processing, it is assumed that the index factors can form continuous broken lines within the range of their attribute values ​​and quantized values, and the inflection points of these broken lines are the standard reference values ​​of the index factors in the table. Based on this, a continuous broken line function for each index factor is established, and then the quantized value of each index factor is calculated using the transformation function of the fuzzy membership function method.

[0035] like Figure 3 As shown, the transformation function of the indicator factors is illustrated using the daily average treatment capacity (positive indicator) and distance from the national control section (negative indicator) as examples. Figure 'a' shows the transformation function of the daily average treatment capacity, and Figure 'b' shows the transformation function of the distance from the national control section. In the constructed indicator system, the feedback and trend of the selected indicator factors on pollution source risk can be divided into positive feedback (or positive effect) and negative feedback (or negative feedback). If the original indicator positively affects pollution source risk, it becomes a positive indicator or a benefit-type indicator; if the original indicator negatively affects pollution source risk, it becomes a negative indicator or a cost-type indicator. The quantified values ​​after dimensionless processing of all original indicator factors can eliminate the influence of dimensions and unify them within a certain range, i.e., between [0,1]. The closer the quantified value is to 1, the greater the pollution source risk; conversely, the smaller the value is. The relevant transformation function is: For positive indicators: (2) For negative indicators: (3) in, , These are the attribute values ​​of the indicator factor before and after dimensionless processing, respectively. , These are the upper and lower limits of the standard reference value for the indicator factor; , These are the upper and lower limits of the quantified value of the indicator factor, respectively.

[0036] Step S4: Establish an optimal combination weighting model based on the analytic hierarchy process, entropy weighting method, and coefficient of variation method, and optimize each index factor through combined weights; To obtain the optimal weight set that best balances subjective and objective information, this invention constructs the following combined weighting model: The Analytic Hierarchy Process (AHP) combines the decision-maker's experience and judgment with a judgment matrix to compare indicators at the same level pairwise, determining their relative importance and ultimately assigning indicator weights. Its main steps are divided into the following sub-steps: By conducting in-depth analysis of practical problems, the various factors or indicators are divided into several levels according to a top-down structure, and a relevant hierarchical structure is established. The pairwise comparison method is used to compare the indicators of the next level that belong to the factors of the previous level, to obtain the attribute values, and to construct the corresponding judgment matrix to prepare for the next step of calculation. Calculate the eigenvalues ​​and vectors, and then perform a consistency check on them. If the check fails, reconstruct the judgment matrix. Calculate the combined weight vector of the lowest-level indicators to the target and perform a consistency check. If it fails, the corresponding judgment matrix needs to be reconstructed.

[0037] Entropy weight method: The entropy weight method is used to calculate the information entropy of each indicator, and then the information entropy is used to measure the amount of information contained in the indicator. By assigning weights to the indicator factors, objective indicator weights can be obtained.

[0038] First, build One proposal to be evaluated and A matrix consisting of several indicators to be evaluated is used to obtain a judgment matrix after dimensionless processing. , The expression is: (4) The formula for calculating information entropy is: (5) In the formula, , indicating the serial number of the scheme to be evaluated; , indicating the serial number of the indicator to be evaluated; Representing the The first scheme Quantitative values ​​of each indicator factor; Representing the Information entropy of each indicator.

[0039] The formula for determining the entropy weight based on the information entropy of the indicator is: (6) In the formula, For the first The entropy weight of each indicator factor.

[0040] Coefficient of variation method: In the constructed evaluation index system, the degree of difference in index values ​​reflects the actual gap between the evaluated objects, thereby determining the weight of the index. The coefficient of variation and weight calculation of the index can be expressed as: (7) (8) In the formula, Indicates the serial number of the indicator to be evaluated; This refers to the number of indicators to be evaluated; For the first Standard deviation of each indicator; It is the first The average of the indicators; It is the first The coefficient of variation of each indicator; No. The coefficient of variation weight of each indicator.

[0041] Therefore, a combined weighting calculation method based on the analytic hierarchy process (AHP), entropy weighting, and coefficient of variation (COP) is introduced. This method integrates the two aforementioned combinations to establish an optimal combined weighting model for optimizing index weights. On one hand, the entropy weighting method suffers from an equilibration defect when calculating weights; the COP method can compensate for this. The weights determined by combining these two objective weighting methods are more reasonable than either method alone.

[0042] After calculating the weights of the indicators, the weights of each indicator are optimized by combining the weights.

[0043] It should be noted that determining index weights using the combined weighting method requires considering the weights calculated by each of the included weighting methods simultaneously, and then calculating the combined weight. There are generally two commonly used combined weighting methods: the "multiplicative" combination method and the "additive" combination method. Essentially, these represent the normalization process after weight multiplication and the mathematical linear weighting, respectively. The weighting formula can be expressed as: Multiplication combination method: (9) "Addition" combination method: (10) In the formula, The first weighting method is used to calculate the... The weight of each indicator; The second weighting method is used to calculate the first... The weight of each indicator; This is the preference coefficient; No. The combined weights of each indicator.

[0044] Combining subjective and objective weighting methods can take into account both subjective experience and objective raw indicator information, resulting in a more reasonable calculated combined weight. The optimal combined weighting model is as follows: (11) In the formula, Indicates the number of indicators to be evaluated; The preference coefficient is set to 0.5. , , These represent the calculations of the first, second, and third steps using the analytic hierarchy process (AHP), entropy weight method, and coefficient of variation method, respectively. The weight of each indicator; Indicates the first The optimal combination of weights for each indicator.

[0045] Step S5: Calculate the comprehensive risk index and level classification of each pollution source, generate a risk list containing the risk index and level of each pollution source, combine the assessment results with the geographic information system to realize the visualization of risk space and intuitively identify high-risk cluster areas.

[0046] Comprehensive Risk Index Calculation: The comprehensive risk index (R) for each pollution source is calculated using a linear weighted comprehensive model. (12) in, Let i be the risk index of the i-th pollution source. The optimal combination weight for the j-th indicator is... Let be the standardized value of the j-th indicator for the i-th pollution source.

[0047] Risk Level Classification: Using the natural breakpoint method or equal interval method, combined with management needs, the risk index is divided into 5 levels: Level I (High Risk): 0.8 ≤ R ≤ 1.0 Level II (Higher Risk): 0.6 ≤ R < 0.8 Level III (General Risk): 0.4 ≤ R < 0.6 Level IV (Lower Risk): 0.2 ≤ R < 0.4 Level V (Low Risk): 0 ≤ R < 0.2 Results output and visualization: Generate a risk list containing risk indices and levels for each pollution source.

[0048] By combining the assessment results with a Geographic Information System (GIS), the risk level of each pollution source is marked on an electronic map of the reservoir area using dots of different colors (such as green, yellow, orange, and red) and / or sizes. This enables spatial visualization of the risks, intuitive identification of high-risk clusters, and provides decision support for differentiated and precise environmental supervision.

[0049] To make the technical solution of the present invention clearer and easier to understand, and to facilitate understanding and implementation by those skilled in the art, the specific implementation process of the present invention will be described in detail below with reference to a typical application scenario of a hydropower station reservoir area and the accompanying drawings.

[0050] 1. Calculation of evaluation indicators; Based on the main characteristics and operational status of pollution sources in the reservoir area of ​​a hydropower station, as well as the results of hydrological and water environment simulation calculations, the factors of each evaluation index are calculated and determined.

[0051] (1) Average daily processing capacity According to the wastewater treatment plant design data and the construction and operation data of the enterprise, the average daily treatment capacity of the main pollution sources discharge outlets 1, 2, 3, 4 and 5 are 24,000 t / day, 79,200 t / day, 30,000 t / day, 40,000 t / day and 200 t / day, respectively.

[0052] (2) Distance from national monitoring section According to the requirements of ecological and environmental zoning management, the hydropower station dam site is approximately 2.1 km from the downstream national monitoring section. Based on the river section lengths of the main pollution sources in the hydropower station reservoir area from the dam site, the distances of sewage outlets 1, 2, 3, 4, and 5 from the downstream national monitoring section are determined to be 18.24 m, 16.76 m, 11.44 m, 4.63 m, and 4.07 m, respectively.

[0053] (3) COD and ammonia nitrogen emission intensity The results of water pollution diffusion simulations show that under the most unfavorable operating conditions, the pollution zone near the pollution source is relatively large, and the water environment risk is also greater. Therefore, the pollution source emission concentration in March during the dry season was selected as the assessment indicator factor for COD and ammonia nitrogen emission intensity. Meanwhile, to further explore the water environment risk caused by pollution source emissions under accident conditions, the maximum COD and ammonia nitrogen concentrations of the wastewater treatment plant influent in March during the dry season were used to consider the pollution source emission intensity in accident emissions. According to the operation logs of the wastewater treatment plant and the operating enterprises, the maximum COD concentrations of the influent water quality during the dry season of March for outlets 1, 2, 3, 4, and 5 were 278.46 mg / L, 335.87 mg / L, 173.38 mg / L, 211.53 mg / L, and 313.78 mg / L, respectively, and the maximum ammonia nitrogen concentrations were 26.36 mg / L, 32.27 mg / L, 17.65 mg / L, 20.09 mg / L, and 43.41 mg / L, respectively.

[0054] (4) Length and width of the pollution zone Using the emission intensity of COD and ammonia nitrogen from the pollution sources as boundary conditions, the extent of the pollution zone along the shoreline of each pollution source under the most unfavorable operating conditions is calculated. The larger value of the length and width of the COD and ammonia nitrogen pollution zone is taken as the length and width of the pollution zone. That is, the lengths of the pollution zones in the waters near discharge outlets 1, 2, 3, 4, and 5 are 846m, 2539m, 627m, 1268m, and 371m, respectively, and the widths of the pollution zones are 34m, 45m, 25m, 33m, and 14m, respectively.

[0055] (5) Average flow velocity and water depth Based on the simulation results of the most unfavorable hydrological conditions, the average flow velocities at sewage outlets 1, 2, 3, 4, and 5 are 0.23 m³ / s, 0.21 m³ / s, 0.16 m³ / s, 0.13 m³ / s, and 0.08 m³ / s, respectively, and the average water depths are 10.33 m, 10.87 m, 11.41 m, 12.23 m, and 12.78 m, respectively.

[0056] Table 2 Original values ​​of pollution source risk assessment index factors in hydropower station reservoir area

[0057] 2. Classification of evaluation indicators; Currently, there is relatively little research on the evaluation standards and level classification of indicators for pollution source risk assessment. Therefore, for the established pollution source risk assessment indicator system for hydropower station reservoir areas, a scientific and reasonable evaluation and level classification of each indicator is necessary, referring to relevant research in other disciplines or fields. In this study, the indicators of the reservoir area pollution source risk assessment indicator system are divided into five levels according to the magnitude of the risk: high, relatively high, moderate, relatively low, and low, corresponding to five evaluation levels, and each indicator factor is further classified into levels. The main principles referenced include the following aspects: Based on relevant national, regional, and industry regulations or standards, the various indicator factors are classified into levels according to their standard delineation provisions; Refer to research findings that have reached a consensus or are widely used in related disciplines, as well as the relevant results when the selected indicators are applied to other studies; The relevant statistical data of indicators within the study area, as well as the relative range of the indicators, are used as the reference criteria for classification. For some qualitative indicators and classification standards, it is necessary to consult scholars and experts in relevant fields and combine the actual situation of the region and the indicators to finally form the classification standards for indicator factors.

[0058] The classification standards and levels of risk assessment indicators for pollution sources in the reservoir area of ​​hydropower stations are detailed in Table 3.

[0059] Table 3. Classification Standards and Levels of Pollution Source Risk Assessment Indicators in Hydropower Station Reservoir Area

[0060] It should be noted that the upper and lower limits of the average daily treatment capacity were determined and classified according to the statistical results of the city's sewage treatment facilities.

[0061] The classification is based on the distance between the upstream power station dam site and the downstream national-level monitoring section of the river.

[0062] The upper limits of COD and ammonia nitrogen emission concentrations are determined based on the design, construction, and operation data of wastewater treatment plants and enterprises. The lower limits of COD and ammonia nitrogen emission concentrations are determined based on the relevant requirements for compliance with emission standards, and then the emission levels are classified.

[0063] Based on the water pollution diffusion simulation results, the pollution zone along the shore of discharge outlet 1 is relatively large under the most unfavorable operating conditions. Therefore, the upper limits of COD and ammonia nitrogen emission concentrations under the most unfavorable operating conditions are used as the boundary conditions for discharge outlet 1 to calculate the upper limits of the pollution zone length and width, and the lower limits of the pollution zone length and width are calculated as the boundary conditions for discharge outlet 1 under the most unfavorable operating conditions. The pollution zones are then classified into different levels.

[0064] According to long-term runoff monitoring data, the maximum flow rate of the hydropower station in March, during the dry season, is 2250 m³ / h. 3 / s, based on which the average flow rate and upper limit of water depth of the pollution source section in the reservoir area of ​​the hydropower station are calculated, the lower limit value is calculated based on the minimum discharge flow rate of the upstream power station, and the level is classified.

[0065] 3. Dimensionless processing of evaluation indicators; The first step in conducting a risk assessment of pollution sources in a hydropower station reservoir area is to dimensionlessly process the calculated values ​​of the original indicator factors. The evaluation is then based on these dimensionlessly processed values. The dimensionless processing involves two aspects: data normalization and convergence. The main purpose is to address the differences and comparability of the indicator properties, enabling the summation of the quantified values. After dimensionless processing, the quantified values ​​of all indicator factors will be mapped to the [0,1] interval. Then, an appropriate method is selected for subsequent comprehensive evaluation and analysis.

[0066] The fuzzy membership function method was used to perform dimensionless processing on the original indicator factors. During the dimensionless processing, it was assumed that the indicator factors could form continuous broken lines within the range of their attribute values ​​and quantified values, with the inflection points of these broken lines representing the standard reference values ​​of the indicator factors in the table. Based on this, continuous broken line functions for each indicator factor were established. Then, the quantified values ​​of each indicator factor were calculated using the transformation function of the fuzzy membership function method. The transformation functions of the indicator factors are illustrated using daily average processing capacity (positive indicator) and distance from the national control section (negative indicator) as examples. Table 4 shows the quantified values ​​of each indicator factor in the risk assessment of pollution sources in the hydropower station reservoir area.

[0067] Table 4 Quantitative Values ​​of Pollution Source Risk Assessment Indicators in Hydropower Station Reservoir Area

[0068] 4. Calculation of indicator weights; An optimal combination weighting model based on the analytic hierarchy process (AHP), entropy weight method, and coefficient of variation method was used to calculate the optimal combination weights of the indicator factors, as shown in Table 5. Figure 4 As shown.

[0069] As shown in the table, in the criterion element layer, the weights of scale, location, emission intensity, pollution belt characteristics, and hydrological characteristics are 0.3271, 0.1724, 0.0923, 0.3119, and 0.0963, respectively. Among them, the pollution source scale element, which measures the pollutant generation and emission capacity, has the largest weight, indicating that the characteristics of the pollution source itself and its emission features play a dominant role in pollution source risk. The pollution belt characteristics element has the second largest weight, but it is not much different from the scale element, indicating that the pollution belt range, which directly reflects the magnitude of pollution source risk, is also equally important. Next are location, hydrological characteristics, and emission intensity. Furthermore, the sum of the weights of scale and pollution belt characteristics exceeds 0.5, making them the main factors influencing pollution source risk. For specific assessment indicators, the sum of the weights of daily average treatment capacity, pollution belt length, and distance from national control sections is close to 0.7, indicating that these are the main indicators influencing the magnitude of pollution source risk.

[0070] Table 5 Calculation Results of Optimal Combination Weights of Pollution Source Risk Assessment Indicators in Hydropower Station Reservoir Area

[0071] 5. Evaluation Results and Analysis.

[0072] The ultimate goal of the pollution source risk assessment in the hydropower station reservoir area is to obtain a comprehensive pollution source risk assessment result by integrating all selected evaluation index factors, and to use the assessment result to reflect the degree of risk of each pollution source. The higher the risk index, the greater the risk of the pollution source; the lower the risk index, the lower the risk of the pollution source. The pollution source risk assessment results in the hydropower station reservoir area are divided into 5 levels, and the specific level classification criteria are shown in Table 6.

[0073] Table 6. Risk Level Classification of Pollution Sources in Hydropower Station Reservoir Area

[0074] The main pollution sources are classified and rated according to the classification standards, as shown in Table 7 and... Figure 5 As shown, the results of the pollution source risk assessment are presented.

[0075] For discharge outlet 1, the emission intensity factor is rated "relatively high," which is the main factor determining the risk level, indicating that the pollution source risk can be reduced by improving the influent water quality. The remaining factors are rated as average or low. For discharge outlet 2, the scale and pollution zone characteristics factors are rated "high," indicating that its pollutant emission scale and pollution zone range are relatively large, and the risk level is relatively high. The emission intensity, hydrological characteristics, and location factors are rated as "relatively high," "average," and "low," respectively. For discharge outlet 3, the location factor is rated "relatively high," while the remaining factors are rated as average or below, indicating that the distance from the national monitoring section is an important indicator affecting its risk. For discharge outlets 4 and 5, both location factors are rated "high," and their risk level is mainly affected by their distance from the national monitoring section. At the same time, discharge outlet 5's emission intensity factor is also rated "high," which is also an important factor affecting the pollution source risk.

[0076] In summary, for the risk assessment of pollution sources in the reservoir area of ​​hydropower stations, the hydrological characteristic elements are all rated as general or below. The scale and location elements cannot be changed due to the inherent properties of the pollution sources. Therefore, the main factors affecting the risk of pollution sources are the emission intensity and pollution zone characteristics. The risk of pollution sources can be reduced by improving the influent water quality, upgrading the production process, and optimizing the design of the discharge outlets.

[0077] Table 7. Assessment Results of Pollution Source Element Indices

[0078] Based on the quantified values ​​of each indicator factor and the calculation of the optimal combination weight, the pollution source risk index is calculated, and the pollution source risks are classified and ranked, as shown in Table 8. Figure 6As shown in the figure. According to the assessment results, the risk indices of sewage outlet 1, sewage outlet 2, sewage outlet 3, sewage outlet 4 and sewage outlet 5 are 0.356, 0.768, 0.351, 0.511 and 0.328, respectively, and the assessment levels are "lower", "higher", "lower", "average" and "lower", respectively. Among them, sewage outlet 2 has the highest risk, followed by sewage outlet 4, sewage outlet 1, sewage outlet 3 and sewage outlet 5.

[0079] Table 8. Risk Assessment Results of Pollution Sources in the Hydropower Station Reservoir Area

[0080] According to a second aspect of the present invention, a water environment risk assessment system based on multi-source information and numerical simulation is provided, comprising: The indicator construction module is used to build a multi-level, multi-dimensional risk assessment indicator system; The data acquisition and simulation module is used to collect multi-source data and perform dynamic indicator simulation. The indicator processing module is used to perform dimensionless processing on indicator factors. The weight optimization module is used to establish the optimal combination weighting model and calculate the indicator weights; The risk assessment and visualization module is used to calculate the comprehensive risk index, classify risk levels, and combine it with a geographic information system to achieve visual display.

[0081] It is understood that the water environment risk assessment system based on multi-source information and numerical simulation provided by the present invention corresponds to the water environment risk assessment method based on multi-source information and numerical simulation provided in the foregoing embodiments. The relevant technical features of the water environment risk assessment system based on multi-source information and numerical simulation can be referred to the relevant technical features of the water environment risk assessment method based on multi-source information and numerical simulation, and will not be repeated here.

[0082] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0083] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0084] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

[0085] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A water environment risk assessment method based on multi-source information and numerical simulation, characterized in that, Includes the following steps: Construct a multi-level, multi-dimensional risk assessment indicator system; Multi-source data collection was conducted, and the various indicator factors of the pollution source risk assessment indicator system in the reservoir area were classified into levels according to the magnitude of the risk, and dynamic indicator simulation based on mechanism model was carried out. The original index factors are dimensionless by using the fuzzy membership function method. An optimal combination weighting model is established based on the analytic hierarchy process, entropy weighting method and coefficient of variation method, and the factors of each index are optimized by combining weights. Calculate the comprehensive risk index and level classification for each pollution source, generate a risk list containing the risk index and level of each pollution source, combine the assessment results with the geographic information system to achieve a visual display of risk space and intuitively identify high-risk cluster areas.

2. The water environment risk assessment method based on multi-source information and numerical simulation according to claim 1, characterized in that, The multi-level, multi-dimensional risk assessment indicator system includes an objective layer, a criterion layer, and an indicator factor layer; the objective layer includes the comprehensive environmental risk index of point source pollution in the reservoir area. The criteria layer includes scale elements, location elements, emission intensity elements, pollution zone characteristic elements, and hydrological characteristic elements; the indicator factor layer includes: daily average treatment capacity, distance from national control section, COD emission concentration, ammonia nitrogen emission concentration, pollution zone length, pollution zone width, average flow velocity at the discharge outlet section, and average water depth above the discharge outlet section.

3. The water environment risk assessment method based on multi-source information and numerical simulation according to claim 1, characterized in that, The process of classifying the various indicator factors of the reservoir area pollution source risk assessment indicator system according to the magnitude of the risk, and conducting dynamic indicator simulation based on a mechanism model, includes: A hydrodynamic-water quality coupled model was constructed, and characteristic operating conditions were set. Each pollution source was generalized as a constant point source, and the emission concentration was input for simulation. The length of the pollution zone, the width of the pollution zone, the average flow velocity of the discharge outlet section, and the average water depth were extracted as dynamic indicators. From the simulation results, the maximum length and maximum width of the pollution zone formed by each pollution source under the characteristic operating conditions, as well as the average flow velocity and average water depth of the discharge outlet section, were extracted.

4. The water environment risk assessment method based on multi-source information and numerical simulation according to claim 3, characterized in that, The characteristic operating conditions include the most unfavorable operating conditions, which correspond to the minimum ecological flow discharged from the upstream cascade hydropower stations or the flow corresponding to the minimum operating water level maintained during the reservoir impoundment period.

5. The water environment risk assessment method based on multi-source information and numerical simulation according to claim 1, characterized in that, The step of using the fuzzy membership function method to perform dimensionless processing on the original index factors includes: Assuming that the index factors can form continuous broken lines within the range of their attribute values ​​and quantified values, and that the inflection points of these broken lines are the standard reference values ​​of the index factors in the table, a continuous broken line function for each index factor is established. Then, the quantified value of each index factor is calculated using the transformation function of the fuzzy membership function method.

6. The water environment risk assessment method based on multi-source information and numerical simulation according to claim 1, characterized in that, The weight calculation formula for the optimal combination weighting model is expressed as follows: In the formula, Indicates the number of indicators to be evaluated; For preference coefficients, ; , , These represent the calculations of the first, second, and third steps using the analytic hierarchy process (AHP), entropy weight method, and coefficient of variation method, respectively. The weight of each indicator; Indicates the first The optimal combination of weights for each indicator.

7. The water environment risk assessment method based on multi-source information and numerical simulation according to claim 1, characterized in that, The comprehensive risk index for each pollution source is calculated using a linear weighted comprehensive model, expressed by the following formula: in, Let i be the risk index of the i-th pollution source. The optimal combination weight for the j-th indicator is... Let be the standardized value of the j-th indicator for the i-th pollution source.

8. The water environment risk assessment method based on multi-source information and numerical simulation according to claim 1, characterized in that, The risk level classification adopts the natural breakpoint method or the equal interval method to divide the risk index into multiple levels, namely high risk, relatively high risk, moderate risk, relatively low risk and low risk.

9. The water environment risk assessment method based on multi-source information and numerical simulation according to claim 1, characterized in that, The visualization display achieves visualization of the spatial distribution of risks by marking the risk levels of each pollution source on a GIS map with different colors or dot symbols.

10. A water environment risk assessment system based on multi-source information and numerical simulation, used in the water environment risk assessment method based on multi-source information and numerical simulation as described in any one of claims 1 to 9, characterized in that, include: The indicator construction module is used to build a multi-level, multi-dimensional risk assessment indicator system; The data acquisition and simulation module is used to collect multi-source data and perform dynamic indicator simulation. The indicator processing module is used to perform dimensionless processing on indicator factors. The weight optimization module is used to establish the optimal combination weighting model and calculate the indicator weights; The risk assessment and visualization module is used to calculate the comprehensive risk index, classify risk levels, and combine it with a geographic information system to achieve visual display.

Images