CN117892983A

CN117892983A - Method and system for determining offshore area land pollution load distribution

Info

Publication number: CN117892983A
Application number: CN202410292640.8A
Authority: CN
Inventors: 陈仲晗; 邢瑜; 苏思琪; 刘帅帅; 杨静
Original assignee: South China Institute of Environmental Science of Ministry of Ecology and Environment
Current assignee: South China Institute of Environmental Science of Ministry of Ecology and Environment
Priority date: 2024-03-14
Filing date: 2024-03-14
Publication date: 2024-04-16
Anticipated expiration: 2044-03-14
Also published as: CN117892983B

Abstract

The invention discloses a method and a system for determining the distribution of land pollution loads in an offshore area, which comprises the following steps: determining the sea area range and acquiring research data; reading the research data, acquiring concentration relations among all monitoring stations based on the positions of the monitoring stations and the water quality distribution data, and calculating the concentration of the preset pollutants of all monitoring stations in the sea area under the condition that the area proportion of the good water quality reaches a threshold value; acquiring response parameters of the sea-entering pollution sources, constructing and using a hydrodynamic water quality model, and calculating response parameters of the concentration of the preset pollutants of each monitoring station to sea-entering flux; and calculating the sharing rate of each land pollution to each monitoring station, and determining the maximum allowable emission and the reduction of the sea pollution source based on the pollutant concentration target value of each monitoring station position. The land pollution load distribution in the offshore area can be scientifically, reasonably and dynamically realized; effectively control the water quality standard reaching rate and improve the area ratio of the excellent water quality.

Description

Method and system for determining offshore area land pollution load distribution

Technical Field

The invention relates to a method for determining water environment pollutants, in particular to a method for determining land pollution load distribution of an offshore area.

Background

One of the main influencing factors of the environmental quality of the offshore area is the input of land pollutants, and the land pollutants mainly enter the sea through the approaches of river entering, direct-discharge sea pollution sources and the like, so as to cause pollution and damage to the water quality, sediment, organisms and the like of the offshore area to different degrees.

In order to effectively control the impact of land-based contaminants on the environmental quality of the offshore area, it is desirable to rationally determine the environmental capacity of the offshore area and the land-based contaminant load distribution. Land pollutant load distribution refers to the distribution of land pollutant load to various emission sources or areas according to certain principles and methods under the condition of meeting the environmental capacity limit of the offshore area. The offshore area environment capacity and the land pollutant load distribution are important bases for land pollution control and offshore area environment management, and are scientific bases for setting emission standards and emission reduction measures.

The calculation is carried out by adopting a hydrodynamic water quality model, a box model, a sharing rate method and the like. Although these methods reflect the size and distribution of the environmental capacity of the offshore area, there are problems and limitations, mainly the following: firstly, the spatial distribution characteristics of the environmental quality of the offshore area are ignored, only the water quality standard condition of single or few points is considered, and the overall water quality condition of the offshore area cannot be comprehensively reflected; secondly, the diversity and complexity of the sea-entering pollution sources are ignored, only the sea-entering river is considered as the main sea-entering pollution source, and the influence of other sea-entering pollution sources such as direct sea-entering pollution sources, wharfs, farms and the like on the environmental capacity of offshore areas cannot be fully considered; and thirdly, the dynamic relation between the offshore pollution source and the offshore area environmental capacity is ignored, only the environmental capacity calculation under the static or steady state condition is considered, and the method can not adapt to the actual condition of the quality change of the offshore pollution source and the offshore area environmental.

In addition, the existing method often underestimates the environmental capacity of the bay, neglects the basic characteristics of pollutant migration, and further has insufficient scientific and objective requirements on land pollution emission reduction.

Therefore, research innovation is required in order to solve at least part of the problems.

Disclosure of Invention

The invention aims to: a method for determining land pollution load distribution in offshore areas is provided to solve the problems in the prior art. Still further, an offshore area land pollution load distribution determination system is provided.

The technical scheme is as follows: the method for determining the land pollution load distribution of the offshore area comprises the following steps:

s1, determining a sea area range, and acquiring research data, wherein the research data at least comprises monitoring station positions, water quality distribution data, topographic data, river flow, meteorological data, tide level tide data and land pollution data;

s2, reading the research data, obtaining concentration relations among all monitoring stations based on the positions of the monitoring stations and water quality distribution data, and calculating the concentration of the preset pollutants of all monitoring stations in the sea area under the condition that the area proportion of good water quality reaches a threshold value;

s3, acquiring response parameters of the sea pollution sources, constructing and using a hydrodynamic water quality model, and calculating response parameters of the concentration of the preset pollutants of each monitoring station to the sea flux;

And S4, calculating the sharing rate of each land pollution to each monitoring station, and determining the maximum allowable emission and the reduction of the sea pollution source based on the pollutant concentration target value of each monitoring station position.

According to one aspect of the application, the step S1 is further:

s11, determining a sea area range, selecting a preset sea area boundary according to the geographical position, morphological characteristics and hydrodynamic conditions of the sea area, and performing grid division;

step S12, collecting traditional data including monitoring station positions, water quality distribution data, topographic data, river flow, meteorological data, tide level tide data and land pollution data;

and S13, acquiring remote sensing data, and acquiring the spatial distribution and time change data of pollutants according to the remote sensing data.

According to one aspect of the present application, the step S2 is further:

step S21: reading research data, reading the position and water quality distribution data of a monitoring station from traditional data and remote sensing data, and storing the data in a data matrix with a preset format;

step S22: according to longitude and latitude coordinates or grid coordinates of the positions of the monitoring stations, calculating the distances among the monitoring stations, the monitoring stations and the coastline, and storing the distances in a two-dimensional matrix to form a distance matrix;

Step S23: calculating the average concentration of pollutants of each monitoring station according to the water quality distribution data, and storing the average concentration in a one-dimensional array to form a concentration matrix;

step S24: and (3) solving the concentration of the preset pollutants of each monitoring station in the sea area range by using a least square method according to the condition that the area ratio of the good water quality reaches the threshold value by using a reverse solving distance inverse ratio method.

According to one aspect of the present application, the step S3 is further:

step S31: establishing a two-dimensional or three-dimensional hydrodynamic water quality model according to the sea area range and grid division, and setting model parameters and boundary conditions;

step S32: operating simulation, inputting a traditional data source and a remote sensing data source, setting different pollution source emission and water quality standards, operating a preset simulation scheme, and simulating the water level, tide and variation of a sea area through a two-dimensional or three-dimensional hydrodynamic water quality model to form a simulation result;

step S33: and calculating response parameters of the concentration of the preset pollutants of each monitoring station to the sea flux according to the simulation results.

According to one aspect of the present application, the step S4 is further:

step S41: dividing the sea pollution sources into different categories according to the positions and types of the sea pollution sources, and calculating the total discharge and average concentration of each pollution source;

Step S42: according to the result of the hydrodynamic water quality model, calculating a correlation coefficient matrix between the concentration of the preset pollutants of each monitoring station and the discharge amount of each class of sea pollution sources;

step S43: solving the sharing rate of each monitoring station of each type of sea-entering pollution source according to the correlation coefficient matrix and the concentration of the preset pollutants of each monitoring station by utilizing a multiple linear regression method;

step S44: calculating the contribution rate of each class of sea-entering pollution sources to the total pollutant load in the sea area range according to the sharing rate of each class of sea-entering pollution sources to each monitoring station;

step S45: and determining the maximum allowable emission and reduction amount of the sea-entering pollution sources according to the contribution rate of the sea-entering pollution sources of each category to the total pollutant load in the sea area.

According to one aspect of the application, the step S24 further includes:

step S241: remote sensing data and ocean current data are obtained, wherein the remote sensing data comprise sea surface temperature, chlorophyll, suspended matters and turbidity, and the ocean current data comprise water level, tide and flow direction;

step S242: preprocessing remote sensing data and ocean current data, wherein the preprocessing comprises quality control, radiation correction, geometric correction, atmosphere correction and noise removal;

Step S243: carrying out spatial analysis on the remote sensing data and the ocean current data to divide subareas of the ocean area, and calculating the average value and variance of the pollutant concentration of each subarea, wherein the spatial analysis comprises spatial interpolation, spatial statistics and spatial clustering;

step S244: and calculating a correction coefficient of each sub-area according to the ratio of the average value and the variance of the sub-area to the average value and the variance of the whole sea area so as to reflect the difference degree of pollutant concentration distribution of the sub-area and the whole sea area.

According to one aspect of the application, the method further includes step S25 of solving a concentration of the predetermined contaminant of each monitoring station in the sea area by using a bayesian network:

step S251, constructing a directed acyclic graph model according to hydrodynamic conditions of the sea area, migration and transformation processes of pollutants and uncertainty factors, wherein the directed acyclic graph model represents causal relationships between the concentration of the preset pollutants of each monitoring station and other variables;

step S252, according to pre-stored data and priori knowledge, a probability distribution is assigned to each node, and the probability that the node takes different values under the condition of a given father node is represented;

step S253, calculating posterior probability distribution of the concentration of the preset pollutants of each monitoring station by using conditional probability and joint probability in the Bayesian network, namely, the probability that the concentration of the preset pollutants of the monitoring station takes different values under the condition of giving observation values or evidences of other variables; and then selecting the maximum posterior estimation or Bayesian estimation as a final result according to the posterior probability distribution.

According to an aspect of the application, the step S4 further includes:

s4a, dividing the sea pollution sources into different categories, and calculating the total discharge and average concentration of each pollution source;

s4b, regarding each monitoring station in the sea area range as a game participant, and constructing a non-cooperative game model, wherein the strategy of each monitoring station is to select a pollutant concentration target value, and the income of each monitoring station is the probability that the water quality reaches the standard;

step S4c, calculating a correlation coefficient matrix between the pollutant concentration of each monitoring station and the discharge amount of each sea-entering pollution source according to the result of the hydrodynamic water quality model, and taking the correlation coefficient matrix as a parameter of a game model;

s4d, solving Nash equilibrium of the game model by utilizing an optimal reaction dynamic or evolution stability strategy, namely obtaining an optimal pollutant concentration target value of each monitoring station;

and S4e, reversely pushing out the maximum allowable emission and the reduction amount of the sea pollution sources of each category according to the optimal pollutant concentration target value of each monitoring station.

According to an aspect of the application, the step S31 further includes:

step S31a, constructing a FABM framework model, defining the type, unit and dimension of each basic variable, and specifying the dependency and conversion relation between the basic variables, wherein the basic variables comprise physical variables and biological geochemical variables, the physical variables comprise water level, tide, temperature and salinity, and the biological geochemical variables comprise nutrient salt and chlorophyll a;

Step S31b, selecting a hydrodynamic water quality model and a bio-geochemical model, and coupling the hydrodynamic water quality model and the bio-geochemical model into a FABM framework model, and configuring a calculation domain, grid division, boundary conditions, initial conditions and parameter values;

step S31c, invoking computational cores of a hydrodynamic water quality model and a bioelectrochemical model in the FABM framework model, performing iterative computation according to time steps, performing data exchange and information transfer after each time step, and updating the values of various variables;

and S31d, comparing the simulation result with the observed data, and evaluating the fitting degree and error source of the model.

According to another aspect of the present application, an offshore area land pollution load distribution determination system comprises:

at least one processor; and

a memory communicatively coupled to at least one of the processors; wherein,

the memory stores instructions executable by the processor for execution by the processor to implement the offshore area land pollution load distribution determination method of any one of the above-described aspects.

The beneficial effects are that: according to the actual water quality condition of the sea area and the response characteristics of the sea-entering pollution source, determining the pollutant concentration target value of each monitoring station and the maximum allowable emission and reduction amount of the sea-entering pollution source, thereby realizing the scientificalness, rationalization and dynamics of offshore area land pollution load distribution; can effectively control the water quality standard reaching rate of the coastal area, improve the area ratio of excellent water quality and improve the ecological environment of the coastal area. Overcomes the deficiencies of the prior art and the advantages of the related art will be described below in connection with the detailed description.

Drawings

Fig. 1 is a flow chart of the present invention.

Fig. 2 is a flowchart of step S1 of the present invention.

Fig. 3 is a flow chart of step S2 of the present invention.

Fig. 4 is a flowchart of step S3 of the present invention.

Fig. 5 is a flowchart of step S4 of the present invention.

Detailed Description

As shown in fig. 1, a method for determining the land pollution load distribution of an offshore area is provided, which comprises the following steps:

In the embodiment, the target value of the pollutant concentration of each monitoring station and the maximum allowable emission and reduction amount of the sea pollution source can be determined according to the actual water quality condition of the sea area and the response characteristics of the sea pollution source, so that the scientificalness, rationalization and dynamics of offshore area land pollution load distribution are realized; the water quality standard reaching rate of the coastal area can be effectively controlled, the excellent water quality area proportion is improved, the ecological environment of the coastal area is improved, and the biodiversity of the coastal area is protected; the method can balance the relationship between the distribution of the land pollution load of the offshore area and the economic and social development, and promote the land pollution control of the offshore area and the sustainable development of the area. The method solves the problems that the assessment modes in the prior art are inconsistent, the environmental capacity of the bay is underestimated, the basic characteristics of pollutant migration are ignored, and further the requirements on land pollution emission reduction are not scientific and objective enough.

As shown in fig. 2, according to an aspect of the present application, the step S1 is further:

and S11, determining a sea area range, selecting a preset sea area boundary according to the geographical position, morphological characteristics and hydrodynamic conditions of the sea area, and performing grid division.

Specifically, a reasonable calculation domain is determined according to the characteristics of the sea area, so that the establishment and operation of a subsequent hydrodynamic water quality model are facilitated, the precision and efficiency of the model can be improved, and the calculation amount and error are reduced.

the physical, chemical and biological characteristics of the sea area and the pollutant emission condition of each river are obtained, so that the subsequent forcing, calibration and verification of the hydrodynamic water quality model are facilitated. The reliability and applicability of the model can be improved, and the input and output data of the model can be increased.

By utilizing the remote sensing technology, the traditional data are supplemented and verified, and the space-time change rule of the sea area is reflected, so that the optimization and prediction of the subsequent hydrodynamic water quality model are facilitated. The integrity and sensitivity of the model can be improved, and the application range and prediction capability of the model are expanded.

In the implementation, the sea area can be meshed according to the topography and the water depth of the sea area by adopting triangular or quadrilateral grids, so that the grid density is inversely proportional to the water depth, namely, the shallower the water depth is, the denser the grid is, and then a grid partitioning diagram of the sea area is created. For traditional data acquisition, the data may be acquired from a department or institution or may be self-conducted for field investigation and sampling. These data can be used to describe the physical, chemical and biological characteristics of the sea area, as well as the pollutant emissions from each of the ocean-going rivers. For remote sensing data, the remote sensing data may be obtained from a related department or institution or may be obtained using its own remote sensing device. These data can be used to supplement and validate traditional data and reflect the law of temporal and spatial variations in the sea area.

As shown in fig. 3, according to an aspect of the present application, the step S2 is further:

in the actual implementation process, the water quality monitoring data of the urban offshore area can be obtained from an environmental protection department, wherein the water quality monitoring data comprise the position (longitude and latitude coordinates) and water quality indexes (such as COD, BOD, inorganic nitrogen, active phosphate and other pollutant concentrations) of each monitoring station. And acquiring remote sensing image data of the city coastline. And the acquired data are arranged into a standard format, such as a CSV or Excel format, so that subsequent processing is facilitated. The monitoring station position data and water quality data are combined into a data matrix, wherein rows represent different monitoring stations and columns represent different parameters (position coordinates, water quality index).

The position and water quality distribution data of the monitoring station in the sea area are acquired from different data sources and are converted into a unified data format, so that subsequent data analysis and calculation are facilitated. The readability and usability of the data can be improved, and the redundancy and error of the data can be reduced.

Longitude and latitude coordinates or grid coordinates of the monitoring station are extracted. And determining the distance between the monitoring station and the nearest coastline by using a GIS tool. A two-dimensional matrix is constructed in which both rows and columns represent different monitoring stations. And calculating the distance between each pair of monitoring stations and filling the distance to the position corresponding to the distance matrix.

According to the coordinates of the positions of the monitoring stations, the space distance between the monitoring stations and the horizontal distance (the distance parallel to the longitude and latitude line) between the monitoring stations and the coastline are calculated, so that the application of the subsequent distance inverse proportion method is facilitated. The spatial information and the relevance of the data can be improved, and the dimension and the complexity of the data are increased.

contaminant concentration data for each monitoring station is extracted from the data matrix. A one-dimensional array is constructed and the average concentration of contaminants at each monitoring station is recorded.

And calculating the average concentration of pollutants of each monitoring station according to the water quality distribution data, so that the subsequent calculation of the area ratio of the excellent water quality is facilitated. The pollution degree and the water quality category of the data can be improved, and the attribute and the characteristic of the data are increased.

And (3) establishing a model according to a distance inverse proportion method, and assuming an inverse proportion relation between the pollutant concentration of the monitoring station and the distance from the monitoring station to the pollutant source. And solving model parameters by using a least square method, and determining the pollutant concentration of each monitoring station in the sea area range.

And according to the condition that the area ratio of the good water quality reaches the threshold value, solving the concentration of the preset pollutants of each monitoring station in the sea area range by using a least square method so as to facilitate the calculation of the target parameters of each station. Target values and constraint conditions of data can be improved, and optimization and solving capabilities of the data are improved. The accuracy of pollutant distribution estimation is improved by combining a distance inverse proportion method and a least square method.

In the prior art, the bay area is taken as a whole, the pollutant concentration of each grid point is determined by adopting a distance inverse proportion method, and the pollutant concentration is not accurate enough under the influence of ocean currents, seabed topography and various factors, so the following technical scheme is provided. In the technical scheme, the sea area is divided by remote sensing data, and different correction coefficients are given to different areas at different moments, so that the prediction accuracy of the grid concentration is improved.

According to one aspect of the application, the step S24 further includes:

remote sensing data including sea surface temperature, chlorophyll, suspended matter and turbidity can be obtained from platforms such as satellites or unmanned aerial vehicles, reflecting biological, chemical and physical characteristics of the sea area. Ocean current data, including water level, current and flow direction, can also be obtained from buoys or hydrologic stations, etc., which reflect the dynamic characteristics of the ocean. These data are all important factors affecting contaminant transport and diffusion.

because the remote sensing data and the ocean current data may have quality problems, such as missing values, abnormal values, noise and the like, quality control is required to be performed on the remote sensing data and the ocean current data, and unqualified data is removed or repaired. Meanwhile, operations such as radiation correction, geometric correction, atmospheric correction and the like are required to be carried out on the remote sensing data so as to eliminate the influence of factors such as instrument errors, earth curvature, atmospheric scattering and the like, and enable the remote sensing data to truly reflect the sea area condition. In addition, noise removal of the remote sensing data and the ocean current data is required, such as using a filter or wavelet transform, to improve the signal-to-noise ratio of the data.

Step S243: carrying out spatial analysis on the remote sensing data and the ocean current data to divide subareas of the ocean area, and calculating the average value and variance of the pollutant concentration of each subarea, wherein the spatial analysis comprises spatial interpolation, spatial statistics, spatial clustering and the like;

in order to better analyze the contaminant concentration profile of the sea area, it is necessary to spatially analyze the remote sensing data and the ocean current data to divide the sub-areas of the sea area and calculate the mean and variance of the contaminant concentration of each sub-area. Discrete point data may be converted to continuous surface data using spatial interpolation methods such as Kriging or IDW, etc., to obtain a contaminant concentration profile throughout the sea. Spatial statistical methods, such as Moran's I or Geary's C, etc., can be used to evaluate the spatial autocorrelation and heterogeneity of contaminant concentrations to find high and low value regions of contaminant concentration. Spatial clustering methods, such as K-means or DBSCAN, may be used to divide regions with similar contaminant concentration characteristics into one sub-region and assign a label to each sub-region.

Step S244: and for each preset time period, calculating a correction coefficient of each subarea according to the ratio of the average value and the variance of the subarea to the average value and the variance of the whole sea area so as to reflect the difference degree of the pollutant concentration distribution of the subarea and the whole sea area.

In order to more accurately determine the land pollution load born by each sub-area, the correction coefficient of each sub-area is calculated according to the difference degree of the pollutant concentration distribution of the sub-area and the whole sea area. The correction coefficient may be calculated using the following equation: c (C) _i =（μ/μ _i ）×（σ _i /σ）；

Wherein C is _i Is the correction coefficient of the ith sub-area, mu _i Is the mean value of the pollutant concentration, sigma, of the ith sub-zone _i Is the variance of the contaminant concentration of the ith sub-area, μ is the average of the contaminant concentration of the entire sea area, σ is the variance of the contaminant concentration of the entire sea area. The larger the correction coefficient is, the higher the pollutant concentration of the subarea is, and the more land pollution load is born; the smaller the correction factor, the lower the contaminant concentration in the sub-region, assuming less land-based pollution load.

In summary, in this embodiment, the remote sensing data and the ocean current data are utilized to comprehensively consider the biological, chemical, physical and dynamic characteristics of the ocean area, so as to more comprehensively analyze the pollutant concentration distribution situation of the ocean area. The quality and reliability of the data are improved by preprocessing, spatial analysis, correction coefficient calculation and other methods, so that the pollutant concentration level and the difference degree of each sub-area can be more accurately determined. And according to the correction coefficient of each sub-area, the land pollution load is reasonably distributed, so that the target of the ratio of the excellent water quality area of the offshore area is more effectively realized.

In other words, sea pollution is not only related to the input of pollutants, but is also affected by sea currents and other environmental factors. The acquisition of remote sensing data and ocean current data provides key clues to contaminant distribution. The spread and distribution of contaminants in the sea area can be more fully understood, providing a basis for making accurate estimates. And carrying out spatial analysis on the remote sensing data and the ocean current data by using a GIS tool. The subareas of the sea area are divided by the technologies of spatial interpolation, spatial statistics, spatial clustering and the like, and the average value and variance of the pollutant concentration of each subarea are calculated. The distribution of contaminants in the sea area is often non-uniform and requires careful spatial analysis to reveal such distribution characteristics. The space analysis can identify the polluted hot spot area, and provides important basis for pollution control and management. The calculation of the correction coefficient can evaluate the pollution condition of each sub-area more accurately, and provides scientific basis for pollution source identification and pollution treatment.

In a further embodiment, the process of using spatial statistical methods to evaluate the spatial autocorrelation and heterogeneity of a concentration of a contaminant further comprises:

step S243a, selecting a predetermined spatial statistics index. The spatial statistics are a number for quantifying the autocorrelation and heterogeneity of spatial data, and commonly used spatial statistics include Moran's I, geary's C, getis-Ord G, and the like. Different indexes have different calculation formulas and meanings, and proper indexes need to be selected according to data characteristics and analysis purposes. In general, moran's I and Geary's C are used to measure global spatial autocorrelation, i.e., whether the concentration of contaminants throughout the sea exhibits a tendency to aggregate or disperse; getis-Ord G is a measure of local spatial autocorrelation, i.e., whether a region has a contaminant concentration above or below the average level of its surrounding regions.

Step S243b, determining a spatial weight matrix. The spatial weight matrix is a matrix for describing the correlation between spatial data, and reflects the adjacency and the similarity of the spatial data. There are various methods for constructing the spatial weight matrix, such as a distance-based method, a direction-based method, a neighborhood-based method, and the like. A suitable method needs to be selected based on the data distribution and spatial characteristics. Generally, a distance-based approach refers to the elements of the spatial weight matrix being inversely proportional to the distance between the spatial data, i.e., the closer the distance, the greater the weight; the direction-based method means that the elements of the spatial weight matrix are in direct proportion to the direction between the spatial data, i.e. the more consistent the direction, the greater the weight; the neighborhood-based method refers to that elements of the spatial weight matrix only take 0 or 1, and indicates whether spatial data belongs to a certain neighborhood, such as a K neighbor or a hook neighborhood.

Step S243c, calculating the value of the spatial statistics index. According to the calculation formula of the selected space statistics index, the space weight matrix and the pollutant concentration data can be utilized to calculate the value of the space statistics index. Different indexes have different value ranges and meanings, and the values of the indexes need to be interpreted according to analysis purposes. In general, moran's I has a value range of [ -1,1], and when Moran's I approaches 1, it indicates that spatial data exhibits a strong positive correlation, i.e., a high value region is adjacent to a high value region and a low value region is adjacent to a low value region; when Moran's I approaches-1, the spatial data is shown to exhibit a strong negative correlation, i.e., a high value region and a low value region are adjacent; when Moran's I approaches 0, it is shown that the spatial data exhibits randomness, i.e., there is no apparent spatial regularity for the high-value regions and the low-value regions. The range of values for Geary's C is [0,2], indicating that the spatial data exhibits a strong positive correlation when Geary's C approaches 0, a strong negative correlation when Geary's C approaches 2, and a randomness when Geary's C approaches 1. The value range of Getis-Ord G is [0,1], when Getis-Ord G is close to 1, the concentration of pollutants in a certain area is higher than the average level of the surrounding area, namely the area is a hot spot area; when the Getis-Ord G approaches 0, it means that the contaminant concentration in a region is lower than the average level in its surrounding region, i.e., the region is a cold spot region.

In another embodiment of the present application, a spatial clustering method is used to divide a region with similar contaminant concentration characteristics into one sub-region and assign a label to each sub-region. The spatial clustering method is a method for grouping based on similarity and continuity of spatial data, and can be used for finding out the internal structure and rule of the spatial data. The spatial clustering method comprises the following substeps:

step S243i, selecting a proper spatial clustering algorithm. There are various spatial clustering algorithms, such as K-means, DBSCAN, SOM, etc. Different algorithms have different principles and advantages and disadvantages, and a proper algorithm needs to be selected according to the data characteristics and the analysis purpose. In general, K-means is a distance-based algorithm that divides spatial data into K clusters such that the sum of squares of data within each cluster from its cluster center is minimized and the data distance between each cluster is maximized; DBSCAN is a density-based algorithm that divides spatial data into different clusters such that the data density within each cluster is above a certain threshold and the data density between each cluster is below a certain threshold; SOM is a neural network based algorithm that maps spatial data onto a low-dimensional grid such that each grid cell represents a cluster, with high data similarity within each cluster and low data similarity between each cluster.

Step S243ii, determining parameters of the spatial clustering algorithm. There are various parameters of the spatial clustering algorithm, such as the number of clusters, the threshold of density, the size of the grid, etc. Appropriate parameters need to be determined based on the data characteristics and analysis purposes. In general, the number of clusters can be determined according to the number of subareas to be divided, and the optimal number of clusters can be selected according to some evaluation indexes, such as contour coefficients or Calinski-Harabasz indexes; the threshold of the density can be determined according to the data distribution and the density distribution, and can be selected according to some heuristic methods, such as a K-distance graph or a minimum spanning tree, etc.; the size of the grid may be determined based on the data dimensions and scale, or the size of the grid may be selected to be optimal based on some optimization method, such as cross-validation or information criteria.

Step S243iii, executing a spatial clustering algorithm and obtaining a result. According to the selected spatial clustering algorithm and parameters, spatial clustering can be performed on the spatial data, and labels of clusters to which each data point belongs are obtained. These tags may be used as tags for sub-regions, thereby dividing a region with similar contaminant concentration characteristics into one sub-region. The mean and variance of the contaminant concentration for each sub-zone can also be calculated for each sub-zone region to facilitate subsequent correction factor calculation and land-based pollution load distribution. The concentration of contaminants in each sub-region may be summarized and analyzed using some descriptive statistical method, such as mean, variance, standard deviation, maximum, minimum, median, quartile, etc. Some visualization methods, such as bar graphs, box graphs, scatter graphs, etc., may also be used to demonstrate and compare the contaminant concentrations of each sub-region. In this way, the profile and degree of variation in contaminant concentration for each sub-region can be more clearly seen.

step S251, constructing a directed acyclic graph model according to hydrodynamic conditions of the sea area, migration and transformation processes of pollutants and uncertainty factors, wherein the directed acyclic graph model represents causal relationships between the concentration of the preset pollutants of each monitoring station and other variables; the type and the number of the variables, and the direction and the strength of the causal relationship are determined according to the actual situation. In general, variables can be divided into the following categories: hydrodynamic condition variables such as water level, tidal current, flow direction, etc., which affect the transport and diffusion of contaminants (e.g., inorganic nitrogen, active phosphate). Inorganic nitrogen migration and conversion variables, such as inorganic nitrogen generation, consumption, sedimentation, volatilization, etc., affect the change in inorganic nitrogen concentration. Uncertainty factor variables, such as meteorological conditions, human activity, measurement errors, etc., that affect the randomness and credibility of the inorganic nitrogen; inorganic nitrogen concentration variables of the monitoring station, such as A, B, C, D, etc., which are target variables to be predicted, are also output variables.

If a node is a discrete variable, such as flow direction or human activity, the probability distribution of the node is represented by using a plurality of distributions, namely the probability that the node takes each possible value is given, such as the probability that the flow direction is east, south, west, north is 0.2, 0.3, 0.4, 0.1, etc.; if a node is a continuous variable, such as water level or inorganic nitrogen concentration, etc., a normal distribution or other distribution is used to represent the probability distribution of the node, i.e., the mean and variance of the node are given, such as the mean of water level is 5 meters, the variance is 0.5 square meters, etc.; if a node is an observation or evidence, such as the inorganic nitrogen concentration of a monitoring station, a certain value is used to represent the probability distribution of the node, i.e. to give an actual measurement of the node, such as the inorganic nitrogen concentration of a monitoring station of 0.8 mg/l, etc. Parameters of the probability distribution of each node are determined according to pre-stored data and prior knowledge, such as statistical analysis according to historical data or experience judgment of an expert. Some learning methods, such as maximum likelihood estimation or bayesian estimation, may also be used to automatically learn the parameters of the probability distribution of each node from the data. These learning methods are implemented using tools such as MATLAB or Python, etc. Some symbols, such as P or θ, are used to represent parameters of probability distribution of each node, such as P (flow direction=east) =0.2 or θ (water level) =5, and so on.

If the structure and scale of the bayesian network are simple, the posterior probability distribution is directly calculated using methods of accurate reasoning, such as variable elimination or belief propagation. The principle of the methods is that a Bayesian rule and a probability chain rule are utilized to convert posterior probability distribution into a series of calculation of conditional probability and joint probability, and then parameters of the posterior probability distribution are solved according to parameters of probability distribution of each node. These methods are implemented using MATLAB or Python tools, etc. A symbol such as P or θ is used to represent a parameter of the posterior probability distribution, such as P (inorganic nitrogen concentration |water level=5) =0.6 or θ (inorganic nitrogen concentration |water level=5) =0.8, or the like. Then, according to the parameters of the posterior probability distribution, the maximum posterior estimation or Bayesian estimation is selected as a final result. The maximum posterior estimation means that when a value with the highest probability in the posterior probability distribution is selected as the final result, for example, P (inorganic nitrogen concentration |water level=5) =0.6, inorganic nitrogen concentration=0.6 mg/l is selected as the final result; bayesian estimation refers to selecting an expected value of the posterior probability distribution as a final result, such as θ (inorganic nitrogen concentration |water level=5) =0.8, and selecting inorganic nitrogen concentration=0.8 mg/l as a final result.

If the structure and scale of the Bayesian network is complex, the posterior probability distribution is approximately calculated using methods of approximate reasoning, such as Markov chain Monte Carlo or variational reasoning, etc. The principle is to use some random or optimization process to generate some samples or distributions to approximate the posterior probability distribution. These methods are implemented using MATLAB or Python. X or Q is used to represent a sample or distribution approximating a posterior probability distribution, such as X (inorganic nitrogen concentration |water level=5) = {0.5,0.6,0.7,0.8,0.9} or Q (inorganic nitrogen concentration |water level=5) =n (0.8,0.1), or the like. Then, based on the samples or distributions that approximate the posterior probability distribution, a maximum posterior estimate or bayesian estimate is selected as the final result. Maximum a posteriori estimation means that when a value having the highest probability in the approximate posterior probability distribution is selected as a final result, such as X (inorganic nitrogen concentration |water level=5) = {0.5,0.6,0.7,0.8,0.9} the inorganic nitrogen concentration=0.9 mg/l is selected as a final result; bayesian estimation refers to selecting an expected value of the approximate posterior probability distribution as the final result, such as Q (inorganic nitrogen concentration |water level=5) =n (0.8,0.1), and selecting inorganic nitrogen concentration=0.8 mg/l as the final result.

In the embodiment, a directed acyclic graph model is constructed by utilizing hydrodynamic conditions of the sea area, migration and transformation processes of inorganic nitrogen and uncertainty factors, and the causal relationship between the inorganic nitrogen concentration of each monitoring station and other variables is represented, so that influence factors and change rules of the inorganic nitrogen concentration of the sea area are analyzed more scientifically. According to the pre-stored data and the priori knowledge, a probability distribution is assigned to each node, and the probability that the node takes different values under the condition of a given father node is represented, so that the uncertainty and the credibility of the inorganic nitrogen concentration of the sea area are described more objectively. And calculating posterior probability distribution of inorganic nitrogen concentration of each monitoring station by using conditional probability and joint probability in the Bayesian network, namely, the probability that the inorganic nitrogen concentration of the monitoring station takes different values under the condition of given observation values or evidences of other variables, thereby more accurately predicting the distribution condition and future trend of the inorganic nitrogen concentration of the sea area.

As shown in fig. 4, according to an aspect of the present application, the step S3 is further:

Each grid cell represents a calculation unit of a hydrodynamic water quality model, each arrow represents a flow field direction of the hydrodynamic water quality model, and each dot represents a position of a monitoring station. And determining the accuracy and the scale of sea area range and grid division, and the type and the complexity of the hydrodynamic water quality model according to the professional knowledge and the actual situation. In general, sea area coverage and meshing should be able to cover the sea area of interest and monitoring stations, as well as be able to reflect the topography and hydrologic characteristics of the sea area; the hydrodynamic water quality model can simulate the water level, tide, flow direction, temperature, salinity, inorganic nitrogen and other variables of the sea area, and consider hydrodynamic conditions of the sea area, migration and transformation processes of the inorganic nitrogen, uncertainty factors and the like. Hydrodynamic water quality models are built and run using tools such as MIKE or EFDC, etc. Some symbols, such as H or Q, etc., are used to represent parameters and boundary conditions of the hydrodynamic water quality model, such as H (water level) =2 meters or Q (flow) =10 cubic meters/second, etc.

The data of hydrodynamic conditions, migration and transformation processes of inorganic nitrogen and uncertainty factors of sea areas, such as water level, tide, flow direction, temperature, salinity, inorganic nitrogen, meteorological conditions, artificial activities and the like, are obtained from the traditional data sources and the remote sensing data sources and are used as input data of a hydrodynamic water quality model. The quality and reliability of the data, as well as the preprocessing and interpolation methods of the data, are determined from the pre-stored data and a priori knowledge. Some tools, such as MATLAB or Python, etc., are used to process and input data. Some symbols, such as D or X, are used to represent the value or distribution of the data, such as D (water level) =2 meters or X (inorganic nitrogen) =n (0.8,0.1), etc.

According to the analysis purpose and the actual situation, different pollution source discharge and water quality standards are set and used as control variables of the hydrodynamic water quality model. The scope and limitation of the pollution source emission and the water quality standard, and the influence and meaning of the pollution source emission and the water quality standard are determined according to professional knowledge and policy regulations. The control variables are set and adjusted using MIKE or EFDC, etc. Some symbols, such as E or S, etc., are used to represent the values or distributions of the control variables, such as E (pollution source emissions) =5 tons/day or S (water quality standard) =0.5 mg/liter, etc. According to analysis requirements and calculation capacities, running a preset simulation scheme, and simulating sea water level, tide, flow direction, temperature, salinity, inorganic nitrogen and other variables, and the variation and trend of the variables through a two-dimensional or three-dimensional hydrodynamic water quality model to form a simulation result. The quantity and the content of the simulation schemes, and the accuracy and the effectiveness of the simulation results are determined according to the expertise and the actual conditions. Some tools, such as MIKE or EFDC, are used to run and output simulation results. Some symbols, such as R or Y, are used to represent the value or distribution of the simulation result, such as R (water level) =2.5 meters or Y (inorganic nitrogen) =n (0.9,0.2), etc.

And determining a response function of the concentration of the preset pollutants of each monitoring station to the sea entering flux according to the actual situation, namely, a function representing the relation between the concentration of the preset pollutants of the monitoring station and the sea entering flux. Some mathematical models, such as linear models, nonlinear models, dynamic models, etc., are used to represent the form and parameters of the response function. The form and parameters of the response function are expressed using the symbols F or β, etc., such as F (sea flux) =β (concentration of predetermined contaminant), etc. Based on the simulation results, parameters of the response function, i.e., values representing the degree of response of the concentration of the predetermined contaminant of the monitoring station to the sea flux, are estimated using some fitting method, such as a least square method, a maximum likelihood method, a bayesian method, or the like. These fitting methods are implemented using MATLAB or Python tools, etc. The value or distribution of the parameter of the response function is expressed using a symbol of β or θ or the like, such as β (concentration of predetermined contaminant) =0.5 or θ (concentration of predetermined contaminant) =n (0.5, 0.1) or the like. By means of the two-dimensional or three-dimensional hydrodynamic water quality model, the water level, tide, flow direction, temperature, salinity, inorganic nitrogen and other variables of the sea area are simulated, so that the hydrodynamic water quality condition and the change trend of the sea area are more comprehensively analyzed. And calculating response parameters of the concentration of the preset pollutants of each monitoring station to the sea flux according to the simulation result, so that the contribution degree and the influence degree of the concentration of the preset pollutants of each monitoring station to the pollution load of the sea area are more accurately estimated.

According to an aspect of the application, the step S31 further includes:

the type, unit and dimension of the basic variables, and the dependency relationship and conversion relationship between the basic variables are determined according to the actual conditions. In general, the types of basic variables can be classified as scalar, vector, or tensor.

the type and complexity of the hydrodynamic water quality model and the bio-geochemical model should be capable of simulating the water level, tide, flow direction, temperature, salinity, nutrient salt, chlorophyll a and other variables of the sea area, and considering the hydrodynamic conditions of the sea area, the migration and transformation process of inorganic nitrogen, uncertainty factors and the like; the computational domain, meshing, boundary conditions, initial conditions and parameter values should be capable of covering the sea area of interest and the monitoring stations, as well as reflecting the topography and hydrologic characteristics of the sea area. Some tools, such as MIKE or EFDC, are used to select and couple hydrodynamic water quality models and bio-geochemical models, as well as to configure computational domains, meshing, boundary conditions, initial conditions, and parameter values. Some symbols, such as M or C, etc., are used to represent the type and complexity of the hydrodynamic water quality model and the bio-geochemical model, as well as the computational domain, meshing, boundary conditions, initial conditions, and parameter values, such as M (hydrodynamic water quality model) =mike or C (computational domain) =100×100×10 (meters), etc.

the size and number of time steps, and the termination conditions for iterative computations are determined based on the analysis requirements and computational power. The size and the number of the time steps and the influence and the meaning of the termination condition of the iterative calculation are determined according to the actual situation. In general, the size and number of time steps should be such as to ensure the stability and accuracy of the calculation and to meet the analysis period and frequency, and the termination condition of the iterative calculation may be the reaching of a predetermined number of time steps or the reaching of a predetermined error range, etc. The time step and termination conditions for the iterative computation are determined and set using tools such as FABM or XML. Some symbols, such as T or E, are used to represent the size and number of time steps, and the termination conditions for iterative calculations, such as T (time step) =1 hour or E (error range) =0.01 mg/l, etc.

And (3) invoking computational cores of the hydrodynamic water quality model and the bio-geochemical model in the FABM framework model, performing iterative computation according to time steps, and simulating variables such as water level, tide, flow direction, temperature, salinity, nutrient salt, chlorophyll a and the like of the sea area, and the variation and trend of the variables. Principles and methods of computational cores of hydrodynamic water quality models and bio-geochemical models, and their inputs and outputs, are determined based on expertise and reality. In general, the principles and methods of computational cores of hydrodynamic water quality models and bio-geochemical models should be able to be based on equations and laws of physics, chemistry and biology, as well as taking into account hydrodynamic conditions in the sea area, migration and transformation processes of inorganic nitrogen, uncertainty factors, and the like. Some tools, such as MIKE or EFDC, etc., are used to invoke and run the computational cores of hydrodynamic water quality models and bio-geochemical models. Some symbols such as I or 0 are used to represent the input and output of computational cores of hydrodynamic water quality models and bio-geochemical models, such as (water level) =d (water level) or O (inorganic nitrogen) =r (inorganic nitrogen), etc. After each time step, data exchange and information transfer are carried out, and the values of the variables are updated. The manner and content of data exchange and information transfer, and their impact and meaning, are determined according to expertise and practical conditions. In general, the manner and content of data exchange and information transfer should be such as to ensure consistency and coordination between the computational cores of the hydrodynamic water quality model and the bio-geochemical model, as well as the accuracy and validity of the values of the individual variables. Some tools, such as FABM or XML, are used for data exchange and information transfer. Some symbols such as U or M are used to represent the manner and content of data exchange and information transfer, such as U (inorganic nitrogen) =0 (inorganic nitrogen) or M (inorganic nitrogen) =f (inorganic nitrogen), and the like.

And obtaining sea area observation data such as water level, tide, flow direction, temperature, salinity, ammonia nitrogen, chlorophyll a and the like from the traditional data source and the remote sensing data source as reference data of simulation results. The quality and reliability of the observed data, and the preprocessing and interpolation method of the observed data are determined according to the prestored data and the priori knowledge. Some tools, such as MATLAB or Python, etc., are used to process and input the observed data. Some symbols, such as D or X, etc., are used to represent the value or distribution of the observed data, such as D (water level) =2.5 meters or X (ammonia nitrogen) =n (0.7, 0.1), etc. According to analysis purposes and actual conditions, proper evaluation indexes and methods, such as root mean square errors, correlation coefficients, deviation percentages and the like, are selected to evaluate the comparison of simulation results and observed data, namely, the fitting degree of a model and the numerical value of error sources. And determining the influence and meaning of the evaluation index and the method, and the advantages, disadvantages and application range of the evaluation index and the method according to the professional knowledge and the actual situation. The values of the evaluation index and method are calculated and output using some tools, such as MATLAB or Python, etc. The values of the evaluation index and method are represented using some symbols such as E or R, etc., such as E (root mean square error) =0.2 mg/l or R (correlation coefficient) =0.8, etc.

By constructing the FABM framework model, defining the types, units and dimensions of the basic variables and specifying the dependency and conversion relationships between the basic variables, a universal, flexible, extensible and couplable platform for hydrodynamic water quality and bio-geochemical models is realized, so that different hydrodynamic water quality models and bio-geochemical models can be more conveniently selected and integrated, and the properties and relationships of the variables can be more flexibly defined and modified. The embodiment can more comprehensively analyze the hydrodynamic water quality, the bio-geochemical condition and the change trend of the sea area.

As shown in fig. 5, according to an aspect of the present application, the step S4 is further:

generally, the location and type of the source of offshore pollution can be divided into coastal or near-coastal, industrial or agricultural or living, etc.; the basis and method of classifying them into different categories may be based on the nature, scale, extent of impact, etc. of the source of pollution. GIS or Excel tools are used to acquire and process data of the sea pollution source. The symbol P or C is used to denote the emission or concentration of the source of marine pollution, such as P (industry) =10 tons/day or C (industry) =0.8 mg/liter, etc.

the concentration of the predetermined contaminant at each monitoring station may be obtained from the results of the hydrodynamic water quality model as a row vector of the correlation coefficient matrix. The location and number of each monitoring station, and the type and unit of predetermined contaminant are determined according to the actual situation. In general, the location and number of each monitoring station should be capable of covering the sea area of interest and the source of pollution, as well as reflecting the water quality distribution and variation in the sea area; the type and unit of the predetermined contaminant may be ammonia nitrogen, total phosphorus, chemical oxygen demand, etc., as well as milligrams per liter, tons per day, etc. The results of the hydrodynamic water quality model are obtained and output using tools such as MIKE or EFDC. The concentration of the predetermined contaminant of each monitoring station is represented using a symbol such as R or Y, such as R (a) =0.9 mg/l or Y (a) =n (0.9,0.2), or the like.

The discharge amounts of the sea-entering pollution sources of the respective categories are acquired from step S41 as column vectors of the correlation coefficient matrix. The quantity and the name of each type of sea-entering pollution source, and the type and the unit of the discharge amount are determined according to professional knowledge and actual conditions. In general, the number and designation of each type of source of marine pollution should be able to reflect the structure and characteristics of the source of sea and to distinguish between the effects and contributions of the different sources of pollution; the type and unit of emissions may be ammonia nitrogen, total phosphorus, chemical oxygen demand, etc., as well as tons/day, kg/day, etc. GIS or Excel tools are used to acquire and process data of the sea pollution source. The symbol P or C is used to represent the discharge amount of each type of marine pollution source, such as P (industry) =10 tons/day or C (industry) =0.8 mg/liter.

The correlation coefficient between the concentration of the predetermined contaminant at each monitoring station and the discharge of each type of sea-going source, i.e. a value representing a linear or nonlinear relationship between them, is calculated using some statistical method, such as pearson correlation coefficient or spearman correlation coefficient, etc. The type and the range of the correlation coefficient, and the influence and the meaning of the correlation coefficient are determined according to the expertise and the actual situation. In general, the type of correlation coefficient may be linear or nonlinear, may range from-1 to 1, and the influence and meaning may be positive or negative correlation, strong or weak correlation, etc. A matrix of correlation coefficients is calculated and output using MATLAB or Python or the like. The value of the correlation coefficient matrix is represented using a symbol such as R or ρ, for example, R (a, industry) =0.8 or ρ (a, industry) =0.7, or the like.

and taking the concentration of the preset pollutants of each monitoring station as a dependent variable, taking the discharge amount of each type of sea-entering pollution sources as an independent variable, and establishing a multiple linear regression model, namely an equation representing the linear relation between the concentration of the preset pollutants of each monitoring station and the discharge amount of each type of sea-entering pollution sources. The form and parameters of the multiple linear regression model, as well as the assumptions and conditions of the multiple linear regression model, are determined based on expertise and practice. In general, the form of the multiple linear regression model may be y=β0+β1x1+β2x2+ … +βnxn+epsilon, where Y is the dependent variable, X1, X2, …, xn is the independent variable, β0, β1, β2, …, βn is the regression coefficient, epsilon is the error term; the assumption and condition of the multiple linear regression model may be that the expectation of the error term is zero, the variance is constant, uncorrelated, obeying normal distribution, etc. Multiple linear regression models are built and configured using MATLAB or Python, among other tools. The form and parameters of the multiple linear regression model are represented using the symbols Y or β, such as Y (a) =β0+β1p (industry) +β2p (agriculture) +β3p (life) +epsilon, etc.

And solving regression coefficients of a multiple linear regression model, namely numerical values representing the influence degree of each category of sea-entering pollution sources on the concentration of the preset pollutants of each monitoring station by utilizing a correlation coefficient matrix and the concentration of the preset pollutants of each monitoring station and utilizing some solving methods, such as a least square method, a maximum likelihood method, a Bayesian method and the like. The principle and the steps of the solving method, and the advantages, disadvantages and application range of the solving method are determined according to the professional knowledge and the actual situation. Generally, the principles and steps of the solution method should be able to be based on statistical or optimization theory and method, as well as assumptions and conditions that consider the multiple linear regression model, etc.; the advantages and disadvantages and the application range of the solving method can be that the least square method is simple and feasible, but sensitive to abnormal values, the maximum likelihood method can estimate variance, but needs to meet normal distribution, the Bayesian method can introduce priori information, but needs to select proper priori distribution and the like. MATLAB or Python tools are used to implement and output the results of the solution method. The values or distributions of regression coefficients are represented using symbols such as β or θ, for example, β1=0.5 or θ1=n (0.5, 0.1), or the like.

And calculating the sharing rate of each type of sea-entering pollution sources to each monitoring station according to the regression coefficient of the multiple linear regression model, namely, calculating the numerical value representing the contribution proportion of each type of sea-entering pollution sources to the concentration of the preset pollutants of each monitoring station. And determining a calculation formula and a range of the sharing rate, and influence and meaning of the sharing rate according to professional knowledge and actual conditions. In general, the calculation formula for the contribution rate may be the contribution rate = regression coefficient x independent/dependent variable, ranging from 0 to 1, the greater the contribution rate may be, meaning that the greater the contribution of the category of marine pollution sources to the concentration of the predetermined pollutant at the monitoring station, and vice versa. The value of the contribution rate is calculated and output using MATLAB or Python or the like tools. The value of the sharing rate is represented by a symbol such as F (a, industry) =0.6 or Φ (a, industry) =0.6.

and calculating the total pollutant load of each class of the sea-entering pollution sources according to the emission and average concentration of each class of the sea-entering pollution sources, namely, calculating the value representing the contribution of each class of the sea-entering pollution sources to the total pollutants in the sea area. And determining a calculation formula and a unit of the total pollutant load, and the influence and the meaning of the total pollutant load according to the professional knowledge and the actual situation. In general, the calculation formula of the total pollutant load may be total pollutant load=emission amount×concentration, and units may be ton/day, kg/day, etc., and the influence and meaning may be that the larger the total pollutant load is, the greater the contribution of the type of sea-going pollution source to the total pollutant in the sea area is. And calculating the total pollutant load of each monitoring station according to the concentration of the preset pollutant of each monitoring station and the result of the hydrodynamic water quality model, namely, calculating the numerical value representing the contribution quantity of each monitoring station to the total pollutant in the sea area range. And determining a calculation formula and a unit of the total pollutant load, and the influence and the meaning of the total pollutant load according to the professional knowledge and the actual situation. In general, the calculation formula of the total pollutant load may be total pollutant load=concentration×flow, and the units may be ton/day, kg/day, etc., and the influence and meaning may be that the larger the total pollutant load is, the greater the contribution of the monitoring station to the total pollutant in the sea area is. And calculating the contribution rate of each type of sea-entering pollution source to the total pollutant load in the sea area range according to the total pollutant load of each type of sea-entering pollution source and the total pollutant load of each monitoring station, namely, calculating the numerical value representing the contribution proportion of each type of sea-entering pollution source to the total pollutant in the sea area range. And determining a calculation formula and a range of the contribution rate, and influence and meaning of the contribution rate according to professional knowledge and actual conditions. In general, the calculation formula of the contribution rate may be that the contribution rate=total pollutant load/total pollutant load in the sea area range, and the range may be between 0 and 1, and the influence and meaning may be that the larger the contribution rate is, the larger the contribution of the sea-entering pollution source of the category to the total pollutant in the sea area range is.

And determining target values and limiting values of the total pollutant load in the sea area range, namely, numerical values representing control targets and control standards of the total pollutant in the sea area range according to analysis purposes and actual conditions. The magnitude and units of the target values and the limit values, and the influence and meaning of the target values and the limit values are determined according to the expertise and the policy specifications. In general, the size and units of the target and limit values may be ton/day, kg/day, etc., and the effect and meaning may be that the smaller the target and limit values, the more stringent the control of the total pollutants in the sea area, and vice versa.

And determining the maximum allowable emission and reduction of the sea-entering pollution sources according to the contribution rate of the sea-entering pollution sources of each category to the total pollutant load in the sea area range and the target value and the limiting value of the total pollutant load in the sea area range, namely, the numerical value representing the control target and the control standard of the sea-entering pollution sources of each category. And determining a calculation formula and a unit of the maximum allowable emission amount and the reduction amount, and influence and meaning of the maximum allowable emission amount and the reduction amount according to professional knowledge and actual conditions. In general, the calculation formula of the maximum allowable discharge amount and the reduction amount may be maximum allowable discharge amount=target value×contribution rate/concentration, reduction amount=discharge amount-maximum allowable discharge amount, unit may be ton/day, kg/day, etc., and the influence and meaning may be that the smaller the maximum allowable discharge amount and the reduction amount, the more strict the control of the type of sea-going pollution source,

According to an aspect of the application, the step S4 further includes:

s4a, dividing the sea pollution sources into different categories, and calculating the total discharge and average concentration of each pollution source; the classification and quantitative analysis of the sea-entering pollution sources are realized, so that the structure and the characteristics of the sea-entering pollution sources and the emission condition of pollutants of various pollution sources are more clearly known.

S4b, regarding each monitoring station in the sea area range as a game participant, and constructing a non-cooperative game model, wherein the strategy of each monitoring station is to select a pollutant concentration target value, and the income of each monitoring station is the probability that the water quality reaches the standard; the water quality target and the income of each monitoring station are optimally analyzed, so that the water quality control target and the standard of the sea area are more reasonably formulated, and the competition and cooperation relation among the monitoring stations is considered.

Step S4c, calculating a correlation coefficient matrix between the pollutant concentration of each monitoring station and the discharge amount of each sea-entering pollution source according to the result of the hydrodynamic water quality model, and taking the correlation coefficient matrix as a parameter of a game model; the quantitative analysis of the relation between the water quality of each monitoring station and the discharge amount of each sea-entering pollution source is realized, so that the hydrodynamic water quality of the sea area and the migration and conversion process of pollutants and the influence degree of each sea-entering pollution source on the water quality of each monitoring station are reflected more accurately.

S4d, solving Nash equilibrium of the game model by utilizing an optimal reaction dynamic or evolution stability strategy, namely obtaining an optimal pollutant concentration target value of each monitoring station; by analyzing the solution of the water quality targets of each monitoring station, the optimal solution of the water quality control of the sea area, the optimal strategy and the maximum income of each monitoring station are more effectively found out.

And S4e, reversely pushing out the maximum allowable emission and the reduction amount of the sea pollution sources of each category according to the optimal pollutant concentration target value of each monitoring station. By analyzing the control targets and the control standards of the sea-entering pollution sources, measures for managing and controlling the sea-entering pollution sources and emission reduction requirements and responsibilities of the sea-entering pollution sources are more scientifically formulated.

In another embodiment of the present application, step S45 further includes balancing reduction and cost between the sea pollution sources by using a dynamic optimization method, so as to improve the distribution efficiency:

step S45a, determining a cost reduction function of each type of sea pollution source according to actual conditions, namely an equation representing the relation between the reduction amount and the reduction cost of each type of sea pollution source.

The form and parameters of the cut cost function, and the impact and meaning of the cut cost function, are determined according to policy specifications. In general, the form of the cost reduction function may be linear or nonlinear, the parameter may be a unit cost reduction, a marginal cost reduction, or the like, and the influence and meaning may be that the steeper the cost reduction function, the higher the cost reduction representing the type of sea-going pollution source.

Step S45b, a dynamic optimization model is established according to the maximum allowable emission and the reduction amount of each type of sea-entering pollution source and the reduction cost function of each type of sea-entering pollution source, namely an equation representing the optimization problem between the reduction amount and the reduction cost of each type of sea-entering pollution source.

And determining an objective function and constraint conditions of the dynamic optimization model, and assumptions and conditions of the dynamic optimization model according to the expertise and the actual conditions. In general, the objective function of the dynamic optimization model may be to minimize the total cut cost for each type of marine pollution source, the constraint may be that the emissions of each type of marine pollution source do not exceed a maximum allowable emissions, and that the total pollutant load within the sea area does not exceed a target or limit value, etc.; the assumptions and conditions of the dynamic optimization model may be that the cut-out and cut-out costs for each class of seagoing pollution sources are continuously differentiable, the cut-out and cut-out costs for each class of seagoing pollution sources are independent, and the cut-out and cut-out costs for each class of seagoing pollution sources are time-varying.

And step S45c, solving an optimal solution of the dynamic optimization model by utilizing a plurality of solving methods, such as a gradient descent method, a Lagrange multiplier method, a dynamic programming method and the like, namely obtaining the optimal reduction amount and the minimum reduction cost of the sea-entering pollution sources of each category.

The principle and the steps of the solving method, and the advantages, disadvantages and application range of the solving method are determined according to the actual situation. Generally, the principles and steps of the solution method should be able to be based on the theory and method of optimization or dynamic planning, and consider objective functions and constraints of the dynamic optimization model, etc.; the advantages and disadvantages and the application range of the solving method can be that the gradient descent method is simple and feasible, but is possibly in local optimum, the Lagrange multiplier method can process constraint conditions, certain regular conditions are required to be met, the dynamic programming method can process time change, no post-effect is required to be met, and the like.

In another embodiment of the present application, step S45 further includes:

step S45i, determining the maximum allowable entering mass of pollutants entering the sea and river to the bay based on the first environmental parameter and the second environmental parameter;

based on the first environmental parameter, determining a quantitative response relationship between the mass of the inland river pollutants and the quality of the bay water according to the constructed bay three-dimensional hydrodynamic force and the bio-geochemical process; calculating the maximum bearing capacity of the bay pollutant based on the quantitative response relationship; determining a first pollutant distribution weight corresponding to each sea-going river based on the second environmental parameters; and based on the maximum bearing capacity, the quantitative response relation and the first pollutant distribution weight corresponding to each sea-going river, respectively calculating to obtain the maximum allowable mass of each sea-going river. Based on the third environmental parameter, determining the maximum allowable inland volume of each tributary to the corresponding inland river pollutant according to the maximum allowable inland volume;

Step S45ii, calculating and obtaining pollutant degradation coefficients of all branches based on the third environmental parameter;

determining a second pollutant distribution weight corresponding to each tributary based on the pollutant degradation coefficient of each tributary; and respectively calculating and obtaining the maximum allowable inland volume of each tributary based on the maximum allowable inland volume and the second pollutant distribution weight corresponding to each inland river.

Step S45iii, determining the maximum allowable generation amount of pollutants from each pollution source to the corresponding tributary according to the maximum allowable river entering amount based on the fourth environmental parameter; determining a relationship between each source of pollution production and the source of pollution to tributary reduction based on the fourth environmental parameter; and calculating the maximum allowable production amount of each pollution source based on the maximum allowable river entering amount and the relation between the pollutant production amount of each pollution source and the reduction amount of the pollution source from the pollution source to the branch flow.

Wherein, the bay river basin is divided into a first river basin from each inland river to the bay, a second river basin from each tributary to each inland river, and a third river basin from each source pollution source to each tributary. First environmental parameters: the environmental parameters of the bay include the water level, tide, water temperature, salinity, dissolved oxygen, pH value, nutrient salts such as nitrogen, phosphorus, silicon and the like, concentration and distribution of pollutants such as organic matters, heavy metals and the like of the bay; second environmental parameters: environmental parameters of the first river basin, including the flow rate, water quality category, pollutant type, load and the like of each river entering; third environmental parameter: environmental parameters of the second river basin, including flow rate, water quality category, pollutant type, load, etc. of each tributary; fourth environmental parameter: the environmental parameters of the third basin include the location, type, scale, emissions, removal rate, etc. of each source contamination source.

at least one processor; and

a memory communicatively coupled to at least one of the processors; wherein,

The preferred embodiments of the present invention have been described in detail above, but the present invention is not limited to the specific details of the above embodiments, and various equivalent changes can be made to the technical solution of the present invention within the scope of the technical concept of the present invention, and all the equivalent changes belong to the protection scope of the present invention.

Claims

1. The method for determining the land pollution load distribution of the offshore area is characterized by comprising the following steps of:

2. The method for determining the distribution of the pollution load of the offshore area and land according to claim 1, wherein said step S1 is further:

3. The method for determining the distribution of the pollution load of the offshore area and land according to claim 2, wherein said step S2 is further:

4. A method for determining the distribution of the pollution load of the offshore area according to claim 3, wherein said step S3 is further:

step S32: operating simulation, inputting a traditional data source and a remote sensing data source, setting different pollution source emission and water quality standards, operating preset simulation schemes, and simulating water level, tide and variation of a sea area through a two-dimensional or three-dimensional hydrodynamic water quality model to form a simulation result;

5. The method for determining the distribution of the pollution load to the offshore area according to claim 4, wherein said step S4 is further:

6. The offshore area land pollution load distribution determination method of claim 5, wherein said step S24 further comprises:

7. The method for determining the distribution of the pollution load of the offshore area and land as claimed in claim 5, further comprising the step of solving the concentration of the predetermined pollutants of each monitoring station in the sea area by using a bayesian network in step S25:

8. The offshore area land pollution load distribution determination method of claim 5, wherein said step S4 further comprises:

9. The offshore area land pollution load distribution determination method of claim 5, wherein said step S31 further comprises:

step S31a, constructing a FABM framework model, defining the type, unit and dimension of each basic variable, and specifying the dependency and conversion relation between the basic variables, wherein the basic variables comprise physical variables and biological geochemical variables, the physical variables comprise water level, tide, temperature and salinity, and the biological geochemical variables comprise nutrient salts and chlorophyll a;

10. An offshore area land pollution load distribution determination system, comprising:

at least one processor; and

a memory communicatively coupled to at least one of the processors; wherein,

the memory stores instructions executable by the processor for performing the offshore area land pollution load distribution determination method of any one of claims 1 to 9.