CN111797579B - Backward probability model method for tracing water environmental pollution sources - Google Patents
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Abstract
The invention discloses a backward probability model method for tracing a water body environment pollution source, which comprises the following steps: 1. carrying out generalization and hydrodynamic simulation on the water body subjected to pollution source tracing; 2. generating backward probability fields of all space nodes at corresponding time nodes through backward probability model calculation; 3. generating a conditional probability field based on the concentration value of a single monitoring point according to the Bayesian theorem, and finally synthesizing a total conditional probability field of multiple monitoring points of different backward time nodes; 4. determining pollution source release time after calculation; 5. drawing a conditional probability field of the time node, and determining the position of a pollution source; 6. after the pollution source release time and the pollution source position are determined, the contribution rate of each quality value to the probability value of the point is calculated according to a normal distribution rule, and the quality value with high contribution rate is more likely to be the quality of the pollution source, so that the problems that the pollution source of the water body is difficult to trace back, and three indexes of the time, the position and the quality of the instantaneous emission pollution source release cannot be determined at the same time are solved.
Description
Technical Field
A backward probability model method for tracing water environmental pollution sources belongs to the field of environmental treatment, in particular to the field of water environment treatment.
Background
The water environment pollution of water bodies such as rivers, sea areas or lake reservoirs is caused by pollutant discharge. In order to protect the river water environment, the government establishes a manual water body inspection mechanism, but the time and space coverage are limited only by manpower; meanwhile, a laying monitoring network is actively explored, but the conditions of long and narrow river channels for allowing station establishment are limited, the manufacturing cost is high, and the monitoring of the whole river reach cannot be realized temporarily. Therefore, the phenomenon of stealing the wastewater into the river channel is often restricted, and the management department is basically not in charge of the theft.
The water environment protection experts acquire more achievements on the tracing direction research of the pollution sources, the main stream method is to establish a water body hydrodynamic water quality numerical model, and the water environment protection experts are combined with the optimization method to search the discharge position or the discharge time of the pollution sources on the basis of the existing water quality monitoring data. However, the global search method requires a large number of times to drive the model to simulate calculation, and is long in time consumption, more in consumed calculation resources and impractical to use. In contrast, the emerging backward probability model method can be improved into a backward probability model by means of a numerical simulation tool, the model generates a probability field of a suspected pollution source at each monitoring point (which can be in space-time inconsistency), finally, the space-time probability distribution of the pollution source is generated by utilizing the Bayesian theory in combination with the monitoring data, and the place with high probability value is likely to be the emission place of the pollution source. The technical scheme utilizes the theoretical basis of a backward probability model to improve the FVCOM model, namely a model-open numerical simulation tool, into a probability generation tool with a pollution source identification function, the FVCOM is not researched by the modification code at present, and the traditional backward probability model theory only identifies the pollution source discharge position at a given time or the pollution source discharge time at the given position, and the technical scheme provides three indexes for simultaneously determining the instantaneous discharge pollution source discharge time, position and quality.
Disclosure of Invention
Technical problems: the technical scheme solves the problems that tracing the pollution source of the water body is difficult, and three indexes of time, position and quality of instantaneous emission pollution source release cannot be determined at the same time.
The technical content is as follows: a backward probability model method for tracing a water environmental pollution source comprises the following steps: 1. generalizing the water body for tracing the pollution source: simulating an accurate hydrodynamic process of the water body according to a normal model construction program; exporting hydrodynamic processes of all nodes of the model, and sequencing the hydrodynamic processes according to time backward to generate backward hydrodynamic conditions; selecting a proper numerical simulation model for tracing the water environment of a water pollution source, modifying the source code of the model, reading the backward hydrodynamic condition of the model, and changing the water quality module into a backward probability model; 2. the concentration variable at the backward time node corresponding to the monitoring time drives the backward probability model according to 1, and then the backward probability fields of all the space nodes at the time node are generated;
3. after different probability fields are obtained by driving backward probability models according to different time monitoring points, assuming that a quality field meets a normal distribution rule, and combining the single probability fields into a total conditional probability field by taking a monitoring concentration value as a condition through a Bayesian theorem;
4. the sum of the factor value assurance probabilities in the total conditional probability field formula is 1, the factor value represents the possible probability of the pollution source release time, the smaller the factor value is, the larger the reciprocal value is, and the greater the probability that the backward driving time value is the pollution source release time is represented;
5. after the pollution source release time is determined, a conditional probability field of the time node is drawn, and the probability that the space node with a high probability value is the pollution source release position is larger;
6. after the release time and the position of the pollution source are determined, the contribution rate of each quality value to the probability value of the point is calculated according to a normal distribution rule, and the quality value with high contribution rate is more likely to be the quality of the pollution source.
Preferably, the numerical simulation model for tracing the water environment of the water pollution source selects an FVCOM ocean hydrodynamic force and water quality simulation model.
Preferably, the formulas based on the backward probability model methodology for sequentially determining the pollution source release time, position and quality are as follows:
preferably, the method for tracing the backward probability model of the water pollution source comprises the following specific operation steps:
firstly, acquiring hydrodynamic boundary conditions such as river bottom elevation, flow velocity and flow quantity, performing numerical modeling on the water body environment of a research area, and acquiring a corresponding space flow field in a period by using a FVCOM;
II, storing the flow field data into a dat file in a binary format according to the FVCOM set format;
III, after the flow field is obtained through the FVCOM simulation, the FVCOM water quality calculation module is independently extracted through the Fortran language according to the FVCOM code programming architecture, and the flow field data reading module is automatically programmed according to the saved flow field format, so that the water quality module can independently operate, and the programmed flow field data reading format is as follows:
READ(10)(U(I,K),V(I,K),K＝1,KBM1)
READ(10)D(I),(WTS(I,K),KH(I,K),K＝2,KBM1)
IV, after flow field data are read, according to the independent water quality module, the pollutant concentration change field under the known flow field can be simulated by calling the FVCOM module:
CALL VISCOF_H
CALL ADV_S
CALL VDIF_TS(SF1)
the three original modules respectively calculate vortex viscosity coefficient, convection effect and diffusion effect;
v, the water quality module is extracted independently, on the basis of the existing flow field, the FVCOM integral model with huge calculation amount is not required to be driven, the instant point source emission situation at any position can be simulated, and the subsequent backward probability model is also based on the improvement of the module;
VI, the water quality module forward simulates a concentration field, if the concentration field is subjected to backward treatment, a backward simulation result is a probability field according to an accompanying partial differential equation:
that is, the initial condition is 1 total probability, and the value of each node simulated in the backward direction is the probability value that the point is a pollution source;
and VII, reversing the flow field according to the deduction result of the accompanying equation, taking 1 as an initial condition, carrying out backward direction in time, and simulating a backward probability field as a result, wherein the mode of realizing the backward probability field in an independent water quality module is as follows, namely, firstly, pointing a reading pointer to the tail of a flow field data dat file:
DO IT＝1,NSTEPS
DO I＝1,N
READ(10)
END DO
DO I＝1,M
READ(10)
END DO
END DO
then, the pointer is moved back by the BACKSPACE to read the hydrodynamic parameters of the corresponding position:
DO I＝1,N
BACKSPACE(10)
END DO
DO I＝1,M
BACKSPACE(10)
END DO
DO I＝1,N
READ(10)(U(I,K),V(I,K),K＝1,KBM1)
END DO
after the hydrodynamic data of one time node is read, the flow field is reversed:
U＝-U；V＝-V；WTS＝-WTS
finally, the native module is driven to complete simulation of backward probability:
CALL VISCOF_H
CALL ADV_S
CALL VDIF_TS(SF1)
and VIII, driving a backward probability model on a position with a monitoring concentration value and a time node based on field actual measurement data to obtain spatial probability distribution fields of different backward time nodes, and calculating a conditional probability spatial distribution field by combining specific monitoring time, monitoring point position and concentration data and using a Bayesian theory:
the probability field spatial distribution calculated under the known conditions such as concentration and the like is more directional;
the β value in ix, above equation is a parameter that guarantees that the sum of probability fields is equal to 1, i.e. the probability is 100%, and is a variable about backward time, each backward time node corresponds to a β value, and the larger the value, the more likely the corresponding backward time node is the time of emission of pollution sources, and the specific quantitative formula is as follows:
calculating beta values of different backward time nodes, and comparing the beta values to determine the backward time of the pollution source emission with the maximum probability; after the pollution source discharge time is determined, the space distribution of the conditional probability field can be calculated according to a Bayesian formula in VIII, and then the pollution source discharge position with high probability is determined; after the pollution source position is determined, the formula in VIII is used for knowing and integrating the quality, the quality is dispersed according to normal distribution, and the contribution rate of each discrete quantity to the whole backward time conditional probability distribution field is calculated:
the mass value with the large contribution rate is determined as the amount of pollution source emission.
The beneficial effects are that: the invention relates to a backward probability model method for tracing water environmental pollution sources, which is characterized in that a backward probability model is improved by means of a numerical simulation tool, a probability field of a suspected pollution source is generated at each monitoring point position (which can be in space-time inconsistency), and finally, the space-time probability distribution of the pollution source is generated by utilizing a Bayesian theory in combination with monitoring data, wherein the place with high probability value is likely to be the emission place of the pollution source; the method solves the problems that the tracing of the water pollution source is difficult, and three indexes of the time, the position and the quality of the instantaneous discharge pollution source release cannot be determined at the same time.
Drawings
FIG. 1 is a schematic diagram of a backward probability model method for tracing a water environmental pollution source;
FIG. 2 is a diagram of a data model at A in FIG. 1 of a backward probability model method for tracing a water environmental pollution source according to the present invention;
FIG. 3 is a flow chart of a backward probability model method for tracing water environmental pollution sources.
DETAILED DESCRIPTION OF EMBODIMENT (S) OF INVENTION
The following selects the preferred embodiment of the technical scheme to explain the technical scheme in detail.
A backward probability model method for tracing a water environmental pollution source comprises the following steps:
1. generalizing the water body for tracing the pollution source: simulating an accurate hydrodynamic process of the water body according to a normal model construction program; exporting hydrodynamic processes of all nodes of the model, and sequencing the hydrodynamic processes according to time backward to generate backward hydrodynamic conditions; selecting a proper numerical simulation model for tracing the water environment of a water pollution source, modifying the source code of the model, reading the backward hydrodynamic condition of the model, and changing the water quality module into a backward probability model; 2. the concentration variable at the backward time node corresponding to the monitoring time drives the backward probability model according to 1, and then the backward probability fields of all the space nodes at the time node are generated;
3. after different probability fields are obtained by driving backward probability models according to different time monitoring points, assuming that a quality field meets a normal distribution rule, and combining the single probability fields into a total conditional probability field by taking a monitoring concentration value as a condition through a Bayesian theorem;
4. the sum of the factor value assurance probabilities in the total conditional probability field formula is 1, the factor value represents the possible probability of the pollution source release time, the smaller the factor value is, the larger the reciprocal value is, and the greater the probability that the backward driving time value is the pollution source release time is represented;
5. after the pollution source release time is determined, a conditional probability field of the time node is drawn, and the probability that the space node with a high probability value is the pollution source release position is larger;
6. after the release time and the position of the pollution source are determined, the contribution rate of each quality value to the probability value of the point is calculated according to a normal distribution rule, and the quality value with high contribution rate is more likely to be the quality of the pollution source.
The numerical simulation model for tracing the water environment of the water pollution source selects the FVCOM ocean hydrodynamic force and water quality simulation model. The technical scheme utilizes the theoretical basis of a backward probability model to improve the FVCOM model, namely a model-open numerical simulation tool, into a probability generation tool with a pollution source identification function, the FVCOM is not researched by the modification code at present, and the traditional backward probability model theory only identifies the pollution source discharge position at a given time or the pollution source discharge time at the given position, and the technical scheme provides three indexes for simultaneously determining the instantaneous discharge pollution source discharge time, position and quality. The formula based on the backward probability model methodology for sequentially determining the pollution source release time, position and quality is as follows:
the backward probability model method for tracing the water pollution source comprises the following specific operation steps:
and I, acquiring hydrodynamic boundary conditions such as river bottom elevation, flow velocity and flow quantity, and the like, carrying out numerical modeling on the water body environment of the research area, and obtaining a corresponding space flow field in a period by using the FVCOM.
II, storing the flow field data (node information, flow velocity components and the like) into a dat file in a binary format according to the FVCOM setting format.
III, after the flow field is obtained through the FVCOM simulation, the FVCOM water quality calculation module is independently extracted through the Fortran language according to the FVCOM code programming architecture, and the flow field data reading module is automatically programmed according to the saved flow field format, so that the water quality module can independently operate, and the programmed flow field data reading format is as follows:
READ(10)(U(I,K),V(I,K),K＝1,KBM1)
READ(10)D(I),(WTS(I,K),KH(I,K),K＝2,KBM1)
IV, after flow field data are read, according to the independent water quality module, the pollutant concentration change field under the known flow field can be simulated by calling the FVCOM module:
CALL VISCOF_H
CALL ADV_S
CALL VDIF_TS(SF1)
the three primary modules calculate vortex viscosity coefficient, convection effect and diffusion effect respectively.
V, the water quality module is extracted independently, on the basis of the existing flow field, the FVCOM integral model with huge calculation amount is not required to be driven, the instant point source emission situation at any position can be simulated, and the subsequent backward probability model is also based on the improvement of the module.
VI, the water quality module forward simulates a concentration field, if the concentration field is subjected to backward treatment, a backward simulation result is a probability field according to an accompanying partial differential equation:
that is, the initial condition is a 1 total probability, and the value of each node modeled in the backward direction will be the probability value that the point is the source of contamination.
And VII, reversing the flow field according to the deduction result of the accompanying equation, taking 1 as an initial condition, carrying out backward direction in time, and simulating a backward probability field as a result, wherein the mode of realizing the backward probability field in an independent water quality module is as follows, namely, firstly, pointing a reading pointer to the tail of a flow field data dat file:
DO IT＝1,NSTEPS
DO I＝1,N
READ(10)
END DO
DO I＝1,M
READ(10)
END DO
END DO
then, the pointer is moved back by the BACKSPACE to read the hydrodynamic parameters of the corresponding position:
DO I＝1,N
BACKSPACE(10)
END DO
DO I＝1,M
BACKSPACE(10)
END DO
DO I＝1,N
READ(10)(U(I,K),V(I,K),K＝1,KBM1)
END DO
after the hydrodynamic data of one time node is read, the flow field is reversed:
U＝-U；V＝-V；WTS＝-WTS
finally, the native module is driven to complete simulation of backward probability:
CALL VISCOF_H
CALL ADV_S
CALL VDIF_TS(SF1)
and VIII, driving a backward probability model on a position with a monitoring concentration value and a time node based on field actual measurement data to obtain spatial probability distribution fields of different backward time nodes, and calculating a conditional probability spatial distribution field by combining specific monitoring time, monitoring point position and concentration data and using a Bayesian theory:
the probability field spatial distribution calculated under the known conditions such as concentration and the like is more directional;
the β value in ix, above equation is a parameter that guarantees that the sum of probability fields is equal to 1, i.e. the probability is 100%, and is a variable about backward time, each backward time node corresponds to a β value, and the larger the value, the more likely the corresponding backward time node is the time of emission of pollution sources, and the specific quantitative formula is as follows:
calculating beta values of different backward time nodes, and comparing the beta values to determine the backward time of the pollution source emission with the maximum probability; after the pollution source discharge time is determined, the space distribution of the conditional probability field can be calculated according to a Bayesian formula in VIII, and then the pollution source discharge position with high probability is determined; after the pollution source position is determined, the formula in VIII is used for knowing and integrating the quality, the quality is dispersed according to normal distribution, and the contribution rate of each discrete quantity to the whole backward time conditional probability distribution field is calculated:
the mass value with the large contribution rate is determined as the amount of pollution source emission.
The above embodiments are preferred embodiments of the inventive arrangements, and not all embodiments of the arrangements. Any other technical solution based on or suggested by this solution, and which can be obtained by a person skilled in the art without the need for inventive work, is within the scope of protection of this solution.
Claims (1)
1. A backward probability model method for tracing a water environmental pollution source is characterized by comprising the following steps: comprises the following steps:
1. generalizing the water body for tracing the pollution source: simulating an accurate hydrodynamic process of the water body according to a normal model construction program; exporting hydrodynamic processes of all nodes of the model, and sequencing the hydrodynamic processes according to time backward to generate backward hydrodynamic conditions; selecting a proper water environment numerical simulation model, modifying a source code of the water environment numerical simulation model, enabling the water environment numerical simulation model to read backward hydrodynamic conditions, changing a water quality module into a backward probability model, and selecting a FVCOM ocean hydrodynamic force and water quality simulation model from the water environment numerical simulation model;
2. the concentration variable at the backward time node corresponding to the monitoring time drives the backward probability model according to 1, and then the backward probability fields of all the space nodes at the time node are generated;
3. after different probability fields are obtained by driving backward probability models according to different time monitoring points, assuming that a quality field meets a normal distribution rule, and combining the single probability fields into a total conditional probability field by taking a monitoring concentration value as a condition through a Bayesian theorem;
4. the sum of the factor value assurance probabilities in the total conditional probability field formula is 1, the factor value represents the possible probability of the pollution source release time, the smaller the factor value is, the larger the reciprocal value is, and the greater the probability that the backward driving time value is the pollution source release time is represented;
5. after the pollution source release time is determined, a conditional probability field of the time node is drawn, and the probability that the space node with a high probability value is the pollution source release position is larger;
6. after the release time and the position of the pollution source are determined, calculating the contribution rate of each quality value to the probability value of the point according to a normal distribution rule, wherein the quality value with high contribution rate is more likely to be the quality of the pollution source;
the backward probability model for tracing the water pollution source comprises the following specific operation steps:
i, acquiring river bottom elevation, flow velocity and flow hydrodynamic boundary conditions, carrying out numerical modeling on the water body environment of a research area, and obtaining a corresponding space flow field in a period by using the FVCOM;
II, storing the flow field data into a dat file in a binary format according to the FVCOM set format;
III, after the flow field is obtained through the FVCOM simulation, the FVCOM water quality calculation module is independently extracted through the Fortran language according to the FVCOM code programming architecture, and the flow field data reading module is automatically programmed according to the stored flow field format, so that the water quality module can independently operate;
IV, after flow field data are read, according to independent water quality modules, pollutant concentration change fields under a known flow field can be simulated by calling three modules of CALL VISCOF_H, CALL ADV_S and CALL VDIF_TS (SF 1), wherein the three modules of CALL VISCOF_H, CALL ADV_S and CALL VDIF_TS (SF 1) are respectively used for calculating vortex viscosity coefficients, convection effects and diffusion effects;
v, the water quality module is extracted independently, on the basis of the existing flow field, the FVCOM integral model with huge calculation amount is not required to be driven, the instant point source emission situation at any position can be simulated, and the subsequent backward probability model is also based on the improvement of the module;
VI, the water quality module forward simulates a concentration field, if the concentration field is subjected to backward treatment, the value of each node simulated backward is a probability value of the point as a pollution source according to an accompanying partial differential equation, namely that the initial condition is 1 total probability;
VII, reversing a flow field according to a deduction result of an accompanying equation, taking 1 as an initial condition, carrying out backward direction in time, simulating the result to be a backward probability field, then carrying out pointer backward movement by using a BACKSPACE to read hydrodynamic parameters of corresponding positions, and finally, driving three modules of CALL VISCOF_H, CALL ADV_S and CALL VDIF_TS (SF 1) to finish simulation of backward probability; the method is characterized in that based on field actual measurement data, a backward probability model is driven on a position with a monitoring concentration value and a time node to obtain space probability distribution fields of different backward time nodes, at the moment, a conditional probability space distribution field can be calculated through Bayesian theory by combining specific monitoring time, monitoring point position and concentration data, and the calculated probability field space distribution under the condition of known concentration has better directivity;
IX, when the backward time variable, each backward time node corresponds to a value, and the larger the value is, the more likely the corresponding backward time node is the time of emission of the pollution source;
x, calculating the values of different backward time nodes, and comparing the values of each different backward time node to determine the backward time of the pollution source emission with the maximum probability; after the pollution source discharge time is determined, the space distribution of the conditional probability field can be calculated through a Bayesian formula, and then the pollution source discharge position with high probability is determined; after the pollution source position is determined, calculating the contribution rate of each quality value to the probability value of the point according to a normal distribution rule, wherein the quality value with the large contribution rate is determined as the emission amount of the pollution source.
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| CN107145737A (en) * | 2017-04-28 | 2017-09-08 | 南京工业大学 | One kind is based on the markovian unsteady state flow reverse recognizer of pollution sources off field |
| CN108897964A (en) * | 2018-07-09 | 2018-11-27 | 重庆大学 | A kind of Bayesian statistics source tracing method of sewage network discharge beyond standards industrial wastewater |
| CN109885804A (en) * | 2019-01-23 | 2019-06-14 | 大连理工大学 | A kind of air monitoring and source discrimination method based on monitoring car |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105956664A (en) * | 2016-04-27 | 2016-09-21 | 浙江大学 | Tracing method for sudden river point source pollution |
| CN107145737A (en) * | 2017-04-28 | 2017-09-08 | 南京工业大学 | One kind is based on the markovian unsteady state flow reverse recognizer of pollution sources off field |
| CN108897964A (en) * | 2018-07-09 | 2018-11-27 | 重庆大学 | A kind of Bayesian statistics source tracing method of sewage network discharge beyond standards industrial wastewater |
| CN109885804A (en) * | 2019-01-23 | 2019-06-14 | 大连理工大学 | A kind of air monitoring and source discrimination method based on monitoring car |
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