CN117275602A - Source intensity estimation and tracing method for river basin agricultural non-point source pollution - Google Patents
Source intensity estimation and tracing method for river basin agricultural non-point source pollution Download PDFInfo
- Publication number
- CN117275602A CN117275602A CN202310671215.5A CN202310671215A CN117275602A CN 117275602 A CN117275602 A CN 117275602A CN 202310671215 A CN202310671215 A CN 202310671215A CN 117275602 A CN117275602 A CN 117275602A
- Authority
- CN
- China
- Prior art keywords
- point
- source
- agricultural non
- point source
- water quality
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 68
- 238000012544 monitoring process Methods 0.000 claims abstract description 111
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 82
- 239000003344 environmental pollutant Substances 0.000 claims abstract description 52
- 231100000719 pollutant Toxicity 0.000 claims abstract description 52
- 238000011160 research Methods 0.000 claims abstract description 15
- 238000004364 calculation method Methods 0.000 claims description 13
- 230000008859 change Effects 0.000 claims description 9
- 239000010865 sewage Substances 0.000 claims description 9
- 230000008569 process Effects 0.000 claims description 6
- 238000009792 diffusion process Methods 0.000 claims description 5
- 238000005259 measurement Methods 0.000 claims description 5
- 238000004458 analytical method Methods 0.000 claims description 4
- 230000015556 catabolic process Effects 0.000 claims description 4
- 238000006731 degradation reaction Methods 0.000 claims description 4
- 239000006185 dispersion Substances 0.000 claims description 4
- 230000004044 response Effects 0.000 claims description 4
- 238000011144 upstream manufacturing Methods 0.000 claims description 4
- 239000011159 matrix material Substances 0.000 claims description 2
- 238000004445 quantitative analysis Methods 0.000 abstract description 4
- 238000003903 river water pollution Methods 0.000 abstract description 4
- 230000003068 static effect Effects 0.000 description 6
- 238000007619 statistical method Methods 0.000 description 5
- 238000004422 calculation algorithm Methods 0.000 description 4
- OAICVXFJPJFONN-UHFFFAOYSA-N Phosphorus Chemical compound [P] OAICVXFJPJFONN-UHFFFAOYSA-N 0.000 description 3
- 238000009826 distribution Methods 0.000 description 3
- 230000033001 locomotion Effects 0.000 description 3
- 229910052698 phosphorus Inorganic materials 0.000 description 3
- 239000011574 phosphorus Substances 0.000 description 3
- 238000003911 water pollution Methods 0.000 description 3
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 description 2
- 238000013473 artificial intelligence Methods 0.000 description 2
- 230000015572 biosynthetic process Effects 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000007613 environmental effect Effects 0.000 description 2
- 239000010842 industrial wastewater Substances 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 239000007787 solid Substances 0.000 description 2
- 239000000126 substance Substances 0.000 description 2
- 239000002351 wastewater Substances 0.000 description 2
- 241000257303 Hymenoptera Species 0.000 description 1
- 230000002159 abnormal effect Effects 0.000 description 1
- 238000012271 agricultural production Methods 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000035605 chemotaxis Effects 0.000 description 1
- 230000006866 deterioration Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 239000003337 fertilizer Substances 0.000 description 1
- 230000002068 genetic effect Effects 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000002844 melting Methods 0.000 description 1
- 230000008018 melting Effects 0.000 description 1
- 244000005700 microbiome Species 0.000 description 1
- 230000005012 migration Effects 0.000 description 1
- 238000013508 migration Methods 0.000 description 1
- 229910052757 nitrogen Inorganic materials 0.000 description 1
- 239000000575 pesticide Substances 0.000 description 1
- 238000001556 precipitation Methods 0.000 description 1
- 238000002922 simulated annealing Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
- G16C20/30—Prediction of properties of chemical compounds, compositions or mixtures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/02—Agriculture; Fishing; Forestry; Mining
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Tourism & Hospitality (AREA)
- Mathematical Physics (AREA)
- Economics (AREA)
- Human Resources & Organizations (AREA)
- Computational Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Strategic Management (AREA)
- Primary Health Care (AREA)
- Pure & Applied Mathematics (AREA)
- Marketing (AREA)
- Data Mining & Analysis (AREA)
- General Health & Medical Sciences (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Agronomy & Crop Science (AREA)
- Mining & Mineral Resources (AREA)
- Algebra (AREA)
- Software Systems (AREA)
- Chemical & Material Sciences (AREA)
- Operations Research (AREA)
- Marine Sciences & Fisheries (AREA)
- Animal Husbandry (AREA)
- Databases & Information Systems (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Crystallography & Structural Chemistry (AREA)
- Bioinformatics & Computational Biology (AREA)
- Development Economics (AREA)
- Educational Administration (AREA)
- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
The invention discloses a source intensity estimation and tracing method for river basin agricultural non-point source pollution, which is used for calculating river water quality monitoring point agricultural non-point source contribution and source thereof. The current river water pollution tracing method is mainly focused on the aspect of point source pollution tracing research, and research results aiming at agricultural non-point source pollution tracing are relatively few. According to the method, a two-dimensional river water quality model is utilized to calculate the contribution value of fixed point source emission to the concentration of the monitoring point, then the concentration contribution value of the agricultural non-point source in the downstream monitoring point is calculated according to the actually measured pollutant mass concentration value of the river water quality monitoring point and the river pollutant background value, finally the position coordinates of the agricultural non-point source are combined, the two-dimensional river water quality model is utilized to reversely calculate the strength of each agricultural non-point source, and then the water quality model is utilized to positively calculate the concentration contribution value and the proportion of each agricultural non-point source to the downstream monitoring point of the river. The method can effectively solve the problems of rapid estimation of the source intensity of the agricultural non-point source in the river basin and rapid tracing and quantitative analysis of the agricultural non-point source.
Description
Technical Field
The invention relates to the field of river basin non-point source pollution, in particular to a source intensity estimation and tracing method for river basin agricultural non-point source pollution.
Background
With the rapid development of social economy and the improvement of the living standard and the demands of people, the discharge amount of industrial and agricultural wastewater, domestic sewage and agricultural water is increased year by year, so that the river environment pollution is increased. According to investigation, river pollution mainly comes from four major categories of domestic sewage and wastewater, industrial wastewater, agricultural sewage and natural rainfall. The water environment deterioration problem caused by serious river pollution not only affects the normal development of society, but also seriously threatens the physical health of people and the ecological safety of living environment. Sudden water pollution accidents often have great influence on river water environment, and have great harmfulness and long-term influence. In order to prevent and reduce the loss caused by water pollution accidents, the environmental protection department needs to carry out daily water quality inspection work on the water areas in jurisdiction, and once the water quality is abnormal, the pollution source needs to be rapidly determined in the first time so as to pertinently formulate an emergency treatment scheme.
The water pollution can be divided into point source pollution and surface source pollution, the point source pollution refers to pollution caused by the discharge of dissolved and solid pollutants at a fixed sewage outlet, and mainly refers to pollution caused by the centralized discharge of domestic sewage, medical and industrial wastewater, overflow sewage and the like. The non-point source pollution refers to pollution caused by the fact that dissolved, solid and gaseous pollutants are converged into a water body along with precipitation or snow melting from a non-specific place. Such as pollution caused by the inflow of chemical fertilizers and pesticides applied in agricultural production into water body through rain wash.
The current river water pollution tracing method is mainly focused on the aspect of point source pollution tracing research, and the point source pollution tracing method mainly comprises a static tracing method and an active tracing method.
The static tracing method for the point source pollution mainly comprises the steps of identifying and expanding around source items, and the basic principle of the static tracing method can be summarized to solve the optimal control problem that the difference value between an actual water quality monitoring value and a model output value is minimum, namely inverting the pollutant discharge position, the discharge intensity and the discharge time by using the observation value of a fixed monitoring station according to the migration and transformation rule of pollutants in a river channel. The static traceability belongs to an inverse problem in environmental hydraulics and has nonlinearity and discomfort compared with the known pollution source and hydrologic water quality parameters to predict the diffusion condition and concentration distribution of river pollutants. The static tracing method is mainly divided into a deterministic method, a statistical method and an artificial intelligence method. The deterministic method comprises optimization methods such as a regularization method, a trial-and-error method, a least square method and a linear programming method, and the like, and the currently proposed deterministic method mainly comprises the steps of directly or indirectly constructing a traceability optimization model to find an inverse problem optimal solution, but because of single solving information and discomfort of the inverse problem, the deterministic method is easily interfered by an observation error and an error of the model, and a remarkable deviation exists in a traceability result. The statistical method mainly comprises a geoscience statistical method, a Bayes method, a reverse position probability density function-based method and the like, the statistical method can give out the range and probability information of the pollution source position, and compared with a deterministic method, the statistical method has better fault-tolerant capability and better anti-disturbance performance on the observation error and the model self error. Artificial intelligence methods include inversion methods based on genetic algorithms, based on simulated annealing algorithms, based on differential evolution algorithms, and based on neural networks.
The active tracing method for the point source pollution mainly utilizes the mobile equipment to carry corresponding sensors, and actively searches in a river pollution zone according to a set rule until the position of a discharge source is found. Compared with a static tracing method, the information acquired by the active tracing method is richer in variety and better in real-time performance, the motion state of the mobile device can be continuously adjusted according to various data, the environment adaptability and the anti-interference capability are good, and the pollution source position can be locked more accurately. Active traceability methods which have been proposed at present include chemical trending methods, information trending methods, machine vision assistance methods, model-based evaluation methods and the like. Russell and the like are inspired by ants searching for food, and a pollution source positioning method is provided, wherein a robot provided with a concentration sensor is utilized to search for a pollution source through Z-shaped movement. Russell is inspired by chemotaxis of microorganisms, and a vortex worm algorithm is provided, and can realize the circulating motion of a robot in an equilibrium concentration field so as to acquire more field source information.
The current river water pollution tracing method is mainly focused on the aspect of point source pollution tracing research, and the point source pollution is easy to trace and treat because the pollution source has only one or a few points. The agricultural non-point source pollution has the characteristics of wide distribution range, random formation process, multiple influencing factors and difficult monitoring mode, so that the determination of the agricultural non-point source pollution source is always a difficult problem of watershed agricultural pollution tracing and quantitative analysis, and the research results for agricultural non-point source pollution tracing are relatively less at present.
The invention discloses a source intensity estimation and tracing method for river basin agricultural non-point source pollutants, which aims at the actual emission characteristics of the river basin agricultural non-point source pollutants, wherein the agricultural non-point source pollutants mainly comprise total phosphorus TP, total nitrogen TN and the like. According to the method, a two-dimensional river water quality model is utilized to calculate the contribution value of fixed point source emission to the concentration of the monitoring point, then the concentration contribution value of the agricultural non-point source in the downstream monitoring point is calculated according to the actually measured pollutant mass concentration value of the river water quality monitoring point and the river pollutant background value, finally the position coordinates of the agricultural non-point source are combined, the two-dimensional river water quality model is utilized to reversely calculate the strength of each agricultural non-point source, and then the water quality model is utilized to positively calculate the concentration contribution value and the proportion of each agricultural non-point source to the downstream monitoring point of the river. The method can effectively solve the problems of rapid estimation of the source intensity of the agricultural non-point source in the river basin and rapid tracing and quantitative analysis of the agricultural non-point source.
Disclosure of Invention
The invention aims to provide a source intensity estimation and tracing method for river basin agriculture non-point source pollution. The object of the present invention is achieved by the following technique.
The method is provided for solving the problem of tracing the river basin agricultural non-point source pollution, and is convenient to express, and the agricultural non-point source total phosphorus TP is taken as an example, and other pollutant tracing methods are the same as TP. The basic principle of the invention is as follows: the actual measurement TP concentration value of the river downstream water quality monitoring point comprises three parts: the TP contribution value of the fixed point source emission to the monitoring point TP, the TP contribution value of the agricultural surface source emission to the monitoring point TP and the TP background concentration value can be obtained through monitoring, the TP contribution value of the agricultural surface source to the monitoring point TP can be obtained by subtracting the TP contribution value of the fixed point source emission to the monitoring point TP and the TP background value from the TP concentration value of the water quality monitoring point TP, then the agricultural surface source contribution of the monitoring point TP is used for back calculation to obtain the agricultural surface source emission intensity, and further the TP concentration contribution value and the TP contribution proportion of each agricultural surface source to the river water quality monitoring point TP are calculated.
Step 1: characteristic parameters such as a fixed point source, an agricultural surface source, background concentration, water quality monitoring points and the like in a research river basin are determined, and the characteristic parameters comprise the position P of a fixed point source discharge outlet i I=1, 2,3, …, I is the number of fixed point sources, and the fixed point sources have strong emission source S i I=1, 2,3, …, I; river basin inner face source position A j J=1, 2,3, …, J is the number of agricultural non-point sources; river background concentration C h The method comprises the steps of carrying out a first treatment on the surface of the Water quality monitoring point position W k K=1, 2,3, …, K is the number of water quality monitoring points, and the mass concentration of pollutant in the water quality monitoring points is C k ，k＝1,2,3,…,K；
Step 2: calculating the river background concentration C h ，C h Sewage with value of upstream monitoring point of research water areaDye mass concentration;
step 3: calculating the contribution value of the ith fixed point source to the pollutant mass concentration of the monitoring point k, wherein specific calculation formulas are shown in (1), (2), (3) and (4):
C ik (x,y,t)＝ΔC 1 +ΔC 2 +ΔC 3 +C h (1)
wherein C is ik The unit of the contribution value of the pollutant mass concentration of the ith fixed point source to the monitoring point k is mg/L; t is time, and the unit is s; x is x ik 、y ik The unit is m for the horizontal and vertical distance from the kth monitoring point to the ith fixed point source; u is the velocity component in the x direction in m/s; v is the velocity component in the y direction in m/s; d (D) x Is the dispersion coefficient in the x direction, and the unit is m 2 /s；D y Is the dispersion coefficient in the y direction, the unit is m 2 S; k is the degradation coefficient, and the unit is s -1 ；S i The emission rate of the pollutant is the ith fixed point source, and the unit is g/s; b is the distance between the sewage pipes and the bank; b is river width; n is the number of boundary reflections; h is river depth; ΔC 1 A mass concentration delta generated at that point for the true source; ΔC 2 The mass concentration increase produced at this point for the off-shore boundary reflection (off-shore virtual source); ΔC 3 The mass concentration increase generated at this point for far shore boundary reflection (far shore virtual source); c (C) h Is the river background concentration; the distance between the near-shore virtual source and the real source is 2nB, and the distance between the far-shore virtual source and the real source is 2nB-2nB; ΔC 1 、ΔC 2 、ΔC 3 Can be divided into 3 parts: a longitudinal convection diffusion term, a transverse convection diffusion term, and a degradation term;
step 4: the contribution value of the agricultural non-point source to the pollutant mass concentration of the monitoring point k is calculated, and a specific calculation formula is as follows:
wherein C is tk The unit of the contribution value of the J agricultural non-point sources to the pollutant mass concentration of the kth monitoring point is mg/L; c (C) jk The unit of the contribution value of the pollution mass concentration of the jth agricultural non-point source to the monitoring point k is mg/L; c (C) k The mass concentration of the pollutant actually monitored by the kth monitoring point is in mg/L; c (C) ik The contribution value of the pollutant mass concentration of the ith fixed point source to the monitoring point k is given; c (C) h Is the river background concentration;
step 5: the emission source intensity of the jth agricultural non-point source is calculated reversely, and in the process of tracing the agricultural non-point source, the focus is on the reverse calculation of the emission source intensity of the agricultural non-point source, and the monitoring point k measures the contribution value C of the agricultural non-point source in the pollutant concentration tk And the agricultural non-point source position coordinates are used as known information, the agricultural non-point source intensity is solved through a reverse model, A is assumed to be a change coefficient, the change coefficient is used for quantitatively describing the response change relation of the monitoring point k pollutant mass concentration to the agricultural non-point source j, and the agricultural non-point source intensity is Q j J=1, 2,3, …, J, and the agricultural non-point source contribution value in the measured concentration of each water quality monitoring point is the sum of the non-point source J contribution concentrations, and the process can be described as:
A·Q＝C tk (6)
expanding equation (6) into a matrix form yields:
wherein, the calculation formula of the change coefficient A is as follows:
wherein the meaning of the variables in the formula is consistent with the formulas (1) to (4)，A kj For monitoring the response change coefficient of the mass concentration of the pollutant at point k to the agricultural non-point source j, x jk 、y jk The unit is m for the horizontal and vertical distance from the kth monitoring point to the jth agricultural non-point source;
when the number K of the water quality monitoring points is equal to the number J of the agricultural non-point sources, the equation set (7) can be directly solved to obtain the emission source intensity Q of each agricultural non-point source j If the number K of the water quality monitoring points is larger than the number J of the agricultural non-point sources, a group of overdetermined equations is formed, and the constructed overdetermined equations can be solved by a least square method; in a practical scenario, the number K of general water quality monitoring points is equal to the number J of agricultural non-point sources;
step 6: agricultural non-point source analysis of water quality monitoring points k, the agricultural non-point source contribution value of each water quality monitoring point is the linear superposition sum of the pollutant concentrations contributed by each agricultural non-point source, and the emission source intensity Q of each agricultural non-point source can be calculated through the step 5 j Then the concentration contribution value C of the jth agricultural surface source to the monitoring point k can be calculated in the forward direction through the two-dimensional water quality model formulas (1) to (4) jk The expression can be simplified as:
C jk ＝f(Q j ；Para) (8)
c in the formula jk The contribution value of the j-th agricultural surface source to the pollutant concentration of the monitoring point k is calculated, f is a two-dimensional river water quality model, para is a water flow water quality parameter, so that the agricultural surface source sources of different monitoring points and the contribution value of the agricultural surface source sources to the concentration of the monitoring point can be calculated, and the contribution proportion P of the different agricultural surface sources j to the pollutant concentration of the monitoring point k can be calculated jk The calculation formula is as follows:
P jk ＝C jk /C k (9)
the invention has the beneficial effects that:
the current river water pollution tracing method is mainly concentrated on the aspect of point source pollution tracing research, and the point source pollution is relatively easy to trace and treat because the pollution source has one or a few points; the agricultural non-point source pollution has the characteristics of wide distribution range, random formation process, various influencing factors and difficult monitoring mode, and the analysis of the agricultural non-point source pollution source is always a difficult problem of tracing the river basin agricultural pollution, and the invention provides a source intensity estimation and tracing method of the river basin agricultural non-point source pollution aiming at the actual emission characteristics of the river basin agricultural non-point source pollution, so that the problems of tracing and quantitative analysis of the river basin agricultural pollution are solved;
the invention can quantitatively calculate the contribution ratio of the fixed point source, the agricultural non-point source and the background concentration in the actual measurement pollutant concentration of the river water quality monitoring point;
the method can quantitatively calculate the specific sources of the agricultural non-point sources in the actual measurement concentration of the river water quality monitoring point and the contribution value of each agricultural non-point source to the monitoring point.
Drawings
FIG. 1 is a schematic flow chart of a source intensity estimation and tracing method of river basin agriculture non-point source pollution;
FIG. 2 is a diagram showing the spatial relationship among a fixed point source, an agricultural non-point source and water quality monitoring points in an embodiment of the present invention。
Detailed Description
In order to make the objects, features and advantages of the present invention more comprehensible, embodiments accompanied with figures are described in detail below;
fig. 1 is a schematic flow chart of a source intensity estimation and tracing method for river basin agricultural non-point source pollution in the embodiment, and a specific implementation process includes the following steps: _cell
Step 1: characteristic parameters such as a fixed point source, an agricultural surface source, background concentration, water quality monitoring points and the like in a research river basin are determined, and the characteristic parameters comprise the position P of a fixed point source discharge outlet i I=1, 2,3, …, I is the number of fixed point sources, i=3, the fixed point source discharge source is strong S i I=1, 2,3, …, I; river basin inner face source position A j J=1, 2,3, …, J is the number of agricultural non-point sources, j=3; river background concentration C h The method comprises the steps of carrying out a first treatment on the surface of the Water quality monitoring point position W k K=1, 2,3, …, K is the number of water quality monitoring points, k=4, and the mass concentration of total phosphorus TP in the pollutant of the water quality monitoring points is C k K=1, 2,3, …, K; the spatial relationship diagram of the fixed point source, the agricultural non-point source and the water quality monitoring point is shown in fig. 2, and the specific positions are shown in table 1:
TABLE 1 relative position coordinates of fixed Point Source, agricultural non-point Source and Water quality monitoring Point
| Sequence number | Type(s) | Relative position coordinates x/m | Relative position coordinates y/m |
| 1 | Fixed point source P 1 | 300 | 100 |
| 2 | Fixed point source P 2 | 370 | 0 |
| 3 | Fixed point source P 3 | 450 | 0 |
| 4 | Agricultural non-point source A 1 | 150 | 100 |
| 5 | Agricultural non-point source A 2 | 250 | 0 |
| 6 | Agricultural non-point source A 3 | 350 | 100 |
| 7 | Water quality monitoring point W 1 | 800 | 70 |
| 8 | Water quality monitoring point W 2 | 850 | 30 |
| 9 | Water quality monitoring point W 3 | 900 | 70 |
| 4 | Water quality monitoring point W 4 | 950 | 30 |
Step 2: calculating the river background concentration C h ，C h Take the value as the TP mass concentration of the upstream monitoring point of the research water area, C h ＝0.15mg/L；
Step 3: calculating the contribution value of the ith fixed point source to the pollutant mass concentration of the monitoring point k, wherein specific calculation formulas are shown in (1), (2), (3) and (4), and the contribution value of the fixed point source emission to the actually measured TP concentration of the water quality monitoring point is shown in Table 2:
table 2 contribution value of fixed Point Source discharge to Water quality monitoring Point TP
Step 4: calculating a contribution value of an agricultural non-point source to the pollutant mass concentration of a monitoring point k, wherein the specific calculation adopts a formula (5), and the calculation result is summarized in a table 3;
TABLE 3 actual measurement concentration of water quality monitoring point TP and contribution value of fixed point source and agricultural non-point source (mg/L)
| Water quality monitoring point | W 1 | W 2 | W 3 | W 4 |
| Measured concentration C k | 7.42 | 6.93 | 7.19 | 5.64 |
| Background concentration C h | 0.15 | 0.15 | 0.15 | 0.15 |
| FixingPoint source C ik | 2.51 | 4.03 | 2.89 | 3.02 |
| Agricultural non-point source C jk | 4.76 | 2.75 | 4.15 | 2.47 |
Step 5: reversely calculating the emission source intensity of the jth agricultural non-point source by using formulas (6) - (7), wherein the emission source intensity of the 3 agricultural non-point sources is Q 1 ＝1382.45mg/s，Q 2 ＝1198.43mg/s，Q 3 ＝1296.68mg/s；
Step 6: agricultural non-point source analysis of the water quality monitoring point k is carried out, and the contribution value C of the j-th agricultural non-point source to the actually measured concentration of the monitoring point k is calculated in the forward direction through two-dimensional water quality model formulas (1) to (4) jk Contribution value and proportion P of different agricultural non-point sources j to pollutant concentration of monitoring point k jk Listed in table 4:
TABLE 4 contribution values (mg/L) and ratios (%)
The present invention is not limited to the above-mentioned embodiments, and any person skilled in the art should make changes or substitutions within the scope of the present invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (6)
1. The source intensity estimation and tracing method for river basin agricultural non-point source pollution is characterized by comprising the following steps of:
step 1: determining characteristic parameters such as a fixed point source, an agricultural surface source, background concentration, water quality monitoring points and the like in a research river basin;
step 2: select river background concentration C h ，C h The value is the pollutant mass concentration of the upstream monitoring point of the research water area;
step 3: calculating a contribution value of fixed point source emission to the concentration of the monitoring point by using a two-dimensional river water quality model;
step 4: calculating the concentration contribution value of the agricultural non-point source in the downstream monitoring point according to the actually measured pollutant mass concentration value of the river water quality monitoring point and the river pollutant background value;
step 5: combining the position coordinates of the agricultural non-point sources, and reversely calculating the strength of each agricultural non-point source by using a two-dimensional river water quality model;
step 6: and (5) forward calculating the concentration contribution value and the proportion of each agricultural non-point source to the downstream monitoring point of the river by using the water quality model.
2. The method for estimating and tracing the source intensity of river basin agricultural non-point source pollution according to claim 1, wherein in step 1, characteristic parameters such as a fixed point source, an agricultural non-point source, a background concentration, a water quality monitoring point and the like are determined in a research river basin, and specifically the method comprises a fixed point source discharge port position P i I=1, 2,3, …, I is the number of fixed point sources, and the fixed point sources have strong emission source S i I=1, 2,3, …, I; river basin inner face source position A j J=1, 2,3, …, J is the number of agricultural non-point sources; river background concentration C h ，C h The value is the pollutant mass concentration of the upstream monitoring point of the research water area; water quality monitoring point position W k K=1, 2,3, …, K is the number of water quality monitoring points, and the mass concentration of pollutant in the water quality monitoring points is C k ，k＝1,2,3,…,K。
3. The method for estimating and tracing the source intensity of river basin agricultural non-point source pollution according to claim 1, wherein in the step 3, the contribution value of fixed point source emission to the concentration of the monitoring point is calculated by using a two-dimensional river water quality model, and the specific steps are as follows: calculating the contribution value of the ith fixed point source in the research flow field to the pollutant mass concentration of the water quality monitoring point k, wherein specific calculation formulas are shown in (1), (2), (3) and (4):
C ik (x,y,t)＝ΔC 1 +ΔC 2 +ΔC 3 +C h (1)
wherein C is ik The unit of the contribution value of the pollutant mass concentration of the ith fixed point source to the monitoring point k is mg/L; t is time, and the unit is s; x is x ik 、y ik The unit is m for the horizontal and vertical distance from the kth monitoring point to the ith fixed point source; u is the velocity component in the x direction in m/s; v is the velocity component in the y direction in m/s; d (D) x Is the dispersion coefficient in the x direction, and the unit is m 2 /s；D y Is the dispersion coefficient in the y direction, the unit is m 2 S; k is the degradation coefficient, and the unit is s -1 ；S i The emission rate of the pollutant is the ith fixed point source, and the unit is g/s; b is the distance between the sewage pipes and the bank; b is river width; n is the number of boundary reflections; h is river depth; ΔC 1 A mass concentration delta generated at that point for the true source; ΔC 2 The mass concentration increase produced at this point for the off-shore boundary reflection (off-shore virtual source); ΔC 3 The mass concentration increase generated at this point for far shore boundary reflection (far shore virtual source); c (C) h Is the river background concentration; the distance between the near-shore virtual source and the real source is 2nb, and the distance between the far-shore virtual source and the real source is2nB-2nb；ΔC 1 、ΔC 2 、ΔC 3 Can be divided into 3 parts: longitudinal convective diffusion term, transverse convective diffusion term, and degradation term.
4. The method for estimating and tracing the source intensity of river basin agricultural non-point source pollution according to claim 1, wherein in the step 4, the concentration contribution value of the agricultural non-point source in the downstream monitoring point is calculated according to the pollutant mass concentration value actually measured by the river water quality monitoring point and the river pollutant background value, and the specific steps are as follows:
the contribution value of the agricultural non-point source to the pollutant mass concentration of the monitoring point k is calculated, and a specific calculation formula is as follows:
wherein C is tk The unit of the contribution value of the J agricultural non-point sources to the pollutant mass concentration of the kth monitoring point is mg/L; c (C) jk The unit of the contribution value of the pollution mass concentration of the jth agricultural non-point source to the monitoring point k is mg/L; c (C) k The mass concentration of the pollutant actually monitored by the kth monitoring point is in mg/L; c (C) ik The contribution value of the pollutant mass concentration of the ith fixed point source to the monitoring point k is given; c (C) h Is the river background concentration.
5. The method for estimating and tracing the source intensity of river basin agricultural non-point source pollution according to claim 1, wherein in step 5, the source intensity of each agricultural non-point source is calculated reversely by using a two-dimensional river water quality model in combination with the position coordinates of the agricultural non-point source, and the method comprises the following specific steps:
step 51: contribution value C of agricultural non-point source in actual measurement of pollutant concentration at monitoring point k tk And the agricultural non-point source position coordinates are used as known information, and the A is assumed to be a change coefficient to quantitatively describe the response change relation of the pollutant mass concentration of the monitoring point k to the agricultural non-point source j, wherein the agricultural non-point source strength is Q j J=1, 2,3, …, J, and the agricultural non-point source contribution value in the measured concentration of each water quality monitoring point is the sum of the superposition of the non-point source J contribution concentrationsThe process can be described as:
A·Q ＝C tk (6)
expanding equation (6) into a matrix form yields:
wherein, the calculation formula of the change coefficient A is as follows:
wherein the meanings of variables are consistent with the formulas (1) to (4), A kj For monitoring the response change coefficient of the mass concentration of the pollutant at point k to the agricultural non-point source j, x jk 、y jk The unit is m for the horizontal and vertical distance from the kth monitoring point to the jth agricultural non-point source;
step 52: k is larger than or equal to J, when the number K of water quality monitoring points is equal to the number J of agricultural non-point sources, the equation set (7) can be directly solved in a column-wise manner to obtain the emission source intensity Q of each agricultural non-point source j If the number K of the water quality monitoring points is larger than the number J of the agricultural non-point sources, a group of overdetermined equations is formed, and the constructed overdetermined equations can be solved by a least square method.
6. The method for estimating and tracing the source intensity of river basin agricultural non-point source pollution according to claim 1, wherein in the step 6, the concentration contribution value and the proportion of each agricultural non-point source to the downstream monitoring point of the river are calculated forward by using a water quality model, and the specific steps are as follows:
step 61: agricultural non-point source analysis of water quality monitoring points k, wherein the agricultural non-point source contribution value of each water quality monitoring point is the concentration of pollutants contributed by each agricultural non-point sourceThe linear superposition sum can calculate the emission source intensity Q of each agricultural non-point source through the step 5 j ；
Step 62: then the concentration contribution value C of the jth agricultural non-point source to the monitoring point k can be calculated in the forward direction through the two-dimensional water quality model formulas (1) to (4) jk The expression can be simplified as:
C jk ＝f(Q j ；Para) (8)
c in the formula jk The contribution value of the j-th agricultural non-point source to the pollutant mass concentration of the monitoring point k is represented by f, the two-dimensional river water quality model is represented by f, and the water quality parameter of water flow is represented by Para;
step 63: thereby solving the agricultural non-point source sources of different monitoring points and the concentration contribution values to the monitoring points, and the contribution proportion P of the different agricultural non-point sources j to the pollutant concentration of the monitoring point k jk The calculation formula is as follows:
P jk ＝C jk /C k (9)。
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310671215.5A CN117275602A (en) | 2023-06-07 | 2023-06-07 | Source intensity estimation and tracing method for river basin agricultural non-point source pollution |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310671215.5A CN117275602A (en) | 2023-06-07 | 2023-06-07 | Source intensity estimation and tracing method for river basin agricultural non-point source pollution |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117275602A true CN117275602A (en) | 2023-12-22 |
Family
ID=89199671
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310671215.5A Pending CN117275602A (en) | 2023-06-07 | 2023-06-07 | Source intensity estimation and tracing method for river basin agricultural non-point source pollution |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117275602A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117408856A (en) * | 2023-12-13 | 2024-01-16 | 浙江省生态环境监测中心(浙江省生态环境信息中心) | Pollutant tracing method and device, storage medium and electronic equipment |
| CN117829037A (en) * | 2024-03-06 | 2024-04-05 | 北京益普希环境咨询顾问有限公司 | Groundwater pollution source tracing identification method and system |
-
2023
- 2023-06-07 CN CN202310671215.5A patent/CN117275602A/en active Pending
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117408856A (en) * | 2023-12-13 | 2024-01-16 | 浙江省生态环境监测中心(浙江省生态环境信息中心) | Pollutant tracing method and device, storage medium and electronic equipment |
| CN117829037A (en) * | 2024-03-06 | 2024-04-05 | 北京益普希环境咨询顾问有限公司 | Groundwater pollution source tracing identification method and system |
| CN117829037B (en) * | 2024-03-06 | 2024-05-14 | 北京益普希环境咨询顾问有限公司 | Groundwater pollution source tracing identification method and system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN117275602A (en) | Source intensity estimation and tracing method for river basin agricultural non-point source pollution | |
| CN105956664B (en) | A kind of river point source pop-up threat source tracing method | |
| Zheng et al. | Prediction of harmful algal blooms in large water bodies using the combined EFDC and LSTM models | |
| Mirzaei et al. | Comparison of interpolation methods for the estimation of groundwater contamination in Andimeshk-Shush Plain, Southwest of Iran | |
| Kisi et al. | River suspended sediment modelling using a fuzzy logic approach | |
| Kim et al. | Augmentation of limited input data using an artificial neural network method to improve the accuracy of water quality modeling in a large lake | |
| Nourani et al. | Estimation of Suspended Sediment Load Using Artificial Intelligence‐Based Ensemble Model | |
| Khalil et al. | A statistical approach for the assessment and redesign of the Nile Delta drainage system water-quality-monitoring locations | |
| Zhang et al. | Water quality analysis and prediction using hybrid time series and neural network models | |
| EP4303785A1 (en) | Reservoir flow rate regulating method and apparatus, electronic device, and storage medium | |
| Abba et al. | Neuro-fuzzy ensemble techniques for the prediction of turbidity in water treatment plant | |
| CN111753417B (en) | Parameter sensitivity analysis method based on uncertainty separation | |
| Ferdowsi et al. | A new combination approach for optimal design of sedimentation tanks based on hydrodynamic simulation model and machine learning algorithms | |
| Emami et al. | Modeling groundwater quality using three novel hybrid support vector regression models | |
| Kılıç et al. | Water quality prediction of Asi River using fuzzy based approach | |
| Kim et al. | Three-dimensional augmentation for hyperspectral image data of water quality: an integrated approach using machine learning and numerical models | |
| Wang et al. | Prediction method of cyanobacterial blooms spatial-temporal sequence based on deep belief network and fuzzy expert system | |
| Plew et al. | CLUES Estuary-a tool for predicting estuary water quality | |
| Busari et al. | Chlorophyll a predictions in a Piedmont Lake in Upstate South Carolina using machine-learning approaches | |
| Ayanshola et al. | Modelling of turbidity variation in a water treatment plant | |
| Wan et al. | Water environment mathematical model mathematical algorithm | |
| Ozbay et al. | Investigation of cluster analysis in surface water in Yesilirmak river | |
| Bomani | SOFT COMPUTING APPLICATION TO PREDICT IRRIGATION WATER QUALITY INDEX, CASE STUDY OF OUED HAMMAM NORTH-EAST ALGERIA | |
| CN115330132B (en) | Method for water quality distribution reverse-time inversion of wide and shallow river in sudden pollution accident | |
| Guzel et al. | Technical Report No: ND19-02 |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |