CN112149871B - Pollutant point source analysis method based on combination of GIS (geographic information System) space statistics and random simulation - Google Patents
Pollutant point source analysis method based on combination of GIS (geographic information System) space statistics and random simulation Download PDFInfo
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Abstract
The invention belongs to the field of groundwater environment simulation, and discloses a pollutant point source analysis method based on combination of GIS (geographic information System) space statistics and random simulation, which is used for point source pollutant evaluation and pollution source analysis in urban dense areas. Firstly, establishing a numerical model of the migration of pollutants in underground water, providing key information of hydrology and geology and land utilization conditions for judging the easy pollution by adopting an evidence weighted analysis method based on a GIS spatial statistics technology, and further evaluating the environmental quality and the vulnerability of the underground water; and then analyzing a pollutant capture area or a pollutant source area by adopting a random simulation technology based on a zero-space Monte Carlo particle tracking method. The method provided by the invention can effectively identify the fragile area of groundwater pollution and analyze and define a potential pollution point source, can provide regional key information for long-term monitoring of groundwater pollution, and becomes an important tool for making a groundwater management and protection strategy by a non-expert decision maker.
Description
Technical Field
The invention relates to a pollutant point source analysis method based on GIS space statistics and random simulation, and belongs to the field of underground water environment.
Background
On a global scale, the rate of urban development and population migration from rural to urban areas has increased dramatically over the last century, with statistics showing that about 54% of the world population lives in urban areas in 2016 and that this data will rise to about 66% by 2050. Under the background of urbanization development, social economy continues to rapidly increase, and groundwater plays a key role in promoting the urbanization development as an important fresh water resource. However, as urban populations grow and human activities grow, available groundwater resources are of increasing concern, both quantitatively and qualitatively. Intensive water supply requirements in urban and industrial development place tremendous pressure on the quality and quantity of underground water, on the one hand, the release of pollution sources generated by long-term industrial activities further deteriorates the quality of underground water, and on the other hand, the unsustainable management of underground water resources has serious social and economic impacts, besides causing huge impacts on the environment, threatening human health and increasing the cost of water withdrawal. Therefore, in order to adapt to rapid urbanization and the increasing demand of safe drinking water in urban areas, the future adjustment is how to make a new scientific groundwater development and utilization strategy so as to more fully manage and protect groundwater resources.
In order to solve the above problems, numerical simulation and a spatial statistical method based on the GIS are important means and tools for evaluating the environmental quality of underground water. Particle tracking methods are commonly used to simulate convective dominant pollutant transport processes and identify pollution interception or pollution source zones by reverse time reverse particle tracking along the streamlines. Given the uncertainty of hydrogeological parameters (such as permeability coefficients), conventional particle tracking methods are often combined with the zero-space monte carlo method to seek to account for the effects of permeability coefficient uncertainty on the identification of potential pollution sources. The GIS spatial statistical method based on the evidence weighted analysis technology is successfully applied to aquifer pollution vulnerability evaluation based on key hydrogeology and land utilization information. The invention provides a pollutant point source analysis method based on combination of GIS space statistics and random simulation, which is used for point source pollutant evaluation and analysis in urban dense areas. Firstly, establishing a numerical model of the migration of pollutants in underground water, providing key information for judging the hydrology and geology which are easy to pollute and the utilization condition of land by adopting an evidence weighted analysis method based on a GIS spatial statistic technology, and further evaluating the environmental quality and the vulnerability of the underground water; the random simulation adopts a zero-space Monte Carlo particle tracking method and is used for a pollutant capture area or a pollutant source area. The method provided by the invention can effectively identify the vulnerable pollution area and define the potential pollution source, can provide the key information of the area for underground water pollution monitoring, and becomes an important tool for non-expert decision makers to make underground water management and protection strategies.
Disclosure of Invention
The purpose of the invention is as follows: the invention aims to solve the technical problem of providing a pollutant point source analysis method based on the combination of GIS space statistics and random simulation.
The technical scheme is as follows: the invention adopts the following technical scheme for solving the technical problems: a pollutant point source analysis method based on GIS space statistics and random simulation is disclosed, which comprises the following steps:
the method comprises the steps of firstly, constructing an underground water flow model based on an MODFLOW underground water flow simulation program, correcting to obtain a permeability coefficient field, and simulating to obtain the underground water level and the flow field of a research area; establishing a deterministic pollutant migration simulation model by adopting an MT3DMS solute migration simulation program based on an underground water flow model, delineating the potential position of a pollution source based on a land utilization change related graph in a research area, simulating the space-time distribution of the pollutants in an aquifer in the next preset year, and taking the simulated pollutant space-time distribution data as concentration observation data for subsequently identifying the pollution source;
secondly, identifying and capturing a hydraulic interception area of the pollution plume by analyzing the pollutant concentration distribution of the research area simulated and calculated in the first step under the condition of supposing that the position of the pollution point source is uncertain;
thirdly, evaluating the interaction relation between the spatial distribution of the hydraulic capture area influenced by the pollution plumes and potential factors influencing the vulnerability of the pollution of the diving aquifer by adopting an evidence weighted analysis method, taking the observation well position in the hydraulic capture area as a training point by adopting an evidence weighted analysis technology, calculating the influence degree of a priori probability and a specific variable on the pollution, wherein the specific variable is also a prediction factor, the prediction factor comprises a hydrogeology factor and a land utilization change factor, each prediction factor is summarized into different values, the spatial association degree of each prediction factor and the pollution is quantized by carrying out spatial statistical analysis, a posterior probability graph based on the influence weight of each prediction factor is generated, and a high-value area with the calculated posterior probability more than 80% is a pollution vulnerable area;
step four, the influence of the variability of the permeability coefficient field on the distribution of the pollution paths can be estimated and explored by adopting a zero-space Monte Carlo particle tracking method, observation wells in a hydraulic capture area of pollution plumes are identified and captured to serve as initial positions of released particles and pollution path simulation, a plurality of groups of pollution paths are obtained based on the simulation and are used for describing a frequency graph of the particles passing through a model grid unit, and the probability that an area where a high frequency value is located contains a pollution point source is high;
and step five, combining the prediction factor influence weight posterior probability distribution information acquired by different methods in the step three and the step four with the frequency distribution information of the particle tracing crossing region, and fusing the combined information into single information to be output so as to identify the pollution source information, and simultaneously comparing the pollution source identification result with the calculation result of the deterministic pollution migration model in the step one, so that the reliability of describing a pollution point source and a pollution fragile region based on a GIS space statistics and random simulation combined method can be accurately evaluated, and the closer the results of the two are, the stronger the reliability of the method provided by the invention is.
Further, in the first step, international MODFLOW and MT3DMS programs are adopted to simulate the underground water flow and pollutant solute transport process of the research area, and model identification technology is adopted to correct hydrogeological parameters of the underground aquifer of the research area.
Further, in the third step, an evidence weighted analysis method is adopted to evaluate the interaction relationship between the space distribution of the hydraulic capture area influenced by the pollution plumes and potential factors influencing the vulnerability of the pollution of the diving aquifer, and the method comprises the following steps:
step 1: selecting a pollutant solute monitoring network of a research area, observing a horizon as a diving aquifer influenced by pollutants, locking a monitoring well influenced by pollution plumes in the monitoring network based on the time-space distribution of the pollutants in the next preset year simulated and calculated in the step one, determining the monitoring well as a training point for weighted analysis of evidence, and setting the rest monitoring wells which are not influenced by the pollution plumes as control points for later algorithm verification;
step 2: determining main control factors, namely prediction factors, influencing the migration and distribution of pollution plumes in a research area, wherein the prediction factors comprise hydrogeological factors: underground water level buried depth, underground water flow rate and supply amount, potential positions of pollution point sources, and converting each prediction factor into grid map data according to the horizontal grid subdivision size of the model;
step 3; randomly generating training point subsets by adopting a training point reduction tool in an ArcGIS spatial data model base ArcSDM, wherein the number of subset monitoring points is 75% of the total number of the training points determined in the step 1, ensuring that the inside of each subdivision unit area contains at most 1 training point, then carrying out evidence weighted analysis on each group of training point subsets by adopting a prediction factor set in the step 2, outputting a posterior probability graph of each group of training subsets, and representing that the area is more easily influenced by the interception of pollution plumes when the posterior probability is higher;
wherein, EM is the integrated prediction result, AUCi represents the success rate value of the ith subset prediction output result Mi, and n is the number of the training subsets. Reclassifying the integrated prediction results into five levels of posterior probabilities based on a geometric interval method, further dividing the region affected by the pollution plume into five levels: 0.9 to 1.0 is excellent; preferably 0.8 to 0.9; 0.7 to 0.8 percent; 0.6-0.7 is common; 0.5-0.6 difference.
Further, in the fourth step, the influence of the variability of the permeability coefficient field on the pollution path distribution can be estimated and explored by adopting a zero-space monte carlo particle tracking method, and the specific method is as follows:
step 1: randomly generating 500 aquifer permeability coefficient fields for realization, releasing pollution particles in the monitoring well influenced by the pollution plume determined in the step two to serve as an initial position of pollution path simulation, performing reverse particle tracking simulation by adopting an MODPATH program, and calculating a pollution path under the realization of each group of permeability coefficients;
step 2: counting the number of particles passing through a single model unit in each simulation, thereby calculating the total number n of the particles passing through the single model unit in all the simulations i,j And the crossing frequency, i, j represents the ith row and the jth column of the subdivision unit, and the calculation formula of the particle crossing frequency of each model unit is as follows:
wherein n is tot Is the total number of particles released, n sim Representing the number of simulations;
and 3, step 3: dividing the crossing frequency value obtained in the step 2 into five grades: greater than 60% high frequency region; 41% -60% higher frequency region; 21% -40% medium frequency region; 5% -20% of low frequency region; less than 5% of the very low frequency region and plotting the cross-over frequency of the region of interest, higher frequency values indicate a higher probability of containing a point source of contamination.
Has the advantages that: compared with the prior art, the technical scheme of the invention has the following beneficial technical effects:
the invention breaks through the traditional point source analysis method only considering single factor, provides a point source pollutant evaluation and pollution source analysis method in urban dense areas which integrates the GIS-based space statistical technology and adopts the evidence weighted analysis method and the random simulation technology based on the zero-space Monte Carlo particle tracking method, compares and analyzes the posterior probability distribution information of the prediction factor influence weights obtained by different methods and the frequency distribution information of the particle tracing crossing areas, and finally integrates the information into key output information which can effectively identify the fragile area of groundwater pollution and analyze and demarcate the potential pollution point source, thereby becoming an important tool for a non-expert decision maker to make groundwater management and protection strategy.
Drawings
FIG. 1 is a flow chart of a pollutant point source analysis technology based on GIS spatial statistics and random simulation;
FIG. 2 is a flow of an evidence weighted analysis technique;
FIG. 3 is a posterior probability map based on evidence weighted analysis. The posterior probability is divided into one grade to five grades, and the probability of finding the polluted influence area is represented;
fig. 4 is a graph of the frequency of the contaminant particles crossing each model cell. The high frequency region depicts the source of the potential contamination points.
FIG. 5 is a graph (a) showing the possible spatial distribution regions of a point source of contamination and the regions where the probability of the contamination plume being affected is large; (b) the spatial distribution of the pollution streamlines and the simulated pollution plume are shown, where the location of the letters represents the source of the pollution point used for the simulation.
Detailed Description
The technical process of the invention for analyzing the point source of the pollutant based on the combination of GIS space statistics and random simulation is shown in figure 1. Firstly, constructing an underground water flow and pollutant model based on MODFLOW and MT3DMS underground water flow and solute migration simulation programs, determining a pollution point source (source intensity space-time value) by adopting a random and subjective method based on a study area land utilization change related diagram, simulating the space-time distribution of pollutants in an aquifer in the past 30 years, and assuming concentration observation data for subsequently identifying the pollution source; carrying out spatial statistical analysis by adopting an evidence weighting technology to quantify the spatial correlation degree of each prediction factor and the pollution occurrence, and generating a posterior probability graph based on the influence weight of each prediction factor, wherein a posterior probability high-value area is an area more likely to be influenced by pollution plumes; estimating the influence of the variability of the permeability coefficient field on the pollution path distribution by adopting a zero-space Monte Carlo particle tracking method, and using the method for depicting a frequency graph of the particles passing through the independent unit, wherein the probability that a high-frequency value area contains a pollution point source is high; and combining the information acquired by different methods, and fusing the information into single information to be output so as to identify the pollution source information.
The technical solution of the present invention is further illustrated by the following specific examples:
2.1 case analysis
The method adopts a pollution example disclosed by the American geological survey bureau as a research case, and the simulation calculation area is located in the Massachusetts Military protection area (MMR) of the northeastern China, which is established at the beginning of the 20 th century and has an area of about 89km 2 And is located near the Town of Fallcous (Town of Falmouth) of the corner of Ked (Cape Cod). The study was located in the Chemical-Spill 10 (CS-10) region of the south-east corner of MMR, and was used primarily as a feeding base for the united states air force. The area of the CS-10 polluted source region is about 0.15km 2 The main component of the contaminant is Trichloroethylene (TCE). The pollution plume range of the underground water is about 5km long and 2km wide, the deepest depth reaches 43m, the average depth reaches 37m below the ground surface, and the average depth reaches 18m below the diving surface.
The water flow model of the CS-10 area is established on the basis of MODFLOW, the whole research area is divided into 159 columns, 161 rows and 21 layers, and the total simulation area is 57km 2 . The step size in the horizontal direction is 34m, the step size is larger as the step size is closer to the boundary, the thickness change of each layer in the vertical direction is larger, the minimum can be smaller than 1.5m, and the maximum can be larger than 15 m. The lateral boundary is a given water head boundary and is obtained by calculating and then interpolating a regional water flow model; the upper part is a fixed flow boundary, and the change range of the supply amount is 0.41-0.86 cm/yr; the lower water barrier is a zero flow boundary. The permeability coefficient K in the water flow model is obtained by interpolating the actually measured value, the variation range is large, the permeability coefficient K can be smaller than 3m/d in a silt aquifer, and the permeability coefficient K can be larger than 91m/d in a coarse sand aquifer. Groundwater currents in this region flow mainly in a horizontal direction from south to southwest. The hydraulic slope of the level averages 0.001. The solute transport model is based on MT3 DMS. The model adopts the same mesh subdivision as a MODFLOW water flow model, and the lateral direction and the vertical direction are fixed flux boundaries. On the boundary, the diffusion term is not considered but only the convection term is considered, and if the flow rate is 0, the solute transport is also equal to 0.
TABLE 1 relevant input parameters in CS-10 study area groundwater flow and solute transport models
2.2 evidence weighting method analysis posterior probability results
The statistical and physical relationship between the prediction factors (groundwater flow speed, groundwater level burial depth, replenishment quantity and land use change) and the spatial distribution of the pollution plume is significant through analysis, and a posterior probability map (fig. 3) can be generated through GIS spatial analysis based on evidence weighting technology. By calculating a weighted average based on the AUC statistics, the prediction probabilities of the 20 posterior probability maps are combined into one set model, which can more accurately represent the contribution of the prediction output. The ensemble model is reclassified for five classes of posterior probabilities, with the range of the fifth class of probabilities defining the regions that are more likely to be affected by the contaminant plume and the first class of regions indicating a low probability of finding a point source of contamination. As shown in fig. 3, the maximum probability value mainly affects the southwest of the study area.
2.3 null space Monte Carlo particle tracking method particle crossing probability results
While deterministic models can invert a set of parameter sets that minimize error variance by correction, the implication of applying basic particle tracking methods to a single correction model is unexplored with respect to the effect of parameter uncertainty and can produce misleading results due to the ambiguity of the inversion. In order to solve the problem, a particle tracking method is combined with the zero-space Monte Carlo simulation, uncertainty of a model prediction simulation result is introduced, and the influence of various realizations of a permeability coefficient field on pollution path distribution is researched.
A particle-crossing frequency map is generated by calculating the particle frequency across each model cell, followed by reclassification, into five levels, each reflecting a particular range of frequency values expressed in percentage (fig. 4). Although a high percentage of frequencies indicates a high probability (> 60%) of finding potential source regions, it must be emphasized that every cell contained in the strip should be considered as a potential region for contaminant release, even those with a low probability (< 20%).
2.4 two methods information merging and analysis
The posterior probability information obtained by the evidence weighting analysis is merged with the particle crossing probability information obtained by the zero-space Monte Carlo particle tracking method to generate an integrated probability distribution map, which comprises an area with high probability of being intercepted by the pollution plume and an area more likely to contain the pollution point source (figure 4). Comparison of the frequency grading obtained by the zero-space monte carlo particle tracking method with the results of simulating pollution point sources shows that the method is very effective in capturing certain pollution source regions. The strips correctly highlight the presence of the point sources of contamination "a", "c", "d" and "g" (frequency of 5% -40%), whereas the location of the point source of contamination "b" is in a low frequency region (< 5%), resulting in the inability to capture and identify the location of unknown sources. Of all simulated pollution point sources, only the pollution source "f" and the corresponding pollution plume are not fully delineated by the path lines, which move in different directions, reflecting the northeast as a potential source of pollution.
Even if it is difficult to accurately determine the source of the contamination in some areas, the frequency map accurately depicts the extent of the simulated plume and indicates that the monitoring grid is not well covered and may contain contamination sources (e.g., the west and northeast sectors). This information is very helpful for the decision maker to optimize the spatial configuration of the monitoring network.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
Claims (4)
1. A pollutant point source analysis method based on GIS space statistics and random simulation is characterized by comprising the following steps:
the method comprises the steps of firstly, constructing an underground water flow model based on an MODFLOW underground water flow simulation program, correcting to obtain a permeability coefficient field, and simulating to obtain the underground water level and the flow field of a research area; establishing a deterministic pollutant migration simulation model by adopting an MT3DMS solute migration simulation program based on an underground water flow model, delineating the potential position of a pollution source based on a land utilization change related graph in a research area, simulating the space-time distribution of the pollutants in an aquifer in the next preset year, and taking the simulated pollutant space-time distribution data as concentration observation data for subsequently identifying the pollution source;
step two, under the condition of supposing that the position of a pollution point source is uncertain, identifying and capturing a hydraulic power interception region of the pollution plume through analyzing the pollutant concentration distribution of the research region simulated and calculated in the step one;
thirdly, evaluating the interaction relation between the space distribution of the hydraulic capture area influenced by the pollution plumes and potential factors influencing the vulnerability of the pollution of the diving aquifer by adopting an evidence weighted analysis method, taking an observation well position in the hydraulic capture area as a training point by adopting an evidence weighted analysis technology, calculating the influence degree of prior probability and specific variables on the pollution, wherein the specific variables are also prediction factors, the prediction factors comprise hydrogeology factors and land utilization change factors, each prediction factor is summarized into different values, the space association degree of each prediction factor and the pollution is quantified by carrying out space statistical analysis, generating a posterior probability graph based on the influence weight of each prediction factor, and a high-value area with the calculated posterior probability being more than 80% is a pollution vulnerable area;
step four, estimating and exploring the influence of the variability of a permeability coefficient field on the distribution of the pollution paths by adopting a zero-space Monte Carlo particle tracking method, identifying and capturing an observation well in a hydraulic capture area of the pollution plume as an initial position for releasing particles and simulating the pollution paths, obtaining a plurality of groups of pollution paths based on the simulation, and describing a frequency graph of the particles passing through a model grid unit;
and step five, combining the posterior probability distribution information of the influence weight of the prediction factors obtained by different methods in the step three and the step four with the frequency distribution information of the particle tracing crossing area, and fusing the posterior probability distribution information and the frequency distribution information into single information to be output so as to identify the pollution source information.
2. The method for analyzing point sources of pollutants based on GIS space statistics and random simulation as claimed in claim 1, wherein in step one, international MODFLOW and MT3DMS programs are used to simulate the underground water flow and pollutant solute transport process of the research area, and model identification technology is used to correct hydrogeological parameters of underground aquifers of the research area.
3. The method for analyzing the point source of the pollutants based on the combination of GIS spatial statistics and stochastic simulation as claimed in claim 1, wherein an evidence weighted analysis method is adopted in the third step to evaluate the interaction relationship between the spatial distribution of the hydraulic capture area affected by the pollution plumes and the potential factors affecting the vulnerability of the diving aquifer pollution, and the steps are as follows:
step 1: selecting a pollutant solute monitoring network of a research area, observing a horizon as a diving aquifer influenced by pollutants, locking a monitoring well influenced by pollution plumes in the monitoring network based on the time-space distribution of the pollutants in the next preset year simulated and calculated in the step one, determining the monitoring well as a training point for weighted analysis of evidence, and setting the rest monitoring wells which are not influenced by the pollution plumes as control points for later algorithm verification;
step 2: determining main control factors, namely prediction factors, influencing the migration and distribution of pollution plumes in a research area, wherein the prediction factors comprise hydrogeological factors: converting each prediction factor into grid map data according to the horizontal mesh subdivision size of the model;
step 3; randomly generating training point subsets by adopting a training point reduction tool in an ArcGIS spatial data model base ArcSDM, wherein the number of subset monitoring points is M% of the total number of the training points determined in the step 1, ensuring that each subdivision unit area contains at most 1 training point, then carrying out evidence weighted analysis on each group of training point subsets by adopting a prediction factor set in the step 2, and outputting a posterior probability graph corresponding to each group of training subsets;
step 4; combining the posterior probability prediction results of the training subsets output in the step 3 into an independent integrated simulation result by adopting a weighted average method based on area statistics under a curve, wherein the calculation method comprises the following steps:
wherein EM is an integrated prediction result, AUCi represents a success rate value of the ith subset prediction output result Mi, n is the number of training subsets, the integrated prediction result is reclassified into five grades of posterior probabilities based on a geometric interval method, and the region influenced by the pollution plume is further divided into five grades: 0.9 to 1.0 is excellent; preferably 0.8 to 0.9; 0.7 to 0.8 percent; 0.6-0.7 is common; 0.5-0.6 difference.
4. The method for analyzing the point source of the pollutant based on the combination of the GIS spatial statistics and the stochastic simulation, according to the claim 1, wherein the influence of the variability of the permeability coefficient field on the pollution path distribution can be estimated and explored by adopting a zero-space Monte Carlo particle tracking method in the fourth step, and the specific method is as follows:
step 1: randomly generating 500 aquifer permeability coefficient fields for realization, releasing pollution particles in the monitoring well influenced by the pollution plume determined in the step two to serve as an initial position of pollution path simulation, performing reverse particle tracking simulation by adopting an MODPATH program, and calculating a pollution path under the realization of each group of permeability coefficients;
step 2: counting the number of particles passing through a single model unit in each simulation, thereby calculating the total number n of particles passing through the single model unit in all the simulations i,j And the crossing frequency, i, j represents the ith row and the jth column of the subdivision unit, and the calculation formula of the particle crossing frequency of each model unit is as follows:
wherein n is tot Is the total number of particles released, n sim Representing the number of times of simulation;
and 3, step 3: dividing the crossing frequency value obtained in the step 2 into five grades: greater than 60% high frequency region; 41% -60% higher frequency region; 21% -40% of medium frequency region; 5% -20% of low frequency region; and (3) a very low frequency region of less than 5 percent, and drawing a graph of the crossing frequency of the research region, wherein the higher frequency value region indicates that the pollution point source is more likely to be contained.
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Application publication date: 20201229 Assignee: Zhejiang Benzheng Environmental Protection Technology Co.,Ltd. Assignor: HOHAI University Contract record no.: X2024320000012 Denomination of invention: A pollutant point source analysis method based on the combination of GIS spatial statistics and random simulation Granted publication date: 20220913 License type: Common License Record date: 20240524 |