In-situ groundwater remediation method and device based on microbial agents

Abstract

The present invention relates to a method and device for in-situ groundwater remediation based on a microbial agent. The method comprises: connecting monitoring points according to groundwater flow direction to form a directed graph; performing a breadth-first traversal of the directed graph, and dividing diffusion edges between nodes into one or more diffusion edges based on remediation load; and determining a setting range for the microbial agent based on the directed graph; wherein the setting range includes one or more nodes. When remediation resources such as monitoring data are limited, the present invention enables differentiated and adaptive optimization of the microbial agent device layout within a three-dimensional space, dynamically adapting to the microbial agent's action cycle and pollutant diffusion mode, thereby improving the in-situ remediation efficiency of the microbial agent device.

Description

Technical Field

[0001] The present invention belongs to the technical field of groundwater remediation, and in particular relates to an in-situ groundwater remediation method and device based on microbial agents. Background Art

[0002] Microbial remediation is most suitable for biodegradable organic pollutants, such as petroleum hydrocarbons (benzene, diesel), chlorinated solvents (trichloroethylene), and nitrates. For example, hydrocarbons can be oxidized by aerobic bacteria, while chlorinated hydrocarbons can be dechlorinated by anaerobic bacteria. In practical applications, the effectiveness of microbial agents must be verified through both laboratory and field pilot trials. For example, in the case of a diesel spill at a gas station, the injection of petroleum-degrading bacteria (Pseudomonas) combined with a slow-release oxygenator reduced pollutant concentrations by 90% within three months. Similarly, at a chlorinated hydrocarbon-contaminated site, the addition of dechlorinating bacteria (Dehalococcoides) and sodium lactate achieved complete dechlorination within a year. If environmental conditions are less than ideal (such as low temperatures or low-permeability formations), biostimulation (nutrient addition) or engineering measures (electrokinetic assistance) can be combined to improve remediation efficiency. In-situ groundwater remediation using microbial agent systems is economical and safe. Microbial remediation costs are generally lower than chemical oxidation or extraction treatment. It is particularly suitable for large-scale, low-concentration contamination, poses no risk of secondary contamination, and is suitable for ecologically sensitive areas (such as near drinking water sources). Some microbial agents have a limited duration of action, but their effectiveness can be significantly prolonged through scientific bacterial selection and environmental optimization. Their applicability must be determined based on the type of pollutant, site conditions, and remediation objectives to ensure the technology is both feasible and cost-effective.

[0003] Microbial inoculants are closely related to pollutant dispersion and require a rigorous monitoring data base. ArcGIS or Surfer can be used to create groundwater contaminant isoconcentration maps to facilitate microbial inoculant device setup. However, using these automated tools to determine pollutant dispersion requires high monitoring data quality. The quality and spatial distribution of input data directly impact the accuracy and reliability of the resulting maps, which contradicts the suitability of microbial inoculants for remediating large-scale, low-concentration contamination. Obtaining the dispersion of low-concentration contaminants over large areas requires significant monitoring costs. For two-dimensional maps, at least 5–8 monitoring points are required to generate basic contour lines, but this results in lower accuracy. For three-dimensional distribution maps, ≥10 monitoring points are required, with stratified data at different depths. For reliable mapping, small sites (<1 km²) require 15–20 monitoring points. Large sites (>1 km²) require at least 10 points per square kilometer, or an increase based on the complexity of the contamination plume. Furthermore, the spatial distribution of monitoring points is highly demanding. Monitoring points must uniformly cover the contamination plume: they should be located at the source, plume core, plume head, and background areas. If the plume bifurcates or migrates in opposite directions, additional monitoring points will be required, significantly increasing the complexity of implementing microbial inoculants. Furthermore, the use of microbial inoculant devices requires significant flexibility, requiring dynamic adaptation during the implementation of control strategies. This is because the duration of action of microbial inoculants in groundwater remediation is typically limited, depending on the characteristics of the bacterial strain, environmental conditions, and the nature of the contaminant. Liquid inoculants injected directly into the aquifer typically maintain microbial activity for one to three months, after which they gradually become inactivated due to nutrient depletion, environmental stress, or hydraulic erosion. Solid, slow-release inoculants (such as biochar or gel microspheres encapsulating microorganisms) can extend the duration of action to six to twelve months, achieving long-term remediation by slowly releasing microorganisms and nutrients. If sustained effects are required, regular monitoring and replenishment of microbial agents or nutrients are required, which makes large-scale accurate monitoring more difficult and the economic cost is very high. However, with the daily use and protection of groundwater, there are also many monitoring points that can provide real-time pollutant monitoring data. Although these monitoring data cannot reach the level of accurately drawing diffusion distribution maps, they can be used to roughly determine the scope of the pollution prevention and control area and provide the approximate scope of the pollutant source and the pollution plume head area. This provides a basis for the layout and effective use of microbial agent devices. The pollution plume head area shows a sharp transition of pollutant concentration from high (pollution plume core area) to low (uncontaminated background water body). It is a key part for controlling the spread of pollution and an area that can be preliminarily determined through limited monitoring data. Reasonable layout of microbial agents at the pollution head can effectively prevent the expansion of the pollution range, which is more economical and flexible than setting up biological curtains or reaction walls to treat high-concentration core areas.For the reasons mentioned above, the prior art often uses a fixed spacing setting method for the setting method of microbial agent devices, for example: setting the grid width to 5~20m for grid arrangement, and uniformly reducing the grid spacing when the permeability is poor or the pollutants are difficult to degrade. This setting method treats the groundwater control area as a two-dimensional plane, but in fact, the groundwater control area is a three-dimensional area, and the pollutant concentration in each area is different. How to make differential and adaptive layouts of microbial agent devices within a three-dimensional space under limited monitoring data, so as to quickly adapt to the desired control strategy, is a technical problem to be solved. Based on the above problems, the present invention can make differential and adaptive optimization layouts of microbial agent devices within a three-dimensional space under the condition of limited repair resources such as monitoring data, and dynamically adapt to the action cycle of microbial agents and the diffusion mode of pollutants, thereby improving the in-situ repair efficiency of microbial agent devices. Summary of the Invention

[0004] In order to solve the above problems in the prior art, the present invention proposes an in-situ groundwater remediation method and device based on microbial agents, the method comprising:

[0005] Step S1: Connect the monitoring points according to the groundwater flow direction to form a directed graph; the nodes are monitoring points, and the edges are diffusion edges from upstream monitoring points to downstream monitoring points;

[0006] Step S2: Perform a breadth-first traversal of the directed graph and divide the diffusion edges between nodes into one or more diffusion edges based on the repair load; the repair load indicates the amount of repair in the prevention and control space represented by the diffusion edge; the corresponding groundwater diffusion space is divided into one or more spatial grids with the same repair load;

[0007] Step S3: Determine the setting range of the microbial agent based on the directed graph; the setting range includes one or more nodes; specifically:

[0008] Step S31: Obtain any node in the directed graph and determine a set of lengths consisting of the lengths of the shortest paths from each starting node to the node; the length is the number of diffusion edges from the starting node to the node;

[0009] Step S32: Calculate the eigenvalue of the length set, and use the eigenvalue of the length set as the diffusion coefficient of the node;

[0010] Step S33: placing the node into the repair set corresponding to the diffusion coefficient interval into which its diffusion coefficient falls;

[0011] Step S34: selecting one or more remediation sets as target remediation sets according to the pollutant type and / or pollution occurrence stage targeted by the remediation strategy;

[0012] Step S35: Select all nodes in the target restoration set or select some nodes in the target restoration set at even intervals to form a setting range; and deploy microbial agents around the setting range.

[0013] Furthermore, the monitoring points conduct dynamic monitoring of pollutant concentrations, groundwater depth, and groundwater flow direction.

[0014] Furthermore, some of the nodes in the evenly spaced selected target repair set constitute a setting range, specifically: under the constraint of the number of setting positions of the repair strategy, some of the nodes in the evenly spaced selected target repair set constitute a setting range.

[0015] Furthermore, the characteristic value is the mean or minimum value of the lengths in the length set.

[0016] Furthermore, an association relationship between the repair set and its diffusion coefficient interval is preset.

[0017] Furthermore, the diffusion coefficient intervals do not overlap.

[0018] Furthermore, the duration of action of the microbial agents deployed once is limited.

[0019] A microbial agent-based in-situ groundwater remediation system is provided, wherein the microbial agent-based in-situ groundwater remediation system is used to implement the above-mentioned microbial agent-based in-situ groundwater remediation method.

[0020] A microbial agent-based in-situ groundwater remediation device is provided, wherein the microbial agent-based in-situ groundwater remediation device is used to implement the above-mentioned microbial agent-based in-situ groundwater remediation method.

[0021] A microbial agent-based in-situ groundwater remediation platform is provided, wherein the microbial agent-based in-situ groundwater remediation platform is used to implement the above-mentioned microbial agent-based in-situ groundwater remediation method.

[0022] The beneficial effects of the present invention include:

[0023] (1) Based on the use of directed graphs to describe the action area of ​​microbial agents, the three-dimensional space range is divided into uniform and consistent grids based on the control load, which supports further quantitative analysis of the control area, realizes the optimization of the layout of microbial agent devices with differentiation and adaptability within the three-dimensional space, and dynamically adapts to the action cycle of microbial agents and the diffusion mode of pollutants, optimizes the layout of microbial agent devices, and thus improves the remediation efficiency;

[0024] (2) Based on directed graph analysis, the diffusion coefficient corresponding to the node is obtained to adapt to the prevention and control strategy; while ensuring that the density of the pollutant inoculant device is increased as the pollution plume continues to expand in space, the barrier size will be increased. At the same time, as the restoration time progresses, as the pollutants continue to spread, and as the plume area continues to retreat and expand, the barrier of the microbial inoculant device will be retreated, forming a dynamic adaptive process that follows the progress of pollutant diffusion. This dynamic adaptive process is also adapted to the effective time length of the microbial inoculant device, avoiding the poor restoration effect caused by insufficient action time of the inoculant and migration of the inoculant. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application, but do not constitute an improper limitation of the present invention. In the drawings:

[0026] Figure 1 Schematic diagram of the in-situ groundwater remediation method based on microbial agents provided by the present invention. DETAILED DESCRIPTION

[0027] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. The exemplary embodiments and descriptions are only used to explain the present invention but are not intended to limit the present invention.

[0028] The present invention proposes an in-situ groundwater remediation method and device based on microbial agents, as shown in the attached Figure 1 As shown, the method includes the following steps:

[0029] Step S1: Obtain the pollutant parameters of each monitoring point in the prevention and control area; connect the monitoring points according to the groundwater flow direction to form a directed graph G; the nodes in the directed graph are monitoring points, and the edges are diffusion edges from upstream monitoring points to downstream monitoring points.

[0030] Preferably: the monitoring points can dynamically monitor information such as pollutant concentration, groundwater depth, and flow direction; since the absolute relationship between pollutant parameters between monitoring points and the actual location of pollution sources are not sought, the directed graph can be used to describe multi-source pollution and cross-groundwater flow and pollutant diffusion patterns, and perform parallel control of multi-source pollution accordingly; therefore, the control area can be an area where microbial agent devices need to be deployed based on remediation strategies, and the control area can be determined across administrative regions; when there is no upstream and downstream relationship between two nodes, there is no order relationship between the two nodes; the control area targeted by the microbial agent is mainly the diffusion area containing the pollution plume, so the starting node can be a node located in the upstream area and there is no order relationship between it and other nodes.

[0031] Preferably: when constructing a directed graph, the initial node (the upstream starting point of pollutant diffusion) and the target node (the end point of pollutant diffusion, the position where the pollutant parameter drops to close to 0) are determined according to the geographical information of the control area and the existing groundwater distribution information in the control area, and the diffusion edge between the nodes is further determined according to the existing groundwater flow direction information; wherein: the geographical information of the control area includes information such as the geographical location distribution, landform, transportation, facilities, buildings, meteorology, water system and vegetation of the control area; the diffusion edge corresponds to the control area and the three-dimensional space in which it is located, and the geographical information of the diffusion edge can represent the geographical information of the three-dimensional space within a certain range of the control space or the two-dimensional space containing the diffusion edge to a certain extent.

[0032] Alternatively: when the existing information on the direction of groundwater flow is insufficient, after determining the initial node and the target node, diffusion edges are set between all adjacent nodes that may have pollutant diffusion relationships based on the insufficient existing groundwater flow direction information; and further, the relationship information between the pollutant parameters of any two monitoring points with diffusion edges is analyzed to determine whether to retain the diffusion edge. When there is a correlation (or strong correlation) between the pollutant parameters of the two in the historical monitoring data, the diffusion edge is retained, otherwise, the diffusion edge is deleted; the existence of a correlation means that there has been a correlation between the pollutant parameter information obtained from multiple dynamic monitorings; and a strong correlation indicates that there has been a correlation between the two multiple times, or there is always a correlation; in this way, a directed graph with relatively dense coverage can be formed when limited or sparse monitoring points are deployed, without being restricted to the specific flow direction and formation relationship information of groundwater.

[0033] Preferably: the control area is the preliminarily determined pollution plume head, which is the front-end area of ​​the migration and diffusion of pollutants in the groundwater pollution plume, that is, the front boundary of the pollution zone in the direction of groundwater flow; when the pollutants diffuse in the groundwater control area, the pollutant concentration in the pollution plume head area transitions sharply from high (pollution plume core area) to low (uncontaminated background water body), which is the key part for controlling the spread of pollution and the area that can be preliminarily determined through limited monitoring data; the reasonable deployment of microbial agents in the pollution head can effectively prevent the expansion of the pollution range, which is more economical and more flexible than setting up biological curtains or reaction walls to control high-concentration core areas.

[0034] Step S2: Traverse the directed graph and divide the diffusion edges between nodes into one or more diffusion edges according to the repair load; specifically: perform breadth-first traversal on the directed graph and divide the diffusion edges between nodes into one or more diffusion edges based on the repair load; the corresponding groundwater diffusion space is divided into one or more spatial grids with consistent repair loads; the diffusion edge is divided into one diffusion edge, that is, the diffusion edge does not need to be divided; and when the diffusion edge is divided into multiple diffusion edges, new nodes will be generated along with the division of the diffusion edge, and the directed graph will be updated based on the new nodes and the diffusion edges generated by the division; the newly generated diffusion edge and its nodes are used to replace the diffusion edge before the division to obtain the updated directed graph.

[0035] The step S2 specifically includes the following steps:

[0036] Step S21: Put all starting nodes into a queue to be processed; nodes without incoming edges are starting nodes; nodes without outgoing edges are leaf nodes.

[0037] Preferably: the queue to be processed is initialized to be empty; the starting node is or is not the location of the pollution source; however, the starting node is closer to the location of the pollution source.

[0038] Preferably: when there are multiple starting nodes, all starting nodes are put into the queue to be processed in a random sorting order.

[0039] Alternative: When there are multiple starting nodes, the starting nodes are sorted according to the level of their pollutant parameters, their distance from the center of all starting nodes, or their distance from the center of the control area, so that the ones sorted in the former are queued first, and those sorted in the latter are queued later; in the subsequent gridding process, the diffusion edges processed first form a denser grid because it is difficult to reach consistency; thus, they have more advantages in the subsequent remediation process.

[0040] Step S22: Take a node from the head of the queue to be processed as the current node; if the queue to be processed is empty, the gridding ends; after the gridding ends, the diffusion edge is divided into one or more diffusion edges with the same repair load; a new node is formed at the division position.

[0041] Step S23: obtaining an unprocessed child node of the current node as the current child node; and placing the current child node at the end of the queue to be processed.

[0042] Preferred: Prioritize unprocessed child nodes that are close to the current node to achieve rapid convergence.

[0043] Step S24: taking the diffusion edge between the current node and the current child node as the current diffusion edge.

[0044] Step S25: Divide the current diffusion edge into one or more diffusion edges with the same repair load; the repair load indicates that the diffusion edge represents the repair amount of the control space.

[0045] The step S25 specifically includes the following steps:

[0046] Step S251: Initialize the set to be divided as an empty set, and put the current diffusion edge into the set to be divided;

[0047] Step S252: obtaining an unprocessed diffusion edge;

[0048] Preferably, if the length of the unprocessed diffusion edge is less than a length threshold, it is set as processed and the process returns to step S252 to be re-acquired; the length threshold is a preset value, for example, 5m;

[0049] Step S253: Divide the unprocessed diffusion edge into two diffusion edges according to the edge length. ;in: is the starting node of the diffusion edge, is the end node of the diffusion edge; the edge length is the distance between the two endpoint nodes.

[0050] Preferably, attributes are set for the new nodes generated after the division; the attributes include pollutant parameters at the node position, node position, etc.; and can be obtained by actual measurement or calculation or prediction based on attribute information at the endpoint node.

[0051] Step S255: Determine whether the diffusion edges obtained by division meet the constraint conditions, and put the diffusion edges that do not meet the constraint conditions into the set to be divided; the diffusion edges that meet the constraint conditions are newly generated diffusion edges; the constraint condition is that the repair load size represented by the diffusion edge is within the repair load size threshold range.

[0052] Preferably: the repair load size is one of the control space size represented by the diffusion edge, the diffusion edge length, the comprehensive calculated value of the diffusion edge and the pollutant parameter concentration, the comprehensive calculated value of the control space size represented and the pollutant parameter concentration, etc.

[0053] Preferably, the space size threshold range and the pollutant concentration range threshold are preset values, for example: the space size threshold range is , that is, it is related to the grid density and groundwater depth. The setting method of the threshold is related to the geographical location and climate of the prevention and control area. In humid areas such as coastal areas and river banks, the groundwater depth is 0.5~5 m, in semi-arid areas such as plains and farmlands, the groundwater depth is 5~20 m, in arid areas such as deserts and plateaus, the groundwater depth is 20~100 m or deeper, and the groundwater depth in urban areas is 3~15 m. Therefore, for different regions, the spatial size threshold range is different. Of course, the deeper the groundwater depth, the larger the threshold range; for example; the concentration of benzene is 0.01 mg / L, the concentration of petroleum hydrocarbons is 0.1 mg / L~1 mg / L; the threshold value of the comprehensive calculation value of the diffusion edge and pollutant parameter concentration is equal to .

[0054] Preferably, step S255 further includes: determining whether the diffusion edge obtained by the division satisfies one of the following formulas (1) (2) and one of (3) to (5); if not, the diffusion edge is placed in the set to be divided; the diffusion edge that satisfies the requirements is a newly generated diffusion edge, and no further division is performed; wherein: is the diffusion edge length; for The depth of groundwater, for Pollutant concentration at the site; are the upper and lower limits of the consistency range; the consistency is judged by the following formula, making it easy to achieve the consistency of the load capacity, spatial transition, and repair device settings of the divided grid space;

[0055] .

[0056] Preferred: is the default value; for example: .

[0057] Step S256: determine whether there are any unprocessed diffusion edges, if yes, return to step S252;

[0058] Step S26: If the current node still has unprocessed child nodes, return to step S23; otherwise, return to step S22.

[0059] Step S3: Determine the setting range of the microbial agent based on the directed graph; specifically: for any node in the directed graph, determine the length of the shortest path from any starting node to the node; determine the diffusion coefficient of the node based on the length; divide the node into one or more repair sets based on the diffusion coefficient; select one or more repair sets from the repair set to cooperate with the target repair set based on the repair strategy, and select one or more nodes from the target repair set to constitute the setting range.

[0060] The step S3 specifically includes the following steps:

[0061] Step S31: Get any node c in the directed graph and determine the length of the shortest path from each starting node o to the node c The length of the set; is the number of diffusion edges from the starting node o to the node c.

[0062] Preferably: when there is one starting node, the node with a step length of 1 from the starting node is used as the starting node.

[0063] Step S32: determining the diffusion coefficient of the node based on the length set; specifically, calculating the eigenvalue of the length set, and using the eigenvalue of the length set as the diffusion coefficient of the node.

[0064] Preferably, the characteristic value is one or more of the mean, minimum, maximum, sum, etc. of the lengths in the length set;

[0065] Step S33: putting the node into the repair set corresponding to the diffusion coefficient interval into which its diffusion coefficient falls.

[0066] Preferably, the diffusion coefficient is divided into one or more diffusion coefficient intervals, and a correlation relationship between the repair set and its diffusion coefficient interval is preset; and the diffusion coefficient intervals are non-overlapping.

[0067] Step S34: selecting one or more repair set cooperation target repair sets from the repair set based on the repair strategy; specifically: selecting one or more repair set cooperation target repair sets according to the pollutant type and / or pollution occurrence stage targeted by the repair strategy.

[0068] The method selects one or more remediation sets as target remediation sets according to the type of pollutants targeted by the remediation strategy; specifically: for petroleum hydrocarbon pollution, select remediation sets with small diffusion coefficient interval values ​​and large diffusion coefficient interval values; for chlorinated hydrocarbon pollution, select remediation sets with medium diffusion coefficient interval values; for nitrate pollution, select remediation sets with small diffusion coefficient interval values; for heavy metal pollution, all remediation sets are used as target remediation sets.

[0069] Preferred: for petroleum hydrocarbon pollution, use Pseudomonas and slow-release oxygen agent; for chlorinated hydrocarbon pollution, use Dehalococcoides and sodium lactate; for nitrate pollution, use Paracoccus and methanol; for heavy metal pollution, use sulfate-reducing bacteria.

[0070] The method comprises selecting one or more remediation sets as target remediation sets according to the pollution occurrence stage targeted by the remediation strategy; specifically, in the early remediation stage when pollutants have not yet migrated on a large scale, selecting a remediation set with a small diffusion coefficient interval value; in the mid-term and late remediation stages when the pollution has spread, selecting a remediation set with a moderate or large diffusion coefficient interval value; of course, when remediation resources are not limited, a wide range of microbial agent devices can be deployed without considering the selection of remediation sets.

[0071] Step S35: Select one or more nodes from the target repair set to form a setting range; specifically: select all nodes in the target repair set or select some nodes in the target repair set at even intervals to form the setting range.

[0072] Alternatively, the step S35 is specifically as follows: under the constraint of the number of setting positions of the repair strategy, some nodes in the target repair set are evenly spaced to form a setting range.

[0073] Alternatively, the step S35 is specifically as follows: based on the setting position quantity constraint of the repair strategy and the progress time of the repair strategy, one or more nodes are selected from the target repair set to form a setting range, and the distribution of the one or more nodes in the target repair set is adjusted as time progresses.

[0074] The specific steps include:

[0075] Step S351: Obtain the setting position quantity constraint N of the repair strategy, the target repair set quantity K, and the current repair strategy progress time t; wherein: the progress time is the number of unit time lengths; for example: if the unit time length is 1 month, then t=2 means that the progress time length is 2 months; wherein: the smaller the number of the target repair set k, the smaller the corresponding diffusion coefficient interval value.

[0076] Step S352: Divide the position quantity constraint N into K positive integers based on the progress time; expressed as ; segmentation makes .

[0077] Step S353: For the kth target repair set, select evenly spaced Nodes are used to set the position; after the selection is completed, the selected Nodes constitute the setting range; microbial inoculant devices are set around each node position in the setting range; then, while ensuring that the density of the pollutant inoculant devices is continuously expanded in space and the barrier size increases, as the remediation time progresses, as the pollutants continue to spread, and as the plume area continues to retreat and expand, the microbial inoculant device barrier will retreat, forming a dynamic adaptability process that follows the progress of pollutant diffusion. This dynamic adaptation process is also adapted to the effective time length of the microbial inoculant device, avoiding poor remediation effect caused by insufficient action time of the inoculant and migration of the inoculant.

[0078] The microbial agent is set around each node position in the setting range; specifically: the setting method of the microbial agent is selected based on the groundwater depth at the node position; the microbial agent is set in a longitudinal arrangement at the node position with a large groundwater depth, so that the microbial agent diffuses radially; and the microbial agent is set in a horizontal arrangement at the node position with a small groundwater depth, thereby adapting to the spatial remediation load represented by the expanded edge; and a suitable arrangement position is selected within the range near the node position to arrange the microbial agent device.

[0079] The step S3 further includes: when the repair strategy is time-constrained or location-number-constrained, determining the fastest setting range of the microbial agent based on the directed graph; specifically, obtaining the shortest path from the start node to the target node, and using the node positions on the shortest path as the fastest setting range; specifically, including the following steps:

[0080] Step S3X1: Select the target node; set the fastest setting range to empty; specifically: set all leaf nodes as target nodes.

[0081] Alternatively, the leaf nodes falling within the key prevention and control area are used as target nodes; the key prevention and control area is the repair focus area of ​​the repair strategy.

[0082] Alternatively, select some leaf nodes from the leaf nodes as target nodes at intervals.

[0083] Step S3X2: Get any starting node; get the shortest path from the starting node to each target node, and put the shortest path into the shortest path set; when there is no path from the starting node to a target node, the path length is infinite.

[0084] Step S3X3: Obtain the shortest path with the shortest length from the shortest path set (the path length is the number of diffuse edges in the path). When the starting node and the target node of the shortest path with the shortest length are not within the fastest setting range, all nodes on the path of the shortest path with the shortest length are placed into the fastest setting range; the shortest path with the shortest length is deleted from the shortest path set; repeat this step until the shortest path set is empty; the fastest setting range is managed by a set, so repeated placement of nodes will not result in an increase in the number of nodes in the set.

[0085] Of course, nodes can be reselected and positions can be screened within the fastest setting range based on the actual needs of the repair strategy. For example, nodes can be reselected from the perspectives of spacing distance, actual setting difficulty, etc., which will not be elaborated here.

[0086] Preferably, the microbial agent is arranged on a functional agent carrier to form a microbial agent device; the functional agent carrier is a porous material such as biochar, activated carbon, zeolite, etc., and a nutrient solution storage tank containing N, P, K and other elements necessary for microbial growth is also provided; in some water quality environments, it can be arranged in a sustained-release capsule to slowly release microorganisms and prolong the action time; when laying out the microbial agent device, a high-pressure injection pump can be selected according to the depth of groundwater to inject the liquid agent into the deep aquifer, or an osmotic diffuser such as a porous pipe or screen can be used to evenly release the agent to avoid local blockage.

[0087] Based on the same inventive concept, the present invention also provides a microbial inoculant system, which is used to implement the above-mentioned microbial inoculant method.

[0088] Based on the same inventive concept, the present invention also provides a microbial inoculant device, which is used to implement the above-mentioned microbial inoculant method.

[0089] A computer program (also referred to as a program, software, software application, script, or code) can be written in any form of programming language, including assembly or interpreted languages, declarative or procedural languages, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, object, or other unit suitable for use in a computing environment. A computer program may, but need not, correspond to a file in a file system. A program can be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program, or in multiple collaborative files (e.g., files storing one or more modules, subroutines, or code portions). A computer program can be deployed to execute on one computer or on multiple computers located at one site or distributed across multiple sites and interconnected by a communication network.

[0090] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0091] The present invention is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0092] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0093] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0094] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. An in-situ groundwater remediation method based on microbial agents, characterized in that: The method comprises: Step S1: Connect the monitoring points according to the groundwater flow direction to form a directed graph; the nodes are monitoring points, and the edges are diffusion edges from upstream monitoring points to downstream monitoring points; Step S2: Perform breadth-first traversal on the directed graph and divide the diffusion edges between nodes into one or more diffusion edges based on the repair load; The restoration load indicates that the diffusion edge represents the restoration amount of the control space; the corresponding groundwater diffusion space is divided into one or more spatial grids with the same restoration load; the diffusion edge is divided into one diffusion edge, that is, the diffusion edge does not need to be divided; when the diffusion edge is divided into multiple diffusion edges, new nodes are generated along with the division of the diffusion edge, and the directed graph is updated based on the new nodes and the diffusion edges generated by the division; the newly generated diffusion edge and its nodes are used to replace the diffusion edge before the division to obtain the updated directed graph; Step S3: Determine the setting range of the microbial agent based on the directed graph; the setting range includes one or more nodes; specifically: Step S31: Obtain any node in the directed graph and determine a length set consisting of the lengths of the shortest paths from each starting node to the node; The length is the number of diffusion edges from the starting node to the node; Step S32: Calculate the eigenvalue of the length set, and use the eigenvalue of the length set as the diffusion coefficient of the node; Step S33: placing the node into the repair set corresponding to the diffusion coefficient interval into which its diffusion coefficient falls; Step S34: selecting one or more remediation sets as target remediation sets according to the pollutant type and / or pollution occurrence stage targeted by the remediation strategy; Step S35: Select all nodes in the target restoration set or select some nodes in the target restoration set at even intervals to form a setting range; and deploy microbial agents around the setting range.

2. The in-situ groundwater remediation method based on microbial agents according to claim 1, characterized in that: The monitoring points conduct dynamic monitoring of pollutant concentrations, groundwater depth, and groundwater flow direction.

3. The in-situ groundwater remediation method based on microbial agents according to claim 2, characterized in that: Part of the nodes in the evenly spaced selected target repair set constitutes a setting range, specifically: under the setting position quantity constraint of the repair strategy, part of the nodes in the evenly spaced selected target repair set constitutes a setting range.

4. The in-situ groundwater remediation method based on microbial agents according to claim 3, characterized in that: The characteristic value is the mean or minimum value of the lengths in the length set.

5. The in-situ groundwater remediation method based on microbial agents according to claim 4, characterized in that: The association relationship between the repair set and its diffusion coefficient interval is preset.

6. The in-situ groundwater remediation method based on microbial agents according to claim 5, characterized in that: The diffusion coefficient intervals do not overlap.

7. The in-situ groundwater remediation method based on microbial agents according to claim 6, characterized in that: The duration of action of microbial agents deployed once is limited.

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