Site distribution sampling method for groundwater environment background value investigation

Abstract

The invention relates to the technical field of underground water distribution sampling, in particular to a distribution sampling method for underground water environment background value investigation, which comprises the following steps: S1, determining an investigation range and a target aquifer, screening historical data, confirming a background value index, dividing hydrogeological partitions according to data, and calculating the minimum sample size required in a statistical unit; s2, analyzing hydrochemical characteristics of each unit to obtain key hydrological nodes, monitoring blind areas and point distribution cost information in each unit, and optimizing point distribution planning in each regional statistical unit, and S3, collecting water samples at corresponding depth intervals by using a layered water sampler according to groundwater at different depths, detecting conventional indexes of the water samples by using a water quality analyzer, and determining the water quality of the groundwater. A detection result is recorded; according to the method, the minimum sample size is calculated through the sample data set, the sample representativeness is improved, point distribution planning is optimized, point distribution is more targeted, point distribution is optimized by analyzing water chemical characteristics to obtain various information, and monitoring comprehensiveness and rationality are guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of groundwater sampling site layout, and specifically relates to a sampling method for groundwater environmental background value investigation. Background Art

[0002] Groundwater is an important part of water resources and plays an irreplaceable role in maintaining the balance of the ecosystem, ensuring industrial and agricultural production, and providing domestic water for residents. However, with the continuous intensification of human activities, groundwater faces an increasingly serious threat of pollution. In order to accurately evaluate the pollution status of groundwater and distinguish the influence of natural background values and human pollution, the investigation of groundwater environmental background values is particularly important.

[0003] Some past sampling site layout methods often based on administrative divisions or simple grid divisions, without fully considering the complexity of the groundwater system. For example, in some areas with complex terrain and diverse geological structures, simple grid sampling site layout may miss some representative areas. In mountainous areas, for example, the runoff direction and recharge relationship of groundwater are greatly affected by topography and landform, and a single grid sampling site layout cannot accurately capture the water quality characteristics on different groundwater flow paths.

[0004] Many traditional sampling methods do not well combine the dynamic change characteristics of groundwater. The water level and water quality of groundwater may fluctuate greatly in different seasons and different years. For example, in the rainy season and dry season, the recharge volume, runoff velocity, and dilution and diffusion of pollutants of groundwater are all different.

[0005] For long-term groundwater environmental background value investigation, traditional sampling site layout lacks sustainable planning and is likely to affect the layout cost. Some sampling sites may be able to obtain certain data in the short term, but over time, due to changes in factors such as regional development and groundwater exploitation, these sampling sites will gradually lose their representativeness, and traditional methods do not anticipate this situation and it is difficult to adjust the sampling site layout plan in a timely manner. Summary of the Invention

[0006] In order to solve the above problems, the present invention provides a sampling method for groundwater environmental background value investigation.

[0007] The technical solution of the present invention is: a sampling method for groundwater environmental background value investigation, including the following steps:

[0008] S1. Data acquisition

[0009] S1-1. Determine the investigation scope and target aquifer, collect and collate regional geological, hydrogeological data and water quality inspection reports. At the same time, identify pollution sources, screen historical hydrochemical data, and eliminate data with unknown horizons, human pollution, abnormal macro components, and unbalanced cations and anions. Select background value indicators for macro components, comprehensive indicators, high-background trace and ultra-trace components.

[0010] S1-2. Divide the hydrogeological sub-regions based on geological structures and groundwater recharge-discharge conditions to obtain a statistical unit sub-region map. Calculate and statistically determine the minimum sample size for each unit, and the sample size for a single statistical unit should be no less than 6. The formula for calculating the minimum sample size of the unit is: where: N is the sample size / unit; t is the t-value in the t-distribution table for a selected confidence level of 95% or degrees of freedom df = N - 2; Cv is the coefficient of variation of the sample data set, that is, the ratio of the sample standard deviation to the sample mean. When multiple indicators are involved, the coefficient of variation of the total dissolved solids can be used as the coefficient of variation of the data set; m is the allowable error. The allowable error for confined water sample data is within 30%, and the allowable error for unconfined groundwater sample data is within 20%.

[0011] S2. Analyze the hydrochemical characteristics of each unit based on the data obtained in S1 to obtain key hydrographic nodes, monitoring blind spots, and sampling point cost information within each unit. Optimize the sampling point planning within each regional statistical unit to maximize the coverage of key hydrographic nodes, minimize monitoring blind spots, and rationalize sampling point costs.

[0012] S3. Use a layered water sampler to collect water samples at different depths of groundwater according to the corresponding depth intervals. Use a water quality analyzer to detect the conventional indicators of the water samples and record the detection results.

[0013] Note: The t-value at the selected confidence level involved in the formula is based on statistical principles. The confidence level is generally set at 95% in this method, which means that in multiple samplings, 95% of the intervals constructed by the samples contain the population parameters; the GPS positioning is used to determine the sampling point location, and the number, longitude and latitude are marked and recorded on site. The layered water sampler collects water samples at different depth intervals, marks the sampling point number, time, and depth information and detects the conventional indicators to ensure the accuracy and traceability of the sampling points, which is helpful for analyzing the spatio-temporal variation characteristics of groundwater.

[0014] Furthermore, the key hydrographic nodes are any one or any combination of two or more of the recharge area, discharge area, and strong runoff zone.

[0015] Note: Paying attention to the recharge area can help understand the source water quality of groundwater and is useful for judging the initial purity of groundwater. For example, when studying the groundwater in a water source protection area, the water quality monitoring in the recharge area can determine whether there are potential pollution sources upstream; the discharge area can reflect the final water quality state of groundwater after a series of geological processes and possible pollution impacts. For instance, by monitoring the discharge area near a lake, the potential impact of substances carried by groundwater on the lake ecosystem can be observed; the strong runoff zone is crucial for understanding the water quality changes in the main flow path of groundwater. It can serve as a channel for the rapid diffusion of pollutants. Identifying this area helps track the direction of pollution diffusion. When considered in combination, it can more comprehensively cover different parts of the groundwater system, including the water quality conditions from the source to the end and the main flow path, providing multi-dimensional data for accurate statistical characterization of background values.

[0016] Further, the specific method of S2 includes: S2-1: Based on the objective function Z1, maximize the coverage of the key hydrographic nodes described in S1-2. The objective function Z1 is: In the formula: Z1 is the sum of the coverage degrees of all key hydrographic nodes, n is the key hydrographic node, h k is the k-th key hydrographic node, C hk is the coverage coefficient of the k-th key hydrographic node; preferentially set monitoring points near the key hydrographic nodes to maximize Z1;

[0017] S2-2: Based on the objective function Z2, minimize the monitoring blind areas described in S1-2. The objective function Z2 is: In the formula: Z2 is the sum of the uncovered degrees of all monitoring blind areas, m is the total number of monitoring blind areas; b l is the l-th monitoring blind area, l ranges from 1 to m, used to traverse all monitoring blind areas; U bl is the measure of the uncovered degree of the l-th monitoring blind area, and the calculation method is: In the formula: M bl is the set of positions (i,j) related to the l-th monitoring blind area, to minimize Z2;

[0018] S2-3: Based on the objective function Z3, balance the cost described in S1-2. The objective function Z3 is: Z3 = N × ∑ i ∑ j c ij In the formula: N = ∑ i ∑ j x ij , N represents the total number of monitoring points, i and j are the indices or coordinates of positions; c ij is the cost of setting a monitoring point at position (i,j); adjust the positions of the monitoring points to minimize Z3.

[0019] Explanation: In step S2-1, the objective function is used to maximize the coverage of key hydrogeological nodes. Key hydrogeological nodes play an important role in controlling groundwater circulation and water quality changes. Setting monitoring points near these nodes first can capture dynamic information in key areas, ensuring the study of groundwater circulation patterns and potential water quality change trends. In step S2-2, the layout aims to minimize the monitoring blind spots. The definition of monitoring blind spots is clarified and incorporated into the layout plan through the objective function, calculating and minimizing the total degree of non-coverage in blind spots to avoid monitoring loopholes and ensure the comprehensiveness of the investigation and the reliability of the results. In step S2-3, the cost is balanced based on the objective function. Considering the total number of layout points and location costs, the positions of monitoring points are adjusted to minimize the total cost, taking into account both the investigation quality and economic cost to improve the sustainable effect in large-scale investigations.

[0020] Furthermore, in S3, sampling equipment is prepared according to research requirements and cost budgets. The sampling equipment includes water samplers, water quality analysis instruments, and water level gauges.

[0021] Explanation: Selecting a suitable water sampler according to research requirements can ensure the collection of representative water samples, thus accurately analyzing the composition of groundwater. Water level gauges help to understand the changes in groundwater levels, which is very important for studying the dynamic balance of groundwater, the direction of water flow, and the interaction with surface water. For example, when studying the impact of seasonal water level changes on groundwater quality, the data from water level gauges is indispensable and provides more comprehensive data support for the entire groundwater research on the premise of controllable costs.

[0022] Furthermore, the monitoring blind spots in S2-2 refer to areas with large variations in regional hydrogeological conditions, where the buried depth of the groundwater level exceeds 80 - 100m, areas with complex strata and geological structures, complex aquifer system structures, unstable spatial distribution of aquifers, and complex groundwater recharge, runoff, discharge conditions, or hydrodynamic characteristics. The cost in S2-3 refers to the expenses caused by the number of layout points, drilling and well construction, sampling, and testing and analysis.

[0023] Explanation: For areas with large variations in regional hydrogeological conditions and a buried depth of the groundwater level exceeding 80 - 100m, monitoring this area can promptly detect the scope and degree of pollution diffusion, which has important warning significance for taking pollution prevention and control measures. Areas with complex strata and geological structures, complex aquifer system structures, unstable spatial distribution of aquifers, and complex groundwater recharge, runoff, discharge conditions, or hydrodynamic characteristics are regarded as monitoring blind spots because the groundwater flow paths are complex in these areas, and there may be some special hydrogeological phenomena. Monitoring this area can help to deeply understand the water quality characteristics of groundwater under complex geological conditions and the impact of special geological structures on the migration and transformation of pollutants, contributing to a more comprehensive understanding of the entire groundwater environment. Reasonably determining the number of layout points can not only ensure the collection of sufficient data to accurately represent the background value of the groundwater environment but also avoid unnecessary cost waste caused by unreasonable layout.

[0024] Further, for the C hk The calculation method is as follows: In the formula: x ij is a decision variable, indicating whether to set a monitoring point at the position (i, j), and x ij ∈{0, 1}, x ij = 1 means setting a monitoring point, x ij = 0 means not setting a monitoring point; N hk is the set of positions (i, j) related to the k-th key hydrological node h k

[0025] Note: This calculation method clarifies the quantification method of whether to set a monitoring point. By defining the decision variable x ij , the setting situation of monitoring points at each position can be clearly represented, making the point layout decision more scientific and systematic, and facilitating scientific point layout planning; the introduction of the position set N hk related to the key hydrological node links the setting of monitoring points with the key hydrological node, so that the monitoring points can be reasonably arranged according to the importance and characteristics of the key hydrological node, ensuring effective monitoring in places related to the key areas of the groundwater environment and improving the accuracy of background value statistical representation.

[0026] Further, ensure that there are at least 6 sampling points in each of the above-mentioned regional statistical units, that is, where is the s-th regional statistical unit, and is the set of positions related to the s-th regional statistical unit.

[0027] Note: Specifying the lower limit of the number of sampling points in each regional statistical unit can ensure that there is sufficient data to characterize the characteristics of groundwater in a relatively independent area. 6 sampling points can, to a certain extent, reflect the diversity of groundwater quality in this area and avoid data one-sidedness caused by too few sampling points;

[0028] Setting sampling points based on regional statistical units takes into account the differences in groundwater in different types of regions. For example, different soil types, geological structures, or land use methods may affect groundwater quality, which helps to more comprehensively and meticulously analyze the groundwater environmental background values in different regions.

[0029] Further, in the S2, it also includes dynamically adjusting the point layout according to the background value information in S1, dividing the rainy season and the dry season according to seasons, setting the weights of the objective functions, ω 1r ω 2r ω 3r are the weights of the objective functions Z1, Z2, and Z3 in the rainy season, and ω 1r + ω 2r + ω 3r ​= 1; ω 1d , ω 2d , ω 3d are the weights of the objective functions Z1, Z2, and Z3 during the dry season, and ω 1d + ω 2d + ω 3d = 1.

[0030] Note: Adjusting the weights of the objective functions in the rainy season and dry season takes into account the impact of seasonal factors on the groundwater environment. For example, in the rainy season, since the importance of the recharge area is more prominent during this period, that is, the increased precipitation leads to a significant increase in the recharge volume of the recharge area. By adjusting the weights, more monitoring resources are allocated to the recharge area, and more monitoring points are added in and around the recharge area, that is, increasing the weight of Z1, which helps to better grasp the changes in water quality and water volume during the groundwater recharge process by rainwater; in the dry season, according to the characteristics of the dry season, that is, more attention is paid to the maintenance of the groundwater level and the stability of water quality, etc., the weights are adjusted, so as to optimize the layout plan. While maintaining a certain monitoring intensity for key hydrological nodes, more attention is paid to the monitoring of areas that may affect water quality stability, such as areas prone to pollutant accumulation, that is, increasing the weight of Z2, so that the layout plan can adapt to the changes in the groundwater environment in different seasons and improve the accuracy and effectiveness of monitoring.

[0031] Furthermore, the weight ω 1r in the rainy season is dynamically corrected through real-time meteorological data. When the real-time rainfall exceeds the threshold, ω 1r is increased to 0.6, and ω 3r is decreased to 0.1; based on the groundwater dynamic model to predict the water level decline rate in the dry season, if the monthly decline > 5%, then ω 2d is increased to 0.5.

[0032] Note: Combining real-time meteorological data with the groundwater dynamic model, by dynamically adjusting the monitoring weights in the rainy season and dry season, it breaks through the limitations of the traditional fixed weight mode. When the rainfall suddenly increases in the rainy season or the water level drops rapidly in the dry season, the system can automatically adjust the allocation of monitoring resources, achieve reasonable planning, give priority to covering key areas, and significantly improve the real-time performance and adaptability of the monitoring plan, solving the problem of insufficient monitoring accuracy of traditional methods under extreme meteorological conditions.

[0033] Furthermore, according to the coefficient of variation Cv(t) of the real-time monitoring data, the sample size N is dynamically corrected, and the correction formula is: where Cv(t) represents the coefficient of variation that changes with time or season, ΔN is the dynamic correction term. When Cv(t) increases by more than 20% compared with the previous period, ΔN = 2, otherwise ΔN = 0.

[0034] Description: Dynamically correct the sample size based on the coefficient of variation. The correction is achieved through real-time data feedback, addressing the defect that traditional static sample size formulas cannot adapt to the dynamic changes of groundwater and significantly improving the monitoring accuracy. Innovatively introduce the coefficient of variation Cv(t) as the basis for dynamically correcting the sample size. By monitoring the change in the degree of dispersion of real-time data, flexibly adjust the number of sampling points. Compared with traditional static sample size calculations, this method can automatically increase the sample size to improve monitoring accuracy when groundwater data fluctuates greatly, and save costs when the data is stable, realizing the intelligentization of the monitoring plan and the optimal allocation of resources, and solving the defect that traditional methods cannot adapt to the dynamic changes of groundwater.

[0035] The beneficial effects of the present invention are as follows:

[0036] The processing of multi-source data is rigorous. Collect geological and hydrogeological data and water quality inspection reports, identify pollution sources, strictly screen historical hydrochemical data and select background value indicators to ensure high-quality and representative data, laying a foundation for subsequent analysis; divide hydrogeological zones based on geological structures and groundwater recharge-discharge conditions, obtain regional statistical units and analyze hydrochemical characteristics to obtain key hydrogeological nodes, monitoring blind spots and information on the cost of layout points, breaking through the limitations of traditional rough division and providing an accurate basis for layout planning; determine the minimum sample size of the unit according to different types of groundwater, which helps to ensure that the collected sample size can accurately reflect the overall situation when conducting groundwater-related research or monitoring; through the coefficient of variation of the sample data set, the degree of dispersion of the data can be reflected. When multiple indicators are involved, the coefficient of variation of the total dissolved solids can also be used as the coefficient of variation of the data set. This flexibility enables the formula to adapt to various types of groundwater data situations and can play a role in both single-index and multi-index research, ensuring the scientificity of sample size determination. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 is the minimum sample density table for background value investigation in the embodiment of the present invention (number / 100km 2 );

[0038] Figure 2 is the local longitudinal section simulation diagram of the pore water in loose rocks in Zhuhai in Application Examples 1 and 2 of the present invention;

[0039] Figure 3 is the local longitudinal section simulation diagram of groundwater recharge in Zhuhai in Application Examples 1 and 2 of the present invention;

[0040] Figure 4 is the local longitudinal section simulation diagram of groundwater discharge in Zhuhai in Application Examples 1 and 2 of the present invention;

[0041] Figure 5 is the schematic diagram of hydrogeological zoning in Zhuhai in Application Examples 1 and 2 of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0042] To further elaborate on the methods adopted and the effects achieved by the present invention, the technical solutions of the present invention will be clearly and completely described below in conjunction with experiments.

[0043] Example:

[0044] A method for site selection and sampling for groundwater environmental background value investigation, comprising the following steps:

[0045] S1. Data acquisition

[0046] S1-1. Determine the investigation scope and target aquifer, select the smallest hydrogeological sub-region or the centralized drinking water source protection area as the investigation area, and clarify the target aquifer according to the monitoring horizon and the exploited horizon; collect and sort out regional geological, hydrogeological data and water quality test reports, and at the same time identify pollution sources, screen historical hydrochemical data, and eliminate data with unknown horizons, affected by human pollution, abnormal macro-components, imbalance between anions and cations, and unreliable test results, and select background value indicators, including macro-components, comprehensive indicators, and high-background trace and ultra-trace components;

[0047] S1-2. Divide the hydrogeological sub-regions based on geological structures and groundwater recharge-discharge conditions to obtain a statistical unit partition map, calculate the minimum sample size of the statistical unit, and the sample size of a single statistical unit shall be no less than 6. The formula for calculating the minimum sample size of the unit is: In the formula: N is the sample size / unit; t is the t value in the t distribution table when the selected confidence level is 95% or the degree of freedom df = N - 2; Cv is the coefficient of variation of the sample data set, that is, the ratio of the sample standard deviation to the sample mean. When multiple indicators are involved, the coefficient of variation of the total dissolved solids can be used as the coefficient of variation of the data set; m is the allowable error. The allowable error of the confined water sample data is within 30%, and the allowable error of the unconfined groundwater sample data is within 20%;

[0048] The minimum sample density value of each research area is determined according to hydrogeological survey data. For areas where hydrogeological surveys have not been carried out, confined aquifers, and special areas such as swamp plains, desert rock deserts, alpine canyons, and permafrost areas on the plateau, the minimum sample density is determined according to the working accuracy of the 1:250,000 scale. The calculation of the minimum sample density refers to Figure 1 ;

[0049] S2. Analyze the hydrochemical characteristics of each unit based on the data obtained in S1 to obtain the key hydrographic nodes, monitoring blind spots, and site selection cost information within each unit, and optimize the site selection plan within each regional statistical unit to maximize the coverage of key hydrographic nodes, minimize monitoring blind spots, and rationalize the site selection cost;

[0050] S3. Use a layered water sampler to collect water samples according to groundwater at different depths at corresponding depth intervals. Use a water quality analyzer to detect the conventional indicators of the water samples, record the detection results, and draw a groundwater hydrochemical type map, a contour map of the distribution of total dissolved solids, and a contour map of the distribution of each main background value indicator within the investigation scope. The data point density used for drawing shall not be less than 1 per 100 km 2 ;

[0051] Overlay the statistical unit zoning map with the contour maps of the distribution of each indicator. When the hydrochemical characteristics of the samples near the boundary of a statistical unit are similar to those of the adjacent unit but differ significantly from other samples within the unit, the boundary of the statistical unit should be adjusted and the above samples should be classified into the adjacent statistical unit for statistics. After adjustment, it should be verified whether the remaining samples meet the requirements of the sampling point density and the minimum sample size. When the requirements are not met, supplementary sample collection and testing should be carried out.

[0052] Example 2: This example is basically the same as Example 1, except that the analysis method in S2 includes:

[0053] S2-1: Based on the objective function Z1, maximize the coverage of the key hydrological nodes described in S1-2. The objective function Z1 is: In the formula: Z1 is the sum of the coverage degrees of all key hydrological nodes, n is the key hydrological node, h k is the kth key hydrological node, C hk is the coverage coefficient of the kth key hydrological node; preferentially set monitoring points near the key hydrological nodes to maximize Z1; the key hydrological nodes are recharge areas and discharge areas; the calculation method of the C hk is: In the formula: x ij is a decision variable, indicating whether to set a monitoring point at the position (i, j), and x ij ∈{0, 1}, x ij = 1 indicates that a monitoring point is set, x ij = 0 indicates that no monitoring point is set; N hk is the set of positions (i, j) related to the kth key hydrological node h k ; Ensure that there are at least 6 sampling points in each of the above-mentioned regional statistical units, that is where g s is the s-th regional statistical unit, L gs is the set of positions (i, j) related to the s-th regional statistical unit g s ;

[0054] S2-2: Based on the objective function Z2, minimize the monitoring blind areas described in S1-2. The objective function Z2 is: Where: Z2 is the sum of the uncovered degrees of all monitoring blind areas, m is the total number of monitoring blind areas; b l is the l-th monitoring blind area, where l ranges from 1 to m and is used to traverse all monitoring blind areas; U bl is the measure of the uncovered degree of the l-th monitoring blind area, and the calculation method is: Where: M bl is the set of positions (i, j) related to the l-th monitoring blind area, which minimizes Z2; the monitoring blind area is an area with large changes in regional hydrogeological conditions, a deep groundwater level exceeding 80 - 100 m, complex strata and geological structures, a complex aquifer system structure, unstable spatial distribution of aquifers, and complex groundwater recharge or runoff and discharge conditions or hydrodynamic characteristics; the cost is the cost caused by the number of sampling points, equipment purchase, installation and maintenance;

[0055] S2 - 3. Balance the cost described in S1 - 2 based on the objective function Z3, and its objective function Z3 is: Z3 = N × ∑ i ∑ j c ij Where: N = ∑ i ∑ j x ij , N represents the total number of sampling points, and i and j are the indices or coordinates of the positions; c ij is the cost of setting a sampling point at the position (i, j); adjust the positions of the monitoring points to minimize Z3; in S3, prepare sampling equipment according to research requirements and cost budgets, and the sampling equipment includes water samplers, water quality analysis instruments, and water level gauges.

[0056] Example 3: This example is basically the same as Example 2, except that in S2, it also includes dynamically adjusting the sampling points according to the background value information described in S1, dividing the rainy season and dry season according to seasons, setting the weights of the objective functions, ω 1r , ω 2r , ω 3r are the weights of the objective functions Z1, Z2, and Z3 in the rainy season, and ω 1r + ω 2r + ω 3r = 1; ω 1d , ω 2d , ω 3d are the weights of the objective functions Z1, Z2, and Z3 in the dry season, and ω 1d + ω 2d + ω 3d = 1; dynamically correct the rainy season weight ω 1r through real-time meteorological data. When the real-time rainfall exceeds the threshold, increase ω 1r to 0.6 and decrease ω 3r to 0.1; predict the dry season water level decline rate based on the groundwater dynamic model. If the monthly decline > 5%, then increase ω2d to 0.5; according to the coefficient of variation Cv(t) of real-time monitoring data, the sample size N is dynamically corrected, and the correction formula is: Wherein, Cv(t) represents the coefficient of variation that changes with time or season, ΔN is a dynamic correction term, and when Cv(t) increases by more than 20% compared with the previous cycle, ΔN=2, otherwise ΔN=0.

[0057] Application Example 1:

[0058] The sampling scope is now Zhuhai City's administrative area, and regional statistical units are obtained; the regional environmental background content of natural components such as macro-components, micro-components and trace components in groundwater is identified to provide a basis for targeted groundwater pollution prevention and control work in Zhuhai City;

[0059] S1. Data Acquisition

[0060] S1-1, combined with relevant hydrogeological data of Zhuhai City, such as Figures 2 - 5 As shown, based on the relevant geological and hydrogeological reports and maps, historical groundwater environmental monitoring data, and the identification of pollution sources, the groundwater background value survey zones in Zhuhai City were determined, the points were reasonably and preliminarily arranged, and the survey data were used to carry out on-site confirmation and monitoring data analysis of the groundwater environmental background survey points in Zhuhai, confirm the representativeness of the survey points, supplement well sampling, and conduct test analysis to determine the groundwater environmental background value in Zhuhai City;

[0061] According to the hydrogeological conditions of Zhuhai City, the survey scope and target aquifers were determined and divided into three statistical units: intermountain valley pore water, coastal plain pore water and bedrock fissure water; intermountain valley pore water: mainly low mineralization, weak acidity, high Fe and Mn content, which may be related to the regional geological background such as granite weathering; coastal plain pore water: affected by seawater intrusion, Cl - 、Na + The content is high, and the total dissolved solids TDS is significantly higher than other units; bedrock fissure water: the SiO2 content is high, reflecting the long-term interaction between groundwater and surrounding rock in the bedrock area;

[0062] S1-2. Hydrogeological zoning based on geological structure and groundwater recharge conditions, such as Figure 2As shown, a statistical unit partition map is obtained, the minimum sample size of the unit is calculated and statistically analyzed, and the minimum sample size required for each unit is calculated according to the formula for the minimum sample size. For the phreatic aquifers of pore water in mountain valleys, pore water in coastal plains, and fissure water in bedrock, m = 20%. The coefficient of variation Cv of each unit is taken as the empirical value of 0.5. When the confidence level is selected as 95% in the t-distribution table, the t-value is obtained, and the minimum sample size is initially obtained. The initial planned number of sampling points is appropriately adjusted according to the distribution area of each calculation unit. Among them, the distribution area of pore water in mountain valleys is small, while the distribution areas of pore water in coastal plains and fissure water in bedrock are large. The results are shown in Table 1;

[0063] Table 1: Minimum sample size of each regional unit

[0064]

[0065] The total land area of Zhuhai City is 1,736.46 square kilometers. For the phreatic aquifer, the accuracy of the existing hydrogeological survey work is calculated according to 1:50,000, and calculated at about 6 per 100 km 2 The number of sampling points for phreatic water in the land area of Zhuhai City is close to 100, and the calculation of the minimum sample density refers to Figure 1 ;

[0066] According to the requirements of the minimum sample size within the calculation unit, groundwater sampling points are arranged. Combining with the requirements of the sampling point density for the investigation of the regional groundwater environmental background value, groundwater sampling points are supplemented, and the groundwater environmental background value in the study area is comprehensively analyzed and studied;

[0067] According to the requirements of the sampling point density and the work needs, the number of sampling points is determined. The specific locations of the sampling points should avoid areas that have been polluted or are suspected of being polluted, as well as areas affected by seawater intrusion, geothermal aquifers, salt-bearing strata, etc.; the number of sampling points after supplementary investigation and layout is shown in Tables 2 and 3. Among them, 47 investigation points for fissure water in bedrock rely on existing mountain spring monitoring points within the working area;

[0068] Table 2: Number of sampling points after supplementation in each regional unit

[0069]

[0070]

[0071] Table 3: Total types and numbers of sampling points

[0072] Monitoring target layer Mountain spring water monitoring point Newly built well Number of sample points Phreatic water 47 47 94

[0073] S2. Analyze the hydrochemical characteristics of each unit based on the data obtained in S1. As shown in Tables 4 and 5, obtain the key hydrological nodes, monitoring blind spots, and sampling point cost information within each unit, and optimize the sampling point layout within each regional statistical unit to maximize the coverage of key hydrological nodes, minimize monitoring blind spots, and rationalize sampling point costs.

[0074] Table 4: Distribution Table of Key Hydrological Nodes

[0075]

[0076] Table 5: Distribution Table of Monitoring Blind Spots

[0077]

[0078] S3. Use a layered water sampler to collect water samples at corresponding depth intervals according to groundwater at different depths, and use a water quality analyzer to detect the conventional indicators of the water samples and record the detection results.

[0079] Application Example 2:

[0080] Based on Example 2, continue to optimize the sampling point layout within a single unit for Application Example 1. For the pore water unit in the mountain valley, the recharge area is determined to be the area near the water source in the upper reaches of the valley, with an area of approximately 100 square kilometers. For the convenience of coordinates, it is divided into 50 sub-blocks of 1 - 3 square kilometers; the discharge area is the low-lying area at the confluence of the lower reaches of the valley and the river, with an area of approximately 50 square kilometers. For the convenience of coordinates, it is divided into 30 sub-blocks of 0.5 - 2 square kilometers.

[0081] Select sub-block (5,1) near the water source entrance and with rich vegetation in the recharge area, that is, the 5th sub-block along the length direction of the recharge area and the 1st sub-block along the width direction, and sub-block (25,1) with a lower terrain conducive to water collection to set monitoring points, that is, x [5,1] = 1, x [25,1] = 1; According to the data, the area weight of sub-block (5,1) is 0.8, and the area weight of sub-block (25,1) is 0.9; In the discharge area, set a monitoring point in sub-block (1,5), that is, near the junction of the wetland and the river, that is, x [1,5] = 1, and the area weight of this sub-block is 0.9; For the coverage coefficient C h1 of the key hydrological node h1 in the recharge area of the pore water in the mountain valley is 1.7; The Ch2 of the discharge area h2 is 0.9, then

[0082] The method for determining the area weight in the recharge area in this application example: If the vegetation is rich and the terrain is low-lying at the point, the vegetation coverage is high and the terrain is low-lying, and the surface water infiltration ability is strong, that is, the recharge effect is significant, then the area weight is 1.

[0083] The vegetation is abundant at the point location, the terrain is flat, and the vegetation coverage is high. However, the flat terrain results in weak surface water collection ability, that is, the recharge effect is medium, so the area weight is 0.9;

[0084] The vegetation is sparse at the point location, and the terrain is low-lying. The low-lying terrain is conducive to surface water collection. However, the sparse vegetation leads to weak infiltration ability, that is, the recharge effect is medium to low, so the area weight is 0.8;

[0085] The vegetation is sparse at the point location, and the terrain is flat. The sparse vegetation and flat terrain result in weak surface water infiltration ability, that is, the recharge effect is poor, so the area weight is 0.6;

[0086] The point location is an artificial surface such as a hardened road surface. The artificial surface hinders surface water infiltration and has almost no recharge effect, so the area weight is 0.3;

[0087] The method for determining the area weight in the discharge area of this application example: There is a spring emergence point at the point location, and the discharge is concentrated, that is, it has a significant impact on the groundwater system, so the area weight is 1;

[0088] The point location is at the confluence of rivers, and the discharge is concentrated, that is, the water volume is large and the water quality changes significantly, so the area weight is 0.9;

[0089] The point location is a wetland or swamp, and the discharge is relatively concentrated, that is, the ecological function is important but the discharge volume is medium, so the area weight is 0.8;

[0090] The point location is a wetland or swamp, and the discharge is dispersed, that is, the ecological function is important but the discharge volume is small, so the area weight is 0.7;

[0091] The point location is in a slow seepage area, and the discharge intensity is weak, that is, it has little impact on the groundwater system, so the area weight is 0.3;

[0092] Analysis found that the central part of the recharge area and the transition area connecting to the discharge area are not fully covered. The (10,1) sub-block is located near the center of the recharge area, and the groundwater flows through here; the (20,1) sub-block is near the transition zone between the recharge area and the discharge area, and the changes in groundwater quality and water level here have an important impact on the water quality and water volume balance of the entire basin. Therefore, it is planned to add monitoring points in the (10,1) and (20,1) sub-blocks of the recharge area;

[0093] The (1,10) of the discharge area is located near the center of the internal wetland of the discharge area, which is a key area of the wetland ecosystem and is sensitive to changes in groundwater discharge volume and water quality. The (1,15) sub-block is near the edge of the discharge area where the water flow is slow and pollutants are likely to accumulate. It is planned to add monitoring points in the (1,10) and (1,15) sub-blocks of the discharge area; that is, x [10,1] = 1, x [20,1] = 1, x [1,10]=1,x [1,15] =1; According to the survey data, the area weight of the newly added sub-block (10,1) is 0.8, and the area weight of the sub-block (20,1) is 0.7; for the drainage area, the area weight of the sub-block (1,10) is 0.8, and the area weight of the sub-block (1,15) is 0.7, then C h1 3.2; C h2 is 2.4; then Z1 increases to 5.6;

[0094] The industrial cluster is located in the middle of the basin, close to one side of the river. Based on field surveys and water flow simulations, the area with large changes in hydrogeological conditions and groundwater levels buried more than 80-100m was determined as an area 2 km long and 0.5 km wide along the river direction based on topography, soil type and water flow velocity. Due to the uneven topography and soil in this area, it was divided into 10 sub-blocks of 100-400 square meters. When dividing the sub-areas, factors such as the location of industrial waste discharge outlets, the impact of wind direction on pollutant diffusion, and differences in soil permeability were taken into account;

[0095] Through detailed geological surveys, we found a complex area of ​​strata and geological structures. Granite and shale are interlaced in this area, forming cracks and faults. The area is about 1.5 square kilometers. According to the geological structure characteristics, it is divided into 15 sub-blocks of 80-300 square meters. When dividing the units, we combined the geological survey results and divided them according to the trend, density and other factors of the cracks and faults.

[0096] During the initial deployment, no monitoring points were set up in areas where the regional hydrogeological conditions changed greatly and the groundwater level was buried more than 80-100m. For the blind area b1 where the regional hydrogeological conditions changed greatly and the groundwater level was buried more than 80-100m, the area weight of each sub-area was about 1. No monitoring points were set up in the blind area b1.

[0097] In the complex stratigraphic and geological structure area, monitoring points were set only in the complex area sub-block (1,1) with relatively stable geological structure at the edge. This is because in the preliminary survey, considering the safety of construction and equipment installation, the relatively stable edge area was given priority. For the blind area b2 in the complex stratigraphic and geological structure area, the area weight of the sub-block (1,1) is 0.9, U b2 =(1-0.9)+(1-0)×14=14.1; in the calculation process, the (1-0) part means that except for the (1,1) unit, the other 14 sub-blocks were not monitored initially, and the area weight of each sub-block was 1, then

[0098] In this application example, the method for determining the area weight within the areas with large variations in regional hydrogeological conditions, areas where the depth of the groundwater level exceeds 80 - 100 m, areas with complex strata and geological structures, complex aquifer system structures, unstable spatial distribution of aquifers, and complex groundwater recharge, runoff, discharge conditions or hydrodynamic characteristics:

[0099] If the point is located in a fault zone where groundwater flows rapidly and the hydraulic connection is strong, the area weight is 1;

[0100] If the point is located in a karst conduit where groundwater flow is concentrated and water quality changes significantly, the area weight is 1;

[0101] If the point is located in a deep karst area where monitoring is difficult and data acquisition needs to be strengthened, the area weight is 1;

[0102] If the point is located in a fault zone where groundwater flow and hydraulic connection are moderate, the area weight is 0.9;

[0103] If the point is located in a karst conduit where groundwater flow is relatively concentrated and water quality changes moderately, the area weight is 0.7;

[0104] If the point is located in a fissure - developed area where the risk of pollutant migration is high and key attention is required, the area weight is 0.6;

[0105] If the point is located in a dense rock formation where groundwater flow is slow and the impact on the system is small, the area weight is 0.3;

[0106] For areas with large variations in hydrogeological conditions and areas where the depth of the groundwater level exceeds 80 - 100 m, the (3,1) sub - block is located on the main path of pollutant diffusion, which is determined through comprehensive analysis of pollutant diffusion simulation and factors such as wind direction and water flow;

[0107] (5,1) sub - block is close to the pollutant accumulation area, where the terrain and water flow conditions are prone to pollutant accumulation; (7,1) sub - block is near the downstream boundary of the diffusion area, which is crucial for monitoring whether pollutants flow out of this area; It is planned to add monitoring points in (3,1) sub - block, (5,1) sub - block, and (7,1) sub - block. At this time, U b1 needs to be recalculated. The area weight of the newly added sub - block (3,1) is 0.9, the area weight of sub - block (5,1) is 0.8, and the area weight of sub - block (7,1) is 0.7. Then U b1 =(1 - 0)×7+(1 - 0.9)+(1 - 0.8)+(1 - 0.7)=7.6;

[0108] For areas with complex strata and geological structures, the (3,3) sub-block is located inside the complex area and near the fracture development area, where the fractures may be channels for pollutant migration; the (3,5) sub-block is near the center of the complex area and is representative of the water quality changes in the entire complex area; the (5,3) sub-block is close to the fault zone, and the fault zone may affect the flow of groundwater and the diffusion of pollutants; the (5,5) sub-block is at another key part of the complex area, and the geological changes here have a greater impact on the groundwater quality; it is planned to add monitoring points in the (3,3) sub-block, (3,5) sub-block, (5,3) sub-block, and (5,5) sub-block.

[0109] At this time, U b2 needs to be recalculated. The area weight of the newly added sub-block (3,3) is 0.8, the area weight of the sub-block (3,5) is 0.7, the area weight of the sub-block (5,3) is 0.8, and the area weight of the sub-block (5,5) is 0.7. Then U b2 =(1 - 0.9)+(1 - 0.8)+(1 - 0.7)+(1 - 0.8)+(1 - 0.7)+(1 - 0)×10 = 11.1. Then

[0110] For the layout planning of sampling points in the pore water unit of the mountain valley, the costs of sampling points in different sub-blocks in each area do not vary significantly. Therefore, no relevant adjustments are made to the cost changes affected by the change of the sampling point location; under the same number of additional points, the impact on the objective function before and after adding the points (10,1), (20,1) sub-blocks in the recharge area and the (1,10), (1,15) sub-blocks in the discharge area is the smallest. Considering the cost, the addition plan for the sampling points in these four places, namely the (10,1), (20,1) sub-blocks in the recharge area and the (1,10), (1,15) sub-blocks in the discharge area, is cancelled when implementing the addition plan, and the addition of other points is carried out according to the addition plan.

Claims

1. A sampling method for groundwater environmental background value investigation, characterized in that, Including the following steps: S1. Data acquisition S1-1. Determine the investigation scope and target aquifer, collect and sort out regional geological, hydrogeological data and water quality test reports. Meanwhile, identify pollution sources, screen historical hydrochemical data, eliminate data with unknown horizons, affected by human pollution, abnormal macro-components, and unbalanced cations and anions, and select background value indicators of macro-components, comprehensive indicators, high-background trace and ultra-trace components; S1-2. Divide the hydrogeological zoning based on geological structures and groundwater recharge-discharge conditions to obtain the statistical unit zoning map, calculate and count the minimum sample size of the statistical unit, and the sample size of a single statistical unit should be no less than 6. The formula for calculating the minimum sample size of the unit is: Where: N is the sample size / unit; t is the t-value in the t-distribution table for a selected confidence level of 95% or degrees of freedom df = N - 2; Cv is the coefficient of variation of the sample data set, that is, the ratio of the sample standard deviation to the sample mean. When multiple indicators are involved, the coefficient of variation of the total dissolved solids can be used as the coefficient of variation of the data set; m is the allowable error. The allowable error of the confined water sample data is within 30%, and the allowable error of the unconfined groundwater sample data is within 20%. S2. Analyze the hydrochemical characteristics of each unit based on the data obtained in S1, obtain key hydrogeological nodes, monitoring blind spots and sampling cost information within each unit, and optimize the sampling layout plan within each regional statistical unit to maximize the coverage of key hydrogeological nodes, minimize monitoring blind spots, and rationalize sampling costs; S3. Use a layered water sampler to collect water samples from groundwater at different depths according to corresponding depth intervals, and use a water quality analyzer to detect the conventional indicators of the water samples and record the detection results.

2. The method for site selection and sampling for groundwater environmental background value investigation according to claim 1, characterized in that, The key hydrogeological nodes are any one or any combination of two or more of the recharge area, discharge area and strong runoff zone area.

3. The sampling method for groundwater environmental background value investigation according to claim 1, characterized in that, The specific method of S2 includes: S2-1: Based on the objective function Z1, maximize the coverage of the key hydrological nodes described in S1-2. The objective function Z1 is: In the formula: Z1 is the sum of the coverage degrees of all key hydrological nodes, n is the key hydrological node, and h k is the k-th key hydrological node, and C hk is the coverage coefficient of the k-th key hydrological node; preferentially set monitoring points near the key hydrological nodes to maximize Z1; S2-2: Minimize the monitoring blind area described in S1-2 based on the objective function Z2, and the objective function Z2 is as follows: Where: Z2 is the sum of the uncovered degrees of all monitoring blind areas, m is the total number of monitoring blind areas; b l is the l-th monitoring blind area, where l ranges from 1 to m and is used to traverse all monitoring blind areas; U bl is the measure of the uncovered degree of the l-th monitoring blind area, and the calculation method is as follows: Where: M bl is the set of positions (i, j) related to the l-th monitoring blind area, which minimizes Z2; S2-3. Balance the cost described in S1-2 based on the objective function Z3, and the objective function Z3 is: Z3 = N × ∑ i ∑ j c ij In the formula: N = ∑ i ∑ j x ij , N represents the total number of points, i and j are the indices or coordinates of the positions; c ij is the cost of placing a point at the position (i, j); adjust the positions of the monitoring points to minimize Z3.

4. A sampling point layout method for groundwater environmental background value investigation according to claim 1, characterized in that In S3, prepare sampling equipment according to research requirements and cost budgets. The sampling equipment includes a water sampler, water quality analysis instruments and a water level gauge.

5. A sampling method for groundwater environmental background value investigation according to claim 3, characterized in that The monitoring blind spots in S2-2 are areas with large changes in regional hydrogeological conditions, deep groundwater levels exceeding 80-100m, complex strata and geological structures, complex aquifer system structures, unstable spatial distribution of aquifers, and complex groundwater recharge, runoff, discharge conditions or hydrodynamic characteristics; the cost in S2-3 is the cost caused by the number of sampling points, drilling and well construction, sampling and test analysis.

6. The method for site selection and sampling for groundwater environmental background value investigation according to claim 3, wherein, The described C hk is calculated as follows: In the formula: x ij is a decision variable, indicating whether to set a monitoring point at the position (i, j), and x ij ∈{0, 1}, x ij = 1 means setting a monitoring point, and x ij = 0 means not setting a monitoring point; N hk is the set of positions (i, j) related to the k-th key hydrological node h k .

7. A sampling point layout method for groundwater environmental background value investigation according to claim 1, characterized in that In S2, it also includes dynamically adjusting the distribution points according to the background value information described in S1, dividing the rainy season and the dry season according to seasons, and setting the weights of the objective functions, ω 1r , ω 2r , ω 3r are the weights of the objective functions Z1, Z2, and Z3 during the rainy season, and ω 1r +ω 2r +ω 3r = 1; ω 1d , ω 2d , ω 3d are the weights of the objective functions Z1, Z2, and Z3 during the dry season, and ω 1d +ω 2d +ω 3d = 1.

8. A sampling method for groundwater environmental background value investigation according to claim 7, characterized in that Dynamically correct the rainy season weight ω through real-time meteorological data 1r , when the real-time rainfall exceeds the threshold, increase ω 1r to 0.6 and decrease ω 3r to 0.1; predict the dry season water level decline rate based on the groundwater dynamic model. If the monthly decline > 5%, then increase ω 2d to 0.

5.

9. A sampling point layout method for groundwater environmental background value investigation according to claim 7, characterized in that According to the coefficient of variation Cv(t) of the real-time monitoring data, the sample size N is dynamically corrected, and the correction formula is: Among them, Cv(t) represents the coefficient of variation changing with time or season, and ΔN is a dynamic correction term. When Cv(t) increases by more than 20% compared with the previous cycle, ΔN = 2, otherwise ΔN = 0.

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