Water quality sampling decision method for optimization of groundwater washing well duration and suppression of dead water interference

Abstract

The present application relates to the technical field of water quality detection, and particularly relates to a water quality sampling decision method for optimization of well washing duration and inhibition of dead water interference. The method collects water temperature and conductivity by a multi-parameter sensor at a preset step length, calculates water density, obtains buoyancy frequency square based on static water calibration noise standard deviation, and constructs a vertical density profile. According to the first and second threshold values, strong density stratification, weak mixed transition and full mixed zone are divided, and the dead water thickness is calculated by using induced flow rate and convection velocity integral. The historical concentration is obtained to quantify the cross contamination risk. The wellbore inner diameter, Peclet number and dead water volume are combined to solve the targeted displacement volume, and the well washing duration is optimized by the targeted displacement volume. Sampling is performed after well washing for the optimized duration. The present application quantifies stratification and risk, overcomes the defects of traditional lack of quantitative criteria, can target regulation and control displacement and flushing, inhibits dead water interference, and improves sampling reliability and efficiency.

Description

Technical Field

[0001] This invention relates to the field of water quality testing technology, and in particular to a water quality sampling decision-making method for optimizing groundwater well flushing time and suppressing dead water interference. Background Technology

[0002] Well flushing and water quality sampling are core components of groundwater environmental monitoring systems. The aim is to obtain representative water samples from the wellbore that accurately reflect the chemical characteristics of the target aquifer. However, in actual well flushing operations, the water in the wellbore is often not in a homogeneous mixed state. Instead, due to temperature gradients, differences in solute concentrations, and the residual effects of historical well flushing disturbances, a vertical density stratification structure is formed. This results in some sections of the water having extremely weak flow and slow renewal and replacement, known as stagnant water zones. These stagnant water zones have limited material exchange with the aquifer water, but they can continuously release historically accumulated pollutants during the well flushing process, thereby interfering with the water quality indicators of subsequent sampling. Currently, the commonly used well-washing termination criteria at home and abroad include displacement methods based on a fixed wellbore volume multiple, such as 3 to 5 times the wellbore water volume, and the method of stabilizing three consecutive readings of field monitoring parameters. However, these existing technologies all treat the wellbore as a completely mixed reactor, ignoring the non-uniform effect of the local stagnation zone caused by vertical density stratification on the displacement efficiency. They also lack quantitative characterization methods for the thickness, stability, and pollutant exchange potential between the dead water zone and the aquifer. As a result, the determination of the well-washing endpoint often relies on the operator's subjective experience or an overly conservative fixed-volume flush, making it difficult to effectively identify the risk of slow release of pollutants in the weakly mixed transition zone.

[0003] Therefore, there is an urgent need for a water quality sampling decision-making method that optimizes well flushing time and suppresses dead water interference. This method can accurately quantify the thickness of dead water interference and the risk of cross-contamination within the well, and optimize well flushing time and sampling timing accordingly to improve the representativeness and reliability of groundwater quality sampling data. Summary of the Invention

[0004] To overcome the shortcomings of insufficient sampling representativeness and difficulty in controlling the risk of cross-contamination during the well flushing process of groundwater monitoring wells due to the lack of quantitative criteria, this invention provides a water quality sampling decision method for optimizing groundwater well flushing time and suppressing dead water interference.

[0005] The technical implementation scheme of the present invention is: a water quality sampling decision method for optimizing groundwater well flushing time and suppressing dead water interference, comprising the following steps: S1: Collect the physicochemical characteristics data of the water layer in the monitoring well, and construct a vertical profile of the wellbore based on the physicochemical characteristics data of the water layer; S2: Based on the vertical profile of the well shaft, identify the type of water retention zone within the well shaft and calculate the dead water interference thickness; S3: Obtain historical water sample data and aquifer hydrogeological data from monitoring wells to quantify the potential risk of cross-contamination of stagnant water. S4: Obtain the wellbore structure data of the monitoring well, combine the water retention section type and dead water interference thickness to solve the targeted displacement volume, and optimize the well washing time; S5: Decision on the timing of water quality sampling based on the optimized well washing time.

[0006] Preferably, the acquisition of water layer physicochemical characteristic data from the monitoring well, and the construction of a vertical profile of the wellbore based on the water layer physicochemical characteristic data, includes: acquiring water layer physicochemical characteristic data from bottom to top using a multi-parameter sensor at a preset step size; the water layer physicochemical characteristic data includes measured water temperature and measured conductivity; obtaining water temperature, water density, and conductivity under standard conditions as reference temperature, reference water density, and reference conductivity; calculating the water density at different depths within the monitoring well; calculating the standard deviation of the density gradient background noise caused by sensor measurements using a static water calibration method; calculating the vertical density gradient based on the water density at different depths; calculating the square of the buoyancy frequency at different depths based on the water density at different depths, the standard deviation of the density gradient background noise caused by sensor measurements, and the vertical density gradient; and constructing a vertical profile of the wellbore.

[0007] Preferably, the calculation of the squared buoyancy frequency at different depths based on water density at different depths, the standard deviation of background noise from the density gradient caused by sensor measurements, and the vertical density gradient includes: The formula for water density is: ; In the formula, For depth The density of the water at that location For reference water density, The coefficient of thermal expansion of water is denoted as . For depth The measured water temperature at the location, For reference temperature, The conductivity density conversion factor, For depth Measured conductivity at the location Reference conductivity; The formula for the square of the buoyancy frequency is: ; In the formula, For depth The square of the buoyancy frequency at that point It is the acceleration due to gravity. For reference water density, For depth Vertical density gradient at that location This represents the standard deviation of the background noise from the density gradient caused by sensor measurements.

[0008] Preferably, the step of identifying the type of water retention zone within the wellbore based on the vertical profile of the wellbore and calculating the dead water interference thickness includes: the water retention zone type includes a high-density stratified retention zone, a weak mixing transition zone, and a fully mixed zone; a first buoyancy frequency threshold and a second buoyancy frequency threshold are preset; the distribution zone of the square of the buoyancy frequency is obtained based on the vertical profile of the wellbore; the distribution zone with the square of the buoyancy frequency less than the first buoyancy frequency threshold is classified as a fully mixed zone; the distribution zone with the square of the buoyancy frequency greater than or equal to the first buoyancy frequency threshold and less than the second buoyancy frequency threshold is classified as a weak mixing transition zone; the distribution zone with the square of the buoyancy frequency greater than or equal to the second buoyancy frequency threshold is classified as a high-density stratified retention zone; the vertical flow velocity induced by the well-washing pump at different depths is obtained; the characteristic convection velocity caused by the density difference is calculated based on the water density at different depths; the corresponding depths at the top and bottom of the high-density stratified retention zone and the weak mixing transition zone are obtained; and the equivalent dead water interference thickness is calculated.

[0009] Preferably, the step of obtaining the corresponding depths at the top and bottom of the sections of the high-density stratified retention zone and the weak-mixing transition zone, and calculating the equivalent dead water interference thickness, includes: The formula for the equivalent dead water interference thickness is: ; In the formula, For the equivalent dead water interference thickness, The depth corresponding to the top of the segment. The depth corresponding to the bottom of the section. For well washing pumps at depth The induced vertical flow velocity at that location, For depth Characteristic convection velocities caused by density differences For the characteristic function, when The value is 1 otherwise the value is 0. It is the integral variable.

[0010] Preferably, the step of acquiring historical water sample data and aquifer hydrogeological data of the monitoring well to quantify the potential risk quality of cross-contamination of dead water includes: obtaining the settling time, the initial concentration of pollutants at different depths in the historical water sample, and the cross-sectional area per unit depth of the monitoring well based on the historical water sample data; obtaining the effective hydrodynamic dispersion coefficient of pollutants in the dead water layer, the pollutant degradation rate constant, and the surface flux of characteristic leached substances of the well casing material through experiments based on the aquifer hydrogeological data; and calculating the potential risk quality of cross-contamination of dead water.

[0011] Preferably, the calculation of the potential risk of cross-contamination in stagnant water includes: The potential risk of cross-contamination in stagnant water is posed by the following quality formula: ; In the formula, The potential risk of cross-contamination in stagnant water quality, For the equivalent dead water interference thickness, The surface flux of characteristic leachates of well casing material. The effective hydrodynamic dispersion coefficient of pollutants in the dead water layer. This refers to the settling time. Pi For the depth of historical water samples The initial concentration of pollutants at the location, The pollutant degradation rate constant, This refers to the cross-sectional area per unit depth of the monitoring well. It is the integral variable.

[0012] Preferably, the step of acquiring the wellbore structure data of the monitoring well, calculating the targeted displacement volume based on the water retention zone type and dead water interference thickness, and optimizing the well washing time includes: obtaining the initial characteristic pollutant concentration of the dead water layer based on the high-density stratified retention zone, setting a preset threshold for the desired dead water residual pollutant concentration, obtaining the wellbore inner diameter based on the wellbore structure data, calculating the Pecklet number of the wellbore water flow based on the characteristic convection velocity caused by the density difference and the wellbore inner diameter, calculating the dead water geometric volume based on the wellbore inner diameter and the dead water interference thickness, obtaining the maximum allowable cross-contamination background mass by collecting blank samples from the analysis equipment, calculating the targeted displacement volume based on the dead water geometric volume, the initial characteristic pollutant concentration of the dead water layer, the Pecklet number of the wellbore water flow, and the desired dead water residual pollutant concentration threshold, obtaining the actual flow rate of the well washing pump and the basic time for pipeline flushing, and optimizing the well washing time based on the targeted displacement volume.

[0013] Preferably, the step of obtaining the actual flow rate of the well-washing pump and the baseline time of pipeline flushing, and optimizing the total well-washing time in conjunction with targeted displacement volume, includes: The formula for targeted displacement volume is: ; In the formula, To target displacement volume, For the geometric volume of stagnant water, The Pecklet number is the flow rate of the wellbore. The initial characteristic pollutant concentration of the dead water layer. The desired threshold for residual pollutant concentration in stagnant water; The formula for well washing time is: ; In the formula, To optimize the well washing time, To target displacement volume, This refers to the actual flow rate of the well-washing pump. This is the base time for pipeline flushing. The potential risk of cross-contamination in stagnant water quality, To determine the maximum permissible background quality for cross-contamination during sampling analysis, It is a sensitivity index.

[0014] Preferably, the step of deciding on the timing of water quality sampling based on the optimized well-washing time includes: after cleaning the monitoring well according to the optimized well-washing time, collecting water quality samples for testing.

[0015] The beneficial effects of this invention are: This invention utilizes high-parameter sensors for vertical profile measurement, combined with precise conversion of water density, conductivity, and temperature, and quantitative calculation of the square of buoyancy frequency. This allows for an objective and continuous characterization of the vertical density stratification structure within the monitoring well casing. It overcomes the crude assumption in traditional well-washing operations that the well casing is treated as a homogeneous mixture, providing a reliable data foundation for subsequent identification of stagnant sections and quantification of dead water thickness. This fundamentally improves the judgment bias caused by neglecting temperature-salinity coupling stratification.

[0016] This invention divides wellbore water into a high-density stratified retention zone, a weakly mixed transition zone, and a fully mixed zone based on a buoyancy frequency threshold. It also introduces an equivalent dead water interference thickness index and incorporates the competitive relationship between the flow velocity induced by the well-washing pump and the convection velocity driven by density difference into the integral calculation. This enables the quantitative definition of dead water areas that are difficult to replace during actual well washing. Compared with existing empirical methods that rely solely on turbidity or conductivity plateau values, this invention can more accurately locate the risk zone of residual pollution, providing a direct spatial scale basis for subsequent calculation of targeted displacement volume.

[0017] This invention innovatively integrates multiple parameters in risk assessment, including historical water sample pollutant concentration, pollutant degradation rate constant, effective dispersion coefficient of dead water layer, and surface flux of leached substances characteristic of well casing material. It constructs a potential risk quality model for cross-contamination in dead water, considering the degradation and residue of historically accumulated pollutants during retention, and also covering the leaching contribution of the well casing material itself under long-term static conditions. This model can comprehensively reflect the pollution load released by the dead water zone during well washing, making up for the shortcomings of existing technologies that only focus on the water quality of the aquifer and ignore the pollution sources inside the well casing. Attached Figure Description

[0018] Figure 1This is a flowchart of the water quality sampling decision-making method for optimizing groundwater well washing time and suppressing dead water interference according to the present invention; Figure 2 This is a schematic diagram of the targeted displacement volume calculation process of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Water quality sampling decision-making methods for optimizing groundwater well flushing time and suppressing dead water disturbance, such as Figure 1 and Figure 2 As shown, it includes the following steps: S1: Collect the physicochemical characteristics data of the water layer in the monitoring well, and construct a vertical profile of the wellbore based on the physicochemical characteristics data of the water layer; The water layer physicochemical characteristics data of the monitoring well are collected from bottom to top by a multi-parameter sensor at a preset step size. The water layer physicochemical characteristics data include measured water temperature and measured conductivity. Water temperature, water density and conductivity under standard conditions are obtained as reference temperature, reference water density and reference conductivity. The water density at different depths in the monitoring well is calculated. The standard deviation of the density gradient background noise caused by the sensor measurement is calculated by the static water calibration method. The vertical density gradient is calculated according to the water density at different depths. Based on the water density at different depths, the standard deviation of the density gradient background noise caused by the sensor measurement and the vertical density gradient, the square of the buoyancy frequency at different depths is calculated, and the vertical profile of the well is constructed.

[0021] The formula for water density is: ; In the formula, For depth The density of the water at that location For reference water density, The coefficient of thermal expansion of water is denoted as . For depth The measured water temperature at the location, For reference temperature, The conductivity density conversion factor, For depth Measured conductivity at the location Reference conductivity; The formula for the square of the buoyancy frequency is: ; In the formula, For depth The square of the buoyancy frequency at that point It is the acceleration due to gravity. For reference water density, For depth Vertical density gradient at that location This represents the standard deviation of the background noise from the density gradient caused by sensor measurements.

[0022] It should be further explained that a multi-parameter water quality analyzer equipped with a high-precision temperature probe and a conductivity probe is used to collect the physicochemical characteristics data of the water layer of the monitoring well point by point from the bottom of the wellbore upwards at a preset step size. The preset step size is usually set to 0.1 meters to 0.2 meters. After obtaining the measured water temperature sequence and measured conductivity sequence at each depth, the data enters the preprocessing stage of the physicochemical characteristics data of the water layer.

[0023] Reference water temperature, reference water density, and reference conductivity values ​​under standard conditions are retrieved from the standard material database for the area where the current monitoring well is located. These reference values ​​are generally the average of groundwater background monitoring statistics over many years in the area. There is often a difference between the actual measured water temperature and the reference water temperature, and the measured conductivity also deviates from the reference conductivity. Since water density changes with temperature and total dissolved solids according to specific physical laws, the actual water density at each sampling point is calculated using a water density formula at each depth. The water density formula converts the raw water temperature and conductivity values ​​measured by multi-parameter sensors into the actual water density at each depth. Its logic is that groundwater density is not a constant value, but decreases with increasing temperature and increases with increasing total dissolved solids (i.e., conductivity). The formula uses the reference water density under standard conditions as a benchmark, subtracts the density decrease caused by temperature increase by multiplying the deviation of the measured water temperature from the reference temperature by the coefficient of thermal expansion of the water, and accumulates the dissolved matter by multiplying the deviation of the measured conductivity from the reference conductivity by the conductivity-density conversion coefficient. The increase in density is used to obtain the true water density at the current depth. In the formula for water density, the coefficient of thermal expansion of water is set as follows: A set of monitoring well water samples are collected on-site and placed in a constant-temperature water bath. The actual density values ​​are measured at different temperature points, such as 5°C, 15°C, 25°C, and 35°C. The absolute value of the slope of density change with temperature is obtained after linear regression, which is the actual coefficient of thermal expansion of the water sample. For water samples without a constant-temperature water bath, the coefficient of thermal expansion of pure water in the corresponding temperature range as specified in international standards is used. The conductivity-density conversion coefficient is set as follows: At least five sets of water samples with different dilution ratios are collected on-site. The conductivity and density of each set are measured. Then, conductivity is used as the abscissa and density as the ordinate for linear fitting. The slope of the fitted line is the actual conductivity-density conversion coefficient of the monitoring well water sample. After calculating the actual water density value at each depth point using this formula, the density change rate between adjacent depths, i.e., the vertical density gradient, is further obtained. The vertical density gradient is calculated by dividing the difference in water density between two adjacent depth points by the aforementioned preset step size.

[0024] The standard deviation of the density gradient background noise caused by sensor measurements is calculated using the static water calibration method. The specific implementation method of the static water calibration method is as follows: The multi-parameter sensor is completely immersed in a homogenized water tank that has been thoroughly stirred and mixed on site. At least sixty sets of water temperature and conductivity data are continuously collected at the same sampling frequency as the well shaft measurement. The standard deviations of these sixty sets of water temperature data and conductivity data are calculated. Then, these two standard deviations are multiplied by the partial derivatives of the water density formula with respect to temperature and conductivity, i.e., the coefficient of thermal expansion and the conductivity-density conversion coefficient, respectively, to obtain the total standard deviation of the density measurement value. Finally, the density standard deviation is divided by the preset step size used in the well shaft measurement to obtain the standard deviation of the density gradient background noise. This standard deviation represents the minimum true density gradient limit that can be distinguished under the coupling effect of the sensor system's own accuracy and external random interference. After completing the calculation of the vertical density gradient and the determination of the standard deviation of the density gradient background noise, the measured value of the vertical density gradient, the reference water density value, the gravitational acceleration value, and the aforementioned standard deviation of the density gradient background noise at each depth point are substituted into the formula for the square of the buoyancy frequency to calculate the square of the buoyancy frequency for each depth.

[0025] The buoyancy frequency square formula is used to quantify the stability of water at various depths within a wellbore against vertical mixing. When the vertical density gradient is negative, meaning the density increases with depth, the water mass, after being disturbed, will return to its original position under gravity, forming stable density stratification, and the buoyancy frequency square is positive. When the density gradient is zero or positive, the water tends to mix or is unstable. The formula uses the gravitational acceleration multiplied by the absolute value of the vertical density gradient and divided by the reference water density as the basic framework of the classical buoyancy frequency square, and introduces an exponential function for correction. When the actual absolute value of the density gradient is less than the standard deviation of the density gradient background noise caused by sensor measurements, the calculated value of the buoyancy frequency square is automatically weakened, separating the measurement noise from the true stratification signal and improving misjudgments caused by insufficient equipment accuracy. A curve is plotted with depth as the ordinate and buoyancy frequency square as the abscissa, forming the vertical profile of the wellbore. This profile can visually reflect the continuous change in the stable stratification of water density within the wellbore with depth.

[0026] S2: Based on the vertical profile of the well shaft, identify the type of water retention zone within the well shaft and calculate the dead water interference thickness; The water retention zone types include high-density stratified retention zones, weakly mixed transition zones, and fully mixed zones. A first buoyancy frequency threshold and a second buoyancy frequency threshold are preset. Distribution zones with the square of the buoyancy frequency are obtained based on the vertical profile of the wellbore. Zones with the square of the buoyancy frequency less than the first buoyancy frequency threshold are classified as fully mixed zones. Zones with the square of the buoyancy frequency greater than or equal to the first buoyancy frequency threshold but less than the second buoyancy frequency threshold are classified as weakly mixed transition zones. Zones with the square of the buoyancy frequency greater than or equal to the second buoyancy frequency threshold are classified as high-density stratified retention zones. The vertical flow velocity induced by the well-washing pump at different depths is obtained. The characteristic convection velocity caused by the density difference is calculated based on the water density at different depths. The corresponding depths at the top and bottom of the high-density stratified retention zones and weakly mixed transition zones are obtained, and the equivalent dead water interference thickness is calculated.

[0027] The formula for the equivalent dead water interference thickness is: ; In the formula, For the equivalent dead water interference thickness, The depth corresponding to the top of the segment. The depth corresponding to the bottom of the section. For well washing pumps at depth The induced vertical flow velocity at that location, For depth Characteristic convection velocities caused by density differences For the characteristic function, when The value is 1 otherwise the value is 0. It is the integral variable.

[0028] It should be further explained that, according to groundwater dynamics and stratified flow theory, the larger the square of the buoyancy frequency, the faster the water density increases with depth at that depth, the stronger the gravitational restoring force on vertical displacement, and the more difficult water exchange; conversely, the smaller the square of the buoyancy frequency, the more homogeneous the mixing tends to be. Based on this principle, a first buoyancy frequency threshold and a second buoyancy frequency threshold are preset. The square values ​​of the buoyancy frequency at all depth points calculated in step S1 are arranged in ascending order, and a cumulative frequency distribution curve of the square of the buoyancy frequency is plotted. The square value of the buoyancy frequency corresponding to 30% of the cumulative frequency is taken as the first buoyancy frequency threshold, and the square value of the buoyancy frequency corresponding to 70% of the cumulative frequency is taken as the second buoyancy frequency threshold. If there is a clear plateau segment on the cumulative frequency distribution curve, the starting value of the plateau segment is taken as the first threshold and the ending value is taken as the second threshold.

[0029] After setting the preset threshold, the buoyancy frequency square of each depth point is scanned one by one from the vertical profile of the wellbore. Adjacent depth points with the same interval are merged into a continuous segment. The specific division rules are as follows: all continuous depth ranges with buoyancy frequency square less than the first buoyancy frequency threshold are classified as fully mixed zones. In this area, the water density gradient is extremely weak, and vertical mixing is not inhibited by density. Continuous depth ranges with buoyancy frequency square greater than or equal to the first buoyancy frequency threshold and simultaneously less than the second buoyancy frequency threshold are classified as weakly mixed transition zones. In this area, the density gradient can produce a certain stabilizing effect, but a strong stratified structure has not yet been formed. Continuous depth ranges with buoyancy frequency square greater than or equal to the second buoyancy frequency threshold are classified as strong density stratified retention zones. In this area, the density increases significantly with depth, the buoyancy frequency square value is high, and vertical water exchange is strongly inhibited. This is the most important potential source of stagnant water.

[0030] After completing the above-mentioned section type division, the equivalent dead water interference thickness is calculated for each identified high-density stratified retention zone and weak mixing transition zone. The equivalent dead water interference thickness is directly calculated as zero for the fully mixed zone, as there is no density suppression effect. The vertical velocity distribution induced by the well-washing pump at various depths within the wellbore and the characteristic convection velocity distribution caused by density differences are obtained. The well-washing pump is lowered to the predetermined pumping depth and pumping is started. Miniature propeller-type flow meters or electromagnetic flow probes are placed along the wellbore axis at different depths within the wellbore to directly measure the vertical velocity components at each depth. The spacing of the measurement points along the depth direction is consistent with the preset step size in step S1. For the characteristic convection velocity caused by density difference, take the absolute value of the difference between the water density at depth z obtained in step S1 and the density at the next preset step above depth z, multiply it by the gravitational acceleration, divide it by the water density at that depth, multiply it by the distance between that depth and the adjacent strong density stratification boundary, and finally perform a square root operation on the product. The result is the characteristic convection velocity caused by density difference at that depth.

[0031] After obtaining the distribution data of the two types of velocities along the depth, for each stagnation section that needs to be calculated, the corresponding depths at the top and bottom of the section are extracted and denoted as the upper and lower limits of integration, respectively. The induced vertical flow velocity and characteristic convection velocity at each depth point are then substituted into the integral formula for the equivalent dead water disturbance thickness for integration calculation. The formula's logic lies in the fact that the thickness of the dead water zone is not simply a geometric thickness determined by the density stratification boundary, but should be determined by a weighted average of the relative strengths between the forced convection induced by pumping and the natural convection caused by density differences. When the induced vertical velocity at a certain depth is greater than the characteristic convection velocity, the pumping effect is sufficient to overcome the density stabilization effect, and the water at that point can be effectively displaced, contributing almost zero to the dead water thickness. When the induced vertical velocity is less than the characteristic convection velocity, density-driven flow dominates, and water stagnation is severe; the dead water thickness at that point should be included. The indicative function in the formula further requires that the integral be included only when the square of the buoyancy frequency at that depth is greater than zero, i.e., when stable stratification exists, thus excluding the artificially inflated thickness in unstable or mixed states. After performing the above integral calculations for each strong density stratification stagnation zone and weak mixing transition zone, the calculation results of each zone are summed to obtain the equivalent dead water interference thickness of the entire monitoring well.

[0032] S3: Obtain historical water sample data and aquifer hydrogeological data from monitoring wells to quantify the potential risk of cross-contamination of stagnant water. Based on the historical water sample data, the settling time, the initial concentration of pollutants at different depths in the historical water samples, and the cross-sectional area per unit depth of the monitoring well are obtained. Based on the hydrogeological data of the aquifer, the effective hydrodynamic dispersion coefficient of pollutants in the dead water layer, the pollutant degradation rate constant, and the surface flux of characteristic leached substances of the well casing material are obtained through experiments. The potential risk of cross-contamination in the dead water is calculated.

[0033] The potential risk of cross-contamination in stagnant water is posed by the following quality formula: ; In the formula, The potential risk of cross-contamination in stagnant water quality, For the equivalent dead water interference thickness, The surface flux of characteristic leachates of well casing material. The effective hydrodynamic dispersion coefficient of pollutants in the dead water layer. This refers to the settling time. Pi For the depth of historical water samples The initial concentration of pollutants at the location, The pollutant degradation rate constant, This refers to the cross-sectional area per unit depth of the monitoring well. It is the integral variable.

[0034] It is necessary to further explain that the historical water sample data of the monitoring well is obtained. This historical water sample data usually comes from the water quality analysis records collected and stored in the database during each routine monitoring or site survey of the monitoring well. This includes the types and concentrations of target pollutants in water samples at different depths during each sampling. Two key pieces of information are extracted from these historical records: first, the settling time, which refers to the total time from the end of the last well flushing or sampling disturbance and the return of the well to a settled state until the start of the current planned well flushing operation; and second, the initial concentration of pollutants at different depths in the historical water samples. This concentration is taken as the average concentration of pollutants in the same depth segment in the most recent one or more historical monitoring sessions, serving as a reference value for the initial concentration of pollutants accumulating in the dead water layer. The cross-sectional area per unit depth of the monitoring well is obtained from the well completion file. This area is calculated by multiplying the square of the well's inner diameter by pi and then dividing by four. Since the monitoring well is usually a cylindrical structure, the cross-sectional area per unit depth remains basically constant in the vertical direction of the well. If there are variable diameter or blocked sections in the well, the values ​​are taken in segments according to the actual well diameter distribution.

[0035] After processing historical water sample data, aquifer hydrogeological data was obtained, and three key parameters were determined experimentally. The first parameter is the effective hydrodynamic dispersion coefficient of pollutants in the dead aquifer. This coefficient reflects the spatial propagation ability of pollutants within the dead aquifer due to the combined effects of molecular diffusion and micro-convection. Its value is related to the tortuosity of the porous medium within the dead aquifer, the diffusion characteristics of the pollutant molecules themselves, and the micro-flow channels remaining within the dead aquifer. Through indoor soil column dispersion experiments, undisturbed core samples from the aquifer retained during well construction or disturbed soil samples collected in the field were used to fill dispersion columns. Using the actual hydrochemical conditions of the dead aquifer section in the field as the background solution, tracer pollutants were injected, and the breakthrough curve was measured. The effective hydrodynamic dispersion coefficient was then obtained by fitting the data. The second parameter is the pollutant degradation rate constant, which describes the rate at which the target pollutant decreases under static retention conditions due to biodegradation, hydrolysis, and natural redox attenuation processes. This is achieved by collecting water samples from a dead water area for indoor incubation experiments. The residual concentration after different incubation times is measured under simulated field temperature and dissolved oxygen conditions, and then the degradation rate constant is obtained by fitting a first-order degradation kinetic equation. The third parameter is the surface flux of characteristic dissolved substances from the well casing material. This parameter reflects the mass rate at which characteristic dissolved substances are released from the surface of the well casing material per unit time and per unit area into adjacent water bodies. This parameter is obtained through a static immersion experiment: a small section of pipe material of the same material and specifications as the well casing is taken and immersed in blank water with similar chemical properties to the monitored well water. The immersion is carried out under static conditions at the same temperature as the well casing for a duration equal to the static retention time at the site. The concentration increment of characteristic dissolved substances in the immersion solution is then measured. Combined with the immersion area and volume of the sample, the surface flux of characteristic dissolved substances can be calculated.

[0036] After obtaining all the above parameters, numerical integration was performed according to the potential risk mass formula for cross-contamination in dead water. The formula's logic lies in decomposing the pollution sources in the dead water zone into two independent and coexisting contributions: the first contribution comes from characteristic substances slowly dissolved into the dead water layer by the well casing material during long-term static immersion. Its release is driven by the surface flux of these characteristic dissolved substances and propagates outwards in the dead water layer through molecular diffusion. The distance of diffusion is determined by the product of the effective hydrodynamic dispersion coefficient and the static residence time. The second contribution comes from the portion of pollutants that remained in the dead water layer after natural decay during static residence. Its remaining concentration decays exponentially according to first-order degradation kinetics. The sum of the concentrations from both sources is multiplied by the cross-sectional area per unit depth of the monitoring well and integrated along the entire dead water thickness to obtain the total risk pollution mass. A larger mass indicates a heavier pollution load to be removed during well washing. A higher potential risk mass value indicates that if the dead water cannot be fully replaced during well washing, the residual pollutant mass will severely interfere with subsequent samples, requiring a longer washing time or a larger displacement volume for removal.

[0037] S4: Obtain the wellbore structure data of the monitoring well, combine the water retention section type and dead water interference thickness to solve the targeted displacement volume, and optimize the well washing time; The initial characteristic pollutant concentration of the dead water layer is obtained based on the high-density stratified retention zone. A threshold for the desired residual pollutant concentration in the dead water is preset. The wellbore inner diameter is obtained based on the wellbore structure data. The Pecklet number of the wellbore water flow is calculated based on the characteristic convection velocity caused by the density difference and the wellbore inner diameter. The dead water geometric volume is calculated based on the wellbore inner diameter and the dead water interference thickness. The maximum allowable cross-contamination background mass is obtained by collecting blank samples from the analysis equipment. The targeted displacement volume is calculated based on the dead water geometric volume, the initial characteristic pollutant concentration of the dead water layer, the Pecklet number of the wellbore water flow, and the desired residual pollutant concentration threshold in the dead water. The actual flow rate of the well-washing pump and the basic time of pipeline flushing are obtained. The well-washing time is optimized by combining the targeted displacement volume.

[0038] The formula for targeted displacement volume is: ; In the formula, To target displacement volume, For the geometric volume of stagnant water, The Pecklet number is the flow rate of the wellbore. The initial characteristic pollutant concentration of the dead water layer. The desired threshold for residual pollutant concentration in stagnant water; The formula for well washing time is: ; In the formula, To optimize the well washing time, To target displacement volume, This refers to the actual flow rate of the well-washing pump. This is the base time for pipeline flushing. The potential risk of cross-contamination in stagnant water quality, To determine the maximum permissible background quality for cross-contamination during sampling analysis, It is a sensitivity index.

[0039] It needs to be further explained that, such as Figure 2 As shown, the initial characteristic pollutant concentration of the dead water layer is obtained from the identified high-density stratified retention zone. This concentration represents the most representative pollution level within the dead water zone. Since the high-density stratified retention zone is the part of the wellbore with the worst flowability and the most difficult water renewal, it is also the section most difficult to displace during well washing. Technicians select the maximum pollutant concentration in the water sample within this section as the initial characteristic pollutant concentration and preset a desired dead water residual pollutant concentration threshold. This threshold is usually determined based on the target accuracy and detection limit requirements for subsequent sampling and analysis of the monitoring well, for example, it is set to twice the method detection limit or one-tenth of the target water quality standard limit.

[0040] The wellbore inner diameter is obtained from the well structure design drawings or field measurement data. Based on the characteristic convection velocity caused by the density difference and the wellbore inner diameter, technicians calculate the Pecklet number of the wellbore flow. The Pecklet number is a dimensionless parameter used to characterize the relative importance of convective and diffusion mass transfer within the wellbore. It is calculated by multiplying the characteristic convection velocity by the wellbore inner diameter and dividing by the effective hydrodynamic dispersion coefficient. A high Pecklet number indicates that convection is dominant, and the displacement of the dead water zone is mainly driven by pump-forced flow. A low Pecklet number indicates that diffusion is not negligible, and the reverse diffusion of pollutants from the dead water zone to the mainstream zone accelerates pollutant release.

[0041] The dead water geometric volume is calculated based on the wellbore inner diameter and the equivalent dead water interference thickness calculated in step S2. The dead water geometric volume represents the physical space occupied by the dead water zone within the wellbore. It is calculated by multiplying the cross-sectional area per unit depth of the wellbore by the equivalent dead water interference thickness. The cross-sectional area per unit depth of the wellbore is obtained by multiplying the square of the wellbore inner diameter by pi and then dividing by four. Since the equivalent dead water interference thickness covers multiple discontinuous sections, technicians need to multiply the equivalent thickness of each independent section by the cross-sectional area per unit depth before summing them to obtain the total geometric volume of all dead water sections within the wellbore. Simultaneously with the calculation of the dead water geometric volume, a blank sample is collected from the analysis equipment to obtain the maximum permissible cross-contamination background mass for sampling and analysis. An analytical equipment blank sample is a full-procedure blank measurement of pure water or clean groundwater free of the target contaminant, performed before the start of on-site well washing and sampling operations, using the exact same sampling bottles, preservatives, transportation conditions, and laboratory analytical procedures as the formal sampling. The result of this measurement represents the background signal level introduced by the analytical equipment and operating procedures themselves, and its value represents the lowest background response value achievable under ideal, uncontaminated conditions. This background response value is then converted into mass units according to the sample volume and the limit of quantitation of the analytical method.

[0042] After completing the preparation of the above basic parameters, the targeted displacement volume is calculated by substituting the dead water geometric volume, the Peckley number of the wellbore flow, the initial characteristic pollutant concentration of the dead water layer, and the desired residual pollutant concentration threshold into the targeted displacement volume formula. The logic of the targeted displacement volume formula is to decompose the pollution sources in the dead water zone into two independent and coexisting contributions: the first contribution comes from characteristic substances slowly dissolved into the dead water layer by the wellbore material during long-term static immersion. Its release is driven by the surface flux of the characteristic dissolved substances and propagates outward in the dead water layer through molecular diffusion. The distance of diffusion is determined by the product of the effective hydrodynamic dispersion coefficient and the static residence time. The second contribution comes from the portion of pollutants that have historically remained in the dead water layer after natural decay during static residence. Its remaining concentration decays exponentially according to first-order degradation kinetics. The sum of the concentrations from both sources is multiplied by the cross-sectional area per unit depth of the monitoring well and integrated along the entire dead water thickness to obtain the total risk pollution mass.

[0043] After the targeted displacement volume was determined, the technicians moved on to the well flushing time optimization phase. This involved obtaining two field operational parameters: the actual flow rate of the well flushing pump and the baseline flushing time. The actual flow rate of the well flushing pump is not the rated flow rate on the pump nameplate, but rather the actual pumping flow rate measured on-site using a flow meter. The baseline flushing time refers to the shortest flushing time required after the main dead water displacement phase to completely replace the remaining contaminated water sample in the pipeline with clean water. This time depends on the pipeline length, inner diameter, and flushing flow rate, and is estimated by dividing the pipeline volume by the flushing flow rate. The well-washing time is calculated using a formula that converts the targeted displacement volume into actual operating time, and then adds an additional flushing time considering the risk of cross-contamination. This involves two independent time accumulations: the first is the main pumping time required to extract the entire targeted displacement volume at the actual flow rate of the well-washing pump—this is the basic time consumption for well washing; the second is an additional compensation flushing time for situations where the potential risk of cross-contamination from stagnant water exceeds permissible levels. This compensation time is scaled by multiplying the baseline pipeline flushing time by a power of the sensitivity exponent of the ratio of risk quality to the maximum permissible background quality. When the risk quality is less than or equal to the permissible background quality, the ratio is less than or equal to one, and the compensation time is no greater than the baseline time. When the risk quality significantly exceeds the standard, the ratio is much greater than one, and the compensation time is amplified exponentially, providing additional flushing margin in high-risk situations. Sensitivity indices, used as adjustment indices, are determined using the analytic hierarchy process (AHP). A three-tiered hierarchical structure is constructed: the target layer represents reasonable values ​​for the sensitivity indices; the criteria layer includes three dimensions: the monitoring purpose of the monitoring well, the characteristics of the target pollutants, and feedback on the well washing effect; and the scheme layer represents discrete alternative sensitivity index levels. Pairwise judgment matrices are constructed for the target layer, and the relative importance of the three criteria is assigned using a nine-level scaling method. The largest eigenvalue and corresponding eigenvector of the judgment matrix are calculated, and the weight coefficients of the three criteria are obtained after normalization. The consistency ratio is then calculated, and the test is passed when the consistency ratio is less than 0.1.

[0044] S5: Decision on the timing of water quality sampling based on the optimized well washing time.

[0045] After cleaning the monitoring well according to the optimized well-washing time, water quality samples were collected for testing.

[0046] It should be further explained that the well-washing duration optimized in step S4 is used as the direct control parameter for on-site operations, and the final well-washing and sampling operations are initiated. Before officially starting the well-washing pump, technicians first check the status of all on-site equipment, including the well-washing pump's cable connections, pipeline sealing, flow meter zeroing calibration, and the normal operation of multi-parameter sensors, ensuring that the equipment can operate continuously and stably within the expected optimized duration. After starting the well-washing pump, timing begins, with the timing endpoint being the time corresponding to the optimized well-washing duration. When the timing reaches the optimized well-washing duration, the well-washing pump is shut off and pumping stops. At this point, after the targeted displacement volume of water in the wellbore, the contaminant concentration in the dead water zone has decreased from the initial characteristic contaminant concentration to below the expected dead water residual contaminant concentration threshold. Simultaneously, the potential risk of cross-contamination in the dead water has also decreased to near or below the maximum cross-contamination background quality allowed for sampling analysis after compensation treatment with additional flushing time. Following on-site operating procedures, the well is left to stand for a period of time to allow the water in the wellbore to return to a stable state, and water samples are collected for subsequent water quality testing.

[0047] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the scope of protection of the present invention. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the essence and scope of the technical solutions of the present invention.

Claims

1. A water quality sampling decision-making method for optimizing groundwater well flushing time and suppressing dead water disturbance, characterized by: Includes the following steps: S1: Collect the physicochemical characteristics data of the water layer in the monitoring well, and construct a vertical profile of the wellbore based on the physicochemical characteristics data of the water layer; S2: Based on the vertical profile of the well shaft, identify the type of water retention zone within the well shaft and calculate the dead water interference thickness; S3: Obtain historical water sample data and aquifer hydrogeological data from monitoring wells to quantify the potential risk of cross-contamination of stagnant water. S4: Obtain the wellbore structure data of the monitoring well, combine the water retention section type and dead water interference thickness to solve the targeted displacement volume, and optimize the well washing time; S5: Decision on the timing of water quality sampling based on the optimized well washing time.

2. The water quality sampling decision-making method for optimizing groundwater well flushing time and suppressing dead water interference according to claim 1, characterized in that, The process of acquiring and monitoring the water layer physicochemical characteristics data and constructing a vertical profile of the wellbore based on the acquired water layer physicochemical characteristics data includes: acquiring water layer physicochemical characteristics data from bottom to top using a multi-parameter sensor at a preset step size; the water layer physicochemical characteristics data includes measured water temperature and measured conductivity; obtaining water temperature, water density, and conductivity under standard conditions as reference temperature, reference water density, and reference conductivity; calculating the water density at different depths within the monitoring well; calculating the standard deviation of the density gradient background noise caused by sensor measurements using a static water calibration method; calculating the vertical density gradient based on the water density at different depths; and calculating the square of the buoyancy frequency at different depths based on the water density at different depths, the standard deviation of the density gradient background noise caused by sensor measurements, and the vertical density gradient; and constructing a vertical profile of the wellbore.

3. The water quality sampling decision-making method for optimizing groundwater well flushing time and suppressing dead water interference according to claim 2, characterized in that, The calculation of the squared buoyancy frequency at different depths based on water density at different depths, the standard deviation of background noise from density gradients caused by sensor measurements, and the vertical density gradient includes: The formula for water density is: ； In the formula, For depth The density of the water at that location For reference water density, The coefficient of thermal expansion of water is denoted as . For depth The measured water temperature at the location, For reference temperature, The conductivity density conversion factor, For depth Measured conductivity at the location Reference conductivity; The formula for the square of the buoyancy frequency is: ； In the formula, For depth The square of the buoyancy frequency at that point It is the acceleration due to gravity. For reference water density, For depth Vertical density gradient at that location This represents the standard deviation of the background noise from the density gradient caused by sensor measurements.

4. The water quality sampling decision-making method for optimizing groundwater well flushing time and suppressing dead water interference according to claim 2, characterized in that, The step of identifying the water retention zone type within the wellbore based on the wellbore's vertical profile and calculating the dead water interference thickness includes: the water retention zone type includes a high-density stratified retention zone, a weak mixing transition zone, and a fully mixed zone; a first buoyancy frequency threshold and a second buoyancy frequency threshold are preset; the distribution zones of the square of the buoyancy frequency are obtained based on the wellbore's vertical profile; the distribution zones with the square of the buoyancy frequency less than the first buoyancy frequency threshold are classified as fully mixed zones; the distribution zones with the square of the buoyancy frequency greater than or equal to the first buoyancy frequency threshold and less than the second buoyancy frequency threshold are classified as weakly mixed transition zones; the distribution zones with the square of the buoyancy frequency greater than or equal to the second buoyancy frequency threshold are classified as high-density stratified retention zones; the vertical flow velocity induced by the well-washing pump at different depths is obtained; the characteristic convection velocity caused by the density difference is calculated based on the water density at different depths; the corresponding depths at the top and bottom of the high-density stratified retention zone and the weakly mixed transition zone are obtained; and the equivalent dead water interference thickness is calculated.

5. The water quality sampling decision-making method for optimizing groundwater well flushing time and suppressing dead water interference according to claim 4, characterized in that, The process of obtaining the corresponding depths at the top and bottom of the high-density stratified retention zone and the weakly mixed transition zone, and calculating the equivalent dead water interference thickness, includes: the formula for the equivalent dead water interference thickness is: ； In the formula, For the equivalent dead water interference thickness, The depth corresponding to the top of the segment. The depth corresponding to the bottom of the section. For well washing pumps at depth The induced vertical flow velocity at that location, For depth Characteristic convection velocities caused by density differences For the characteristic function, when The value is 1 otherwise the value is 0. It is the integral variable.

6. The water quality sampling decision-making method for optimizing groundwater well flushing time and suppressing dead water interference according to claim 1, characterized in that, The acquisition of historical water sample data and aquifer hydrogeological data from the monitoring well, and the quantification of the potential risk quality of cross-contamination in stagnant water, includes: obtaining the settling time, the initial concentration of pollutants at different depths in the historical water sample, and the cross-sectional area per unit depth of the monitoring well based on the historical water sample data; obtaining the effective hydrodynamic dispersion coefficient of pollutants in the stagnant water layer, the pollutant degradation rate constant, and the surface flux of characteristic leached substances of the well casing material through experiments based on the aquifer hydrogeological data; and calculating the potential risk quality of cross-contamination in stagnant water.

7. The water quality sampling decision-making method for optimizing groundwater well flushing time and suppressing dead water interference according to claim 6, characterized in that, The calculation of the potential risk of cross-contamination in stagnant water includes: The potential risk of cross-contamination in stagnant water is posed by the following quality formula: ； In the formula, The potential risk of cross-contamination in stagnant water quality, For the equivalent dead water interference thickness, The surface flux of characteristic leachates of well casing material. The effective hydrodynamic dispersion coefficient of pollutants in the dead water layer. This refers to the settling time. Pi For the depth of historical water samples The initial concentration of pollutants at the location, The pollutant degradation rate constant, This refers to the cross-sectional area per unit depth of the monitoring well. It is the integral variable.

8. The water quality sampling decision-making method for optimizing groundwater well flushing time and suppressing dead water interference according to claim 2, characterized in that, The process of acquiring wellbore structure data, calculating the targeted displacement volume based on the water retention zone type and dead water interference thickness, and optimizing well washing time includes: obtaining the initial characteristic pollutant concentration of the dead water layer based on the high-density stratified retention zone, setting a preset threshold for the desired residual pollutant concentration in the dead water, obtaining the wellbore inner diameter based on the wellbore structure data, calculating the Pecklet number of the wellbore water flow based on the characteristic convection velocity caused by density difference and the wellbore inner diameter, calculating the dead water geometric volume based on the wellbore inner diameter and the dead water interference thickness, obtaining the maximum allowable cross-contamination background mass by collecting blank samples from the analysis equipment, calculating the targeted displacement volume based on the dead water geometric volume, the initial characteristic pollutant concentration of the dead water layer, the Pecklet number of the wellbore water flow, and the desired residual pollutant concentration threshold in the dead water, obtaining the actual flow rate of the well washing pump and the basic flushing time of the pipeline, and optimizing the well washing time based on the targeted displacement volume.

9. The water quality sampling decision-making method for optimizing groundwater well flushing time and suppressing dead water interference according to claim 8, characterized in that, The process of obtaining the actual flow rate of the well-washing pump and the baseline time of pipeline flushing, combined with the optimization of the total well-washing time by targeting displacement volume, includes: The formula for targeted displacement volume is: ； In the formula, To target displacement volume, For the geometric volume of stagnant water, The Pecklet number is the flow rate of the wellbore. The initial characteristic pollutant concentration of the dead water layer. The desired threshold for residual pollutant concentration in stagnant water; The formula for well washing time is: ； In the formula, To optimize the well washing time, To target displacement volume, This refers to the actual flow rate of the well-washing pump. This is the base time for pipeline flushing. The potential risk of cross-contamination in stagnant water quality, To determine the maximum permissible background quality for cross-contamination during sampling analysis, It is a sensitivity index.

10. The water quality sampling decision-making method for optimizing groundwater well flushing time and suppressing dead water interference according to claim 1, characterized in that, The decision on the timing of water quality sampling based on the optimized well-washing time includes: after cleaning the monitoring well according to the optimized well-washing time, collecting water quality samples for testing.

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