A method for evaluating the diffusion trend of groundwater pollution at solid waste stacking sites

Abstract

The present invention provides a method for evaluating the diffusion trend of groundwater pollution at solid waste accumulation sites, which relates to the technical field of water resource pollution assessment and includes: obtaining monitoring data of a target area; obtaining the data volume of the monitoring data to obtain a first index feature, and judging whether the trend change of the monitoring data is relatively stable to obtain a second index feature, and judging whether the data distribution of the monitoring data meets a preset statistical hypothesis to obtain a third index feature, and judging whether the monitoring data has seasonal characteristics to obtain a fourth index feature; screening a preset evaluation method for the diffusion trend of groundwater pollution according to all the above index features to obtain an optimal evaluation method; using the optimal evaluation method to analyze and evaluate the diffusion trend of groundwater pollution of the monitoring data to obtain an evaluation result. The present invention can evaluate the diffusion trend of groundwater at the site more efficiently, flexibly and at low cost, and provide ideas and methods for precise risk control of groundwater at polluted sites.

Description

Technical Field

[0001] The present invention relates to the technical field of water resource pollution assessment, and in particular to a method for assessing the diffusion trend of groundwater pollution at a solid waste dump site. Background Art

[0002] In the process of conducting groundwater risk assessment at solid waste dump sites, the traditional method usually uses groundwater monitoring data to compare with the relevant specified indicator limits or regional groundwater background levels (i.e., single comparison method) to assess the risk level. For example, for groundwater in suspected contaminated areas and land boundaries, pollutants are evaluated using one standard limit; groundwater pollutants at groundwater sensitive targets are evaluated using another standard limit.

[0003] Relevant regulations also require that after the storage site or landfill is closed, the groundwater monitoring system should continue to operate normally, with a monitoring frequency of at least once every six months, until the groundwater quality does not exceed the groundwater background level for two consecutive years. After the tailings pond is closed, if no leachate is generated for two consecutive years or the generated leachate can be discharged stably and meet the standards without treatment, and the groundwater quality does not exceed the water quality of the upstream monitoring wells or the regional groundwater background level for two consecutive years, its environmental supervision level may no longer be divided. After the tailings pond is closed, the operation and management units of the tailings pond shall take measures to ensure that the groundwater quality monitoring wells continue to operate normally, and continue to monitor the groundwater quality in accordance with relevant regulations until the downstream groundwater quality does not exceed the upstream groundwater quality or the regional groundwater background level for two consecutive years.

[0004] In general, the traditional single comparison method has many limitations and irrationalities, and imposes strict requirements on groundwater risk supervision, which significantly increases the cost of groundwater risk management at solid waste dump sites. Summary of the invention

[0005] In order to overcome the shortcomings of the prior art, the purpose of the present invention is to provide a method for assessing the pollution diffusion trend of groundwater at solid waste dump sites, which can more efficiently, flexibly and at low cost assess the pollution diffusion trend of groundwater at the site, and provide ideas and methods for precise risk control of groundwater at polluted sites.

[0006] To achieve the above object, the present invention provides the following solutions:

[0007] A method for assessing the diffusion trend of groundwater pollution at solid waste dump sites, comprising:

[0008] Obtain monitoring data for target areas;

[0009] Obtain the data volume of the monitoring data to obtain the first index feature, and determine whether the trend change of the monitoring data is relatively stable to obtain the second index feature, and determine whether the data distribution of the monitoring data meets the preset statistical hypothesis to obtain the third index feature, and determine whether the monitoring data has seasonal characteristics to obtain the fourth index feature;

[0010] Screen the preset groundwater pollution diffusion trend evaluation methods according to the first index feature, the second index feature, the third index feature and the fourth index feature to obtain the optimal evaluation method;

[0011] Use the optimal evaluation method to analyze and evaluate the groundwater pollution diffusion trend of the monitoring data to obtain the evaluation result.

[0012] Preferably, determining whether the trend change of the monitoring data is relatively stable to obtain the second index feature includes:

[0013] Use the Z-Score method to calculate the Z-score of each data point in the monitoring data;

[0014] Determine the data points with the Z-score exceeding ±3 as outliers;

[0015] If the proportion of outliers among all the data points exceeds 10%, then determine the second index feature as a significant and unstable trend change, otherwise determine the second index feature as a stable trend change.

[0016] Preferably, the statistical hypotheses include: polynomial regression, exponential regression, and logarithmic regression.

[0017] Preferably, determining whether the monitoring data has seasonal characteristics to obtain the fourth index feature includes:

[0018] Draw a time series graph based on the monitoring data;

[0019] Check whether there is a periodic pattern in the monitoring data according to the time series graph. If the monitoring data repeats a similar pattern within a specific time period, then determine the fourth index feature as having seasonal characteristics, otherwise determine the fourth index feature as not having seasonal characteristics.

[0020] Preferably, the groundwater pollution diffusion trend evaluation methods include: analytical model method, regression analysis method, Theil-Sen slope estimation method, and Mann-Kendall trend test method.

[0021] Preferably, screening the preset groundwater pollution diffusion trend evaluation method according to the first index feature, the second index feature, the third index feature, and the fourth index feature to obtain an optimal evaluation method, including:

[0022] Judge whether the first index feature is less than a first preset threshold to obtain a first judgment result. If the first judgment result is yes, determine the analytical model method as the optimal evaluation method. If the first judgment result is no, judge whether the fourth index feature has seasonal characteristics to obtain a second judgment result. If the second judgment result is yes, determine the Mann-Kendall trend test method as the optimal evaluation method;

[0023] If the second judgment result is no, judge whether the second index feature has a stable trend change to obtain a third judgment result. If the third judgment result is no, determine the Theil-Sen slope estimation method or the Mann-Kendall trend test method as the optimal evaluation method according to the first index feature;

[0024] If the third judgment result is yes, judge whether the third index feature meets the preset statistical hypothesis to obtain a fourth judgment result. If the fourth judgment result is yes, determine the regression analysis method as the optimal evaluation method. If the fourth judgment result is no, determine any one of the Theil-Sen slope estimation method or the Mann-Kendall trend test method as the optimal evaluation method.

[0025] Preferably, determining the Theil-Sen slope estimation method or the Mann-Kendall trend test method as the optimal evaluation method according to the first index feature, including:

[0026] Judge whether the first index feature is less than a second preset threshold. If so, determine the Theil-Sen slope estimation method as the optimal evaluation method; if not, determine the Mann-Kendall trend test method as the optimal evaluation method.

[0027] According to the specific embodiments provided by the present invention, the following technical effects of the present invention are disclosed:

[0028] The present invention provides a method for evaluating the diffusion trend of groundwater pollution at solid waste stacking sites, including: obtaining monitoring data of a target area; obtaining the data volume of the monitoring data to obtain a first index feature, and judging whether the trend change of the monitoring data is relatively stable to obtain a second index feature, and judging whether the data distribution of the monitoring data meets a preset statistical hypothesis to obtain a third index feature, and judging whether the monitoring data has seasonal characteristics to obtain a fourth index feature; screening a preset evaluation method for the diffusion trend of groundwater pollution according to the first index feature, the second index feature, the third index feature and the fourth index feature to obtain an optimal evaluation method; using the optimal evaluation method to analyze and evaluate the diffusion trend of groundwater pollution of the monitoring data to obtain an evaluation result. The present invention can evaluate the diffusion trend of groundwater at the site more efficiently, flexibly and at low cost, and provide ideas and methods for precise risk control of groundwater in polluted sites. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings without creative efforts.

[0030] Figure 1 It is a flowchart of the method provided by the embodiment of the present invention;

[0031] Figure 2 It is a schematic diagram of the judgment logic provided by the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0032] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0033] The purpose of the present invention is to provide a method for evaluating the diffusion trend of groundwater pollution at solid waste stacking sites, which can evaluate the diffusion trend of groundwater at the site more efficiently, flexibly and at low cost, and provide ideas and methods for precise risk control of groundwater in polluted sites.

[0034] In order to make the above objects, features and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below with reference to the drawings and specific embodiments.

[0035] Figure 1The flowchart of the method provided by the embodiments of the present invention is as follows. Figure 1 As shown, the present invention provides a method for evaluating the diffusion trend of groundwater pollution at solid waste stacking sites, including:

[0036] Step 100: Obtain the monitoring data of the target area;

[0037] Step 200: Obtain the data volume of the monitoring data to obtain the first index feature, and judge whether the trend change of the monitoring data is relatively stable to obtain the second index feature, and judge whether the data distribution of the monitoring data meets the preset statistical hypothesis to obtain the third index feature, and judge whether the monitoring data has seasonal characteristics to obtain the fourth index feature;

[0038] Step 300: Screen the preset groundwater pollution diffusion trend evaluation method according to the first index feature, the second index feature, the third index feature and the fourth index feature to obtain the optimal evaluation method;

[0039] Step 400: Use the optimal evaluation method to analyze and evaluate the diffusion trend of groundwater pollution in the monitoring data to obtain an evaluation result.

[0040] Preferably, judging whether the trend change of the monitoring data is relatively stable to obtain the second index feature includes:

[0041] Calculate the Z-score of each data point in the monitoring data by using the Z-Score method;

[0042] Determine the data points with the Z-score exceeding ±3 as abnormal points;

[0043] If the proportion of abnormal points among all the data points exceeds 10%, then determine the second index feature as significantly unstable in trend change, otherwise determine the second index feature as stable in trend change.

[0044] Specifically, in this embodiment, the Z-Score method is used to identify the number of outliers: The Z-score is an index that measures the number of standard deviations between a data point and the average value. Generally, data points with a Z-score exceeding ±3 are regarded as outliers. If the proportion of outliers in the data exceeds 10%, it can generally be considered that the trend change is significantly unstable.

[0045] Preferably, the statistical hypotheses include: polynomial regression, exponential regression and logarithmic regression.

[0046] Preferably, judging whether the monitoring data has seasonal characteristics to obtain the fourth index feature includes:

[0047] Draw a time series graph according to the monitoring data;

[0048] Check whether there is a periodic pattern in the monitoring data according to the time series graph. If similar patterns repeatedly appear in the monitoring data within a specific time period, determine that the fourth index feature has seasonal characteristics; otherwise, determine that the fourth index feature does not have seasonal characteristics.

[0049] Optionally, in this embodiment, a time series graph is plotted to visually check whether there is a periodic pattern in the data. If similar patterns repeatedly appear in the data within a specific time period, this indicates the presence of seasonal characteristics.

[0050] Preferably, the method for evaluating the diffusion trend of groundwater pollution includes: the analytical model method, the regression analysis method, and the trend test method (Theil - Sen slope estimation method, Mann - Kendall trend test method).

[0051] Specifically, the analytical model method uses solute lateral migration analytical models in groundwater (such as the steady - state Domenico model, the unsteady - state Domenico model, the Ogata Banks model) to evaluate the spatio - temporal variation law of pollutant concentration in the aquifer. When the amount of monitoring data n is small (n < 6), the analytical model method is selected. Based on the regional geology, hydrogeological conditions, and pollution data of the solid waste storage yard, carry out pollution simulation and prediction analysis, simulate the pollution occurrence process, and then select the regression analysis method or the trend test method (Theil - Sen slope estimation method, Mann - Kendall trend test method) to evaluate the pollution diffusion trend after expanding a certain amount of data.

[0052] Furthermore, the regression analysis method and the trend test method evaluate the diffusion of groundwater pollution by analyzing the trend (increasing or decreasing trend) of long - term monitoring data of pollutant concentration in groundwater.

[0053] Among them, the regression analysis method aims to determine the correlation between the dependent variable and certain independent variables, establish a regression equation (function expression) with a good correlation, and extrapolate it to predict the change trend of the dependent variable. Common regression analysis methods include linear regression, polynomial regression, exponential regression, and logarithmic regression, etc.

[0054] The conditions for judging the stability of the diffusion trend by the regression analysis method are as follows: at a 95% confidence level (i.e., P - value < 0.05), if the slope of the trend line is significantly greater than 0, it indicates that the concentration of groundwater pollutants shows an upward trend; if the slope of the trend line is significantly less than 0, it indicates that the concentration of groundwater pollutants shows a downward trend; if the slope of the trend line has no significant difference from 0, it indicates that the concentration of groundwater pollutants shows a steady state. When the concentration of pollutants in groundwater shows a steady state or a downward trend, it can be judged that the groundwater risk is small.

[0055] Among them, the trend test method determines the direction or amplitude of the increase or decrease by comparing the long-term monitoring data of the pollutant concentration in groundwater, so as to master the change trend of groundwater monitoring data or discover abnormal changes. Typical trend test methods are Theil-Sen slope estimation method and Mann-Kendall trend test method.

[0056] The Theil-Sen slope estimation method, also known as Sen slope estimation, is a robust non-parametric statistical trend calculation method. This method calculates the slope between pairwise data pairs in the time series and takes the median of the slopes as the overall trend of the time series change. This method has high calculation efficiency, is insensitive to outliers, and is suitable for trend analysis of small sample data (6 ≤ data volume n < 10).

[0057] The Mann-Kendal (MK) trend test is a non-parametric time series trend test method. It does not require the measured values to follow a normal distribution, is not affected by missing values and outliers, and is suitable for significant tests of trends (monotonic increase or decrease trends) in long time series data. This method is widely used in fields such as environmental monitoring, climate change, and water resource management. The Mann-Kendall trend test method has certain requirements for the data volume n (usually requires n ≥ 10).

[0058] When the trend test method (i.e., Theil-Sen slope estimation method and Mann-Kendall trend test method) is used for the groundwater pollution diffusion trend of solid waste piles, the stable judgment conditions for the diffusion trend are as follows: at the 95% confidence level (i.e., P value < 0.05), if the pollutant concentration shows an increasing or decreasing trend over time, it indicates that the groundwater pollutant concentration shows an upward or downward trend; if the trend of the pollutant concentration over time is not significant, it is considered that the diffusion has tended to be stable. When the pollutant concentration in groundwater shows a steady state or a downward trend, it can be judged that the groundwater risk is small.

[0059] The advantages and disadvantages of each method can be seen in Table 1.

[0060] Table 1 Comparison table of each method in this embodiment

[0061]

[0062] As Figure 2 shown, in this embodiment, according to the first index feature, the second index feature, the third index feature, and the fourth index feature, the preset groundwater pollution diffusion trend evaluation method is screened to obtain the optimal evaluation method, including:

[0063] Determine whether the first index feature is less than the first preset threshold to obtain a first judgment result. If the first judgment result is yes, determine the analytical model method as the optimal evaluation method. If the first judgment result is no, then determine whether the fourth index feature has seasonal characteristics to obtain a second judgment result. If the second judgment result is yes, determine the Mann-Kendall trend test method as the optimal evaluation method; According to the preset standard, 4 to 8 quarters of monitoring data are required to determine whether the groundwater remediation effect meets the standard (the preset standard can refer to the "Technical Guidelines for Groundwater Remediation and Risk Control of Polluted Sites" (HJ25.6-2019)). At the same time, considering that the regression analysis method usually requires at least 5 data points for fitting analysis, the first preset threshold in this embodiment is set to 6.

[0064] If the second judgment result is no, then determine whether the second index feature has a stable trend change to obtain a third judgment result. If the third judgment result is no, then determine the Theil-Sen slope estimation method or the Mann-Kendall trend test method as the optimal evaluation method according to the first index feature; Exemplarily, determining the Theil-Sen slope estimation method or the Mann-Kendall trend test method as the optimal evaluation method according to the first index feature includes: determining whether the first index feature is less than the second preset threshold. If so, determine the Theil-Sen slope estimation method as the optimal evaluation method; if not, determine the Mann-Kendall trend test method as the optimal evaluation method. The Mann-Kendall trend test method requires the data volume n≥10, so the second preset threshold in this embodiment is set to 10. When the data volume n is large (n≥10), the Mann-Kendall trend test method is selected; when the data volume n is large (6≤n<10), the Theil-Sen slope estimation method is selected.

[0065] If the third judgment result is yes, then determine whether the third index feature meets the preset statistical hypothesis to obtain a fourth judgment result. If the fourth judgment result is yes, determine the regression analysis method as the optimal evaluation method. If the fourth judgment result is no, then determine any one of the Theil-Sen slope estimation method or the Mann-Kendall trend test method as the optimal evaluation method.

[0066] Specifically, when there are many outliers (i.e., the change trend is unstable), this embodiment recommends using the Theil-Sen slope estimation method or the Mann-Kendall trend test method. The difference between the two is that the Theil-Sen slope estimation method is for less data volume, while the Mann-Kendall trend test method is for more data volume. In addition, only the Mann-Kendall trend test method can consider the seasonal characteristics of the data.

[0067] As another alternative embodiment, in this embodiment, whether the data distribution satisfies certain statistical assumptions or there is a monotonic change trend: Determine whether it conforms to a certain regression model (i.e., satisfies certain statistical assumptions) according to the distribution characteristics of the data and prior knowledge. If the fitting significance is good (e.g., P < 0.05), then the regression analysis method is preferably used (because quantitative prediction can be performed). If the fitting significance is poor, then further analyze whether there is a monotonic change trend in the data. If the data has a monotonic change trend, then the Theil-Sen slope estimation method or the Mann-Kendall trend test method is used (the difference between the two is that the former is for less data volume, while the latter is for more data volume); when the data has no monotonic change trend and the data distribution satisfies certain statistical assumptions, then the regression analysis method is used (such as polynomial regression, exponential regression, logarithmic regression, etc.).

[0068] Furthermore, when there are no monitoring wells or the monitoring data is extremely scarce, it is recommended to use the analytical model method. Based on the regional geological and hydrogeological empirical parameters, combined with the pollution data of the solid waste landfill, carry out pollution simulation and prediction analysis, simulate the pollution occurrence process, evaluate and determine the pollution change trend, and then select the regression analysis method or the trend test method for diffusion trend evaluation after expanding a certain amount of data.

[0069] In addition, if the data characteristics are not obvious, multiple methods can be used simultaneously, and finally, comprehensive analysis and judgment are made according to the results of different evaluation methods.

[0070] The beneficial effects of the present invention are as follows:

[0071] The present invention can effectively solve the limitations and irrationalities existing in the traditional single comparison method, and can more efficiently, flexibly and low-costly evaluate the pollution diffusion trend of the groundwater in the site, providing ideas and methods for the precise risk control of the groundwater in the polluted site.

[0072] In this specification, each embodiment is described in a progressive manner. The key point of each embodiment is to illustrate the differences from other embodiments. The same or similar parts among the embodiments can be referred to each other.

[0073] In this article, specific examples are used to elaborate on the principles and implementation modes of the present invention. The descriptions of the above embodiments are only used to help understand the method of the present invention and its core idea; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation modes and application scopes. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A method for assessing the diffusion trend of groundwater pollution at solid waste dump sites, characterized in that: include: Obtain monitoring data for target areas; Obtaining the data volume of the monitoring data to obtain a first indicator feature, and determining whether the trend change of the monitoring data is relatively stable to obtain a second indicator feature, and determining whether the data distribution of the monitoring data meets a preset statistical hypothesis to obtain a third indicator feature, and determining whether the monitoring data has seasonal characteristics to obtain a fourth indicator feature; Screening a preset groundwater pollution diffusion trend evaluation method according to the first indicator feature, the second indicator feature, the third indicator feature, and the fourth indicator feature to obtain an optimal evaluation method; Analyzing and evaluating the groundwater pollution diffusion trend of the monitoring data using the optimal evaluation method to obtain an evaluation result; Determining whether the monitoring data has seasonal characteristics to obtain the fourth indicator characteristics includes: Draw a time series graph based on the monitoring data; Checking whether there is a periodic pattern in the monitoring data according to the time series graph, if the monitoring data repeatedly shows a similar pattern within a specific time period, determining the fourth indicator feature as having a seasonal feature, otherwise determining the fourth indicator feature as not having a seasonal feature; The groundwater pollution diffusion trend evaluation method includes: analytical model method, regression analysis method, trend test method; the trend test method includes: Theil-Sen slope estimation method and Mann-Kendall trend test method; The preset groundwater pollution diffusion trend evaluation method is screened according to the first indicator feature, the second indicator feature, the third indicator feature and the fourth indicator feature to obtain the optimal evaluation method, including: Determine whether the first indicator feature is less than a first preset threshold value to obtain a first judgment result. If the first judgment result is yes, determine the analytical model method as the optimal evaluation method. If the first judgment result is no, determine whether the fourth indicator feature has seasonal characteristics to obtain a second judgment result. If the second judgment result is yes, determine the Mann-Kendall trend test method as the optimal evaluation method. If the second judgment result is no, then judging whether the second indicator feature is a stable trend change, and obtaining a third judgment result; if the third judgment result is no, then determining the Theil-Sen slope estimation method or the Mann-Kendall trend test method as the optimal evaluation method according to the first indicator feature; If the third judgment result is yes, then determine whether the third indicator characteristic meets the preset statistical hypothesis to obtain the fourth judgment result. If the fourth judgment result is yes, then determine the regression analysis method as the optimal evaluation method. If the fourth judgment result is no, then determine either the Theil-Sen slope estimation method or the Mann-Kendall trend test method as the optimal evaluation method.

2. The method for assessing the diffusion trend of groundwater pollution at solid waste dump sites according to claim 1 is characterized in that: Determining whether the trend change of the monitoring data is relatively stable to obtain a second indicator feature includes: The Z-score method is used to calculate the Z score of each data point in the monitoring data; Determine the data points whose Z scores exceed ±3 as outliers; If the proportion of abnormal points in all the data points exceeds 10%, the second indicator feature is determined as a significantly unstable trend change; otherwise, the second indicator feature is determined as a stable trend change.

3. The method for assessing the diffusion trend of groundwater pollution at solid waste dump sites according to claim 1, characterized in that: The statistical assumptions include: polynomial regression, exponential regression and logarithmic regression.

4. The method for assessing the diffusion trend of groundwater pollution at solid waste dump sites according to claim 1, characterized in that: Determining the Theil-Sen slope estimation method or the Mann-Kendall trend test method as the optimal evaluation method according to the first indicator feature includes: Determine whether the first indicator feature is less than a second preset threshold value. If so, determine the Theil-Sen slope estimation method as the optimal evaluation method; if not, determine the Mann-Kendall trend test method as the optimal evaluation method.

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