A method for determining ecological risk weights of polluted land

Abstract

The present invention relates to a method for determining the ecological risk weight of a polluted plot disclosed in the present invention. The method for determining the ecological risk weight of pollutants includes the following steps: selecting different biological toxicity indicators of pollutants for measurement, and constructing a database of pollutant biological toxicity indicators; calculating the ecological environmental impact of pollutants under different biological toxicity indicators; constructing a decision tree for determining and analyzing the pollutant weights, and performing pruning optimization based on the characteristics of the applicable plot; and obtaining the ecological risk weight of the corresponding pollutant based on the decision tree determined and analyzed after optimization. The method for determining the ecological risk weight of pollutants described in the present invention is applicable to the risk assessment process of the ecological environmental risk of polluted plots. The weight determination method is comprehensive and objective, systematic and complete, highly extensible, and feasible.

Description

Technical Field

[0001] The present invention relates to the technical field of ecological environmental risk control and management, and in particular to a method for determining the ecological risk weight of a polluted land. Background Art

[0002] In the field of ecological and environmental risk control and management, stakeholders such as polluting enterprises, developers, governments, and local residents often need to scientifically evaluate the ecological and environmental risks of polluted plots in order to make relevant decisions on the subsequent risk control and governance and remediation of polluted plots. In other words, establishing a plot risk assessment system based on indicators and methods is the basis and prerequisite for plot risk control and governance and remediation. Existing technologies often use multi-level assessment endpoints and the combined toxic effects of multiple metals to comprehensively assess the ecological risks of polluted plots and analyze the causes and dominant factors of ecological risks. However, in the "evidence-weight method" of the ecological risks of heavy metal polluted plots, only the indicator weights of some pollutants are disclosed, and the method and specific steps for assigning pollutant weights in heavy metal polluted plots or non-heavy metal polluted plots are not disclosed. Summary of the Invention

[0003] The purpose of the present invention is to provide a method for determining the ecological risk weight of a polluted land under the type of heavy metal polluted land or non-heavy metal polluted land.

[0004] To achieve the above object, the present invention provides the following solutions:

[0005] A method for determining the ecological risk weight of a polluted plot, the method comprising:

[0006] Obtain different biotoxicity indicators of pollutants and build a database of biotoxicity indicators of pollutants;

[0007] Calculate the ecological and environmental impact of the pollutants under different biological toxicity indicators;

[0008] Constructing a decision tree for the pollutant weight determination analysis, and performing pruning optimization based on applicable land plot characteristics to obtain an optimized decision tree;

[0009] According to the optimized decision tree, the ecological risk weights of the corresponding pollutants are obtained.

[0010] Optionally, the biological toxicity indicators are no-observed-effect concentration, minimum-effect concentration, maximum acceptable toxicity concentration, half-maximal effect concentration, and median lethal concentration; the measurement and acquisition of biological toxicity indicators refers to obtaining 3 or more biological toxicity indicators of corresponding pollutants through experimental methods, literature retrieval, and database search methods, and different biological toxicity indicators should contain data of at least 5 different organisms.

[0011] Optionally, the fields of the database are pollutants, biological toxicity indicators, test environment, affected organisms, and indicator concentrations.

[0012] Optionally, the ecological and environmental impact of the pollutant under different biological toxicity indicators is the pollutant concentration corresponding to a 5% cumulative probability obtained by species sensitivity curve fitting calculation.

[0013] Optionally, the sensitivity curve fitting method includes Log-normal, Log-logistical and BurrIII fitting methods.

[0014] Optionally, constructing a decision tree for the pollutant weight determination analysis and performing pruning optimization based on applicable land plot characteristics to obtain an optimized decision tree specifically includes:

[0015] Obtaining the corresponding index value of each different biological toxicity index according to the basic data of each different biological toxicity index;

[0016] Constructing a weight determination analysis decision subtree, wherein the weight determination analysis decision subtree includes a root node and multiple child nodes, the analysis decision subtree is divided into a plot feature analysis decision subtree and a biological toxicity decision subtree, the root node of the plot feature analysis decision subtree is the plot feature, and the child nodes are all jump nodes; the root node of the biological toxicity decision subtree is the biological toxicity index type, and the child nodes are jump nodes, which are used to connect with other decision subtrees, indicating all situations that do not meet the index value range, and the remaining child nodes are respectively the different biological toxicity index values;

[0017] Connect multiple decision subtrees according to the applicable plot characteristics. The connection order is to first connect all plot characteristic analysis decision subtrees and then connect all biological toxicity decision subtrees. The connection method is to connect the jump node of one decision subtree to the root node of another decision subtree. Different decision subtrees can be used multiple times during the connection.

[0018] According to the characteristics of the applicable plots, the decision tree formed after connection is pruned and optimized.

[0019] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects: the method for determining the ecological risk weight of a polluted plot of land described in the present invention obtains different biological toxicity indicators of pollutants by measuring, calculates the ecological environmental impact of pollutants under different biological toxicity indicators, constructs a decision tree for the pollutant weight determination analysis and performs pruning optimization to obtain the pollutant ecological risk weight of the pollutant in the evidence weight method evaluation system. The weight determination method is comprehensive and objective, systematic and complete, has strong scalability and good feasibility. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0021] Figure 1 This is a flow chart of the method for determining the ecological risk weight of a polluted land provided in Example 1 of the present invention;

[0022] Figure 2 The weight determination analysis decision subtree described in the first embodiment of the present invention;

[0023] Figure 3 This is the species sensitivity curve of heavy metal arsenic described in Example 2 of the present invention;

[0024] Figure 4 The weight determination and analysis decision tree after pruning optimization described in the second embodiment of the present invention. DETAILED DESCRIPTION

[0025] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0026] The object of the present invention is to provide a pipeline flow velocity detection device capable of improving detection accuracy.

[0027] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0028] Example 1

[0029] like Figure 1 As shown, this embodiment proposes a method for determining the ecological risk weight of polluted land based on decision tree analysis, and the specific implementation method adopted is as follows:

[0030] S1: Select different biotoxicity indicators of pollutants for measurement and obtain, and build a pollutant biotoxicity indicator database;

[0031] (1) The results of indoor simulation experiments show that the pollutants have no observed effect concentration (NOEC), minimum effect concentration (LOEC), maximum acceptable toxicity concentration (MATC), half maximum effect concentration (EC) on specific organisms under the characteristics of the applicable plot. 50), median lethal concentration (LC50) 50 ) and other biological toxicity evaluation parameters;

[0032] (2) Obtain specific toxicity parameters by collecting relevant domestic and foreign literature reports and consulting domestic and foreign biotoxicology experimental database data;

[0033] The data and information obtained through indoor simulation experiments, literature retrieval, and domestic and foreign public databases will be organized in the format of pollutants, biological toxicity indicators, test environment, affected organisms, and indicator concentrations to form a dedicated database for subsequent species sensitivity curve fitting calculations and decision tree construction and analysis.

[0034] S2: Calculate the ecological and environmental impacts of pollutants under different biological toxicity indicators;

[0035] The ecological effect curve (SSD) and HC5 value of the corresponding pollutants were calculated by Log-normal, Log-logistical and Burr III fitting methods, and the evaluation weight parameters were analyzed and determined according to the predetermined HC5 value range;

[0036] The calculation methods and data requirements of different fitting methods are shown in Table 1 below:

[0037] Table 1 Calculation methods and data requirements for different fitting methods

[0038]

[0039]

[0040] Among them, when there are multiple effect concentrations for the same toxicity endpoint, the average value of the effect concentration is used to construct the subsequent ecological effect curve; when there are multiple toxicity endpoints for the same species, the minimum value is used to construct the ecological effect curve, and the HC5 value is the pollutant concentration corresponding to a 5% cumulative probability on the SSD fitting curve. The smaller the HC5 value, the higher the ecological risk of the pollutant.

[0041] S3: Construct a decision tree for the pollutant weight determination analysis and perform pruning optimization based on applicable plot characteristics. The steps are as follows:

[0042] S31: According to the basic data of each different biological toxicity index, the corresponding index value of each different biological toxicity index is obtained. 50 The relationship between the corresponding index value and the calculated HC5 value is as follows:

[0043]

[0044] S32: Figure 2As shown, a weight determination analysis decision subtree is constructed, wherein the weight determination analysis decision subtree includes a root node and multiple child nodes. The analysis decision subtree can be divided into a plot feature analysis decision subtree and a biological toxicity decision subtree. The root node of the plot feature analysis decision subtree is the plot feature, and the child nodes are all jump nodes. The corresponding relationship between the applicable plot features and branches is shown in Table 2 below; the root node of the biological toxicity decision subtree is the biological toxicity index type, and child node 1 is a jump node for connecting to other decision subtrees, indicating all situations that do not meet the index value range. The remaining child nodes are respectively the different biological toxicity index values, and the corresponding relationship is determined by the aforementioned step S31;

[0045] Table 2 Correspondence between plot characteristics and branches

[0046]

[0047] S33: Connect multiple decision subtrees based on applicable land plot characteristics. The connection order is to first connect all land plot characteristic analysis decision subtrees and then connect all biological toxicity decision subtrees. The connection method is to connect the jump node of one decision subtree to the root node of another decision subtree. Different decision subtrees can be used multiple times during the connection.

[0048] S34: Based on the characteristics of the applicable plots, the decision tree formed after connection is pruned and optimized to form a final decision tree.

[0049] S4: Determine the decision tree for analysis based on the optimized weights to obtain the ecological risk weights of the corresponding pollutants.

[0050] Example 2

[0051] Heavy metal arsenic was selected as a case study object. Based on indoor simulation experiments and the USEPA ECOTOX biological toxicity database of the United States, the ecological risk weight of the contaminated land was determined through decision tree analysis, thus providing a scientific basis for ecological environmental risk assessment.

[0052] (1) An indoor simulation experiment was conducted. Twelve 1-liter beakers were prepared. 600 grams of soil with different concentrations of heavy metal arsenic and 12 well-developed earthworms were placed in the beakers. The soil included three treatments: N1, N2, and N3. The soil moisture was adjusted to 25% of the dry weight, and four replicates were performed for each treatment. Before the earthworms were placed in the soil, they were placed on moist filter paper for 24 hours to empty their stomach contents. During the indoor experiment, the ambient temperature was maintained at around 20 degrees, the humidity was 75%, and the treatment was 12 hours of light + 12 hours of darkness. After 14 and 28 days, the earthworms were removed from the soil for heavy metal accumulation and ecotoxicological analysis, and the no observed effect concentration (NOEC), minimum effect concentration (LOEC), maximum acceptable toxicity concentration (MATC), and half-maximum effect concentration (EC) of earthworms under arsenic stress were obtained.50 ), median lethal concentration (LC50) 50 ) and other biological toxicity index evaluation concentrations. The sensitive organism earthworms were replaced with other larvae and invertebrates to obtain relevant biological toxicity index evaluation results for multiple organisms.

[0053] (2) The evaluation results of relevant biological toxicity indicators of relevant organisms were obtained from the U.S. EPA's Ecotoxicology Database (ECOTOX) using heavy metal arsenic as the search condition.

[0054] (3) The evaluation results of biological toxicity indicators from indoor simulation experiments, literature research, and ecotoxicology databases were organized into a data table, as shown in Table 3 below. The fields in the data table include pollutants, biological toxicity indicators, test environment, affected organisms, and indicator concentrations.

[0055] Table 3 Statistical data of biological toxicity index evaluation of heavy metal arsenic

[0056]

[0057]

[0058] (4) Using the Log-normal method, the half-maximal effect concentration (EC 50 ) to fit the species sensitivity curve, the results are as follows Figure 3 The same method was used to fit and calculate the remaining biological toxicity indicators in turn and obtain the pollutant concentration (HC5 value) corresponding to the 5% cumulative probability. The final HC5 values ​​are shown in Table 4 below:

[0059] Table 4 HC5 values ​​of heavy metal arsenic and their confidence intervals

[0060]

[0061] (5) Determine the branch decision conditions and decision values ​​of the decision subtree. Two branches of the land feature analysis decision subtree and four branches of the biological toxicity decision subtree are constructed. They are expressed as follows:

[0062]

[0063]

[0064]

[0065] (6) Based on the applicable plot characteristics, multiple decision subtrees are connected to form the final decision tree, where the subtrees are arranged in the order of first arranging the plot characteristic analysis decision subtree and then arranging the biological toxicity decision subtree.

[0066] (7) Figure 4As shown in the figure, the decision trees formed after connection are pruned and optimized according to the characteristics of the applicable plots to form the final decision tree. The jump nodes of the last layer of the biological toxicity decision subtree are merged into the nodes with the corresponding index value of 1, and the jump nodes of the remaining subtrees are replaced by the root nodes of the connected subtrees.

[0067] (8) According to the decision tree determined by the optimized weight analysis, the ecological risk weight of heavy metal arsenic is 1.

[0068] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0069] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only intended to help understand the method and core concept of the present invention. At the same time, those skilled in the art will find that the specific implementation methods and application scopes may vary based on the concept of the present invention. In summary, the contents of this specification should not be construed as limiting the present invention.

Claims

1. A method for determining the ecological risk weight of a polluted plot, characterized in that: The determination method includes: Obtain different biotoxicity indicators of pollutants and build a database of biotoxicity indicators of pollutants; Calculate the ecological and environmental impact of the pollutants under different biological toxicity indicators; The ecological effect curves and HC5 values ​​of the corresponding pollutants were calculated using the Log-normal, Log-logistical, and Burr III fitting methods. The evaluation weight parameters were then analyzed and determined based on the predetermined HC5 value range. When multiple effect concentrations existed for the same toxicity endpoint, the average of the effect concentrations was used to construct the subsequent ecological effect curve. When multiple toxicity endpoints existed for the same species, the minimum value was used to construct the ecological effect curve. The HC5 value was the pollutant concentration corresponding to a 5% cumulative probability on the SSD fitting curve. A smaller HC5 value indicated a higher ecological risk for the pollutant. Constructing a decision tree for the pollutant weight determination analysis, and performing pruning optimization based on applicable land plot characteristics to obtain an optimized decision tree; The step of constructing a decision tree for determining and analyzing pollutant weights and performing pruning optimization based on applicable land plot characteristics to obtain an optimized decision tree specifically includes: Obtaining the corresponding index value of each different biological toxicity index according to the basic data of each different biological toxicity index; Constructing a weight determination analysis decision subtree, wherein the weight determination analysis decision subtree includes a root node and multiple child nodes, the analysis decision subtree is divided into a plot feature analysis decision subtree and a biological toxicity decision subtree, the root node of the plot feature analysis decision subtree is the plot feature, and the child nodes are all jump nodes; the root node of the biological toxicity decision subtree is the biological toxicity index type, and the child nodes are jump nodes, which are used to connect with other decision subtrees, indicating all situations that do not meet the index value range, and the remaining child nodes are respectively the different biological toxicity index values; Connect multiple decision subtrees according to the applicable plot characteristics. The connection order is to first connect all plot characteristic analysis decision subtrees and then connect all biological toxicity decision subtrees. The connection method is to connect the jump node of one decision subtree to the root node of another decision subtree. Different decision subtrees can be used multiple times during the connection. According to the characteristics of the applicable plots, the decision tree formed after connection is pruned and optimized; According to the optimized decision tree, the ecological risk weights of the corresponding pollutants are obtained.

2. A method for determining ecological risk weights of contaminated land according to claim 1, characterized in that: The biological toxicity indicators are no-observed-effect concentration, minimum-effect concentration, maximum acceptable toxicity concentration, half-maximal-effect concentration, and median lethal concentration; the measurement and acquisition of biological toxicity indicators refers to obtaining three or more biological toxicity indicators of corresponding pollutants through experimental methods, literature retrieval, and database search methods, and different biological toxicity indicators should contain data of at least five different organisms.

3. The method for determining the ecological risk weight of a polluted land according to claim 1, characterized in that: The fields of the database are pollutants, biological toxicity indicators, test environment, affected organisms, and indicator concentrations.

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