CN110990786A - Comprehensive evaluation method for heavy metal polluted soil environment - Google Patents
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Abstract
The invention discloses a comprehensive evaluation method of a heavy metal polluted soil environment, which comprises the following steps: selecting indexes from plants, soil physicochemical properties, soil animals and soil microorganisms; sampling from an area polluted by heavy metal and an area which is not artificially damaged in the same area, measuring each index, and performing normalization processing on measured original data; respectively carrying out correlation analysis on the indexes of each layer, eliminating the indexes with correlation larger than t, taking the rest indexes as preferred indexes, and taking the data of the preferred indexes as sample space; clustering the normalized data of the area polluted by the heavy metal in the sample space by using a K-Means clustering method to obtain a clustered group; and calculating the mean value of the normalized data of each preferable index of the sample in the area which is not artificially damaged in the same region as the optimal point, calculating the Euclidean distance from the optimal point to the clustering center of each group, and respectively and sequentially grading the corresponding points according to the Euclidean distance to complete comprehensive evaluation.
Description
Technical Field
The invention relates to the technical field of pollution safety evaluation, in particular to a comprehensive evaluation method for a heavy metal polluted soil environment.
Background
Along with the high-speed development of economy in China, the ecological environment pollution and the soil pollution caused in the industrial pollution and resource exploitation process cannot be ignored, the national soil point standard exceeding rate is 16.1%, and particularly, the heavy metal pollution of soil can affect a food chain through different ways: 1200 tons of grains in China are polluted by heavy metal every year, about 200 hundred million RMB are lost, and the health of human beings is influenced more or less. In 2016, 5 months, China issued a soil pollution control action plan (abbreviated as 'ten items of soil'), and along with the implementation of 'ten items of soil', the contaminated soil remediation business of China gradually moves to the right.
At present, two methods of physical-chemical remediation and phytoremediation are mainly used for remediation of heavy metal contaminated soil. The plant restoration method has the advantages of low investment, environmental friendliness and the like, and is preferred to be applied. Both types of repair approaches have received attention and have been invested in research to achieve certain results. However, how to repair and restore the soil polluted by heavy metals, whether the soil polluted by heavy metals meets a preset target or standard, whether the soil repaired by the land can be reused or not, whether the soil ecosystem and the human health are threatened or not, whether further restoration means are required to be implemented or not, and the like, all of which require the restoration effect evaluation of the soil repaired.
The traditional soil heavy metal pollution evaluation is mainly visual evaluation of heavy metal removal effect, such as a single-factor index method, an internal Merlot comprehensive pollution index method, a ground accumulation index method, a potential ecological hazard index method and the like, but the risks of the soil to peripheral plants, animals, human bodies and the like cannot be comprehensively represented only by the total amount of pollutants in the soil, so that the comprehensive and credible evaluation of the remediation effect is not facilitated, and the land is not favorably recycled after remediation.
Plants, animals and microorganisms vary widely in their distribution of resources in terms of growth, survival and reproduction, and produce a wide variety of forms and functions even in a small spatial range. Recently, research begins to take the combination of soil physicochemical properties, plant responses and microorganisms in pairs or the combination of the soil physicochemical properties, the plant responses and the microorganisms as indexes for evaluating the soil remediation effect, and compared with the traditional soil evaluation, the evaluation is comprehensive, but only by the comparison of various data, the evaluation is not systematic. Soil animal functional characteristics, such as appearance characteristics, fertility characteristics, and the like, have been proven as tools for ecosystem evaluation, and trait-based methods have been successfully used for predicting soil animal responses to abiotic stress, but are rarely used for evaluating the effect of heavy metal remediation.
A Wangshizhong team establishes a comprehensive system of soil physicochemical properties, plant response and microorganism response, normalizes data by using a mathematical method, controls the result score between 0 and 1, and intuitively and systematically compares the repairing effect of the plant repairing method under different soil heavy metals. However, the system has many indexes, and the formula corresponding to each index to be used for calculation is complicated. In addition, the evaluation result depends on subjective judgment of people, and the accuracy and the reasonability of the evaluation result are all deficient.
In conclusion, a comprehensive evaluation method for the soil environment polluted by heavy metals, which is more comprehensive, stronger in applicability and more objective, needs to be developed.
Disclosure of Invention
The invention provides a comprehensive evaluation method of a heavy metal polluted soil environment, aiming at solving the problems that the evaluation result of the existing comprehensive evaluation method of the soil environment depends on the subjective judgment of people and the accuracy and the reasonability of the evaluation result are all deficient.
In order to achieve the purpose of the invention, the technical scheme is as follows: a comprehensive evaluation method of a heavy metal polluted soil environment comprises the following steps:
s1: selecting indexes from four layers of plants, soil physicochemical properties, soil animals and soil microorganisms;
s2: sampling from an area polluted by heavy metal and an area which is not artificially damaged in the same area, measuring each index, and then carrying out normalization processing on original data measured by each index;
s3: respectively carrying out correlation analysis on the indexes of each layer, eliminating the indexes with correlation larger than t, taking the rest indexes as preferred indexes, and taking the data of the preferred indexes as sample space;
s4: clustering the normalized data of the area polluted by the heavy metal in the sample space by using a K-Means clustering method to obtain a clustered group;
s5: calculating the mean value of the normalized data of each preferable index of the sample in the area which is not damaged by people in the same area as the optimal point, calculating the Euclidean distance from the optimal point to the clustering center of each group, giving the best score to the data in the group with the minimum Euclidean distance, giving the worst score to the data in the group with the maximum Euclidean distance, and giving the scores of other groups according to the Euclidean distance to finish comprehensive evaluation.
Preferably, in step S1, the plant indexes include plant species diversity, plant density, and plant heavy metal content.
Further, in step S1, the indexes of the soil physicochemical properties include soil pH, soil heavy metal content, heavy metal available state content, and nutrient element content.
Still further, in step S1, the soil animal index includes soil animal diversity and soil animal functional characteristics.
Still further, in step S1, the soil microorganism indicators include microorganism diversity, microorganism amount, and enzyme activity.
Further, in step S2, the raw data measured by each index is normalized, specifically, the value of the index not belonging to the interval between [0 and 1] is converted into the value in the interval between [0 and 1] by the following formula;
wherein the formula expression is as follows:
wherein, XiValue of an index for the ith sample, XminIs the minimum value of the index in all samples, XmaxIs the maximum value of the index in all samples, YiIs XiAnd (5) normalizing the result.
Still further, in step S3, the correlation analysis adopts the cosine theorem, that is:
calculating cosine correlation of two index vector spaces by using cosine theorem, wherein K is a certain level and K isiIs an index i, K of a certain leveljIs an index j of a certain level.
Still further, in step S4, the K-Means clustering method specifically includes the following steps:
s401: determining a k value, namely clustering the data set to obtain k sets;
s402: randomly selecting k data points from the data set as a centroid;
s403: calculating the Euclidean distance between each point in the data set and each centroid, and dividing the point with the minimum Euclidean distance to the centroid into a set to which the centroid belongs;
s404: dividing all data into sets to obtain k sets;
s405: recalculating the centroid of each set;
s406: if the distance between the newly calculated centroid and the original centroid is smaller than or equal to a certain set threshold value, the clustering is considered to reach the expected result, and the algorithm is terminated;
s407: and if the distance between the newly calculated centroid and the original centroid is larger than a certain set threshold value, returning to the steps S403-S406 for iteration.
The invention has the following beneficial effects:
in the evaluation method, the indexes of plants, soil physicochemical properties, soil animals and soil microorganisms are comprehensively evaluated, and a K-Means clustering machine learning method is adopted instead of the traditional human-based subjective judgment method, so that the method is suitable for comprehensively, accurately and scientifically evaluating the condition of the heavy metal polluted soil environment.
Drawings
Fig. 1 is a flowchart of the comprehensive evaluation method of the heavy metal contaminated soil environment described in example 1.
Detailed Description
The invention is described in detail below with reference to the drawings and the detailed description.
Example 1
As shown in fig. 1, a comprehensive evaluation method of a heavy metal contaminated soil environment, the comprehensive evaluation method comprises the following steps:
step S1: selecting indexes from four layers of plants, soil physicochemical properties, soil animals and soil microorganisms;
the indexes of the plants comprise plant species diversity, plant density and plant heavy metal content.
The indexes of the physical and chemical properties of the soil comprise the pH value of the soil, the heavy metal content of the soil, the effective state content of the heavy metal and the content of nutrient elements.
The indexes of the soil animals comprise species diversity and soil animal functional characteristics, and the body length of the soil animals is selected to represent the soil animal functional characteristics in the embodiment.
The indicators of the soil microorganisms include microbial diversity, microbial biomass, and enzyme activity.
Step S2: sampling from an area polluted by heavy metal and an area which is not artificially damaged in the same area, measuring each index, and then carrying out normalization processing on original data measured by each index;
the method comprises the following steps of (1) carrying out normalization processing on original data measured by each index, specifically converting the value of the index which does not belong to the interval between [0 and 1] into the value in the interval between [0 and 1] through the following formula;
wherein the formula expression is as follows:
wherein, XiValue of an index for the ith sample, XminIs the minimum value of the index in all samples, XmaxIs the maximum value of the index in all samples, YiIs XiAnd (5) normalizing the result.
Step S3: because the indexes of the same type may have commonalities, correlation analysis is carried out on the indexes participating in evaluation. In the embodiment, the indexes of each layer are subjected to correlation analysis respectively, the indexes with the correlation larger than t are eliminated, the rest indexes are used as the optimal indexes, and the data of the optimal indexes are used as sample space;
in step S3, the correlation analysis uses the cosine theorem, that is:
calculating cosine correlation of two index vector spaces by using cosine theorem, wherein K is a certain level and K isiIs an index i, K of a certain leveljIs an index j of a certain level.
When the correlation is calculated, the cosine value of an included angle between two index vectors is closer to 1, and the correlation of the two indexes is higher; the lower the correlation between two indices if the cosine of the angle between the two index vectors is closer to 0. The value of 0< t <1 as described in this embodiment can be set empirically.
S4: clustering the normalized data of the area polluted by the heavy metal in the sample space by using a K-Means clustering method to obtain a clustered group;
in step S4, the K-Means clustering method specifically includes the following steps:
s401: determining a k value, namely clustering the data set to obtain k sets;
s402: randomly selecting k data points from the data set as a centroid;
s403: calculating the Euclidean distance between each point in the data set and each centroid, and dividing the point with the minimum Euclidean distance to the centroid into a set to which the centroid belongs;
s404: dividing all data into sets to obtain k sets;
s405: recalculating the centroid of each set;
s406: if the distance between the newly calculated centroid and the original centroid is smaller than or equal to a certain set threshold value, the clustering is considered to reach the expected result, and the algorithm is terminated;
s407: and if the distance between the newly calculated centroid and the original centroid is larger than a certain set threshold value, returning to the steps S403-S406 for iteration.
S5: calculating the mean value of normalized data of each preferable index of the sample in the area which is not damaged by people in the same region as the optimal point, calculating the Euclidean distance from the optimal point to the clustering center of each group, giving the best score to the data in the group with the minimum Euclidean distance, giving the worst score to the data in the group with the maximum Euclidean distance, and giving the scores of other groups according to the Euclidean distance, namely, the score given by the embodiment is better for the data with the smaller Euclidean distance, and the score given by the data with the larger corresponding Euclidean distance is worse. Thereby completing the comprehensive evaluation.
It should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.
Claims (8)
1. A comprehensive evaluation method of a heavy metal polluted soil environment is characterized by comprising the following steps: the comprehensive evaluation method comprises the following steps:
s1: selecting indexes from four layers of plants, soil physicochemical properties, soil animals and soil microorganisms;
s2: sampling from an area polluted by heavy metal and an area which is not artificially damaged in the same area, measuring each index, and then carrying out normalization processing on original data measured by each index;
s3: respectively carrying out correlation analysis on the indexes of each layer, eliminating the indexes with correlation larger than t, taking the rest indexes as preferred indexes, and taking the data of the preferred indexes as sample space;
s4: clustering the normalized data of the area polluted by the heavy metal in the sample space by using a K-Means clustering method to obtain a clustered group;
s5: calculating the mean value of the normalized data of each preferable index of the sample in the area which is not damaged by people in the same area as the optimal point, calculating the Euclidean distance from the optimal point to the clustering center of each group, giving the best score to the data in the group with the minimum Euclidean distance, giving the worst score to the data in the group with the maximum Euclidean distance, and giving the scores of other groups according to the Euclidean distance to finish comprehensive evaluation.
2. The comprehensive evaluation method of a heavy metal contaminated soil environment according to claim 1, characterized in that: in step S1, the plant indexes include plant species diversity, plant density, and plant heavy metal content.
3. The comprehensive evaluation method of a heavy metal contaminated soil environment according to claim 1, characterized in that: in step S1, the indexes of the physical and chemical properties of the soil include soil pH, soil heavy metal content, heavy metal available state content, and nutrient element content.
4. The comprehensive evaluation method of a heavy metal contaminated soil environment according to claim 1, characterized in that: in step S1, the soil animal index includes species diversity and soil animal functional characteristics.
5. The comprehensive evaluation method of a heavy metal contaminated soil environment according to claim 1, characterized in that: in step S1, the soil microorganism indicators include microorganism diversity, microorganism amount, and enzyme activity.
6. The method for comprehensively evaluating a heavy metal-contaminated soil environment according to any one of claims 2 to 5, characterized in that: in step S2, the raw data measured by each index is normalized, specifically, the value of the index not between [0,1] is converted into the value in the [0,1] interval by the following formula;
wherein the formula expression is as follows:
wherein, XiValue of an index for the ith sample, XminIs the minimum value of the index in all samples, XmaxIs the maximum value of the index in all samples, YiIs XiAnd (5) normalizing the result.
7. The comprehensive evaluation method of a heavy metal contaminated soil environment according to claim 6, characterized in that: in step S3, the correlation analysis uses the cosine theorem, that is:
calculating cosine correlation of two index vector spaces by using cosine theorem, wherein K is a certain level and K isiIs a certain oneIndex of bedding i, KjIs an index j of a certain level.
8. The comprehensive evaluation method of a heavy metal contaminated soil environment according to claim 7, characterized in that: in step S4, the K-Means clustering method specifically includes the following steps:
s401: determining a k value, namely clustering the data set to obtain k sets;
s402: randomly selecting k data points from the data set as a centroid;
s403: calculating the Euclidean distance between each point in the data set and each centroid, and dividing the point with the minimum Euclidean distance to the centroid into a set to which the centroid belongs;
s404: dividing all data into sets to obtain k sets;
s405: recalculating the centroid of each set;
s406: if the distance between the newly calculated centroid and the original centroid is smaller than or equal to a certain set threshold value, the clustering is considered to reach the expected result, and the algorithm is terminated;
s407: and if the distance between the newly calculated centroid and the original centroid is larger than a certain set threshold value, returning to the steps S403-S406 for iteration.
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