CN105631203A - Methods to Identify Sources of Heavy Metal Pollution in Soil - Google Patents

Abstract

The invention discloses a method for identifying heavy metal pollution sources in soil based on a K-means clustering method. The method uses a K-means clustering-principal component analysis pollution source identification composite model to identify pollution sources, and can quickly and accurately trace the origin of heavy metal pollutants source, giving the contribution rate of each pollutant, providing reliable technical support for environmental management departments to deal with pollution accidents and controlling pollution risks, and overcoming the inability of existing technologies to give the contribution of specific emission sources to receptors and the impact on pollution control. The defect that the work has no practical guiding significance.

Description

Methods to Identify Sources of Heavy Metal Pollution in Soil

technical field

The invention belongs to the technical field of heavy metal pollution source analysis, and in particular relates to a method for identifying heavy metal sources in soil based on a K-means cluster analysis method.

Background technique

Pollution source identification technology is a method to identify, analyze and evaluate the source of pollutants. The current pollution source identification technology can be roughly divided into three types: inventory analysis method, diffusion model and receptor model. The inventory analysis method is a source apportionment method to establish a list model by observing and simulating the source emissions, emission characteristics, and geographical distribution of pollutants; the diffusion model is a predictive model, which is input by inputting the emission data of each pollution source and Relevant parameter information is used to predict the spatial and temporal changes of pollutants; the receptor model is a type of technology that determines the contribution rate of each pollution source through chemical and microscopic analysis of the receptor sample, and its ultimate goal is to identify the pollution source that contributes to the receptor , and quantitatively calculate the share rate of each pollution source.

At present, there are few studies on the identification of heavy metal pollution sources in soil. The main pollution source identification method is to achieve qualitative comparison through graphic observation of source spectrum and factor loading, or to achieve semi-quantitative comparison by calculating the deviation between source spectrum and factor loading. Most of these methods do not consider the nonlinear characteristics of pollution source spectrum, and the identification results cannot truly reflect the corresponding relationship between factor loads and pollution source spectrum.

Contents of the invention

The present invention aims to provide a method for identifying the source of heavy metals in soil based on the K-means cluster analysis method, which overcomes the defect that the traditional heavy metal pollution source analysis method cannot give the contribution rate of specific emission sources.

The inventor provides the following technical solutions.

A method for identifying heavy metal pollution sources in soil, the operation steps comprising:

Step 1, determine the investigation area of heavy metal pollution sources;

Combined with the overall urban planning and industrial layout, select areas with more than three types of heavy metal emission pollution sources and at least five types of heavy metal pollutants discharged including lead, mercury, chromium, arsenic, and cadmium as the investigation area.

Step 2: Conduct investigation in the determined investigation area of heavy metal pollution sources. The investigation process includes:

(1) Collection of basic data

Through the collection and analysis of relevant materials (such as public complaints, pollution source census database, pollution source files, environmental monitoring data, environmental impact assessment reports, etc.), grasp the distribution of heavy metal pollution industries and enterprises in the survey area, and select representative ones with more prominent influence. Industry enterprises, to determine the list of pollution sources to be further investigated;

(2) Field investigation (monitoring)

Carry out on-the-spot investigation of major heavy metal pollutants in the survey area (including on-site layout, sampling and analysis and testing). According to the production process, production process, pollutant generation mechanism and discharge form of pollution sources and other factors, and refer to the pollution source investigation specifications, determine the distribution and sampling methods. Monitoring indicators include component concentration indicators.

(3) Data processing and analysis

For the data obtained from the field survey, combined with the characteristics of the data itself and the purpose of the survey, scientific statistical methods are used for classification and statistical analysis.

(4) The processed and analyzed data will establish a pollution source information database.

Step 3. Based on the regional investigation of heavy metal pollution sources, analyze the impact of pollution sources on the environment under different circumstances;

Different situations include: ① a single pollution source is located in an environmentally sensitive point; ② multiple pollution sources of different types are located in an environmentally sensitive point; ③ multiple pollution sources of the same type are located in an environmentally sensitive point. In the first case, according to the relative positional relationship between the pollution source and the environmental sensitive point, formulate the corresponding monitoring plan, and analyze the influence degree of the pollution source on the environmental sensitive point; in the second case, analyze and judge according to the characteristic pollutants of each pollution source; The third case is more complicated. It is necessary to test the source strength of the pollution source and determine the influence of each pollution source in combination with a mathematical model.

Step 4, identify the heavy metal characteristic markers in various emission sources;

According to the quantity and value of the main heavy metal pollutants obtained from the field survey, the content of each heavy metal component in the target pollution is considered comprehensively, that is, the objective index, and the pollution type is determined according to its source component spectrum.

Various emission sources include: industrial "three wastes", vehicle exhaust, municipal solid waste, agricultural sludge, organic fertilizers, pesticides and chemical fertilizers.

Step 5: Apply the K-means clustering method and use Matlab software programming to convert the site layout and monitoring data in the field survey into a quantitative matrix that can be accepted by the computer, standardize the data, eliminate the influence of dimensions, and obtain a standardized matrix;

Step six, build a model for identifying heavy metal pollution sources based on the K-means clustering method, including

(1) Classification of pollution sources by K-means cluster analysis method

The first step, preprocessing and initialization

The second step is to output training sample pairs

The core idea of the K-means algorithm is to divide n data objects into k clusters, so that the sum of squares from the data points in each cluster to the cluster center is the smallest. The algorithm processing process:

Input: the number of clusters k, a data set containing n data objects.

Output: k clusters.

(1) Choose k objects among n data objects as the initial clustering center

(2) For each remaining object, assign it to the nearest cluster according to its distance from the center of each cluster.

(3) Using the formula (i=1,2,…,n;j=1,2,…,k) recalculate each class center and use the formula Calculate the criterion function value at this time

(4) Calculate the new allocation method: Assume In class n, if (in ), the sample Assign to class m, and then calculate the value of the criterion function after assignment at this time

(5) if Stop calculation, otherwise c=c+1, repeat (3) (4) (5) steps

For processing large data sets, the K-means algorithm is relatively scalable and efficient, where n is the number of all objects, k is the number of clusters, and t is the number of iterations. Usually k<<t and t<<n. When the K-means algorithm is used for clustering, when the resulting clusters are dense and the differences between clusters are obvious, its clustering effect is better.

(2) Using principal component analysis to calculate the contribution rate of each category of pollution sources

The first step, data standardization

Including data review, selection of pollutant variables and standardization of receptor concentration data.

Data review: including identification, judgment and processing of undetected items, missing items, and outliers.

The signal-to-noise ratio is introduced. If the signal-to-noise ratio of a certain pollutant is too small or the proportion below the detection limit is large, it cannot be used for factor analysis.

Data normalization:

in: ; (j=1,2,...,p)

The second step is to calculate the correlation coefficient matrix of the sample

in,

The third step is to calculate the eigenvalues and corresponding eigenvectors of the correlation coefficient matrix

Eigenvalues:

Feature vector:

Step seven, use the constructed heavy metal pollution source identification model to identify heavy metal pollution sources, including:

(1) Use the principal component factor analysis to extract the factor loading matrix and factor score matrix, and determine the number of principal component factors.

(2) Consider the identification of factor loads based on the component spectrum of pollution sources as the identification of multi-parameter patterns, and use K-mean cluster analysis to identify pollution sources.

(3) Finally, the identified classification model is used to calculate the contribution rate of pollution sources of factor loads, and realize the source apportionment of heavy metal characteristic pollutants.

The selection of heavy metal pollutants described in the present invention follows the following principles: (1) Heavy metal substances that are restricted to be discharged in domestic and foreign regulations and standards; (2) Exist widely in various pollution sources, or are characteristic pollutants of the industry; (3) Heavy metal substances with reliable monitoring methods. According to the above principles, five kinds of heavy metal pollutants that are more harmful to the human body are screened out, namely lead, mercury, chromium, arsenic, and cadmium.

The advantages of the present invention are:

(1) This method can quickly and accurately trace the source of heavy metal pollutants. It has strong practicability and wide application value. It provides a reliable technical guarantee for environmental management departments to deal with pollution accidents and control pollution risks.

(2) The traditional pollutant source apportionment technology can only roughly give the category of pollution sources that contribute more to the environmental receptors, but cannot give the contribution of specific emission sources to the receptors, and lacks practical guidance for pollution prevention and control. The method of the invention comprehensively reveals the emission composition characteristics of heavy metal sources, and screens out characteristic markers that can indicate pollution sources.

(3) The present invention provides technical support for formulating regional pollution control strategies and improving regional environmental quality, so that when environmental management departments face pollution problems in the future, they can quickly identify pollution sources through systematic and complete source analysis methods and corresponding data information systems , so as to prevent and control pollution.

Description of drawings

Figure 1 is a flow chart of the method of the present invention.

detailed description

The content of the present invention will be described in further detail below in conjunction with specific embodiments.

Example

Step 1. The Jinjiang River Basin is used as the investigation area for heavy metal pollution.

Step 2, the data comes from the heavy metal content in the sediments of the Jinjiang River Basin, and the surface sediment samples of 10 stations were collected with a grab sampler.

Step 3: According to the survey area, determine the heavy metals to be investigated as As, Hg, Cd, Cr, and Pb, and take a total of 10 monitoring points.

Steps 4 to 7 refer to the following data analysis, in which data standardization processing and clustering analysis are completed with the help of SPSS statistical analysis software and programs edited by Matlab. The main analysis is as follows:

This data analysis mainly carried out K-means cluster analysis on As, Hg, Cd, Cr, and Pb, and the results are as follows:

Table 1 and Table 2 are the initial classification center and final classification center respectively, which are actually 2 types of classification (these 2 types are separated by spss statistical analysis software, 1 refers to the first type, 2 refers to the second type ) standard concentration.

Table 1 Initial Classification Center

Table 2 Final classification center

Table 3 is the variance analysis table to analyze whether each clustering variable is statistically significant. It can be seen from the table that the p-value (Sig.) corresponding to the two clustering variables in this example is very small, and the two clustering variables can be determined Variables are meaningful for the classification of the data in this case.

Table 3 variance analysis table

K-means clustering divides the heavy metal pollution sources in this example into two categories.

This data analysis mainly conducts principal component analysis on As, Hg, Cd, Cr, and Pb, and the results are shown in Table 4: Correlation coefficient and corresponding P value:

Table 4 Correlation coefficient matrix

See Table 5. The statistical information of the principal components includes the order of the characteristic roots from large to small. The characteristic root of the first principal component is 3.576, which explains 71.518% of the total; although the characteristic root of the second principal component is 0.701<1, it is close to 1 , so it is also selected to explain the overall 14.016%, and the cumulative contribution rate reaches 85.535%. In this example, the first two principal components should be selected.

Table 5 Total Variance Explanation

See Table 6, the number of principal components is determined to be 2, then analyze the data again and input 2 when selecting the principal components to obtain the factor loading matrix (ie factor loading matrix). It can be seen that the first principal component mainly contains the information of the original variables As, Cd, and Pb, which means that the pollution sources of the first principal component are mainly chemical fertilizers, pesticides and domestic sewage sources. The second principal component contains the main information of Hg, that is, the pollution sources are mainly industrial wastewater such as chlor-alkali, plastic, battery, and electronics.

Table 6 Factor loading matrix

Claims (3)

1. A method for identifying heavy metal pollution sources in soil, characterized in that the operating steps comprise: Step 1, determine the investigation area of heavy metal pollution sources; Step 2: Conduct investigation in the determined investigation area of heavy metal pollution sources. The investigation process includes: (1) Collection of basic data (2) Field investigation Carry out on-the-spot investigation of major heavy metal pollutants in the investigation area, including on-site layout, sampling and analysis and testing; (3) Data processing and analysis Classify and organize and statistically analyze the data obtained from field surveys; (4) Establish a pollution source information database with the processed and analyzed data; Step 3. Based on the regional investigation of heavy metal pollution sources, analyze the impact of pollution sources on the environment under different circumstances; Different situations include: ① a single pollution source is located in an environmentally sensitive point; ② multiple pollution sources of different types are located in an environmentally sensitive point; ③ multiple pollution sources of the same type are located in an environmentally sensitive point; Step 4, identify the heavy metal characteristic markers in various emission sources; Step 5: Apply the K-means clustering method and use Matlab software programming to convert the site layout and monitoring data in the field survey into a quantitative matrix that can be accepted by the computer, standardize the data, eliminate the influence of dimensions, and obtain a standardized matrix; Step six, build a model for identifying heavy metal pollution sources based on the K-means clustering method, including (1) Classification of pollution sources by K-means cluster analysis method The first step, preprocessing and initialization The second step is to output training sample pairs The core idea of the K-means algorithm is to divide n data objects into k clusters, so that the sum of squares from the data points in each cluster to the cluster center is the smallest. The algorithm processing process: Input: the number of clusters k, a data set containing n data objects; Output: k clusters; (1) Choose k objects among n data objects as the initial clustering center (2) For each remaining object, assign it to the nearest cluster according to its distance from the center of each cluster; (3) Using the formula (i=1,2,…,n;j=1,2,…,k) recalculate each class center and use the formula Calculate the criterion function value at this time (4) Calculate the new allocation method: Assume In class n, if (in ), the sample Assign to class m, and then calculate the value of the criterion function after assignment at this time (5) if Stop calculation, otherwise c=c+1, repeat (3) (4) (5) steps For processing large data sets, the K-means algorithm is relatively scalable and efficient, n is the number of all objects, k is the number of clusters, and t is the number of iterations; usually k<<t and t<<n; When using the K-means algorithm to cluster, when the resulting clusters are dense and the differences between clusters are obvious, its clustering effect is better; (2) Using principal component analysis to calculate the contribution rate of each category of pollution sources The first step, data standardization Including data review, selection of pollutant variables and standardization of receptor concentration data; Data review: including identification, judgment and processing of undetected items, missing items, and outliers; Introduce the signal-to-noise ratio. If the signal-to-noise ratio of a certain pollutant is too small or the proportion below the detection limit is large, it cannot be used for factor analysis; Data normalization: in: ; (j=1,2,...,p) The second step is to calculate the correlation coefficient matrix of the sample in, The third step is to calculate the eigenvalues and corresponding eigenvectors of the correlation coefficient matrix Eigenvalues: Feature vector: Step seven, use the constructed heavy metal pollution source identification model to identify heavy metal pollution sources, including: (1) Use the principal component factor analysis to extract the factor loading matrix and factor score matrix, and determine the number of principal component factors; (2) Consider the identification of factor loads based on the component spectrum of pollution sources as the identification of multi-parameter patterns, and use K-mean cluster analysis to identify pollution sources; (3) Finally, the identified classification model is used to calculate the contribution rate of pollution sources of factor loads, and realize the source apportionment of heavy metal characteristic pollutants. 2. The method for identifying heavy metal pollution sources in soil according to claim 1, wherein the basic data in step 2 include public complaints, pollution source census databases, pollution source files, environmental monitoring data, and environmental impact assessment reports. 3. The method for identifying heavy metal pollution sources in soil according to claim 1, characterized in that, the various emission sources described in step 4 include: industrial "three wastes", automobile exhaust, municipal solid waste, agricultural sludge, organic fertilizer, Pesticides and fertilizers.