CN105631203A - Method for recognizing heavy metal pollution source in soil - Google Patents
Method for recognizing heavy metal pollution source in soil Download PDFInfo
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- CN105631203A CN105631203A CN201510988451.5A CN201510988451A CN105631203A CN 105631203 A CN105631203 A CN 105631203A CN 201510988451 A CN201510988451 A CN 201510988451A CN 105631203 A CN105631203 A CN 105631203A
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Abstract
The invention discloses a method for recognizing a heavy metal pollution source in soil on the basis of a K-means clustering method. The method is applied to the pollution source recognition on a pollution source recognition composite model through K-means clustering-main ingredient analysis; the sources of heavy metal contaminants can be fast and accurately traced; the contribution rate of each contaminant is given; the reliable technical guarantee is provided for an environment management department to deal with pollution accidents and to control the pollution risk; and the defects that in the prior art, the contribution rate of a concrete discharging source on a receptor cannot be given, and practical guidance significance cannot be realize on the pollution control work are overcome.
Description
Technical field
The invention belongs to heavy metal pollution Source apportionment field, be specifically related to a kind of method being identified of heavy metal in soil being originated based on K-means clustering method.
Background technology
Polluter identification technology is a kind of method that the source to pollutant differentiates, resolves and evaluates. Current polluter identification technology substantially can be divided into three kinds: inventory analysis method, diffusion model and receptor model. Inventory analysis method is by observing and the source emission amount of simulating pollution thing, discharge characteristics and discharge geographical distribution etc., setting up a kind of Source Apportionment of list model; Diffusion model belongs to prediction type model, and it is by inputting the emissions data of each polluter and relevant parameter information predicts the change in time and space situation of pollutant; Receptor model then passes through the chemistry to acceptor sample and microscopic analysis, it is determined that a class technology of each polluter contribution rate, and its final purpose is to identify the contributive polluter of receptor, and the share rate of each polluter of quantitative Analysis.
The current research for heavy metal in soil polluter identification is little, and main polluter recognition methods is through the figure to source spectrum and factor loading and observes and realize qualitative comparison, or realizes sxemiquantitative by the deviation of calculating source spectrum and factor loading and compare. These methods do not account for polluting the nonlinear characteristic of source spectrum more, and recognition result can not truly reflect factor loading and pollute the corresponding relation of source spectrum.
Summary of the invention
The present invention is to provide for a kind of method being identified of heavy metal in soil being originated based on K-means clustering method, overcomes tradition heavy metal pollution Source Apportionment and can not provide the defect of concrete emission source contribution rate size.
Inventor provide techniques below scheme.
A kind of method identifying heavy metal in soil polluter, operating procedure includes:
Step one, it is determined that the survey area of source of heavy metal pollution;
Can in conjunction with overall city planning and industrial sector layout, select heavy metals emission pollutant source type more than the heavy metal type of 3 classes, discharge at least include lead, hydrargyrum, chromium, arsenic, this 5 class Main Heavy Metal of cadmium region as survey area.
Step 2, investigates in the source of heavy metal pollution survey area determined, fact-finding process includes:
(1) basic data is collected
By to the collection of related data (such as masses' complaint, Pollutant source investigation data base, polluter archives, environmental monitoring data, environment impact assessment statement etc.) and finishing analysis, grasp the distribution of survey area heavy metal pollution industry and enterprise, therefrom filter out industry and enterprise representative, that impact is comparatively prominent, it is determined that the polluter list that investigate further;
(2) on-site inspection (monitoring)
Main Heavy Metal in survey area is carried out on-site inspection (including scene to layout, sample and analyze test). According to factors such as the production technology of polluter, production procedure, the generation mechanism of pollutant and form of export, with reference to investigation of pollution sources specification, it is determined that layout and the method for sampling. Monitoring index includes constituent concentration index.
(3) data process&analysis
The data that on-site inspection is obtained, in conjunction with data unique characteristics and survey objective, adopt the statistical method of science to carry out taxonomic revision and statistical analysis.
(4) data after Treatment Analysis are set up pollution sources information data base.
Step 3, on the basis of source of heavy metal pollution survey area investigation, analyzes the polluter impact on environment under different situations;
Different situations include: 1. single polluter is positioned at environment sensitive spot; 2. multiple different types of polluter are positioned at environment sensitive spot; 3. multiple same kind of polluter are positioned at environment sensitive spot. The 1. in kind situation, and the relative position relation according to polluter Yu environment sensitive spot is formulated corresponding monitoring scheme, analyzed the influence degree of the environmentally sensitive point of polluter; The, 2. in kind situation, is analyzed differentiating according to the characteristic contamination matter of each polluter; The 3. to plant situation more complicated, need to the source strength of polluter be tested, and combined mathematical module judge each polluter affect size.
Step 4, identifies the Characteristics of Heavy Metals marker in all kinds of emission source;
Value according to the Main Heavy Metal that on-site inspection obtains, considers content and the objective indicator of each heavy metal components in target stains, determines its pollution type according to its derived components spectrum.
All kinds of emission sources include: Industrial " three Waste ", vehicle exhaust, domestic waste, agricultural sludge, fertilizer, agriculture chemical.
Step 5, applies K-means clustering method, adopts Matlab software programming, is layouted at scene in on-site inspection and Monitoring Data is converted into the quantization matrices that computer can accept, and is standardized data processing, and eliminates dimension impact, obtains normalized matrix;
Step 6, builds the model of the heavy metal pollution identifing source based on K-means clustering method, including
(1), K-means clustering method carries out the category division of polluter
The first step, pretreatment and initialization
Second step, exports training sample pair
The core concept of K-means algorithm is that n data object is divided into k cluster, makes the data point in each cluster minimum to the quadratic sum of this cluster centre, algorithm process process:
Input: cluster number k, comprises the data set of n data object.
Output: k cluster.
(1) k object in optional n data object is as initial cluster center
(2) to remaining each object, according to its distance with each bunch of center, it is assigned to nearest bunch.
(3) formula is utilized(i=1,2 ..., n; J=1,2 ..., k) recalculate each class center, and use formulaCalculate criterion function value now
(4) the new method of salary distribution is calculated: assumeIn class n, if(wherein), by sampleIt is assigned in class m, then calculates the criterion function value after now distribution
(5) ifStop calculating, otherwise c=c+1, repeat (3) (4) (5) step
To processing large data sets, K-means algorithm is relatively telescopic and high efficiency, and n is the number of all objects, and the number that k is bunch, t is the number of times of iteration. Usual k < < t and t < < n. When clustering with K-means algorithm, when result bunch is intensive, and bunch and bunch between when distinguishing obvious, its Clustering Effect is better.
(2), quote principal component analysis and carry out the contribution rate calculating that polluter is of all categories
The first step, data normalization processes
Including the examination & verification of data, the selection of pollutant variable and three processes of acceptor density data normalization.
The examination & verification of data: include not detecting item, lacks item, the identification of exceptional value, judgement and process.
Introduce signal to noise ratio, if certain pollutant signal to noise ratio is too small or large percentage lower than detection limit, then cannot be used for carrying out factorial analysis.
Data normalization:
Wherein:;(j=1,2,��,p)
Second step, calculates the correlation matrix of sample
Wherein,
3rd step, calculates the eigenvalue of correlation matrix and corresponding characteristic vector
Eigenvalue:
Characteristic vector:
Step 7, utilizes the source of heavy metal pollution model of cognition built to carry out the identification of source of heavy metal pollution, including:
(1) utilize Principal Component Factor Analysis to extract and there is factor loading matrix and factor score matrix, it is determined that main constituent factor number.
(2) again the factor loading identification based on polluter component spectrum is treated as the identification problem of multiparameter model, utilize K-mean cluster analysis to carry out the identification of polluter.
(3) calculating of the polluter contribution rate of the disaggregated model realization factor load identified finally is utilized, it is achieved the source resolution of Characteristics of Heavy Metals pollutant.
Heavy metal pollutants of the present invention selects to follow following principle: the heavy metal substance of limiting emission in (1) regulation, standard both at home and abroad; (2) it is widely present in all kinds of polluter, or the characteristic contamination of industry; (3) there is the heavy metal substance of reliable monitoring method. According to above principle, filter out heavy metal contaminants 5 kinds that harm is bigger, namely lead, hydrargyrum, chromium, arsenic, cadmium.
Advantages of the present invention is:
(1) the method can review the source of heavy metal contaminants fast and accurately, practical, has application value widely, provides reliable technical guarantee for environmental management department reply contamination accident, control pollution risk.
(2) traditional contamination sources analytic technique can only substantially provide the polluter classification that the contribution of environment receptor is bigger, and can not provide the size that receptor is contributed by concrete emission source, lacks the actual directive significance to prevention and cure of pollution work. The method of the invention, discloses heavy metal source emission composition characteristic comprehensively, filters out the signature identification thing that can indicate that pollution source.
(3) present invention improves offer technical support for formulating regional pollution control way and regional environmental quality, make from now on environmental management department in the face of pollution problem time, system, complete Source Apportionment and corresponding data information system can be passed through, identify rapidly polluter, thus carrying out the prevention and control polluted.
Accompanying drawing explanation
Fig. 1 is the flow chart of the method for the invention.
Detailed description of the invention
Below in conjunction with specific embodiment, content of the present invention is described in further detail.
Embodiment
Step one, using basin, Jinjiang as Investigation of Heavy Metals region.
Step 2, Data Source content of beary metal in the deposit of basin, Jinjiang, 10 erect-position surface sediment samples with grab type samplers sample.
Step 3, according to survey area, it is determined that the heavy metal of investigation is As, Hg, Cd, Cr, Pb, takes 10 monitoring sites altogether.
Step 4��seven see following data analysis, and the program that wherein data normalization process and cluster analysis situation etc. are edited by SPSS statistical analysis software and Matlab completes, under Main Analysis situation is shown in:
As, Hg, Cd, Cr, Pb are mainly carried out K-means cluster analysis by this data analysis, and result is as follows:
Table 1 and table 2 respectively preliminary classification center and final classification center, be actually the concentration of 2 kinds of classification (this 2 class is branched away, and 1 refers to the first kind, and 2 refer to Equations of The Second Kind) standard by spss statistical analysis software.
Table 1 preliminary classification center
The final classification center of table 2
Table 3 is analysis of variance table, whether statistically significant analyzes each clustering variable, and as can be seen from the table, the p value (Sig.) corresponding to 2 clustering variable of this example is only small, it is possible to determine that these 2 variablees are meaningful to the classification of present case data.
Table 3 analysis of variance table
The source of heavy metal pollution of this example is divided into 2 big classes by K-means cluster.
As, Hg, Cd, Cr, Pb are mainly carried out principal component analysis by this data analysis, and result is table 4 such as: correlation coefficient and corresponding P value:
Table 4 correlation matrix
In Table 5, the statistical information of main constituent includes the sequential arrangement that characteristic root is descending, and first principal component characteristic root is 3.576, and it explains overall 71.518%; Although Second principal component, characteristic root is 0.701 < 1 but close to 1, so also choosing into, explaining overall 14.016%, now contribution rate of accumulative total reaches 85.535%, and this example should choose the first two main constituent.
Table 5 population variance is explained
In Table 6, main constituent number is defined as 2, then data are analyzed when choosing main constituent again and input 2, obtain this factor loading matrix (i.e. Factor load-matrix). Visible first principal component mainly comprises former variables A s, Cd, Pb information, namely shows that first principal component polluter is mainly fertilizer and pesticide and sewage source. Second principal component, contains the main information of Hg, and namely polluter is mainly the industrial wastewaters such as chlor-alkali, plastics, battery, electronics.
Table 6 Factor load-matrix
Claims (3)
1. the method identifying heavy metal in soil polluter, it is characterised in that operating procedure includes:
Step one, it is determined that the survey area of source of heavy metal pollution;
Step 2, investigates in the source of heavy metal pollution survey area determined, fact-finding process includes:
(1) basic data is collected
(2) on-site inspection
Main Heavy Metal in survey area is carried out on-site inspection, layouts including scene, sample and analyze test;
(3) data process&analysis
The data that on-site inspection is obtained carry out taxonomic revision and statistical analysis;
(4) data after Treatment Analysis are set up pollution sources information data base;
Step 3, on the basis of source of heavy metal pollution survey area investigation, analyzes the polluter impact on environment under different situations;
Different situations include: 1. single polluter is positioned at environment sensitive spot; 2. multiple different types of polluter are positioned at environment sensitive spot; 3. multiple same kind of polluter are positioned at environment sensitive spot;
Step 4, identifies the Characteristics of Heavy Metals marker in all kinds of emission source;
Step 5, applies K-means clustering method, adopts Matlab software programming, is layouted at scene in on-site inspection and Monitoring Data is converted into the quantization matrices that computer can accept, and is standardized data processing, and eliminates dimension impact, obtains normalized matrix;
Step 6, builds the model of the heavy metal pollution identifing source based on K-means clustering method, including
(1), K-means clustering method carries out the category division of polluter
The first step, pretreatment and initialization
Second step, exports training sample pair
The core concept of K-means algorithm is that n data object is divided into k cluster, makes the data point in each cluster minimum to the quadratic sum of this cluster centre, algorithm process process:
Input: cluster number k, comprises the data set of n data object;
Output: k cluster;
(1) k object in optional n data object is as initial cluster center
(2) to remaining each object, according to its distance with each bunch of center, it is assigned to nearest bunch;
(3) formula is utilized(i=1,2 ..., n; J=1,2 ..., k) recalculate each class center, and use formulaCalculate criterion function value now
(4) the new method of salary distribution is calculated: assumeIn class n, if(wherein), by sampleIt is assigned in class m, then calculates the criterion function value after now distribution
(5) ifStop calculating, otherwise c=c+1, repeat (3) (4) (5) step
To processing large data sets, K-means algorithm is relatively telescopic and high efficiency, and n is the number of all objects, the number that k is bunch, and t is the number of times of iteration; Usual k < < t and t < < n; When clustering with K-means algorithm, when result bunch is intensive, and bunch and bunch between when distinguishing obvious, its Clustering Effect is better;
(2), quote principal component analysis and carry out the contribution rate calculating that polluter is of all categories
The first step, data normalization processes
Including the examination & verification of data, the selection of pollutant variable and three processes of acceptor density data normalization;
The examination & verification of data: include not detecting item, lacks item, the identification of exceptional value, judgement and process;
Introduce signal to noise ratio, if certain pollutant signal to noise ratio is too small or large percentage lower than detection limit, then cannot be used for carrying out factorial analysis;
Data normalization:
Wherein:;(j=1,2,��,p)
Second step, calculates the correlation matrix of sample
Wherein,
3rd step, calculates the eigenvalue of correlation matrix and corresponding characteristic vector
Eigenvalue:
Characteristic vector:
Step 7, utilizes the source of heavy metal pollution model of cognition built to carry out the identification of source of heavy metal pollution, including:
(1) utilize Principal Component Factor Analysis to extract and there is factor loading matrix and factor score matrix, it is determined that main constituent factor number;
(2) again the factor loading identification based on polluter component spectrum is treated as the identification problem of multiparameter model, utilize K-mean cluster analysis to carry out the identification of polluter;
(3) calculating of the polluter contribution rate of the disaggregated model realization factor load identified finally is utilized, it is achieved the source resolution of Characteristics of Heavy Metals pollutant.
2. the method for identification heavy metal in soil polluter according to claim 1, it is characterised in that basic data described in step 2 includes masses' complaint, Pollutant source investigation data base, polluter archives, environmental monitoring data, environment impact assessment statement.
3. the method for identification heavy metal in soil polluter according to claim 1, it is characterised in that described in step 4, all kinds of emission sources include: Industrial " three Waste ", vehicle exhaust, domestic waste, agricultural sludge, fertilizer, agriculture chemical.
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| CN106096268A (en) * | 2016-06-12 | 2016-11-09 | 西藏自治区高原生物研究所 | A kind of appraisal procedure of rubbish environmental pollution |
| CN106612834A (en) * | 2016-09-18 | 2017-05-10 | 北京市土肥工作站 | Method and device for determining special fertilizer formula based on regional soil nutrient resources |
| CN107367530A (en) * | 2016-08-30 | 2017-11-21 | 北京航空航天大学 | A kind of water environment pollution type method for quickly identifying based on physics and chemistry bioelectrochemical system |
| CN108693084A (en) * | 2017-04-06 | 2018-10-23 | 富士电机株式会社 | Generating source analytical equipment and generating source analysis system |
| CN109900682A (en) * | 2019-03-22 | 2019-06-18 | 临沂大学 | A kind of topsoil heavy metal pollution source quantitative identification method calculated based on enrichment factor value |
| CN110096490A (en) * | 2018-10-18 | 2019-08-06 | 苏州科技大学 | Contaminated site database and its construction method |
| CN110163290A (en) * | 2019-05-28 | 2019-08-23 | 北华航天工业学院 | A method of pollution sources are parsed based on quick clustering and Chemical mass balance mode |
| CN110175647A (en) * | 2019-05-28 | 2019-08-27 | 北华航天工业学院 | A kind of pollution source discrimination clustered based on principal component analysis and K-means |
| CN110390494A (en) * | 2019-08-13 | 2019-10-29 | 成都理工大学 | The source tracing method of " three nitrogen " in the household refuse landfill sites underground water of farming region |
| CN110674570A (en) * | 2019-09-04 | 2020-01-10 | 山西大学 | Reverse distance model construction method for calculating contribution rates of different pollution sources to Pb pollution |
| CN111783299A (en) * | 2020-06-30 | 2020-10-16 | 中国环境科学研究院 | Combined use method of sediment heavy metal source analytical model |
| CN111932146A (en) * | 2020-09-01 | 2020-11-13 | 平安国际智慧城市科技股份有限公司 | Method and device for analyzing pollution cause, computer equipment and readable storage medium |
| CN112085081A (en) * | 2020-09-02 | 2020-12-15 | 董萍 | Sewage component detection method and system |
| CN112461816A (en) * | 2020-10-23 | 2021-03-09 | 同济大学 | Industrial sludge identification method and system based on heavy metal fingerprints |
| CN112557612A (en) * | 2020-11-20 | 2021-03-26 | 中南大学 | Method for analyzing heavy metal pollution source and pollution boundary of underground water in metal mining area by using water system sediments |
| CN113657698A (en) * | 2020-05-12 | 2021-11-16 | 中国环境科学研究院 | Basin partition pollution source identification method based on multivariate statistics and receptor model |
| CN113706127A (en) * | 2021-10-22 | 2021-11-26 | 长视科技股份有限公司 | Water area analysis report generation method and electronic equipment |
| CN113780820A (en) * | 2021-09-13 | 2021-12-10 | 宝航环境修复有限公司 | Soil surface layer ecological construction method and device for soil pollution risk management and control |
| CN114689818A (en) * | 2020-12-30 | 2022-07-01 | 中国科学院沈阳应用生态研究所 | Method for confirming homology analysis of heavy metal pollutants in polluted site |
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| CN106096268A (en) * | 2016-06-12 | 2016-11-09 | 西藏自治区高原生物研究所 | A kind of appraisal procedure of rubbish environmental pollution |
| CN107367530B (en) * | 2016-08-30 | 2019-12-31 | 北京航空航天大学 | Method for quickly identifying water environment pollution type based on physicochemical-bioelectrochemical system |
| CN107367530A (en) * | 2016-08-30 | 2017-11-21 | 北京航空航天大学 | A kind of water environment pollution type method for quickly identifying based on physics and chemistry bioelectrochemical system |
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| CN114689818B (en) * | 2020-12-30 | 2024-01-16 | 中国科学院沈阳应用生态研究所 | Confirmation method for homology analysis of heavy metal pollutants in polluted site |
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