CN113470765A

CN113470765A - Soil heavy metal source analysis method

Info

Publication number: CN113470765A
Application number: CN202110725906.XA
Authority: CN
Inventors: 胡月明; 苏辉跃; 王璐
Original assignee: Guangzhou South China Institute Of Natural Resources Science And Technology
Current assignee: Guangzhou South China Institute Of Natural Resources Science And Technology
Priority date: 2021-06-29
Filing date: 2021-06-29
Publication date: 2021-10-01

Abstract

The invention discloses a soil heavy metal source analysis method, which comprises the following steps: monitoring and stationing a research area, collecting a soil sample, processing the soil sample, and measuring the content of heavy metal in the soil sample; performing descriptive statistical analysis; determining the spatial distribution characteristics of the heavy metals by using a statistical method, and identifying a pollution source; analyzing the soil heavy metal pollution source and the contribution rate thereof by adopting a positive definite matrix factor analysis model; obtaining a clear pollution source and contribution rate; determining the degree of heavy metal sources by adopting a geological accumulation index and a pollution load index; determining the heavy metal content, toxicity and future pollution condition of the soil by adopting the potential ecological risk index and the sampling point accumulated risk index; comprehensively obtaining an evaluation result; finally, the analysis of the heavy metal source of the soil in the research area is realized. The method can solve the problem of subjective judgment in the pollution source investigation process, reduce uncertainty and improve the objectivity and accuracy of soil pollution source analysis.

Description

Soil heavy metal source analysis method

Technical Field

The invention belongs to the technical field of farmland ecosystems, relates to the technical field of soil heavy metal pollution source analysis, and particularly relates to a soil heavy metal source analysis method.

Background

The farmland soil is an important component of a farmland ecosystem and is a direct provider of nutrients required by the growth of farmland crops; the soil quality and the environmental conditions thereof are important foundations of agricultural product safety, and directly influence the urban and rural ecological environment protection and sustainable development situation. Soil is widely considered to be the most vulnerable part to heavy metal contamination because of its surface layer's ability to readily bind heavy metals, and in addition, soil acts as a filtration system to reduce toxicity by absorbing or degrading different types of contaminants. Nevertheless, excessive accumulation of contaminants in the soil still poses a great threat to human health and the natural environment. At present, the risk assessment of heavy metal (including heavy metal and heavy metal-like) pollution of soil, especially the heavy metal pollution degree, possible sources and ecological environment in farmland soil, has become the focus of people's attention. The method has the advantages that the pollution characteristics of the heavy metals in the soil are known, the environmental risk and the ecological risk of the soil are evaluated, the basic premise of preventing and controlling the soil pollution is provided, and important information is provided for making a decision for repairing the polluted soil. Therefore, the detection of soil pollution has become a primary task for governments of various countries.

The analysis of the pollution source can be divided into a receptor model and a diffusion model according to whether the study object is a pollution area or a pollution source, and an isotope labeling method based on the isotope composition difference of heavy metals between soil and the pollution source (Coulter, 2004; Song et al, 2006; liuna et al, 2010; Belis et al, 2013). The diffusion model is to track the processes of migration, transformation, diffusion and the like of heavy metals by taking pollution sources as research objects, so as to obtain the contribution rate of the pollution sources to the heavy metals (Yunker et al, 2002), and the contribution condition of the pollution sources at a certain time and a certain place is predicted by inputting the emission data of each pollution source, the distance from the emission source, meteorological conditions and the physicochemical properties of pollutants. The diffusion model is a prediction model, and the relationship between the emission source and the environment is researched through a quantitative method. However, due to the fact that the accuracy of the pollutant emission source list is not high, a large prediction error occurs as a result, and the method cannot be used for an open source with difficult source strength determination.

Compared with the diffusion model, the receptor model is not influenced by the emission condition of the pollution source, weather, geography and the like, and does not depend on the saliency of a certain condition excessively, namely the strength and weakness of the source are not strict. The receptor model is a digital model and a method for analyzing different sources and contribution rates of pollutants in a receptor, and is mainly based on linear relations between heavy metals and various pollution sources.

The Chemical Mass Balance method (CMB) compares a previously known combination of characteristics of a contamination source component with an observed combination of a receptor component, and determines a source of a main substance affecting the receptor and a contribution rate thereof. In the model, the contribution rate of the pollution source is determined by analyzing the content of pollutants in the receptor and the source component spectrum emitted by the pollution source and fitting the pollution source and the receptor by utilizing multiple linear regression. However, the chemical mass balance method model is difficult to popularize and apply because the source component spectrum is difficult to detect. The multivariate statistical method can supplement the defects of the chemical mass balance method, and the multivariate statistical methods can be combined with each other.

Absolute principal component analysis-multiple linear regression (APCA-MLR) is a multiple linear regression between the de-normalized principal component scores generated by maximum-rotation PCA and the measured values of the contaminants used to estimate the contribution rates of the individual sources of contamination. In this model, principal component analysis finds the principal factors through data dimensionality reduction, identifies the major pollution sources, and then determines the contribution rate of each pollution source by performing multivariate linear regression using the absolute factor scores and pollutant content. The model requires a large amount of data to support and must artificially discriminate to which source the principal component obtained from the principal component analysis belongs, which makes the result somewhat subjective.

The Positive definite Matrix factor analysis model (PMF) is a factor analysis method based on the least square method and optimized by using the standard deviation of data, which decomposes a pollutant concentration Matrix of a receptor into a factor contribution Matrix and a factor component spectrum Matrix so as to quantitatively analyze the source and the contribution rate of heavy metal, and the Positive definite Matrix model normalizes the prediction error of each element of the Matrix by introducing known uncertainty information and solves the contribution and the component spectrum of a source class under the condition of non-negative limitation. For example, the scholars take the golden product and the riverside water source area of Wu Zhongxia as a research object, and quantitatively identify the heavy metal sources and the contribution rates of all the sources of the soil by adopting a positive definite matrix analysis method on the basis of comprehensively analyzing the soil medium characteristics of the water source area. The PMF model has more pollution source analyses of polycyclic aromatic hydrocarbons in the atmosphere and soil, and the problems of difficult matching of the pollution source and overestimation or underestimation of the pollution contribution rate are caused due to the fact that the migration and conversion rule of heavy metals in the soil is greatly different from the pollutants in the atmosphere. Therefore, when the receptor model is applied to soil pollution source analysis, certain improvement needs to be carried out according to the migration and transformation characteristics of the heavy metal.

The Unmix model resolves pollution sources and contribution rates by assuming that there is some linear relationship between actual data and sources, when the number and composition of the sources are unknown. The Unmix model is based on a dual principle, uses the mass conservation law of substances to take each sample point of a receptor as a multi-dimensional space, each dimension in the multi-dimensional space represents a measured element, and then reduces the dimension of a data space by a principal component analysis method to analyze the contribution rate of a source. The Unmix model has the advantages of no requirement for data distribution form, no need for determining the number of pollution sources, no need for information such as source component spectrum and uncertainty, but the Unmix model requires a large number of data samples and has a high requirement for data quality, and the experience of data Herry indicates that 200-3000 samples can obtain 5 sources and 2000-3000 samples can obtain 7 sources, so that the requirement of the model for data volume is very high.

In summary, the prior art also has the following disadvantages:

1. the existing single receptor model has strong subjectivity on heavy metal source analysis, and uncertainty is easily generated on the aspect of source contribution.

2. The main pollution sources are identified differently in the analysis results of different receptor models in the same research area, so that the judgment of the pollution sources is inaccurate.

Disclosure of Invention

In view of the defects of the prior art, the invention provides the soil heavy metal source analysis method, which can solve the problem of subjective judgment in the pollution source investigation process and reduce uncertainty.

In order to achieve the purpose, the invention adopts the technical scheme that:

the embodiment of the invention provides a method for analyzing a heavy metal source in soil, which comprises the following steps:

s10, monitoring and stationing the research area, collecting soil samples, processing the soil samples, and measuring the content of heavy metals in the soil samples;

s20, performing descriptive statistical analysis on the measured data of the content of the heavy metals in the soil sample;

s30, determining the spatial distribution characteristics of the heavy metals by using a geostatistical method according to the description statistical analysis result of the step S20, and identifying a pollution source;

s40, analyzing the soil heavy metal pollution source and the contribution rate thereof by adopting a positive definite matrix factor analysis model according to the description statistical analysis result of the step S20;

s50, obtaining a clear pollution source and contribution rate according to the pollution source identified in the step S30, the pollution source analyzed in the step S40 and the contribution rate of the pollution source;

s60, determining the degree of the heavy metal source by adopting a geological accumulation index and a pollution load index according to the description statistical analysis result of the step S20; determining the heavy metal content, toxicity and future pollution condition of the soil by adopting the potential ecological risk index and the sampling point accumulated risk index; comprehensively obtaining an evaluation result;

s70, according to the clear pollution source and contribution rate in the step S50 and the evaluation result in the step S60, the analysis of the soil heavy metal source in the research area is realized.

Further, still include: the step S30 includes:

performing statistical analysis on the maximum value, the minimum value, the average value, the standard deviation and the variance of the contents of the multiple heavy metals according to the description of the step S20, and using K-S to test whether the contents of the heavy metals conform to normal distribution;

and carrying out logarithmic transformation standardization on the content data which do not conform to normal distribution, drawing a soil heavy metal space distribution map by using a common Kriging method, and identifying a pollution source.

Further, still include: the step S40 includes:

decomposing the data matrix X of the content of the heavy metals in the soil sample into two matrixes: one is as follows: factor contribution G and factor distribution F; the other is a residual matrix E; the formula is as follows:

in the formula, X_ijThe content of the jth heavy metal component of the ith sample; p is the number of factors; k is the number of pollution sources; g_ikContribution of a pollution source k to the ith sample, namely a sharing rate matrix of the pollution source; f_kjThe content of the jth heavy metal component in the pollution source k is the content of the jth heavy metal component in the pollution source k, namely a pollution source component matrix; e_ijIs a residual error matrix;

defining an objective function Q;

(7) in the formula of U_ijUncertainty of jth heavy metal component of ith sample; e.g. of the type_ijThe j heavy metal component residual error item of the ith sample is obtained; n is the number of samples; m is the number of heavy metal species;

iterative calculation is carried out based on a least square algorithm, an original matrix X is continuously decomposed to obtain optimal matrixes G and F, and the optimal target enables Q to tend to a degree of freedom value.

Further, when the content of each heavy metal component is less than or equal to the corresponding method detection limit, the uncertainty is:

when the content of each heavy metal component is larger than the detection limit of the corresponding method, the uncertainty is as follows:

wherein, sigma is the relative standard deviation of the content of each heavy metal component; c is the content of each heavy metal component; MDL is the method detection limit.

Further, still include: the step S50 includes:

determining the most appropriate factor quantity according to the optimized minimum and stable Q value;

analyzing the spatial distribution of the heavy metals which are mainly influenced in the factor quantity;

determining whether a point source contamination is present;

when point source pollution is determined to exist and a clear pollution source exists, a pollution source list is obtained;

when point source pollution does not exist, judging whether similar space distribution exists or not;

if so, deducing the pollution source according to the characteristics of the heavy metal to obtain a pollution source list;

and when the judgment result is negative, obtaining a pollution source list through the spatial distribution characteristics of the step S30.

Further, in step S60, the geological accumulation index is used to evaluate the effect of human activities on heavy metal pollution, and is graded by comparing the test concentration in soil with the background value of natural geochemistry, and the calculation formula is:

in the formula (I), the compound is shown in the specification,

the content of the heavy metal j measured in the ith sample is mg/kg;

the value is the local soil environment background value of the jth heavy metal, and K is a constant.

Further, in the step S60, the pollution load index reflects the contribution degree of each heavy metal to the comprehensive pollution of all heavy metals, and the calculation formula is as follows:

in the formula (I), the compound is shown in the specification,

the content of

heavy metals

1 and 2 … j measured in the 1 st sample is unit mg/kg;

the value is the local soil environment background value of the 1 st and 2 … j heavy metals; j is the number of the heavy metal component, and in this case, j is m.

Further, in step S60, the potential ecological risk index is used for evaluating the ecological risk of the soil heavy metal based on the content and toxicity of the soil heavy metal, reflects the comprehensive influence of multiple pollutants, and divides the potential hazard degree by a quantitative method, and the calculation formula is as follows:

the calculation formula of the cumulative risk index of the sampling point is as follows:

wherein the content of the first and second substances,

toxicity response coefficient for a single heavy metal i, wherein: as is 10, Cu Pb 5, Cd 30, Zn 1, Cr 2, Ni 5.

Compared with the prior art, the invention has the following beneficial effects:

the embodiment of the invention provides a soil heavy metal source analysis method which comprises the following steps:

(1) pollution source analysis based on geostatistical and positive definite matrix factor analysis model

And constructing a soil heavy metal pollution source analysis system based on the combination of positive definite matrix factor analysis and ground statistics. The defects of strong subjectivity and inaccurate pollution source judgment of the current soil pollution source analysis method are overcome, the positive definite matrix factor analysis model is applied to soil pollution source analysis, and the objectivity and the accuracy of the source analysis model in the field of soil pollution source analysis are improved by combining a land statistical analysis method.

(2) Construction of soil heavy metal pollution risk evaluation system based on geological accumulation index and ecological risk

And selecting an evaluation method with complementary advantages to construct a soil heavy metal ecological risk evaluation system according to the characteristics of each ecological risk evaluation method. When the types of elements are more, the pollution condition of each element needs to be comprehensively evaluated. And (3) evaluating and grading the pollution degree by contrast with the grading index of the soil accumulated pollution index by taking the background value of the soil element as a reference, considering the background value variation factor caused by the sedimentary rock action and also considering the action of artificial activities. The potential ecological hazard index method reflects the toxic effect and potential hazard degree of heavy metals to the ecological environment. And (3) evaluating the ecological hazard degree and grade of a single heavy metal and the comprehensive ecological hazard and grade of all heavy metals by using the soil background value and the heavy metal toxicity response coefficient as reference values and contrasting potential ecological hazard grading indexes.

Drawings

Fig. 1 is a flowchart of a soil heavy metal source analysis method according to an embodiment of the present invention;

FIG. 2 is a flowchart illustrating a process of analyzing and determining a heavy metal pollution source in soil according to an embodiment of the present invention;

FIG. 3 is a distribution diagram of locations of a research area and sampling points according to an embodiment of the present invention;

FIG. 4a is a graph showing the factor distribution of a PMF model according to an embodiment of the present invention;

FIG. 4b is a schematic diagram of contributions of different factors to heavy metal accumulation according to an embodiment of the present invention;

FIG. 5 is a schematic diagram of a heavy metal accumulation index and a risk potential index provided by an embodiment of the present invention;

FIG. 6a is a graph of heavy metal loading provided by an embodiment of the present invention;

fig. 6b is a graph of a clustering lineage provided in an embodiment of the present invention.

Detailed Description

In order to make the technical means, the creation characteristics, the achievement purposes and the effects of the invention easy to understand, the invention is further described with the specific embodiments.

In the description of the present invention, it should be noted that the terms "upper", "lower", "inner", "outer", "front", "rear", "both ends", "one end", "the other end", and the like indicate orientations or positional relationships based on those shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.

In the description of the present invention, it is to be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "disposed," "connected," and the like are to be construed broadly, such as "connected," which may be fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.

The method for analyzing the heavy metal source in the soil, which is provided by the invention, is shown in figure 1 and comprises the following steps:

In the embodiment, the method can be used for more objectively analyzing the possible pollution source of the soil, and the geostatistical method is combined with the receptor model, so that the subjective judgment of investigators in the pollution source investigation process is reduced, and the uncertainty is reduced. Further, the source of the heavy metal pollutants in the soil is determined to be the basis of pollution evaluation, and a more targeted countermeasure can be provided for alleviating and controlling adverse changes of the soil environment.

The above steps are described in detail below:

and Step1, monitoring and stationing the research area, collecting soil samples, processing the soil samples, and measuring the content of heavy metals in the soil samples.

Step2 descriptive statistical analysis (maximum, minimum, mean, standard deviation, etc.) was performed on the heavy metal data and correlation between total soil heavy metal and available heavy metal was revealed using Pearson correlation test.

Step3, determining the space distribution characteristics of the heavy metals by using a geostatistical method, using Kolmogorov-Smirnov (K-S) to test whether the concentration of the heavy metals accords with normal distribution, carrying out logarithmic transformation standardization on the concentration data which do not accord with the normal distribution, and using an Ordinary Kriging method Ordinary Kriging (OK) to draw the space distribution diagram of the heavy metals in the soil.

And Step4, analyzing the soil heavy metal pollution source and the contribution rate thereof by adopting a positive definite matrix factor analysis model. The relevant definition and formula of the positive definite matrix factor analysis model are as follows:

PMF is a data analysis method based on a factor analysis principle, and the errors in the chemical components of the receptor are determined by using weights, and then the main pollution source and the contribution rate of the receptor are determined by a least square method. The matrix of sampled heavy metal sample data is decomposed into two matrices, a factor contribution G (i × k) and a factor distribution F (k × j), and a residual matrix E (i × j). The formula is as follows:

in the formula, X_ijIs the concentration of the jth chemical component of the ith sample; p is the number of factors; k is the number of pollution sources; g_ikIs the contribution of the pollution source k to the ith sample, namely a sharing rate matrix of the pollution source; f_kjIs the concentration of the jth heavy metal component in the source k, i.e. the pollution source component matrix; e_ijIs the residual matrix.

The PMF defines an objective function Q:

in the formula of U_ijRepresenting the uncertainty of the jth chemical composition of the ith sample; e.g. of the type_ijThe j heavy metal component residual error item of the ith sample is obtained; n is the number of samples; m is the number of heavy metal species.

The PMF model is based on a least square algorithm to carry out iterative calculation, and an original matrix X is continuously decomposed to obtain optimal matrices G and F, wherein the optimization aim is to enable Q to tend to a freedom value, namely i X j.

In the PMF model, concentration data and uncertainty data are required to be input, and the calculation method of the uncertainty data comprises the following steps:

when the concentration of each element is less than or equal to the corresponding MDL (method detection limit), the uncertain value is:

when the concentration of each element is greater than the corresponding MDL (method detection limit), the uncertain value is:

wherein σ is the relative standard deviation; c is the element concentration; MDL is the method detection limit.

Step5, for example, firstly inputting heavy metal concentration data and uncertainty data into EPAPMF 5.0 software, and determining the most appropriate factor quantity according to the minimum and stable Q value; then, whether a high-value area is concentrated in a certain area or not and whether obvious pollution sources (such as pollution discharge factories, mines, metal processing factories and the like) exist nearby the high-value area or not are judged from the spatial distribution of the heavy metals by analyzing the spatial distribution of the main influence heavy metals in the factors; when the heavy metals are not point source pollution, whether the heavy metals have similar spatial distribution needs to be judged, and if the heavy metals have similar spatial distribution, a pollution source can be deduced according to the characteristics of the heavy metals; otherwise, the pollution source is judged through the spatial distribution characteristics of the heavy metal (as shown in fig. 2).

And Step6, determining the degree of the heavy metal source and the future pollution condition of the heavy metal by using the geological accumulation index and the potential ecological risk index. Geological accumulation Index (Index of geological accumulation, I)_geo) And Pollution Load Index (PLI) as follows:

(1) index of geologic accumulation

The geological accumulation index is used for evaluating the influence of artificial activities on heavy metal pollution, and is graded by comparing the test concentration in soil with a natural geochemical background value, and the calculation formula is as follows:

in the formula (I), the compound is shown in the specification,

the content of the heavy metal j measured in the ith sample is mg/kg;

The Pollution Load Index (PLI) can visually reflect the contribution degree of each heavy metal to the comprehensive pollution of all heavy metals^[15]The calculation formula is as follows:

wherein the content of the first and second substances,

is the concentration of heavy metal j (mg/kg) measured in sample i,

the value is the local soil environment background value of the jth heavy metal, and K is a constant, usually 1.5; j is the number of the heavy metal component, and in this case, j is m. . For example, when the research is in the Wuqing district of Tianjin, the environmental background value and the national soil environmental quality standard are the evaluation standards (GB 15618-.

(2) Potential ecological risk index

The Potential Ecological Risk Index (PERI) is used for evaluating the ecological risk of the heavy metal in the soil based on the content and toxicity of the heavy metal in the soil, reflects the comprehensive influence of multiple pollutants, and divides the potential hazard degree by a quantitative method. Potential ecological risk index

And the cumulative Risk (RI) of the sampling point is calculated as:

wherein the content of the first and second substances,

as is 10, Cu Pb 5, Cd 30, Zn 1, Cr 2, Ni 5, which are the toxicity response coefficients of the individual heavy metals (j). Ecological risk factors and evaluation criteria are shown in table 1.

Cumulative index method I of Table 1(a)_geoValues and evaluation criteria; (b) potential ecological risk index PLI and evaluation standard; (c) potential risk index and evaluation criteria thereof

And Step7, combining the analysis result of the heavy metal pollution source and the ecological risk evaluation result to construct a soil heavy metal pollution source analysis evaluation research system.

The technical solution of the present invention is further illustrated by a specific example below:

step1 study area distribution and data collection:

the research area is located in the Wuqing area in the northwest of Tianjin, and the area is located at the lower end of the North China alluvial plain, has mild topography and inclines from the north, the west and the south to the sea entering direction of the southeast sea river. The region belongs to the continental monsoon climate in the temperature zone, the annual average temperature is 11.6 ℃, and the rainfall is 500-610 mm. The northwest wind prevails all year round, and the annual average wind speed is 2-4 m.s^-1. The Wuqing district is a typical urban and rural transition district and is also a main intensive farming district in Tianjin city. Beijing pollution discharge river, Beijing canal and YongyunThe sewage discharge rivers such as the fixed river and the like are Wuqing and are the main irrigation sources of the area. The soil type of the research area mainly comprises moisture soil, loamy moisture soil, sandy moisture soil, glutinous moisture soil, salinized moisture soil, wet moisture soil, limestone leaching moisture soil and the like, and the soil layer is deep, has various utilization modes and has the characteristic of multiple adaptability. The vegetable is mainly spinach, tomato and Chinese cabbage.

According to the standard sampling procedure in technical Specification for soil Environment monitoring (HJ/T166-2004), samples of surface soil (0-20cm) were collected on site. In short, according to the characteristics of the cultivated land area, the topographic characteristics, the soil type and the vegetable variety, a field block in a representative grid is selected as a sampling point, and meanwhile, a pollution source with obvious characteristics is avoided. Collecting about 10 mixed samples of a field block with large area and complex terrain according to an S-shaped sampling method, and positioning the mixed samples on the most representative point positions; the field block with small area and single landform is used for collecting mixed samples according to a five-point method, and coordinates are positioned on a middle point position. When sampling soil, firstly removing plant residues on the surface layer (about 1cm), then collecting soil on the plough layer (about 1-20cm) by using a stainless steel soil drill, carefully removing the soil contacting with the metal surface of the soil drill, finally thoroughly mixing in a sealing bag for about 1kg, writing a label and storing. Meanwhile, a portable high-precision global positioning instrument is used for recording the precise coordinates of each field block for later-stage mapping analysis. The vegetable sampling follows the corresponding principle, namely, the vegetables are collected from the same position of the soil sample in a sealed bag, marked and stored. And dried with an instrument at 60 ℃ for two hours on the same day, the roots and overground parts were separated, crushed and stored in moisture-proof sealed bags for further analysis. Meanwhile, the soil sample is air-dried and pretreated, including removing impurities such as weeds and root systems, and the stone grains are filtered by adopting a 2mm sieve. 100g of the heavy metal is ground until the heavy metal completely passes through a nylon sieve with the aperture of 100 meshes, and the heavy metal is uniformly mixed for measuring the content of the heavy metal.

Irrigation water sampling rules comply with technical specifications for surface water and sewage monitoring (HJ/T-91-2002), and water samples are randomly collected from irrigation ditches. Prior to analysis, the samples were stored in a cooler to minimize biodegradation and volatilization.

Organic fertilizer (cow dung, chicken manure, pig manure and the like) used by the fertilizer collection sampling points is stored in a polyvinyl chloride bag before analysis so as to prevent the sample from being interfered by others.

Accurately weighing 0.50g of prepared soil sample, and performing microwave digestion (Milestone ETHOS UP) on heavy metal in the soil by using aqua regia-perchloric acid to be detected; the vegetables and organic fertilizer are completely made of HNO₃-H₂O₂Digesting to be tested by a method of 6: 3; preparing 500 ml water samples for metal element test, and adding 5 ml HNO into each water sample₃(1+1), and then shaking up to be measured. Wherein, the contents of heavy metals Pb, Cu, Cr, Ni and Zn in soil, organic fertilizer and irrigation water are measured by a flame atomic absorption spectrometer (Analytik Jena novAA 350), and the contents of heavy metals Cr, Pb, Cu, Ni and Zn in vegetables are measured by an inductively coupled plasma emission spectrometer; the Cd content of the soil-vegetable-organic fertilizer-irrigation water is determined by a graphite furnace atomic absorption spectrometer (Analytik Jena ZEEnit 650P), and As is determined by an atomic fluorescence spectrometer (AFS-933, Jitian Beijing). The pH value of the soil is measured by leaching according to the water-soil ratio of 2.5:1(v/w) by adopting a pH meter, and the organic matter of the soil is measured by adopting a potassium dichromate oxidation-external heating method. In the process of analyzing the total amount of heavy metals, an environmental standard substance soil GBW07430 (institute of geophysical and geochemical survey, Chinese geological academy of sciences) is taken as a quality control sample, the recovery rate of each heavy metal element content of the obtained quality control sample is within the range of 91-107%, parallel samples are added among a certain number of samples, and the standard deviation of the parallel samples is within 9%; as shown with reference to fig. 3.

Step2, performing descriptive statistical analysis on the heavy metal data;

table 2 shows descriptive statistics of soil characteristics (pH and SOM content) and heavy metal concentrations in soil samples. It can be seen that the soil has an average pH of 7.69(6.63-8.89), with 4.21% of the soil samples having a pH below 7.0. In addition, the SOM content is 7.17-42.48 g/kg^-1In the mean value of 19.30 g.kg^-1This is consistent with the mean SOM content in national soils (19.8 g.kg)^-1)。

The average value of different heavy metals and the heavy metal content difference in the soil is Zn (113.64 mg. kg)^-1)、Cr(69.33mg·kg^-1)、Pb(46.28mg·kg^-1)、Cu(17.26mg·kg^-1)、Ni(19.89mg·kg^-1)、As(1.08mg·kg^-1)、Cd(0.06mg·kg^-1) The Risk Screening Value (RSV) of the agricultural land soil risk control standard of China soil environmental quality (GB 15618-. Compared with agricultural soil Risk Screening Values (RSV) with different pH values, the total overproof rates of Cd, Pb, As, Cu and Zn concentrations in the soil are respectively 5.26%, 2.11%, 1.05%, 2.11% and 2.11%, more specifically, As and Pb pollution (100%) occurs in the soil with the pH being more than 7.5, Cu and Zn overproof samples all occur in neutral soil (pH being 6.5 or more and less than or equal to 7.5), and Cd pollution occurs in the neutral soil (pH being 6.5 or more and less than or equal to 7.5) and alkaline soil (pH being more than or equal to 7.5) and is respectively 3.16% and 2.11%. And then, comparing with the background value of the soil in the Wuqing area, finding that the average concentration of heavy metal in the soil exceeds the background value of the area, although the average concentration of heavy metal Cd in the soil is lower, the heavy metal Cd in the soil exceeds the background value of the soil in the area, and the maximum concentration of Cd in the soil is 31.44 times of the background value. Thus, the major polluting element in this area is Cd followed by Pb, Zn and Cu. Researchers show that the high Coefficient of Variation (CV) indicates that the spatial distribution of heavy metals in the area is uneven, and data analysis shows that the coefficient of variation of Cd, Zn, Cu and Pb is 136.15%, 69.35%, 66.55% and 63.25% respectively, which indicates that the degree of variation among sampling points is high, thereby indicating that the soil in the area is influenced by serious human activities.

Table 2 soil characteristics and heavy metal concentration descriptive statistics (n 92)

Note: SD: standard deviation; CV: and (4) a variance coefficient.

Step3, determining the spatial distribution characteristics of the heavy metals by using a geostatistical method;

spatial variation of heavy metals can be used to identify hot spot areas and identify potential sources of soil heavy metals. The average error (ME) of the ordinary Kriging interpolation is close to 0, and the predicted value is proved to be accurate. The Root Mean Square Standard Error (RMSSE) value was between 0.980 and 1.003, indicating that the standard error is accurate and the spatial distribution of heavy metals results are shown in fig. 4. The high value areas of Cu and Cd are respectively in the northeast and the middle part, and obvious point source pollution is presented; the spatial distribution characteristics of Cr, Ni and As are similar, and the high-value zone is positioned in the southwest part; pb and Zn present non-point source pollution, Pb affects almost the whole Wuqing district, and Zn high value district is mainly in the northwest and southwest parts.

Step4, analyzing the soil heavy metal pollution source and the contribution rate thereof by adopting a positive definite matrix factor analysis model;

and (4) analyzing by a PMF model, determining the most appropriate factor number according to the minimum and stable Q value, and finally obtaining 6 factors. At the same time, the residual values for most soil samples were between-3.0 and 3.0, and R²>0.94 indicates that the correlation between metals is strong. The result of the PMF run is shown in fig. 4a, which is a factor distribution diagram of the PMF model, wherein the left axis represents the content of each heavy metal in the factor, and the right axis represents the contribution rate of each heavy metal in the factor. 4b is a schematic diagram showing the contribution of different factors to the accumulation of heavy metals, the abscissa represents the heavy metal species, such as copper, zinc, etc.; the ordinate represents the contribution ratio of factors affecting heavy metals, such as Cadmium, which is mainly composed of

factors

1, 4, 5, and 6, wherein the factor 1 accounts for 62.4%, and the factor is the pollution source species. Colloquially, it is said that the Cadmium is simultaneously affected by 4 sources of contamination, of which the source 1 of contamination has a major effect on the accumulation of heavy metals.

Step5, analyzing the soil heavy metal pollution source;

as can be seen from the spatial distribution map of the heavy metal, the spatial distribution of Cd is relatively concentrated, and point source pollution is presented. Plots with high concentrations of Cd are located in the southwest part of the area where there are many industrial activities, such as the alloy processing, printing and dyeing industries, and intensive industrial activities are one of the important sources of Cd. Areas with high Pb content are close to the main roads with developed traffic, whereas automobile exhaust usually contains a large amount of Pb, which is also generally regarded as a sign of traffic.

The trends of Cr, Ni and As are highly consistent and downstream in the river in the area indicate that Cr, Ni and As have the same pollution source and there is intensive industrial activity near the high value region, so it is concluded that the possible sources of Cr, As and Ni are a mixed source of matrix and river irrigation water. Cu pollution is concentrated, a high-value area is in the northeast of the area, and the facility agriculture in the area develops rapidly and is a main area for vegetable planting. Perennial fertilization and irrigation lead to enrichment of Cu, which is often the hallmark of organic fertilizers, forming non-point source pollution, and therefore it is inferred that the main cause of Cu enrichment is agricultural fertilization. The spatial distribution of Zn in the soil is in the southwest and northwest parts of the area, Zn has similar spatial distribution to Pb, and high-value points are close to the main roads and the vicinity of rivers of traffic density. Mechanical wear of automobile parts (e.g., brake pads, fuel tanks, head pads) and automobile parts can release Zn to the environment. Therefore, it is inferred that traffic emissions and agricultural drainage irrigation may be the main cause of Zn enrichment.

The first factor (F1) is also the largest factor (27.8% of the total contribution), dominated by Cd, which exceeds 62.4% by weight. The source of Cd pollution in agricultural soils may be artificial pollution such as sewage, sludge, fertilizer use and atmospheric sedimentation produced by industry or municipalities. The possible pollution source of Cd is inferred to be industrial emission according to the fact that Cd presents point source pollution instead of area source pollution on the spatial distribution characteristic. In addition, the contents of heavy metals in irrigation water and organic fertilizers in a research area are analyzed, and the fact that the content of the heavy metals in the irrigation water does not exceed the maximum metal content allowed by the irrigation water (Table 3) (GB5084-2005) is found, and the content of Cd in the organic fertilizers is 0.04-0.59 mg-kg^-1Meanwhile, according to the organic fertilizer industry standard (NY525-2012), the content of Cd in organic fertilizer samples in Wuqing district does not exceed the standard (as shown in Table 4). Therefore, factor 1 is attributed to an industrial source.

TABLE 3 heavy metal content in irrigation water

Note: SD: standard deviation; CV: a variance coefficient; standard values for the different elements in irrigation water were obtained from GB5084-2005 (MEPRC, 2004).

The second factor (F2) accounted for 14.5% of the total contribution, characterized primarily As (31.9%) and Pb (24.2%), which may be attributed to the use of pesticides (i.e. pesticides/insecticides). At present, pesticides containing As and Pb are forbidden by most countries, but the As and Pb in soil still accumulate in a large amount due to the difficult degradability of heavy metals. As is used in large quantities for the production of pesticides such As insecticides, herbicides and the like, and causes the accumulation of As in soil. In addition, the phosphate fertilizer contains As, and the application of the phosphate fertilizer enriches As in soil^[27]. And Pb is mainly used for manufacturing a heat stabilizer in the agricultural mulching film, for example, the Pb content in soil and plants is treated by different mulching film residue amounts, and the result shows that the Pb content in the soil and the plants with high mulching film residue amount is higher than that with low mulching film residue amount. Thus, factor 2 may be a mixed source of pesticide fertilizer and mulch.

The third factor (F3) consisted primarily of Ni (33.2%), Cu (27.4%), Cr (19.7%) and As (20.5%), accounting for 17.1% of the total contribution. Ni, Cr and Cu are generally regarded as indexes of natural sources, which has been proved by many scientists, for example, by adopting a multivariate statistical analysis method to determine the main sources of Ni, Co, Cr, Fe and Al in agricultural soil in the Iranian Fahan industrial area as geological causes; and the average concentrations of these four heavy metals were below the respective background values. According to I_geoAnd PLI, and the pollution level, only a small amount of Cu and As causes harm to the environment, which is also due to the enrichment of Cu and As in the soil of the area caused by long-term use of large amount of pesticides and livestock manure, and the conclusion is also consistent with the

factors

1 and 4. Thus, the third factor is determined to be a natural source.

The fourth factor (F4) mainly contributed Zn (35.9%) and Cu (35.1%) to 16.4% of the total contribution. Zn and Cu are transferred to animal manure as an inherent component of additives to daily livestock feed, and thus are generally markers for livestock manure applications. As shown in table 4, the organic fertilizer contains a large amount of Zn and Cu, which also confirms that factor 4 is the source of the organic fertilizer.

TABLE 4 heavy metal content in organic fertilizer

Note: SD: standard deviation; CV: a variance coefficient; the standard values of different elements in the fertilizer are obtained from NY525-2012(MEPRC, 2004).

The fifth factor (F5) accounted for 15.9% of the total contribution, which was related to Pb (45.7%) and Cd (17.3%). It is well known that Pb is often artificially a sign of traffic in soil, and automobile exhaust emissions are the main route for Pb to enter soil. Although the use of Pb-containing gasoline was prohibited in china since 2000, the use of Pb-containing gasoline for decades has resulted in severe contamination of Pb in the soil around roads. For Cd, it is also present in the tires and fuel of automobiles, and Cd-containing dust enters the soil from fuel combustion and tire wear. With the increase of the distance from the road, the concentration of Pb and Cd shows a descending trend, and the two heavy metal high-value areas are located near main roads of the area, such as expressways, national roads and provincial roads, in combination with spatial distribution characteristics. In summary, a factor of 5 may be defined as traffic emissions.

The sixth factor (F6) is the lightest in specific gravity and is composed mainly of Cr (21.0%), Ni (12.9%) and Pb (11.5%). According to the spatial distribution diagram of the heavy metals in the soil, Cr and Ni have similar spatial distribution characteristics, the high-value zone is positioned at the middle and lower reaches of a river and mainly concentrates at the lower reaches of a northern canal and a Beijing pollution discharge river, more factories are distributed in the middle and lower reaches of the northern canal, the pollution of the areas is mainly the transmission of the sewage discharge of the factories through the northern canal, the heavy metal pollution enrichment is caused by the irrigation of farmlands, and the factor 6 is not a main pollution contributor and only accounts for 8.2 percent of the total contribution. It was therefore concluded that factor 6 is sewage irrigation.

Step6, determining the current situation of ecological risk and pollution condition by adopting a geological accumulation index and a potential ecological risk index;

in order to quantitatively evaluate the pollution state in the area, cumulative indexes I are respectively calculated_geoAnd a pollution load index PLI according to I_geoAnd PLI, and the results are shown in FIG. 5, where the box chart is mainly used to reflectThe characteristics of the original data distribution can also be compared with the characteristics of a plurality of groups of data distributions. Wherein in fig. 5, a part a represents a heavy metal cumulative index; section b represents the potential risk index. The average value of the earth cumulative indexes is Pb in sequence>Cd>Cu>As>Zn>Ni>Cr, which indicates that Pb, Cd, Cu and As are accumulated in soil more than other heavy metals, 13.68%, 12.63%, 3.15% and 1.05% of soil samples containing Pb, Cd, Cu and As are in moderate pollution (I)_geo>1) To extremely strong pollution (I)_geo<5) In the meantime. In addition, 85.26% and 14.74% of the soil samples were at moderate contamination (1 < PLI.ltoreq.2) and high contamination (2 < PLI.ltoreq.5) levels.

The potential ecological risk index for heavy metals in soil ranges from 0.91 to 942.67, indicating that heavy metals accumulated in soil pose a risk to the local ecosystem. Of these, 1.05% of the soil samples had a strong ecological risk, while the medium ecological risk, the strong ecological risk accounted for 9.47% and 2.10%, respectively. According to the individual pollution risk indexes of different heavy metals, according to Cd>As>Pb>Cu>Ni>Cr>Zn decreases in order, this is in accordance with I_geoThere were similar evaluation results. In general, Cd, As, Pb and Cu are the major toxic elements in soil, which constitute potential risks to the local environment, and Cd, As are major contributors to overall ecological risk.

Step7, combining the analysis result of the heavy metal pollution source and the ecological risk evaluation result to construct a soil heavy metal pollution source analysis evaluation research system;

the potential pollution sources are determined according to the analysis result of the pollution source analysis: industrial activities (Cd), pesticides and fertilizers and mulch effects (As and Pb), natural sources (Ni, Cu, Cr and As), organic fertilizers (Zn and Cu), traffic emissions (Pb and Cd) and sewage irrigation (Cr, Ni and Pb). Combining with the current situation of ecological risks and pollution condition analysis, determining that the main pollution risks of Wuqing areas in Tianjin City are industrial pollution and excessive agricultural activities, and therefore, the safety discharge of industrial wastewater, waste gas and waste residues is strictly controlled in the working center of Wuqing areas in Tianjin City for strengthening the control value of industrial discharge; secondly, strengthen the guide and rationally apply chemical fertilizer and fertilizer, control the safe use of pesticide.

Referring to fig. 6a, a heavy metal load graph is shown, each point in the scatter graph represents each original variable, an x-axis value is a correlation coefficient of the variable and a first principal component, and a y-axis value is a correlation coefficient of the variable and a second principal component, so that the closer the point is to which axis, the more the variable is related to the corresponding principal component; the value represents the correlation coefficient size.

Referring to fig. 6b, which is a graph of clustering analysis pedigree, the numbers are relative distances of each category, and are the result of resetting according to the distance proportion. The relative distance of this class can tell us approximately the change in distance between classes. For good clustering results, the distance between classes should be as large as possible, for example, in the case of the dendrogram shown in fig. 6b, when the distance between two classes is at the level of 25 when the classes are clustered into 2, and when the distance between the classes is rapidly close to the level of 15 when the classes are clustered into 3, the more the clusters are, the closer the classes are, the more the features of the classes are blurred.

The principal component analysis result shows that the principal component 1 explains 43.27% of total variance, the principal component 1 comprises four elements of Cr, Ni, As and Pb, the statistical description shows that the whole body of Cr and Ni does not exceed the background value of Wuqing, only has individual high-value points, and the comparison of the spatial distribution situation shows that the Cr and Ni have similar distribution rules, so that the Cr pollution source can be divided into a mapped mother substance and an industrial source; the concentration of Ni element is normal as a whole, only individual places exceed the background value, and the concentration of Ni element in the research area is below the background value, so that the source of Ni is inferred to be the matrix of the soil; the spatial distribution of As element concentration is compared to find that no obvious point source pollution exists, and other researches show that the As pollution sources are mostly the matrix of the soil and the use of arsenic-containing pesticides, so that the fact that the element concentration of As in the Wuqing district is increased due to agricultural activities (arsenic-containing pesticides) is inferred; the spatial distribution of Pb shows that the Pb concentration is mostly above background values over the study area, indicating that the study area is heavily contaminated with Pb. The main source of lead is industrial and mining enterprises for smelting, manufacturing and using lead products, particularly lead-containing wastewater, waste gas and waste residues discharged in the non-ferrous metal smelting process, 4 non-ferrous metal processing enterprises exist in a research area by searching POI data of the Wuqing area and a pollution enterprise list of the Wuqing area, and therefore the source of the Pb is inferred to be an industrial source.

The main component 2 explains the total variance of 27.01%, including Cu and Zn, which have similar spatial distribution characteristics from the spatial distribution diagram, thus the main source of Cu and Zn is caused by applying organic fertilizer.

In the source analysis model, principal component analysis can only analyze three possible pollution sources, and can only roughly distinguish artificial sources from natural sources; the positive definite matrix factor analysis model can analyze the possibility of more pollution sources, and can more accurately find out the possible pollution sources of heavy metals in the facility vegetable field by combining the statistical spatial characteristics.

The invention can realize the objective judgment of the analysis of the soil heavy metal pollution source. Compared with the existing method, the method can identify the pollution source of the heavy metal more accurately, and reduce the inaccuracy caused by subjective judgment of workers.

The foregoing shows and describes the general principles and broad features of the present invention and advantages thereof. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims

1. A method for analyzing a heavy metal source in soil is characterized by comprising the following steps:

2. The method for analyzing a heavy metal source in soil according to claim 1, further comprising: the step S30 includes:

3. The method for analyzing a heavy metal source in soil according to claim 2, further comprising: the step S40 includes:

defining an objective function Q;

4. The method for analyzing a heavy metal source in soil according to claim 3, further comprising: when the content of each heavy metal component is less than or equal to the corresponding method detection limit, the uncertainty is as follows:

5. The method for analyzing a heavy metal source in soil according to claim 4, further comprising: the step S50 includes:

determining whether a point source contamination is present;

6. The method for analyzing heavy metal sources in soil according to claim 5, wherein in step S60, the geological accumulation index is used for evaluating the influence of human activities on heavy metal pollution, and is classified by comparing the tested concentration in soil with the natural geochemical background value, and the calculation formula is as follows:

in the formula (I), the compound is shown in the specification,

the content of the heavy metal j measured in the ith sample is mg/kg;

7. The method for analyzing a heavy metal source in soil according to claim 6, wherein in the step S60, the pollution load index reflects the contribution degree of each heavy metal to the comprehensive pollution of all heavy metals, and the calculation formula is as follows:

in the formula (I), the compound is shown in the specification,

the content of heavy metals 1 and 2 … j measured in the 1 st sample is unit mg/kg;

8. The method for analyzing soil heavy metal sources according to claim 7, wherein in the step S60, the potential ecological risk index is used for evaluating the ecological risk of the soil heavy metal based on the content and toxicity of the soil heavy metal, reflects the comprehensive influence of multiple pollutants, and is divided into potential hazard degrees by a quantitative method, and the calculation formula is as follows:

wherein the content of the first and second substances,

toxicity response coefficient for a single heavy metal j, wherein: as is 10, Cu Pb 5, Cd 30, Zn 1, Cr 2, Ni 5.