CN108918815B - Method for predicting heavy metal risk of soil - Google Patents
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Abstract
The invention provides a method for predicting heavy metal risks in soil, which is used for estimating the contents of various heavy metals in the soil in a target area based on a sequential condition simulation method and synthesizing the contents of various heavy metals based on a Hakanson potential ecological risk index method to generate a visual soil heavy metal risk map. Compared with the traditional kriging interpolation method, the sequential condition simulation method can better reflect the uncertainty of heavy metal distribution, overcomes the smoothing effect of the traditional kriging interpolation method, and has better predictability; the Hakanson potential ecological risk index method can scientifically integrate the heavy metal content of various soils to obtain more accurate assessment risk; the high risk area can be observed from the visual soil heavy metal risk graph more intuitively, and preventive measures can be taken in time, so that the method has good practicability.
Description
Technical Field
The invention belongs to the field of geostatistics, and particularly relates to a method for predicting heavy metal risk in soil.
Background
With the rapid development of urbanization and industrial and mining industries and the large use of pesticide organic fertilizers, more and more heavy metals enter the farmland soil environment through sewage irrigation, atmospheric sedimentation, rainfall runoff and other ways. The content of heavy metals in the farmland is accumulated day by day, the heavy metals are difficult to be degraded by microorganisms in the soil, and once the content of the heavy metals exceeds a specific threshold value, the heavy metals cause serious harm to the safety of crops, the ecological environment and the human health. Aiming at the harm stress of the heavy metals in farmland soil, the risk area of the heavy metals in the farmland is identified in advance, and scientific guidance and basis can be provided for regional farmland environment protection, heavy metal pollution early warning, risk management and control and the like.
The existing method generally comprises two steps when judging the risk of the soil heavy metal area, firstly, the spatial distribution of the soil heavy metal content is predicted, then, the risk threshold value is determined according to the evaluation method, and then whether the area is at risk or not is judged.
The method comprises the steps that accurate spatial distribution mapping of soil heavy metals in a region required by farmland heavy metal risk identification is used as a support, aggregation characteristics of the soil heavy metals are analyzed through spatial distribution and trends, and whether the soil is risky or not is judged by comparing predicted content with a risk threshold. Currently, the most common method in soil property prediction research is kriging interpolation, and there are generally two ways to identify the heavy metal risk of soil: firstly, the method directly utilizes an interpolation method to carry out space prediction on the heavy metal content of the soil, obtains the space distribution of the heavy metal content and then compares the space distribution with a threshold value, and a multi-purpose linear Kriging model is used, such as an Ordinary Kriging (OK); secondly, uncertainty evaluation is used for determining the spatial distribution of the heavy metal content in the region exceeding the threshold probability, and a nonlinear Kriging model is used, such as Indicator Kriging (IK).
The pollution evaluation can define the degree of heavy metal pollution of the soil, and the threshold value of heavy metal pollution is determined by grading, so that the hazard level of the heavy metal in the soil is divided. There are many methods and criteria for rating heavy metal contamination, and they can be mainly classified into an index method, a model index method, and other evaluation methods.
The existing commonly used prediction method has certain limitations and defects, on one hand, the kriging interpolation is the optimal unbiased estimation on the non-sampling point, and the requirement on the distribution of data is strict. However, the content of heavy metals in the soil is not only related to the texture of the soil itself, but also related to the influence of human factors, which causes the distribution of the heavy metals in the soil to present extremely high spatial heterogeneity. Abnormal values also exist in actual sampling, so that data are distributed in a biased mode and have large variation coefficients and bias values. This makes the data often not fully compliant with kriging requirements. Data is generally subjected to normal distribution by nonlinear transformation, but is difficult to convert to the original scale; or the abnormal value is removed by utilizing a variant cloud picture, a triple standard deviation criterion and the like, but the abnormal value is objectively existed, and the direct neglect of the abnormal value can have a great influence on the predicted result. Meanwhile, the Kriging interpolation also has a smoothing effect, the smoothing degree is influenced by the distribution of the sampling points, and the farther the sampling points are away, the stronger the smoothing effect of the interpolation is. On a large scale, due to limitations of uneven farmland distribution, space, time, labor cost and the like, uniform and dense sampling is difficult to achieve, and the generated smoothing effect may cause important information loss in a soil heavy metal content abnormal area.
On the other hand, the index method is simple and easy to understand and grasp and operate, but has a certain problem that it is difficult to comprehensively and comprehensively express the pollution degree of the soil by using the single-factor index method. The internal Meiro comprehensive pollution index method has subjective factors for artificial estimation, and easily exaggerates the influence of high-concentration heavy metal pollution. The cumulative index method ignores the correlation among elements, has different pollution capacities and biological effectiveness of all metals, is difficult to compare the environmental quality among the elements or among regions, and has subjectivity when selecting the k value.
Disclosure of Invention
The invention provides a method for predicting heavy metal risks in soil, which is used for estimating the content of various heavy metals in the soil in a target area based on a sequential condition simulation method and synthesizing the content of various heavy metals based on a Hakanson potential ecological risk index method to generate a visual soil heavy metal risk map. Compared with the traditional kriging interpolation method, the sequential condition simulation method can better reflect the uncertainty of heavy metal distribution, overcomes the smoothing effect of the traditional kriging interpolation method, and has better predictability; the Hakanson potential ecological risk index method can scientifically integrate the heavy metal content of various soils to obtain more accurate assessment risk; the high risk area can be observed from the visual soil heavy metal risk graph more intuitively, and preventive measures can be taken in time, so that the method has good practicability.
The invention provides a method for predicting heavy metal risk in soil, which comprises the following steps:
determining a soil heavy metal risk prediction target area;
selecting sampling points in the target area and sampling soil;
measuring the heavy metal content of the soil of the sampling point;
deducing soil heavy metal content data of non-sampling points and generating a spatial distribution map of the heavy metal content of the target area based on the soil heavy metal content data of the sampling points;
and calculating the ecological risk index of the target area based on the spatial distribution map and generating a soil heavy metal risk evaluation map.
In a preferred embodiment, the target area is a region where both agricultural and industrial production are present.
In a preferred embodiment, the soil sampling is carried out on the sampling points based on a quincunx point arrangement method.
In a preferred embodiment, the soil heavy metal comprises copper, zinc, lead, cadmium, chromium, arsenic, mercury.
In the preferred embodiment, soil heavy metal content data of non-sampling points are deduced according to the soil heavy metal content data of the sampling points based on a sequential indication simulation method.
In a preferred embodiment, the sequential indication simulation method comprises the following steps:
carrying out indication transformation on the heavy metal content data of the sampling points;
respectively calculating an indicated semi-variation function of the heavy metal content data under each threshold condition;
establishing a prior condition cumulative distribution function based on the indication semi-variant function;
dividing the target region into grids of the same resolution, defining a random path passing through all grids, and randomly extracting a value from the conditional cumulative distribution function at a first position grid as a simulation value;
and applying the simulation value to the prior condition cumulative distribution function of the next position grid, randomly extracting a value from the prior condition cumulative distribution function of the next position grid as a simulation value, and repeatedly executing the step until all grids are simulated.
In a preferred embodiment, the calculating the ecological risk index of the target area comprises the following steps:
calculating potential ecological risk indexes of single heavy metals in the soil of the target area;
and calculating the comprehensive potential ecological risk index of the heavy metals in the soil of the target area.
In a preferred embodiment, the calculation formula of the potential ecological risk index of the single metal is
In a preferred embodiment, the calculation formula of the comprehensive potential ecological risk index of the heavy metals is
Wherein,
is the potential ecological hazard coefficient of the ith heavy metal,
the toxicity response coefficient of the ith heavy metal; and RI is the comprehensive potential ecological hazard index.
The invention provides a method for predicting heavy metal risks in soil, which is used for estimating the content of various heavy metals in the soil in a target area based on a sequential condition simulation method and synthesizing the content of various heavy metals based on a Hakanson potential ecological risk index method to generate a visual soil heavy metal risk map. Compared with the traditional kriging interpolation method, the sequential condition simulation method can better reflect the uncertainty of heavy metal distribution, overcomes the smoothing effect of the traditional kriging interpolation method, and has better predictability; the Hakanson potential ecological risk index method can scientifically integrate the heavy metal content of various soils to obtain more accurate assessment risk; the high risk area can be observed from the visual soil heavy metal risk graph more intuitively, and preventive measures can be taken in time, so that the method has good practicability.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 shows a flow chart of a soil heavy metal risk prediction method according to an embodiment of the invention;
FIG. 2 is a schematic diagram illustrating a quincunx dotting method according to an embodiment of the present invention;
FIG. 3 shows a sample point profile for an embodiment of the present invention;
FIG. 4 shows a descriptive statistical plot of soil heavy metals at a sampling point in the practice of the invention;
FIG. 5 shows a schematic diagram of a semi-variogram image;
FIG. 6 shows an indicative hemivariogram image of Cu at each threshold condition;
FIG. 7 shows a table of optimal parameters indicating semi-variant functions for each threshold condition;
FIG. 8 shows the cumulative distribution function of Cu in deciles;
FIG. 9 is a schematic diagram illustrating a working interface and results of sequential pointing simulation according to an embodiment of the present invention;
FIG. 10 shows a soil heavy metal spatial distribution map of an embodiment of the invention;
FIG. 11 shows soil heavy metal potential ecological risk classification criteria for an embodiment of the invention;
FIG. 12 shows the soil heavy metal risk potential index of an embodiment of the present invention;
FIG. 13 shows a SISIM and IK misjudgment rate comparison data table of an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the invention provides a method for predicting heavy metal risks in soil, which solves the defects of serious screening and smoothing effects of sampling abnormal values in the prediction of the spatial distribution of the content of heavy metals in the traditional method, indicates the abnormal values to calculate an indicated semi-variation function through the indicative processing of data in the early stage, and simultaneously simulates gridding of a research area by utilizing sequential indication to reduce the influence of the smoothing effects; the prediction result is subjected to risk area identification by adopting a Hakanson potential ecological risk index method with wide consideration factors and is presented by a visualization means, so that the method has good accuracy, intuition and practicability.
In statistics, there is a spatial autocorrelation when the value of a variable at some position in space depends on the value of the variable at other positions. In a system, some variables are often not random when considering space or time, and therefore, when calculating the characteristics of the variables, the spatial structure of the variables needs to be calculated in addition to the statistics such as the mean, variance, etc. of the variables. Variables that depend on the location of the spatial distribution are referred to as regionalized variables. For a variable Z, such as the content Z (i) of a heavy metal in the example of the invention, which varies according to the different spatial positions i, the variation of this variable is determined by three portions: the overall value f (i) in the large scale range is used for representing the variation trend, the spatial dependence s (i) in the local small range and the error, and can be represented by the formula Z (i) ═ f (i) + s (i) + l +.
The two main characteristics of the regionalized variable are randomness and structure, wherein the randomness is that the value of the regionalized variable is random at a certain local point; structural means that for the entire region there is a global or average structure, and the values of adjacent regionalized variables have the correlation expressed by the structure.
For example, in a small rangeThe distribution of heavy metal i is different from that of the large area, and the heavy metal content in the small area may be higher or lower than the average value of the large area, which is random. In particular, this small range may be defined by Z (i)1) Representing a state, each observation can be considered as Z (i)1) One implementation of (a); likewise, another small range may be represented by Z (i)2) That is, each observation can be considered as Z (i)2) One implementation of (a); at the same time, Z (i)1) And Z (i)2) There is some quantitative relationship.
Specifically, for a random process, to fully characterize it, its distribution function or distribution density must be given, but such a distribution is generally difficult to find. The focus of the research is therefore on its mathematical characteristics, which mainly include: mathematical expectation, correlation function, variance, covariance mean square value; where the mathematical expectation is a first order moment and the following four numerical features are all second order moments.
When a random process is a second moment process, the numerical characteristics of the second moment can be studied, so that the random process can be analyzed correspondingly, such as judging whether the random process is stable or not.
It is not necessary to find the complete distribution function or distribution density function of the regionalized variable, and the first two moments, the first moment and the second moment, of the regionalized variable distribution function or distribution density function are sufficient to provide an approximate solution to the problem under consideration in most cases, such as the heavy metal content distribution data according to embodiments of the present invention. On the other hand, the data measured in the field is not sufficient to obtain a distribution function or a distribution density function with a complete localization variable, and therefore, it is not necessary to obtain a distribution function or a distribution density function with a complete localization variable.
Furthermore, if only the first two moments of the random function are used, the two random functions may be considered to be identical if the first and second moments of the two random functions are identical.
The first moment is an average value function of a regionalized variable and is defined as E [ Z (x) ];
the second moment in one regional variable is three in total and is a variance function of the regional variable, a covariance function of the regional variable and a variation function respectively; the variance function is also called a half variance, and is a half of the variance of two-point differences of a regional variable.
In practical applications, the above function is often estimated from several measurements. For example, the half-variance function needs to be calculated by mathematically expecting the value of E [ Z (x) -Z (x + h) ], so several implementation data for Z (x) and Z (x + h) are necessary.
However, in the soil space research, only one pair of data is obtained at the point x and the point x + h, and a second pair of data cannot be obtained at the same point in the space, so that in order to overcome the difficulty, some assumptions and limitations need to be provided for regionalized variables.
Assuming that the mean value of the variable is a constant within the local division and does not change with the position; assuming that the covariance C [ Z (x), Z (y) of sample points x and y exists and depends only on the distance of sample points x and y, Z is second order stationary, with the result that the mean and covariance of Z do not vary spatially.
Thus, at a certain local area in space, for a certain point x in space0A plurality of points at a distance h can be regarded as Z (x)0) So that statistical inferences and valuation predictions may be made.
Further, if the random function z (x) has the following implication assumptions:
the mean value is present and is independent of x, i.e., E [ Z (x)) ] m, 0 for any E [ Z (x + h) -Z (x)) ];
for any distance h, the variable [ Z (x + h) -Z (x) ] has a finite variance that does not depend on x;
then for any x and h, the following holds:
Var[Z(x+h)-Z(x)]＝E{[Z(x+h)-Z(x)]22 γ (h), where γ (h) is called a semi-variogram, which is used to characterize the spatial variogram structure, or spatial continuity, of random variables.
Therefore, stationarity mainly includes two types, wherein one type means that the mean value is stable, and the mean value is assumed to be constant and to surround the position; another class is adapted to second order stationarity related covariance functions and intrinsic stationarity related semi-variant functions; the covariance stationary is that the covariance of any two points with the same distance and direction is assumed to be the same, the covariance is related only to the values of the two points and not to their positions; inlier stationarity assumes that the variance of any two points with the same distance and direction is the same.
In the model prediction stage, the soil heavy metal hazard prediction method provided by the embodiment of the invention mainly utilizes the data of sampling points and the structurality of a half-variation function, adopts a method of sequentially indicating kriging, estimates the data of non-sampling points, and finally generates a soil heavy metal hazard risk map through a Hakanson potential ecological risk index method, thereby judging a heavy metal risk area.
Fig. 1 shows a flow chart of a soil heavy metal risk prediction method according to an embodiment of the invention. The embodiment of the invention provides a method for predicting heavy metal hazards in soil, which mainly comprises the steps of data acquisition, model prediction and risk assessment, and the specific implementation steps are as follows:
s101: determining a target area for predicting the heavy metal hazard of soil;
the heavy metal pollution refers to environmental pollution caused by heavy metals or compounds thereof, and is mainly caused by human factors such as mining, waste gas emission, sewage irrigation, use of products with heavy metals exceeding standards and the like, so that the target area of the method for predicting the risk of the heavy metal damage of the soil provided by the embodiment of the invention is mainly an area where agricultural production and industrial production exist simultaneously.
Considering that the risk prediction of heavy metal hazards is used for providing basis for risk control of heavy metal pollution and the like, the target area is divided into administrative regions to select application favorable for achievement. The target area of the embodiment of the invention is selected as Guangzhou city urban area which is located in the middle east of Guangdong province and has total area of 1616.47km2The landform is mainly the southern delta and the valley plain, and the cultivated land soil is mainly red soil and oozing type paddy soil.
The urban area is a main production base of agricultural products such as grains in the Zhujiang Delta and the like, and also has the pillar industries such as the component manufacturing industry, the clothing manufacturing industry and the like. The research on the heavy metal content of farmland soil in the region and the spatial distribution condition of the risk thereof has important reference significance for protecting the safety of agricultural products in the region.
S102: selecting sampling points from the target area and sampling soil;
after the prediction target area of the heavy metal hazard of the soil is determined, the data of the heavy metal content of the soil in the target area needs to be collected. Taking into account the size of the target area, it is not practical to completely determine the heavy metal content of the soil in the target area, and therefore, sampling within the target area is required.
In the specific implementation, the method comprises two steps of determining the sampling point and sampling soil for the sampling point, and the heavy metal content data of the sampling point is obtained through the determination of the soil sampling sample.
In the embodiment of the invention, land utilization data of an urban area is collected, and sampling points are designed according to the distribution condition of farmlands and the distribution condition of industrial and mining enterprises in a research area. The sampling points cover the whole research area as much as possible, particularly places with intensive human industrial activities, mainly comprise places such as a raw material mining area, a factory concentration area, a logistics concentration area, a waste treatment area and the like, and most of the human activity concentration areas are based on a road network or can be considered to have good road network conditions in most of the human activity concentration areas by combining the characteristics of modern production, so that the heavy metal content of the road network and the surrounding areas of the road network is higher than that of the other areas, the sampling points have certain representativeness, and the road network in a target area can be used as a trunk line of the sampling points to sample nearby the road network.
In the soil sampling implementation process, a sample is collected from surface soil, the surface soil is 0-20 cm away from the ground surface, and a quincunx point distribution method is selected as a sampling method.
Fig. 2 shows a schematic diagram of a quincunx dot arrangement method according to an embodiment of the invention. The quincunx point arrangement method is that after a soil sample is taken from one point, four points are selected for sampling by radiating 10m to the periphery based on the point, the soil samples at the five points are made into a mixed sample as a sampling result of an initial sampling point, GPS coordinate positioning is carried out on the mixed sample, and longitude and latitude coordinates of the mixed sample are confirmed. The soil sample collected by the quincunx point arrangement method can avoid the specificity of local minimum areas and avoid extreme values of data.
Figure 3 shows a sample point profile for an embodiment of the present invention. Specifically, the embodiment of the invention collects the land utilization data of the urban area in 2010, analyzes the distribution conditions of farmland and industrial and mining areas, considers the operability, time, labor cost and the like of sampling, and finally determines that 204 agricultural land sampling points are arranged in the urban area on the basis of the road network of the urban area, and the number of the soil samples obtained by sampling is 204.
S103: determining the heavy metal content of the sample at the sampling point;
after soil sampling, the heavy metal content of a sample needs to be measured, in the specific implementation, the air-dried sample is screened by a 2mm sieve to prepare a soil sample, and the contents of total copper, total zinc, total lead, total cadmium, total chromium, total arsenic and total mercury are respectively measured. The embodiment of the invention does not necessarily introduce the determination method of the heavy metal elements, which can be specially used for detecting specific heavy metal elements in different mineral producing areas or areas with mineral product transit and processing industries.
Further, arsenic As and mercury Hg are 1:1 HCl-HNO3Preparing a solution to be detected, and detecting by a reduction gasification-atomic fluorescence photometer method; the copper Cu, zinc Zn, lead Pb, cadmium Cd and chromium Cr adopt HF-HNO3-HClO4Preparing a solution to be detected by a digestion method, and detecting Cd and Pb by a graphite furnace atomic absorption spectrometry except for the detection of Cd and Pb by a graphite furnace atomic absorption spectrometry; specifically, the atomic absorption spectrometer (graphite furnace) may be Z-2000 available from HITACHI, Japan, and the flame atomic absorption spectrometer may be Z-5300 available from HITACHI, Japan; during detection, the national soil standard substance GSS-1 is added for quality control.
Through determination, the recovery rate of each heavy metal is within the range of 92-104%, the relative standard deviation is within the allowable range of +/-10%, and the component analysis result is reliable and can be adopted in the subsequent steps.
It should be noted that the data finally obtained for each set of sampling points at least includes the coordinates of the sampling points and the heavy metal contents of total copper, total zinc, total lead, total cadmium, total chromium, total arsenic and total mercury, and the unit of the heavy metal content is mg-kg-1。
Figure 4 shows a descriptive statistical map of soil heavy metals at a sampling point implemented by the present invention. The data of sampling points are subjected to preliminary statistical analysis, and the maximum value, the minimum value, the mean value, the standard deviation, the variation coefficient, the skewness, the kurtosis and the K-S test value of 7 heavy metals Cu, Zn, Pb, Cd, Cr, As and Hg are calculated by SPSS statistical software in the research. And carrying out descriptive statistical analysis on 7 heavy metal elements in farmland soil of the city-increasing area. The average contents of Cu, Zn, Pb, Cd, Cr, As and Hg were 12.71 mg/kg-1、39.63mg·kg-1、42.56mg·kg-1、0.08mg·kg-1、35.44mg·kg-1、8.84mg·kg-1And 0.11mg kg-1。
The variation degree of the variable can be roughly estimated by the variation coefficient, the degree of the artificial influence on the sample can be reflected to a certain degree, the variation coefficient is weak when being less than 10%, medium when being between 10% and 100%, and strong when being more than 100%.
The skewness reflects the symmetry of the geographic data distribution, and when the skewness is not 0, the skewness indicates that extreme values exist in the data.
Kurtosis reflects how concentrated the geographic data is near the mean.
The K-S test is a test of distribution goodness of fit and is a method for testing whether geographic data conform to normal distribution.
The SPSS statistical software carries out descriptive statistics, and the distribution of other heavy metals except Zn and Pb has certain right bias and abnormal values. The variation coefficient of 7 heavy metal elements in the soil in the research area is 29-87%, and the variation coefficient belongs to medium variation. The coefficient of variation of the heavy metals Cu, Cd, As and Hg is relatively large, which indicates that the distribution of the 4 heavy metals is greatly influenced by human factors. The pH of the soil environment in the research area is 5.4 on average, and the comparison between the Ridgeon Delta of soil heavy metal risk evaluation screening value (DB 44/T1415-2014) and the soil environment quality standard (GB15618-1995) shows that 7 heavy metal elements do not exceed the soil environment quality secondary standard, but are slightly higher than the soil background value, which indicates that the farmland soil heavy metal in the research area has potential risk to crops and ecological environment.
The steps are data acquisition stages, and heavy metal content data of sampling points in the target area are obtained after data acquisition. And the next executed step is a step related to target area model prediction, and the acquired sampling point heavy metal content data is used for estimating the heavy metal content of the non-sampling points in the target area so as to make risk prediction.
The embodiment of the invention adopts a sequential indication simulation method in space simulation to predict the space distribution of the heavy metal, integrates the respective advantages of a sequential random simulation algorithm and an indication Kriging method, and has stronger adaptability in the random simulation method.
The basic idea of the sequential indication simulation method is that a target area is divided into grids with uniform resolution, prior information is obtained for the grids which are not sampled by using an indication kriging method, the indication kriging does not participate in model random simulation calculation, a posterior probability model is constructed for spatial simulation according to the conditional probability distribution of a Conditional Cumulative Distribution Function (CCDF) of sample data, finally random values are taken from the distribution as simulation reality to obtain a simulation value, the simulation is carried out until the last grid by using a sequential algorithm, and the uncertainty evaluation result is obtained by carrying out given n times of simulation realization.
The specific implementation steps are as follows:
s104: carrying out indication transformation on the heavy metal content data of the sampling points;
specifically, the prior information of the Conditional Cumulative Distribution Function (CCDF) is obtained according to the idea of indicating kriging.
The instruction transformation steps are as follows: let { Z (x)i) I 1, 2.. n } is a set of sampled data, given K thresholds, it is usually determined using quantiles, the more quantiles the more accurate the reconstruction of the CCDF isCoded as indicator variables of 0 and 1, with the formula:
specifically, according to the embodiment of the invention, a decile number is selected, namely 9 threshold values are set, the heavy metal content data of the sampling point is subjected to indication transformation, and the decile number with the heavy metal content of 0.1-0.9 is taken as the threshold value by using SPSS statistical software.
Specifically, the embodiment of the invention counts the sampling data of the heavy metal copper at the sampling point, and the maximum value and the minimum value of the sampling data are 37.16 mg-kg-1And 2.61mg kg-1In the embodiment of the present invention, when k is 9 thresholds and quantiles are 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, and 0.9, the threshold is set according to the quantile in the range between 2.61 and 37.16, and Z is set for each thresholdk＝1＝6.30、Zk＝2＝9.73、Zk＝3＝13.21、Zk＝4＝16.60、Zk＝5＝20.07、Zk＝6＝23.40、Zk＝7＝26.94、Zk＝8＝30.32、Zk＝933.71 in mg/kg-1(ii) a It should be noted that the threshold value should remain unchanged when the indicating transformation formula is executed at the same time in the target region; aiming at different heavy metal species, the quantile is unchanged, the threshold value space is changed, and the threshold value corresponding to the same quantile is also changed.
After the threshold is confirmed, the indication conversion can be carried out, and the formula is as follows:
in a specific implementation, the indication function can be further described by a conditional probability:
when X is presentaWhere a is1, 2,3 … and n denotes the sampling point, and where in the present example n is 204, for the sampling point,
I(Xa；Z)＝P{Z(Xa)≤Z|Z(Xa)＝Za}
in this case, the index function estimation value of a certain point X to be estimated can be expressed as:
I*(X；Z)＝P{Z(X)≤Z|Z(Xa)＝Za,a＝1,2,L,n}
for a sampling point, the indication value can be interpreted as the probability that the true value of the point is less than or equal to the threshold value when the measured value of the point is known as Za, and for the point to be estimated, the indication function estimation value can be interpreted as the information around the known point to be estimated, that is, the probability that the true value of the point is less than or equal to the threshold value when the measured value of the sample is sampled.
Specifically, the simulation accuracy can be checked by adopting cross validation, random sampling with the ratio of 2:8 is adopted, 40 sampling points are selected as independent validation points and do not participate in modeling, and the rest 164 sampling points are used as training samples.
S105: respectively calculating an indicated semi-variation function of the heavy metal content data under each threshold condition;
fig. 5 shows a schematic diagram of a semi-variogram. The semi-variant function is a measure of the spatial variability of the regionalized variable, and reflects the characteristics that the degree of spatial variation changes with distance, so that the spatial correlation of the regionalized variable can be quantitatively described.
The mathematical expression of the semi-variogram γ (h) is:
wherein x is one point in the target area, and x + h is a point in the target area with a distance h from x; z (x) is a value obtained by dividing x, that is, data of the content of a certain heavy metal in the embodiment of the present invention, Z (x + h) is a value obtained at x + h, and E is a constant. Generally, h is named as a step size, and half-variogram functions of different step sizes hi (i is1, 2 …, n) are counted in the same direction to obtain a group of different experimental half-variogram values γ (h)i). With h as the abscissa, γ (h)i) A set of { h, γ (h) obtained for the ordinatei) Points are called half variogram.
Several main parameters in the hemivariogram are a, co and c, respectively. Wherein, a represents a variation range (range), reflects the range of regional variables having correlation in space, data having correlation in the variation range are not correlated outside the variation range, and the data are different for different models, for example, for spherical and linear models, A indicates that the soil property has the maximum correlation distance of a spatial variation structure; the maximum correlation distances for the gaussian and exponential models are 1.73A and 3A, respectively. The size of the variation range is limited by the observation scale, and the smaller the distance between sampling points is, the greater the similarity, namely the spatial correlation is in the variation range. When h > A, the spatial correlation of the regionalized variable z (x) does not exist, i.e., when a point is more distant from a known point than a distance, the point data cannot be used for interpolation or extrapolation.
co represents the nugget effect (nugget effect) to describe the degree of mutation that occurs in regionalized variables over small distances. Theoretically, when the distance between sampling points is 0, the half-variance function value should be 0, and due to the measurement error and the spatial variance, when the sampling points are very close, the half-variance function value is not 0, that is, the block golden value exists, and the block golden effect is the variance of the observation value of the non-spatial property. Mathematically, the lump gold effect is equivalent to a purely random portion of the variable.
c is the base station value (sil), and when h approaches the verified distance (a), the limit of the half-variance is equal to the variance of the variable, beyond which the attribute variables can be considered spatially independent. Reflecting the total variability of the variables in space, the larger the base station value is, the larger the fluctuation degree of the data is, and the larger the parameter change amplitude is.
The ratio of the lump to the base is called the basal effect, the larger this value is, it is more caused by random components that the spatial variation is, otherwise, it is caused by the specific geographic process or the combination of multiple processes
Geostatistices classify theoretical models of variogram into three major classes: one is a base station value model, which comprises a spherical model, an index model, a Gaussian model, a linear base station value model and a pure block gold effect model; one type is a base-free model, and comprises a power function model, a linear base-free value model and a parabolic model; yet another type is the void effect model; specific function graphs may be referred to in the art.
Fig. 6 shows an indicated hemivariogram image of Cu under each threshold condition, fig. 7 shows an optimum parameter table of the indicated hemivariogram under each threshold condition, and fig. 8 shows a cumulative distribution function of Cu in deciles. The calculation of the hemivariogram requires setting the step size, the step size number, the step size tolerance, and the search direction and direction tolerance, respectively. The step length is the space interval distance of the sampling points, the step length number is the step length number contained under the maximum variable range a, and the step length tolerance is the allowable search radius error; the step size and the number of steps determine how the half-and-half variogram values are grouped. Since the distribution of the sampling points is non-uniform, the total number of the step sizes is determined by adopting an average nearest neighbor method, namely, the distance between each point position and the nearest neighbor point position of each point position is measured, and then the average value of all the nearest neighbor distances is calculated. The step number is obtained according to an empirical rule of removing the step from half of the maximum distance between the point pairs, and half of the step is generally taken as the step tolerance. The search direction is a search direction set for anisotropy in spatial data interpolation, and is generally searched in the E-W direction, the S-N direction, the SW-NE direction, and the SE-NW direction, and the direction tolerance is an allowable search direction error.
The available step size is about 2500m, the number of steps is10, and the step size tolerance is 1250 m. Respectively calculating the indicated half-variation functions in the directions of 0 degree, 45 degrees, 90 degrees and 135 degrees and under isotropy, wherein the direction tolerance is 22.5 degrees, adjusting the obtained indicated half-variation function parameters to adopt a proper model to ensure that the fitting result is optimal, calculating the indicated Kriging estimation under the condition of being less than each threshold value by utilizing GS +9.0, and reconstructing the F of the CCDFz(x)。
S106: dividing the target area into grids of the same resolution, defining a random path through all grid nodes and locating the grids at a first location from Fz(x)Randomly extracting a value as an analog value;
estimation value of conditional cumulative distribution function at target grid node
A simple kriging method relying on information indicative of a semi-variogram results:
λiin order to be the weight, the weight is,
denotes zkExpected values for samples below threshold, weights were determined by the simple kriging method:
γI(x0，xi；zk) And gammaI(x0，xi；zk) Are each zkPoint x at thresholdiAnd point xjIndicates a half variance value and a point x0And point xjIndicates a semi-variance value.
S107: and adding the simulation value into the condition data set, adding the simulation value into the modeling of the grid prior condition cumulative distribution function Fz (x) at the next position under the condition of a new data set, continuously extracting a value from the condition distribution function at the grid, and repeating the process until all grid nodes are simulated, thereby completing one-time simulation realization.
S108: repeatedly executing the step S106 and the step S107 until the simulation times reach a preset value;
from the number n1 of n simulations of each mesh that is greater than a given threshold, the ratio of n1 to n is the probability that the mesh is greater than the threshold in the n simulations.
FIG. 9 is a schematic diagram illustrating a working interface and results of sequential pointing simulation according to an embodiment of the present invention.
In the embodiment of the invention, the size of a research area is considered, the area is about 60km multiplied by 50km, and the grid division calculation and visualization operation time are considered, the research area is divided into grids of 600 multiplied by 500, and the grid size of each grid is 100m multiplied by 100 m. A random path passing through all grids is set through a SISIM module in SGeMSv2.5b software, and 1200 times of simulation realization is carried out on the grids by utilizing the input of indicating half-variation function parameters including an indicating threshold value, a fitting model, a variation range a and the like.
S109: generating a spatial distribution map of the heavy metal content of the target area;
fig. 10 shows a soil heavy metal spatial distribution diagram according to an embodiment of the invention. And (3) estimating to obtain a heavy metal content value predicted by the grid through E-type in post-processing, and introducing Arcgis10.2 into the grid by using an ASCII file to convert the heavy metal content value into grid data to obtain the spatial distribution condition of the heavy metal content.
Through steps S105 to S1109, estimated data of spatial distribution of various heavy metals in the target region can be obtained, and the following steps are related to risk assessment. The risk evaluation can define the degree of heavy metal damage to the soil according to the toxicity mechanism of the heavy metal to evaluation objects such as ecological environment, human health and the like, and determine a heavy metal risk threshold value through classification, so as to divide the damage grade of the heavy metal in the soil.
The Hakanson potential ecological risk index method is a method for effectively dividing the potential hazard degree of heavy metals, and for an atypical research area, the method can consider the effects of environment, ecology and the like, migration rules and toxicology principles of the heavy metals; the method not only pays attention to the hazard of single heavy metal, but also considers the comprehensive hazard of multiple heavy metals; and the influence of regional difference is weakened by combining the background value of the research area, and the method is suitable for researching the heavy metal risk degree of farmland soil in regional scale.
S110: calculating an ecological risk index of the target area;
according to the Hakanson index method, the potential ecological risk indexes of a single heavy metal in the soil in the research area and the comprehensive potential ecological risk indexes of multiple heavy metals in the soil can be respectively used
And RI, the formula is:
in the formula:
as individual heavy metal risk factor, CiIs the measured value of the concentration of the heavy metal in the surface soil,
reference values (typically soil background values or secondary standards);
is the potential ecological hazard coefficient of the ith heavy metal,
the toxicity response coefficient of the ith heavy metal; and RI is the comprehensive potential ecological hazard index.
Fig. 11 shows the soil heavy metal potential ecological risk classification standard of the embodiment of the invention, and fig. 12 shows the soil heavy metal potential risk index of the embodiment of the invention. Respectively calculating ecological hazard indexes of single heavy metal for 204 sampling points in a research area, wherein the soil reference value adopts the background upper limit and the toxicity coefficient of heavy metal elements in the soil of the Zhujiang Delta
The toxicity coefficients of heavy metals (Hg 40, Cd 30, As 10, Cu 5, Pb 5, Cr 2, Zn 1) calculated by the prior study were used As evaluation criteria. According to the observation result, the risk index Er: hg is a mercury vapor>Cd>As>Pb>Cu>Cr>Two heavy metal elements Zn, Hg and Cd have the possibility of more than moderate potential ecological risks.
S111: and generating a soil heavy metal risk evaluation graph.
Fig. 12 shows a comprehensive evaluation chart of the risk of heavy metals in soil according to the embodiment of the invention. And calculating the comprehensive risk index values of Hg, Cd and 7 heavy metals of each grid by using the grid values of the farmland soil heavy metal results predicted by sequential indication simulation, and obtaining a soil heavy metal risk evaluation graph of the research area, wherein the graph is used as a basis for identification of the farmland soil heavy metal risk area.
Specifically, since there may be a deviation between the grid data generated by simulation and the sampling point data, the final simulation prediction result may have a misjudgment, and the accuracy of the risk identification may be evaluated by using an erroneous judgment Rate (ER). The misjudgment conditions are generally divided into two types, namely, a non-risk area is judged as a risk area, and a certain level of risk area is judged as a non-risk area or other levels of risk areas.
In the formula: n is a radical of1Number of samples to determine a non-risk area as a risk area, N2And N is the total number of the sample points to judge the risk area of a certain level as a non-risk area or the sample points of the risk areas of other levels.
FIG. 13 shows a SISIM and IK misjudgment rate comparison data table of an embodiment of the present invention. By comparing the data distribution of 41 verification sampling points with the simulation result of Sequential Indication Simulation (SISIM), points with wrong classification are found, and the misjudgment rate (ER) of the points is calculated respectively.
The Hg potential ecological risk index, Cd potential ecological risk index and comprehensive potential ecological risk index calculated by Sequential Indication Simulation (SISIM) are between 4.88 and 17.07 percent and are better than 9.76 to 19.51 percent of Indication Kriging (IK), and the precision of the index is within an acceptable range.
The embodiment of the invention provides a method for predicting heavy metal risk in soil, which adopts a sequential indication simulation method combined with a GIS (geographic information system) to predict the spatial distribution of the heavy metal content in the farmland soil. Each realization of sequential indication simulation only utilizes the conditional probability distribution information of the sampling points, the smoothing effect is low, and the contribution of abnormal values to high-value areas is not ignored, so that the simulation result of the sequential indication simulation is closer to the real condition; the method has the advantages that the heavy metal risk area of the farmland soil is defined and classified according to the Hakanson potential ecological risk index method, the spatial distribution of the heavy metal content of the farmland soil is visually presented, the heavy metal risk area of the soil is clearly and visually observed, and the method has good practicability.
The soil heavy metal risk prediction method provided by the embodiment of the invention is described in detail, a specific example is applied in the method to explain the principle and the implementation mode of the invention, and the description of the embodiment is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (4)
1. A method for predicting heavy metal risk in soil is characterized by comprising the following steps:
determining a soil heavy metal risk prediction target area;
selecting sampling points in the target area and sampling soil;
measuring the heavy metal content of the soil of the sampling point;
deducing soil heavy metal content data of non-sampling points and generating a spatial distribution map of the heavy metal content of the target area based on the soil heavy metal content data of the sampling points;
calculating an ecological risk index of the target area and generating a soil heavy metal risk evaluation chart based on the spatial distribution chart, wherein:
deducing soil heavy metal content data of the non-sampling points according to the soil heavy metal content data of the sampling points based on a sequential indication simulation method;
the sequential indication simulation method comprises the following steps:
the method comprises the following steps of performing indication transformation on the heavy metal content data of the sampling point, wherein the indication transformation on the heavy metal content data of the sampling point comprises the following steps:
obtaining prior information of the conditional cumulative distribution function according to the idea of indicating Kriging, and setting { Z (x)i) I 1, 2.. n } is a set of sampled data, and given K thresholds, a quantile is usually used to determine, and the more quantiles, the more accurate the reconstruction of the CCDF, the encoded indicator variables are 0 and 1;
the method comprises the following steps of selecting decimals, namely setting 9 thresholds, carrying out indication transformation on heavy metal content data of sampling points, using SPSS statistical software to take the decimals with the heavy metal content of 0.1-0.9 as the thresholds, and carrying out the indication transformation after confirming the thresholds, wherein the formula is as follows:
the indicator function is described by conditional probability:
when X is presentaWhere a is1, 2,3 …, n denotes the sample point, n is 204, for the sample point,
I(Xa；Z)＝P{Z(Xa)≤Z|Z(Xa)＝Za}
in this case, the value of the estimation of the indicator function at a certain point X to be estimated is represented as:
I*(X；Z)＝P{Z(X)≤Z|Z(Xa)＝Za,a＝1,2,…,n}
for a sampling point, the indication value can be interpreted as the probability that the true value of the point is less than or equal to the threshold value when the measured value of the point is known as Za, and for a point to be estimated, the indication function estimation value can be interpreted as the information around the known point to be estimated, that is, the probability that the true value of the point is less than or equal to the threshold value when the measured value of the sample is sampled;
respectively calculating the indicated semimutation function of the heavy metal content data under each threshold condition, wherein:
the mathematical expression indicating the half-variogram γ (h) is:
wherein x is one point in the target area, and x + h is a point in the target area with a distance h from x; z (x) is a value at x, certain heavy metal content data, Z (x + h) is a value at x + h, E is a constant, h is named as a step length, and indicated half-variation functions of different step lengths hi (i is1, 2 …, n) are counted in the same direction to obtain a group of different indicated half-variation function values gamma (h)i) With h as the abscissa, γ (h)i) A set of { h, γ (h) obtained for the ordinatei) Points are referred to as indicative of the half variogram;
establishing a prior condition cumulative distribution function based on the indication semi-variant function;
dividing the target region into grids of the same resolution, defining a random path passing through all grids, and randomly extracting a value from the conditional cumulative distribution function at a first position grid as a simulation value;
using the simulation value in the prior condition cumulative distribution function of the next position grid, randomly extracting a value from the prior condition cumulative distribution function of the next position grid as a simulation value, and repeatedly executing the step until all grids are simulated;
the step of calculating the ecological risk index of the target area comprises the following steps:
calculating potential ecological risk indexes of single heavy metals in the soil of the target area;
calculating a comprehensive potential ecological risk index of the heavy metals in the soil of the target area;
the potential ecological risk index calculation formula of the single heavy metal is
the comprehensive potential ecological risk index calculation formula of the heavy metals is
Wherein,
as the potential ecological risk coefficient of the ith heavy metal,
the toxicity response coefficient of the ith heavy metal; RI is the composite potential ecological risk index.
2. The soil heavy metal risk prediction method of claim 1, wherein the target area is an area where both agricultural production and industrial production exist.
3. The soil heavy metal risk prediction method of claim 1, wherein the soil sampling is performed on the sampling points based on a quincunx point arrangement method.
4. The soil heavy metal risk prediction method of claim 1, wherein the soil heavy metal comprises copper, zinc, lead, cadmium, chromium, arsenic, mercury.
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| 汉源铅锌矿区土壤重金属空间变异及其污染风险评价;毛竹;《中国优秀硕士学位论文全文数据库 工程科技I辑》;20100615(第06期);第24页第3段，第24页4.3.1.1序贯指示模拟算法-第25页第3行，第25页倒数第1-2行，第26页倒数第1-2行，第27页第1-2行 * |
| 麦麦提吐尔逊•艾则孜等.博斯腾湖流域绿洲农田土壤重金属污染及潜在生态风险评价.《地理学报》.2017,第72卷(第9期), * |
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