CN108918815A - A kind of heavy metal-polluted soil Risk Forecast Method - Google Patents
A kind of heavy metal-polluted soil Risk Forecast Method Download PDFInfo
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Abstract
The present invention provides a kind of heavy metal-polluted soil Risk Forecast Method, estimated based on various types of content of beary metal of the sequential condition simulation method to soil in target area, and based on the comprehensive various content of beary metal of Hakanson potential ecological risk index method, visualization heavy metal-polluted soil risk map is generated.For traditional kriging analysis method, sequential condition simulation method can more embody the uncertainty of heavy metal distribution, overcome the smoothing effect of traditional kriging analysis method, have more good predictability；Hakanson potential ecological risk index method can science the various heavy metal content in soil of synthesis, obtain accurately assessment risk；High risk zone intuitively more being observed from visualization heavy metal-polluted soil risk map, and making precautionary measures in time, there is good practicability.
Description
Technical field
The invention belongs to Geostatistical fields, and in particular to arrive a kind of heavy metal-polluted soil Risk Forecast Method.
Background technique
With a large amount of uses of urbanization, the fast development of industry and mining and pesticide organic fertilizer, more and more heavy metals
Enter agricultural land soil environment by approach such as sewage irrigation, atmospheric sedimentation, precipitation runoffs.The content of heavy metal is increasingly tired in farmland
Product, and heavy metal is difficult to by the microbial degradation in soil, content of beary metal is once more than specific threshold, will be pacified to crops
Entirely, ecological environment and human health cause to seriously endanger.For the herbivore stress of farmland soil heavy metals, farmland weight is identified in advance
The risk zones of metal can provide the finger of science to the protection of the farm environment in region, heavy metal pollution prewarning and risk management and control etc.
It leads and foundation.
Existing method is generally divided into two steps when judging heavy metal-polluted soil Regional Risk, is to heavy metal content in soil first
The prediction of spatial distribution, then determines the threshold value of risk further according to evaluation method, and then judges whether this area is risky.
Heavy metals in farmland risk identification needs the accurate spatial distribution drawing of heavy metal-polluted soil in region to pass through as support
Spatial distribution and trend compare to analyze its aggregation features and predict content and risk threshold value to judge soil with the presence or absence of risk
Property.Currently the most commonly used is kriging analysis method in soil attribute forecasting research, identify that heavy metal-polluted soil risk generally has two
Kind approach：First is that directly carrying out spatial prediction to heavy metal content in soil using interpolation method, the space of content of beary metal is obtained
It is compared after distribution with threshold value, linear Krieger model is mostly used, such as Ordinary Kriging Interpolation (Ordinary Kriging, OK)；Two
It is to determine that region content of beary metal is more than the spatial distribution of threshold probability, is mostly used non-linear Krieger using uncertain assessment
Model, such as Indicator Kriging (Indicator Kriging, IK).
Pollution evaluation can define the degree of The Heavy Metal Contaminated Soil, and the threshold value of heavy metal pollution is determined by being classified,
To divide the hazard rating of heavy metal in soil.Ranking method and standard to heavy metal pollution have very much, can be divided mainly into
Index method, model index method and other evaluation methods.
For existing common prediction technique there are certain limitation and defect, one side kriging analysis is to non-sampled point
More strict requirements are distributed with to data in Best unbiased estimator.But the content of heavy metal in soil not only with soil sheet
Body quality is related, also related with the influence of human factor, this causes the distribution of heavy metal-polluted soil that high special heterogeneity is presented.
There is also exceptional values in actual samples, make data at partial velocities, have the biggish coefficient of variation and degree of bias value.This makes
Data are often not in full conformity with the requirement of Krieger.Generally make data Normal Distribution by nonlinear transformation, but is difficult to
Be converted to archeus；Or using variation cloud atlas, three times standard deviation criterion etc. come excluding outlier, but exceptional value objective reality,
Large effect may be generated to the result of prediction by directly ignoring it.There is also smoothing effects for kriging analysis simultaneously, put down
Slippage degree is influenced by sampling point distributions, and sampled point is stronger at a distance of the smoothing effect of its remoter interpolation.And under big scale,
Due to farmland is unevenly distributed, space, time and manpower cost etc. it is restricted, be difficult to accomplish uniformly intensive sampling, generate
Smoothing effect may cause heavy metal content in soil exceptions area important information lose.
On the other hand, index method form is easily understood, and is easily mastered and operates, but has certain problems:Such as
Single factor index number technique is difficult to comprehensively, synthetically show the pollution level of soil.Water quality assessment and prediction, artificially estimate because
Subband has subjectivity, is easy to exaggerate the influence of high concentration heavy metal pollution.Ground adds up index method and has ignored correlation between element,
The different pollution capacities and biological effectiveness of each metal, it is difficult between comparison element or interregional environmental quality, the meeting when choosing k value
With subjectivity.
Summary of the invention
The present invention provides a kind of heavy metal-polluted soil Risk Forecast Methods, based on sequential condition simulation method to target area
The various types of content of beary metal of interior soil is estimated, and is based on the comprehensive various huge sum of moneys of Hakanson potential ecological risk index method
Belong to content, generates visualization heavy metal-polluted soil risk map.For traditional kriging analysis method, sequential condition simulation method is more
Add the uncertainty that can embody heavy metal distribution, overcome the smoothing effect of traditional kriging analysis method, has more good
It is predictive；Hakanson potential ecological risk index method can science the various heavy metal content in soil of synthesis, it is more accurate to obtain
Assessment risk；High risk zone intuitively more can be observed from visualization heavy metal-polluted soil risk map, and make in time
Precautionary measures have good practicability.
The present invention provides a kind of heavy metal-polluted soil Risk Forecast Methods, the described method comprises the following steps：
Determine heavy metal-polluted soil risk profile target area；
Sampled point is selected in the target area and carries out soil sampling；
Measure the heavy metal content in soil of the sampled point；
Heavy metal content in soil data based on the sampled point derive the heavy metal content in soil data of non-sampled point simultaneously
Generate the spatial distribution map of the target area content of beary metal；
Based on the spatial distribution map, calculates the Ecological risk index of the target area and generate heavy metal-polluted soil risk
Evaluation figure.
Preferred embodiment, the target area are to exist simultaneously agricultural production and industrial area.
Preferred embodiment carries out soil sampling to the sampled point based on quincunx method of layouting.
Preferred embodiment, the heavy metal-polluted soil include copper, zinc, lead, cadmium, chromium, arsenic, mercury.
Preferred embodiment is based on Sequential Indicator Simulation Method, according to the heavy metal content in soil number of the sampled point
According to the heavy metal content in soil data for deriving non-sampled point.
Preferred embodiment, the Sequential Indicator Simulation Method include the following steps：
Instruction transformation is carried out to the content of beary metal data of the sampled point；
Calculate separately instruction semivariable function of the content of beary metal data under each threshold condition；
Priori conditions cumulative distribution function is established based on the instruction semivariable function；
The target area is divided into the grid of same resolution ratio, defines a random walk Jing Guo all grid,
And a value is randomly selected from conditional cumulative distribution function at first position grid as the analogue value；
The analogue value is used for the priori conditions cumulative distribution function of the next position grid, from the elder generation of the next position grid
It tests in conditional cumulative distribution function and randomly selects a value as the analogue value, repeat the step until all grid have been simulated
Finish.
Preferred embodiment, the Ecological risk index for calculating the target area include the following steps：
Calculate the potential ecological risk index of single heavy metal in the target area soil；
Calculate the synthesis potential ecological risk index of various heavy in the target area soil.
The potential ecological risk index calculation formula of preferred embodiment, the single metal is
For individual event heavy metal risk factor, CiFor topsoil heavy metal concentration measured value,For reference value.
The synthesis potential ecological risk index calculation formula of preferred embodiment, the various heavy is
Wherein,For the potential ecological risk factors of the i-th heavy metal species,For the Toxic Response Factor of the i-th heavy metal species；
RI is comprehensive potential index of Hakanson's ecological harm.
The present invention provides a kind of heavy metal-polluted soil Risk Forecast Methods, based on sequential condition simulation method to target area
The various types of content of beary metal of interior soil is estimated, and is based on the comprehensive various huge sum of moneys of Hakanson potential ecological risk index method
Belong to content, generates visualization heavy metal-polluted soil risk map.For traditional kriging analysis method, sequential condition simulation method is more
Add the uncertainty that can embody heavy metal distribution, overcome the smoothing effect of traditional kriging analysis method, has more good
It is predictive；Hakanson potential ecological risk index method can science the various heavy metal content in soil of synthesis, it is more accurate to obtain
Assessment risk；High risk zone intuitively more can be observed from visualization heavy metal-polluted soil risk map, and make in time
Precautionary measures have good practicability.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
Some embodiments of invention for those of ordinary skill in the art without creative efforts, can be with
Other attached drawings are obtained according to these attached drawings.
Fig. 1 shows the heavy metal-polluted soil Risk Forecast Method flow chart of the embodiment of the present invention；
Fig. 2 shows the quincunx method schematic diagrames of layouting of the embodiment of the present invention；
Fig. 3 shows the sampling point distributions figure of the embodiment of the present invention；
Fig. 4 shows the sampled point heavy metal-polluted soil descriptive statistic figure that the present invention is implemented；
Fig. 5 shows semivariable function image schematic diagram；
Fig. 6 shows the instruction semivariable function image of Cu under each threshold condition；
Fig. 7 shows the optimal parameter table that semivariable function is indicated under each threshold condition；
Fig. 8 shows the cumulative distribution function of Cu under tenths；
Fig. 9 shows the working interface and result schematic diagram of Sequential Indicator Simulation of the embodiment of the present invention；
Figure 10 shows the heavy metal-polluted soil spatial distribution map of the embodiment of the present invention；
Figure 11 shows the heavy metal-polluted soil potential information requirement grade scale of the embodiment of the present invention；
Figure 12 shows the heavy metal-polluted soil potential risk index of the embodiment of the present invention；
Figure 13 shows the False Rate correlation data table of the SISIM and IK of the embodiment of the present invention.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts all other
Embodiment shall fall within the protection scope of the present invention.
The embodiment of the invention provides a kind of heavy metal-polluted soil Risk Forecast Methods, solve conventional method in content of beary metal
Sampling exceptional value present in spatial distribution prediction screens out the instruction by early period to data with the serious defect of smoothing effect
Exceptional value instructionization is carried out the calculating of instruction semivariable function by processing, while using Sequential Indicator Simulation to the lattice in research area
Networking reduces the influence of smoothing effect；The extensive Hakanson potential ecological risk index of Consideration is used to prediction result
Method is carried out risk zones identification and is presented with visualization means, has good accuracy, intuitive and practicability.
In statistics, when value of the variable in some positions in space is dependent on value of the variable in other positions, just deposit
In spatial autocorrelation.In ground system, some variables are considering space or when the time, variable be frequently not it is random, thus,
When calculating the feature of these variables, in addition to statistics such as the mean value of variable, variances, it is also necessary to calculate the space structure of variable.According to
Rely and is known as regionalized variable in the variable of spatial distribution position.For a variable Z, such as certain heavy metal species of the embodiment of the present invention
Content Z (i) changes with different spatial i, and the variation of the variable is decided by three parts：Totality within the scope of large scale
Value f (i) can use formula Z (i)=f for indicating the spatial dependence s (i) and error ε of variation tendency, local a small range
(i)+s (i)+ε is indicated.
The two major features of regionalized variable are randomness and structural, and randomness refers to for the certain point of part, area
When the value of domain variable at random；Structural to refer to, for whole region, there are a totality or average structure, phases
The value of adjacent regionalized variable has correlativity expressed by the structure.
For example, the distribution of heavy metal i is different with big region in small range, the content of beary metal of a small range can
It can be higher than, lower than the mean value in big region, and this is random.Specifically, the small range can use Z (i1) indicate certain state,
Each observation may be considered Z (i1) one realization；Equally, another small range can use Z (i2) indicate, Mei Geguan
Measured value is regarded as Z (i2) one realization；Meanwhile Z (i1) and Z (i2) there is certain quantitative relations.
Specifically, to be fully described by its feature for random process, it is necessary to which distribution function or the distribution for providing it are close
Degree, but this distribution generally is difficult to acquire.So the emphasis of research is its mathematical feature, these mathematical features mainly include：
Mathematic expectaion, correlation function, variance, covariance mean-square value；Wherein, mathematic expectaion is first moment, and next four word feature is all
It is second moment.
When a random process is second order moment process, we can study its second moment numerical characteristic, thus to
Machine process is analyzed accordingly, for example is judged whether steady etc..
The complete distribution function of regionalized variable or distribution density function seek also It is not necessary to, regionalized variable distribution
Two squares of head of function or distribution density function, first order and second order moments be enough to provide in most cases studied a question it is close
Like solution, the content of beary metal distributed data as involved in the embodiment of the present invention.On the other hand, the data surveyed on the spot are also not enough to
The complete distribution function of regionalized variable or distribution density function are acquired, so the complete distribution function of regionalized variable or distribution
Density function is sought being not necessarily to.
In addition, if two squares of head of random function are only used, if the first order and second order moments of two random functions are
It is identical, then it is considered that the two random functions are identical.
First moment is the mean function of a regionalized variable, is defined as E [Z (x)]；
There are three second moment in one regionalized variable is total to, respectively the variance function of regionalized variable, compartmentalization become
Covariance function, the variogram of amount；Wherein, variogram is also known as semivariance, is a regionalized variable two o'clock difference side
The half of difference.
In practical applications, it is made an estimate with superior function often through several measured values.For example, semi-variance function needs to lead to
The value for crossing mathematic expectaion E [Z (x)-Z (x+h)] is calculated, and therefore, necessity has several realization data of Z (x) He Z (x+h).
However, often can only obtain a pair of of data in point x and point x+h, it is impossible to same in space in soil space research
It a little goes to obtain second pair of data again, therefore, in order to overcome this difficulty, needs to propose some hypothesis and limit to regionalized variable
It is fixed.
It is assumed that the mean value of variable is a constant within being locally divided into, do not change with position；It is assumed that the association of sample point x and y
Variance C [Z (x), Z (y)] exists, and is solely dependent upon the distance of sample point x and y, then Z is second-order stationary, the result is that the mean value of Z
It does not change spatially with covariance.
In this way, in a certain subrange in space, to space certain point x0, at a distance of the multiple points for being h, can regard Z as
(x0) multiple realizations, so as to carry out statistical inference and valuation prediction.
Further, if random function Z (x) has following intrinsic hypothesis：
Mean value exists and is not dependent on x, i.e. E [Z (x)]=m, for any E [Z (x+h)-Z (x)]=0；
For any distance h, variable [Z (x+h)-Z (x)] has a limited variance, which is not dependent on x；
Then for arbitrary x and h, following formula is set up：
Var [Z (x+h)-Z (x)]=E { [Z (x+h)-Z (x)]2The γ (h) of }=2, wherein γ (h) is known as semivariable function,
Semivariable function is used to characterize the spatial variability structure or spatial continuity of stochastic variable.
Therefore, stationarity mainly includes two classes, and one type refers to that mean value is steady, it is assumed that mean value is constant, and with position
It sets and surrounds and watches；It is another kind of to accumulate suitable for the related second-order stationary of covariance function and in related with semivariable function steadily；Covariance
The covariance for steadily assuming that any two points in distance having the same and direction is identical, value phase of the covariance with two o'clock
Close and it is unrelated with their position；Inside accumulate and steadily assumes that the variance of any two points with same distance and direction is identical
's.
Heavy metal-polluted soil hazard prediction method provided in an embodiment of the present invention mainly utilizes sampled point in the model prediction stage
Data and semivariable function it is structural, using the method for sequential Indicator Kriging, the data of non-sampled point are estimated,
Heavy metal-polluted soil is generated eventually by Hakanson potential ecological risk index method and endangers risk map, to judge heavy metal risk
Region.
Fig. 1 shows the heavy metal-polluted soil Risk Forecast Method flow chart of the embodiment of the present invention.The embodiment of the present invention provides
A kind of heavy metal-polluted soil hazard prediction method mainly includes the step of three data acquisition, model prediction and risk assessment aspects
Suddenly, specific implementation step is as follows：
S101：Determine the target area of heavy metal-polluted soil hazard prediction；
It is heavy metal-polluted to have a finger in every pie the environmental pollution as caused by heavy metal or its compound, mainly by mining, exhaust gas discharge, sewage
It irrigates and using caused by the human factors such as heavy metals exceeding standard product, therefore, heavy metal-polluted soil provided in an embodiment of the present invention endangers
The target area of Risk Forecast Method application predominantly exists simultaneously agricultural production and industrial area.
Foundation, target area are provided in view of Heavy Metal Pollution risk profile is used to risk management and control of heavy metal pollution etc.
Domain is advantageously selected for the application of achievement with administrative division.The target area of the embodiment of the present invention is selected as Guangzhou Zengcheng area,
It is located at the Guangdong Province central and east, gross area 1616.47km2, landforms are mostly based on the delta in south and valley plain, arable land
Soil is educated with red earth and infiltration based on type rice soil.
Zengcheng area is the agricultural product such as Delta of the Pearl River grain major production base, while possessing components manufacturing industry, clothes
The pillar industries such as manufacturing industry.The farmland soil heavy metals content in the region and its space distribution situation of risk are studied, for protecting
The agricultural product security of shield this area has important reference significance.
S102：Sampled point is selected out of described target area and carries out soil sampling；
After determining heavy metal-polluted soil hazard prediction target area, need to collect the heavy metal content in soil number in target area
According to.In view of the range size of target area, the soil in target area is measured completely content of beary metal be it is unpractical, because
This, needs to sample in target area.
In specific implementation, it is divided into the determination of sampled point and two steps of soil sampling is carried out to sampled point, by soil
The measurement of sample obtains the content of beary metal data of sampled point.
In embodiments of the present invention, the land use data in Zengcheng area is collected first, according to farmland in research area
Distribution situation and the distribution situations of industrial and mining enterprises design sampled point.Sampled point covers entire research area as far as possible, especially
It is the more intensive place of human industry's activity, mainly includes raw material production zone, factory concentrated area, logistics concentration zones
The places such as domain, waste processing region, in conjunction with the characteristics of modern production, most of mankind's activity close quarters are mostly based on road network
, or can consider that the road network condition of most of mankind's activity close quarters is better, and therefore, the weight around road network and road network
Tenor is higher compared with remaining region, has certain representativeness, can be dry using the road network in target area as sampled point
Line is sampled near road network.
In soil sampling implementation process, sample picks up from topsoil, and topsoil is 0~20cm of span ground level
Soil, the method for sampling select quincunx method of layouting.
Fig. 2 shows the quincunx method schematic diagrames of layouting of the embodiment of the present invention.The quincunx method of layouting refers to takes soil on one point
After earth sample, 10m is radiated based on the point around, four points is selected to be sampled, aggregate sample is made in the pedotheque of five points
Product carry out GPS coordinate positioning as the sampled result being originally sampled a little, and to it, confirm its latitude and longitude coordinates.By quincunx
The pedotheque of method of layouting acquisition can avoid the specificity in local minimum region, and data is avoided to generate extremum.
Fig. 3 shows the sampling point distributions figure of the embodiment of the present invention.Increase specifically, the embodiment of the present invention has collected 2010
The land use data in city analyzes its farmland and industrial and mineral land used distribution situation, at the same consider sampling operability, the time and
Human cost etc., it is final to determine based on increasing City Road Network, 204 farming land sampled points are arranged altogether in Zengcheng area, sample institute
Totally 204 parts of soil sample number obtained.
S103：Measure the sample content of beary metal of the sampled point；
It needs to be measured the content of beary metal of sample after soil sampling, in specific implementation, to the sample after air-drying, mistake
Pedotheque is made in the sieve of 2mm, measures total copper, total zinc, total lead, total cadmium, total chromium, total arsenic, total mercury content respectively, needs to illustrate
Be, due to had no in the research area of the embodiment of the present invention special orefield, special mineral products transfer or special mineral products processing etc.
Therefore place need to only measure common heavy metal element.For different orefields or there is mineral products transfer, secondary industry
Region, special detection can be carried out for specific heavy metal element, the embodiment of the present invention not introduce its measuring method one by one.
Further, arsenic As and mercury Hg uses 1:1 HCl-HNO3Prepare liquid is prepared, by reduction and gaseous-atomic fluorescence light
Spend the measurement of meter method；Copper Cu, zinc Zn, lead Pb, cadmium Cd, chromium Cr use HF-HNO3-HClO4Digestion method prepares prepare liquid, except Cd, Pb are adopted
With outside graphite furnace atomic absorption spectrometry, remaining is using atomic absorption spectrophotometry measurement；Specifically, Atomic absorption
The Z-2000 of Japanese HITACHI company can be used in spectrometer (graphite furnace), and Japan can be used in flame atomic absorption spectrophotometer
The Z-5300 of HITACHI company；When detection, country reference material of soil GSS-1 is added and is controlled for quality.
After measured, each heavy metal recovery rate is in 92%~104% range, and relative standard deviation is in allowed band ± 10%
Within, composition analysis result is more reliable, for subsequent step use.
It needs to be illustrated, finally show that the data of every group of sampled point include at least sample point coordinate and total copper, total
Zinc, total lead, total cadmium, total chromium, total arsenic, total mercury content of beary metal, the unit of content of beary metal is generally mgkg-1。
Fig. 4 shows the sampled point heavy metal-polluted soil descriptive statistic figure that the present invention is implemented.The data of sampled point are carried out
Rough estimates analysis, this research are utilized respectively SPSS statistical software statistics meter to 7 heavy metal species Cu, Zn, Pb, Cd, Cr, As, Hg
Maximum value, minimum value, mean value, standard deviation, the coefficient of variation, the degree of bias, kurtosis and K-S test value are calculated.To Zengcheng area agricultural land soil 7
Being described property of Heavy Metallic Elements statistical analysis.The average content of Cu, Zn, Pb, Cd, Cr, As, Hg are respectively 12.71mg
kg-1、39.63mg·kg-1、42.56mg·kg-1、0.08mg·kg-1、35.44mg·kg-1、8.84mg·kg-1With
0.11mg·kg-1。
Wherein, the size of the coefficient of variation can rough estimate variable degree of variation, can reflect to a certain extent sample by
The degree of man's activity, the coefficient of variation are weak variability less than 10%, and the coefficient of variation is medium variation between 10% -100%
Property, it is strong variability that the coefficient of variation, which is greater than 100%,.
The degree of bias reflects the symmetry of geodata distribution, when not being 0, indicates that there are extremums for data.
Kurtosis reflects geodata in the intensity of mean value attachment.
K-S inspection is a kind of inspection of fitting of distribution goodness, for examining whether geodata meets the side of normal distribution
Method.
It can be obtained by being described property of SPSS statistical software statistics, the distribution of other heavy metals all exists centainly in addition to Zn, Pb
Right avertence and exceptional value.The coefficient of variation of 7 Heavy Metallic Elements of soil in area is studied between 29%-87%, is belonged to medium
Variation.Heavy metal Cu, the coefficient of variation of Cd, As and Hg are relatively large, illustrate that the distribution of this 4 heavy metal species is affected by human factors
It is larger.The pH average out to 5.4 of area's soil environment is studied, by comparing《The heavy metal-polluted soil risk assessment screening value Delta of the Pearl River》
(DB 44/T1415-2014) with《Standard of soil environment quality》(GB15618-1995) it finds, 7 Heavy Metallic Elements all do not surpass
Cross soil environment quality secondary standard, but be slightly above soil background, illustrate to study area's farmland soil heavy metals to crop and
There are potential risks for ecological environment.
Above step is data acquisition phase, and after being acquired by data, the heavy metal for obtaining sampled point in target area contains
Measure data.Next the step of the step of executing is related target area model prediction, is contained with the sampled point heavy metal obtained
Data are measured, estimation is carried out to make risk profile to the content of beary metal of sampled point non-in target area.
The embodiment of the present invention is pre- come the spatial distribution for carrying out heavy metal using the Sequential Indicator Simulation Method in spatial simulation
It surveys, it combines the respective advantage of sequential stochastic simulation algorithm Yu Indicator Kriging method, adapts in Method of Stochastic
Property is stronger.
The basic ideas of Sequential Indicator Simulation Method are that target area is divided into the grid of unified resolution first, to not adopting
The grid of sample obtains prior information using Indicator Kriging method, and Indicator Kriging is not involved in the calculating of model stochastic simulation, root
According to the conditional probability distribution of the conditional cumulative distribution function (CCDF) of sample data, constructs posterior probability model and carry out spatial mode
Quasi-, finally random value is used as simulation reality to obtain the analogue value from the distribution, is simulated always using SEQUENTIAL ALGORITHM to last one
A grid obtains the result of uncertain assessment by carrying out given n times simulated implementation.
Specific implementation steps are as follows：
S104：Instruction transformation is carried out to the content of beary metal data of the sampled point；
Specifically, obtaining conditional cumulative distribution function (CCDF) this prior information according to the thought of Indicator Kriging.
Indicate that shift step is as follows：If { Z (xi), i=1,2 ..., n } it is one group of sampled data, K threshold value is given, is led to
It is determined frequently with quantile, the reconstruction of its more CCDF of quantile is more accurate, is encoded into 0 and 1 indicator variable, formula
For：
Specifically, the embodiment of the present invention chooses decile, that is, 9 threshold values are set, to the content of beary metal number of sampled point
According to instruction transformation is carried out, take the quantile of each content of beary metal 0.1~0.9 as threshold value using SPSS statistical software.
Specifically, the sampled data of the heavy metal copper of statistic sampling point of the embodiment of the present invention, maximum value and minimum value point
It Wei not 37.16mgkg-1And 2.61mgkg-1, the embodiment of the present invention takes k=9 threshold value, be 0.1 with quantile, 0.2,
0.3,0.4,0.5,0.6,0.7,0.8,0.9, then the range intervals between 2.61 to 37.16 are taken into threshold value by quantile, respectively
For ZK=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=9=33.71, unit mgkg-1；It should be noted that being executed in target area with primary
When indicating transformation for mula, threshold value should be remained unchanged；For different heavy metal types, quantile is constant, and threshold value valued space changes
Become, the corresponding threshold value of same quantile can also change.
After confirming threshold value, instruction transformation can be carried out, formula is：
In specific implementation, also indicator function can be described with conditional probability：
Work as Xa, a=1,2,3 ..., n indicate sampled point, n=204 of the embodiment of the present invention, for sampled point,
I(Xa；Z)=P { Z (Xa)≤Z|Z(Xa)=Za}
At this point, certain waits that the indicator function estimated value for estimating point X can be expressed as：
I*(X；Z)=P Z (X)≤Z | Z (Xa)=Za, a=1,2, L, n }
For sampled point, indicated value may be interpreted as the known point measured value be Za when, the true value of the point is less than
Equal to the probability of threshold value, and for point to be estimated, indicator function estimated value may be interpreted as known wait estimate a peripheral information, that is, adopt
When all measured value, the true value of the point is less than or equal to the probability of threshold value.
Specifically, simulation precision can be examined by using cross validation, using 2:8 ratio random sampling chooses 40
A sampling point is not involved in modeling as individual authentication point, remaining 164 point is as training sample.
S105：Calculate separately instruction semivariable function of the content of beary metal data under each threshold condition；
Fig. 5 shows semivariable function schematic diagram.Semivariable function is a kind of measurement of regionalized variable Spatial Variability,
The feature that spatial variability degree changes with distance is reflected, so as to the spatial coherence of quantitative description regionalized variable.
The mathematic(al) representation of semivariable function γ (h) is：
Wherein, x is wherein, x+h is a bit that distance x distance is h in target area in target area；Z (x) is x
The value removed, i.e. certain content of beary metal data of the embodiment of the present invention, Z (x+h) are the value at x+h, and E is a constant.One
As, h is named as step-length, in the same direction, is counted to the semivariable function of different step-length hi (i=1,2 ..., n), can
Obtain a different set of experiment semivariable function value γ (hi).Using h as abscissa, γ (hi) be obtained one group of ordinate h,
γ(hi) point referred to as semi-variogram.
Several major parameters in semi-variogram are respectively a, co, c.Wherein, a indicates to become journey (range), reflects area
Domain variable spatially has the range of correlation, and data have correlation within the scope of becoming journey, is becoming except journey range
Data are irrelevant, different for different models, and such as spherical and linear model, A shows that soil property exists
The maximal correlation distance of spatial variability structure；For Gauss model and exponential model maximal correlation distance be then respectively 1.73A and
3A.Become Cheng great little to be limited by observing result, become within the scope of journey, the distance between sampling point is smaller, similitude, i.e. spatial coherence
It is bigger.Work as h>When A, the spatial coherence of regionalized variable z (x) is not present, i.e., becomes journey when certain point is greater than at a distance from known point
When, which cannot be used for interpolation or extrapolation.
Co indicates nugget effect (nugget effect), occurs in the distance of very little describing regionalized variable
Mutation content.Theoretically, when the distance between sampled point is 0, semivariable function value should be 0. since there are measurement errors and sky
Between make a variation, when so that sampled point is very close, their semivariable function value is not 0, that is, there are block gold number, nugget effect right and wrong
The variation of spatial property observation.Mathematically, nugget effect is equivalent to the pure random partial of variable.
C is base station value (sill), when h tends to spacing (A) verified, the limiting value of semivariance, and equal to the variance of variable,
Becoming journey more than this can consider attribute variable space independence.Reflect that the total variability size of variable spatially, base station value are bigger
Illustrate that the degree of fluctuation of data is bigger, the amplitude of Parameters variation is bigger.
For the ratio of block gold and base station substrate effect, this value is bigger, and illustrating spatial variability more is that random element causes
, it is otherwise then as caused by specific geographical process or multiple process synthesis
The theoretical model of variation function is divided into three categories by Geostatistical：One kind is that have base station value model, including spherical mould
Type, Gauss model, linearly has base station value model and pure nugget effect model at exponential model；One kind is no base station model, including power
Function model, linearly without base station value model, parabola model；It is cave effects model there are also one kind；Specific functional arrangement can join
It is referred to according to the prior art.
Fig. 6 shows the instruction semivariable function image of Cu under each threshold condition, and Fig. 7 shows and indicates under each threshold condition
The optimal parameter table of semivariable function, Fig. 8 show the cumulative distribution function of Cu under tenths.The calculating of semivariable function needs
Step-length, step-length number, step-length tolerance and the direction of search and direction tolerance is set separately.Step-length be sampled point space interval away from
From step-length number is the number of steps for including under maximum change journey a, and step-length tolerance is the search radius error allowed；Step-length and step-length
Number determines how double of semivariable function value and is grouped.Due to sampling point distributions be it is heterogeneous, using average arest neighbors side
Method determines step-length sum, that is, measures the distance between each point and its nearest neighbor point position, then calculates all these nearest
The average value of neighborhood distance.Step-length number is obtained according to the rule of thumb that the half of maximum distance between point pair removes step-length, generally takes step
Long half is as step-length tolerance.The direction of search refers to the searcher set for anisotropy existing for spatial data interpolation
To generally being scanned for the direction E-W, the direction S-N, the direction SW-NE and the direction SE-NW, direction tolerance refers to permission
Direction of search error.
It is about 2500m that step-length, which can be obtained, and step-length number is 10, and step-length tolerance is 1250m.Calculate separately 0 °, 45 °, 90 ° and 135 °
Instruction semivariable function under direction and isotropism, direction tolerance is 22.5 °, and adjusts obtained instruction semivariation letter
Number parameter keeps fitting result best using appropriate model, is estimated using the Indicator Kriging that GS+9.0 calculating is less than each threshold condition
Meter, rebuilds the F of CCDFz(x)。
S106：Target area is divided into the grid of same resolution ratio, defines one by the random of all network computations
Path and at first position grid from Fz(x)In randomly select a value as the analogue value；
Estimated value of the conditional cumulative distribution function in target mesh nodeDependence refers to
Show that the simple Kriging method of semivariable function information obtains：
λiFor weight,Indicate zkThe desired value of sample under threshold value, weight are determined by simple Kriging method：
γI(x0, xi；zk) and γI(x0, xi；zk) it is z respectivelykPoint x at threshold valueiWith point xjBetween instruction semivariance and
Point x0With point xjBetween indicate semivariance.
S107：The analogue value is added to condition data to concentrate, under conditions of new data set, is added to next
In the modeling of the grid priori conditions cumulative distribution function Fz (x) of position, continue to extract from conditional distribution function at grid
One value, the process of repetition have been modeled to all network computations, that is, complete a simulated implementation.
S108：Step S106 and step S107 are repeated, until number realization reaches preset value；
According to the frequency n 1 for being greater than given threshold value in each grid statistics n times, the ratio of n1 and n are exactly the grid in n times
It is greater than the probability of threshold value in simulation.
Fig. 9 shows the working interface and result schematic diagram of Sequential Indicator Simulation of the embodiment of the present invention.
The embodiment of the present invention considers the size in research area, area be about 60km × 50km and grid division calculate and
Operation time is visualized, research zoning is divided into 600 × 500 grid, the size of each grid grid is 100m × 100m.It is logical
SISIM module in SGeMSv2.5b software is crossed, a random walk by all grids is set, utilizes instruction semivariable function
Parameter input, including instruction threshold value, model of fit, change journey a etc., carry out 1200 simulated implementations to grid.
S109：Generate the spatial distribution map of the content of beary metal of target area；
Figure 10 shows the heavy metal-polluted soil spatial distribution map of the embodiment of the present invention.Pass through the E-type estimation in post-processing
The content of beary metal value of grid prediction is obtained, Arcgis10.2 is imported using ascii text file and switchs to raster data, obtain heavy metal
The space distribution situation of content.
By step S105~S1109, each heavy metal species spatial distribution estimation data of target area can be obtained, need below
It to be carried out being risk assessment correlation step.Risk assessment can be according to heavy metal to the evaluation pair such as ecological environment, human health
The toxicity mechanism of elephant defines degree of the soil by Heavy Metal Pollution, heavy metal risk threshold value is determined by being classified, to divide soil
The hazard rating of heavy metal in earth.
Hakanson potential ecological risk index method is a kind of method for effectively marking off the potentially hazardous degree of heavy metal,
For atypia research area, it can consider the effects such as the environment of heavy metal, ecology, Transport and toxicology principle；
It had both paid close attention to the harmfulness of single heavy metal, it is contemplated that the comprehensive harm of various heavy；Binding area background value, it is weak
The influence for having changed area differentiation is suitable for the farmland soil heavy metals degree of risk research of regional scale.
S110：Target area Ecological risk index calculates；
According to Hakanson index method, in survey region in soil single heavy metal potential ecological risk index and soil
The synthesis potential ecological risk index of various heavy can be used respectively in earthIt is indicated with RI, calculation formula is：
In formula：For individual event heavy metal risk factor, CiFor topsoil heavy metal concentration measured value,For reference value
(generally soil background or secondary standard)；For the potential ecological risk factors of the i-th heavy metal species,For the i-th heavy metal species
Toxic Response Factor；RI is comprehensive potential index of Hakanson's ecological harm.
Figure 11 shows the heavy metal-polluted soil potential information requirement grade scale of the embodiment of the present invention, and Figure 12 shows this hair
The heavy metal-polluted soil potential risk index of bright embodiment.The harm of single Heavy Metal Ecological is calculated separately to research 204, area sampling point
Index, soil reference value use the Heavy Metals in Soils of Pearl River Delta background values of elements upper limit, toxic factorUsing existing research
The heavy metal toxicity coefficient (Hg=40, Cd=30, As=10, Cu=5, Pb=5, Cr=2, Zn=1) of calculating is Appreciation gist.
Observing result can find, the risk index Er of the embodiment of the present invention：Hg>Cd>As>Pb>Cu>Cr>Two heavy metal species of Zn, Hg and Cd
There are the possibility of the medium above potential information requirement for element.
S111：Generate heavy metal-polluted soil risk assessment figure.
Figure 12 shows the heavy metal-polluted soil Risk Comprehensive Evaluation figure of the embodiment of the present invention.It is predicted using Sequential Indicator Simulation
Farmland soil heavy metals result grid grid value, the integrated risk of Hg, Cd and 7 heavy metal species for calculating each grid refers to
Numerical value obtains research area's heavy metal-polluted soil risk assessment figure, in this, as the foundation of farmland soil heavy metals risk area identification.
Specifically, there may be deviations for Grid square and sample point data due to being generated by simulation, cause final
Simulation and forecast result is likely to occur erroneous judgement, and False Rate (Error Rate, ER) can be used to evaluate the accuracy of identification risk.
It is judged situation by accident and is generally divided into two kinds, first is that non-risk area is determined as risk area, second is that a certain rank risk area is determined as
Non- risk area or other rank risk areas.
In formula：N1For the number of samples that non-risk area is determined as to risk area, N2For a certain rank risk area is determined as non-wind
The number of samples of danger zone or other rank risk areas, N are total number of samples.
Figure 13 shows the False Rate correlation data table of the SISIM and IK of the embodiment of the present invention.By by 41 verifying samples
Point data distribution compares with the analog result of Sequential Indicator Simulation (SISIM), finds out the point of classification error, calculate separately them
False Rate (ER).
By the calculated Hg potential ecological risk index of Sequential Indicator Simulation (SISIM), Cd potential ecological risk index, comprehensive
Potential ecological risk index is closed between 4.88-17.07%, is relatively better than the 9.76%-19.51% of Indicator Kriging (IK),
Its precision belongs to acceptable range.
The embodiment of the invention provides a kind of heavy metal-polluted soil Risk Forecast Method, using Sequential Indicator Simulation Method with
GIS is combined, and predicts the spatial distribution of farmland soil heavy metals content, is compared with conventional method, Ordinary Kriging Interpolation interpolation
Sampled point of the result of middle future position by neighborhood and the distance to sampled point are influenced, while generating smoothing effect.And sequential finger
Show each information for realizing the conditional probability distribution just with sampled point of simulation, smoothing effect is low, does not ignore exception
It is worth the contribution to content high level region, this makes its analog result closer to the truth；According to the potential life of Hakanson
State risk exponential delimited farmland soil heavy metals risk zones and be classified, to the spatial distribution of farmland soil heavy metals content
It has carried out visualization to present, clear and intuitive observes heavy metal-polluted soil risk zones, has good practicability.
It is provided for the embodiments of the invention heavy metal-polluted soil Risk Forecast Method above to be described in detail, herein
Apply that a specific example illustrates the principle and implementation of the invention, the explanation of above example is only intended to help
Understand method and its core concept of the invention；At the same time, for those skilled in the art, according to the thought of the present invention,
There will be changes in the specific implementation manner and application range, in conclusion the content of the present specification should not be construed as to this
The limitation of invention.
Claims (9)
1. a kind of heavy metal-polluted soil Risk Forecast Method, which is characterized in that the described method comprises the following steps：
Determine heavy metal-polluted soil risk profile target area；
Sampled point is selected in the target area and carries out soil sampling；
Measure the heavy metal content in soil of the sampled point；
Heavy metal content in soil data based on the sampled point derive the heavy metal content in soil data of non-sampled point and generate
The spatial distribution map of the target area content of beary metal；
Based on the spatial distribution map, calculates the Ecological risk index of the target area and generate heavy metal-polluted soil risk assessment
Figure.
2. heavy metal-polluted soil Risk Forecast Method as described in claim 1, which is characterized in that the target area is while depositing
In agricultural production and industrial area.
3. heavy metal-polluted soil Risk Forecast Method as described in claim 1, which is characterized in that based on quincunx method of layouting to institute
It states sampled point and carries out soil sampling.
4. heavy metal-polluted soil Risk Forecast Method as described in claim 1, which is characterized in that the heavy metal-polluted soil includes
Copper, zinc, lead, cadmium, chromium, arsenic, mercury.
5. heavy metal-polluted soil Risk Forecast Method as described in claim 1, which is characterized in that be based on Sequential Indicator Simulation side
Method derives the heavy metal content in soil data of non-sampled point according to the heavy metal content in soil data of the sampled point.
6. heavy metal-polluted soil Risk Forecast Method as claimed in claim 5, which is characterized in that the Sequential Indicator Simulation Method
Include the following steps：
Instruction transformation is carried out to the content of beary metal data of the sampled point；
Calculate separately instruction semivariable function of the content of beary metal data under each threshold condition；
Priori conditions cumulative distribution function is established based on the instruction semivariable function；
The target area is divided into the grid of same resolution ratio, defines a random walk Jing Guo all grid, and
A value is randomly selected at first position grid from conditional cumulative distribution function as the analogue value；
The analogue value is used for the priori conditions cumulative distribution function of the next position grid, from the priori item of the next position grid
A value is randomly selected in part cumulative distribution function as the analogue value, repeats the step until the simulation of all grid finishes.
7. heavy metal-polluted soil Risk Forecast Method as claimed in claim 5, which is characterized in that described to calculate the target area
Ecological risk index include the following steps：
Calculate the potential ecological risk index of single heavy metal in the target area soil；
Calculate the synthesis potential ecological risk index of various heavy in the target area soil.
8. heavy metal-polluted soil Risk Forecast Method as claimed in claim 7, which is characterized in that the potential life of the single metal
State risk index calculation formula is
For individual event heavy metal risk factor, CiFor topsoil heavy metal concentration measured value,For reference value.
9. heavy metal-polluted soil Risk Forecast Method as claimed in claim 8, which is characterized in that the synthesis of the various heavy
Potential ecological risk index calculation formula is
Wherein,For the potential ecological risk factors of the i-th heavy metal species,For the Toxic Response Factor of the i-th heavy metal species；RI is comprehensive
Close potential index of Hakanson's ecological harm.
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