CN108932978A - The pollutant health risk assessment method of Kernel-based methods simulation and analysis of uncertainty - Google Patents
The pollutant health risk assessment method of Kernel-based methods simulation and analysis of uncertainty Download PDFInfo
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Abstract
The invention discloses the pollutant health risk assessment methods of a kind of simulation of Kernel-based methods and analysis of uncertainty, this method combines health risk assessment with groundwater pollutant transition process, utilizes spatial and temporal distributions of the TOUGH2 software simulation organic pollutant in underground water.In order to improve the reliability of evaluation result, considers the influence that contaminant transportation uncertainty evaluates human health risk, inverting identification is carried out to key parameter medium permeability by Markov chain Monte Carlo simulation method.This method provides decision information by the uncertain reliability for improving risk evaluation results of analysis Contaminants Transport simulation for the management and prevention and treatment of contaminated site.
Description
Technical field
The present invention relates to the health risks of the Typical Organic Pollutants of a kind of simulation of Kernel-based methods and analysis of uncertainty to comment
Valence method belongs to pollutant health risk assessment technical field.
Background technique
In many shortage of water resources areas, underground water is resident living, irrigation and industrial important water source.But with
The fast development of social economy, underground water pollution have become the important environmental problem for threatening the ecosystem and human health.Meanwhile
The various more complicated Typical Organic Pollutants of physicochemical property occur, such as tetrachloro-ethylene, Polychlorinated biphenyls, these pollutant density
It is bigger than water and not soluble in water, people are increased to the difficulty of underground water pollution monitoring and preventing and controlling.In order to reduce in underground water
Harm of the typical pollutant to human health, and decision information is provided for the improvement of contaminated site, for Typical Organic Pollutants
Health risk assessment have become the important component of groundwater environment management.
Human health risk (HHR) evaluation refers to that assessing the current or following human body is potentially contaminated chemicals in environment
A possibility that influence of matter is to endanger health generation, nineteen eighty-three American Academy of Sciences proposes four steps of health risk assessment
Method: harm identification, dose response assessment, exposure assessment, risk characterization.Environmental Protection Agency proposes " super base within 1989
Golden project human health risk assessment guidelines ", the human body proposed in directive/guide in life the increase of cancer stricken probability a possibility that
(ILCR) it is widely used in measuring risk evaluation results, maximum acceptable value-at-risk is 1 × 10-6:
R (x)=1-exp [- ADD (x) × CPF] (I)
Wherein, R (x) represents the ILCR value at any one of research area control plane；ADD indicates average daily pollutant exposure
Amount；CPF indicates potential carcinogen metabolic rate；The expression formula of ADD can be write again:
Wherein, IR represents daily drink amount；BW represents weight；ED represents exposure duration；EF represents exposure frequency；AT representative
Service life,Represent the mean concentration on control plane.
Concentration distribution of pollutants is the important factor in order of human health risk evaluation, needle in conventional health risk assessment
Sampling on the spot is crossed to pollutant concentration data multi-pass and lab analysis obtains, this method is simple and fast, but does not account for
Transport And Transformation process of the pollutant in underground water.A kind of health risk assessment method of Kernel-based methods simulation is gradually by people at present
Approve, this method has coupled groundwater pollutant migration models and health risk assessment model, have studied pollutant when
The empty regularity of distribution.
The simulation process of groundwater pollutant migration will receive the interference of various uncertain factors, not such as model parameter
Certainty, the uncertainty of model structure and the error for observing data.These uncertainties frequently can lead to simulate the dirt come
It is not high to contaminate object concentration reliability, so that final health risk assessment result precision is poor, easily causes groundwater management and determines
The fault of plan, waste of manpower, material resources.
Summary of the invention
Above-mentioned the deficiencies in the prior art are directed to, the purpose of the present invention is to provide a kind of simulation of Kernel-based methods and are not known
Property analysis pollutant health risk assessment method, with realize reduce health risk assessment result uncertainty, pass through research
The uncertainty of contaminant transportation model increases the reliability of human health risk evaluation model.
In order to achieve the above objectives, The technical solution adopted by the invention is as follows:
The pollutant health risk assessment method of a kind of simulation of Kernel-based methods and analysis of uncertainty of the invention, including step
It is rapid as follows:
1) coupling groundwater pollutant migration numerical model and human health risk evaluation model；
2) the concentration variation to pollutant in underground water is simulated；
3) it using the parameter uncertainty in Markov chain Monte-Carlo method processing simulation process, will not know
Property analysis after parameter substitute into groundwater pollutant migration numerical model and human health risk evaluation model calculates, obtain final
Evaluation result.
Preferably, the step 1) specifically: transition process of the pollutant in underground water is obtained, by groundwater pollutant
Migration numerical model is coupled with human health risk evaluation model, establishes groundwater pollutant transport model.
Preferably, the groundwater pollutant in the step 1) migrates numerical model, is established using multiphase flow Darcy's law
Its equilibrium equation:
Wherein, t represents the time,Represent porosity, SβRepresent the saturation degree of β phase, ρβThe density of β phase is represented,Represent κ
Mass fraction of the matter in β phase, VnRepresentative basin, ΓnRepresentative basin area, k represent absolute permeability, krβRepresent the phase of β phase
To permeability, μβRepresent the viscosity of β phase；PβThe Fluid pressure of β phase is represented, g represents acceleration of gravity, and n represents interior normal vector, qκ
Represent the rate of generation or consumption of calorie in unit volume.
Preferably, the step 2) specifically: simulate non-dissolution phase organic pollutant from pollution sources using TOUGH2 software
To the transition process of underground water, at the same after predicting a period of time underground water pollutant space distribution rule.
Preferably, the step 3) specifically includes: Markov chain Monte-Carlo method is based on bayesian theory processing ground
The uncertainty being lauched in numerical simulation, basis are Bayesian formulas:
In formula, θ represents the stochastic variable in groundwater parameter, the prior distribution density of p (θ) representation parameter, and p (y | θ) generation
The likelihood function of table parameter is meant that the similarity when model parameter is θ, between model output and existing observational data y；p
The a posteriori distribution density of (θ | y) representation parameter；
The Markov Chain of Stationary Distribution π (x) is obeyed by establishing, and carries out random sampling in it is distributed, entire
The abundant search to objective function probability distribution space is realized in markovian evolutionary process, key parameter is carried out anti-
It asks.
Preferably, the step 3) is specific further include: Markov chain Monte-Carlo method passes through sampling algorithm experiment pair
The simulation of goal systems is refused adaptive (DREAMzs) sampling algorithm using delay and is handled the prior information of model.
Preferably, the step 3) is specific further include: delay refusal Adaptive sampling arithmetic is divided into warming up period and formally drills
Change phase two parts, its step are as follows:
Warming up period:
(a) prior information for generating model parameter, obtains one group of initial sample [θ by prior distributioni, i=1 ... ...,
N], N indicates parallel markovian quantity, starting point of the initial sample respectively as N chain, θiIndicate the current shape of the i-th chain
State,Represent j-th of element of the i-th chain current state, θ=θ [θ1,……,θd], d represents the ginseng of the model identified
Number dimension；
(b) t=1, t represent time, CRm=m/nCR, m=1 ... ..., nCR, CR is the crossover probability that subspace develops, nCR
It is set in advance, pm=1/nCR, indicate CRmCorresponding probability, Lm=0, it is to update pmParameter；
(c) likelihood function is selected, the probability density L [M (θ of every chain starting point is calculatedi| I, Z)], i=1 ..., N；
(d) alternative point x is generated for chain ii:
In formula, δ is for generating the different chain logarithm alternatively put；r1(j), r2(n) ∈ [1 ... ..., N], and r1(j)≠r2
(n) ≠ i, j=1 ..., δ；E, ε are the random numbers being uniformly distributed from d dimension with normal distribution；γ (δ, d ') is jump scale,
The substitution value for being d when subspace develops depending on δ and d ', d '；
(e) it is based on multinomial distribution p (p1,……,pcn), from 1 ... ..., nCRMiddle sampling obtains m；
(f) crossover probability CR=m/n is enabledCR, Lm=Lm+1；
(g) it under the probability of 1-CR, usesIt is rightEach element substituted, random number of the u between [0,1]；Work as CR=
1, it is not rightIt is substituted:
(h) alternative point x is calculatediProbability density and acceptance probability α (θi, xi):
(i) judge whether to receive xi；If α >=u receives xiFor the sample of chain i, otherwise refuse；
(j) normalized square of skip distance Δ is calculatedm:
In formula, rjIndicate the standard deviation of the current jth dimension of N chain；
It (k) is chain 1 ..., N repeats step (d)-(j)；
(l) CR value probability distribution updates:
(m) t=t+1；
(n) step (k)-(m) is repeated until t meets warming up period Period Length；
(o) IQR is counted, and removes outer layer chain；Calculate the mean value W of the posterior density logarithm of 50% sample after every chain
[W1,……,WN], IQR=Q3-Q1, Q1、Q3Respectively indicate 1/4 and 3/4 quantile of N chain W；As W < Q1When -2IQR, referred to as
Outer layer chain；There is chain to be identified as outer layer chain when warming up period, then needs to implement another warm-up phase, then carry out IQR detection, until not having
There is outer layer chain；
It is formal to develop the phase:
(p) comprising step (a)-(i) in above-mentioned warming up period；
It (q) is chain 1 ..., N repeats step (a)；
(r) t=t+1；
(s) step (b)-(c) is repeated L times, L is the evolution number of parallel chain, is arranged in advance；
(t) convergence test is carried out using rear 50% sample of every chain；
If (u) reaching convergence for every dimension sample standard deviation of inverted parameters, stop developing, if not up to, returning to step
Suddenly (d).
Beneficial effects of the present invention:
Method of the invention can be reliable from the raising health risk assessment of contaminant transportation model uncertainty angle is reduced
Property, " source-path-receptor " this risk assessment process can be more fully portrayed, the value-at-risk ultimately caused is determined
Amount evaluation.And influence of the following a period of time pollutant to human health risk can be assessed, it is contaminated site management and risk
Decision provides foundation.
Detailed description of the invention
Fig. 1 is two-dimentional sandbox schematic diagram；
Fig. 2 is pollutant distribution situation schematic diagram in simulation initial time sandbox；
Fig. 3 a is Permeability Parameters k1A posteriori distribution density schematic diagram；
Fig. 3 b is Permeability Parameters k2A posteriori distribution density schematic diagram；
Fig. 4 is the probability density function schematic diagram that sandbox tests value-at-risk.
Specific embodiment
For the ease of the understanding of those skilled in the art, the present invention is made further below with reference to embodiment and attached drawing
Bright, the content that embodiment refers to not is limitation of the invention.
Embodiment: by the permeability k of two-dimentional sandbox experiment reverse medium, and the ILCR value at sandbox right margin is calculated.
Two-dimentional sandbox length (x) 60cm, wide (y) 45cm, thickness (z) 1.6cm.It is set as determining head boundary at x=0 and x=60cm, in sandbox
Flow rate of water flow 1m/d, pollutant decanting point are located at x=30cm, y=40cm, z=0.8cm, as shown in Figure 1.Simulate start time
Lucky pollutant stops injection, and distribution such as Fig. 2, risk control face takes place of the rightmost side close to right margin.Black is empty in figure
16 × 38 (x × y) a observation points have uniformly been laid in line box.In view of the heterogeneity during back-up sand, although in reality
Test and set solid black lines box in chronogram and do not lay lenticular body, but pollutant in this position not on the right side of the lenticular body of lower section
It streams, conjecture solid black lines boxed area has local heterogeneity.Therefore have for the permeability of two-dimentional sandbox inverting
Two: one be sandbox background matter permeability k1, one is permeability k at black box2.For the experiment of two-dimentional sandbox
Specific step is as follows for health risk assessment process:
(1) according to two-dimentional sandbox waterpower and ambient condition, the heterogeneous artesian aquifer underground water dynamics mould of two dimension is established
Type and groundwater pollutant migration models；
(2) choosing permeability k is parameter to be estimated, and generates its prior information, it is assumed that the prior probability obedience of k is uniformly distributed,
k1∈[1.0×10-10, 20 × 10-10], k2∈[1.0×10-11, 10 × 10-11]；
(3) 3 Markov Chains, i.e. N=3 are set.Crossover probability parameter nCR=3, different chain logarithm δ=3 of different evolution,
Jump scale γ is set as 1.0 by every 5 iteration；
(4) it is directed to unknown parameter permeability k, using 608 observation point pollutant saturation degrees in sandbox as identification number
According to using the progress model parameter inverting of DREAMzs-MCMC method；Wherein the total the number of iterations of all chains of warming up period is 5000, formally
Total the number of iterations of simulation phase all chains is 20000；
(5) analog sample is collected, a posteriori distribution density of permeability k is obtained, such as Fig. 3 a and Fig. 3 b, k1Distribution collection
In 2.4 × 10-10~2.7 × 10-10, k2Distribution it is consistent with priori, but concentrate on 1.0 × 10-11~4.0 × 10-11；
We are by k1、k2A posteriori distribution density as the input value in contaminant transportation model, substitute into step (1) and obtain at control plane
Pollutant concentration a posteriori distribution density；
(6) concentration distribution density is substituted into health risk assessment model:
The probability density function of value-at-risk is obtained, as shown in figure 4, the distribution of value-at-risk is 1.356 × 10-5~1.363 × 10-5。
K in conjunction with empirical data, in available sandbox1、k2Empirical value k1=1.35 × 10-10, k2=3.66 × 10-11, the value-at-risk under the conditions of empirical value is calculated, result is 1.357 × 10-6, it is found that it is located at the left side of probability density function,
And the value-at-risk maximum probability after analysis of uncertainty is likely located at 1.362 × 10-6Near.Obviously, it is obtained by parameter empirical value
To result will cause human health risk is underestimated, so as to cause the fault in decision.Therefore, during risk assessment
It is significant to final evaluation result to obtain exact input parameter area.
The uncertainty of health risk assessment can be effectively reduced in the method for the present invention, is water environment protection and contaminated site pipe
It manages decision and support is provided, manpower and material resources is avoided to waste.
There are many concrete application approach of the present invention, the above is only a preferred embodiment of the present invention, it is noted that for
For those skilled in the art, without departing from the principle of the present invention, it can also make several improvements, this
A little improve also should be regarded as protection scope of the present invention.
Claims (7)
1. a kind of pollutant health risk assessment method of Kernel-based methods simulation and analysis of uncertainty, which is characterized in that including
Steps are as follows:
1) coupling groundwater pollutant migration numerical model and human health risk evaluation model；
2) the concentration variation to pollutant in underground water is simulated；
3) using the parameter uncertainty in Markov chain Monte-Carlo method processing simulation process, uncertain point will be carried out
Parameter after analysis substitutes into groundwater pollutant migration numerical model and human health risk evaluation model calculates, and is finally evaluated
As a result.
2. the pollutant health risk assessment method of Kernel-based methods simulation according to claim 1 and analysis of uncertainty,
It is characterized in that, the step 1) specifically: obtain transition process of the pollutant in underground water, groundwater pollutant is migrated
Numerical model is coupled with human health risk evaluation model, establishes groundwater pollutant transport model.
3. the pollutant health risk assessment side of Kernel-based methods simulation according to claim 1 or 2 and analysis of uncertainty
Method, which is characterized in that the groundwater pollutant in the step 1) migrates numerical model, establishes it using multiphase flow Darcy's law
Equilibrium equation:
Wherein, t represents the time,Represent porosity, SβRepresent the saturation degree of β phase, ρβThe density of β phase is represented,κ matter is represented in β
Mass fraction in phase, VnRepresentative basin, ΓnRepresentative basin area, k represent absolute permeability, krβRepresent the opposite infiltration of β phase
Rate, μβRepresent the viscosity of β phase；PβThe Fluid pressure of β phase is represented, g represents acceleration of gravity, and n represents interior normal vector, qκIt represents single
The rate of generation or consumption of calorie in the volume of position.
4. the pollutant health risk assessment method of Kernel-based methods simulation according to claim 1 and analysis of uncertainty,
It is characterized in that, the step 2) specifically: simulate non-dissolution phase organic pollutant from pollution sources to ground using TOUGH2 software
The transition process being lauched, at the same after predicting a period of time underground water pollutant space distribution rule.
5. the pollutant health risk assessment method of Kernel-based methods simulation according to claim 1 and analysis of uncertainty,
It is characterized in that, the step 3) specifically includes: Markov chain Monte-Carlo method is based on bayesian theory and handles underground water
Uncertainty in numerical simulation, basis are Bayesian formulas:
In formula, θ represents the stochastic variable in groundwater parameter, the prior distribution density of p (θ) representation parameter, and p (y | θ) represent ginseng
Several likelihood functions is meant that the similarity when model parameter is θ, between model output and existing observational data y；p(θ|y)
The a posteriori distribution density of representation parameter；
The Markov Chain of Stationary Distribution π (x) is obeyed by establishing, and carries out random sampling in it is distributed, in entire Ma Er
The abundant search to objective function probability distribution space is realized in the evolutionary process of section's husband's chain, and reverse is carried out to key parameter.
6. the pollutant health risk assessment method of Kernel-based methods simulation according to claim 5 and analysis of uncertainty,
It is characterized in that, the step 3) is specific further include: Markov chain Monte-Carlo method is tested by sampling algorithm to target
The simulation of system is handled the prior information of model using delay refusal Adaptive sampling arithmetic.
7. the pollutant health risk assessment method of Kernel-based methods simulation according to claim 6 and analysis of uncertainty,
It is characterized in that, the step 3) is specific further include: delay refusal Adaptive sampling arithmetic is divided into warming up period and formal evolution phase
Two parts, its step are as follows:
Warming up period:
(a) prior information for generating model parameter, obtains one group of initial sample [θ by prior distributioni, i=1 ... ..., N], N table
Show parallel markovian quantity, starting point of the initial sample respectively as N chain, θiIndicate the current state of the i-th chain,Generation
J-th of element of table the i-th chain current state, θ=θ [θ1,……,θd], d represents the parameter dimension of the model identified；
(b) t=1, t represent time, CRm=m/nCR, m=1 ... ..., nCR, CR is the crossover probability that subspace develops, nCRIn advance
Setting, pm=1/nCR, indicate CRmCorresponding probability, Lm=0, it is to update pmParameter；
(c) likelihood function is selected, the probability density L [M (θ of every chain starting point is calculatedi| I, Z)], i=1 ... ..., N；
(d) alternative point x is generated for chain ii:
In formula, δ is for generating the different chain logarithm alternatively put；r1(j), r2(n) ∈ [1 ... ..., N], and r1(j)≠r2(n)≠i,
J=1 ..., δ；E, ε are the random numbers being uniformly distributed from d dimension with normal distribution；γ (δ, d ') is jump scale, depends on δ
The substitution value for being d when subspace develops with d ', d '；
(e) it is based on multinomial distribution p (p1,……,pcn), from 1 ... ..., nCRMiddle sampling obtains m；
(f) crossover probability CR=m/n is enabledCR, Lm=Lm+1；
(g) it under the probability of 1-CR, usesIt is rightEach element substituted, random number of the u between [0,1]；Work as CR=1, no
It is rightIt is substituted:
(h) alternative point x is calculatediProbability density and acceptance probability α (θi, xi):
(i) judge whether to receive xi；If α >=u receives xiFor the sample of chain i, otherwise refuse；
(j) normalized square of skip distance Δ is calculatedm:
In formula, rjIndicate the standard deviation of the current jth dimension of N chain；
It (k) is chain 1 ..., N repeats step (d)-(j)；
(l) CR value probability distribution updates:
(m) t=t+1；
(n) step (k)-(m) is repeated until t meets warming up period Period Length；
(o) IQR is counted, and removes outer layer chain；Calculate the mean value W [W of the posterior density logarithm of 50% sample after every chain1,……,
WN], IQR=Q3-Q1, Q1、Q3Respectively indicate 1/4 and 3/4 quantile of N chain W；As W < Q1When -2IQR, referred to as outer layer chain；In advance
There is chain to be identified as outer layer chain when the hot phase, then needs to implement another warm-up phase, then carry out IQR detection, until there is no outer layer chain；
It is formal to develop the phase:
(p) comprising step (a)-(i) in above-mentioned warming up period；
It (q) is chain 1 ..., N repeats step (a)；
(r) t=t+1；
(s) step (b)-(c) is repeated L times, L is the evolution number of parallel chain, is arranged in advance；
(t) convergence test is carried out using rear 50% sample of every chain；
If (u) reaching convergence for every dimension sample standard deviation of inverted parameters, stop developing, if not up to, returning to step
(d)。
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| CN110503296A (en) * | 2019-07-08 | 2019-11-26 | 招联消费金融有限公司 | Test method, device, computer equipment and storage medium |
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| CN114329905A (en) * | 2021-12-03 | 2022-04-12 | 国家电投集团科学技术研究院有限公司 | Method and device for evaluating reliability of full-range analog machine and computer equipment |
| CN114444272A (en) * | 2021-12-31 | 2022-05-06 | 华中科技大学 | Bayesian hierarchical model-based food pollutant exposure and health hazard dose response relation model establishment method |
| CN114444272B (en) * | 2021-12-31 | 2024-04-12 | 华中科技大学 | Dose response relation model establishment method for food pollutant exposure and health hazard based on Bayesian hierarchical model |
| CN116151488A (en) * | 2023-04-19 | 2023-05-23 | 中科三清科技有限公司 | Pollution data analysis method, system and equipment |
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