CN107300550A - A kind of method based on BP neural network model prediction atmosphere heavy metal concentration - Google Patents
A kind of method based on BP neural network model prediction atmosphere heavy metal concentration Download PDFInfo
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Abstract
The invention discloses a kind of method based on BP neural network model prediction atmosphere heavy metal concentration, comprise the following steps：1) typical small survey region selection；2) survey region meteorological data is obtained；3) survey region atmosphere particle concentration data acquisition；4) survey region atmosphere particle concentration data detection；5) data analysis；6) model construction；7) heavy metal concentration in regional atmospheric particulate matter to be evaluated is carried out according to constructed BP neural network model to predict.The present invention sets up the fast prediction model of urban atmosphere heavy metal by the response relation between BP neural network and each Monitoring factors, so as to which the concentration to atmosphere heavy metal is estimated, a kind of new approaches are provided to the pollution prevention of China's urban atmosphere heavy metal, with extremely important realistic meaning.
Description
Technical field
The present invention relates to atmosphere pollution studying technological domain, it is specifically related to a kind of big based on BP neural network model prediction
The method of gas heavy metal concentration.
Background technology
Heavy metal-polluted in air is infected with natural source and artificial two kinds of source, by universe celestial body act on and the earth on it is various
Geologic process and make some heavy metal elements enter air in belong to natural source, the heavy metal artificially originated is mainly industrial life
A large amount of pernicious gases and dust containing heavy metal that production, motor vehicle exhaust emission and auto tire wear are produced etc..With motor vehicle
Increasingly increase, the atmosphere heavy metal pollution produced therewith more serious, there are some researches show atmosphere heavy metal pollution is to human body
Have significant impact in terms of health, animals and plants ecotoxicological, these heavy metal elements include lead, copper, cadmium, arsenic, chromium, nickel,
The elements such as selenium, mercury, in addition to nutrient iron, aluminium influential on sea hydrobiont.Harm of the heavy metal to environment is depended on first
It is the content in environment again in the physicochemical properties of its own, when these heavy metals run up to certain journey in animal body
When spending, i.e., the growing of animal, physiological loading can be directly affected, until causing the death of animal, while heavy metal
It can be absorbed by the body along food chain by digestive system, to the very harmful of crowd.Therefore to heavy metal concentration in air
Side's monitoring is just particularly important.
The monitoring of current heavy metal largely carries out pre-concentration sampling using film or ram, then (atom is inhaled by AAS
Receive spectrum), the measurement of the detecting instrument such as ICP-MS (inductivity coupled plasma mass spectrometry), AAS and ICP-MS can detect many simultaneously
Heavy Metallic Elements, test limit is low, and sensitivity is high.But be due to this off-line analysis mode temporal resolution it is not high, it is difficult to
The need for meeting research heavy metal element Transport And Transformation, and ICP-MS is expensive, and complex operation is also not susceptible to promote the use of.
It is also a kind of semicontinuous detecting instrument sampled based on film using the detecting instrument of XRF (X-ray fluorescence spectra analysis) technology, though
So temporal resolution is improved to a certain extent, and its detection belongs to Non-Destructive Testing, can carry out multiple element and detect simultaneously, but
Its expensive price causes the instrument to be not easy to popularization.So existing heavy metal monitoring technology is difficult to meet in real time, on a large scale
Monitoring, heavy metal on-line checking is a great problem of present atmosphere heavy metal research field.
At present, although many cities have been set up the monitoring station of Atmospheric particulates, but due to there is weather condition, it is artificial
Factor and other the reason for can not manipulate, the data of many websites can all be lacked or disconnected section, and for a huge sum of money in Atmospheric particulates
The research work of the monitoring analysis of category just more lacks.Correlative study by multiple linear regression model, artificial neural network,
SVMs etc. linearly or nonlinearly mathematical method be successfully established atmosphere pollution (including SO2, CO, NO2, O3,
PM2.5 etc.) forecast model, but on predict Heavy Metals In Atmospheric Particles concentration research be rarely reported.Therefore, to typical case
Heavy metal pollution species, time space distribution, pollution sources are identified and set up assessment China city in urban atmosphere particulate matter
The fast prediction model of atmosphere heavy metal pollution feature, has extremely important to the pollution prevention of China's urban atmosphere heavy metal
Meaning.
The content of the invention
Purpose be to provide a kind of method based on BP neural network model prediction atmosphere heavy metal concentration, for predict with
The concentration of assessment area atmosphere heavy metal, support is provided for work such as the formulations of corresponding prevention and control measure.
The technical scheme is that：
A kind of method based on BP neural network model prediction atmosphere heavy metal concentration, comprises the following steps：
1) typical small survey region selection：
Survey region of the selection with typical representative, typical small research area should possess following items essential characteristic：The
One, the region possesses typical traffic activity, construction and life discharge, and a large amount of gases of generation, particulate matter, road are raised
Dirt；Second, there is high energy-consuming enterprises and disposal of pollutants rich and influential family in the region, the air pollution in the region is by industrial pollution short distance
The influence of transmission, has hidden danger to health；3rd, the meteorological data in the region is complete, and modeling precision is high；4th,
The regional atmospheric sample is easily obtained；
2) survey region meteorological data is obtained：According to step 1) requirement select survey region, sampled point is determined, using normal
The meteorological data of automatic monitor station near atmospheric particulate cascade sampling device collection Atmospheric particulates, synchronous recording sampled point is advised,
Including：Temperature, humidity, air pressure and wind speed；
3) survey region atmosphere particle concentration data acquisition：The concentration of analysis test Heavy Metals In Atmospheric Particles, is carried
Take mainly includes with analysis heavy metal element：Al, As, Cd, Cr, Cu, Fe, Ni, Pb and Zn；Extracting method is：By 1/8 size
Sample filter membrane is cut into fine strip shape in after digestion tube, and total metalses are extracted using nitration mixture HNO3-HCl-HF-HClO4, are then treated
Survey GOLD FROM PLATING SOLUTION and belong to the concentration of element using inductive coupling plasma emission spectrograph and inductivity coupled plasma mass spectrometry survey
It is fixed；
4) survey region atmosphere particle concentration data detection：Using Grubbs methods to surveyed Atmospheric particulates initial data
Middle special datum is tested and rejected；The normal distribution-test of data is carried out using SPSS19.0；
5) data analysis：Analyze Heavy Metals In Atmospheric Particles concentration and meteorological data and corresponding particle diameter Atmospheric particulates are dense
The dependency relation of degree；By SPSS23.0 by 14 heavy metal species concentration in Atmospheric particulates and meteorological data (temperature, humidity, gas
Pressure, wind speed) and particle diameter atmosphere particle concentration progress multiple linear correlation analysis is corresponded to, relative coefficient is higher, shows two
Person's correlation is better；
6) model construction：Using meteorological factor and pollutant data as the input factor, atmosphere heavy metal concentration for output because
Son, builds the model based on BP neural network model prediction atmosphere heavy metal concentration, and related to measured value according to predicted value
The quality of property and error judgment model construction；
7) heavy metal concentration in regional atmospheric particulate matter to be evaluated is carried out according to constructed BP neural network model to predict：
According to the concentration of the meteorological factor in city period Heavy Metals In Atmospheric Particles corresponding with the pollutant data prediction city.
Further, in such scheme, the BP networks (Fig. 2) are multitiered networks, are divided into input layer, hidden layer and defeated
Go out between layer, each layer and carry out full connection；It realize multilayer study imagination, when given one input pattern of network, it by
Input layer unit passes to implicit layer unit, is then sent through output layer unit after successively being handled through implicit layer unit, is produced after processing
One output mode, if output response has error with desired output pattern, is unsatisfactory for requiring, is then transferred to error back propagation,
Error amount is successively reversely transmitted along connecting path and each layer connection weight is corrected, when each training mode, which is all met, to be required,
Then study terminates；In hands-on, first have to provide one group of training sample, each training sample therein by input sample and
Ideal output is to composition；When all reality outputs of network are consistent with its ideal output, training terminates；Otherwise, error is passed through
The inverse method propagated makes the preferable output of network consistent with reality output to correct weights；When repetition learning until sample lump is missed
Poor (such as following formula) reaches some required precision, i.e. E<Stop during ε (previously given precision), and record the power after now adjusting
Value.
Yk is desired output in formula, and ck is reality output, and m is learning sample number.
Further, in such scheme, during modeling, 70% is randomly selected as training data, is left 30% and is made
For checking data；To ensure the representativeness of training data, region, season, the data of day and night are randomly selected, and include it
In very big and minimum；Among 100 times successfully model, mould is simulated in maximum being once used as of selection training pattern coefficient correlation
Type, heavy metal concentration is carried out and prediction.
Further, in such scheme, whether the model for judging to build has the specific measurement index of universality
Have：
1. coefficient correlation (R), for reflecting dependency relation level of intimate, the fitness for measurement model between variable
Energy；
2. mean absolute error (MAE), is that the absolute value of all single analogues value and the deviation of measured value is averaged, is used for
The prediction effect of assessment models；
3. root-mean-square error (MSE), is the square root of the average value of the quadratic sum of error, for judging the reliable of the model
Property.
Further, in such scheme, after model training is finished, the meteorological data (wind of Free Region random time section
Speed, air pressure, temperature, humidity) and correspondence particle diameter atmosphere particle concentration as input the factor, with the model prediction trained this when
The concentration of section, survey region correspondence atmosphere heavy metal.
The beneficial effects of the invention are as follows：The dirt of Heavy Metals In Atmospheric Particles under the Various Seasonal of city is probed into present invention analysis
Feature is contaminated, while the routine monitoring factors such as meteorological data are collected, by the response between BP neural network and each Monitoring factors
Relation sets up the fast prediction model of urban atmosphere heavy metal, so that the concentration to atmosphere heavy metal is estimated, it is corresponding
The work such as the formulation of prevention and control measure provide support, establish the fast prediction model for assessing China's urban atmosphere heavy metal pollution,
A kind of new approaches are provided to the pollution prevention of China's urban atmosphere heavy metal, with extremely important realistic meaning.
Brief description of the drawings
Fig. 1 is the techniqueflow block diagram of the present invention；
Fig. 2 is the flow chart of BP neural network model construction；
Fig. 3 is atmosphere particle concentration and meteorological data；
Fig. 4 is the variation tendency of Various Seasonal atmosphere heavy metal concentration；
Fig. 5 Nanjing meteorological data of 2015, PM2.5 concentration and the heavy metal concentration of prediction.
Embodiment
Modeled by taking the celestial woods school district of Nanjing University as an example, concentration of the application model to heavy metal in the PM2.5 in 2015 of Nanjing
Predicted.
Nanjing is the second largest city of China Yangtze River Delta Area, the important comprehensive Industrial Complex of China, East China
The particularly important transport hub in area.The quick Process of Urbanization Construction in Nanjing is generated than more serious air pollution in recent years,
Essentially from a large amount of gases produced by industrial production, traffic activity, construction and life discharge, particulate matter, dust on the roads
Deng.The main economic development zone in Nanjing, technological development zone etc. are around whole Nanjing, the wherein chemical work in the Nanjing of direct north
Industry garden is the compact district of chemical enterprise, summarizes the main high energy-consuming enterprises in Nanjing and disposal of pollutants rich and influential family.Nanjing University
Celestial woods school district is located in Xian Lin university cities, but also relatively near (5.6 kilometers) according to Nanjing petrochemical industry.It can be seen that, the air in this region is dirty
Dye will be influenceed by industrial pollution short-distance transmission, have huge hidden danger to health, should be by government's dependent part
Door and the great attention of the public.
Sampler has used classification cutting head (2.5 μm, 5 μm, 10 μm and 100 μm), and sampling media is high purity quartz filter membrane
(QM-Whatman, 20.3cm × 25.4cm), sampling flow is 100L/min, and sampler atmospheric connection height is left away from ground 1.5m
It is right.The sampling time of celestial woods be 22 days-May 7 April in 2014 (spring), 9-July of July 26 (summer), October 13-
October 22 (autumn), 27 days 18 days-January of January in 2015 (winter), total acquire 156 samples altogether, and synchronous recording is certainly
The meteorological data of dynamic monitoring station, including temperature, humidity, air pressure and wind speed etc..The equal constant temperature and humidity 24h (temperature of filter membrane before and after sampling
25 DEG C, humidity 50%) and weigh, to determine quality, it is finally stored in drying box to pretreatment.Every filter membrane is bisected into 8 parts,
Wherein 1 part is used for total amount and determines, and 7 parts give over to other experiment use in addition.
Statistical analysis, Nanjing atmospheric particle concentration and meteorological factor are as shown in figure 3, result is shown：Nanjing is big
Aerated particle thing is more gathered in fine particle PM2.5, account for the 49% of total amount.In addition, celestial woods PM2.5 concentration is 78 μ
G/m3, the sample for having 38% has exceeded the average daily limit value of national standard (75 μ g/m3).Generally speaking, celestial woods winter PM2.5 and
Heavy metal concentration is significantly higher than summer in PM2.5-5.The variation tendency of atmosphere heavy metal concentration is as shown in figure 4, result is shown：Greatly
The concentration in part atmosphere heavy metal winter is significantly higher than summer.
For checking mode input parameter and the internal association of metal, correlation analysis is carried out with SPSS23.0, is as a result shown：
Metal concentration, in notable negatively correlated, is in notable positive correlation with air pressure, most of metal is with humidity without significantly correlated with wind speed, temperature
Property.Wind speed has certain diluting effect to atmosphere pollution, and wind speed is more high more is easily reduced atmosphere particle concentration.In general,
Temperature is higher, and atmospheric pressure is lower, and air convection movement is more obvious, and Atmospheric particulates diffusion rate is faster so that Atmospheric particulates
The concentration of middle heavy metal concentration is also lower.Metal concentration and PM are in notable positive correlation, have common denominator such as between this and they
Industrial discharge, transportation emission, daily culinary art, biomass combustion etc. are relevant.
The processing handled well is uniformly called in through the compiled program of MATLAB 2013a instruments, carry out model training and
Checking, obtained final result is as shown in table 1：Training pattern r values are maintained at 0.423-0.765 (PM2.5), 0.540-0.672
(PM2.5-5), between 0.543-0.682 (PM5-10) and 0.560-0.684 (PM10-100), model checking r is maintained at
0.502-0.752 (PM2.5), 0.559-0.786 (PM2.5-5), 0.560-0.772 (PM5-10) and 0.622-0.783
(PM10-100) between.The result of comprehensive R values, MAE values and RMSE value, it can be seen that the predicted value and actual value correlation of model compared with
Good, the model is relatively reliable.
Table 1：Coefficient correlation, mean absolute error and root mean square between BP neural network pattern die analog values and measured value are missed
Difference
After model training is finished, by Nanjing annual meteorological datas (wind speed, air pressure, temperature, humidity) in 2015 and
PM2.5Concentration predicts the concentration of period correspondence atmosphere heavy metal as the input factor, as a result shows：2015, Nanjing
PM2.5Middle heavy metal concentration is ordered as Al>Fe>Zn>Pb>Cu>As>Cr>Ni>Cd, winter>Spring>Autumn>Summer, pollute most tight
The period of weight is in December and January.Wherein, typical element As and Pb annual tendency chart is as shown in Figure 5.
It is described above, only it is the case study on implementation of the present invention, is not the limitation for making other forms to the present invention, it is any
Those skilled in the art are changed or are modified as the equivalent of equivalent variations possibly also with the technology contents of the disclosure above
Embodiment.But it is every without departing from technical solution of the present invention content, the technical spirit according to the present invention implements what is made to more than
Any simple modification, equivalent variations and remodeling, still belong to the protection domain of technical solution of the present invention.
Claims (6)
1. a kind of method based on BP neural network model prediction atmosphere heavy metal concentration, it is characterised in that comprise the following steps：
1) typical small survey region selection：
Survey region of the selection with typical representative, typical small research area should possess following items essential characteristic：First, should
Region possesses typical traffic activity, construction and life discharge, and produces a large amount of gases, particulate matter, dust on the roads；The
Two, there is high energy-consuming enterprises and disposal of pollutants rich and influential family in the region, the air pollution in the region is by industrial pollution short-distance transmission
Influence, there is hidden danger to health；3rd, the meteorological data in the region is complete, and modeling precision is high；4th, the area
Domain atmospheric sample is easily obtained；
2) survey region meteorological data is obtained：According to step 1) requirement select survey region, sampled point is determined, using conventional big
Aerated particle thing andersen sampler gathers the meteorological data of automatic monitor station near Atmospheric particulates, synchronous recording sampled point, including：
Temperature, humidity, air pressure and wind speed；
3) survey region atmosphere particle concentration data acquisition：Analysis test Heavy Metals In Atmospheric Particles concentration, extract and
Analysis heavy metal element mainly includes：Al, As, Cd, Cr, Cu, Fe, Ni, Pb and Zn；Extracting method is：By the sample of 1/8 size
Filter membrane is cut into fine strip shape in after digestion tube, and total metalses are extracted using nitration mixture HNO3-HCl-HF-HClO4, then to be measured molten
The concentration of metallic element is determined using inductive coupling plasma emission spectrograph and inductivity coupled plasma mass spectrometry in liquid；
4) survey region atmosphere particle concentration data detection：Using Grubbs methods to special in surveyed Atmospheric particulates initial data
Different value is tested and rejected；The normal distribution-test of data is carried out using SPSS19.0；
5) data analysis：Analyze Heavy Metals In Atmospheric Particles concentration and meteorological data and corresponding particle diameter atmosphere particle concentration
Dependency relation；By SPSS23.0 by 14 heavy metal species concentration in Atmospheric particulates and meteorological data (temperature, humidity, air pressure, wind
Speed) and particle diameter atmosphere particle concentration progress multiple linear correlation analysis is corresponded to, relative coefficient is higher, shows that both are related
Property is better；
6) model construction：Using meteorological factor and pollutant data as the input factor, atmosphere heavy metal concentration is the output factor, structure
The model based on BP neural network model prediction atmosphere heavy metal concentration is built, and according to predicted value and the correlation and mistake of measured value
The quality that poor judgment models are built；
7) heavy metal concentration in regional atmospheric particulate matter to be evaluated is carried out according to constructed BP neural network model to predict：According to
The concentration of the meteorological factor in city period Heavy Metals In Atmospheric Particles corresponding with the pollutant data prediction city.
2. a kind of method based on BP neural network model prediction atmosphere heavy metal concentration according to claim 1, it is special
Levy and be, the BP networks are multitiered networks, be divided between input layer, hidden layer and output layer, each layer and carry out full connection；It is real
The imagination of multilayer study is showed, when given one input pattern of network, it passes to implicit layer unit by input layer unit, through hidden
Output layer unit is then sent through after successively being handled containing layer unit, an output mode is produced after processing, if output response and phase
Hope output mode have error, be unsatisfactory for requiring, be then transferred to error back propagation, error amount is successively reversely transmitted along connecting path
And each layer connection weight is corrected, when each training mode, which is all met, to be required, then study terminates；In hands-on, first have to
One group of training sample is provided, each training sample therein is by input sample and preferable output to constituting；When all realities of network
When border exports consistent with its ideal output, training terminates；Otherwise, network is made to correct weights by the method for error Back-Propagation
Ideal output is consistent with reality output；When repetition learning until sample set overall error (such as following formula) reaches some required precision, i.e. E
<Stop during ε (previously given precision), and record the weights after now adjusting.
<mrow>
<mi>E</mi>
<mo>=</mo>
<mfrac>
<mn>1</mn>
<mn>2</mn>
</mfrac>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>k</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>m</mi>
</munderover>
<msup>
<mrow>
<mo>(</mo>
<msup>
<mi>y</mi>
<mi>k</mi>
</msup>
<mo>-</mo>
<msup>
<mi>c</mi>
<mi>k</mi>
</msup>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
</mrow>
Yk is desired output in formula, and ck is reality output, and m is learning sample number.
3. a kind of method based on BP neural network model prediction atmosphere heavy metal concentration according to claim 2, it is special
Levy and be, during modeling, randomly select 70% as training data, be left 30% as checking data；Successfully built at 100 times
Among mould, selection training pattern coefficient correlation it is maximum once as simulation model, heavy metal concentration is carried out and prediction.
4. a kind of method based on BP neural network model prediction atmosphere heavy metal concentration according to claim 2, it is special
Levy and be, during modeling, randomly select 70% as training data, be left 30% as checking data；To ensure to train number
According to representativeness, randomly select region, season, the data of day and night, and include it is therein greatly and minimum；100
It is secondary successfully model among, selection training pattern coefficient correlation it is maximum once as simulation model, heavy metal concentration carry out with
Prediction.
5. a kind of method based on BP neural network model prediction atmosphere heavy metal concentration according to claim 1, it is special
Levy and be, whether the specific measurement index with universality has the model for judging to build：Coefficient correlation (R), average absolute
Error (MAE) and root-mean-square error (MSE).
6. a kind of method based on BP neural network model prediction atmosphere heavy metal concentration according to claim 1, it is special
Levy and be, after model training is finished, the meteorological data (wind speed, air pressure, temperature, humidity) of Free Region random time section and correspondingly
Particle diameter atmosphere particle concentration is as the input factor, with the model prediction trained the period, survey region correspondence air weight
The concentration of metal.
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