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In order to improve the air quality in the urban andindustrial area the European Environment Commission,starting with 1996, imposed restrictions regarding thegas or suspension powders emissions and elaboratedseveral directives that establish the maximumconcentration level for different airborne pollutants: such

as NOx, SO2, O3, CO (CO2), the total contents forvolatiles organics compounds (TOC, including benzene,chlorinate derivates, formaldehyde etc.) and other solidparticles in suspension state with diameter

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particles existing in an urban location near an industrialarea, from Constanta considered a risky one (withindustrial profile such as petroleum refinery and thepetroleum product manufacturing) and adding the streetintense traffic from January 2006 August 2007.

The used model for the prevision of chemicalcompounds near emissions points: Artificial NeuralNetworks (ANN), one of the artificial intelligencebranches, is used with great success for the

approximation of nonlinear function for its specificity tolearn from past experiments, when inputs and outputsare feed to it. Considering these data ANN organizesitself and simulates the real influences between inputsand outputs, in order to determine future unknownoutputs starting from known inputs.

The current known data are: air pollutantsconcentration (APC) with values measured in twoemission points (A, B) from the considered industrialrisk area (IA), linearization coefficient (LC), calculatedwith a mathematical function with variables such as thedata values of the pollutants concentration measured inIA and having as a objective the best ANN training.Considering this the ANN was used for the futureprevision of the APC values in the IA without the needof measuring real values.

The method has three steps: the analyses of APCvalues, ANN architecture, ANN training . Every stepwith their characteristics is presented bellow.

For the pollution analysis and implicit the evaluationof air quality from the studied industrial area the maingases (SO

2, H2S, NO2, CO) and the fine powders weremonitored through daily and specific time measurementswith the help of a mobile laboratory equipped with modernanalysis system for the air emissions, with four modernanalyzers and with a meteorological station, interfacedwith a PC, that offers the possibility for online watch of

the registered concentrations values [9-10].NITROGEN OXIDE ANALYZER (ThermoEnvironmental Instruments) combines the optical,mechanical and chemical characteristics, and itsfunctioning is based on chemiluminescence principle(chemiluminescence NO-NO2-NOx analyzer).

CARBON OXIDE ANALYZER (ThermoEnvironmental Instruments) functions on the infraredabsorption principle combining the last generationtechnologies with classical technologies (gas filtercorrelation CO analyzer).

SULPHUROUS HYDROGEN ANALYZER(Thermo Environmental Instruments) is assembled from

a converter that transforms the sulphurous hydrogen insulphur dioxide, measuring simultaneous two differentairborne noxious through the ultraviolet absorptionprinciple ( pulsed fluorescence H

2S-SO

2analyzer).

Portable Environmental Particulate Airmonitor-Haz-Dust EPAM-5000 (manufactured fromEnvironmental Devices Corporation) is a portablemicroprocessor - based particulate monitor suitable forambient, environmental, and indoor air quality

investigations. Highly sensitive, this monitor uses lightscattering to measure particle concentration and provideimmediate real-time determinations and data recordingsof airborne particle concentration in milligrams per cubicmeter (mg/m3). Interchangeable size-selective samplingheads allow PM10, PM2.5, or PM1.0 monitoring. ThePAM-5000 unique aerodynamic particle sizing and 47-mm in-line filter cassette holder (optional accessory)loaded with the appropriate filter provide concurrentgravimetric sampling. Sample for up to 24 h on onebattery and store up to 15 months of monitoring data.Data can be downloaded to and stored on a PC forfurther analysis.

Through statistical measuring and analyses of theprobes, compound concentration were determined forgas and fine particle emissions and imissions with valuessmaller than the concentration limits imposed by the localenvironmental agencies (eg.: .283 mg/m3 for NOx, 0.75mg/m3 for SO2, 14 mg/m

3 for CO) and 50 g/m3 for thesuspensions powders (PM

10) European limit [8].

Experimental partCPA values analyses

The values fed to the ANN in order to develop thetraining session (table 1) are the following :

- APC values measured from the first emission point(A), in the risk area (IA);

- APC values measured from the second emissionpoint (B) in the risk area (IA);- the linearization coefficient, calculated (LC);- the APC values measured in target urban area (UA).The ANN inputs consist from the neurons

corresponding to each input data imposed from thebeginning. The input neurons are the first ANN neuronswithout any previous layers. Thus each input neuron islinked with the next neuron from the next first layer ofthe hidden neurons. The ANN must make a nonlinearassociation between the input data: APC valuesmeasured from emission point A and B and the

Table 1VALUES OF THE MEASURED APC

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linearization coefficient (LC) and the output data: APCvalues measured in target urban area (UA).

The ANN output consist from one layer of one neuroncorresponding to the APC values measured in targeturban area (UA) and linked to each neuron from thelast anterior layer, the hidden layer.

The final ratio for the input and output data wereestablished as follows: 68% for the training session, 16%for the validation session and 16% for the testing session.

ANN architectureIn order to make the prevision through ANN the trial

version of Alyuda NeuroIntelligence was used, thespecial ANN software proved its utility and easy useover other software.

Considering the quantity of experimental values, thenumber of data series and the nature of the presentproblem, the feedforward ANN architecture was chosen.

For the training algorithm, having in mind the functionapproximation, we have to choose the backpropagationalgorithm for training (Levenberg-Marquardt LM).

Considering the number of input and output data thenumber of neurons from the input and respectively outputlayers were determined equal with the number of data.

Thus there were imposed three neurons in the input layerand one neuron in the output layer.

The number of neurons from the hidden layer can becalculated with the Rogers formula, Hecht Kolmogorov theory or the Radial Gaussian Floodsystem. On the other hand the number of hidden neuronscan be determined through the simulation of the ANNstructure looking for the one that offers the smallestANN training error. In the end it was used the last

possibility simulating several ANN structures with ahidden neurons between numbers 5 and 12, organized inone or two layers.

Due to the data heterogeneity the most satisfyingresult obtained in the structure with eight neuronsorganized in two layers (fig. 1).

Fig. 1. ANN architecture [3-5-3-1]

Fig. 2. Networkserrors for the

training session

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The backpropagation algorithm uses specific functionfor the ANN neurons. Considering the possibilitiesoffered by the software the neurons activation functionswere chosen as follows: for the hidden neurons theactivation function used was the hyperbolic tangent (thelogistic or linear function could also be chosen) and forthe output neurons (output layer) was chosen the linearactivation function (the logistic or hyperbolic tangentfunction could also be chosen).

Based on the past experience training RNA organizesits internal structure in order to determine the smallesterror between the simulated and the real data. This erroris based on the type of training algorithm. In order tominimize the ANN error, for the training session, LMalgorithm uses the sum-of-squares error functionminimization:

(1)

were:dj value of the real datay

j value of the simulated data

Through successive trainings the ANN changes thestructure values in order to minimize this error.

ANN trainingThe training session purpose is to determine the best

ANN structure that would be finally used.The modifiedANN structure elements are the weights between eachneuron from the adjacent layers (fig. 1). In order toestablish the best operational weights as the results oftraining it is necessary to set a number of parametersfor the best algorithm processing. These parameters are:

- the propagation coefficient = 1 settles the speedthat the weights are modified through the entire ANNstructure;

- the learning rate = 0.1 the training learn speed;- the maxim absolute error = 1 . 10-6 the maximum

value of the error that will stop the training process (fig.2);

-the number of successive train sessions = 4 numberof consecutive sessions after which the training were

stopped;-the manual randomization rates = 0.1 the valuethat the weights are modified with, after every iteration(because of the heterogeneity of values for CO and H

2S

the manual randomization rates were modified to: 0.2,being the only modification to the general method).

Results and discussions The result of imposed conditions to the training

process for each APC, the results determined by theANN are presented in table 2 and figures 2 and 3.

The testing procedure determines mainly thecomparison between the results of training the outputs(the simulated UA values) and the targets of the realUA. The testing results can be easily observed in thetable 3 and figure 4.

As it can be seen for the absolute error and for theabsolute relative error, the differences between thetargets and the outputs are very small.

The differences between the simulated UA and realUA values are very small (max: 7% for H2S and min: -0.0094 for NO2). The ratio values that excide the 1%value are determined by the only difficulty encountered:the heterogeneity of measured vales for CO and H2S.

Table 3

COMPARISON BETWEEN TARGET AND OUTPUT FOR UA

Table 2

TRAINING STOPS CONDITIONS

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In figure 4 are represented the facts that the output

data are almost the same with the target data and mostimportant are situated on the plotted target tangentinterval.

Because of the small differences between these twocompared values it is difficult to determine an accurate

Table 4

SIMULATED VS. REAL UA VALUES

plot where the graphical representation of the target to

be differentiated from the graphical representation ofthe output.After the training session is follows the last test for

the ANN before it can be finally use in practice. In this

Fig. 3. Dataset errors foreach AP training session.

Absolute errors vs. numberof iterations

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Fig. 4. Absolute data errors for the training session (random values).

last step a new set of data is feed to trained ANN,different from those used for this training phase.

This new set contains A, B and LC values and notUA values (but real UA values are used for thecomparison).

Considering now that the ANN is trained and ready it

does not matter if the new set of data contains one ormore values for each of the inputs. The results of anexample of the query session can be seen in table 4.

ConclusionThe authors present a provisioning model that uses

Artificial Neural Network (ANN) for the concentrationof some inorganic air pollutants in two industrial locationsand an urban area from Constanta.

The ANN use, starting from the analysis and endingwith the testing and query, can be considered a successfor the objective proposed. Even working with differentvariables and data that can not be simulated withdeterministic algorithms the results of the research canbe considered in the whether to chose the ANN for futureuse for prevision and control of the chemical compoundsthat pollutes the atmosphere. The used ANN is the firstone from a more elaborated research, but because of its

success, we considered that it can be used for futureproblems which can be more complex.

I order to overcome the problems raised by theheterogeneity of CO and H

2S the ANN model can be

easily modified of the best results (minimizing thenetwork error) and also the training parameters (as the

propagation coefficient and the learning rate can beslightly changed).The use of at ANN from this paper is a quick and

efficient form of evaluation of the measured environmentparameters.

Thus it is recommended the connection between localstations for environment control, in order to offercontinuous data for an immediate processing and for arapid ecological decision by the competent agencies.

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Manuscript received: 31.01.2008

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