Approximation of the carbon monoxide concentration resulting from the road traffic using experimental measurements.

Tarulescu, Stelian ; Tarulescu, Radu ; Soica, Adrian 等

1. INTRODUCTION

Carbon monoxide is a colorless, odorless gas that enters the bloodstream through the lungs. It reduces the amount of oxygen that reaches organs and tissues. Exposure to high levels of carbon monoxide can cause cardiovascular or respiratory problems for sensitive people such as children and the elderly. Rapid urbanization, industrialization and population growth have led to an increase in number of vehicles that cause air pollution. It is estimated that road traffic contributes 60 [%] of air pollution in urban areas.

A case by case assessment is required to predict the air quality in urban situations, so as to evolve certain traffic management measures to maintain the air quality levels with in the tolerable limits. Brasov city from Romania has been chosen as the study area. In the central area of the Brasov City can be found the biggest concentration of the carbon monoxide, where the majority in traffic is composed by the vehicles equipped with gasoline engines, where the traffic conditions are admitting their functioning frequently at uneconomical regimes, with partial loads, low engine speeds and uncompleted burnings of the fuel. This study was used to predict carbon monoxide concentrations from road traffic at five intersections from Brasov city. Two data groups required by the model: traffic parameters and emission parameters were collected at each intersection and have been used as the inputs to the model.

2. THE STUDIED AREA

The city of Brasov is a medieval city, but in the same time is a powerful industrial and commercial centre. The historical centre of the city is the most frequented area by the tourist and also by the local population. This area is characterized by tall buildings, narrow streets and very significant road traffic.

[FIGURE 2 OMITTED]

The analyzed route was: Muresenilor Street, Gheorghe Baritiu Street, Poarta Schei Street, Nicolae Balcescu Steet, Eroilor Boulevard.

From this intersections, just intersection 4 (Dobgogeanu Gherea Street + Nicolae Balcescu Street) have traffic lights.

3. ROAD TRAFFIC AND CARBON MONOXIDE MEASUREMENT METHODOLOGY

For intersection's analysis there were collected data about the road traffic and data about the chemical pollution (CO concentration) in the neighborhood of the road. The most common and handy method is the manual collecting of the road traffic data, with the help of an observer team, each member of this team writing down a specific element of the road traffic.

The volume of the traffic flow was determined by counting the total number of the vehicles, which passed through the intersection during one hour (8.00-9.00 and 15.00-16.00) in all ways. For measuring the concentration of the chemical pollutants from the studied area it will be used a team of two persons. In order to determine the pollution degree of this area, it was used an OLDHAM MX21 Plus portable multi-gas detector. The measurement unit for the pollutant is [ppm]--parts per million.

The measurements were made for each of the 5 intersections of the route. Simultaneously there were taken the values of traffic flow and the values of CO concentration. The four distinct situations, in function of season and time interval in which the measurement was made are: cold season (winter), morning rush hour (8.00-9.00); cold season (winter), evening rush hour (15.00-16.00); warm season (summer), morning rush hour (8.00-9.00); warm season (summer), evening rush hour (15.00-16.00).

The concentration variation of the chemical pollutant (CO [ppm]) specific to the areas near the road' infrastructure for the two analyzed time intervals is presented in the next graphic (for one intersection):

[FIGURE 3 OMITTED]

4. CARBON MONOXIDE APPROXIMATION MODEL

Using the measured data from the intersections, it can be established an average pollution level for each of these ones. For each intersection it will be analyzed only the points which are near the road, excluding the points which are far from the road or placed after green areas or other objectives. For the CO concentration it will be established an average value, expressed in the corresponding measuring unit. The average will be a rounded arithmetical mean, which will contain all the values obtained in the measurement points, but without the maximum and the minimum value.

[CO.sub.average] = [n.summation over (i=1)] [p.sub.i] - min([p.sub.i]) - max(p.sub.i])/n - 2 (1)

where: [CO.sub.average] = the average value of the analyzed pollutant; pi = the value of the pollutant in each of the analyzed points; n = the number of analyzed points for each intersection.

In order to realize the model there were made tables with the traffic values and the values of the CO concentration, in function of the intersections of the analyzed route. For calculus were used the equations corresponding to the determined linear curves, using the values obtained experimentally. The working page of the mathematical model was made grouping the four analyzed situations, for the analyzed route. For each of these situations, the intersections were sorted increasingly by the number of etalon vehicles.

Next to each intersection there were written the average values of the CO concentration, to represent in a chart the dependence between these one and the number of etalon vehicles. The obtained curves were calculated for each representation of the experimental values obtaining a theoretical curve given by a linear regression.

For this analyzed chemical compound, in order to realize a unitary mathematical model, it can be written equations of pollution concentration variation depending on etalon vehicles number measured in one hour time interval.

[R.sup.2] = Statistical measure of how well a regression line approximates real data points; an R-squared of 1.0 (100 [%]) indicates a perfect fit. [V.sub.e] = The number of etalon vehicles.

Next it will be presented the resulted curves and equations from the analysis, for the studied pollutant, for all studied situations.

[FIGURE 4 OMITTED]

5. CONCLUSION

The mathematical model can be used for different routes and situations and introducing a number of etalon vehicles for an intersection can be estimated the CO pollution level.

From this study which as realized on the base of the data obtained experimentally can be observed some characteristics of the pollution made by traffic flow:

* Substantial increments of the chemical compounds concentrations resulted from the fossil fuels burning are in the case of transitory functioning of internal combustion engines.

* The time interval and the season influence visibly the chemical pollutant compounds.

* The traffic's flow composition (cars, trucks, buses, trolleybuses) but also the traffic volume values (expressed by the Traffic capacity = etalon vehicles \ hour) have a determinant role over the city's CO pollution level.

6. REFERENCES

Adamko, N.; Kavicka, A. & Klima, V. (2007). Agent Based Simulation of Transportation Logistic Systems, DAAAM International Scientific Book, B. Katalinic (Ed.), Published by DAAAM International, ISBN 3-901509-60-7, Vienna, Austria

Hrubina, K.; Wessely, E.; Macurova, A. & Balcak, S. (2008). Classification of the Models and the Mathematical Models, DAAAM International Scientific Book, Published by DAAAM International, ISBN 978-3-901509-66-7, Vienna, Austria

Shishir, L. & Patil, S. (2001). Monitoring of atmospheric behavior of NOx from vehicular traffic, Environmental Monitoring Assessment, Vol. 68, Springer, Netherlands

Tarulescu, S.; Tarulescu, R. & Soica, A. (2008). Mathematical model of pollution compounds calculus in function of traffic capacity from urban areas, WSEAS International Conference on Multivariate Analysis and its Application in Science and Engineering,, ISBN: 978-960-6766-65-7, Istanbul, Turkey

*** Oldham MX 21 PLUS, Technical Documentation

Tab. 1. Regression equations for one distinct situation

Season Time Regression equation [R.sup.2]
 Interval deviation

Winter 8.00- CO = 1,69299 + 7,97803 x 0,97268
 9.00 [10.sup.-4] x [V.sub.E]

Fig. 1. Percentage of the CO emissions by source category

CO Emissions by Source Category

Industrial processes 8%
Fuel Combustion 6%
Miscellaneous 10%
Transportation 76%

Note: Table made from pie chart.