The Methodology of Chemical Pollutants Approximation Model

Transcription

1 The Methodology of Chemical Pollutants Approximation Model STELIAN TARULESCU, CORNELIU COFARU Department of Mechanical Engineering Transilvania University of Brasov Eroilor Boulevard, No 29, Brasov ROMANIA Abstract: - Urban air pollution from road transport is a growing concern in a large number of developing country cities and Brasov is not an exception. Exposure to pollutants such as carbon monoxide, volatile organic compounds and ozone has been found to be associated with increases in hospital admissions for cardiovascular and respiratory disease and mortality in many cities in Europe and other continents. The transit traffic in the historical center of this city produces big concentrations of carbon monoxide, nitrogen oxides, ozone and volatile organic compounds. In order to realize a prediction model for the main air pollutants, it is necessary to analyze all the intersections from this area. It is necessary to find the optimal way for reduce the pollution in Brasov and one solution is to determine the actual stage of chemical pollution and accomplishment of an approximation model for chemical pollutants in function of traffic flows. In this study is presented the methodology (8 steps) for a chemical pollutants approximation model. The all eight steps of approximation model methodology are presented in the paper. Key-Words: - Methodology, pollution, model, chemical, traffic. 1 Introduction Transportation involves the combustion of fossil fuels to produce energy translated into motion. Pollution is created from incomplete carbon reactions, unburned hydrocarbons or other elements present in the fuel or air during combustion. The principal pollutants from the transport sector responsible for adverse health effects include lead, various types of particulate matter, ozone (formed from atmospheric reactions of oxides of nitrogen (NO x ) and volatile organic compounds (VOC)), and various toxic VOC, nitrogen dioxide, carbon monoxide, ammonia and sulphur dioxide. However, the proportion of these various pollutants attributable to the transport sector varies significantly across different cities. Pollution from transportation also causes damage to crops, farmland, forests, lakes, rivers, streams and swampy biosystems. This damage is mainly due to the effects of acidification (from nitrates and sulphuric acid), eutrophication (from excessive nitrate levels) and migratory ozone [1]. Almost all motorized transportation today involves the combustion of fossil fuels, which produces energy to be transformed into motion. This combustion is the reaction of the hydrogen and carbon present in the fuels with oxygen in the air to produce in the ideal world water vapors (H 2 O) and carbon dioxide (CO 2 ). Neither of these products is damaging to human health. However, CO 2 is the principal gas responsible for the greenhouse effect, an increase in the average temperature of the planet resulting from the trapping of solar energy, with which the increased presence of this gas in the atmosphere is associated. These by-products directly damage human health, but they can also react in the atmosphere, producing secondary transport pollutants such as sulphuric acid, sulphates and ozone, which also damage human health. The type and extent of secondary-pollutant production is heavily dependent on local atmospheric and climate conditions. Atmosphere and climate, together with urban form, population densities and street densities, also influence the extent to which populations are exposed to primary and secondary pollutants [3]. 2 Chemical pollution approximation model presentation Solution of the basic problem depends upon the identification of the sources of pollution and elucidation of how these sources relate to various land-use activities. With such knowledge control measures through supervised urban planning, zoning, and land use regulations might be provided. Alterations in space and time might thus reduce pollution loads through civic action. Brasov city is the capital of Brasov county in Romania, located in the center of the country, in a mountain area, at 180 km north from Bucharest. The city is about 110 km 2 in area (276 km 2 with the surrounding areas). It is the fifth important town in Romania. ISSN: ISBN:

2 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 [4]. It is necessary to find the optimal solution for reduce the pollution in Brasov and the firs step is to determine the actual stage of chemical pollution. Also, very important is to accomplish a prediction model for chemical pollutants approximation in function of traffic flows [5]. The approximation model developed in this study related land use and environmental inputs (experimental values from chemical measurements) to pollution outputs (pollution level in urban areas). 3 Methodology for the pollutant concentrations approximation model For accomplish the mathematical model of pollutant approximation it can be used the following methodology: Step 1 Choosing the area that will be analyzed. The first stage of this step is choosing the routes. In the metropolitan areas the most sensitive places are the residential areas, commercial areas, historical zone of the city and the main institutions. In this case the selected route is the one that cross through historical center of the city and make the connection to the residential areas. In this study, for the Brasov city was selected the town s historical center area. The analyzed route was: Iuliu Maniu Street, Nicolae Iorga Street, Lunga Street, Nicolae Balcescu Street, 15 Noiembrie Street, Castanilor Street. The second stage of this step is choosing the intersections. For this route was selected 6 intersection from which four are with traffic lights and two are marked with traffic signs. The six intersections are: Intersection 1 - Castanilor Street + Iuliu Maniu Street; Intersection 2 - Alexandru Ioan Cuza Street + Agrişelor Street + Iuliu Maniu Street; Intersection 3 - Nicolae Iorga Street + Lungă Street; Intersection 4 - Lunga Street + Eroilor Boulevard + Muresenilor Street; Intersection 5 - Eroilor Boulevard + Vlad Tepes Street + Nicolae Balcescu Street + 15 Noiembrie Boulevard; Intersection 6-15 Noiembrie Boulevard + Castanilor Street. On three of those the access of the road traffic is made from the residential areas to the historical center (intersections 1, 2 and 3), and on the other three there is an opposed flux (Fig. 1). LUNGA Street 3 NICOLAE IORGA Strret 4 5 EROILOR Boulevard VLAD TEPES Street EROILOR Boulevard AGRISELOR Street PIETII Street IULIU MANIU Street Fig. 1 Analyzed intersections 2 GRADINARILOR Street 15 NOIEMBRIE Boulevard 1 IULIU MA CASTANILOR Street The third stage of this step is assignation of the measurement points. For each intersection ware selected several measurement points for the chemical pollutants monitoring. Next is presented as an example the scheme of an intersection (Intersection 5 - Eroilor Boulevard + Vlad Tepes Street + Nicolae Balcescu Street + 15 Noiembrie Boulevard), with the chosen measurement points in order to make the measurements Nicolae BALCESCU Street 7 Vlad TEPES Street NOIEMBRIE Boulevard Fig. 2 The points where the measurements were done The fourth stage of this step is determination of the time intervals for the measurements and choosing the time for measurements (season, month, week and day). The measurements ware made in function of the traffic flows and meteorological conditions. The four distinct situations, in function of season and time interval in which the measurement was made are: ISSN: ISBN:

3 cold season (winter), morning rush hour ( ); cold season (winter), evening rush hour ( ); warm season (summer), morning rush hour ( ); warm season (summer), evening rush hour ( ). Step 2 Accomplish the traffic flows and chemical pollution measurements. The first stage of this step is represented by the traffic flow measurements. The method used for the traffic flow measurement 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. For a certain input with variable time signals it is established the following data measurement in order to analyze the intersection: traffic volume, number of vehicles which are passing the stop line, for each traffic direction (forward, left, right), for each vehicle category. In this case, with special turning moves there are necessary more persons, the maximum number being of 4: one for each entrance [3]. The volume of the traffic flow was determined by counting the total number of the vehicles, which passed through the intersection during one hour ( and ) in all ways. The second stage of this step is chemical pollution measurements. For measuring the concentration of the chemical pollutants from the studied area it will be used a team of two persons. The two persons will use the necessary equipment (portable gas analyzer) and will write the specific values of the measurement points [7]. For the chemical measurements was used the MultiRae IR gas monitor. The MultiRae IR combines a PID and carbon dioxide sensor with O 2, LEL and 1 specific toxic gas sensor in one compact monitor with sampling pump. With this gas analyzer can be determined the concentration of the following pollutants: CO, NO, NO 2, H 2 S, VOC, O 3, SO 2. Fig. 3 The MultiRae IR portable multi-gas detector The measurements were made for each of the 6 intersections of the route. Simultaneously there were taken the values of traffic flow and the values of main chemical compounds pollutants: CO, VOC, O 3, NO, NO 2 and H 2 S. Step 3 Data centralization. Accomplishment or creation of a data base for traffic and chemical pollution values are the stages representative for this step. In order to realize this data base, all the traffic flows data and chemical pollution data registered for the studied intersections, ware collected in work sheets, using Microsoft Excel software. Fig. 4 Example of a data base working page, for a single intersection (chemical pollutants values) Step 4 Data analysis. The first stage of this step is to identify the chemical pollution level for each intersection. Using the measured data from the intersections, an average pollution level for each of these ones can be established. For each intersection, only the points which are near the road were analyzed, excluding the points far from the road or placed after green areas or other objectives. For each pollutant an average value, was established expressed in the corresponding measuring unit. The average was a rounded arithmetical mean, which contained all the values obtained in the measurement points, but without the maximum and the minimum value. n p min( p ) max( p ) = i = 1 i i i X average (1) n 2 Where: X average = the average value of the analyzed pollutant; p i = the value of the pollutant in each of the analyzed points; n = the number of analyzed points for each intersection. The second stage of this step is pollutants analysis in order to accomplish the mathematical model. From the six pollutants for which were made measurements, there were analyzed only three and these are: carbon monoxide (CO), volatile organic compounds (VOC) and ozone (O 3 ). The rest of the pollutants were not analyzed for the ISSN: ISBN:

4 following reasons: Nitrogen monoxide (NO) the values of the NO concentration are for most of the intersections minimum (1 [ppm]) Suppurated hydrogen (H 2 S) the values of the H 2 S concentration varies very little from one season to another, and is not specific to vehicles. Nitrogen dioxide (NO 2 ) the values of the NO 2 concentration varies depending on the season and on the time interval when the measurements were made. The values are between 0.1 and 0.2 [ppm] for most of the cases. Though, it could not be established a dependency of the NO 2 concentration in function of the etalon vehicle number. The values measured vary randomly in function of the weight of different categories of vehicles from the road traffic, but also in function of geometrical parameters of each intersection. Step 5 Obtain the curves of chemical pollutants variation in function of the traffic flow. The first stage of this step is assignation of the approximation method for each pollutant. In order to realize the model, tables with the traffic values and the values of the three pollutants were made, depending on the intersections of the analyzed route. The equations corresponding to the determined regression curves were used, for calculus for each pollutant, using the values obtained experimentally. Studding the existent documentation (local and international) for the chemical pollution approximation in function of traffic flow, it can be used the regression. To simplify the mathematical model, for this study, can be used the linear regression to approximate CO and O 3 and exponential regression to approximate VOC. For VOC was chosen exponential regression because of the big variation of the determined values for analyzed intersections. The second stage of this step represents creating the work pages using the analyzed data, using OriginPro software. The working page of the mathematical model was made by 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. For each of the studied pollutants their variations were determined according to the etalon vehicles number. The taken values vary randomly depending on the weight of the different vehicles categories from the road traffic, but depending on the geometrical parameters of each intersection. For each of the four situations, the intersections were arranged increasingly after the number of etalon vehicles. Next to each intersection the average values of the two pollutants were written, in a chart the dependence between these two and the number of etalon vehicles to be represented. The third stage of this step is obtaining the variation chemical pollutants curves. The obtained curves were calculated for each representation of the experimental values (obtained from measurements), obtaining a theoretical curve given by a regression equation. It was whished to obtain a theoretical curve very closed to the curves obtained with the experimental values. For each situation, the resulted theoretical curves were described through linear or exponential regression equations [2], [4], [6]. The resulted curves and equations from the analysis will be presented, for each of the three studied pollutants, for a single situation. For exemplification, the CO, VOC and O 3 variations for warm season, at the evening rush hour will be presented. Fig. 5 The variation of the CO concentration in function of the etalon vehicles number Fig. 6 The variation of the VOC concentration in function of the etalon vehicles number ISSN: ISBN:

5 Fig. 7 The variation of the O 3 concentration in function of the etalon vehicles number Step 6 Establish of the work pages for the mathematical model. The first stage of this step is to accomplishment of the tables from the final work pages. For this analyzed chemical compounds, in order to realize a unitary mathematical model, equations of pollution concentration variation depending on etalon vehicles number measured in one hour time interval can be written. CO theoretical = , V (2) 25343,53 VOC = 15,922 e 12,629 (3) theoretical V e E Fig. 8 Presentation of mathematical model results for one of the four situations, for the analyzed route Step 7 Verification of the mathematical model. This step involves the accomplishment of the verification measurements in similar condition with initial measurements and including of this data in the work pages. For model verification, in one of the route intersections ware made chemical pollution measurements. The selected intersection was intersection 2. In the next picture is presented the VOC variation. The seventh point represents the verification VOC value. It can be concluding that the original approximation regression fits this new data. 6 O3 = 0, , V (4) E theoretical Where: CO theoretical, VOC theoretical and O 3theoretical = the theoretical values of the CO, VOC and O 3 concentrations describing the variations of the mathematical model curves; V E = the number of etalon vehicles. The second stage of this step is to accomplishment of the graphics for each studied route. After the introduction of the formulas and the graphical representation of the three pollutants, the result is the theoretical curves corresponding to the used equations. In figure 11 the table and the corresponding diagrams for route 1, in the warm season and the evening rush hour ( ) are presented. The corresponding number for each intersection, the traffic values (etalon vehicles), the average values for the chemical pollutants concentration (determined using the data obtained experimentally) and the pollutants values obtained through calculus, using the equation of each pollutant compound in the table are presented. Fig. 9 Verification of the mathematical model Step 8 Using the mathematical model. The pollutant prediction model can be used for different routes and situations and introducing a number of etalon vehicles for several intersections, the pollution level for three chemical pollutants can be estimated. ISSN: ISBN:

6 Fig.10 Utilization of mathematical model for CO, VOC and O 3 concentration estimation, depending on the etalon vehicle number for a route 4 Conclusion In order to design effective approaches to pollution management from mobile sources, it is important to diagnose urban air pollution problems, determine the impact of mobile sources, and identify affordable and sustainable solutions. The first step is to find how serious outdoor air pollution is in the city and the nature of the pollution problem. This entails monitoring air quality and comparing ambient concentrations with national air quality standards or, in their absence, internationally recognized health-based air quality guidelines [2]. The prediction model can be used to approximate the air pollution level in urban areas. The values of CO, VOC and O 3 concentrations regarding to the number of etalon vehicles in one hour interval (for morning or evening rush hour) can be determined. From this study realized on the base of the data obtained experimentally some characteristics of the pollution made by traffic flow can be observed: 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 meteorological conditions (temperature, wind s speed and direction, humidity, air pressure) influence the pollutants values; 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 pollution level; intersection and main street s geometry on which is developed the city s transitory traffic influences significantly the pollution level; the biggest impact over the air quality, from the areas designated to pedestrians, is given by the traffic road; the pollutant emissions from the vehicles being maximal near the roads, at the height of the human respiratory organs. Where transport activities are a major contributor to a specific air pollution problem, it is important to determine in what ways these activities can be reduced. Transport sector emissions can be reduced through a variety of changes to the overall transport system: efficiency improvements in the urban transport system, changes in modal shares through infrastructure investments or land use policy, or through fiscal policies that can affect fuel and vehicle technology choice, fuel consumption, and vehicle use. References: [1] GORHAM, R., Air pollution from ground transportation Division for Sustainable Development Department of Economic and Social Affairs, S.U.A., [2] GWILLIAM, K., KOJIMA, M., JOHNSON, T., Reducing Air Pollution from Urban Transport The international Bank for Reconstruction and development, S.U.A., [3] COFARU, C., Pollution legislation in road transportation, Transilvania University of Brasov, [4] TARULESCU, S., TARULESCU, R., SOICA, A., 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: , Istanbul, Turkey [5] Philippopoulos, K., Deligiorgi, D., Karvounis, G., Tzanakou, M., Pollution Dispersion Modeling at Chania, Greece, under Various Meteorological Conditions WSEAS International Conference on Energy, Environment, Ecosystems And Sustainable Development (EEESD '09), ISBN: , Athens, Greece [6] ZABALZA, J., OGULEI, D., Study of urban atmospheric pollution in Navarre, Environmental Monitoring and Assessment, Vol. 134, Springer, Netherlands, ISSN: ISBN: