A Gas Dispersion Model for Traffic-Congested Urban Areas

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1 A Gas Dispersion Model for Traffic-Congested Urban Areas MARIA CLAUDIA SURUGIU, MARIUS MINEA, IONEL PETRESCU Department of Telematics and Electronics for Transports "POLITEHNICA" University of Bucharest - Faculty of Transports 313, Splaiul Independentei, BUCHAREST, ROMANIA claudia.surugiu@upb.ro, mariusminea@yahoo.com, npcrank@yahoo.com Abstract: - Traffic management systems are usually employed for reducing traffic congestion and increasing its safety. Recently, interest for using also these systems in reducing emissions and pollution produced by road vehicles has increased. In this paper, some mathematical models and assessment techniques regarding air quality in urban areas with dense road traffic are presented. Measurements and modelling techniques may be employed in a combined manner, in order to assess the air quality, there where these pollutants levels range between the upper and the lower thresholds of evaluation. For obtaining the gas dispersion scheme, a set of different models resulted from specific algorithms has been employed, with the purpose to compare the Gaussian and empiric models. Key-Words: -Air quality, Gaussian model, empiric model, gas dispersion, emissions monitoring, and road traffic modelling 1 Introduction Intelligent Transport Systems is a concept regarding the usage of an integrated set of technologies and sciences, such as information technology, automation and communications with the purpose of increasing the road traffic safety, reducing fuel consumption, stress and increasing speed. These technologies are widespread on a large area, increasing efficiency and safety of road transportation in a percentage which may be comprised between 15 and 0 percent [6]. Traffic congestion is a very important and acute problem of nowadays for many groups of actors that perform in the field of transports: fleet managers, transport operators, traffic administrators, public transport administrators, traffic participants or travellers. Increasing the actual transport systems is a main purpose of the Intelligent Transport Systems at the international level. At the European Union level, there are specific, clearly defined programs to deploy in an integrated, harmonized and standardised manner ITS on specific transport corridors, known as the TEN-T 1. These specific corridors are conceived to include ITS technologies developed for specific modes of transportation: - For road transportation, ITS (urban traffic management, public transport management, highway management, advanced vehicle systems etc.); - For railway transportation, the ERTMS/ETCS solutions; - For water transportation (Rhine-Main- Danube corridor #7), the RIS 3 solutions; All these solutions are, in fact, telematic technologies deployed in a harmonized manner, integrated and able to exchange information between different modes of transportation. Coming back to road transportation, traffic congestion may be reduced by improving transport network efficiency, by redirecting transport request from road to other modes of transportation, such as railway or waterway. Employing the wide palette of technologies offered by the Intelligent Transport Systems has an indirect, but positive effect on the environment. Most of the benefits are based on the principle that fewer vehicles on the road mean less fuel consumption and less CO emissions [7]. Systems that make such thing possible include: ERTMS/ETCS European Rail Traffic Management, European Train Control System 1 TEN-T Trans-European Network for Transports 3 RIS River Information Services ISBN:

2 Navigation devices and information services regarding traffic, that are able to bring information to traffic participants and vehicles drivers concerning the most efficient routes that may be used to avoid time losses due to traffic and to change directions; Information regarding parking, that are directly delivered to the vehicle driver, depending on his location, instead of determining him to make several detours until finding an acceptable parking place; Real-time information transmitted to travellers or commuters, regarding multimodal transport, which is able to encourage usage of public transportation system. Sustained mobility cannot be obtained without protecting and enhancing environmental protection. Specific services that directly address to the environment are the following: Pollution monitoring; Information regarding air quality; Strategies for management and administration of transport/traffic request; Access control in areas with a high level of pollution. Direct measurement of the impact on the environment can be focused only on specific, local measures, such as air quality in road junctions or other points of interest. The evaluation is terrain configuration and road geometry dependent. There is no rule to be generated so that is universally valid. Emissions modelling Traffic management systems are usually employed for reducing traffic congestion and increasing safety of traffic participants. It appears that recently, interest in using such systems for reducing emissions and air pollution has increased. There are two main objectives that can be mentioned for this purpose: In cases where a function of the traffic management system addresses safety problems or traffic congestion, this specific problem shall be designed in such a manned that it could not lead to an increase in emissions; Special systems are to be designed for reducing emissions. In the framework of a complex process, designing and optimisation of traffic management systems with low emissions is based on the understanding of specific links with other systems, because the traffic management system itself does not directly affect emissions rate. The initial impact of a traffic management system is oriented on the driver and often involves a consistent set of decisions. Depending on the traffic management system type, the driver is free to chose if he/she respects the speed limit, if he/she decides a route change or even a transport mode shifting, in case the public transport system denotes a priority in traffic compared to private traffic. Any changing in driver decisions has as result a different mode of vehicle operation. There are several modalities in which such changes may occur. For example, a change in the total distance run may appear, a change in traffic composition or in the travel speeds profiles and any of these changes or a combination of them may affect the emissions rate. A change in the emissions rate leads to a modification in the local air pollution, but in an un-proportional manner, because air pollution level is influenced by many other factors, such as non-traffic sources of pollution, general substances concentrations, photochemical transformations and, of course, weather..1 Currently dispersion models employed in Romania Models currently employed in Romania, in an operative mode, for computing pollutants concentration are those based on Gaussian solutions of the diffusion equation. These models can be applied to the following types of sources: momentary and continuous sources, linear sources and surface sources. These models are applicable on one or on several of such sources. However, models that take into account the pollutants chemical reactions in the atmosphere are not yet employed. Likewise, there are no currently used methods that expand to mezzoscale, due to the lack of necessary meteorological data. Restrictions in selecting models that may be used currently in Romania are imposed by the following existing circumstances: Existing meteorological data are not accessible in an automated manner, constantly and in real time; High altitude meteorological data is available only for three weather stations; The complex shape of the country s relief limits the possibility of modelling. These limitations impose, for the current problems, not for research activities, employment of some models that are based on standard meteorological ISBN:

3 data, at the Earth s surface. These must be introduced in a format that does not need a permanent connection with the meteorological network. The employed models consider the same analytical form of the equation for a specific type of source, being diversified according the modelling of the following input parameters: Over heighten; Wind speed profile; Standard deviations; Stability class. The modelling of the input parameters depends on the source geometry and the surface topography see figure 1. The estimation of pollutants concentration results from a chain of models, built from a dynamic model of emissions, a waiting algorithm and, eventually, a dispersion model. For the dispersion model traffic modelling data can be employed, to see how the vehicles queue length influence the pollutant emissions quantity in a junction. KINEMATIC DATA TRAFFIC LIGHT SETTING TRAFFIC FLOWS INTERSECTIONS GEOMETRY CAR FLEET COMPOSITIONS METEOCLIMATIC DATA DYNAMIC EMISSION FACTORS ESTIMATION TRAFFIC PARAMETERS CALCULATION MODAL EMISSIONS CALCULATION DISPERSION CALCULATION VEHICLE PARAMETRES MODAL EMISSIONS CO CONCENTRATINS Fig.1 Different approaches for emissions modelling required data.1.1 Traffic modelling One of the available measures to reduce traffic congestion and to improve air quality and people s mobility is presented in the Institute of Transportation Engineers (lte 4 ) Report, from Here it is suggested that the main cause of traffic congestion is the large number of individuals that employ private car to travel in metropolitan areas, flowing from large dispersed locations to central urban areas, even there where an adequate road network (in terms of traffic capacity) does not exist [], [3]. Specific actions that may be undertaken are divided on five components, as following: 1. Obtaining a maximum possible from the actual highway system via: a. Usage of Intelligent Transportation Systems (ITS); b. Ramp metering at the entrances in urban areas (on highways); c. A good management of the local network of local streets (noise management, parking management etc.); d. Law enforcement.. Building of new transport capacities (new arterials, highways, improving the existing ones); 3. Ensuring an efficient management of the transit services (encouraging usage of transit services); 4. Transport demand management via: a. Strategies to avoid traffic congestions; b. Diminishing of existing congestions; 5. Financing and institutional measures: a. Fuel and pollution tolling, road toll etc.; b. A good manner to organise institutions collaboration in the field of road transportation. The predetermined traffic signalling associates to traffic flow a correct path according to a synchronisation plan. The Webster method [6] is employed to determine an optimal signalling cycle. Because the traffic actuated signalling acts as a fixed signalling when all exists are congested, the Webster methodology can be used to compute the cycle length and green time for traffic actuated controllers. Webster showed that the minimal delay in a junction is obtained when the length of the signalling cycle results from the following equation: 4 ITE - Institute of Transportation Engineers - ISBN:

4 1,5 L 5 C n 1 y i i 1 (1) Where: C optimal signalling cycle length (in seconds); L total time lost in a signalling cycle (in seconds); y i critical traffic volume on lane (i stage, volume/hour) / saturation flowing (volume/hour); n number of stages. Total time lost may be denoted as the time that is not used by any stage for vehicles discharging. The total lost time is given by: L n i 1 l i R () Where: l i time lost in stage i, that is generally 4 seconds; R total periods within a cycle, where the signals display red. The effective green time available at a cycle is given by: G te = C L (3) In order to obtain minimum general delay, the total green effective time must be distributed in different stages according to y values, for obtaining effective green time for a single stage. yi Gei Gie (4) y1 y yn The total green time for a stage (without the amber time) is obtained from: G ai = G ei + l i π i (5) Where π i is the total green time for stage i..1. Models for pollutants dispersion There is a various number of sophisticated and complex methods used for estimating air pollution levels. These techniques include simple, linear Gaussian models, along with more elaborated numerical models [1]. The databases used in different models can be divided in the following categories: Meteorological information: o Wind speed and direction; o Temperature; o Humidity; o Wind gusts and temperature fluctuations; Information regarding traffic: o Traffic volumes; o Vehicles speed; o Traffic composition; Terrain information: o Normal (plain) level terrains; o Terrains at high altitudes; o Rough terrains; Measurement periodicity. In order to develop viable models of pollutants air dispersion, phenomena such as pollutants transport and diffusion are to be analysed, after their generation at source. The air concentration of an emitted pollutant will evolve in time and space according to a law in the following form: dc dt * c (6) Where c = c(x, y, z, t) represent air concentration of the pollutant, σ is a diffusion coefficient or a dispersion parameter. c is the Laplacian of the concentration, according to spatial coordinates x,y,z. Equation (6) is in fact the diffusion rate of a pollutant in certain simplifying hypothesis air without turbulences, by neglecting some partials of superior order relative to space coordinates etc. Normally, the atmospheric air is characterized by the presence of turbulences, which determines that, beside the molecular diffusion process (relatively simply modelled by equation (6)) the air masses also account for their movement (determined by several factors, such as temperature, humidity etc.). If the winds act constantly and in a laminar regime of flowing, then dispersion modelling is still possible by considering simple diffusion in case of instantaneous emissions, because in this situation the mass of air performs a translation movement. Otherwise, a travelling speed of the wind twists or tourbillions has to be considered. The dispersion modelling consists in developing the equation (6) according to the type of the polluting source and the atmospheric stability. For modelling air pollutants sourced by road transportation, a Gaussian approach is recommended [4], [6]. The Gaussian model has been obtained using algorithms implemented in OCD (Offshore and Coastal Dispersion Model) and CALINE/4 models. The OCD dispersion module, which estimates pollutants concentrations in signalised junctions, is called LINE. This module is able to compute pollutant concentrations in the final point beginning ISBN:

5 from a start (finite) line, which is approximated with a vector of points. Then concentrations are calculated employing a Gaussian equation to each point apart. A coordination transformation is necessary for the points situated at both ends, in order to apply the Gaussian equation to all receiver-street configurations, also taking into account wind direction. Then concentrations of all streets are eventually summed in order to observe the global effect to the whole junction []. Using the algorithm implemented in CALINE/4 needs reshaping the input interface. The purpose was to use estimations given by modal and vector-type emissions as entry data for the dispersion model. After introducing input data (emission factors, structure of the vehicle queue, the operational mode of measurement, the geometrical features of the junction and all atmospheric variables), pollutant concentrations at the receiving point could be estimated by employing a Gaussian equation. A third algorithm is applied then for the pollutant dispersion in the junction, in order to estimate concentrations at the reception point of the junction [3],[4]. This algorithm is result of an empirical approach, employing a box-type of model, simplified, applied to each street that connects with the analysed junction. This model has used the following type of equation: C=Q S /[(U+0,5)*x 0 ] (7) Where: C [mg/m 3 ] represents concentration; Q S [mg/m*h]: average rate of global emissions in time unit and per vehicle, concerning the whole area of the junction; U [m/s]: wind speed (at roofs level); x 0 [m]: average distance from the sensor to the middle of the road. The Qs parameter has been estimated after obtaining modal rates of emissions and the metric lengths of each of the five segments of road, employing the EMITX module. Starting from these values, global emissions can be then calculated for each of the road sectors, in [mg/m 3 ]. The value of Qs is obtained summarizing all these values, from all junction arms. As previously mentioned, calculation of the concentration in the receiving point is obtained employing a simplified box-type model, where the wideness of the spatial domain where the pollutants dispersion take place equals the wideness of each intersection arm. According to this model, an estimate of the concentration near a sensor close to the road can be calculated as a ratio between the global emissions Qs and the product between the wind speed U + 0,5 and the average distance travelled by each gas cloud from source to sensor..1. Results For all dispersion models linear correlation coefficients r has been obtained, sorted after the sector generating the wind. Table 1 Correlation coefficients r Model type /Wind sector OCD CALINE/4 Local Box North East South West In Table 1 it can be observed that the OCD model presents a weak correlation level, the estimation being very sensitive to changes in wind direction. When wind alternated direction from south and east, an almost null (0.0) correlation has been obtained, while when winds blows from the other directions (especially from west side), a better correlation is obtained. These results suggest that the LINE module is able to correlate results only when wind blows in the direction of the sensor. The CALINE/4 model shows a better correlation, although not all r values are acceptable. Again, problems start to appear when wind blew from east and south, but the overall correlation was better than in OCD model case. The best value has been obtained when wind direction was from west. The empiric model has offered the best coefficients of correlation, except the west sector, where the other models of dispersion obtained better values. The values for r ranged from 0.40 to Once calibrated, the dispersion models have been applied for a time period, in order to test their performances. The results are presented in figure, where a comparison between the CALINE/4 and the empiric models has been accomplished. The OCD model could not be used because its performances are very weak. It can be observed in the figure that for the total, the empiric model is somehow better than the Gaussian one, excepting the west sector, where significant differences between the two models have been observed. ISBN:

6 mg/m3 mg/m3 Advances in Automatic Control North CALINE/ East South Sector LOCAL BOX MODEL North -0.3 East South West 0.15 West Sector Fig. Comparison between CALINE/4 model performances and local box model Regarding the southern, northern and eastern sectors, the box model delivered average errors ranging between -0.3 and mg/m 3, while the CALINE/4 model values are situated between 0.44 and 0.96 mg/m 3. Similarly the standard variations are bigger for the Gaussian model ( mg/m 3 ) than for the other model ( mg/m 3 ). The best estimates for CALINE/4 model are obtained in the north sector, with values close to those of the empiric model, while the worst results are present in the south sector, the best sector of the empiric model. Finally, the empiric model results are better than the Gaussian ones. 3. Conclusion The empiric dispersion model for estimating pollutant concentrations in signalised junctions, based on a simplified box-type model proved to be more precise than the Gaussian OCD and CALINE/4 models. Any dispersion model needs to be validated in various urban environments and for the whole range of meteorological conditions occurring in the real atmosphere. This database is useful for testing and validation others street canyon models for the same location and to compare the results obtained. A limitation of these measurements campaign is that no flow or turbulence measurements within the street canyon are available and the concentration measurements do not contain particulate matter. Acknowledgment The work has been funded by the Sectorial Operational Programme Human Resources Development of the Ministry of European Funds through the Financial Agreement POSDRU/159/1.5/S/ References: [1] Benson, P.E., CALINE4: A dispersion model for predicting air pollution concentrations near roadways, California Department of Transportation, Sacramento, [] Ch. Colberga, B. Tonab, W. Stahelb, M. Meierc, J. Staehelina, Comparison of a road trafficemission model (HBEFA) with emissions derived from measurements in the Gubrist road tunnel, Switzerland, Atmospheric Environment, 39, , 005. [3] EMEP EEA Emission Inventory Guidebook, Key Category Analysis and Methodological Choice, 009 [4] ETC/ACC - ETC-ACC Air Emissions Spreadsheet for Indicators 004. European Environment Agency, 005. [5] G. M. Raducan, Pollutant dispersion modelling with OSPM in a street canyon from Bucharest,Romanian Reports in Physics, Vol. 60, No. 4, P , 008. [6] Horowitz, J.L., Air quality for urban transportation planning, MIT Press, Cambridge Mass., 198 [7] Ionel, I. Science and Motor Vehicles. Numerical analysis of traffic influence on air quality, JUMV 001. Belgrade, Yugoslav Society of Automotive Engineers, pp [8] Maria Claudia Surugiu, Minea M, Grafu F.D, Sisteme inteligente de transport aplicatii, editura MATRIXROM, 007. [9] Maria Claudia SURUGIU, Elena MAGHIARI - Emissions monitoring and traffic management system, 8th International Conference on Technology and quality for sustained development, TQSD 008, pag.1 7. ISBN: