Traffic performance-based air quality management

Transcription

1 Traffic performance-based air quality management M. Granberg & J. Niittymaki Helsinki University of Technology, Laboratory of Transportation, Finland. Abstract Traffic is a major strain on air quality in cities, as most of the hazardous emissions in urban areas are caused by it. Road traffic causes such emissions as COz, NO,, NOz, HC and CO. Road traffic induces also particles, of which especially small particles are extremely hazardous to health. Accurate modelling of traffic and traffic-generated pollution is important in estimating the exposure caused to people. Being able to correctly model traffic and its emissions helps in finding different traffic control strategies that could be useful in improving the situation in extensive air quality management systems. Granberg et a1 [l] presented a combined application of traffic microsimulation and street canyon dispersion models, which was tested on a case study in Helsinki. The purpose of the study presented here is to continue on the same field and to introduce the traffic-based pollution and concentration model used in Helsinki area as well to investigate the connection between urban transport and air quality. The study will also handle the exposure of people to pollution, and shed light on its consequences. The current air quality measurement systems used in Helsinki area will be introduced. Finally, some traffic control measures and their effect on emissions will be tested and presented.

2 1 Introduction Traffic monitoring gives information on traffic flows and the functioning of traffic control and management. Air quality monitoring can be used e.g. to find out trends on how emission concentrations have developed, and to study how various areas differ from each other. Air quality monitoring can also be considered as the basis for measures needed to be executed. In most European cities traffic is a major cause of emissions and pollution. This means that the performance of traffic flows has a strong effect on air quality. The continuing urbanisation in both developing and developed countries adds to the deterioration of air quality as well as exposes more and more people to hazardous emissions. Traffic induces such pollutants as carbon monoxide (CO), hydrocarbons (HC), nitrogen oxides (NO,) and particles. Especially rush hours pose particular problems in terms of environmental pollution because during the peak hours the concentration of emissions is usually higher (Niittymaki et a1 [2]. The cause-consequence relationship of traffic and urban air quality will be studied in this paper. The air-quality measurement system used in Helsinki area will also be introduced, and an integrated traffic simulation-operational street pollution model will be presented. This study is a part of a DIANA (Development of Integrated Air Pollution Modelling Systems) project, the aim of which is to develop a comprehensive modelling system for the needs of urban planning. 2 Traffic as a pollutant The share of traffic generated emissions varies in different cities. However, it is evident that traffic imposes a major strain on urban air quality all over the world. One of the reasons for the impact of traffic-based emissions being so hazardous is the height at which they are generated; vehicular emissions are caused at street level, and thus have straight access to alveolar air. Although the emissions of individual vehicles have decreased in accordance to improving technical solutions, the continuing increase of traffic diminishes the effect of these improvements. In addition, technical improvements have not yet bettered the situation notably in many developing countries, as there the vehicles are usually older, function less efficiently and thus pollute more than in developed countries. The impurities of air quality can be seen to have three types of consequences. 1. global, such as the green house effect 2. regional, such as acidification of water systems or high concentration of ozone in the lower atmosphere 3. local, such as health risks and reduced quality of environment caused by increased concentration of different types of pollution The continuing intensification of the green house effect has caused concern all over the world. The Kyoto convention, held in December 1997, imposed an aim

3 to the industrialised countries of reducing green house gases by 5% from the level of the year 1990 by The share of green house gases generated by traffic varies depending on the country and area. In Finland approximately 20% of carbon dioxide and 49% of hydrocarbons are caused by traffic. This means that also traffic-generated emissions need to be restrained if the levels set in Kyoto are to be reached. (MINTC [3].) Most of the pollution in the Helsinki metropolitan area originates from power production and traffic. Traffic has a major influence on the quality of air, though, because its emissions are caused at ground level. On the average, the quality of air in the metropolitan area of Finland is fairly good. However, occasionally the concentrations of nitrogen dioxide and particles rise to hazardously high levels. This happens especially near busy, crowded routes. In extensive follow-up studies it has been noted that particle concentration of air has a surprisingly strong correlation with morbidity and death rate. Recent epidemiological studies and health effect assessments have indicated that the most severe adverse health effects of ambient air pollution are consistently associated with inhalable particles (PM10; 50% size cut-off at 10 pm), and possibly even more strongly with fine particles (PM2.5; 50% size cut-off at 2.5 pm) including also diesel exhaust particles ( pm). These adverse health effects include hospital admissions of respiratory and cardiovascular patients, increased daily deaths and shortened life expectancy. The ambient air particulate matter (PM) pollution in Finnish cities varies a lot depending on the season. In cold winter periods, the 24-hour concentrations of PM2.5 and PMlO are usually low and close to each other suggesting that this PM is, to a large extent, from vehicle exhausts, of which diesel engines especially. In dry spring periods, the 24-hour concentrations of PMlO are several times higher than in winter due to a large contribution of resuspended sand and other types of dusts originating from e.g. asphalt, tyres, studs etc, and raised into the atmosphere by traffic. The PM pollution during both seasons has been shown to be associated with changes in lung functioning among asthmatic children in Finland. (Salonen et al [4]) 3 Air quality monitoring in Helsinki area (Aarnio et a1 [S]) There are five so-called multi-component stations and three portable measurement stations in the metropolitan area of Finland. The location and emissions measured at these stations are depicted in Figure l. The stations monitor the concentrations of sulphur dioxide (SOz), nitrogen oxides (NO,, NOz), carbon monoxide (CO), ozone (03), TSP (Total Suspended Particles), breathable particles (PMlo) and fine particles (PM2,5). The location of the measurement stations have been chosen so that they would describe different types of urban environments. The Tiiiilo-measurement station, for example, represents a very busy downtown area, while the Vallilla-measurement station describes the air quality of Helsinki region in more general. The data gathered from this measurement network has been succesfully used in developing emission models for different kinds of environments.some results of a case study

4 conducted in Tijolo are considered also in this paper. The case study is presented in more detail in (Granberg et a1 [l]) and (Niittyrnaki et a1 [2]). Figure 1: The measurement network of the metropolitan area of Finland. 4 DIANA-project and its sub-models in brief This paper belongs to a DIANA-project, a project that aims at developing a comprehensive modelling system for the uses of urban planning. The sub-models of DIANA are shown in Figure 2. The bolded arrows represent what has already been done, and the dashed arrows what is meant to be done in the future. This paper concentrates on air quality management. HUTSIM, a traffic micro-simulation program, is used as a sub-system of modelling traffic in DIANA. The development of HUTSIM was started in the late 1980's at Helsinki University of Technology in the Laboratory of Transportation. HUTSIM is a well-calibrated microscopic simulation model with stochastic nature and main principles of object-oriented modelling, rule-based dynamics, time-scanning updating and graphical user-interface. HUTSIM was originally designed the simulation of traffic signal control in mind, and a real signal controller is used as a part of the simulation system. The enhancement of HUTSIM continues. (Kosonen [6].) EMCA, a sub-program of HUTSIM, is used in calculating the emissions caused in the HUTSIM-model. EMCA calculates the emissions in a given model based

5 on vehicle information saved in the output files produced by HUTSIM. Based on these data and on pre-determined emission matrices, the emission quantities are calculated. For a more detailed description of these programs, see (Niittymaki et al [2]) and (Kosonen [6]). Figure 2: The sub-models of DIANA (Niittymaki et a1 [2]). OSPM (Operational Street Pollution Model) is used in the modelling of flow and dispersion in a street canyon. It is developed by Hertel and Berkowicz (Hertel et al [7] and [g]). The OSPM model is a practical street pollution model based on simplified description of flow and dispersion conditions in street canyons. Concentrations of exhaust gases are computed using a combination of a plume model for the direct contribution from street traffic, and a box model for the recirculating part of pollutants in the street. The simplified parameterisation of the flow and dispersion conditions in a street canyon has been deduced from extensive analysis of experimental data and model tests. (Niittymaki et a1 [2]) CAR-FM1 (Contaminants in the Air from a Road - Finnish Meteorological Institute) model, developed at Finnish Meteorological Institute, is used in approximating the emission dispersion and convection of road transport and street-traffic. The model can also be applied in evaluating emissions' chemical transformation in air. The model has been tested empirically by comparing predicted results with data from two field experiments. (Kukkonen et a1 [9]) UDM-FM1 (Urban Dispersion Modelling System - Finnish Meteorological Institute), developed also by FMI, is used in the assessment of urban air quality. The system includes different types of emission sources for point, line, area and volume sources based on Gauss' dispersion equations. The model has been tested empirically by comparing predictions with results fiom measurement networks of several Finnish towns. (Kukkonen et a1 [9])

6 HZRLAM (HIgh Resolution Limited Area Model) is used at FM1 in producing short-range weather predictions. The HIRLAM model has been used operationally since At present, four 48-hour regional forecasts and four 36- hour mesoscale forecasts are produced daily for Northern Europe with HIRLAM. FM1 is a participant in the international HIRLAM project contributing mainly to the development of data assinulation system and physical parameterisations of the model. (FM1 [l011 The functioning principle of the DIANA system is depicted in Figure 3. esb Traffic flow 4 I Control Figure 3: Traffic based air quality management. This type of traffic based air quality management could for example be used, when the air quality reaches a pre-determined limit. Then certain traffic control strategies could be taken into action. These types of control strategies could mean e.g. limiting the amount of traffic allowed to city area or appointing a rushhour or environmental fee for cars intending to the area. 5 Results 5.1 Case study of Toolo A case study of Toolo (in Helsinki) was conducted to test the DIANA modelling system. The street modelled (Runeberginkatu; Runeberg Street) is a busy downtown street with uniform building structures over a distance of 175 metres. The case study of Toolo is described in more detail in (Granberg et a1 [l]) and (Niittymaki et a1 [2]). One of the achieved results was that the percentage of heavy-duty vehicles (HDV's) has a significant influence on the amount of emissions caused. On a typical working day in March the percentage of heavy duty vehicles varied depending on the hour from 1% to 24% but was mostly under 10% with the average of 7%. However, from the example of Figures 4 and

7 5, it can be seen that the HDV's influence on the total of (NO,) emissions caused is higher than their share in percentage would suggest. I Tlmo of day 1 I Figure 4: The percentage of heavy-duty vehicles on Runeberg Street on a typical working day. Figure 5: NO, emissions of heavy-duty vehicles and light vehicles on Runeberg Street on a typical working day. The EU's COST 346 -project approaches this issue. The topic of COST 346 is emissions and fuel consumption from HDV-traffic. The objectives of COST are to develop an improved methodology for estimating pollutant emissions and fuel consumption from HDV-traffic in Europe, and developing a method that would also make it possible to estimate the emissions from single vehicles as well as from vehicle fleets. The Laboratory of Transportation of Helsinki University of Technology is involved in COST 346. (COST 346 [l l]) In the COST 346 program it was estimated that 35% of C02 emissions, 60% of NOx emissions and 75% of particles are caused by road freight. This would suggest that reducing the emissions and share of heavy-duty vehicles should have an influence on urban air quality. (COST 346 [l l])

8 5.2 Traffic control measures and emissions A case study conducted in Gothenburg, Sweden, in 1998, showed the effect of humps and speed limit reduction on emissions. The study was conducted on two streets, of which the other was located on the central area of the city (Vastra Hamngatan) and the other on semi-central urban area (KungsladugArdsgatan). The aim was to compare the exhaust pollution and fuel consumption related to the traffic flow before and after speed reducing humps were applied, as well as to study the effect of the humps on traffic efficiency. The study was conducted with the traffic microsimulation program HUTSIM. Some of the results are shown in Figure 6 and Table 1 and 2. l no humps, humps, no humps, humps, 50kmlh 50kmh 30kmlh 30km/h Traffic control strategy Figure 6: The effect of different traffic control strategies on the amount of emissions caused on semi-central urban area (Kungsladugirdsgatan). The results show that, if humps are installed when the speed limit is 50 krnh they increase the emissions caused significantly. When humps are installed to a street with a speed limit of 30 kmh, on the other hand, they do not increase the emissions hazardously. Reducing vehicle speeds in urban areas might have strong positive effects on traffic safety. However, it should be noted that environmental solutions are often not in accordance with traffic safety aspects, nor efficiency aspects either, and reducing speeds in urban areas might in some situations add to pollution quite significantly.

9 Table 1: The effect of different control strategies on traffic and fuel consumption on semi-central urban area (Kungsladug&dsgatan) with a speed limit of 50krnlh. Speed lirnit Average delay Average speed Fuel consumption 50knlih no humps 4.6 (sec) (litres) humps S difference (+430.4%) (-29.1%) (+19.0%) Table 2: The effect of different control strategies on traffic and fuel consumption on semi-central urban area (KungsladugHdsgatan) with a speed limit of 30kmlh. Speed li~nit Average delay Average speed Fuel consumption 30kdh no humps 18 6 (sec) 22.9 (kdh) (litres) humps difference +1.7 (+9.1%) -0.4 (-1.7%) (+3.0%) 6 Conclusions As traffic increases in urban areas, it is becoming evident that some kind of air quality management would be needed in many cities. Traffic based air quality modelling gives a concrete tool for such management. By applications such as presented here it is possible to test the effects of certain traffic control measures on air quality. Furthermore, traffic control measures and their impacts are often strongly sight specific, which means that it is extremely difficult, if not impossible, to generalise results of one place to another. A dynamic and adaptable modelling system could therefore be most useful in environmental assessment. Even though effects of different traffic control measures can vary significantly, traffic engineers do not have endless possibilities in solving environmental problems. The functioning of traffic signals can for example be optimised so that the traffic flow arriving to the signals is handled efficiently. The number of vehicles is given, though. In traffic performance-based management, on the other hand, the emphasis should be on combining traffic control, adjustment and regulation comprehensively.

10 References [l] Granberg, M,, Niittymaki, J., Karppinen, A., Kukkonen J., Combined application of traftic microsimulation and street canyon dispersion models, and evaluation of the modelling system against measured data. 9 p, Published in the Conference Proceedings of Urban Transport 2000 (26-28 July 2000). pp [2] Niittymaki, J., Granberg, M., Karppinen, A., Development of Integrated Air Pollution Modelling Systems for Urban Planning - DIANA project. Paper accepted for poster session in Transportation Research Board 80'~ Annual Meeting in Washington D.C., January 7-1 1, pp [3] A web page of MINTC (Ministry of Transport and communication Finland) Accessed [4] Salonen, R.O., Halinen, A., Pennanen, A., Sillanpaa, M. Toxic effects of urban air and diesel exhaust particles on the restipratory tract (PAMTOX). Part of a Finnish Research Programme on Environmental Health. An extended abstract on the web page of National Public Health Institute: Accessed [5] Aarnio,P., Hiimekoski, K., Koskentalo, T., Ilmanlaatu plakaupunkiseudulla (Air quality in the metropolitan area of Finland). YTV (Helsinki Metropolitan Area Council) Cl998:l. 25pp [6] Kosonen I., HUTSIM - Simulation Tool for Traffic Signal Control Planning. Helsinkfi University of Technology, Transportation Engineering, Publication No p [7] [2] Hertel, 0. and Berkowicz, R., Modelling Pollution from Traffic in a Street Canyon. Evaluation of Data and Model Development. DMU Luft- A129. National Environmental Research Institute. Roskilde. 77 p [8] [3] Hertel, 0. and Berkowicz, R., Modelling NO2 Concentrations in a Street Canyon. DMU Luft A-131. National Environmental Research Institute, Roskilde. 3 1 p [9] Kukkonen, J., Karppinen, A., Hakonen, J., Pohjola, M,, Pesonen, R., Pietarila, H., Aarnio, P,, Koskentalo, T., Elolhde, T. Leviamismallien kaytto ilmanlaadun arvioinnissa ja hallinnassa (The use of dispersion models in the evaluation and management of air quality) YTV (Helsinki Metropolitan Area Council) B/2000:9. 25pp [l01 Finnish Meteorological Institute (FMI). Atmospheric Modelling. The webpage of FMI: Accessed [l 11COST 346. Emissions and Fuel Consumption from Heavy Duty Vehicles. COST 346 Powerpoint Presentation. COST pp