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An integrated simulation system for traffic induced air pollution
Abstract
In recent years the growing traffic demand combined with an increase in exhaust gas emissions is the main reason for a permanent decrease in air quality in urban areas. Especially during hot summer days, mainly traffic emissions are responsible for providing precursor substances for the ozone reaction. They account for approximately 70% of all emissions. In order to facilitate investigations analysing this situation, local authorities in environmental protection and urban planning agencies are interested in performing emission and air pollution simulation as well as scenario analysis by means of model based simulation systems. Therefore a realistic modelling of the physical behaviour of the atmosphere as well as the exact description of the emissions is necessary. Up to now mainly traffic countings combined with different statistical methods have been used to calculate these emissions. The obtained results are often incorrect and do not reflect the dynamic behaviour of the traffic flow. Traffic flow models provide a more promising approach. Currently, in the European Community funded SIMTRAP project, an integrated system for traffic flow information, air pollution modelling and decision support will be developed in a distributed High Perfomance Computing Network (HPCN), and subsequently tested in a number of European sites. SIMTRAP centres around two well-established core components: the air pollution model DYMOS and the mesoscopic dynamic traffic simulation tool DYNEMO. The project aims to integrate both modules in a remote HPCN environment in order to enable the detailed simulation of an area of sufficient geographical extent. Interpretation and visualization of results will take place in a local 3D GIS system. Communication will take place using existing computer networks and protocols.
... The VT-micro model was then added as a subroutine within the INTEGRATION (Rakha and Ahn, 2004). Another example is the SIMulation of Traffic induced Air Pollution (SIMTRAP) project which represents an integrated simulation platform for traffic related air pollution (Schmidt and Schafer, 1998). For the SIMTRAP project, researchers integrated mesoscopic traffic flow model with air pollution model using remote High Performance Computing Network (HPCN) and tested the system in different European cities (Schmidt and Schafer, 1998). ...
... Another example is the SIMulation of Traffic induced Air Pollution (SIMTRAP) project which represents an integrated simulation platform for traffic related air pollution (Schmidt and Schafer, 1998). For the SIMTRAP project, researchers integrated mesoscopic traffic flow model with air pollution model using remote High Performance Computing Network (HPCN) and tested the system in different European cities (Schmidt and Schafer, 1998). The Air pollution model Dynamic Model for Smog analysis (DYMOS) and a mesoscopic traffic simulation tool Dynamic Net Model (DYNEMO) were the two main components integrated in the SIMTRAP project. ...
... The Air pollution model Dynamic Model for Smog analysis (DYMOS) and a mesoscopic traffic simulation tool Dynamic Net Model (DYNEMO) were the two main components integrated in the SIMTRAP project. DYMOS is a simulation system that contains a air pollutant transport model and a air-chemistry model (Schmidt and Schafer, 1998;Heimann, 1985;Gery et al., 1988). The traffic simulation model DYNEMO was developed as a mesoscopic model in which the mean speed and traffic density relationship is used as an input for each section of the network (Schwerdtfeger, 1984). ...
This paper presents an integrated simulator “CUIntegration” to evaluate routing strategies based on energy and/or traffic measures of effectiveness for any Alternative Fuel Vehicles (AFVs). The CUIntegration can integrate vehicle models of conventional vehicles as well as AFVs developed with MATLAB-Simulink, and a roadway network model developed with traffic microscopic simulation software VISSIM. The architecture of this simulator is discussed in this paper along with a case study in which the simulator was utilized for evaluating a routing strategy for Plug-in Hybrid Electric Vehicles (PHEVs) and Electric Vehicles (EVs). The authors developed a route optimization algorithm to guide an AFV based on that AFV driver’s choice, which included; finding a route with minimum (1) travel time, (2) energy consumption or (3) a combination of both. The Application Programming Interface (API) was developed using Visual Basic to simulate the vehicle models/algorithms developed in MATLAB and direct vehicles in a roadway network model developed in VISSIM accordingly. The case study included a section of Interstate 83 in Baltimore, Maryland, which was modeled, calibrated and validated. The authors considered a worst-case scenario with an incident on the main route blocking all lanes for 30 min. The PHEVs and EVs were represented by integrating the MATLAB-Simulink vehicle models with the traffic simulator. The CUIntegration successfully combined vehicle models with a roadway traffic network model to support a routing strategy for PHEVs and EVs. Simulation experiments with CUIntegration revealed that routing of PHEVs resulted in cost savings of about 29% when optimized for the energy consumption, and for the same optimization objective, routing of EVs resulted in about 64% savings.
... The newly emitted NO molecules rapidly react with O 3 molecules [17,18], leading to the formation of nitrogen dioxide (NO 2 ) and molecular oxygen [19] during the morning and evening at the traffic site. This phenomenon underscores the significant impact of vehicular emissions on ground-level ozone (GLO) chemistry [12] and air quality dynamics in traffic-prone areas [20,21]. Emissions of NO from vehicles in and around traffic areas have been observed to cause a significant reduction in O 3 concentration through the process of ozone titration. ...
... As a result, this chemical process leads to a noticeable decrease in the concentration of GLO during the morning and evening at traffic study sites. This phenomenon highlights the significant impact of vehicular emissions on shaping the chemistry of GLO [20], and on the dynamics of air quality in traffic areas [21]. The study highlights the significant impact and considerable influence of traffic volume on GLO concentrations, unveiling a clear and pronounced negative correlation between the two. ...
... System dynamics has been applied to study the influence of transportation on environmental pollution in several studies. Lu et al. (1997) and Schmidt and Schäfer (1998) were one of the first using system dynamics method to deal with the air pollution issue from a technical point. Schmidt and Schäfer (1998) argued that traffic emissions accounted for about 70% of all emissions. ...
... Lu et al. (1997) and Schmidt and Schäfer (1998) were one of the first using system dynamics method to deal with the air pollution issue from a technical point. Schmidt and Schäfer (1998) argued that traffic emissions accounted for about 70% of all emissions. They used simulation software to simulate emissions and air pollution in urban areas and carried out scenario analysis of urban management policies. ...
As demand for air transport increases worldwide, the contribution of aviation to air quality is also increasing. As a result, growing public and political pressure is likely to target air transport further to reduce its greenhouse gas emissions. The main challenge for policymakers is to reduce greenhouse gas emissions from aviation, while maintaining the mobility of passengers and time-sensitive cargo, and meet the future air transport needs of developing and emerging countries. This paper discusses three general policies to reduce emissions from commercial aviation: (1) improve technical efficiency, (2) adopt policy restrictions, (3) update the fleet construction by introducing new type of aircraft. To assess the impact of these policies on total emissions, a system dynamics approach is used to capture system interactions and delayed feedbacks in the air transport system and to allow scenario testing through simulation. This study found that the policy mix can effectively reduce CO2 emissions, and the impact of policy restrictions is greater than the impact of improvement in technological efficiency. This study could provide a reference for policy makers to reduce CO2 emissions from the aviation industry.
... The main building blocks of such a simulation framework are (1) the structure of the modelling chain which contains traffic, emission, air quality and water quality models ( Figure 2) and (2) the interfaces between the output from a model and the input to the next model. Examples of modelling systems to link traffic flow, emission and air quality modelling tools have been described by Lim et al. (2005), Schmidt and Schäfer (1998) and Hatzopoulou (2010). Modelling the effects of atmospheric pollutant dispersion on water quality has been studied mostly at regional scales to address the impact of air pollutants on ecosystems (e.g., Burian et al., 2002, Vijayaraghavan et al., 2010, Gunawardena, 2012. ...
... If models have been developed, applied and, to some extent, evaluated for each of those components, the integration of all those components to simulate the impact of road traffic on air and water pollution has been very limited to date. Some modelling systems linking traffic flow, emissions of air pollutants and air quality modelling tools have been developed (e.g., Lim et al. 2005;Schmidt and Schäfer 1998;Hatzopoulou 2010). Although a large amount of work has been conducted to link atmospheric pollution to surface water contamination at regional and global scales for environmental issues such as acid deposition, mercury contamination and nutrients inputs causing eutrophization of water bodies, little attention has been paid to linking air and water pollution in urban areas. ...
Road traffic emissions are a major source of pollution in cities. Modeling of air and stormwater pollution due to on-road vehicles is essential to understand the processes that lead to the pollution and to provide the necessary information for the development of effective public policies to reduce pollution. The objective of this thesis is to evaluate the feasibility and relevance of modeling chains to simulate the impact of road traffic on air and stormwater pollution. The first part of the thesis consisted in assessing the state of the art of modeling tools available for the different relevant phenomena (traffic, emissions, atmospheric dispersion, and stormwater quality), highlighting challenges associated with the integration of the different models to create a consistent modeling chain in terms of pollutants and spatio-temporal scales. Two examples of modeling chains have been proposed, one static with hourly time-steps, the other based on a dynamic approach for traffic and its associated pollution. In the second part of the thesis, different interface tools have been developed to link models and construct modeling chains. These modeling chains were tested with different case studies: (1) coupling traffic and emissions for the simulation of an urban street using a dynamic model of traffic with instantaneous and time-averaged emission models, (2) coupling on-road emissions and atmospheric dispersion/deposition near a freeway, (3) coupling traffic, emissions and atmospheric dispersion/chemistry near a freeway, (4) coupling emissions and atmospheric dispersion/deposition in a suburban neighborhood (5) coupling atmospheric deposition and stormwater quality for an urban catchment, and finally (6) a complete modeling chain with traffic / emissions / air and stormwater quality models for urban catchment drainage. This work allows one to identify different possibilities of model integration to calculate air and stormwater pollution due to road traffic in urban areas. Moreover, it provides a solid basis for the future development of integrated numerical models of urban pollution
... System dynamics allows the researchers to combine different facets of socio-economic problems from several points of view. Lu and Turco (1997) and Schmidt and Schafer (1998) are the first studies which utilized system dynamics approach for air pollution from a technical point of view. Schmidt and Schafer (1998) evaluated the transport induced emission which was approximately 70% of all emissions. ...
... Lu and Turco (1997) and Schmidt and Schafer (1998) are the first studies which utilized system dynamics approach for air pollution from a technical point of view. Schmidt and Schafer (1998) evaluated the transport induced emission which was approximately 70% of all emissions. They simulated the emissions and air pollutions in urban areas via simulation software. ...
... Consequently roads are frequently congested, creating economical, social, and ecological challenges. Moreover, in recent epidemiological studies of the effects of combustion-related (mainly traffic-generated) air pollution, NO 2 was shown to be associated with adverse health effects [23], [24]. Furthermore, road traffic exhaust emissions account for 40% of volatile organic compounds, more than 70% of NO x , and over 90% of CO in most European cities [23], and about 45% of the pollutants released in the US [21]. ...
... Moreover, in recent epidemiological studies of the effects of combustion-related (mainly traffic-generated) air pollution, NO 2 was shown to be associated with adverse health effects [23], [24]. Furthermore, road traffic exhaust emissions account for 40% of volatile organic compounds, more than 70% of NO x , and over 90% of CO in most European cities [23], and about 45% of the pollutants released in the US [21]. Frequent and longer congested traffic conditions make this even worse. ...
In this paper we present a model-based traffic flow control approach to improve both traffic flow and emissions in a traffic network. A model predictive control (MPC) is implemented using a microscopic car-following traffic flow model and an average-speed-based emission model. We consider reduction of total time spent (TTS) and total emissions (TE) as performance measures of the control strategy. Moreover, with the help of simulations we illustrate that a traffic control strategy, particularly an MPC strategy, aiming at the reduction of the TTS does not necessarily reduce the level of emissions. In particular, when the traffic flow is congested, we demonstrate that a traffic control strategy that addresses TTS (or improvement of the traffic flow) alone can cause an increment in the level of emissions and vice versa. Therefore, in this paper we explain how to integrate both requirements so that a balanced trade-off is obtained.
... Moreover, traffic jams also cause emissions and the related adverse effects on human health. Recent studies have shown that NO 2 has adverse health effects [11]. In most European cities, road traffic exhaust emissions account for more than 70% of NO x [11]. ...
... Recent studies have shown that NO 2 has adverse health effects [11]. In most European cities, road traffic exhaust emissions account for more than 70% of NO x [11]. Similarly, in the US road traffic exhaust emission contribute about 45% of the released pollutants [8]. ...
Although traffic congestion is a pressing problem that drivers face every day, improving the traffic flow does not always create a healthy environment to the people residing in the neighborhood of the freeway. Improved traffic flow neither means efficient fuel consumption of the vehicles. Moreover, reduction of total emissions or travel times in a traffic network does not always guarantee reduction in the area-wide emission levels, because there are many other factors that affect the area-wide emissions. In particular, the direction and speed of wind are important factors that play a significant role in the area-wide emission levels. Therefore, in this paper, we systematically model the effect of wind on the area-wide emission levels and design a model-based traffic controller to reduce the dispersion of emissions. More specifically, a model predictive control (MPC) is used to integrate various variable speed limits in order to provide a balanced trade-off between the area-wide emissions and the travel times. Furthermore, we present a case study to demonstrate the proposed control approach.
... Generally, the solution is using supercomputers, clusters, or grid systems [7][8][9][10][11][12][13][14][15][16]. These systems are built by connecting numerous processors, either by some sort of direct link or by a network connection. ...
The Graphics Processing Unit (GPU) is a powerful tool for parallel computing. In the past years the performance and capabilities of GPUs have increased, and the Compute Unified Device Architecture (CUDA) - a parallel computing architecture - has been developed by NVIDIA to utilize this performance in general purpose computations. Here we show for the first time a possible application of GPU for environmental studies serving as a basement for decision making strategies. A stochastic Lagrangian particle model has been developed on CUDA to estimate the transport and the transformation of the radionuclides from a single point source during an accidental release. Our results show that parallel implementation achieves typical acceleration values in the order of 80-120 times compared to CPU using a single-threaded implementation on a 2.33 GHz desktop computer. Only very small differences have been found between the results obtained from GPU and CPU simulations, which are comparable with the effect of stochastic transport phenomena in atmosphere. The relatively high speedup with no additional costs to maintain this parallel architecture could result in a wide usage of GPU for diversified environmental applications in the near future.
... As a result, this chemical transformation results in a noticeable decrease in the concentration of GLO [22] during the morning and evening at the traffic site. This phenomenon highlights the significant role of vehicular emissions in influencing GLO chemistry [23], and air quality dynamics in traffic areas [24], [25]. During afternoon, increased O3 concentration was observed due to decreased traffic volume. ...
... As a result, this chemical transformation results in a noticeable decrease in the concentration of ground-level ozone (GLO) [22] during the morning and evening at the traffic site. This phenomenon highlights the significant role of vehicular emissions in influencing GLO chemistry [23], and air quality dynamics in traffic ICEPTP 118-4 areas [24], [25]. But during afternoon, increased O3 concentration was observed due to decreased number of vehicles at the traffic site. ...
This study focuses on ozone (O3) pollution resulting from road traffic in India (special focus on National Highways), where diesel and petrol are major fuels used for transportation system which are major contributors to O3 forming precursors such as NOx and VOC emissions. Data is collected by using a Serinus 10 ozone analyzer and a portable weather station Kestrel 5500. Using Multiple Linear Regression (MLR), O3 concentration levels are predicted along NH-16 in Kharagpur, West Bengal, India. The MLR model performance is assessed by R-squared, and F-test, along with AIC and BIC tests which are evidencing that MLR is the most suitable model, accurately predicting O3 pollution levels. The study reveals that the 8-h average O3 concentrations (117.24 µg m-3) exceed NAAQS 2009 (100 µg m-3) and WHO 2021 (100 µg m-3) standards. Higher traffic volume correlates negatively (r =-0.87) with lower O3 levels. Moderate southeast winds elevate O3 levels and transport pollutants away from the traffic area. Urgent action is needed, including comprehensive O3 pollution assessment on India's national highways and policy measures to mitigate it.
... In many regions, urban traffic emissions have become a major source of air pollutants, including CO 2 , NO x , and so on. In the epidemiological studies of the effects of combustion-related (mainly traffic-generated) air pollution, NO 2 was shown to be associated with adverse health effects [25]. Furthermore, road traffic exhaust emissions account for 40% of volatile organic compounds, more than 70% of NO x , and over 90% of CO in most European cities, and for about 45% pollutants released in the US [26]. ...
With the help of accurate parking navigation systems, sharing vacant private parking spaces with public travelers may have the potential of reducing the number of cruising vehicles and contribute to traffic emission reduction. However, the quantitative effects of such a measure are still relatively unknown in traffic management. In this paper, we firstly established an optimal allocation model of shared parking spaces, which is a pure integer linear programming model of maximizing the number of served public vehicles to avoid cruising for parking. Further, the parking space allocation model was expanded to an estimation model that quantifies the effect of emission reduction. Secondly, a branch-and-cut algorithm was introduced as the core algorithm to solve the proposed models. Finally, detailed sensitivity analysis, based on empirical data collected by electronic parking toll collections and questionnaire surveys in Beijing, China, evaluated the proposed model and algorithm. The results indicate that shared parking can both effectively reduce the cruising time and the number of vehicles, but also has significantly a positive effect on emission reduction. This research is helpful to provide theoretical support for alleviating parking pressure and environmental problems.
... A number of prior researches have been conducted to simulate the air pollution problem and provide a systemic outlook for practitioners. Schmidt offered an integrated simulation system for simulating the air pollution in relation to traffic [3]. Kessler also developed a more detailed simulation model of air pollution based on nested models in North Rhine-Westphalia [4]. ...
Industrialization and urbanization have brought along repercussions such as air pollution in urban areas. Air pollution has turned to a major concern for societies as it brings about a wide variety of problems. This reality illustrates the need for defining and formulating the air pollution problem properly. In this regard, variables have to be identified and analyzed systematically. Therefore, in this practical study, data gathering was conducted, and interviews were carried out for identifying the effective factors. Then, the interrelations among these variables were modeled, and the trend was analyzed using the VENSIM software. The investigation was carried out in the megacity of Tehran. This metropolitan has been greatly affected by air pollution due to its special geographical situation. Finally, dynamic relationship between variables and their contribution to the whole system was simulated. The result of the simulation shows that the "public transport" as the leverage variable has the most influence on air pollution among other effective variables.
... In the literature there are many studies in which air pollution has been examined and analyzed with geospatial methods (Elbir, 2004;Fedra and Haurie, 1999;Lin and Lin, 2002;David et al., 1997;Briggs et al., 2003;Chen et al., 2015;Hee-Jae and Myung-Jin, 2014;Xiao et al., 2014;Rohde and Muller, 2015;Matejicek, 2005;Song, 2008). In order to improve the air quality, geographical information system (GIS) based decision support systems have also been developed for urban areas (Guerrero et al., 2008;Lim et al., 2005;Puliafito et al., 2003;Schmidt and Schafer, 1998;Jensen et al., 2001). Air quality modeling and quality mapping by GIS, preparation of emission inventory and scenario analysis for air pollution reduction are the main components of this spatio-temporal urban air quality management system. ...
It was aimed to characterize spatial variations of air pollutants in Marmara region, Turkey for determining contribution to air pollution status in this study. We used spatial data analysis for measured sulfur dioxide (SO2) and particulate matter (PM10) concentrations recorded in Marmara, which is the most industrialized region of Turkey. GIS technique was used for monitoring air pollution and spatial analyses of these pollutants measured with the period during between October 1, 2013 and March 31, 2014 known as winter (heating) season obtained from 61 air quality monitoring stations located in this region. Spatial distribution maps for these pollutants were generated to determine emission patterns for the study area with the aid of geostatistical techniques. Additionally standard and spatial regression models were employed on the measured emissions to reveal possible factors of air quality in the region using standard ordinary least squares (OLS) and spatially autoregressive (SAR) regression models. The two regression models revealed that all the four explanatory meteorological variables (i.e. temperature, wind speed, humidity and atmospheric pressure) used to depict the pollution levels in relation to air quality. After the definition of the final model parameters, the model was fit to the entire data set and the residuals were examined for the presence of spatial autocorrelation with Moran’s I. Compared to the OLS technique, SAR is found to be more appropriate when dependent variables exhibit spatial autocorrelation resulting in a valid model.
... In-depth analysis of 'speed' and 'acceleration' in specific situations (e.g. stop-and-go at traffic lights and overcrowded roads) using traffic-flow models can provide reliable emission estimates (Pandian et al., 2009;Schmidt and Schäfer, 1998). Underestimation of vehicle speed or flow rate may lead to a drastic increase in emissions (Negrenti, 1999). ...
Road vehicles are a major source of airborne nanoparticles (<100 nm) and particulate matter (PM), including PM10 (≤10 μm), PM2.5 (≤2.5 μm) and PM1 (≤1 μm) emissions. Over 99% of particles, by number, are reprsented by particles below 300 nm in diameter in polluted urban environments. The small size of particles in the nano-size range enables them to enter deeper into the lungs, causing both acute and chronic adverse health effects such as asthma, cardiovascular and ischemic heart diseases. The issue of air pollution becomes more prominent at urban traffic hot-spots such as traffic intersections (TIs), where pollution pockets are created due to frequently changing driving conditions. Recent trends suggest an exponential increase in travel demand and travelling time in the UK and elsewhere over the years, which indicate a growing need for the accurate characterisation of exposure at TIs since exposure at these hot-spots can contribute disproportionately high to overall commuting exposure. Based on field observations, this thesis aims (i) to investigate the traffic driving conditions in which TIs become a hotspot for nanoparticles and PM, (ii) to estimate the extent of road that is affected by high particle number concentrations (PNCs) and PM due to presence of a signal, (iii) to assess the vertical and horizontal variations in PNC and PMC at different TIs, (iv) to estimate the associated in-cabin and pedestrian exposure at TIs, and finally (v) to predict PNCs by using freely available models of air pollution at TIs.
For this thesis, two sets of experiments (i.e. mobile- and fixed-sites) were carried out to measure airborne nanoparticles and PM in the size range of (0.005-10 μm) using a fast response differential mobility spectrometer (DMS50) and a GRIMM particle spectrometer (1.107 E). Mobile measurements were made on a circle passing through 10 TIs and fixed-site measurements were carried out at two different types of TIs (i.e. 3- and 4-way). Dispersion modelling was then performed by using California Line Source (CALINE4) and California Line Source for Queueing and Hotspot Calculations (CAL3QHC) at TIs.
Several important findings were then extrapolated during the analysis. These findings indicated that congested TIs were found to become hot-spots when vehicle accelerate from idling conditions. The average length of road in longitudinal direction that is affected by high PNCs and PM was found to be highest (148 m; 89 to –59 m from the center of a TI) at a 3-way TI with built up area and lowest at 4-way TI with built-up area (79 m; 46 to –33 m). Vertical PNCs, horizontal PNCs and PM profiles followed an exponential decay. Exponential decay of PNCs in the vertical direction was much sharper at the 4-way TI than at the 3-way TI. Based on tracer gas method, particle number emission factors (PNEFs) during congested and free flow driving conditions were also estimated. The results showed that the PNEF during congested conditions can be up to 9 times higher than those during free flow conditions at a TI. In-cabin and pedestrian exposure during delay conditions was up to 7 and 7.3 times higher than exposure during free flow conditions at TIs. The modelling exercise showed that model choice to predict PNCs depends on the type of TI, size range of particles, receptor height and distance from the TI. Key findings of the proposed study could assist in validating and refining the capabilities of existing models for exposure assessment to PNCs at TIs. The proposed study will assist to enhance the scientific understanding of the problem as well as develop a database, showing the contribution of exposure at TIs towards the overall daily exposure during commuting in diverse city environments.
... In this paper we are interested in the numerical modeling of air pollutant transport from a road network (eventually including other pointwise sources) [21,26,17,27,8,16,28,10]. In addition to a novel mathematical formulation coupling traffic flow in networks and pollution dispersion models, a full computational algorithm is required to conduct this analysis, because of the complex variables involved, including vehicle emissions, traffic volume, meteorology, and terrain geometry. ...
As it is well known, traffic flow is the main pollution source in many urban areas, where the number of vehicles ranges from many thousands to millions. Thus, estimating the pollution emission rate due to traffic flow in big cities is a very hard task. To approach this environmental issue, in this paper we propose a methodology that consists of combining the 1D Lighthill–Whitham–Richards traffic model for road networks with a classical 2D advection–diffusion–reaction pollution model for the atmosphere. Here, the pollution model uses a source term that takes into account the traffic flow contamination by means of a Radon measure supported on a road network within an urban domain. Furthermore, we establish the existence of solution of the coupled model, and detail a complete numerical algorithm to compute it (mainly, interfacing a finite volume scheme based on the supply–demand method for the traffic model, with a characteristics-Lagrange finite element method for the pollution model). Finally, several numerical experiences for a real urban domain (the Guadalajara Metropolitan Area in Mexico) are presented.
... In order to facilitate the analysis of this situation, environmental protection authorities are interested in performing emission and air pollution simulation as well as scenario analysis by means of model based simulation systems (Winiwarter et al [4]). Traffic flow models provide a promising approach (Schmidt et al [5], Xia et al [6]), including calculations of air pollutant emissions from all transport sectors (Symeomidis et al [7]). This paper presents a methodology to estimate atmospheric emissions from road transport including the development of a tailored software tool. ...
Atmospheric emissions from road transport have increased all around the world since 1990 more rapidly than from other pollution sources. Moreover, they contribute to more than 25% of total emissions in the majority of the European Countries. This situation confirms the importance of road transport when complying with emission ceilings (e.g. Kyoto Protocol and National Emissions Ceilings Directive). A methodology has been developed to evaluate the effect of transport measures on atmospheric emissions (EmiTRANS). Its application to Spain in the horizon of 2020 allows the quantification of the effect of several measures on emission reductions. This quantification was done through scenario development. Several scenarios were calculated considering technical measures (e.g. vehicle scrapping systems, higher penetration of hybrid and electric vehicles, fuel substitution, etc.) and non-technical measures (mileage reduction, implementation of Low Emission Zones and/or Congestion Charges in main cities, reduction of average speeds, logistical improvements that affects heavy duty vehicle load factors, etc.). The scenarios show the effect of each measure on NO x , SO 2 , CO, PM 10 , PM 2.5 , VOC, CO 2 and CH 4 emissions. The main conclusion is the necessity to combine both technical and non-technical measures to increase global effectiveness. In the analysis of specific pollutants, there is a great dispersion on reductions effect: technical measures are more effective to reduce air pollutants while non-technical measures are better options to reduce greenhouse effect gases (even though they also reduce air pollutants in a less efficient way).
... Population increase is a determining factor for air quality degradation observed in recent years ____________ [563][564][565][566][567][568][569][570][571][572][573][574][575][576] since the exponential growth of world population causes an exponential rising in demand for energy [1]; and the primary source of the energy is petrolium based fuels [2,3]. Accordingly in urban atmosphere, emissions from motor vehicles are the largest contributor to urban air quality [4,5]. In Turkey, the conventional parameters measured in the network include SO 2 and PM 10 mass. ...
In order to accurate and precise determination of Volatile Organic Compounds (VOCs) from vehicular emissions were developed method including a) adsorption of VOCs on different adsorbents followed by thermal desorption and gas chromatography (GC) with flame ionization detector (FID) quantification, b) validation of the sampling and analytical method. The adsorption efficiency of Tenax and Chromosorb 106 adsorbents were eveluated for 104 VOCs to select of adequate sorbent for passive sampling. Since most of the anthropogenic VOCs emitted from urban location are associated with the vehicle experiments were performed near road side. The results obtained showed the both the adsorbents almost same efficieny for selected VOCs emitted from vehicular exhaust. The VOC levels measured in this study were comparable with European cities and lower than Asian cities. The differences of VOC levels measured with different cities are due to traffic composition of the cities and their characteristics of fuel.
... Some modelling systems linking traffic flow, emissions of air pollutants and air quality modelling tools have been developed (e.g. Lim et al. 2005;Schmidt and Schäfer 1998;Hatzopoulou and Miller 2010). Although a large amount of work has been conducted to link atmospheric pollution to surface water contamination at regional and global scales for environmental issues such as acid deposition, mercury contamination and nutrients inputs causing eutrophization of water bodies, little attention has been paid to linking air and water pollution in urban areas. ...
... Some modelling systems linking traffic flow, emissions of air pollutants and air quality modelling tools have been developed (e.g. Lim et al. 2005; Schmidt and Schäfer 1998; Hatzopoulou and Miller 2010). Although a large amount of work has been conducted to link atmospheric pollution to surface water contamination at regional and global scales for environmental issues such as acid deposition, mercury contamination and nutrients inputs causing eutrophization of water bodies, little attention has been paid to linking air and water pollution in urban areas. ...
Road traffic pollutants are a cause of major concern in urban areas. In fact, traffic generated emissions are degrading air quality up to the point where the physical health of the citizens is directly threatened (in particular NOx, Sox, metals, microparticles..). Moreover, they contribute to a local and global deterioration of other environmental media by affecting for example water quality (in particular metals, PAH, aliphatic hydrocarbons). Even if all traffic pollutants are not a matter of concern for both air and water, some of them are in common between these media or linked by their vectors (for example particles). It justifies the need to try to treat jointly air and surface water impacts of road traffic emissions. The present work focuses on the development of a modeling platform linking traffic, emission, atmospheric dispersion and water fate of road pollutants. At the moment, the modeling chain begins with the traffic model output that serves as input for the pollutant emission model. Then the emission is considered as a lineic source and the pollutant dispersion is simulated with a Gaussian dispersion model in the atmosphere. Wet and dry depositions link that to the build-up and to the pollutant transport on urban surfaces during rainy events using an urban hydrological model. The main scientific challenge lies in evaluating the appropriate scales at which the global platform can give reliable results and by adopting different strategies to model each component of the model depending on the chosen spatial and temporal scales. Priority pollutants are sometimes different between the air and water environment, which may cause insufficient data and knowledge of pollutant emissions for water pollutants. Therefore, different emission or traffic components are available on the platform, depending on the scales or the nature of the pollutants that are simulated. Finally, the resolutions of the atmospheric and hydrological models are adapted to the emission knowledge and to the scale of observations. To illustrate the talk, some results from different case studies will be shown (Cours Lafayette, Viry Chatillon and Sucy catchments). At any time, the interest of these results with regard to the urban environment will be discussed. In particular, some scenarios may be tested with the platform: the impact of changes in the composition of the vehicle park or the occurrence of different meteorological conditions or urban design options on air or water quality. In conclusion, this attempt to implement a full modeling chain highlights the lake and the need of appropriate data in this field and opens the perspective of a new coupled traffic-air-water modeling field in urban areas.
... Most researchers all over the world seldom put their focus on integration of traffic simulation model and diffused model of vehicle pollution. Schmidt M. has performed dispersion simulation of vehicle emission by integrating air pollution dispersion model-DYMOS and mesoscale traffic simulation model -DYNEMO (Schmidt, 1998). And Addison P. S. used the output of PARAMICS as instantaneous source to simulate the dispersion process of vehicle emission in street canyon (Addison, 2000). ...
Management and control of vehicle exhaust is extremely urgent due to the growth trend of vehicle population. Prediction of vehicular exhausts dispersion is one of the indispensability links of atmospheric environment treatment. However, the prediction of air pollution induced by vehicle exhaust needs detailed data of traffic flow forecasting which is difficult to obtain. In order to simulate and forecast the distribution of vehicle exhaust more exactly as traffic demand changes, this paper presents a method that transforms future traffic demand into traffic flow in PARAMICS, and uses the independently developed UTESAS (Urban Traffic Environment Simulation and Assessment System) to forecast the concentration distribution of vehicular exhausts. There are three approaches. Firstly, the road network of simulation is set up for PARAMICS. Secondly, the project of traffic simulation is performed to predict the traffic flow. Thirdly, traffic flow data is used as input of UTESAS to simulate the dispersion, and then the spatial distribution of forecasted vehicle exhaust is shown in GIS module. Finally, this paper gives a case of application. The method has been used to simulate and predict the hourly variety of concentration of carbon monoxide of the road network near Tianhe Sports Center in Guangzhou during evening rush hour in workdays of June 2007. This simulation has been carried out under the typical meteorological condition of evening climax of Guangzhou.
... In-depth analysis of 'speed' and 'acceleration' in specific situations (e.g. stop-andgo at traffic lights and overcrowded roads) using the traffic-flow models can provide reliable emission estimates (Pandian et al., 2009; Schmidt and Sch€ afer, 1998). Underestimation of vehicle speed or flow rate may lead to drastic increase in emissions (Negrenti, 1999). ...
... GESIMA had been used to simulate the water substance field over Nairobi City, Kenya. GMD Institute of Computer Architecture and Software Technology, Berlin, developed a parallel air pollution simulation system for mesoscale applications namely dynamic models for smog analysis which can be used to model air pollution and vehicular traffic emissions when running on high-performance computing platforms (Schmidt and Schafer 1998). Martin et al. (2003) studied the emission of pollutants produced by Endesa Power Plant of As Pontes in the northwest of Spain using sulphur transport Eulerian model 2 (STEM-II) program. ...
The objective of this paper is to provide a comprehensive theoretical review with regard to history, existing approaches, recent developments, major research, associated computational methods, and applications of air quality models. A wide range of topics is covered, focusing on sources of air pollution, primary and secondary pollutants, atmospheric chemistry, atmospheric chemical transport models, computer programs for dispersion modelling, online and offline air quality modelling, data assimilation, parallel computing, applications of geographic information system in air quality modelling, air quality index, as well as the use of satellite and remote sensing data in air quality modelling. Each of these elements is comprehensively discussed, covered, and reviewed with respect to various literature and methods related to air quality modelling and applications. Several major commercial and noncommercial dispersion packages are extensively reviewed and detailed advantages and limitations of their applications are highlighted. The paper includes several comparison summaries among various models used in air quality study. Furthermore, the paper provides useful web sites, where readers can obtain further information regarding air quality models and (or) software. Lastly, current generation of air quality models and future directions are also discussed. This paper may serve as a compendium for scientists who work in air quality modelling field. Some topics are generally treated; therefore, the paper may also be used as a reference source by many scientists working with air quality modelling.
... Examples of decision support systems used in major European cities such as Paris, Stockholm, Lisbon, Milano, Berlin, Geneva, Vienna, Oslo and Athens for local authorities are the Swedish AirViro (SMHI, 2009), the Austrian AirWare (Fedra and Haurie, 1999), the Norwegian AirQUIS (Bohler et al., 2002) and the Swedish EnviMan (Tarodo, 2003) systems. A few more decision support systems for specific air quality management studies are in operation in the world (Fine and Ambrosiano, 1997;Schmidt and Schafer, 1998;Jensen et al., 2001;Lin and Lin, 2002). ...
... Some modelling systems linking traffic flow, emissions of air pollutants and air quality modelling tools have been developed (e.g. Lim et al. 2005;Schmidt and Schäfer 1998;Hatzopoulou and Miller 2010). Although a large amount of work has been conducted to link atmospheric pollution to surface water contamination at regional and global scales for environmental issues such as acid deposition, mercury contamination and nutrients inputs causing eutrophization of water bodies, little attention has been paid to linking air and water pollution in urban areas. ...
Methods for simulating air pollution due to road traffic and the associated effects on stormwater runoff quality in an urban environment are examined with particular emphasis on the integration of the various simulation models into a consistent modelling chain. To that end, the models for traffic, pollutant emissions, atmospheric dispersion and deposition, and stormwater contamination are reviewed. The present study focuses on the implementation of a modelling chain for an actual urban case study, which is the contamination of water runoff by cadmium (Cd), lead (Pb), and zinc (Zn) in the Grigny urban catchment near Paris, France. First, traffic emissions are calculated with traffic inputs using the COPERT4 methodology. Next, the atmospheric dispersion of pollutants is simulated with the Polyphemus line source model and pollutant deposition fluxes in different subcatchment areas are calculated. Finally, the SWMM water quantity and quality model is used to estimate the concentrations of pollutants in stormwater runoff. The simulation results are compared to mass flow rates and concentrations of Cd, Pb and Zn measured at the catchment outlet. The contribution of local traffic to stormwater contamination is estimated to be significant for Pb and, to a lesser extent, for Zn and Cd; however, Pb is most likely overestimated due to outdated emissions factors. The results demonstrate the importance of treating distributed traffic emissions from major roadways explicitly since the impact of these sources on concentrations in the catchment outlet is underestimated when those traffic emissions are spatially averaged over the catchment area.
... Rouphail et al [18] showed that NOx, CO, and hydrocarbon emissions are at least twice as high when a vehicle is in control delay mode as opposed to cruising mode. The study found that the highest emission rates were observed during vehicle acceleration while the lowest occurred in idle mode as shown in Schmidt and Schafer 1998 [19] found that the best measure of vehicle emissions at an intersection can be found by understanding the dynamic flow of the traffic. Good analysis of fleet speed and acceleration i.e. stop and go vs. cruise situations can find better results than traffic counts alone. ...
Carbon monoxide levels were monitored at intersections, bus stops, in
enclosed parking garages, and in vehicles. Variation in CO levels was
then compared with traffic variables. The effect of traffic volume,
traffic delay, site location, time of day and meteorological variables
were investigated during ambient testing. Incoming and outgoing vehicle
volume as well as the effect of the time of day were studied during
garage testing. Finally CO variation with vehicle speed, acceleration,
road grade and vehicle specific power (VSP), a variable that measures a
vehicles engine load per unit mass were investigated during in vehicle
tests. The type of vehicle, the surrounding environment and time of day
were also considered. Two studies were performed at two different
locations. One study was done in Singapore during the fall of 2009 and
one in Cincinnati where tests were done from the winter to the summer of
2010. Similar tests were performed at both locations. Ambient monitoring
in Singapore was performed around the NUS (National University of
Singapore) campus at bus stops within the campus and around the
perimeter of the campus. Ambient testing in Cincinnati was done during
winter and spring time at a large intersection. An enclosed parking
garage was studied at both locations as well as in vehicle tests.
Singapore buses were studied while personal vehicles and city buses were
studied in Cincinnati. Consistent correlations between CO and traffic
counts were not seen for the most part at ambient testing sites. A 5
minute interval was used and test periods were typically 1 to 2 hours
long. Bus delay at busy bus stops showed consistent positive
correlations with CO at the Singapore site. Vehicle delay counted by
hand at intersections showed a positive correlation in some cases but
was not consistently over each test period. The most consistent pattern
around CO concentrations was a peak just after an acceleration period of
a traffic cycle (after a green light for an approach with a large
queue). Morning tests showed the highest CO levels during the ambient
tests. Ambient CO levels ranged from 0 to 1.5 ppm in most cases at both
locations. A 3 day test near a major highway showed that CO
concentrations during peak periods were elevated when compared to non
peak periods. Better correlations between traffic and CO were observed
in the parking garages. High levels of traffic both at the 5 minute
interval and across testing periods consistently showed the highest CO
levels. CO measurements at the Singapore car park were done just outside
the enclosed garage and although low levels of CO were observed the
correlation with traffic was the highest of any of the test sites. Good
correlations were also seen at the parking garage in Cincinnati although
variations in idling time, a wider range of vehicle ages and a more
complex entrance may have lead to less consistent correlations than
those observed in Singapore. The highest levels were observed in the
evening hours as cold start emissions and long idle times led to higher
levels of CO. Elevated levels of CO were observed in all the vehicles
tested relative to the ambient environment. CO increased by 0.2 to 0.6
ppm in most cases. A direct correlation with speed, acceleration, road
grade and VSP was not observed over a 5 second interval that was tested.
Large buses such as a double decker and double long buses in Singapore
showed higher levels of CO than normal 12 meter long buses that were
taken. The highest levels of CO were seen during cold starts in the
morning in personal cars. CO was shown to exponentially decrease with
distance driven in the morning which is the same pattern seen by other
studies when looking at direct vehicle emissions.
... Examples of decision support systems used in major European cities such as Berlin, Geneva, Vienna, Oslo and Athens for local authorities are the Austrian AirWare (Fedra and Haurie, 1999), the Norwegian AirQUIS (Bohler et al., 2002) and the Swedish EnviMan (Tarodo, 2003) systems. A few more decision support systems for specific air quality management studies are in operation in the world (Fine and Ambrosiano, 1996;Schmidt and Schafer, 1998;Jensen et al., 2001;Lin and Lin, 2002). ...
Decision support systems used for urban air quality management provide to improve planning and operational decision making processes by providing useful and scientifically sound information to the public officials, planners and scientists, and possibly the general public. The Environmental Engineering Department of Dokuz Eylul University, in cooperation with the Municipality of Izmir, has newly started an Eureka project (EU1388). In this project, such an expert Decision Supper? System, with the name of Airware, will be used. This study summarizes the architecture and decision support approach of this system.
... In fact road transportation is one of the major contributors to man-made polluting emissions. In European cities it has been estimated that more than 40% of the hydrocarbon, more than 70% of the nitrogen oxides, and over 90% of the carbon monoxide are accounted for by road transport [175]. Approximately 15% of the world's emissions of carbon dioxide 1 , the principal global warming gas, is generated by motor vehicles [131]. ...
Computationally efficient dynamic fuel consumption, emissions, and dispersion of emissions models are developed. Fast and practically feasible model-based controller is proposed. Using the developed models, the controller steers the traffic flow in such a way that a balanced trade-off between the travel times, fuel consumption, and emissions (to a target zone) is achieved. Simulation-based case studies are used to compare and illustrate the models and control approaches proposed.
... More and more studies focused on urban traffic exhaust polluting the air environment in the recent years. Gulliver [6,7] and Colvile [8] performed researches on traffic micro-environment car exhaust pollution concentration characteristics; many scholars used model methods to simulate or assess the impact of car exhaust on air condition [9,10,11,12], and many studied the influence of exhaust on people's health [13,14]. However, there're few researches on the internal relationship of urbanization and traffic, and the influence on the number of motor vehicles and air environment quality. ...
With the speed-up of urbanization, the number of motor vehicles has increased rapidly, which is the main urban air pollutant source because of too much emitted exhaust gas. Based on the motor vehicle exhaust emissions in Shandong Province in 2006, using trend extrapolation and scenario analysis, predict the motor vehicle exhaust emissions for future planning. The results show that the motor vehicles in 2015and 2020 are 27.52 million and 34.53 million, which is 1.9times and 2.38 times of the motor vehicles in 2006, respectively. For the specific air pollutants from motor vehicles exhaust in 2020, SO2, NOx, PM2.5 and PM10 will reach 28.4 thousand tons, 356.7 thousand tons, 10.8 thousand tons and 12.2 thousand tons, which will be 3 times, 1.2 times, 5 times and 5 times of the emissions in 2006. The urban air pollutions caused by motor vehicles exhaust will be very serious.
... New bespoke technologies are constantly being developed to improve the accuracy of incoming information. Global positioning systems interfaced in a GIS can now be used to monitor traffic (Taylor et al, 2000) and high performance 3D GIS models of air pollution and traffic simulation are being developed (Schmidt & Schafer, 1998; McHugh et al, 1997; Moreselli et al, 1997). For example, Zakarin & Mikarimova (2000) numerically model urban air pollution using GIS as an interface; a common approach of displaying emissions inventory data produced using dispersion models (e.g Fedra & Haurie, 1999; Prabha & Mursch-Radlgrubber, 1999). ...
The proliferation of 'commercial off the shelf' geographical information systems into the scientific community has resulted in the widespread use of spatial climate data in a variety of applications. This paper presents a review of the role of geographical information systems in climatology and meteorology by i) discussing methods used to derive and refine spatial climate data and ii) reviewing the bespoke application of GIS and spatial climate datasets in agriculture, ecology, forestry, health and disease, weather forecasting, hydrology, transport, urban environments, energy and climate change.
... Examples of researches have been done in major European cities such as Berlin, Geneva, Vienna, Oslo, Athens, Austrian AirWare (Fedra and Haurie, 1999), the Norwegian AirQUIS (Bohler et al., 2002) and the Swedish EnviMan (Tarodo, 2003). A few more decision support systems for specific air quality management studies are in operation in the world (Fine and Ambrosiano, 1996;Schmidt and Schafer, 1998;Jensen et al., 2001;and Lin, 2002). ...
Air pollution is overflowing in big cities, especially in areas where pollution sources and the human population are concentrated. Economic growth and industrialization caused the increasing emissions of air polluting. Then, the quantities of polluting have increased dramatically; the evaluation of a suitable method for predicting and monitoring the pollution will be very important. To prevent or minimize damages of atmospheric pollution, optimum predicting methods are urgently needed which can rapidly and reliably detect and quantify air quality. One of the important spatial analyses for this application in GIS environment is surface simulation using Gostatistical methods. These lead us through creating a statistically valid surface which subsequently is used in GIS models for optimum decision making. Analysis create predicted surface for unmeasured points (Which we have not enough information on them) in study area. For This purpose, Ground stations and MODIS image of Tehran are used for collecting online air pollution information. Then, different geostatistical methods have been used for finding out the optimum prediction method for air pollution, based on received observations. These methods are performed based on spatial relationships (spatial similarity) among the measured points. In here, we use fractal and simple semivariogram for calculating correlation between points and determining which one of them is better for our application. We tested that the fractal dimension which measured by the spatial correlation length is more reliable based on autocorrelation and structural analysis. After that, we proved co-Kriging interpolation is more accurate by producing and evaluating prediction standard error maps.
... In the broadest sense, a DST is any guidance, procedure, or analysis tool that can be used to help support a deci- sion6789 . Within HENVINET, a DST is a tool that supports decision makers to make decisions in the E&H sector, in particular to propose actions and policies for reducing the burden of environmental stressors on human health. ...
The HENVINET Health and Environment Network aimed to enhance the use of scientific knowledge in environmental health for policy making. One of the goals was to identify and evaluate Decision Support Tools (DST) in current use. Special attention was paid to four “priority” health issues: asthma and allergies, cancer, neurodevelopment disorders, and endocrine disruptors.
We identified a variety of tools that are used for decision making at various levels and by various stakeholders. We developed a common framework for information acquisition about DSTs, translated this to a database structure and collected the information in an online Metadata Base (MDB).
The primary product is an open access web-based MDB currently filled with 67 DSTs, accessible through the HENVINET networking portal http://www.henvinet.eu and http://henvinet.nilu.no. Quality assurance and control of the entries and evaluation of requirements to use the DSTs were also a focus of the work.
The HENVINET DST MDB is an open product that enables the public to get basic information about the DSTs, and to search the DSTs using pre-designed attributes or free text. Registered users are able to 1) review and comment on existing DSTs; 2) evaluate each DST’s functionalities, and 3) add new DSTs, or change the entry for their own DSTs.
Assessment of the available 67 DSTs showed: 1) more than 25% of the DSTs address only one pollution source; 2) 25% of the DSTs address only one environmental stressor; 3) almost 50% of the DSTs are only applied to one disease; 4) 41% of the DSTs can only be applied to one decision making area; 5) 60% of the DSTs’ results are used only by national authority and/or municipality/urban level administration; 6) almost half of the DSTs are used only by environmental professionals and researchers. This indicates that there is a need to develop DSTs covering an increasing number of pollution sources, environmental stressors and health end points, and considering links to other ‘Driving forces-Pressures-State-Exposure-Effects-Actions’ (DPSEEA) elements. Of interest to both researchers and decision makers should be the standardization of the way DSTs are described for easier access to the knowledge, and the identification of coverage gaps.
... Due to the increase in fuel consumption and in the frequency and duration of traffic congestion as a consequence of increasing numbers of vehicles in the fleet, the emissions of road traffic systems have increased enormously. For example in most European cities road traffic emissions account for 40% volatile organic compounds, more than 70% of NO x , and over 90% of CO [15]. Moreover, the relationship between dispersion and emissions of the traffic flow is complicated. ...
This paper has two main contributions. First, it presents a simple area-wide emission (or dispersion) model for a freeway traffic networks. The model takes the variation of the wind speed and direction into account. Second, it presents a nonlinear parameterized MPC controller for freeway traffic systems. Next, the proposed model and control approach are illustrated with a simulation-based case study. The simulation results show improved traffic performance with respect to the uncontrolled system.
... 5 Good analysis of 'speed' and 'acceleration' in specific situations (e.g. stop-and-go at traffic lights, stop-and-go scenarios in overcrowded roads) using means of traffic-flow models can provide better emission estimates (Schmidt and Schafer, 1998). ...
Urban air quality is generally poor at traffic intersections due to variations in vehicles’ speeds as they approach and leave. This paper examines the effect of traffic, vehicle and road characteristics on vehicular emissions with a view to understand a link between emissions and the most likely influencing and measurable characteristics. It demonstrates the relationships of traffic, vehicle and intersection characteristics with vehicular exhaust emissions and reviews the traffic flow and emission models. Most studies have found that vehicular exhaust emissions near traffic intersections are largely dependent on fleet speed, deceleration speed, queuing time in idle mode with a red signal time, acceleration speed, queue length, traffic-flow rate and ambient conditions. The vehicular composition also affects emissions. These parameters can be quantified and incorporated into the emission models. There is no validated methodology to quantify some non-measurable parameters such as driving behaviour, pedestrian activity, and road conditions
... Examples of decision support systems used by the local authorities in major European cities such as Stockholm, Lisbon, Milano, Berlin, Geneva, Vienna, Paris, Oslo and Athens are the Swedish AirViro (SMHI, 2009), the Austrian AirWare (Fedra and Haurie, 1999), the Norwegian AirQUIS (Bohler et al., 2002) and the Swedish EnviMan (Tarodo, 2003) systems. A few more decision support systems for specific air quality management studies are in operation around the world (Guerrero et al., 2008;Lim et al., 2005;Elbir, 2004;Puliafito et al., 2003;Fine and Ambrosiano, 1996;Schmidt and Schafer, 1998;Jensen et al., 2001;Lin and Lin, 2002;Finzi et al., 1991). ...
To improve the real-time and precision of vehicle emission monitoring and estimation for supporting variable and speedy response on vehicle emission control work, a real-time modeling and visualization system for vehicle emissions on an urban road network (RTVEMVS) was designed and built with Web platform by the development of three modules: real-time data collection, real-time modeling and real-time visualization. The real-time mapping of vehicle emission by vehicle types and road links and real-time“precise vehicles emissions impact estimation” technology which can trace key roads and vehicles induced high air pollution were innovated on system to provide the sensitive perception for government decision makers on speedy vehicles emission control. The detailed vehicle information, traffic flow, speed on each road link and weather data were accessed and fused in real-time from multiple urban sensor systems together with road network information. Integrating an emission calculation model with a high spatiotemporal resolution and the AREMOD dispersion model was running on real-time in RTVEMVS. Based on the Web interface and WebGIS technology (ArcGIS API for JavaScript), real-time vehicle emissions and estimation results can be visualized on a map. The RTEMVS was demonstrated by application on Chongqing of China, hourly based-links vehicle emission by vehicle types on road network and the top 10 of key road links and vehicles induced high air pollution per hour were clearly reveal and visually viewed, and indicate that the real-time system with real-time mapping and precise estimation can provide direct support to the variable vehicle emission control. The RTVEMVS is a useful platform for supporting decision-making in the precise control of urban vehicle emissions pollution.
The proliferation of ‘commercial off-the-shelf’ geographical information systems into the scientific community has resulted in the widespread use of spatial climate data in a variety of applications. This paper presents a review of the role of geographical information systems in climatology and meteorology by (i) discussing methods used to derive and refine spatial climate data and (ii) reviewing the bespoke application of GIS and spatial climate datasets in agriculture, ecology, forestry, health and disease, weather forecasting, hydrology, transport, urban environments, energy and climate change.
Cities development and excessive usage of cars has caused serious environmental
issues such as air pollution. Air pollution of Tehran, as the most populated city of
Iran, has increased considerably in recent years. About 70 to 80 percent of
Tehran’s air pollution is a result of transportation activities. This paper presents a
system dynamics approach to assess long term effect of 6 transportation demand
management policies on modal shares including metro, bus, taxi and car in a 30
year basis and amount of produced NOx, SO2, CO and HC by vehicles are
compared in different scenarios with respect to different emissions of old and new
vehicles. Results of this study show that investment on transit network
development and trip rate reduction as a result of technology improvement and
diversity of land use are best policies for increasing public transit share and
reducing produced pollutants by vehicles respectively. Removing all old vehicles
from transportation system of Tehran, shows 74% reduction in vehicle emissions.
A CyberGIS approach is presented in this chapter where microscopic traffic simulation and gas dispersion simulation systems are combined in order to estimate atmospheric pollution for different scenarios. The combination of these two simulation models allows for detailed investigations of different situations such as the investigation of pollution impacts of different traffic infrastructure variants, as well as for prediction of expected pollution and whether pollutant thresholds will be exceeded. For different case studies, real data about traffic movements provided by the state government, a digital terrain model of the area as well as real measurements of atmospheric data have been used. The evaluation of the approach shows that variations in the settings, regarding traffic or atmospheric conditions, lead to different patterns of observed pollution. The CyberGIS environment described is used to run multiple simulations on a distributed cyberinfrastructure, where the high-end computational resources are available on servers in Europe and in North America.
To efficiently and effectively monitor and mitigate air pollution in the urban environment, it is of paramount importance to integrate into a unified whole air pollutant concentration databases coming from different sources including the ground-based stations, mobile sensors, remote sensing, atmospheric-chemical-transport models and social media for the analysis and unraveling of the complex air pollution processes in space and time. This study constructs and implements for the first time a prototype of the fully integrated air pollution decision support system (APDSS) that put together in an integrated manner all relevant multi-scale, multi-type and multi-source data for decision-making on urban air pollution. The prototype contains the main system that handles the multi-source, multi-type and multi-scale databases, queries, visualization and data mining algorithms and the integrated modules that individually and holistically capitalize on the power of the ground-based stations, ground and aerial mobile sensors, satellite-borne remote-sensing technologies, atmospheric-chemical-transport models and social media. It renders a solid scientific foundation and system development methodology for the study of the spatiotemporal air pollution profiles crucial to the mitigation of urban air pollution. Real-life applications of the prototype are employed to illustrate the functionality of the APDSS.
This paper presents diesel public vehicles' pollutant models based on vehicle's speed for main public vehicles' pollutants including CO, NOx, PM 10 , VOC, CH 4 , HC and SO 2 .The traffic emissions are calculated in three route's slope conditions for horizontal, downhill and uphill which are modeled based on an instantaneous emission model integrated with a macroscopic traffic simulation model. The emission models are based on empirical measurements. Public vehicle emissions relate to the vehicle type, the instantaneous speed and vehicle's route slope conditions. Integrated emission models are applied for diesel public vehicles, which are classified into three category including urban buses, rural buses and minibuses so after presenting emission models, accuracy of them are evaluated by empirical measurements. Some models which are not good compatible with empirical data, have been changed by describing new emission form and new models validity are evaluated by empirical data. Consequently, by considering correlation coefficients can be evaluated new emission models are valid.
This paper constructs a system dynamics model for simulating the impact of different strategies on urban traffic’s energy consumption and carbon emissions. Based on a case study in Beijing, the model includes three subsystems: (1) urban traffic, (2) population and economy, and (3) energy consumption and carbon emissions. First, the model is used to decompose the impact of different vehicles on energy consumption and carbon emissions. Decomposition results show that private cars have the most significant impact on urban traffic’s energy consumption and carbon emissions; however, total vehicle kilometers traveled by private cars are the smallest among four trip modes. Then, the model is used to simulate different urban traffic policies. Policies are categorized as follows: (a) driving restrictions on vehicle registration numbers, (b) a scheme for vehicle registrations via a lottery system, and (c) development of public transportation infrastructures. Scenario simulation results show that all those measures can reduce energy consumption and carbon emissions. Though the last strategy (c) contains several delays, its effect is more stable and far-reaching. Finally, some recommendations about easing traffic pressure and reducing traffic emissions are given.
A study was carried out to establish correlations between traffic variables and air quality in urban areas. CO was monitored because the primary source was automobiles and it could cause negative health effects, e.g., reduced alertness, headaches, and nausea to people exposed for extended periods of time. A study was initially done in Singapore at the National University of Singapore from August to October 2009. A follow up study was then conducted at the University of Cincinnati, OH, in February 2010. The focus was put on the main entrance/exit of the garages where the highest CO concentrations were observed. Almost all traffic in and out of the garages consisted of gasoline-powered passenger cars. At the Singapore site, some light duty diesel powered trucks did enter the garage, but their visits were infrequent. Higher CO occurred when traffic is exiting the garage most likely due to vehicles coming out at a cold start as opposed to vehicles entering the garage after the vehicle has been running for some time. This is an abstract of a paper presented at the 103rd AWMA Annual Conference and Exhibition (Calgary, Alberta 6/22-25/2010).
Modelling approaches for simulating air and stormwater pollution due to on-road vehicles are reviewed and discussed. Models for traffic, emissions, atmospheric dispersion, and stormwater contamination are studied with particular emphasis on their couplings to create a modelling chain. The models must be carefully selected according to the requirements and level of detail of the integrated modelling chain. Although a fair amount of research has been conducted to link air pollution and road traffic, many questions related to spatio-temporal scales, domains of validity, consistency among models, uncertainties of model simulation results, and interfaces between models remain open. The aim of this work is to review the current status of the relationships between traffic, emissions, air quality, and water quality models, to recommend modelling approaches and to propose some directions for improving the state of the science. The difficulties and challenges associated with model coupling are illustrated with specific examples.
Supported by general revenues from the State of Texas 16. Abstract This report presents a framework for analyzing the air quality impact of transportation sector and selecting appropriate Intelligent Transportation Systems (ITS) strategies to reduce mobile source emissions. First within a GIS framework, the mobile source emissions are estimated on the basis of vehicle fleet composition, emission factors and traffic characteristics. Then, a concise four-step method is proposed to select ITS strategies to reduce traffic emissions according to the Federal Highway Administration (FHWA) ITS planning process version 2.1. Following the four-step method, the appropriate ITS strategies can be identified and their potential benefits and impact can be evaluated. In this study, the emission problems are defined based on the emissions modeling within a GIS framework. The ITS strategies are screened under the guidance of the National ITS Architecture. The identified ITS strategies are evaluated by doing experiments with ITS Deployment Analysis System (IDAS). A case study was performed in Austin, TX. It shows that the proposed emissions modeling method and the ITS strategy selection method is very helpful for regional ITS planning and evaluation. The methods and results from this report will be very useful for decision-makings in ITS investments and deployments.
Models for simulating air quality due to vehicles and their effect on runoff quality in an urban environment and their coupling are examined. In order to achieve this aim, the selection of models (traffic, emission, atmospheric dispersion, stormwater) must be carefully made according to the special requirements and level of details needed for the integrated system. Therefore a variety of these models are reviewed. Although a fair amount of research has been conducted in the past to link air pollution and road traffic, many questions related to spatio-temporal scales, domains of validity, consistency among models and interfaces between models remain open. Furthermore, the link between traffic emissions, atmospheric deposition and the contamination of water runoff in urban areas has not been treated yet in a comprehensive manner. The aim of this study is to review the current status of the relationships between traffic, emissions and air and water quality models, to recommend an integrated modelling approach and to propose some directions for advancing the state of the art.
The purpose of this article is to describe determinants and spatial patterns of atmospheric carbon dioxide (CO2) in Phoenix, Arizona. Specifically, we use geographic information systems (GIS) and regression-based analyses to identify the human and biological factors that contribute to spatial and temporal variations in near-surface (2-meter height) atmospheric CO2 levels. We use these factors to create estimated surfaces of CO2 concentrations for the area. We evaluate the surfaces using records of CO2 from independent monitoring stations and transects. To investigate the temporal patterns and variations in CO2 concentrations, we estimate CO2 surfaces for the early mornings and the afternoons, on weekdays when traffic is heavy and spatially focused and on weekends when it is lighter and more spatially dispersed. Findings suggest there is a distinct relationship between the structure of Phoenix CO2 levels and spatial patterns of human activities and vegetation densities. Morning CO2 levels are higher than afternoon levels and correspond closely to the density of traffic, population, and employment. The spatial structure of human activity explains the pattern of CO2 better on weekdays than on weekends. CO2 surfaces reflect declining densities of human activity with distance from the city center, the pattern of irrigated agriculture in the Phoenix area, and riparian habitats on the urban fringe. Spatial and temporal patterns of CO2 concentrations are useful in understanding urban climate and ecosystem processes.
Urban air quality is a major concern throughout the world. In the UK, local authorities are now required to improve air quality in their respective area. In most urban areas, emissions from traffic are a major contributor of harmful pollutants such as nitrogen oxides (NOX) and particulate matter (PM). Tools such as transportation and dispersion models are needed to predict any potential atmospheric pollution problems and to test the effectiveness of any air quality action plans. This paper describes a new framework to link existing air quality tools and the implementation of this framework through the development of prototype software IMPAQT (Integrated Modular Program for Air Quality Tools). IMPAQT aims to aid transport or environmental planners by increasing the efficiency of air quality assessments. IMPAQT was applied to several case studies using a countywide transportation model, an advanced atmospheric dispersion model and a desktop GIS. It was used to carry out urban air quality assessments and to test traffic scenarios. The laborious and time-consuming data preparation work involved in air quality studies was completed efficiently and in a shorter time compared with the methodology currently adopted by local authorities.
The fluid-dynamic traffic model of Kerner and Konhäuser [Phys. Rev. E 48, 2335 (1993); 50, 54 (1994)] is extended by an equation for the vehicles' velocity variance. It is able to describe the observed increase of velocity variance immediately before a traffic jam develops. Another modification takes into account the finite amount of space that each vehicle needs. As a consequence, the improved traffic model does not produce densities that exceed the maximum vehicle density or negative velocities, like former models did.
Written by the team that developed the software, this tutorial is the definitive resource for scientists, engineers, and other computer users who want to use PVM to increase the flexibility and power of their high-performance computing resources.
Written by the team that developed the software, this tutorial is the definitive resource for scientists, engineers, and other computer users who want to use PVM to increase the flexibility and power of their high-performance computing resources. PVM introduces distributed computing, discusses where and how to get the PVM software, provides an overview of PVM and a tutorial on setting up and running existing programs, and introduces basic programming techniques including putting PVM in existing code. There are program examples and details on how PVM works on UNIX and multiprocessor systems, along with advanced topics (portability, debugging, improving performance) and troubleshooting.
PVM (Parallel Virtual Machine) is a software package that enables the computer user to define a networked heterogeneous collection of serial, parallel, and vector computers to function as one large computer. It can be used as stand-alone software or as a foundation for other heterogeneous network software. PVM may be configured to contain various machine architectures, including sequential processors, vector processors, and multicomputers, and it can be ported to new computer architectures that may emerge.
Part 1 Introduction: heterogeneous network computing trends in distributed computing PVM overview other packages. Part 2 The PVM system. Part 3 Using PVM: how to obtain the PVM software setup to use PVM setup summary starting PVM common startup problems running PVM programs PVM console details host file options. Part 4 Basic programming techniques: common parallel programming paradigms workload allocation porting existing applications to PVM. Part 5 PVM user interface: process control information dynamic configuration signalling setting and getting options message passing dynamic process groups. Part 6 Program examples: fork-join dot product failure matrix multiply one-dimensional heat equation. Part 7 How PVM works: components messages PVM daemon libpvm library protocols message routing task environment console program resource limitations multiprocessor systems. Part 8 Advanced topics: XPVM porting PVM to new architectures. Part 9 Troubleshooting: geting PVM installed getting PVM running compiling applications running applications debugging and tracing debugging the system. Appendices: history of PVM versions PVM 3 routines.
The paper gives a survey about air pollution modelling and simulation, and especially results and experience with view to parallel computing. Meteorological and air pollutant transport models in different scales (Eulerian, Lagrangian models) are considered. The numerical algorithms for solving the equations and principles of decomposition on the algorithm level, and the used decomposition method of the model domain are named. Parallel hardware as test-bed (MANNA, workstation cluster, transputer system, CM-5) and software tools for parallelizing the models (PARMACS, PVM, FORGE) are mentioned. Results and experience of the application in the area of Berlin-Brandenburg are discussed as well as runtime measurements.
It is shown that, in an initially homogeneous traffic flow, a region of high density and low average velocity of cars can spontaneously appear, if the density of cars in the flow exceeds some critical value. This region-a cluster of cars-can move with constant velocity in the opposite direction or in the direction of the flow, depending on the selected parameters and the initial conditions of the traffic flow. Based on numerical simulations, the kinetics of cluster formation and the shape of stationary moving clusters are found. The results presented can explain the appearance of a ``phantom traffic jam,'' which is observed in real traffic flow.
Development and testing of the CBM-IV for urban and regional modeling
- M W Gery
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- J P Killus
Gery, M.W., Whitten, G.Z., Killus, J.P., 1988. Development and test-ing of the CBM-IV for urban and regional modeling. U.S. Environ-mental Protection Agency, EPA-600/3-88-012.
Ein Dreischichten-Modell zur Berechnung mesoskaliger Wind- und Immissionsfelder über komplexem Gelände
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Heimann, D., 1985. Ein Dreischichten-Modell zur Berechnung mesos-kaliger Wind-und Immissionsfelder u ¨ber komplexem Gelä. Ph.D. thesis, University of Munich, Germany. Helbing, D., 1995. Improved fluid dynamic model for vehicular traffic. Phys. Rev. E.
A concept for the parallel simulation of traffic flow, traffic emissions and air pollutants dispersion in urban areas. Paper to present at European Simulation Meeting on Simulation Tools and Abgasemissionsfaktoren von PKW in der Bun-desrepublik Deutschland. UBA-FB 91-042
- A Sydow
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Sydow, A., J. Lindemann, Lux, Th., Schä, R.-P., 1995. A concept for the parallel simulation of traffic flow, traffic emissions and air pollutants dispersion in urban areas. Paper to present at European Simulation Meeting on Simulation Tools and Applications, Gyö, Hungary. TU V Rheinland, 1987. Ermittlung von Abgasemissionsfaktoren von Personenkraftwagen in der Bundesrepublik Deutschland im Bezugsjahr 1985. UBA-FB 104 05 347. Erich Schmidt Verlag, Berlin. TU V Rheinland, 1994. Abgasemissionsfaktoren von PKW in der Bun-desrepublik Deutschland. UBA-FB 91-042. Erich Schmidt Ver-lag, Berlin. TU V Rheinland, 1995. Abgasemissionsfaktoren von Nutzfahrzeugen in der Bundesrepublik Deutschland fü das Bezugsjahr 1990. UBA-FB 95-049. Erich Schmidt Verlag, Berlin.
A concept for the parallel simulation of traffic flow, traffic emissions and air pollutants dispersion in urban areas
- A Sydow
- J Lindemann
- Th Lux
- R.-P Schäfer
Abgasemissionsfaktoren von Nutzfahrzeugen in der Bundesrepublik Deutschland für das Bezugsjahr
- Tüv Rheinland
Ermittlung von Abgasemissionsfaktoren von Personenkraftwagen in der Bundesrepublik Deutschland im Bezugsjahr 1985. UBA-FB 104 05 347
- Tüv Rheinland
Abgasemissionsfaktoren von PKW in der Bundesrepublik Deutschland
- Tüv Rheinland