Integrated Traffic and Emission Simulation: a Model Calibration Approach Using Aggregate Information

Environmental impacts of road traffic have attracted increasing attention in project-level traffic planning and management. The conventional approach considers emission impact analysis as a separate process in addition to traffic modeling. This paper first introduces our research effort to integrate traffic, emission, and dispersion processes into a common distributed computational framework, which makes it efficient to quantify and analyze correlations among dynamic traffic conditions, emission impacts, and air quality consequences. A model calibration approach is particularly proposed when on-road or in-lab instantaneous emission measurements are not directly available. Microscopic traffic simulation is applied to generate dynamic vehicle states at the second-by-second level. Using aggregate emission estimation as standard reference, a numerical optimization scheme on the basis of a stochastic gradient approximation algorithm is applied to find optimal parameters for the dynamic emission model. The calibrated model has been validated on several road networks with traffic states generated by the same simulation model. The results show that with proper formulation of the optimization objective function, the estimated dynamic emission model can capture the trends of aggregate emission patterns of traffic fleets and predict local emission and air quality at higher temporal and spatial resolutions.     

Diurnal models of traffic-generated CO for Penang,Malaysia

Georgetown of Penang, an old city, is noted for its narrow streets. The existing traffic dispersal system is utterly inadequate to cope with the ever increasing number of cars and motorcycles on the road. The principal objective of this study is to build prediction models of CO to be employed as one of the planning tools in the future design of Penang urban traffic dispersal system. This study involves the monitoring of kerbside CO levels at selected sites and the fitting of hourly-averaged CO data to linear regression models incorporating the residual effect of CO emission due to traffic in the earlier periods and also different categories of vehicles. The best overall regression model appears to be the one based upon the total traffic count of motorcycles. This can be accounted for by the fact that the traffic counts of motorcycles and cars are highly correlated in most cases and that the emissions of CO from motorcycles are more readily detected as they travel closer to the kerb. The inclusion of residual CO in the models significantly improves the correlation coefficient from about 0.4 to about 0.7.     

Emission-based static traffic assignment models

Tailpipe emissions in the road transportation system are a major source of air pollution and greenhouse gases. One of the possible approaches is to influence drivers’ routing decisions such that the emissions and fuel consumption is minimized. In order to evaluate such condition, we develop environmental traffic assignment (E-TA) models based on user equilibrium (UE) and system optimal (SO) behavioral principles. Extending the traditional travel time-based UE and SO principles to E-TA is not straightforward because, unlike travel time, the rate of emissions increases with the increase in vehicle speed beyond a certain point. The results of various TA models show a network-wide traffic control strategy in which vehicles are routed according to SO-based E-TA, can reduce system-wide emissions. However, a system in which drivers make routing decisions to minimize their own emissions (E-UE system) results in a paradoxical situation of increased individual as well as system-wide emissions.     

上海市机动车污染实时排放预警系统设计与应用

研究上海市机动车污染的动态排放测算和网格化动态排放清单构建,在实时的交通数据和交通环境监测数据的基础上,结合交通模型、机动车排放清单模型等业务模型和算法,依托大数据存储、可视化和GIS等技术,开发了上海市机动车污染物实时排放预警系统,实现了上海市全市道路的机动车动态排放测算、交通环境政策实施情景模拟和网格化排放清单,更新频率为每30 min一次,包含PM、NOx、CO、SO2、VOCs等污染物和9种车型。系统建成后直接服务于首届中国国际进口博览会,为大气污染排放实时总量跟踪评估、污染源管控措施分析及监测成因分析等提供了有力的实时数据和技术支撑。     

New research assessing the effect of engine operating conditions on regulated emissions of a 4-stroke motorcycle by test bench measurements

In the latest years the effect of powered two-wheelers on air polluting emissions is generally noteworthy all over the world, notwithstanding advances in internal combustion engines allowed to reduce considerably both fuel consumption and exhaust emissions of SI engines. Nowadays, in fact, these vehicles represent common means of quotidian moving, serving to meet daily urban transport necessities with a significant environmental impact on air quality. Besides, the emissive behavior of the two-wheelers measured under fixed legislative driving standards (and not on the local driving conditions) might not be sufficiently representative of real world motorcycle riding.The purpose of this investigation is a deeper research on emissive levels of in-use motorcycles equipped with last generation SI engines under real world driving behavior. In order to analyze the effect of vehicle instantaneous speed and acceleration on emissive behavior, instantaneous emissions of CO, HC and NO_X were measured in the exhaust of a four-stroke motorcycle, equipped with a three-way catalyst and belonging to the Euro-3 legislative category. Experimental tests were executed on a chassis dynamometer bench in the laboratories of the National Research Council (Italy), during the Type Approval test cycle, at constant speed and under real-world driving cycles. This analytical-experimental investigation was executed with a methodology that improves vehicles emission assessment in comparison with the modeling approaches that are based on fixed legislative driving standards. The statistical processing results so obtained are very useful also in order to improve the database of emission models commonly used for estimating emissions from road transport sector, then they can be used to evaluate the environmental impact of last generation medium-size motorcycles under real driving behaviors.     

System Dynamics Modeling and Policy Simulation for Urban Traffic: a Case Study in Beijing

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 case study predicting environmental impacts of urban transport planning in China

Predicting environmental impacts is essential when performing an environmental assessment on urban transport planning. System dynamics (SD) is usually used to solve complex nonlinear problems. In this study, we utilized system dynamics (SD) to evaluate the environmental impacts associated with urban transport planning in Jilin City, China with respect to the local economy, society, transport, the environment and resources. To accomplish this, we generated simulation models comprising interrelated subsystems designed to utilize changes in the economy, society, road construction, changes in the number of vehicles, the capacity of the road network capacity, nitrogen oxides emission, traffic noise, land used for road construction and fuel consumption associated with traffic to estimate dynamic trends in the environmental impacts associated with Jilin's transport planning. Two simulation scenarios were then analyzed comparatively. The results of this study indicated that implementation of Jilin transport planning would improve the current urban traffic conditions and boost the local economy and development while benefiting the environment in Jilin City. In addition, comparative analysis of the two scenarios provided additional information that can be used to aid in scientific decision-making regarding which aspects of the transport planning to implement in Jilin City. This study demonstrates that our application of the SD method, which is referred to as the Strategic Environmental Assessment (SEA), is feasible for use in urban transport planning.     

Simulation of Urban-Scale Air Pollution Patterns in Luxembourg: Contributing Sources and Emission Scenarios

The aim of this study is to investigate the air pollution situation in an urban area in southwestern Luxembourg and to simulate annual NO₂ and PM₁₀ concentrations in response to changes in meteorological conditions and emissions using a Gaussian dispersion model. Simulations are carried out for the years 1998–2006. Emission scenarios related to road transport and nonindustrial combustion are performed in order to predict changes of air pollution levels. Road transport is by far the most important local emission source in the study area. Scenarios with more stringent emission standards for vehicles, less traffic, and fewer heavy-duty vehicles lead to reductions of NO_x and primary PM₁₀ emissions. As a result, the annual NO₂ concentrations are decreasing in most parts of the study area and are below the European annual limit value of 40 μg?m^?3. In contrast, a scenario with increased use of wood pellets for domestic heating shows an increase in urban PM₁₀ concentration. The year-to-year variability of meteorological conditions accounts for the same magnitude of absolute NO₂ and PM₁₀ concentration changes as the emission scenarios. The comparison with measurements located in the study area shows that the model is able to predict urban-scale annual average air pollution. The proposed application results show that the model can be appropriate for policy-driven air quality management and planning queries.     

Modelling of Motor Vehicle Fuel Consumption and Emissions Using a Power-Based Model

The performance of a power based fuel consumption and exhaust emissions model for spark ignition vehicles has been evaluated using a large Australian database derived from testing a wide range of in-use cars on a chassis dynamometer. It was also applied to results of on-road fuel consumption measurement using a "floating" car which was driven back and forth on hilly roadways in Sydney with a length of 8.6 km. The model is found to predict the fuel consumption well over the standard drive cycles and also for the floating car. Average exhaust emissions were also well predicted, but, as would be expected, vehicle-to-vehicle correlation is impossible due to the well-known high variability of emissions between nominally identical vehicles.     

Simulating the production and dispersion of environmental pollutants in aerosol phase in an urban area of great historical and cultural value

Mathematical models were developed to simulate the production and dispersion of aerosol phase atmospheric pollutants which are the main cause of the deterioration of monuments of great historical and cultural value. This work focuses on Particulate Matter (PM) considered the primary cause of monument darkening. Road traffic is the greatest contributor to PM in urban areas. Specific emission and dispersion models were used to study typical urban configurations. The area selected for this study was the city of Florence, a suitable test bench considering the magnitude of architectural heritage together with the remarkable effect of the PM pollution from road traffic. The COPERT model, to calculate emissions, and the street canyon model coupled with the CALINE model, to simulate pollutant dispersion, were used. The PM concentrations estimated by the models were compared to actual PM concentration measurements, as well as related to the trend of some meteorological variables. The results obtained may be defined as very encouraging even the models correlated poorly: the estimated concentration trends as daily averages moderately reproduce the same trends of the measured values.     

Mean Vehicle Speed Distributions for the Spatiotemporal Estimation of Exhaust Emissions

Link Emissions Models estimate traffic-related air pollution emissions at the individual road link level and inform governmental policies for air quality management. The current South Australian Link Emissions Model (CLEM) assumes constant spatiotemporal traffic flow at a single fixed mean speed, a potential limitation as the variability of exhaust emissions with vehicle speed has been established in the literature.We extend CLEM to eliminate the assumption of constant traffic flow, through the derivation of mean Australian vehicle speed distributions for different road types. Specifically, we successfully model the vehicle speed profile data from the second National In-Service Emissions study using Nearest Neighbour Kernel Density Estimation. We propose a mean speed Distribution Link Emissions Model (DLEM) for exhaust emission estimation based on the derived mean speed distributions. DLEM is an augmented, enhanced version of CLEM, accommodating a range of vehicle speeds and road types. The performance of the extended model, DLEM, is analysed in comparison to the current model, CLEM, through a case study analysis of vehicle exhaust emissions on a typical arterial road in Adelaide, South Australia. Results indicate use of DLEM and, by extension, mean vehicle speed distributions, has a strong impact on emission estimation. In particular, the fixed speed model, CLEM, may be substantially underestimating exhaust emissions of carbon monoxide, non-methane volatile organic compounds and particulate matter less than 2.5 μm in diameter. These are common exhaust pollutants that have been extensively linked with adverse health effects including respiratory morbidity and premature mortality.     

Vehicular particulate matter emissions in road tunnels in Sao Paulo, Brazil

In the metropolitan area of S?o Paulo, Brazil, ozone and particulate matter (PM) are the air pollutants that pose the greatest threat to air quality, since the PM and the ozone precursors (nitrogen oxides and volatile organic compounds) are the main source of air pollution from vehicular emissions. Vehicular emissions can be measured inside road tunnels, and those measurements can provide information about emission factors of in-use vehicles. Emission factors are used to estimate vehicular emissions and are described as the amount of species emitted per vehicle distance driven or per volume of fuel consumed. This study presents emission factor data for fine particles, coarse particles, inhalable particulate matter and black carbon, as well as size distribution data for inhalable particulate matter, as measured in March and May of 2004, respectively, in the Janio Quadros and Maria Maluf road tunnels, both located in S?o Paulo. The Janio Quadros tunnel carries mainly light-duty vehicles, whereas the Maria Maluf tunnel carries light-duty and heavy-duty vehicles. In the Janio Quadros tunnel, the estimated light-duty vehicle emission factors for the trace elements copper and bromine were 261 and 220 microg km(-1), respectively, and 16, 197, 127 and 92 mg km(-1), respectively, for black carbon, inhalable particulate matter, coarse particles and fine particles. The mean contribution of heavy-duty vehicles to the emissions of black carbon, inhalable particulate matter, coarse particles and fine particles was, respectively 29, 4, 6 and 6 times higher than that of light-duty vehicles. The inhalable particulate matter emission factor for heavy-duty vehicles was 1.2 times higher than that found during dynamometer testing. In general, the particle emissions in S?o Paulo tunnels are higher than those found in other cities of the world.     

An empirical study of estimating vehicle emissions under cordon and distance based road user charging in Leeds, UK

This paper presents the impact of road user charging (RUC) on vehicle emissions through application of traffic assignment and pollutant emission models. It presents results of an analysis of five RUC schemes on vehicle emissions in Leeds, UK for 2005. The schemes were: a ￡3 inner ring road cordoncharge; a double cordon with a ￡2 inner ring road and a ￡1 outer ring road charge; and distance charges of 2, 10 and 20 p/km levied for travel within the outer cordon. Schemes were compared to a no charge option and results presented here. Emissions are significantly reduced within the inner cordon, whilst beyond the cordon, localised increases and decreases occur. The double cordon exhibits a similar but less marked pattern. Distance charging reduces city-wide emissions by 10% under a 2 p/km charge, 42–49% under a 10 p/km charge and 52–59% under a 20 p/km charge. The higher distance charges reduce emissions within the charge zone, and are also associated with elevated emissions outside the zone, but to a lesser extent than that observed for cordon charging.     

Application and Quality Assessment of an Instantaneous Vehicle Emission Model at Fleet Level

A developed instantaneous emission model is applied to predict emission factors for small vehicle fleets for quality assessment. Extensive vehicle measurements of pre-Euro-1 gasoline, Euro-3 gasoline, and Euro-2 diesel vehicles are available. The data were used to develop individual vehicle emission models for each car. The prediction quality for each vehicle category was determined by averaging the results obtained from the individual vehicle models. The results show that the prediction quality is improved in comparison with the individual vehicles, even with a small number of vehicles in a specific category. This indicates that the errors in the individual models are mainly random and that prediction quality, when applied to fleets of cars, is exceptionally high.     

北京市典型道路交通噪声排放特征

采用北京市道路交通噪声自动监测系统2013—2017年采集的等效连续A声级数据,对城市快速路、城市主干线、城市次干线、城市支路的代表性站点噪声排放情况进行了统计分析,结果显示,北京市不同等级的道路噪声排放具备一定的特征,排放水平从大到小依次为城市快速路城市主干线城市支路和城市次干线,道路噪声随时间变化存在较为一致的周期性排放特征,24 h变化特征比较明显。个别道路排放特征存在特异性,如城市主干线道路的一个代表监测站点噪声监测值出现了逐年下降趋势,分析发现,北京市非首都功能疏解对其噪声值的下降有一定贡献。采取一定的规划和管理措施有助于减少道路交通噪声的排放。     

Assessing the effects of noise abatement measures on health risks: A case study in Istanbul

In recent decades, noise pollution caused by industrialization and increased motorization has become a major concern around the world because of its adverse effects on human well-being. Therefore, transportation agencies have been implementing noise abatement measures in order to reduce road traffic noise. However, limited attention is given to noise in environmental assessment of road transportation systems. This paper presents a framework for a health impact assessment model for road transportation noise emissions. The model allows noise impacts to be addressed with the health effects of air pollutant and greenhouse gas emissions from road transportation. The health damages assessed in the model include annoyance, sleep disturbance, and cardiovascular disease in terms of acute myocardial infarction. The model was applied in a case study in Istanbul in order to evaluate the change in health risks from the implementation of noise abatement strategies. The noise abatement strategies evaluated include altering pavement surfaces in order to absorb noise and introducing speed limits. It was shown that significant improvements in health risks can be achieved using open graded pavement surfaces and introducing speed limits on highways.     

道路机动车活动水平调查及其污染物排放测算应用——上海案例研究

采用上海市车辆数据和机动车年检站实地调查方式获取车辆技术及分布，用GPS设备采集机动车行驶工况，通过交通流量录像的现场计数的方法获得道路车流分布。调查结果显示，在用汽车排放水平85％以上都达到或超过国Ⅱ标准；在低速、怠速工况区间内，社会车辆的比功率比例最低；道路轻型客车（含出租车）日平均车流量占总车流量的80％以上。机动车活动水平的详细调查将有助于上海实际道路机动车排放清单的精确计算，并获取主要的排放源及其影响因素。     

A Numerical and Experimental Study of Pollutant Dispersion in a Traffic Tunnel

Three-dimensional turbulent flow and dispersion of gaseous pollutants carbon monoxide (CO) and nitrogen oxides (NOx) in a road tunnel was modeled using the standard k–ε turbulence model and solved numerically using the finite volume method. Vehicle emissions were estimated from the measured traffic flow rates and modeled as banded line sources along the tunnel floor. The effects of fan ventilation and piston effect of moving vehicles on the airflow and pollutant dilution were examined. The numerical results reveal that a peak velocity exists near the tunnel floor due to the piston effect of vehicles. The cross-sectional concentrations of air pollutants are non-uniformly distributed and concentrations rise with downstream distance. The piston effect of vehicles can alone provide 25%–34% dilution of air pollutants in the tunnel, compounded 43%–70% dilution effect according to the ventilation condition.     

Dynamic global warming impact assessment integrating temporal variables: Application to a residential building in China

Climate warming is a global concern, and buildings have been recognized as a major contributor because the carbon emissions during their entire life cycles constitute a large share of the total value (almost 40% globally). Many life cycle assessments of buildings have been conducted to quantify the associated global warming impacts. However, few studies have considered the potential temporal variation over the long lifetimes of buildings. In this study, various temporal variables were combined to evaluate the dynamic life cycle global warming impact of a residential building in China. First, annual material and energy consumption data throughout the entire life cycle were acquired from questionnaire survey, statistical reports and literatures. Five dynamic variables (household size, usage behavior, replacement and improvement of components, waste treatment, and energy mix) and their effects on consumption levels were considered. Second, a dynamic inventory analysis tool (DyPLCA) was used to transform the temporal consumption data into dynamic greenhouse gas quantities. The global warming effects of these emissions were quantified using a dynamic characterization tool (DynCO₂). Finally, emission reduction targets for future decades were used to weight the severities of the impacts at different times. The dynamic instantaneous and cumulative global warming impacts of the building were calculated. Dynamic and static assessment results were compared. We also analyzed the contribution of each dynamic variable to the final results and found that the dynamic variables had very different effects (ranging from −42.00% to 45.34%). This study provided an operable dynamic assessment model and available dynamic data for the global warming impact assessment of buildings in China.     

2020年江苏省机动车排放污染分布特征及与大气质量耦合性研究

依据生态环境部2021年6月发布的《排放源统计调查产排污核算方法和系数手册》,结合本地实测数据,在对汽油车颗粒物(PM)排放系数进行测算的基础上,核算了2020年江苏省机动车PM、氮氧化物(NO_(X))、挥发性有机物(VOC_(S))的排放总量,分析了机动车排放污染分布特征及与大气质量的耦合关系。结果表明:2020年江苏省机动车PM、NO_(X)、VOC_(S)排放量分别为0.5×10^(4),3.71×10^(5),1.17×10^(5) t。从区域分布来看,苏州、南京、无锡3市的3项污染物排放总量及NO_(X)、VOC_(S)排放量均位列前3位,PM排放量位列前3位的是苏州、徐州、无锡。从车型、燃料类型和排放阶段来看,国Ⅳ及以下排放标准的汽油小型客车是机动车VOC_(S)排放控制的重点,国Ⅲ排放标准的重型柴油货车是机动车PM和NO_(X)排放控制的重点。分析区域机动车PM排放量与大气中PM_(2.5)来源解析结果的耦合关系,其间存在不同程度的正相关性,控制机动车污染对改善大气环境会产生积极成效,南京、徐州和盐城3市的成效会尤为明显。