Validation of temporal and spatial consistency of facility- and speed-specific vehicle-specific power distributions for emission estimation: A case study in Beijing,China

Vehicle-specific power (VSP) has been found to be highly correlated with vehicle emissions. It is used in many studies on emission modeling such as the MOVES (Motor Vehicle Emissions Simulator) model. The existing studies develop specific VSP distributions (or OpMode distribution in MOVES) for different road types and various average speeds to represent the vehicle operating modes on road. However, it is still not clear if the facility- and speed-specific VSP distributions are consistent temporally and spatially. For instance, is it necessary to update periodically the database of the VSP distributions in the emission model? Are the VSP distributions developed in the city central business district (CBD) area applicable to its suburb area? In this context, this study examined the temporal and spatial consistency of the facility- and speed-specific VSP distributions in Beijing. The VSP distributions in different years and in different areas are developed, based on real-world vehicle activity data. The root mean square error (RMSE) is employed to quantify the difference between the VSP distributions. The maximum differences of the VSP distributions between different years and between different areas are approximately 20% of that between different road types. The analysis of the carbon dioxide (CO₂) emission factor indicates that the temporal and spatial differences of the VSP distributions have no significant impact on vehicle emission estimation, with relative error of less than 3%.

Implications: The temporal and spatial differences have no significant impact on the development of the facility- and speed-specific VSP distributions for the vehicle emission estimation. The database of the specific VSP distributions in the VSP-based emission models can maintain in terms of time. Thus, it is unnecessary to update the database regularly, and it is reliable to use the history vehicle activity data to forecast the emissions in the future. In one city, the areas with less data can still develop accurate VSP distributions based on better data from other areas.     

Developing a high-resolution vehicular emission inventory by integrating an emission model and a traffic model: Part 1--Modeling fuel consumption and emissions based on speed and vehicle-specific power

To improve the accuracy and applicability of vehicular emission models, this study proposes a speed and vehicle-specific power (VSP) modeling method to estimate vehicular emissions and fuel consumption using data gathered by a portable emissions monitoring system (PEMS). The PEMS data were categorized into discrete speed-VSP bins on the basis of the characteristics of vehicle driving conditions and emissions in Chinese cities. Speed-VSP modal average rates of emissions (or fuel consumption) and the time spent in the corresponding speed-VSP bins were then used to calculate the total trip emissions (or fuel consumption) and emission factors (or fuel economy) under specific average link speeds. The model approach was validated by comparing it against measured data with prediction errors within 20% for trip emissions and link-speed-based emission factors. This analysis is based on the data of light-duty gasoline vehicles in China; however, this research approach could be generalized to other vehicle fleets in other countries. This modeling method could also be coupled with traffic demand models to establish high-resolution emissions inventories and evaluate the impacts of traffic-related emission control measures.     

Real-World In-Use Activity,Fuel Use,and Emissions for Nonroad Construction Vehicles: A Case Study for Excavators

Abstract

A study design was developed and demonstrated for deployment of a portable emission measurement system (PEMS) for excavators. Excavators are among the most commonly used vehicles in construction activities. The PEMS measured nitric oxide, carbon monoxide, hydrocarbons, carbon dioxide, and opacity-based particulate matter. Data collection, screening, processing, and analysis protocols were developed to assure data quality and to quantify variability in vehicle fuel consumption and emissions rates. The development of data collection procedures was based on securing the PEMS while avoiding disruption to normal vehicle operations. As a result of quality assurance, approximately 90% of the attempted measurements resulted in valid data. On the basis of field data collected for three excavators, an average of 50% of the total nitric oxide emissions was associated with 29% of the time of operation, during which the average engine speed and manifold absolute pressure were significantly higher than corresponding averages for all data. Mass per time emission rates during non-idle modes (i.e., moving and using bucket) were on average 7 times greater than for the idle mode. Differences in normalized average rates were influenced more by intercycle differences than intervehicle differences. This study demonstrates the importance of accounting for intercycle variability in real-world in-use emissions to develop more accurate emission inventories. The data collection and analysis methodology demonstrated here is recommended for application to more vehicles to better characterize real-world vehicle activity, fuel use, and emissions for nonroad construction equipment.     

Real-world emissions of in-use off-road vehicles in Mexico

Off-road vehicles used in construction and agricultural activities can contribute substantially to emissions of gaseous pollutants and can be a major source of submicrometer carbonaceous particles in many parts of the world. However, there have been relatively few efforts in quantifying the emission factors (EFs) and for estimating the potential emission reduction benefits using emission control technologies for these vehicles. This study characterized the black carbon (BC) component of particulate matter and NOx, CO, and CO₂ EFs of selected diesel-powered off-road mobile sources in Mexico under real-world operating conditions using on-board portable emissions measurements systems (PEMS). The vehicles sampled included two backhoes, one tractor, a crane, an excavator, two front loaders, two bulldozers, an air compressor, and a power generator used in the construction and agricultural activities. For a selected number of these vehicles the emissions were further characterized with wall-flow diesel particle filters (DPFs) and partial-flow DPFs (p-DPFs) installed. Fuel-based EFs presented less variability than time-based emission rates, particularly for the BC. Average baseline EFs in working conditions for BC, NOx, and CO ranged from 0.04 to 5.7, from 12.6 to 81.8, and from 7.9 to 285.7 g/kg-fuel, respectively, and a high dependency by operation mode and by vehicle type was observed. Measurement-base frequency distributions of EFs by operation mode are proposed as an alternative method for characterizing the variability of off-road vehicles emissions under real-world conditions. Mass-based reductions for black carbon EFs were substantially large (above 99%) when DPFs were installed and the vehicles were idling, and the reductions were moderate (in the 20–60% range) for p-DPFs in working operating conditions. The observed high variability in measured EFs also indicates the need for detailed vehicle operation data for accurately estimating emissions from off-road vehicles in emissions inventories.

Implications: Measurements of off-road vehicles used in construction and agricultural activities in Mexico using on-board portable emissions measurements systems (PEMS) showed that these vehicles can be major sources of black carbon and NO_X. Emission factors varied significantly under real-world operating conditions, suggesting the need for detailed vehicle operation data for accurately estimating emissions inventories. Tests conducted in a selected number of sampled vehicles indicated that diesel particle filters (DPFs) are an effective technology for control of diesel particulate emissions and can provide potentially large emissions reduction in Mexico if widely implemented.     

Real-world in-use activity, fuel use, and emissions for nonroad construction vehicles: a case study for excavators

A study design was developed and demonstrated for deployment of a portable emission measurement system (PEMS) for excavators. Excavators are among the most commonly used vehicles in construction activities. The PEMS measured nitric oxide, carbon monoxide, hydrocarbons, carbon dioxide, and opacity-based particulate matter. Data collection, screening, processing, and analysis protocols were developed to assure data quality and to quantify variability in vehicle fuel consumption and emissions rates. The development of data collection procedures was based on securing the PEMS while avoiding disruption to normal vehicle operations. As a result of quality assurance, approximately 90% of the attempted measurements resulted in valid data. On the basis of field data collected for three excavators, an average of 50% of the total nitric oxide emissions was associated with 29% of the time of operation, during which the average engine speed and manifold absolute pressure were significantly higher than corresponding averages for all data. Mass per time emission rates during non-idle modes (i.e., moving and using bucket) were on average 7 times greater than for the idle mode. Differences in normalized average rates were influenced more by intercycle differences than intervehicle differences. This study demonstrates the importance of accounting for intercycle variability in real-world in-use emissions to develop more accurate emission inventories. The data collection and analysis methodology demonstrated here is recommended for application to more vehicles to better characterize real-world vehicle activity, fuel use, and emissions for nonroad construction equipment.     

Real-world fuel use and gaseous emission rates for flex fuel vehicles operated on E85 versus gasoline

Flex fuel vehicles (FFVs) typically operate on gasoline or E85, an 85%/15% volume blend of ethanol and gasoline. Differences in FFV fuel use and tailpipe emission rates are quantified for E85 versus gasoline based on real-world measurements of five FFVs with a portable emissions measurement system (PEMS), supplemented chassis dynamometer data, and estimates from the Motor Vehicle Emission Simulator (MOVES) model. Because of inter-vehicle variability, an individual FFV may have higher nitrogen oxide (NO_x) or carbon monoxide (CO) emission rates on E85 versus gasoline, even though average rates are lower. Based on PEMS data, the comparison of tailpipe emission rates for E85 versus gasoline is sensitive to vehicle-specific power (VSP). For example, although CO emission rates are lower for all VSP modes, they are proportionally lowest at higher VSP. Driving cycles with high power demand are more advantageous with respect to CO emissions, but less advantageous for NO_x. Chassis dynamometer data are available for 121 FFVs at 50,000 useful life miles. Based on the dynamometer data, the average difference in tailpipe emissions for E85 versus gasoline is ?23% for NO_x, ?30% for CO, and no significant difference for hydrocarbons (HC). To account for both the fuel cycle and tailpipe emissions from the vehicle, a life cycle inventory was conducted. Although tailpipe NO_x emissions are lower for E85 versus gasoline for FFVs and thus benefit areas where the vehicles operate, the life cycle NO_x emissions are higher because the NO_x emissions generated during fuel production are higher. The fuel production emissions take place typically in rural areas. Although there are not significant differences in the total HC emissions, there are differences in HC speciation. The net effect of lower tailpipe NO_x emissions and differences in HC speciation on ozone formation should be further evaluated.

Implications: Reported comparisons of flex fuel vehicle (FFV) tailpipe emission rates for E85 versus gasoline have been inconsistent. To date, this is the most comprehensive evaluation of available and new data. The large range of inter-vehicle variability illustrates why prior studies based on small sample sizes led to apparently contradictory findings. E85 leads to significant reductions in tailpipe nitrogen oxide (NO_x) and carbon monoxide (CO) emission rates compared with gasoline, indicating a potential benefit for ozone air quality management in NO_x-limited areas. The comparison of FFV tailpipe emissions between E85 and gasoline is sensitive to power demand and driving cycles.     

Hot emission model for mobile sources: application to the metropolitan region of the city of Santiago, Chile

Depending on the final application, several methodologies for traffic emission estimation have been developed. Emission estimation based on total miles traveled or other average factors is a sufficient approach only for extended areas such as national or worldwide areas. For road emission control and strategies design, microscale analysis based on real-world emission estimations is often required. This involves actual driving behavior and emission factors of the local vehicle fleet under study. This paper reports on a microscale model for hot road emissions and its application to the metropolitan region of the city of Santiago, Chile. The methodology considers the street-by-street hot emission estimation with its temporal and spatial distribution. The input data come from experimental emission factors based on local driving patterns and traffic surveys of traffic flows for different vehicle categories. The methodology developed is able to estimate hourly hot road CO, total unburned hydrocarbons (THCs), particulate matter (PM), and NO(x) emissions for predefined day types and vehicle categories.     

A comparative analysis of using trip-based versus link-based traffic data for regional mobile source emissions estimation

The current method used for calculating vehicle emissions integrates travel data and associated vehicle emission factors. Travel data from traditional travel demand models are normally link-based (e.g., volumes on roadway segments), while California emission factors are trip-based (i.e., average emission factors over an entire trip), creating a mismatch in the modeling interface. Using dynamic simulation for trip assignment, we present a new modeling framework that consistently provides both trip-based and link-based VMT-speed distributions. Using the Sacramento Metropolitan Area and Kern County in California, we demonstrate the feasibility of this new method and quantify the effects of using trip-based versus link-based travel data on regional peak period emission inventories. The comparison results indicate that for the base scenario in both studied regions, the link-based method generally results in higher emissions than the trip-based method. The sensitivities of the link and trip-based methods to road network variations also appear dissimilar. The link-based emissions are more sensitive to facility-related changes, while the trip-based emissions are more sensitive to demand-related changes. This suggests that greater care may need to be taken to specify the effects of this modeling interface issue within the transportation conformity process and subsequent mobile emissions analysis.     

Motor Vehicle Lead Embssbons Bo the Unfed States: An Analysis of Important Determinants,Geographic Patterns and Future Trends

This paper presents a simple method which utilizes composite emission factors to estimate motor vehicle lead emissions for large areas. Composite emission factors incorporate information on vehicle lead emission rates, sales-weighted average fuel economies, annual vehicle travel fractions, and average gasoline lead concentrations. The lead emissions estimation procedure takes as given estimates of motor vehicle travel and, hence, can be applied to any region or road system for which current or projected travel estimates are available. Estimates of motor vehicle lead emissions for major highway systems in individual states and national forecasts of motor vehicle lead emissions for six potential scenarios regarding the future use of lead additives in gasoline are presented to demonstrate the application of the method.     

Testing Re-entrained Aerosol Kinetic Emissions from Roads : a new approach to infer silt loading on roadways

PM₁₀ and PM_2.5 emissions from roadways are currently estimated using the silt loading on the road surface as a surrogate for the emissions potential of road dust. While the United States Environmental Protection Agency prescribes this method in AP-42, there is considerable cost associated with silt loading measurements; it is feasible to sample only a small portion of a roadway network. A new approach for measuring the concentration of suspendable PM₁₀ above road surfaces has been developed to obtain a more spatially representative estimate of a road's potential to emit dust. The Testing Re-entrained Aerosols Kinetic Emissions from Roads (TRAKER) system uses real-time aerosol sensors mounted on a vehicle to measure the concentration of dust suspended from the road while the vehicle is in motion. When coupled with a Global Positioning System (GPS) instrument, TRAKER can be used to efficiently survey the changes in suspendable particles due to varying road conditions over a large spatial domain.In a recent study on paved roads in Las Vegas, the TRAKER system was compared with collocated silt loading measurements. The TRAKER system was also used to survey the relative amounts of suspendable road dust on approximately 300 miles of paved roads. The system provides a unique perspective on road dust sources and their spatial distribution.Results of this study indicated that the difference of the PM₁₀ concentrations measured behind the tire and on the hood is exponentially related to vehicle speed. This was an interesting finding because current AP-42 road dust emissions estimation methods do not include vehicle speed as a factor in the emissions calculations. The experiment also demonstrated that the distribution of suspendable material on roadways is highly variable and that a large number of samples are needed to represent road dust emissions potential on an urban scale for a variety of road and activity conditions.     

On-road comparison of a portable emission measurement system with a mobile reference laboratory for a heavy-duty diesel vehicle

On-road comparisons were made between a mobile emissions laboratory (MEL) meeting federal standards and a portable emissions measurement system (PEMS). These comparisons were made over different conditions; including road grade, vibration, altitude, electric fields, and humidity with the PEMS mounted inside and outside of the tractor's cab. Brake-specific emissions were calculated to explore error differences between the MEL and PEMS during the Not-To-Exceed (NTE) engine operating zone. The PEMS brake-specific NOx (bsNOx) NTE emissions were biased high relative to the MEL and, in general, were about 8% of the 2007 in-use NTE NO_x standard of 2.68 g kW^?1 h^?1 (2.0 g hp^?1 h^?1). The bsCO₂ emissions for the PEMS were also consistently biased high relative to the MEL, with an average deviation of +4% ± 2%. NMHC and CO emissions were very low and typically less than 1% of the NTE threshold. This research was part of a comprehensive program to determine the “allowance” when PEMS are used for in-use compliance testing of heavy-duty diesel vehicles (HDDVs).     

Modeling the impacts of traffic emissions on air toxics concentrations near roadways

The dispersion formulation incorporated in the U.S. Environmental Protection Agency's AERMOD regulatory dispersion model is used to estimate the contribution of traffic-generated emissions of select VOCs – benzene, 1,3-butadiene, toluene – to ambient air concentrations at downwind receptors ranging from 10-m to 100-m from the edge of a major highway in Raleigh, North Carolina. The contributions are computed using the following steps: 1) Evaluate dispersion model estimates with 10-min averaged NO data measured at 7 m and 17 m from the edge of the road during a field study conducted in August, 2006; this step determines the uncertainty in model estimates. 2) Use dispersion model estimates and their uncertainties, determined in step 1, to construct pseudo-observations. 3) Fit pseudo-observations to actual observations of VOC concentrations measured during five periods of the field study. This provides estimates of the contributions of traffic emissions to the VOC concentrations at the receptors located from 10 m to 100 m from the road. In addition, it provides estimates of emission factors and background concentrations of the VOCs, which are supported by independent estimates from motor vehicle emissions models and regional air quality measurements. The results presented in the paper demonstrate the suitability of the formulation in AERMOD for estimating concentrations associated with mobile source emissions near roadways. This paper also presents an evaluation of the key emissions and dispersion modeling inputs necessary for conducting assessments of local-scale impacts from traffic emissions.     

A methodology for the estimation of road transport air emissions in urban areas of Italy

This paper focuses on the top–down approach for estimating road transport emissions at a local level. A bottom–up approach is preferable where the data and information required by estimation methodologies are available at regional territorial level. In the absence of this regional data, emissions are adapted from national to smaller levels by means of proxy variables. This study highlights the importance of an improvement of the top–down methodology and identifies a corrective index to better characterise road transport emissions at a local level. A set of indicators related to transport activities is selected in order to identify homogeneous areas in the Italian territory. For each area, COPERT (Computer Programme to estimate Emissions from Road Traffic) methodology is applied to estimate the atmospheric emissions of different pollutants; the same methodology is used to calculate road transport emissions at a national level. The results, calculated according to vehicle category and driving mode, are compared with those deriving from a spatial disaggregation of national data by means of simple surrogate variables.     

Use of n-fold cross-validation to evaluate three methods to calculate heavy truck annual average daily traffic and vehicle miles traveled

Reliable estimates of heavy-truck volumes in the United States are important in a number of transportation applications including pavement design and management, traffic safety, and traffic operations. Additionally, because heavy vehicles emit pollutants at much higher rates than passenger vehicles, reliable volume estimates are critical to computing accurate inventories of on-road emissions. Accurate baseline inventories are also necessary to forecast future scenarios. The research presented in this paper evaluated three different methods commonly used by transportation agencies to estimate annual average daily traffic (AADT), which is used to determine vehicle miles traveled (VMT). Traffic data from continuous count stations provided by the Iowa Department of Transportation were used to estimate AADT for single-unit and multiunit trucks for rural freeways and rural primary highways using the three methods. The first method developed general expansion factors, which apply to all vehicles. AADT, representing all vehicles, was estimated for short-term counts and was multiplied by statewide average truck volumes for the corresponding roadway type to obtain AADT for each truck category. The second method also developed general expansion factors and AADT estimates. Truck AADT for the second method was calculated by multiplying the general AADT by truck volumes from the short-term counts. The third method developed expansion factors specific to each truck group. AADT estimates for each truck group were estimated from short-term counts using corresponding expansion factors. Accuracy of the three methods was determined by comparing actual AADT from count station data to estimates from the three methods. Accuracy of the three methods was compared using n-fold cross-validation. Mean squared error of prediction was used to estimate the difference between estimated and actual AADT. Prediction error was lowest for the method that developed separate expansion factors for trucks. Implications for emissions estimation using the different methods are also discussed.     

Estimation of vehicular emissions by capturing traffic variations

Increase in traffic volumes and changes in travel-related characteristics increase vehicular emissions significantly. It is difficult, however, to accurately estimate emissions with current practice because of the reliance on travel forecasting models that are based on steady state hourly averages and, thus, are incapable of capturing the effects of traffic variations in the transportation network. This paper proposes an intermediate model component that can provide better estimates of link speeds by considering a set of Emission Specific Characteristics (ESC) for each link. The intermediate model is developed using multiple linear regression; it is then calibrated, validated, and evaluated using a microscopic traffic simulation model. The improved link speed data can then be used to provide better estimates of emissions. The evaluation results show that the proposed emission estimation method performs better than current practice and is capable of estimating time-dependent emissions if traffic sensor data are available as model input.     

Sensitivity analysis and evaluation of microFacCO: a microscale motor vehicle emission factor model for CO emissions

This paper presents a sensitivity analysis of a microscale emission factor model (MicroFacCO) for predicting real-time site-specific motor vehicle CO emissions to input variables, as well as a limited field study evaluation of the model. The sensitivity analysis has shown that MicroFacCO emission estimates are very sensitive to vehicle fleet composition, speed, and ambient temperature. For the present U.S. traffic fleet, the CO emission rate (g/mi) is increased by more than 500% at 5 mph in comparison with a speed greater than 40 mph and by approximately 67% at ambient temperatures of 45 degrees F and > or = 95 degrees F in comparison with an ambient temperature of 75 degrees F. The input variable "emission failure standard rate" is more sensitive to estimating emission rates in the 1990s than in the 2000s. The estimation of emission rates is not very sensitive to relative humidity. MicroFacCO can also be applied to examine the contribution of emission rates per vehicle class and model year. The model evaluation is presented for tunnel studies at five locations. In general, this evaluation study found good agreement between the measured and the modeled emissions. These analyses and evaluations have identified the need for additional studies to update the high-speed (>35 mph) air conditioning (A/C) correction factor and to add effects due to road grades. MicroFacCO emission estimates are very sensitive to the emission standard failure rate. Therefore, the model performance can be greatly improved by using a local emission standard failure rate.     

Impact of freeway weaving segment design on light-duty vehicle exhaust emissions

In the United States, 26% of greenhouse gas emissions is emitted from the transportation sector; these emisssions meanwhile are accompanied by enormous toxic emissions to humans, such as carbon monoxide (CO), nitrogen oxides (NO_x), and hydrocarbon (HC), approximately 2.5% and 2.44% of a total exhaust emissions for a petrol and a diesel engine, respectively. These exhaust emissions are typically subject to vehicles’ intermittent operations, such as hard acceleration and hard braking. In practice, drivers are inclined to operate intermittently while driving through a weaving segment, due to complex vehicle maneuvering for weaving. As a result, the exhaust emissions within a weaving segment ought to vary from those on a basic segment. However, existing emission models usually rely on vehicle operation information, and compute a generalized emission result, regardless of road configuration. This research proposes to explore the impacts of weaving segment configuration on vehicle emissions, identify important predictors for emission estimations, and develop a nonlinear normalized emission factor (NEF) model for weaving segments. An on-board emission test was conducted on 12 subjects on State Highway 288 in Houston, Texas. Vehicles’ activity information, road conditions, and real-time exhaust emissions were collected by on-board diagnosis (OBD), a smartphone-based roughness app, and a portable emission measurement system (PEMS), respectively. Five feature selection algorithms were used to identify the important predictors for the response of NEF and the modeling algorithm. The predictive power of four algorithm-based emission models was tested by 10-fold cross-validation. Results showed that emissions are also susceptible to the type and length of a weaving segment. Bagged decision tree algorithm was chosen to develop a 50-grown-tree NEF model, which provided a validation error of 0.0051. The estimated NEFs are highly correlated with the observed NEFs in the training data set as well as in the validation data set, with the R values of 0.91 and 0.90, respectively.

Implications: Existing emission models usually rely on vehicle operation information to compute a generalized emission result, regardless of road configuration. In practice, while driving through a weaving segment, drivers are inclined to perform erratic maneuvers, such as hard braking and hard acceleration due to the complex weaving maneuver required. As a result, the exhaust emissions within a weaving segment vary from those on a basic segment. This research proposes to involve road configuration, in terms of the type and length of a weaving segment, in constructing an emission nonlinear model, which significantly improves emission estimations at a microscopic level.     

Use of n-Fold Cross-Validation to Evaluate Three Methods to Calculate Heavy Truck Annual Average Daily Traffic and Vehicle Miles Traveled

Abstract

Reliable estimates of heavy-truck volumes in the United States are important in a number of transportation applications including pavement design and management, traffic safety, and traffic operations. Additionally, because heavy vehicles emit pollutants at much higher rates than passenger vehicles, reliable volume estimates are critical to computing accurate inventories of on-road emissions. Accurate baseline inventories are also necessary to forecast future scenarios. The research presented in this paper evaluated three different methods commonly used by transportation agencies to estimate annual average daily traffic (AADT), which is used to determine vehicle miles traveled (VMT). Traffic data from continuous count stations provided by the Iowa Department of Transportation were used to estimate AADT for single-unit and multiunit trucks for rural freeways and rural primary highways using the three methods. The first method developed general expansion factors, which apply to all vehicles. AADT, representing all vehicles, was estimated for short-term counts and was multiplied by statewide average truck volumes for the corresponding roadway type to obtain AADT for each truck category. The second method also developed general expansion factors and AADT estimates. Truck AADT for the second method was calculated by multiplying the general AADT by truck volumes from the short-term counts. The third method developed expansion factors specific to each truck group. AADT estimates for each truck group were estimated from short-term counts using corresponding expansion factors. Accuracy of the three methods was determined by comparing actual AADT from count station data to estimates from the three methods. Accuracy of the three methods was compared using n-fold cross-validation. Mean squared error of prediction was used to estimate the difference between estimated and actual AADT. Prediction error was lowest for the method that developed separate expansion factors for trucks. Implications for emissions estimation using the different methods are also discussed.     

An empirical method for predicting exhaust emissions of regulated pollutants from future vehicle technologies

A methodology is presented for estimating emissions of passenger cars and light commercial vehicles complying with future European Union emission standards, which introduces appropriate reductions over the emission factors of existing vehicle technologies. For three-way catalyst gasoline vehicles, future real-world emissions are assumed to decrease by the same ratio as emission standards. Additionally, distinction is made between emissions during the thermally stabilised emission control system operation and emissions during the cold-start phase, where reductions are mainly due to the decreasing light-off time of future catalyst technologies. In case of diesel vehicles, some of the emission standards, such as 1993 CO, did not represent the actual emission level of vehicles at the time. Therefore, reductions brought over the 1993 emission factor are based both on relevant emission standards reductions and on technological considerations. In a second step, the derived emission factors are corrected to account for vehicle age and fuel quality effects. Vehicle age is introduced in the calculation via emission degradation functions of the total vehicle-accumulated mileage. The impact of improved fuels on the emissions of existing and future vehicle technologies is also modelled by applying correction factors depending on fuel specifications. A number of examples are given by applying the methodology on forecast activity data for different European countries to illustrate the expected effects of future vehicle technologies and fuels.     

A vehicle emissions system using a car simulator and a geographical information system: Part 1--System description and testing

A methodology for estimating vehicular emissions comprising a car simulator, a basic traffic model, and a geographical information system is capable of estimating vehicle emissions with high time and space resolution. Because of the extent of the work conducted, this article comprises two sections: In Part 1 of this work, we describe the system and its components and use examples for testing it. In Part 2 we will study in more detail the emissions of the sample fleet using the system and will make comparisons with another emission model. The experimental data for the car simulator is obtained using on-board measuring equipment and laboratory Fourier transform IR (FTIR) measurements with a dynamometer following typical driving cycles. The car simulator uses this data to generate emission factors every second. These emission factors, together with information on car activity and velocity profiles of highways and residential and arterial roads in Mexico City in conjunction with a basic traffic model, provide emissions per second of a sample fleet. A geographical information system is used to localize these road emissions.