Improved spatial allocation methodology for on-road mobile emissions

Emissions from automobiles and trucks operating on public roads represent a major portion of the air pollutants included in emission inventories. When emission data are prepared for air quality modeling studies, such as those supporting development of a State Implementation Plan, an emission processor matches the spatial and temporal resolution of the emissions to the requirements of the modeling study. However, the spatial location of vehicular emissions is not known and must be estimated. This paper presents a methodology for determining the spatial distribution of the roads belonging to a road class using geospatial data functions, such as those commonly provided by a geographic information system. Vehicle-miles traveled (VMT) are then allocated to medium-resolution (12 x 12-km) and fine-resolution (4 x 4-km) modeling grids using both this methodology and the existing top-down methodology, which uses population density. The results show a significant difference in the spatial distribution of VMT between these two methodologies. Based upon these results, we recommend using the road class-specific methodology in lieu of the population methodology for spatially allocating vehicular emissions for medium- and finer-resolution modeling grids.     

Effects of improved spatial and temporal modeling of on-road vehicle emissions

Numerous emission and air quality modeling studies have suggested the need to accurately characterize the spatial and temporal variations in on-road vehicle emissions. The purpose of this study was to quantify the impact that using detailed traffic activity data has on emission estimates used to model air quality impacts. The on-road vehicle emissions are estimated by multiplying the vehicle miles traveled (VMT) by the fleet-average emission factors determined by road link and hour of day. Changes in the fraction of VMT from heavy-duty diesel vehicles (HDDVs) can have a significant impact on estimated fleet-average emissions because the emission factors for HDDV nitrogen oxides (NOx) and particulate matter (PM) are much higher than those for light-duty gas vehicles (LDGVs). Through detailed road link-level on-road vehicle emission modeling, this work investigated two scenarios for better characterizing mobile source emissions: (1) improved spatial and temporal variation of vehicle type fractions, and (2) use of Motor Vehicle Emission Simulator (MOVES2010) instead of MOBILE6 exhaust emission factors. Emissions were estimated for the Detroit and Atlanta metropolitan areas for summer and winter episodes. The VMT mix scenario demonstrated the importance of better characterizing HDDV activity by time of day, day of week, and road type. More HDDV activity occurs on restricted access road types on weekdays and at nonpeak times, compared to light-duty vehicles, resulting in 5-15% higher NOx and PM emission rates during the weekdays and 15-40% lower rates on weekend days. Use of MOVES2010 exhaust emission factors resulted in increases of more than 50% in NOx and PM for both HDDVs and LDGVs, relative to MOBILE6. Because LDGV PM emissions have been shown to increase with lower temperatures, the most dramatic increase from MOBILE6 to MOVES2010 emission rates occurred for PM2.5 from LDGVs that increased 500% during colder wintertime conditions found in Detroit, the northernmost city modeled.     

Developing a high-resolution vehicular emission inventory by integrating an emission model and a traffic model: Part 2--A case study in Beijing

A grid-based, bottom-up method has been proposed by combining a vehicle emission model and a travel demand model to develop a high-resolution vehicular emission inventory for Chinese cities. Beijing is used as a case study in which the focus is on fuel consumption and emissions from hot-stabilized activities of light-duty gasoline vehicles (LGVs) in 2005. The total quantity of emissions, emission intensity, and spatial distribution of emissions at 1- by 1-km resolution are presented and compared with results from other inventory methods commonly used in China. The results show that the total daily fuel consumption and vehicular emissions of carbon dioxide, carbon monoxide, hydrocarbons, and oxides of nitrogen from LGVs in the Beijing urban area in 2005 were 1.95 x 10(7) L, 4.28 x 10(4) t, 1.97 x 10(3) t, 0.28 x 10(3) t, and 0.14 x 10(3) t, respectively. Vehicular fuel consumption and emissions show spatial variations that are consistent with the traffic characteristics. The grid-based inventory developed in this study reflects the influence of traffic conditions on vehicle emissions at the microscale and may be applied to evaluate the effectiveness of traffic-related measures on emission control in China.     

A Vehicle Emissions System Using a Car Simulator and a Geographical Information System: Part 1—System Description and Testing

Abstract

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.     

Resolving local-scale emissions for modeling air quality near roadways

A large body of literature published in recent years suggests increased health risk due to exposure of people to air pollution in close proximity to roadways. As a result, there is a need to more accurately represent the spatial concentration gradients near roadways to develop mitigation strategies. In this paper, we present a practical, readily adaptable methodology, using a "bottom-up" approach to develop a detailed highway vehicle emission inventory that includes emissions for individual road links. This methodology also takes advantage of geographic information system (GIS) software to improve the spatial accuracy of the activity information obtained from a Travel Demand Model. In addition, we present an air quality modeling application of this methodology in New Haven, CT. This application uses a hybrid modeling approach, in which a regional grid-based model is used to characterize average local ambient concentrations, and a Gaussian dispersion model is used to provide texture within the modeling domain because of spatial gradients associated with highway vehicle emissions and other local sources. Modeling results show substantial heterogeneity of pollutant concentrations within the modeling domain and strong spatial gradients associated with roadways, particularly for pollutants dominated by direct emissions.     

Modelling the spatial distribution of SO2 and NOx emissions in Ireland

The spatial distributions of sulphur dioxide (SO2) and nitrogen oxides (NOx) emissions are essential inputs to models of atmospheric transport and deposition. Information of this type is required for international negotiations on emission reduction through the critical load approach. High-resolution emission maps for the Republic of Ireland have been created using emission totals and a geographical information system, supported by surrogate statistics and landcover information. Data have been subsequently allocated to the EMEP 50 x 50-km grid, used in long-range transport models for the investigation of transboundary air pollution. Approximately two-thirds of SO2 emissions in Ireland emanate from two grid-squares. Over 50% of total SO2 emissions originate from one grid-square in the west of Ireland, where the largest point sources of SO2 are located. Approximately 15% of the total SO2 emissions originate from the grid-square containing Dublin. SO2 emission densities for the remaining areas are very low, < 1 t km-2 year-1 for most grid-squares. NOx emissions show a very similar distribution pattern. However, NOx emissions are more evenly spread over the country, as about 40% of total NOx emissions originate from road transport.     

Developing a High-Resolution Vehicular Emission Inventory by Integrating an Emission Model and a Traffic Model: Part 2—A Case Study in Beijing

Abstract

A grid-based, bottom-up method has been proposed by combining a vehicle emission model and a travel demand model to develop a high-resolution vehicular emission inventory for Chinese cities. Beijing is used as a case study in which the focus is on fuel consumption and emissions from hot-stabilized activities of light-duty gasoline vehicles (LGVs) in 2005. The total quantity of emissions, emission intensity, and spatial distribution of emissions at 1- by 1-km resolution are presented and compared with results from other inventory methods commonly used in China. The results show that the total daily fuel consumption and vehicular emissions of carbon dioxide, carbon monoxide, hydrocarbons, and oxides of nitrogen from LGVs in the Beijing urban area in 2005 were 1.95 × 10⁷ L, 4.28 × 10⁴ t, 1.97 × 10³ t, 0.28 × 10³ t, and 0.14 × 10³ t, respectively. Vehicular fuel consumption and emissions show spatial variations that are consistent with the traffic characteristics. The grid-based inventory developed in this study reflects the influence of traffic conditions on vehicle emissions at the microscale and may be applied to evaluate the effectiveness of traffic-related measures on emission control in China.     

Use of Subsampled Traffic Data to Estimate Roadway Emissions,Including Conversion to MOBILE6 Vehicle Classifications

Abstract

Air quality is degraded by many factors, among which the emissions from on‐road vehicles play a significant role. Timely and accurate estimate of such emissions becomes very important for policy‐making and effective control measures. However, lack of traffic data and outdated emission software make this task difficult. This research has demonstrated a new method that facilitates the vehicular emission inventories at the local level by using shorter-time Highway Performance Monitoring System (HPMS) traffic data along with the latest U.S. Environment Protection Agency (EPA) emission modeling software, MOBILE6. The conversion methodology was developed for converting readily available HPMS traffic volume data into EPA MOBILE-based traffic classifications, and a corresponding software program was written for automating the process. EPA MOBILE6 model was used to obtain emissions of nitrogen oxides (NO_x), volatile organic compound (VOC), and cabon monoxide (CO) emitted by the parent traffic and subsampled traffic data, and these emissions were additionally compared. The case study has shown that the difference of the magnitude between the emission estimates produced by certain subsampled and parent traffic data are minor, indicating that subsampled HPMS data can be used for reporting parent traffic emissions. It was also observed that traffic emissions follow a Weibull distribution, and NO_x emissions were more sensitive to the traffic data composition than VOC and CO. Lastly, use of average emission values of 20 or 30 consecutive minutes appears to be valid for representing hourly emissions.     

A Modal Approach to Vehicular Emissions and Fuel Consumption Model Development

Abstract

This study reports on the analysis of emissions and fuel consumption from motor vehicles using a modal approach. The four standard driving modes are idling, accelerating, cruising, and decelerating. On‐road data were collected using instrumented test vehicles traveling many times through the urban areas of Hong Kong. A model was developed for estimating vehicular fuel consumption and emissions as a function of instantaneous speed and driving mode. Piecewise interpolation functions were proposed for each nonidling driving mode. Idling emission and fuel consumption rates were estimated as negative exponential functions of idling time. Preliminary modeling results showed good agreements for the test vehicles and indicated that the on‐road measurements are feasible for the development of modal emission and fuel consumption models.     

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.     

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.     

Daily Variability of Motor Vehicle Emissions Derived from Traffic Counter Data

ABSTRACT

This work studied the daily variability of mobile sources in rural and urban areas, in and around the Atlanta Metropolitan Area. Traffic counter data collected during the 1992 Southern Oxidants Study Atlanta Intensive Study were used to analyze the spatial and temporal distribution of traffic volume. A simple method to study the daily variability of mobile emissions from the different types of urban and rural roads is presented. The method is based on hourly traffic volume data and emission factors and it has been generalized to describe the daily variability of mobile emissions for urban and rural areas and for the whole modeling domain. Implications of this study for improving mobile emission inventories are also discussed.     

The measurement of roadway PM10 emission rates using atmospheric tracer ratio techniques

In this work, stationary and mobile point source tracer release techniques have been used to determine PM₁₀ emission rates from four-lane commercial/residential paved roads under sanded and unsanded conditions, and from unpaved roads relative to site-specific vehicular and ambient parameters. Measured street (4 + lanes; ? 10,000 vehicles per day) emission factors for unsanded and sanded roads were 40 and 20% lower, respectively, than the EPA approved reference value. The sanded road emission factor was approximately 40% higher than that for the unsanded road. These results indicate a consistent relationship between PM₁₀ and relative humidity under unsanded conditions. There is some evidence to suggest that street sweeping has a measurable effect on PM,, emission reduction during periods of low relative humidity (i.e. ? 30%). Within the constraints imposed by the variable experimental conditions, the emission factors determined for unpaved roads agreed reasonably well with the unpaved road empirical formula. Limited correlations were observed with ambient meteorological parameters. The capability of the “upwind-dowiawind” concentration modeling method to predict accurate emission was tested using a Gaussian dispersion model (SIMFLUX). Predictions agreed well with the experimentally determined emission factors.     

Improving the accuracy of emission inventories with a machine-learning approach and investigating transferability across cities

ABSTRACT

This study presents a novel method for integrating the output of a microscopic emission modeling approach with a regional traffic assignment model in order to achieve an accurate greenhouse gas (GHG, in CO_2-eq) emission estimate for transportation in large metropolitan regions. The CLustEr-based Validated Emission Recalculation (CLEVER) method makes use of instantaneous speed data and link-based traffic characteristics in order to refine on-road GHG inventories. The CLEVER approach first clusters road links based on aggregate traffic characteristics, then assigns representative emission factors (EFs), calibrated using the output of microscopic emission modeling. In this paper, cluster parameters including number and feature vector were calibrated with different sets of roads within the Greater Toronto Area (GTA), while assessing the spatial transferability of the algorithm. Using calibrated cluster sets, morning peak GHG emissions in the GTA were estimated to be 2,692 tons, which is lower than the estimate generated by a traditional, average speed approach (3,254 tons). Link-level comparison between CLEVER and the average speed approach demonstrates that GHG emissions for uncongested links were overestimated by the average speed model. In contrast, at intersections and ramps with more congested links and interrupted traffic flow, the average speed model underestimated GHG emissions. This proposed approach is able to capture variations in traffic conditions compared to the traditional average speed approach, without the need to conduct traffic simulation.

Implications: A reliable traffic emissions estimate is necessary to evaluate transportation policies. Currently, accuracy and transferability are major limitations in modeling regional emissions. This paper develops a hybrid modeling approach (CLEVER) to bridge between computational efficiency and estimation accuracy. Using a k-means clustering algorithm with street-level traffic data, CLEVER generates representative emission factors for each cluster. The approach was validated against the baseline (output of a microscopic emission model), demonstrating transferability across different cities .     

Spatial surrogate for domestic combustion’s air emissions: A case study from Silesian Metropolis,Poland

The aim of this study is improving currently applied methodology for spatial disaggregation, as well as mapping air emission inventories by taking into account the auxiliary spatial data on population density. District heating infrastructure occurring in more populated areas changes distinctly the spatial distribution of estimated air emission; however, it does not change the initial estimate. That means the total, disaggregated value is constant. Considered sources of domestic combustion are located in the central part of the Silesian Metropolis, in the southern part of Poland. A large part of this area is strongly urbanized and supplied with heat (hot water) from the district heating system. Data on population density help to determine the area within which the dwellers use heat energy and hot water supplied by the heating infrastructure, apart from heating with small domestic boilers and stoves. This causes the domestic combustion’s emission impact within the distinguished area to be significantly lower in comparison to the official guidelines on air emission inventories. The important differences in spatial air emissions distributions calculated using a top-down approach are found for strongly urbanized areas supplied partly with heat and hot water from the district heating network. This fact should be taken into account when preparing detailed, high-resolution emission inventories for air regional and local quality modeling.

Implications: The spatial issues connected with elaboration of the high-resolution emission inventories are presented for the example of the populated area of the Silesian Metropolis (Poland). Spatial distribution of the population density is used to determine the area supplied with heat and hot water from the district heating system. It changes distinctly the spatial distribution of the air emission from small residential combustion sources.     

Measurements and modelling of PM2.5 concentrations near a major road in Kuopio,Finland

A particle measurement campaign was conducted in a suburban environment near a major road in Kuopio, Central Finland from 3 August to 9 September 1999. The mass concentrations of fine particles (PM_2.5) were measured simultaneously at distances of 12, 25, 52 and 87 m from the centre of a major road at a height of 1.8 m, using identical samplers. The concentration measurements were conducted during 16 daytime hours (from 6.00 a.m. to 10.00 p.m.) for 27 days. Traffic flows and relevant meteorological parameters were measured on-site; meteorological measurements from a nearby synoptic weather station were also utilised. We also suggest a preliminary model for predicting the concentrations of PM_2.5 and apply this model in order to analyse the measured data. The regionally and long-range transported contribution was evaluated on the basis of a semi-empirical mathematical model utilising as input values the daily sulphate, nitrate and ammonium measurements at the EMEP stations (Co-operative programme for monitoring and evaluation of the long-range transmission of air pollutants in Europe). The influence of primary vehicular emissions from the nearest roads was evaluated using a roadside emission and dispersion model, CAR-FMI, in combination with a meteorological pre-processing model, MPP-FMI. The contribution of non-exhaust particulate matter emissions (including resuspension of particulate matter from road surfaces) was estimated simply to be directly proportional to the concentrations originating from primary vehicular emissions. Comparison of the predicted results and measurements yields information on the relative importance of various source categories of the measured concentrations of PM_2.5. The regionally and long-range transported contribution, the primary and non-exhaust vehicular emissions, and other sources were estimated to contribute on average 41±6%, 33±6% and 26±7% of the observed PM_2.5 concentrations, respectively. The model presented could also be applied in other European cities for analysing the source contributions to measured fine particulate matter concentrations.     

PM Emissions Emanating from Limited-Access Highways

ABSTRACT

Motor vehicle contributions to primary particulate matter (PM) emissions include exhaust, tire wear, brake and clutch wear, and resuspended road dust. Relatively few field studies have been conducted to quantify fleetaverage exhaust emissions for actual on-road conditions. Therefore, direct measurements of motor vehicle-related PM emissions are warranted. In this study, PM₁₀ and PM_2.5 mass concentrations were measured near two major highways in the St. Louis area over the period from February–April 1997. Samplers were deployed both upwind and downwind of the roadways to capture the transport and dispersion of PM with distance from the roadway. The observed microscale concentration fields were compared to estimates using the PART5 emission factor model together with the CALINE4 highway dispersion model. Traffic- induced PM mass concentrations observed downwind of the roadway were always less than PART5/CALINE4 predictions; average percent differences for observed traffic-induced mass concentrations compared to predicted values were ?34% for PM_2.5 and -70% for PM₁₀. In most cases, the observed PM concentration decay with increasing distance from the roadway was steeper than predicted by dispersion modeling. Motor vehicle-induced emission factors were reconstructed by fitting CALINE4 to the observed concentration data with the emission factor as the sole adjustable parameter. Reconstructed fleet-average motor vehicle emission factors for the urban interstate highway were 0.03–0.04 g/VMT for both PM_2.5 and PM₁₀, while the fleet-average emission factors for the rural interstate highway were 0.2 and 0.3 g/VMT for PM_2.5 and PM₁₀, respectively.     

Modeling emissions for three-dimensional atmospheric chemistry transport models

Poor air quality is still a threat for human health in many parts of the world. In order to assess measures for emission reductions and improved air quality, three-dimensional atmospheric chemistry transport modeling systems are used in numerous research institutions and public authorities. These models need accurate emission data in appropriate spatial and temporal resolution as input. This paper reviews the most widely used emission inventories on global and regional scales and looks into the methods used to make the inventory data model ready. Shortcomings of using standard temporal profiles for each emission sector are discussed, and new methods to improve the spatiotemporal distribution of the emissions are presented. These methods are often neither top-down nor bottom-up approaches but can be seen as hybrid methods that use detailed information about the emission process to derive spatially varying temporal emission profiles. These profiles are subsequently used to distribute bulk emissions such as national totals on appropriate grids. The wide area of natural emissions is also summarized, and the calculation methods are described. Almost all types of natural emissions depend on meteorological information, which is why they are highly variable in time and space and frequently calculated within the chemistry transport models themselves. The paper closes with an outlook for new ways to improve model ready emission data, for example, by using external databases about road traffic flow or satellite data to determine actual land use or leaf area. In a world where emission patterns change rapidly, it seems appropriate to use new types of statistical and observational data to create detailed emission data sets and keep emission inventories up-to-date.

Implications: Emission data are probably the most important input for chemistry transport model (CTM) systems. They need to be provided in high spatial and temporal resolution and on a grid that is in agreement with the CTM grid. Simple methods to distribute the emissions in time and space need to be replaced by sophisticated emission models in order to improve the CTM results. New methods, e.g., for ammonia emissions, provide grid cell–dependent temporal profiles. In the future, large data fields from traffic observations or satellite observations could be used for more detailed emission data.     

Modelling the spatial distribution of ammonia emissions in the UK

Ammonia emissions (NH3) are characterised by a high spatial variability at a local scale. When modelling the spatial distribution of NH3 emissions, it is important to provide robust emission estimates, since the model output is used to assess potential environmental impacts, e.g. exceedance of critical loads. The aim of this study was to provide a new, updated spatial NH3 emission inventory for the UK for the year 2000, based on an improved modelling approach and the use of updated input datasets. The AENEID model distributes NH3 emissions from a range of agricultural activities, such as grazing and housing of livestock, storage and spreading of manures, and fertilizer application, at a 1-km grid resolution over the most suitable landcover types. The results of the emission calculation for the year 2000 are analysed and the methodology is compared with a previous spatial emission inventory for 1996.     

Development and Preliminary Evaluation of a Particulate Matter Emission Factor Model for European Motor Vehicles

ABSTRACT

Although modeling of gaseous emissions from motor vehicles is now quite advanced, prediction of particulate emissions is still at an unsophisticated stage. Emission factors for gasoline vehicles are not reliably available, since gasoline vehicles are not included in the European Union (EU) emission test procedure. Regarding diesel vehicles, emission factors are available for different driving cycles but give little information about change of emissions with speed or engine load. We have developed size-specific speed-dependent emission factors for gasoline and diesel vehicles. Other vehicle-generated emission factors are also considered and the empirical equation for re-entrained road dust is modified to include humidity effects. A methodology is proposed to calculate modal (accelerating, cruising, or idling) emission factors. The emission factors cover particle size ranges up to 10 um, either from published data or from user-defined size distributions.

A particulate matter emission factor model (PMFAC), which incorporates virtually all the available information on particulate emissions for European motor vehicles, has been developed. PMFAC calculates the emission factors for five particle size ranges [i.e., total suspended particulates (TSP), PM₁₀, PM₅, PM₂₅, and PM₁] from both vehicle exhaust and nonexhaust emissions, such as tire wear, brake wear, and re-entrained road dust. The model can be used for an unlimited number of roads and lanes, and to calculate emission factors near an intersection in user-defined elements of the lane. PMFAC can be used for a variety of fleet structures. Hot emission factors at the user-defined speed can be calculated for individual vehicles, along with relative cold-to-hot emission factors. The model accounts for the proportions of distance driven with cold engines as a function of ambient temperature and road type (i.e., urban, rural, or motorway).

A preliminary evaluation of PMFAC with an available dispersion model to predict the airborne concentration in the urban environment is presented. The trial was on the A6 trunk road where it passes through Loughborough, a medium-size town in the English East Midlands. This evaluation for TSP and PM₁₀ was carried out for a range of traffic fleet compositions, speeds, and meteorological conditions. Given the limited basis of the evaluation, encouraging agreement was shown between predicted and measured concentrations.