Emission Factor Model Research Articles

Modeling the Effect of New Commuter Bus Service on Demand and Impact on Greenhouse Gas Emissions: Application to Greater Boston

The transportation sector is considered one of the major contributors to greenhouse gas (GHG) emissions in metropolitan areas. Any efforts to reduce these emissions require strategic management of multiple transportation modes. This paper presents a method to identify opportunities to reduce GHG emissions by expanding commuter bus services and providing incentives to shift commuters from private cars to transit. The approach uses a nested multinomial logit model for mode choice in a region where modes include driving alone, carpooling, walking, cycling, and using four possible transit modes (ferry, commuter rail, rapid transit, and bus) by walk-access or drive-access. A model of existing conditions was calibrated with data from the Boston metropolitan area. Using an emission factor model based on average speeds from the California Air Resources Board, the net effect of a new commuter bus service on GHG emissions from transportation is estimated. Potential GHG reductions are weighed against the capital and operating costs of new transit services to quantify the cost-effectiveness of new commuter bus services for isolated origin-destination pairs. This modeling framework is used to optimize fares and bus frequency to identify the corridors with the most cost-effective potential for GHG reduction. Results are presented for the Boston region, demonstrating the feasibility of implementation and the potential magnitude of benefits for cost-effectively reducing GHG emissions associated with transportation. The method is general and can be applied in other cities around the world.

Potential emission reductions by converting agricultural residue biomass to synthetic fuels for vehicles and domestic cooking in China

Vehicle exhaust and transported biomass burning emissions are important air pollution sources in many urban areas, and domestic cooking with biomass fuels causes indoor air pollution in many rural areas. Using agricultural waste-generated synthetic fuels can reduce emissions both from vehicles and biomass burning. To estimate the potential benefits of synthetic diesel in Beijing, the emission factor model for the Beijing vehicle fleet was applied to estimate exhaust emissions for the 2015–2030 period. Compared with 100% petroleum diesel, a 20% synthetic diesel blend reduced diesel fleet emissions by 24% for carbon monoxide, 30% for total hydrocarbons, 5.5% for nitrogen oxides, and 19% for fine particulate matter with an aerodynamic diameter of ≤2.5μm (PM2.5) while using 100% synthetic diesel decreased emissions by 36% for carbon monoxide, 48% for total hydrocarbons, 10% for nitrogen oxides, and 34% for PM2.5. The use of biomass for producing synthetic fuels rather than burning in the field also reduces air pollution. Over 60g of PM2.5 agricultural open-field burning emissions are avoided per liter of synthetic fuel produced. Replacing solid crop residues with synthetic liquid fuels in household cooking would reduce PM2.5 emissions by more than 90%.

Variability in Real-World Activity Patterns of Heavy-Duty Vehicles by Vocation

Vehicle activity is an integral component in the estimation of mobile source emissions and the study of emission inventories. In the Environmental Protection Agency’s (EPA’s) Motor Vehicle Emission Simulator (MOVES) model and the California Air Resources Board’s (CARB’s) Emission Factor (EMFAC) model, vehicle activity is defined for source types, in which vehicles within a source type are assumed to have the same activity. In both of these models, source types for heavy-duty vehicles are limited in number and the assumption that the activity within these source types is similar may be inaccurate. The focus of this paper is to improve vehicle emission estimates by improving characterization of heavy-duty vehicle activity using vehicle vocation. This paper presents results and analysis from the collection of real-world activity data of 90 vehicles from 19 vehicle categories made up from a combination of vehicle vocation, gross vehicle weight, and geographical area— namely, line haul—out of state; line haul—in state; drayage—Northern California; drayage—Southern California; agricultural—Southern Central Valley; heavy construction; concrete mixers; food distribution; beverage distribution; local moving; airport shuttle; refuse; urban buses; express buses; freeway work; sweeping; municipal work; towing; and utility repair. Results show that real-world activity patterns of heavy-duty vehicles vary greatly by vocation and in some cases by geographic region. Vocation-specific activity information can be used to update assumptions in EPA’s MOVES model or CARB’s EMFAC model to address this variability in emission inventory development.

Spatiotemporal Analysis of Carbon Emissions and Carbon Storage Using National Geography Census Data in Wuhan, China

Mapping changes in carbon emissions and carbon storage (CECS) with high precision at a small scale (urban street-block level) can improve governmental policy decisions with respect to the construction of low-carbon cities. In this study, a methodological framework for assessing the carbon budget and its spatiotemporal changes from 2015 to 2017 in Wuhan is proposed, which is able to monitor a large area. To estimate the carbon storage, a comprehensive coefficient model was adopted with carbon density factors and corresponding land cover types. Details regarding land cover were extracted from the Geographic National Census Data (GNCD), including forests, grasslands, croplands, and gardens. For the carbon emissions, an emission-factor model was first used and a spatialization operation was subsequently performed using the geographic location that was obtained from the GNCD. The carbon emissions that were identified in the study are from fossil-fuel consumption, industrial production processes, disposal of urban domestic refuse, and transportation. The final dynamic changes in the CECS, in addition to the net carbon emissions, were monitored and analyzed, yielding temporal and spatial maps with a high-precision at a small scale. The results showed that the carbon storage in Wuhan declined by 2.70% over the three years, whereas the carbon emissions initially increased by 0.2%, and subsequently decreased by 3.1% over this period. The trend in the net carbon emission changes was similar to that of the carbon emissions, demonstrating that the efficiency of carbon reduction was improved during this period. Precise spatiotemporal results at the street-block level can offer insights to governments that are engaged in urban carbon cycle decision making processes, improving their capacities to more effectively manage the spatial distribution of CECS.

The diminishing importance of nitrogen dioxide emissions from road vehicle exhaust

The direct emission of nitrogen dioxide (NO2) from road vehicle exhaust has been an important contributor to near-road ambient concentrations of NO2 in many European cities. Diesel vehicles and their use of emission control technologies such as Diesel Oxidation Catalysts, have dominated the emission of NO2 from road vehicles. In this work, we summarise findings from recent vehicle emission remote sensing measurements in the UK that provide detailed information on the emissions of NO2 and total NOx(NO2 + NO). We show that while new diesel cars and light commercial vehicles are associated with high (typically 30%) proportions of NO2/NOx, the amount of absolute NOx and NO2 emitted by most Euro 6 vehicles has decreased substantially and that absolute emissions of NO2 have been reducing since around 2007. Additionally, we find that the amount of NO2 decreases as the vehicle mileage increases. Taken together, these factors have led to substantial reductions in emissions of NO2 in recent years from light duty diesel vehicles, which has contributed to reduced roadside NO2 concentrations. There is a need however for commonly used emission factor models to account for these changes in emissions of NO2.

Towards a two-part train traffic emissions factor model for airborne wear particles

In 2017 a new railway tunnel containing two stations opened in Stockholm, Sweden. A series of field measurements were carried out on the platforms in this tunnel before and after it was opened for normal traffic. These measurements were used to investigate the contribution of airborne particle emissions from wear processes to total train emissions. This field data was used to develop a two-part train traffic emission factor model for PM10. The two parts are the accumulative effect term (relating to operating distance such as wheel-rail contact and overhead electric line sliding contact) and a brake effect term (relating to the number of braking operations such as brake disc and brake pad contact). The results show that operating a single trial train at a higher than normal frequency on an otherwise empty platform increases the platform particulate concentration until the concentration reaches a steady value. The model suggests that brake emissions account for about 50% of the total emissions measured in the tunnels.

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

ABSTRACTIn 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 (NOx), 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.

Review of modifications to indirect land use change modeling and resulting carbon intensity values within the California Low Carbon Fuel Standard regulations

Abstract The California Low Carbon Fuel Standard (LCFS) regulations were recently re-adopted (2015) with numerous revisions to the 2009 LCFS. Many aspects of the California Air Resources Board's (CARB) approach to modeling indirect land use change (ILUC) emissions have changed, including significant updates to the Global Trade Analysis Project (GTAP) agro-economic model and use of a new emission factor (EF) database derived from the Agro-Economic Zone Emission Factor (AEZ-EF) model. These revisions have resulted in significantly reduced Carbon Intensity (CI) values for many biofuel pathways, making them more attractive to meet greenhouse gas (GHG) reduction targets. The objective of this work was to investigate the most significant changes made to CARB's approach of modeling ILUC, the reasons for these changes, and their impacts on calculated CI values of biofuels. In this effort, land use change (LUC) estimates from GTAP and the AEZ-EF emission factors (EF) were compared to previous values. A new subset of land type, referred to as has been incorporated into the GTAP-BIO model, and now constitutes the bulk of LUC for corn ethanol, soy biodiesel and sugarcane ethanol. However, since EFs of cropland-pasture are not well established, the AEZ-EF model simply applies an EF that is equivalent to 50% of the EF for conversion of pasture to cropland for the same AEZ region. In addition, differences in carbon accounting between perennial crops and annual crops contribute significantly to large reductions in sugarcane CI. These influential assumptions require further investigation.

The impact of considering land intensification and updated data on biofuels land use change and emissions estimates

BackgroundThe GTAP model has been used to estimate biofuel policy induced land use changes and consequent GHG emissions for more than a decade. This paper reviews the history of the model and database modifications and improvements that have occurred over that period. In particular, the paper covers in greater detail the move from the 2004 to the 2011 database, and the inclusion of cropland intensification in the modeling structure.ResultsThe results show that all the changes in the global economy and agricultural sectors cause biofuels induced land use changes and associated emissions can be quite different using the 2011 database versus 2004. The results also demonstrate the importance of including land intensification in the analysis. The previous versions of GTAP and other similar models assumed that changes in harvested area equal changes in cropland area. However, FAO data demonstrate that it is not correct for several important world regions. The model now includes land intensification, and the resulting land use changes and emission values are lower as would be expected.ConclusionsDedicated energy crops are not similar to the first generation feedstocks in the sense that they do not generate the level of market-mediated responses which we have seen in the first-generation feedstocks. The major market-mediated responses are reduced consumption, crop switching, changes in trade, changes in intensification, and forest or pasture conversion. These largely do not apply to dedicated energy corps. The land use emissions for cellulosic feedstocks depend on what we assume in the emissions factor model regarding soil carbon gained or lost in converting land to these feedstocks. We examined this important point for producing bio-gasoline from miscanthus. Much of the literature suggests miscanthus actually sequesters carbon, if grown on the existing active cropland or degraded land. We provide some illustrative estimates for possible assumptions. Finally, it is important to note the importance of the new results for the regulatory process. The current California Air Resources Board carbon scores for corn ethanol and soy biodiesel are 19.8 and 29.1, respectively (done with a model version that includes irrigation). The new model and database carbon scores are 12 and 18, respectively, for corn ethanol and soy biodiesel. Thus, the current estimates values are substantially less than the values currently being used for regulatory purposes.

An emission processing system for air quality modelling in the Mexico City metropolitan area: Evaluation and comparison of the MOBILE6.2-Mexico and MOVES-Mexico traffic emissions.

This article describes the High-Elective Resolution Modelling Emission System for Mexico (HERMES-Mex) model, an emission processing tool developed to transform the official Mexico City Metropolitan Area (MCMA) emission inventory into hourly, gridded (up to 1km2) and speciated emissions used to drive mesoscale air quality simulations with the Community Multi-scale Air Quality (CMAQ) model. The methods and ancillary information used for the spatial and temporal disaggregation and speciation of the emissions are presented and discussed. The resulting emission system is evaluated, and a case study on CO, NO2, O3, VOC and PM2.5 concentrations is conducted to demonstrate its applicability. Moreover, resulting traffic emissions from the Mobile Source Emission Factor Model for Mexico (MOBILE6.2-Mexico) and the MOtor Vehicle Emission Simulator for Mexico (MOVES-Mexico) models are integrated in the tool to assess and compare their performance. NOx and VOC total emissions modelled are reduced by 37% and 26% in the MCMA when replacing MOBILE6.2-Mexico for MOVES-Mexico traffic emissions. In terms of air quality, the system composed by the Weather Research and Forecasting model (WRF) coupled with the HERMES-Mex and CMAQ models properly reproduces the pollutant levels and patterns measured in the MCMA. The system's performance clearly improves in urban stations with a strong influence of traffic sources when applying MOVES-Mexico emissions. Despite reducing estimations of modelled precursor emissions, O3 peak averages are increased in the MCMA core urban area (up to 30ppb) when using MOVES-Mexico mobile emissions due to its VOC-limited regime, while concentrations in the surrounding suburban/rural areas decrease or increase depending on the meteorological conditions of the day. The results obtained suggest that the HERMES-Mex model can be used to provide model-ready emissions for air quality modelling in the MCMA.

Estimating changes in transport CO2 emissions due to changes in weather and climate in Sweden

There is a considerable body of studies on the relationship between daily transport activities and CO2 emissions. However, how these emissions vary in different weather conditions within and between the seasons of the year is largely unknown. Because individual activity–travel patterns are not static but vary in different weather conditions, it is immensely important to understand how CO2 emissions vary due to the change of weather. Using Swedish National Travel Survey data, with emission factors calculated through the European emission factor model ARTEMIS, this study is a first attempt to derive the amount of CO2 emission changes subject to the change of weather conditions. A series of econometric models was used to model travel behaviour variables that are crucial for influencing individual CO2 emissions. The marginal effects of weather variables on travel behaviour variables were derived. The results show an increase of individual CO2 emissions in a warmer climate and in more extreme temperature conditions, whereas increasing precipitation amounts and snow depths show limited effects on individual CO2 emissions. It is worth noting that the change in CO2 emissions in the scenario of a warmer climate and a more extreme temperature tends to be greater than the sum of changes in CO2 emissions in each individual scenario. Given that a warmer climate and more extreme weather could co-occur more frequently in the future, this result suggests even greater individual CO2 emissions than expected in such a future climate.

Impact analysis of traffic-related air pollution based on real-time traffic and basic meteorological information

With the rapid development of urbanization and motorization in China, traffic-related air pollution has become a major component of air pollution which constantly jeopardizes public health. This study proposes an integrated framework for estimating the concentration of traffic-related air pollution with real-time traffic and basic meteorological information and also for further evaluating the impact of traffic-related air pollution. First, based on the vehicle emission factor models sensitive to traffic status, traffic emissions are calculated according to the real-time link-based average traffic speed, traffic volume, and vehicular fleet composition. Then, based on differences in meteorological conditions, traffic pollution sources are divided into line sources and point sources, and the corresponding methods to determine the dynamic affecting areas are also proposed. Subsequently, with basic meteorological data, Gaussian dispersion model and puff integration model are applied respectively to estimate the concentration of traffic-related air pollution. Finally, the proposed estimating framework is applied to calculate the distribution of CO concentration in the main area of Beijing, and the population exposure is also calculated to evaluate the impact of traffic-related air pollution on public health. Results show that there is a certain correlation between traffic indicators (i.e., traffic speed and traffic intensity) of the affecting area and traffic-related CO concentration of the target grid, which indicates the methods to determine the affecting areas are reliable. Furthermore, the reliability of the proposed estimating framework is verified by comparing the predicted and the observed ambient CO concentration. In addition, results also show that the traffic-related CO concentration is higher in morning and evening peak hours, and has a heavier impact on public health within the Fourth Ring Road of Beijing due to higher population density and higher CO concentration under calm wind condition in this area.

High-resolution simulation of link-level vehicle emissions and concentrations for air pollutants in a traffic-populated eastern Asian city

Abstract. Vehicle emissions containing air pollutants created substantial environmental impacts on air quality for many traffic-populated cities in eastern Asia. A high-resolution emission inventory is a useful tool compared with traditional tools (e.g. registration data-based approach) to accurately evaluate real-world traffic dynamics and their environmental burden. In this study, Macau, one of the most populated cities in the world, is selected to demonstrate a high-resolution simulation of vehicular emissions and their contribution to air pollutant concentrations by coupling multimodels. First, traffic volumes by vehicle category on 47 typical roads were investigated during weekdays in 2010 and further applied in a networking demand simulation with the TransCAD model to establish hourly profiles of link-level vehicle counts. Local vehicle driving speed and vehicle age distribution data were also collected in Macau. Second, based on a localized vehicle emission model (e.g. the emission factor model for the Beijing vehicle fleet – Macau, EMBEV–Macau), this study established a link-based vehicle emission inventory in Macau with high resolution meshed in a temporal and spatial framework. Furthermore, we employed the AERMOD (AMS/EPA Regulatory Model) model to map concentrations of CO and primary PM2.5 contributed by local vehicle emissions during weekdays in November 2010. This study has discerned the strong impact of traffic flow dynamics on the temporal and spatial patterns of vehicle emissions, such as a geographic discrepancy of spatial allocation up to 26 % between THC and PM2.5 emissions owing to spatially heterogeneous vehicle-use intensity between motorcycles and diesel fleets. We also identified that the estimated CO2 emissions from gasoline vehicles agreed well with the statistical fuel consumption in Macau. Therefore, this paper provides a case study and a solid framework for developing high-resolution environment assessment tools for other vehicle-populated cities in eastern Asia.

Estimation of NMVOC emissions using artificial neural networks and economical and sustainability indicators as inputs.

This paper describes the development of an artificial neural network (ANN) model based on economical and sustainability indicators for the prediction of annual non-methane volatile organic compounds (NMVOCs) emissions in China for the period 2005-2011 and its comparison with inventory emission factor models. The NMVOCs emissions in China were estimated using ANN model which was created using available data for nine European countries, which NMVOC emission per capita approximately correspond to the Chinese emissions, for the period 2004-2012. The forward input selection strategy was used to compare the significance of particular inputs for the prediction of NMVOC emissions in the nine selected EU countries and China. The final ANN model was trained using only five input variables, and it has demonstrated similar accuracy in predicting NMVOC emissions for the selected EU countries that were used for the development of the model and then for China for which the input dataset was previously unknown to the ANN model. The obtained mean absolute percentage error (MAPE) values were 8% for EU countries and 5% for China. Also, the temporal trend of NMVOC emissions predicted in this study is generally consistent with the trend obtained using inventory emission models. The proposed ANN approach can represent a viable alternative for the prediction of NMVOC emissions at the national level, in particular for developing countries which are usually lacking emission data.

Real-world activity, fuel use, and emissions of diesel side-loader refuse trucks

Abstract Diesel refuse trucks have the worst fuel economy of onroad highway vehicles. The real-world effectiveness of recently introduced emission controls during low speed and low engine load driving has not been verified for these vehicles. A portable emission measurement system (PEMS) was used to measure rates of fuel use and emissions on six side-loader refuse trucks. The objectives were to: (1) characterize activity, fuel use, and emissions; (2) evaluate variability between cycles and trucks; and (3) compare results with the MOVES emission factor model. Quality assured data cover 210,000 s and 550 miles of operation during which the trucks collected 4200 cans and 50 tons of waste material. The average fuel economy was 2.6 mpg. Trash collection contributed 70%–80% of total fuel use and emissions. The daily activity Operating Mode (OpMode) distribution and cycle average fuel use and emissions is different from previously used cycles such as Central Business District (CBD), New York Garbage Truck (NYGT), and William H. Martin (WHM). NOx emission rates for trucks with selective catalytic reduction were over 90% lower than those for trucks without. Similarly, trucks with diesel particulate filters had over 90% lower particulate matter (PM) emissions than trucks without. Compared to unloaded trucks, loaded truck averaged 18% lower fuel economy while NOx and PM emissions were higher by 65% and 16%, respectively. MOVES predicted values are highly correlated to empirical data; however, MOVES estimates are 37% lower for NOx and 300% higher for PM emission rates. The data presented here can be used to develop more representative cycles and improve emission factors for side-loader refuse trucks, which in turn can improve the accuracy of refuse truck emission inventories.

An Exploration of CO2 Emission Models for Horizontal Curves on Freeways

Global climate change attributed by CO2 emissions could seriously impact people’s daily lives. According to the statistics, road transport is one of the largest contributors to greenhouse gases. Simultaneously, the increasing number of vehicles, especially vehicle mileages traveled, will continue to induce more CO2 emissions. It is believed that the driving behavior is one of the most important factors that impact the CO2 emissions. However, some other factors may also contribute to the CO2 emissions, for instance, the geometric elements of a roadway. This paper explores the relationship between the horizontal curve radius and CO2 emissions on freeways from field tests under realworld driving conditions through the development of an Emission Factor Model (EFM). The prediction results were also compared with the measured CO2 emissions based on the default emission rates generated from Motor Vehicle Emission Simulator (MOVES). The modeling results indicate that the EFM has better performance for the estimation of CO2 emissions factors for horizontal curves on freeways under free flow conditions with a predicting error of 5.5%, which is much smaller than the one from a method directly estimated from MOVES (16.8%).

Ultrafine-Particle Emission Factors as a Function of Vehicle Mode of Operation for LDVs Based on Near-Roadway Monitoring.

This paper presents ultrafine-particle (UFP) emission factors (EFs) as a function of vehicle mode of operation (free flow and congestion) using (1) concurrent 5 min measurements of UFPs and carbon monoxide (CO) concentration, wind speed and direction, traffic volume and speed near a roadway that is restricted to light-duty vehicles (LDVs) and (2) inverse dispersion model calculations. Short-term measurements are required to characterize the highly variable and rapidly changing UFP concentration generated by vehicles. Under congestion conditions, the UFP vehicle EFs increased from 0.5 × 10(13) to 2 × 10(13) (particles km(-1) vehicle(-1)) when vehicle flow increased from 5500 to 7500 vehicles/h. For free-flow conditions, the EF is constant at 1.5 × 10(13) (particles km(-1) vehicle(-1)). The analysis is based on the assumption that air-quality models adequately describe the dilution process due to both traffic and atmospheric turbulence. The approach used to verify this assumption was to use an emission factor model to determine EFs for CO and then estimate dilution factors using measured CO concentrations. This procedure eliminates the need to rely only on air quality models to generate dilution factors. The EFs are suitable for fleet emissions under real-world traffic conditions.

Development of database of real-world diesel vehicle emission factors for China

A database of real-world diesel vehicle emission factors, based on type and technology, has been developed following tests on more than 300 diesel vehicles in China using a portable emission measurement system. The database provides better understanding of diesel vehicle emissions under actual driving conditions. We found that although new regulations have reduced real-world emission levels of diesel trucks and buses significantly for most pollutants in China, NOx emissions have been inadequately controlled by the current standards, especially for diesel buses, because of bad driving conditions in the real world. We also compared the emission factors in the database with those calculated by emission factor models and used in inventory studies. The emission factors derived from COPERT (Computer Programmer to calculate Emissions from Road Transport) and MOBILE may both underestimate real emission factors, whereas the updated COPERT and PART5 (Highway Vehicle Particulate Emission Modeling Software) models may overestimate emission factors in China. Real-world measurement results and emission factors used in recent emission inventory studies are inconsistent, which has led to inaccurate estimates of emissions from diesel trucks and buses over recent years. This suggests that emission factors derived from European or US-based models will not truly represent real-world emissions in China. Therefore, it is useful and necessary to conduct systematic real-world measurements of vehicle emissions in China in order to obtain the optimum inputs for emission inventory models.

In-use activity, fuel use, and emissions of heavy-duty diesel roll-off refuse trucks

The objectives of this study were to quantify real-world activity, fuel use, and emissions for heavy duty diesel roll-off refuse trucks; evaluate the contribution of duty cycles and emissions controls to variability in cycle average fuel use and emission rates; quantify the effect of vehicle weight on fuel use and emission rates; and compare empirical cycle average emission rates with the U.S. Environmental Protection Agency’s MOVES emission factor model predictions. Measurements were made at 1 Hz on six trucks of model years 2005 to 2012, using onboard systems. The trucks traveled 870 miles, had an average speed of 16 mph, and collected 165 tons of trash. The average fuel economy was 4.4 mpg, which is approximately twice previously reported values for residential trash collection trucks. On average, 50% of time is spent idling and about 58% of emissions occur in urban areas. Newer trucks with selective catalytic reduction and diesel particulate filter had NOx and PM cycle average emission rates that were 80% lower and 95% lower, respectively, compared to older trucks without. On average, the combined can and trash weight was about 55% of chassis weight. The marginal effect of vehicle weight on fuel use and emissions is highest at low loads and decreases as load increases. Among 36 cycle average rates (6 trucks × 6 cycles), MOVES-predicted values and estimates based on real-world data have similar relative trends. MOVES-predicted CO2 emissions are similar to those of the real world, while NOx and PM emissions are, on average, 43% lower and 300% higher, respectively. The real-world data presented here can be used to estimate benefits of replacing old trucks with new trucks. Further, the data can be used to improve emission inventories and model predictions.Implications: In-use measurements of the real-world activity, fuel use, and emissions of heavy-duty diesel roll-off refuse trucks can be used to improve the accuracy of predictive models, such as MOVES, and emissions inventories. Further, the activity data from this study can be used to generate more representative duty cycles for more accurate chassis dynamometer testing. Comparisons of old and new model year diesel trucks are useful in analyzing the effect of fleet turnover. The analysis of effect of haul weight on fuel use can be used by fleet managers to optimize operations to reduce fuel cost.

Particle number emission factors and volatile fraction of particles emitted from on-road gasoline direct injection passenger vehicles

Particle number emission factors, and the volatility of the particles, are measured on-road for five gasoline direct injection vehicles over a wide range of operating conditions including urban and highway driving conditions. Two condensation particle counters (CPC) were used to measure nascent and non-volatile (thermodenuded) particle concentrations for transient urban and highway tests. To measure the non-volatile concentration and also the volatility of the particles, a thermodenuder was employed to remove the semi-volatile material from the aerosol sample. Rapid accelerations were also studied in more detail by measuring the particle size distributions in real-time using a differential mobility spectrometer (DMS). The ratio of semi-volatile particles to total particle number is generally higher during acceleration followed by the idle operating mode. The number emission factors (for particles larger than 2.5 nm) ranged between 5.46 × 1011–3.50 × 1012/km for freshly emitted (nascent) particles and between 2.87 × 1011–3.31 × 1012/km for non-volatile (thermodenuded) particles. More particles per kilometer are produced during acceleration compared to cruise conditions where the non-volatile particle number emission factor for acceleration is 2.3 and 1.8 times higher than vehicle cruise for urban and highway driving cycles, respectively. Particle number emission factor models are also presented in terms of particle emission rate as a function of vehicle tractive power and also as a function of vehicle specific power as defined for the US Environmental Protection Agency's MOVES model.