scholarly journals Designing On-Road Vehicle Test Programs for the Development of Effective Vehicle Emission Models

2005 ◽  
Vol 1941 (1) ◽  
pp. 51-59 ◽  
Author(s):  
Theodore Younglove ◽  
George Scora ◽  
Matthew Barth
Keyword(s):  
Estimation Error ◽  
Vehicle Emission ◽  
Emission Measurement ◽  
Mobile Source ◽  
Emission Data ◽  
Data Set ◽  
Mobile Vehicle ◽  
Emission Models ◽  
Road Emission ◽  
Source Emission

Mobile source emission models for years have depended on laboratory-based dynamometer data. Recently, however, portable emission measurement systems (PEMS) have become commercially available and in widespread use, and make on-road real-world measurements possible. As a result, the newest mobile source emission models (e.g., U.S. Environmental Protection Agency's mobile vehicle emission simulator) are becoming increasingly dependent on PEMS data. Although on-road measurements are made under more realistic conditions than laboratory-based dynamometer test cycles, they introduce influencing variables that must be carefully measured for properly developed emission models. Further, test programs that simply measure in-use driving patterns of randomly selected vehicles will result in models that can effectively predict current-year emission inventories for typical driving conditions. However, when predicting more aggressive transportation operations than current typical operations (e.g., higher speeds, accelerations), the model predictions will be less certain. In this paper, various issues associated with on-road emission measurements and modeling are presented. Further, an example on-road emission data set and the reduction in estimation error through the addition of a short aggressive driving test to the in-use data are examined. On the basis of these results, recommendations are made on how to improve the on-road test programs for developing more robust emission models.

Influence of Passenger Load on Diesel Bus Emissions

2014 ◽  
Vol 694 ◽  
pp. 13-18 ◽  
Author(s):  
Qian Yu ◽  
Tie Zhu Li ◽  
Yan Ming Ren ◽  
Na Zhu ◽  
Fang Qian
Keyword(s):  
Measurement System ◽  
Real World ◽  
High Speed ◽  
Emission Factors ◽  
Emission Measurement ◽  
Emission Rates ◽  
Emission Data ◽  
Portable Emission Measurement System ◽  
Emission Models ◽  
Road Emission

The purpose of this paper is to analyze the influence of passenger load on diesel bus emissions based on the real-world on-road emission data collected by the Portable Emission Measurement System (PEMS). It is also analyzed whether passenger load affect the accuracy of emission models based on VSP. The results indicate that the influence of passenger load on emission rates of CO2, CO, NOX and HC is various with different speed and acceleration ranges. As for the distance-based emission factors of CO2, CO, NOX and HC, per-passenger emission factors decrease with the rise of passenger load. In addition, it is found that the influence of passenger load can be omitted properly in the emission models of low and middle speed bins. But that can lead to an error reaching up to 49% if the influence of passenger load is neglected in the models of high speed bins.

On-Road Vehicle Emission and Activity Data Collection and Evaluation in Houston, Texas

2005 ◽  
Vol 1941 (1) ◽  
pp. 60-71 ◽  
Author(s):  
Fengxiang Qiao ◽  
Lei Yu ◽  
Michal Vojtisek-Lom
Keyword(s):  
Vehicle Emissions ◽  
Vehicle Emission ◽  
Emission Measurement ◽  
Passenger Cars ◽  
Emission Data ◽  
Activity Data ◽  
Road Vehicle ◽  
System Data ◽  
Vehicle Activity ◽  
Road Emission

The newly developed on-road emission measurement device OEM-2100 was used to collect emissions in the Houston, Texas, area. The device can measure second-by-second fuel consumption and emissions of nitrogen oxides, hydrocarbons, carbon monoxide, carbon dioxide, and particulate matter. A total of 459.0 mi of on-road tests and 813.9 min of idling tests were conducted on three passenger cars and two trucks under 170 different test conditions (170 bags placed). Global Positioning System data were recorded simultaneously in line with the emission data. Data were analyzed by a six-step data processing procedure. The bag-based analysis indicated that vehicle emissions varied strongly, not only with vehicle activity data but also with roadway facility types and vehicle specifications. Spatial distributions of tested emissions illustrated how the emissions altered along the driving routes. The tested vehicle emissions were compared with the MOBILE6.2 estimates, and significant differences were found for all vehicles and for most testing conditions. Among the roadway facility types, the largest difference was on arterial roads, where the tested on-road emissions were higher than MOBILE6.2 estimates. As for idling conditions, the tested emissions were much higher than MOBILE6.2 estimates and indicates a need for further investigation of idling emissions. The large amount of emission and vehicle activity data collected initiated a useful database in Houston with promising potential uses. More on-road vehicle emission tests are necessary to obtain more accurate and reliable local vehicle emission individuality and to establish a richer on-road emission database.

Comparison of the MOVES2010a, MOBILE6.2, and EMFAC2007 mobile source emission models with on-road traffic tunnel and remote sensing measurements

2012 ◽  
Vol 62 (10) ◽  
pp. 1134-1149 ◽  
Author(s):  
Eric M. Fujita ◽  
David E. Campbell ◽  
Barbara Zielinska ◽  
Judith C. Chow ◽  
Christian E. Lindhjem ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Road Traffic ◽  
Mobile Source ◽  
Traffic Tunnel ◽  
Emission Models ◽  
Source Emission

scholarly journals TNO-MACC_II emission inventory; a multi-year (2003–2009) consistent high-resolution European emission inventory for air quality modelling

2014 ◽  
Vol 14 (20) ◽  
pp. 10963-10976 ◽  
Author(s):  
J. J. P. Kuenen ◽  
A. J. H. Visschedijk ◽  
M. Jozwicka ◽  
H. A. C. Denier van der Gon
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
High Resolution ◽  
Emission Inventory ◽  
Point Sources ◽  
Air Quality Modelling ◽  
Emission Data ◽  
Data Set ◽  
Spatially Distributed ◽  
Reported Data

Abstract. Emissions to air are reported by countries to EMEP. The emissions data are used for country compliance checking with EU emission ceilings and associated emission reductions. The emissions data are also necessary as input for air quality modelling. The quality of these "official" emissions varies across Europe. As alternative to these official emissions, a spatially explicit high-resolution emission inventory (7 × 7 km) for UNECE-Europe for all years between 2003 and 2009 for the main air pollutants was made. The primary goal was to supply air quality modellers with the input they need. The inventory was constructed by using the reported emission national totals by sector where the quality is sufficient. The reported data were analysed by sector in detail, and completed with alternative emission estimates as needed. This resulted in a complete emission inventory for all countries. For particulate matter, for each source emissions have been split in coarse and fine particulate matter, and further disaggregated to EC, OC, SO4, Na and other minerals using fractions based on the literature. Doing this at the most detailed sectoral level in the database implies that a consistent set was obtained across Europe. This allows better comparisons with observational data which can, through feedback, help to further identify uncertain sources and/or support emission inventory improvements for this highly uncertain pollutant. The resulting emission data set was spatially distributed consistently across all countries by using proxy parameters. Point sources were spatially distributed using the specific location of the point source. The spatial distribution for the point sources was made year-specific. The TNO-MACC_II is an update of the TNO-MACC emission data set. Major updates included the time extension towards 2009, use of the latest available reported data (including updates and corrections made until early 2012) and updates in distribution maps.

On-Road Gaseous Emission Characteristics of China IV CNG Bus in Shanghai

2013 ◽  
Vol 690-693 ◽  
pp. 1864-1871 ◽  
Author(s):  
Di Ming Lou ◽  
Si Li Qian ◽  
Zhi Yuan Hu ◽  
Pi Qiang Tan
Keyword(s):  
Emission Factor ◽  
High Speed ◽  
Idle Time ◽  
Operating Conditions ◽  
Vehicle Emission ◽  
Emission Characteristics ◽  
Emission Measurement ◽  
Gaseous Emissions ◽  
Average Speed ◽  
Portable Emission Measurement System

In this paper, on-road CO, THC, NOX, CO2 gaseous emissions characteristics of china IV CNG bus were analyzed based on on-road vehicle emission test in the peak and non-peak hours of city traffic in Shanghai using a portable emission measurement system (PEMS). The experimental results reveal that: compared with the condition results in the non-peak hours, it (conditions in the peak hours) have lower average speed, longer idle time and shorter high speed time; the NOX emission factor and rate in the peak hour reduced by 5.66% and 70.2%; the CO, HC, CO2 emissions factors are increased by 47.2%, 32.6%, 20.8%, and the CO, HC, CO2 emissions rates reduced by 1.94%, 26.5%, 48.7% respectively, compared with that in the non-peak hours; The CO, HC, NOX, CO2 emissions factors all decreased as bus speed increased, while they increased as bus acceleration increased; the gaseous emissions rates all increased as bus speed increased; both the emissions factors and emissions rates contributions are highest at accelerations, higher at cruise speeds, and the lowest at decelerations for non-idling buses; the emissions rates under the condition of idling is lowest; gaseous emissions contribution under the various operating conditions has displayed certain correlations with the percentage of the time for different operating conditions.

scholarly journals Application of a Small Unmanned Aerial System to Measure Ammonia Emissions from a Pilot Amine-CO2 Capture System

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6974
Author(s):  
Travis J. Schuyler ◽  
Bradley Irvin ◽  
Keemia Abad ◽  
Jesse G. Thompson ◽  
Kunlei Liu ◽  
...  
Keyword(s):  
Carbon Capture ◽  
Pilot Scale ◽  
Unmanned Aerial Systems ◽  
Emission Measurement ◽  
Atmospheric Gases ◽  
Emission Data ◽  
Emission Sampling ◽  
Comparison Of The Results ◽  
Aerial Systems ◽  
The Impact

The quantification of atmospheric gases with small unmanned aerial systems (sUAS) is expanding the ability to safely perform environmental monitoring tasks and quickly evaluate the impact of technologies. In this work, a calibrated sUAS is used to quantify the emissions of ammonia (NH3) gas from the exit stack a 0.1 MWth pilot-scale carbon capture system (CCS) employing a 5 M monoethanolamine (MEA) solvent to scrub CO2 from coal combustion flue gas. A comparison of the results using the sUAS against the ion chromatography technique with the EPA CTM-027 method for the standard emission sampling of NH3 shows good agreement. Therefore, the work demonstrates the usefulness of sUAS as an alternative method of emission measurement, supporting its application in lieu of traditional sampling techniques to collect real time emission data.

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

2019 ◽  
Vol 2673 (9) ◽  
pp. 51-61 ◽  
Author(s):  
George Scora ◽  
Kanok Boriboonsomsin ◽  
Thomas D. Durbin ◽  
Kent Johnson ◽  
Seungju Yoon ◽  
...  
Keyword(s):  
Real World ◽  
Motor Vehicle ◽  
Activity Patterns ◽  
Geographical Area ◽  
Food Distribution ◽  
Vehicle Emission ◽  
Heavy Duty ◽  
Mobile Source ◽  
Vehicle Activity ◽  
Heavy Duty Vehicles

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.

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