On-Road Vehicle Emission Measurement via Remote Sensing

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.

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

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.690-693.1864 ◽

2013 ◽

Vol 690-693 ◽

pp. 1864-1871 ◽

Cited By ~ 1

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.

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Meso-scale on-road vehicle emission inventory approach: a study on Dhaka City of Bangladesh supporting the ‘cause-effect’ analysis of the transport system

Environmental Monitoring and Assessment ◽

10.1007/s10661-016-5151-4 ◽

2016 ◽

Vol 188 (3) ◽

Cited By ~ 5

Author(s):

Asif Iqbal ◽

Andrew Allan ◽

Rocco Zito

Keyword(s):

Transport System ◽

Emission Inventory ◽

Vehicle Emission ◽

Dhaka City ◽

Road Vehicle ◽

Effect Analysis ◽

Meso Scale

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Distance-based emission factors from vehicle emission remote sensing measurements

The Science of The Total Environment ◽

10.1016/j.scitotenv.2020.139688 ◽

2020 ◽

Vol 739 ◽

pp. 139688 ◽

Cited By ~ 2

Author(s):

Jack Davison ◽

Yoann Bernard ◽

Jens Borken-Kleefeld ◽

Naomi J. Farren ◽

Stefan Hausberger ◽

...

Keyword(s):

Remote Sensing ◽

Emission Factors ◽

Vehicle Emission

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On-road vehicle emission inventory and its spatio-temporal variations in North China Plain

Environmental Pollution ◽

10.1016/j.envpol.2020.115639 ◽

2020 ◽

Vol 267 ◽

pp. 115639

Author(s):

Peiyu Jiang ◽

Xi Zhong ◽

Lingyu Li

Keyword(s):

North China Plain ◽

North China ◽

Emission Inventory ◽

Temporal Variations ◽

Vehicle Emission ◽

Road Vehicle ◽

Spatio Temporal

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Have vehicle emissions of primary NO2 peaked?

Faraday Discussions ◽

10.1039/c5fd00162e ◽

2016 ◽

Vol 189 ◽

pp. 439-454 ◽

Cited By ~ 36

Author(s):

David C. Carslaw ◽

Tim P. Murrells ◽

Jon Andersson ◽

Matthew Keenan

Keyword(s):

Remote Sensing ◽

Urban Areas ◽

Vehicle Emissions ◽

Remote Sensing Data ◽

Vehicle Emission ◽

Oxides Of Nitrogen ◽

Vehicle Exhaust ◽

Road Vehicles ◽

Limit Values ◽

Sensing Data

Reducing ambient concentrations of nitrogen dioxide (NO2) remains a key challenge across many European urban areas, particularly close to roads. This challenge mostly relates to the lack of reduction in emissions of oxides of nitrogen (NOx) from diesel road vehicles relative to the reductions expected through increasingly stringent vehicle emissions legislation. However, a key component of near-road concentrations of NO2 derives from directly emitted (primary) NO2 from diesel vehicles. It is well-established that the proportion of NO2 (i.e. the NO2/NOx ratio) in vehicle exhaust has increased over the past decade as a result of vehicle after-treatment technologies that oxidise carbon monoxide and hydrocarbons and generate NO2 to aid the emissions control of diesel particulate. In this work we bring together an analysis of ambient NOx and NO2 measurements with comprehensive vehicle emission remote sensing data obtained in London to better understand recent trends in the NO2/NOx ratio from road vehicles. We show that there is evidence that NO2 concentrations have decreased since around 2010 despite less evidence of a reduction in total NOx. The decrease is shown to be driven by relatively large reductions in the amount of NO2 directly emitted by vehicles; from around 25 vol% in 2010 to 15 vol% in 2014 in inner London, for example. The analysis of NOx and NO2 vehicle emission remote sensing data shows that these reductions have been mostly driven by reduced NO2/NOx emission ratios from heavy duty vehicles and buses rather than light duty vehicles. However, there is also evidence from the analysis of Euro 4 and 5 diesel passenger cars that as vehicles age the NO2/NOx ratio decreases. For example the NO2/NOx ratio decreased from 29.5 ± 2.0% in Euro 5 diesel cars up to one year old to 22.7 ± 2.5% for four-year old vehicles. At some roadside locations the reductions in primary NO2 have had a large effect on reducing both the annual mean and number of hourly exceedances of the European Limit Values of NO2.

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

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198105194100107 ◽

2005 ◽

Vol 1941 (1) ◽

pp. 51-59 ◽

Cited By ~ 9

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.

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