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1.
Sci Total Environ ; 739: 139688, 2020 Oct 15.
Article in English | MEDLINE | ID: mdl-32758932

ABSTRACT

Vehicle emission remote sensing has the potential to provide detailed emissions information at a highly disaggregated level owing to the ability to measure thousands of vehicles in a single day. Fundamentally, vehicle emission remote sensing provides a direct measure of the molar volume ratio of a pollutant to carbon dioxide, from which fuel-based emissions factors can readily be calculated. However, vehicle emissions are more commonly expressed in emission per unit distance travelled e.g. grams per km or mile. To express vehicle emission remote sensing data in this way requires an estimate of the fuel consumption at the time of the emission measurement. In this paper, an approach is developed based on vehicle specific power that uses commonly measured or easily obtainable vehicle information such as vehicle speed, acceleration and mass. We test the approach against 55 independent comprehensive PEMS measurements for Euro 5 and 6 gasoline and diesel vehicles over a wide range of driving conditions and find good agreement between the method and PEMS data. The method is applied to individual vehicle model types to quantify distance-based emission factors. The method will be appropriate for application to larger vehicle emission remote sensing databases, thus extending real-world distance-based vehicle emissions information.

2.
J Vis Exp ; (159)2020 05 22.
Article in English | MEDLINE | ID: mdl-32510496

ABSTRACT

The current particle size threshold of the European Particle Number (PN) emission standards is 23 nm. This threshold could change because future combustion engine vehicle technology may emit large amounts of sub-23 nm particles. The Horizon 2020 funded project DownToTen (DTT) developed a sampling and measurement method to characterize particle emissions in this currently unregulated size range. A PN measurement system was developed based on an extensive review of the literature and laboratory experiments testing a variety of PN measurement and sampling approaches. The measurement system developed is characterized by high particle penetration and versatility, which enables the assessment of primary particles, delayed primary particles, and secondary aerosols, starting from a few nanometers in diameter. This paper provides instruction on how to install and operate this Portable Emission Measurement System (PEMS) for Real Drive Emissions (RDE) measurements and assess particle number emissions below the current legislative limit of 23 nm.


Subject(s)
Automobile Driving , Environmental Monitoring/methods , Nanoparticles/analysis , Nanoparticles/chemistry , Particle Size , Vehicle Emissions/analysis , Air Pollutants/analysis , Air Pollutants/chemistry , Environmental Monitoring/instrumentation , Laboratories , Particulate Matter/analysis , Particulate Matter/chemistry
3.
Sci Total Environ ; 444: 161-76, 2013 Feb 01.
Article in English | MEDLINE | ID: mdl-23268143

ABSTRACT

The emissions from hot driving conditions, in which the exhaust-after-treatment systems are working properly, continue to decrease, which is why the emissions of cold starts have gained in importance. Traffic emission models are used to estimate and predict vehicle fleet emissions and the air quality of countries, regions, cities, etc. In addition to the statistical input of fleet activities, these models are mostly based on the use of separate emission sub-models for hot driving and cold start driving. In reality, the cold start models are almost entirely empirical and of limited accuracy. In this work, a model is developed that is based on physical reasoning, i.e., it is based on energy balances. Because many details, such as the thermal conductivities and the engine control decisions, are unknown, the model must be able to address different simplifications. The model can be parameterized with as few as two tests per vehicle. It is applied to several car samples (six to eight vehicles each) of different technical generations and shows reliable prediction for any combination of the driving pattern (including gradient), the ambient temperature, the stop time before the ride and the duration of the ride (if shorter than the warm-up phase).


Subject(s)
Air Pollutants/analysis , Carbon Dioxide/analysis , Models, Theoretical , Vehicle Emissions/analysis , Automobile Driving
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