Dataset Information

On-road emissions of passenger cars beyond the boundary conditions of the real-driving emissions test.

ABSTRACT: Passenger cars are an important source of air pollution, especially in urban areas. Recently, real-driving emissions (RDE) test procedures have been introduced in the EU aiming to evaluate nitrogen oxides (NOx) and particulate number (PN) emissions from passenger cars during on-road operation. Although RDE accounts for a large variety of real-world driving, it excludes certain driving situations by setting boundary conditions (e.g., in relation to altitude, temperature or dynamic driving). The present work investigates the on-road emissions of NOx, NO₂, CO, particle number (PN) and CO₂ from a fleet of 19 Euro 6b, 6c and 6d-TEMP vehicles, including diesel, gasoline (GDI and PFI) and compressed natural gas (CNG) vehicles. The vehicles were tested under different on-road driving conditions outside boundaries. These included 'baseline' tests, but also testing conditions beyond the RDE boundary conditions to investigate the performance of the emissions control devices in demanding situations. Consistently low average emission rates of PN and CO were measured from all diesel vehicles tested under most conditions. Moreover, the tested Euro 6d-TEMP and Euro 6c diesel vehicles met the NOx emission limits applicable to Euro 6d-TEMP diesel vehicles during RDE tests (168?mg/km). The Euro 6b GDI vehicle equipped with a gasoline particulate filter (GPF) presented PN emissions?11 #/km. These results, in contrast with previous on-road measurements from earlier Euro 6 vehicles, indicate more efficient emission control technologies are currently being used in diesel and gasoline vehicles. At the same time, the results suggest that particular attention should be given to CO and PN emissions of certain types of vehicles when driven under dynamic conditions, and possibly additional work is necessary. In particular, the emissions of CO (measured in this study during the regulated RDE test, but without an emission limit associated to it) or PN from PFI vehicles (presently not covered by the Euro 6 standard) showed elevated results in some occasions. Emissions of CO were up to 7.5 times higher when the more dynamic tests were conducted and the highest PN emissions were measured from a PFI gasoline vehicle during dynamic driving. Although based on a limited sample of cars, our work points to the relevance of a technology- and fuel-neutral approach to vehicle emission standards, whereby all vehicles must comply with the same emission limits for all pollutants.

SUBMITTER: Suarez-Bertoa R

PROVIDER: S-EPMC6722398 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

On-road emissions of passenger cars beyond the boundary conditions of the real-driving emissions test.

Suarez-Bertoa Ricardo R Valverde Victor V Clairotte Michael M Pavlovic Jelica J Giechaskiel Barouch B Franco Vicente V Kregar Zlatko Z Astorga Covadonga C

Environmental research 20190702

Passenger cars are an important source of air pollution, especially in urban areas. Recently, real-driving emissions (RDE) test procedures have been introduced in the EU aiming to evaluate nitrogen oxides (NOx) and particulate number (PN) emissions from passenger cars during on-road operation. Although RDE accounts for a large variety of real-world driving, it excludes certain driving situations by setting boundary conditions (e.g., in relation to altitude, temperature or dynamic driving). The p ...[more]

PMID: 31377567

Similar Datasets

Project description:BackgroundAutonomous driving is a growing research area that brings benefits in science, economy, and society. Although there are several studies in this area, currently there is no a fully autonomous vehicle, particularly, for off-road navigation. Autonomous vehicle (AV) navigation is a complex process based on application of multiple technologies and algorithms for data acquisition, management and understanding. Particularly, a self-driving assistance system supports key functionalities such as sensing and terrain perception, real time vehicle mapping and localization, path prediction and actuation, communication and safety measures, among others.MethodsIn this work, an original approach for vehicle autonomous driving in off-road environments that combines semantic segmentation of video frames and subsequent real-time route planning is proposed. To check the relevance of the proposal, a modular framework for assistive driving in off-road scenarios oriented to resource-constrained devices has been designed. In the scene perception module, a deep neural network is used to segment Red-Green-Blue (RGB) images obtained from camera. The second traversability module fuses Light Detection And Ranging (LiDAR) point clouds with the results of segmentation to create a binary occupancy grid map to provide scene understanding during autonomous navigation. Finally, the last module, based on the Rapidly-exploring Random Tree (RRT) algorithm, predicts a path. The Freiburg Forest Dataset (FFD) and RELLIS-3D dataset were used to assess the performance of the proposed approach. The theoretical contributions of this article consist of the original approach for image semantic segmentation fitted to off-road driving scenarios, as well as adapting the shortest route searching A* and RRT algorithms to AV path planning.ResultsThe reported results are very promising and show several advantages compared to previously reported solutions. The segmentation precision achieves 85.9% for FFD and 79.5% for RELLIS-3D including the most frequent semantic classes. While compared to other approaches, the proposed approach is faster regarding computational time for path planning.

Project description:Cars have a high share of global transport-related CO2 emissions. To model the market diffusion of new energy carriers and powertrains like electric vehicles, fleet turnover models are commonly used. A decisive influence factor for the substitution dynamics of such transformations is the survival rate of the national car fleet of a country. It represents the likelihood of a car reaching a certain lifespan. Due to a lack of data, current methods to estimate such survival probabilities neglect the imports and exports of used cars. Existing studies are limited to countries with a predominant market of new cars, compared to low numbers of imported and exported used cars. In this study, we resolve this marked simplification and propose a new method to estimate survival probabilities for countries with a high number of imported and exported used cars. Empirical data on the car stock, on inflows of new and used cars, and on outflows of exported and scrapped cars are gathered from 71 national statistics offices. Survival rates of the car fleets of 31 European countries are derived, for which we find a pronounced regional variability. Average lifespans of cars vary from 8.0 to 35.1 years, with a mean of 18.1 years in Western and 28.4 years in Eastern European countries, revealing the high impact of cross-border flows of cars. The study also shows that survival rate estimates can be improved significantly even in the absence of reliable data if a combination of a Weibull and a Gaussian distribution is used. It is likely that the predictive power of existing models (regarding the future environmental impact of car fleets) could be improved significantly if these findings were considered accordingly. The findings of this study can directly be included in fleet turnover and policy assessment models. They also enable the analysis of economic and environmental spillover effects from the imports and exports of used cars between countries. Supplementary Information The online version contains supplementary material available at (10.1186/s12544-020-00464-0).

Dataset Information

On-road emissions of passenger cars beyond the boundary conditions of the real-driving emissions test.

Publications

On-road emissions of passenger cars beyond the boundary conditions of the real-driving emissions test.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets