Dataset Information

NOx, NH3, N2O and PN real driving emissions from a Euro VI heavy-duty vehicle. Impact of regulatory on-road test conditions on emissions.

ABSTRACT: Euro VI emission standards for heavy-duty vehicles (HDVs) introduced for the first time limits for solid particle number (PN) and NH3 emissions. EU regulation also includes a Portable Emissions Measurement System (PEMS) based test at type approval, followed by in-service conformity (ISC) testing. A comprehensive study on the real-time on-road emissions of NOx, NH3, N2O and PN from a Euro VI HDV equipped with a Diesel Oxidation Catalyst (DOC), a Diesel Particle Filter (DPF), a Selective Catalytic Reduction (SCR) system and an Ammonia Oxidation Catalyst (AMOX) is presented. Our analyses revealed that up to 85% of the NOx emissions measured during the tests performed are not taken into consideration if the boundary conditions for data exclusion set in the current legislation are applied. Moreover, it was found that the highest NOx emissions were measured during urban operation. Analyses show that a large fraction urban of operation is not considered when 20% power threshold as boundary condition is applied. They also show that cold start emissions account for a large fraction of the total NOx emitted. Low emissions of PN (2.8×1010 to 6.5×1010#/kWh) and NH3 (1.0 to 2.2ppm) were obtained during the on-road tests, suggesting effectiveness of the vehicle's after-treatment (DPF and AMOX). Finally, a comparison between speed-based (as currently defined by Euro VI legislation) and land-use-based (using Geographic Information System (GIS)) calculation of shares of operation was performed. Results suggest that using GIS to categorize the shares of operation could result in different interpretations depending on the criteria adopted for their definition.

SUBMITTER: Mendoza-Villafuerte P

PROVIDER: S-EPMC5617885 | biostudies-other | 2017 Dec

REPOSITORIES: biostudies-other

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NOx, NH3, N2O and PN real driving emissions from a Euro VI heavy-duty vehicle. Impact of regulatory on-road test conditions on emissions.

Mendoza-Villafuerte Pablo P Suarez-Bertoa Ricardo R Giechaskiel Barouch B Riccobono Francesco F Bulgheroni Claudia C Astorga Covadonga C Perujo Adolfo A

The Science of the total environment 20170727

Euro VI emission standards for heavy-duty vehicles (HDVs) introduced for the first time limits for solid particle number (PN) and NH3 emissions. EU regulation also includes a Portable Emissions Measurement System (PEMS) based test at type approval, followed by in-service conformity (ISC) testing. A comprehensive study on the real-time on-road emissions of NOx, NH3, N2O and PN from a Euro VI HDV equipped with a Diesel Oxidation Catalyst (DOC), a Diesel ...[more]

PMID: 28763652

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Project description:BackgroundHeavy goods vehicle drivers are an influential driving population in Nepal, with over 90% of goods in the country are transported by road. Due to the time spent on the road, drivers have long periods of exposure to the risk of crash involvement. The study explores the perceptions and experiences of heavy goods vehicle drivers and representatives from their professional association regarding road danger.MethodsWe conducted semi-structured interviews with fifteen heavy goods vehicle drivers regularly driving on the East-West highway of Makwanpur District, Nepal. A focus group was conducted with eleven members from a major transportation entrepreneur's association in Nepal. The focus group and interviews were audio-recorded, transcribed, translated, and analyzed using thematic analysis.ResultsFour themes were developed- assumptions of blame; perceptions of safety culture in the trucking industry; influence of road infrastructure; and behaviours of road users. The road and traffic environment, enforcement, and the safety culture in the heavy vehicle industry not only influenced the attitudes of the road users towards traffic safety but also legitimized and encouraged behaviours that affect safety. General and industry-related road safety improvements suggested by participants included: making provision for heavy good vehicles parking areas, separating the highway with a median strip, improving crash investigation capacity, conducting road safety awareness and training programs, strictly enforcing the speed limit and laws about driving under the influence of alcohol/drugs, and formulating strategies to create a safe, supportive working environment in the heavy vehicle industry.ConclusionHeavy goods vehicle drivers and members of the professional association can provide rich information regarding the barriers and facilitators of road risk in Nepal. Their perceptions and opinions can contribute to devising interventions at individual, societal, organizational, and governmental levels, and inform efforts to develop a positive safety culture within the heavy vehicle transport industry.

Project description:Exposure to vehicular emissions has been linked to numerous adverse health effects. In response to the arising concerns, near-road monitoring is conducted to better characterize the impact of mobile source emissions on air quality and exposure in the near-road environment. An intensive measurement campaign measured traffic-related air pollutants (TRAPs) and related data (e.g., meteorology, traffic, regional air pollutant levels) in Atlanta, along one of the busiest highway corridors in the US. Given the complexity of the near-road environment, the study aimed to compare two near-road monitors, located in close proximity to each other, to assess how observed similarities and differences between measurements at these two sites inform the siting of other near-road monitoring stations. TRAP measurements, including carbon monoxide (CO) and nitrogen dioxide (NO2), are analyzed at two roadside monitors in Atlanta, GA located within 325m of each other. Both meteorological and traffic conditions were monitored to assess the temporal impact of these factors on traffic-related pollutant concentrations. The meteorological factors drove the diurnal variability of primary pollutant concentration more than traffic count. In spite of their proximity, while the CO and NO2 concentrations were correlated with similar diurnal variations, pollutant concentrations at the two closely sited monitors differed, likely due to the differences in the siting characteristics reducing the dispersion of the primary emissions out of the near-road environment. Overall, the near-road TRAP concentrations at all sites were not as elevated as seen in prior studies, supporting that decreased vehicle emissions have led to significant reductions in TRAP levels, even along major interstates. Further, the differences in the observed levels show that use of single near-road observations will not capture pollutant levels representative of the local near-road environment and that additional approaches (e.g., air quality models) are needed to characterize exposures.

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: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 < 6 × 1011 #/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.

Dataset Information

NOx, NH3, N2O and PN real driving emissions from a Euro VI heavy-duty vehicle. Impact of regulatory on-road test conditions on emissions.

Publications

NO<sub>x</sub>, NH<sub>3</sub>, N<sub>2</sub>O and PN real driving emissions from a Euro VI heavy-duty vehicle. Impact of regulatory on-road test conditions on emissions.

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