Seyed Reza Safavi

Bio: Seyed Reza Safavi is an academic researcher from Sharif University of Technology. The author has contributed to research in topics: Natural gas vehicle & European union. The author has an hindex of 1, co-authored 1 publications receiving 1 citations.

Effect of Tampering on On-Road and Off-Road Diesel Vehicle Emissions

Abstract: Illegal manipulation (i.e., tampering) of vehicles is a severe problem because vehicle emissions increase orders of magnitude and significantly impact the environment and human health. This study measured the emissions before and after representative approaches of tampering of two Euro 6 Diesel light-duty passenger cars, two Euro VI Diesel heavy-duty trucks, and a Stage IV Diesel non-road mobile machinery (NRMM) agricultural tractor. With tampering of the selective catalytic reduction (SCR) for NOx, the NOx emissions increased by more than one order of magnitude exceeding 1000 mg/km (or mg/kWh) for all vehicles, reaching older Euro or even pre-Euro levels. The tampering of the NOx sensor resulted in relatively low NOx increases, but significant ammonia (NH3) slip. The particle number emissions increased three to four orders of magnitude, reaching 6–10 × 1012 #/km for the passenger car (one order of magnitude higher than the current regulation limit). The tampered passenger car’s NOx and particle number emissions were one order of magnitude higher even compared to the emissions during a regeneration event. This study confirmed that (i) tampering with the help of an expert technician is still possible, even for vehicles complying with the current Euro standards, although this is not allowed by the regulation; (ii) tampering results in extreme increases in emissions.

An Intelligent Visualisation Tool to Analyse the Sustainability of Road Transportation

Abstract: Road transport is an integral part of economic activity and is therefore essential for its development. On the downside, it accounts for 30% of the world’s GHG emissions, almost a third of which correspond to the transport of freight in heavy goods vehicles by road. Additionally, means of transport are still evolving technically and are subject to ever more demanding regulations, which aim to reduce their emissions. In order to analyse the sustainability of this activity, this study proposes the application of novel Artificial Intelligence techniques (more specifically, Machine Learning). In this research, the use of Hybrid Unsupervised Exploratory Plots is broadened with new Exploratory Projection Pursuit techniques. These, together with clustering techniques, form an intelligent visualisation tool that allows knowledge to be obtained from a previously unknown dataset. The proposal is tested with a large dataset from the official survey for road transport in Spain, which was conducted over a period of 7 years. The results obtained are interesting and provide encouraging evidence for the use of this tool as a means of intelligent analysis on the subject of developments in the sustainability of road transportation.