In this study CO2 and NOx emissions from 149 Euro 5 and 6 diesel, gasoline and hybrid passenger cars were compared using a Portable Emissions Measurement System (PEMS). The models sampled accounted for 56% of all passenger cars sold in Europe in 2016. We found gasoline vehicles had CO2 emissions 13-66% higher than diesel. During urban driving, the average CO2 emission factor was 210.5 (sd. 47) gkm-1 for gasoline and 170.2 (sd. 34) gkm-1 for diesel. Half the gasoline vehicles tested were Gasoline Direct Injection (GDI). Euro 6 GDI engines <1.4ℓ delivered ~17% CO2 reduction compared to Port Fuel Injection (PFI). Gasoline vehicles delivered an 86-96% reduction in NOx emissions compared to diesel cars. The average urban NOx emission from Euro 6 diesel vehicles 0.44 (sd. 0.44) gkm-1 was 11 times higher than for gasoline 0.04 (sd. 0.04) gkm-1. We also analysed two gasoline-electric hybrids which out-performed both gasoline and diesel for NOx and CO2. We conclude action is required to mitigate the public health risk created by excessive NOx emissions from modern diesel vehicles. Replacing diesel with gasoline would incur a substantial CO2 penalty, however greater uptake of hybrid vehicles would likely reduce both CO2 and NOx emissions. Discrimination of vehicles on the basis of Euro standard is arbitrary and incentives should promote vehicles with the lowest real-world emissions of both NOx and CO2.

Real world emissions of oxides of nitrogen (NOx) often greatly exceed those achieved in the laboratory based type approval process. In this paper the real world emissions from a substantial sample of the latest Euro 6 diesel passenger cars are presented with a focus on NOx and primary NO2. Portable Emissions Measurement System (PEMS) data is analysed from 39 Euro 6 diesel passenger cars over a test route comprised of urban and motorway sections. The sample includes vehicles installed with exhaust gas recirculation (EGR), lean NOx traps (LNT), or selective catalytic reduction (SCR). The results show wide variability in NOx emissions from 1 – 22 times the type approval limit. The average NOx emission, 0.36 (sd. 0.36) g km-1, is 4.5 times the Euro 6 limit. The average fraction primary NO2 (fNO2) is 44 (sd. 20) %. Higher emissions during the urban section of the route are attributed to an increased number of acceleration events. Comparisons between PEMS measurements and COPERT speed dependent emissions factors show PEMS measurements to be on average 1.6 times higher than COPERT estimates for NOx and 2.5 times for NO2. However, by removing the 5 most polluting vehicles average emissions were reduced considerably

Air pollution problems persist in many cities throughout the world, despite drastic reductions in regulated emissions of criteria pollutants from vehicles when tested on standardised driving cycles. New vehicle emissions regulations in the European Union and United States require the use of OBD and portable emissions measurement systems (PEMS) to confirm vehicles meet specified limits during on-road operation. The resultant in-use testing will yield a large amount of OBD and PEMS data across a range of vehicles. If used properly, the availability of OBD and PEMS data could enable greater insight into the nature of real-world emissions and allow detailed modelling of vehicle energy use and emissions. This paper presents a methodology to use this data to create engine maps of fuel use and emissions of nitrous oxides (NOx), carbon dioxide (CO2) and carbon monoxide (CO). Effective gear ratios, gearbox shift envelopes, candidate engine maps and a set of vehicle configurations are simulated over driving cycles using the ADVISOR powertrain simulation tool. This method is demonstrated on three vehicles – one truck and two passenger cars – tested on a vehicle dynamometer and one driven with a PEMS. The optimum vehicle configuration and associated maps were able to reproduce the shape and magnitude of observed fuel use and emissions on a per second basis. In general, total simulated fuel use and emissions were within 5% of observed values across the three test cases. The fitness of this method for other purposes was demonstrated by creating cold start maps and isolating the performance of tailpipe emissions reduction technologies. The potential of this work extends beyond the creation of vehicle engine maps to allow investigations into: emissions hot spots; real-world emissions factors; and accurate air quality modelling using simulated per second emissions from vehicles operating in over any driving cycle

The transportation sector plays a vital role in enabling the movement of people, goods, and services, but it is also a major contributor to energy consumption and greenhouse gas emissions. Accurate modeling of fuel consumption and pollutant emissions is critical for effective transportation management and environmental sustainability. This study investigates the use of real-world driving data from gasoline and diesel vehicles to model fuel consumption and exhaust emissions (${CO}_2$ and ${NO}_x$). The models were developed using ensemble bagged and decision tree algorithms with inputs derived from both vehicle speed and GPS speed data. The results demonstrate high predictive accuracy, with the ensemble bagged model consistently outperforming the decision tree model across all datasets. Notably, GPS speed-based models showed comparable performance to vehicle speed-based models, indicating the feasibility of using GPS data for real-time predictions. Furthermore, the combined gasoline and diesel engine dataset improved the accuracy of ${CO}_2$ emission predictions, while the gasoline-only dataset yielded the highest accuracy for fuel consumption. These findings underscore the potential of integrating GPS-based machine learning models into Intelligent Transportation Systems (ITS) to enhance real-time monitoring and policymaking. Future research should explore the inclusion of heavy-duty vehicles, additional pollutants, and advanced modeling techniques to further improve predictive capabilities.