Several methods are currently used to track the bio-component of co-processed fuels including energy/mass balance, yield methods and radiocarbon techniques. The methods used to track or estimate the bio-component of fuels produced when bio and fossil feedstocks are processed together (co-processed) in oil refineries were analysed in detail, together with their advantages and disadvantages. Some methods, such as radiocarbon methods that allow the direct measurement of the bio-content in a fuel, have been criticised due to low accuracy at low blends. However, these reservations have tended to misinterpret the options available for carbon dating and to discount recent improvements in these tests. As much higher co-pressing mixtures are anticipated if published national decarbonisation targets are to be achieved, any challenges at very low co-processing ratios affecting the accuracy of the radiocarbon methods should not be an issue. Energy/mass balance and yield methods might be supplemented with carbon-tracking to determine the real the biogenic content.

Remote sensing technology has been used for gasoline vehicle gaseous emissions monitoring for nearly 30 years. However, the application effect of the remote sensing detection of diesel vehicle tailpipe emission concentrations is unsatisfactory. Therefore, several approaches were proposed to analyze the remote sensing results for gaseous exhaust emissions from diesel vehicles, including the concentration ratios of gaseous emission components to carbon dioxide (CO2) and fuel-based emission factors. Based on our experimental results, these two metrics have some high values in low-speed or low-load conditions of vehicles, which introduces uncertainty when evaluating vehicle emission levels. Therefore, an inversion calculation method originally developed for remote sensing light duty diesel vehicle gaseous emissions was used for the remote sensing of nitrogen monoxide (NO) tailpipe concentrations in heavy duty diesel vehicles, and validated by PEMS tested emission results. For the first time, the above three options for evaluating the NOx emission level of diesel vehicles, including the concentration ratio of NO to CO2, the fuel-based NO emission factor and the estimated tailpipe NO emission concentration were investigated, and some influencing factors were also discussed. The remote sensing tailpipe NO emission concentration can be directly used to evaluate diesel vehicle NO emission levels compared with the two other metrics.

Portable Emission Measurement Systems (PEMS) are commonly used to measure absolute (mass per unit distance) emissions of a range of pollutants from road vehicles under real driving conditions. Because measuring large numbers of vehicles with PEMS is impractical, this paper investigates how vehicle emission remote sensing device (RSD) can supplement the use of PEMS. We simulate whether remote sensing measurements can accurately predict a vehicle's real-world distance-specific nitrogen oxides (NOX) emissions using RSD without measuring its exhaust flow rate. The approach uses readily available type-approval carbon dioxide (CO2) emission data together with average real-world divergences from studies based on user-reported fuel economy data. We find that at least 30 RS measurements from a given vehicle's journey are needed to reach a mean absolute error of 30% compared to a large reference data set of individual PEMS measurements. With that condition met, it is concluded that estimates agree well with actual NOX emissions from cars and the applied method does not introduce a systematic bias. It is also found that the accuracy of estimates for distance-specific NOX emissions does not significantly improve when more than 300 remote-sensing samples are available, with a mean absolute error converging to 23%. We conclude that this method could be used to screen large car fleets and identify vehicles or group of vehicles that are likely grossly exceeding air pollution standards.

Plug-in hybrid electric vehicles (PHEVs) combine an electric motor with an internal combustion engine and can reduce greenhouse gas emissions from transport if mainly driven on electricity. The environmental benefit of PHEVs strongly depends on usage and charging behaviour. However, there is limited evidence on how much PHEVs actually drive on electricity and how much conventional fuel they use in real-world operation. Here, we provide the first systematic empirical analysis of real-world usage and fuel consumption (FC) of approximately 100 000 vehicles in China, Europe, and North America. We find that real-world mean CO2 emissions of PHEVs are between 50 and 300 g CO2 km−1 depending on all-electric range, user group and country. For private vehicles, real-world CO2 emissions are two to four times higher than test cycle values. The high CO2 emissions and FC mainly result from low charging frequency, i.e. less than once per driving day. Our results demonstrate the importance of real-world vehicle emission measurements and indicate the need to adjust current PHEV policies, i.e. official emission values need to better reflect realistic electric driving shares and incentives need to put more emphasis on frequent charging.

Transportation is the fastest-growing source of greenhouse gas (GHG) emissions and energy consumption globally. While the convergence of shared mobility, vehicle automation, and electrification has the potential to drastically reduce transportation impacts, it requires careful integration with rapidly evolving electricity systems. Here, we examine these interactions using a U.S.-wide simulation framework encompassing private electric vehicles (EVs), shared automated EVs (SAEVs), charging infrastructure, controlled EV charging, and a grid economic dispatch model to simulate personal mobility exclusively using EVs. We find that private EVs with uncontrolled charging would reduce GHG emissions by 46% compared to gasoline vehicles. Private EVs with fleetwide controlled charging would achieve a 49% reduction in emissions from baseline and reduce peak charging demand by 53% from the uncontrolled scenario. We also find that an SAEV fleet 9% the size of today's active vehicle fleet can satisfy trip demand with only 2.6 million chargers (0.2 per EV). Such an SAEV fleet would achieve a 70% reduction in GHG emissions at 41% of the lifecycle cost as a private EV fleet with controlled charging. The emissions and cost advantage of SAEVs is primarily due to reduced vehicle manufacturing compared with private EVs.

This chapter introduces conventional emissions monitoring and modeling practices used in the on-road transportation sector. It describes both laboratory-based dynamometer and real-world emissions monitoring practices including portable emissions measurement systems (PEMS) and vehicle emissions remote sensing systems (VERSS) and identifies key events that helped to shape conventional practices. It also describes commonly used macro, meso, and microscale emissions modeling methods and the changing nature of the relationship between monitoring and modeling activities. Finally, it considers concerns regarding the effectiveness of existing practices to support ongoing efforts to reduced vehicle fleet emissions, and opportunities to do better in future.

The current study presents a detailed analysis of the gaseous emissions, focusing on CO2 and NOx, of diesel vehicles under several operating conditions. An assessment is also made on the impact and effectiveness of the Real Driving Emissions (RDE) test, which is mandatory by the European Union (EU) type approval regulation for passenger cars since September 2017. The method followed comprises emissions measurement tests on three Euro 6 diesel vehicles, under laboratory and various on-road operation conditions. Chassis dynamometer tests in the laboratory showed that emissions over the current type approval test (World-wide harmonized Light-duty Test Procedure or WLTP), and over the former one (New European Driving Cycle or NEDC), poorly reflect real-world levels. However, the most demanding CADC testing comes closer to real drive emissions. Comparison of driving conditions on the chassis dynamometer over different driving cycles and on the road reveals that the emission performance substantially varies between different tests, even for apparently similar operation conditions. The NOx emissions reduction strategy of pre-RDE monitoring Euro 6 vehicles seems to be optimized for the NEDC driving conditions, which are not representative of the real-world driving conditions. The real-world emissions during normal driving conditions are effectively captured with the new RDE test, however driving the vehicle dynamically, at conditions outside the RDE regulation boundaries, results to disproportional high emissions. This is a significant shortcoming which might be critical for populations living on hilly areas or those close to specific micro-environments, such as highway entrance ramps, traffic lights, etc.

CO2 emissions of light-duty vehicles are certified over standardised, laboratory-based conditions and reported to the consumers. Such tests reflect specific operating conditions that differ from what an individual driver experiences. Vehicle simulation can bridge the gap and help provide customised, vehicle and trip-specific values. This study investigates the potential of using a simulation-based approach for calculating CO2 emissions over real-world operation, when limited information and test-data are available. The methodology introduced in the European vehicle certification regulation since 2017 is used as a basis. Seven vehicles were tested over multiple on-road trips and in some cases on a chassis dyno. First, the analysis focused on the accuracy of the simulations when only limited information for the vehicle and its components are used. Subsequently, the model was calibrated on test data. The first case presented an error between 1.0% and 4.4% depending on the test, while the standard deviation was 10.0%. When using WLTP for calibration, the average error dropped to −2.9% to −0.2%, and the standard deviation decreased to 2.0%. When calibrating over on-road tests, the average error was 0.7% for the on-road tests and 4.5% for the WLTP.