Detecting candidate high NOx emitting light commercial vehicles using vehicle emission remote sensing.

Vehicle emission remote sensing devices have been widely used for monitoring and assessing the real-world emission performance of vehicles. They are also well-suited to identify candidate high emitting vehicles as remote sensing surveys measure the on-road, real-driving emissions (RDE) of a high proportion of the operational vehicle fleet passing through a testing site. This study uses the Gumbel distribution to characterize the fuel-specific NOx emission rates (g·kg-1) from diesel vans (formally referred to as light commercial vehicles or LCVs) and screen candidate high emitting vehicles. Van emission trends of four European countries (Belgium, Sweden, Switzerland and the UK) from Euro 3 to Euro 6a/b have been studied, and the impact of road grade on candidate Euro 6a/b high-emitters is also evaluated. The measurements of Euro 6a/b fleets from four countries are pooled together, and a consistent 4% of candidate high-emitters are found in both class II and class III Euro 6a/b vans, accounting for an estimated 24% and 21% total NOx emissions respectively. The pooled four country data is differentiated by vehicle models and manufacture groups. Engine downsizing of Euro 6a/b class II vans is suspected to worsen the emission performance when vehicles are driven under high engine load. The VW Group is found to be the manufacture with cleanest NOx emission performance in the Euro 6a/b fleets. By distinguishing high-emitters from normally behaving vehicles, a more robust description of fleet behaviour can be provided and high-emitting vehicles targeted for further testing by plume chasing or in an inspection garage. If the vehicle is found to have a faulty, deteriorated or tampered emission after-treatment system, the periodic vehicle inspection safety and environmental performance certificate could be revoked.

Early observations on the impact of the COVID-19 lockdown on air quality trends across the UK.

UK government implemented national lockdown in response to COVID-19 on the 23-26 March 2020. As elsewhere in Europe and Internationally, associated restrictions initially limited individual mobility and workplace activity to essential services and travel, and significant air quality benefits were widely anticipated. Here, break-point/segment methods are applied to air pollutant time-series from the first half of 2020 to provide an independent estimate of the timings of discrete changes in NO, NO2, NOx, O3, PM10 and PM2.5 time-series from Automatic Urban Rural Network (AURN) monitoring stations across the UK. NO, NO2 and NOx all exhibit abrupt decreases at the time the UK locked down of (on average) 7.6 to 17 µg·m-3 (or 32 to 50%) at Urban Traffic stations and 4 to 5.7 µg·m-3 (or 26 to 46%) at Urban Background stations. However, after the initial abrupt reduction, gradual increases were then observed through lockdown. This suggests that the return of vehicles to the road during early lockdown has already offset much of the air quality improvement seen when locking down (provisional estimate 50 to 70% by 01 July). While locking down O3 increased (7 to 7.4 µg·m-3 or 14 to 17% at Urban stations) broadly in line with NO2 reductions, but later changes suggest significant non-lockdown contributions to O3 during the months that followed. Increases of similar magnitudes were observed for both PM10 (5.9 to 6.3 µg·m-3) and PM2.5 (3.9 to 5.0 µg·m-3) at both Rural and Urban stations alike, but the distribution of changes suggests the lockdown was not an obvious direct source of changes in levels of either of these species during this period, and that more complex contributions, e.g. from resuspension and secondary aerosol, may be more likely major drivers for these changes.

A novel method for comparing passenger car fleets and identifying high-chance gross emitting vehicles using kerbside remote sensing data.

The quantification and comparison of NOX emission from in-situ car fleets, and identification of the highest emitters is an ongoing challenge. This challenge will become more important as new and increasingly complex emissions removal systems penetrate the market. We combine real-world data with new-to-the-field statistical methods to describe fleet-scale emissions behaviours and identify candidate gross-emitter vehicles. 19,605 passenger cars were observed using a Remote Sensing Device across Aberdeen in 2015. Of these, 736 were Euro 6 Passenger Cars. The distribution of observed pollutant per unit of fuel burnt ratios for most fuel type and Euro standards followed an asymmetrical shape best characterised by the Gumbel distribution. The Gumbel distribution approach was not able to fully replicate the distribution of measurements of petrol or Euro 6 diesel cars due to the presence of a subset of high-emitting outliers, ranging from the 13th percentile for Euro 3 petrol to the 2nd percentile for Euro 6 petrol, with Euro 6 diesel having a 5th percentile outlier value. No outlier fraction was observed for pre-Euro 6 diesels. The off-model fractions resembled Gumbel distributed data and in some cases could be modelled as a separate distribution with the fleet behaving as a superposition of them. It is shown that VSP was not directly linked to this behaviour and it is hypothesised that it is caused by the emissions control systems operating sub-optimally. The reasons for sub-optimal operation are beyond the scope of this paper but may be linked to air-fuel mixture sensors, cold-start running and deterioration of the catalytic converter. Larger data-sets with more Euro 6 passenger cars are required to fully test this. Application of this methodology to larger data sets from more widely deployed remote sensing devices will allow observers to identify potentially problematic vehicles for further investigation into their emission control systems.

Real-world CO2 and NOX emissions from refrigerated vans.

Refrigerated vans used for home deliveries are attracting attention as online grocery shopping in the UK is expanding rapidly and contributes to the increasing greenhouse gas (CO2) and nitrogen oxides (NOX) emissions. These vans are typically 3.5-tonne gross weight vehicles equipped with temperature-controlled units called Transport Refrigeration Units (TRUs), which are usually powered off the vehicles' engine. It is obvious that vehicles with added weight of TRUs consume more fuel and emit more NOX, let alone the vehicles' diesel engines are also powering the refrigeration units, which further elevates the emissions. This research uses an instantaneous vehicle emission model PHEM (version 13.0.3.21) to simulate the real-world emissions from refrigerated vans. A validation of PHEM is included using data from laboratory (chassis dynamometer) tests over a realistic driving profile (the London Drive Cycle), to assess its ability to quantify the impact of changing vehicle weights and carrying loads. The impact of the TRU weight, greater frontal area increasing aerodynamic drag and refrigeration load on van emissions is then estimated by PHEM. The influence of ambient temperature, cargo weight and driving condition on CO2 and NOX emission from refrigerated van are also assessed. Overall CO2 emissions of vans with TRUs are found to be 15% higher than standard vehicles, with NOX emissions estimated to be elevated by 18%. This confirms the need to take into account the impact of additional engine load when predicting van emissions in this and other sectors such as ambulances which are relatively heavy, high powered vehicles. Moreover, findings of the impact of TRUs on fuel consumptions can be used to optimize fuel-saving strategies for refrigerated vans and test cases for alternative low- or zero-emission technologies, to support progress to a sustainable net-zero society.

Distance-based emission factors from vehicle emission remote sensing measurements.

Vehicle emission remote sensing has the potential to provide detailed emissions information at a highly disaggregated level owing to the ability to measure thousands of vehicles in a single day. Fundamentally, vehicle emission remote sensing provides a direct measure of the molar volume ratio of a pollutant to carbon dioxide, from which fuel-based emissions factors can readily be calculated. However, vehicle emissions are more commonly expressed in emission per unit distance travelled e.g. grams per km or mile. To express vehicle emission remote sensing data in this way requires an estimate of the fuel consumption at the time of the emission measurement. In this paper, an approach is developed based on vehicle specific power that uses commonly measured or easily obtainable vehicle information such as vehicle speed, acceleration and mass. We test the approach against 55 independent comprehensive PEMS measurements for Euro 5 and 6 gasoline and diesel vehicles over a wide range of driving conditions and find good agreement between the method and PEMS data. The method is applied to individual vehicle model types to quantify distance-based emission factors. The method will be appropriate for application to larger vehicle emission remote sensing databases, thus extending real-world distance-based vehicle emissions information.

Impact of transport electrification on critical metal sustainability with a focus on the heavy-duty segment.

The majority of transport electrification studies, examining the demand and sustainability of critical metals, have focused on light-duty vehicles. Heavy-duty vehicles have often been excluded from the research scope due to their smaller vehicle stock and slower pace of electrification. This study fills this research gap by evaluating the lithium resource impacts from electrification of the heavy-duty segment at the global level. Our results show that a mass electrification of the heavy-duty segment on top of the light-duty segment would substantially increase the lithium demand and impose further strain on the global lithium supply. The significant impact is attributed to the large single-vehicle battery capacity required by heavy-duty vehicles and the expected battery replacement needed within the lifetime of heavy-duty vehicles. We suggest that the ambition of mass electrification in the heavy-duty segment should be treated with cautions for both policy makers and entrepreneurs.

Interinstrument comparison of remote-sensing devices and a new method for calculating on-road nitrogen oxides emissions and validation of vehicle-specific power.

Emissions of nitrogen oxides (NOx) by vehicles in real driving environments are only partially understood. This has been brought to the attention of the world with recent revelations of the cheating of the type of approval tests exposed in the dieselgate scandal. Remote-sensing devices offer investigators an opportunity to directly measure in situ real driving emissions of tens of thousands of vehicles. Remote-sensing NO2 measurements are not as widely available as would be desirable. The aim of this study is to improve the ability of investigators to estimate the NO2 emissions and to improve the confidence of the total NOx results calculated from standard remote-sensing device (RSD) measurements. The accuracy of the RSD speed and acceleration module was also validated using state-of-the-art onboard global positioning system (GPS) tracking. Two RSDs used in roadside vehicle emissions surveys were tested side by side under off-carriageway conditions away from transient pollution sources to ascertain the consistency of their measurements. The speed correlation was consistent across the range of measurements at 95% confidence and the acceleration correlation was consistent at 95% confidence intervals for all but the most extreme acceleration cases. VSP was consistent at 95% confidence across all measurements except for those at VSP ≥ 15 kW t-1, which show a small underestimate. The controlled distribution gas nitric oxide measurements follow a normal distribution with 2σ equal to 18.9% of the mean, compared to 15% observed during factory calibration indicative of additional error introduced into the system. Systematic errors of +84 ppm were observed but within the tolerance of the control gas. Interinstrument correlation was performed, with the relationship between the FEAT and the RSD4600 being linear with a gradient of 0.93 and an R2 of 0.85, indicating good correlation. A new method to calculate NOx emissions using fractional NO2 combined with NO measurements made by the RSD4600 was constructed, validated, and shown to be more accurate than previous methods. IMPLICATIONS: Synchronized remote-sensing measurements of NO were taken using two different remote-sensing devices in an off-road study. It was found that the measurements taken by both instruments were well correlated. Fractional NO2 measurements from a prior study, measurable on only one device, were used to create new NOx emission factors for the device that could not be measured by the second device. These estimates were validated against direct measurement of total NOx emission factors and shown to be an improvement on previous methodologies. Validation of vehicle-specific power was performed with good correlation observed.

Factors influencing particle number concentrations, size distributions and modal parameters at a roof-level and roadside site in Leicester, UK.

Measurements of urban particle number concentrations and size distributions in the range 5-1000 nm were taken at elevated (roof-level) and roadside sampling sites on Narborough Road in Leicester, UK, along with simultaneous measurements of traffic, NO(x), CO and 1,3-butadiene concentrations and meteorological parameters. A fitting program was used to determine the characteristics of up to five modal groups present in the particle size distributions. All particle modal concentrations peaked during the morning and evening rush hours. Additional events associated with the smallest mode, that were not observed to be connected to primary emissions, were also present suggesting that this mode consisted of newly formed secondary particles. These events included peaks in concentration which coincided with peaks in solar radiation, and lower concentrations of the larger modes. Investigation into the relationships between traffic flow and occupancy indicated three flow regimes; free-flow, unstable and congested. During free-flow conditions, positive linear relationships existed between traffic flow and particle modal number concentrations. However, during unstable and congested periods, this relationship was shown to break-down. Similar trends were observed for concentrations of the gas phase pollutants NO(x), CO and 1,3-butadiene. Strong linear relationships existed between NO(x), CO, 1,3-butadiene concentrations, nucleation and Aitken mode concentrations at both sampling locations, indicating a local traffic related emission source. At the roadside, both nucleation and Aitken mode are best represented by a decreasing exponential function with wind speed, whereas at the roof-level this relationship only occurred for Aitken mode particles. The differing relationships at the two sampling locations are most likely due to a combination of meteorological factors and distance from the local emission source.