Real-World Contribution of Electrification and Replacement Scenarios to the Fleet Emissions in West Midland Boroughs, UK

Mapping urban mobility using vehicle telematics to understand driving behaviour

Telematics data, primarily collected from on-board vehicle devices (OBDs), has been utilised in this study to generate a thorough understanding of driving behaviour. The urban case study area is the large metropolitan region of the West Midlands, UK, but the approach is generalizable and translatable to other global urban regions. The new approach of GeoSpatial and Temporal Mapping of Urban Mobility (GeoSTMUM) is used to convert telematics data into driving metrics, including the relative time the vehicle fleet spends idling, cruising, accelerating, and decelerating. The telematics data is also used to parameterize driving volatility and aggressiveness, which are key factors within road safety, which is a global issue. Two approaches to defining aggressive driving are applied and assessed, they are vehicle jerk (the second derivative of vehicle speed), and the profile of speed versus acceleration/deceleration. The telematics-based approach has a very high spatial resolution (15-150 m) and temporal resolution (2 h), which can be used to develop more accurate driving cycles. The approach allows for the determination of road segments with the highest potential for aggressive driving and highlights where additional safety measures could beneficially be adopted. Results highlight the strong correlation between vehicle road occupancy and aggressive driving.

Detecting high emitting vehicle subsets using emission remote sensing systems

It is often assumed that a small proportion of a given vehicle fleet produces a disproportionate amount of air pollution emissions. If true, policy actions to target the highly polluting section of the fleet could lead to significant improvements in air quality. In this paper, high-emitter vehicle subsets are defined and their contributions to the total fleet emission are assessed. A new approach, using enrichment factor in cumulative Pareto analysis is proposed for detecting high emitter vehicle subsets within the vehicle fleet. A large dataset (over 94,000 remote-sensing measurements) from five UK-based EDAR (emission detecting and reporting system) field campaigns for the years 2016-17 is used as the test data. In addition to discussions about the high emitter screening criteria, the data analysis procedure and future issues of implementation are discussed. The results show different high emitter trends dependent on the pollutant investigated, and the vehicle type investigated. For example, the analysis indicates that 23 % and 51 % of petrol and diesel cars were responsible for 80 % of NO emissions within that subset of the fleet, respectively. Overall, the contributions of vehicles that account for 80 % of total fleet emissions usually reduce with EURO class improvement, with the subset fleet emissions becoming more homogenous. The high emitter constituent was more noticeable for pollutant PM compared with the other gaseous pollutants, and it was also more prominent for petrol cars when compared to diesel ones.

Impacts of emergency health protection measures upon air quality, traffic and public health: evidence from Oxford, UK

Emergency responses to the COVID-19 pandemic led to major changes in travel behaviours and economic activities in 2020. Machine learning provides a reliable approach for assessing the contribution of these changes to air quality. This study investigates impacts of health protection measures upon air pollution and traffic emissions and estimates health and economic impacts arising from these changes during two national 'lockdown' periods in Oxford, UK. Air quality improvements were most marked during the first lockdown with reductions in observed NO₂ concentrations of 38% (SD ± 24.0%) at roadside and 17% (SD ± 5.4%) at urban background locations. Observed changes in PM_2.5, PM₁₀ and O₃ concentrations were not significant during first or second lockdown. Deweathering and detrending analyses revealed a 22% (SD ± 4.4%) reduction in roadside NO₂ and 2% (SD ± 7.1%) at urban background with no significant changes in the second lockdown. Deweathered-detrended PM_2.5 and O₃ concentration changes were not significant, but PM₁₀ increased in the second lockdown only. City centre traffic volume reduced by 69% and 38% in the first and second lockdown periods. Buses and passenger cars were the major contributors to NO₂ emissions, with relative reductions of 56% and 77% respectively during the first lockdown, and less pronounced changes in the second lockdown. While car and bus NO₂ emissions decreased during both lockdown periods, the overall contribution from buses increased relative to cars in the second lockdown. Sustained NO₂ emissions reduction consistent with the first lockdown could prevent 48 lost life-years among the city population, with economic benefits of up to £2.5 million. Our findings highlight the critical importance of decoupling emissions changes from meteorological influences to avoid overestimation of lockdown impacts and indicate targeted emissions control measures will be the most effective strategy for achieving air quality and public health benefits in this setting.

A comparison of light-duty vehicles' high emitters fractions obtained from an emission remote sensing campaign and emission inspection program for policy recommendation

Urban transportation is one of the leading causes of air pollution in big cities. In-use emissions of vehicles are higher than the emission control certification levels. The current study uses a roadside remote sensing emission monitoring campaign to investigate (a) fraction of high emitters in the light-duty vehicle (LDV) fleet and their contributions to the total emissions, (b) emission inspection (I/M) programs' effectiveness, and (c) alternate fuel (natural gas) encouragement policy. LDVs consist of passenger or freight transport vehicles with four wheels equivalent to classes M1 and N1 of European union vehicle classifications. The motivation is to assess the current emission inspection program's success rate and study the impact of the increased natural gas vehicle market share policy. It is also meant to present and validate remote sensing as a possible backup method to the current I/M program. The emission remote sensing campaign was conducted to measure emissions of CO, HC, and NO of the LDV fleet. Fleet age, engine size, and fuel type (gasoline or natural gas) were extracted and correlated with emissions. It was found that CO and HC emissions are five times higher for cars more than fifteen years old of age compared to those less than five years old. Analyses of high-emitters showed that almost 20% of the fleet were high-emitters and responsible for roughly half of CO, HC, and NO emissions. The correlation between the I/M program and the remote sensing to identify high-emitters was weak. Which indicates the need for an improved I/M program. It shows that even a limited remote sensing campaign is beneficial as a complementary monitoring tool to the I/M program. The study showed the same fraction of high-emitters in natural gas (methane) vehicles, despite the national policies to increase natural gas vehicle fraction in the market for reduced emissions.