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Connected and autonomous vehicles (CAV) have seen a rapid development over the past decade. However, wide diffusion of high level CAVs is still decades to come, and will depend on many technological, policy and public acceptance factors. Merging a traditional Bass Diffusion Model with a discrete choice model in a system dynamics approach, this study modelled CAV diffusion from 2020 to 2070 in the UK, considering mode choices of CAV private car, CAV car/ride sharing and CAV bus, their possible impacts on road network performance and sustainability, and the feedback of these impacts to CAV diffusion. Results of this study suggest that without interventions CAV diffusion will start to increase rapidly from 2035, and reach market saturation of 98% in around 2057. CAV diffusion will lead to reductions in average travel time, average travel cost, carbon emission and traffic accident. Training campaign, which prepares the general public to be ready for CAVs, is more effective in accelerating CAV diffusion than marketing campaign, which mainly targets the innovators and early adopters. Promoting shared CAVs and CAV public transport can contribute to more sustainable and more affordable mobility with CAVs, although this may lead to smaller CAV market size in terms of CAV sale, and the market size may reduce at a higher rate than sustainability enhancement. • Diffusion of CAVs was simulated using system dynamics modelling based on Bass model. • CAVs of different modes, as well as wider impacts of CAV diffusion, were considered. • Interventions to accelerate CAV diffusion and to optimise CAV impacts were tested. • Training campaign for general public is important for accelerating CAV diffusion. • Promoting shared CAVs and CAV public transport can optimise CAV impacts. [ABSTRACT FROM AUTHOR]

Abstract: There is a high interest in quantifying temporal trends in surface ozone concentrations as they serve to quantify the impacts of the anthropogenic precursor reductions and to assess the effects of emission control strategies. In this paper ozone trends for nearly 2 decades (1993 to 2011) at both rural and urban sites have been analysed, using ground level ozone data from 5 urban and 15 rural sites, which are part of the UK AURN. This study analyses ozone trends at various percentiles, in addition to traditional mean trends using quantile regression, TheilSen function, and changepoint analysis. Ozone trends show significant variability at different statistical metrics (e.g., mean, median, maximum and selected quantiles). Maximum trends were negative, whereas median and mean trends were positive during the study period (1993–2011) at both rural and urban sites. Urban and rural trends show different rates of change and indicate that urban decrement (the difference in ozone concentration between rural and urban areas) has been decreasing over the period. Ozone trends were negative during the last 8years (2004–2011), which could have been caused by the stabilisation of NOx concentration during this period. Furthermore, 3 changepoints were detected in the temporal trend using Pruned Exact Linear Time (PELT) search algorithm, which provides further insight into the ozone temporal trends. [Copyright &y& Elsevier]

Abstract: The motor-vehicle is accountable for emitting a substantial concoction of air quality objective pollutants and carcinogenic hydrocarbons within close proximity to urbanised residential districts. The spatial extent of health impacts associated with road-transport pollutants have traditionally been explored through the examination of artificially created buffers, defined by subjective distances from specified major road links. Within this paper an alternative approach is presented using boundary statistics, which describe naturally occurring shifts of magnitude in socio-environmental and health outcomes across the wider urban area. In contrast, previous distance-threshold investigations have used arbitrarily sized buffers placed upon predetermined locations in response to environmental attributes, without considering the combined influence of additional social burdens. The demographically diverse City of Leicester, situated within the heart of the United Kingdom''s major road-transport network, was selected to showcase such methods. Descriptive multilevel modelling strategies accommodating for generalised spatial structures across Leicester, globally associated issues of deprivation, road-transport emissions and ethnic minorities with increased respiratory risks. Getis-Ord Gi* spatial pattern recognition statistics identified the existence of localised variations, with inner city neighbourhoods tending to house children of ethnic minority groups whom experience disproportionately large environmental and respiratory health burdens. Crisp polygon wombling boundary detection across Leicester appeared to broadly complement the Gi* statistics, identifying naturally occurring boundaries in road-transport emissions to result in elevated children''s respiratory admissions within a distance of 283 m (P < 0.05). The designated threshold was identified to reduce in relation to certain ethnic groups, thus suggesting environmental injustices likely prevail within the model British multicultural City of Leicester. The study''s findings have applications within healthcare management and urban planning for locating vulnerable populaces and for minimising health risks in future road network designs. [Copyright &y& Elsevier]

This study quantifies the change in traffic flow on the M25 motorway in the UK due to the COVID-19 outbreak. Moreover, the impact of the change in traffic flow on non-exhaust PM 2.5 and PM 10 emissions for different categories of vehicle was explored. During the year of the COVID-19 outbreak (March 2020 to February 2021), the total traffic flows of passenger cars (PCs), light goods vehicles (LGVs), heavy goods vehicles (HGVs), and long HGVs on the M25 motorway decreased by 38.6%, 27.6%, 15.9% and 7.2%, respectively, in comparison to the previous year. Correspondingly, the total mass of non-exhaust emissions (PM 2.5 and PM 10) of PCs, LGVs, HGVs, and long HGVs reduced by 38.7%, 27.3%, 16.2% and 7%, respectively. The traffic flows per year before and during the COVID-19 outbreak of long HGVs were 87.2% and 80.7% less than those of PCs. Correspondingly, the long HGVs emitted 10.2% less but 36.3% more PM 2.5 emissions, as well as 10.9% and 66.7% more PM 10 emissions than the latter, indicating that long HGVs contribute much more to non-exhaust particles than PCs. In addition, it was found that resuspension of road dust on the M25 motorway was the largest contributor to air pollution among non-exhaust emissions, followed by road wear, tyre wear, and brake wear particles. [Display omitted] • Traffic flow was quantified on the M25 before and during the year of the outbreak. • Total non-exhaust PM 2.5 and PM 10 mass of four categories of vehicles were evaluated. • Long HGVs emitted the most non-exhaust particles, followed by HGVs, LGVs and PCs. • Resuspension of road dust was the largest contributor to non-exhaust particles. [ABSTRACT FROM AUTHOR]

Electric bikes (e-bikes) represent an increasing share of urban mobility due to their small sizes and clean fuels. This study developed an environmental life cycle assessment model to evaluate e-bikes powered by lithium-ion battery, compared with a petrol car and a battery electric vehicle (BEV) for commuting. System boundary included vehicle lifecycle and fuel lifecycle. The model also included emissions from tyre and brake wear, and noise impacts in a case study in the UK. Results showed that BEVs and e-bikes reduce CO 2 , and this reduction can increase with change in electricity mix and battery recycling. Advantages of e-bikes are not proportional to the weight of the vehicle. Non-exhaust emissions are significant to respiratory effects and human toxicity, so are noise reductions due to the use of electric vehicles. The model and data should help to conduct similar studies elsewhere in the world and to develop policies for electric vehicles. [ABSTRACT FROM AUTHOR]

Figure. Graphical abstract (LCS = low-cost sensors; Ref = reference; AQ = air quality; Met = meteorology; GAM = generalised additive model; RF = random forest; QRM = quantile regression model; NO 2 = nitrogen dioxide). Air quality is monitored before intervention, then the intervention is implemented, and finally air quality is monitored again, which is analysed and modelled employing different machine learning approaches. [Display omitted] In this paper, the impact of smart traffic interventions on air quality was assessed in Thatcham, West Berkshire, UK. The intervention linked NO 2 levels with the cycle time of the traffic lights. When NO 2 levels exceeded a certain threshold, the strategy was triggered, which reduced the traffic congestion by turning the traffic lights green. Eight Earthsense Zephyrs air quality sensors and nine inductive-loop traffic detectors were installed in Thatcham to simultaneously monitor the air quality and traffic flows, respectively. Compared to the pre-intervention period, the observed NO 2 concentrations decreased in June, July and August and increased in September 2021, however, this does not reveal the true effect of smart traffic intervention. Using the observed data on the days with- and without-exceedances, we developed two machine learning models to predict the Business-as-usual (BAU) air quality level, i.e., a generalised additive model for average concentration and a quantile regression model for peak concentration. Our results demonstrated that average predicted concentrations (BAU) were lower than the observed concentrations (with intervention) by 12.45 %. However, we found that peak concentrations decreased by 20.54 %. [ABSTRACT FROM AUTHOR]