How can the built environment affect the impact of autonomous vehicles' operational behaviour on air quality?

Autonomous vehicles (AVs) are pointed out as the technology that will reshape the concept of mobility, with significant implications for the economy, the environment, and society. This fact will bring new challenge to cities urban planning. Research to anticipate the AVs impacts, maximizing their benefits and reduce trade-offs are currently crucial. This work investigates the potential challenges and benefits of gradually replace internal combustion engine human driven vehicles with different penetration rates of AVs - 10%, 30%, 50%, 70%, 90%, and 100% - in urban roads of different characteristics, either in terms of traffic singularities or volumes, and its related implications on air quality. For that purpose, two urban areas with distinct features, Porto and Aveiro, were selected as case studies, and a modelling setup composed of a traffic model, an emission model, and a local air quality model was applied. The results revealed that the AVs benefits are directly linked with the urban design and the road characteristics. In the Aveiro case study, the AVs promoted positive changes with average reductions in daily NOx emissions (compared with the baseline scenario, without AVs) ranging between -2.1% (for C10%) and -7.7% (for C100%). In line with the emissions impacts, positive effects were found on air quality, with average reductions of NO2 concentrations up to -4% (for C100%). In Porto urban area, slight differences in NOx emissions were obtained (<2%), which implied no changes in the air quality levels. The distinct impact of AVs in the study areas is mostly explained by the traffic light coordination system and directional split distributions in the main roads. These results provide valuable insights to support decision-makers in the definition of strategies that allow the integration of these new emerging technologies in the road infrastructure, considering the features of the urban design, traffic profile and road characteristics.

Nanotechnology in automotive industry: research strategy and trends for the future-small objects, big impacts.

The goal of this paper is to emphasize and present briefly the nanotechnology science and its potential impact on the automotive industry in order to improve the production of recent models with an optimization of the safety performance and a reduction in the environmental impacts. Nanomaterials can be applied in car bodies as light weight constructions without compromising the stiffness and crashwortiness, which means less material and less fuel consumption. This paper outlines the progress of nanotechnology applications into the safety features of more recent vehicle models and fuel efficiency, but also emphasis the importance of sustainable development on the application of these technologies and life cycle analysis of the considered materials, in order to meet the society trends and customers demands to improve ecology, safety and comfort.

Autonomous vehicles opportunities for cities air quality.

The impacts of autonomous vehicles (AV) on safety, energy and atmospheric emissions have been recognised to be important issues, but an air quality impact assessment is missing. In this study, by using a numerical modelling approach, the impact of AV on the air quality of a medium-sized Portuguese urban area was evaluated. For that, the air pollutants nitrogen oxides (NOx) and carbon dioxide (CO2) were considered and three scenarios were developed: i) a baseline scenario; ii) an autonomous scenario, assuming an AV market penetration rate of 30%; and iii) an electric autonomous scenario, taking into account that those 30% of AV are pure battery electric cars. A modelling system composed by a road traffic model, a road transport emission model and a Computational Fluid Dynamics air quality model was used. The autonomous scenario promoted an increase of both NOx (+1.8%) and CO2 (+0.7%) emissions, while the electric autonomous scenario resulted in emission reductions of about 30% for both air pollutants. In terms of air quality, distinct patterns were found: i) the autonomous scenario promoted both increases and decreases of NOx concentrations; and ii) the electric autonomous scenario promoted a widespread reduction of NOx concentrations (with an average value of -4%). Overall the results showed that AV have the potential to improve urban air quality, but, further research is needed to enrich the findings of this work.

Multinomial logistic regression for prediction of vulnerable road users risk injuries based on spatial and temporal assessment.

Urban area's rapid growth often leads to adverse effects such as traffic congestion and increasing accident risks due to the expansion in transportation systems. In the frame of smart cities, active modes are expected to be promoted to improve living conditions. To achieve this goal, it is necessary to reduce the number of vulnerable road users (VRUs) injuries. Considering injury severity levels from crashes involving VRUs, this article seeks spatial and temporal patterns between cities and presents a model to predict the likelihood of VRUs to be involved in a crash. Kernel Density Estimation was applied to identify blackspots based on injury severity levels. A Multinomial Logistic Regression model was developed to identify statistically significant variables to predict the occurrence of these crashes. Results show that target spatial and temporal variables influence the number and severity of crashes involving VRUs. This approach can help to enhance road safety policies.

Interaction between motor vehicles and bicycles at two-lane roundabouts: a driving volatility-based analysis.

Drivers' instantaneous decisions regarding speed and acceleration/deceleration, as well as the time rate of acceleration change (jerk) can result in a volatility driving behaviour with significant impact on cyclist safety. The contribution of this article is the assessment of driving volatility in motor vehicle (MV)-bicycle interactions at two-lane roundabouts. Traffic flow and bicycle GPS data were collected from two two-lane roundabouts. Then, traffic, emissions and safety models were used to evaluate volatility impacts on safety, pollutant emissions and traffic performance. The findings showed jerk have an impact on driving volatility between MVs and bicycles, regardless of roundabout design with higher amplitude of variation for MVs. However, MVs had higher acceleration-deceleration variation than bicycles.

Assessment of potential improvements on regional air quality modelling related with implementation of a detailed methodology for traffic emission estimation.

The accuracy and precision of air quality models are usually associated with the emission inventories. Thus, in order to assess if there are any improvements on air quality regional simulations using detailed methodology of road traffic emission estimation, a regional air quality modelling system was applied. For this purpose, a combination of top-down and bottom-up approaches was used to build an emission inventory. To estimate the road traffic emissions, the bottom-up approach was applied using an instantaneous emission model (Vehicle Specific Power - VSP methodology), and an average emission model (CORINAIR methodology), while for the remaining activity sectors the top-down approach was used. Weather Research and Forecasting (WRF) and Comprehensive Air quality (CAMx) models were selected to assess two emission scenarios: (i) scenario 1, which includes the emissions from the top-down approach; and (ii) scenario 2, which includes the emissions resulting from integration of top-down and bottom-up approaches. The results show higher emission values for PM10, NOx and HC, for scenario 1, and an inverse behaviour to CO. The highest differences between these scenarios were observed for PM10 and HC, about 55% and 75% higher (respectively for each pollutant) than emissions provided by scenario 2. This scenario gives better results for PM10, CO and O3. For NO2 concentrations better results were obtained with scenario 1. Thus, the results obtained suggest that with the combination of the top-down and bottom-up approaches to emission estimation several improvements in the air quality results can be achieved, mainly for PM10, CO and O3.

Impact of land use on urban mobility patterns, emissions and air quality in a Portuguese medium-sized city.

The main objective of this work was to evaluate the impact of urban development trends in mobility patterns of a medium sized Portuguese city and air quality consequences, using a sequential modeling process, comprising i) land use and transportation, TRANUS model; ii) road traffic air pollutants emissions, TREM model and; iii) air quality, TAPM model. This integrated methodology was applied to a medium sized Portuguese city. In order to evaluate the implementation of the methodology, a preliminary study was performed, which consisted on the comparison of modeled mobility patterns and CO and PM(10) concentrations with measured data used in the definition of the current scenario. The comparison between modeled and monitored mobility patterns at the morning peak hour for a weekday showed an RMSE of 31%. Regarding CO concentrations, an underestimation of the modeled results was observed. Nevertheless, the modeled PM(10) concentrations were consistent with the monitored data. Overall, the results showed a reasonable consistency of the modeled data, which allowed the use of the integrated modeling system for the study scenarios. The future scenarios consisted on the definition of different mobility patterns and vehicle technology characteristics, according to two main developing trends: (1) "car pooling" scenario, which imposes a mean occupancy rate of 3 passengers by vehicle and (2) the "Euro 6" scenario, which establishes that all vehicles accomplish at least the Euro 6 standard technology. Reductions of 54% and 83% for CO, 44% and 95% for PM(10), 44% and 87% for VOC and 44% and 79% for NO(x) emissions were observed in scenarios 1 and 2, respectively. Concerning air quality, a reduction of about 100 µg m(-3) of CO annual average concentration was observed in both scenarios. The results of PM(10) annual concentrations showed a reduction of 1.35 µg m(-3) and 2.7 µg m(-3) for scenarios 1 and 2 respectively.