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.

Analysis of spatial factors, time-activity and infiltration on outdoor generated PM2.5 exposures of school children in five European cities.

Atmospheric particles are a major environmental health risk. Assessments of air pollution related health burden are often based on outdoor concentrations estimated at residential locations, ignoring spatial mobility, time-activity patterns, and indoor exposures. The aim of this work is to quantify impacts of these factors on outdoor-originated fine particle exposures of school children. We apply nested WRF-CAMx modelling of PM2.5 concentrations, gridded population, and school location data. Infiltration and enrichment factors were collected and applied to Athens, Kuopio, Lisbon, Porto, and Treviso. Exposures of school children were calculated for residential and school outdoor and indoor, other indoor, and traffic microenvironments. Combined with time-activity patterns six exposure models were created. Model complexity was increased incrementally starting from residential and school outdoor exposures. Even though levels in traffic and outdoors were considerably higher, 80-84% of the exposure to outdoor particles occurred in indoor environments. The simplest and also commonly used approach of using residential outdoor concentrations as population exposure descriptor (model 1), led on average to 26% higher estimates (15.7 µg/m3) compared with the most complex model (# 6) including home and school outdoor and indoor, other indoor and traffic microenvironments (12.5 µg/m3). These results emphasize the importance of including spatial mobility, time-activity and infiltration to reduce bias in exposure estimates.

Climate-Change Adaptation Framework for Multiple Urban Areas in Northern Portugal.

Climate change is increasingly exerting pressure with intensified impacts in the short-, medium-, and long-term. Cities are highly vulnerable to the impacts of climate change, and it is recognized that they play a significant role in the European Strategy on adaptation to climate change. This study intends to develop a climate adaptation framework to identify effective measures that will be evaluated using a multi-urban area located in the north of Portugal, as a case study. The climate adaptation framework was developed following the Urban Adaptation Support Tool (AST), adapted to the Portuguese reality. The Weather Research and Forecasting (WRF) model was used to provide future projections with a high level of spatial resolution over the study area, increasing the accuracy of the identification of future climatic vulnerabilities. The results show a tendency for an increase of extreme weather events associated with the increase of both temperature and annual accumulated precipitation variables. A set of both urban and rural measures to promote a sustainable development path to climate adaptability and increase cities resilience to climate change are presented and discussed.

Modelling air quality levels of regulated metals: limitations and challenges.

Toxic metals as arsenic (As), cadmium (Cd), nickel (Ni), and lead (Pb) exist in the atmosphere as particulate matter components. Their concentration levels in the European Union (EU) are regulated by European legislation, which sets target and limit values as annual means, and by the World Health Organization (WHO) that defines guidelines and reference values for those metal elements. Modelling tools are recommended to support air quality assessment regarding the toxic metals; however, few studies have been performed and those assessments rely on discrete measurements or field campaigns. This study aims to evaluate the capability of air quality modelling tools to verify the legislation compliance concerning the atmospheric levels of toxic elements and to identify the main challenges and limitations of using a modelling assessment approach for regulatory purposes, as a complement to monitoring. The CAMx air quality model was adapted and applied over Porto and Lisbon urban regions in Portugal at 5 × 5-km2 and 1 × 1-km2 horizontal resolution for the year 2015, and the results were analysed and compared with the few measurements available in three locations. The comparison between modelled and measured data revealed an overestimation of the model, although annual averages are much lower than the regulated standards. The comparison of the 5-km and 1-km resolutions' results indicates that a higher resolution does not necessarily imply a better performance, pointing out uncertainties in emissions and the need to better describe the magnitude and spatial allocation of toxic metal emissions. This work highlighted that an increase of the spatial and temporal coverage of monitoring sites would allow to improve the model design, contribute to a better knowledge on toxic metals atmospheric emission sources and to increase the capacity of models to simulate atmospheric particulate species of health concern.

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.

A characterisation model to address the environmental impact of green water flows for water scarcity footprints.

The development of methods to assess the potential environmental impact of green water consumption in life cycle assessment has lagged behind those for blue water use, which are now routinely applied in industrial and policy-related studies. This represents a critical gap in the assessment of land-based production systems and the ability to inform policy related to the bio-economy. Combining satellite remote sensing and meteorological data sets, this study develops two new sets of spatially-differentiated and globally applicable characterisation factors (CFs) to assess the environmental impact of green water flows in LCA. One set of CFs addresses the impact of shifts in water vapour flow by evapotranspiration on blue water availability (CFWS) and the other set of CFs addresses moisture recycling within a basin (CFWA). Furthermore, as an additional and optional step, these two indicators are combined into an aggregated green water scarcity indicator, representing the global variability of green water scarcity. The values obtained for CFWA show that there are significant changes in green water flows that were returned to the atmosphere in Alaska (covered by open shrublands) and in some central regions of China (covered by grasslands and barren or sparsely vegetated land), where precipitation levels are lower than 10 mm/yr. The results obtained for CFWS indicate that severe perturbations in surface blue water production occur, particularly in central regions of China (covered by grasslands), the southeast of Australia (covered by evergreen broadleaf forest) and in some central regions of the USA (covered by grassland and evergreen needleleaf forest). The application of the green water scarcity CFs enables the evaluation of the potential environmental impact due to green water consumption by agricultural and forestry products, informing both technical and non-technical audiences and decision-makers for the purpose of strategic planning of land use and to identify green water protection measures.

Atmospheric Emissions from Forest Biomass Residues to Energy Supply Chain: A Case Study in Portugal.

During the past decades, pressures on global environment and energy security have led to an increasing demand on renewable energy sources and diversification of the world's energy supply. The Portuguese energy strategy considers the use of Forest Biomass Residues (FBR) to energy as being essential to accomplish the goals established in the National Energy Strategy for 2020. However, despite the advantages pointing to FBR to the energy supply chain, few studies have evaluated the potential impacts on air quality. In this context, a case study was selected to estimate the atmospheric emissions of the FBR to the energy supply chain in Portugal. Results revealed that production, harvesting, and energy conversion processes are the main culprits for the biomass energy supply chain emissions (with a contribution higher than 90%), while the transport processes have a minor importance for all the pollutants. Compared with the coal-fired plants, the FBR combustion produces lower greenhouses emissions, on a mass basis of fuel consumed; the same is true for NOX and SO2 emissions.

Employing the metabolic "branch point effect" to generate an all-or-none, digital-like response in enzymatic outputs and enzyme-based sensors.

Here, we demonstrate a strategy to convert the graded Michaelis-Menten response typical of unregulated enzymes into a sharp, effectively all-or-none response. We do so using an approach analogous to the "branch point effect", a mechanism observed in naturally occurring metabolic networks in which two or more enzymes compete for the same substrate. As a model system, we used the enzymatic reaction of glucose oxidase (GOx) and coupled it to a second, nonsignaling reaction catalyzed by the higher affinity enzyme hexokinase (HK) such that, at low substrate concentrations, the second enzyme outcompetes the first, turning off the latter's response. Above an arbitrarily selected "threshold" substrate concentration, the nonsignaling HK enzyme saturates leading to a "sudden" activation of the first signaling GOx enzyme and a far steeper dose-response curve than that observed for simple Michaelis-Menten kinetics. Using the well-known GOx-based amperometric glucose sensor to validate our strategy, we have steepen the normally graded response of this enzymatic sensor into a discrete yes/no output similar to that of a multimeric cooperative enzyme with a Hill coefficient above 13. We have also shown that, by controlling the HK reaction we can precisely tune the threshold target concentration at which we observe the enzyme output. Finally, we demonstrate the utility of this strategy for achieving effective noise attenuation in enzyme logic gates. In addition to supporting the development of biosensors with digital-like output, we envisage that the use of all-or-none enzymatic responses will also improve our ability to engineer efficient enzyme-based catalysis reactions in synthetic biology applications.