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.

Classification of Critical Levels of CO Exposure of Firefigthers through Monitored Heart Rate.

Smoke inhalation poses a serious health threat to firefighters (FFs), with potential effects including respiratory and cardiac disorders. In this work, environmental and physiological data were collected from FFs, during experimental fires performed in 2015 and 2019. Extending a previous work, which allowed us to conclude that changes in heart rate (HR) were associated with alterations in the inhalation of carbon monoxide (CO), we performed a HR analysis according to different levels of CO exposure during firefighting based on data collected from three FFs. Based on HR collected and on CO occupational exposure standards (OES), we propose a classifier to identify CO exposure levels through the HR measured values. An ensemble of 100 bagged classification trees was used and the classification of CO levels obtained an overall accuracy of 91.9%. The classification can be performed in real-time and can be embedded in a decision fire-fighting support system. This classification of FF' exposure to critical CO levels, through minimally-invasive monitored HR, opens the possibility to identify hazardous situations, preventing and avoiding possible severe problems in FF' health due to inhaled pollutants. The obtained results also show the importance of future studies on the relevance and influence of the exposure and inhalation of pollutants on the FF' health, especially in what refers to hazardous levels of toxic air pollutants.

Assessment of health benefits related to air quality improvement strategies in urban areas: An Impact Pathway Approach.

Air pollution is, increasingly, a concern to our society given the threats to human health and the environment. Concerted actions to improve air quality have been taken at different levels, such as through the development of Air Quality Plans (AQPs). However, air quality impacts associated with the implementation of abatement measures included in AQPs are often neglected. In order to identify the major gaps and strengths in current knowledge, a literature review has been performed on existing methodologies to estimate air pollution-related health impacts and subsequent external costs. Based on this review, the Impact Pathway Approach was adopted and applied within the context of the MAPLIA research project to assess the health impacts and benefits (or avoided external costs) derived from improvements in air quality. Seven emission abatement scenarios, based on individual and combined abatement measures, were tested for the major activity sectors (traffic, residential and industrial combustion and production processes) of a Portuguese urban area (Grande Porto) with severe particular matter (PM10) air pollution problems. Results revealed a strong positive correlation between population density and health benefits obtained from the assessed reduction scenarios. As a consequence, potential health benefits from reduction scenarios are largest in densely populated areas with high anthropic activity and, thus, where air pollution problems are most alarming. Implementation of all measures resulted in a reduction in PM10 emissions by almost 8%, improving air quality by about 1% and contributing to a benefit of 8.8 million €/year for the entire study domain. The introduction of PM10 reduction technologies in industrial units was the most beneficial abatement measure. This study intends to contribute to policy support for decision-making on air quality management.

Emission Inventories and Particulate Matter Air Quality Modeling over the Pearl River Delta Region.

The Pearl River Delta (PRD) region is located on the southeast coast of mainland China and it is an important economic hub. The high levels of particulate matter (PM) in the atmosphere, however, and poor visibility have become a complex environmental problem for the region. Air quality modeling systems are useful to understand the temporal and spatial distribution of air pollution, making use of atmospheric emission data as inputs. Over the years, several atmospheric emission inventories have been developed for the Asia region. The main purpose of this work is to evaluate the performance of the air quality modeling system for simulating PM concentrations over the PRD using three atmospheric emission inventories (i.e., EDGAR, REAS and MIX) during a winter and a summer period. In general, there is a tendency to underestimate PM levels, but results based on the EDGAR emission inventory show slightly better accuracy. However, improvements in the spatial and temporal disaggregation of emissions are still needed to properly represent PRD air quality. This study's comparison of the three emission inventories' data, as well as their PM simulating outcomes, generates recommendations for future improvements to atmospheric emission inventories and our understanding of air pollution problems in the PRD region.

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.