Abstract
Natural gas (NG) is commonly used in kitchens, powering stoves, ovens, and other appliances. While it is known for its efficiency and convenience, NG contributes to the release of nitrogen dioxide (NO2) and can have significant implications for human health. In this study, the importance of the use of NG in kitchens on human exposure to NO2 was analyzed. An extensive literature review in the field was conducted, and the NO2 levels were assessed in kitchens with NG cookers in Aveiro and electric cookers in Porto, both in Portugal. Higher levels of NO2 were found in kitchens in Aveiro, where NO2 levels outdoors are lower than in Porto. This pollutant can spread to other rooms, especially when ventilation is lacking, which is particularly concerning during colder seasons and at night. As around 70% of the time is spent at home, this can have a significant impact on human exposure to NO2. Therefore, although Aveiro has low levels of NO2 outdoors, its population may be exposed to much higher levels of this pollutant than the Porto population, a city with air quality issues, but predominantly using electric cookers. This finding emphasizes the need for the stricter regulation of NG use indoors to protect human health and also suggests a shift in human health protection policies from mere monitoring/control of outdoor air quality to a comprehensive assessment of human exposure, including exposure to indoor air quality.

Abstract
The impacts of the e-commerce growth have increased the urgency in designing and adopting new alternative delivery strategies. In this context, it is important to consider the particularities of each city like its terrain conditions. This article aims at exploring the impact of road slopes on parcel delivery operations, and how they condition the adoption and implementation of alternative, more sustainable delivery strategies. To this end, a microscopic traffic simulator was used to evaluate different delivery strategies including ICE vans, electric vans, and cargo bikes in three different slope scenarios. This evaluation was based on a medium-sized European city and conducted by comparing the same parcel delivery route at three levels: operational (route length, duration, and waiting time), energy consumption, and emissions. The results revealed that as the road slopes increased, more time was needed to deliver all packages, waiting times grew longer, and vehicles' energy consumption and emissions levels intensified. From the flat terrain to the most sloped terrain, there was an increase in duration of around 5% for traditional and electric vans, 35% for large cargo bikes, and 14% for small cargo bikes. The ICE van suffers a 105% increase in waiting time; the electric van 71%; the large cargo bike 68% and the small cargo bike 52%. Energy consumption also varied, with ICE vans and small cargo bikes consuming nearly 30% more energy, while electric vans and large cargo bikes consumed 4% and 60% more energy, respectively. The ICE van's emissions of CO, HC, PMx, NOx, and CO2 are 13%, 10%, 1%, 20%, and 29% higher, respectively. Moreover, in flatter terrains, the better strategies are the electric van or a large cargo bike, while in more sloped terrains, the most adequate one is the electric van. These findings suggest that the electric van is the best overall strategy for different terrains and different decision-making profiles, ranking first in more than 70% of the profiles across all three terrains.

Abstract
The efficient last-mile delivery of goods involves complex challenges in optimizing driver sectors and routes. This problem tends to be large-scale and involves several criteria to meet simultaneously, such as creating compact sectors, balancing the workload among drivers, minimizing the number of undelivered packages and reducing the dissimilarity of sectors on different days. This work proposes a Decision Support System (DSS) that allows decision-makers to select improved allocation strategies to define sectors. The main contribution is an interactive DSS tool that addresses a many-objective (more than 3 objectives) sectorization problem with integrated routing. It establishes a global allocation strategy and uses it as a benchmark for the created daily allocations and routes. A Preference-Inspired Co-Evolutionary Algorithm with Goal vectors using Mating Restriction (PICEA-g-mr) is employed to solve the many-objective optimization problem. The DSS also includes a visualization tool to aid decision-makers in selecting the most suitable allocation strategy. The approach was tested in a medium-sized Metropolitan Area and evaluated using resource evaluation metrics and visualization methods. The proposed DSS deals effectively and efficiently with the sectorization problem in the context of last-mile delivery by producing a set of viable and good-quality allocations, empowering decision-makers in selecting better allocation strategies. Focused on enhancing service efficiency and driver satisfaction, the DSS serves as a valuable tool to improve overall service quality.

Abstract
This work presents a deep learning framework for analyzing urban mobility by extracting knowledge from messages collected from Twitter. The framework, which is designed to handle large-scale data and adapt automatically to new contexts, comprises three main modules: data collection and system configuration, data analytics, and aggregation and visualization. The text data is pre-processed using NLP techniques to remove informal words, slang, and misspellings. A pre-trained, unsupervised word embedding model, BERT, is used to classify travel-related tweets using a unigram approach with three dictionaries of travel-related target words: small, medium, and big. Public opinion is evaluated using VADER to classify travel-related tweets according to their sentiments. The mobility of three major cities was assessed: London, Melbourne, and New York. The framework demonstrates consistently high average performance, with a Precision of 0.80 for text classification and 0.77 for sentiment analysis. The framework can aggregate sparse information from social media and provide updated information in near real-time with high spatial resolution, enabling easy identification of traffic-related events. The framework is helpful for transportation decision-makers in operational control, tactical-strategic planning, and policy evaluation. For example, it can be used to improve the management of resources during traffic congestion or emergencies.

Abstract
In this paper is presented firstly the traffic and emission characterization of Via de Cintura Interna (VCI), an urban highway at Oporto city, Portugal, with more than 4 000 vehicles/hour during rush hours. Emission estimates were carried through on the basis of emission factors to road transport published in the Atmospheric Emission Inventory Guidebook. A weighed emission factor has beer, calculated for nitrogen oxides (NOx) and vehicle class, according to the Portuguese fleet composition (vehicles age, type of engine and average speed). Furthermore, during a three-week period, an outdoor nitrogen dioxide (NO2) monitoring campaign was carried out in a domain around the VCl (100 m for each side), in particular near residential buildings. The results demonstrate that higher NO2 concentrations are seen in the sub-domain with higher circulation of heavy-duty vehicles and where buildings are adjacent to VCI hindering pollutant dispersion. Meteorological conditions, such as wind intensity and direction, temperature and solar radiation were monitorized too. The NO2 concentrations obtained by the monitoring campaign have been used to create scenarios of population exposure to NO2, having taken into account the time-activity patterns of residents. It was verified that higher exposures occur when the population lives in Boavista, in contrast with the favourable scenario that corresponds to life in Prelada and those working in Espinho city. The work and results presented in this paper are a part of the methodology used in the scope of the ImpactAir Project. This project, started in 2003 in Oporto city, has the main objective of evaluating the impact of urban highway (VCI) traffic emissions on air quality and the health of the local population.

Abstract
This paper focuses on the integration of multiple computational tools towards the objective of assessing emission impacts of different routes. Data from real life GPS tracks was integrated with traffic emission modelling for multiple pollutants (NOx, HC, CO and PM10) to investigate different routing strategies. The main conclusion is that different pollutants dictate different best routes. Hence, strategies for assigning relative weights to pollutants are devised in order to be able to select the best environment-friendly route. © 2012 ACM.