Abstract
The main purpose of this paper is to present a preliminary study on the impact of stratospheric ozone on tropospheric ozone levels under specific atmospheric dynamical conditions. It is well accepted that stratospheric ozone can be the source of part of the tropospheric ozone. Previous studies indicate that the mechanism responsible for this ozone intrusion occurs generally in several steps or just in a single step, usually associated with strong upward motion. In the first part of this paper, the methodology used in order to identify particular short-term episodes, potentially associated to the abovementioned phenomenon, is presented. Several episodes have been studied occurring during 14 years of ozone data collection, recorded by the Portuguese ozone network. Then, an analysis of the dynamical atmospheric conditions associated to previously identified episodes have been developed in order to verify the possibility of stratospheric contribution for the observed ozone level in each episode. Two of these episodes show a relatively good relationship between synoptical patterns related to stratospheric intrusions and backward trajectories. For these cases, the possibility of stratospheric origin should not be discarded before further study is developed.

Abstract
We present new results on the performance of Minimum Error Entropy (MEE) decision trees, which use a novel node split criterion. The results were obtained in a comparive study with popular alternative algorithms, on 42 real world datasets. Carefull validation and statistical methods were used. The evidence gathered from this body of results show that the error performance of MEE trees compares well with alternative algorithms. An important aspect to emphasize is that MEE trees generalize better on average without sacrifing error performance.

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
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
Machine learning algorithms offer the capability to analyze large volumes of real-time data, providing transport authorities with valuable insights into traffic conditions, congestion hotspots, and incident detection from diverse data sources. However, these algorithms face challenges related to data quality and reliability. We conducted a comparative analysis of machine-learning models that can be used to identify and filter transportation content from social media or other sources that can provide small and concise text. The filtrated result can then feed models and/or tools used to improve and automate traffic control, operational management, and tactical management decision-making. We consider factors such as run time, generalization capacity, and performance metrics as criteria to assess their suitability for different decision levels. The analysis is supported by a dataset consisting of Twitter content. The predictions from three groups of algorithms are evaluated: traditional machine learning algorithms (Support Vector Machines, Logistic Regression, and Random Forest), a fine-tuned Google BERT model, and Google BERT models without training (BERT-base and BERT-large). The tests are performed using New York, London, and Melbourne data. The findings of this research aim to assist decision-makers in making informed choices when selecting the most appropriate method to filtrate information subsequently used for models that contribute to different traffic management tasks.