As urban areas grow with the increase in population, so do the problems associated with these areas, such as an increase in atmospheric emissions. Since urban morphology has an effect on the environment, it is necessary to design future urban morphologies to accommodate the expected growth and mitigate the associated problems. By employing an emission distribution methodology based on the relationship between land use and emission activity sectors, including transport and road traffic emissions modelling (with PTV-VISUM and TREM, respectively), this study aims to identify urban morphologies that have the potential to minimize atmospheric emissions for future multi-core regions. This study assesses three urban morphology scenarios, focused on Aveiro, Portugal, where two represent urban compaction - Focused City scenario and Independent City scenario -, and one represents an extreme version of the current urban dispersion. The impact of urban scenarios was compared against the current urban morphology. Results indicate that, for the compact urban morphologies, the Focused City scenario showed a small increase in emissions, and the Independent City scenario led to a decrease in emissions, especially for NOx (-16 %), as it is the pollutant most affected by road traffic emissions. As for the Disperse City scenario, it showed the highest overall increase, as it greatly increased the vehicle volume and total distance travelled. These results highlight the need for policy and behavioral changes to accompany the changes to urban morphology, and for special attention to be paid to the location of activity sectors when designing the different urban morphologies. This study contributes novel insights by applying a comprehensive methodology that integrates land use, activity sectors, and road traffic emissions modelling. By assessing the urban morphology\'s impact on air pollutant emissions, it is possible to inform urban planners of future urban planning strategies.

The aim of the present study was to investigate eye temperature modifications after road transport in athletic horses habituated to travel. Eight adult Italian saddle horses traveled 100 km and, two weeks later, 300 km. Eye temperature (ET), rectal temperature (RT) and serum cortisol concentration were assessed before (T1), after (T2) and 60 min (T3) after the road transport. ET was evaluated with infrared thermography (IRT) in three regions of interest: EL1 (medial canthus), EL2 (central cornea) and EL3 (lateral canthus). Two-way analysis of variance (ANOVA) for repeated measures showed statistically higher values at T2 and T3 for EL1 (p < 0.01), EL2 (p < 0.01) and EL3 (p < 0.01) following the 100 km journey. RT (p < 0.01) showed higher values at T2 and T3 after the 100 km journey and higher values at T2 (p < 0.01) following the 300 km journey. ET values were positively correlated with RT at T1, T2 and T3 following the 100 km journey and at T2 following the 300 km journey and positively correlated with serum cortisol concentration at T1, T2 and T3 following the 100 km journey and at T2 and T3 following the 300 km journey. Eye temperature monitoring with IRT allows quick and practical strategies to monitor an animal\'s physiological state and welfare during daily activities.

The aim of this study was to assess the changes of pro-inflammatory interleukins in 10 horses subjected to road transport practices (distance of 150 km) from the training site (Messina, Sicily) to the competition centre in Syracuse (Sicily). Blood sampling and interleukins analysis were performed during a round trip transportation (transport 1 and transport 2). In particular, blood samples were collected before the transport took place (Pre), five minutes later (Post) and one hour later (Post 1 h), for each transport, in order to assess the serum concentration of IL-1α, IL-1β, IL-2 and IL-6. The results showed that the serum concentration of IL-1α decreased at Post and Post 1 h compared to the values obtained at rest condition (P < 0.05). The other interleukins analysed (i.e. IL-1β, IL-2 and IL-6) showed increased levels at Post than Rest and Post 1 h in transport 1 (P < 0.05). In transport 2 the analysed parameters showed no change throughout the analysed time points (P > 0.05); however, higher levels of IL-1α at Pre and higher IL-1β, IL-2 and IL-6 values at Post were found in transport 1 than transport 2 (P < 0.05). The increase in pro-inflammatory cytokines after transport 1 suggests the triggering of the inflammatory event and this may show that, although horses are animals accustomed to transport, this is a stressful event that could activate the well-orchestrated inflammation cascade, albeit physiological and temporary, as highlighted by the lower serum concentrations of the investigated interleukins found in transport 1 than transport 2 and by the lack of significant differences in the serum concentrations of the investigated interleukins among the time points of transport 2. It must be taken into account that enrolled animals are well-trained and healthy athletic horses participating to a jumper competition, thus, such inflammation did not occur thanks to a good balance between pro-inflammatory and anti-inflammatory cytokines which allowed a prompt restoration of homeostasis eventually impaired by the stressful event.

This study aimed to evaluate changes in body temperature in athletic horses during two different road transport distances. Six Italian Saddle horses were subjected to a 100 and 300 km transport during different times of day (am and pm). Rectal and cutaneous temperatures were recorded before (T0), immediately (following 5 min- T1) and 1 hour (T2) after transport by means of a rectal digital thermometer and a thermal infrared camera (FLIR T440) respectively, for the evaluation of left and right side of four body regions: jugular, shoulder, croup and inner thigh. There were no differences between left and right sides, inner thigh or rectal temperatures when comparing the transport distance, time points or time of day. At T0, jugular (P < 0.0001), shoulder (P < 0.01) and croup (P < 0.01) average temperatures were higher in the pm compared to those in the am in both journeys. At T1, jugular (P < 0.01) and croup (P < 0.01) temperatures were lower in the pm compared to am following the 300 km journey. Jugular temperature (P < 0.0001) was higher following the 300 km compared to the 100 km journey at each time point (T1 and T2) at both times of day (am and pm). Shoulder (P < 0.0001) and croup temperatures (P < 0.0001) were higher at T2 after the 300 km journey than at T2 after the 100 km journey). The current results suggested a difference between the two distances and the time of day appeared to have as great effect on ocular temperature as road transport distance.

BACKGROUND: The decarbonization of road transport is a precondition for achieving carbon neutrality. Battery-electric vehicle technology can make this a reality. In this bias, the objective of the article is to shed light on the ongoing debate about the potentially important role of the adoption of electric vehicles in the transport of microalgae- based products to help them advance to a cleaner life cycle.
METHODS: Five routes, including unimodal and multimodal conditions, were defined to assess the carbon emissions of the transport system and, more specifically, of road transport. The headquarters of market-leading microalgae manufacturers were selected as the origin of the routes and, as the destination, regions that sustain them.
RESULTS: The results reveal the supremacy of road transport of microalgae-based products using electric vehicles powered by nuclear, hydroelectric, and wind, followed by biomass and photovoltaic energy. They also show that the positive impact of wind, water, and photovoltaic energy on the climate, added to the lower battery charging costs and the greater opportunity to generate revenue from the sale of carbon credits, make their tradeoffs.
CONCLUSIONS: The exquisite results of this study convey key messages to decision-makers and stakeholders about the role of electromobility in building a zero-carbon delivery route.

Road networks constitute a vital component of modern society, facilitating rapid transportation and driving economic activities by enabling the smooth movement of goods and people. However, the expansion of road systems carries significant environmental considerations, particularly regarding its impact on groundwater quality. Thus, it is crucial to understand the complex relationship between groundwater quality and the road traffic system. This paper aims to identify the impact of road transport systems on groundwater quality using a data-driven approach. Specifically, road network and groundwater chemistry data in Texas were obtained from an open data portal. This study was carried out in two phases: the explainable artificial intelligence (XAI) modeling phase and the multivariate analysis phase. In the XAI modeling phase, a prediction model was developed using eXtreme Gradient Boosting (XGB), with groundwater chemistry parameters as output features and road transport attributes as input features, i.e., elevation, annual average daily traffic, distance, lane-miles, speed limit and well depth. Furthermore, the relationships between groundwater chemistry parameters and road transport attributes were examined using feature importance and accumulated local effect (ALE). In the multivariate phase, Piper diagrams and principal component analysis (PCA) were utilized to identify the source of the selected groundwater chemistry parameters from the XAI models. The results of the prediction model showed that five groundwater chemistry parameters were significantly impacted by road transport systems with below a mean absolute percentage error of 0.20, including, pH, temperature, aluminum (Al), bicarbonate (HCO3-), and alkalinity. Additionally, XAI models were developed to understand the relationship between the road transport attributes on five selected parameters. The findings collectively indicated that the Texas groundwater qualities are greatly impacted by road transport systems within a distance of 50-meters and a well depth of 100-meters. This study provides a novel contribution to monitoring point sources of groundwater pollution using XAI techniques.

Artificial Intelligence (AI) is being increasingly implemented within road transport systems worldwide. Next generation of AI, Artificial General Intelligence (AGI) is imminent, and is anticipated to be more powerful than current AI. AGI systems will have a broad range of abilities and be able to perform multiple cognitive tasks akin to humans that will likely produce many expected benefits, but also potential risks. This study applied the EAST Broken Links approach to forecast the functioning of an AGI system tasked with managing a road transport system and identify potential risks. In total, 363 risks were identified that could have adverse impacts on the stated goals of safety, efficiency, environmental sustainability, and economic performance of the road system. Further, risks beyond the stated goals were identified; removal from human control, mismanaging public relations, and self-preservation. A diverse set of systemic controls will be required when designing, implementing, and operating future advanced technologies.Practitioner summary: This study demonstrated the utility of HFE methods for formally considering risks associated with the design, implementation, and operation of future technologies. This study has implications for AGI research, design, and development to ensure safe and ethical AGI implementation.

The way dangerous goods are transported on roads within the European Union is regulated by international agreements that have been transposed into national legislation. Unlike European policies that focus on preventing major accidents involving dangerous substances in the industry, the component of territorial planning in areas exposed to technological hazards is not given similar attention when it comes to transport routes of dangerous goods. Since both the transport of hazardous materials and the activities of large industrial operators involve the handling of the same types of dangerous substances, they share similar associated hazards. Within this framework, a question arises as to whether decision-makers should accord greater consideration to the land use policies in the areas adjacent to transportation routes. In this study, the case of Romania was analyzed in detail, with the objective to firstly identify some particularities in how the primary road infrastructure is developed in relation to other European countries. Since the territorial compatibility near large industrial operators is established based on specific regulations at the national level, but there is no similar regulation for the transport of dangerous substances, another objective was to adapt and implement a methodology for the analysis of the territorial compatibility in the proximity of the national roads network. The proposed methodology utilizes open-source data and Geographic Information Systems (GIS) for analysis. It also involves the extensive application of existing rules on territorial compatibility for technological risks, specifically in the context of hazmat transport. The results of this study indicate that the primary road transport network in Romania has different characteristics compared to that of other countries, which determines a higher level of exposure to the hazards specific to the road transport of hazardous materials. Additionally, from an analysis of gasoline and diesel transport on national roads and the implementation of a territorial compatibility matrix, we observed instances of territorial incompatibility in the current state, particularly in areas close to the road, especially for scenarios with a relatively high accident frequency. Thus, the way future large road infrastructure development projects address risks related to dangerous goods transportation and the implementation of environmentally conscious land management strategies can contribute to society\'s sustainable development.

Here, we introduce Traffic Ear, an acoustic sensor pack that determines the engine noise of each passing vehicle without interrupting traffic flow. The device consists of an array of microphones combined with a computer vision camera. The class and speed of passing vehicles were estimated using sound wave analysis, image processing, and machine learning algorithms. We compared the traffic composition estimated with the Traffic Ear sensor with that recorded using an automatic number plate recognition (ANPR) camera and found a high level of agreement between the two approaches for determining the vehicle type and fuel, with uncertainties of 1-4%. We also developed a new bottom-up assessment approach that used the noise analysis provided by the Traffic Ear sensor along with the extensively detailed urban mobility maps that were produced using the geospatial and temporal mapping of urban mobility (GeoSTMUM) approach. It was applied to vehicles travelling on roads in the West Midlands region of the UK. The results showed that the reduction in traffic engine noise over the whole of the study road was over 8% during rush hours, while the weekday-weekend effect had a deterioration effect of almost half. Traffic noise factors (dB/m) on a per-vehicle basis were almost always higher on motorways compared the other roads studied.

Road transport is experiencing disruptive change from new first-of-a-kind technologies. While such technologies offer safety and operational benefits, they also pose new risks. It is critical to proactively identify risks during the design, development and testing of new technologies. The Systems Theoretic Accident Model and Processes (STAMP) method analyses the dynamic structure in place to manage safety risks. This study applied STAMP to develop a control structure model for emerging technologies in the Australian road transport system and identified control gaps. The control structure shows the actors responsible for managing risks associated with first-of-a-kind technologies and the existing control and feedback mechanisms. Gaps identified related to controls (e.g. legislation) and feedback mechanisms (e.g. monitoring for behavioural adaptation). The study provides an example of how STAMP can be used to identify control structure gaps requiring attention to support the safe introduction of new technologies.
This paper considers emerging risks associated with new technologies in the road transport system. It demonstrates a novel approach using STAMP to identify gaps in control and feedback mechanisms within the existing control structure which should be addressed to mitigate risk.