Nanomaterials have been the focus of intense study and growth in the modern era across the globe because of their outstanding qualities, which are brought about by their nanoscale size; for instance, increased adsorption and catalysis capabilities plus significant reactivity. Multiple investigations have verified the fact that nanoparticles may successfully remove a variety of pollutants from water, and, as a result, they have been utilized in the treatment of both water and wastewater. Therefore, the current research intent is to examine the nonlinear heat source/sink influence on the 3D flow of water-based silver nanoparticles incorporated in an Eyring–Powell fluid across a deformable sheet with concentration pollutants. Silver particles have been used intensively to filter water, due to their potent antibacterial properties. The leading equations involving partial differential equations are renewed into the form of ordinary ordinary differential equations through utilizing the appropriate similarity technique. Then, these converted equations are solved by utilizing an efficient solver bvp4c. Visual displays and extensive exploration of the different impacts of the non-dimensional parameters on the concentration, temperature, and velocity profiles are provided. Also, the important engineering variables including skin friction, the rate of heat, and mass transfer are examined. The findings suggest that the mass transfer rate declines due to pollutant parameters. Also, the results suggest that the friction factor is uplifted by about 15% and that the heat transfer rate, as well as the mass transfer rate, declines by about 21%, due to the presence of the nanoparticle volume fraction. We believe that these results may improve the flow rate of nanofluid systems, improve heat transfer, and reduce pollutant dispersal.

Investigations of attitudes towards food waste and preferences of policies that affect sustainability has increased rapidly over the past few decades. Most research on food waste, however, has been undertaken mainly in the developed countries with very few in the developing countries. It is very important therefore to investigate food waste and attitudes towards sustainability in developing countries, especially since a huge amount of carbon emissions and other pollutants occur in the developing countries. Saudi Arabia is a rich developing country that sets its 2030 visions to tackle issues related to sustainability. Many strong policies and programs are set in place to fulfil these visions. Such attitudes towards food waste diverges between cultures as a result of differences in background, income, family structure, age, etc. The contribution of this study is that it intends to investigate attitudes and preferences on policies and programs that aim at achieving sustainability and reducing food waste for young university females in Saudi Arabia. The methodology that is adopted in this study is to design a questionnaire, arranged in 5 sections and 23 questions, to collect data from 199 students at Princess Norah Bint Abdulrahman University. The aim of the questionnaire was to evaluate attitudes and preferences of female students towards food waste in Saudi Arabia, and in particular in the city of Riyadh. A number of policies were selected for the investigation, including three categories of policies: hospitality policies, education and raising awareness, and legislation policies. The participants expressed their opinions towards policies and programs that aim at improving sustainability and reducing food waste. Each policy was assessed in terms of its level of importance and its effectiveness in achieving sustainability. Two indices were calculated to compare and assess the perception of the suggested policies: the Perceived Effectiveness Index (PEI) and the Endorsement Index (EI). The results show that while some policies are perceived as both effective and supported for implementation, some policies are perceived as most effective; however, participants were not very supportive of their implementation. Hospitality policies, such as encouraging food waste recycling and providing the option to take away leftover food, were ranked highest in terms of effectiveness to achieve the target. These findings seem to echo the Saudi society’s attitudes of sustainable behaviour and positive attitudes towards food waste recycling. It should also be noted here that while the Saudi society is still a growing and developing society, it does care significantly about food waste and sustainability. The results are encouraging, and further investigations are urgently needed to better understand determinants of food waste at a household level in developing countries.

The Kingdom of Saudi Arabia (KSA) is known for its high car ownership and usage and its high GDP per capita. This, combined with a low provision of public transportation (PT) systems, has resulted in perceptual attitudes of high dependency on private car travel. The level of awareness of the benefits of reducing car use and increasing travel by more sustainable options has a great impact on social change and behaviour. The Kingdom is currently progressing towards a new phase of “national reform” through implementation of strategic sustainable programs. Riyadh city is constructing a massive metro-system, which is nearing completion and operation. The public is aware of the national agenda, the construction of transportation projects, and of the social changes needed to realize the new vision of the country. This paper aims to assess travel behaviours and attitudes of Saudi nationals towards public transportation. A discrete choice model of the intent to use public transportation is calibrated as a function of social and attitudinal factors, using online survey data. The analysis was carried out using an ordered logit model (OLR) which is derived from the theory of random utility. The results show that, overall, a higher support for public transportation modes was stated by young females, lower income groups, and university graduates. The level of awareness of the national agenda appears to be increasing the level of support for public transportation. The paper is the first investigation of willingness to use public transportation in Riyadh, at a crucial time of national reform; the choice of mode of travel appears to be determined by the travellers’ social and economic characteristics and the level of awareness of the country’s agenda.

Electric vehicles (EVs) have become popular in reducing the negative impact of ICE automobiles on the environment. EVs have been predicted to be an important mode of mass transit around the globe in recent years. Several charging stations in island and remote areas are dependent on off-grid power sources and renewable energy. Solar energy is used in the daytime as it is based on several environmental components. The creation of efficient power trackers is necessary for solar arrays to produce power at their peak efficiency. To deliver energy during emergencies and store it in case there is an excess, energy storage systems are required. It has long been known that reliable battery management technology is essential for maintaining precise battery charge levels and avoiding overcharging. This study suggests an ideal deep-learning-assisted solar-operated off-board smart charging station (ODL-SOOSCS) design method as a result. The development of on-board smart charging for mass transit EVs is the main goal of the ODL-SOOSCS technique that is being described. In the ODL-SOOSCS approach described here, a perovskite solar film serves as the generating module, and the energy it generates is stored in a module with a hybrid ultracapacitor and a lithium-ion battery. Broad bridge converters and solar panels are incorporated into the deep belief network (DBN) controller, which doubles as an EV charging station. An oppositional bird swarm optimization (OBSO) algorithm is used as a hyperparameter optimizer to improve the performance of the DBN model. Moreover, an MPPT device is exploited for monitoring and providing maximal output of the solar panel if the power sources are PV arrays. The proposed system combines the power of metaheuristic optimization algorithms with deep learning techniques to create an efficient and smart charging station for mass transport passenger vehicles. This integration of two powerful technologies is a novel approach toward solving the complex problem of charging electric vehicles in mass transportation systems. The experimental validation of the ODL-SOOSCS technique is tested on distinct converter topologies. A widespread experimental analysis assures the promising performance of the ODL-SOOSCS method over other current methodologies.

The introduction of autonomous vehicles has been considered as a possible option for reducing traffic congestion in many transport studies. Many types of models, in particular car-following microsimulation models have been adopted in most studies. The impacts of autonomous vehicles (AVs) on congestion, however, have not yet been concluded. This could be because different researchers use different forms of car-following models to assess these impacts, or because the utilised modelling approaches and their parameters are different in different studies. In particular, two of the important parameters that are associated with car-following models are the used values for maximum acceleration and the average desired time gaps. While the values of these parameters can be adjusted and controlled by the ACC controllers in the AV, they can also be controlled by the users. Therefore, assigning unrealistic values to these parameters could well result in unrealistic conclusions. This paper investigated the impacts of the maximum acceleration and the average desired time gaps on congestion levels using the loss-time indicator. The analysis was carried out on the Hanshin expressway in Japan and was tested and assessed using the Helly (FACC) car-following microsimulation model. This includes estimating the values of the desired time gap from real traffic time-gap distributions. The Hanshin expressway is an urban toll highway of 273 km that extends from Osaka to Kobe, representing the Hanshin area in Japan. The Hanshin highway serves a huge traffic volume that consists of private and freight vehicles that operate within the Hanshin area. This area represents one of three major municipal areas in Japan including Tokyo and Nagoya. A total of 740,000 vehicles per day travel on the expressway. As a result, there is significant congestion on the Hanshin expressway. There have been various plans put in place to ease congestion ranging from building new roads to the implementation of traffic-demand-management measures. However, the predictions of the impacts of such measures do not provide any evidence that they would ease traffic congestion. Other possible measures that could be investigated for easing traffic congestion include technology-based solutions such as autonomous vehicles. The modelling results recommend that the results obtained from microsimulation models should be taken with care, and good attention should be paid to the parameters used and their values in the model. The values assigned to driving-behaviour parameters, the maximum values of acceleration, and the time-gap settings, for example, control the final outcomes of the models.

Car ownership and use in the Kingdom of Saudi Arabia (KSA) are very high due to the high income, the low fuel prices and the near absence of public transportation in the Kingdom. Currently, the Kingdom is going through a massive transformation and entering a new era of national reforms. One of the main aims of the national reforms is to reduce car dependency and enhance more sustainable options of travel such as public transportation in the KSA. In order to achieve such an aim, there are two hurdles: Firstly, there must be a provision of decent public transportation options, and secondly, there is a need to influence travel behaviour and encourage the shift from private cars to public transportation. For example, in Riyadh city, an impressive metro system is being constructed and will start operation in 2023. To influence travel behaviour, travel demand management measures (TDM), in particular pricing measures, can be adopted and implemented, in order to help and support achieving the target. The main aim of this paper, therefore, is to assess the attitudes of Saudi nationals towards—and willingness to accept—pricing measures and their possible impacts on their travel behaviour in the city of Riyadh. The methodology includes collecting data using an online survey on travel behaviour and attitudes in Riyadh and calibrating multinomial logit modal choice models. The participants in the survey were asked to report their support of the pricing measures for the objective of reducing congestion in the city, improving road safety or reducing travel time. The results show the highest support towards pricing measures for improving road safety, reducing travel times and, lastly, reducing congestion in the city.