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Micropollutants in the aquatic environment pose threats to both ecosystems and human health. Traditional wastewater treatment plants (WWTP) reduce some micropollutants, especially those who adhere to sludge or suspended matter. The hydrophilic micropollutants, on the other side, which may be non-biodegradable and resistant to UV-treatment etc. are typically transported untreated into the water recipients. This paper contains a literature study on the state of the art of advanced wastewater treatment technologies for reducing micropollutants such as pharmaceutical degradation products, personal care products, surfactants and industrial chemicals including heavy metals.

This literature study is completed using the most extensive and expansive literature database in the World to date, Google Scholar (GS). Published papers in recognized scientific journals are sought out in GS, and for relevance for this literature study, papers published here from 2016 and onwards (the last 5 years) have been chosen to eliminate irrelevant studies.

The result of the study is that there are many promising technologies on the market or emerging; however, no one solution treats every micropollutant equally well. Since advanced technologies often require expensive investments for municipalities and companies, it is important to identify which micropollutants pose the highest risk towards human health and the environment, because choosing systems to eliminate them all is not economically wise, and even choosing a system combining the existing technologies can be more expensive than states, municipalities and private companies are capable of investing in.

The research is limited to published papers on GS, which may omit certain papers published in closed databases not sharing their work on GS.

The practical implications are that practitioners cannot find go-to solutions based on the conclusions of the research and thus need to use the results to investigate their own needs further in order to make the wisest decision accordingly. However, the paper outlines the state of the art in advanced wastewater treatment and explains the benefits and downsides of the technologies mentioned; however, more research in the field is required before practitioners may find a proper solution to their specific issues.

The social implications are that the consequences of introducing a removal of micropollutants from the water environment can ultimately effect the citizens/consumers/end-users through added costs to the tariffs or taxes on advanced wastewater treatment, added costs on everyday goods, wares and products and added costs on services that uses goods, wares and products that ultimately produces micropollutants affecting the water environment.

This paper presents a much needed state of the art regarding the current advanced technologies to mitigate micropollutants in wastewater. The overview the paper provides supports politics on national as well as international levels, where larger unions such as the EU has stated that advanced wastewater treatment will be the next step in regulating pollutants for aquatic outlet.

The purpose of this paper is to provide evidence on the importance of cross-border cooperation in the quality assurance of transnational education (TNE) by offering a comparative overview of how TNE is quality assured by both sending and receiving countries. Through this comparative analysis, it will be possible to appreciate the diversity of approaches to TNE. The paper also shows that for the foreseeable future, progress towards effective and effiicient quality assurance of TNE cannot rest on the hope of developing an internationally agreed framework, but it must rest on strengthening cooperation between quality assurance agencies operating within different national frameworks.

This paper looks at four key sending countries of TNE, the UK, Australia, the USA and Germany, considering the main drivers for out-going TNE, its main features and how it is quality assured. It then considers the same aspects from the perspective of receiving countries of TNE, looking at four key receiving locations, China, Dubai, Singapore and Hong Kong. It also offers an overview of some recent international initiatives aimed at fostering cross-border cooperation in the quality assurance of TNE.

In the backdrop of this overview of different approaches to TNE, this paper will conclude that for the foreseeable future it is not possible to develop an internationally agreed view and approach to TNE and its quality assurance. This conclusion allows the author to focus on the importance of cross-border cooperation amongst quality assurance agencies in sending and receiving countries for the effective and efficient quality assurance of TNE.

This is the first comparative study of different countries’ approaches to TNE and its quality assurance, taking into account both sending and receiving countries. It allows pointing to the key different features in different countries’ approaches and illustrates how these often relate to the underlining national strategic priorities and drivers for TNE (either in-bound or out-bound). It also allows the international community to realise that in the absence of a viable shared and agreed international framework for the regulation and quality assurance of TNE, it is of paramount importance to focus efforts on inter-agency cross-border cooperation in order to ensure that TNE continues to be of good quality and relevant to the respective communities.

An individual’s driving style significantly affects overall traffic safety. However, driving style is difficult to identify due to temporal and spatial differences and scene heterogeneity of driving behavior data. As such, the study of real-time driving-style identification methods is of great significance for formulating personalized driving strategies, improving traffic safety and reducing fuel consumption. This study aims to establish a driving style recognition framework based on longitudinal driving operation conditions (DOCs) using a machine learning model and natural driving data collected by a vehicle equipped with an advanced driving assistance system (ADAS).

Specifically, a driving style recognition framework based on longitudinal DOCs was established. To train the model, a real-world driving experiment was conducted. First, the driving styles of 44 drivers were preliminarily identified through natural driving data and video data; drivers were categorized through a subjective evaluation as conservative, moderate or aggressive. Then, based on the ADAS driving data, a criterion for extracting longitudinal DOCs was developed. Third, taking the ADAS data from 47 Kms of the two test expressways as the research object, six DOCs were calibrated and the characteristic data sets of the different DOCs were extracted and constructed. Finally, four machine learning classification (MLC) models were used to classify and predict driving style based on the natural driving data.

The results showed that six longitudinal DOCs were calibrated according to the proposed calibration criterion. Cautious drivers undertook the largest proportion of the free cruise condition (FCC), while aggressive drivers primarily undertook the FCC, following steady condition and relative approximation condition. Compared with cautious and moderate drivers, aggressive drivers adopted a smaller time headway (THW) and distance headway (DHW). THW, time-to-collision (TTC) and DHW showed highly significant differences in driving style identification, while longitudinal acceleration (LA) showed no significant difference in driving style identification. Speed and TTC showed no significant difference between moderate and aggressive drivers. In consideration of the cross-validation results and model prediction results, the overall hierarchical prediction performance ranking of the four studied machine learning models under the current sample data set was extreme gradient boosting > multi-layer perceptron > logistic regression > support vector machine.

The contribution of this research is to propose a criterion and solution for using longitudinal driving behavior data to label longitudinal DOCs and rapidly identify driving styles based on those DOCs and MLC models. This study provides a reference for real-time online driving style identification in vehicles equipped with onboard data acquisition equipment, such as ADAS.

The purpose of this paper is to focus on the removal of hexavalent chromium [Cr(VI)] from wastewater by using activated carbon-supported Fe catalysts derived from walnut shell prepared using a wetness impregnation process. The different conditions of preparation such as impregnation rate and calcination conditions (temperature and time) were optimized to determine their effects on the catalyst’s characteristics.

The catalyst samples were characterized using thermogravimetric analysis, scanning electron microscopy and Fourier transform infrared spectroscopy. The adsorption of Cr(VI) by using using activated carbon supported Fe catalysts derived from walnut shell as an adsorbent and catalyst was investigated under different adsorption conditions. The parameters studied were contact time, adsorbent dose, solution pH and initial concentrations.

Results showed that higher adsorption capacity and rapid kinetics were obtained when the activated walnut shell was impregnated with Fe at 5 per cent and calcined under N₂ flow at 400°C for 2 h. The adsorption isotherms data were analyzed with Langmuir and Freundlich models. The better fit is obtained with the Langmuir model with a maximum adsorption capacity of 29.67 mg/g for Cr(VI) on Fe5-AWS at pH 2.0.

A comparison of two kinetic models shows that the adsorption isotherms system is better described by the pseudo-first-order kinetic model.