Persulfate (PS)-activated, iron-based heterogeneous catalysts have attracted significant attention as a potential advanced and sustainable water purification system. Herein, a novel Fe₃O₄ impregnated graphene oxide (Fe₃O₄@GO)-activated persulfate system (Fe₃O₄@GO+K₂S₂O₈) was synthesized by following a sustainable protocol and was tested on real wastewater containing dye pollutants. In the presence of the PS-activated system, the degradation efficiency of Rhodamine B (RhB) was significantly increased to a level of ≈95% compared with that of Fe₃O₄ (≈25%). The influences of different operational parameters, including solution pH, persulfate dosage, and RhB concentration, were systemically evaluated. This system maintained its catalytic activity and durability with a negligible amount of iron leached during successive recirculation experiments. The degradation intermediates were further identified through reactive oxygen species (ROS) studies, where surface-bound SO₄⁻ was found to be dominant radical for RhB degradation. Moreover, the degradation mechanism of RhB in the Fe₃O₄@GO+K₂S₂O₈ system was discussed. Finally, the results indicate that the persulfate-activated Fe₃O₄@GO catalyst provided an effective pathway for the degradation of dye pollutants in real wastewater treatment.

Unwanted odour emissions are considered air pollutants that may cause detrimental impacts to the environment as well as an indicator of unhealthy air to the affected individuals resulting in annoyance and health related issues. These pollutants are challenging to handle due to their invisibility to the naked eye and can only be felt by the human olfactory stimuli. A strategy to address this issue is by introducing an intelligent processing system to odour monitoring instrument such as artificial neural network to achieve a robust result. In this paper, a review on the application of artificial neural network for the management of environmental odours is presented. The principal factors in developing an optimum artificial neural network were identified as elements, structure and learning algorithms. The management of environmental odour has been distinguished into four aspects such as measurement, characterization, control and treatment and continuous monitoring. For each aspect, the performance of the neural network is critically evaluated emphasizing the strengths and weaknesses. This work aims to address the scarcity of information by addressing the gaps from existing studies in terms of the selection of the most suitable configuration, the benefits and consequences. Adopting this technique could provide a new avenue in the management of environmental odours through the use of a powerful mathematical computing tool for a more efficient and reliable outcome.

Small wastewater treatment plants are often localized nearby tourist areas. Odour emissions are a major environmental issue in these plants and are considered to be the main cause of disturbance noticed by the exposed population. Odour measurement is carried out using analytical or sensorial methods. Sensorial analysis, being assigned to the “human sensor”, is the cause of a considerable uncertainty. In this study, a novel procedure based on highly innovative analytical tool was used to identify and characterise the odour sources and the volatile substances that cause annoyance in a SWWTP located in a sensitive area, with the aim to remove the subjective component in the measure of the odours and define the induced impact. At the same time key odour compounds are detected, and the relationship between their concentration and the performances of the plant are investigated. The sources and the main chemical substances responsible for the olfactory annoyances were identified. Results highlight the applicability of the highly innovative tool in odour emission monitoring. Around 39 different substances were detected, with almost half being smell relevant components as well as responsible. Dimethyl disulphide was identified as key compound connected to the efficiency of the process.

Water is needed for food processing facilities to carry out a number of tasks, including moving goods, washing, processing, and cleaning operations. This causes them to produce wastewater effluent, and they are typically undesirable since it contains a high volume of suspended solids, bacteria, dyestuffs, salts, oils, fats, chemical oxygen demand and biological oxygen demand. Therefore, treatment of food industry wastewater effluent is critical in improving process conditions, socio-economic benefits and our environmental. This short review summarizes the role of available membrane technologies that have been employed for food wastewater treatment and analyse their performance. Particularly, electrospun nanofiber membrane technology is revealed as an emerging membrane science and technology area producing materials of increasing performance and effectiveness in treating wastewater. This review reveals the challenges and perspectives that will assist in treating the food industry wastewater by developing novel membrane technologies.