V (2019) An overview of the problems posed by plastic products and the role of extended producer responsibility in Europe.

Apart from the health aspects and the high death toll, the COVID-19 pandemic has, since its official recognition in March 2020 caused may social and economic problems. It has also led to many environmental ones. For instance, the lockdowns have led to higher levels of consumption of packaged products, and of take-away food. This paper reports on an international study on the increased consumption and subsequent changes in the amounts of waste produced since the COVID-19 pandemic. The results show that 45-48% of the respondents observed an increased consumption of packed food, fresh food, and food delivery. One of the main reasons for the increased waste generation during the lockdown was the fact that people have spent more time at home. In addition, increases of 43% and 53% in food waste and plastic packaging. Drawing from comparisons on the amount of domestic waste produced before and during the pandemic, the findings suggest that some specific types of municipal waste have visibly increased, putting additional pressure on waste management systems. This characterises one of non-intended effects of the COVID-19 pandemic. The results from this study provide useful insights to city administrations and municipal utilities on consumption patterns during emergency situations. This, in turn, may support more systemic and strategic measures to be taken, so as to curtail the increase of household waste during pandemic situations.

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In this study, an evaluation of food waste generation was conducted, using images taken before and after the daily meals of people aged between 20 and 30 years in Serbia, for the period between January 1st and April 31st in 2022. A convolutional neural network (CNN) was employed for the tasks of recognizing food images before the meal and estimating the percentage of food waste according to the photographs taken. Keeping in mind the vast variates and types of food available, the image recognition and validation of food items present a generally very challenging task. Nevertheless, deep learning has recently been shown to be a very potent image recognition procedure, while CNN presents a state-of-the-art method of deep learning. The CNN technique was implemented to the food detection and food waste estimation tasks throughout the parameter optimization procedure. The images of the most frequently encountered food items were collected from the internet to create an image dataset, covering 157 food categories, which was used to evaluate recognition performance. Each category included between 50 and 200 images, while the total number of images in the database reached 23,552. The CNN model presented good prediction capabilities, showing an accuracy of 0.988 and a loss of 0.102, after the network training cycle. The average food waste per meal, in the frame of the analysis in Serbia, was 21.3%, according to the images collected for food waste evaluation.