Improvement of the current waste management is one of the main challenges for most municipalities in Croatia, mainly due to legal obligations set in different European Union (EU) directives regarding waste management, such as reduction of waste generation and landfilling, or increase of separately collected waste and recycling rates. This paper highlights the current waste management in the city of Zagreb by analyzing the waste generation, collection, and disposal scenario along with the regulatory and institutional framework. Since the present waste management system mainly depends upon landfilling, with the rate of separate waste collection and recycling far from being adequate, it is necessary to introduce a new system that will take into account the current situation in the city as well as the obligations imposed by the EU. Namely, in the coming years, the Waste Framework and Landfill Directives of the European Union will be a significant driver of change in waste management practices and governance of the city of Zagreb. At present, the yearly separate waste collection makes somewhat less than 5 kg per capita of various waste fractions, i.e., far below the average value for the (28) capital cities of the EU, which is 108 kg per capita. This is possible to achieve only by better and sustainable planning of future activities and facilities, taking into account of environmental, economic, and social aspects of waste management. This means that the city of Zagreb not only will have to invest in new infrastructure to meet the targets, but also will have to enhance public awareness in diverting this waste at the household level. The solution for the new waste management proposed in this paper will certainly be a way of implementing circular economy approach to current waste management practice in the city of Zagreb. Implications: Municipal waste management in the developing countries in the EU (new eastern EU members) is often characterized by its limited utilization of recycling activities, inadequate management of nonindustrial hazardous waste, and inadequate landfill disposal. Many cities in Eastern Europe and Zagreb as well are facing serious problems in managing municipal wastes due to the existing solid waste management system that is found to be highly inefficient. The proposed scenario for city of Zagreb in the paper is an innovative upgrading of municipal waste management based on the waste management hierarchy and circular economy approach.

The application of municipal sewage sludge as fertilizer in the production of non-food energy crops is an environmentally and economically sustainable approach to sewage sludge management. In addition, the application of municipal sewage sludge to energy crops such as Miscanthus x giganteus is an alternative form of recycling nutrients and organic material from waste. Municipal sewage sludge is a potential source of heavy metals in the soil, some of which can be removed by growing energy crops that are also remediation agents. Therefore, the objective of the research was to investigate the effect of municipal sewage sludge applied at three different rates of 1.66, 3.22 and 6.44 t/ha on the production of Miscanthus. Based on the analyses conducted on the biomass of Miscanthus fertilized with sludge from the wastewater treatment plant in three fertilization treatments, it can be concluded that the biomass of Miscanthus is a good feedstock for the process of direct combustion. Moreover, the application of the largest amount of municipal sewage sludge during cultivation had no negative effect on the properties of Miscanthus biomass. Moreover, the cellulose and hemicellulose content of Miscanthus is ideal for the production of second-generation liquid biofuels. Fertilizer treatments had no effect on the content of cellulose and lignin, while a significant statistical difference was found for hemicellulose.

Invasive plant species (IAS), with their numerous negative ecological, health, and economic impacts, represent one of the greatest conservation challenges in the world. Reducing the negative impacts and potentially exploiting the biomass of these plant species can significantly contribute to sustainable management, protect biodiversity, and create a healthy environment. Therefore, the main objective of this study was to evaluate the nutritional potential, phytochemical status, and antioxidant capacity of nine alien invasive plant species: Abutilon theophrasti, Amaranthus retroflexus, Ambrosia artemisiifolia, Datura stramonium, Erigeron annuus, Galinsoga ciliata, Reynoutria japonica, Solidago gigantea, and Sorghum halepense. Multivariate statistical methods such as cluster and PCA were performed to determine possible connections and correlations among selected IAS depending on the phytochemical content. According to the obtained results, R. japonica was notable with the highest content of vitamin C (38.46 mg/100 g FW); while E. annuus (1365.92 mg GAE/100 g FW) showed the highest values of total polyphenolic compounds. A. retroflexus was characterized by the highest content of total chlorophylls (0.26 mg/g) and antioxidant capacity (2221.97 µmol TE/kg). Therefore, it can be concluded that the selected IAS represent nutrient-rich plant material with significant potential for the recovering of bioactive compounds.

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 1 January and 31 April 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.