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Municipal waste classification is significant for effective recycling and waste management processes that involve the classification of diverse municipal waste materials such as paper, glass, plastic, and organic matter using diverse techniques. Yet, this municipal waste classification process faces several challenges, such as high computational complexity, more time consumption, and high variability in the appearance of waste caused by variations in color, type, and degradation level, which makes an inaccurate waste classification process. To overcome these challenges, this research proposes a novel Channel and Spatial Attention-Based Multiblock Convolutional Network for accurately classifying municipal waste that utilizes a unique attention mechanism for enhancing feature learning and waste classification accuracy. In this research, the data augmentation technique is utilized to improve the size and diversity of the images, which creates a new municipal waste image from the existing image. After the augmentation process, the data preprocessing is performed through normalization, resizing, and dividing the images into smaller patches. In the feature extraction phase, each image patch's detailed representation is created by integrating and extracting the image's relevant features from the embedding space. Finally, the proposed Channel and Spatial Attention-Based Multiblock Convolutional Network predicts the types of municipal waste that are represented in each image patch by classifying the input images into diverse kinds of waste categories. The experimental validation exposed that the proposed Channel and Spatial Attention-Based Multiblock Convolutional Network effectively classifies the municipal waste images and achieves a higher accuracy of 98.73%, lower mean absolute error of 0.048, and lower root mean square error of 0.087 when compared to existing municipal waste classification strategies. These results prove that the proposed Channel and Spatial Attention-Based Multiblock Convolutional Network framework is more accurate, reliable, and well-suited to real-time municipal waste classification. [ABSTRACT FROM AUTHOR]

The composition of plastic and multilayer packaging waste was assessed in the mixed municipal solid waste (MSW) streams of the Kaunas (Lithuania), Daugavpils (Latvia) and Tallinn (Estonia) municipalities. For the analysis of samples in the mixed MSW streams, the authors used manual sorting and a visual recognition method. Composition analysis of plastic and multilayer packaging waste from separately collected waste of multi-family and single-family households was performed in the Kaunas and Tallinn municipalities. For the analysis of samples in the separately collected waste streams, the research group combined manual sorting and near-infrared (NIR) spectroscopy methods. The findings reveal that the percentage distribution of plastic and multilayer packaging waste within the municipal solid waste (MSW) stream is relatively consistent across the municipalities of Kaunas, Daugavpils and Tallinn, comprising 40.16%, 36.83% and 35.09%, respectively. However, a notable variation emerges when examining separately collected plastic and multilayer packaging waste streams. In this category, the proportion of plastic and multilayer packaging within the total separately collected packaging waste stream ranges from 62.05% to 74.7% for multi-family residential buildings and from 44.66% to 56.89% for single-family residential buildings. The authors provided further insights for the enhanced recycling potential of different plastic materials through improved sorting. [ABSTRACT FROM AUTHOR]

Waste has emerged as a pressing concern for the environment, primarily stemming from the processes of urbanization and industrialization. The substantial volumes of waste generated pose a serious threat to the environment, as they spread out harmful substances in the soil and release methane emissions into the atmosphere. To effectively address this issue, this study explores the impact of municipal and industrial waste, as well as waste-related innovation on the load capacity factor (LCF) from 2005 to 2020. For this purpose, the augmented mean group method and the half panel jackknife causality approach were conducted by using panel data from 17 European countries. The empirical findings show that (1) the load capacity curve (LCC) hypothesis is confirmed; (2) municipal and industrial waste have a detrimental effect on the LCF; and (3) innovation in waste management practices have no discernible impact on the LCF. In light of these findings, this study emphasizes the importance of efficient waste management for European countries to exploit the potential of waste as a valuable resource rather than a cause of pollution., (© The Author(s) 2025. Published by Oxford University Press on behalf of the Society of Environmental Toxicology and Chemistry. All rights reserved. For permissions, please email: journals.permissions@oup.com.)