Waste of electrical and electronic equipment (WEEE) has gained significant attention recently because of increasing consumption and related environmental impacts. This work focuses on Lithuania and analyses the trends of WEEE generation and management during the period 2008-2015. Attitudes and behaviour of students and pupils (main future consumers) regarding WEEE are also determined in this work. The analysis shows that the generation and collection of WEEE in Lithuania have been on the rise since the global economic crisis. In total, approximately 16260 metric tonnes of WEEE were collected in 2015 in Lithuania. Most of the collected WEEE consisted of large home appliances and information technology and telecommunication equipment. In addition, the survey highlights that some small WEEE is still discarded together with municipal waste. Results also report that there is a need for more information about WEEE as such, as well as the need for more collection points and possibly a refund system. The study suggests that those aspects could be of importance for the efficiency of WEEE management systems and related policy implementation.

Download full-text PDF

Source
http://dx.doi.org/10.1177/0734242X18806999DOI Listing

Publication Analysis

Top Keywords

weee
9
waste electrical
8
electrical electronic
8
electronic equipment
8
equipment trends
4
trends awareness
4
awareness youths
4
lithuania
4
youths lithuania
4
lithuania waste
4

Similar Publications

A Study on the Battery Recycling Process and Risk Estimation.

Int J Environ Res Public Health

December 2024

Department of Environmental and Safety Engineering, Ajou University, Suwon 16499, Republic of Korea.

The demand for the use of secondary batteries is increasing rapidly worldwide in order to solve global warming and achieve carbon neutrality. Major minerals used to produce cathode materials, which are key raw materials for secondary batteries, are treated as conflict minerals due to their limited reserves, and accordingly, research on the battery recycling industry is urgent for the sustainable secondary battery industry. There is a significant risk of accidents because there is a lack of prior research data on the battery recycling process and various chemicals are used in the entire recycling process.

View Article and Find Full Text PDF

Current industrial separation and sorting technologies struggle to efficiently identify and classify a large part of Waste of Electric and Electronic Equipment (WEEE) plastics due to their high content of certain additives. In this study, Raman spectroscopy in combination with machine learning methods was assessed to develop classification models that could improve the identification and separation of Polystyrene (PS), Acrylonitrile Butadiene Styrene (ABS), Polycarbonate (PC) and the blend PC/ABS contained in WEEE streams, including black plastics, to increase their recycling rate, and to enhance plastics circularity. Raman spectral analysis was carried out with two lasers of different excitation wavelengths (785 nm and 1064 nm) and varying setting parameters (laser power, integration time, focus distance) with the aim at reducing the fluorescence.

View Article and Find Full Text PDF

On the analysis of adapting deep learning methods to hyperspectral imaging. Use case for WEEE recycling and dataset.

Spectrochim Acta A Mol Biomol Spectrosc

December 2024

TECNALIA, Basque Research and Technology Alliance (BRTA), Parque Tecnológico de Bizkaia, C/ Geldo. Edificio 700, E-48160, Derio - Bizkaia, Spain; University of the Basque Country, Plaza Torres Quevedo, 48013 Bilbao, Spain.

Article Synopsis
  • Hyperspectral imaging is increasingly using deep learning techniques instead of traditional methods for processing, but many researchers don't adequately analyze how spectral and spatial information interact.
  • The paper assesses how different combinations of spatial (texture) and spectral (light wavelength) features affect deep learning models for image segmentation, focusing on performance, energy usage, and speed.
  • Results indicate that integrating spatial data with spectral information enhances segmentation accuracy, but not all spectral wavelengths are needed for optimal efficiency, and adapting existing RGB image models for hyperspectral tasks needs further innovation.
View Article and Find Full Text PDF

In the recycling of metal-containing wastes such as end-of-life vehicles (ELV), residues are generated in the mechanical pre-treatment stage. Beside organics which is the main part of the residues, they also contain metals that physical separation has not been able to separate. As the current treatment of residues is disposal through thermal processing, the process is not optimized from the point of view of metal's recovery.

View Article and Find Full Text PDF

Heavy metal pollution from standardized e-waste dismantling activities: Pollution index, risk assessment and intervening measures.

J Hazard Mater

November 2024

National Observation and Research Station of Coastal Ecological Environments in Macao, Macao Environmental Research Institute, Faculty of Innovation Engineering, Macau University of Science and Technology, 999078, Macao.

Due to the e-waste complexity and recycling technology limitations, more and more attention has been paid to the inevitable pollution in the standardized recycling process. This study systematically explores the heavy metals (HMs) (from surface dust) pollution characteristics and exposure risk of a typical e-waste dismantling plant (EDP) in South China. Further, this study explores the real level of health risks through valence analysis of a typical HM (Cr), and seeks effective measures to reduce exposure risk through behavioral science methods.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!