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Estimation of E-waste Generation, Residential Behavior, and Disposal Practices from Major Governorates in Jordan.

Sudki HamdanMotasem N Saidan
Published in: Environmental management (2020)
Estimating the generation of e-waste in governorates is critically needed for sustainable and environmentally sound e-waste management in Jordan. The main objectives of the present study are to quantify and evaluate the annual e-waste generation in all governorates in Jordan and disposal practices. The present study comprises the information of e-waste as classified by the European Union Directive including six main categories (16 United Nations University key items). The survey targeted 15,883 households (12.52% females and 87.48% males), where primary data on e-waste generation and disposal methods were gathered, assessed, and quantified. Subsequently, the survey-based data collected from the study sample have been extrapolated to quantify an e-waste generation inventory for Jordan and the disposal methods using ArcGIS mapping. The study-extrapolated findings reveal that ~8,735,187 e-waste items (13 ktons) had been turned into e-waste and discarded by all households in 2018 in the 12 governorates in Jordan. Moreover, dumping of e-waste is still the dominant disposal method practiced by 58.4% of households in Jordan. The other disposal practices showed that granting of the waste EEE to others has the share of 16.6%; selling (10.7%); delivering the waste EEE for environmentally sound recycling (6.8%); and others practices represented 7.4%. Furthermore, the present study has played a vital role in e-waste awareness dissemination since the findings of the present study have been modeled and shown online by the Department of Statistics, Jordan through the link ( https://arcg.is/1KzvjO ). Finally, the challenges, barriers, and prospects of e-waste management in Jordan have been explored in the present study.
Keyphrases
  • municipal solid waste
  • heavy metals
  • sewage sludge
  • healthcare
  • primary care
  • life cycle
  • dna methylation
  • high resolution
  • machine learning
  • gene expression
  • cross sectional
  • health information
  • genome wide
  • data analysis