Is Municipal Solid Waste composition affected by Demographics or Seasons?

• John Chandler ^[1]
  1. [1] A.J. Chandler & Associates Ltd.
• Localización: Proceedings of the 8th International Workshop on Compositional Data Analysis (CoDaWork2019): Terrassa, 3-8 June, 2019 / Juan José Egozcue Rubí (aut.), Jan Graffelman (aut.), María Isabel Ortego Martínez (aut.), 2019, ISBN 978-84-947240-2-2, págs. 12-18
• Idioma: inglés
• Enlaces
  • Texto Completo Libro (pdf)
• Resumen

  • Residual wastes, material remaining after recycling and organics diversion, require disposal. One alternative is to create Solid Recovered Fuels [SRF] for energy intensive industries. Frequently, potential users of SRF require that the material be fully characterised come with guarantees of energy values and contamination levels. With limited data on compositional variations of residual wastes, particularly as it may pertain to an SRF product, a municipality in southern Ontario, Canada, required a detailed study of the composition and quality of their residual wastes. The wastes are collected separately from both single-family [CS] and high-rise residential [MR] properties and the study afforded an opportunity to distinguish between these waste streams, as well as addressing seasonal differences by sorting materials at different times of the year. Changing priorities during the study resulted in differences in the sorting categories for the 2 phases reported here. Moreover, components that were common to both phases showed different quantities by source and season. The question arose, were the differences significant? Not unexpectedly, the sort data was not normally distributed and applying classical statistical techniques was not successful. Enter, compositional data analysis techniques. When applying these techniques, it became evident that working with high dimensional data increases the complexity of the analyses and the difficulty of interpreting the results. This extended abstract covers some of the methods used to date. From these it was concluded that the dimensionality of the data should be further reduced to attempt to address the question raised above. The final presentation will include the analysis of the less complex data set.