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Editor in Chief: RAFFAELLO COSSU


  • Laurent Spreutels - Department of Chemical Engineering , Polytechnique Montreal , Canada
  • Martin Héroux - Department of Environment , City of Montreal , Canada
  • Robert Legros - Department of Chemical Engineering , Polytechnique Montreal , Canada

DOI 10.31025/2611-4135/2020.13995

Released under CC BY-NC-ND

Copyright: © 2019 CISA Publisher

Editorial History

  • Received: 11 Jul 2019
  • Revised: 29 Apr 2020
  • Accepted: 04 May 2020
  • Available online: 24 Jul 2020


Comprehensive models were developed to predict waste generation for different collection streams. Taking into account the dwelling-type distribution encountered during the different waste collections, it was possible to better capture the waste generation variability. Using the same approach, collection and transportation cost models were also developed. This series of models were validated using data from the Urban Agglomeration of Montreal (UAM), which is composed of 33 districts with widely different scales of population and dwelling characteristics. The unknown parameters of the models were identified through mean square regressions applied on the real data available for the case-study. For example, values of 1.364, 1.019 and 0.500 t/(dwelling.yr) were identified for the total quantity of wastes generated in single-family, duplex and other dwelling, respectively. Using the same approach, it was possible to determine collection time as a function of the dwelling-type distribution along the collection route. Values of 28.7 s, 11.4 s and 5.22 s were identified as the collection time per dwelling for single-family, duplex and other dwelling, respectively. Equipped with a combination of fitted parameters and reported values from the literature, the models were used as predictive tools. Three features are illustrated in this paper: 1) the simulation of various scales for the generation, diversion and specific collection cost; 2) the effect of adding a new collection stream; 3) the effect of an increase of the citizen participation to a specific collection stream. Predicted results enable decision-makers to have access to very useful information.



Akther, A., Ahamed, T., Takigawa, T., Noguchi, R (2016). GIS-based multi-criteria analysis for urban waste management. Journal of the Japan Institute of Energy 95(5):457–467

Goel S., Ranjan V.P., Bardhan B., Hazra T. (2017). Forecasting Solid Waste Generation Rates. In: Sengupta D., Agrahari S. (eds) Modelling Trends in Solid and Hazardous Waste Management. Springer, Singapore

Grossman D, Hudson JF, Marks DH (1974). Waste generation models for solid waste collection. Journal of the Environmental Engineering Division, 100, 1219–1230

Lagneau, J. (2018). Étude multi-échelles des coûts de gestion de la matière résiduelle organique au Québec (Masters thesis, École Polytechnique de Montréal). Retrieved from

Montréal (2017). Portrait 2016 des matières résiduelles de l’agglomération de Montréal. Retrieved from

Purcell M, Magette WL (2009). Prediction of household and commercial BMW generation according to socio-economic and other factors for the Dublin region. Waste Management, 29, 1237–1250

Tanguy, A., Villot, J., Glaus, M., Laforest, V., & Hausler, R. (2017). Service area size assessment for evaluating the spatial scale of solid waste recovery chains: A territorial perspective. Waste Management, 64, 386–396.
DOI 10.1016/j.wasman.2017.03.027