Issue 8, p. 3 (2018)


Theory of Sampling (TOS) applied to characterisation of Municipal Solid Waste (MSW)—a case study from France

  • Philippe Wavrer  
 Corresponding Author
CASPEO, 3, avenue Claude Guillemin, 45060 Orléans Cedex, France
[email protected]
 Search for papers by this author

Knowing the composition of household waste is a prerequisite for effective implementation of municipal solid waste (MSW) management facilities. To meet increasing regulations, facilities in terms of collection, sorting and treatment are becoming more sophisticated and expensive: performance reliability partly depends on a valid, representative knowledge of waste composition. In France, the current method of characterisation of household waste is MODECOMTM, a guide to organise and manage analysis campaigns with the primary objective of evaluating the recyclable or the packaging material content of waste, or to determine the variations and characteristics related to the nature of housing, for example. Implementation of this methodology leads to primary MSW samples, which are successively screened and sorted into a set of standard categories. Although it is possible to determine the composition of household waste in this fashion, at the end of these operations looms the question of its accuracy. Even if the mass of fully sorted MSW samples (usually around 500 kg) may seem high, this is actually extremely small compared to the total lot from which it was sampled (several hundreds of tons, sometimes much more). The Theory of Sampling of particulate materials (TOS), as initially developed by Pierre Gy in the context of the mineral industry, is quite applicable also to household waste. In particular, it allows an estimate of the Fundamental Sampling Error (FSE) to be calculated for each of the sorted categories. From real-world examples of French MSW characterisations, this contribution shows which data are needed and how the FSE formulas are implemented, illustrating how it is possible to ascribe individual total error estimates for each category. This general overview will help local implementation efforts.




Abstract Views: