an official journal of: published by:
an official journal of: published by:
Editor in Chief: RAFFAELLO COSSU

A NOVEL METHOD TO CALCULATE THE SIZE OF REPRESENTATIVE WASTE SAMPLES BASED ON PARTICLES SIZE

  • Giovanni Beggio - Department of Civil, Environmental and Architectural Engineering, University of Padova, Italy
  • Pierre Hennebert - INERIS, France

Released under CC BY-NC-ND

Copyright: © 2021 CISA Publisher


Abstract

A novel approach to determine the size of samples of granular wastes is proposed, forwarding the concept of the “number of particles”, as previously introduced by the authors. To be representative with a minimum error, it was demonstrated that at least 100 particles showing the presence of the measurand, shall be collected in the sample. Waste particles are usually characterized by size-concentration relationships. However, in waste sampling standards they are not explicitly considered when estimating the size of the sample. In this context, this paper extends this requirement to the number of particles “rare is size”, belonging to the less represented size fraction in the waste to be characterized. The number of particles is then transformed into a mass by a formulation that avoids using unrealistic assumptions on particles features. Results derived from the application of the two formulations on 5 different types of waste show that their equivalency relies on how similar are, the proportions of particles rare in concentration and rare in size in the batch to analyse. Here, preliminary knowledge on particles physical features and distribution of the measurand is key to derive coherent values for mass of samples. Finally, the need to perform on-site size reduction is discussed for cases where the application of both the conventional and novel approaches could have leaded to unpractical management of too large-sized waste samples.

Keywords


Editorial History

  • Received: 18 Feb 2022
  • Revised: 23 Mar 2022
  • Accepted: 24 Mar 2022
  • Available online: 31 Mar 2022

References

Bonifazi, G., Gasbarrone, R., Serranti, S., 2021. Detecting contaminants in post-consumer plastic packaging waste by a nir hyperspectral imaging-based cascade detection approach. Detritus 15, 94–106.
DOI 10.31025/2611-4135/2021.14086

Bunge, R., 2019. Recovery of metals from waste incinerator bottom ash

CEN, 2015. EN 15002. Characterization of waste - Preparation of test portions from the laboratory sample

CEN, 2006. CEN/TR 15310-1. Characterization of waste — Sampling of waste materials — Part 1: Guidance on selection and application of criteria for sampling under various conditions

CENELEC, 2015. CLC/TS 50625-3-1. Requirements for the collection, logistics and treatment of WEEE - Part 3-1: Specification relating to depollution

Danish Standards, 2013. DS 3077. Representative sampling - Horizontal standard

Gy, P., 2004. Sampling of discrete materials: II. Quantitative approach - Sampling of zero-dimensional objects. Chemom. Intell. Lab. Syst. 74, 25–38.
DOI 10.1016/j.chemolab.2004.05.015

Haynes, R.J., Belyaeva, O.N., Zhou, Y.F., 2015. Particle size fractionation as a method for characterizing the nutrient content of municipal green waste used for composting. Waste Manag. 35, 48–54.
DOI 10.1016/j.wasman.2014.10.002

Hennebert, P., 2020. Concentrations of brominated flame retardants in plastics of electrical and electronic equipment , vehicles , construction , textiles and non-food packaging : a review of occurrence and management. Detritus 12, 34–50.

Hennebert, P., Beggio, G., 2021. Sampling and sub-sampling of granular waste: size of a representative sample in terms of number of particles. Detritus 17, 30–41.

Khodier, K., Viczek, S.A., Curtis, A., Aldrian, A., O’Leary, P., Lehner, M., Sarc, R., 2020. Sampling and analysis of coarsely shredded mixed commercial waste. Part I: procedure, particle size and sorting analysis. Int. J. Environ. Sci. Technol. 17, 959–972.
DOI 10.1007/s13762-019-02526-w

Kroell, N., Chen, X., Maghmoumi, A., Koenig, M., Feil, A., Greiff, K., 2021. Sensor-based particle mass prediction of lightweight packaging waste using machine learning algorithms. Waste Manag. 136, 253–265.
DOI 10.1016/j.wasman.2021.10.017

Pivato, A., Beggio, G., Raga, R., Soldera, V., 2019. Forensic assessment of HP14 classification of waste: evaluation of two standards for preparing water extracts from solid waste to be tested in aquatic bioassays. Environ. Forensics 20, 275–285.
DOI 10.1080/15275922.2019.1630517

Priya, A., Hait, S., 2021. Characterization of particle size-based deportment of metals in various waste printed circuit boards towards metal recovery. Clean. Mater. 1, 100013.
DOI 10.1016/j.clema.2021.100013

Viczek, S.A., Kandlbauer, L., Khodier, K., Aldrian, A., Sarc, R., 2021a. Sampling and analysis of coarsely shredded mixed commercial waste. Part II: particle size-dependent element determination. Int. J. Environ. Sci. Technol.
DOI 10.1007/s13762-021-03567-w

Viczek, S.A., Khodier, K., Kandlbauer, L., Aldrian, A., Redhammer, G., Tippelt, G., Sarc, R., 2021b. The particle size-dependent distribution of chemical elements in mixed commercial waste and implications for enhancing SRF quality. Sci. Total Environ. 776, 145343.
DOI 10.1016/j.scitotenv.2021.145343

Wavrer, P., 2018. Theory of Sampling (TOS) applied to characterisation of Municipal Solid Waste (MSW)—a case study from France. TOS Forum 2013, 3.
DOI 10.1255/tosf.101

Weissenbach, T., Sarc, R., 2022. Particle-specific characterisation of non-hazardous, coarse-shredded mixed waste for real-time quality assurance. J. Environ. Manage. 301, 113878.
DOI 10.1016/j.jenvman.2021.113878