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Optimum sample size in refuse analysis
Thesis   Open access

Optimum sample size in refuse analysis

Elias Musa
Masters by Research, Murdoch University
1980
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Abstract

Refuse survey is important in the collection of information on the physical and chemical characteristics of the solid waste, which can aid solid waste personnels in formulating and deciding collection policies, disposal methods, processing and recovery options of solid waste. As the population involved in the generation of solid waste is normally large, an optimum sample size has to be decided that could provide a reasonable accuracy in the estimation of the population characteristics. The purpose of this work was to determine the number of samples required to give a standard deviation of ten percent of the mean for the average household rate and percentage composition by weight of the major components of the waste. The first part of the work was to analyse the solid waste data acquired from the Victorian EPA which would lead to the determination of the number of samples required for the field studies that followed. As a result of the analysis, 205 samples were collected from 3 localities in the City of Melville and 197 samples were collected from the Shire of Kalamunda. The collection was done by sampling at a pre-determined interval direct from the households ahead of the municipal collection crew. The samples were then brought to the disposal site to be sorted by hand into eleven categories. The data was analysed by using the SPSS package to determine mean, standard deviation and standard error at various sample sizes for household generation rate and percentage composition by weight of various components of the waste. The results showed that the number of samples chosen, not the total sampling weight, was important in refuse analysis. The number of samples to be selected to give a precision set above, depended on the value of the coefficient of variation (standard deviation/mean) of the population. However, as the mean and standard deviation showed no significant improvement as the sample size was increased from about 40 to 200, the precision set earlier appeared to be arbitrary. A sample size of 40 seems to be adequate for approximating the population mean within a reasonable degree of accuracy for the household generation weight and the percentage composition of major components.

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UN Sustainable Development Goals (SDGs)

This output has contributed to the advancement of the following goals:

#12 Responsible Consumption & Production

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