Журнал гражданской и экологической инженерии

Robust Statistical Analysis for MSW Characterization Studies

Abstract

Roger Painter, Valetta Watson and Ahmad Kheder

Tennessee State University conducted a pilot municipal solid waste study for Tennessee Department of Environment at two Tennessee municipal solid waste disposal facilities. A major goal for the pilot study was to develop and demonstrate statistical analysis methodologies to be used in a future statewide municipal solid waste study. In municipal solid waste studies the costs of sorting and weighing a sufficient number of samples to obtain reasonably precise estimates is prohibitive for some waste constituents. This issue regarding the number of municipal solid waste samples was addressed using a real-time iterative analysis that involved simultaneous tracking of the mean, trimmed mean, and median of the sample populations. Sampling was terminated for a particular waste category based on observation of dimensioning incremental improvement in 90 percent confidence intervals for the mean and median. This approach was adopted to take advantage of the robustness of efficiency of the mean for waste categories with near normal distribution and the robustness of validity of the median for grossly non-normally distributed categories. The trimmed mean was included in the analysis as an intermediate estimator to the mean and median with regard to loss of sample information. The convergence of the three estimators for nearly normal data and the trimmed mean intermediate relationship to the mean and median provided excellent field guidance regarding the tradeoff between precision and sampling cost. This approach also provides the option of making statistical inference on the median for grossly non-normal waste subcategories when additional sampling to designate the mean is not an option.