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The Florida Department of Environmental Protection, referred to as FDEP (www.dep.state. .us/), has conducted waste reduction and recycling research. A noteworthy project of FDEP was a recycling economic information study. The goal of the study was to document the size of the recycling and reuse industries in Florida. As part of the study, recycling level estimates were derived via surveys on Florida businesses. The surveys collected the recycling and reuse rates of solid waste materials in Florida.
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15.1 Introduction
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This chapter discusses the characterization of U.S. businesses and government agencies based on solid waste stream composition percentages. The purpose of characterization was to determine and group the business types that generate similar solid waste materials. Figure 15.1 displays the characterization process. Standard Industrial Classi cation (SIC) codes were used as the grouping basis to describe types of business functions, as described in Chap. 14. The waste records gathered from each SIC code group were consolidated and the mean and variance waste composition percentages were calculated for each material. SIC code groups have historically been used by U.S. environmental regulators to classify businesses based on solid waste generation. The usage of SIC codes for this purpose was statistically validated and is discussed in Sec. 15.3 of this chapter. After the successful validation of using SIC codes, multivariate cluster analysis was conducted on the SIC code group waste composition data to reduce groups further. Twenty-two of these nal clusters, referred to as waste groups, were formed that statistically generate similar compositions of solid waste. In the next chapter, these waste groups are analyzed to determine the signi cant variables that in uence annual solid waste quantities.
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15.2 Data Consolidation and Initial Characterization
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Several preparations were completed before beginning the solid waste characterization process and waste group determination. Speci cally, the 438 waste records (companies) collected from the national survey included 65 SIC code groups. Each waste record was placed in 1 of 65 data matrices; with each matrix representing each of the 65 SIC code
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Activity Group 438 businesses and government agencies into their 65 SIC code groups
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Purpose Reduce 438 businesses and government agencies into their 65 SIC code groups Characterize 65 groups using standardized parameters (means and standard deviations)
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Characterize 65 SIC code groups by waste composition data
Conduct multivariate cluster analysis on population parameters to reduce data groups by clustering similar SIC code groups
Reduce to 22 clusters (waste groups)
Analyze 22 waste groups using multivariable regression analysis to develop a model to quantify and evaluate solid waste generation
Business waste characterization process.
groups for which data was collected. To initially characterize the solid waste data, mean and variance composition percentages for each material in the 65 SIC code groups (matrices) were calculated using the individual company records collected from the national survey. Materials comprising less than 2 percent of all SIC code group waste streams were not included in the calculations to simplify the analysis. Material composition percentage means and variances of the following materials were calculated.
Biohazard wastes Construction and demolition debris (sand, stone, and concrete) Fabric and textiles Food waste Glass Metal Old corrugated containers (cardboard) Chemicals, sludge, and used oil Organic wastes (agricultural) Paper (excluding cardboard) Plastic Rubber Wood Yard waste
The means and variances of the solid waste composition percentages from the 65 SIC code groups can be seen in Figs. 15.2 to 15.4. The next section provides a further analysis of this data.
SIC Division Structure Mean Waste Stream Compostition Percentage BioHaz.mean BioHaz.var CandD.mean CandD.var Fabric.mean Fabric.var Food.mean Food.var Glass.mean Glass.var Metal.mean Metal.var OCC.mean OCC.var OilChem.mean OilChem.var Organic.mean Organic.var Paper.mean
Paper.var
Plastic.mean
Plastic.var
Rubber.mean 5.1 9.0 6.1 8.0 8.2 9.0 2.1 14.0 0.0 16.0 0.0 17.0 2.1 7.0 2.6 10.0 2.2 8.0 4.0 6.0 6.0 4.0 4.1 5.0 2.1 2.3 2.1 2.1 0.9 1.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 14.0 3.3 13.0 3.9 0.0 0.0 0.0 5.0 0.0 0.0 0.0 1.2 0.0 12.0 13.0 26.0 13.0 3.8 10.0 4.5 9.0 9.8 17.0 5.3 19.0 3.2 3.2 6.4 6.2 0.0 0.0 0.0 0.0 0.0 0.0 14.0 4.9 19.0 4.3 0.0 3.1 2.7 0.0 0.0 0.0 0.0 0.0 0.0 14.0 3.4 5.0 15.0 4.0 6.0 65.0 18.5 5.0 51.0 16.0 6.0 19.0 6.1 14.0 18.0 4.8 15.0 20.0 5.3 13.0 8.0 1.9 9.0 1.2 1.6 1.5 1.8 3.5 3.7 2.9 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 9.0 2.2 8.0 1.8 0.0
SIC Group Number Number of Firms in Group 6 5 2 3 3 3 8 7 6 8 8 5 7 7 6 8 5 6 5 4 6 0.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0 3.0 3.0 4.0 6.0 5.0 6.0 3.0 0.8 0.8 0.9 1.3 2.0 1.4 1.7 0.8 0.8 0.0 0.0 0.0 0.0 18.0 5.0 5.0 1.9 0.0 2.0 2.0 0.0 0.0 2.0 2.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 22.0 0.0 20.0 0.0 0.0 0.0 0.0 6.2 0.0 5.6 1.0 0.0 5.0 0.0 21.0 0.0 4.0 0.3 4.0 1.4 23.0 5.9 5.0 1.4 1.5 9.3 2.2 3.0 4.0 1.0 0.0 0.8 12.0 1.1 11.0 0.3 4.0 0.0 7.0 7.0 0.0 0.0 21.0 5.9 1.0 0.3 4.0 1.0 4.0 1.1 10.0 3.0 5.0 3.9 6.0 3.9 5.0 1.5 6.0 2.3 10.0 1.6 11.0 0.6 13.0 3.2 6.0 0.6 11.0 2.9 7.0 0.0 3.0 0.9 11.0 0.0 3.0 0.9 13.0 0.6 7.0 1.7 7.0 0.6 6.0 1.5 8.0 0.6 7.0 1.6 7.0 0.0 61.0 13.8 8.0 0.0 59.0 13.1 9.0 1.2 1.4 1.2 1.4 2.4 2.6 1.0 1.0 0.0 0.0 0.0 0.5 0.7 0.0 0.0 0.0 0.0 0.0 8.0 7.0 9.0 0.0 0.0 2.2 2.0 2.7 0.0 0.0 0.0 0.0 0.0 0.0 13.0 0.0 12.0 0.0 5.0 0.0 5.0 0.0 5.0 4.4 3.1 1.5 2.0 1.3 1.0 1.0 0.0 2.0 2.0 0.3 10.0 0.3 9.0 0.0 9.0 0.6 13.0 0.6 10.0 2.3 2.4 3.2 2.9 2.2 9.0 9.0 8.0 8.0 8.0 2.2 2.2 1.9 1.9 1.9 0.0 0.0 0.0 3.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0 1.4 0.0 1.7 0.0 2.6 0.0 3.1 0.0 1.7 20.0 1.9 18.0 1.7 19.0 1.9 3.0 2.2 2.0 0.0 0.0 0.0 0.0 0.0 0.0 13.0 3.2 1.0 0.3 9.0 2.7 9.0 2.3 0.0 0.0 22.0 0.0 21.0 0.0 20.0 0.0 6.0 1.2 0.0 0.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
02 07 08 10 12
16 17 20 22
Description Agricultural production crops Agricultural production livestock and animal specialties Agricultural services Forestry Metal mining Coal mining Building construction general contractors and operative builders Heavy construction other than building construction contractors Construction special trade contractors Food and kindred products Textile mill products Apparel and other finished products made from fabrics and similar materials
24 25 26 27 28 29 30 33
Lumber and wood products, except furniture Furniture and fixtures Paper and allied products Printing, publishing, and allied industries Chemicals and allied products Petroleum refining and related industries Rubber and miscellaneous plastics products Primary metal industries Fabricated metal products, except machinery and transportation equipment
0.0 11.0 0.0 11.0 0.0 0.0 0.0 0.0 5.0 0.0 6.1 0.0 4.9 0.0 0.8 0.0 0.7
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Figure 15.2 Means and variances of waste stream composition percentages for the 65 SIC code groups utilized to conduct characterization/cluster analysis. Waste stream composition data (SIC code 01 to 34).
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