Commercial Waste
Characterization Study
Mecklenburg County
JANUARY 2006
Mecklenburg County
COMMERCIAL WASTE CHARACTERIZATION STUDY
Table of Contents
Letter of Transmittal
Table of Contents
List of Tables
List of Figures
Section 1 - Summary
1.1 Overview.................................................................................................. 1-1
1.2 Methodology ............................................................................................ 1-1
1.3 Limitations ............................................................................................... 1-1
1.4 Conclusions and Recommendations ........................................................ 1-2
Section 2 - Business/Commercial Sector
2.1 Overview.................................................................................................. 2-1
2.1.1 Large Companies ......................................................................... 2-1
2.1.2 Employee Size ............................................................................. 2-2
2.2 Conclusion ............................................................................................... 2-3
Section 3 - Counties with Similar Commercial Sectors
3.1 Overview.................................................................................................. 3-1
3.1.1 King County, Washington ........................................................... 3-1
3.1.2 Hennepin County, Minnesota ...................................................... 3-1
3.1.3 Wake County, North Carolina ..................................................... 3-1
3.1.4 Ramsey County, Minnesota ......................................................... 3-2
3.1.5 Palm Beach County, Florida ........................................................ 3-2
3.2 Results...................................................................................................... 3-4
Section 4 - Preliminary Waste Characterization
4.1 Methodology ............................................................................................ 4-1
4.2 Results.................................................................................................... 4-13
Section 5 - Commercial Waste Disposal Quantities
5.1 Overview.................................................................................................. 5-1
5.2 Methodology ............................................................................................ 5-1
5.3 Results...................................................................................................... 5-3
5.4 Conclusion ............................................................................................... 5-5
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Table of Contents
Section 6 - Recommendations
6.1 Overview.................................................................................................. 6-1
6.2 Recommendations.................................................................................... 6-1
6.2.1 Food Waste .................................................................................. 6-1
6.2.2 Uncoated OCC............................................................................. 6-2
6.2.3 Untreated Wood Waste................................................................ 6-2
6.2.4 Film Plastics................................................................................. 6-2
6.2.5 Other Ferrous ............................................................................... 6-2
6.3 Follow-Up Study Recommendations....................................................... 6-3
List of Tables
1-1
1-2
2-1
2-2
2-3
3-1
3-2
4-1
4-2
4-3
4-4
5-1
5-2
6-1
6-2
Comparison of Commercial Waste Characterization ......................................1-2
Mecklenburg County, Solid Waste Disposal Rates by Industry Sector...........1-5
Mecklenburg County, Largest Companies (by Employee Count)...................1-2
Mecklenberg County Employee Range (by Number of
Establishments) ................................................................................................2-3
Mecklenberg County, Largest Industries (by Employee Count) .....................2-4
Mecklenberg County, County Commercial Sector Comparisons,
Number of Employees by Industry Sector.......................................................3-3
Mecklenburg County, County Populations......................................................3-4
King County, Washington, Commercial Waste Characterization (by
weight) .............................................................................................................4-3
Hennepin County, Minnesota, Commercial Waste Characterization
(by weight) .......................................................................................................4-6
Wake County, North Carolina, Commercial Waste Characterization
(by weight) .......................................................................................................4-9
Comparison of Commercial Waste Characterization ....................................4-14
Mecklenburg County, Solid Waste Disposal Rates by Industry Sector...........5-3
Industry Sector Abbreviation Key ...................................................................5-6
Mecklenburg County, Commercial Waste Characterization ...........................6-1
Mecklenburg County, Commercial Waste Characterization by
Employment Sector..........................................................................................6-3
List of Figures
5-1 Mecklenburg County, Solid Waste Daily Disposal Rates by Industry
Sector ...............................................................................................................5-4
5-2 Mecklenburg County, Solid Waste Annual Disposal Quantities by
Industry Sector .................................................................................................5-5
ii
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Table of Contents
This report has been prepared for the use of Mecklenburg County for the specific purposes
identified in the report. Use of the report and its contents for other purposes is prohibited
without prior approval of Mecklenburg County.
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iii
Section 1
SUMMARY
1.1 Overview
R. W. Beck, Inc. was retained by Mecklenburg County (County) to conduct a "paper
study" to develop a quantitative and qualitative description of Mecklenburg County's
commercial waste stream. The objectives included estimating the relative contribution
of commercial solid waste by business type, projecting the overall composition of the
commercial solid waste disposed, and providing recommendations on materials to
target through diversion programs.
1.2 Methodology
To conduct the commercial waste characterization study the R.W. Beck project team:
„
Characterized the County's business/commercial sector including the types of
businesses and number of employees;
„
Identified a set of counties with similar business/commercial sectors;
„
Developed a preliminary commercial waste characterization by using existing
commercial waste characterization data from similar counties;
„
Estimated the quantities of waste disposed by business sector; and
„
Developed recommendations for additional diversion targets and follow up
investigations.
1.3 Limitations
The commercial waste characterization study should be considered a planning study
that provides an estimated quantitative and qualitative characterization of the County
commercial waste stream. No actual field studies were conducted to adjust and/or
confirm the estimates. It should be noted that our analysis has not included the
estimated quantities of construction and demolition materials disposed. The County
programmatically manages these materials separately from other commercial waste
sources.
The commercial waste characterization was developed by comparing and adjusting
existing commercial waste characterizations from counties with similar business
sectors. These existing characterizations -- for Hennepin, King, and Wake Counties -were conducted using industry-accepted field sorting and analytical techniques. The
reliability of the County commercial waste characterization estimate is tied directly to
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Section 1
the similarity of the business sectors and their waste management practices of the three
counties selected for comparison.
As for the estimated disposal quantities of the County commercial sector, the estimates
rely on per-employee estimates by business sector that were generated by the
California Integrated Waste Management Board (CIWMB). These estimates were
based on a comprehensive set of more than 1200 generator-based field sorts used to
statistically estimate per employee business sector disposal rates. The per-employee
disposal rates were then applied to the County employee counts by business sector.
The limitations of this approach are related to the extent of the similarities and
differences between business sector activity in California as compared to the County.
Generally speaking, California has aggressive materials diversion programs. No field
data presently exists for the County to compare per-employee disposal patterns with
the CIWMB-generated information.
Overall the study outcomes offer a reasonable estimate of the commercial quantities
disposed and the relative composition of the County’s disposed commercial solid
waste.
1.4 Conclusions and Recommendations
Provided below are Tables 1-1 and 1-2. Table 1 represents the commercial waste
characterization and Table 2 represents a relative comparison of the quantities of
materials disposed by industry sector.
Table 1-1
Comparison of Commercial Waste Characterization
Material Categories
Paper
Plastic
Newsprint (ONP)
High Grade Office
Magazines/Catalogs
Uncoated OCC recyclable
Uncoated OCC nonrecyclable
Coated OCC
Boxboard
Mixed Paper - recyclable
Mixed Paper nonrecyclable
TOTAL PAPER
PET Bottles
HDPE Bottles
PVC
Polystyrene
1-2 R. W. Beck
Mean
Mecklenburg
Estimated
Tonnage
(Mean)
2.6%
3.6%
1.3%
15,762
21,520
7,994
9.0%
54,448
0.5%
0.1%
1.0%
5.0%
2,887
484
6,093
29,957
7.2%
30.3%
0.5%
0.5%
0.0%
0.5%
43,133
182,278
2,769
2,970
58
2,968
Mecklenburg
Estimated
Tonnage
(Low)
Mecklenburg
Estimated
Tonnage
(High)
157,945
211,305
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SUMMARY
Table 1-1
Comparison of Commercial Waste Characterization
Material Categories
Film - transport packaging
Other Film
Other Containers
Other non-containers
TOTAL PLASTIC
Metals
Aluminum Beverage
Containers
Other Aluminum
Ferrous Containers
Other Ferrous
Other Non-Ferrous
TOTAL METALS
Glass
Clear Containers
Green Containers
Brown Containers
Other Glass
TOTAL GLASS
Organic Yard Waste - Grass and
Materials Leaves
Yard Waste - woody
material
Food Waste
Wood Pallets
Treated Wood
Untreated Wood
Diapers
Other Organic Material
TOTAL ORGANIC
MATERIALS
Problem Televisions
Materials Computer Monitors
Computer
Equipment/Peripherals
Electric and Electronic
Products
Batteries
Other
TOTAL PROBLEM
MATERIALS
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Mean
Mecklenburg
Estimated
Tonnage
(Mean)
Mecklenburg
Estimated
Tonnage
(Low)
Mecklenburg
Estimated
Tonnage
(High)
0.2%
5.1%
0.3%
5.7%
12.8%
1,202
30,884
1,984
34,313
77,148
66,814
91,011
0.5%
0.3%
0.8%
5.1%
0.8%
7.5%
1.0%
0.4%
0.6%
0.7%
2.7%
3,046
1,808
4,573
30,909
5,035
45,371
6,038
2,478
3,536
4,106
16,158
26,431
66,332
15,169
16,854
2.4%
14,506
0.0%
10.5%
2.6%
3.7%
6.5%
0.8%
4.2%
0
63,001
15,869
22,361
39,196
5,008
25,356
30.8%
0.0%
0.0%
185,299
0
0
177,272
195,024
0.5%
2,764
0.9%
0.0%
0.7%
5,210
278
4,232
2.1%
12,484
6,019
21,308
R. W. Beck 1-3
Section 1
Table 1-1
Comparison of Commercial Waste Characterization
Material Categories
HHW
Other
Waste
Latex Paint
Oil Paint
Unused Pesti/Fungi/
Herbicides
Unused Cleaners and
Solvents
Compressed Fuel
Containers
Automotive - Antifreeze
Automotive - Used oil
filters
Other
TOTAL HHW
Textiles
Carpet
Sharps and Infectious
Waste
Rubber
Construction & Demolition
Debris
Household Bulky Items
Empty HHW Containers
Miscellaneous
TOTAL OTHER WASTE
TOTAL
1-4 R. W. Beck
Mean
Mecklenburg
Estimated
Tonnage
(Mean)
0.0%
0.2%
0
964
0.0%
0
0.0%
102
0.0%
0.0%
24
0
0.0%
0.2%
0.4%
1.5%
1.8%
0
1,446
2,539
9,121
10,663
0.0%
1.0%
201
6,011
4.0%
1.5%
0.0%
3.5%
13.4%
24,144
8,906
201
21,343
80,590
601,862
Mecklenburg
Estimated
Tonnage
(Low)
Mecklenburg
Estimated
Tonnage
(High)
625
5,778
59,229
96,910
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SUMMARY
Table 1-2
Mecklenburg County
Solid Waste Disposal Rates by Industry Sector
Industry Sector
Retail Trade
Manufacturing
Health Care and Social Assistance
Accommodation and Food Services
Wholesale Trade
Finance and Insurance
Professional, Scientific, and Technical Services
Educational Services
Other Services (except Public Administration)
Transportation and Warehousing
Administrative and Support and Waste
Management and Remediation Services
Information
Real Estate and Rental and Leasing
Public Administration
Arts, Entertainment and Recreation
Utilities
Agriculture, Forestry, Fishing and Hunting
Mining
Management of Companies and Enterprises
Grand Total
Employee
Count
66,021
65,315
50,099
44,892
41,480
37,949
36,620
27,986
24,488
20,792
Disposal Rate
lbs/Employee/Day
8.61
7.11
6.90
15.62
4.93
1.64
5.85
4.39
5.50
8.41
Annual Disposal
Quantities
Tons
103,717
84,721
63,109
127,931
37,332
11,385
39,120
22,444
24,575
31,915
17,924
5.79
18,926
15,733
15,582
13,836
6,303
4,067
3,678
756
162
493,678
6.47
2.60
2.19
6.08
1.64
1.94
9.86
1.64
6.74
18,567
7,385
5,534
6,994
1,220
1,301
1,361
49
607,584
We recommend the County focus the development of additional diversion policies and
programs on food waste, untreated wood waste, and film plastics. The Retail Trade,
Accommodation and Food Services represent primary sources for additional
commercial waste diversion opportunities.
The County should give some
consideration to conducting commercial generator-based field sorts. These field sorts
should be focused upon likely generators of food waste, untreated wood waste, and
film plastics.
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R. W. Beck 1-5
Section 2
BUSINESS/COMMERCIAL SECTOR
2.1 Overview
Mecklenburg County (County) has over 800 various business sectors with
approximately 534,850 employees as identified in the InfoUSA data provided by the
Charlotte Chamber of Commerce. Historically, the leading businesses and industries
have been manufacturing and finance-related, although employment is well
distributed. Charlotte-Mecklenburg is one of the nation’s leading distribution and
transportation hubs, due to its convenient location at the intersection of Interstate 77
and Interstate 85. In 2003, it was reported that the County was home to over 500
corporate headquarters1.
In completing the first task, R.W. Beck used data provided by the Charlotte Chamber
of Commerce to derive and summarize the information provided below. The data
supplied included a list of businesses located in the County with pertinent information
on each of the over 33,000 businesses including but not limited to the number of
employees, industry type, and square footage.
2.1.1 Large Companies
The County has a number of large local and national businesses within its boundaries,
as well as several corporate headquarters. Of the top five County businesses based on
number of employees, two are commercial banks with corporate headquarters located
within the County. Wachovia Bank has 55 locations in the County and currently
employs 11,947. Bank of America has 53 locations, including their corporate
headquarters, and presently employees approximately 8,105. The second largest
commercial entity located in the County is Presbyterian Hospital of the General
Medical and Surgical Hospital business sector. Presbyterian Hospital has over 10,714
employees in 52 different locations within the County. Depicted below in Table 2 -1
is a list of the top 25 largest companies in the County based on the number of
employees.
1
Source: Tony Crumbley, Mecklenburg County Chamber of Commerce, telephone interview August 18, 2005.
www.charlottechamber.com/content.cfm?category_level_id=133&content_id=190.
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Section 2
Table 2-1
Mecklenburg County
Largest Companies (by Employee Count)
Rank
Company Name
Employee Size
1
WACHOVIA CORP
11,947
2
PRESBYTERIAN HOSPITAL
10,714
3
BANK OF AMERICA CORP
8,105
4
DUKE ENERGY CORP
6,052
5
BELK INC
3,382
6
CAROLINAS MEDICAL CENTER-MERCY
3,360
7
T J MAXX
3,260
8
US AIRWAYS INC
3,015
9
MERITA BAKERY
3,012
10
UNIVERSITY OF NORTH CAROLINA
3,007
11
KNIGHT PUBLISHING CO
3,007
12
CONTINENTAL GENERAL TIRE INC
3,006
13
ROYAL & SUNALLIANCE USA
3,003
14
SCHNEIDER NATIONAL
3,003
15
BEACON MEDICAL PRODUCTS
3,000
16
SOLECTRON TECHNOLOGY INC
3,000
17
WINN-DIXIE
2,210
18
YMCA
2,170
19
CHARLOTTE POLICE PATROL DIST
1,723
20
DAVIDSON COLLEGE
1,500
21
HERFF JONES CO
1,163
22
PIEDMONT NATURAL GAS CO
1,125
23
PRICEWATERHOUSE COOPERS
1,125
24
GOODWILL INDUSTRIES
1,005
25
CHARLOTTE PIPE & FOUNDRY CO
925
2.1.2 Employee Size
Based on the data from InfoUSA, more than 50 percent of the business establishments
in the County employ 1-4 individuals as reflected below in Table 2-2. Of the four
companies with an employee size over 5,000 identified above in Table 2-1, three have
establishments with 5,000 or more employees. An establishment is defined by NAICS
as a single physical location. Table 2-2 below characterizes the size of the County’s
business establishments by employment range. Of the 33,455 establishments reported
in the County, only 707 are establishments with more than 100 employees.
2-2 R. W. Beck
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BUSINESS/COMMERCIAL SECTOR
Information for some of the companies as it relates to the number of employees was
not available and is identified as such in the table below.
Table 2-2
Mecklenburg County
Employee Range (by Number of Establishments)
Employee Range
Number of Establishments
1-4
17,749
5-9
7,080
10-19
3,758
20-49
2,581
50-99
932
100-249
545
250-499
109
500-999
34
1000-4999
16
5000-9999
3
Not Available
648
GRAND TOTAL
33,455
2.2 Conclusion
Overall, the business mix in the County is quite diverse. Only the two industry
categories of Retail Trade and Manufacturing each comprise more than 10 percent of
the total number of employees in the County. The Finance and Insurance industry
categories (which would include Wachovia Corp. and Bank America - two of the top
five largest companies) comprise only 7 percent of the total number of employees in
the County. Table 2-3 below characterizes the number of employees by industry type
for all employees within the County.
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R. W. Beck 2-3
Section 2
Table 2-3
Mecklenburg County
Largest Industries (by Employee Count)
Rank
Industry
Employee Count
Percentage of
Employed
1
Retail Trade
66,021
12%
2
Manufacturing
65,315
12%
3
Health Care and Social Assistance
50,099
9%
4
Accommodation and Food Services
44,892
8%
5
Wholesale Trade
41,480
8%
6
Construction
41,180
8%
7
Finance and Insurance
37,949
7%
8
Professional, Scientific, and Technical Services
36,620
7%
9
Educational Services
27,986
5%
10
Other Services (except Public Administration)
24,488
5%
11
Transportation and Warehousing
20,792
4%
12
Administrative and Support and Waste Management
and Remediation Services
17,924
3%
13
Information
15,733
3%
14
Real Estate and Rental and Leasing
15,582
3%
15
Public Administration
13,836
3%
16
Arts, Entertainment, and Recreation
6,303
1%
17
Utilities
4,067
1%
18
Agriculture, Forestry, Fishing and Hunting
3,678
1%
19
Mining
756
0%
20
Management of Companies and Enterprises
162
0%
534,858
100%
Grand Total
2-4 R. W. Beck
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Section 3
COUNTIES WITH SIMILAR COMMERCIAL SECTORS
3.1 Overview
Using industry quantification data available from the U.S. Census Bureau1 and
descriptive demographic information, R.W. Beck compared a potential list of similar
local governments to Mecklenburg County to initiate Task 2. Based on discussions
with Mecklenburg County staff and available waste characterization data, the list of
local governments considered included King County, Washington; Hennepin and
Ramsey Counties in Minnesota; Tallahassee, Florida; Fairfax County, Virginia;
Indianapolis, Indiana; Nashville, Tennessee; Wake County, North Carolina; and Palm
Beach County, Florida. Using general industry mix and demographic information as
primary metrics for this comparison, a short list of five U.S. counties were identified
for further comparison.
A brief descriptive profile of each of the local governments on the short list is
provided below.
3.1.1 King County, Washington
Historically King County’s economy was centered on forest product manufacturing.
More recently, King County has grown into a diversified export based economy. King
County historically has had a strong economic standing in the high tech industry as
well as the services and trade industry.2
3.1.2 Hennepin County, Minnesota
Hennepin County has a diverse industry base. The real estate, finance, healthcare and
food service industries are among a few industries with recent employment growth.3
3.1.3 Wake County, North Carolina
Wake County has a large research based industry comprised of one of the nation’s
largest research parks and three universities. Helping to fuel the thriving retail sales
1
Source: U.S. Census Bureau
www.census.gov/index.html
2
Source: Southwest King County Chamber of Commerce website
www.swkcc.org/kingcounty.asp
3
Source: Hennepin County Growth Oversight Plan
www.co.hennepin.mn.us
B1603
Section 3
and entrepreneurial success, are several major industries in pharmaceuticals, computer
software and hardware, telecommunications and biotechnology. 4
3.1.4 Ramsey County, Minnesota
Ramsey County is home to several Fortune 500 companies and high tech leaders.
Ramsey County’s economy is considered increasingly diverse with a wealth of
businesses from manufacturing to medical technology. Growth areas for employees
include health care, personal care and service, and construction. 5
3.1.5 Palm Beach County, Florida
Palm Beach County has a strong retail service and distribution business industry
supporting a flourishing tourist industry. Palm Beach’s convenient location is optimal
for serving the State of Florida, as well as international clients.6
In order to identify local governments with similar commercial sectors to
Mecklenburg County, two factors were considered. The first factor was the business
mix for each of the considered counties. Using NAICS industry classification and the
number of employees currently employed under each classification, R.W. Beck
characterized the business mix for each shortlisted County. The number of employees
that fall under 22 industry categories were then estimated. From these employee
counts, an estimate was derived for the percentage of employees of the total county
workforce that were employed in that industry. Comparing the percentages for the
various industry sectors provided a measure of how similar each of the counties were
to Mecklenburg County. Table 3-1 illustrates the five counties division of
employment through the 22 industry categories and the comparison to Mecklenburg
County. The column labeled DIF represents the difference in percentage of employees
for the various sectors when compared to Mecklenburg County.
The second factor that was taken into account in determining which counties are the
most similar was the population data for each county. Table 3-2 shows the list of
counties and their estimated population.
4
Source: Wake County Economic Development website
www.raleigh-wake.org
5
Source: Saint Paul Area Chamber of Commerce
www.saintpaulchamber.com/ed/ramsey_county.asp
6
Source: North Palm Beach Chamber of Commerce
http://www.npbchamber.com
3-2 R. W. Beck
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COUNTIES WITH SIMILAR COMMERCIAL SECTORS
Table 3-1
Mecklenburg County
County Commercial Sector Comparisons
Number of Employees by Industry Sector
INDUSTRY SECTOR
MECKLENBURG
King
County, WA
DIF
Hennepin
County, MN
DIF
Wake
County, NC
DIF
Ramsey
County, MN
Palm
Beach, FL
DIF
DIF
Retail Trade
66,021
12%
111,870
12%
0.00
97,827
13%
0.00
47352
14%
0.02
32,276
14%
0.02
64,342
14%
0.02
Manufacturing
65,315
12%
105,637
11%
0.01
83,351
11%
0.02
9573
3%
0.09
33,621
15%
0.02
20,727
5%
0.08
Health Care and Social Assistance
50,099
9%
94,392
10%
0.01
93,708
12%
0.03
35078
10%
0.01
0
0%
0.09
59,101
13%
0.04
Accommodation and Food Services
44,892
8%
78,308
8%
0.00
64,236
8%
0.00
29,946
9%
0.00
22,060
10%
0.01
47,776
11%
0.02
Wholesale Trade
41,480
8%
59,489
6%
0.01
57,738
7%
0.00
17685
5%
0.03
16,116
7%
0.01
20,384
4%
0.03
Construction
41,180
8%
50,658
5%
0.02
29,610
4%
0.04
27663
8%
0.00
12,089
5%
0.02
32,409
7%
0.01
Finance and Insurance
37,949
7%
54,121
6%
0.01
75,977
10%
0.03
14233
4%
0.03
15,826
7%
0.00
26,630
6%
0.01
Professional, Scientific,
and Technical Services
36,620
7%
74,938
8%
0.01
63,792
8%
0.01
29440
9%
0.02
14,581
6%
0.01
32,633
7%
0.00
Educational Services
27,986
5%
14,406
2%
0.04
9,194
1%
0.04
18202
5%
0.00
8,738
4%
0.01
5,890
1%
0.04
Other Services
(except Public Administration)
24,488
5%
45,179
5%
0.00
31,627
4%
0.01
13473
4%
0.01
11,257
5%
0.00
20,444
4%
0.00
Transportation and Warehousing
20,792
4%
45,638
5%
0.01
30,023
4%
0.00
9569
3%
0.01
7,574
3%
0.01
6,231
1%
0.03
Administrative and Support and
Waste Management
and Remediation Services
17,924
3%
58,393
6%
0.03
52,528
7%
0.03
27955
8%
0.05
17,790
8%
0.04
62,013
14%
0.10
Information
15,733
3%
66,286
7%
0.04
26,233
3%
0.00
15109
4%
0.01
7,837
3%
0.00
12,352
3%
0.00
Real Estate and Rental and Leasing
15,582
3%
23,615
3%
0.00
19,295
2%
0.00
7442
2%
0.01
5,358
2%
0.01
13,577
3%
0.00
Public Administration
13,836
3%
0
0%
0.03
0%
0.03
24007
7%
0.04
0
0%
0.03
0
0%
0.03
Arts, Entertainment, and Recreation
6,303
1%
19,511
2%
0.01
12,351
2%
0.00
4,407
1%
0.00
4,388
2%
0.01
14,246
3%
0.02
Utilities
4,067
1%
868
0%
0.01
3,009
0%
0.00
200
0%
0.01
756
0%
0.00
2,547
1%
0.00
Agriculture, Forestry,
Fishing and Hunting
3,678
1%
2,347
0%
0.00
529
0%
0.01
589
0%
0.01
283
0%
0.01
7,381
2%
0.01
Mining
756
0%
558
0%
0.00
0
0%
0.00
222
0%
0.00
0
0%
0.00
0
0%
0.00
Management of Companies
and Enterprises
162
0%
23,809
3%
0.03
30,931
4%
0.04
8058
2%
0.02
19,578
9%
0.08
6,213
1%
0.01
0.28
781,959
0.30
340,203
0.37
230,128
0.39
454,896
Grand Total
B1603
534,858
930,023
0.45
R. W. Beck 3-3
Section 3
Table 3-2
Mecklenburg County
County Populations
County
Population
Mecklenburg County, NC
771,617
Ramsey County, MN
499,498
Wake County, NC
719,520
Hennepin County, MN
1,120,897
Palm Beach, FL
1,243,320
King County, WA
1,777,143
Source: U.S. Census Bureau,2000.
3.2 Results
When comparing the five short listed counties to Mecklenburg County, the counties
with the most similar commercial sectors overall are King County, Washington and
Hennepin County, Minnesota. Similar to Mecklenburg County, the top three industry
sectors in King and Hennepin County are Retail Trade, Manufacturing and Health
Care and Social Assistance.
Though the mix of businesses is similar to Mecklenburg County, King, and Hennepin
Counties’ populations were greater than Mecklenburg in 2000. King County had over
a million more residents than Mecklenburg County, while Hennepin County had over
300,000 more residents.
Ramsey County Minnesota and Wake County North
Carolina have two of the three same top industries as does Mecklenburg County.
Moreover, Wake County is the closest in population to Mecklenburg County, with a
difference of around fifty-two thousand residents. Thus, Wake County also should be
considered for comparison.
Of the five counties, the County least similar to Mecklenburg County would be Palm
Beach County, Florida. Palm Beach has over a half million more residents than
Mecklenburg County and has the highest percentage difference by industry sector
when compared to Mecklenburg’s business mix.
Based on the above, R.W. Beck recommended that Mecklenburg County consider
King County, Hennepin County, and Wake County for a direct comparison of the
commercial sector.
3-4 R. W. Beck
B1603
Section 4
PRELIMINARY WASTE CHARACTERIZATION
4.1 Methodology
Per feedback from the Mecklenburg County staff in a September 8 memorandum to R.
W. Beck, Mecklenburg County selected the following three local governments and
available waste characterizations for use as part of the third task to develop a
preliminary waste characterization:
„
King County, Washington;
„
Hennepin County, Minneapolis; and
„
Wake County, North Carolina.
These three local governments were selected from a shortlist of local governments
considered similar to Mecklenburg County. These local governments have a business
mix similar to Mecklenburg County when comparing the number of employees by
business sector as reflected in Section 3. Furthermore, all three local governments
have had effective commercial recycling programs for more than a decade. For each
of these local governments, R.W. Beck was able to obtain applicable commercial
waste characterizations.
R. W. Beck conducted waste characterizations for both Wake County and the Twin
City Metropolitan Area, which includes, Hennepin County, within the last five to
seven years. King County completed a waste characterization study in 2000 and
shared the results of their study with R. W. Beck. All three studies included field
sampling and sorting with the development of waste characterizations for various
generators including the non-residential (commercial) waste stream.
For Wake County, the commercial waste stream included all commercial
establishments, industry, and institutions such as hospitals and schools. The definition
of the commercial waste that was used for this study excluded multi-family generated
materials even though in many instances solid waste from these generators may be
commingled with solid wastes generated by commercial entities. In completing the
Wake County study, the results of the commercial waste characterization results were
developed both separately and in the aggregate by isolating the unique loads of
materials containing primarily construction and demolition materials or industrial
byproducts.
In completing the Minnesota Statewide Waste Characterization study, the definition of
the commercial waste stream used was similar to the Wake County study with the
exclusion of multi-family generated materials. Landfill transaction data were
reviewed prior to the sorting events and surveys were conducted during the sampling
B1603
Section 4
process to assist in excluding the sampling of primarily multi-family, industrial byproducts, and C&D loads.
As for King County, the commercial waste stream appears to be more broadly defined
than the other two studies. The sampling and sorting methodology appears to have
included “mixed loads” of materials as part of the non-residential waste stream results.
Mixed loads include both residential and commercially generated materials. However,
based on the review of the detailed study, it appears that very few of these types of
samples were included as part of the results.
Provided below in Tables 4-1 through 4-3 are the commercial waste characterization
results from the waste characterization studies for each of these local governments.
4-2 R. W. Beck
B1603
PRELIMINARY WASTE CHARACTERIZATION
Table 4-1
King County, Washington
Commercial Waste Characterization 1
(by weight)
Material Categories
Paper
Plastic
Metal
B1603
Mean
+/-
1
Newspaper
3.5%
0.6%
2
OCC/Kraft
9.0%
1.9%
3
Low Grade Recyclable
6.6%
1.0%
4
High Grade Printing
1.5%
0.4%
5
Computer Paper
0.5%
0.4%
6
Bleached Polycoats
0.4%
0.1%
7
Paper/Other Materials
2.2%
0.6%
8
Other Paper
5.8%
1.0%
TOTAL PAPER
29.5%
1
PET #1 Bottles
0.4%
0.1%
2
HDPE #2 Bottles
0.4%
0.1%
3
Other Containers
0.6%
0.1%
4
Polystyrene Foam
0.7%
0.1%
5
Film and Bags
6.2%
1.1%
6
Other Packaging
0.5%
0.1%
7
Plastic Products
1.4%
0.3%
8
Plastic/Other Materials
0.9%
0.3%
TOTAL PLASTIC
11.1%
1
Aluminum Cans
0.5%
2
Other Aluminum
0.3%
0.1%
3
Tinned Food Cans
0.9%
0.3%
4
Other Ferrous
3.6%
1.4%
5
Other Nonferrous
0.1%
0.1%
6
Mixed Metals/Materials
1.8%
0.5%
TOTAL METALS
7.2%
0.2%
R. W. Beck 4-3
Section 4
Table 4-1
King County, Washington
Commercial Waste Characterization 1
(by weight)
Material Categories
Glass
Mean
+/-
1
Clear Containers
1.2%
0.2%
2
Green Containers
0.4%
0.1%
3
Brown Containers
0.6%
0.1%
4
Other Glass
0.6%
0.4%
TOTAL GLASS
2.8%
Organics
1
Dimension Lumber
3.0%
1.0%
(Wood/
2
Treated Wood
1.7%
0.9%
Yard/
3
Contaminated Wood
0.4%
0.3%
Food)
4
Roofing/Siding
0.1%
0.1%
5
Stumps
0.0%
0.0%
6
Large Prunings
0.4%
0.6%
7
Yard Wastes
4.7%
2.1%
8
Other Wood
5.2%
1.8%
9
Food Wastes
13.4%
2.5%
TOTAL ORGANIC MATERIALS
28.9%
Other
1
Textiles/Clothes
1.9%
0.6%
Organics
2
Carpet/Upholstery
2.5%
1.0%
3
Disposable Diapers
1.3%
0.3%
4
Rubber Products
0.7%
0.4%
5
Tires
0.7%
0.6%
6
Animal Carcasses
0.0%
0.0%
7
Animal Feces
1.0%
0.5%
8
Miscellaneous Organics
1.2%
1.2%
TOTAL OTHER ORGANICS
9.3%
4-4 R. W. Beck
B1603
PRELIMINARY WASTE CHARACTERIZATION
Table 4-1
King County, Washington
Commercial Waste Characterization 1
(by weight)
Material Categories
Mean
+/-
Other
1
Const/Demo Wastes
2.6%
1.1%
Wastes
2
Ashes
0.3%
0.3%
3
Nondistinct Fines
2.5%
1.7%
4
Gypsum Wallboard
1.9%
0.5%
5
Furniture/Mattresses
1.7%
1.2%
6
Small Appliances
0.9%
0.5%
7
Miscellaneous Inorganics
1.2%
0.7%
TOTAL OTHER WASTES
11.1%
1
Used Oil
0.0%
0.0%
2
Vehicle Batteries
0.0%
0.0%
3
Household Batteries
0.1%
0.1%
4
Latex Paint
0.0%
0.0%
5
Oil-Based Paint
0.1%
0.2%
6
Solvents/Thinners
0.0%
0.0%
7
Adhesives/Glues
0.1%
0.1%
8
Cleaners and Corrosives
0.0%
0.0%
9
Pesticides/Herbicides
0.0%
0.0%
10
Gas/Fuel Oil
0.0%
0.0%
11
Antifreeze
0.0%
0.0%
12
Medical Waste
0.1%
0.0%
13
Other Hazardous
0.0%
0.0%
TOTAL HHW
0.4%
HHW
100.3%
1
Based on results from Waste Monitoring Program Report (August 2000) completed by the King County Department
of Natural Resources.
B1603
R. W. Beck 4-5
Section 4
Table 4-2
Hennepin County, Minnesota
Commercial Waste Characterization 1
(by weight)
Material Categories
Paper
Plastic
Metals
Mean
Lower
Bound
Upper Bound
1
2
Newsprint (ONP)
High Grade Office
2.6%
4.2%
1.9%
2.8%
3.5%
6.3%
3
Magazines/Catalogs
2.7%
1.6%
4.2%
4
Uncoated OCC - recyclable
10.2%
8.2%
13.3%
5
Uncoated OCC - nonrecyclable
0.4%
0.3%
0.5%
6
Coated OCC
0.2%
0.1%
0.5%
7
Boxboard
1.5%
1.2%
2.2%
8
Mixed Paper - recyclable
6.1%
4.6%
7.5%
9
Mixed Paper - nonrecyclable
7.3%
5.8%
9.2%
TOTAL PAPER
35.1%
30.2%
40.8%
1
2
PET Bottles/Jars - clear
PET Bottles/Jars - colored
0.3%
0.1%
0.2%
0.1%
0.4%
0.1%
3
Other PET
0.0%
0.0%
0.1%
4
HDPE Bottles - natural
0.3%
0.2%
0.5%
5
HDPE Bottles - colored
0.1%
0.1%
0.2%
6
PVC
0.0%
0.0%
0.1%
7
Polystyrene
0.8%
0.6%
1.0%
8
Film - transport packaging
0.6%
0.4%
0.9%
9
Other Film
3.0%
2.5%
3.9%
10
Other Containers
0.3%
0.2%
0.5%
11
Other non-containers
6.7%
5.2%
8.6%
TOTAL PLASTIC
12.3%
10.3%
14.8%
1
2
Aluminum Beverage Containers
Other Aluminum
0.4%
0.6%
0.4%
0.4%
0.6%
0.8%
3
Ferrous Containers
0.7%
0.5%
1.4%
4
Other Ferrous
2.6%
1.9%
3.8%
5
Other Non-Ferrous
0.0%
0.0%
0.1%
TOTAL METALS
4.4%
3.5%
6.1%
4-6 R. W. Beck
B1603
PRELIMINARY WASTE CHARACTERIZATION
Table 4-2
Hennepin County, Minnesota
Commercial Waste Characterization 1
(by weight)
Material Categories
Glass
Organic
Materials
Problem
Materials
HHW
B1603
Mean
Lower
Bound
Upper Bound
1
2
Clear Containers
Green Containers
0.9%
0.4%
0.7%
0.2%
1.3%
0.6%
3
Brown Containers
0.4%
0.2%
0.6%
4
Other Glass
1.1%
0.6%
1.8%
TOTAL GLASS
2.7%
2.0%
3.9%
1
2
3
Yard Waste - Grass and Leaves
Yard Waste - woody material
Food Waste
1.3%
0.0%
10.8%
0.9%
0.0%
8.2%
2.1%
0.0%
14.2%
4
Wood Pallets
7.9%
5.4%
11.6%
5
Treated Wood
4.1%
2.9%
6.5%
6
Untreated Wood
3.5%
2.2%
5.5%
7
Diapers
0.3%
0.2%
0.4%
8
Other Organic Material
1.5%
1.0%
2.1%
TOTAL ORGANIC MATERIALS
29.5%
25.1%
35.0%
1
2
3
Televisions
Computer Monitors
Computer Equipment/Peripherals
0.0%
0.0%
0.4%
0.0%
0.0%
0.1%
0.0%
0.0%
0.5%
4
Electric and Electronic Products
1.1%
0.6%
1.5%
5
Batteries
0.0%
0.0%
0.1%
6
Other
0.1%
0.0%
0.3%
TOTAL PROBLEM MATERIALS
1.7%
0.9%
2.1%
1
2
Latex Paint
Oil Paint
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
3
Unused Pesti/Fungi/Herbicides
0.0%
0.0%
0.0%
4
Unused Cleaners and Solvents
0.1%
0.0%
0.2%
5
Compressed Fuel Containers
0.0%
0.0%
0.0%
6
Automotive - Antifreeze
0.0%
0.0%
0.0%
7
Automotive - Used oil. filters
0.0%
0.0%
0.0%
8
Other
0.0%
0.0%
0.1%
TOTAL HHW
0.1%
0.1%
0.2%
R. W. Beck 4-7
Section 4
Table 4-2
Hennepin County, Minnesota
Commercial Waste Characterization 1
(by weight)
Material Categories
Other
Waste
Lower
Bound
Upper Bound
1
2
3
Textiles
Carpet
Sharps and Infectious Waste
1.5%
2.8%
0.0%
1.0%
1.7%
0.0%
1.9%
4.2%
0.0%
4
Rubber
0.8%
0.4%
1.4%
5
Construction & Demolition Debris
2.1%
1.2%
3.5%
6
Household Bulky Items
2.7%
1.9%
4.4%
7
Empty HHW Containers
0.1%
0.1%
0.2%
8
Miscellaneous
4.2%
3.0%
5.8%
TOTAL OTHER WASTE
14.2%
11.0%
18.2%
TOTAL
1
Mean
100.0%
Based on results from Minnesota Statewide Waste Characterization Study (May 2000) completed by R. W. Beck for the Minnesota
Pollution Control Agency and Solid Waste Management Coordinating Board.
4-8 R. W. Beck
B1603
PRELIMINARY WASTE CHARACTERIZATION
Table 4-3
Wake County, North Carolina
Commercial Waste Characterization 1,2
(by weight)
Material Categories
Paper
Plastic
B1603
Mean
Lower
Bound
Upper
Bound
1
2
Newspaper
Magazines
1.5%
1.0%
1.0%
0.6%
2.2%
1.5%
3
High Grade/ Office
4.2%
2.7%
6.1%
4
Recyclable OCC and Kraft Bags
7.7%
5.7%
10.1%
5
Non-Recyclable OCC
0.8%
0.4%
1.4%
6
Uncoated Boxboard
1.3%
0.8%
1.8%
7
Mixed Recyc Paper
2.0%
1.4%
2.8%
8
Non-Recyclable Paper
5.5%
3.8%
7.5%
TOTAL PAPER
24.1%
18.8%
29.9%
1
2
#1 PET Soft Drink Cont. >or= 1 liter
#1 PET Soft Drink Cont. < 1 liter
0.0%
0.2%
0.0%
0.1%
0.1%
0.2%
3
#1 PET Custom Bottles. >or= 1 liter
0.0%
0.0%
0.0%
4
#1 PET Custom Bottles. < 1 liter
0.1%
0.1%
0.1%
5
#1 PET Jar and Cont.
0.0%
0.0%
0.0%
6
#1 PET Subtotal
0.3%
0.2%
0.4%
7
#2 HDPE Natural Bottles
0.2%
0.1%
0.2%
8
#2 HDPE Pigmented Bottles
0.1%
0.1%
0.2%
9
#2 HDPE Tubs/Cont.
0.1%
0.1%
0.2%
10
#2 HDPE Subtotal
0.4%
0.2%
0.5%
11
#3 PVC Bottles
0.0%
0.0%
0.0%
12
#3 PVC Tubs/Cont.
0.0%
0.0%
0.0%
13
#3 PVC Subtotal
0.0%
0.0%
0.0%
14
#4 LDPE Bottles
0.0%
0.0%
0.0%
15
#4 LDPE Tubs/Cont.
0.0%
0.0%
0.0%
16
#4 LDPE Subtotal
0.0%
0.0%
0.0%
17
#5 PP Bottles
0.0%
0.0%
0.0%
18
#5 PP Tubs/Cont.
0.0%
0.0%
0.1%
19
#5 PP Subtotal
0.1%
0.0%
0.1%
20
#6 PS Bottles/Cont.
0.0%
0.0%
0.0%
21
#7 Other Plastic Bottles/Cont.
0.0%
0.0%
0.0%
22
Other Plastic Products
7.3%
4.9%
10.2%
23
Film/Wrap/Bags
5.9%
4.2%
7.8%
TOTAL PLASTIC
13.9%
10.3%
18.0%
R. W. Beck 4-9
Section 4
Table 4-3
Wake County, North Carolina
Commercial Waste Characterization 1,2
(by weight)
Metals
Glass
Material Categories
Mean
Lower
Bound
Upper
Bound
1
2
Aluminum Beverage Containers
Ferrous Food
0.3%
0.4%
0.2%
0.2%
0.4%
0.6%
3
Other Ferrous Scrap
8.9%
5.2%
13.5%
4
Other Non-Ferrous Scrap
0.3%
0.2%
0.4%
TOTAL METALS
9.9%
6.2%
14.3%
1
2
Clear
Green
0.6%
0.2%
0.4%
0.1%
0.9%
0.3%
3
Blue
0.0%
0.0%
0.0%
4
Brown
0.5%
0.3%
0.9%
5
Other Mixed Cullet
0.1%
0.1%
0.2%
TOTAL GLASS
1.5%
0.9%
2.2%
Yard Waste
0.9%
0.5%
1.5%
Food Waste
Non-Treated Wood
6.9%
18.2%
4.3%
11.2%
10.0%
26.5%
4
Treated Wood
5.1%
3.0%
7.7%
5
Diapers
0.6%
0.3%
1.0%
6
Other Organic Material
2.6%
1.1%
4.7%
TOTAL ORGANIC MATERIALS
34.3%
n/a
n/a
All Electrical and Household Appl
Computer Equipment
Other Durables
0.3%
0.7%
1.7%
0.2%
0.4%
0.8%
0.5%
1.2%
2.8%
TOTAL PROBLEM MATERIALS
2.7%
n/a
n/a
1
2
Automotive Products
Paints and Solvents
0.3%
0.1%
0.2%
0.0%
0.5%
0.1%
3
Pesticides, Herbicides & Fungicides
0.0%
0.0%
0.0%
4
Household Cleaners
0.0%
0.0%
0.0%
5
Batteries (lead -acid)
0.0%
0.0%
0.0%
6
Batteries (other)
0.0%
0.0%
0.0%
7
Other (HHM containers w/prod inside)
0.0%
0.0%
0.0%
8
Light Bulbs
0.0%
0.0%
0.1%
TOTAL HHM
0.4%
n/a
n/a
Organic 1
Materials
2
3
Problem 1
Materials 2
3
HHM
4-10 R. W. Beck
B1603
PRELIMINARY WASTE CHARACTERIZATION
Table 4-3
Wake County, North Carolina
Commercial Waste Characterization 1,2
(by weight)
Material Categories
Other
Waste
TOTAL
Mean
Lower
Bound
Upper
Bound
1
2
3
Textiles and Leather
Sharps
Rubber
0.9%
0.0%
0.5%
0.5%
0.0%
0.3%
1.3%
0.0%
0.7%
4
Construction & Demolition Debris
9.6%
4.9%
15.5%
5
Other Inorganic
2.2%
1.1%
3.8%
TOTAL OTHER WASTE
13.2%
n/a
n/a
100.1%
Based on results from Waste Characterization Study (June, 1999) completed by R. W. Beck for Wake County Solid Waste
Management Division.
2 N/A refers to the lower and upper ranges not being available because the material categories are composed of modified
material subcategories.
1
B1603
R. W. Beck 4-11
Section 4
R.W. Beck compared the three commercial waste characterizations provided above to
assess the variability of the commercial waste stream. The intent was to identify
values that may represent a preliminary commercial characterization for Mecklenburg
County. Because the three characterization studies reflected some differences in the
material categories, some of the material subcategories had to be reordered into other
material categories to ensure compatible comparisons. The Hennepin County
categories were selected as the base set of categories because the breadth of the list of
material categories was adequate to provide compatibility between the results.
The following adjustments were made to the subcategories for each of the
characterizations.
The following categories were combined:
„
Hennepin County:
„
“PET Bottles/Jars – clear” and “PET Bottles/Jars – colored” were combined
into one sub-category, “PET Bottles”.
„
“HDPE Bottles – natural” and “HDPE Bottles – colored” were combined into
one sub-category, “HDPE Bottles”.
The means (averages) of the following sub-categories were reordered as follows:
„
King County:
„
Computer Paper was added to High Grade Office under “Paper”.
„
Bleached Polycoats were added to Mixed Paper – nonrecyclable under
“Paper”.
„
Paper/Other Materials were added to Mixed Paper – nonrecyclable under
“Paper”.
„
Other Paper was added to Mixed Paper – nonrecyclable under “Paper”.
„
Other Packaging was added to Other non-containers under “Plastic”.
„
Plastics/Other Materials were added to Other non-containers under “Plastic”.
„
Mixed Metals/Materials were added to Other Non-Ferrous under “Metals”.
„
Dimension Lumber, Contaminated Wood, Roofing/Siding, Large Prunings,
and Other Wood were moved from “Organics (Wood/Yard/Food) to Other
Organic Material under “Organic Materials”.
„
Disposable Diapers were moved from “Other Organics” to Diapers under
“Organic Materials”.
„
Tires were moved from “Other Organics” to Rubber under “Other Waste”.
„
Animal Feces were added to Other Organic Material under “Organic
Materials”.
„
Ashes, Nondistinct Fines, and Misc. Inorganics were moved from “Other
Wastes” to Miscellaneous under “Other Waste”.
4-12 R. W. Beck
B1603
PRELIMINARY WASTE CHARACTERIZATION
„
„
Gypsum Wallboard was moved from “Other Waste” to Construction &
Demolition Debris under “Other Waste”.
„
Furniture/Mattresses were moved from “Other Wastes” to Household Bulky
Items under “Other Waste”.
„
Small Appliances were moved from “Other Wastes” to Electric and Electronic
Products under “Problem Materials”.
„
Household Batteries were moved from “HHW” to Batteries under “Problem
Materials”.
„
Adhesives/glues were moved from “HHW” to Other under “HHW”.
„
Medical Waste was moved from “HHW” to Sharps and Infectious Waste
under “Other Waste”.
Wake County:
„
Plastics #5 PP Subtotal was moved to Other Containers under “Plastic”.
„
Automotive Products were moved from “HHM” to Other under “HHW”.
„
Paints and Solvents were moved from “HHM” to Oil Paint under “HHW”.
In addition, when comparing the results between the three studies for the material
categories of “untreated wood” and “construction & demolition materials” much
variability exists. To promote compatibility between the results from the studies,
R.W. Beck adjusted the following:
„
For the King County results, the estimated percentage of materials comprising the
dimensional lumber category was removed from the Other Organic Materials
category and placed in the Untreated Wood category. Without this change, the
Untreated Wood category was represented by 0% as the mean. With this change,
the Untreated Wood category was adjusted to 3%.
„
For the Wake County results, both the Untreated Wood category and Construction
& Demolition Materials category were adjusted downwardly to reflect the
estimated percentage that these material categories comprised of the commercial
waste stream, without the sampled loads comprised exclusively of construction
and demolition materials. This adjustment was needed to address the variability
in these categories resulting from the large quantities of construction and
demolition materials entering the Wake County disposal facility at the time the
study was conducted. As a result, the Untreated Wood category was reduced
from a mean of 18.2% to 13.0% and the Construction & Demolition Materials
category was reduced from a mean of 9.6% to 5.4%.
4.2 Results
The mean for the categories and subcategories was calculated for each of the three
waste characterizations to compare the results. The mean percentage by weight for
each of the material categories and subcategories was then applied to the total
quantities of commercial MSW disposed as reported by Mecklenburg County for
B1603
R. W. Beck 4-13
Section 4
fiscal year 2003-2004. The estimated quantities of commercial MSW disposed was
approximately 601,900 tons. Table 4-4 below reflects the results from applying the
percentages by weight for the material categories to the tonnage disposed.
Table 4-4
Comparison of Commercial Waste Characterization
Material Categories
Paper
Plastic
Metals
Glass
Newsprint (ONP)
High Grade Office
Magazines/Catalogs
Uncoated OCC recyclable
Uncoated OCC nonrecyclable
Coated OCC
Boxboard
Mixed Paper - recyclable
Mixed Paper nonrecyclable
TOTAL PAPER
PET Bottles
HDPE Bottles
PVC
Polystyrene
Film - transport packaging
Other Film
Other Containers
Other non-containers
TOTAL PLASTIC
Aluminum Beverage
Containers
Other Aluminum
Ferrous Containers
Other Ferrous
Other Non-Ferrous
TOTAL METALS
Clear Containers
Green Containers
Brown Containers
Other Glass
TOTAL GLASS
4-14 R. W. Beck
Hennepin
1 Avg.
King1
Avg.
Wake1
Avg.
Mean
Mecklenburg
Estimated
Tonnage
(Mean)
2.6%
4.2%
2.7%
3.5%
2.0%
0.0%
1.8%
4.5%
1.3%
2.6%
3.6%
1.3%
15,762
21,520
7,994
10.2%
9.0%
8.0%
9.0%
54,448
0.4%
0.2%
1.5%
6.1%
0.0%
0.0%
0.0%
6.6%
1.1%
0.0%
1.6%
2.3%
0.5%
0.1%
1.0%
5.0%
2,887
484
6,093
29,957
7.3%
35.1%
0.4%
0.4%
0.0%
0.8%
0.6%
3.0%
0.3%
6.7%
12.2%
8.4%
29.5%
0.4%
0.4%
0.0%
0.7%
0.0%
6.2%
0.6%
2.8%
11.1%
5.8%
26.2%
0.6%
0.7%
0.0%
0.0%
0.0%
6.2%
0.1%
7.6%
15.1%
7.2%
30.3%
0.5%
0.5%
0.0%
0.5%
0.2%
5.1%
0.3%
5.7%
12.8%
43,133
182,278
2,769
2,970
58
2,968
1,202
30,884
1,984
34,313
77,148
0.4%
0.6%
0.7%
2.6%
0.0%
4.4%
0.9%
0.4%
0.4%
1.1%
2.7%
0.5%
0.3%
0.9%
3.6%
1.9%
7.2%
1.2%
0.4%
0.6%
0.6%
2.8%
0.6%
0.0%
0.7%
9.2%
0.6%
11.0%
0.9%
0.5%
0.8%
0.4%
2.5%
0.5%
0.3%
0.8%
5.1%
0.8%
7.5%
1.0%
0.4%
0.6%
0.7%
2.7%
3,046
1,808
4,573
30,909
5,035
45,371
6,038
2,478
3,536
4,106
16,158
Mecklenburg
Estimated
Tonnage
(Low)
Mecklenburg
Estimated
Tonnage
(High)
157,945
211,305
66,814
91,011
26,431
66,332
15,169
16,854
B1603
PRELIMINARY WASTE CHARACTERIZATION
Table 4-4
Comparison of Commercial Waste Characterization
Material Categories
Organic Yard Waste - Grass and
Materials Leaves
Yard Waste - woody
material
Food Waste
Wood Pallets
Treated Wood
Untreated Wood
Diapers
Other Organic Material
TOTAL ORGANIC
MATERIALS
Problem Televisions
Materials Computer Monitors
Computer
Equipment/Peripherals
Electric and Electronic
Products
Batteries
Other
TOTAL PROBLEM
MATERIALS
HHW
Latex Paint
Oil Paint
Unused Pesti/Fungi/
Herbicides
Unused Cleaners and
Solvents
Compressed Fuel
Containers
Automotive - Antifreeze
Automotive - Used oil
filters
Other
TOTAL HHW
B1603
Hennepin
1 Avg.
King1
Avg.
Wake1
Avg.
Mean
Mecklenburg
Estimated
Tonnage
(Mean)
1.3%
4.7%
1.2%
2.4%
14,506
0.0%
10.8%
7.9%
4.1%
3.5%
0.3%
1.5%
0.0%
13.4%
0.0%
1.7%
3.0%
1.3%
8.3%
0.0%
7.2%
0.0%
5.4%
13.0%
0.9%
2.9%
0.0%
10.5%
2.6%
3.7%
6.5%
0.8%
4.2%
0
63,001
15,869
22,361
39,196
5,008
25,356
29.5%
0.0%
0.0%
32.4%
0.0%
0.0%
30.5%
0.0%
0.0%
30.8%
0.0%
0.0%
185,299
0
0
0.4%
0.0%
1.0%
0.5%
2,764
1.1%
0.0%
0.1%
0.9%
0.1%
0.0%
0.6%
0.0%
2.0%
0.9%
0.0%
0.7%
5,210
278
4,232
1.7%
0.0%
0.0%
1.0%
0.0%
0.1%
3.5%
0.0%
0.4%
2.1%
0.0%
0.2%
12,484
0
964
0.0%
0.0%
0.0%
0.0%
0
0.1%
0.0%
0.0%
0.0%
102
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
24
0
0.0%
0.0%
0.1%
0.0%
0.1%
0.2%
0.0%
0.6%
1.0%
0.0%
0.2%
0.4%
0
1,446
2,539
Mecklenburg
Estimated
Tonnage
(Low)
Mecklenburg
Estimated
Tonnage
(High)
177,272
195,024
6,019
21,308
625
5,778
R. W. Beck 4-15
Section 4
Table 4-4
Comparison of Commercial Waste Characterization
Material Categories
Other
Waste
Textiles
Carpet
Sharps and Infectious
Waste
Rubber
Construction & Demolition
Debris
Household Bulky Items
Empty HHW Containers
Miscellaneous
TOTAL OTHER WASTE
Hennepin
1 Avg.
King1
Avg.
Wake1
Avg.
Mean
Mecklenburg
Estimated
Tonnage
(Mean)
1.5%
2.8%
1.9%
2.5%
1.2%
0.0%
1.5%
1.8%
9,121
10,663
0.0%
0.8%
0.1%
1.4%
0.0%
0.8%
0.0%
1.0%
201
6,011
2.1%
2.7%
0.1%
4.2%
14.2%
4.5%
1.7%
0.0%
4.0%
16.1%
5.4%
0.0%
0.0%
2.5%
9.8%
4.0%
1.5%
0.0%
3.5%
13.4%
24,144
8,906
201
21,343
80,590
601,862
TOTAL
1
Mecklenburg
Estimated
Tonnage
(Low)
Mecklenburg
Estimated
Tonnage
(High)
59,229
96,910
Represent modified commercial waste characterizations based on previous studies.
The commercial characterization results and its variability can be measured by
reviewing the confidence intervals/standard deviations for each of the material
categories and subcategories. For purposes of our preliminary comparison, we have
not evaluated the statistical significance between the means of the material categories
when comparing the three studies. However, we have provided a mean tonnage and
upper and lower bound tonnage estimates using the high and low means from the three
studies for the primary material categories. These values do reflect a preliminary
range of quantities by material category. Comparing statistical significance is
problematic based on limited available data.
An alternative approach would be to review the upper and lower confidence intervals
for all of the subcategories and establish a percentage by weight range for each
subcategory by selecting the absolute number representing the highest upper and
lowest lower bound for each individual subcategory. The results would reflect a
greater level of variability at the materials subcategory level than what is now shown
now in Table 4-4. This approach would have limited value because the ranges would
be so large that they would provide little useful information.
Another alternative approach would be to determine statistical significance at the
primary material category level only and use the median as the measure at the material
subcategory level. Unfortunately, the King County results did not include standard
deviations by primary material category to allow one to compare and determine
statistical significance between the results for all three studies.
4-16 R. W. Beck
B1603
Section 5
COMMERCIAL WASTE DISPOSAL QUANTITIES
5.1 Overview
The purpose of this section is to provide a preliminary commercial waste
quantification by business category for Mecklenburg County (“the County”). R.W.
Beck (“Beck”) was able to determine estimates of solid waste disposed by each
business type in the County using a database developed from a 1999 study conducted
by the California Integrated Waste Management Board (“CIWMB”). The CIWMB
database is comprised of disposal rate estimates grouped by Standard Industrial
Classification (“SIC”) business groupings.
The CIWMB study randomly collected 1,207 samples of commercial waste generator
locations in order to calculate the disposal rates for each SIC business grouping. Beck
conducted research to identify other viable sources of waste disposal information by
business type, but to date, Beck has not identified other potentially useful sources for
generation rate estimates that are compatible with the data available for Mecklenburg
County. To date, the 1999 CIWMB study is the most recent study of its kind that
presents data that fits with R.W. Beck’s estimation methodology.
The CIWMB groupings consist of one or more SIC codes that have been grouped
together in a discretionary fashion by CIWMB. A key assumption associated with the
disposal rates is that businesses belonging to a particular employment category
generate waste at a similar rate. This is the basis for the methodology developed by
R.W. Beck to produce estimates of total waste generated by employment sector.
5.2 Methodology
The following is a complete delineation of the methodology employed to develop
waste disposal estimates for Mecklenburg County.
1. County employment data was extracted from the Info USA database, and then
tallied and divided into 20 North American Industry Classification System
(“NAICS”) Industry Sectors. For the purposes of computing Task 5 estimates,
these categories were redistributed back into sub-sectors in order to match the
NAICS sub-sectors with the SIC classifications as specified by CIWMB. R.W.
Beck then catalogued a database of all NAICS sub-sector employment totals.
2. Each SIC business sector, which is the level of aggregation at which the
disposal rate data was available, was allocated to its appropriate NAICS sector
and sub-sector. This was accomplished through the use of the CIWMB SIC
B1603
Section 5
business sector list, along with Internet cross-reference sources that tie SIC and
NAICS codes together.
3. Based on the allocation in Step 2, a weighted average of tonnage disposed per
employee per year was computed for each NAICS grouping. This was based
on the assignment of each SIC business sector disposal rate to one or more
corresponding NAICS sub-sectors. The percentage of total employees in each
sub-sector was the basis for this weighted average. A weighted average
provides a more precise estimate of aggregate disposal, because it takes
employee distribution into account at the most specific level possible.
4. The disposal estimates computed in Step 3 were then extrapolated to a tons
disposed per year by employment sector using the employee totals catalogued
in Step 1. The disposal rates presented in the Results Section below are
estimates of waste disposal for the County by employment sector (which are
simply the aggregation of a specific set of NAICS codes) on an annual basis.
The results of R.W. Beck’s analysis must be tempered with the following caveats:
„
Synchronization of SIC business groupings as grouped by the CIWMB data
source and NAICS codes involved a certain amount of judgment. While R.W.
Beck has adhered to the cross-reference Internet sources wherever possible,
certain SIC codes have been excluded from CIWMB’s analysis. In these cases,
the most appropriate substitute has been used to designate the associated NAICS
grouping. In other cases, SIC business groupings as defined by CIWMB were
inclusive of SIC codes that were cross-referenced to belong to slightly different
NAICS groupings. Judgment calls have been made with respect to these
instances, so as to ensure that the most sensible business grouping disposal rate
estimate was used in each case.
„
NAICS employment data provides a range of employee count for each specific
business. In order to perform the analysis, averages of each work area range were
used to compute aggregated estimates of employment counts by sector. To the
extent that these averages overrepresented or underrepresented the true
employment totals in the County, the results below must be interpreted as an
approximation of waste disposed on average.
„
The CIWMB disposal rates are drawn from an empirical study performed in a
specific region of the nation. This study employed a relatively large sample size,
and contains reasonable groupings of businesses from which to determine
generation rates. While the reported generation rates resulting from the CIWMB
study are an appropriate proxy for use in the County, it cannot be known for
certain whether this data is representative of actual waste generation behavior in
Mecklenburg County. An empirical study would need to be conducted in order to
take regional factors that may be indigenous to Mecklenburg County into account,
some of which may ultimately impact the results.
5-2 R. W. Beck
B1603
COMMERCIAL WASTE DISPOSAL QUANTITIES
5.3 Results
Table 5-1 presents the results of the analysis by NAICS industry sector with employee
counts and lists associated disposal rates and projected annual disposal quantities.
As evidenced by the results in Table 5-1, the industry sector with the highest disposal
rate is Accommodation and Food Services. It is logical that this industry sector would
have a high disposal rate, due to the type of work activity associated with this industry
and the amount of waste employees in this sector produce while completing these
activities. Even though the Accommodation and Food Services sector does not have
the largest labor force, it is estimated to be the largest generator of solid waste due to
the level of its per employee disposal rate.
Table 5-1
Mecklenburg County
Solid Waste Disposal Rates by Industry Sector
INDUSTRY SECTOR
Retail Trade
Manufacturing
Health Care and Social Assistance
Accommodation and Food Services
Wholesale Trade
Finance and Insurance
Professional, Scientific, and Technical Services
Educational Services
Other Services (except Public Administration)
Transportation and Warehousing
Administrative and Support and Waste
Management and Remediation Services
Information
Real Estate and Rental and Leasing
Public Administration
Arts, Entertainment and Recreation
Utilities
Agriculture, Forestry, Fishing and Hunting
Mining
Management of Companies and Enterprises
Grand Total
Employee
Count
66,021
65,315
50,099
44,892
41,480
37,949
36,620
27,986
24,488
20,792
Disposal Rate
lbs/Employee/Day
8.61
7.11
6.90
15.62
4.93
1.64
5.85
4.39
5.50
8.41
Annual Disposal
Quantities
Tons
103,717
84,721
63,109
127,931
37,332
11,385
39,120
22,444
24,575
31,915
17,924
5.79
18,926
15,733
15,582
13,836
6,303
4,067
3,678
756
162
493,678
6.47
2.60
2.19
6.08
1.64
1.94
9.86
1.64
6.74
18,567
7,385
5,534
6,994
1,220
1,301
1,361
49
607,584
The average disposal rate for commercial waste was calculated at 6.74
lbs/employee/day. Figure 5-1 graphically depicts the comparison between the various
B1603
R. W. Beck 5-3
Section 5
industry sector disposal rates. Disposal rates range from 1.64 to 15.62
lbs/employee/day.
Figure 5-1
Mecklenburg County
Solid Waste Daily Disposal Rates by Industry Sector
18.00
Generation Rate (lbs/Employee/Day)
16.00
14.00
12.00
10.00
8.00
6.00
4.00
2.00
Industry Sector
Figure 5-2 depicts the estimated quantities disposed by industry sector. Note that the
Accommodations and Food Service sector also is estimated to dispose of more lbs/day
of materials than other industry sectors. However, the relative order of the other
industry sectors varies as a function of both the number of employees and the
employee disposal rate.
5-4 R. W. Beck
B1603
Management
Utilities
Finance
Agriculture
Public
Administration
Real Estate
Educational
Services
Wholesale Trade
Other Services
Administrative
Technical Services
Arts
Information
Health Care
Manufacturing
Transportation
Retail Trade
Mining
Food Services
0.00
COMMERCIAL WASTE DISPOSAL QUANTITIES
Figure 5-2
Mecklenburg County
Solid Waste Daily Disposal Quantities by Industry Sector
800,000
Solid Waste Disposal (lbs/day)
700,000
600,000
500,000
400,000
300,000
200,000
100,000
Management
Utilities
Agriculture
Mining
Public
Administration
Arts
Real Estate
Finance
Information
Administrative
Educational
Services
Other Services
Transportation
Wholesale Trade
Technical Services
Health Care
Manufacturing
Retail Trade
Food Services
0
Industry Sector
5.4 Conclusion
The historical data received from Mecklenburg County depicted the estimated
quantities of commercial solid waste disposed in 2004 at 601,925 tons of waste.
Using the calculated disposal rates for Task 5, R.W. Beck estimated a disposed
quantity of approximately 607,584 tons of commercial solid waste. This small
difference is the likely result of the use of empirically estimated per-sector disposal
rates and estimated employment size for the various industry sectors. It should be
noted that our analysis has not included the estimated quantities of construction and
demolition materials disposed. This portion of the solid waste stream was excluded
from these estimates because the County programmatically manages these materials
separately from other commercial waste sources.
This analysis has provided a systematic approach to estimating commercial waste
disposal by isolating employment sectors and extrapolating quantities using
empirically validated generation rate estimates. These estimates, along with the waste
composition estimates, provided in earlier tasks of this study, should serve to guide the
County in determining future areas of focus for commercial waste reduction and
recycling programs.
B1603
R. W. Beck 5-5
Section 5
Table 5-2
Industry Sector Abbreviation Key
Abbreviations
Administrative
Agriculture
Arts
Educational Services
Finance
Food Services
Health Care
Information
Management
Manufacturing
Mining
Other Services
Public Administration
Real Estate
Retail Trade
Technical Services
Transportation
Utilities
Wholesale Trade
5-6 R. W. Beck
Industry Sector
Administrative and Support and Waste Management and Remediation
Services
Agriculture, Forestry, Fishing and Hunting
Arts, Entertainment and Recreation
Educational Services
Finance and Insurance
Accommodation and Food Services
Health Care and Social Assistance
Information
Management of Companies and Enterprises
Manufacturing
Mining
Other Services(except Public Administration)
Public Administration
Real Estate and Rental and Leasing
Retail Trade
Professional, Scientific and Technical Services
Transportation and Warehousing
Utilities
Wholesale Trade
B1603
Section 6
RECOMMENDATIONS
6.1 Overview
In Section 4, the composition of Mecklenburg County’s commercial waste stream was
projected based on data from selected communities. The results of this analysis
identified the following materials as the five most prevalent in the County’s solid
waste stream:
Table 6-1
Mecklenburg County
Commercial Waste Characterization
Material
1
Estimated Tons
Disposed per Year
Food waste
63,001
Uncoated OCC
54,448
Untreated wood waste
39,196
Film plastics
32,0861
Other ferrous
30,909
Includes transport packaging film and other film plastics
From a technical standpoint, all of these materials, when relatively free of
contaminants, have recovery and recycling potential. The recovery potential for each
of these materials is discussed below, as well as R. W. Beck’s recommendations on
which of these materials should be the focus of future County efforts to reduce and
recycle commercial solid waste.
6.2 Recommendations
6.2.1 Food Waste
Many U.S. communities have established commercial food waste composting
programs, as well as some have established residential food waste recycling programs.
Food waste can be used in farming applications or composted on site or centrally, via
various methods including in vessel or vermi-composting for a variety of applications.
In addition, some food from commercial sources is fit for human or animal
consumption and can be diverted for such purposes, thereby being diverted from the
B1603
Section 6
waste stream. R. W. Beck recommends food waste as an appropriate target for
additional efforts by Mecklenburg County to reduce and recycle business waste. We
recommend that the County investigate associated issues, barriers, opportunities and
potential strategies for County implementation.
6.2.2 Uncoated OCC
Markets for uncoated OCC are currently very strong and are expected to remain
strong. It is likely that the above estimate for OCC in the County waste stream is high,
given that the data from the three comparison communities were obtained in 1999 and
2000 when markets were not as favorable. Given this information, and that
Mecklenburg County has a business recycling ordinance that specifies the recycling of
OCC to promote recovery and recycling of this material, we do not recommend OCC
as a target for additional County waste reduction efforts. However, we encourage the
County to monitor existing OCC recycling efforts and enforce the existing business
recycling ordinance.
6.2.3 Untreated Wood Waste
Untreated wood waste such as pallets, crates, and wood scrap can be recovered and
recycled via chipping for use in landscaping and ground cover applications, chipping
for fuel use, and in making furniture and other wood products (such as finger jointed
lumber and wall board). The extent to which such activities occur is largely driven by
the economics associated with disposal of these materials. We recommend that clean
wood waste be targeted by future County business waste recycling programs.
6.2.4 Film Plastics
Increasingly, a number of U.S. communities and some states, such as Rhode Island,
are targeting film plastics for recycling from both commercial and residential sources.
Film plastics include grocery bags, trash bags, storage bags, pallet wrap, and other
sheet fiber plastic. Residential plastic film targeted for recovery and recycling is
typically plastic grocery bags, which are often collected through grocery stores.
Certain companies making plastic lumber products, such as Trex, claim that they
cannot get enough supply of film plastics and are actively seeking more of film
plastics. Given the amount of film plastics in the waste stream, and the market
demand for this material, R. W. Beck recommends that Mecklenburg County give
consideration to targeting this material for recovery in its business waste recycling
program. This material in particular, would merit further study to determine key
sources, quality concerns, barriers and specific opportunities prior to establishing any
program for recovery and recycling.
6.2.5 Other Ferrous
This is a catch-all category for ferrous materials that may or may not be recyclable,
depending on the materials that are commingled with the ferrous metal. Items in this
category include ferrous metal besides containers, such as clothes hangers, sheet metal
6-2 R. W. Beck
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RECOMMENDATIONS
products, etc. Given the market value of ferrous metals and the extent to which
businesses routinely recycle such metal, the opportunity to capture substantial
additional ferrous metals for recycling due to new program efforts is likely to be
limited. Therefore, we do not recommend this material as a target for the county’s
business recycling program at this time.
In Section 5, two employment sectors were identified as being the largest sources of
waste generated by commercial businesses, based on the number of employees by
industry sector and estimated tons per employee for each sector. These are as follows:
Table 6-2
Mecklenburg County
Commercial Waste Characterization by Employment Sector
Employee Sector
Annual Estimated
Disposed Tonnage
Accommodation and Food Services
127,931
Retail Trade
103,717
Retail Trade and Accommodation and Food Service sectors are primary generators of
food waste and plastic film. R. W. Beck recommends that the County focus its target
of opportunities to reduce and recycle the target waste materials mentioned above
from these sources. In addition, two other sectors—Educational Services, and Health
Care and Social Assistance—often have food service establishments thereby making
them appropriate targets for food waste recycling programs. These institutions
generate substantially less waste than those mentioned above, but may be more
receptive to participating in waste reduction and recycling programs. For these
reasons, we recommend including them along with Accommodation and Food
Services and Retail Trade (food retail in particular) to recycle food waste.
6.3 Follow-Up Study Recommendations
Based upon the analysis completed above, the planning level results should be
confirmed through field studies. We would recommend that the County conduct
generator-based field audits for targeted businesses. Specifically, conducting waste
audits for a sampling of businesses in the Retail Trade and Accommodation and Food
Services should be considered.
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