Greenhouse Gas Reduction Potential through
MSW Management
Comparative analysis of MSW management strategies
Adam Silbert
4/28/10
Table of Contents
Introduction ............................................................................................................................... 3
Approach ..................................................................................................................................... 4
Models..................................................................................................................................................... 4
Representative MSW Waste Streams........................................................................................... 5
Development of Scenarios ............................................................................................................ 14
Recycling Scenarios .................................................................................................................................... 14
Results ....................................................................................................................................... 17
Landfill Mass and Volume ............................................................................................................. 17
Electrical Power Production........................................................................................................ 19
Potential Landfill Methane Production ................................................................................... 20
Energy Requirements .................................................................................................................... 21
GHG Emissions .................................................................................................................................. 22
Custom Scenario............................................................................................................................... 23
Discussion ................................................................................................................................ 23
Conclusions .............................................................................................................................. 24
Works Cited ............................................................................................................................. 25
Appendix ................................................................................................................................... 26
2
Introduction
Over the past century municipal solid waste management (MSW) has transformed
from simple consolidation of refuse in landfills to the highly complex management
system in place today to handle the ever growing waste management needs of
increasing populations. Consistent with the global agenda for the reduction of
greenhouse gas (GHG) emissions, it is crucial to examine the impact of waste
management strategies on these gases. The purpose of this study was to examine a
variety of MSW streams and management scenarios and to evaluate their impacts.
There are a variety of waste management strategies and each has its own benefits
and pitfalls. Landfills are often the cheapest option, but they require large amounts
of space and may contain materials that might allow toxic leachate to enter soil and
groundwater. Combustion in waste-to-energy (WTE) facilities is an alternative to
landfills, thus recovering energy and sending only the ash to the landfills. However,
this alternative results in concerns over its emissions and their effect on air quality.
Either of these strategies would typically be coupled plans for composting and/or
recycling. In this study, the strategies were evaluated for their impacts on landfill
masses, volumes and methane production, electrical power production from WTE,
GHG emissions and energy consumption.
This study used two spreadsheet models, MSWFLOW (Haith, 1999) and WARM (U.S.
EPA Climate Change Division, 2006) to calculate impacts. The waste streams
reflected a range of communities, and included statewide averages, major urban and
suburban communities and a national average, as developed for the U.S.
Environmental Protection Agency. To maintain consistency with the EPA national
data, the waste streams were generally based on residential, commercial and
institutional solid wastes.
This study recognizes that waste streams will have some degree of variation across
the country due to environmental or other reasons. Where observed differences in
management impacts have a correlation with waste composition, management
scenarios can cater to the specific needs of an area. Observed similarities may
indicate which management strategies are more resilient to differences in waste
composition, and may thus apply to a greater proportion of the US. Based on these
results, the study developed combination management scenarios, which are suitable
for national and regional application.
The remainder of this report includes explanations of the two models, MSWFLOW
and WARM, and how and why they are used in the study. This report explains how
the representative waste streams were chosen and adjusted for consistency with
the models. Selection of waste management scenarios are described, including
methods used to determine recycling levels. Results are presented that
demonstrate how waste streams are affected by the management scenarios, and
3
these are followed by a discussion of these results and conclusions of the study. An
appendix at the end of the report presents the waste stream inputs used in model
runs.
Approach
Models
The MSWFLOW model was designed to analyze the impacts of a solid waste
management strategy on a specified waste stream. The model examines the wastes
based on product types, which necessitates a relatively detailed waste
characterization. Based on assumed physical and chemical properties of the
product materials, the model calculates landfill mass and volume as well as landfill
gas production (CO2 and CH4). The model also calculates a heating value that is
converted to a theoretical electrical power production. Recovery amounts are
determined based on percentages provided by the user for each waste product. The
percentages of products not recovered by the Materials Recovery Facility (MRF) and
sent either to a landfill or WTE facility is specified by the user. The MSWFLOW
results are valuable for landfill operators, particularly with respect to space
requirements and in anticipating the impact of the resultant greenhouse gas
production and disposition strategies. Additionally the model can be used to
anticipate the amount of materials a MRF can reasonably expect to receive as well as
the amount of energy a WTE may potentially produce.
The WARM model was designed by the EPA to provide a basis for comparison of
GHG emissions and energy impacts for different materials based on different waste
management scenarios. This model employs a life-cycle methodology that
calculates the GHG emissions of a material through all phases of its life cycle by
comparing its origins from either virgin materials or recycled inputs. The model
takes into account such factors as fossil fuels required for transport and also
calculates the difference in GHG emissions for energy production obtained from
WTE or landfill gas combustion as compared to the burning of fossil fuels. The
materials used for comparison in the WARM model make up more than 65% of the
total waste stream. These materials were selected using three criteria:
1. The quantity generated
2. The difference in energy use for manufacturing the product from virgin
versus recycled inputs
3. The potential contribution of the material to CH4 generation in landfills
It is important to note that the WARM model is comparative. The results of WARM
analysis are obtained by examining two waste management scenarios and are
therefore valuable in understanding the relative efficacy of different waste
strategies. The MSFLOW model produces absolute data from one waste
management option. By employing both MSFLOW and WARM modeling, this study
4
shows that it is possible to carry out a comprehensive waste management
methodology that integrates hard data on waste/landfill volumes, energy
production, and a comparative analysis of GHG emissions as an integral part of the
planning process.
Representative MSW Waste Streams
Waste stream data used in the study were selected to represent a range of U.S.
conditions, including national, statewide and regional MSW generation. The data
were obtained from five waste generation studies, representing U.S. national
averages, as determined by the US EPA (Office of Solid Waste, 2007), Erie County,
NY (Northeast-Southtowns Solid Waste Management Board, 2003), the state of
Illinois (CDM, 2009), New York City (Department of Sanitation New York, 2005), and
Sonoma County, CA (R3 Consulting group, 2003). Each study contains MSW data
from residential and commercial sources only, excepting the Sonoma County data.
The Sonoma County data contains waste from construction and demolition sources.
This waste stream will provide results on how the management scenarios are
affected by construction and demolition components of waste, namely wood and
steel. The MSWFLOW model was based on the product categories used in the
USEPA national averages, as shown in Table 1.
5
Table 1. US Per-capita Generation of MSW in 2007 (Office of Solid Waste, 2008)
Product
Percent by mass
DURABLE GOODS
Major Appliances
Small Appliances
Furniture & Furnishings
Carpets & Rugs*
Rubber Tires*
Batteries, Lead-Acid
Misc Durables
1.39
0.54
3.60
1.21
1.89
0.98
7.88
Total Durable
17.48
NONDURABLE GOODS
Newspapers*
Books
Magazines*
Office Papers*
Directories*
Standard (A) Mail*
Other Commercial Printing
Tissue Paper & Towels
Paper Plates & Cups
Plastic Plates & Cups
Trash Bags
Disposable Diapers
Other Nonpackaging Paper
Clothing & Footware
Towels, Sheets, Pillowcases
Misc Nondurables
4.23
0.52
0.98
2.31
0.27
2.27
2.41
1.35
0.50
0.33
0.41
1.44
1.71
3.20
0.42
1.60
Total Nondurable
23.96
(continued)
6
Table 1. (continued)
Product
Percent by mass
CONTAINERS & PACKAGING
Glass Pkg
Beer & Soft Drink Bottles*
Wine & Liquor Bottles*
Food & Other Bottles & Jars*
Total Glass
2.97
0.64
0.80
4.41
Total Steel
0.00
0.94
0.09
1.03
Total Aluminum
0.55
0.02
0.16
0.72
Total Paper Pkg
12.02
0.19
2.13
0.06
0.44
0.00
0.54
15.37
Steel Pkg
Beer & Soft Drink Cans*
Food & Other Cans*
Other Steel Pkg
Aluminum Pkg
Beer & Soft Drink Cans*
Other Cans*
Foil & Closures
Paper & Paperboard Pkg
Corrugated Boxes*
Milk Cartons
Folding Cartons
Other Paperboard Pkg
Bags & Sacks
Wrapping Papers
Other Paper Pkg
Plastics Pkg
Soft Drink Bottles
Milk Bottles
Other Containers
Bags & Sacks
Wraps
Other Plastics Pkg
Total Plastics
Wood Pkg*
Other Misc Pkg
0.39
0.32
1.44
0.39
1.22
1.49
5.25
5.47
0.12
Total Containers & Pkg
32.37
Total Other Wastes
12.18
12.56
1.44
26.19
OTHER WASTES
Food Wastes*
Yard Waste*
Misc Inorganic Wastes
*Products that are the inputs into the WARM model
7
Compared to the national data obtained from the U.S. EPA, the other waste stream
data did not always include the same amount of detail, leaving some categories
blank and/or classifying large quantities of waste in ‘other’ or ‘miscellaneous’
categories. In these situations the waste was appropriated proportionally using the
waste generation rates from the national average, and the distribution percentages
are listed in Table 3. The “Report Product” column in the tables is the product listed
as it is found in the original waste generation report. The “US EPA Product” column
represents the products that the nonspecific “report product” best translates into.
The last column shows how the report product is distributed among the
corresponding EPA products. Complete distributions of products for each waste
stream are provided in the Appendix.
The materials breakdown of the MSW streams (Table 2) illustrates the disparity
among the waste streams; values marked by an asterisk differ substantially from the
median values. Both the state of Illinois and NYC have low yard waste at 6.4% and
4.9% respectively compared to the median 14.2%, presumably due to a landfill ban
on yard waste in the state of Illinois since 1990 and NYC, being a highly urbanized
environment, producing very little yard waste. These two waste streams also share
a high proportion of plastics, 16.7% and 17.5% respectively, compared to the
median 11.7%. Plastics have a high potential for energy production from WTE. The
food waste proportion will have a large impact on the scenarios that involve
composting and thus, it is important to observe major aberrations in this category.
The food waste proportion for NYC, 20.5%, is significantly higher than the median at
12.2% and the proportion for Sonoma County is significantly lower at 5.2%. The
NYC waste stream has a very low proportion of cardboard at 7.1%, compared to the
median 14.4%. The Sonoma County waste stream includes construction and
demolition debris and as a result, the waste stream’s proportion of wood and steel
are unusually high. The Sonoma County waste stream is 16.7% wood compared to
7.6% for the national data. The waste stream contains 14.5% steel, calculated as
Ferrous Packaging, compared to the median value of 3.6%.
8
Table 2. Materials Composition of Waste Streams
Material
Organic
Food Waste
Paper
Cardboard
Plastics
Textiles
Rubber
Leather
Yard Waste
Wood
Total Organic
Inorganic
Glass
Ferrous Pkg
Other Ferrous
Aluminum Pkg
Other Aluminum
Other Metal
Misc
Total Inorganic
Erie
County,
NY
State
of
Illinois
NYC,
NY
Sonoma
County,
CA
12.2
17.5
14.4
11.7
4.6
2.1
0.8
12.6
7.6
83.4
10.2
24.4
15.5
10.2
4.2
1.2
0.2
14.2
4.3
84.3
13.0
18.8
19.4
16.7*
6.3
1.6
0.8
6.4*
0.6
83.6
20.5*
26.3
7.1
17.5*
5.8
0.6
0.4
4.9*
1.6
84.6
5.2*
16.0
12.1
8.0
3.5
1.6
0.2
14.8
16.7
78.0
5.2
1.0
5.0
0.7
0.6
0.7
3.4
16.6
6.3
1.7
3.2
2.1
0.2
0.5
1.6
15.7
4.0
3.7
1.4
1.3
0.1
0.4
5.4
16.4
5.4
3.6
2.0
0.9
0.2
0.1
3.1
15.4
3.3
14.5*
3.2
0.3
0.4
0.2
0.1
22.0
National
*Values which differ substantially from National values.
One of the simplifications for the WARM model is that it uses a selection of the total
products in the waste stream, namely those that have the most potential for GHG
production. The product inputs required by the WARM model can be determined
from those used in the MSWFLOW model. The products in Table 1 that are labeled
with * are inputs in the WARM model. There is an exception, where the product list
of plastic products (containers, bags, etc) does not convert directly to the plastic
materials (HDPE, LDPE, PET). With exception of the State of Illinois, each of the
waste generation reports contained data on the materials breakdown of plastics as
well as a product breakdown. For the State of Illinois, the plastic was input as
“Mixed Plastic” in the WARM model, which are composed of HDPE, LDPE and PET
and are estimated by taking a weighted average of the 2003 recovery rates for the
three plastic types.
9
Table 3a. Allocation of Waste Categories to EPA National Categories for
Erie County, NY
Report Product
US EPA Product
Other Ferrous
Major appliances
Small appliances
Other Ferrous
Furniture & Furnishings
Wood Packaging
Carpets & Rugs
Clothing & Footware
Towels, Sheets,
Pillowcases
Rubber Tires
Clothing & Footware
68.95
26.48
4.57
39.68
60.32
25.00
66.24
Books
Directories
Standard (A) Mail
Other Commercial Printing
9.43
4.93
41.59
44.05
Tissue Paper & Towels
Paper Plates & Cups
Plastic Plates & Cups
Trash Bags
Disposable Diapers
Soft Drink Bottles
Milk Bottles
Other Containers
Bags & Sacks
Wraps
Other Plastics Pkg
Beer & Soft Drink Bottles
Wine & Liquor Bottles
Food & Other Bottles &
Jars
Milk Cartons
Folding Cartons
Other Paperboard Pkg
72.77
27.23
4.46
5.55
19.34
5.24
4.25
19.39
5.24
16.49
20.06
67.22
14.56
Bags & Sacks
Other Paper Pkg
45.06
54.94
Wood
Textiles
Rubber
Other Letter &
Printing
Disposable Paper
Goods
Total Plastics
Total Glass
Paperboard
Other Paper
Packaging
%
8.76
0.37
0.63
18.22
8.09
89.48
2.43
10
Table 3b. Allocation of Waste Categories to EPA National Categories for
State of Illinois
Report Product
US EPA Product
Other Textiles
Carpets & Rugs
Towels, Sheets,
Pillowcases
Books
Directories
Standard (A) Mail
Other Commercial
Printing
Tissue Paper & Towels
Paper Plates & Cups
Other Nonpackaging
Paper
Other Paperboard Pkg
Bags & Sacks
Other Paper Pkg
Beer & Soft Drink
Bottles
Wine & Liquor Bottles
Food & Other Bottles &
Jars
Other Cans
Foil & Closures
74.06
Plastic Plates & Cups
Soft Drink Bottles
Milk Bottles
Other Containers
Wraps
Other Plastics Pkg
6.38
7.49
6.08
27.74
23.59
28.71
Mixed Paper-Recyclable
Compostable Paper
Other Paper
Total Glass
Other Aluminum
Total Plastics (excluding
bags)
%
25.94
5.12
2.68
22.60
23.94
13.38
5.01
17.02
0.57
4.36
5.32
67.22
14.56
18.22
8.89
91.11
11
Table 3c. Allocation of Waste Categories to EPA National Categories for
New York City, NY
Report Product
US EPA Product
Appliances: Ferrous
Appliances: Non-Ferrous
Appliances: Plastic
Phone Books/Paperbacks
Major Appliances
Small Appliances
72.26
27.74
Books
Directories
Magazines
Standard (A) Mail
Other Commercial
Printing
Tissue Paper &
Towels
65.69
34.31
14.00
32.44
Trash Bags
Soft Drink Bottles
Milk Bottles
Other Containers
Bags & Sacks
Wraps
Other Plastics Pkg
Beer & Soft Drink
Bottles
Wine & Liquor
Bottles
Food & Other
Bottles & Jars
7.28
6.87
5.58
25.44
6.87
21.63
26.33
Folding Cartons
Other Paperboard
Pkg
Other Paper Pkg
78.22
Mixed Grade Low Paper
Total Plastic(excluding
disposable plastic)
Total Glass
Compostable/Soiled
Paper/Waxed OCC/Kraft
%
34.36
19.21
67.22
14.56
18.22
2.12
19.66
12
Table 3d. Allocation of Waste Categories to EPA National Categories for
Sonoma County, CA
Report Product
US EPA Product
Wood
Furniture & Furnishings
Wood Pkg
Carpets & Rugs
Clothing & Footware
Towels, Sheets,
Pillowcases
Books
Directories
Other Commercial Printing
Tissue Paper & Towels
Paper Plates & Cups
Other Nonpackaging Paper
Milk Cartons
Folding Cartons
Other Paperboard Pkg
Bags & Sacks
Other Paper Pkg
Plastic Plates & Cups
Trash Bags
Soft Drink Bottles
Milk Bottles
Other Containers
Bags & Sacks
Wraps
Other Plastics Pkg
Textiles/Leather
Other Paper
Total Plastic
%
39.68
60.32
25.00
66.24
8.76
5.10
2.66
23.83
13.32
4.99
16.94
1.90
21.05
0.57
4.34
5.29
5.53
6.88
6.49
5.27
24.04
6.49
20.44
24.87
13
Development of Scenarios
The four basic MSW management strategies included in the study are: Landfill,
Waste to Energy, Recycling and Composting. They were combined into the
following six management scenarios:
(1)
(2)
(3)
Send all waste to landfill
Send all waste to WTE, landfill resultant ash
Send all compostable materials to composting facility, landfill noncompostable products/materials
(4)
Send all compostable materials to composting facility, WTE noncompostable products/materials, landfill resultant ash
(5)
Send 60% of recyclable materials to MRF, landfill remaining waste
(6)
Send 60% of recyclable materials to MRF, WTE remaining waste, landfill
resultant ash
In combination with the five different MSW waste streams, this produced a total of
30 distinct situations to be evaluated.
Recycling Scenarios
The MSWFLOW model includes data on a theoretical “high” gross recovery, which
are the projected 2010 recovery rates estimated by EPA in 1996 (Office of Solid
Waste and Emergency Response, 1996), where percent gross recovery is the
amount of the product that is sent to a MRF. The MSWFLOW model calculates net
recovery from gross recovery using data on residuals from MRFs. When the “high”
gross recovery rates are applied to the products distribution of the 2007 waste
stream, the gross recovery is 42%. This report examines extremes in management
strategies to evaluate the impact one scenario has over another. It would be
unreasonable to assert that any municipality could achieve 100% gross recovery,
but some local municipalities such as San Francisco, CA have achieved 70% gross
recovery. With this value as a reference point, a recovery rate of 60% was
determined to be very high, but theoretically achievable. The 60% recovery rate
was calculated based on the “high” gross recovery rate, where each product
recovery rate is multiplied by some factor that produce an overall gross recovery of
60%. Table 3 includes the “high” recovery rates as found in the MSWFLOW model
and the recovery rates of the respective products for the individual waste streams
that result in 60% total recycling. The multiplicative factor to increase the recycling
rate to 60% is included at the top of the table. For the purposes of calculations in
WARM, compostable products consist of only yard waste and food discards.
14
Table 4. Recycling Rates for Waste Streams
Multiplicative Factor
Product
“high”
recovery
rates
National
Erie
County,
NY
State of
Illinois
NYC,
NY
Sonoma
County,
CA
1.5
1.3
1.5
2.4
1.3
Recovery rates necessary to achieve 60% total
recycling
DURABLE GOODS
Major Appliances
Small Appliances
Furniture & Furnishings
Carpets & Rugs
Rubber Tires
Batteries, Lead-Acid
Misc Durables
79
11
11
11
37
99
26
100.0
16.8
16.8
16.8
56.5
100.0
39.7
100.0
14.0
14.0
14.0
47.0
100.0
33.0
100.0
16.5
16.5
16.5
55.6
100.0
39.1
100.0
26.1
26.1
26.1
87.7
100.0
61.6
100.0
14.0
14.0
14.0
46.9
100.0
33.0
94
37
53
66
32
38
100.0
56.5
80.9
100.0
48.8
58.0
100.0
47.0
67.4
83.9
40.7
48.3
100.0
55.6
79.7
99.3
48.1
57.2
100.0
87.7
100.0
100.0
75.8
90.1
100.0
46.9
67.2
83.7
40.6
48.2
42
5
5
5
5
5
64.1
7.6
7.6
7.6
7.6
7.6
53.4
6.4
6.4
6.4
6.4
6.4
63.2
7.5
7.5
7.5
7.5
7.5
99.6
11.9
11.9
11.9
11.9
11.9
53.3
6.3
6.3
6.3
6.3
6.3
5
26
7.6
39.7
6.4
33.0
7.5
39.1
11.9
61.6
6.3
33.0
26
5
39.7
7.6
33.0
6.4
39.1
7.5
61.6
11.9
33.0
6.3
NONDURABLE GOODS
Newspapers
Books
Magazines
Office Papers
Directories
Standard (A) Mail
Other Commercial
Printing
Tissue Paper & Towels
Paper Plates & Cups
Plastic Plates & Cups
Trash Bags
Disposable Diapers
Other Nonpackaging
Paper
Clothing & Footware
Towels, Sheets,
Pillowcases
Misc Nondurables
(continued)
15
Table 4. Continued
Erie
Sonoma
State of
NYC,
County,
County,
Illinois
NY
NY
CA
Recovery rates necessary to achieve 60% total
recycling
National
Product
CONTAINERS &
PACKAGING
Glass Pkg
Beer & Soft Drink Bottles
Wine & Liquor Bottles
Food & Other Bottles &
Jars
“high”
recovery
rates
58
58
88.5
88.5
73.7
73.7
87.2
87.2
100.0
100.0
73.6
73.6
58
88.5
73.7
87.2
100.0
73.6
Steel Pkg
Beer & Soft Drink Cans
Food & Other Cans
Other Steel Pkg
74
74
74
100.0
100.0
100.0
94.0
94.0
94.0
100.0
100.0
100.0
100.0
100.0
100.0
93.9
93.9
93.9
Aluminum Pkg
Beer & Soft Drink Cans
Other Cans
Foil & Closures
79
79
79
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
Paper & Paperboard Pkg
Corrugated Boxes
Milk Cartons
Folding Cartons
Other Paperboard Pkg
Bags & Sacks
Wrapping Papers
Other Paper Pkg
75
32
32
32
32
32
32
100.0
48.8
48.8
48.8
48.8
48.8
48.8
95.3
40.7
40.7
40.7
40.7
40.7
40.7
100.0
48.1
48.1
48.1
48.1
48.1
48.1
100.0
75.8
75.8
75.8
75.8
75.8
75.8
95.1
40.6
40.6
40.6
40.6
40.6
40.6
Plastics Pkg
Soft Drink Bottles
Milk Bottles
Other Containers
Bags & Sacks
Wraps
Other Plastics Pkg
68
47
32
16
16
16
100.0
71.7
48.8
24.4
24.4
24.4
86.4
59.7
40.7
20.3
20.3
20.3
100.0
70.7
48.1
24.1
24.1
24.1
100.0
100.0
75.8
37.9
37.9
37.9
86.3
59.6
40.6
20.3
20.3
20.3
Wood Pkg
37
56.5
47.0
55.6
87.7
46.9
16
65
24.4
99.2
20.3
82.6
24.1
97.8
37.9
100.0
20.3
82.5
OTHER WASTES
Food Wastes
Yard Waste
16
Results
The percentage distributions of products for each of the five waste streams, as
shown in the Appendix, were applied to a city of 100,000 people using the 2007
mean per capita MSW production of 765 kg/yr from Office of Solid Waste (2008).
The MSWFLOW model outputs are shown in Figures 1-4 which display landfill mass,
landfill volume, power output from a WTE facility and landfill methane (CH4)
generation respectively. The WARM model outputs are shown in Figures 5 & 6
displaying energy savings and reduction in GHG emissions respectively. Because the
WARM model is a comparative model, each scenario is displayed as compared to the
“Landfill All” scenario.
Landfill Mass and Volume
The landfill mass and volume (Figures 1 & 2) as effected by a given management
scenario show little variation among the waste streams. However, both landfill mass
and volume spike up to 28% over the median for Sonoma County for each WTE
scenario (WTE All, Compost + WTE, Recycle + WTE). This is likely due to the
unusually high level of steel in the Sonoma County waste stream as noted in Table 2,
due to the contributions of waste from construction and demolition sources. For the
NYC waste stream, the “Recycle + Landfill” scenario lowers landfill mass 11% and
landfill volume 15% from the median. Likewise, for the NYC waste stream the
“Recycle + WTE” scenario lowers both landfill mass and volume 20% from the
median. These results stem from the NYC waste stream having a very high
proportion of food waste and a very low proportion of cardboard as shown in Table
2. Because of these factors, the mass and volume recycled in the NYC recycle
scenarios are greater than the mass and volume of the other respective waste
streams.
Compared to the “Landfill All” scenarios, the “Compost + Landfill” scenarios show
mass reductions from 18-24% and volume reductions from 7-10%. This indicates
that a landfill operator would not benefit from a composting option, which would
lower the income from tipping fees with little effect on the size requirements of the
landfill.
The Illinois waste stream exhibits higher landfill volumes for landfill-based
management scenarios. This is likely a result of the Illinois waste stream’s high
plastic content (16.7%), which has a relatively low density.
17
Figure 1. Landfill Mass for a U.S. city of 100,000 as affected by Waste Stream
and Management Scenario.
Landfill Mass
Mass/day (Mg/d)
250
200
Landfill All
WTE All
150
Compost + Landfill
100
Compost + WTE
Recycle + Landfill
50
Recycle + WTE
0
National
Erie Co
Illinois
NYC
Sonoma Co
Figure 2. Landfill Volume for a U.S. city of 100,000 as affected by Waste Stream
and Management Scenario.
Landfill Volume
Volume/day (m^3/d)
600
500
Landfill All
400
WTE All
Compost + Landfill
300
Compost + WTE
200
Recycle + Landfill
100
Recycle + WTE
0
National
Erie Co
Illinois
NYC
Sonoma Co
18
Electrical Power Production
The electrical power output from a WTE facility (Figure 3) indicates power outputs
vary significantly with waste stream. Across the waste streams, the data indicates
that management strategies involving composting may reduce power output by as
much as 10%. Management scenarios that include recycling demonstrate
reductions in power production from 50-60% although, in this case, the effect is
least in Sonoma County and greatest in NYC. The Sonoma County waste stream has
a proportionally high amount of steel and a low amount of cardboard and paper
products as noted in Table 2, due to the waste contribution of construction and
demolition sources. The maximum energy production from WTE for Sonoma
County is 7% smaller than the median due to the lack of paper products, which burn
very well, and abundance of steel, which does not burn at all. The power produced
for the “Recycle + WTE” scenario is nearly equal across the waste streams, excepting
an 11% dip below the median for NYC. This demonstrates how Sonoma County’s
unusual waste stream strongly effects its maximum energy production but has little
effect on energy production for a recycling scenario, due to the high potential for
steel and paper to be recycled. This indicates that many of the products that have
the potential for energy production also have the greatest potential for recycling.
The 11% drop for the “Recycle + WTE” scenario for NYC correlates with the data
shown in Figures 1 and 2, that the NYC recycle scenario involves recycling a high
proportion of products that have high energy content.
The Illinois waste stream has the greatest potential for energy production. This
stems from their relatively high amount of plastic products and textiles as seen in
Table 2, which have larger heating values than most other MSW products.
Figure 3. Electrical Power produced by waste combustion (WTE) for a U.S. city
of 100,000 as affected by Waste Stream and Management Scenario.
Power
160
Power (MW/d)
140
120
100
WTE All
80
Compost + WTE
60
Recycle + WTE
40
20
0
National
Erie Co
Illinois
NYC
Sonoma Co
19
Potential Landfill Methane Production
The MSWFLOW model calculates the potential amount of landfill methane
production (Figure 4) which assumes complete anaerobic degradation of the waste
stream and 100% gas collection efficiency. The NYC waste stream has the lowest
potential methane production for each scenario. Table 2 demonstrates that the NYC
waste stream has a higher proportion of food waste and a lower proportion of
cardboard. To investigate the possible implications of high food waste and low
cardboard, the MSWFLOW model was applied using the National waste stream, and
the proportions were modified for increasing levels of food waste and decreasing
levels of cardboard. The potential landfill methane decreased with increases in food
waste and with decreases in cardboard. This establishes that the NYC waste stream
has the lowest potential methane production due to its high food waste and low
cardboard content.
As compared to the “Landfill All” scenario, the potential methane production is
reduced 15-19 % with the addition of composting and 54-68% with recycling. In
both cases, reduction is largest in the NYC waste stream.
Figure 4. Potential Methane (CH4) generation for a U.S. city of 100,000 as
affected by Waste Stream and Management Scenario compared to Landfilling
Landfill Methane
Volume/day (m^3/d)
70.0
60.0
50.0
40.0
Landfill All
30.0
Compost + Landfill
Recycle + Landfill
20.0
10.0
0.0
National
Erie Co
Illinois
NYC
Sonoma Co
20
Energy Requirements
The energy savings (Figure 5) compared to landfilling vary greatly with waste
stream. The life-cycle analysis includes energy uses from manufacturing through
disposal, and as a result, depends highly on the composition of the waste stream.
Each waste stream does follow similar trends for the modeled scenarios. For all of
the waste streams, the “Recycle + WTE” scenario achieved the highest energy
savings, followed closely by the “Recycle + Landfill” scenario, indicating that a waste
management strategy that maximizes recycling has major potential to impact
energy savings. This is due to the life-cycle analysis, whereby making products out
of recycled materials, energy can be saved that would otherwise have been used to
create the product from virgin inputs.
The “Compost + Landfill” scenario achieves few energy savings. Since these results
are compared to a total landfill scenario, this indicates that composting has a
negligible effect on energy saving. Further, when comparing the “WTE All” scenario
with the “Compost + WTE” scenario, the compost has a negative impact. This is due
to the energy that would have come from combustion of the compostable materials
and replacement of the burning of fossil fuels. Thus, composting cannot be
considered an energy-saving MSW management strategy.
The Illinois waste stream achieves the highest energy savings. These results are
similar to those observed in Figure 3 where Illinois also had the highest electrical
power production. This is in a large part due to the Illinois waste stream containing
high amounts of plastic and carpets. These materials have high energy savings per
ton recycled, as shown in Appendix Figure A-1, and also have high heating values.
Figure 5. Reduction in energy requirements for a U.S. city of 100,000 as
affected by Waste Stream and Management Scenario compared to Landfilling
Energy Savings
National
Erie Co
Illinois
NYC
Sonoma Co
900
800
700
MWh/d
600
500
400
WTE ALL
Compost + Landfill
Compost + WTE
300
Recycle + Landfill
200
Recycle + WTE
100
0
-100
21
GHG Emissions
The reductions in GHG emissions (Figure 6) across waste streams follow the pattern
observed by energy savings in Figure 5. This is not surprising as that most energy
use is related to burning of fossil fuels, which also produces GHG. For each waste
stream, the “Recycle + Landfill” and “Recycle + WTE” scenarios achieve the greatest
reduction in GHG emissions. This indicates that, as with results found in energy
savings, recycling has the greatest impact on reduction of GHG emissions. This is
due to the life cycle analysis whereby making products out of recycled materials
GHG’s can be saved that would otherwise have been emitted if the product were
made from virgin inputs.
The “Compost + Landfill” scenarios have relatively small impacts across the waste
streams excepting NYC. This can be explained by the high amount of food discards
(20.5%) in the NYC waste stream. This causes the composting scenarios to have a
more significant effect than they would otherwise, because the WARM model
calculates carbon storage in the soil from composting. This high proportion of food
waste is also responsible for the lower maximum achievable reduction in GHG
emissions, because the waste streams are of equal mass, the NYC waste stream will
have less recyclables as result of the waste stream having more food waste.
The Illinois waste stream has the highest potential for reduction in GHG emissions,
which correlates directly with the data observed for energy savings. This is due to
the high proportion of plastics and carpets in the Illinois waste stream. These
materials have a large difference in GHG emissions depending on whether they are
made from virgin vs. recycled inputs.
Figure 6. Reduction in GHG emissions for a U.S. city of 100,000 as affected by
Waste Stream and Management Scenario compared to Landfilling
Reduction in GHG Emissions
90.0
National
Erie Co
Illinois
NYC
Sonoma Co
MTCE/d
80.0
70.0
WTE ALL
60.0
Compost + Landfill
50.0
Compost + WTE
40.0
Recycle + Landfill
30.0
Recycle + WTE
20.0
10.0
0.0
22
Custom Scenario
Using the results on how waste streams respond to the different waste management
scenarios, a scenario was constructed for use with the national average waste
stream. This scenario is constructed with the purpose of maximizing reduction in
GHG emissions and achieving energy savings. The results demonstrate that the best
way to achieve these goals is to maximize recycling and WTE as alternatives to
landfilling. With this in mind, this scenario has modified the recycling rates for each
material, setting the recycling rates to over 95% for those products that yield the
greatest reduction in GHG emissions and improve energy savings. These targeted
materials were carpet, plastics, magazines/third class mail, corrugated boxes and
aluminum. By targeting these materials for recycling and maximizing WTE, both the
per day reduction in GHG emissions and the per day energy savings increase 10%.
The amount of power produced in a WTE facility almost doubles, and the overall
recycling rate drops to 53%.
Discussion
The MSWFLOW model produces results in absolute form, calculating waste flow
characteristics based on the composition of the waste input. The results from the
MSWFLOW model demonstrate that many waste streams have very similar
responses to a given waste management strategy and noticeable deviations occur
with major variations in the waste stream. In contrast, the WARM model produces
results in a comparative form only, utilizing a life-cycle analysis to perform its
calculations. The results from the WARM model show a relatively high degree of
variability, by attributing the energy required and the GHG emissions to the
materials as calculated in the life-cycle analysis.
In the interest of energy savings and reduction of GHG emissions, maximizing
recycling rates is the best management strategy. Building and operating an MRF can
be a costly undertaking, due to a combination of technology and manpower
requirements for the essential sorting process. The carbon credits from recycling
could be sold to a power or processing plant that would face costly remediation
measures to achieve the mandated reduction in GHG emissions. A waste stream
similar to that of NYC, particularly containing a high proportion of food discards,
will be able to achieve relatively high reduction in GHG emissions though a waste
management strategy that includes composting. Composting efforts have little
impact on GHG emissions otherwise, and have essentially no impact on energy
savings.
23
Conclusions
1. One purpose of this study is to allow waste management planners to
incorporate reduction of GHG emissions and energy savings into waste
management strategies. For each waste stream, the two scenarios that
included recycling exhibited the largest reduction in GHG emissions and the
greatest energy savings. These scenarios also produced the least amount of
power from combustion and landfill methane. Composting has the least
significant impact on reduction of GHG emissions and had energy savings.
2. The Illinois waste stream exhibits the maximum potential for reduction in
GHG emissions, energy savings and power produced from combustion from
the scenarios that maximize recycling. This is due in a large part to the high
proportion of plastics and carpets. Therefore, on a national scale, the local
municipalities that have matching MSW characteristics could be targeted for
recycling programs to achieve the greatest overall GHG emission reduction
and energy savings.
3. The NYC waste stream represents a waste stream with a high proportion of
food waste. Initial expectations were that the high food waste would result
in high landfill methane. The results were contrary to these expectations,
demonstrating that a high proportion of food waste favors carbon dioxide
production over methane production. The high food waste proportion of
20.5% creates a significant potential for reduction of GHG emissions by
composting, as compared to average waste streams.
4. The life cycle analysis provided by the EPA clearly shows how required
energy inputs can differ between materials, as well has the differences
between materials being made from virgin vs. recycled inputs. This study
demonstrates the power that these materials can have as components of a
waste stream. As demonstrated by the customized scenario, the most
favorable alternative for a waste management strategy is to achieve
maximum recycling for the short list of materials that have significant energy
savings (aluminum, carpet, plastics).
24
Works Cited
Department of Sanitation New York City (DSNY), NYC Waste Characterization Study,
2005,
http://www.nyc.gov/html/nycwasteless/html/recycling/waste_char_study.shtml
Haith, D.A. 1998. Materials balance for municipal solid waste management. Journal
of Environmental Engineering 124(1):67-75
Illinois Recycling Association, 2009, Illinois Commodity/Waste Generation and
Characterization Study, Oak Park, IL
NorthEast-Southtowns (N.E.S.T.) 2003. Solid Waste Management Board, NorthEast
Southtowns Regional Solid Waste Management Plan, Buffalo, NY, March 21, 2003
Office of Solid Waste. 20078. Municipal Solid Waste in the United States:2007 Facts
and Figures. U.S. Environmental Protection Agency, Washington D.C.
Office of Solid Waste and Emergency Response (OSWER). (1996). "Characterization
of municipal solid waste in the United States: 1995 update." Rep. No. EPA530-R-96001. U.S. Environmental Protection Agency, Washington, D.C.
R3 Consulting Group. 2003. Sonoma County 2003 Solid Waste Generation Study.
Sonoma County Waste Management Agency, Santa Rosa. CA.
U.S. EPA Climate Change Division. 2006. Solid waste management and greenhouse
gases: A life-cycle assessment of emissions and sinks (3rd ed). EPA530-R-06-004.
U.S. Environmental Protection Agency, Washington DC.
25
Appendix
Table A-1. MSW input percentages for the MSWFLOW model
National
Erie Co, NY
Illinois
NYC, NY
Sonoma Co, CA
% by mass
% by mass
% by mass
% by mass
% by mass
DURABLES
Major Appliances
1.39
2.37
0.86
1.38
0.58
Small Appliances
0.54
0.91
0.39
0.53
1.40
Furniture & Furnishings
3.60
2.03
1.01
2.41
7.92
Carpets & Rugs
1.21
0.51
4.46
1.43
0.77
Rubber Tires
1.89
1.49
1.32
0.32
2.14
Batteries, Lead-Acid
0.98
0.73
0.73
0.08
0.23
Misc Durables
7.88
0.00
0.00
0.00
0.00
Newspapers
4.23
6.84
6.15
8.75
2.84
Books
0.52
0.69
0.39
0.72
0.41
Magazines
0.98
1.04
3.00
1.68
0.58
Office Papers
2.31
3.04
2.06
1.04
2.72
Directories
0.27
0.36
0.20
0.37
0.22
Standard (A) Mail
2.27
3.07
1.72
3.89
3.63
Other Commercial Printing
2.41
3.25
1.82
4.12
1.94
Tissue Paper & Towels
1.35
1.40
1.02
2.30
1.08
Paper Plates & Cups
0.50
0.52
0.38
0.50
0.41
Plastic Plates & Cups
0.33
0.48
0.88
0.59
0.36
Trash Bags
0.41
0.60
1.18
1.13
0.45
Disposable Diapers
1.44
2.08
2.13
3.71
0.49
Other Nonpackaging Paper
1.71
2.53
1.30
0.80
1.38
Clothing & Footware
3.20
3.87
2.38
3.60
2.05
Towels, Sheets, Pillowcases
0.42
0.18
0.85
1.58
0.27
Misc Nondurables
1.60
0.00
0.00
0.00
0.00
NONDURABLE GOODS
(Continued)
26
Table A-1. (Continued)
National
Erie Co, NY
Illinois
NYC, NY
Sonoma Co, CA
% by mass
% by mass
% by mass
% by mass
% by mass
CONTAINERS & PACKAGING
Glass Pkg
Beer & Soft Drink Bottles
2.97
4.04
2.64
3.50
1.84
Wine & Liquor Bottles
0.64
0.88
0.57
0.76
0.40
Food & Other Bottles & Jars
0.80
1.10
0.72
0.95
0.50
Beer & Soft Drink Cans
0.00
0.00
0.00
0.00
0.00
Food & Other Cans
0.94
1.55
1.23
1.39
3.05
Other Steel Pkg
0.09
0.16
2.51
2.25
11.47
Beer & Soft Drink Cans
0.55
0.97
0.71
0.24
0.18
Other Cans
0.02
0.10
0.05
0.07
0.01
Foil & Closures
0.16
1.07
0.54
0.63
0.13
12.02
12.55
17.26
1.35
10.22
Milk Cartons
0.19
0.23
0.24
0.46
0.15
Folding Cartons
2.13
2.59
1.85
5.12
1.71
Other Paperboard Pkg
0.06
0.07
0.04
0.14
0.05
Bags & Sacks
0.44
0.72
0.33
0.81
0.35
Wrapping Papers
0.00
0.00
0.00
0.00
0.00
Other Paper Pkg
0.54
0.88
0.40
1.29
0.43
Soft Drink Bottles
0.39
0.56
1.04
1.07
0.42
Milk Bottles
0.32
0.46
0.84
0.87
0.34
Other Containers
1.44
2.09
3.84
3.96
1.56
Bags & Sacks
0.39
0.56
0.58
1.07
0.42
Wraps
1.22
1.77
3.27
3.37
1.32
Other Plastics Pkg
1.49
2.16
3.98
4.10
1.61
Wood Pkg
5.47
3.09
0.00
0.22
12.05
Other Misc Pkg
0.12
0.00
0.00
0.00
0.00
Food Wastes
12.18
10.18
13.03
20.54
5.16
Yard Waste
12.56
14.22
6.40
4.90
14.77
1.44
0.00
3.67
0.00
0.00
Steel Pkg
Aluminum Pkg
Paper & Paperboard Pkg
Corrugated Boxes
Plastics Pkg
OTHER WASTES
Misc Inorganic Wastes
27
Table A-2. MSW input percentages for the WARM model
National
Erie Co, NY
Illinois
NYC, NY
Sonoma Co, CA
% by mass
% by mass
% by mass
% by mass
% by mass
Aluminum Cans
0.56
1.08
0.77
0.31
0.19
Steel Cans
0.94
1.55
1.23
1.39
3.05
-
-
-
-
-
Glass
4.41
6.01
3.93
5.21
2.74
HDPE
2.17
2.33
-
1.15
0.29
LDPE
2.46
2.72
-
0.01
1.39
PET
1.45
0.96
-
1.42
0.23
Copper Wire
Corrugated Cardboard
12.02
12.55
17.26
1.35
10.22
Magazines/Third-class Mail
3.26
4.11
4.72
5.57
4.21
Newspaper
4.23
6.84
6.15
8.75
2.84
Office Paper
2.31
3.04
2.06
1.04
2.72
Phonebooks
0.27
0.36
0.20
0.37
0.22
-
-
-
-
-
5.47
3.09
-
0.22
12.05
-
-
-
-
-
Food Scraps
12.18
10.18
13.03
20.54
5.16
Yard Trimmings
12.56
14.22
6.40
4.90
14.77
Grass
-
-
-
-
-
Leaves
-
-
-
-
-
Branches
-
-
-
-
-
Mixed Paper (general)
Mixed Paper (primarily
residential)
Mixed Paper (primarily from
offices)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
Mixed Metals
-
-
-
-
-
Mixed Plastics
-
-
13.54
-
-
Mixed Recyclables
-
-
-
-
-
Mixed Organics
-
-
-
-
-
Textbooks
Dimensional Lumber
Medium-density Fiberboard
Mixed MSW
-
-
-
-
-
1.21
0.51
4.46
1.43
0.77
Personal Computers
-
-
0.45
0.32
-
Clay Bricks
-
-
-
-
-
Concrete1
-
-
-
-
-
Fly Ash2
-
-
-
-
-
Tires3
1.89
1.49
1.32
0.32
2.14
Total
67.39
71.02
75.52
54.30
62.98
Carpet
28
Figure A-1. Energy Savings per Ton Recycled* (U.S. EPA Climate Change Division,
2006)
*Assumes recycled materials would otherwise have been landfilled. Aggregate
refers to concrete recycled as aggregate.
29