^F|ll EPA/600/R-019/094 | June 2019 | www.epa.gov/research
otrH
United States
Environmental Protection
Agency
Life Cycle Assessment and
Cost Analysis of Municipal
Wastewater T reatment
Expansion Options for Food
Waste Anaerobic Co-Digestion
Office of Research and Development
Washington, D.C.
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&EPA
United States
Environmental Protection
Agency
Life Cycle Assessment and Cost
Analysis of Municipal Wastewater
Treatment Expansion Options for Food
Waste Anaerobic Co-Digestion
Ben Morelli, Sarah Cashman, Sam Arden
Eastern Research Group, Inc. (ERG)
110 Hartwell Ave
Lexington, MA 02421
Prepared for:
Cissy Ma, Jason Turgeon, Jay Garland, Diana Bless
U.S. Environmental Protection Agency
National Exposure Research Laboratory
National Risk Management Research Laboratory
Office of Research and Development
26 W. Martin Luther King Drive
Cincinnati, OH 45268
May 30, 2019
Draft Report: EPA Contract No. EP-C-16-015, Task Order 0003
Report Revisions: EPA Contract No. EP-C-15-010, Work Assignment 2-32;
EPA Contract No. EP-C-17-041, Work Assignment 1-67
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Although the information in this document has been funded by the United States Environmental
Protection Agency under Contract EP-C-16-015 to Eastern Research Group, Inc. (Draft Report),
EPA Contract No. EP-C-15-010 to Pegasus Technical Services, Inc. (Report Revisions - 1) and
EPA Contract No. EP-C-17-041 to Eastern Research Group, Inc. (Report Revisions - 2), it does
not necessarily reflect the views of the Agency and no official endorsement should be inferred.
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Abstract
ABSTRACT
This study presents results of a life cycle assessment (LCA) and life cycle cost
assessment (LCCA) of a case-study wastewater treatment facility (WWTF) in Massachusetts, the
Greater Lawrence Sanitary District (GLSD). The GLSD WWTF is a medium-sized facility that
treats an average municipal sewage flowrate of 23.5 million gallons per day (MGD). The WWTF
is currently (2017-2018) in the process of installing additional anaerobic digestion (AD) capacity
and a combined heat and power (CHP) system to expand energy recovery. The AD and CHP
expansion project will allow GLSD to accept up to 92,000 gallons per day of source separated
organic (SSO) waste, avoiding landfill and waste-to-energy disposal of food waste, while
considerably boosting biogas production.
A scenario and sensitivity analysis were included to understand the effect of SSO
acceptance rate, AD performance, avoided disposal processes and LCCA parameters on
environmental impact and life cycle cost results. Results associated with two co-digestion
feedstock scenarios were compared to results for baseline (2016) WWTF operation, prior to co-
digestion and the AD and CHP expansion. Results are presented for both a low and base AD
performance scenario. Base results consider avoided food waste disposal processes that
correspond to 2016 end-of-life disposal pathways in Massachusetts, where approximately 68 and
32 percent of food waste were incinerated and landfilled, respectively. The cost analysis
compares the above LCA scenarios across two cost scenarios to establish a low and base
estimate of system operating costs over a 30-year period.
The study develops life cycle inventory data for the GLSD WWTF based on plant
records, engineering design documents and process models of the WWTF. The report presents
results for eight environmental impact categories.
Results demonstrate that adoption of SSO co-digestion in combination with the AD and
CHP expansion project reduce plant-wide environmental impacts and system operating cost in
six of eight environmental impact categories when base AD performance is maintained. Water
use is negative, indicating an environmental benefit in all scenarios due to on-site and industrial
effluent reuse programs. Eutrophication potential is the only impact category that increases
because of anaerobic co-digestion in the base AD performance scenario. Eutrophi cation impact
was found to increase by between 10 and 24 percent, depending upon the scenario.
Results in all other impact categories respond positively (i.e. yielding reductions in net
environmental impact) to anaerobic co-digestion. Reductions in fossil fuel depletion, cumulative
energy demand and global warming potential can be particularly dramatic due to their strong link
with avoided energy products and disposal processes that yield environmental credits within the
analysis. Biogas is a source of non-fossil and low-carbon energy that displaces fossil fuel
consumption in the Northeast Regional grid mix as well as on-site natural gas combustion. Net
present value (NPV) results decrease moderately with AD expansion under the base scenario.
These reductions in system NPV correspond to payback periods for the AD expansion and CHP
installation project of ten to 27 years in the base AD performance scenario, depending on the
cost and SSO acceptance scenario. Payback periods of less than the 30 year analysis period were
not identified within the low AD performance scenario.
l
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List of Acronyms
LIST OF ACRONYMS
AD
Anaerobic digester/digestion
AEC
Alternative energy credit
AP
Acidification potential
ASP
Aerated static pile
BODs
Biological oxygen demand, 5-day
CAS
Conventional activated sludge
cBOD
Carbonaceous biological oxygen demand
CED
Cumulative energy demand
CHP
Combined heat and power
COD
Chemical oxygen demand
DMR
Discharge monitoring report
DO
Dissolved oxygen
DTH
Dekatherms
EOL
End-of-life
EP
Eutrophication potential
EPA
Environmental Protection Agency (U.S.)
ERG
Eastern Research Group, Inc.
FDP
Fossil fuel depletion potential
GBT
Gravity belt thickener
GHG
Greenhouse gas
GLSD
Greater Lawrence Sanitary District
gpd
Gallons per day
GWP
Global warming potential
HP
Horsepower
HRT
Hydraulic retention time
ICE
Internal combustion engine
IPCC
Intergovernmental Panel on Climate Change
ISO
International Standardization Organization
km
Kilometer
kW
Kilowatt
kWh
Kilowatt-hour
LCA
Life cycle assessment
LCCA
Life cycle cost assessment
LCI
Life cycle inventory
LCIA
Life cycle impact assessment
LMOP
Landfill methane outreach program
MA
Massachusetts
MassDEP
Massachusetts Department of Environmental Protection
MCF
Methane correction factor
Mg
Megagram
MGD
Million gallons per day
MLSS
Mixed liquor suspended solids
MSW
Municipal solid waste
MSWDST
Municipal solid waste decision support tool
NIST
National Institute of Standards and Technology
11
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List of Acronyms
NMVOC
Non-methane volatile organic compounds
NPDES
National Pollutant Discharge Elimination System
NPV
Net present value
PM
Particulate matter
PMFP
Particulate matter formation potential
QAPP
Quality assurance project plan
RAS
Return activated sludge
REC
Renewable energy credit
SCADA
Supervisory control and data acquisition
SFP
Smog formation potential
SOTE
Standard oxygen transfer efficiency
SRT
Solids retention time
SSO
Source separated organics
TKN
Total kjeldahl nitrogen
TN
Total nitrogen
TP
Total phosphorus
TPY
Tons per year
TRACI
Tool for the Reduction and Assessment of Chemical and Environmental Impacts
TSS
Total suspended solids
U.S.
United States
U.S. LCI
United States Life Cycle Inventory Database
VOC
Volatile organic compound
vs
Volatile solids
VSR
Volatile solids reduction
vss
Volatile suspended solids
WAS
Waste activated sludge
WTE
Waste-to-Energy
WU
Water use
WWTF
Wastewater treatment facility
LIST OF CHEMICAL SYMBOLS
C
Carbon
ch4
Methane
CO
Carbon monoxide
co2
Carbon dioxide
h2
Hydrogen, gas
h20
Water
h2s
Hydrogen sulfide
no3
Nitrate
N02
Nitrite
N
Nitrogen
n2
Nitrogen, gas
N20
Nitrous oxide/dinitrogen monoxide
nh3
Ammonia
NOx
Nitrogen oxides
111
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List of Acronyms
O3 Ozone
P Phosphorus
P2O5 Phosphorus pentoxide
SOx Sulfur oxides
iv
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Table of Content
TABLE OF CONTENTS
Page
ES. Executive Summary 1
ES.l Introduction 1
ES.2 Methodology 2
ES.2.1 Scenario and Sensitivity Analysis 3
ES.2.2 Life Cycle Inventory Development 4
ES.3 Results Summary 5
ES.2.3 Key Findings 7
1. Introduction and Study Goal 1-1
2. Study Scope 2-1
2.1 Functional Unit 2-1
2.2 System Definition and Boundaries 2-1
2.3 Study Site Description 2-3
2.3.1 New England Case-Study Wastewater Treatment Plant 2-4
2.3.2 Introduction to Waste Scenarios and Sensitivity Analysis 2-7
2.4 Metrics and Life Cycle Impact Assessment Scope 2-8
3. LCI Methodology 3-1
3.1 Data Sources and Modeling Approach 3-1
3.2 Influent Water Quality, Septage and SSO Characteristics 3-1
3.3 GLSD WWTF Life Cycle Inventory Development 3-3
3.3.1 External Waste Processing and Transport 3-5
3.3.2 Influent Pump Station 3-5
3.3.3 Preliminary and Primary Treatment 3-5
3.3.4 Biological Treatment 3-6
3.3.5 Secondary Clarification 3-8
3.3.6 Plant Water and Disinfection 3-8
3.3.7 Sludge Thickening and Dewatering 3-8
3.3.8 Anaerobic Digestion 3-10
3.3.9 On-Site Combustion Units 3-14
3.3.10 Biosolids Pelletization 3-16
3.3.11 Land Application of Pelletized Biosolids 3-17
3.3.12 Effluent Release 3-19
3.3.13 Facilities 3-19
3.3.14 Avoided Waste Processes 3-20
3.4 Background LCI Databases 3-22
3.5 LCI Limitations and Data Quality 3-22
4. LCCA Methodology 4-1
4.1 LCCA Data Sources 4-1
4.2 LCCA Methods 4-1
4.2.1 Total Capital Costs 4-1
v
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Table of Contents
TABLE OF CONTENTS (Continued)
Page
4.2.2 Cost Escalation 4-2
4.2.3 Total Annual Costs 4-2
4.2.4 Net Present Value 4-3
4.2.5 AD and CHP Expansion Payback Period 4-3
4.2.6 LCCA Cost Scenario Parameters 4-4
4.3 Treatment Group and Unit Process Costs 4-5
4.3.1 General Facility and Administration (Full Plant
treatment group) 4-5
4.3.2 Preliminary and Primary Treatment 4-6
4.3.3 Biological Treatment 4-7
4.3.4 Secondary Clarification 4-7
4.3.5 Sludge Thickening and Dewatering 4-8
4.3.6 Plant Water and Disinfection 4-8
4.3.7 Anaerobic Digestion and CHP 4-9
4.3.8 Biosolids Pelletization and Sale 4-10
5. LCA and LCCA Results by Treatment Group 5-1
5.1 Guide to Results Interpretation 5-1
5.2 Eutrophication Potential 5-2
5.3 Cumulative Energy Demand 5-3
5.4 Global Warming Potential 5-5
5.5 Acidification Potential 5-8
5.6 Fossil Depletion Potential 5-9
5.7 Smog Formation Potential 5-10
5.8 Particulate Matter Formation Potential 5-10
5.9 Water Use 5-11
5.10Life Cycle Cost Assessment 5-12
6. Scenario and Sensitivity Analysis 6-1
6.1 Anaerobic Digestion Performance 6-1
6.2 SSO Avoided End-of-Life Disposal 6-4
6.3 Summary Results - All Impact Categories and EOL Scenarios 6-8
6.4 Normalized LCIA Results 6-13
6.5 LCCA Cost Scenarios 6-14
7. Conclusions 7-1
8. References 8-1
vi
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List of Appendices
LIST OF APPENDICES
Appendix A: Composting and Land Application of Food Waste: A Comparison with
Anaerobic Co-Digestion at a Wastewater Treatment Facility
Appendix B: Detailed LCI Calculations and Background Information
Appendix C: LCCA Supporting Information and Detailed Results
Appendix D: LCIA Process Results
Appendix E: Data Quality Documentation
vii
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List of Tables
LIST OF TABLES
Page
Table ES-1. Environmental Impact and Cost Metrics 2
Table ES-2. Feedstock Scenario Waste Treatment Volumes (gpd) 3
Table ES-3. Anaerobic Digestion Performance Scenario Parameters 3
Table ES-4. Midpoint Impacts for the Baseline and Feedstock-AD Performance Scenario
(per m3 wastewater treated) 7
Table 2-1. Average Influent Composition of GLSD WWTF 2-1
Table 2-2. New England Electrical Grid Mix 2-2
Table 2-3. Typical Biogas Composition 2-5
Table 2-4. Feedstock Scenario Waste Treatment Volumes (gpd) 2-7
Table 2-5. Anaerobic Digestion Performance Scenario Parameters 2-8
Table 2-6. Environmental Impact and Cost Metrics 2-9
Table 2-7. Description of LCA Impact Categories 2-9
Table 2-8. Assignment of Unit Processes to Treatment Group for Results Presentation 2-11
Table 3-1. Septage, Municipal Solids and SSO Characteristics 3-2
Table 3-2. Scenario Effluent Composition and Permit Requirements 3-2
Table 3-3. 2016 Plant Electricity Use Allocated to Unit Processes 3-4
Table 3-4. Transport Calculations for Incoming External Waste and SSO 3-5
Table 3-5. Primary Clarifier Operational Parameters (GPS-X™ output) 3-6
Table 3-6. Aeration Tank Standard Oxygen Transfer Efficiency 3-7
Table 3-7. Biological Treatment Operational Parameters (GPS-X™ output) 3-7
Table 3-8. Secondary Clarifier Operational Parameters (GPS-X™ output) 3-8
Table 3-9. Thickening and Dewatering Annual Electricity Consumption (kwh) 3-9
Table 3-10. Thickening and Dewatering Operational Parameters (GPS-X™ Output) 3-9
Table 3-11. Anaerobic Digester Design and Operational Parameters 3-10
Table 3-12. Anaerobic Digestion Performance Scenarios Parameters and Biogas
Production 3-12
Table 3-13. Facility Energy Demand and Production - Base AD Scenario 3-13
Table 3-14. Facility Energy Demand and Production - Low AD Scenario 3-13
viii
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List of Tables
LIST OF TABLES (Continued)
Page
Table 3-15. Flaring Emissions, Short Tons Per Year (TPY) and per m3 Biogas 3-15
Table 3-16. CHP Engine Emissions, Short Tons Per Year (TPY) and per m3 Biogas 3-15
Table 3-17. Glycol Boiler Emissions, Short Tons Per Year (TPY) and per m3 Biogas 3-16
Table 3-18. Pellet Drier Emissions, Short Tons Per Year (TPY) and per m3 Biogas 3-16
Table 3-19. Pellet Production and Nutrient Content 3-17
Table 3-20. Biosolid Fertilizer Pellet Specifications 3-17
Table 3-21. Comparison of Chemical and Pelletized Biosolid Nutrient Applications 3-18
Table 3-22. Estimated Agricultural Emissions 3-18
Table 3-23. Effluent Emissions by Feedstock Scenario 3-19
Table 3-24. National and Massachusetts Average Landfill Gas Management Practice1 3-20
Table 3-25. National and Massachusetts Average WTE Facility Specifications 3-21
Table 3-26. Cost Data Quality Criteria 3-23
Table 3-27. Life Cycle Inventory Data Quality Criteria1'2 3-23
Table 4-1. Capital Costs of the Anaerobic Digester and CHP Expansion Project 4-4
Table 4-2. Low and Base Cost Scenario Parameters 4-4
Table 4-3. LCCA Treatment Groups 4-5
Table 4-4. Annual Cost Summary by Feedstock Scenario for the Full Plant Treatment
Group - Base Cost Scenario 4-6
Table 4-5. Cost Summary by Feedstock Scenario for the Preliminary and Primary
Treatment Group - Base Cost Scenario 4-7
Table 4-6. Annual Cost Summary by Feedstock Scenario for the Biological Treatment
Group - Base Cost Scenario 4-7
Table 4-7. Annual Cost Summary by Feedstock Scenario for the Secondary Clarifier
Treatment Group - Base Cost Scenario 4-8
Table 4-8. Annual Cost Summary by Feedstock Scenario for the Sludge Thickening and
Dewatering Treatment Group - Base Cost Scenario 4-8
Table 4-9. Annual Cost Summary by Feedstock Scenario for the Plant Water and
Disinfection Treatment Group - Base Cost Scenario 4-8
Table 4-10. Annual Cost Summary by Feedstock Scenario for the AD and CHP
Treatment Group - Base Cost Scenario 4-10
IX
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List of Tables
LIST OF TABLES (Continued)
Page
Table 4-11. Annual Cost Summary by Feedstock Scenario for the AD and CHP
Treatment Group - Base Cost Scenario 4-11
Table 6-1. Net Impact Results for all Feedstock and AD Performance Scenarios (per m3
Wastewater Treated) 6-9
Table 6-2. Relative Impact Results for all Feedstock and AD Performance Scenarios
(Relative to Baseline Scenario) 6-11
Table 6-3. 2008 U.S. Normalization Factors and Per Capita Annual Impacts 6-13
Table 6-4. Estimated Annual Contribution of Municipal Wastewater Treatment Per
Capita Impact in Seven Impact Categories 6-14
Table 6-5. AD and CHP System Payback Period (years) 6-15
x
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List of Figures
LIST OF FIGURES
Page
Figure ES-1. Presentation of co-digestion scenario LCIA results relative to baseline
(2016) LCIA impacts 6
Figure 2-1. General system boundaries for case-study wastewater treatment plant 2-3
Figure 2-2. Process flow diagram of Greater Lawrence Sanitary District wastewater
treatment facility 2-6
Figure 3-1. Allocation of electricity to process units 3-3
Figure 5-1. Eutrophication potential results by treatment group 5-3
Figure 5-2. Cumulative energy demand results by treatment group 5-4
Figure 5-3. Cumulative energy demand results by process category 5-5
Figure 5-4. Global warming potential results by treatment group 5-6
Figure 5-5. Global warming potential results by process category 5-7
Figure 5-6. Acidification potential results by treatment group 5-8
Figure 5-7. Fossil depletion potential results by treatment group 5-9
Figure 5-8. Smog formation potential results by treatment group 5-10
Figure 5-9. Particulate matter formation potential results by treatment group 5-11
Figure 5-10. Water use results by treatment group 5-12
Figure 5-11. Base life cycle costs by cost category for the case-study wastewater
treatment facility 5-13
Figure 6-1. Eutrophi cation potential by treatment group for all feedstock and AD
performance scenarios 6-2
Figure 6-2. Global warming potential by treatment category for all feedstock and AD
performance scenarios 6-3
Figure 6-3. Cumulative energy demand by treatment group for all feedstock and AD
performance scenarios 6-4
Figure 6-4. Global warming potential results by treatment group for all feedstock and
avoided EOL scenarios 6-6
Figure 6-5. Cumulative energy demand by treatment group for all feedstock and avoided
EOL scenarios 6-7
Figure 6-6. Life cycle cost assessment summary showing results for each Feedstock-AD
performance scenario by cost scenario 6-16
XI
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Executive Summary
ES. EXECUTIVE SUMMARY
ES.l Introduction
Communities and states throughout the United States (U.S.) are leveraging diverse
strategies to manage and transform waste streams to avoid landfilling or incineration by
increasing recycling and alternative beneficial uses (U.S. EPA 2017a). The waste ban on organic
materials disposed of by large commercial and industrial waste generators in Massachusetts is a
specific example of the strategies available (Commonwealth of Massachusetts 2017a). The waste
ban motivates institutions to compete and cooperate to identify and enact beneficial alternative
disposal methods for diverted organic materials. The U.S. Environmental Protection Agency
(U.S. EPA) is working with states to develop best practices based on local experience, providing
guidance and objective information that other communities can use to make important
management decisions.
This study investigates the potential benefits and burdens of anaerobically digesting
diverted organic materials in the context of a case-study wastewater treatment facility (WWTF)
in Massachusetts, the Greater Lawrence Sanitary District (GLSD). Life cycle assessment (LCA)
and life cycle cost assessment (LCCA) tools were used to examine how the environmental
impacts and cost of wastewater treatment are affected when large-scale co-digestion of organic
waste is introduced to an existing WWTF. The organic waste is expected to be primarily fruit
and vegetable waste, referred to as source separated organics (SSO), from regional commercial
and institutional sources.
The GLSD WWTF treats municipal sewage and septic waste for several communities in
Massachusetts. The plant treats an average flowrate of approximately 23 million gallons per day
(MGD), with a permitted capacity of 52 MGD. The treatment process uses primary
sedimentation, conventional activated sludge (CAS) preceded by an anoxic zone and secondary
clarification to meet biochemical oxygen demand (BODs) and total suspended solids (TSS)
permit requirements. The facility was not designed for nutrient removal and has no permit
requirements for nitrogen or phosphorus. Sludge processing at the facility consists of dewatering,
anaerobic digestion (AD) and biosolids drying and pelletization. Pelletized biosolids are used
locally as an agricultural amendment. In response to the Massachusetts organic waste ban, the
GLSD WWTF is undergoing a series of renovations to increase AD capacity and expand on-site
energy recovery with the installation of a combined heat and power (CHP) system.
This study's objectives are to:
• Calculate the baseline environmental benefits and burdens of wastewater treatment
with AD for a typical mid-sized WWTF;
• Quantify the comparative environment benefits and burdens associated with
expanding AD capacity for the co-digestion of SSO;
• Determine the energy recovery potential of AD, and evaluate the environmental and
cost benefits of offsetting external electricity and heat generation and alternative
organic waste disposal methods such as landfilling or incineration for waste-to-energy
(WTE); and
ES-1
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Executive Summary
• Determine the life cycle costs associated with the upgraded treatment plant over a 30-
year timespan, compare to the baseline scenario prior to co-digestion and calculate a
discounted payback period for the AD expansion and CHP project.
ES.2 Methodology
The study employs standard LCA and LCCA methods to simultaneously understand the
environmental and economic impacts of expanding AD capacity and energy recovery for the co-
digestion of SSO. The analysis complies with the guidelines established for conducting an LCA
study in ISOs 14040 and 14044 (ISO 2006a; ISO 2006b). The LCCA results were generated
using methods developed by the National Institute of Standards and Technology (NIST) (Fuller
and Petersen 1996).
A scenario analysis was used to characterize the energy recovery potential for two co-
digestion scenarios at two levels of AD performance. Impact results for the co-digestion and AD
performance scenarios were compared against a historical (2016), baseline scenario that is
representative of typical plant operations prior to co-digestion. The studies functional unit is the
treatment of one cubic meter (m3) of municipal wastewater. The acceptance of SSO material has
a negligible effect on the volume of waste treated by the facility and was therefore excluded from
the definition of the functional unit.
Table ES-1 summarizes the impact category metrics calculated for each scenario. Most of
the life cycle impact assessment (LCIA) metrics were estimated using the Tool for the Reduction
and Assessment of Chemical and environmental Impacts (TRACI), version 2.1 (Bare et al. 2003;
Bare 2011). Global warming potential was estimated using the 100-year characterization factors
provided by the Intergovernmental Panel on Climate Change (IPCC) 4th Assessment Report
(Pachauri and Rei singer 2007). The ReCiPe LCIA method was used to characterize water use
and fossil fuel depletion potential (Goedkoop et al. 2009). To provide another perspective on
energy, cumulative energy demand was estimated using a method adapted from the Ecoinvent
Centre (Hischier et al. 2010).
Table ES-1. Environmental Impact and Cost Metrics
Metric
Method
I nit
Cost - Net present value (NPV)
LCCA
U.S. Dollars (2016)
Global warming potential (GWP)
TRACI 2.1
kg CO2 equivalent (eq.)
Eutrophication potential (EP)
TRACI 2.1
kg N eq.
Particulate matter formation potential (PMFP)
TRACI 2.1
kg PM2.5 eq.
Smog formation potential (SFP)
TRACI 2.1
kg 03 eq.
Acidification potential (AP)
TRACI 2.1
kg S02 eq.
Water use (WU)
ReCiPe
m3
Fossil fuel depletion potential (FDP)
ReCiPe
kg oil eq.
Cumulative energy demand (CED)
Ecoinvent
MI
ES-2
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Executive Summary
ES.2.1 Scenario and Sensitivity Analysis
A scenario analysis was used to generate and compare impact results for two co-digestion
and AD performance scenarios against a historical (2016), baseline scenario that is representative
of typical plant operations prior to co-digestion. Table ES-2 lists the volume, in gallons per day
(gpd), of solid streams destined for digestion in each of the three feedstock scenarios. Source
separated organics are received by the facility and pumped directly into the digesters from a
temporary holding tank. The partial and full capacity scenarios were designed to represent 50
percent and 100 percent capacity utilization of the AD capacity available for SSO co-digestion.
Table ES-2. Feedstock Scenario Waste Treatment Volumes (gpd)
\\ nslc Source
linsdinc
I'iti liitl C'iipiicilv
Scenario
l ull Ciipiicily
Scenario
Thickened primary and
WAS
1.7E+5
1.8E+5
1.9E+5
Septage
8.0E+4
8.0E+4
8.0E+4
Trucked-in municipal
solids
8.0E+3
8.0E+3
8.0E+3
SSO
-
4.6E+4
9.2E+4
Each of the co-digestion feedstock scenarios were evaluated for two AD performance
scenarios that determine biogas production and availability for energy recovery. Table ES-3 lists
the main parameters that were varied between the low and base (expected) AD performance
scenarios.
Table ES-3. Anaerobic Digestion Performance Scenario
Parameters
I'sinimclcr Vimc
I'ocilslock
Scenario
Low
Al)
Base
Al)
Percent volatile solids reduction1
(% of influent VS)
Baseline
n.a.
55%
Partial capacity
61%
69%
Full capacity
63%
72%
Biogas yield2 (standard ft3/lb of VS
destroyed)
Baseline
n.a.
17.4
Partial capacity
15.0
18.4
Full capacity
15.0
18.5
Flaring rate
All
20%
10%
1 The low AD performance scenario assumes a 50% volatile solids reduction for
municipal solids and a 70% reduction for SSO.
2 Biogas yield values for the base AD scenario were based on GPS-X™ model
output (Hydromantis 2017). The low AD performance scenario biogas yield
estimate was based on CAPDETWorks™ defaults (Harris, et al. 1982).
The study includes a sensitivity analysis that examines the impact of assumptions related
to avoided food waste end-of-life (EOL) disposal options on environmental impact results. The
main LCA results presented in Section 5 include the effects of avoided landfill disposal and
WTE combustion for the SSO material according to recent (2016) estimates of EOL disposal for
municipal solid waste (MSW) in the state of Massachusetts. It was estimated that 32 percent of
food waste is diverted from landfill disposal, while the remaining 68 percent of food waste
ES-3
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Executive Summary
avoids combustion in WTE facilities. The sensitivity analysis also presents results based on
national average avoided disposal processes and hypothetical scenarios where 100 percent of
food waste is diverted from landfills or WTE facilities.
To evaluate sensitivity to cost parameters, a low cost scenario was evaluated in addition
to the base cost scenario. The cost scenarios vary discount rate and revenue unit costs such as
electricity and SSO tipping fees. Low and base cost parameter values were specified to yield a
reasonable range of estimated life cycle costs. Further detail on specific LCCA parameters is
provided in Section 4.2.6.
Appendix A presents the results of a secondary analysis that directly compares five
alternative food waste treatment and disposal options including, AD, windrow composting,
aerated static pile composting, landfill disposal and WTE combustion.
ES.2.2 Life Cycle Inventory Development
The analysis is a case-study of an existing WWTF, and life cycle inventory (LCI) data
were based primarily on plant records, engineering documents, budget information and
conversations with the plant manager and operations supervisor. Results for the partial and full
capacity scenarios were additionally based on modeling performed in the wastewater treatment
simulation software GPS-X™ (Hydromantis 2017). The main sources of data include:
• Air permit application for the AD and CHP expansion (2016) (Cousens 2016);
• CAPDETWorks™ design and costing software (Hydromantis 2014);
• Discharge Monitoring Report information (2016) (U.S. EPA 2016);
• Engineering report assessing the feasibility of several AD expansion, CHP and SSO
acceptance scenarios (2013) (CDM Smith 2013);
• Engineering energy evaluation (2009) (PES and UTS 2009);
• GPS-X™ model results (Hydromantis 2017).
• Plant purchasing records for: electricity, natural gas, chemicals, potable water and grit
disposal (2016);
• Plant influent and effluent quality and quantity records (2016);
• National Pollutant Discharge Elimination System (NPDES) permit (valid 2010-
publication) (U.S. EPA and MADEP 2005);
• The Municipal Solid Waste Decision Support Tool (MSW DST) (RTI International
2012);
The primary LCI data for wastewater treatment processes include electricity, natural gas
and chemical use. Purchasing records were used to quantify chemical consumption in the
baseline scenario and standard dosage rates were applied to estimate values for the partial and
full capacity scenarios. Biogas production was estimated in the base AD performance scenario
using the GPS-X™ model and was allocated among the potential uses based on a hierarchy
established by GLSD that prioritizes biogas use in the pelletization facility. The quantity of
biogas not required for biosolids drying is combusted in the CHP facility, producing net-metered
electricity and thermal energy available for on-site use. Emissions data from air permit
applications was used to develop LCI data for on-site combustion equipment including the CHP
ES-4
-------
Executive Summary
engines, boiler, pellet drier and flares. The MSW DST model was used to develop LCI data for
avoided EOL disposal options. The U.S. LCI and Ecoinvent 2.2 inventory databases were used to
model background production processes such as electricity generation, chemical and
infrastructure materials, and transportation (Frischknecht et al. 2005; NREL 2012). Data quality
estimates for the developed inventory and background processes are documented in Appendix E.
LCI data compiled from these sources, using the methods described in this report, was
modeled in the openLCA software program version 1.6.3 (GreenDelta 2016).
ES.3 Results Summary
Figure ES-1 presents LCIA results for both co-digestion feedstock and AD performance
scenarios relative to baseline LCIA results. Baseline LCIA results have been standardized to
equal 100 for all impact categories and are depicted in the figure as a dashed red line. Each bar
represents an individual feedstock-AD performance scenario impact result. Bars that extend
above the baseline represent an increase in environmental impact for that category due to the AD
expansion and associated SSO co-digestion. Bars that fall between the baseline and x-axis
represent a net decrease in impact potential because of SSO co-digestion. Bars with a negative
net value, falling below the x-axis, indicate scenario results that yield a net environmental
benefit.
Eutrophication potential (EP) impacts increase in all co-digestion scenarios. Increases of
less than 15 percent also occur for acidification potential (AP) in the partial capacity-low AD
performance scenario, when increased facility material and energy demands are not fully
compensated for by avoided product benefits. Particulate matter formation potential (PMFP) and
smog formation potential (SFP) and fossil depletion potential (FDP) yield slight increases in
environmental impact for the partial capacity-low AD performance scenario for the same reason.
Water use potential varies negligibly between scenarios, due to the results being driven
by effluent reuse which remains constant across scenarios. The WWTF reuses approximately 10
percent of treated effluent to satisfy their own non-potable water demands, avoiding potable
water purchases and impacts. The plant also sells a fraction of treated effluent, approximately 3
percent, to an industrial partner for reuse.
Cumulative energy demand (CED), FDP and global warming potential (GWP) impact
results drop rapidly as more SSO is accepted and as AD performance increases due to increased
energy recovery. Net benefits are possible for all three impact categories when base AD
performance is achieved.
Figure ES-1 also presents relative system net present value (NPV) for the base cost
scenario introduced in Section 4.2.6. Using base cost assumptions, both low AD performance
scenarios yield modest increases in system cost over a 30-year time horizon of between one and
five percent. Base AD performance scenarios yield four and 10 percent reductions in system
NPV for the partial and full capacity feedstock scenarios, respectively. These reductions in
system NPV correspond to payback periods for the AD expansion and CHP installation project
of 27 and 14 years for the partial and full capacity scenarios. Low cost LCCA scenario
assumptions improve system economic performance, reducing system payback periods to 19 and
10 years for the partial and full capacity scenarios, respectively.
ES-5
-------
Executive Summary
150
100
« 50
0
03
| 0
| -50
1 -100
CQ
o -150
8 -200
<3
^ -250
-300
-350
Acronyms: AP - acidification potential, CED - cumulative energy demand, EP - eutrophication potential, FDP - fossil fuel depletion potential,
GWP - global wanning potential, PMFP - particulate matter formation potential, SFP - smog formation potential, WU - water use
Figure ES-1. Presentation of co-digestion scenario LCIA results relative to baseline (2016)
LCIA impacts.
Table ES-4 presents midpoint LCA impact results that corresponded to the relative result
values presented in Figure ES-1. Acidification potential impact is reduced by 35 and 50 percent
by accepting SSO material according to the partial and full capacity-base AD performance
scenario assumptions, respectively. A review of detailed process results, in Appendix D, reveals
that over 80 percent of this impact reduction is due to avoided energy products.
Cumulative energy demand decreases from a maximum of 5.0 MJ per m3 of wastewater
treated in the baseline scenario to a minimum of -6.4 MJ per m3 for the full capacity-base AD
performance scenario. In this scenario the WWTF avoids more energy use than is required for its
own operation, and becomes a net exporter of electricity, producing an annual surplus of over six
million kWh. Eutrophication potential results increase by between 10 and 25 percent across the
analyzed scenarios. The low AD performance scenario was based on the conservative
assumption that 80 percent of nitrogen influent to the digesters is solubilized, thereby returning
to primary and secondary treatment processes. Fossil depletion potential results reveal that the
use of biogas as an energy source leads to a net reduction in fossil fuel consumption. Increased
biogas production attributable to co-digestion and the installation of CHP eliminates the need for
on-site natural gas combustion in both the partial and full capacity scenarios. Avoided energy
products associated with digestion more than offset increased facility energy demand,
substituting natural gas and grid electricity with a non-fossil energy alternative.
Baseline - Prior to co-digestion
Net
increase
Net
decrease
Net
benefit
~ Partial Capacity - Base AD S Partial Capacity - Low AD
~ Full Capacity - Base AD B Full Capacity - Low AD
ES-6
-------
Executive Summary
Table ES-4. Midpoint Impacts for the Baseline and Feedstock-AD Performance Scenario
(per in3 wastewater treated)
l-'iTilslock - A1) I'crl'oniiiiiHT Scenario
1111 p;icl
Ciiloiiorv
I nils
liiisclinc
(iipncilv
- IJjise A l>
I'iirtiiil
- l ow Al)
lull
C'ii|);u-il\
- IJjise Al)
lull
- Low Al)
AP
kg S02 eq
1.0E-3
6.6E-4
1.1E-3
5.4E-2
1.1E-3
CED
MJ
5.0
-1.7
3.7
-6.4
1.2
EP
kg N eq
0.02
0.03
0.03
0.03
0.03
FDP
kg oil eq
0.05
-0.07
0.02
-0.15
-0.04
GWP
kg C02 eq
0.36
0.01
0.19
-0.28
-0.05
PFMP
kg PM2 5 eq
5.4E-5
1.8E-5
5.6E-5
-4.5E-6
4.4E-5
SFP
kg 03 eq
0.02
8.3E-3
0.02
3.7E-3
0.02
WU
m3 H2O
-0.13
-0.12
-0.12
-0.12
-0.12
NPV
Million $(2016)
314
301
329
282
317
Global warming potential decreases from a maximum of 0.36 kg CC>2-eq. per m3 of
wastewater in the baseline scenario to a minimum of -0.28 kg CC>2-eq. per m3 in the full capacity
scenario. Avoided energy production credits are the largest contributor to reductions in net GWP,
yielding an environmental credit of -0.80 kg CO2 eq. per m3 wastewater treated in the full
capacity scenario. The GWP benefits of avoiding landfill disposal are also considerable, -0.33 kg
CO2 eq. per m3 wastewater treated, and are primarily attributable to avoided methane emissions.
The partial and full capacity-base AD performance scenarios yield 50 and 75 percent
reductions in SFP relative to the baseline scenario. Avoided electricity production is responsible
for the greatest reduction in SFP. The partial and full capacity scenarios yield 65 and 110 percent
reductions in PMFP relative to the base feedstock scenario. Review of detailed process results
reveals that avoided natural gas combustion yields the greatest reduction in PMFP.
ES.3.1 key Findings
• Reductions in environmental impact or the generation of environmental benefits are
possible in seven of eight impact categories, except for eutrophication potential,
which increases by between 10 and 25 percent depending upon the scenario.
• While the magnitude of impact reductions and benefits was found to be sensitive to
feedstock scenarios, AD performance scenarios and avoided EOL disposal processes,
the general trend of realizing reduced impact following the introduction of co-
digestion was consistent over the full range of sensitivity scenarios.
• For medium-scale WWTFs with a ready source of SSO, or similar high strength
organic waste, investment in AD capacity and CHP systems provides an opportunity
to reduce net environmental impact, while reducing energy expenditures over time.
• The AD expansion and energy recovery project can yield a reliable economic
payback period for both feedstock scenarios assuming base AD performance.
Economic benefits were not identified under conditions of low AD performance and
capacity utilization.
ES-7
-------
Section 1—Introduction and Study Goal
1. INTRODUCTION AND STUDY GOAL
Communities and states throughout the United States (U.S.) are leveraging diverse
strategies to manage and transform waste streams to avoid landfilling or incineration by
increasing recycling and alternative beneficial uses (U.S. EPA 2017a). The waste ban on organic
materials disposed of by large commercial and industrial waste generators in Massachusetts is a
specific example of the strategies available (Commonwealth of Massachusetts 2017a). The waste
ban motivates institutions to compete and cooperate to identify and enact beneficial alternative
disposal methods for diverted organic materials. The U.S. Environmental Protection Agency
(U.S. EPA) is working with states to develop best practices based on local experience, providing
guidance and objective information that other communities can use to make important
management decisions. These decision-making processes must broadly consider local
perspectives and available financing in addition to environmental objectives.
This report is intended to support that decision-making process. This report will provide
valuable information to wastewater treatment personnel, municipalities and local or state
officials as they look to reduce the environmental impact of the wastewater treatment sector,
identify good opportunities for resource and energy recovery, and seek organic waste disposal
practices that either minimize impact or generate environmental benefits. The report is also
intended to directly benefit the case-study wastewater treatment facility, the Greater Lawrence
Sanitary District (GLSD), as they complete their AD expansion project and transition to its long-
term management.
Several alternative disposal methods for organic waste are common, including food
donation, use as animal feed, composting and anaerobic digestion (AD). This study investigates
the potential benefits and burdens of digesting diverted organic materials in the context of a case-
study wastewater treatment facility in Massachusetts. Life cycle assessment (LCA) and life cycle
cost assessment (LCCA) tools are used to examine how the environmental impacts and cost of
wastewater treatment are affected when large-scale co-digestion of organic waste is introduced to
an existing wastewater treatment plant (WWTF). The organic waste is expected to be primarily
fruit and vegetable waste, referred to as source separated organics (SSO), from commercial and
institutional sources. Side-by-side use of LCA and LCCA techniques allows a broad range of
environmental and economic indicators to be considered, with the aim of facilitating a reasoned
and informed decision-making process that does not unknowingly shift burdens from one
sustainability indicator to another.
LCA is a widely-accepted technique to assess the environmental aspects and potential
impacts associated with products, processes, or services. It provides a "cradle-to-grave" analysis
of environmental impacts and benefits that can better inform and assist in selecting the most
environmentally preferable choice among various options. The steps for conducting an LCA
include (1) identifying goal and scope, (2) compiling a life cycle inventory (LCI) of relevant
energy and material inputs and environmental releases, (3) evaluating the potential
environmental impacts associated with identified inputs and releases and (4) interpreting the
results to help inform decision-making.
LCCA is a complementary process to LCA for evaluating the total economic costs of an
asset by analyzing initial costs and discounted future expenditures over the life cycle of an asset
1-1
-------
Section 1—Introduction and Study Goal
(Varnier and Saidur 2004). It is used to evaluate differences in cost and the timing of costs
between alternative projects.
The GLSD WWTF treats municipal sewage and septic waste for several communities in
Massachusetts. The plant treats an average flowrate of approximately 23 million gallons per day
(MGD), with a permitted capacity of 52 MGD. The treatment process uses primary
sedimentation, conventional activated sludge (CAS) preceded by an anoxic zone and secondary
clarification meet biochemical oxygen demand (BODs) and total suspended solids (TSS) permit
requirements. The facility is not designed for nutrient removal and has no permit requirements
for nitrogen or phosphorus. Sludge processing at the facility consists of dewatering, AD and
biosolids drying and pelletization. Pelletized biosolids are used locally as an agricultural
amendment. In response to the Massachusetts organic waste ban, the GLSD WWTF is
undergoing a series of renovations to increase AD capacity and expand on-site energy recovery.
This study's objectives are to:
• Calculate the baseline environmental benefits and burdens of wastewater treatment
with AD for a typical mid-sized WWTF;
• Quantify the comparative environment benefits and burdens associated with
expanding AD capacity for the co-digestion of SSO;
• Determine the energy recovery potential of AD, and evaluate the environmental and
cost benefits of offsetting external electricity and heat generation and alternative
organic waste disposal methods such as landfilling or incineration for waste-to-energy
(WTE); and
• Determine the life cycle costs associated with the upgraded treatment plant over a 30-
year timespan, compare to the baseline scenario prior to co-digestion and calculate a
discounted payback period for the AD expansion and combined heat and power
(CHP) project.
The metrics planned for use in this assessment are cost and a suite of LCA-related impact
categories in addition to traditional wastewater quality parameters. The life cycle impact
assessment (LCIA) categories include global warming potential, eutrophication potential,
particulate matter formation potential, smog formation potential, acidification potential and fossil
depletion potential. Water use and cumulative energy demand inventory indicators are also
included. The specific impact categories and associated methods considered are introduced in
more detail in Section 2.4.
1-2
-------
Section 2—Study Scope
2. STUDY SCOPE
This study design follows the guidelines for LCA provided by ISO 14040 and 14044
(ISO 2006b; ISO 2006a) and LCCA practices outlined in the National Institute of Standards and
Technology (NIST) guidelines (Fuller and Petersen 1996). The following subsections describe
the scope of the study based on the treatment system configurations selected and the functional
unit used for comparison, as well as the system boundaries, LCIA methods and datasets used.
2.1 Functional Unit
A functional unit provides the basis for comparing results in a LCA. The key
consideration in selecting a functional unit is to ensure the treatment system configurations are
compared on a fair and transparent basis and provide an equivalent end service to the
community. The functional unit for this study is the treatment of one cubic meter of municipal
wastewater with the influent wastewater characteristics shown in Table 2-1. Impact results are
standardized per cubic meter of the 23.5 MGD average flowrate (approximately 32.4 million
cubic meters per year). The quantity of waste treated by the facility varies depending upon the
investigated waste scenario. However, the minor increase in waste volume treated, attributable to
accepted SSO, is not considered in the definition of the functional unit given that its contribution
to facility level volumetric flow is less than 0.5 percent of total waste treated. Waste scenarios
are described in detail in Section 3.2. The main results section presents results per cubic meter of
wastewater treated.
Table 2-1. Average Influent Composition of GLSD WWTF
Chsimclorislic
\ ill III'
I nil
Total Suspended Solids (TSS)
251
mg/L
Volatile Solids (VS)
75%
-
Carbonaceous Biological Oxygen Demand (cBOD)
184
mg/L
Total Kjeldahl Nitrogen (TKN)
35
mg/L N
Ammonia (NH3)
20
mg/L N
Total Phosphorus (TP)
4.85
mg/L P
Nitrite (NO2)
0
mg/L N
Nitrate (NO3)
0
mg/L N
Organic Nitrogen
15
mg/L N
Temperature
15.6
°C
2.2 System Definition and Boundaries
System boundaries include all on-site wastewater and sludge treatment processes
necessary to treat the average flowrate of 23.5 MGD of municipal wastewater. The beginning of
the wastewater treatment system is the influent pump station, which contributes significantly to
the facilities overall energy demand. Also included within the system boundary is final discharge
of the treated effluent and disposal of pelletized biosolids via land application. A general system
diagram that depicts system boundaries for all scenarios is presented in Figure 2-1.
2-1
-------
Section 2—Study Scope
The main inventory elements considered in this study include electricity consumption and
generation, on-site fuel combustion, water use and consumable materials. Only select
infrastructure elements associated with the AD and combined heat and power (CHP) expansion
are modeled to understand the relative impacts from the new infrastructure components.
Infrastructure materials include unit concrete, rebar, excavation and sub-grade coarse aggregate.
All included infrastructure components are expected to have a useful lifespan that extends
beyond the 40-year study timeframe (Harris, et al. 1982), which eliminates the need to consider
material replacement of infrastructure in the environmental analysis. Pumps, electronics, other
in-unit mechanical equipment and end-of life (EOL) disposal of plant infrastructure are excluded
from the system boundary. Other studies have shown that for activated sludge systems
infrastructure and EOL demolition contributions to life cycle energy demand are low as
compared to the operational phase (Emmerson et al. 1995), which provides justification for the
simplified treatment of infrastructure elements. Process greenhouse gas emissions (GHG)
resulting from biological treatment and effluent release, fugitive methane releases from AD and
emissions from pellet land application are estimated and included in the calculation of impacts.
The electrical grid mix for the New England region is used in the analysis and is depicted in
Table 2-2 (van Welie 2017).
Table 2-2. New England Electrical Grid Mix
Kncriiv Source
Percent of (Jriil (ienemtion
Biomass
6.2
Coal
2.4
Natural Gas
50
Hydroelectric
7.1
Nuclear
31
Solar
0.62
Wind
2.4
Reference: (van Welie 2017)
The analysis includes consideration of avoided electricity and heat production associated
with biogas utilization and avoided fertilizer production associated with biosolids pellet land
application. The study also investigates the impact of avoided EOL disposal processes for SSO
such as disposal in a landfill or WTE incineration. The plant reuses approximately 10 percent of
treated effluent for cleaning, chemical delivery and other non-potable uses, avoiding the use of
treated drinking water. A small fraction of treated effluent (approximately 165 million gallons
per year) is purchased for reuse by a local industrial partner. Avoided products and waste
processes lead to the generation of environmental credits, decreasing the environmental impact
of the treatment system. Figure 2-1 shows that production of the constituents that make up the
wastewater such as treated drinking water and human and industrial sources of organic material
are excluded from the system boundary. The environmental impact of generating these materials
is not attributable to wastewater treatment.
2-2
-------
Section 2—Study Scope
End-of-Life Disposal of j
Plant Infrastructure
Sewage System
Electrical and
Mechanical
Infrastructure Production
Influent
Wastewater
Septage
Electricity and
Natural Gas
• Generation
• Distribution
Raw Material
Extraction and
Processing
Chemical
Manufacturing
Select
Infrastructure
Manufacturing
Avoided
Potable
Water
J
1 t
Source Separated Organics,
Municipal Solids
Avoided
Electricity &
Heat
Production
Avoided
Fertilizer
Production
Primary
Treatment
Biological
Treatment
Sludge
Processing
Anaerobic
Digestion
Biosolids
Pelletization
Pellets
Land
Application
T
Effluent Release
Avoided
Waste
KEY
System J
Excluded Unit
Wastewater
Foreground Unit
Background
Boundary ,
Process
Treatment Plant
Processes
Unit Processes
» Emissions to Nature
T - Transport
Figure 2-1. General system boundaries for case-study wastewater treatment plant.
2.3 Study Site Description
The Greater Lawrence Sanitary District (GLSD) WWTF provides wastewater treatment
services for five communities with a combined population of over 200,000 people. The facility
has a design flowrate of 52 MGD and a peak flow capacity of 135 MGD and treats an average
flowrate of 23.5 MGD. The plant also accepts around 90,000 gallons per day (gpd) of trucked in
septage and thickened biosolids from small WWTFs in the region. Thickened septage, primary
sludge and waste activated sludge (WAS) are collectively referred to as municipal solids
throughout this report. The plant has been in operation since 1971 having undergone a series of
updates since initial construction. The existing AD facility began operation in 2002 and is paired
with a thermal drying facility that produces pelletized biosolids for use as an agricultural or
horticultural amendment. The WWTF is required to meet effluent BODs, TSS, pH, chlorine
residual, fecal coliform and dissolved oxygen (DO) permit requirements (U.S. EPA and MADEP
2005).
In 2017, the facility pursued upgrades to expand AD capacity to allow for co-digestion of
SSO. The term SSO refers to organic material that is separated from conventional landfill or
recycling waste streams at the point of generation. The SSO for the GLSD WWTF is an
engineered feedstock composed primarily of fruit and vegetable waste that undergoes additional
processing steps to reduce contamination, ensuring consistent composition that will support
stable digester performance. The move to expand co-digestion capacity was driven by a
commercial organics disposal ban implemented in 2014 in Massachusetts (Commonwealth of
2-3
-------
Section 2—Study Scope
Massachusetts 2017a). The facility also began installation of a CHP system in 2017 allowing the
facility to produce both heat and electricity from biogas.
2.3.1 New England Case-Study Wastewater Treatment Plant
2.3.1.1 Primary and Secondary Treatment System
Figure 2-2 is a process flow diagram of the GLSD WWTF. Preliminary treatment
consists of aerated grit chambers, bar screens and two parallel 175' diameter primary clarifiers.
Primary sludge is dewatered in one of four gravity thickeners. Effluent of the preliminary
treatment processes flows via gravity into a completely mixed, plug-flow anoxic reactor before
entering a series of four parallel, plug-flow activated sludge basins. The anoxic reactor is not
intended for denitrification, having no internal recycle, and is operated to minimize nitrification
and associated energy demand. The activated sludge system is operated with a low solids
retention time and mixed liquor suspended solids (MLSS) concentration. Effluent from the
activated sludge tank flows into one of four 175' diameter secondary clarifiers. Waste activated
sludge is sent to two gravity belt thickeners for dewatering before being combined with
thickened primary sludge, trucked in municipal solids and SSO for pumping to the ADs
(depending on scenario). Return activated sludge (RAS) is pumped back to the anoxic unit at a
recycle rate that is approximately 78 percent of the average influent flow rate, or 18 MGD.
2.3.1.2 Anaerobic Digestion
Anaerobic digestion is the main sludge processing step, which uses a methanogenic
process to break down volatile suspended solids (VSS) contained within the sludge. Biogas is
produced from this degradation process. Table 2-3 illustrates a typical composition for biogas
generated at a municipal WWTF. Feedstocks for AD include primary solids, WAS, trucked-in
septage, trucked-in municipal solids and SSO.
The baseline scenario WWTF (2016) is equipped with three 1.5-million gallon
mesophilic AD units. A fourth identical unit is being constructed (2017-2018) to allow for the
co-digestion of regionally supplied SSO waste. Each vessel has a diameter of 85 feet and a
sidewall depth of 38.5 feet. The vessels run at a constant temperature of 95°F. Sludge influent to
the ADs is heated to match the reactor temperature prior to introduction into the vessel. Biogas is
the primary source of thermal energy used to provide process heat for AD and the on-site control
buildings, thereby off-setting natural gas usage. Natural gas use is required to provide a small
portion of facility heat demand in some scenarios. It was assumed that CHP thermal energy
production exceeding facility demand is wasted as there are no current plans to utilize this
energy. Each digester is equipped with a floating cover that allows for a maximum storage
capacity of 146,000 cubic feet (ft3) of biogas. Two bowl-style centrifuges are used to dewater
digested biosolids to reach a target solids concentration of greater than 25 percent (mass
fraction), before entering the thermal drying and pelletization facility.
2-4
-------
Section 2—Study Scope
Table 2-3. Typical Biogas Composition
liio<>;is C omponent
Kxpcclcd Kitniic1
Methane (CH4)
60-70%
Carbon Dioxide (CO2)
30-40%
Water Vapor (H2O)
<7%
Nitrogen (N2)
Hydrogen (H2)
Hydrogen Sulfide (H2S)
1 dry basis, by volume (Wiser et al. 2010)
2.3.1.3 Biosolid Thermal Drying and Pelletization
The drying facility is contracted to accept thickened biosolids to produce a pelletized
agricultural amendment. The maximum facility capacity allows for the daily processing of 38 dry
short tons of thickened biosolids. The facility requires a significant input of thermal and
electrical energy requiring 8,500 MJ and 350 kWh per dry short ton of biosolids processed.
Biogas or natural gas is combusted in a rotary drum dryer, which is used to reduce the moisture
content of biosolids to between two and three percent. Pellets are screened to ensure a consistent
product size and conveyed to a hopper to await shipment. A trucking distance of 121 km was
assumed based on the distance between the GLSD facility and Massachusetts's main agricultural
region. Pellets are spread on agricultural fields, where they replace chemical fertilizers.
Pelletized biosolids contain on average four and two percent nitrogen (N) and phosphorus (P) as
N and P2O5, respectively.
2-5
-------
Section 2—Study Scope
Source Separated Organics
Figure 2-2. Process flow diagram of Greater Lawrence Sanitary District wastewater treatment facility.
Nitrous Oxide
Emissions from
Receiving Stream
2-6
-------
Section 2—Study Scope
2.3.2 Introduction to Waste Scenarios and Sensitivity Analysis
The analysis modeled three waste acceptance scenarios. The baseline scenario represents
2016 conditions prior to commencing acceptance of SSO material for co-digestion and enhanced
energy recovery. The partial and full capacity scenarios are differentiated to isolate the effect of
AD infrastructure capacity utilization on environmental impact and life cycle cost. The
sensitivity analysis examines the effect of AD performance, avoided SSO disposal and cost
parameters on environmental and economic indicators. Appendix A includes the results of an
additional analysis where AD of food waste is compared to composting as an alternative EOL
disposal strategy.
2.3.2.1 Waste Acceptance (Feedstock) Scenarios
Within the baseline feedstock scenario, energy recovery was limited to heat generation
for AD and facility heating and biosolids pelletization. The partial and full capacity feedstock
scenarios, referred to also as the partial and full capacity scenarios, assume that 50 and 100
percent of available SSO capacity are utilized, respectively. The 50 percent utilization scenario
was included to reflect the concern that SSO availability may reduce over time as demand for
organic wastes increase. Table 2-4 illustrates all sources and quantities of organic waste
processed by the facility according to feedstock scenario.
Table 2-4. Feedstock Scenario Waste Treatment Volumes (gpd)
Wsisle Source
liiisoline
I'iti liitl C'iipiicilv
l ull Ciipiicilv
Thickened primary and WAS
1.7E+5
1.8E+5
1.9E+5
Septage
8.0E+4
8.0E+4
8.0E+4
Trucked-in municipal solids1
8.0E+3
8.0E+3
8.0E+3
SSO
-
4.6E+4
9.2E+4
1 Trucked-in municipal solids refers to thickened primary and WAS from small, regional WWTFs.
2.3.2.2 Anaerobic Digester Performance Sensitivity
Two AD performance scenarios were modeled. The parameters used to represent digester
performance are the expected volatile solids reduction (VSR) and biogas yield per unit of
digested volatile solids (VS). The values presented in Table 2-5 refer to the composite waste
stream that is fed to the digesters and considers the variable digestibility of SSO as compared to
average characteristics of the municipal solids stream. The low AD performance scenario
additionally incorporates a low estimate of biogas utilization, assuming that 80 percent of biogas
was used for biosolids pelletization and CHP. The remaining 20 percent of biogas is flared. Low
AD performance parameters were only applied to the partial and full capacity feedstock
scenarios, as the baseline scenario represents historical performance. The base AD performance
scenario assumes 90 percent utilization of produced biogas. Both scenarios consider potential
losses of methane from the floating lid, estimating that five percent of biogas methane is lost to
the atmosphere, untreated (UNFCCC 2012).
2-7
-------
Section 2—Study Scope
Table 2-5. Anaerobic Digestion Performance Scenario Parameters
I'sirsinicler Name
IVeilslock Scenario
Low Al)
Base A1)
Percent VSR1 (% of influent VS)
Baseline
n.a.
55%
Partial Capacity
61%
69%
Full Capacity
63%
72%
Biogas Yield2 (standard f3/lb of VS
destroyed)
Baseline
n.a.
17.4
Partial Capacity
15.0
18.4
Full Capacity
15.0
18.5
1 The low AD performance VSR assumes a 50% reduction for municipal solids and 70% for SSO.
2 Biogas yields for the base AD performance scenario were based on GPS-X™ model output
(Hydromantis 2017). Low AD performance biogas yield was based on CAPDETWorks™ defaults
(Harris, etal. 1982).
Table Acronyms: VS - volatile solids, VSR-volatile solids reduction
2.3.2.3 Avoided Source Separated Organic Disposal Sensitivity
The avoided SSO disposal analysis expands the system boundaries to calculate the net
benefits and burdens of displacing alternative disposal routes for the SSO material used as a
digester feedstock. Baseline results, presented in Section 5, utilize typical SSO disposal routes
for organic material in Massachusetts prior to the landfill ban (MA disposal mix scenario). In the
MA disposal mix scenario, 68 percent of SSO was assumed to be diverted from WTE facilities,
while the remaining 32 percent is diverted from landfills (Fischer 2017). In the national disposal
mix scenario, 18 percent of MSW is combusted in WTE facilities and the remaining 82 percent
is disposed of in landfills (U.S. EPA 2014). Additionally, the sensitivity analysis generates
comparative results excluding the effect of avoided SSO disposal and considering 100 percent
displacement of landfill and WTE disposal pathways. Appendix A includes the results of an
additional analysis where anaerobic digestion of food waste is compared to composting as an
alternative EOL management strategy.
2.3.2.4 Life Cycle Cost Assessment Parameter Sensitivity
To evaluate sensitivity to cost parameters, A low cost scenario was evaluated in addition
to a base (expected) cost scenario. The cost scenarios vary discount rate and revenue unit costs
such as electricity and SSO tipping fees. Low and base cost parameter values are specified to
yield a reasonable range of estimated life cycle costs. Further detail on specific LCCA
parameters is provided in Section 4.2.6.
2.4 Metrics and Life Cycle Impact Assessment Scope
Table 2-6 summarizes the metrics calculated for each scenario. The life cycle cost of
operating the baseline and upgraded system configurations was estimated using standard
approaches for LCCA, with more detail on the costing methodology provided in Section 4. Most
of the environmental metrics were estimated using the Tool for the Reduction and Assessment of
Chemical and environmental Impacts (TRACI), version 2.1 (Bare et al. 2003; Bare 2011).
TRACI is an LCIA method developed by the U.S. EPA to assess local, regional and global
2-8
-------
Section 2—Study Scope
impacts. It incorporates a compilation of methods representing current best practice for
estimating human health and ecosystem impacts based on U.S. conditions and emissions
information provided by LCI data. Global warming potential was estimated using the 100-year
characterization factors provided by the Intergovernmental Panel on Climate Change (IPCC) 4th
Assessment Report (Pachauri and Reisinger 2007). In addition to TRACI, the ReCiPe LCIA
method was used to characterize water use and fossil fuel depletion potential (Goedkoop et al.
2009), impacts which are not included in the current version of TRACI. ReCiPe's water
depletion potential impact assessment method was altered to exclude cooling water and turbine
water for hydroelectricity production. To provide another perspective on energy use and
generation, cumulative energy demand (CED) was estimated using a method adapted from the
Ecoinvent Centre (Hischier et al. 2010). CED includes the energy content of all non-renewable
and renewable energy resources utilized at the WWTF and throughout upstream supply-chains.
As specified in the Ecoinvent CED method, the energy content of biogas was not inventoried, as
it enters the facility as a waste product. Table 2-7 provides a description of each impact category.
Table 2-6. Environmental Impact and Cost Metrics
Metric
Method
I nit
Cost
LCCA
U.S. Dollars (2016)
Global warming potential (GWP)
TRACI 2.1
kg CO2 equivalent (eq.)
Eutrophication potential (EP)
TRACI 2.1
kg N eq.
Particulate matter formation potential
(PMFP)
TRACI 2.1
kg PM2 5 eq.
Smog formation potential (SFP)
TRACI 2.1
kg 03 eq.
Acidification potential (AP)
TRACI 2.1
kg S02 eq.
Water use (WU)
ReCiPe (adapted)
m3
Fossil fuel depletion potential (FDP)
ReCiPe
kg oil eq.
Cumulative energy demand (CED)
Ecoinvent
MI
Table 2-7. Description of LCA Impact Categories
InipiH't/liiMMitorv
Ciileiiorv
Description
I nit
Eutrophication
potential (EP)
Luliopliicalion assesses die potential unpads i'10111 exeessn e
loading of macro-nutrients to the environment and eventual
deposition in waterbodies. Excessive macrophyte growth resulting
from increased nutrient availability can directly affect species
composition or lead to reductions in oxygen availability that harm
aquatic ecosystems. Pollutants covered in this category are
phosphorus and nitrogen based chemical species. The method used
is from TRACI 2.1, which is a general eutrophication method that
characterizes limiting nutrients in both freshwater and marine
environments, phosphorus and nitrogen respectively, and reports a
combined impact result.
kg N eq.
2-9
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Section 2—Study Scope
Table 2-7. Description of LCA Impact Categories
IllipiH't/lllMMllorV
Csitefiorv
Description
I nit
Global warming
potential (GWP)
The GWP impact category represents the heat trapping capacity of
GHGs over a 100-year period. All GHGs are characterized as kg
CO2 eq. using the TRACI2.1 method. TRACIGHG
characterization factors align with the IPCC 4th Assessment Report
for a 100-year time horizon (Pachauri and Reisinger 2007).
kg C02 eq.
Cumulative energy
demand (CED)
The CED inventory indicator accounts for the total use of non-
renewable fuels (natural gas, petroleum, coal and nuclear) and
renewable fuels (such as biomass and hydroelectricity). Energy is
tracked based on the higher heating value of the fuel utilized from
point of extraction, with all energy values summed together and
reported on a MJ basis.
MJ
Water use (WU)
Water use results are based on the volume of fresh water inputs to
the life cycle of products within the WWTF supply-chain. Water
use is an inventory category and does not characterize the relative
water stress related to water withdrawals. This category has been
adapted from the water depletion potential category in the ReCiPe
impact assessment method.
m3
Particulate matter
formation potential
(PMFP)
Particulate matter formation potential results in human health
impacts such as effects on breathing and respiratory systems,
damage to lung tissue, cancer and premature death. Primary
pollutants (including PM2 5) and secondary pollutants (e.g. NOx)
leading to particulate matter formation are characterized as kg
PM2 5 eq. based on the TRACI 2.1 impact assessment method.
kg PM2.5
eq.
Acidification
potential (AP)
Acidification potential quantifies the acidifying effect of substances
on their environment. Acidification can damage or shift sensitive
plant and animal populations and lead to damaging effects on
human infrastructure (i.e. acid rain) (Norris 2003). Important
emissions leading to terrestrial acidification include sulfur dioxide
(SO2), NOx and NH3. Results are characterized as kg SO2 eq.
according to the TRACI 2.1 impact assessment method.
kg S02 eq.
Smog formation
potential (SFP)
Smog formation potential results determine the formation of
reactive substances that cause harm to human respiratory health
and can lead to reduced photosynthesis and vegetative growth
(Norris 2003). Results are characterized in units of kg of ozone
(O3) eq. according to the TRACI 2.1 impact assessment method.
Some key emissions leading to SFP include carbon monoxide
(CO), CH4andNOx.
kg 03 eq.
Fossil fuel
depletion potential
(FDP)
Fossil fuel depletion potential quantifies the consumption of fossil
fuels, primarily coal, natural gas and crude oil. All fuels are
characterized in units of kg oil eq. based on the heating value of the
fossil fuel, according to the ReCiPe impact assessment method.
kg oil eq.
LCIA results are grouped according to treatment group for results presentation in all
LCIA impact categories (Table 2-8).
2-10
-------
Section 2—Study Scope
Table 2-8. Assignment of Unit Processes to Treatment Group for Results Presentation
TiTiiliiK'iil (iroup
I nil Process Vimc
Influent pump station
Influent pump station
Preliminary/primary
Screening and grit removal
Primary clarification
Biological treatment
Pre-anoxic tank
Aeration basins
Secondary clarification
Plant water and disinfection1
Plant water and disinfection
Sludge dewatering
Gravity belt thickener
Gravity thickener
Centrifuge
Anaerobic digestion and CHP2
SSO transport and processing
Anaerobic digestion
Combined heat and power
Pellet drying
Biosolids drying and pelletization
Land application3
Land application of biosolids pellets
Effluent release
Effluent release; to surface water
Building operation
Administration building utilities
1 Includes avoided drinking water treatment
2 Includes avoided electricity and natural gas and avoided SSO EOL disposal
3 Includes avoided fertilizer production
Results are also presented according to process categories for global warming potential
(GWP) and CED. All unit processes in the LCA model were assigned to the process categories
listed below:
• Avoided electricity, CHP.
• Avoided fertilizer.
• Avoided natural gas, CHP.
• Avoided SSO disposal.
• Avoided water.
• Chemicals.
• Effluent release.
• Electricity.
• Grit disposal.
• Infrastructure.
• Land application.
• Natural gas.
• On-site combustion.
• Potable water use.
• Transport.
• Unit process emissions.
2-11
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Section 3—LCI Methodology
3. LCI METHODOLOGY
This chapter covers the data sources, assumptions and parameters used to establish the
LCI values used in this study.
3.1 Data Sources and Modeling Approach
The analysis is a case-study of an existing WWTF that was based primarily on plant
records, engineering documents, budget information, conversations with the plant manager and
operations supervisor and the wastewater treatment simulation software GPS-X™ (Hydromantis
2017). GPS-X™ was used to estimate changes in plant operating conditions when the facility
expands AD capacity to accept SSO feedstock. The main sources of data include:
• Air permit application for the AD and CHP expansion (2016) (Cousens 2016);
• CAPDETWorks™ design and costing software (Hydromantis 2014);
• Discharge Monitoring Report (DMR) information (2016) (U.S. EPA 2016);
• Engineering report assessing the feasibility of several AD expansion, CHP and SSO
acceptance scenarios (2013) (CDM Smith 2013);
• Engineering energy evaluation (2009) (PES and UTS 2009);
• GPS-X™ model results (Hydromantis 2017).
• Plant purchasing records for: electricity, natural gas, chemicals, potable water and grit
disposal (2016);
• Plant influent and effluent quality and quantity records (2016);
• National Pollutant Discharge Elimination System (NPDES) permit (valid 2010-
publication) (U.S. EPA and MADEP 2005);
• The Municipal Solid Waste Decision Support Tool (MSW DST) (RTI International
2012);
The above information, in addition to literature cited throughout this document, was used
to define the system boundaries for the analysis and to parameterize the GPS-X™ model. Model
results were compared against known plant data. For the partial and full capacity scenarios, the
GPS-X™ model was adjusted to account for added AD capacity and the quantity of accepted
SSO waste. Model output was used to calculate the effect of additional nutrient and BOD loading
that results from returning centrifuge supernatant to the primary and secondary treatment units.
Details of the modeling process and adjustments made to GPS-X™ model results are presented
in Section 3.3.
LCI data compiled from these sources, using the methods described in this report, was
modeled in the OpenLCA software program for results generation (GreenDelta 2016).
3.2 Influent Water Quality, Septage and SSO Characteristics
The characteristics associated with the influent municipal wastewater are the same for all
scenarios (Table). Influent flowrate, BOD and TSS represent the average daily value recorded at
the GLSD WWTF during 2016. Other influent parameters are representative of medium strength,
residential wastewater (Tchobanoglous et al. 2014). The temperature of influent and effluent
3-1
-------
Section 3—LCI Methodology
wastewater varies throughout the year but was set at 60°F (15.6 °C) in the GPS-X™ model as a
representative annual average.
In addition to influent wastewater from the municipal sewer system, the treatment plant
also processes trucked-in septage waste, municipal solids from small WWTFs and an engineered
SSO waste stream. The characteristics and accepted quantities of each waste stream are listed in
Table 3-1 and Table 3-4, respectively. Septage is treated with municipal sewage waste and is
subject to primary and secondary treatment. Trucked-in municipal solids are transported to the
facility and are pumped directly from temporary holding tanks into the digesters, as is SSO. The
SSO scenarios analyzed in the sensitivity analysis were previously presented in Section 2.3.2.1.
Table 3-1. Septage, Municipal Solids and SSO Characteristics
( hiinu'lerislic
I'eedslock
I nil
Septsifie1
Trucked Municipnl Solids2
sso-(
TSS
15,000
22,500
137,000
mg/L
vss
10,000
16,500
124,000
mg/L
VSS/TSS
67
73
90
%
Total Nitrogen4
750
600
3,750
mg N/L
Total P5
375
210
620
mg P/L
Chemical Oxygen
Demand (COD)5
17,000
29,000
216,000
mg COD/L
Density
1,020
1,030
1,050
kg/m3
1 (U.S. EPA 1984)
2 (Tchobanoglous et al. 2014), assumes 67 percent primary solids and 37 percent WAS by mass.
3 Personal communication with Lauren Fillmore (Fillmore 2017)
4 Fraction of TKN in TSS is 0.05
5 Based on GPS-X™ default TP and COD fractions of influent TSS
Baseline effluent characteristics, listed in Table 3-2, were calculated using 2016 DMR
data. Effluent constituent concentrations for the partial and full capacity scenarios were
estimated using percent removal values corresponding to baseline plant operations and scenario
specific loading estimates drawn from the GPS-X™ model. Table 3-2 also lists GLSD's state
pollutant discharge elimination system permit requirements.
Table 3-2. Scenario Effluent Composition and Permit Requirements
Ch;ti;uleiislic
li:isoli no
l>:irti:il
C':ip:u-il\
lull
Ciipiicilv
I nil
Kllluenl
Limits
I nil
TSS
6.05
6.28
6.55
mg/L
30
mg/L, average
monthly
BOD
17.7
18.0
18.5
mg O2/L
30
mg/L, average
monthly
TN
20.6
22.4
23.8
mg/L N
no permit requirements
TKN
19.9
21.7
23.1
mg/L N
nh,
22.5
24.5
26.0
mg/L NH3
no3
2.85
3.10
3.30
mg/L NO3
Organic
nitrogen
1.44
1.57
1.67
mg/LN
3-2
-------
Section 3—LCI Methodology
Table 3-2. Scenario Effluent Composition and Permit Requirements
Characteristic
Baseline
Partial
Capacity
Full
Capacity
Unit
Effluent
Limits
Unit
TP
0.367
0.378
0.389
mg/L P
3.3 GLSD WWTF Life Cycle Inventory Development
Process configuration and key operational parameters used to establish the GPS-X™
model were provided by facility staff. Facility records of electricity use were provided for the
year 2016 and were allocated to units based on supervisory control and data acquisition
(SCADA) system data for the years 2007/2008 according to the breakout established in Figure
3-1. This is the most recent period for which detailed electricity consumption data was available
by unit process.
7%
13%
H Biological Treatment ~ Control Buildings
¦ Secondary Clarification ~ Anaerobic Digestion
H Sludge Thickening and Dewatering H Sludge Drying
~ Plant Water & Disinfection H Preliminary and Primary Treatment
Figure 3-1. Allocation of electricity to process units.
Table 3-3 reports plant-level electricity consumption for 2016. Changes in electricity
consumption associated with the partial and full capacity waste scenarios are described
throughout Section 3.3.
3-3
-------
Section 3—LCI Methodology
Table 3-3. 2016 Plant Electricity Use Allocated to Unit Processes
Electricity User
% of Total Electricity Use
Usage (kwh)
Influent pump station
31%
5.716.065
Electricity User
% of Plant Electricity Use
(excluding pump station)
Usage (kwh)
Control buildings
2%
234,702
Preliminary and primary treatment
7%
821,458
Biological treatment
35%
4,341,991
Secondary clarification and RAS pumping
8%
1,056,160
Anaerobic digestion
13%
1,642,915
Sludge thickening/dewatering
11%
1,408,213
Sludge drying
16%
2,047,752
Plant water & disinfection
8%
938,809
Plant Electricity Use (excluding influent pump station) 12,492,000
Total Electricity Use (including influent pump station) 18,208,065
Equipment power consumption associated with existing unit processes was input into
GPS-X™ to generate estimates of electricity consumption for components common to all three
feedstock scenarios. We then use the relationship between actual and modeled electricity
consumption in the baseline feedstock scenario to adjust model estimates of energy consumption
for the partial and full-capacity scenarios using Equation 1. This approach allows GPS-X™ to be
used to estimate increased electricity consumption within the SSO feedstock scenarios, while
linking these estimates directly to recorded plant electricity use.
ElectricityLCAiXy = Electricity GpS_Xxy x
B as eline actuai x
Baseline gps-x,x
Equation 1
where:
ElectricityLCA,x,y = LCA model electricity consumption of unit x for the >' feedstock
scenario
El ectri city gps-x,x,y = GPS-X™ estimated electricity consumption of unit x in the >'
feedstock scenario
Baselineactuai,x = Actual electricity consumption of unit x (2016) in the baseline
feedstock scenario
Baselineops-x,x = GPS-X™ estimated electricity consumption of unit x in the
baseline feedstock scenario
The following subsections describe the detailed operational LCI developed for the
WWTF by unit process on an annual basis. Annual inputs and outputs were allocated to the
functional unit by dividing annual input and output quantities by the number of cubic meters of
wastewater treated per year. Environmental benefits and burdens, including those generated due
to treatment of additional SSO waste, were standardized to the average flowrate of 23.5 MGD.
3-4
-------
Section 3—LCI Methodology
3.3.1 External Waste Processing and Transport
Septage and municipal solids are trucked to the WWTF primarily from communities
served by the facility, assuming a 25-kilometer (km) transport distance. SSO material is trucked
48 km from a processing facility located within the Boston metropolitan area. Raw food waste is
collected from commercial and institutional facilities. A 25-km transport distance was assumed
for movement of raw food waste to the SSO processing facility. Table 3-4 summarizes truck
transport requirements for incoming organic waste. A food waste bulk density of 1.8 kg/gallon
(475 kg/m3) was used to calculate the transport weight to the SSO processing facility (RTI
International 2012). A water addition of 2.3 kg per gallon of SSO was estimated based on an
assumed 31 percent solids content of raw food waste (RTI International 2012) and a 13 percent
solids content of the engineered SSO product. Energy required to grind and pump the SSO slurry
was estimated based on specifications for a small scale commercial food grinder and an assumed
pumping head of 20 meters, which yields an estimated electricity requirement of 762,000 kWh
per year for the full capacity scenario (approximately 3% of WWTF electricity use).
Table 3-4. Transport Calculations for Incoming External Waste and SSO
SiTiiiirio
Wsisle Type1
Qiiiinlily (<>pd)
Mjiss
Oneiric
(ons/iliiv)
Imnsport
llistillHT (km)
Tmnsport
(lkm/\T):
All scenarios
Septage
80,000
308
25
2.81E+6
Municipal solids
8,000
31.2
25
2.85E+5
Baseline
scenario
Food waste
-
-
-
-
SSO
-
-
-
-
Partial capacity
scenario
Food waste
42,900
77.0
25
7.03E+5
SSO
46,000
183
48
3.23E+6
Full capacity
scenario
Food waste
85,700
154
25
1.41E+6
SSO
92,000
367
48
6.46E+6
1 Food waste is an input to SSO. It is SSO that is an input to the WWTF.
2 tkm = ton-kilometers
3.3.2 Influent Pump Station
The influent pump station used 5.7 million kWh in 2016, corresponding to an electricity
consumption of 0.176 kWh/m3. An activated carbon tower is used for odor control at the influent
pump station. The tower contains 1,200 ft3 (35 m3) of activated carbon. To develop the LCI
quantity, it was assumed that the activated carbon was replaced every three years and has a
density of 480 kg/m3. Influent pump station LCI quantities remain constant across scenarios.
3.3.3 Preliminary and Primary Treatment
Preliminary treatment consists of aerated grit removal and bar screening. The case-study
facility provided records of grit disposal for 2016. A total annual grit production of 404 metric
tons was allocated evenly to annually treated wastewater leading to a grit disposal requirement of
0.012 kg grit/m3 of treated wastewater. Preliminary and primary treatment were allocated seven
percent of plant electricity consumption as reported in Table 3-3, which equates to 821,000 kWh
or 0.025 kWh/m3 of treated wastewater. Electricity use for grit removal and primary clarification
3-5
-------
Section 3—LCI Methodology
remains constant across the three feedstock scenarios, due to the minor change in influent
flowrate across scenarios (less than 0.5 percent). The WWTF spends approximately $11,000 per
year on potassium permanganate, which is used for odor control. Potassium permanganate is
purchased as 97.5% KMnC>4 for a unit cost of $3.25 per pound ($7.16 per kg), leading to an
annual KMnC>4 consumption of approximately 1500 kg.
Table 3-5. Primary Clarifier Operational Parameters (GPS-X™ output)
Psi rsiineter
liiisoline
I'iirliiil Ciipiicilv
l ull C itp;uil\
I nils
Influent flowrate
9.6E+4
9.6E+4
9.7E+4
m3/d
Influent TSS
3.2E+2
3.3E+2
3.4E+2
mg/L
Influent cBOD
2.0E+2
2.0E+2
2.1E+2
mg
O2/L
Influent TN
44
48
51
mg N/L
Influent phosphorus
12
13
13
mg P/L
TSS removal efficiency
55
56
57
%
cBODs removal efficiency
42
43
45
0/
/o
TN removal efficiency
20
20
20
0/
/o
TP removal efficiency
15
15
17
0/
/o
3.3.4 Biological Treatment
Biological treatment consists of a plug-flow anoxic tank, followed by a series of four
plug-flow aeration basins. The biological treatment unit was allocated 35 percent of plant
electricity consumption as reported in Table 3-3, which equates to 4.3 million kWh or 0.134
kWh/m3 of treated wastewater for the baseline scenario. Electricity consumption for the partial
and full capacity scenarios was estimated based on GPS-X™ estimated increases in BOD
loading to aeration tank of three and six percent, respectively. The model was set to maintain a
DO concentration of two mg O2/L. All modeling assumes a standard oxygen transfer efficiency
(SOTE) of 0.23, which is based on the annual average plant SOTE as reported in a plant energy
evaluation and documented in Table 3-6.
Table 3-6 and Table 3-7 document design and operational parameters for the aeration
basins. The anoxic and aerobic reactors have a combined hydraulic retention time (HRT) of 4.6
hours. A solids retention time (SRT) of two days was used in the GPS-X™ model (Table 3-7).
SRT controls the MLSS concentration via the RAS flow rate from the secondary clarifiers. A
low SRT is maintained to minimize nitrification, avoiding the associated oxygen demand and
aeration energy costs.
3-6
-------
Section 3—LCI Methodology
Table 3-6. Aeration Tank Standard Oxygen Transfer Efficiency
Standard Oxygen Transfer
Month
Kfficiency (SOU!)
January
0.38
February
0.38
March
0.38
April
0.30
May
0.20
June
0.15
July
0.10
August
0.13
September
0.13
October
0.18
November
0.23
December
0.23
Average
0.23
Table 3-7. Biological Treatment Operational Parameters (GPS-X™ output)
Parameter
Baseline
Partial Capacity
l ull Capacity
I nits
Influent flowrate1
9.26E+4
9.27E+4
9.29E+4
m3/day
Influent TKN
36.3
39.8
42.4
mg N/L
Influent TP
7.27
7.42
7.53
mg P/L
Influent cBOD
115
115
115
mg O2/L
Influent COD
246
246
246
mg COD/L
MLSS concentration
1.11E+3
1.13E+3
1.15E+3
mg/L
Nitrous oxide emissions2
3.09
3.39
3.62
metric tons/yr
Methane emissions2'3
119
119
119
metric tons/yr
1 The influent flowrate excludes the RAS flow.
2 Nitrous oxide and methane emissions are calculated based on TKN and BOD values from GPS-X™.
3 Methane emissions increase only slightly in the partial and full capacity scenarios. This increase is obscured by the
use of three significant figures.
Process GHG emissions of methane and nitrous oxide were estimated for the biological
treatment unit based on influent TKN and BOD concentrations. Nitrous oxide emissions were
estimated by applying an emission factor of 0.0016 kg N20-N/kg influent TKN (Chandran
2012), indicating that 0.16 percent of influent N is released as N2O. Methane emissions were
calculated using a theoretical maximum methane generation rate (B0) of 0.6 kg CHVkg influent
BOD, which reflects methane emissions under anaerobic conditions (IPCC 2006). The
theoretical maximum methane generation rate was adjusted downwards using the IPCC method
and a methane correction factor (MCF) of 0.044. The MCF value was calculated using a methane
emission factor of 11 g CHVkg influent chemical oxygen demand (COD) reported by Daelman et
al. (2013) and discussed in Appendix B. The MCF estimates the share of theoretical methane
generation potential that will be realized by the study system. Table 3-7 presents annual emission
estimates by feedstock scenario. No chemical use is required for the biological treatment process.
3-7
-------
Section 3—LCI Methodology
3.3.5 Secondary Clarification
Electricity consumption for this unit includes clarifier drive energy, RAS pumping and
WAS pumping. Annual electricity demand for secondary clarification is 1.1 million kWh per
year or 0.033 kWh/m3 of treated wastewater. Electricity use was not scaled depending on the
feedstock scenario due to the minimal change in flowrates as displayed in Table 3-8.
Table 3-8. Secondary Clarifier Operational Parameters
(GPS-X™ output)
I'simmclcr
liiisdinc
Piirliiil ( ;i|):icitv
l ull Ciipiicilv
I nils
Influent flowrate
1.60E+5
1.61E+5
1.61E+5
m3/d
RAS flowrate
6.79E+4
6.79E+4
6.79E+4
m3/d
WAS flowrate
5.24E+3
5.24E+3
5.23E+3
m3/d
Surface
overflow rate
13.0
13.1
13.1
m3/(m2.d)
Solids loading
rate
26.7
27.1
27.7
kg/(m2.d)
Influent TSS
1.12E+3
1.13E+3
1.15E+3
mg/L
Influent cBOD
373
380
389
mg/L
Effluent TSS
6.69
6.71
6.74
mg/L
Effluent cBOD
10.9
10.3
9.73
mg/L
WAS TSS
2.44E+3
2.48E+3
2.53E+3
mg/L
3.3.6 Plant Water and Disinfection
Effluent is chlorinated and dechlorinated following secondary clarification. A portion of
treated effluent is utilized both on and off-site in several reuse applications. The WWTF reuses
between two and three MGD (approximately 10 percent) of effluent for on-site applications such
as chemical delivery and wash water. Plant records indicate that on average an additional 0.46
MGD of treated wastewater was purchased and reused off-site by a local industrial partner. The
electricity requirement for disinfection and plant water distribution is eight percent of annual
plant consumption or 0.029 kWh/m3 of treated wastewater.
Sodium hypochlorite is used for disinfection. Plant records indicate that 1.6 million
pounds (734 metric tons) of 15 percent sodium hypochlorite were used in 2016. Sodium bisulfite
is used for dechlorination. Plant records indicate that 746 thousand pounds (338 metric tons) of
38 percent sodium bisulfite were used in 2016. Electricity and chemical use associated with these
unit processes were held constant across scenarios.
3.3.7 Sludge Thickening and Dewatering
The sludge thickening and dewatering process includes operation of gravity thickeners,
gravity belt thickeners (GBT) and centrifuges. Together the sludge thickening and dewatering
processes consume 11 percent of plant electricity or 1.41 million kWh in the baseline scenario.
Baseline electricity consumption of the gravity thickeners was calculated assuming operation of
two out of four thickening units and a collector drive power of five horsepower (HP) per unit
(7.5 kW total). Pumping energy requirements were based on an assumed hydraulic head of 40
3-8
-------
Section 3—LCI Methodology
feet and a pump efficiency of 60 percent (Tarallo et al. 2015). Electricity requirements for the
GBTs were estimated based on a combined equipment power requirement of 21.6 kW, which
includes the belt drive, polymer pump, mixer, wash booster pump and thickened WAS mixer.
Pumping energy requirements for the GBT were based on an assumed hydraulic head of 50 feet
and a pump efficiency of 60 percent (Tarallo et al. 2015). One of two centrifuges are typically in
operation and have a combined power requirement of 142 kW for the motor and backdrive.
Centrifuge pumping energy requirements were based on an assumed hydraulic head of 30 feet
and a pump efficiency of 60 percent. These values were input into GPS-X™ and yielded an
estimated energy consumption for the three processes of 1.55 million kWh for the baseline
scenario (Table 3-9). For partial and full capacity scenarios, thickener power consumption was
increased proportionally to the increase in the flowrate of solids to each unit as estimated by
GPS-X™ and shown in Table 3-10. Equation 1 was used to adjust GPS-X™ estimated electricity
consumption for use in the LCA model.
The solids capture rate for the centrifuge was based on plant specific performance as
reported in the 2009 energy evaluation (PES and UTS 2009). Solids capture rates for the gravity
thickener and GBT were set at the GPS-X™ default values, 90 and 95 percent respectively.
Polymer use in the baseline scenario was based on plant purchasing records. For the co-digestion
feedstock scenarios, GBT polymer use was estimated assuming a polymer addition of five kg dry
polymer per metric ton of dry solids processed (Tchobanoglous et al. 2014). The centrifuge
polymer requirement was estimated assuming a polymer addition of 19.5 kg dry polymer per
metric ton of dry solids processed, as reported in the facilities energy feasibility study (CDM
Smith 2013).
Table 3-9. Thickening and Dewatering Annual Electricity Consumption (kwh)
CI'S-Y"'
Output
liiisclinc1
Partial
Capacity
l ull Capacity
Gravity thickener
74,200
67,600
71,800
76,600
Gravity belt thickener
196,000
178,000
181,000
185,000
Centrifuge
1,240,000
1,130,000
1,460,000
1,800,000
Centrifuge pumping
39,200
35,700
46,200
57,100
Total electricity use
1,550,000
1,410,000
1,760,000
2,100,000
1 Scaled baseline electricity consumption matches 2016 plant records.
Table 3-10. Thickening and Dewatering Operational Parameters (GPS-X™ Output)
I nit Process
I'ara meter
Baseline
Partial Capacity
l ull Ciipiicity
I nits
Primary solids flowrate
5.68E+3
5.68E+3
5.68E+3
m3/day
Influent TSS
2.96E+3
3.14E+3
3.35E+3
mg/L
Solids loading rate
37.9
40.3
43.0
kg/(m2.d)
Gravity
Supernatant flowrate
5.30E+3
5.28E+3
5.25E+3
m3/day
Thickener
Supernatant TSS
296
314
335
mg/L
Supernatant TN
44.2
48.6
52.2
mg N/L
Supernatant TP
8.60
8.93
9.27
mg P/L
Solids capture
90.7
90.7
90.8
%
3-9
-------
Section 3—LCI Methodology
Table 3-10. Thickening and Dewatering Operational Parameters (GPS-X™ Output)
I nit Process
I'iir.i meter
liiiseline
I'iirtiiil (;ip;icil\
l ull Ciipiicitv
I nits
Thickened solids
381
405
432
m3/day
Thickened Solids TSS
4.00E+4
4.00E+4
4.00E+4
mg/L
WAS flowrate
5.24E+3
5.24E+3
5.23E+3
m3/day
Influent TSS
2.44E+3
2.48E+3
2.53E+3
mg/L
Supernatant flowrate
4.97E+3
4.96E+3
4.95E+3
m3/day
Supernatant TSS
122
124
126
mg/L
Gravity Belt
Supernatant TN
30.4
33.9
36.5
mg N/L
Thickener
Supernatant TP
6.50
6.66
6.77
mg P/L
Solids capture
95.3
95.3
95.3
%
Thickened solids
271
274
280
m3/day
Thickened Solids TSS
4.50E+4
4.50E+4
4.50E+4
mg/L
Polymer use
2.33E+4
2.37E+4
2.41E+4
kg/year
Dige state flowrate
682
883
1.09E+3
m3/day
Influent solids
1.73E+4
2.44E+4
3.27E+4
dry kg/day
Centrate flowrate
633
815
999
m3/day
Centrate TSS
4.30E+3
4.69E+3
5.10E+3
mg/L
Centrifuge
Centrate TN
1.17E+3
1.36E+3
1.40E+3
mg N/L
Centrate TP
381
328
294
mg P/L
Solids capture
84.2
84.3
84.4
0/
/O
Thickened solids
1.45E+4
2.05E+4
2.76E+4
dry kg/day
Thickened Solids TSS
3.00E+5
3.00E+5
3.00E+5
mg/L
Polymer use
9.83E+5
1.34E+6
1.15E+6
kg/year
3.3.8 A naerobic Digestion
Thickened primary sludge, WAS and trucked municipal solids are blended and pumped
into one of three mesophilic ADs in the baseline scenario. A fourth digester was added to
accommodate the SSO material accepted in the partial and full capacity AD performance
scenarios. No dewatering is required for the trucked municipal solids or SSO material. Table
3-11 lists basic design parameters for the three AD feedstock scenarios.
Table 3-11. Anaerobic Digester Design and Operational Parameters
Description
li;iseline
I'iirtiiil C:ip;icil\
l-'iill Ciipiicitv
I nit
Anaerobic digesters
3
4
4
count
Tank diameter
85
85
85
feet
Sidewall depth
39
39
39
feet
Tank volume
1,520,000
1,520,000
1,520,000
gallons per tank
Total storage volume (all tanks)
4,560,000
6,080,000
6,080,000
gallons
Effective volume, total
4,200,000
5,600,000
5,600,000
gallons
3-10
-------
Section 3—LCI Methodology
Table 3-11. Anaerobic Digester Design and Operational Parameters
Description
liitscline
I'iirtinl Ciipiicity
l-'iill C ;t|):uil\
I nil
Average feed percent solids
4.0%
5.9%
7.0%
solids
Average feed VSS %
70%
79%
82%
ratio
VS loading
4.31E+4
9.20E+4
1.41E+5
lb VSS/day
Effective HRT1
23
24
19
days
1 Calculated using effective volume.
Incoming solids are heated to 95°F using a heat exchanger. Biogas is the preferred fuel
source. A glycol boiler system was used to provide thermal energy in the baseline feedstock
scenario. CHP thermal energy is preferred for the partial and full capacity scenarios and was
found to be sufficient to heat digester solids for all except the partial capacity-low AD
performance scenario, where a small quantity of supplementary natural gas is required. Section
3.3.9 includes a description of units used for on-site biogas combustion.
Ferric chloride is added to each digester at a rate of 1.6 gallons per digester per hour (34
percent solution) for biogas H2S control. The ADs were allocated 13 percent of plant electricity
consumption, 1.64 million kWh, in the baseline scenario as reported in Table 3-3. Each digester
tank is equipped with one central and three external mixers. Two of the three glycol pumps are
allocated to the digesters, with the third providing pumping for building heat delivery. This
equipment has a total power demand of 216 and 276 kW in the baseline and SSO feedstock
scenarios, respectively. Equation 1 was used to scale GPS-X™ estimated electricity
consumption.
LCA results were generated for two AD performance scenarios, reflecting expected
(base) and low digester performance. Estimated VSR and biogas yield are varied within the two
AD performance scenarios, affecting biogas production and resulting energy generation. Table
3-12 presents these parameter values along with estimates of biogas production. Volatile solids
reduction is higher for the partial and full capacity scenarios due to the increased digestibility of
fruit and vegetable waste (SSO) as compared to primary sludge and WAS (EBMUD 2008). The
low AD performance VSR was calculated assuming 50 and 70 percent reductions for municipal
solids and SSO, respectively. The composite VSR for the base AD performance scenario is an
output of the GPS-X™ model, corresponding to 55 and 79 percent reductions for municipal
solids and SSO. Methane content of biogas is relatively consistent across scenarios ranging from
59.2 to 59.9 percent methane (by volume) depending upon the scenario. Biogas production
increases by approximately 230 and 350 percent between the baseline and full capacity feedstock
scenarios for the low and base AD performance scenarios, respectively. Available biogas
quantity reflects the portion of biogas that is lost as fugitive methane emissions. Fugitive
methane emissions were estimated based on an IPCC emission factor for "floating gas holders
with no external water-seal" (UNFCCC 2012).
3-11
-------
Section 3—LCI Methodology
Table 3-12. Anaerobic Digestion Performance Scenarios Parameters and Biogas
Production
Al) SiTiuirio
Description
liiisoline
I'itrliitl
(;ip:uitv
lull
(iipiicilv
I nils
Base
VS reduction
55",.
M%
72%
of influent VS
Low
M%
63%
Base
Biogas yield
174
18.4
18.5
ft3/lb VSS
destroyed
Low
15
15
Both
Methane content of biogas
59.2
59.4
59.9
% v/v
Both
Fugitive methane losses
5%
of total
Base
Biogas production
4.13E+5
1.17E+6
1.87E+6
ft3/day
Low
X 40E+5
1.34E+6
Base
Available biogas
5
1 [ 1E+6
1.78E+6
Low
7.98E+5
1.28E+6
After exiting the digesters, biogas is cleaned and pressurized before entering the CHP
system. Condensation is used to remove excess moisture from the biogas. Iron sponge filters
were added during the CHP upgrade to further reduce the presence of sulfur in biogas, which can
lead to corrosion of biogas cleaning and CHP equipment as well as undesirable sulfur oxide
emissions. Activated carbon filters are used to removed siloxane from the biogas. Biogas is
pressurized to four or five psi before entering the CHP engines.
Gas storage is limited to the space available within each digester underneath the floating
covers. Due to the timing of biogas production and facility energy demand and CHP
maintenance or malfunction, the facility does not expect to utilize 100 percent of available
biogas. The term available biogas refers to biogas production minus fugitive losses. The base and
low AD performance scenarios assume 90 and 80 percent utilization of available biogas,
respectively. The portion of biogas that is not used for facility heat or electricity production was
assumed to be combusted in one of two on-site biogas flares. Use of biogas in the pellet drying
facility is prioritized over other uses. The pellet drying facility is not set up to utilize thermal
energy from the CHP system, requiring direct combustion of biogas in the pellet dryers. In the
baseline feedstock scenario, a small quantity of natural gas was required to supplement biogas to
satisfy the heat demand of pelletization. The balance of available biogas is combusted in the
CHP system in the partial and full capacity scenarios. Table 3-13 and Table 3-14 summarize
biogas utilization and facility energy demand for the base and low AD scenarios, respectively.
Heat demand and provision are both expressed in terms of fuel energy (primary energy), i.e.
prior to the application of equipment conversion efficiencies. Thermal energy production of the
CHP system was calculated assuming a thermal conversion efficiency of 39 percent (Wiser et al.
2010), and is expressed in fuel energy equivalents assuming a boiler thermal conversion
efficiency of 80 percent. The heat content of biogas is 550 BTU/ft3 (20.5 MJ/m3), which is on the
lower end of the reported range for biogas from WWTFs (Ong et al. 2017).
3-12
-------
Section 3—LCI Methodology
Table 3-13. Facility Energy Demand and Production - Base AD Scenario
Category
Description
Baseline
Partial
Capacity
lull
Capacity
I nil
Biogas
utilization
Biotas iiLi 11/alion
s:%
Mil",,
yu%
of available
biogas1
Flaring rate
18%
10%
10%
Pellet dryer use
53%
30%
25%
Boiler use
29%
0%
0%
CHP use
n.a.
60%
65%
Energy
demand
Pellet dryer heat demand
4.4E+7
7.0E+7
9.4E+7
MJ/year
(fuel energy)
Digester heat demand
2.8E+7
3.6E+7
4.3E+7
Facility heat demand
1.4E+7
1.4E+7
1.4E+7
Electricity demand
1.8E+7
2.0E+7
2.1E+7
kWh/year
(delivered)
Biogas
energy
production
and use
Available biogas energy1
6.9E+7
2.1E+8
3.4E+8
MJ/year
(fuel energy)
Flare energy losses
1.5E+7
2.4E+7
3.8E+7
Pellet dryer heat, from biogas
4.4E+7
7.0E+7
9.4E+7
CHP heat, from biogas
n.a.
6.9E+7
1.2E+8
Digester heat, from biogas
2.4E+7
3.6E+7
4.3E+7
Facility heat, from biogas
-
1.4E+7
1.4E+7
Wasted CHP heat, from biogas
n.a.
1.9E+7
6.2E+7
Electricity, from biogas
n.a.
1.6E+7
2.7E+7
kWh/year
(delivered)
Electricity, excess production
n.a.
-
6.1E+6
Energy
use
summary
Biogas energy recovery2
78%
81%
71%
of produced
biogas
energy2
Electricity demand satisfaction
-
80%
100%
of total
facility
demand
Heat demand satisfaction
79%
100%
100%
1 Available biogas refers to biogas production minus fugitive losses.
2 Includes energy losses associated with fugitive emissions.
Table 3-14. Facility Energy Demand and Production - Low AD Scenario
Category
Description
Partial Capacity
l ull Capacity
I nit
Biogas
utilization
Biogas utilization
80%
80%
of available
biogas
Flaring rate
20%
20%
Pellet dryer use
41%
35%
CHP use
39%
45%
Energy
demand
Pellet dryer heat demand
7.0E+7
9.4E+7
MJ/year (fuel
energy)
Digester heat demand
3.6E+7
4.3E+7
Facility heat demand
1.4E+7
1.4E+7
Electricity demand
2.0E+7
2.1E+7
kWh/year
(delivered)
Biogas
energy
Available biogas energy1
1.7E+8
2.7E+8
MJ/year (fuel
energy)
Flare energy losses
3.4E+7
5.4E+7
3-13
-------
Section 3—LCI Methodology
Table 3-14. Facility Energy Demand and Production - Low AD Scenario
( iilciiorv
Description
Partial C apacity
l-'iill Capacity
I nit
production
and use
Pol lot dry or heal, from biogas
7.0E+7
y.4L 7
CHP heat, from biogas
2.9E+7
5.7E+7
Digester heat, from biogas
3.2E+7
4.3E+7
Facility heat, from biogas
-
1.4E+7
Electricity, from biogas
7.3E+6
1.4E+7
kWh/year
(delivered)
Electricity, excess production
-
-
Energy use
summary
Biogas energy recovery2
74%
72%
of produced
biogas energy2
Electricity demand satisfaction
37%
64%
of total facility
demand
Heat demand satisfaction
85%
100%
1 Available biogas refers to biogas production minus fugitive losses.
2 Includes energy losses associated with fugitive emissions.
Infrastructure requirements for the new AD unit and CHP buildings were estimated based
on unit dimensions using generalized CAPDETWorks™ design equations (Harris, et al. 1982).
Earthwork, wall and slab concrete, sub-grade gravel and additional piping requirements were
included. Combined heat and power building materials were estimated using generalized
building LCI information based on building volume assuming 14-foot ceiling height and 12,000
square feet of floor area.
3.3.9 On-Site Combustion Units
Biogas and natural gas are combusted in several on-site combustion units: flare, pellet
dryer, glycol boiler, building heat boiler and CHP engine. Table 3-13 and Table 3-14 describe
the use of on-site combustion equipment for each feedstock and AD performance scenario.
Building heat boilers combust only natural gas and are used exclusively in the baseline feedstock
scenario. Emissions from the building heat boiler were approximated using a natural gas boiler
unit process adapted from Ecoinvent 2.2. Other combustion unit emissions were based on values
reported in an air permit application specific to the GLSD WWTF.
The design capacity of the flare is 800 standard cubic feet per minute (scfm) of biogas.
The reported volatile organic compound (VOC) destruction rate is 99 percent. The permit
application reports estimated annual emissions when the flare combusts 7.3 million m3 of biogas.
This information was used to calculate flare emission factors in kg/m3 biogas combusted, as
reported in Table 3-15. Methane emissions were estimated using the reported VOC destruction
rate. A worst-case estimate of non-methane volatile organic compounds (NMVOCs) and
methane emissions was estimated assuming a 95 percent destruction rate, based on facility
testing that indicates a potential discrepancy between ideal and realized flare performance (Shah
et al. 2011). The worst-case emission factors were analyzed as part of the low AD performance
scenario.
3-14
-------
Section 3—LCI Methodology
Table 3-15. Flaring Emissions, Short Tons
Per Year (TPY) and per m3 Biogas
I'olliiliinl
Emissions
CITY)
Emissions (k<>/iir'
bio»:is combusted)
Vliogcn oxides (\0\)
v 32
1 i5i:-3
V olatile organic compounds (VOCs), base AD
performance
6.52
8.07E-4
Volatile organic compounds (VOCs), low AD
performance
n.a.
4.04E-3
Sulfur dioxide (SO2)
10.2
1.26E-3
Particulate matter (PM)
4.69
5.81E-4
Carbon monoxide (CO)
28.0
3.47E-3
Methane (CH4), base AD performance
n.a.
3.90E-3
Methane (CH4), low AD performance
n.a.
1.95E-2
Two new internal combustion engines are used for CHP generation. Each engine has a
design capacity of 12.9 MMBtu/hr (13.6 GJ/hr), which equates to a biogas combustion rate of
390 scfm (11.4 m3/minute) per engine. The engines utilize oxidation catalyst and selective
catalytic reduction emission control technologies to minimize VOC and CO and nitrogen oxide
emissions, respectively. The oxidation catalyst system is expected to remove 50 and 96 percent
of VOC and CO emissions by weight, respectively. The selective catalytic reduction system is
expected to remove 98.2% of NOx emissions by weight. The permit application reports
estimated annual emissions when the flare is operating at design capacity. Table 3-16 reports
CHP emission factors in kg/m3 biogas combusted.
Table 3-16. CHP Engine Emissions, Short
Tons Per Year (TPY) and per m3 Biogas
I'olllllillll
Emissions ( I P'S )
Emissions (kg/nr'
hio»its combusted)
NOx
2.10
1.64E-4
VOCs
20.2
1.58E-3
S02
0.18
1.41E-5
PM
0.44
3.44E-5
CO
13.7
1.07E-3
nh,
0.82
6.41E-5
CH41
n.a.
4.30E-3
N201
n.a.
1.02E-4
1 Values are based on Ecoinvent 2.2 unit process: "natural gas,
burned in cogen one MWe lean burn", and are converted to be
on a per m3 biogas basis.
The facility has three dual-fuel glycol boilers that are used to provide digester heat in the
baseline feedstock scenario. Each boiler has a design capacity of 8.31 MMBtu/hr (8.77 GJ/hr).
3-15
-------
Section 3—LCI Methodology
The units are not equipped with pollution control devices. Emission factors were calculated
based on design capacity and annual emissions (Table 3-17).
Table 3-17. Glycol Boiler Emissions, Short
Tons Per Year (TPY) and per m3 Biogas
I'olliiliinl
Emissions ( I P'S )
Emissions (k*»/m 4
bio»;is combusted)
NOx
1.36
4.08E-4
VOCs
0.310
9.29E-5
S02
1.72
5.15E-4
PM
0.400
1.20E-4
CO
0.800
2.40E-4
n2o
n.a.
1.02E-5
ch4
n.a.
4.10E-5
The facility operates two pellet driers each with a design capacity of 15 MMBtu/hr (15.8
GJ/hr), which equates to a maximum annual biogas combustion rate of 13.5 million m3 per year.
Pellet driers are equipped with cyclone separators and scrubber/condensers for emission control.
Emission factors were calculated based on design capacity and estimated annual emissions
(Table 3-18).
Table 3-18. Pellet Drier Emissions, Short Tons Per
Year (TPY) and per m3 Biogas
I'olllllillll
Emissions ( I P'S )
Emissions (k*»/m 4
bio»:is combusted)
NOx
10.5
1.12E-3
VOCs
1.93
2.05E-4
S02
11.4
1.21E-3
PM
5.58
5.93E-4
CO
7.76
8.25E-4
Arsenic (As)
2.48E-4
2.64E-8
Cadmium (Cd)
1.56E-3
1.66E-7
N20
n.a.
2.05E-6
ch4
n.a.
4.10E-5
3.3.10 Biosolids Pelletization
An on-site biosolids pelletization facility is operated by an outside contractor to turn
dewatered biosolids from the centrifuge into a dry, stabilized agricultural amendment. Table 3-19
lists the quantity of centrifuge cake processed per day, associated pellet production and pellet
nutrient content. Biosolids pelletization requires 8,500 MJ of thermal energy and 350 kWh of
electrical energy per dry short ton of biosolids processed. Sludge drying requires 16, 20 and 25
percent of facility electricity demand, excluding influent pump station electricity requirements,
for the baseline, partial and full capacity feedstock scenarios, respectively (Table 3-3). Section
3-16
-------
Section 3—LCI Methodology
3.3.8 discusses the ability of biogas to satisfy pellet drying energy demand. Section 3.3.9
describes pellet drying equipment and emissions.
Table 3-19. Pellet Production and Nutrient Content
I'iinimeler
li;iseline
I'itrliitl
C;i|):icilv
lull
Ciipiicilv
I nils
Ccnlnl'u^c cakc. dr\ mass
1 45i: 4
:<)5i: 4
: 7m: 4
kg.
Pellet production
5.16E+6
7.30E+6
9.8IE+6
kg/year
Pellet N Content
2.06E+5
2.92E+5
3.92E+5
kg/year as N
Pellet P Content
4.51E+4
6.37E+4
8.56E+4
kg/year as P
3.3.11 Land Application of Pelletized Biosolids
Pelletized biosolids were assumed to be transported an average of 121 km to farm fields
for application as a fertilizer and soil amendment. Table 3-20 lists basic biosolids pellet
specifications. Nutrient content information was provided by the drying facility. Carbon content
was estimated assuming a carbon to nitrogen ratio of 7:1 (Parnaudeau et al. 2004; Rigby et al.
2016).
Table 3-20. Biosolid Fertilizer Pellet
Specifications
P.inimeler
\ :illie
I nils
Moisture content
2.5%
moisture
Nitrogen content
4%
by weight
Phosphorus content
2%
by weight
Potassium content
0%
by weight
Carbon content
28%
by weight
Fertilizer pellets are loaded into a manure spreader and distributed on agricultural fields
at the average 2015 U.S. nitrogen (N) and phosphorus (P2O5) application rate for winter wheat
(NASS 2016). It was assumed that 1.06 liters of diesel fuel are required to spread one ton of
pellets (ROU 2007). Pellets are applied such that they provide 61 lb N per acre (68.4 kg N/ha)
and 31 lb P2O5 per acre (34.9 kg P20s/ha) of plant available nutrients. The estimate of plant
available nutrients is equivalent to the fertilizer replacement value. A nitrogen fertilizer
replacement value for the pelletized biosolids of 55 percent was used in this analysis (Smith and
Durham 2002; Rigby et al. 2016). The value is based on the total quantity of mineralized
nitrogen available over a three-year period. Negligible additional mineralization typically occurs
after three years when biosolids are applied at typical agronomic rates (Rigby et al. 2016). A
fertilizer replacement value of 95 percent was used for P2O5 (Boldrin et al. 2009). Table 3-21
compares typical application rates for chemical fertilizers to the pelletized biosolid nutrient
application rates used in this study, designed to achieve equivalent plant availability. The
pelletized biosolids are also a source of carbon.
3-17
-------
Section 3—LCI Methodology
Table 3-21. Comparison of Chemical and Pelletized Biosolid
Nutrient Applications
Psi rsinieter
Chemiciil l-'erlili/er
Application (k»/h;t/\ i )
Pellctizeil liiosolid
Application (k»/h;t/\ i )
I nits
Nitrogen
68.4
124
as N
Phosphorus
34.7
62.2
as P2O5
Potash
43.7
-
as K2O
Carbon
-
870
as C
The benefits of avoided fertilizer production were estimated assuming the replacement of
urea and single superphosphate for the plant available portion of pellet nutrients. Urea and single
superphosphate are 46 and 21 percent N and P2O5 by weight, respectively.
Typical agricultural emissions such as nitrous oxide (N2O), NOx, NH3, NO3 and P have
been calculated based on a conservative estimate of the potential net change in agricultural
emissions that could occur by replacing inorganic fertilizers with organic alternatives. Field
emissions of nutrients can vary over a wide range depending upon application method and
timing, soil type and a variety of climatic factors. The methods used to estimate field emissions
are based on total nutrient application rates and therefore lead to higher estimates of agricultural
emissions due the increased total nutrient application rate (Table 3-21) of pelletized biosolids
that is required to achieve equivalent plant available nutrient applications. Impacts were assessed
based on the net change in agricultural emissions that would be expected based on the assumed
fertilizer replacement rates.
Table 3-22 lists the calculated emission per kg of land applied nutrient. Phosphorus
emissions to surface water and groundwater were estimated using the Ecoinvent methodology
(Nemecek and Kagi 2007). Ammonia emissions were estimated assuming that 8.5 percent of
applied nitrogen is released as NH3 (Goedkoop et al. 2009). Nitrogen oxide emissions were
estimated assuming that 21 percent of land applied nitrogen is lost via this route (Nemecek and
Kagi 2007). Nitrate and N2O emissions were estimated using the IPCC method (De Klein et al.
2006). Direct N2O emissions associated with land use were excluded as these emissions remain
consistent regardless of fertilizer type. The carbon sequestration estimate assumes that 0.32 kg of
CO2 are sequestered per kg of carbon land applied, equating to a long-term carbon sequestration
rate of 9 percent (Boldrin et al. 2009).
Environmental impacts based on these values should be viewed as reasonable estimates,
however significant variability in these values is expected in practice.
Table 3-22. Estimated Agricultural Emissions
Pa rsi meter
Viilue
I nils
NH3, to air
0.103
kg NHs/kg applied N
N2O, to air
0.025
kg N20/kg applied N
NOx, to air1
0.011
kg NOx/kg applied N
NO3, to water
1.33
kg NOs/kg applied N
P, to groundwater
2.99E-3
kg P/kg applied P
3-18
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Section 3—LCI Methodology
Table 3-22. Estimated Agricultural Emissions
P.inimeler
Viilue
I nils
P, to surface water
0.087
kg P/kg applied P
C, Sequestered
0.32
kg CC^/kg C applied
1 Excludes nitrous oxide.
3.3.12 Effluent Release
Table 3-23 lists effluent quality for each of the three feedstock scenarios in the base AD
performance scenario. Baseline effluent characteristics were calculated using 2016 DMR data
(U.S. EPA 2016). Effluent concentrations for the partial and full capacity scenarios were
estimated using percent removal values corresponding to baseline plant operations and scenario
specific primary clarifier loading estimates drawn from the GPS-X™ model. The WWTF has no
permitted nutrient requirements and is not operated for nutrient removal.
GPS-X™ modeling indicates that the partial and full capacity scenarios yield nine and 16
percent, respective, increases in nitrogen load to the primary clarifier. This increased load
corresponds to 61 and 55 percent of SSO nitrogen content for the partial and full capacity
scenarios. The low AD performance scenario assumes that 80 percent of SSO nitrogen is
returned to the primary clarifier as part of a sensitivity analysis to quantify the effect on
eutrophication potential (EP) impact. The 80 percent nitrogen return flow estimate was based on
the reasoning that organic nitrogen is solubilized in proportion to the realized volatile solid
reduction in the AD, which can be 75-80 percent for fruit and vegetable waste (EBMUD 2008).
Nitrous oxide emissions from receiving streams were calculated based on the IPCC
guideline that 0.005 kg of N2O-N are emitted per kg of nitrogen discharged to the aquatic
environment. Details of that calculation are presented in Appendix B.
Table 3-23. Effluent Emissions by Feedstock Scenario
I'iinimeler
liiiseline
Partial
Capacity
lull
Capacity
I nils
Flowrate
8.73E+4
8.75E+4
8.76E+4
m3/day
TSS, to water
6.05
6.28
6.55
mg/L
BOD5, to water
17.7
18.0
18.5
mg O2/L
TN, to water
20.6
22.4
23.8
mg N/L
NH3-N, to water
18.5
20.1
21.4
mg N/L
NO3-N, to water
0.644
0.701
0.746
mg N/L
Organic N, to water
1.44
1.57
1.67
mg N/L
TP, to water
0.367
0.378
0.389
mg P/L
N2O, to air
0.162
0.176
0.187
mg N2O/L
3.3.13 Facilities
Administrative, laboratory and maintenance facilities (i.e. control buildings) were
allocated two percent of annual electricity consumption in the baseline scenario. This value was
3-19
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Section 3—LCI Methodology
increased by five percent in the SSO scenarios to account for the additional electricity
requirements of the new CHP building. Building heating requirements were allocated 72 percent
of natural gas purchases based on the energy efficiency evaluation (PES and UTS 2009). The
plant purchased approximately 17,000 Dekatherms (DTH) of natural gas in 2016. Control
buildings were allocated 100 percent of purchased potable water consumption, which totals 3.87
million gallons per year. Potable water consumption was estimated based on the annual water
bill of $17,236 and a unit water cost of $3.10 per 100 ft3 (municipal rate schedule). For facility
heating, the analysis assumed that CHP heat is the preferred building heat source following
installation of the new facilities. CHP heat production is sufficient to provide 100 percent of
building heat requirements for all except the partial capacity-low AD performance scenario, for
which supplementary natural gas is required. Building heat requirements were held constant
across scenarios.
3.3.14 A voided Waste Processes
Several states have implemented a landfill ban on organic waste including Massachusetts,
Vermont and Connecticut (Henricks 2014). Food waste diverted from the landfill is expected to
be alternatively disposed of via AD, composting or as animal feed among other options. This
study examines the net environmental impact of shifting SSO EOL treatment from disposal in
landfills and WTE facilities to beneficial reuse as an AD feedstock. Baseline results, presented in
Section 5, are based on 2016 Massachusetts waste diversion, where 32 percent of diverted SSO
avoids landfill disposal. The remaining 68 percent of food waste is diverted from WTE facilities.
Appendix A includes the results of an additional analysis where AD of food waste is compared
to composting as an alternative EOL management strategy.
3.3.14.1 Avoided Food Waste Landfilling
Avoided burdens were calculated relative to national average and Massachusetts landfills,
which were differentiated by the share of landfills that practice energy recovery, flaring or
venting of generated landfill gas as depicted in Table 3-24.
Table 3-24. National and Massachusetts Average
Landfill Gas Management Practice1
(J:is Mji nil"oiiu'iiI 1'met ice
Niilioiiiil1
M.issiichuseUs2
Flaring
24%
19%
Energy recovery
68%
81%
Venting
8%
-
1 (U.S. EPA 2017b; U.S. EPA 2017c)
2 (Commonwealth of Massachusetts 2017b)
The share of Massachusetts landfills that employ energy recovery are reported in the
Master List of Solid Waste Facilities in Massachusetts (Commonwealth of Massachusetts
2017b). In total there were 14 operational landfills in the State of Massachusetts in 2014. Seven
of the operational landfills were equipped with energy recovery. The remaining facilities were
assumed to flare their landfill gas. The share of landfill gas produced at each facility was
calculated based on the mass of waste landfilled in 2014, the most recent available data year.
3-20
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Section 3—LCI Methodology
The share of national landfills that employ each gas management practice was determined
by analyzing landfill and energy project level technical data collected by EPA's Landfill
Methane Outreach Program (LMOP) (U.S. EPA 2017c; U.S. EPA 2017b). The 2017 LMOP
database lists 2,452 U.S. landfills, of which 1,165 are open. Only open facilities were considered
in this analysis. The database reports landfill gas generation for 822 of the open facilities. All
active landfills with landfill gas estimates were classified as energy recovery, flare or venting
facilities. Project level LMOP data (2017) lists 849 operational energy recovery projects.
Facilities that do not report an operational energy recovery project and have a gas collection and
flaring facility were classified as flare facilities. All other facilities were assumed to vent landfill
gas.
Of the 822 open facilities, 361 report energy recovery projects, accounting for 68 percent
of reported landfill gas generation. While 223 facilities were classified as venting, they account
for only eight percent of reported landfill gas generation. The remaining 238 facilities were
classified as flare facilities and account for 24 percent of reported landfill gas generation.
The LCI for landfill disposal of food waste, or SSO, was developed using the Municipal
Solid Waste Decision Support Tool (MSW DST), version 1.0 (RTI International 2012). Separate
LCIs were generated for facilities venting, flaring and recovering energy from landfill gas. The
MSW DST tool models each landfill over a 100-year period. All scenarios assume no gas capture
system is in place during the first two years of operation. Years two to thirty are specified such
that landfill gas is either vented, flared or piped to an internal combustion engine (ICE) electrical
generator, according to the scenario. All landfill gas is vented following year 30 once gas
production has slowed. The assumed electrical efficiency of the landfill ICE is 33 percent. Heat
from the ICE is not recovered for reuse. The oxidation rate of methane that escapes through the
cover material was held constant across scenarios and was set to 0.038 (unitless) (U.S. EPA
2015a). The LCI for each facility category was combined using the gas management practices
specified in Table 3-24 as weighting factors. The full LCI is available in Appendix B, Table B-l.
3.3.14.2 Avoided Food Waste Incineration
Avoided burdens were calculated relative to national average and Massachusetts WTE
facilities, which were differentiated based on plant heat rate and emissions per unit of waste
combusted as presented in Table 3-25.
The full LCI is available in Appendix B, Table B-2.
Table 3-25. National and Massachusetts Average WTE Facility Specifications
WTK I'jirjimotor
Niilioiiiil1
MjissjichusoUs2
Food waste heat value (BTU/lb)
1,800
1,800
Plant heat rate (BTU/kWh)
18,000
19,214
Sulfur dioxide (ppmv @ 7% oxygen, dry)
8.0
2.8
Hydrochloric acid (ppmv @7% oxygen, dry)
8.9
8.9
Nitrogen oxides (ppmv @ 7% oxygen, dry)
1.4E+2
56
Carbon monoxide (ppmv @ 7% oxygen, dry)
26
4.8
Particulate matter (mg/dscm @7% oxygen, dry)
4.0
0.63
3-21
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Section 3—LCI Methodology
Table 3-25. National and Massachusetts Average WTE Facility Specifications
WTK I'jirjimotor
Niilioiiiil1
MjissjichusoUs2
Dioxins/furans (mg/dscm @7% oxygen, dry)
4.5
4.5
Methane (lb emitted/ton MSW)
3.0E-3
3.0E-3
Ammonia (lb emitted/ton MSW)
-
8.0E-3
Hydrocarbons (lb emitted/ton MSW)
-
-
1 MSW D ST default values
2 Variations from MSW DST defaults are based on records for the North Andover Massachusetts, Wheelabrator
WTE facility
3.4 Background LCI Databases
In addition to the primary data sources described in the preceding sections, several
background LCI databases were used to model life cycle impacts of upstream processes such as
electricity generation and distribution, transportation, and manufacturing of chemical and
material inputs. Ecoinvent 2.2 served as the basis for many of the upstream infrastructure inputs,
chemical and avoided fertilizer manufacturing (Frischknecht et al. 2005). The U.S. Life Cycle
Inventory (U.S. LCI) database was used to represent the manufacture of some chemical inputs
and most of the electricity unit processes, in cases where applicable U.S. specific processes were
available in the database (NREL 2012). A U.S. EPA LCI database was used for electricity from
solar and wind, transportation processes and additional infrastructure materials (U.S. EPA
2015b).
3.5 LCI Limitations and Data Quality
In accordance with the project's Quality Assurance Project Plan (QAPP) entitled Quality
Assurance Project Plan for Life Cycle Considerations and Systems Analyses of Municipal Water
Sustainability Assessments approved by EPA on March 21, 2017 (ERG 2017), ERG collected
existing data1 to develop the LCA and cost estimates for the GLSD WWTF and associated
scenario/sensitivity analysis. ERG evaluated the collected information for completeness,
accuracy and reasonableness. In addition, ERG considered publication date, accuracy/reliability
and cost completeness when reviewing data quality. Finally, ERG performed developmental and
final product internal technical reviews of the LCA and costing methodology and calculations for
this study.
Table 3-26 presents the data quality criteria ERG used when evaluating collected cost
data. All capital costs associated with the AD and CHP expansion project were drawn from an
engineering feasibility study and are specific to the case-study facility. Current and ongoing
operational, maintenance and material purchase costs were based on budget data from the GLSD
WWTF or facility-specific unit costs that were applied to estimated LCI and LCCA parameters
documented in this report (e.g. electricity production/value).
1 Existing data means information and measurements that were originally produced for one purpose that are
recompiled or reassessed for a different purpose. Existing data are also called secondary data. Sources of existing
data may include published reports, journal articles, LCI and government databases, and industry publications.
3-22
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Section 3—LCI Methodology
Table 3-26. Cost Data Quality Criteria
(„)u;ilil> Criterion: Com
Diilii
Dcscriplion/Dcfinilion
\cccp(;incc Spccificiilions
Current
Report the time period of the data.
Costs are converted to a standard year using
the Bureau of Labor Services 2017
Consumer Price Index (Crawford et al. 2017)
Complete
Ensure all aspects of the technology
costs are reported.
Cost estimates are completed using all input
costs for energy, labor, chemicals and waste
disposal.
Representative
Report if the costs used are
representative of the technology
studied.
Costs are based on data from peer reviewed
literature, vendor information and
engineering software specific to the
technologies studied.
Accurate/Reliable
Document the sources of the data.
Confirm calculations are based on
sound methodology and technically
correct.
Data sources and calculations were
documented and reviewed.
Table 3-27 presents the data quality criteria ERG used when evaluating collected or
developed LCI data. ERG documented qualitative descriptions of the source reliability,
completeness, temporal correlation, geographical correlation and technological correlation in
Appendix E, for EPA's use in determining whether the LCI data are acceptable for use.
Structuring the analysis as a case-study allows for high data quality. Completeness, temporal
correlation, geographic correlation and technological correlation for entries based on plant
records were all assigned data quality scores of one. Plant records were not able to furnish all
information required for the LCI, leading to source reliability scores of two or below for some
data in the baseline scenario. The partial and full capacity scenarios rely on modeling and
engineering/scientific estimation methods that rely on numerous assumptions, leading to a data
quality score of three for source reliability. The same data quality rubric was applied both to LCI
development and the use of unit process data from existing databases listed in Section 3.4. Some
entries in Table E-l have been marked as n.a., not applicable, for LCI development entries that
use engineering/scientific estimation methods that are applied to future, potential co-digestion
scenarios.
Table 3-27. Life Cycle Inventory Data Quality Criteria1'2
lii(lic;ilor
Reporting (rilcriii
Score
Source Reliability3
Data verified based on measurements.
1
Data verified based on some assumptions and/or standard
science and engineering calculations.
2
Data verified with many assumptions, or non-verified but
from quality source.
3
Qualified estimate.
4
Non-qualified estimate.
5
Completeness
Representative data from a sufficient sample of sites over an
adequate period of time.
1
Smaller number of sites, but an adequate period of time.
2
Sufficient number of sites, but a less adequate period of
time.
3
Smaller number of sites and shorter periods or incomplete
data from an adequate number of sites or periods.
4
3-23
-------
Section 3—LCI Methodology
Table 3-27. Life Cycle Inventory Data Quality Criteria1'2
liitlic;il
-------
Section 3—LCI Methodology
• Data Accuracy and Uncertainty. In a complex study with literally thousands of
numeric entries, the accuracy of the data and how it affects conclusions is truly a
difficult subject, and one that does not lend itself to standard error analysis
techniques. The scenario-based sensitivity analysis was conducted in lieu of a formal
uncertainty assessment and was intended to produce results within a range that is
representative of the facilities potential performance. However, there is still
uncertainty and variability associated with individual LCI values, and the reader
should keep this in mind when interpreting the results. Comparative conclusions
should not be drawn based on small differences in impact results.
• Modeled vs. Actual WWTF Performance. Given the complexity of the processes
occurring within the WWTF and the minimal data available for proper
characterization, several assumptions were made regarding the expected effects of
SSO co-digestion on those processes. One source of uncertainty is the effect on
WWTF effluent, especially with respect to nutrient concentrations and eutrophication
potential. For this study, effects were estimated based on past, demonstrated
performance under baseline conditions (see Section 3.3.12 for detailed discussion),
taking into consideration the increase in nutrient loading associated with co-digestion.
Ultimately, evaluation of long-term monitoring data will help answer these questions.
A preliminary analysis has been conducted in Appendix B, though given the limited
data available at the time of publishing, results are inconclusive.
3-25
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Section 4—LCCA Methodology
4. LCCA METHODOLOGY
This section presents the methodology used to develop life cycle costs for the three
feedstock scenarios. Cost data was collected and adjusted from several sources as described in
Section 4.1. Basic LCCA methods are described in Section 4.2. Life cycle cost assessment
results are presented according to two cost scenarios, which span a reasonable range of variation
for parameters that affect estimates of system net present value (NPV). Parameter values for the
low and base cost scenarios are listed in Section 4.2.6.
4.1 LCCA Data Sources
Cost data were obtained from the following sources:
• Annual budget for the GLSD WWTF (2016);
• Engineering report assessing the feasibility of several AD expansion, CHP and SSO
acceptance scenarios (CDM Smith 2013);
• Plant purchasing records for: electricity, natural gas, chemicals, potable water and grit
disposal (2016).
The above information, in addition to literature cited throughout this document, was used
to develop life cycle costs.
4.2 LCCA Methods
The LCCA uses NPV to consider capital costs and annual or otherwise periodic costs
associated with operation, maintenance and material replacement over a 30-year time horizon.
The goal of the LCCA is to compare the present value of several operational alternatives for the
GLSD WWTF. The analysis compares the NPV of operating the WWTF according to historical
patterns, without CHP and SSO acceptance, to alternatives that include varying levels of co-
digestion and AD performance.
4.2.1 Total Capital Costs
Total capital costs include purchased equipment, direct and indirect costs. Direct costs are
physical or material costs associated with capital projects, such as the installation of a new
treatment process. Direct costs include mobilization, site preparation, site electrical, yard piping,
instrumentation and control and lab and administration building. Indirect costs include legal
costs, engineering design fee, inspection costs, contingency, technical services, interest during
construction, profit and miscellaneous cost (Harris, et al. 1982). All capital costs associated with
the AD and CHP expansion project were drawn from a previous energy feasibility study (CDM
Smith 2013) and were inclusive of purchased equipment, direct and indirect costs. As these costs
were inclusive, no additional calculation was required to estimate capital cost.
Additional, ongoing capital costs were estimated as the sum of annual capital
expenditures and ongoing debt service, which is how GLSD budgets for equipment replacement,
non-routine maintenance projects and capital upgrades. Estimated annual capital expenditures
were available for the period from 2015 to 2017. Average capital expenditures over this period
were used to approximate this budget item in all future years. Annual capital expenditures are not
4-1
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Section 4—LCCA Methodology
subject to interest payments and averaged approximately 10 percent of the total annual budget
over the period from 2015 to 2017.
The GLSD provided estimates of their expected debt service payments over a 25-year
period (Table C-4). Debt service data shows that the capital cost of the AD expansion and CHP
project will be paid down over a 20-year period, from 2021 to 2040. These capital costs are
separated out and applied only to the partial and full capacity scenarios. Only the first 10 years of
debt service projections were expected to be accurate, as additional large maintenance or
expansion projects may occur after this 10 year period, but are not currently being projected.
Rather than use only the projected values, which decrease towards the end of the 25-year
projection period, we used the average debt service payment, excluding the CHP project, over
the first 10 years as an estimate of on-going capital debt service. Annual debt service averaged
approximately 18 percent of the total annual budget over the period from 2015 to 2017. Annual
debt service payments include the assessment of interest.
4.2.2 Cost Escalation
Per NIST LCCA guidelines, the analysis does not assume escalation rates beyond the
standard inflation rate for any cost categories except for energy costs (Fuller and Petersen 1996).
The LCCA was performed in constant (non-inflated) dollars and uses a real discount rate
corresponding to the constant dollar method. Electricity and natural gas costs were escalated
according to 2017 annual energy escalation factors specific to fuel type, in the Northeastern U.S.
(Lavappa et al. 2017). Energy escalation factors were applied by multiplying base year energy
cost by the escalation factor corresponding to the appropriate calendar year. Energy escalation
factors are included in Appendix C, Table C-l.
4.2.3 Total Annual Costs
Total annual costs include operation and maintenance labor, materials, chemicals, energy
and plant revenue. Equation 2 was used to calculate total annual costs.
Total Annual Costs = Operation Costs +
Material Costs + Chemical Costs + Energy Costs - Plant Revenue
Equation 2
where:
Total annual costs (2016 $/year) = Total annual operation and maintenance costs
Operation costs (2016 $/year) = Labor and non-material ancillary costs required to
operate the WWTF, including operation, administrative, laboratory labor and routine
equipment maintenance
Materials costs (2016 $/year) = Material and physical service costs (e.g. grit disposal)
costs required to operate and maintain the WWTF, including equipment replacement
Chemical costs (2016 $/year) = Cost of chemicals required for WWTF operation (e.g.,
ferric chloride, polymer)
Energy costs (2016 $/year) = Cost of electricity required for WWTF operation
Plant revenue (2016 $/year) = Revenue received associated with waste tipping fees,
renewable energy credits, alternative energy credits and industrial cost sharing programs
4-2
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Section 4—LCCA Methodology
Operational labor cost associated with primary and secondary treatment remain the same
regardless of the scenario considered. Additional personnel are required to manage the AD and
CHP expansion. Regular plant maintenance is carried out by plant staff and does not require
additional labor costs beyond their annual salary and benefits. Annual operation and maintenance
costs were based on the 2016 budget, which includes costs associated with typical preventive
maintenance.
4.2.4 Net Present Value
Equation 3 was used to calculate system NPV (Fuller and Petersen 1996). A real discount
rate of three percent was used in the base cost scenario.
where:
NPV (2016 $) = Net present value of all costs and revenues necessary to construct and
operate the WWTF
Costx = Cost in future year x
i (%) = Real discount rate
x = number of years in the future
4.2.5 AD and CHP Expansion Payback Period
Equation 4 was used to calculate a discounted payback period for the combined AD
expansion and CHP installation. Payback period measures the duration of time that is required to
recover initial investment cost of a particular project or project alternative (Fuller and Petersen
1996). A payback period will only exist if unit annual revenue exceeds annual cost. System NPV
estimates include capital cost of the AD and CHP expansion project using debt service
projections so that both interest and the timing of payment is accurately modeled. However,
discounted payback period was estimated using capital cost projections from the energy
feasibility study, detailed in Table 4-1.
Net Present Value =
Equation 3
X
Payback period (x) = ^
t=l
Revt — Costt
—t T7— > Capital Investment
(i + iy H
Equation 4
where:
x = Payback period, measured in years
Revt = Revenue in year t
Costt = Operational expenditure in year t
i (%) = Real discount rate
4-3
-------
Section 4—LCCA Methodology
Table 4-1. Capital Costs of the Anaerobic Digester and CHP Expansion Project
C'osl C'iile«iorv
C'iipitiil Cost
(2016 Ss)
New AD tank & ancillary equipment
4,700,000
Digester feed pumps
710,000
Foam control and site improvements
490,000
CHP engines
10,400,000
Siloxane treatment
1,900,000
Waste blending tank
380,000
Collection, flare and safety upgrade
1,800,000
Waste receiving station
380,000
Total
20,760,000
4.2.6 LCCA Cost Scenario Parameters
Cost parameter assumptions can have a significant effect on total life cycle costs or the
cost performance of any unit within the WWTF. Table 4-2 documents parameters used in the low
and base cost scenarios. The low cost scenario corresponds to parameter values that will yield a
lower system NPV than the base cost scenario.
The study period remains consistent across scenarios, while the real discount rate varies
between three and five percent for the base and low cost scenarios, respectively. A lower
discount rate indicates that a higher value is placed on money in the future, which increases the
contribution of future operational costs and material replacement to system NPV. Electricity cost
per kWh was based on plant utility records, including all fees. Electricity revenue is 10 and 14
percent below the purchased electricity cost to account for customer service fees that are not
covered by the Massachusetts net metering program (Commonwealth of Massachusetts 2017c).
Renewable energy credits (RECs) and alternative energy credits (AECs) values are determined
as a function of supply and demand in the marketplace. Base cost REC and AEC values were
provided by GLSD staff. Low cost REC and AEC values were based on personal communication
with the program manager of the Massachusetts renewable and alternative portfolio standard
programs (Wassam 2018). Natural gas price was based on plant utility records and the energy
feasibility study for the base and low cost scenarios, respectively. Expected SSO tipping fees
were based on feedback from GLSD staff.
Table 4-2. Low and Base Cost Scenario Parameters
I'iinimiMcr Ysilue
Low C'osl
linso C ost
Planning period (years)
30
30
Real discount rate (%)
5%
3%
Electricity cost ($/k\Vh)'
0.143
0.143
Electricity, avoided cost ($/kWh)2
0.129
0.123
Renewable energy credit ($/MWh)3
25
12
Alternative energy credit ($/MWh)3
20
14
Natural gas cost ($/DTH)4
10.5
9.88
4-4
-------
Section 4—LCCA Methodology
Table 4-2. Low and Base Cost Scenario Parameters
I'iinimiMcr Ysilue
Low Cost
linso Cost
SSO tipping fee ($/gallon)
0.02
0.005
1 2016 plant utility bills, includes all fees.
2 Low cost value based on the energy feasibility study (CDM Smith 2013).
Base value assumes a 10% reduction in the 2016 utility rate to account for
customer charges and system benefit not offset by net metering.
3 Low cost REC and AEC values based on personal communication with
RPS and APS Program Manager (Wassam 2018). Base cost REC and
AEC values based on correspondence with GLSD staff.
4 Base cost from plant utility records, Low scenario cost based on energy
feasibility study
Table Acronym: DTH - dekatherm = 1,000,000 British thermal units
4.3 Treatment Group and Unit Process Costs
The following sections describe data sources and cost estimation assumptions for
individual unit processes. Electricity, natural gas and chemical costs were allocated to the
treatment groups listed in Table 4-3, as described in Section 3.3 and the following subsections.
Costs that are unable to be allocated to specific unit processes were allocated to the full plant
treatment group when presenting LCCA results.
Table 4-3. LCCA Treatment Groups
TiTiitmiMit Croups
I nil Process Vimc
Full plant
Control building
Preliminary/Primary treatment
Wastewater collection; operation and infrastructure
Influent pump station
Screening and grit removal
Primary clarification
Waste receiving and holding
Biological treatment
Pre-anoxic tank
Aeration basins
Secondary clarification
Secondary Clarification
Plant water and disinfection
Plant Water and Disinfection
Sludge thickening and
dewatering
Gravity belt thickener
Gravity thickener
Centrifuge
AD and CHP
Anaerobic digestion
Combined heat and power
Pellet drying
Biosolids drying and pelletization
4.3.1 General Facility and Administration (Full Plant treatment group)
General facility and administrative costs were based on the 2016 budget. Table 4-4
summarizes annual plant costs that were assigned to the Full Plant treatment group. Plant labor
costs are divided among administration, monitoring (laboratory), maintenance and operations
4-5
-------
Section 4—LCCA Methodology
personnel. Based on budget granularity it was not possible to allocate maintenance and operation
personnel costs to specific unit processes. Fringe benefits, such as health care and workers
compensation are included in plant labor costs.
The facility budget includes a line item for general mechanical and electrical supplies,
which was assumed to include the material requirements for routine preventive maintenance.
Additional preventive maintenance costs for the AD and CHP system were developed
specifically for those unit processes. The majority (72 percent) of purchased natural gas is used
for general building heating. GLSD receives several small sources of revenue including REC
sales from an on-site solar electricity installation as well as industrial surcharge and industrial
cost recovery programs.
Annual capital expenditures and debt service, which were used to estimate life cycle costs
associated with equipment replacement and process upgrades were not able to be assigned to
specific treatment processes based on the available information. Plant staff provided projections
of existing and planned debt service expenditures.
Table 4-4. Annual Cost Summary by Feedstock Scenario for the Full
Plant Treatment Group - Base Cost Scenario
Anniiill Cost
l-'ccdstock SiTiuirio
liiisclino
I'iirliiil Ciipiicitv
l ull C;i|);icilv
Plant labor
$4,709,488
Administrative,
miscellaneous
$737,213
Operations,
miscellaneous
$102,150
Monitoring
$113,530
Materials, general
maintenance
$418,483
Capital projects
$1,800,000
Debt service1
$3,316,494
Electricity
$35,768
$37,556
$37,556
Natural gas
$121,793
-
-
Water
$17,236
Diesel
$19,000
Revenue, solar RECs
$15,000
Revenue, other
$120,200
Miscellaneous, other
$246,000
1 The value shown is for 2016 debt service. Debt service estimates change annually over the course of the
30-year analysis period. See Table C-2 for year-by-year debt service estimates.
4.3.2 Preliminary and Primary Treatment
This treatment group includes the influent pump station, grit removal, bar screen and
primary clarifier. Table 4-5 summarizes annual plant costs that were assigned to the Preliminary
4-6
-------
Section 4—LCCA Methodology
and Primary treatment group. Preliminary and primary treatment was allocated seven percent of
facility electricity expenditures (excluding pump station electricity). The influent pump station
consumes an additional five million kWh per year. Septage and municipal solids tipping fees
were allocated to this treatment group and amount to 1.56 million dollars in the base year (2016).
The Preliminary and Primary treatment group requires annual material inputs of activated carbon
and potassium permanganate as well as grit disposal.
Ongoing maintenance of the new waste receiving station was assessed as two percent of
capital expenditures listed in Table 4-1. One additional staff member was assumed to be required
to cover the additional workload of accepting SSO and increased operational requirements
associated with the AD and CHP expansion. The cost of this employee was included in the AD
and CHP process group.
Table 4-5. Cost Summary by Feedstock Scenario for the Preliminary and Primary
Treatment Group - Base Cost Scenario
Cost Category
Feedstock Scenario
liiiselinc
I'iirtinl Ciipiicity
Full Ciipiicity
Electricity
$935,931
Chemical, potassium permanganate
$11,000
Revenue, septage
$1,500,000
Revenue, municipal solids
$60,000
Material, activated carbon
$12,681
Grit disposal
$47,500
Maintenance, receiving station
-
$7,600
$7,600
4.3.3 Biological Treatment
The biological treatment group accounts for 35 percent of electricity cost in the baseline
scenario. Electricity consumption, and associated cost, was increased in the partial and full
capacity scenarios proportional to increased BOD loading (Table 4-6).
Table 4-6. Annual Cost Summary by Feedstock Scenario for the Biological Treatment
Group - Base Cost Scenario
Cost C':ile«iorv
Feedstock Scenario
Baseline
Partial Capacity
Full Ciipiicitv
Electricity
$621,612
$632,518
$644,981
4.3.4 Secondary Clarification
The four secondary clarifiers and RAS pumping account for eight percent of electricity
cost in the baseline scenario. Electricity consumption for secondary clarification remains
constant across scenarios (Table 4-7).
4-7
-------
Section 4—LCCA Methodology
Table 4-7. Annual Cost Summary by Feedstock Scenario for the Secondary Clarifier
Treatment Group - Base Cost Scenario
C'osl C':iU>«iorv
l-'ccdslock Scoiiiirio
linsclinc
Piirliiil ( iipncilv
l ull C iipiicilv
Electricity
$151,203
4.3.5 Sludge Thickening and Dewatering
Table 4-8 summarizes annual plant costs associated with the Sludge Thickening and
Dewatering treatment group. Sludge thickening and dewatering accounts for 11 percent of
electricity cost in the baseline scenario. Electricity consumption by the gravity thickeners and
GBTs increases slightly as the facility accepts increased quantities of SSO. Centrifuge electricity
consumption was scaled proportionally to the increase in digester solids processed. Polymer use
is required for centrifuges and GBTs and was calculated for each scenario based on the quantity
of solids processed by the respective dewatering process. The unit cost of polymer is $1.49 per
pound ($3.28 per kg).
Table 4-8. Annual Cost Summary by Feedstock Scenario for the Sludge Thickening and
Dewatering Treatment Group - Base Cost Scenario
Cost ( iilcjiorv
l-'ccdslock SiTiiiirio
liitsclinc
I'sirliiil ( ;ip:icilv
l-'ull ( :ip:icilv
1 Electricity
$201,604
$251,643
$300,162
Chemical, polymer
$447,148
$647,377
$843,865
4.3.6 Plant Water and Disinfection
Table 4-9 summarizes annual plant costs associated with the Plant Water and
Disinfection treatment group. All annual costs for this treatment group remain constant across
scenarios. Plant water and disinfection accounts for eight percent of electricity cost in the
baseline scenario. Unit chemical costs in dollars per pound are $0,093 and $0,117 for sodium
hypochlorite and sodium bisulfite, respectively. GLSD receives revenue for the sale of treated
effluent to a local industrial partner.
Table 4-9. Annual Cost Summary by Feedstock Scenario for the Plant Water and
Disinfection Treatment Group - Base Cost Scenario
C 'osl CsHc«»orv
l-'ccdslock Scenario
liitsclinc I'llrli;i 1 (;ip;icit\ l ull (;ip;icil\
Electricity
$134,403
Chemical, sodium hypochlorite
$100,000
Chemical, sodium bisulfite
$100,000
Revenue, effluent sale
$72,000
4-8
-------
Section 4—LCCA Methodology
4.3.7 Anaerobic Digestion and CHP
The costs of unit construction and mechanical equipment associated with the AD
expansion and CHP installation were based on debt service estimates provided by facility staff.
Capital costs include one additional digester, waste blending tank, new digester feed pumps,
upgraded flare and biogas collection system, siloxane treatment system and two 1.6 MW
cogeneration engines. One additional full-time staff member will be required to help with
operation of the AD units and acceptance of the SSO material. Several large maintenance
projects including digester cleaning, fixing a draft tube leak, biogas metering and monitoring,
and foam control improvements were included in the cost estimate. Table 4-10 summarizes
annual plant costs associated with the AD and CHP treatment group.
Ongoing maintenance cost associated with the AD and CHP expansion was estimated to
be two percent of capital cost for AD and biogas processing equipment. CHP engine
maintenance costs were estimated using a factor of $0,019 per kWh of electricity production
(Wiser et al. 2010). These costs were considered in addition to maintenance costs based on the
2016 budget, introduced in Section 4.3.1.
The AD and CHP system accounts for 14 percent of electricity cost in the baseline
scenario. Electricity consumption increases by approximately 28 percent with the addition of a
fourth digester. Net metering in the State of Massachusetts allows the facility to offset the cost of
purchased electricity following installation of the CHP system. Under the Massachusetts net
metering system, electricity production beyond the customers demand is credited to the
electricity bill and can be used to pay for future utility expenditures. Net metered electricity
production off-sets basic service charges, distribution, transmission and transition costs on the
customers electricity bill (Commonwealth of Massachusetts 2017c).
The GLSD's CHP system is classified as a Class III net metering facility, and could be
eligible for payment for excess electricity production, at the discretion of the electric utility
(Commonwealth of Massachusetts 2018). However, this was deemed to be unlikely, therefore
the only financial benefit ascribed to electricity production more than the facilities' demand
comes from the sale of RECs. Only the full capacity-base AD performance scenario is expected
to produce electricity in excess of facility demand (Table 3-13, Table 3-14). The economic value
of net metered electricity was used in the calculation of the discounted payback period for the
AD and CHP expansion project.
The CHP system is eligible to be classified as class I generation unit within the
Massachusetts Renewable Portfolio Standard program (Commonwealth of Massachusetts 2016),
allowing for an additional source of revenue from the sale of environmental benefits associated
with electricity production. Renewable energy credits, corresponding to one MWh of net
electricity production, can be sold at current market prices. The facility is also eligible for
classification as a Renewable Thermal Generation Unit within the Massachusetts Renewable
Portfolio Standard program, which allows for revenue from the sale of AECs for useful thermal
energy. Alternative energy credits, corresponding to one MWh equivalent of thermal energy
production, can also be sold at current market prices. Revenue from the sale of RECs and AECs
was assessed less the internal (parasitic) energy demand (i.e. net production) of the AD and CHP
expansion (DOER 2016).
4-9
-------
Section 4—LCCA Methodology
Revenue from acceptance of SSO material was allocated to the AD and CHP treatment
group. GLSD staff report an SSO tipping fee of between 0.5 and 2 cents per gallon.
The GLSD purchased 124 thousand pounds (56 metric tons) of ferric chloride in 2016 at
a unit cost of $0,255 per pound.
Table 4-10. Annual Cost Summary by Feedstock Scenario for the AD and CHP Treatment
Group - Base Cost Scenario
Cost ( iilcjiorv
l-'ccdslock Scciiiirio
liiisoline
I'nrliiil Ciipiicilv
l ull Ciipiicilv
Additional labor
-
$104,000
$104,000
Electricity
$235,205
$300,539
$300,539
Natural gas
$37,214
-
-
Avoided cost, electricity
-
$2,051,175
$3,545,027
Capital, annual debt service1
-
$1,427,317
$1,428,317
Chemical, defoamant
$20,000
Chemical, ferric chloride
$31,797
$35,772
$39,746
Revenue, SSO tipping fee
-
$83,950
$167,900
Revenue, RECs
-
$162,900
$301,143
Revenue, AECs
-
$292,182
$385,802
Maintenance, CHP2
-
$303,225
$524,061
Maintenance, AD3
-
$175,600
$175,600
1 This cost corresponds to the first annual debt service payment. See Table C-2 for year-by-year debt
service estimates.
2 Maintenance costs were estimated in addition to plant maintenance costs listed in the 2016 budget.
3 Maintenance of ADs in the baseline scenario are included in the Full Plant treatment group.
4.3.8 Biosolids Pelletization and Sale
The pellet drying facility is operated by a separate entity that is contracted to dry
digestate from the GLSD WWTF. No additional offsite material is processed in the pellet drying
facility. An annual base drying fee is paid to the contracting company regardless of the quantity
of solids processed by the facility. An additional fee of $24.97 is charged per wet short ton of
material in excess of 20,000 short tons per year. GLSD is separately responsible for the energy
cost of operating the pellet drying facility. The pellet drying facility accounts for 16 percent of
electricity cost in the baseline scenario. Additional electricity costs were estimated for the partial
and full capacity scenarios based on an additional electricity requirement of 350 kWh per short
dry ton of solids processed. The increased heat demand associated with SSO feedstock scenarios
was also estimated on a per ton basis. Biogas can satisfy 100 percent of pellet drying heat
demand for all feedstock and AD performance scenarios, avoiding expenditures on natural gas.
No revenue is currently generated or expected from the sale of biosolid pellets, however disposal
costs are avoided.
4-10
-------
Section 4—LCCA Methodology
Table 4-11. Annual Cost Summary by Feedstock Scenario for the AD and CHP Treatment
Group - Base Cost Scenario
C'osl C':iU>«iorv
l-'iTilslock Sceiiiirio
liitscliiic
l ull Ciipiicilv
Base drying fee
$2,293,445
Processing charge
$66,462
$300,619
$575,785
Electricity
$293,162
$414,474
$557,033
Natural gas
$6,766
-
-
4-11
-------
Section 5—LCA and LCCA Results by Treatment Group
5. LCA AND LCCA RESULTS BY TREATMENT GROUP
This section presents comparative LCA results for the GLSD WWTF scenarios by impact
category.
5.1 Guide to Results Interpretation
Results for this project were calculated for all combinations of the following parameters.
• Feedstock Scenarios - Results were calculated for baseline, partial capacity and full
capacity feedstock scenarios. The partial and full capacity scenarios demonstrate the
effect of accepting and digesting SSO waste on impact potential of the treatment
system. Feedstock quantities associated with the scenarios are presented in Table 2-4.
• Anaerobic Digestion - Results were calculated for a set of parameters defining low
and base (expected) operational performance of the AD units, as presented in Table
3-12.
• Avoided SSO Disposal - Results were calculated for the MA disposal mix of avoided
SSO disposal processes, avoided landfill only, avoided WTE only and without any
avoided disposal (i.e., avoided disposal is outside system boundaries). Background
information on avoided disposal scenarios is available in Section 3.3.14.
Section 5 presents results for all feedstock scenario options assuming base AD
performance and avoided SSO disposal impacts using the MA disposal mix. Section 6 presents
all results as part of the scenario and sensitivity analysis.
The above model parameters were varied over the ranges defined in Section 2.3.2 to
convey the potential variability in impact results that might be realized by wastewater treatment
systems of the type considered in this analysis. The trends observed and the key variables that
drive environmental impacts as discussed in Sections 5 and 6 can be used by facilities during the
design process to estimate potential impacts and areas for potential improvement by examining
results associated with the parameter combinations that most closely match those of their specific
system of interest.
5-1
-------
Section 5—LCA and LCCA Results by Treatment Group
Throughout this section, results calculated at the unit process level have been aggregated
by treatment group, as shown in Table 2-8. Global warming potential and CED also show
impacts aggregated according to the process categories listed in Section 2.4. Relative change
values, quoted in this section, comparing impact between the baseline scenario and the partial
and full capacity scenarios were calculated relative to the baseline scenario, using Equation 5.
SSO ScenariOj^pact BaselineimpaCf
Relative Change = — 7: —
Baseline jmpac^
Equation 5
5.2 Eutrophication Potential
Eutrophication potential is a critical metric for measuring the comparative environmental
performance of wastewater treatment systems. Figure 5-1 presents EP results organized by
treatment group. Eutrophication potential impacts are presented in g N eq/m3 wastewater treated.
Nitrogen equivalents present the EP of both nitrogen and phosphorus compounds together in a
single unit and are therefore, not comparable to typical parameters used to measure and report
effluent quality. Eutrophication potential is primarily driven by effluent release, with over 92
percent of eutrophication impact attributable to this treatment group for all feedstock scenarios.
Eutrophication potential increases by 10 and 20 percent as more SSO material is
processed in the partial and full capacity scenarios. SSO material contains additional nitrogen
and phosphorus that is returned to the primary and secondary treatment unit processes. In theory,
a fraction of these nutrients is ultimately released in the effluent, contributing to EP. However,
the actual quantity of nitrogen and phosphorus that is returned, and thus ultimately contributes to
effluent concentrations, has not been well studied and is a source of uncertainty in the current
results. Preliminary water quality monitoring is ongoing to determine what affect, if any, the
addition of SSO material may have on final effluent nutrient concentrations (further discussed in
Appendix B).
Land application of pelletized biosolids is the other visible contributor to EP impact,
accounting for between four and six percent of impact. The eutrophication contribution of land
applied biosolid pellets is a source of uncertainty and is included as a conservative estimate of
the net change in EP that could result from switching from chemical fertilizers to organic
nutrient sources. Increased field emissions were based on the 55 percent fertilizer replacement
value, which indicates that greater quantities of nitrogen and phosphorus need to be land applied
for an equivalent crop response due to lower plant availability of nutrients in the pelletized
biosolids.
5-2
-------
Section 5—LCA and LCCA Results by Treatment Group
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~ Influent Pump Station 0 Preliminary/Primary
S Biological Treatment ~ Sludge Dewatering
~ Plant Water and Disinfection 0 Anaerobic Digestion and CHP
~ Pellet Drying 0 Pellet Land Application
H Building Operation H Effluent Release
• Net Impact
Figure 5-1. Eutrophication potential results by treatment group.
5.3 Cumulative Energy Demand
Figure 5-2 and Figure 5-3 present CED results organized according to treatment group
and by process category, respectively. Cumulative energy demand decreases from a maximum of
5.0 MJ per m3 of wastewater treated in the baseline feedstock scenario to a minimum of -6.4 MJ
per mJ for the full capacity scenario. If the full capacity of the digesters is used for co-digestion
of municipal solids and SSO material, the WWTF avoids more energy use than is required for its
own operation.
Figure 5-3 illustrates the 55 percent increase in gross positive CED that is associated with
the full capacity scenario. The increase is largely associated with avoided SSO disposal in
landfills and WTE facilities, both of which are also energy producers. The full capacity scenario
experiences a 20 percent increase in CED associated with plant electricity consumption due to
the processing requirements of the additional organic waste. The figure shows that this increase
in gross, positive CED is offset by avoided electricity production and natural gas combustion.
Avoided electricity production produces the largest CED credit for the partial and full capacity
scenarios, amounting to six and 11 MJ, respectively. Energy content of combusted biogas is
excluded from the analysis for all feedstock scenarios and avoided disposal processes, because it
enters the WWTF as a waste product, while CED measures energy extractions from nature.
5-3
-------
Section 5—LCA and LCCA Results by Treatment Group
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~ Influent Pump Station ~ Prelim in ary/Primary
H Biological Treatment ~ Sludge Dewatering
¦ Plant Water and Disinfection 0 Anaerobic Digestion and CHP
~ Pellet Drying 1 Pellet Land Application
HBuilding Operation 00 Effluent Release
•Net Impact
Figure 5-2. Cumulative energy demand results by treatment group.
The analysis shows that a significant energy resource is forfeited when food waste, in the
form of SSO, is disposed of in landfills and WTE facilities. Each kg of food waste generates
approximately 0.025 kWh of electricity when disposed of in MA landfills due to landfill gas
capture, whereas that same kg of material will generate approximately 0.09 kWh when
incinerated with energy recovery. When food waste is digested in this analysis it generates up to
0.48 kWh per kg of food waste plus recovered thermal energy. It has been shown that the
theoretical energy potential of the organic material in typical domestic wastewater is on the order
of 1.9 kWh/m3 of wastewater treated (McCarty et al. 2011). Through the application of co-
digestion to boost the quantity of volatile solids processed, this system realizes approximately
2.6 MJ of energy recovery per m3 of wastewater treated in the full capacity-base AD
performance scenario.
5-4
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Section 5—LCA and LCCA Results by Treatment Group
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EO Electricity
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E3 Transport
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~ Infrastructure
• Net Impact
Full Capacity
El Chemicals
~ Avoided Fertilizer
0 Avoided SSO Disposal
DD Potable Water Use
~ Oil-Site Combustion
Figure 5-3. Cumulative energy demand results by process category.
5.4 Global Warming Potential
Figure 5-4 presents GWP results organized according to treatment group, while Figure
5-5 presents results according to process category. Global warming potential decreases from a
maximum of 0.36 kg CCh-eq per m3 of wastewater in the baseline feedstock scenario to a
minimum of -0.28 kg CC>2-eq per m3 within the full capacity scenario. Figure 5-4 demonstrates
that the marked decrease in net GWP is largely due to environmental credits associated with AD.
Results by process category show that gross, positive GWP increases as more SSO is accepted.
The GWP of increased material and energy consumption and process emissions associated with
the co-digestion feedstock scenarios are more than offset by avoided product credits. The effect
on net impacts is such that the WWTF achieves a net zero GWP impact in the partial capacity
scenario. Environmental benefits (negative impact results) are realized by accepting and
processing SSO to boost biogas energy and biosolids production in the full capacity-base AD
scenario.
The increases in GWP that occur due to increased process GHG emissions, are primarily
associated with fugitive methane emissions from the digesters. The analysis assumes that five
percent of biogas methane is lost through the floating cover based on guidance provided in Clean
Development Mechanism literature (UNFCCC 2012). Other types of digesters can have lower
rates of fugitive emissions, but Clean Development Mechanism guidance does not suggest a
5-5
-------
Section 5—LCA and LCCA Results by Treatment Group
fugitive emission rate below 2.8 percent. On-site combustion emissions (biogas equipment),
increases in facility electricity demand, and increased nitrous oxide emissions associated with
elevated effluent nitrogen emissions also contribute to increases in gross, positive GWP.
Figure 5-5 shows that avoided natural gas production and combustion is the largest single
contributor to reductions in net GWP, yielding an environmental credit of 0.45 kg CO2 eq. per
m3 wastewater treated in the full capacity scenario. Avoided SSO disposal also contributes
substantially to reductions in net GWP for the co-digestion scenarios. An examination of detailed
process results, presented in Appendix D, reveals that avoided landfill disposal is responsible for
the impact reduction. The GWP credit associated with avoided SSO disposal in landfills
primarily accrues due to avoided landfill gas methane emissions. Avoided WTE incineration
contributes to net GWP at a rate of approximately 0.09 kg CO2 eq. per m3 of wastewater treated
(full capacity). The magnitude of the GWP benefit is therefore strongly dependent on avoiding
landfill disposal, and the assumed gas capture rate of the landfill. The sensitivity results in
Section 6.1 present comparative results that illustrate the effect of avoided SSO disposal
assumptions.
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Section 5—LCA and LCCA Results by Treatment Group
of biogas heat production that can be utilized by the facility contributes to avoided natural gas
consumption. In the full capacity scenario, over 60 terajoules of additional thermal energy is
available for utilization2. Wasted thermal energy constitutes 20 percent of available biogas fuel
energy and 50 percent of CHP thermal output in the full capacity scenario. Further
environmental benefits, not assessed in this report, can be realized in the future if the facility is
able to identify additional uses of thermal energy. The LCA assumes all electricity can be
utilized at the facility or exported to the grid with each kWh of electricity production yielding
one kWh of avoided electricity production. Avoided electricity production leads to GWP credits
of 0.20 and 0.35 kg CO2 eq. per m3 wastewater treated in the partial and full capacity scenarios.
The full capacity scenario is a net exporter of electricity, producing an annual surplus of over six
million kWh. All electricity production was assumed to offset energy from the ISO-NE network
shown in Table 2-2.
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Section 5—EOt and /.(11 Results by Treatment Group
5.5 Acidification Potential
Figure 5-6 presents the impact assessment results for acidification potential organized
according to treatment group. Relative acidification potential impact is reduced by 35 and 50
percent by accepting SSO material according to the partial and full capacity scenario
assumptions, respectively. The figure shows a significant environmental credit attributable to AD
avoided products. A review of detailed process results reveals that over 80 percent of this
environmental credit is due to avoided energy production. Avoided SSO disposal contributes the
remaining 20 percent of acidification potential reductions. Pellet land application is shown to
contribute prominently to acidification potential impact. Acidification potential of land
application is due primarily to field emission of ammonia. A nitrogen fertilizer replacement
value of 55 percent was used to estimate this impact due to lower nutrient availability in
pelletized biosolids as compared to chemical fertilizers as described in Section 3.3.11. Operation
of the pellet dryer also contributes to noticeable increases in acidification potential as the
quantity of dried biosolids increases.
-1.5
Baseline Partial Capacity Full Capacity
~ Influent Pump Station 0 Preliminary/Prim aiy
S3 Biological Treatment ~ Sludge Dewateiing
¦ Plant Water and Disinfection 0 Anaerobic Digestion and CHP
~ Pellet Drying B Pellet Land Application
DO Building Operation 03 Effluent Release
•Net Impact
Figure 5-6. Acidification potential results by treatment group.
5-8
-------
Section 5—LCA and LCCA Results by Treatment Group
5.6 Fossil Depletion Potential
Figure 5-7 presents fossil depletion potential results organized according to treatment
group. The use of biogas as an energy source leads to a net reduction in fossil fuel consumption.
Increased biogas production attributable to co-digestion and the installation of CHP eliminates
the need for on-site natural gas combustion in both the partial and full capacity scenarios. If the
full capacity of the fourth digester is utilized, the facility becomes a net exporter of electricity,
despite a 20 percent increase in facility electricity demand. Combined heat and power electricity
production in the partial capacity scenario satisfies 80 percent of facility electricity demand.
Sixty percent of available biogas is combusted in the CHP system in the partial capacity
scenario, 30 percent is used in the pellet driers and 10 percent is flared. Figure 5-7 reflects the
increase in energy demand for sludge dewatering and pellet drying that accompanies SSO
acceptance. Avoided energy products associated with digestion more than offset increased
facility energy demand, substituting natural gas and grid electricity with a non-fossil energy
alternative.
0.15
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Baseline Partial Capacity Full Capacity
~ Influent Pump Station E3 Preliminary/Primary
B Biological Treatment ~ Sludge Dewatering
~ Plant Water and Disinfection E3 Anaerobic Digestion and CHP
~ Pellet Drying (3 Pellet Land Application
DD Building Operation ~ Effluent Release
#Net Impact
Figure 5-7. Fossil depletion potential results by treatment group.
5-9
-------
Section 5—LCA and LCCA Results by Treatment Group
5.7 Smog Formation Potential
Figure 5-8 presents the smog formation potential results organized according to treatment
group. The partial and full capacity scenarios yield 50 and 80 percent reductions in smog
formation potential relative to the baseline scenario. This is due to the environmental credit
attributable to AD avoided products. Review of detailed process results reveals that avoided
electricity production yields the greatest reduction in smog formation potential. Environmental
impacts and credits are nearly balanced for the AD treatment group in the baseline feedstock
scenario leading to a negligible net contribution to smog formation potential. Emissions
associated with biogas combustion in the pellet drying facility lead to increasing gross, positive
smog formation potential impacts from operation of the WWTF. Avoided products and EOL
disposal methods associated with digestion more than offset increases in smog formation
potential associated with increased pellet production.
25
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Section 5—LCA and LCCA Results by Treatment Group
the environmental credit attributable to AD avoided products. Review of detailed process results
reveals that avoided natural gas combustion yields the greatest reduction in particulate matter
formation potential. Biogas flaring is the largest contributor to particulate matter formation
potential impact within the AD treatment group for the partial and full capacity scenarios.
Emission of particulates and SO2 from biogas combustion in the pellet drier are the dominant
contributor to increasing gross, positive particulate matter formation potential impacts from
operation of the WWTF. Increased electricity demand associated with biosolids dewatering also
contributes to increased gross, positive impact as the facility accepts larger quantities of SSO.
Overall, the trend is towards decreasing net particulate matter formation potential impact as more
SSO is accepted.
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Figure 5-9. Particulate matter formation potential results by treatment group.
5.9 Water Use
Figure 5-10 presents water use results grouped according to treatment group. The avoided
water extraction associated with on-site and industrial reuse of treated wastewater dominates all
other sources of water use within the product system. The quantity of reuse water remains
constant across scenarios and constitutes approximately 13 percent of treated wastewater by
volume. A split axis scale is used to facilitate viewing water use not associated with avoided
potable water consumption. Examination of detailed process results indicates that water use
Baseline Partial Capacity Full Capacity
~ Influent Pump Station 13 Preliminary/Primary
H B i ological T reatment ~ Sludge Dewatering
~ Plant Water and Disinfection 0 Anaerobic Digestion and CHP
~ Pellet Drying H Pellet Land Application
DDBuilding Operation 00Effluent Release
~Net Impact
5-11
-------
Section 5—LCA and LCCA Results by Treatment Group
during SSO slurry production, avoided SSO disposal and electricity consumption are the three
primary contributors to gross, positive water use potential. Avoided electricity production
reduces gross, positive water use potential by approximately 40 percent.
0.01
CC
-------
Section 5—LCA and LCCA Results by Treatment Group
and the CHP system is 14 and 27 years for the full and partial capacity scenarios, respectively.
Payback period is not applicable for the baseline scenario.
This report presents results such that the environmental benefit of energy recovery
accrues to the WWTF, reducing the impact of treating a unit of wastewater. However, when the
facility sells RECs and AECs associated with its energy products, they are selling those benefits
to other facilities. The LCA quantifies this environmental benefit and presents the results per
cubic meter of treated wastewater.
A detailed breakdown of life cycle costs that were used to develop Figure 5-11 are
included in Appendix C, Table C-3 and Table C-4.
o
o
ri
C/5
350
300
250
200
^ 150
o
100
50
301
»H'»»
vKwKvKvKvKv
282
v.v.v.v.v.v.v.v.v
viyivivivivlvlvlv
-50
Baseline
B Capital
~ Annual Chemical
Partial Capacity
~ Annual Operation
El Annual Energy
Full Capacity
ESI Annual Material
• T otal NPV
Figure 5-11. Base life cycle costs by cost category for the case-study wastewater treatment
facility.
5-13
-------
Section 6—Scenario and Sensitivity Analysis
6. SCENARIO AND SENSITIVITY ANALYSIS
Sensitivity and scenario analysis help determine the influence of model assumptions and
parameters on the study findings. The sensitivity and scenario analysis covered in this work were
previously introduced in Section 2.3.2.
6.1 Anaerobic Digestion Performance
Results presented in this section demonstrate the sensitivity of EP, GWP and CED impact
results to assumptions regarding anaerobic digester performance. Section 6.3 includes summary
results for all impact categories.
Figure 6-1 presents comparative EP results for three feedstock and two AD performance
scenarios. The trend in results shows that increasing acceptance of SSO material is likely to
increase the plant's contribution to eutrophication in the absence of nutrient removal and
recovery strategies. The GLSD WWTF is not permitted on nitrogen or phosphorus and is not
designed for nutrient removal. The highest EP is exhibited by the full capacity-low AD
performance and represents a 24 percent increase in EP impact relative to the baseline feedstock
scenario. Nitrogen emission estimates in the low AD performance scenario assume that 80
percent of nutrient content in SSO material is solubilized and returned to the primary and
secondary treatment units leading to increased effluent emissions. In the base AD scenario,
approximately 60 percent of nitrogen in the SSO returns to the primary clarifier in centrifuge
supernatant. A consistent 20 percent increase in phosphorus return was also applied in the low
AD scenario. The difference in EP between the base and low AD performance scenarios is
approximately four percent in both co-digestion scenarios.
6-1
-------
Section 6—Scenario and Sensitivity Analysis
30
1 25
p
z 20
3
% 15
| 10
t+\
J—I
V
2
Mi "
Baseline
Partial Capacity
Full Capacity
Base AD
Base AD Low AD
Base AD Low AD
i i Preliminary.'Primary
i i Sludge Dewatering
ESSS Anaerobic Digestion and CHP
mo Pellet Land Application
i I Effluent Release
Figure 6-1. Eutrophication potential by treatment group for all feedstock and AD
performance scenarios.
Figure 6-2 presents comparative GWP results for three feedstock and two AD
performance scenarios. Both partial capacity scenarios yield net reductions in GWP relative to
the base scenario. The partial capacity-low AD scenario yields a 50 percent reduction in GWP
impact relative to the baseline scenario, while the partial capacity-base AD scenario has very
close to net zero GWP impact. Both full capacity-AD performance scenarios produce GWP
benefits (i.e. net negative impact results). The full capacity-base AD performance scenario leads
to a reduction in GWP of 175 percent relative to the baseline scenario. Both avoided SSO
disposal and avoided energy products contribute considerable reductions in GWP impact. The
magnitude of the avoided SSO disposal impact/credit is the same regardless of AD performance
scenario.
In the partial capacity-base AD performance scenario, the environmental credit
associated with avoided energy products totals 0.50 kg CO2 eq per m3 wastewater treated.
Avoided landfill disposal reduces GWP impact by 0.20 kg CO2 eq per m wastewater treated,
while avoided WTE disposal leads to slight increases in GWP. Although avoided WTE disposal
itself yields slight increases in impact, the benefits of digesting food waste outweigh the forfeited
benefits of WTE incineration.
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is.
Influent Pump Station
B i ol ogi ca I Treatment
Plant Water and Disinfection
Pellet Drying
~ Building Operation
• Net Impact
6-2
-------
Section 6—Scenario and Sensitivity Analysis
The quantity of biogas flared increases to 20 percent in the low AD performance
scenario, increasing on-site combustion emissions and reducing energy production. Fugitive
emissions decrease in the low AD performance scenario, given that emissions were estimated as
a set five percent fraction of biogas produced.
Global warming potential impact per m3 of wastewater treated was found to be sensitive
to assumptions related to capacity utilization and AD performance, however the downward trend
in GWP impact remains consistent regardless of AD performance as SSO co-digestion is adopted
and SSO quantities increase. Overall, Figure 6-2 demonstrates that utilizing the full digester
capacity for SSO co-digestion produces the greatest GWP benefit, by avoiding the use of fossil-
based energy products and methane emissions associated with landfill disposal of food waste.
1
O
cc
>
Zf}
CC
>
0.6
0.4
0.2
2 -0.2
-0.4
cr
o
iN
O
U
ojj -0.6
i—*5
-0.8
Baseline
Partial Capacity
Full Capacity
Base AD
Base AD Low AD
Base AD Low AD
Influent Pump Station
Biological Treatment
Plant Water and Disinfection
Pellet Drying
Building Operation
Net Impact ~
Prel imi nary Primary
I I Sludge Dewatering
I Anaerobic Digestion and CHP
I Pellet Land Application
CHZi Effluent Release
Figure 6-2. Global warming potential by treatment category for all feedstock and AD
performance scenarios.
Figure 6-3 presents comparative CED results for three feedstock and two AD
performance scenarios. All four SSO feedstock scenarios yield lower net CED than was
associated with the WWTF prior to AD expansion and SSO co-digestion. The partial capacity-
low AD performance scenario demonstrates the most modest reduction in CED, reducing net
CED by approximately 30 percent relative to the baseline scenario. In the full capacity-base AD
scenario, a CED environmental benefit is achieved, corresponding to a 225 percent reduction
6-3
-------
Section 6—Scenario and Sensitivity Analysis
relative to the baseline scenario. Like the trend in GWP results, net CED was found to be
sensitive to both feedstock acceptance and assumptions regarding AD performance, however the
relative reduction in CED as SSO co-digestion is adopted is clear in all the developed scenarios.
-20
Baseline
Partial Capacity
Full Capacity
Base AD
Base AD Low AD
Base AD Low AD
Influent Pump Station
Biological Treatment
Plant Water and Disinfection
Pellet Drying
Building Operation
Net Impact I I
i \ Preliminary/Primary
¦ ' Sludge Dewatering
~ Anaerobic Digestion and CHP
E
mmm Pellet Land Application
i i Effluent Release
Figure 6-3. Cumulative energy demand by treatment group for all feedstock and AD
performance scenarios.
6.2 SSO Avoided End-of-Life Disposal
This section presents results of the sensitivity analysis focusing on avoided EOL disposal
processes. Baseline results, presented in Section 5, include the net environmental benefits and
burdens of avoiding SSO EOL disposal according to current disposal practices in Massachusetts
(MA disposal mix). Results excluding avoided EOL processes are labeled "None" in Figure 6-4
and Figure 6-5. The figures also present results for hypothetical scenarios where each kg of SSO
waste digested avoids 100% landfill or 100% WTE disposal routes. Section 6.3 includes
summary results for all impact categories. Appendix A includes the results of an additional
analysis where anaerobic digestion of food waste is compared to composting as an alternative
EOL disposal strategy.
Figure 6-4 presents GWP results by treatment group for all avoided EOL options and the
base AD performance scenario. The magni tude of global warming potential impact results is
strongly affected by selection of the avoided EOL disposal option. Avoiding landfill disposal of
6-4
-------
Section 6—Scenario and Sensitivity Analysis
SSO waste yields a significant environmental credit, which drives the negative impact score
demonstrated for the AD and CHP treatment group. The predominant contribution of landfill
disposal in the national disposal mix, 82 percent, is responsible for the significant decrease in
GWP within this scenario. This avoided GWP burden is primarily due to avoided landfill
fugitive methane emissions. Avoiding WTE disposal of SSO has the opposite effect, which
works to reduce the GWP benefits associated with avoided energy and fertilizer products. This
makes sense intuitively, given that substitution of one energy producing process for another has a
more limited net effect on results. However, the net GWP impact of the AD and CHP treatment
group is negative for all avoided SSO disposal scenarios, indicating that the net benefits of
anaerobically digesting food waste are greater than the benefits associated with WTE
combustion, making AD an environmentally preferable option for the GWP impact category.
Excluding avoided EOL disposal still leads to a net reduction in GWP impact for both the
partial and full capacity feedstock scenarios, indicating that the environmental benefits of SSO
co-digestion are not dependent on avoided EOL disposal credits.
Figure 6-5 presents CED results by treatment group for all avoided EOL options and the
base AD performance scenario. Both the full and partial capacity scenarios yield net reductions
in facility energy demand regardless of assumptions concerning avoided EOL disposal. The
magnitude of net reduction in CED is less sensitive than GWP impact results. Avoiding WTE
disposal tends to reduce CED benefits, due to the energy production associated with WTE
incineration. Avoided landfill disposal has a limited effect on CED.
The national disposal scenario yields a greater CED reduction relative to the MA disposal
mix due to the lower quantity of avoided WTE combustion in the national disposal scenario. The
"None" scenario, that excludes avoided EOL disposal, shows that avoided waste disposal
processes tend to reduce the CED benefits of food waste anaerobic digestion. However, the
benefits of AD considerably outweigh those of the avoided disposal processes, making AD an
environmentally preferable option to either landfill or WTE disposal of food waste from the
perspective of CED.
6-5
-------
Section 6—Scenario and Sensitivity Analysis
-2.0
Feedstock Baseline
Avoided SSO
Partial Capacity
MA Mix National None Landfill - WTE -
Mix
~ Influent Pump Station
~ Sludge Dewatering
¦ Pellet Drying
E3 Effluent Release
100% 100%
Full Capacity
Ma Mix National None Landfill - WTE -
Mix 100% 100%
0 Preliminary/Primary
¦ Plant Water and Disinfection
B Pellet Land Application
• Net Impact ~
Biological Treatment
0 Anaerobic Digestion and CHP
CD Building Operation
Figure 6-4. Global warming potential results by treatment group for all feedstock and avoided EOL scenarios.
6-6
-------
Section 6—Scenario and Sensitivity Analysis
-20
Avoided SSO
Baseline
Partial Capacity
Full Capacity
)
MA Mix National None Landfill -
Mix 100%
WTE -
100%
Ma Mix National None Landfill -
Mix 100%
WTE -
100%
~ Influent Pump Station
~ Sludge Dewatering
¦ Pellet Drying
0 Effluent Release
~ Preliminary/Primary
¦ Plant Water and Disinfection
B Pellet Land Application
• Net Impact ~
S Biological Treatment
0 Anaerobic Digestion and CHP
CD Building Operation
Figure 6-5. Cumulative energy demand by treatment group for all feedstock and avoided EOL scenarios.
6-7
-------
Section 6—Scenario and Sensitivity Analysis
6.3 Summary Results - All Impact Categories and EOL Scenarios
Table 6-1 presents net impact results for all feedstock-AD performance scenarios for each
avoided SSO disposal scenario. Positive impact results for feedstock-AD performance scenarios
that are less than the baseline value indicate a net reduction in environmental impact relative to
the baseline scenario. Negative impact results indicate an environmental benefit due to a
combination of avoided product and SSO disposal process benefits. Table 6-2 presents LCIA
results relative to the baseline scenario. Positive percentages that are less than 100 percent
indicate a net reduction in environmental impact relative to the baseline scenario, while values
greater than 100 percent indicate a net increase in impact. Negative values indicate an
environmental benefit.
6-8
-------
Section 6—Scenario and Sensitivity Analysis
Table 6-1. Net Impact Results for all Feedstock and AD Performance Scenarios (per m3 Wastewater Treated)
Partial
Partial
lull
lull
Capacity. Base
Capacity. Low
Capacity.
Capacity.
Impacl Cale«»orv
KOI. Scenario
Baseline
Al)
Al)
Base Al)
Low Al)
MA Disposal Mix
0.36
0.01
0.19
-0.28
-0.05
Global Warming
National Disposal Mix
n.a.
-0.4
-0.2
-1.1
-0.9
Potential - kg
No Avoided SSO Disposal
n.a.
0.17
0.36
0.06
0.28
CO2 eq
Avoided Landfill - 100%
n.a.
-0.5
-0.3
-1.2
-1.0
Avoided WTE - 100%
n.a.
0.23
0.42
0.18
0.41
MA Disposal Mix
0.02
0.03
0.03
0.03
0.03
Eutrophication
National Disposal Mix
n.a.
0.03
0.03
0.03
0.03
Potential - kg N
No Avoided SSO Disposal
n.a.
0.03
0.03
0.03
0.03
eq
Avoided Landfill - 100%
n.a.
0.03
0.03
0.03
0.03
Avoided WTE - 100%
n.a.
0.03
0.03
0.03
0.03
MA Disposal Mix
5.0
-1.7
3.7
-6.4
1.2
Cumulative
National Disposal Mix
n.a.
-2.5
2.9
-8.03
-0.5
Energy Demand -
No Avoided SSO Disposal
n.a.
-3.4
2.0
-9.77
-2.2
MJ
Avoided Landfill - 100%
n.a.
-2.8
2.6
-8.5
-0.9
Avoided WTE - 100%
n.a.
-1.20
4.2
-5.3
2.2
MA Disposal Mix
0.05
-0.07
0.02
-0.15
-0.04
Fossil Depletion
National Disposal Mix
n.a.
-0.08
0.01
-0.17
-0.06
Potential - kg oil
No Avoided SSO Disposal
n.a.
-0.09
0.00
-0.19
-0.08
eq
Avoided Landfill - 100%
n.a.
-0.08
0.01
-0.18
-0.06
Avoided WTE - 100%
n.a.
-6.4E-2
0.02
-0.14
-0.03
Particulate Matter
Formation
Potential - kg
PM2 5 eq
MA Disposal Mix
5.4E-5
1.8E-5
5.6E-5
-4.5E-6
4.4E-5
National Disposal Mix
n.a.
1.3E-5
5.1E-5
-1.4E-5
3.4E-5
No Avoided SSO Disposal
n.a.
2.0E-5
5.8E-5
-9.0E-8
4.8E-5
Avoided Landfill - 100%
n.a.
1.1E-5
4.9E-5
-1.7E-5
3.1E-5
Avoided WTE - 100%
n.a.
2.1E-5
5.9E-5
1.6E-6
5.0E-5
MA Disposal Mix
1.0E-3
6.6E-4
1.1E-3
5.4E-4
1.1E-3
Acidification
National Disposal Mix
n.a.
5.9E-4
1.1E-3
3.8E-4
9.8E-4
Potential - kg SO2
No Avoided SSO Disposal
n.a.
7.2E-4
1.2E-3
6.6E-4
1.3E-3
eq
Avoided Landfill - 100%
n.a.
5.8E-4
1.1E-3
3.7E-4
9.8E-4
Avoided WTE - 100%
n.a.
7.0E-4
1.2E-3
6.1E-4
1.2E-3
6-9
-------
Section 6—Scenario and Sensitivity Analysis
Table 6-1. Net Impact Results for all Feedstock and AD Performance Scenarios (per m3 Wastewater Treated)
Partial
Partial
lull
lull
Capacity. Base
Capacity. Low
Capacity.
Capacity.
Impiicl CsiU'Sorv
KOI. Scenario
liiisolino
Al)
Al)
Base Al)
Low Al)
MA Disposal Mix
0.02
8.3E-3
0.02
3.7E-3
0.02
Smog Formation
National Disposal Mix
n.a.
4.9E-3
0.01
0.00
1.0E-2
Potential - kg O3
No Avoided SSO Disposal
n.a.
0.01
0.02
5.7E-3
0.02
eq
Avoided Landfill - 100%
n.a.
4.4E-3
1.4E-2
0.00
9.3E-3
Avoided WTE - 100%
n.a.
0.01
0.02
0.01
0.02
MA Disposal Mix
-0.13
-0.12
-0.12
-0.12
-0.12
Water Use - m3
H20
National Disposal Mix
n.a.
-0.12
-0.12
-0.12
-0.12
No Avoided SSO Disposal
n.a.
-0.12
-0.12
-0.12
-0.12
Avoided Landfill - 100%
n.a.
-0.12
-0.12
-0.12
-0.12
Avoided WTE - 100%
n.a.
-0.12
-0.12
-0.12
-0.12
6-10
-------
Section 6—Scenario and Sensitivity Analysis
Table 6-2. Relative Impact Results for all Feedstock and AD Performance Scenarios (Relative to Baseline Scenario)
Impact Cale«»orv
KOI. Scenario
liasdinc
Partial Capacity.
Base A1)
Partial Capacity,
l ow Al)
l-'iill Capacity.
IJasc Al)
l-'iill Capacity,
l ow Al)
Global Warming
Potential
MA Disposal Mix
100%
2%
53%
-76%
-13%
National Disposal Mix
n.a.
-115%
-64%
-311%
-248%
No Avoided SSO Disposal
n.a.
47%
99%
16%
78%
Avoided Landfill - 100%
n.a.
-132%
-81%
-344%
-281%
Avoided WTE- 100%
n.a.
65%
116%
50%
113%
Eutrophication
Potential
MA Disposal Mix
100%
110%
114%
120%
124%
National Disposal Mix
n.a.
110%
114%
119%
124%
No Avoided SSO Disposal
n.a.
110%
114%
120%
124%
Avoided Landfill - 100%
n.a.
110%
114%
119%
124%
Avoided WTE - 100%
n.a.
110%
114%
120%
124%
Cumulative
Energy Demand
MA Disposal Mix
100%
-34%
73%
-126%
24%
National Disposal Mix
n.a.
-50%
57%
-160%
-9%
No Avoided SSO Disposal
n.a.
-68%
39%
-194%
-44%
Avoided Landfill - 100%
n.a.
-55%
52%
-169%
-18%
Avoided WTE - 100%
n.a.
-24%
83%
-106%
44%
Fossil Depletion
Potential
MA Disposal Mix
100%
-148%
40%
-330%
-84%
National Disposal Mix
n.a.
-168%
19%
-371%
-125%
No Avoided SSO Disposal
n.a.
-190%
-2%
-414%
-168%
Avoided Landfill - 100%
n.a.
-174%
14%
-382%
-136%
Avoided WTE - 100%
n.a.
-136%
52%
-305%
-59%
Particulate Matter
Formation
Potential
MA Disposal Mix
100%
33%
103%
-8%
81%
National Disposal Mix
n.a.
24%
94%
-26%
64%
No Avoided SSO Disposal
n.a.
37%
107%
0%
90%
Avoided Landfill - 100%
n.a.
21%
91%
-32%
57%
Avoided WTE - 100%
n.a.
39%
109%
3%
93%
Acidification
Potential
MA Disposal Mix
100%
66%
113%
54%
114%
National Disposal Mix
n.a.
59%
105%
38%
98%
No Avoided SSO Disposal
n.a.
72%
119%
66%
126%
Avoided Landfill - 100%
n.a.
58%
105%
37%
98%
6-11
-------
Section 6—Scenario and Sensitivity Analysis
Table 6-2. Relative Impact Results for all Feedstock and AD Performance Scenarios (Relative to Baseline Scenario)
Impact Cale«»orv
KOI. Scenario
liasdinc
Partial Capacity.
Base A1)
Partial Capacity,
l ow Al)
l-'iill Capacity.
IJasc Al)
l-'iill Capacity,
l ow Al)
Avoided WTE- 100%
n.a.
70%
116%
61%
121%
Smog Formation
Potential
MA Disposal Mix
100%
50%
105%
22%
102%
National Disposal Mix
n.a.
29%
84%
-18%
62%
No Avoided SSO Disposal
n.a.
56%
111%
34%
115%
Avoided Landfill - 100%
n.a.
26%
81%
-25%
55%
Avoided WTE - 100%
n.a.
61%
116%
44%
125%
Water Use
MA Disposal Mix
100%
99%
99%
99%
98%
National Disposal Mix
n.a.
100%
99%
99%
98%
No Avoided SSO Disposal
n.a.
100%
99%
99%
99%
Avoided Landfill - 100%
n.a.
100%
99%
99%
98%
Avoided WTE - 100%
n.a.
99%
99%
99%
98%
6-12
-------
Section 6—Scenario and Sensitivity Analysis
6.4 Normalized LCIA Results
Normalization is a process of standardizing impact results such that the contribution of
impact results associated with the functional unit can be investigated relative to total national or
global impact for a given impact category. Table 6-3 shows normalization factors and U.S.
national per capita impacts in the year 2008. This is the most recent year that LCA normalization
factors are available (Lippiatt et al. 2013; Ryberg et al. 2014). A CED normalization factor was
developed for U.S. 2008 conditions based on reported total primary energy consumption data
(Enerdata 2017). A normalization factor was not available for the fossil depletion potential
impact category; therefore, this category is excluded from the normalization step. The
normalization factor is the total U.S. impact for the specified category in 2008. Impact per person
was estimated by dividing the normalization factor by the U.S. population. The U.S. population
in 2008 was estimated as 304,100,000 people (World Bank 2017). So, for example, the second
row of Table 6-3 indicates that average per capita GHG emissions from all U.S. sources was just
over 24 metric tons of CO2 eq. in 2008.
Table 6-3. 2008 TT.S. Normalization Factors and Per Capita Annual Impacts
lmp;icl ( 1
I nil
Norniiili/iiiion
l-'iicKir (( S-200X)
lmp;icl per Poi son :
Soiiito
Eutrophication potential
kg N eq/yr
6.6E+9
22
(Ryberg et al. 2014)
Global warming potential
kg CO2 eq/yr
7.4E+12
2.4E+4
(Ryberg et al. 2014)
Acidification potential
kg SO2 eq/yr
2.8E+10
92
(Ryberg et al. 2014)
Smog formation potential
kg O3 eq/yr
4.2E+11
1.4E+3
(Ryberg et al. 2014)
Particulate matter formation
potential
kg PM2 5 eq/yr
7.4E+9
24
(Ryberg et al. 2014)
Cumulative energy demand
MJ
9.5E+13
3.1E+5
(Enerdata 2017)
Water Depletion
liter H20 eq/yr
1.7E+14
5.6E+2
(Lippiatt et al. 2013)
1 Normalization factor not available for fossil depletion, so these categories are excluded from normalization step.
2 Impact per person calculated using 2008 population of 304,100,000 (World Bank 2017)
The process of normalization allows us to better assess the significance of impacts by
comparing against absolute benchmarks at the national level. The functional unit for this study is
a cubic meter of wastewater treated. To provide a gross, general context to these numbers, this
presentation of normalized results calculates values based on the range of per capita municipal
wastewater that is generated each year. The average generation of domestic municipal
wastewater in the U.S. was estimated to be between 50 and 89 gallons per person per day
(Tchobanoglous et al. 2014). This is a large range, reflecting the wide variation in use patterns as
determined by factors such as climate, household size, and home and community conservation
measures. This level of daily use translates to an annual domestic wastewater generation rate of
between 70 and 120 cubic meters per person per year. By multiplying impact results calculated
in this study by the annual cubic meters of domestic wastewater treated each year at municipal
wastewater facilities and dividing by per capita normalization factors, we calculated the
approximate annual contribution of domestic wastewater treatment to total per capita impact in
6-13
-------
Section 6—Scenario and Sensitivity Analysis
each of the included impact categories for conditions presented in this study. The calculation
excludes wastewater generated by commercial, public and industrial sources, and therefore
overestimates the impact from individuals and does not reflect the full national burden of
wastewater treatment. Normalized results for the three feedstock and two AD performance
scenarios are presented in Table 6-4 for seven environmental impact categories.
Normalized results show that wastewater treatment makes the largest contribution to
eutrophication per capita emissions. Normalized impact for all other categories is relatively
small, less than one percent, due to environmental credits attributable to avoided energy and
disposal products. Avoided product credits work to reduce net impact, and the associated
normalized impact. Avoided potable water use contributes notably to normalized water use
impact, off-setting between one and three percent of per capita water consumption. Negative
normalized impact results indicate that operation of the WWTF, in the associated operational
mode, reduces national emissions contributing to that impact category (i.e. an environmental
benefit).
Normalized results are by their nature a highly generalized metric that overlooks nuance
in favor of developing a high level indicator that provides guidance pertaining to results
interpretation and the study systems contribution to individual impact categories. Normalized
results should always be considered in this context.
Table 6-4. Estimated Annual Contribution of Municipal Wastewater Treatment
Per Capita Impact in Seven Impact Categories
linimi ( ;ik'iior\ 1.2
liiisolino
I'iii'liiil ( ;ii);ici(\ IVcdslock
Full ( ;ii);ici(\ Iced slock
liiise Al)
Low Al)
liiisc Al)
Low Al)
1 iiiiioplucalioii polenlial
lo 13%
8 lo 14" u
8 lo 15%
9 lo 15%
9 lo 1 (>" u
Global warming potential
0.1 to 0.2%
0%
0.05 to 0.1%
-0.08 to -0.1%
-0.01 to -0.02%
Acidification potential
0.08 to 0.1%
0.05 to 0.1%
0.08 to 0.2%
0.04 to 0.1%
0.09 to 0.2%
Smog formation potential
0.08 to 0.1%
0.04 to 0.1%
0.09 to 0.2%
0.02 to 0.03%
0.09 to 0.2%
Particulate matter formation
potential
0.02 to 0.03%
0.01 to 0.01%
0.02 to 0.03%
0%
0.01 to 0.02%
Water use
-1.6 to-2.8%
-1.5 to -2.8%
-1.5 to-2.7%
-1.5 to-2.7%
-1.5 to -2.7%
Cumulative energy demand
0.1 to 0.2%
-0.04 to -0.1%
0.08 to 0.1%
-0.14 to -0.2%
0.03 to 0.05%
1 Normalization factor not available for fossil depletion, so this category is excluded from normalization step.
2 Negative values indicate reductions in impact as result of WWTF operation.
6.5 LCCA Cost Scenarios
Figure 6-6 presents a summary of life cycle cost results for all feedstock, AD and cost
scenarios by cost category. Discount rate selection is largely responsible for the difference in
NPV magnitude between the low and base cost scenario, while the changes in relative
relationships between cost categories is determined by capacity utilization, tipping fee and
energy revenue assumptions. The shift in relative energy cost/revenue between scenarios is more
dependent on AD capacity utilization and performance than it is on realized tipping fees and
energy prices. Within the base cost scenario, the partial capacity-low AD performance scenario
yields a 33 percent reduction in annual energy expenditures. The partial capacity-base AD
performance scenario however realizes an 83 percent reduction in energy cost, demonstrating
6-14
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Section 6—Scenario and Sensitivity Analysis
that relatively small reductions in biogas yield, VS reduction and biogas utilization can
considerably affect the balance of system costs.
Table 6-5 emphasizes this point through the calculation of payback period for
investments made as part of the AD and CHP expansion project for all feedstock, AD
performance and cost scenarios. The low cost scenario is associated with parameter values that
increase revenue potential of SSO acceptance and energy production, leading to shorter payback
periods. Both the partial and full capacity scenarios have a payback period that is less than the
system lifespan (30 years) in the base AD performance scenario. Neither feedstock scenario
yields a payback period of less than 30 years within the low AD performance scenario. Several
aspects of the low AD performance scenario combine to explain this result.
The most obvious aspects of the low AD performance scenario that reduce system
revenue are the lower rate of volatile solids destruction and lower biogas yield. Together these
two factors reduce biogas production by nearly 30 percent. This result is also a function of
prioritizing biogas first for use in the pellet driers, where heat is the only beneficial end product.
The low AD performance scenario also assumes an elevated flaring rate of 20 percent. When
considered together these factors lead to a 50 percent reduction in the quantity of biogas that
goes to the CHP system in the full capacity scenario. Greater utilization of the CHP system in the
base AD performance scenario provides access to increased revenue potential from the sale of
both RECs and AECs in addition to net metering benefits. The full capacity-base AD
performance scenario shifts the WWTF for a net energy consumer to a net producer of energy.
Additional financial benefits may be realized as a function of the shift from traditional
disposal routes of food waste to its anaerobic digestion at WWTFs. Beneficial use of this former
waste product, has the potential to reduce tipping fees associated with landfill disposal. These
benefits are likely to be captured by waste generators and waste collection and hauling
companies. The plant also avoids disposal fees that would previously have been associated with
biosolids disposal, while local farms get access to a low or no cost source of soil amendment.
Table 6-5. AD and CHP System Payback Period (years)
Sccnsirio
linso C'osl
Low C'osl
1 baseline
None
None
Partial Capacity-Low AD
None
None
Partial Capacity-Base AD
27
19
Full Capacity-Low AD
None
None
Full Capacity-Base AD
14
10
6-15
-------
Section 6—Scenario and Sensitivity Analysis
Figure 6-6. Life cycle cost assessment summary showing results for each Feedstock-AD performance scenario by cost scenario.
C7 cr C7 u-
Base Cost
! Capital E3 Annual Operation ED Annual Material
Low Cost
~ Annual Chemical ~ Annual Energy • Total NPV
6-16
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Section 7—Conclusions
7. CONCLUSIONS
This report describes the effort to use recent purchasing records (2016), engineering
design documents, the MSW DST and the GPS-X™ modeling software to develop LCI data for
GLSD's wastewater treatment processes, biogas combustion units and avoided SSO disposal
processes. Using the developed LCI data in combination with existing inventory data for
upstream production processes the analysis quantifies environmental impacts in eight impact
categories.
LCA results presented in this study highlight the environmental and economic benefits
available to medium-scale WWTFs willing to invest in additional AD capacity, CHP equipment
and personnel for co-digestion of SSO waste. Reductions in environmental impact or the
generation of environmental benefit, judged relative to the baseline scenario prior to co-
digestion, are possible in all impact categories assessed, except eutrophication. The possibility of
achieving reduced environmental impact, even as the facility processes increased quantities of
waste in the form of SSO, is robust for most impact categories, given that reductions are realized
for all (or most) feedstock and AD performance scenarios investigated.
The magnitude of reductions and benefits in most categories is sensitive to AD utilization
and performance and avoided SSO EOL disposal assumptions. In particular, net GWP benefits
are greatest when avoiding landfill disposal of source separated food waste. As U.S. EOL
disposal practices change either in the form or environmental impact of avoided disposal routes,
the applicability of the environmental benefits currently assessed will need to be revisited.
However, given the relatively early stage of U.S. efforts to shift away from landfill and WTE
disposal routes for organic material, these avoided benefits are expected to be reasonable for
many areas of the country for years to come.
The main environmental trade-off identified in this analysis was between increasing
eutrophi cation potential, 10 to 20 percent increase for the partial and full capacity scenarios, and
all other impact categories as the facility processes more SSO. Given the location and permit
requirements of the GLSD WWTF, no specific nutrient removal efforts are made to mitigate
contribution to eutrophi cation potential. Facilities that are bound by nutrient limitations are likely
to require a simultaneous investment in nutrient removal capacity assuming static or decreasing
permitted nutrient effluent quantity. The magnitude of normalized eutrophi cation potential
impact, relative to that of other impact categories, indicates that this aspect of AD expansion for
co-digestion should be carefully considered.
The energy analysis of AD presented in Section 3.3.8 indicates that the level of SSO co-
digestion described in both the partial and full capacity scenarios was able to meet the thermal
energy demand of GLSD's WWTF. In fact, significant excess thermal energy is available (i.e.
currently wasted) in both scenarios. If GLSD, or other municipalities considering similar
projects, can find additional productive uses for all thermal energy, additional environmental and
economic benefits are available. Due to the net-metering program, environmental benefits are
captured for all produced electricity, even the portion that exceeds plant electricity demands.
LCCA results are favorable for the GLSD WWTF, and likely for other medium-scale
treatment facilities with similar energy consumption profiles. All base AD performance scenarios
demonstrate a discounted payback period of less than the expected system lifespan. Payback
7-1
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Section 7—Conclusions
period for the AD and CHP upgrades, assuming full capacity utilization, is 14 or less years in the
base AD performance scenario. Economic benefits were not identified under conditions of low
AD performance and capacity utilization for either cost scenario. Energy expenditures are
significantly reduced in all co-digestion scenarios, yielding a source of net energy revenue within
the full capacity-base AD performance scenarios. Previous work by the authors of this report
demonstrated that economic payback of investment in AD and CHP technology are more
difficult to achieve at the one MGD system scale (Morelli et al. 2017).
The use of biosolids drying and pelletization is a relatively unique aspect of this facility
that should be considered by other facilities looking to translate results to their own context.
Biosolids drying and pelletization is a source of significant energy demand within the facility,
which provides a corresponding opportunity to benefit the economics of digestion paired with
CHP. The most reliable economic benefit of Massachusetts's net metering program comes from
off-setting facility electricity purchasing cost, as discussed in Section 4.3.7. Likewise, AECs are
only applicable to useful thermal energy, i.e. that which is put to use. The significant energy
demand of biosolid drying and pelletization allows the case-study facility to capture economic
benefits that may not be available to all facilities. More importantly, net metering, AEC and REC
or equivalent programs must be available to a specific WWTF for them to capture similar
benefits.
This report presents results such that the environmental benefit of energy recovery
accrues to the WWTF, reducing the impact of treating a unit of wastewater. However, when the
facility sells RECs and AECs associated with its energy products, they are selling those
environmental benefits to other facilities. The LCA quantifies this environmental benefit and
presents the results per cubic meter of treated wastewater. Sale of RECs and AECs does not
diminish the environmental benefit of co-digestion and the AD expansion project but instead
shifts the facility that is able to claim those environmental benefits as an off-set to their
production impacts.
For medium-scale WWTFs with a ready source of SSO, or similar high strength organic
waste, investment in AD capacity and CHP systems provides an opportunity to reduce net
environmental impact, while reducing energy expenditures over time. The analysis demonstrates
sensitivity to capacity utilization and avoided EOL disposal assumptions that should be
considered by facilities as they endeavor to assess applicability of case-study results within their
own context.
7-2
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Section 8—References
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U.S. EPA. 2017c. Landfill Technical Data: Landfill-level data only. Government.
Landfill Methane Outreach Program (LMOP). June.
U.S. EPA, and MADEP. 2005. National Pollutant Discharge Elimination System Permit
No. MA0100447: Greater Lawrence Sanitary District. U.S. Environmental Protection
Agency and Massachusetts Department of Environmental Protection.
Varnier, D. J., and R. Saidur. 2004. Life Cycle Cost Analysis as a Decision Support Tool
for Managing Municipal Infrastructure. In .
Wassam, J. 2018. Personal Communication with John Wassam, RPS and APS Program
Manager.
van Welie, G. 2017. State of the Grid: 2017 January 30.
Wiser, J. R., P. E., J. W. Schettler P. E., and J. L. Willis P. E. 2010. Evaluation of
Combined Heat and Power Technologies for Wastewater Treatment Facilities. EPA 832-
R-10-006. U.S. Environmental Protection Agency.
World Bank. 2017. Population, Total.
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Appendix A - Alternative Food Waste Treatment Comparison
Appendix A:
Composting and Land Application of Food Waste:
A Comparison with Anaerobic Co-Digestion at a Wastewater Treatment
Facility
-------
Appendix A - Alternative Food Waste Treatment Comparison
Appendix A
Composting and Land Application of Food Waste: A Comparison with Anaerobic Co-
Digestion at a Wastewater Treatment Facility
Introduction
As industries and institutions shift from the paradigm of waste disposal to that of resource
recovery, it becomes apparent that EOL decisions shift from a conversation centered on
minimizing impact to one that productively considers opportunities to maximize environmental
benefit. This new paradigm looks at "waste" as a resource.
In light of the Massachusetts landfill and incineration ban on organic materials from large
industrial and institutional producers, several industries are vying for the opportunity to utilize
abundant organic waste streams. In this Appendix to the main report, we analyze composting as
an alternative disposal pathway for SSO waste (i.e., food waste). To do this we isolate inputs,
emissions and costs associated with GLSD's WWTF that can be directly attributed to the
addition of co-digestion capacity and the processing of SSO waste. We then compare co-
digestion impacts and costs against food waste management through windrow and aerated static
pile (ASP) compost systems. LCA results were also generated for food waste landfilling and
WTE combustion as a reference for other regions and waste generators not subject to the waste
ban. No cost data were compiled for the landfill and WTE disposal options.
While five treatment and disposal options are compared in this analysis, as of 2017 the
selection process is not an either-or proposition. The Massachusetts Department of
Environmental Protection (MassDEP) estimates that organic material comprises greater than 25
percent of the solid waste stream. The state's goal to divert 35 percent of food waste by 2020
indicates an annual food waste diversion rate of 350,000 tons per year (MassDEP 2017).
Currently, the generation of food waste is expected to exceed the capacity of compost, AD and
other food waste recycling facilities regionally (Layzer and Schulman 2014). Regardless of the
theoretical surplus, existing facilities may experience supply shortages due to competition or
insufficient food waste collection systems.
A-l
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Appendix A - Alternative Food Waste Treatment Comparison
Analysis Scope
This section introduces the system boundaries of the food waste disposal comparison.
The functional unit for this analysis was defined as treatment of one kg of disposed food waste
(gate-to-grave). Table A-l lists the scenarios included in the food waste disposal comparison.
The baseline scenarios for the compost and AD systems are highlighted in bold.
Table A-l. Summary of Compost Comparison Scenarios
Disposal Method
S\ Mem Pcrformance
Transport Distance
Windrow
Improved Performance
Local
Regional
Base Performance
Local
Regional
Aerated Static Pile
Improved Performance
Local
Regional
Base Performance
Local
Regional
Anaerobic Digestion
Low Performance
Actual
Base Performance
Notes: Baseline scenario values highlighted in bold. The base performance scenario represents the average
environmental impact of composting facility operation. The improved performance scenario represents a well-
managed compost facility with reduced energy use and GHG emissions. Transport scenarios are introduced in Table
A-5 and associated text. The local transport scenario was based on existing facilities in Eastern Massachusetts. The
regional scenario is a hypothetical scenario that assumes local capacity is insufficient to meet compost capacity
demands, requiring further transport.
Food waste is sourced from commercial and industrial sources. When destined for AD,
the food waste is first processed into an engineered bioslurry that removes contamination and
standardizes the material, allowing consistent performance of the digesters. The full capacity AD
scenario processes 92,000 gallons of SSO per day, which corresponds to approximately 154,000
kg of food waste per day, or 0.42 kg of food waste (wet mass) per kg of SSO. Food waste was
assumed to have a solids content of 31 percent (Sundberg et al. 2011).
Windrow composting is currently the most common composting method practiced in
Massachusetts based on our assessment of facilities in the Eastern half of the state. The use of
ASP compost systems is less common but is practiced by at least one facility that accepts
diverted food waste (Cook 2017).
Compost performance scenarios encompass process GHG emissions and energy
consumption estimates. The composting base performance scenarios correspond to average
emission and energy consumption values found in the literature. The improved performance
scenario corresponds to the 25th percentile of emission and energy consumption values. For the
AD unit process, the performance scenarios correspond to the original low and base AD
performance scenarios detailed in the main study report. AD system performance primarily
affects biogas production, the corresponding mix of on-site combustion process use and avoided
energy products.
Local and regional transport distance scenarios were analyzed for both compost methods.
The local transport distance was calculated based on the location of the 20 composting facilities
that are nearest to the Boston metro region (Cook 2017). The regional transport scenario assumes
A-2
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Appendix A - Alternative Food Waste Treatment Comparison
a hypothetical transport distance that is three times the value used in the local scenario and is
included due to questions about the availability of sufficient local composting capacity. Regional
transport assumptions are assessed in a sensitivity analysis.
Compost System Boundaries
Figure A-l depicts system boundaries for both the windrow and ASP composting
systems. Transportation of waste to the compost or WWTF varies among the three options and is
included within the system boundaries.
Both compost management systems require active management to ensure adequate
material degradation, pile temperatures and low to average process emissions. Energy use
estimates required for facility operation were included in the LCI and are discussed in detail in
the section on LCI development in this Appendix. Initial moisture content of the compost pile is
typically established in a range of between 50 to 60 percent weight/weight. The initial carbon to
nitrogen ratio (C:N) of material in the compost pile should also be kept within a standard range
of between 20:1 and 45:1 (Christensen 2009; Brewer et al. 2013), with 30:1 being optimal
(MDAR 2011). Table A-2 lists a typical material composition of food waste.
Due to the high nitrogen and moisture content of food waste, a considerable quantity of
carbon rich organic material is required for successful composting. However, this LCI and the
associated impacts were solely based on the material composition of the food waste, assuming
the other organic materials will be readily available and would be destined for composting
regardless of food waste EOL management decisions.
A-3
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Appendix A - Alternative Food Waste Treatment Comparison
Amendment
Material
Management
Material
Production for
Equipment
Active Odor
Control System
Pre-compost
Contaminant
Screening
Food Waste
Collection &
Transport
Emissions to Air Composting
t
Option A: Windrow Formation & Turning
Material Handling
(c
Option B: ASP
^^^^Emissions to Air
Screening &
Loading
Diesel
Combustion
Electricity
Consumption
Transport to Farm
Land Application
Handling &
Spreading
Emissions to Air & Water
I
i
Avoided Fertilizer Carbon Sequestration
Production
KEY:
Excluded Process
Modeled Process
Process Emissions Carbon Sequestration
I I
Figure A-l. System diagram of composting and land application processes.
Table A-2. Assumed Material Composition and Moisture Content of
Collected Food Waste
Parameter
Units
Value
Source
Carbon Content
% dry mass
44%
(Boldrin et al. 2009; Richard
2014)
Nitrogen Content
% dry mass
2.5%
Calculated from (Richard 2014)
Phosphorus Content
% dry mass
0.90%
(ROU 2007)
C:N Ratio
unitless
18
Calculated
Moisture Content
% wet mass
69%
(Sundberg et al. 2011)
Dry Mass
kg dry/kg wet
31%
Calculated
A-4
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Appendix A - Alternative Food Waste Treatment Comparison
Carbon (C), nitrogen (N) and phosphorus (P) present in the food waste can lead to
pollutant emissions during the composting process and are a source of beneficial nutrients in the
finished compost. Carbon-based compost process emissions of methane (CH4) and carbon
monoxide (CO) were included in the LCI. Carbon dioxide (CO2) emissions are also emitted
during composting but were excluded from the inventory as the carbon is biogenic in origin and
will not contribute to net global warming potential. Compost methane emissions are also derived
from biogenic carbon, but are included because they still contribute to GWP, having a greater
GWP than CO2 per unit carbon. Nitrous oxide (N2O) and ammonia (NH3) emitted from the
compost pile were also estimated for the inventory as a function of food waste N content.
Diesel combustion and electricity consumption required for facility operation were
included in the LCI. Sources of energy consumption within the compost facilities include
material handling, windrow turning, screening, administrative space conditioning and blowers
for the ASP process. Electricity consumption for shredding, prior to composting, was excluded
from the inventory as it is not expected to be required for food waste.
Based on conversations with several local facilities, it does not appear that pre-compost
screening or ventilated odor control strategies are standard in current regional practice. Given
this, such processing steps have been excluded from the analysis. However, many facilities
expressed reservations about accepting additional sources of food waste as residential and
restaurant collection were perceived to be potentially high in contaminants. If this remains the
case, and considerable additional compost processing capacity is pursued, additional contaminant
removal steps may be required in the future.
Based on conversations with Boston-area compost facilities, it does not appear that
installation of leachate management systems is common practice in this region. Alternatively,
facility managers and regulators have indicated that use of grass buffer regions has proved
sufficient to allow adequate infiltration, thereby preventing runoff from compost facilities. No
leachate collection system materials, energy use, or emissions were included in the LCI. Other
authors have noted that leachate production can be considered negligible at well managed
facilities (Komilis and Ham 2004).
Material production and assembly for mechanical equipment (e.g., ASP piping and
compost turner) were excluded from the analysis (ROU 2007; Saer et al. 2013). Mechanical
equipment materials are expected to contribute little to the impact per unit of food waste
processed over the equipment's expected lifespan. The same assumption was used for
mechanical equipment within the WWTF. Infrastructure for the compost administration building
is included in the compost LCIs, as was new infrastructure for the added AD capacity in the
GLSD LCI.
Transportation of finished compost in the base performance scenario was estimated using
the same distance assumption as that originally developed for trucking of pelletized biosolids to
the site of land application, 121 km. Due to uncertainty regarding this assumption, a shorter
transport distance of 60 km was assumed in the improved performance scenario. Compost is
applied as an agricultural amendment, leading to field emissions and avoiding the production and
use of chemical fertilizers. A carbon credit was applied for the estimated fraction of carbon in the
compost that remains in the soil beyond 100 years.
A-5
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Appendix A - Alternative Food Waste Treatment Comparison
Adaptation of GLSD Wastewater Treatment Facility System Boundaries
To directly compare compost and co-digestion food waste management options, the
WWTF low and base AD LCI models from the main study report were adjusted to reflect only
the portion of treatment plant impact that is attributable to SSO processing. Annual input and
output quantities used to develop the original LCI were scaled and recalculated to be based on
the updated functional unit, 1 kg of food waste treated, by dividing annual LCI and cost inputs
and outputs by the annual quantity of food waste within the SSO accepted at the WWTF.
Not all treatment processes at the WWTF are considerably affected by the decision to
accept SSO for co-digestion with municipal solids. Table A-3 summarizes the adjustments made
to individual unit process LCIs throughout the GLSD WWTF. The influent pump station,
preliminary and primary treatment processes, secondary clarification and plant water and
disinfection were assumed not to incur additional operational input requirements as a result of
accepting SSO. The influent pump station, bar screening and grit removal are bypassed
altogether by the SSO material, which is received, stored temporarily and pumped directly into
the AD tanks. The clarifiers, plant water and disinfection processes demand operational energy
and chemical use primarily on a volume basis of wastewater treated, which is only marginally
affected by the decision to accept SSO (less than 0.5 percent of influent water volume).
Table A-3. Adjustment of Unit Process LCI Data for Compost Comparison.
Trciiliiicnl Croup
I nil Process Niiine
( oin post C oin p;iI'ison
Adjustment
Influent pump station
Influent pump station
Excluded
Preliminary/primary
Screening and grit removal
Excluded
Primary clarification
Excluded
Biological treatment
Pre-anoxic tank
Scaled1
Aeration basins
Scaled1
Secondary clarification
Excluded
Plant water and disinfection
Plant water and disinfection
Excluded
Sludge dewatering
Gravity belt thickener
Scaled1
Gravity thickener
Scaled1
Centrifuge
Scaled1
Anaerobic digestion and
CHP
SSO transport and processing
Included
Anaerobic digestion
Scaled2
[Base AD factor - 78%]
I Low AD factor - 69%\
Combined heat and power
Pellet drying
Biosolids drying and pelletization
Scaled1
Land application
Land application of biosolids pellets
Scaled1
Effluent release
Effluent release; to surface water
Scaled1
Building operation
Administration building utilities
Excluded
1 Food Waste LCI value = (Full Capacity LCI value - Baseline LCI value)
2 Food Waste LCI values affected by the installation of CHP are scaled based on food waste's fraction of biogas
production, which are 78 percent and 69 percent in the base and low AD performance scenarios, respectively.
Food Waste LCI value = (Full Capacity LCI value * (BiogasFc-BiogaSbaSe)/BiogasFc)). BiogasFc = biogas
production in the full capacity scenario, Biogasbase = Biogas production in the baseline scenario.
A-6
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Appendix A - Alternative Food Waste Treatment Comparison
Other unit processes, particularly the solids processing and AD units, are directly affected
by food waste acceptance. Previously calculated LCI values for these unit processes were scaled
to reflect the difference in solids acceptance and associated biogas production attributable to
SSO co-digestion and the AD and CHP expansion project. Many of the food waste specific LCI
values were calculated by subtracting baseline LCI quantities (without food waste) from
corresponding full capacity LCI values and dividing by the new reference flow of total food
waste treated. This approach is sufficient for input parameters that are not affected by the
installation of CHP at the WWTF (e.g., AD and aeration electricity consumption, ferric chloride
use and pellet drying heat demand).
The installation of a CHP system at the GLSD WWTF coincided with the decision to
accept SSO waste. Changes to the LCI that result from CHP installation are therefore not wholly
attributable to SSO acceptance, and another approach was required to accurately allocate the
associated LCI values between the SSO and municipal sewage. Installation of CHP at the
WWTF affects the relative fraction of biogas that is utilized in the various on-site combustion
processes. The quantity of biogas produced is itself independent of CHP installation. Biogas
combustion, and the associated avoided energy products, is therefore scaled by the fraction of
biogas production attributable to SSO acceptance, which in the full capacity-base AD
performance scenario is 78 percent. This means that 78 percent of avoided electricity production
was attributed to the additional food waste processed at the GLSD WWTF. Infrastructure
materials associated with the added AD capacity and SSO pre-processing and transport were
allocated completely to SSO.
Finally, it is necessary to remove the avoided EOL burdens for WTE and landfilling from
the AD unit process. In the results presented per kg of food waste, we are directly comparing
EOL treatment options, and avoided disposal burdens can be excluded from the analysis scope,
as they would not differ between the compared AD and composting options. The main analysis
takes a more indirect approach to the comparison of EOL treatment options, instead focusing on
net environmental impact per m3 of treated wastewater.
Using the above approaches, the new inventory isolates the environmental benefits and
burdens of accepting SSO for co-digestion at an existing WWTF, allowing a direct comparison
with windrow and ASP composting that serve as alternative options for food waste EOL
disposal.
Compost - Life Cycle Inventory Development
As of 2017, there were at least 30 composting facilities permitted to handle food waste in
Massachusetts. Table A-4 lists 20 composting facilities that are nearest to the Boston metro
region (Cook 2017), their distance from downtown and an estimate of potentially available
capacity. These facilities were used to estimate the transport distance for the local transport
scenario. Available capacity was estimated to be 30 percent of permitted capacity, based on
conversations with contact persons from several local compost facilities. Total estimated
capacity was based on contact with facility personnel when possible. In the absence of site
specific information, farm-based compost operations were assumed to operate under the general
permit, allowing them to accept 95 metric tons (Mg) of food waste (105 U.S. tons) per week
(MassDEP 2012). For dedicated composting facilities operating on a solid waste permit, a value
A-7
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Appendix A - Alternative Food Waste Treatment Comparison
of 150 Mg/week was assumed as the estimated capacity. Transport distances were estimated
using Google Maps and address information associated with individual compost facilities (Cook
2017). Table A-4 shows that using the above assumptions, 20 composting facilities are required
to process the volume of food waste treated in the full capacity AD scenario. Available capacity
for handling food waste via composting is speculative as the facilities contacted were concerned
about the impact of additional contamination on facility operation and quality of the final
compost. Several facilities indicated that if contamination cannot be controlled, they are not
interested in accepting additional food material.
Table A-4. Estimated Capacity and Distance of Compost Facilities Nearest to
Boston
Facility Number
Estimated
Capacity
(Mg/week)
Available
Capacity
Transport
Distance, km1
Facility l2
95
30%
61
Facility 2
150
30%
55
Facility 32
91
30%
49
Facility 4
95
30%
109
Facility 5
95
30%
71
Facility 6
95
30%
91
Facility 7
150
30%
78
Facility 8
95
30%
62
Facility 9
95
30%
115
Facility 102
64
30%
71
Facility 11
95
30%
34
Facility 122
872
30%
85
Facility 132
30
0%
31
Facility 14
95
30%
69
Facility 15
95
30%
105
Facility 16
95
30%
78
Facility 17
95
30%
22
Facility 182
0
30%
25
Facility 19
95
30%
78
Facility 202
939
30%
39
Estimated Available Capacity3
1,020
Mg/week
Required Available Capacity4
1,080
Mg/week
1 Transport distance was estimated from city center.
2 Indicates a conversation with facility personnel.
3 Estimated available capacity was estimated as the X(estimated capacity*available capacity) for the 20 local
composting facilities. Estimated available capacity does not exactly match required capacity, as it is only
a rough estimate and could in practice move considerably up or down.
4 Required available capacity was based on the food waste quantity processed in the GLSD full capacity AD
scenario.
A-8
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Appendix A - Alternative Food Waste Treatment Comparison
Table A-5 lists distances associated with the two compost transport scenarios. An
estimated collection route distance of 20 km was included for compost and WWTF disposal
scenarios (included in Table A-5). Transport distance for the local transport scenario was
estimated to be 61 km, based on a weighted average of the available capacity data presented in
Table A-4. The regional transport scenario assumes a hypothetical transport distance that is three
times the value used in the local scenario and is intended to represent out-of-state and/or regional
transport.
Table A-5. Total Food Waste Transport Distances
Tmnsporl Sccnsirio
Disliinco (km)
Compost - Local1
81
Compost - Regional
203
1 Total transport distance is the sum of collection route distance (20 km) plus
transport from end-of-route to the compost facility.
Food waste to be composted is received and immediately mixed with absorbent material
to help control odors. Unlike yard waste, it is essential that food waste be mixed with absorbent,
carbonaceous materials quickly (Christensen 2009). No shredding or grinding of source
separated food waste is typically employed. Diesel use for material handling at receiving was
included in the LCI (ROU 2007). Table A-6 presents a summary of calculated LCI values for the
four compost LCA scenarios.
Additional diesel fuel use was included for windrow turning (Komilis and Ham 2004;
ROU 2007; Saer et al. 2013) and loading of finished compost (ROU 2007). Inclusive electricity
consumption factors from Boldrin et al. (2009) were used to estimate electricity use at the
windrow and ASP compost facilities. Electricity consumption estimates include pre- and post-
screening, administrative facility operation and aeration energy for ASP.
Methane emissions from windrow composting were estimated using a calculated average
emission factor of 0.0082 kg CH4-C/kg C entering the compost pile (Hellmann et al. 1997;
Hellebrand 1998; Fukumoto et al. 2003; Pipatti et al. 2006; Amlinger et al. 2008; Boldrin et al.
2009; SYLVIS 2011; Maulini-Duran et al. 2013) or 0.82 percent of carbon in the compost
feedstock. No methane emissions are expected from the ASP system due to the use of active
aeration and biofilter venting to ensure oxidation of any methane that might form in anaerobic
pockets within the compost pile (SYLVIS 2011). Carbon monoxide, nitrous oxide, ammonia and
NMVOC emissions are not expected to be affected by the biofilter and were assumed to be the
same for both composting methods. Sources used to develop process emission LCI values are
presented in footnotes to Table A-6. The 25th percentile of values taken from the cited references
was used to estimate values for the improved performance scenario. A mass loss of 58 percent
was estimated during the compost process (Tiquia et al. 2002; Fukumoto et al. 2003; Razza et al.
2009; Saer et al. 2013), which affects the quantity of compost that is ultimately land applied.
A transport distance of 121 km was assumed from the compost facility to the land
application site, the same as that used for transportation of pelletized biosolids to the site of land
application. A shorter transport distance of 60 km was assumed in the improved performance
scenario.
A-9
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Appendix A - Alternative Food Waste Treatment Comparison
Diesel use was included for field application of compost. Compost application avoids the
production of the chemical fertilizers urea and single superphosphate based on equivalent,
available N and P content. A phosphorus fertilizer replacement value of 95 percent was assumed
(Boldrin et al. 2009). A cumulative fertilizer replacement value of 55 percent was assumed for
compost nitrogen content. The nitrogen fertilizer replacement value assumes that 40 percent of
land applied nitrogen is plant available in year one (Smith and Durham 2002). In years two and
three, an additional 10 percent and 5 percent of nitrogen content mineralize and become plant
available (Rigby et al. 2016).
A-10
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Appendix A - Alternative Food Waste Treatment Comparison
Table A-6. Compost and Land Application Life Cycle Inventory
linsic Input
Detailed I se
Windrow
ASI>
I nils
Base
Performance
Improved
Performance
Base
Performance
Improved
Performance
Composting
per kg wet
feedstock
Diesel use
Material receiving1
4.80E-4
4.80E-4
4.80E-4
4.80E-4
liters
Windrow formation & turning2
4.99E-4
2.99E-4
-
-
liters
Compost loading for transport1
3.00E-5
3.00E-5
3.00E-5
3.00E-5
liters
Electricity use
Total3
9.86E-3
4.94E-3
0.037
0.023
kWh
Process emissions
Ammonia4
4.06E-4
8.35E-5
4.06E-4
8.35E-5
kg NH3
Methane5
1.50E-3
3.10E-4
-
-
kg CH4
Nitrous oxide6
1.54E-4
3.14E-5
1.54E-04
3.14E-05
kg N20
NMVOCs7
1.04E-4
6.85E-5
1.04E-4
6.85E-5
kg NMVOC
Carbon monoxide8
1.27E-4
1.27E-4
1.27E-4
1.27E-4
kg CO
Land Application
per kg compos f
Transport
To agricultural field
0.121
0.060
0.121
0.060
tkm
Diesel use
Compost application1
1.06E-3
7.07E-4
1.06E-3
7.07E-4
liters
Field Emissions
Ammonia, to air10
5.30E-4
3.54E-4
5.30E-4
3.54E-4
kg NH3
Nitrous oxide, to air11
1.91E-4
1.27E-4
1.91E-4
1.27E-4
kg N20
NOx, to air12
8.39E-5
5.59E-5
8.39E-5
5.59E-5
kg NOx
Nitrate, to water11
8.29E-3
5.53E-3
8.29E-3
5.53E-3
kg N03
A-ll
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Appendix A - Alternative Food Waste Treatment Comparison
Table A-6. Compost and Land Application Life Cycle Inventory
Usisic Input
Detailed I se
Windrow
ASI>
I nils
Base
Performance
Improved
Performance
Base
Performance
Improved
Performance
Phosphorous, to surface water12
3.94E-5
2.63E-5
3.94E-5
2.63E-5
kg P
Phosphorous, to groundwater12
1.29E-6
8.62E-7
1.29E-6
8.62E-7
kg P
Avoided products
Urea13
7.63E-3
5.08E-3
7.63E-3
5.08E-3
kg (as N)
Single superphosphate14
0.012
7.84E-3
0.012
7.84E-3
kg (as P2O5)
Carbon Sequestration
Storage beyond 100 years15
0.051
0.119
0.051
0.119
kg C02
1 (ROU 2007)
2 (Komilis and Ham 2004; ROU 2007; Saer et al. 2013)
3 (Boldrin et al. 2009)
4 (Hellebrand 1998; Fukumoto et al. 2003; Maulini-Duran et al. 2013)
5 (Hellmann et al. 1997; Hellebrand 1998; Fukumoto et al. 2003; Pipatti et al. 2006; Amlinger et al. 2008; Boldrin et al. 2009; SYLVIS 2011; Maulini-Duran et
al. 2013)
6 (Hellmann et al. 1997; Hellebrand 1998; Fukumoto et al. 2003; Pipatti et al. 2006; Boldrin et al. 2009; Maulini-Duran et al. 2013)
7 (Maulini-Duran et al. 2013)
8 (Hellebrand 1998)
9 Mass and carbon loss during composting is accounted for in land application LCI values.
10 (Goedkoop et al. 2013)
11 (De Klein et al. 2006)
12 (Nemecek and Kagi 2007)
13 (Smith and Durham 2002; Rigby et al. 2016)
14 (Boldrin etal. 2009)
15 The amount of land applied carbon remaining in soil after 100 years was estimated using sequestration factors from 3 references (ROU 2007; Favoino and
Hogg 2008; Boldrin et al. 2009). Sequestration is estimated as Mg (Xh/Mg C in finished compost and therefore must account for carbon loss during
composting. On average 58 percent of incoming wet mass is lost during composting (Tiquia et al. 2002; Fukumoto et al. 2003; Razza et al. 2009; Saer et al.
2013). A large fraction, 74 percent, of mass loss is due to a reduction in pile moisture content (calculated). The complementary fraction, 16 percent, is
attributed to the loss of carbon as CO2, CH4 and CO. Losses of N and P were assumed to be negligible on a cumulative, wet mass basis.
A-12
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Appendix A - Alternative Food Waste Treatment Comparison
Landfill and WTE Food Waste Disposal Options
Despite the landfill and incineration material ban on large, commercial producers of food
waste in Massachusetts, a considerable fraction of food scraps in the U.S. are still disposed of in
landfills and WTE facilities. Environmental result figures included in this Appendix include
landfill and WTE disposal options to represent this fraction of food waste. Landfill and WTE
LCI data were developed using the MSW DST (RTI International 2012) to model emissions to
air and water as described in report Section 3.3.14. Modeled landfill gas management reflects
current practice in Massachusetts, where 19 percent of gas is flared and 81 percent is used for
energy recovery. Nationally, approximately 24 percent of landfill gas is flared, 68 percent is used
for energy recovery, and 8 percent is vented to the atmosphere. Therefore, the results presented
in this Appendix should be considered conservative from the perspective of global warming
potential.
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Appendix A - Alternative Food Waste Treatment Comparison
Life Cycle Cost Assessment
The life cycle costs of food waste disposal are compared per metric ton of food waste
disposed over a 30-year time horizon using the LCCA methodology described in Section 4.2.
Cost estimates were based on a typical (small) composting facility that operates under a
Massachusetts general permit. This permit allows the facility to process 95 Mg (105 U.S. short
tons) of food waste per week. Food waste can make up no more than 25 percent of the compost
mixture by volume (MassDEP 2012). This corresponds to a total processing capacity of
approximately 13,100 Mg/yr, which includes over 4,900 Mg of food waste. The cost analysis
focuses only on the food waste being processed at the compost facility and excludes costs
associated with yard waste and woody debris processing. Capital costs that apply to both food
and yard waste were allocated to the two materials on a volume basis assuming that 25 percent of
accepted volume is food waste.
In addition to the cost methodology described in Section 4.2, it was necessary to include
several additional cost elements that do not apply in the case of the WWTF retrofit or were
unnecessary due to basing WWTF costs on plant records. In particular, we include interest
during construction and indirect costs associated with establishing a new compost facility. Table
A-7 lists the indirect cost factors applicable to the windrow and ASP compost systems. Indirect
cost factors were applied to the sum of year 1 capital costs.
Table A-7. Indirect Cost Factors for
Composting Systems
Indirect Cost Kleinenls
Indirect Cost l-'jictor
(%)
\ liscellaneous Costs
5%
Legal Costs
2%
Engineering Design Fee
15%
Inspection Costs
2%
Contingency
20%
Technical
2%
Sources: (Hydromantis 2014; AACEI 2016)
Interest during construction was assessed using Equation A-l and a conservative 5
percent interest rate (Komilis and Ham 2004). A two-year construction period was assumed.
Interest During Construction = (Installed Equipment Cost + Indirect Costs)
Interest Rate During Construction
x Construction Period x
2
Equation A-l
The ASP and windrow compost facilities were assumed to process material outdoors
without employing advanced odor and leachate processing systems. These assumptions are
intended to represent existing facilities in Central and Eastern Massachusetts, but they will not
apply in all contexts. For example, a report titled Odor in Commercial Scale Compost: Literature
Review and Critical Analysis indicates that most larger municipal compost facilities in
A-14
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Appendix A - Alternative Food Waste Treatment Comparison
Washington State include indoor handling of the initial composting phase, leachate collection
and advanced odor control (Ma et al. 2013). These facilities are considerably costlier to operate
and are likely to lend themselves to greater process control. Reasons that contribute to these
differences in approach include climate conditions, proximity to urban areas, local and state
regulations, and public vs. private ownership.
The GLSD LCCA input values were adjusted to reflect the updated system boundaries
described earlier in Table A-3.
LCCA results are presented as NPV in dollars per Mg of food waste disposed (2016 $).
All system costs were tabulated on an annual basis. Total system NPV (in 2016 $) was divided
by the quantity of food waste processed over a 30-year period, allowing comparison between the
three food waste management options. The quantity of food waste processed annually is held
constant over the 30-year period. Cost input parameters correspond to the base and low cost
scenario parameters listed in Table 4-2. Table A-8 lists several additional cost parameters
specific to the compost treatment options. Like the cost scenarios defined in the main report, the
low cost scenario refers to the combination of cost parameters that lead to lower system NPV.
The base cost scenario provides a more conservative estimate of life cycle cost. These cost
parameter values are described in detail in the subsequent sections.
Table A-8. Compost Low and Base Cost Parameters
C'osl I'iti itmclcrs
Low Cos!
Usise C 'osl
I nils
Tipping fee, food waste
0.044
0.033
$/kg food waste
Compost value
0.019
0.015
$/kg compost sold
Construction interest rate
3%
5%
of capital cost
An LCI and cost assessment by Komilis and Ham (2004) was the primary source of
composting cost data used in the analysis. Cost estimates in that document originally pertained to
1999 and have been adjusted into current (2016) dollars.
Costs Common to Both Compost Methods
Each of the two hypothetical composting facilities was modeled to handle an identical
quantity of food and yard waste, leading to several life cycle costs that remain constant across
the two systems. Table A-9 summarizes the life cycle costs that apply to both the windrow and
ASP composting systems. Both facilities process approximately 4,900 Mg of food waste
annually, with typical tipping fees ranging from $20 to $40 per Mg of material accepted. The
base and low cost scenarios assume tipping fees of $30 and $40/Mg, respectively. The base cost
tipping fee corresponds to an annual revenue of $164,000.
Each composting facility requires one frontend loader, tub grinder and trommel (rotary)
screen. The capital cost of the tub grinder was excluded from the analysis as it was assumed not
to be required for the food waste, being used to grind woody yard waste. Annual maintenance
costs for the frontend loader and trommel screen were estimated per piece of equipment. A 15-
year service life was applied to mechanical equipment. Each compost facility was assumed to
require a 186 m2 (2,000 ft2) administration building at a cost of $519 per m2 An annual building
maintenance factor of 3 percent of capital cost was applied. Capital and maintenance costs were
A-15
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Appendix A - Alternative Food Waste Treatment Comparison
allocated to the food waste processed using food wastes' share of the total facility material
volume processed, which is 25 percent.
Labor costs were estimated based on labor estimates from Komilis and Ham (2004),
which works out to a labor requirement of 0.57 hours per metric ton of material processed.
Processing 4,900 Mg of food waste per year therefore requires 2,800 hours of labor. The
estimated labor requirement was divided equally between a supervisor and laborer/machine
operator. The supervisor's labor rate is $28.57/hour. Laborers/equipment operators are paid a
rate of $21.16/hour. A 40 percent overhead factor was applied to all labor costs (Komilis and
Ham 2004). Estimated labor rates represent national averages for "first-line supervisors of
transportation and material-moving machine and vehicle operators" and "excavating and loading
machine and dragline operators" for NAICS code 325300 representing the pesticide, fertilizer
and other agricultural chemical manufacturing industry (U.S. DOL 2016).
Each kg of material processed at the composting facility yields between 0.42 and 0.46 kg
of finished compost. The base and low cost scenarios assume values of finished compost of $15
and $19 per metric ton, which corresponds to a cost of between $12 and $16 per cubic yard.
These values are broadly representative of compost produced in the New England region. A
residual production rate of 5 percent was assumed for all incoming food waste, and is disposed
of in a sanitary landfill at a cost of $54 per metric ton.
The base cost construction interest rate of 5 percent was suggested by Komilis and Ham
(2004), while the 3 percent interest rate represents a conservative rate for a State Revolving Fund
(SRF) loan. Current loan interest rates for the Massachusetts State Revolving Fund, administered
by the Massachusetts Department of Environmental Protection, are 2 percent (MassDEP 2015).
Table A-9. Life Cycle Costs Common to both Composting Systems1
Cost Klcmcnt
Cost
Cost l-Ycqiicncy
Labor
107,350
annual
Tipping Fee
(163,457)
annual
Compost Sales
(31,522)
annual
Administrative Building
28,775
year 1
Administrative Building, maintenance
863
annual
Residuals Landfilling
13,480
annual
Loader
54,023
year 1
Loader, maintenance
360
annual
Screen
36,015
year 1
Screen, maintenance
180
annual
1 All costs are scaled to represent only food wastes share of capital and annual costs and revenues.
Windrow Composting Costs
Table A-10 summarizes life cycle costs specific to the windrow composting system. The
windrow composting system requires a specialized compost turner with a capital cost of
$259,000 (scaled to represent food wastes share). The machine has an expected useful lifespan of
15 years. Maintenance cost for the windrow turner was estimated assuming a cost factor of 2
percent of capital costs. Land area required for a windrow facility was estimated using aerial
photos of four windrow facilities in Eastern Massachusetts. The average land area requirement
A-16
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Appendix A - Alternative Food Waste Treatment Comparison
across the four facilities was 3.2 m2/Mg/yr or a total land requirement of 1.6 hectares for food
waste processing. Land cost was estimated using the CAPDETWorks™ default value adjusted to
2016 dollars, or $50,104/hectare ($20,276/acre). Annual property tax was estimated using an
average 2016 Massachusetts commercial property tax rate of $18.59 per thousand dollars of
assessed value (MA DLS 2019). The cost of site grading was applied to the entire facility area at
a unit cost of $18,000 per hectare. Electricity and diesel cost was estimated using the developed
LCI value and assuming electricity and diesel unit costs of 14.3 cents/kWh and 0.63 $/liter (2.38
$2016/gallon) (US EIA 2019). Energy costs were escalated using the energy escalation factors
included in Table C-l.
Table A-10. Windrow Composting Life Cycle Costs
Cost Klcmciil1
Usisc C'osl
l.ow Cost
C'osl I'Ycqucncv
Windrow Turner
64,828
Year 1
Windrow Turner, maintenance
1,297
annual
Land cost
78,245
Year 1
Property taxes
1,455
annual
Site grading
28,122
Year 1
Indirect costs
105,882
Year 1
Electricity
6,993
3,505
annual
Diesel
3,142
2,518
annual
Loan interest
15,882
9,529
Year 1
1 All costs are scaled to represent only food wastes share of capital and annual costs and revenues.
Aerated Static Pile Composting Costs
Table A-l 1 summarizes life cycle costs specific to the ASP compost system. The system
layout was based on the configuration of a 153 m3 (200 cubic yard) pilot scale system operated
in Walla Walla, WA (02 Compost 2015). The system consists of perforated PVC pipe manifolds
lain on top of the ground and connected to a 1.5 HP blower for aeration control. The blowers are
run on a pulse schedule where they typically operate for between one and four minutes in every
20 minutes. The pile will not be able to maintain sufficient temperatures if the blowers are run
continuously. A 150 mm (6 in) pipe is used as a manifold to connect the blower with four 100
mm (4 in) lateral lines each 18 meters in length. The manifold is 7.3 meters (24 ft) in length. Pile
height is approximately 2.4 meters. The pipes are made of SDR 35 PVC, which is commonly
used for sewer mains and is stronger than regular schedule 40 PVC. Pipe cost was estimated
using 2016 cost factors from the RSMeans database (RSMeans 2016). A 30 percent installation
cost factor was applied to bare material cost assuming that facility staff would assemble the
manifolds. Unit cost for the 150 and 100 mm pipe was $23.66 and $17.45 per meter of pipe,
respectively (including fittings).
Each static pile contains 153 m3 of material when the composting process begins,
corresponding to a pile mass of approximately 110 Mg. The ASP system requires approximately
30 days for the active composting phase and 30 days of curing time (02 Compost 2015). The
A-17
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Appendix A - Alternative Food Waste Treatment Comparison
blowers are only required during the active phase. Using these assumptions 5 pipe manifolds
were required to process the incoming food waste. When a pile finishes the active phase, the
blower is moved to a newly formed pile. Manifolds remain in place during the curing phase, to
avoid the additional material handling. One extra blower was specified in the event that one is
down for maintenance. The capital cost per blower is $1400. A 30 percent installation cost factor
and 2% annual maintenance cost factor was applied to the blowers. A total of 10 pipe manifolds
and 6 blowers comprise the capital equipment required for operation of the ASP composting
system. This system configuration is easily scalable. The useful lifespan of piping and blowers
was assumed to be 3 and 5 years, respectively. Electricity and diesel cost were estimated using
the developed LCI values and the same cost assumptions described for windrow composting.
The land area requirement for the ASP composting facility was based on aerial photos of
a facility in Central Massachusetts. The land area requirement was estimated to be 0.59
Mg/m2/year or 0.3 hectare in total to process 4,900 Mg of material. The decreased land area
requirement of ASP composting is corroborated by the pilot study in Walla Walla, Washington,
which concluded that ASP would allow a fourfold increase in the throughput of their existing
(windrow) facility. Land cost, property taxes and site grading we estimated using the developed
land area estimate and cost factors described in the section on windrow composting.
Table A-ll. ASP Composting Life Cycle Costs
Cost Klcmcnl1
linse Cost
Low Cost
("osl
Ircqiicncv
ASP piping
20,708
Year 1
ASP blowers
11,726
Year 1
Blower maintenance
164
annual
Land cost
14,590
Year 1
Property taxes
271
annual
Site grading
5,244
Year 1
Indirect costs
78,246
Year 1
Electricity
26,237
16,310
annual
Diesel
1,588
annual
Loan interest
11,737
7,042
Year 1
1 All costs are scaled to represent only food wastes share of capital and annual costs
and revenues.
GLSD LCCA Cost Adjustments
All GLSD annual and capital costs common to both the baseline and full capacity
scenario were zeroed out for the food waste disposal LCCA comparison. No food waste is
processed in the baseline scenario, so it follows that costs common to both feedstock scenarios
are not attributable to the food waste being processed. Additionally, all cost inputs associated
with unit processes marked as "Excluded" in Table A-3 were zeroed out. Remaining process
costs were assumed to partially reflect the additional system costs and revenues associated with
SSO acceptance. Cost inputs that are independent of the decision to install CHP capacity were
A-18
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Appendix A - Alternative Food Waste Treatment Comparison
adjusted to reflect the cost of food waste processing by subtracting baseline life cycle cost inputs
from the corresponding input value for the full capacity feedstock scenario. Cost input values
that are influenced by the decision to install a CHP system were allocated to food waste based on
the fraction of biogas production contributed by food waste. Food waste contributes
approximately 69 and 78 percent of biogas production in the low and base AD performance
scenarios, respectively.
As described in Section 4.3, the majority of system costs associated with large
maintenance and equipment replacement projects were estimated using plant-provided data on
debt service payments. A portion of debt service payments was allocated to food waste
processing based on the volume fraction of SSO as compared to municipal sewage, which is
approximately 0.4 percent. Maintenance costs associated with the CHP system were allocated
using the food waste biogas production fraction, while maintenance costs for the fourth digester
tank were allocated solely to food waste processing.
Table C-4 includes detailed cost input data for the food waste comparison, allowing
comparison with cost input data associated with LCCA results in the main report.
Food Waste Disposal - Comparative Results
This Appendix provides a comparative analysis of food waste management options
including: AD, windrow and ASP composting, landfilling and WTE combustion. Results are
presented for eight environmental impact categories as well as for life cycle costs for the AD and
two compost treatment options. All AD results correspond to the full capacity scenario, which
assumes that AD infrastructure is fully utilized. LCA results are presented in the order they are
introduced in the main report.
Base performance results, which are representative of estimated average performance, are
presented for AD and compost treatment options. A low performance scenario was evaluated in
the main report to test the sensitivity of LCA and LCCA results to worse than expected
performance of AD treatment units. This scenario has been carried forward into this Appendix.
An improved performance scenario has been evaluated for the compost options, based on the
judgement that a well-managed compost facility should be able to achieve reduced equipment
use and GHG emissions through efficient management. The following model parameters are
varied within the performance scenarios:
• AD
o Volatile solids reduction, biogas yield and rate of on-site biogas utilization (i.e.,
fraction flared)
• Compost
o Equipment energy consumption, process GHG emissions, transport distance to
land application, quantity of sequestered carbon and amount of avoided fertilizer
A-19
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Appendix A - Alternative Food Waste Treatment Comparison
Summary Results
Table A-12 presents impact assessment results per kg of food waste disposed for each of
the five treatment options. Cost results are presented per metric ton. Cost was not assessed for
the landfill and WTE treatment options. Landfill and WTE options are shown as historical
reference points for the environmental results, but these options are no longer available to
commercial food waste generators in Massachusetts given the ban on disposal of commercial
organic food waste. It should also be noted that landfilling and WTE combustion of food waste
remove material from the nutrient cycle; whereas, AD and composting allow continued
beneficial use of the nutrient material. Benefits of long-term nutrient recovery are not fully
captured in the impact categories covered in this LCA.
Figure A-2 presents impact assessment results in a format that allows relative comparison
of the treatment options as a percentage of maximum impact within each environmental or cost
category. Treatment options for which relative net impacts are greater than zero correspond to
environmental impacts and/or economic costs. Treatment options that have relative net impacts
that are less than zero correspond to environmental benefits or revenue. Environmental benefits
indicate that the positive environmental effect of avoided products (e.g., electricity, natural gas,
fertilizer) is greater than the environmental impact of inputs and process emissions associated
with an individual treatment option for that impact category. In both Table and Figure A-2,
lower values indicate treatment options with lower environmental impact.
Figure A-2 indicates that the base performance AD scenario has the lowest environmental
impact or the greatest environmental benefit in six of eight impact categories, and also yields the
lowest NPV per unit of food waste processed. The FDP and CED impact categories, that are
directly related to energy use and production, demonstrate the best relative performance of food
waste anaerobic co-digestion due to biogas energy recovery. The use of a split axis in Figure A-2
visually minimizes the relative FDP and CED benefits of food waste managed via AD, but still
clearly demonstrates its superior performance. Figure A-7 confirms that avoided grid electricity
and natural gas consumption are responsible for the large relative benefits of food waste co-
digestion. WTE combustion is the second best performer in these two impact categories. The
ASP compost option demonstrates the highest FDP and CED impact, followed closely by
windrow composting. WTE combustion, AD and landfilling all capture at least a small fraction
of the energy content present in food waste. However, composting is a net energy consumer, and
the relative energy demand of electricity consumption for ASP composting is greater than that of
diesel use to fuel the windrow turner.
The base performance AD scenario is the only EOL treatment option that generates net
benefits for PMFP, SFP and AP. These three impact categories are strongly linked to combustion
emissions, and only with the higher biogas yield of the base performance scenario do the benefits
of avoided energy products outweigh the impacts associated with on-site combustion and
transportation of heavy food waste and pelletized biosolids. Biogas combustion does produce
pollutant emissions that contribute to these three impact categories. Windrow and ASP compost
systems have the largest environmental impact in these three categories in the base performance
scenario, due to process emissions that occur during composting or land application. Food waste
landfilling also leads to relatively high PMFP and SFP impacts, similar in magnitude to compost
options within the improved performance scenario.
A-20
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Appendix A - Alternative Food Waste Treatment Comparison
Anaerobic digestion of food waste also leads to an environmental benefit in the GWP
impact category, again due to avoided energy products. Food waste landfilling has a
considerably higher carbon footprint than all other food waste EOL disposal options. Modeled
landfill gas management reflects current practice in Massachusetts, where 19 percent of gas is
flared and 81 percent is used for energy recovery. As a reference point, nationally approximately
24 percent of landfill gas is flared, 68 percent is used for energy recovery and 8 percent is vented
to the atmosphere. See report Section 3.3.14 for more detail. The GWP impact of composting is
associated with methane and nitrous oxide emissions in the base performance scenario. All other
food waste EOL treatment scenarios lead to a GWP benefit due to avoided energy products,
fertilizer production and carbon sequestration for the fraction of land applied carbon that remains
in the soil after 100 years.
It is rare to find products, technologies or processes in any comparative environmental
analysis that outperform all other options across all of the included indicators. These variations
in environmental performance force communities, plant personnel and policy makers to grapple
with challenging environmental and cost trade-offs, that challenge the notion of a "best"
available option. An increase in EP associated with food waste AD is the largest environmental
impact of co-digestion. A fraction of nutrient content in the food waste is returned to the primary
and secondary treatment processes, and is ultimately released with the treated effluent. The base
and low AD performance scenarios assume that 55 percent and 80 percent of food waste
nutrients are solubilized during digestion and return to plants headworks. The 55 percent
estimate used in the base performance scenario was based on the result of GPS-X™ model runs,
while the 80% value used in the low performance scenario assumes that VS destruction
correlates with nutrient solubility. Approximately 70 percent of nitrogen and phosphorus
returned to the headworks are released with the effluent, based on GPS-X™ estimates. The two
compost options have the next highest EP, due to emissions associated with land application.
Both landfilling and WTE combustion exhibit negligible EP impact. Water use is also greatest
for the food waste co-digestion due to the water that is used to reduce the solids content of
incoming food waste during the bioslurry production process.
Life cycle cost is presented per metric ton of food waste processed for AD and compost
treatment options. The base performance AD scenario demonstrates the best economic
performance, with revenue of approximately $7.60 per metric ton over the thirty year time
horizon. Dollars are expressed as NPV. The low performance AD scenario has the highest NPV
per metric ton of food waste primarily due to reduced production of electricity from the CHP
system because a larger fraction of overall biogas heat content is required for the pellet drying
process, which bypasses the CHP engine. All compost scenarios lead to net revenue per metric
ton of food waste accepted, which ranges between $1.70 and $4.80 across the base and improved
compost performance scenarios.
A-21
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Appendix A - Alternative Food Waste Treatment Comparison
Table A-12. Summary LCA Results Comparing Food Waste EOL Management Options - per kg of food waste treated
Performance Scenario
Base
Low
Base
Base
Improved
Improved
n.a.
n.a.
Impact Category
I nils
Al)
Al)
Windrow
ASI»
Windrow
ASP
Landfill
wti:
Acidification Potential
kg SO2 eq
-1.1E-4
2.1E-4
1.2E-3
1.2E-3
5.0E-4
4.9E-4
1.4E-4
8.1E-5
Cumulative Energy Demand
MJ
-7.2
-3.2
0.29
0.54
0.22
0.39
-0.20
-0.96
Eutrophication Potential
kg N eq
2.4E-3
3.1E-3
9.5E-4
9.5E-4
6.7E-4
6.7E-4
8.6E-6
6.2E-6
Fossil Depletion Potential
kg oil eq
-0.12
-0.06
7.1E-3
9.1E-3
5.7E-3
7.1E-3
-1.1E-3
-9.8E-3
Global Warming Potential
kg CO2 eq
-0.14
-0.03
0.10
0.07
-0.01
-0.01
0.32
-0.02
Particulate Matter
Formation Potential
kg PM2.5 eq
-2.5E-5
4.0E-7
2.8E-5
2.8E-5
7.4E-6
7.4E-6
7.5E-6
2.9E-6
Smog Formation Potential
kg O3 eq
-3.8E-3
3.0E-3
6.4E-3
6.4E-3
5.1E-3
5.2E-3
4.8E-3
2.0E-3
Water Use
m3 H2O
8.0E-4
1.1E-3
-5.1E-4
-4.7E-4
-3.7E-4
-3.4E-4
-3.4E-5
-1.2E-4
Cost1
$/ Mg
-7.6
10
-3.8
-1.7
-4.8
-3.6
2
n.a.
2
n.a.
1 All cost results presented in this table were developed using the base cost assumptions defined in this Appendix and report Section 4.2.6.
2 Cost per ton for landfill and WTE combustion were not evaluated.
A-22
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Appendix A - Alternative Food Waste Treatment Comparison
100%
50%
« 0%
a -100%
-------
Appendix A - Alternative Food Waste Treatment Comparison
Eutrophication Potential Results
Figure A-3 presents EP impact assessment results for each of the five food waste
treatment options according to the underlying drivers that contribute to impact. Food waste co-
digestion leads to the highest overall EP across both performance scenarios, with effluent release
contributing between 77 percent and 82 percent of gross positive impact. Land application of
pelletized biosolids and compost contribute considerably to EP impact, especially for the
compost treatment options, where it dominates impact assessment results. Avoided fertilizer
production provides modest reductions in net EP impact for the windrow and ASP compost
systems in both performance scenarios. The benefits of avoided fertilizer production are minor
for the AD treatment options. Both landfilling and WTE combustion have negligible relative EP
impact.
The actual quantity of food waste nitrogen and phosphorus that is returned to the plant
headworks and ultimately contributes to effluent concentrations has not been well studied and is
a source of uncertainty in the current results. Preliminary water quality monitoring is ongoing to
determine what effect, if any, the addition of SSO material may have on final effluent nutrient
concentrations (further discussed in the Appendix B Nutrient Supplement Section).
I
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00
z
00
a
11
o
Ph
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O
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a
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w
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0
-0.5
3.1
2.4
mm
1.0
0.67
~~5~
0.67
i-
0.01
0.01
AD
AD
Windrow
ASP
Windrow
ASP Landfill1
WTE1 ,
Base
Low
Base
Base
Improved
Improved
I
0 Avoided Fertilizer
~ Effluent Release
E3 Land Application
~ Transport
H Avoided Electricity, CHP
H Chemicals
~ Infrastructure
CD Potable Water Use
• Net Impact
1 Landfilling and combustion of commercial food waste are prohibited in the State of Massachusetts per regulation 310 CMR 19.000.
B Avoided Natural Gas, CHP
El Electricity
H On-Site Combustion
~ Unit Process Emissions
Figure A-3. Eutrophication potential results by process category for food waste end-of-life
treatment.
A-24
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Appendix A - Alternative Food Waste Treatment Comparison
Cumulative Energy Demand Results
Figure A-4 presents CED inventory results for each of the five treatment options
according to the underlying drivers that contribute to energy demand. Positive contributions to
CED are dominated by electricity consumption for the AD and ASP treatment options.
Transportation of food waste, SSO, pelletized biosolids and finished compost also contribute
visibly to CED. The two AD treatment scenarios lead to net reductions in energy demand due to
their avoided energy products. Biogas combustion is not considered to contribute to energy
demand because it enters EOL treatment facilities as a waste product. Avoided electricity
production also leads to net reductions in energy demand when considering landfilling and WTE
combustion of food waste. The heat fraction of energy associated with biogas combustion at
landfills and WTE plants was assumed not to contribute avoided product benefits. Specific
facilities that cooperate with local industrial partners, or otherwise find beneficial uses for waste
heat would be eligible to receive additional avoided product benefits. The landfill and WTE
treatment options consider disposal in facilities relatively close, 73 km total transit distance, to
the point of waste generation. Increased transport distances would lead to increases in energy
demand for these options.
2.0
-7.2
-3.2
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-------
Appendix A - Alternative Food Waste Treatment Comparison
Global Warming Potential Results
Figure A-5 presents GWP impact assessment results for each of the five treatment
options according to the underlying drivers that contribute to impact. Process emissions of GHGs
are the predominant contributor to GWP impact, especially for food waste landfilling, which has
the highest GWP impact. Process emissions also contribute considerably to the GWP of food
waste co-digestion and both composting options within the base performance scenario. Fugitive
emissions from the AD tank were estimated assuming a 5 percent leakage rate (UNFCCC 2012).
Methane emissions in the base performance windrow scenario are approximately 0.8 percent of
carbon entering the compost pile. No methane emissions are assumed in the ASP system. Nitrous
oxide emissions are approximately 1.3 percent of nitrogen entering the compost pile for both the
ASP and windrow composting methods. On-site combustion of biogas and transportation of food
waste contribute additional visible increases in GWP for the AD treatment route.
Avoided products serve to reduce the net GWP of all five treatment options, to varying degrees.
Avoided energy products are responsible for the net environmental benefit associated with food
waste co-digestion and WTE combustion, and are therefore dependent on biogas replacing
natural gas combustion and grid based electricity consumption. The New England ISO grid is
being replaced in this analysis. Over 80 percent of electricity demand in the 2016 grid mix was
supplied by natural gas and nuclear power plants (Table 2-2). Replacement of dirtier or cleaner
electrical grid mixes will directly affect the realized avoided product benefits. Carbon
sequestration and avoided fertilizer production associated with compost land application are
responsible for the net GWP benefit associated with windrow and ASP compost options in the
improved performance scenario. Land application of the pelletized biosolids yields a negligible
sequestration credit when compared against other processes in the AD life cycle. Higher GHG
emissions and a more conservative assumption regarding the carbon sequestration potential of
compost use lead to net GWP impacts in the base performance compost scenarios. In the base
performance scenario, approximately 8 percent of land applied carbon is sequestered beyond 100
years. This value increases to 19 percent in the improved performance scenario.
A-26
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Appendix A - Alternative Food Waste Treatment Comparison
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-------
Appendix A —Alternative Food Waste Treatment Comparison
Acidification Potential Results
Figure A-6 presents AP impact assessment results for each of the five treatment options
according to the underlying drivers that contribute to impact. A number of sources contribute
emissions that lead to AP impact including process emissions, waste transport, emissions
associated with land application, and on-site combustion of biogas. The base performance
compost scenarios lead to the highest AP impact due to a combination of these process
categories. Process based ammonia emissions are the single largest contributor to composting AP
impact. Avoided fertilizer production reduces composting net AP by between 15 percent and 25
percent across all included scenarios.
The base performance AD scenario is the only EOL treatment option that leads to a net
reduction in AP, due primarily to avoided energy product credits. Landfilling, WTE combustion,
and the low performance AD scenarios all have relatively low AP impacts per kg of food waste
disposed.
%
G
£
cr
-------
Appendix A —Alternative Food Waste Treatment Comparison
Fossil Fuel Depletion Potential Results
Figure A-7 presents FDP inventory results for each of the five treatment options
according to the underlying drivers that contribute to impact. The low and base performance AD
treatment options lead to the largest reductions in FDP across the five treatment options.
Reduced fossil fuel consumption stems primarily from the replacement of fossil energy sources
with the heat and power recovered from biogas combustion. WTE combustion and landfilling
also lead to modest reductions in FDP due to energy recovery at these respective facilities. The
windrow and ASP compost systems have similar, low consumption of fossil fuel resources
attributable primarily to diesel consumption during food waste and compost transport and for
operation of processing equipment.
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-------
Appendix A —Alternative Food Waste Treatment Comparison
Smog Formation Potential Results
Figure A-8 presents SFP impact assessment results for each of the five treatment options
according to the underlying drivers that contribute to impact. Emission of nitrogen oxides during
transportation of food waste, compost and pelletized biosolids is a primary contributor to SFP
impact for all of the EOL treatment options. On-site biogas combustion in the CHP engine and
pellet drying facility also contribute to SFP impact. The base performance AD scenario is the
only EOL treatment option to generate a net SFP benefit, attributable to the avoided energy
products. WTE combustion has the lowest net SFP impact among the remaining treatment
options. Windrow and ASP composting systems have the highest SFP due to a combination of
high transportation related impact and minimal avoided product benefits.
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AD
AD
Windrow
ASP
Windrow
ASP Landfill1
WTE1
Base
Low
Base
Base
Improved
Improved
-
11 Avoided Fertilizer
~ Effluent Release
~ Land Application
~ Transport
~ Avoided Electricity, CHP
ED Chemicals
¦ Infrastructure
0 Potable Water Use
• Net Impact
1 Landfilling and combustion of commercial food waste are prohibited in the State of Massachusetts per regulation 310 CMR 19.000.
0 Avoided Natural Gas, CHP
ED Electricity
Dffl On-Site Combustion
~ Unit Process Emissions
Figure A-8. Smog formation potential results by process category for food waste end-of-life
treatment.
A-30
-------
Appendix A —Alternative Food Waste Treatment Comparison
Particulate Matter Formation Potential Results
Figure A-9 presents PMFP impact assessment results for each of the five treatment
options according to the underlying drivers that contribute to impact. A number of sources
contribute emissions that lead to PMFP impact including process emissions, waste transport,
emissions associated with land application and on-site combustion of biogas. The base
performance compost scenarios lead to the highest PMFP impact due to a combination of these
process categories. Process based ammonia emissions are the single largest contributor to
compost PMFP impact. Avoiding production of the chemical fertilizers urea and single
superphosphate helps reduce the net environmental burden of both compost and AD treatment
options.
The base performance AD scenario leads to an overall net reduction of PMFP impact due
to the combined benefits of avoided fertilizer production and energy products. On-site
combustion of biogas is the largest contributor to AD PMFP impact, followed by land
application of pelletized biosolids. All of the other treatment options, including the low
performance AD scenario and the improved performance compost scenarios yield low, net
positive PMFP impact per kg of food waste treated.
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2.8E-05 2.8E-05
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7.5E-06
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2.9E-06
6.0E-05
4.0E-05
2.0E-05
-2.0E-05
-4.0E-05
-6.0E-05
-8.0E-05
~ Avoided Electricity, CHP
H Chemicals
a Infrastructure
~ Potable Water Use
• Net Impact
1 Landfilling and combustion of commercial food waste are prohibited in the State of Massachusetts per regulation 310 CMR 19.000.
AD
AD
Windrow
ASP
Windrow
ASP Landfill1
WTE1
Base
Low
Base
Base
Improved
Improved
-
~ Avoided Fertilizer
~ Effluent Release
E3 Land Application
~ Transport
IS Avoided Natural Gas, CHP
~ Electricity
0 On-Site Combustion
~ Unit Process Emissions
Figure A-9. Particulate matter formation potential results by process category for food
waste end-of-life treatment.
A-31
-------
Appendix A —Alternative Food Waste Treatment Comparison
Water Use Results
Figure A-10 presents WU inventory results for each of the five treatment options
according to the underlying drivers that contribute to water demand. Water use required to
produce the SSO slurry for co-digestion is the largest contributor to water use across the five
treatment option, and leads to the highest water demand being associated with the low and base
performance AD scenarios. Total water use necessary to process the SSO is approximately 3
percent of plant water use. Water use associated with SSO production is a conservative estimate.
Some or all of the necessary moisture may be sourced from complementary liquid wastes,
leading to direct reductions in estimated water use. Avoided fertilizer production provides a
water use credit for the AD and compost treatment options. Water use during fertilizer
production is primarily associated with sulfuric acid production and electricity generation.
The high moisture content of food waste relative to the desired moisture content of
compost feedstock was assumed to eliminate compost water use for the food waste fraction of
pile feedstock.
2.0E-03
8.0E-04
1.1E-03
I
1.5E-03
£ 1.0E-03
00
b
X 5.0E-04
-------
Appendix A - Alternative Food Waste Treatment Comparison
Life Cycle Cost Assessment Results
Table A-13 presents life cycle costs per metric ton of food waste processed at each
facility type. Cost estimates are provided for all performance scenarios considered in the LCA
results. For information of payback period of the AD and CHP installation refer to Section 6.5.
Overall the base AD performance scenario realizes the most revenue per metric ton of food
waste processed within both the base and low cost scenarios. The low performance AD scenario
assumes a more conservative estimate of biogas production, which considerably lowers revenue
associated with avoided energy products. The low AD performance scenario leads to a cost of
$10 per metric ton of food waste processed in the base cost scenario, and drops to approximately
$0.50 per ton in the low cost scenario. All of the eight composting scenarios lead to revenue that
ranges between $2.50 and $10 per metric ton of food waste treated.
Figure A-l 1 summarizes life cycle costs for each treatment option according to
underlying cost categories. Negative values correspond to net revenue, over a 30-year period, for
the relevant cost category. The AD system has much higher overall costs but also results in
greater cost savings. Costs and revenue for the composting systems are over an order of
magnitude lower compared to the AD system.
Although anaerobic digestion is more capital intensive, it leads to increased revenue
potential from the sale of renewable and alternative energy credits or by avoiding electricity and
natural gas costs. It is this revenue potential, particularly from the renewable and alternative
energy credits, that leads to the lowest life cycle costs per metric ton of food waste processed in
the base AD performance scenario. Capital costs are fixed however, and when the digesters
produce less biogas in the low AD performance scenario the balance of expenditure to revenue
shifts considerably, which leads to an economic loss. Tipping fees are categorized as an
operational cost, which produce a small amount of net revenue in the low cost scenario, negating
other operational costs.
In Figure A-12, results are presented relative to gross positive expenditures to allow
composting and AD cost contribution analysis results to be viewed more clearly on the same axis
scale. This figure shows that revenue is a smaller fraction of total expenditures for both of the
AD scenarios as compared to composting. Still, the AD option leads to the highest revenue per
metric ton of food waste processed, in the case of the base performance scenario as indicated by
the X marks in the figure. Labor cost is included in the annual operation cost category and is
offset by tipping fee revenue and the sale of finished compost. The sale of RECs and AECs leads
to net revenue in the energy cost category for the WWTF.
Table A-13. Life Cycle Cost or Revenue per Metric Ton of Food Waste Treated
Treatment System
I'crlorm :hht Scenario
Life C'vcle Cost (201 (> S/M»)
li:ise Cost
Low Cost
AD
Base
-7.6
-16
Low
10
0.55
Windrow
Base
-4.5
-9.9
Improved
-5.4
-11
ASP
Base
-2.4
-8.5
Improved
-4.3
-10
A-33
-------
Appendix A —Alternative Food Waste Treatment Comparison
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a Capital ED Annual Operation ED Annual Material ~ Annual Chemical ~ Annual Energy • Total NPV
Figure A-ll. Net present value life cycle costs by cost category.
A-34
-------
Appendix A - Alternative Food Waste Treatment Comparison
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Figure A-12. Relative life cycle costs by cost category. Net cost per metric ton of food waste
processed is marked with an X and values correspond to the secondary y-axis.
A-35
-------
Appendix A - Alternative Food Waste Treatment Comparison
Compost LCA Results — Transport Distance Sensitivity
The availability of sufficient composting capacity in Eastern Massachusetts is not certain
given the large quantity of organic material being considered. A regional transportation scenario
was analyzed to determine the sensitivity of LCA results to longer transport distances, should
local composting capacity prove insufficient for the considered waste volumes. The regional
transport scenario assumes a hypothetical transport distance of food waste to the composting
facility that is three times the calculated local transport distance, 203 km (125 miles).
Figure A-13 depicts relative LCA results for the windrow and ASP compost systems for
both performance and transport distance scenarios. All windrow composting results are displayed
in shades of blue with fill patterns that vary according to scenario assumptions. ASP compost
system results are displayed in shades of green. Each pair of bars is labeled with values that
represent the percent increase in impact associated with the regional transportation scenario,
relative to the local transport scenario. Larger increases in LCA results indicate greater
sensitivity to the underlying transport assumptions used in the analysis.
CED, FDP and SFP impact assessment results are most strongly affected by increased
transportation distances, with relative increases in impact that range from 32 percent to 81
percent of impact potential when shifting from the local to regional transport scenario. The WU
and EP impact results are negligibly affected by transport assumptions, less than 1 percent.
Relative increases in impact potential are greater for the improved performance scenario due to
the lower magnitude of impact potential, and the correspondingly greater influence of increased
trucking.
While the impact assessment results are sensitive to transport distance assumptions in
several impact categories, the realized shifts in impact do not have a material effect on the
relative environmental performance of the five EOL treatment options discussed previously.
A-36
-------
Appendix A Alternative Food Waste Treatment Comparison
100%
80%
a
P, 60%
S 40%
3
O
y 20%
-------
Appendix A - Alternative Food Waste Treatment Comparison
Conclusions
This Appendix presents comparative LCA and LCCA results for five food waste EOL
treatment and disposal options including anerobic digestion, windrow composting, aerated static
pile composting, landfill disposal and WTE combustion. LCA results indicate that food waste
treatment via AD produces considerable environmental benefits in six of eight impact categories
assuming base AD performance. These benefits are primarily attributable to replacing grid
electricity and natural gas with power and heat from biogas combustion. Composting treatment
options generate larger environmental impacts in categories strongly dependent on energy
production and consumption such as CED, AP, SFP and PMFP because all other treatment and
disposal options capture at least a small fraction of the energy contained in food waste. Notable
results include the high GWP of food waste disposal in landfills, and the low EP of landfill and
WTE disposal options. WTE combustion performs reasonably well in all impact categories. An
increase in EP is the largest possible tradeoff associated with co-digestion of food waste.
Additional treatment options are available to address this issue.
The results indicate three unique approaches to food waste treatment. Anaerobic
digestion recognizes the energy potential value of food waste, but requires a considerable capital
investment to realize these benefits. The low AD performance scenario results demonstrate the
importance of sound digester management if an overall economic benefit and maximum
environmental benefits or reductions in environmental impact are to be achieved. Compost
LCCA results depict a different strategy for food waste treatment with a much lower capital cost,
that does not capture the energy potential and associated environmental benefit of food waste
energy recovery. Both strategies recover nitrogen and phosphorus contained in the original food
waste and put it to beneficial use as an agricultural amendment. The sale of compost to end
users, is an important revenue stream for composting facilities, while the production and
distribution of pelletized biosolids remains a net cost for the WWTF. Landfills and WTE
disposal represent a third approach that is in line with the traditional approach to waste disposal
where materials are effectively taken out of active circulation, albeit with energy recovery.
The scenarios and sensitivities presented in this Appendix highlight the importance of
careful management of all systems and site specific consideration of important underlying model
parameters where possible. The transportation sensitivity for example indicates strong sensitivity
of several LCA impact categories to underlying assumptions about the distance the food waste or
compost is assumed to travel. Other examples of key parameters that are expected to vary
nationally include realized AD performance, fugitive methane emissions from digesters and
landfills, GHG emissions from compost piles, and long-term carbon sequestration of land applied
soil amendments. Appropriate interpretation of the reported results should consider such factors
when drawing their own conclusions.
A-38
-------
Appendix B - Detailed LCI Calculations and Background Information
Appendix B:
Detailed LCI Calculations
and Background Information
-------
Appendix B - Detailed LCI Calculations and Background Information
Appendix B
Detailed LCI Calculations
and Background Information
Process Emission Calculations
Process GHG emissions were calculated for biological treatment, anaerobic digestion and
effluent release. Carbon dioxide releases from the WWTF were assumed to be biogenic in origin,
do not contribute to global warming potential impact and were therefore excluded from the
analysis. The following sections describe calculation procedures used to estimate process GHG
emissions.
Nitrous Oxide Emissions from Biological Treatment
The methodology for calculating N2O emissions associated with the biological
wastewater treatment unit was based on IPCC guidelines for national inventories (IPCC 2006).
The average N2O emission factor of two MLE treatment systems, 0.16 percent influent TKN
emitted as N2O (EF%), was used to estimate N2O emissions from biological treatment (Chandran
2012).
N2O process emissions = TKN (mg/L) x Flow (gpd) x 3.785 L/gal x 365.25 days/yr x 1x10-6
kg/mg x EF% x 44/28
Equation B-l
Where:
N2O process emissions = N2O emissions from wastewater treatment process (kg N2O /yr)
TKN = Concentration of TKN entering biological treatment process (mg/L)
Flow = Wastewater treatment flow entering biological treatment process (gpd)
EF% = average measured percentage of TKN emitted as N2O
44/28 = molecular weight conversion of N to N2O
Methane Emissions from Biological Treatment
The methodology for calculating methane emissions associated with the biological
wastewater treatment unit was based on IPCC guidelines for national inventories (IPCC 2006).
Methane emissions were estimated using the amount of organic material (i.e., BOD) entering the
unit operations that may exhibit anaerobic activity, an estimate of the theoretical maximum
amount of methane that can be generated from the organic material (Bo), and a methane
correction factor (MCF) that reflects the degree to which theoretical maximum methane
generation rates are realized. In general, the IPCC does not estimate methane emissions from
well managed centralized aerobic treatment systems. However, there is acknowledgement that
some methane can be emitted from pockets of anaerobic activity and the treatment process
evaluated has an anoxic zone preceding the aeration basin. An MCF of 0.05 was used to estimate
CH4 process emissions based on emission measurements from Daelman et al. (2013).
Daelman et al. (2013) reports measured emissions data from a WWTF in the Netherlands.
The Netherlands biological treatment process includes an anoxic zone preceding the aerated
zone. Unlike the Lawrence WWTF the Netherlands treatment process utilizes an internal recycle
B-l
-------
Appendix B - Detailed LCI Calculations and Background Information
between the aerated and anoxic zones. The average reported methane emission factor is 11 g CH4
per kg of influent COD, which converts to an MCF of approximately 0.044 using a Bo of 0.25 kg
CH4/kg COD (IPCC 2006).
Methane process emissions = BOD (mg/L) x Flow (gpd) x 3.785 L/gal
x 365.25 days/yr x 1x10-6 kg/mg x Bo x MCF
Equation B-2
Where:
Methane process emissions = Methane emissions from wastewater treatment process (kg
CH4 /yr)
BOD = Concentration of BOD entering biological treatment process (mg/L)
Flow = Wastewater treatment flow entering biological treatment process (gpd)
Bo = maximum methane producing capacity, 0.6 kg CHVkg BOD (IPCC 2006)
MCF = methane correction factor (fraction)
Nitrous Oxide Emissions from Effluent Release
The methodology for calculating nitrous oxide emissions associated with effluent
discharge is based on the guidance provided in the IPCC Guidelines for national inventories
(IPCC 2006). N2O emissions from domestic wastewater (wastewater treatment) were estimated
based on the amount of nitrogen discharged to aquatic environments from each of the system
configurations, which accounts for nitrogen removed with sewage sludge.
N20effluent = Neffluent x Flow x 3.785 L/gal x 365.25 days/yr
x 1x10-6 kg/mg x EF3 x 44/28
Equation B-3
Where:
N20effluent = N2O emissions from wastewater effluent discharged to aquatic
environments (kg N20/yr)
Neffluent = Nitrogen in wastewater discharged to receiving stream, mg/L
Flow = Effluent flow, MGD
EF3 = Emission factor (0.005 kg N2O -N/kg sewage-N produced)
44/28 = Molecular weight ratio of N2O to N2
Avoided End-Of-Life Disposal Life Cycle Inventory Data
Table B-l and Table B-2 list LCI flow information for the avoided landfill and WTE
disposal processes. Results were generated using the Municipal Solid Waste Decision Support
Tool (MSW DST) (RTI International 2012).
Table B-l. Massachusetts and National Average Landfill LCI, including Waste
Collection
Pni'iimolor
( ompiirlmonl
Niilioiiiil A\er;i«ic
l.iimirill
Miissiichusclls
l.iimirill
I nils
Energy, diesel
input
2.92E-7
2.92E-7
MJ/kg SSO
B-2
-------
Appendix B - Detailed LCI Calculations and Background Information
Table B-l. Massachusetts and National Average Landfill LCI, including Waste
Collection
Piiftiim-K-r
( niii|);iriim*nl
Niilioiiiil .\\crsi}»c
l.iiiKirill
Miissiichusolls
l.iiiKirill
I nils
Electricity
output
0.025
0.021
kWh/kg SSO
Total Particulate Matter
air emission
1.85E-5
1.65E-5
kg/kg SSO
Nitrogen Oxides
air emission
1.15E-4
1.03E-4
kg/kg SSO
Hydrocarbons (non CH4)
air emission
7.00E-6
7.00E-6
kg/kg SSO
Sulfur Oxides
air emission
6.06E-6
5.97E-6
kg/kg SSO
Carbon Monoxide
air emission
3.43E-5
3.37E-5
kg/kg SSO
Carbon Dioxide Biogenic
air emission
0.149
0.144
kg/kg SSO
Carbon Dioxide Fossil
air emission
2.01E-3
2.01E-3
kg/kg SSO
Ammonia (Air)
air emission
2.89E-9
2.89E-9
kg/kg SSO
Lead (Air)
air emission
1.26E-11
1.26E-11
kg/kg SSO
Methane (CH4)
air emission
0.013
0.015
kg/kg SSO
Hydrochloric Acid
air emission
4.14E-6
3.83E-6
kg/kg SSO
Dissolved Solids
water discharge
1.22E-5
1.22E-5
kg/kg SSO
Suspended Solids
water discharge
3.10E-6
3.10E-6
kg/kg SSO
BOD
water discharge
1.45E-7
1.45E-7
kg/kg SSO
COD
water discharge
2.77E-8
2.77E-8
kg/kg SSO
Oil
water discharge
2.10E-7
2.10E-7
kg/kg SSO
Sulfuric Acid
water discharge
4.20E-5
4.20E-5
kg/kg SSO
Iron
water discharge
6.17E-10
6.17E-10
kg/kg SSO
Ammonia (Water)
water discharge
1.74E-9
1.74E-9
kg/kg SSO
Copper
water discharge
3.55E-9
3.55E-9
kg/kg SSO
Cadmium
water discharge
2.69E-14
2.69E-14
kg/kg SSO
Arsenic
water discharge
1.16E-10
1.16E-10
kg/kg SSO
Mercury (Water)
water discharge
1.05E-14
1.05E-14
kg/kg SSO
Phosphate
water discharge
8.76E-15
8.76E-15
kg/kg SSO
Selenium
water discharge
3.19E-10
3.19E-10
kg/kg SSO
Chromium
water discharge
2.74E-14
2.74E-14
kg/kg SSO
Lead (Water)
water discharge
1.16E-10
1.16E-10
kg/kg SSO
Zinc
water discharge
1.35E-12
1.35E-12
kg/kg SSO
B-3
-------
Appendix B - Detailed LCI Calculations and Background Information
Table B-2. Massachusetts and National Average WTE Combustion LCI, including Waste
Collection
Piiriimolor
( ompiirimonl
N;iliun;il \\cr;i!ie
Miissiichusolls
I nils
W i ll ( cimhuslicin
\\ 111 ( cim husl ion
Energy, Diesel
input
5.32E-4
5.32E-4
gal/kg SSO
Natural Gas Use
input
0.020
0.020
MJ/kg SSO
Electricity Production
output
0.087
0.093
kWh/kg SSO
Total Particulate Matter
air emission
8.65E-6
1.16E-5
kg/kg SSO
Nitrogen Oxides
air emission
7.40E-5
1.67E-4
kg/kg SSO
Hydrocarbons (non CH4)
air emission
1.86E-6
1.86E-6
kg/kg SSO
Sulfur Oxides
air emission
1.83E-5
3.14E-5
kg/kg SSO
Carbon Monoxide
air emission
1.75E-5
4.08E-5
kg/kg SSO
Carbon Dioxide Biogenic
air emission
0.212
0.212
kg/kg SSO
Carbon Dioxide Fossil
air emission
4.04E-3
4.04E-3
kg/kg SSO
Ammonia (Air)
air emission
1.66E-6
1.66E-6
kg/kg SSO
Lead (Air)
air emission
3.24E-9
3.24E-9
kg/kg SSO
Methane (CH4)
air emission
3.52E-6
3.52E-6
kg/kg SSO
Hydrochloric Acid
air emission
1.27E-5
1.27E-5
kg/kg SSO
Dissolved Solids
water discharge
3.86E-6
3.86E-6
kg/kg SSO
Suspended Solids
water discharge
1.42E-7
1.42E-7
kg/kg SSO
BOD
water discharge
5.92E-9
5.92E-9
kg/kg SSO
COD
water discharge
2.45E-7
2.45E-7
kg/kg SSO
Oil
water discharge
4.37E-7
4.37E-7
kg/kg SSO
Sulfuric Acid
water discharge
1.16E-8
1.16E-8
kg/kg SSO
Iron
water discharge
6.26E-8
6.26E-8
kg/kg SSO
Ammonia (Water)
water discharge
4.32E-8
4.32E-8
kg/kg SSO
Copper
water discharge
2.83E-14
2.83E-14
kg/kg SSO
Cadmium
water discharge
1.76E-10
1.76E-10
kg/kg SSO
Arsenic
water discharge
7.87E-14
7.87E-14
kg/kg SSO
Mercury (Water)
water discharge
1.35E-14
1.35E-14
kg/kg SSO
Phosphate
water discharge
5.80E-9
5.80E-9
kg/kg SSO
Selenium
water discharge
1.69E-13
1.69E-13
kg/kg SSO
Chromium
water discharge
1.71E-10
1.71E-10
kg/kg SSO
Lead (Water)
water discharge
1.66E-12
1.66E-12
kg/kg SSO
Zinc
water discharge
6.29E-11
6.29E-11
kg/kg SSO
B-4
-------
Appendix B - Detailed LCI Calculations and Background Information
Nutrient Supplement
In order to assess whether the increased SSO loadings, commenced in February 2018,
had any measurable effect on plant nutrient effluent concentrations, ERG conducted a
preliminary analysis of historical water quality data. To try and isolate any effects of the process
change, data for one year prior to the change were grouped (February 2017 through January
2018) and plotted alongside data after the process change, which at the time of data acquisition
extended through June of 2018. Available effluent data for nitrogen and phosphorus are shown in
Figure B-l through Figure B-3, with the delineation in each figure corresponding to February 1,
2018.
3.00
2.50
—I
2.00
E_
X
1.50
O
2
111
1.00
t/i
0.50
0.00
•
•
•
•
•
•
• ••
i
•
•
•
•
•
•
* • . • • •
•
i i i i i i i i i i i
i i i i i i i
F-17 M-17 A-17 M-17 J-17 J-17 A-17 5-17 0-17 N-17 D-17 J-1S F-1S M-18 A-1S M-18 J-1B J-1S A-1S
Figure B-l. Secondary effluent nitrate+nitrite, February 2017 through June 2018.
40.00
35.00
j 30.00
I3 25.00
^ 20.00
5 15.00
™ 10.00
5.00
0.00
F-17M-17A-17M-17 J-17 J-17 A-17 S-17 0-17N-17D-17 J-18 F-18M-18A-18M-18 J-18 J-18 A-18
Figure B-2. Secondary effluent ammonia, February 2017 through June 2018.
0.80
0.70
0.60
—i
bJQ
0.50
£_
Cl
0.40
%
0.30
J)
0.20
0.10
0.00
_J I I I I I I I I I
_l I I I I I
D-16 J-17 F-17 M-17 A-17 M-17 J-17 J-17 A-17 S-17 0-17 N-17 D-17 J-1S F-IB M-18 A-1B M-1S J-1S J-1S
Figure B-3. Secondary effluent total phosphorus, February 2017 through June 2018.
B-5
-------
Appendix B - Detailed LCI Calculations and Background Information
Graphically, effluent nutrient levels appear to have decreased following the February
2018 process change, however all data sets have a considerable degree of variability. Moreover,
when looking at long-term influent data, a seasonal pattern is apparent in the strength of
wastewater received, with the lowest strength water appearing roughly February through April.
This is best illustrated in the BOD and TSS datasets (Figure B-4 and Figure B-5) due to the
greater frequency with which these parameters are measured.
F-17 M-17 A-17 M-17 J-17 J-17 A-17 S-17 0-17 N-17 D-17 J-1B F-1B M-1B A-1B M-1B J-1B J-1B A-1B
Figure B-4. Influent BOD data, February, 2017 through August, 2018.
BOO
700
500
I
m 500
J 400
B.300
- 200
100
0
A
•
•
•
'• JL.
wM*i£ ^ i
j i.
• a
mSt
Wii
*
P5 31
*
i i i
1Tt
i i
i
F-17 M-17 A-17 M-17 J-17 J-17 A-17 S-17 0-17 N-17 D-17 J-18 F-1B M-1B A-1B M-1B J-1B J-1B A-1B
Figure B-5. Influent TSS data, February, 2017 through August, 2018.
Given these dataset limitations, which for effluent nutrient concentrations include sparse
availability, a high degree of variability and incomplete seasonal coverage, t-tests were
performed to test for statistical differences in like datasets. For each parameter above, groupings
were made for pre- and post- process change data with equal seasonal coverage and tested for
equal means. Equal seasonal coverage was established to remove any seasonal effect, though this
also reduced the total sample sizes for some parameters. Seasonal groupings were made where
there were approximately equivalent coverages in 2017 and 2018. Table B-3 summarizes these
groupings and results. For the test that was used (t-test assuming unequal variances in Excel), the
p-value is a measure of the statistical significance of the null hypothesis, which states that the
sample means are equal. In other words, a low p-value would suggest that the sample means are
not statistically equal.
B-6
-------
Appendix B - Detailed LCI Calculations and Background Information
Table B-3. Sample t-test Results, Assuming Unequal Variances
Parameter (m«/l.)
.Months
2017 Mean1
20IS Mean1
Change
P-value
Influent
BOD
I'cb-Aug
108 (129)
180 (125)
7%
0.08
Influent
TSS
Feb-Aug
198 (176)
215 (192)
9%
0.07
Secondary Effluent
NOx
May-Jun
0.795 (3)
0.872 (4)
10%
0.89
Secondary Effluent
nh3
May-Jun
19.6(3)
24.0 (4)
23%
0.16
Secondary Effluent
TP
Feb-Jun
0.469 (5)
0.285 (5)
-39%
0.11
1 value in parentheses refer to number of samples
Table B-3 indicates that none of the sample means are statistically different at the 95%
confidence level (p-value < 0.05). There is however suggestion that BOD and TSS influent
concentrations increased (>90% confidence), ammonia effluent concentrations increased (>80%
confidence) and total phosphorus effluent concentrations decreased (>85% confidence)., though
effluent average concentrations are based on few observations.
The results do not therefore indicate a clear effect of the increased SSO acceptance on
effluent nutrient concentrations. Although ammonia effluent concentrations did appear to
increase, the change is not statistically significant and the change occurred over a time where the
strength of the wastewater influent also increased. Moreover, the observed decrease in total
phosphorus effluent concentrations is contradictory, indicating that the relationship between
influent concentration, SSO load and effluent concentrations is not easily deduced at least from
the current dataset. Additional data collection and comparison of a full year of pre- and post-
process change effluent data may show a stronger relationship.
B-7
-------
Appendix C - LCCA Supporting Information and Detailed Results
Appendix C:
LCCA Supporting Information and Detailed
Results
-------
Appendix C - LCCA Supporting Information and Detailed Results
Appendix C
LCCA Supporting Information and Detailed Results
Table C-l shows the diesel, natural gas and electricity escalation factors that are used in
the LCCA analysis. The escalation factors exclude general inflation and are specific to the
Northeastern United States. Detailed LCCA results are presented in Table C-3 and Table C-4.
Table C-l. Energy Cost Escalation Factors
Distillate 1 iiol Oil
Niiluml (ijis
Kleclricilv
^ c:i r
Kscalnlioii I'nclor
Kscitlitlion l-iiclor'
Ksciiliilion l itcloi-1
2016
1.00
1.00
1.00
2017
1.00
1.00
1.00
2018
1.12
1.02
0.980
2019
1.19
1.08
0.990
2020
1.22
1.18
1.01
2021
1.23
1.23
1.01
2022
1.25
1.26
1.03
2023
1.27
1.28
1.04
2024
1.29
1.30
1.05
2025
1.32
1.30
1.07
2026
1.34
1.29
1.09
2027
1.36
1.29
1.10
2028
1.37
1.31
1.11
2029
1.38
1.33
1.13
2030
1.41
1.35
1.14
2031
1.44
1.35
1.14
2032
1.47
1.36
1.14
2033
1.47
1.36
1.14
2034
1.49
1.38
1.13
2035
1.50
1.42
1.14
2036
1.53
1.45
1.15
2037
1.54
1.47
1.15
2038
1.55
1.48
1.15
2039
1.56
1.50
1.15
2040
1.58
1.49
1.15
2041
1.58
1.49
1.14
2042
1.58
1.50
1.14
2043
1.58
1.51
1.15
2044
1.58
1.53
1.15
2045
1.59
1.56
1.15
1 (Lavappa et al. 2017)
C-l
-------
Appendix C - LCCA Supporting Information and Detailed Results
Table C-2. Annual Estimates of Debt Service Expenditure.
\n;il> sis
^ ciir
^ ciir
IK'hl Sen ice (S 2UI(i) - Pliinl Records
Dehl Sen ice IS 2KI(») - sis
\ iilucs
liiiscline
( IIP-AI)
Projccl
Piirliiil iind
l ull ( :i|)iicil>
liiiselinc
Piirliiil iind l ull
( ii|)iicil>
year 1
2016
$ 3,316,494
$
$3,316,494
$ 3,316,494
$3,316,494
year 2
2017
$ 3,376,809
$
$3,376,809
$ 3,376,809
$3,376,809
year 3
2018
$ 3,255,369
$
$3,255,369
$ 3,255,369
$3,255,369
year 4
2019
$ 3,133,929
$
$3,133,929
$ 3,133,929
$3,133,929
year 5
2020
$ 3,137,225
$
$3,137,225
$ 3,137,225
$3,137,225
year 6
2021
$ 2,651,634
$1,427,317
$4,078,951
$ 2,651,634
$4,078,951
year 7
2022
$ 2,630,064
$1,409,541
$4,039,605
$ 2,630,064
$4,039,605
year 8
2023
$ 2,630,652
$1,411,210
$4,041,862
$ 2,630,652
$4,041,862
year 9
2024
$ 2,583,593
$1,412,916
$3,996,508
$ 2,583,593
$3,996,508
year 10
2025
$ 2,580,475
$1,414,657
$3,995,132
$ 2,580,475
$3,995,132
year 11
2026
$ 2,577,294
$1,416,437
$3,993,730
$ 2,929,625
$4,346,061
year 12
2027
$ 1,302,075
$1,418,256
$2,720,331
$ 2,929,625
$4,347,881
year 13
2028
$ 1,300,708
$1,420,114
$2,720,822
$ 2,929,625
$4,349,739
year 14
2029
$ 1,299,311
$1,422,013
$2,721,324
$ 2,929,625
$4,351,637
year 15
2030
$ 1,297,883
$1,423,952
$2,721,835
$ 2,929,625
$4,353,577
year 16
2031
$ 1,296,425
$1,425,934
$2,722,359
$ 2,929,625
$4,355,559
year 17
2032
$ 1,294,934
$1,427,960
$2,722,893
$ 2,929,625
$4,357,584
year 18
2033
$ 1,293,412
$1,430,028
$2,723,440
$ 2,929,625
$4,359,653
year 19
2034
$ 712,567
$1,432,143
$2,144,709
$ 2,929,625
$4,361,767
year 20
2035
$ 710,842
$1,434,302
$2,145,144
$ 2,929,625
$4,363,927
year 21
2036
$ 553,055
$1,436,509
$1,989,565
$ 2,929,625
$4,366,134
year 22
2037
$ 551,218
$1,438,764
$1,989,982
$ 2,929,625
$4,368,389
year 23
2038
$ 549,341
$1,441,068
$1,990,409
$ 2,929,625
$4,370,693
year 24
2039
$ 547,424
$1,443,422
$1,990,846
$ 2,929,625
$4,373,047
year 25
2040
$ 545,464
$1,445,828
$1,991,292
$ 2,929,625
$4,375,452
year 26
2041
$ 1,805,128
$
$2,946,423
$ 2,929,625
$2,929,625
year 27
2042
$ 1,805,128
$
$2,946,423
$ 2,929,625
$2,929,625
year 28
2043
$ 1,805,128
$
$2,946,423
$ 2,929,625
$2,929,625
year 29
2044
$ 1,805,128
$
$2,946,423
$ 2,929,625
$2,929,625
year 30
2045
$ 1,805,128
$
$2,946,423
$ 2,929,625
$2,929,625
C-2
-------
Appendix C - LCCA Supporting Information and Detailed Results
Table C-3. Summary of Life Cycle Costs
Cosl
Scenario
Sccnsirio
(l-oodslock. Al)
poii'oniiiiiKT)
( iipiliil
Amiiiiil
()|H-r;ilion
Amiiiiil
Milli'riiil
Amiiiiil
( homiciil
Amiiiiil
r.iKTjij
Tul;il
SPY
Base Cost
Baseline
96
106
37
14
61
314
Partial Capacity,
Low AD
115
111
44
18
41
329
Partial Capacity,
Base AD
115
111
47
18
10
301
Full Capacity, Low
AD
115
115
46
23
19
317
Full Capacity, Base
AD
115
115
52
23
-21
282
Low Cost
Baseline
77
85
30
11
49
251
Partial Capacity,
Low AD
91
85
35
15
29
255
Partial Capacity,
Base AD
91
85
38
15
2
230
Full Capacity, Low
AD
91
84
37
18
9
239
Full Capacity, Base
AD
91
84
41
18
-28
207
C-3
-------
Appendix C - LCCA Supporting Information and Detailed Results
Table C-4. Detailed Life Cycle Costs - WWTF1
\l)
Sivii;irin
I Vl'll Slock
Si-iii;irin-
(¦i'lliT;il (osl
IK'hiik'd Ciisl
Ciik-»iir\
I'roww
Desiriplinn
\\ ;isle\\ ;iler
Value-1
I-hiiiI \\ ;isle
V;illle45"
1 nil
All
All
Chemical
Chemicals
AD&CHP
Defoamant
20,000
-
$/yr
All
All
Maintenance
General
AD&CHP
Digester Cleaning
500,000
166,667
$/replacement
All
All
Operation
General
AD&CHP
Draft Tube Leak
20,000
-
$
All
All Future
Scenarios
Operation
Labor
AD&CHP
Additional Staffing
104,000
104,000
$/yr
All
All Future
Scenarios
Maintenance
General
AD&CHP
Maintenance of AD
and biogas
processing
175,600
136,835
$/yr
All
Baseline
Energy
Natural Gas
AD&CHP
Utility Bills
37,214
n.a.
$/yr
All
Baseline
Chemical
Chemicals
AD&CHP
Ferric Chloride
31,797
n.a.
$/yr
All
Baseline
Energy
Electricity
AD&CHP
Utility Bills
235,205
n.a.
$/yr
All
Full
Capacity
Chemical
Chemicals
AD&CHP
Ferric Chloride
39,746
7,949
$/yr
All
Full
Capacity
Operation
Fee Revenue
AD&CHP
SSO
-167,900
-167,900
$/yr
All
Full
Capacity
Energy
Electricity
AD&CHP
Utility Bills
300,539
65,335
$/yr
All
Partial
Capacity
Chemical
Chemicals
AD&CHP
Ferric Chloride
35,772
n.a.
$/yr
All
Partial
Capacity
Operation
Fee Revenue
AD&CHP
SSO
-83,950
n.a.
$/yr
All
Partial
Capacity
Energy
Electricity
AD&CHP
Utility Bills
300,539
n.a.
$/yr
Baseline
Full
Capacity
Energy
Electricity
AD&CHP
Electricity Sale
-3,380,142
-2,633,960
$/yr
Baseline
Full
Capacity
Energy
Renewable
Energy Credit
AD&CHP
Electricity Sale
-301,143
-234,665
$/yr
Baseline
Full
Capacity
Maintenance
General
AD&CHP
CHP Maintenance
524,061
408,372
$/yr
Baseline
Full
Capacity
Energy
Alternative
Energy Credit
AD&CHP
Electricity Sale
-385,802
-300,634
$/yr
Baseline
Partial
Capacity
Energy
Electricity
AD&CHP
Electricity Sale
-1,955,772
n.a.
$/yr
Baseline
Partial
Capacity
Energy
Renewable
Energy Credit
AD&CHP
Electricity Sale
-162,180
n.a.
$/yr
Baseline
Partial
Capacity
Maintenance
General
AD&CHP
CHP Maintenance
303,225
n.a.
$/yr
Baseline
Partial
Capacity
Energy
Alternative
Energy Credit
AD&CHP
Electricity Sale
-292,182
n.a.
$/yr
C-4
-------
Appendix C - LCCA Supporting Information and Detailed Results
Table C-4. Detailed Life Cycle Costs - WWTF1
\l)
Sivii;irin
I Vl'll Slock
Si-iii;irin-
(¦i'lliT;il (osl
IK'hiik'd Ciisl
Ciik-»iir\
I'roww
Desiriplinn
\\ ;isle\\ ;iler
Value-1
I-hiiiI \\ ;isle
V;illle45"
1 nil
Low
Full
Capacity
Energy
Electricity
AD&CHP
Electricity Sale
-1,688,895
-1,169,866
$/yr
Low
Full
Capacity
Energy
Renewable
Energy Credit
AD&CHP
Electricity Sale
-136,144
-94,304
$/yr
Low
Full
Capacity
Energy
Alternative
Energy Credit
AD&CHP
Electricity Sale
-385,802
-267,238
$/yr
Low
Full
Capacity
Maintenance
General
AD&CHP
CHP Maintenance
261,848
181,377
$/yr
Low
Partial
Capacity
Energy
Electricity
AD&CHP
Electricity Sale
-901,215
n.a.
$/yr
Low
Partial
Capacity
Energy
Renewable
Energy Credit
AD&CHP
Electricity Sale
-59,297
n.a.
$/yr
Low
Partial
Capacity
Energy
Alternative
Energy Credit
AD&CHP
Electricity Sale
-221,853
n.a.
$/yr
Low
Partial
Capacity
Maintenance
General
AD&CHP
CHP Maintenance
139,725
n.a.
$/yr
Low
Partial
Capacity
Energy
Natural Gas
AD&CHP
Purchase
40,794
n.a.
$/yr
All
Baseline
Energy
Electricity
Biological
Treatment
Utility Bills
621,612
n.a.
$/yr
All
Full
Capacity
Energy
Electricity
Biological
Treatment
Utility Bills
644,981
23,369
$/yr
All
Partial
Capacity
Energy
Electricity
Biological
Treatment
Utility Bills
632,518
n.a.
$/yr
All
All
Operation
Labor
Full Plant
Administration
Salaries
620,572
-
$/yr
All
All
Operation
Labor
Full Plant
Monitoring Salaries
332,969
-
$/yr
All
All
Maintenance
Labor
Full Plant
Maintenance Salaries
760,210
-
$/yr
All
All
Operation
Labor
Full Plant
Operations Salaries
1,439,598
-
$/yr
All
All
Operation
General
Full Plant
Administrative Costs
737,213
-
$/yr
All
All
Operation
General
Full Plant
Monitoring
113,530
-
$/yr
All
All
Maintenance
General
Full Plant
Maintenance
2,000
-
$/yr
All
All
Operation
General
Full Plant
Operations
102,150
-
$/yr
All
All
Operation
Labor
Full plant
Fringe Benefits
943,621
-
$/yr
All
All
Maintenance
Labor
Full plant
Fringe Benefits
299,753
-
$/yr
All
All
Operation
General
Full Plant
Contingency
230,000
-
$/yr
All
All
Maintenance
Labor
Full Plant
Hired Maintenance
Labor
312,765
-
$/yr
C-5
-------
Appendix C - LCCA Supporting Information and Detailed Results
Table C-4. Detailed Life Cycle Costs - WWTF1
\l)
Sivii;irin
I Vl'll Slock
Si-iii;irin-
(¦i'lliT;il (osl
IK'hiik'd Ciisl
Ciik-»iir\
I'roww
Desiriplinn
\\ ;isle\\ ;iler
Value-1
I-hiiiI \\ ;isle
V;illle45"
1 nil
All
All
Maintenance
Materials
Full Plant
Mechanical and
Electrical Supplies
418,483
-
$/yr
All
All
Operation
Diesel
Full plant
Gasoline for vehicles
19,000
-
$/yr
All
All
Operation
Water
Full plant
Utility Bills
17,236
-
$/yr
All
All
Chemical
Chemicals
Full Plant
Other Chemicals
2,000
-
$/yr
All
All
Energy
Renewable
Energy Credit
Full Plant
Solar Electricity
Plant
-15,000
-
$/yr
All
All
Operation
Fee Revenue
Full Plant
Industrial Surcharge
-110,000
-
$/yr
All
All
Operation
Fee Revenue
Full Plant
Industrial Cost
Recovery
-3,000
-
$/yr
All
All
Operation
Fee Revenue
Full Plant
Other revenue
-7,200
-
$/yr
All
All
Operation
General
Full Plant
Waste Disposal,
Utility
12,000
-
$/yr
All
All
Capital
Capital
Full Plant
Capital Projects
1,800,000
7,004
$/yr
All
Baseline
Energy
Natural Gas
Full plant
Utility Bills
121,793
n.a.
$/yr
All
Baseline
Energy
Electricity
Full plant
Utility Bills
33,601
n.a.
$/yr
All
Full
Capacity
Energy
Electricity
Full plant
Utility Bills
35,281
1,680
$/yr
All
Partial
Capacity
Energy
Electricity
Full plant
Utility Bills
35,281
n.a.
$/yr
Baseline
Full
Capacity
Energy
Natural Gas
Full Plant
Purchase
-
-
$/yr
Baseline
Partial
Capacity
Energy
Natural Gas
Full Plant
Purchase
-
n.a.
$/yr
Low
Full
Capacity
Energy
Natural Gas
Full Plant
Purchase
-
-
$/yr
Low
Partial
Capacity
Energy
Natural Gas
Full Plant
Purchase
121,793
n.a.
$/yr
All
All
Operation
General
Pelletization
Capacity Charge
2,293,445
-
$/yr
All
Baseline
Energy
Natural Gas
Pelletization
Utility Bills
6,766
n.a.
$/yr
All
Baseline
Operation
General
Pelletization
Processing Charge
66,462
n.a.
$/yr
All
Baseline
Energy
Electricity
Pelletization
Utility Bills
293,162
n.a.
$/yr
All
Full
Capacity
Operation
General
Pelletization
Processing Charge
575,785
509,323
$/yr
All
Full
Capacity
Energy
Electricity
Pelletization
Utility Bills
557,033
263,870
$/yr
All
Partial
Capacity
Operation
General
Pelletization
Processing Charge
300,619
n.a.
$/yr
C-6
-------
Appendix C - LCCA Supporting Information and Detailed Results
Table C-4. Detailed Life Cycle Costs - WWTF1
\l)
Sivii;irin
I Vl'll Slock
Si-iii;irin-
(¦i'lliT;il (osl
IK'hiik'd Ciisl
Ciik-»iir\
I'roww
Desiriplinn
\\ ;isle\\ ;iler
Value-1
I-hiiiI \\ ;isle
V;illle45"
1 nil
All
Partial
Capacity
Energy
Electricity
Pelletization
Utility Bills
414,474
n.a.
$/yr
Low
Partial
Capacity
Energy
Natural Gas
Pelletization
Utility Bills
6,766
n.a.
$/yr
All
All
Chemical
Chemicals
Plant Water &
Disinfection
Sodium Flypochlorite
100,000
-
$/yr
All
All
Chemical
Chemicals
Plant Water &
Disinfection
Sodium Bisulfite
100,000
-
$/yr
All
All
Operation
Fee Revenue
Plant Water &
Disinfection
Effluent Sale
-72,000
-
$/yr
All
Baseline
Energy
Electricity
Plant Water &
Disinfection
Utility Bills
134,403
n.a.
$/yr
All
Full
Capacity
Energy
Electricity
Plant Water &
Disinfection
Utility Bills
134,403
-
$/yr
All
Partial
Capacity
Energy
Electricity
Plant Water &
Disinfection
Utility Bills
134,403
n.a.
$/yr
All
All
Chemical
Chemicals
Preliminary and
Primary Treatment
Potassium Perm. -
Odor Control
11,000
-
$/yr
All
All
Operation
General
Preliminary and
Primary Treatment
Grit Disposal
47,500
-
$/yr
All
All
Operation
Fee Revenue
Preliminary and
Primary Treatment
Septage Receiving
Fees
-1,500,000
-
$/yr
All
All
Operation
Fee Revenue
Preliminary and
Primary Treatment
Outside Sludge
-60,000
-
$/yr
All
All
Operation
Materials
Preliminary and
Primary Treatment
Activated Carbon,
Grit
12,681
-
$/yr
All
All Future
Scenarios
Maintenance
General
Preliminary and
Primary Treatment
Additional Receiving
Station
7,600
7,600
$/yr
All
Baseline
Energy
Electricity
Preliminary and
Primary Treatment
Utility Bills
935,931
n.a.
$/yr
All
Full
Capacity
Energy
Electricity
Preliminary and
Primary Treatment
Utility Bills
935,931
-
$/yr
All
Partial
Capacity
Energy
Electricity
Preliminary and
Primary Treatment
Utility Bills
935,931
n.a.
$/yr
All
Baseline
Energy
Electricity
Secondary
Clarification
Utility Bills
151,203
n.a.
$/yr
All
Full
Capacity
Energy
Electricity
Secondary
Clarification
Utility Bills
151,203
-
$/yr
All
Partial
Capacity
Energy
Electricity
Secondary
Clarification
Utility Bills
151,203
n.a.
$/yr
C-7
-------
Appendix C - LCCA Supporting Information and Detailed Results
Table C-4. Detailed Life Cycle Costs - WWTF1
\l)
Sivii;irin
I Vl'll Slock
Si-iii;irin-
(¦i'lliT;il (osl
IK'hiik'd Ciisl
Ciik-»iir\
I'roww
Desiriplinn
\\ ;isle\\ ;iler
Value-1
I-hiiiI \\ ;isli-
\;illli-45"
1 nil
All
Baseline
Chemical
Chemicals
Thickening &
Dewatering
Polymer
447,149
n.a.
$/yr
All
Baseline
Energy
Electricity
Thickening &
Dewatering
Utility Bills
201,604
n.a.
$/yr
All
Full
Capacity
Chemical
Chemicals
Thickening &
Dewatering
Polymer
843,865
396,716
$/yr
All
Full
Capacity
Energy
Electricity
Thickening &
Dewatering
Utility Bills
300,162
98,558
$/yr
All
Partial
Capacity
Chemical
Chemicals
Thickening &
Dewatering
Polymer
647,377
n.a.
$/yr
All
Partial
Capacity
Energy
Electricity
Thickening &
Dewatering
Utility Bills
251,643
n.a.
$/yr
1 Costs presented in this table are annual or year one costs.
2 All Future Scenarios includes both the partial and full capacity feedstock scenarios.
3 Cost data in this column corresponds to LCCA results present in report Sections 5.10 and 6.5.
4 Cost data in this column corresponds to LCCA results presented in report Appendix A.
5 n.a. - not applicable. The baseline and partial capacity scenarios were not evaluated as part of the food waste EOL treatment analysis in Appendix A.
6 Values of zero indicate no change in life cycle cost due to the addition of food waste co-digestion.
C-8
-------
Appendix D - LCIA Process Results
Appendix D:
LCIA Process Results
-------
Appendix D - LCIA Process Results
Appendix D
LCIA Process Results
The tables in this section include detailed LCIA results by treatment group for all feedstock, AD performance and avoided EOL
disposal process scenarios.
Table D-l. Process LCIA Results for the MA Disposal Mix Avoided SSO Disposal Scenario
A1) Scenario
Base
liiISC
Low
liiISC
Low
IVedslock Scenario
liiisclinc
I'iirlinl C:i|);ici(\
I'iirliiil Ciipiicily
l ull Ciipiicily
l ull C;ip:icilv
WWTF, Total
0.36
6.6E-3
0.19
-0.28
-0.05
Land Application
-3.7E-3
-5.4E-3
-5.4E-3
-7.3E-3
-7.3E-3
Preliminary/Primary
0.03
0.03
0.03
0.03
0.03
Pellet Drying
0.03
0.04
0.04
0.05
0.05
Global
Influent Pump Station
0.07
0.07
0.07
0.07
0.07
Warming
Biological Treatment
0.18
0.18
0.18
0.18
0.18
Potential - kg
Sludge Dewatering
0.03
0.04
0.04
0.05
0.05
CO2 eq
Plant Water and Disinfection
-0.02
-0.02
-0.02
-0.02
-0.02
Building Operation
0.03
3.3E-3
0.03
3.3E-3
3.3E-3
Secondary Clarification
0.01
0.01
0.01
0.01
0.01
Effluent Release
0.05
0.05
0.05
0.06
0.06
Anaerobic Digestion and CHP
-0.05
-0.40
-0.24
-0.71
-0.48
WWTF, Total
0.02
0.03
0.03
0.03
0.03
Land Application
9.0E-4
1.3E-3
1.3E-3
1.7E-3
1.7E-3
Preliminary/Primary
2.8E-5
2.8E-5
2.8E-5
2.8E-5
2.8E-5
Eutrophication
Pellet Drying
6.3E-6
9.0E-6
9.2E-6
1.2E-5
1.2E-5
Potential - kg N
Influent Pump Station
7.4E-6
7.4E-6
7.4E-6
7.4E-6
7.4E-6
eq
Biological Treatment
5.6E-6
5.7E-6
5.7E-6
5.8E-6
5.8E-6
Sludge Dewatering
5.9E-5
8.6E-5
8.6E-5
1.1E-4
1.1E-4
Plant Water and Disinfection
-2.9E-5
-2.9E-5
-2.9E-5
-2.9E-5
-2.9E-5
Building Operation
4.7E-6
5.6E-7
4.7E-6
5.6E-7
5.6E-7
D-l
-------
Appendix D - LCIA Process Results
Table D-l. Process LCIA Results for the MA Disposal Mix Avoided SSO Disposal Scenario
A1) Scenario
liilSO
liilSO
Low
liilSO
Low
IVedslock Scciiiirio
liitseline
I'nrliiil Ciipiicily
I'iirliiil Ciipiicily
l ull Ciipiicily
l ull Ciipiicily
Sccondan Cluriilculion
1.4L-6
1.4L-6
1.4L-6
1.4L-6
1.4L-6
Effluent Release
0.02
0.02
0.02
0.03
0.03
Anaerobic Digestion and CHP
-1.3E-5
-1.9E-5
4.9E-6
-5.0E-5
-1.5E-5
WWTF, Total
5.0
-1.7
3.7
-6.4
1.2
Land Application
-0.23
-0.32
-0.32
-0.43
-0.43
Preliminary/Primary
1.1
1.1
1.1
1.1
1.1
Pellet Drying
0.83
1.1
1.1
1.5
1.5
Cumulative
Energy Demand
-MJ
Influent Pump Station
2.2
2.2
2.2
2.2
2.2
Biological Treatment
1.7
1.7
1.7
1.7
1.7
Sludge Dewatering
0.83
1.1
1.1
1.4
1.4
Plant Water and Disinfection
-0.62
-0.62
-0.62
-0.62
-0.62
Building Operation
0.58
0.10
0.59
0.10
0.10
Secondary Clarification
0.41
0.41
0.41
0.41
0.41
Effluent Release
-
-
-
-
-
Anaerobic Digestion and CHP
-1.8
-8.5
-3.6
-14
-6.2
WWTF, Total
0.05
-0.07
0.02
-0.15
-0.04
Land Application
-4.1E-3
-5.9E-3
-5.9E-3
-7.9E-3
-7.9E-3
Preliminary/Primary
0.02
0.02
0.02
0.02
0.02
Pellet Drying
0.01
0.01
0.01
0.02
0.02
Fossil Depletion
Potential - kg oil
eq
Influent Pump Station
0.03
0.03
0.03
0.03
0.03
Biological Treatment
0.02
0.02
0.02
0.02
0.02
Sludge Dewatering
0.01
0.02
0.02
0.02
0.02
Plant Water and Disinfection
-7.5E-3
-7.5E-3
-7.5E-3
-7.5E-3
-7.5E-3
Building Operation
0.01
1.2E-3
0.01
1.2E-3
1.2E-3
Secondary Clarification
4.9E-3
4.9E-3
4.9E-3
4.9E-3
4.9E-3
Effluent Release
-
-
-
-
-
Anaerobic Digestion and CHP
-0.05
-0.16
-0.08
-0.25
-0.14
D-2
-------
Appendix D - LCIA Process Results
Table D-l. Process LCIA Results for the MA Disposal Mix Avoided SSO Disposal Scenario
A1) Scenario
liilSO
liilSO
Low
liilSO
Low
IVedslock Scciiiirio
liitseline
I'nrliiil Ciipiicily
I'iirliiil Ciipiicily
l ull Ciipiicily
l ull Ciipiicily
W W il', lolal
5.4L-5
1.8L-5
5.0L-5
-4.5L-0
4.4L-5
Land Application
1.7E-6
2.3E-6
2.3E-6
3.2E-6
3.2E-6
Preliminary/Primary
1.6E-5
1.6E-5
1.6E-5
1.6E-5
1.6E-5
Pellet Drying
1.8E-5
2.7E-5
2.8E-5
3.7E-5
3.7E-5
Particulate
Matter
Formation
Potential - kg
PM2 5 eq
Influent Pump Station
9.1E-6
9.1E-6
9.1E-6
9.1E-6
9.1E-6
Biological Treatment
6.8E-6
6.9E-6
6.9E-6
7.0E-6
7.0E-6
Sludge Dewatering
1.1E-5
1.5E-5
1.5E-5
1.9E-5
1.9E-5
Plant Water and Disinfection
7.1E-8
7.1E-8
7.1E-8
7.1E-8
7.1E-8
Building Operation
5.7E-6
4.0E-7
5.7E-6
4.0E-7
4.0E-7
Secondary Clarification
1.7E-6
1.7E-6
1.7E-6
1.7E-6
1.7E-6
Effluent Release
-
-
-
-
-
Anaerobic Digestion and CHP
-1.6E-5
-6.1E-5
-2.9E-5
-9.8E-5
-5.0E-5
WWTF, Total
1.0E-3
6.6E-4
1.1E-3
5.4E-4
1.1E-3
Land Application
4.1E-4
5.8E-4
5.8E-4
7.8E-4
7.8E-4
Preliminary/Primary
1.8E-4
1.8E-4
1.8E-4
1.8E-4
1.8E-4
Pellet Drying
1.9E-4
2.8E-4
2.8E-4
3.7E-4
3.7E-4
Acidification
Potential - kg
SO2 eq
Influent Pump Station
1.3E-4
1.3E-4
1.3E-4
1.3E-4
1.3E-4
Biological Treatment
1.0E-4
1.0E-4
1.0E-4
1.0E-4
1.0E-4
Sludge Dewatering
9.6E-5
1.3E-4
1.3E-4
1.7E-4
1.7E-4
Plant Water and Disinfection
-6.3E-6
-6.3E-6
-6.3E-6
-6.3E-6
-6.3E-6
Building Operation
6.7E-5
6.0E-6
6.7E-5
6.0E-6
6.0E-6
Secondary Clarification
2.5E-5
2.5E-5
2.5E-5
2.5E-5
2.5E-5
Effluent Release
-
-
-
-
-
Anaerobic Digestion and CHP
-1.8E-4
-7.6E-4
-3.6E-4
-1.2E-3
-6.2E-4
Smog
Formation
WWTF, Total
0.02
8.3E-3
0.02
3.7E-3
0.02
Land Application
-3.0E-4
-4.3E-4
-4.3E-4
-5.5E-4
-5.5E-4
Preliminary/Primary
3.6E-3
3.6E-3
3.6E-3
3.6E-3
3.6E-3
D-3
-------
Appendix D - LCIA Process Results
Table D-l. Process LCIA Results for the MA Disposal Mix Avoided SSO Disposal Scenario
A1) Scenario
liilSO
liilSO
Low
liilSO
Low
Potential - kg 0
IVedslock Scciiiirio
Pellet Dr\ ing
liitseline
3.0L-3
I'nrliiil Ciipiicily
5.3L-3
I'iirliiil Ciipiicily
5.4L-3
l ull Ciipiicily
7.2L-3
l ull Ciipiicily
7.2L-3
eq
Influent Pump Station
4.6E-3
4.6E-3
4.6E-3
4.6E-3
4.6E-3
Biological Treatment
3.6E-3
3.6E-3
3.6E-3
3.7E-3
3.7E-3
Sludge Dewatering
1.6E-3
2.1E-3
2.1E-3
2.5E-3
2.5E-3
Plant Water and Disinfection
-1.1E-3
-1.1E-3
-1.1E-3
-1.1E-3
-1.1E-3
Building Operation
7.6E-4
2.1E-4
7.7E-4
2.1E-4
2.1E-4
Secondary Clarification
8.6E-4
8.6E-4
8.6E-4
8.6E-4
8.6E-4
Effluent Release
-
-
-
-
-
Anaerobic Digestion and CHP
-3.0E-4
-0.01
-1.7E-3
-0.02
-3.8E-3
WWTF, Total
-0.13
-0.12
-0.12
-0.12
-0.12
Land Application
-3.4E-4
-4.7E-4
-4.7E-4
-6.4E-4
-6.4E-4
Preliminary/Primary
8.4E-5
8.4E-5
8.4E-5
8.4E-5
8.4E-5
Pellet Drying
8.8E-5
1.2E-4
1.2E-4
1.7E-4
1.7E-4
Influent Pump Station
2.5E-4
2.5E-4
2.5E-4
2.5E-4
2.5E-4
Water Use - m3
Biological Treatment
1.9E-4
1.9E-4
1.9E-4
1.9E-4
1.9E-4
H20
Sludge Dewatering
1.1E-4
1.4E-4
1.4E-4
1.8E-4
1.8E-4
Plant Water and Disinfection
-0.13
-0.13
-0.13
-0.13
-0.13
Building Operation
4.7E-4
4.6E-4
4.7E-4
4.6E-4
4.6E-4
Secondary Clarification
4.6E-5
4.6E-5
4.6E-5
4.6E-5
4.6E-5
Effluent Release
-
-
-
-
-
Anaerobic Digestion and CHP
7.0E-5
8.3E-4
1.2E-3
1.7E-3
2.3E-3
Table Acronyms: AD - anaerobic digestion, CHP - combined heat and power, LCIA - life cycle impact assessment, MA - Massachusetts, SSO - source
separated organics, WWTF - wastewater treatment facility
D-4
-------
Appendix D - LCIA Process Results
Table D-2. Process LCIA Results for the National Disposal Mix Avoided SSO Disposal Scenario
A1) Scenario
liiISC
liiise
Low
liiISC
Low
IVedslock Scciiiirio
Baseline
I'nrliiil Ciipiicity
I'itrliitl ( iipncilv
l ull Ciipiicity
I'ull Ciipiicily
WW IF, Total
U.3t>
-U.42
-U.23
-1.1
-u.yu
Land Application
-3.7E-3
-5.4E-3
-5.4E-3
-7.3E-3
-7.3E-3
Preliminary/Primary
0.03
0.03
0.03
0.03
0.03
Pellet Drying
0.03
0.04
0.04
0.05
0.05
Global
Influent Pump Station
0.07
0.07
0.07
0.07
0.07
Warming
Biological Treatment
0.18
0.18
0.18
0.18
0.18
Potential - kg
Sludge Dewatering
0.03
0.04
0.04
0.05
0.05
CO2 eq
Plant Water and Disinfection
-0.02
-0.02
-0.02
-0.02
-0.02
Building Operation
0.03
3.3E-3
0.03
3.3E-3
3.3E-3
Secondary Clarification
0.01
0.01
0.01
0.01
0.01
Effluent Release
0.05
0.05
0.05
0.06
0.06
Anaerobic Digestion and CHP
-0.05
-0.82
-0.67
-1.6
-1.3
WWTF, Total
0.02
0.03
0.03
0.03
0.03
Land Application
9.0E-4
1.3E-3
1.3E-3
1.7E-3
1.7E-3
Preliminary/Primary
2.8E-5
2.8E-5
2.8E-5
2.8E-5
2.8E-5
Pellet Drying
6.3E-6
9.0E-6
9.2E-6
1.2E-5
1.2E-5
Eutrophication
Potential - kg N
eq
Influent Pump Station
7.4E-6
7.4E-6
7.4E-6
7.4E-6
7.4E-6
Biological Treatment
5.6E-6
5.7E-6
5.7E-6
5.8E-6
5.8E-6
Sludge Dewatering
5.9E-5
8.6E-5
8.6E-5
1.1E-4
1.1E-4
Plant Water and Disinfection
-2.9E-5
-2.9E-5
-2.9E-5
-2.9E-5
-2.9E-5
Building Operation
4.7E-6
5.6E-7
4.7E-6
5.6E-7
5.6E-7
Secondary Clarification
1.4E-6
1.4E-6
1.4E-6
1.4E-6
1.4E-6
Effluent Release
0.02
0.02
0.02
0.03
0.03
Anaerobic Digestion and CHP
-1.3E-5
-2.2E-5
1.7E-6
-5.7E-5
-2.1E-5
Cumulative
Energy Demand
-MJ
WWTF, Total
5.0
-2.5
2.9
-8.0
-0.47
Land Application
-0.23
-0.32
-0.32
-0.43
-0.43
Preliminary/Primary
1.1
1.1
1.1
1.1
1.1
Pellet Drying
0.83
1.1
1.1
1.5
1.5
D-5
-------
Appendix D - LCIA Process Results
Table D-2. Process LCIA Results for the National Disposal Mix Avoided SSO Disposal Scenario
A1) Scenario
liiISC
IJnsc
Low
Usise
Low
l-'eedslock Sccnsirio
Baseline
I'nrlinI (:ip;uilv
I'itrliitl ( iipncilv
l ull Ciipiicilv
l ull ( :ip:icilv
Influent Pump Station
2.2
2.2
2.2
2.2
2.2
Biological Treatment
1.7
1.7
1.7
1.7
1.7
Sludge Dewatering
0.83
1.1
1.1
1.4
1.4
Plant Water and Disinfection
-0.62
-0.62
-0.62
-0.62
-0.62
Building Operation
0.58
0.10
0.59
0.10
0.10
Secondary Clarification
0.41
0.41
0.41
0.41
0.41
Effluent Release
-
-
-
-
-
Anaerobic Digestion and CHP
-1.8
-9.3
-4.4
-15
-7.8
Fossil Depletion
Potential - kg oil
eq
WWTF, Total
0.05
-0.08
9.0E-3
-0.17
-0.06
Land Application
-4.1E-3
-5.9E-3
-5.9E-3
-7.9E-3
-7.9E-3
Preliminary/Primary
0.02
0.02
0.02
0.02
0.02
Pellet Drying
0.01
0.01
0.01
0.02
0.02
Influent Pump Station
0.03
0.03
0.03
0.03
0.03
Biological Treatment
0.02
0.02
0.02
0.02
0.02
Sludge Dewatering
0.01
0.02
0.02
0.02
0.02
Plant Water and Disinfection
-7.5E-3
-7.5E-3
-7.5E-3
-7.5E-3
-7.5E-3
Building Operation
0.01
1.2E-3
0.01
1.2E-3
1.2E-3
Secondary Clarification
4.9E-3
4.9E-3
4.9E-3
4.9E-3
4.9E-3
Effluent Release
-
-
-
-
-
Anaerobic Digestion and CHP
-0.05
-0.17
-0.09
-0.27
-0.16
Particulate
Matter
Formation
Potential - kg
PM2 5 eq
WWTF, Total
5.4E-5
1.3E-5
5.1E-5
-1.4E-5
3.4E-5
Land Application
1.7E-6
2.3E-6
2.3E-6
3.2E-6
3.2E-6
Preliminary/Primary
1.6E-5
1.6E-5
1.6E-5
1.6E-5
1.6E-5
Pellet Drying
1.8E-5
2.7E-5
2.8E-5
3.7E-5
3.7E-5
Influent Pump Station
9.1E-6
9.1E-6
9.1E-6
9.1E-6
9.1E-6
Biological Treatment
6.8E-6
6.9E-6
6.9E-6
7.0E-6
7.0E-6
Sludge Dewatering
1.1E-5
1.5E-5
1.5E-5
1.9E-5
1.9E-5
Plant Water and Disinfection
7.1E-8
7.1E-8
7.1E-8
7.1E-8
7.1E-8
D-6
-------
Appendix D - LCIA Process Results
Table D-2. Process LCIA Results for the National Disposal Mix Avoided SSO Disposal Scenario
A1) Scenario
liiISC
IJnsc
Low
Usise
Low
l-'eedslock Sccnsirio
Baseline
I'nrlinI (
I'itrliitl C it|):uil\
l ull Ciipiicilv
l ull ( :ip:icilv
Building Operation
5.7E-t>
4.UE-7
5.7E-t>
4.UE-7
4.UE-7
Secondary Clarification
1.7E-6
1.7E-6
1.7E-6
1.7E-6
1.7E-6
Effluent Release
-
-
-
-
-
Anaerobic Digestion and CHP
-1.6E-5
-6.6E-5
-3.4E-5
-1.1E-4
-6.0E-5
WWTF, Total
1.0E-3
5.9E-4
1.1E-3
3.8E-4
9.8E-4
Land Application
4.1E-4
5.8E-4
5.8E-4
7.8E-4
7.8E-4
Preliminary/Primary
1.8E-4
1.8E-4
1.8E-4
1.8E-4
1.8E-4
Pellet Drying
1.9E-4
2.8E-4
2.8E-4
3.7E-4
3.7E-4
Acidification
Potential - kg
SO2 eq
Influent Pump Station
1.3E-4
1.3E-4
1.3E-4
1.3E-4
1.3E-4
Biological Treatment
1.0E-4
1.0E-4
1.0E-4
1.0E-4
1.0E-4
Sludge Dewatering
9.6E-5
1.3E-4
1.3E-4
1.7E-4
1.7E-4
Plant Water and Disinfection
-6.3E-6
-6.3E-6
-6.3E-6
-6.3E-6
-6.3E-6
Building Operation
6.7E-5
6.0E-6
6.7E-5
6.0E-6
6.0E-6
Secondary Clarification
2.5E-5
2.5E-5
2.5E-5
2.5E-5
2.5E-5
Effluent Release
-
-
-
-
-
Anaerobic Digestion and CHP
-1.8E-4
-8.4E-4
-4.4E-4
-1.4E-3
-7.8E-4
WWTF, Total
0.02
4.9E-3
0.01
-3.1E-3
0.01
Land Application
-3.0E-4
-4.3E-4
-4.3E-4
-5.5E-4
-5.5E-4
Preliminary/Primary
3.6E-3
3.6E-3
3.6E-3
3.6E-3
3.6E-3
Pellet Drying
3.6E-3
5.3E-3
5.4E-3
7.2E-3
7.2E-3
Smog
Formation
Influent Pump Station
4.6E-3
4.6E-3
4.6E-3
4.6E-3
4.6E-3
Biological Treatment
3.6E-3
3.6E-3
3.6E-3
3.7E-3
3.7E-3
Potential - kg O3
Sludge Dewatering
1.6E-3
2.1E-3
2.1E-3
2.5E-3
2.5E-3
eq
Plant Water and Disinfection
-1.1E-3
-1.1E-3
-1.1E-3
-1.1E-3
-1.1E-3
Building Operation
7.6E-4
2.1E-4
7.7E-4
2.1E-4
2.1E-4
Secondary Clarification
8.6E-4
8.6E-4
8.6E-4
8.6E-4
8.6E-4
Effluent Release
-
-
-
-
-
Anaerobic Digestion and CHP
-3.0E-4
-0.01
-5.1E-3
-0.02
-0.01
D-7
-------
Appendix D - LCIA Process Results
Table D-2. Process LCIA Results for the National Disposal Mix Avoided SSO Disposal Scenario
A1) Scenario
liiISC
IJnsc
Low
Usise
Low
l-'eedslock Sccnsirio
Baseline
I'nrlinI (:ip;uilv
I'itrliitl ( ;i|):icilv
l ull Ciipiicilv
l ull ( :ip:icilv
WWTF, Total
-U.13
-U.12
-0.12
-0.12
-0.12
Land Application
-3.4E-4
-4.7E-4
-4.7E-4
-6.4E-4
-6.4E-4
Preliminary/Primary
8.4E-5
8.4E-5
8.4E-5
8.4E-5
8.4E-5
Pellet Drying
8.8E-5
1.2E-4
1.2E-4
1.7E-4
1.7E-4
Influent Pump Station
2.5E-4
2.5E-4
2.5E-4
2.5E-4
2.5E-4
Water Use - m3
Biological Treatment
1.9E-4
1.9E-4
1.9E-4
1.9E-4
1.9E-4
H20
Sludge Dewatering
1.1E-4
1.4E-4
1.4E-4
1.8E-4
1.8E-4
Plant Water and Disinfection
-0.13
-0.13
-0.13
-0.13
-0.13
Building Operation
4.7E-4
4.6E-4
4.7E-4
4.6E-4
4.6E-4
Secondary Clarification
4.6E-5
4.6E-5
4.6E-5
4.6E-5
4.6E-5
Effluent Release
-
-
-
-
-
Anaerobic Digestion and CHP
7.0E-5
7.3E-4
1.1E-3
1.5E-3
2.1E-3
Table Acronyms: AD - anaerobic digestion, CHP - combined heat and power, LCIA - life cycle impact assessment, SSO - source separated organics, WWTF -
wastewater treatment facility
D-8
-------
Appendix D - LCIA Process Results
Table D-3. Process LCIA Results Excluding Avoided SSO Disposal
A1) Scciiiirio
liilSO
liilSO
Low
liilSO
Low
IVoilslock Sccnsirio
liitsoli no
I'iirliiil (;tp;icilv
I'sirlisil C itpitcilx
l ull C'iipiicily
l ull C iipiicily
WWTF, Total
0.36
0.17
0.36
0.06
0.28
Land Application
-3.7E-3
-5.4E-3
-5.4E-3
-7.3E-3
-7.3E-3
Preliminary/Primary
0.03
0.03
0.03
0.03
0.03
Pellet Drying
0.03
0.04
0.04
0.05
0.05
Global
Influent Pump Station
0.07
0.07
0.07
0.07
0.07
Warming
Biological Treatment
0.18
0.18
0.18
0.18
0.18
Potential - kg
Sludge Dewatering
0.03
0.04
0.04
0.05
0.05
CO2 eq
Plant Water and Disinfection
-0.02
-0.02
-0.02
-0.02
-0.02
Building Operation
0.03
3.3E-3
0.03
3.3E-3
3.3E-3
Secondary Clarification
0.01
0.01
0.01
0.01
0.01
Effluent Release
0.05
0.05
0.05
0.06
0.06
Anaerobic Digestion and CHP
-0.05
-0.23
-0.08
-0.38
-0.15
WWTF, Total
0.02
0.03
0.03
0.03
0.03
Land Application
9.0E-4
1.3E-3
1.3E-3
1.7E-3
1.7E-3
Preliminary/Primary
2.8E-5
2.8E-5
2.8E-5
2.8E-5
2.8E-5
Pellet Drying
6.3E-6
9.0E-6
9.2E-6
1.2E-5
1.2E-5
Eutrophication
Potential - kg
N eq
Influent Pump Station
7.4E-6
7.4E-6
7.4E-6
7.4E-6
7.4E-6
Biological Treatment
5.6E-6
5.7E-6
5.7E-6
5.8E-6
5.8E-6
Sludge Dewatering
5.9E-5
8.6E-5
8.6E-5
1.1E-4
1.1E-4
Plant Water and Disinfection
-2.9E-5
-2.9E-5
-2.9E-5
-2.9E-5
-2.9E-5
Building Operation
4.7E-6
5.6E-7
4.7E-6
5.6E-7
5.6E-7
Secondary Clarification
1.4E-6
1.4E-6
1.4E-6
1.4E-6
1.4E-6
Effluent Release
0.02
0.02
0.02
0.03
0.03
Anaerobic Digestion and CHP
-1.3E-5
-1.3E-5
1.0E-5
-4.0E-5
-4.3E-6
Cumulative
Energy
Demand - MJ
WWTF, Total
5.0
-3.4
2.0
-9.8
-2.2
Land Application
-0.23
-0.32
-0.32
-0.43
-0.43
Preliminary/Primary
1.1
1.1
1.1
1.1
1.1
Pellet Drying
0.83
1.1
1.1
1.5
1.5
D-9
-------
Appendix D - LCIA Process Results
Table D-3. Process LCIA Results Excluding Avoided SSO Disposal
A1) Scciiiirio
liilSO
liilSO
Low
liilSO
Low
IVoilslock Sccnsirio
liitsoli no
I'iirliiil (;tp;icilv
I'sirlisil C itpitcilx
l ull C'iipiicily
l ull C iipiicily
Inilucnl Pump SlaLion
Biological Treatment
1.7
1.7
1.7
1.7
1.7
Sludge Dewatering
0.83
1.1
1.1
1.4
1.4
Plant Water and Disinfection
-0.62
-0.62
-0.62
-0.62
-0.62
Building Operation
0.58
0.10
0.59
0.10
0.10
Secondary Clarification
0.41
0.41
0.41
0.41
0.41
Effluent Release
-
-
-
-
-
Anaerobic Digestion and CHP
-1.8
-10
-5.3
-17
-9.6
WWTF, Total
0.05
-0.09
-1.1E-3
-0.19
-0.08
Land Application
-4.1E-3
-5.9E-3
-5.9E-3
-7.9E-3
-7.9E-3
Preliminary/Primary
0.02
0.02
0.02
0.02
0.02
Pellet Drying
0.01
0.01
0.01
0.02
0.02
Fossil
Influent Pump Station
0.03
0.03
0.03
0.03
0.03
Depletion
Biological Treatment
0.02
0.02
0.02
0.02
0.02
Potential - kg
Sludge Dewatering
0.01
0.02
0.02
0.02
0.02
oil eq
Plant Water and Disinfection
-7.5E-3
-7.5E-3
-7.5E-3
-7.5E-3
-7.5E-3
Building Operation
0.01
1.2E-3
0.01
1.2E-3
1.2E-3
Secondary Clarification
4.9E-3
4.9E-3
4.9E-3
4.9E-3
4.9E-3
Effluent Release
-
-
-
-
-
Anaerobic Digestion and CHP
-0.05
-0.18
-0.10
-0.29
-0.18
WWTF, Total
5.4E-5
2.0E-5
5.8E-5
-9.0E-8
4.8E-5
Land Application
1.7E-6
2.3E-6
2.3E-6
3.2E-6
3.2E-6
Particulate
Preliminary/Primary
1.6E-5
1.6E-5
1.6E-5
1.6E-5
1.6E-5
Matter
Pellet Drying
1.8E-5
2.7E-5
2.8E-5
3.7E-5
3.7E-5
Formation
Influent Pump Station
9.1E-6
9.1E-6
9.1E-6
9.1E-6
9.1E-6
Potential - kg
Biological Treatment
6.8E-6
6.9E-6
6.9E-6
7.0E-6
7.0E-6
PM2 5 eq
Sludge Dewatering
1.1E-5
1.5E-5
1.5E-5
1.9E-5
1.9E-5
Plant Water and Disinfection
7.1E-8
7.1E-8
7.1E-8
7.1E-8
7.1E-8
Building Operation
5.7E-6
4.0E-7
5.7E-6
4.0E-7
4.0E-7
D-10
-------
Appendix D - LCIA Process Results
Table D-3. Process LCIA Results Excluding Avoided SSO Disposal
A1) Scciiiirio
liilSO
liilSO
Low
liilSO
Low
IVoilslock Sccnsirio
liitsoli no
I'iirliiil (;tp;icilv
I'sirlisil C itpitcilx
l ull C'iipiicily
l ull C iipiicily
Secondary Clarification
1.7L-0
1.7L-0
1.7L-0
1.7L-0
1.7L-0
Effluent Release
-
-
-
-
-
Anaerobic Digestion and CHP
-1.6E-5
-5.9E-5
-2.7E-5
-9.4E-5
-4.6E-5
WWTF, Total
1.0E-3
7.2E-4
1.2E-3
6.6E-4
1.3E-3
Land Application
4.1E-4
5.8E-4
5.8E-4
7.8E-4
7.8E-4
Preliminary/Primary
1.8E-4
1.8E-4
1.8E-4
1.8E-4
1.8E-4
Pellet Drying
1.9E-4
2.8E-4
2.8E-4
3.7E-4
3.7E-4
Acidification
Potential - kg
SO2 eq
Influent Pump Station
1.3E-4
1.3E-4
1.3E-4
1.3E-4
1.3E-4
Biological Treatment
1.0E-4
1.0E-4
1.0E-4
1.0E-4
1.0E-4
Sludge Dewatering
9.6E-5
1.3E-4
1.3E-4
1.7E-4
1.7E-4
Plant Water and Disinfection
-6.3E-6
-6.3E-6
-6.3E-6
-6.3E-6
-6.3E-6
Building Operation
6.7E-5
6.0E-6
6.7E-5
6.0E-6
6.0E-6
Secondary Clarification
2.5E-5
2.5E-5
2.5E-5
2.5E-5
2.5E-5
Effluent Release
-
-
-
-
-
Anaerobic Digestion and CHP
-1.8E-4
-7.0E-4
-3.0E-4
-1.1E-3
-5.0E-4
WWTF, Total
0.02
9.3E-3
0.02
5.7E-3
0.02
Land Application
-3.0E-4
-4.3E-4
-4.3E-4
-5.5E-4
-5.5E-4
Preliminary/Primary
3.6E-3
3.6E-3
3.6E-3
3.6E-3
3.6E-3
Pellet Drying
3.6E-3
5.3E-3
5.4E-3
7.2E-3
7.2E-3
Smog
Influent Pump Station
4.6E-3
4.6E-3
4.6E-3
4.6E-3
4.6E-3
Formation
Biological Treatment
3.6E-3
3.6E-3
3.6E-3
3.7E-3
3.7E-3
Potential - kg
Sludge Dewatering
1.6E-3
2.1E-3
2.1E-3
2.5E-3
2.5E-3
03 eq
Plant Water and Disinfection
-1.1E-3
-1.1E-3
-1.1E-3
-1.1E-3
-1.1E-3
Building Operation
7.6E-4
2.1E-4
7.7E-4
2.1E-4
2.1E-4
Secondary Clarification
8.6E-4
8.6E-4
8.6E-4
8.6E-4
8.6E-4
Effluent Release
-
-
-
-
-
Anaerobic Digestion and CHP
-3.0E-4
-9.4E-3
-7.0E-4
-0.02
-1.7E-3
D-ll
-------
Appendix D - LCIA Process Results
Table D-3. Process LCIA Results Excluding Avoided SSO Disposal
A1) Scciiiirio
liilSO
liilSO
Low
liilSO
Low
IVoilslock Sccnsirio
liitsoli lie
I'iirliiil (;ip;uilv
I'sirlisil ( iipncilv
l ull C'iipiicily
l ull C iipiicily
W WTF, Total
-U.13
-U.12
-U.12
-U.12
-U.12
Land Application
-3.4E-4
-4.7E-4
-4.7E-4
-6.4E-4
-6.4E-4
Preliminary/Primary
8.4E-5
8.4E-5
8.4E-5
8.4E-5
8.4E-5
Pellet Drying
8.8E-5
1.2E-4
1.2E-4
1.7E-4
1.7E-4
Influent Pump Station
2.5E-4
2.5E-4
2.5E-4
2.5E-4
2.5E-4
Water Use - m3
Biological Treatment
1.9E-4
1.9E-4
1.9E-4
1.9E-4
1.9E-4
H20
Sludge Dewatering
1.1E-4
1.4E-4
1.4E-4
1.8E-4
1.8E-4
Plant Water and Disinfection
-0.13
-0.13
-0.13
-0.13
-0.13
Building Operation
4.7E-4
4.6E-4
4.7E-4
4.6E-4
4.6E-4
Secondary Clarification
4.6E-5
4.6E-5
4.6E-5
4.6E-5
4.6E-5
Effluent Release
-
-
-
-
-
Anaerobic Digestion and CHP
7.0E-5
6.4E-4
1.0E-3
1.4E-3
2.0E-3
Table Acronyms: AD - anaerobic digestion, CHP - combined heat and power, LCIA - life cycle impact assessment, SSO - source separated organics, WWTF -
wastewater treatment facility
D-12
-------
Appendix D - LCIA Process Results
Table D-4. Process LCIA Results for 100% Landfill Avoided SSO Disposal Scenario
A1) Scciiiirio
I'ccdstock Scenario
lijiso
liiisolino
lijiso
I'iirliiil C ;t|):uil\
Low
I'iirliiil C
lijiso
l ull Ciipiicily
Low
l ull ( npncilv
Global
Warming
Potential - kg
CO2 eq
WWTF, Total
0.36
-0.48
-0.29
-1.2
-1.0
Land Application
-3.7E-3
-5.4E-3
-5.4E-3
-7.3E-3
-7.3E-3
Preliminary/Primary
0.03
0.03
0.03
0.03
0.03
Pellet Drying
0.03
0.04
0.04
0.05
0.05
Influent Pump Station
0.07
0.07
0.07
0.07
0.07
Biological Treatment
0.18
0.18
0.18
0.18
0.18
Sludge Dewatering
0.03
0.04
0.04
0.05
0.05
Plant Water and Disinfection
-0.02
-0.02
-0.02
-0.02
-0.02
Building Operation
0.03
3.3E-3
0.03
3.3E-3
3.3E-3
Secondary Clarification
0.01
0.01
0.01
0.01
0.01
Effluent Release
0.05
0.05
0.05
0.06
0.06
Anaerobic Digestion and CHP
-0.05
-0.88
-0.73
-1.7
-1.5
Eutrophication
Potential - kg N
eq
WWTF, Total
0.02
0.03
0.03
0.03
0.03
Land Application
9.0E-4
1.3E-3
1.3E-3
1.7E-3
1.7E-3
Preliminary/Primary
2.8E-5
2.8E-5
2.8E-5
2.8E-5
2.8E-5
Pellet Drying
6.3E-6
9.0E-6
9.2E-6
1.2E-5
1.2E-5
Influent Pump Station
7.4E-6
7.4E-6
7.4E-6
7.4E-6
7.4E-6
Biological Treatment
5.6E-6
5.7E-6
5.7E-6
5.8E-6
5.8E-6
Sludge Dewatering
5.9E-5
8.6E-5
8.6E-5
1.1E-4
1.1E-4
Plant Water and Disinfection
-2.9E-5
-2.9E-5
-2.9E-5
-2.9E-5
-2.9E-5
Building Operation
4.7E-6
5.6E-7
4.7E-6
5.6E-7
5.6E-7
Secondary Clarification
1.4E-6
1.4E-6
1.4E-6
1.4E-6
1.4E-6
Effluent Release
0.02
0.02
0.02
0.03
0.03
Anaerobic Digestion and CHP
-1.3E-5
-2.2E-5
1.5E-6
-5.7E-5
-2.2E-5
Cumulative
Energy Demand
-MJ
WWTF, Total
5.0
-2.8
2.6
-8.5
-0.93
Land Application
-0.23
-0.32
-0.32
-0.43
-0.43
Preliminary/Primary
1.1
1.1
1.1
1.1
1.1
Pellet Drying
0.83
1.1
1.1
1.5
1.5
D-13
-------
Appendix D - LCIA Process Results
Table D-4. Process LCIA Results for 100% Landfill Avoided SSO Disposal Scenario
A1) Scciiiirio
I'ccdstock Scenario
lijiso
liiisolino
lijiso
I'iirliiil C ;t|):uil\
Low
I'iirliiil C
lijiso
l ull Ciipiicily
Low
l ull ( npncilv
Influent Pump Station
2.2
2.2
2.2
2.2
2.2
Biological Treatment
1.7
1.7
1.7
1.7
1.7
Sludge Dewatering
0.83
1.1
1.1
1.4
1.4
Plant Water and Disinfection
-0.62
-0.62
-0.62
-0.62
-0.62
Building Operation
0.58
0.10
0.59
0.10
0.10
Secondary Clarification
0.41
0.41
0.41
0.41
0.41
Effluent Release
-
-
-
-
-
Anaerobic Digestion and CHP
-1.8
-9.6
-4.7
-16
-8.3
Fossil Depletion
Potential - kg oil
eq
WWTF, Total
0.05
-0.08
6.4E-3
-0.18
-0.06
Land Application
-4.1E-3
-5.9E-3
-5.9E-3
-7.9E-3
-7.9E-3
Preliminary/Primary
0.02
0.02
0.02
0.02
0.02
Pellet Drying
0.01
0.01
0.01
0.02
0.02
Influent Pump Station
0.03
0.03
0.03
0.03
0.03
Biological Treatment
0.02
0.02
0.02
0.02
0.02
Sludge Dewatering
0.01
0.02
0.02
0.02
0.02
Plant Water and Disinfection
-7.5E-3
-7.5E-3
-7.5E-3
-7.5E-3
-7.5E-3
Building Operation
0.01
1.2E-3
0.01
1.2E-3
1.2E-3
Secondary Clarification
4.9E-3
4.9E-3
4.9E-3
4.9E-3
4.9E-3
Effluent Release
-
-
-
-
-
Anaerobic Digestion and CHP
-0.05
-0.17
-0.10
-0.28
-0.16
Particulate
Matter
Formation
Potential - kg
PM2 5 eq
WWTF, Total
5.4E-5
1.1E-5
4.9E-5
-1.7E-5
3.1E-5
Land Application
1.7E-6
2.3E-6
2.3E-6
3.2E-6
3.2E-6
Preliminary/Primary
1.6E-5
1.6E-5
1.6E-5
1.6E-5
1.6E-5
Pellet Drying
1.8E-5
2.7E-5
2.8E-5
3.7E-5
3.7E-5
Influent Pump Station
9.1E-6
9.1E-6
9.1E-6
9.1E-6
9.1E-6
Biological Treatment
6.8E-6
6.9E-6
6.9E-6
7.0E-6
7.0E-6
Sludge Dewatering
1.1E-5
1.5E-5
1.5E-5
1.9E-5
1.9E-5
Plant Water and Disinfection
7.1E-8
7.1E-8
7.1E-8
7.1E-8
7.1E-8
Building Operation
5.7E-6
4.0E-7
5.7E-6
4.0E-7
4.0E-7
D-14
-------
Appendix D - LCIA Process Results
Table D-4. Process LCIA Results for 100% Landfill Avoided SSO Disposal Scenario
A1) Scciiiirio
I'ccdstock Scenario
lijiso
liiisolino
lijiso
I'iirliiil C ;t|):uil\
Low
I'iirliiil C
lijiso
l ull Ciipiicily
Low
l ull ( npncilv
Secondary Clarification
1.7E-6
1.7E-6
1.7E-6
1.7E-6
1.7E-ft
Effluent Release
-
-
-
-
-
Anaerobic Digestion and CHP
-1.6E-5
-6.7E-5
-3.5E-5
-1.1E-4
-6.3E-5
Acidification
Potential - kg
SO2 eq
WWTF, Total
1.0E-3
5.8E-4
1.1E-3
3.7E-4
9.8E-4
Land Application
4.1E-4
5.8E-4
5.8E-4
7.8E-4
7.8E-4
Preliminary/Primary
1.8E-4
1.8E-4
1.8E-4
1.8E-4
1.8E-4
Pellet Drying
1.9E-4
2.8E-4
2.8E-4
3.7E-4
3.7E-4
Influent Pump Station
1.3E-4
1.3E-4
1.3E-4
1.3E-4
1.3E-4
Biological Treatment
1.0E-4
1.0E-4
1.0E-4
1.0E-4
1.0E-4
Sludge Dewatering
9.6E-5
1.3E-4
1.3E-4
1.7E-4
1.7E-4
Plant Water and Disinfection
-6.3E-6
-6.3E-6
-6.3E-6
-6.3E-6
-6.3E-6
Building Operation
6.7E-5
6.0E-6
6.7E-5
6.0E-6
6.0E-6
Secondary Clarification
2.5E-5
2.5E-5
2.5E-5
2.5E-5
2.5E-5
Effluent Release
-
-
-
-
-
Anaerobic Digestion and CHP
-1.8E-4
-8.4E-4
-4.4E-4
-1.4E-3
-7.8E-4
Smog
Formation
Potential - kg O3
eq
WWTF, Total
0.02
4.4E-3
0.01
-4.2E-3
9.3E-3
Land Application
-3.0E-4
-4.3E-4
-4.3E-4
-5.5E-4
-5.5E-4
Preliminary/Primary
3.6E-3
3.6E-3
3.6E-3
3.6E-3
3.6E-3
Pellet Drying
3.6E-3
5.3E-3
5.4E-3
7.2E-3
7.2E-3
Influent Pump Station
4.6E-3
4.6E-3
4.6E-3
4.6E-3
4.6E-3
Biological Treatment
3.6E-3
3.6E-3
3.6E-3
3.7E-3
3.7E-3
Sludge Dewatering
1.6E-3
2.1E-3
2.1E-3
2.5E-3
2.5E-3
Plant Water and Disinfection
-1.1E-3
-1.1E-3
-1.1E-3
-1.1E-3
-1.1E-3
Building Operation
7.6E-4
2.1E-4
7.7E-4
2.1E-4
2.1E-4
Secondary Clarification
8.6E-4
8.6E-4
8.6E-4
8.6E-4
8.6E-4
Effluent Release
-
-
-
-
-
Anaerobic Digestion and CHP
-3.0E-4
-0.01
-5.7E-3
-0.03
-0.01
Water Use - m3
H20
WWTF, Total
-0.13
-0.12
-0.12
-0.12
-0.12
Land Application
-3.4E-4
-4.7E-4
-4.7E-4
-6.4E-4
-6.4E-4
D-15
-------
Appendix D - LCIA Process Results
Table D-4. Process LCIA Results for 100% Landfill Avoided SSO Disposal Scenario
A1) Scciiiirio
lijiso
lijiso
Low
lijiso
Low
I'ccilstock Scenario
liiisolino
I'iirliiil (
I'iirliiil (
l ull Ciipiicily
l ull ( ;ip;K-il\
Preliminary/Primary
8.4E-5
8.4E-5
8.4E-5
8.4E-5
8.4E-5
Pellet Drying
8.8E-5
1.2E-4
1.2E-4
1.7E-4
1.7E-4
Influent Pump Station
2.5E-4
2.5E-4
2.5E-4
2.5E-4
2.5E-4
Biological Treatment
1.9E-4
1.9E-4
1.9E-4
1.9E-4
1.9E-4
Sludge Dewatering
1.1E-4
1.4E-4
1.4E-4
1.8E-4
1.8E-4
Plant Water and Disinfection
-0.13
-0.13
-0.13
-0.13
-0.13
Building Operation
4.7E-4
4.6E-4
4.7E-4
4.6E-4
4.6E-4
Secondary Clarification
4.6E-5
4.6E-5
4.6E-5
4.6E-5
4.6E-5
Effluent Release
-
-
-
-
-
Anaerobic Digestion and CHP
7.0E-5
7.1E-4
1.1E-3
1.5E-3
2.1E-3
Table Acronyms: AD - anaerobic digestion, CHP - combined heat and power, LCIA - life cycle impact assessment, SSO - source separated organics, WWTF -
wastewater treatment facility
D-16
-------
Appendix D - LCIA Process Results
Table D-5. Process LCIA Results for 100% WTE Avoided SSO Disposal Scenario
A1) Scciiiirio
lisiso
liiISC
Low
lisiso
Low
l-'ccdslock SiTiiiirio
liiisclinc
I'iti liitl Ciipiicily
Piirlinl Ciipiicily
l ull Ciipiicily
l ull Ciipiicilv
Global
Warming
Potential - kg
CO2 eq
WWTF, Total
0.36
0.23
0.42
0.18
0.41
Land Application
-3.7E-3
-5.4E-3
-5.4E-3
-7.3E-3
-7.3E-3
Preliminary/Primary
0.03
0.03
0.03
0.03
0.03
Pellet Drying
0.03
0.04
0.04
0.05
0.05
Influent Pump Station
0.07
0.07
0.07
0.07
0.07
Biological Treatment
0.18
0.18
0.18
0.18
0.18
Sludge Dewatering
0.03
0.04
0.04
0.05
0.05
Plant Water and Disinfection
-0.02
-0.02
-0.02
-0.02
-0.02
Building Operation
0.03
3.3E-3
0.03
3.3E-3
3.3E-3
Secondary Clarification
0.01
0.01
0.01
0.01
0.01
Effluent Release
0.05
0.05
0.05
0.06
0.06
Anaerobic Digestion and CHP
-0.05
-0.17
-0.02
-0.25
-0.02
Eutrophication
Potential - kg
N eq
WWTF, Total
0.02
0.03
0.03
0.03
0.03
Land Application
9.0E-4
1.3E-3
1.3E-3
1.7E-3
1.7E-3
Preliminary/Primary
2.8E-5
2.8E-5
2.8E-5
2.8E-5
2.8E-5
Pellet Drying
6.3E-6
9.0E-6
9.2E-6
1.2E-5
1.2E-5
Influent Pump Station
7.4E-6
7.4E-6
7.4E-6
7.4E-6
7.4E-6
Biological Treatment
5.6E-6
5.7E-6
5.7E-6
5.8E-6
5.8E-6
Sludge Dewatering
5.9E-5
8.6E-5
8.6E-5
1.1E-4
1.1E-4
Plant Water and Disinfection
-2.9E-5
-2.9E-5
-2.9E-5
-2.9E-5
-2.9E-5
Building Operation
4.7E-6
5.6E-7
4.7E-6
5.6E-7
5.6E-7
Secondary Clarification
1.4E-6
1.4E-6
1.4E-6
1.4E-6
1.4E-6
Effluent Release
0.02
0.02
0.02
0.03
0.03
Anaerobic Digestion and CHP
-1.3E-5
-1.7E-5
6.5E-6
-4.7E-5
-1.2E-5
WWTF, Total
5.0
-1.2
4.2
-5.3
2.2
D-17
-------
Appendix D - LCIA Process Results
Table D-5. Process LCIA Results for 100% WTE Avoided SSO Disposal Scenario
Cumulative
Energy
Demand - MJ
A1) Scciiiirio
lisiso
liiISC
Low
lisiso
Low
l-'ccdslock SiTiiiirio
liiisclinc
I'iti liitl Ciipiicilv
Piirlinl Ciipiicilv
l ull Ciipiicilv
l ull Ciipiicilv
Land Application
-U.23
-U.32
-U.32
-U.43
-U.43
Preliminary/Primary
1.1
1.1
1.1
1.1
1.1
Pellet Drying
0.83
1.1
1.1
1.5
1.5
Influent Pump Station
2.2
2.2
2.2
2.2
2.2
Biological Treatment
1.7
1.7
1.7
1.7
1.7
Sludge Dewatering
0.83
1.1
1.1
1.4
1.4
Plant Water and Disinfection
-0.62
-0.62
-0.62
-0.62
-0.62
Building Operation
0.58
0.10
0.59
0.10
0.10
Secondary Clarification
0.41
0.41
0.41
0.41
0.41
Effluent Release
-
-
-
-
-
Anaerobic Digestion and CHP
-1.8
-8.0
-3.1
-13
-5.2
Fossil
Depletion
Potential - kg
oil eq
WWTF, Total
0.05
-0.06
0.02
-0.14
-0.03
Land Application
-4.1E-3
-5.9E-3
-5.9E-3
-7.9E-3
-7.9E-3
Preliminary/Primary
0.02
0.02
0.02
0.02
0.02
Pellet Drying
0.01
0.01
0.01
0.02
0.02
Influent Pump Station
0.03
0.03
0.03
0.03
0.03
Biological Treatment
0.02
0.02
0.02
0.02
0.02
Sludge Dewatering
0.01
0.02
0.02
0.02
0.02
Plant Water and Disinfection
-7.5E-3
-7.5E-3
-7.5E-3
-7.5E-3
-7.5E-3
Building Operation
0.01
1.2E-3
0.01
1.2E-3
1.2E-3
Secondary Clarification
4.9E-3
4.9E-3
4.9E-3
4.9E-3
4.9E-3
Effluent Release
-
-
-
-
-
Anaerobic Digestion and CHP
-0.05
-0.15
-0.08
-0.24
-0.13
D-18
-------
Appendix D - LCIA Process Results
Table D-5. Process LCIA Results for 100% WTE Avoided SSO Disposal Scenario
A1) Scciiiirio
lisiso
liiISC
Low
lisiso
Low
l-'ccdslock SiTiiiirio
liiisclinc
I'iti liitl Ciipiicily
Piirlinl Ciipiicily
l ull Ciipiicily
l ull Ciipiicilv
WWTF, Total
5.4E-5
2.1E-5
5.yE-5
l.bE-b
5.UE-5
Land Application
1.7E-6
2.3E-6
2.3E-6
3.2E-6
3.2E-6
Preliminary/Primary
1.6E-5
1.6E-5
1.6E-5
1.6E-5
1.6E-5
Pellet Drying
1.8E-5
2.7E-5
2.8E-5
3.7E-5
3.7E-5
Particulate
Influent Pump Station
9.1E-6
9.1E-6
9.1E-6
9.1E-6
9.1E-6
Matter
Formation
Potential - kg
PM2 5 eq
Biological Treatment
6.8E-6
6.9E-6
6.9E-6
7.0E-6
7.0E-6
Sludge Dewatering
1.1E-5
1.5E-5
1.5E-5
1.9E-5
1.9E-5
Plant Water and Disinfection
7.1E-8
7.1E-8
7.1E-8
7.1E-8
7.1E-8
Building Operation
5.7E-6
4.0E-7
5.7E-6
4.0E-7
4.0E-7
Secondary Clarification
1.7E-6
1.7E-6
1.7E-6
1.7E-6
1.7E-6
Effluent Release
-
-
-
-
-
Anaerobic Digestion and CHP
-1.6E-5
-5.8E-5
-2.6E-5
-9.2E-5
-4.4E-5
WWTF, Total
1.0E-3
7.0E-4
1.2E-3
6.1E-4
1.2E-3
Land Application
4.1E-4
5.8E-4
5.8E-4
7.8E-4
7.8E-4
Preliminary/Primary
1.8E-4
1.8E-4
1.8E-4
1.8E-4
1.8E-4
Pellet Drying
1.9E-4
2.8E-4
2.8E-4
3.7E-4
3.7E-4
Acidification
Potential - kg
SO2 eq
Influent Pump Station
1.3E-4
1.3E-4
1.3E-4
1.3E-4
1.3E-4
Biological Treatment
1.0E-4
1.0E-4
1.0E-4
1.0E-4
1.0E-4
Sludge Dewatering
9.6E-5
1.3E-4
1.3E-4
1.7E-4
1.7E-4
Plant Water and Disinfection
-6.3E-6
-6.3E-6
-6.3E-6
-6.3E-6
-6.3E-6
Building Operation
6.7E-5
6.0E-6
6.7E-5
6.0E-6
6.0E-6
Secondary Clarification
2.5E-5
2.5E-5
2.5E-5
2.5E-5
2.5E-5
Effluent Release
-
-
-
-
-
Anaerobic Digestion and CHP
-1.8E-4
-7.2E-4
-3.2E-4
-1.2E-3
-5.5E-4
D-19
-------
Appendix D - LCIA Process Results
Table D-5. Process LCIA Results for 100% WTE Avoided SSO Disposal Scenario
A1) Scciiiirio
lisiso
liiISC
Low
lisiso
Low
l-'ccdslock SiTiiiirio
liiisclinc
I'iti liitl Ciipiicily
Piirlinl Ciipiicily
l ull Ciipiicily
l ull Ciipiicilv
Smog
Formation
Potential - kg
03 eq
WWTF, Total
U.U2
u.ul
U.U2
7.4E-3
U.U2
Land Application
-3.0E-4
-4.3E-4
-4.3E-4
-5.5E-4
-5.5E-4
Preliminary/Primary
3.6E-3
3.6E-3
3.6E-3
3.6E-3
3.6E-3
Pellet Drying
3.6E-3
5.3E-3
5.4E-3
7.2E-3
7.2E-3
Influent Pump Station
4.6E-3
4.6E-3
4.6E-3
4.6E-3
4.6E-3
Biological Treatment
3.6E-3
3.6E-3
3.6E-3
3.7E-3
3.7E-3
Sludge Dewatering
1.6E-3
2.1E-3
2.1E-3
2.5E-3
2.5E-3
Plant Water and Disinfection
-1.1E-3
-1.1E-3
-1.1E-3
-1.1E-3
-1.1E-3
Building Operation
7.6E-4
2.1E-4
7.7E-4
2.1E-4
2.1E-4
Secondary Clarification
8.6E-4
8.6E-4
8.6E-4
8.6E-4
8.6E-4
Effluent Release
-
-
-
-
-
Anaerobic Digestion and CHP
-3.0E-4
-8.5E-3
1.5E-4
-0.01
-4.9E-5
Water Use -
m3 H2O
WWTF, Total
-0.13
-0.12
-0.12
-0.12
-0.12
Land Application
-3.4E-4
-4.7E-4
-4.7E-4
-6.4E-4
-6.4E-4
Preliminary/Primary
8.4E-5
8.4E-5
8.4E-5
8.4E-5
8.4E-5
Pellet Drying
8.8E-5
1.2E-4
1.2E-4
1.7E-4
1.7E-4
Influent Pump Station
2.5E-4
2.5E-4
2.5E-4
2.5E-4
2.5E-4
Biological Treatment
1.9E-4
1.9E-4
1.9E-4
1.9E-4
1.9E-4
Sludge Dewatering
1.1E-4
1.4E-4
1.4E-4
1.8E-4
1.8E-4
Plant Water and Disinfection
-0.13
-0.13
-0.13
-0.13
-0.13
Building Operation
4.7E-4
4.6E-4
4.7E-4
4.6E-4
4.6E-4
Secondary Clarification
4.6E-5
4.6E-5
4.6E-5
4.6E-5
4.6E-5
Effluent Release
-
-
-
-
-
Anaerobic Digestion and CHP
7.0E-5
8.9E-4
1.3E-3
1.9E-3
2.5E-3
Table Acronyms: AD - anaerobic digestion, CHP - combined heat and power, LCIA - life cycle impact assessment, SSO - source separated organics, WTE -
waste-to-energy, WWTF - wastewater treatment facility
D-20
-------
Appendix E - Data Quality Documentation
Appendix E:
Data Quality Documentation
-------
Appendix E - Data Quality Documentation
Appendix E
Data Quality Documentation
Table E-l documents data quality scores corresponding to source reliability, completeness, temporal correlation, geographical
correlation and technological correlation for developed LCI data and background unit processes drawn from existing LCI databases.
Table E-l. Documentation of Data Quality.
I):il:i (.)u:ilil\ Inilkiilor
J"
/
V
'=
V
z_ 3
Ti c
1 ( A
£ -r
= -jj
1 ( \ linil |)IIHCw IlilllH-
priuT--.
=
E
z
•.¦mm-
Si'ciiurin
1111 ii 11 oiil | ii 11 ihiln
l):il:i I)tmriiiliiin
7
-
—
(ir:i|i|ilii:ililc)
lllllll IllIM'
Null-
Baseline
Inventory, eleclricily
Baseline - Utility records:
1
1
1
1
l
electricity, ISO New England
n.a.
Plant records of total
consumption
2016, at user
facility electricity and
natural gas purchases
for 2016
Partial capacity,
Inventory, electricity
Partial and lull capacity - scaled
3
n.a.
n.a.
1
l
electricity, ISO New England
n.a.
see main report text for
Full capacity
consumption
baseline values
2016, at user
details.
Baseline
Inventory, natural gas
Utility records: Plant records of
1
1
1
1
l
Heat, natural gas at industrial
n.a.
consumption
total facility electricity and
fiirnace >100 kW
natural gas purchases for 2016.
Partial capacity,
Inventory, natural gas
Partial and lull capacity - scaled
3
n.a.
n.a.
1
l
Heat, natural gas at industrial
n.a.
see main report text for
Full capacity
consumption
baseline values
fiirnace >100 kW
details.
All
Allocation factors,
Electricity use was allocated to
2
n.a.
3
1
l
n.a.
n.a.
See main report text
electricity
individual unit processes using
for discussion of how
allocation data from a 2009
2016 electricity
energy efficiency evaluation.
consumption records
Values were adjusted to reflect
were used to inform
estimated 2016 pellet drying
electricity use in the
energy demand
partial and lull capacity
scenarios.
Base
Inventory, biogas
Production - GPS-X model,
2
n.a.
n.a.
n.a.
l
Biogas production -
developed
production and use
validated against energy
Anaerobic digestion
for this study
feasibility study
Biogas use - allocated to
Allocation to combustion units -
combustion processes
hierarchy of use coupled with
facility specific heat and
electricity demand of pellet
drying facility.
E-l
-------
Appendix E - Data Quality Documentation
Table E-l. Documentation of Data Quality.
1 >:M:i <.)u;ilil\ Inilk'iilnr
SlllKIMM
1111 ii 11 oiil | ii 11 ihiln
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V
=
7
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/
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w
5.
z_ 3
;l t
Ti c
1 ( \ iinil |iiinr-- mime
1 ( A
priuT--.
MllllTC
lllllll IllIM'
Null-
Base
Inventory, effluent
quality & LCI unit
process
Based on Annual 2016 DMR
Data
1
i
i
1
1
Effluent release; MA case-
study; wastewater treatment
unit; m3 wastewater
developed
for this study
Partial capacity,
Full capacity
Inventory, effluent
quality & LCI unit
process
Scaled 2016 DMR Releases
based on calculated removal
rate accounting for increased
nutrient content of SSO
3
i
i
1
1
Effluent release; MA case-
study; wastewater treatment
unit; m3 wastewater
developed
for this study
All
Inventory, influent
quality
BOD and TSS data were drawn
from plant records for the year
2016
1
i
i
1
1
n.a.
n.a.
All
Inventory, influent
quality
VS, N and P data were based on
representative values from the
literature
2
n.a.
n.a.
2
n.a.
n.a.
n.a.
All
Inventory, activated
carbon
Based on volume data provided
by facility and assumed material
density
2
1
1
1
1
Granular activated carbon
production; MA Case Study
developed
for this study
All
LCI unit process
Granular activated carbon
production; MA Case Study
3
4
4
3
1
Granular activated carbon
production; MA Case Study
developed
for this study
Original study is based
on production of 1 ton
of GAC from
bituminous coal. Study
notes 3 tons of coal,
1600kwh, 330m3 of
natural gas, and 400
km of transport are
required. Study also
notes that transport
distance is arbitrary,
but that the analysis
showed low sensitivity
to this parameter
(Bayer et al. 2005)
All
Inventory, grit disposal
Grit disposal, based on plant
records for 2016
1
1
1
1
1
disposal, inert waste, 5%
water, to inert material
landfill
ecoinvent 2.2
Held constant across
scenarios.
E-2
-------
Appendix E - Data Quality Documentation
Table E-l. Documentation of Data Quality.
1 >:M:i <.)u;ilil\ Inilk'iilnr
SlllKIMM
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=
7
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z_
z_ 3
;l t
Ti c
1 ( \ iinil |iiinr-- mime
1 ( A
priuT--.
MllllTC
lllllll IllIM'
Null-
All
LCI unit process
disposal, inert waste, 5% water,
to inert material landfill
3
3
4
3
1
disposal, inert waste, 5%
water, to inert material
landfill
ecoinvent 2.2
All
Inventory, process GHG
emissions
Estimates of N20 and CH4
process emissions from the
aeration basin, AD and
receiving waters
2
n.a.
n.a.
n.a.
2/3
included in biological
treatment, anaerobic
digestion, and effluent release
n.a.
See report text for
details
All
Inventory, sodium
bisulfite
Plant purchasing records
1
1
1
1
1
Sodium hydrogen Sulfite,
38% in solution
n.a.
All
LCI unit process
Sodium hydrogen Sulfite, 38%
in solution
3
3
4
2
1
Sodium hydrogen Sulfite,
38% in solution
Ecoinvent 3,
adapted
Adapted to US context.
Solution strength only
affects transport
processes per
ecoinvent 2.2
documentation, (i.e.
inventory quantity
refers to pure
chemical).
All
Inventory, sodium
hypochlorite
Plant purchasing records
1
1
1
1
1
sodium hypochlorite, 15% in
H20, at plant
n.a.
All
LCI unit process
sodium hypochlorite, 15% in
H20, at plant
3
3
4
3
1
sodium hypochlorite, 15% in
H20, at plant
ecoinvent 2.2
Solution strength only
affects transport
processes per
ecoinvent 2.2
documentation, (i.e.
inventory quantity
refers to pure
chemical).
All
Inventory, avoided
potable water
Plant staff recommendation -
internal reuse
Plant records (2018) - offsite
industrial reuse
1
1
1
1
1
Drinking Water Treatment;
MA case study
n.a.
Estimated LCI quantity
based on revenue using
value of non-potable
reuse water from
literature.
Base
Inventory, ferric chloride
Plant chemical purchasing
records
1
1
1
1
1
iron (III) chloride, 34% in
H20, at plant
n.a.
Partial capacity,
Full capacity
Inventory, ferric chloride
Scaled baseline value based on
increase in AD capacity
2
n.a.
n.a.
n.a.
1
iron (III) chloride, 34% in
H20, at plant
n.a.
E-3
-------
Appendix E - Data Quality Documentation
Table E-l. Documentation of Data Quality.
1 >:M:i <.)u;ilil\ Inilk'iilnr
SlllKIMM
1111 ii 11 oiil | ii 11 ihiln
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V
=
7
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"E.
w
5.
z_ 3
;l t
Ti c
1 ( \ iinil |iiinr-- iiiinir
1 ( A
priuT--.
MllllTC
lllllll IllIM'
Null-
All
LCI unit process
iron (III) chloride, 34% in H20,
at plant
3
3
4
3
1
iron (III) chloride, 34% in
H20, at plant
ecoinvent 2.2
Solution strength only
affects transport
processes per
ecoinvent 2.2
documentation, (i.e.
inventory quantity
refers to pure
chemical).
All
LCI unit process
electricity, ISO New England
2016, at user
1
1
1
1
1
electricity, ISO New England
2016, at user
this study
2016 grid mix for New
England.
All
LCI unit process
Heat, natural gas at industrial
furnace >100 kW
3
3
4
3
1
Heat, natural gas at industrial
furnace >100 kW
ecoinvent 2.2
All
LCI unit process
Flare, CHP, glycol boiler and
pellet dryer emissions from air
permit application
1
1
1
1
1
Biogas, burned in CHP engine
biogas, burned in flare, US
biogas, burned in glycol
boiler
biogas, burned pellet dryer
developed
for this study
Based on air permit
application emission
quantities specific to
the installed units.
All
LCI unit process
electricity production, from
biomass
3
3
4
2
1
Electricity, biomass, at power
plant, adapted USLCI
US LCI,
adapted
Added Biomass energy
content
All
LCI unit process
electricity production, from coal
3
3
4
2
1
Electricity, bituminous coal,
at power plant, adapted US
LCI
US LCI,
adapted
Replaced Dummy
Flows:
All
LCI unit process
electricity production,
hydropower
3
3
4
3
1
electricity, hydropower, at
reservoir power plant, non-
alpine regions
ecoinvent 2.2
All
LCI unit process
electricity production, natural
gas
3
3
4
2
1
Electricity, natural gas, at
power plant, adapted USLCI
US LCI,
adapted
Replaced 'Dummy
Transport, pipeline,
unspecified' by
'Transport, pipeline,
natural gas'
All
LCI unit process
electricity production, solar
3
3
4
2
1
electricity, solar
EPA
harmonized
database
E-4
-------
Appendix E - Data Quality Documentation
Table E-l. Documentation of Data Quality.
1 >:M:i <.)u;ilil\ Inilk'iilnr
SlllKIMM
1111 ii 11 oiil | ii 11 ihiln
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7
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5.
z_ 3
;l t
Ti c
1 ( \ iinil |iiinr-- iiiinir
1 ( A
priuT--.
MllllTC
lllllll IllIM'
Null-
All
LCI unit process
electricity production, wind
3
3
4
2
1
electricity, wind
EPA
harmonized
database
All
LCI unit process
electricity production, nuclear
3
3
4
2
1
electricity, nuclear, at power
plant, ecoinvent US
ecoinvent
adapted
Partial capacity,
Full capacity
Inventory, steel
Steel
2
4
n.a.
n.a.
n.a.
steel product manufacturing,
average metal working
ecoinvent 2.2
Partial capacity,
Full capacity
Inventory, gravel
Gravel
2
4
n.a.
n.a.
n.a.
gravel, crushed, at WWTP,
MA
ecoinvent
2.2, adapted
substituted regional
electricity grid, added
50 km of transport.
Partial capacity,
Full capacity
Inventory, concrete
Concrete
2
4
n.a.
n.a.
n.a.
ready mixed concrete, 20
MPa, at MA plant
Data
extracted
from U.S.
Portland
Cement
Association's
LCI Report
on Portland
Cement
Concrete
2003
substituted regional
electricity grid
Partial capacity,
Full capacity
Inventory, CHP building
Building construction
2
4
n.a.
n.a.
n.a.
building, multi-story
ecoinvent 2.2
Partial capacity,
Full capacity
Inventory, excavation
Excavation
2
4
n.a.
n.a.
n.a.
excavation, hydraulic digger
ecoinvent 2.2
All
Inventory, tractor use
Tractor, land application
2
3
4
3
2/3
Diesel, combusted in
industrial equipment
EPA
harmonized
database
All
Inventory, pellet
transport
Truck, pellet hauling
2
3
4
2
1
Transport, combination truck,
short-haul, diesel powered,
Northeast
US LCI
All
Inventory, land
application emissions
Emissions associated with
biosolids pellet land application
3
n.a.
n.a.
n.a.
n.a.
Digestate Pellets; Land
Applied; MA case-study; per
m3 wastewater
this study1
See Section 3.3.11
All
Inventory, potassium
permanganate
Facility specific budget and
chemical cost data
2
1
1
1
1
potassium permanganate, at
plant
n.a.
E-5
-------
Appendix E - Data Quality Documentation
Table E-l. Documentation of Data Quality.
1 >:M:i <.)u;ilil\ Inilk'iilnr
SlllKIMM
1111 ii 11 oiil | ii 11 ihiln
l):il:i I)tmriiiliiin
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=
7
/
/
"E.
w
5.
z_ 3
;l t
Ti c
1 ( \ iinil |iiinr-- mime
1 ( A
priuT--.
MllllTC
lllllll IllIM'
Null-
All
LCI unit process
potassium permanganate, at
plant
3
3
4
3
1
potassium permanganate, at
plant
ecoinvent 2.2
All
Inventory, septage and
municipal solids hauling
Plant records of volume
accepted and assumed transport
distance.
2
n.a.
n.a.
n.a.
n.a.
Truck transport, class 8,
heavy heavy-duty (HHD),
diesel, long-haul, load factor
0.5
EPA
harmonized
database
Baseline
Inventory, polymer
Plant records of polymer
purchased
1
n.a.
n.a.
n.a.
n.a.
polyacrylamide S
n.a.
Partial capacity,
Full capacity
Inventory, polymer
Applied chemical dose rates to
GPS-X estimates of solids
processed
2
n.a.
n.a.
n.a.
n.a.
polyacrylamide S
n.a.
Partial capacity,
Full capacity
LCI unit process
Source Separated Organics, at
WWTP
3
5
1
5
5
Source Separated Organics, at
WWTP
developed
for this study
Includes transport,
electricity and water
use. Uses very
generalized
assumptions due to a
lack of other available
data sources.
Partial capacity,
Full capacity
LCI unit process
steel product manufacturing,
average metal working
3
3
4
3
1
steel product manufacturing,
average metal working
ecoinvent 2.2
Partial capacity,
Full capacity
LCI unit process
gravel, crushed, at WWTP, MA
3
3
4
3
1
gravel, crushed, at WWTP,
MA
ecoinvent
2.2, adapted
substituted regional
electricity grid, added
50 km of transport.
Partial capacity,
Full capacity
LCI unit process
ready mixed concrete, 20 MPa,
at MA plant
3
3
4
2
1
ready mixed concrete, 20
MPa, at MA plant
Data
extracted
from U.S.
Portland
Cement
Association's
LCI Report
on Portland
Cement
Concrete
2003
substituted regional
electricity grid
E-6
-------
Appendix E - Data Quality Documentation
Table E-l. Documentation of Data Quality.
1 >:M:i <.)u;ilil\ Inilk'iilnr
SlllKIMM
1111 ii 11 oiil | ii 11 ihiln
1 >:il:i I)tmri|iliiiii
V
=
7
/
/
"E.
w
z_
z_ 3
;l t
Ti c
1 ( \ iinil |iiinr-- mime
1 ( A
priuT--.
MllllTC
lllllll IllIM'
Null-
Partial capacity,
Full capacity
LCI unit process
building, multi-story
3
3
4
3
1
building, multi-story
ecoinvent 2.2
Partial capacity,
Full capacity
LCI unit process
excavation, hydraulic digger
3
3
4
3
1
excavation, hydraulic digger
ecoinvent 2.2
All
LCI unit process
Diesel, combusted in industrial
equipment
3
3
4
2
1
Diesel, combusted in
industrial equipment
EPA
harmonized
database
All
LCI unit process
Transport, combination truck,
short-haul, diesel powered,
Northeast
3
3
4
2
1
Transport, combination truck,
short-haul, diesel powered,
Northeast
US LCI
All
LCI unit process
Digestate Pellets; Land Applied;
MA case-study; per m3
wastewater
2
n.a.
n.a.
n.a.
n.a.
Digestate Pellets; Land
Applied; MA case-study; per
m3 wastewater
this study1
See Section 3.3.11
All
LCI unit process
Drinking Water Treatment; MA
case study
1
2
2
2
3
Drinking Water Treatment;
MA case study
developed
for this study
All
LCI unit process
Heat, natural gas at industrial
furnace >100 kW
3
3
4
2
1
Heat, natural gas at industrial
furnace >100 kW
EPA
harmonized
database
Partial capacity,
Full capacity
LCI unit process
Avoided SSO landfilling - U.S.
2
2
2
2
1
Avoided SSO landfilling -
U.S.
developed
for this study
Based on modeling
fromMSWDST model
Partial capacity,
Full capacity
LCI unit process
Avoided SSO Waste-to-Energy
2
2
2
2
1
Avoided SSO Waste-to-
Energy
developed
for this study
Based on modeling
fromMSWDST
model, supplemented
with emissions data
from local WTE
facility.
Partial capacity,
Full capacity
Inventory, Avoided SSO
landfill
Based on MA waste diversion,
2016/2017
1
n.a.
n.a.
n.a.
n.a.
Avoided SSO landfilling -
U.S.
n.a.
Partial capacity,
Full capacity
Inventory, Avoided SSO
WTE
Based on MA waste diversion,
2016/2017
1
n.a.
n.a.
n.a.
n.a.
Avoided SSO Waste-to-
Energy
n.a.
E-7
-------
SEPA
United States
Environmental Protection
Agency
PRESORTED STANDARD
POSTAGE & FEES PAID
EPA
PERMIT NO. G-35
Office of Research and Development (8101R)
Washington, DC 20460
Official Business
Penalty for Private Use
$300
------- |