ENVIRONMENTAL AND ECONOMIC LIFE CYCLE ASSESSMENT OF
SEWAGE SLUDGE TREATMENT PROCESSES
by
Ziyi Zhuang
B.A.Sc., The University of British Columbia, 2019
A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF
THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF APPLIED SCIENCE
in
THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES
(Civil Engineering)
THE UNIVERSITY OF BRITISH COLUMBIA
(Vancouver)
July 2021
© Ziyi Zhuang, 2021
ii
The following individuals certify that they have read, and recommend to the Faculty of Graduate
and Postdoctoral Studies for acceptance, a thesis entitled:
ENVIRONMENTAL AND ECONOMIC LIFE CYCLE ASSESSMENT OF SEWAGE
SLUDGE TREATMENT PROCESSES
submitted by Ziyi Zhuang in partial fulfillment of the requirements for
the degree of Master of Applied Science
in Civil Engineering
Examining Committee:
Dr. Omar Swei, Assistant Professor, Department of Civil Engineering, UBC
Supervisor
Dr. Loretta Li, Department of Civil Engineering, UBC
Supervisory Committee Member
iii
Abstract
The management of sewage sludge is a major global issue due to the presence of
contaminants in the sludge, such as heavy metals and polybrominated diphenyl ethers (PBDEs),
which are harmful to human health and the environment. Due to these concerns, this study aims to
create a decision-support tool for municipalities when evaluating alternative sludge treatment
techniques. The environmental and economic impacts of four common treatment techniques
(anaerobic digestion, incineration, composting and pyrolysis) and three end-of-life uses (landfill,
agricultural application and energy recovery) are evaluated by the use of life cycle assessment
(LCA) and life cycle costs analysis (LCCA). In order to deliver credible results, the uncertainties
inherent in LCA and LCCA are assessed via probabilistic approaches.
The global warming potential (GWP) for each scenario is studied by using the LCA method.
The results demonstrate that pyrolysis has the lowest (deterministic) GWP after capturing
environmental credits due to energy recovery and fertilizer substitution. Incineration is the worst
option in terms of GWP, primarily due to the greenhouse gas (GHG) emissions from the process.
The findings from the probabilistic analysis indicate that pyrolysis process and agricultural
application of anaerobically digested sludge can achieve net negative GHG emissions under some
circumstances.
The economic assessment shows that composting has the lowest life cycle costs among
these studied technologies due to its low capital investment costs. Incineration is the least preferred
alternative due to its high waste management and transportation costs. The results also indicate
that capital costs are the most dominant contributor to life cycle costs across all technologies.
Pyrolysis process can generate more profits compared to the other alternatives given that valuable
resources, such as energy, fertilizer and fuel, can be recovered from the process.
iv
Overall, by considering both environmental impacts and economic costs, this study
suggests that pyrolysis is the most environmentally optimal and economically affordable sewage
sludge treatment method due to its low life cycle costs and desirable performance in terms of GWP.
The incineration process is the worst option since it is the most expensive option and has the
highest GHG emissions among these considered treatment processes.
v
Lay Summary
This dissertation details a decision-support tool aimed at supporting municipalities in their
selection of appropriate sludge treatment technologies in North America. The environmental and
economic consequences of five alternative scenarios for municipal sewage sludge are evaluated
by using LCA and LCCA. The five scenarios are: (1) anaerobic digestion combined with
landfilling; (2) anaerobic digestion combined with agricultural application; (3) incineration; (4)
pyrolysis; and (5) composting. A Monte Carlo simulation framework that encompasses the
complete life cycle of each technology was developed in order to account for relevant uncertainties
inherent in life cycle inventory and impact factors.
vi
Preface
This thesis is an original, unpublished, and independent work by the author, Ziyi Zhuang.
The research presented in this dissertation was conducted by myself under the direct supervision
of Dr. Omar Swei, an assistant professor at the Department of Civil Engineering at the University
of British Columbia, and Dr. Loretta Li, a professor at the Department of Civil Engineering at the
University of British Columbia. I was responsible for data collection, conducting the literature
review, developing methodology, creating LCA and LCCA models, and analyzing and interpreting
the results. Dr. Loretta Li provided additional support with defining the objective and scope of the
study, building an energy consumption model for targeted technologies, introduction in Chapter 1,
and literature review in Chapter 2. Dr. Omar Swei provided feedback and advice throughout this
thesis.
vii
Table of Contents
Abstract ......................................................................................................................................... iii
Lay Summary ................................................................................................................................ v
Preface ........................................................................................................................................... vi
Table of Contents ........................................................................................................................ vii
List of Tables ................................................................................................................................ ix
List of Figures ................................................................................................................................ x
List of Abbreviations ................................................................................................................... xi
Acknowledgements .................................................................................................................... xiii
Dedication ................................................................................................................................... xiv
Introduction ........................................................................................................................... 1
Literature Review ................................................................................................................. 4
Literature review on current sludge treatment options and end-uses ................................. 4
A brief review of life cycle assessment and life cycle cost analysis .................................. 8
2.2.1 The LCA framework ............................................................................................... 10
2.2.2 Goal and Scope Definition ...................................................................................... 10
2.2.2.1 Functional units ................................................................................................... 11
2.2.2.2 System boundaries............................................................................................... 11
2.2.3 Life cycle inventory analysis .................................................................................. 12
2.2.4 Life cycle impact assessment .................................................................................. 13
2.2.5 Probabilistic LCA and LCCA overview ................................................................. 15
2.2.6 Probabilistic LCA/LCCA for sewage sludge management .................................... 17
Knowledge gaps targeted .................................................................................................. 18
Methodologies ...................................................................................................................... 19
Goal and scope of the thesis.............................................................................................. 19
3.1.1 Anaerobic digestion ................................................................................................ 20
3.1.2 Incineration ............................................................................................................. 21
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3.1.3 Composting ............................................................................................................. 21
3.1.4 Pyrolysis .................................................................................................................. 22
Inventory Analysis ............................................................................................................ 24
Estimating uncertainty of LCI data ................................................................................... 24
Case study application ...................................................................................................... 26
Results and Discussions ...................................................................................................... 27
Life cycle assessment (LCA) -Global warming potential ................................................. 27
Life cycle cost analysis (LCCA) ....................................................................................... 30
Combine economic and environmental results ................................................................. 32
Limitations of the work ..................................................................................................... 34
Comparison with other studies.......................................................................................... 35
Conclusions and Recommendations .................................................................................. 37
Conclusions ....................................................................................................................... 37
Recommendations for future work ................................................................................... 38
References .................................................................................................................................... 40
Appendices ............................................................................................................................................... 55
Appendix A Summary of findings from previous studies ....................................................... 55
Appendix B Summary of assumptions & input data in the energy, LCA & LCCA models for
each scenario…………………………………………………………………………………..66
B.1 Energy models for each scenario ................................................................................... 67
B.1.1 Energy model for anaerobic digestion process ....................................................... 67
B.1.2 Energy model for incineration process.................................................................... 72
B.1.3 Energy model for composting process .................................................................... 74
B.1.4 Energy model for fast pyrolysis process ................................................................. 76
B.2 Environmental Assessment -Global Warming Potential ................................................ 79
B.3 Economic Assessment .................................................................................................... 84
ix
List of Tables
Table 4-1. Probabilistic analysis results of each scenario for global warming potential impact. 30
Table 4-2. Probabilistic analysis results of each scenario for life cycle cost analysis. ................ 32
x
List of Figures
Figure 2-1.Past LCA studies of these four selected sewage sludge management technologies. ... 5
Figure 2-2. Past LCA studies of these three selected sludge disposal methods............................. 7
Figure 3-1. System boundaries for the sludge treatment methods considered in the study. ........ 23
Figure 4-1. Global warming potential of each scenario over its full life cycle. ........................... 28
Figure 4-2. A CDF plot of four treatment scenarios (exclude incineration). ............................... 29
Figure 4-3. Expected Life cycle costs analysis (LCCA) of each scenario. .................................. 31
Figure 4-4. Combined LCA and LCCA results for each scenario. .............................................. 34
xi
List of Abbreviations
AD
AP
CDF
CV
DM
EDIP
EOL
EP
GHG
GWP
HTP
IPU
ISO
LCA
LCC
LCI
LCCA
LCIA
MSWI
MT
OFAT
PAH
PCBs
PCDDs
PFAS
PPCPs
SS
TAD
THSAD
Anaerobic digestion
Acidification potential
Cumulative distribution function
Coefficient of Variation
Dry matter
The Environmental Development of Industrial Products
End-of-life
Eutrophication potential
Greenhouse gas
Global warming potential
Human toxicity potential
The Institue for Product Development
International Organization for Standardization
Life cycle assessment
Life cycle costs
Life cycle inventory
Life cycle cost analysis
Life cycle impact assessment
Municipal solid waste incinerator plants
Million tonnes
One-factor-at-a-time
Polycyclic aromatic hydrocarbon
Polychlorinated biphenyls
Polychlorinated dibenzo-p-dioxins
Per- and polyfluoroalkyl substances
Pharmaceuticals and personal care products
Sewage sludge
Thermophilic anaerobic digestion
Thermophilic high-solids anaerobic digestion
xii
TPs
TS
WWTPs
Toxicity potentials
Total solids
Wastewater treatment plants
xiii
Acknowledgements
I would like to express my deepest appreciation to my research supervisor, Dr. Omar Swei,
who made this work possible. Your great guidance, patience, and encouragement carried me
throughout all stages of my research. I greatly appreciate your time for meeting with me every
week, reviewing my countless pages of writing, and giving me valuable suggestions on the
research. Working under your supervision has been very enjoyable, and I have learned and
improved a lot.
I would also like to thank Dr. Loretta Li for providing her expert opinion on the sewage
sludge treatment processes, helping me with data collection, and reviewing my thesis. Without her
insightful comments and unending support, this work could not be conducted successfully.
Many thanks to Badr A. Mohamed for reviewing my calculations and providing the
guidance on the pyrolysis process.
Last but not least, I would like to thank my family for their continuous support throughout
my years of education, both morally and financially.
xiv
Dedication
To my parents
1
Introduction
Significant quantities of municipal sewage sludge (SS) and biosolids, the by-product of
wastewater treatment plants (WWTPs), pose major environmental and economic challenges to
municipalities (Gallego-Schmid & Tarpani, 2019; Lee et al., 2020; Tarpani & Azapagic, 2018;
Teoh & Li, 2020; Yang et al., 2017; Canadian Council of Ministers of the Environment, 2012).
The major difference between sludge and biosolids is that biosolids have undergone treatment to
decrease or eliminate pathogenic organisms (CCME, 2010). Canada produces more than 2.5
million wet tonnes of waste- and treated sludge (biosolids) every year (Canadian Council of
Ministers of the Environment, 2012). The average annual sludge production in Germany, England,
France, and the United States is 22 million tonnes (MT), 12 MT, 8.5 MT, and 71 MT, respectively
(Xu et al., 2014). The highest sewage sludge production is observed in developed countries
(Grobelak et al., 2019). The production of SS is expected to rise in the future due to increasingly
stringent requirements around wastewater treatment, rapid population growth, and the increased
adoption of secondary and tertiary wastewater treatment processes (Canadian Council of Ministers
of the Environment, 2012; Teoh & Li, 2020). In the European Union, the production of sewage
sludge has increased by more than 50%, from 6.5 MT of dried sludge in 1992 to 10.9 MT in 2015
(Werle & Sobek, 2019). The challenge of the sludge treatment process is further exacerbated by
the tremendous environmental and human health impacts imposed by its mismanagement (Barry
et al., 2019). Relevant concerns include pharmaceuticals and personal care products (PPCPs),
PBDEs, polycyclic aromatic hydrocarbon (PAH) in treated wastewater (North, 2004; Song et al.,
2006; Deng et al., 2015), biosolids and sludge-amended soil (Gorgy et al., 2011; Gorgy et al., 2012;
M. Kim et al., 2017), heavy metals (Kelessidis & Stasinakis, 2012) and reactivation of pathogens
in some biosolid treatment scenarios, and greenhouse gas emissions (Grobelak et al., 2019).
2
The rapid increase in sewage sludge production and its increasing environmental concerns
have motivated municipalities and local government agencies to revisit and improve their existing
management approaches. Conventional SS treatments include anaerobic digestion (Appels et al.,
2011; Vasco-Correa et al., 2018; Li et al., 2017), composting (Murray et al., 2008; Kelessidis &
Stasinakis, 2012; Di Maria et al., 2016) and incineration (Hospido et al., 2005; Murakami et al.,
2009; Hong et al., 2009). Common disposal methods include agricultural application (Kelessidis
& Stasinakis, 2012; Lundin et al., 2004; Singh & Agrawal, 2008) and landfilling (Xu et al., 2014;
Tarpani et al., 2020). Moreover, thermochemical chemical sludge treatment methods such as
pyrolysis are also being developed to maximize energy and resource recovery (Syed-hassan et al.,
2017; Bora et al., 2020).
Sewage sludge from WWTPs, a bio-waste that contains approximately 40% total carbon,
is high in nutrients (50,000 – 130,000 mg/L of nitrogen and 20,000 – 80,000 mg/L of phosphates)
(Sohaili et al., 2012; Li et al., 2019; Shiba & Ntuli, 2017). Because 90% of phosphorus can be
extracted from sewage sludge ashes, SS can be utilized as an adequate phosphate fertilizer (Franz,
2008). Recognizing the nutrients in SS and biosolids, communities in North America (Fytili &
Zabaniotou, 2008) and Australia (Peters & Rowley, 2009; Peters & Lundie, 2001) have used
processed sewage sludge for agricultural application, forestry, and land reclamation. In Canada, of
the 660,000 tonnes of dried sludge produced each year, approximately half of the amount is used
in agricultural application while the rest is incinerated or landfilled (Grobelak et al., 2019). To
address the concerns of contaminants in SS, stricter legal limits on landfilling disposal methods
have been set by governments (Murray et al., 2008). Therefore, converting sewage sludge through
the pyrolysis process into valuable products, such as biochar, sewage-sludge based activated
carbon, fuel and energy, has become increasingly popular (Teoh & Li, 2020). These recovered
3
products can serve other commercial purposes and generate revenue for WWTPs (Tarpani &
Azapagic, 2018). For example, the UK water industry currently generates approximately 800 GWh
of electrical energy from sewage sludge per year (Mills et al., 2014).
There is a need to evaluate the environmental and economic impacts of alternative sewage
sludge treatment technologies typically used in North America. Life cycle assessment (LCA) and
life cycle cost analysis (LCCA) provide useful frameworks to evaluate common treatment
techniques and end-of-life disposal methods. The environmental impacts of different sewage
sludge techniques such as anaerobic digestion and incineration have been extensively analyzed
using the LCA method (Hong et al., 2009; Houillon & Jolliet, 2005; Hospido et al., 2005).
However, few studies have considered the economic impacts of these sewage sludge methods. In
addition, although there are several uncertainties when conducting an LCA and LCCA, the
incorporation of these uncertainties remains uncommon in practice (Alyaseri & Zhou, 2019; Lloyd
& Ries, 2007).
The overall objective of the study is to create a decision-support tool to evaluate both the
environmental and economic impacts of alternative sewage sludge treatment technologies
typically used in North America. This study relies on LCA and LCCA to evaluate four common
treatment techniques (incineration, pyrolysis, anaerobic digestion, and composting) and three end-
of-life disposal methods (landfill, agriculture application, and energy recovery). Due to the
significant uncertainties underlying the model inputs, a novel tool embeds simulation methods to
probabilistically evaluate each technology.
4
Literature Review
Sewage sludge management continues to draw major attention from practitioners and
researchers, owing to the volume generated annually and the presence of harmful contaminants
(Teoh & Li, 2020). Appendix A distills, to the author’s best awareness, some of the major research
contributions in this domain over the last several years. These collected papers are categorized by
different geographic regions (Europe, Asia, and North America). The purpose of this section is to:
(1) investigate the widely used sewage sludge treatment methods and end-uses in recent decades;
and (2) categorize past methodologies that have assessed the environmental and economic impacts
of sewage sludge treatments.
Literature review on current sludge treatment options and end-uses
This study analyzes the environmental and economic impact of four sewage sludge
treatment processes (anaerobic digestion, incineration, composting, and pyrolysis) and three
disposal methods (agricultural application, landfilling, and energy recovery). These techniques
have been selected for two important reasons. First, the first three treatment processes are widely
used worldwide while the latter, pyrolysis, is increasingly used in practice (Fytili & Zabaniotou,
2008; Tarpani et al., 2020; Piao et al., 2016). Based on the studies tabulated in Table A.1 of
Appendix A, the percentage of past LCA studies that have evaluated anaerobic digestion,
incineration, composting and pyrolysis are 70%, 61%, 43%, and 23%, respectively. Second, all
four technologies are able to recover valuable resources, such as nutrients, energy or fuels, which
can help local governments achieve their sustainable development goals. Due to the different
resource recovery potential for each treatment method, the corresponding environmental and
economic consequences of these technologies are explored in details in this study (Tarpani et al.,
2020).
5
Figure 2-1.Past LCA studies of these four selected sewage sludge management technologies.
As shown in Figure 2-1, in Europe, anaerobic digestion (AD) has been extensively studied
in the literature. Anaerobic digestion, which is recognized as a promising method for energy
recovery, can convert the organic substance into biogas and stabilized residues because of the
anaerobic bacterial processes (Grobelak et al., 2019). The biogas can be used as an energy resource,
either for the wastewater treatment plant itself or elsewhere (Rulkens, 2008). The conventional
AD process involves treating sludge with total solids (TS) content of 3%-6% in mesophilic
conditions (i.e., 30-42℃). Compared with the conventional AD, thermophilic anaerobic digestion
(TAD) or thermophilic high-solids anaerobic digestion (THSAD) can deliver better results by
increasing the biogas production with a shorter digestion time due to higher temperature (about
55℃) (Gebreeyessus & Jenicek, 2016; Zhang et al., 2016). However, TAD and THSAD methods
require more energy for heating sludge and maintaining the temperature of the digestors (Li et al.,
2017). From Figure 2-1, incineration is also widely discussed in previous studies, especially in
Europe and Asia. This method can efficiently reduce the sludge volume by up to 96% to stabilised
ash (Vesilind & Ramsey, 1996). Incineration is a process that includes complete oxidation of
0
4
8
12
16
20
Anaerobic digestion
(AD)
Incineration Composting Pyrolysis
Nu
mb
er o
f re
levan
t st
ud
ies
Europe Asia North America
6
organic components in sewage sludge at high temperatures (Braguglia et al., 2003). The process
is increasingly focused on the recovery of energy. The energy produced in the incineration process
can be used for the dewatering process prior to incineration or can be used for generating heat and
electricity (Rulkens, 2008). However, this method is not widely accepted in Europe and North
America, primarily because of its potential environmental impacts, including air emissions and the
final ash disposal, and the expensive treatment system required to deal with emissions from the
incinerator (Rulkens, 2008). Composting is also an effective treatment to reduce pathogenic
organisms and stabilize the organic material in sewage sludge (Lim, 2012). This method is
normally carried out aerobically in a composter, such as an inclined rotating cylinder, for more
than one week. Sawdust or other bulking agents are usually required for the treatment process to
adjust the moisture content and carbon-to-nitrogen ratio. The compost is then transferred to
windrows, where it is left for several weeks. The finished product is normally used for agricultural
application (Tarpani & Azapagic, 2018). However, some persistent organic compounds, such as
polychlorinated biphenyls (PCBs), PAH, polychlorinated dibenzo-p-dioxins (PCDDs), per- and
polyfluoroalkyl substances (PFAS) cannot be completely removed by composting (European
Comission, 2002) and potential leached to the environment land application. Pyrolysis, which is
another thermal treatment technique, has received significant attention recently. In this process,
organic material is decomposed under the influence of heat in an oxygen-free environment (Cao
& Pawłowski, 2012). The benefits of using this method include high reductions in the sludge
volume and heavy metal emissions, as well as recovery of valuable products including bio-oil,
combustible gases, and biochar (Tarpani et al., 2020). The relative yields and properties of the
pyrolysis products are closely related to the applied operating conditions, such as temperature,
heating rate and feeding mode, and feedstock conditions (Park et al., 2008; Pokorna et al., 2009).
7
Furthermore, according to previous studies summarized in Table A.1 of Appendix A, three
major disposal methods are typically evaluated in the LCA literature: agricultural application
(77%), landfilling (75%), and energy recovery (95%). Figure 2-2 compares the number of studies
related to the application of these final disposal methods across Europe, Asia, and North America.
Figure 2-2. Past LCA studies of these three selected sludge disposal methods.
From Figure 2-2, agricultural application is a commonly studied and used disposal method
in Europe. Biosolid application can benefit vegetation and increase their drought tolerance due to
their inherent nutrients, including nitrogen and phosphorous, as well as micronutrients such as
nickel, zinc, and copper (Shammas & Wang, 2009). In the European Union, more than 70% of
sludge is treated thermally by incineration or used for agriculture application (Tarpani et al., 2020).
However, due to concerns related to eutrophication, pathogens, air pollution, and heavy metal
emissions, stricter regulations are typically applied to this disposal method (Fytili & Zabaniotou,
2008). Landfilling is also a prevalent sludge treatment method. However, this method is restricted
in many countries due to environmental and human health concerns. Direct mercury and lead
releases produced during the landfilling process have serious negative repercussions on human
0
4
8
12
16
20
24
Landfill Agricultural application Energy recovery
Nu
mb
er o
f re
lev
an
t st
ud
ies
Europe Asia North America
8
health (Xu et al., 2014). Furthermore, landfilling is found to significantly contribute to global
warming due to the generation of large quantities of methane and carbon dioxide (Usapein &
Chavalparit, 2017). Therefore, during the period 2008-2017, the usage of landfilling decreased by
43% across the 28 European Union member states (European Environment Agency, 2019). The
percentage of waste disposed through landfilling is expected to be reduced to 10% or less of total
waste by 2035 within the European Union (European Environment Agency, 2019). Therefore,
there has been a real shift from landfilling disposal or agricultural application to energy recovery
such as heat and electricity generation. Based on the literature review, almost all researchers take
into account benefits from the recovery of energy during different sewage sludge treatment
processes in their studies. Anaerobic digestion of sludge can generate biogas that can be used as
fossil fuels to produce heat and electricity (Vasco-Correa et al., 2018). Pyrolysis is also a common
method for the production of bio-oil, bio-gas/syngas and biochar (Arazo et al., 2017; Agar et al.,
2018). These products have an energy content of over 30 MJ/kg, which represents the potential to
produce heat, electricity, or transportation fuels (Cao & Pawłowski, 2012). Incineration of sludge
can generate excess heat that can be converted into electricity, and the produced electricity can be
reused in the treatment process (Hong et al., 2009).
A brief review of life cycle assessment and life cycle cost analysis
Life cycle assessment (LCA) is a framework to evaluate the environmental impact of a
product, process, or activity (ISO 14040:2006). LCA involves tracking the environmental releases
over the life cycle of the product, from the extraction of raw materials to the final disposal or
recycling of the product (Lim, 2012). LCA can be used to compare the environmental impacts of
two or more products or processes that perform the same function. A comparable functional unit
is necessary so that the alternatives can be compared on a common basis (Crawford, 2011).
9
A complete LCA model includes an analysis of a broad range of impacts resulting from all
required inputs and all related outputs from every stage in a product’s life cycle (Crawford, 2011).
As a technical approach, this method has been applied to WWTPs since the late 1990s (Lim, 2012).
Table A.2 of Appendix A shows that it has been extensively used to assess the environmental
impacts of different sewage sludge treatments. Typical inflows for an LCA are raw material and
energy inputs whereas outflows from the relevant activities include both the physical product (e.g.,
a tonne of dried biosolids) and a variety of environmental releases and associated impacts (e.g.,
global warming potential). For sewage sludge management, important inputs incorporated in past
studies include the thickened sludge from the WWTP, energy used for the treatment processes,
and other chemical additives. Examples of outputs and releases for sewage sludge management
include various gas emissions, heavy metals emissions, solid wastes, and valuable products (e.g.,
biochar). The LCA process can be effectively conducted by (1) identifying and quantifying the
energy and materials consumed and the products or wastes generated during the process; (2)
recognizing the major impact categories relevant to the sludge treatment, such as GWP, toxicity
potentials (TPs), and eutrophication potential (EP); and (3) identifying and assessing opportunities
for environmental improvements (Hong et al., 2009).
Life cycle cost analysis (LCCA) is regarded as a useful tool to assess the economic
performance of various sewage sludge treatment processes throughout their life cycles (Xu et al.,
2014). This method is similar to LCA but considers costs instead of environmental impacts. It can
be used to effectively evaluate all available options by tracking their fiscal resource requirements
over their lifetime. LCCA aims to help decision-makers identify potential trade-offs between initial
capital investments and long-term operational and maintenance requirements (Greg McNamara,
2018). For sludge treatment processes, Hong et al. (2009) conducted both environmental and
10
economic assessments of the most commonly used management techniques in Japan: dewatering,
composting, drying, incineration, incinerated ash melting and dewatered sludge melting, each with
and without digestion. For the economic assessment, it included costs associated with construction,
energy consumption, equipment and labour, maintenance, and final disposal. Xu et al. (2014)
performed a similar economic assessment for 13 sewage sludge-treatment scenarios in China.
Murray et al. (2008) also evaluated the economic performance of sewage sludge handling options
in China, which included capital, operational, and transportation costs of different treatments.
However, studies related to the economics of sludge treatment methods in North America are
scarce. Barry et al. (2019) conducted an economic assessment of the sludge treatment process, but
the study only analyzed one sludge treatment method, pyrolysis. To address these issues and fill
in the knowledge gaps, this thesis details an LCCA of four common sludge treatment techniques:
anaerobic digestion, composting, incineration and pyrolysis.
2.2.1 The LCA framework
According to the International Organization for Standardization (ISO), LCA includes the
following stages: (1) goal and scope definition; (2) inventory analysis; (3) impact assessment; and
(4) interpretation (ISO 14040:2006).
2.2.2 Goal and Scope Definition
The goal, scope, and depth of this study can be defined by determining the system
boundaries, functional unit, selected impact category, methodology of impact assessment, initial
data requirement, and key assumptions (Lim, 2012). Scope definition is an important step, since it
determines the direction, breadth, and depth of the study.
11
2.2.2.1 Functional units
Identifying the functional unit is a key first step in conducting an LCA. A functional unit
can be defined as a “quantified description of the function of a product that serves as the reference
basis for all calculations regarding impact assessment” (Di Cesare S., Cartone A., 2020). Choosing
a suitable functional unit provides a common basis for facilitating comparisons between
performances of different treatment processes.
In the sludge treatment literature, functional units are chosen based on different purposes
and contexts. Cao and Pawłowski (2012) used a functional unit of 500 m3 liquid raw sewage sludge
per day based on daily production rates of sewage sludge and the scale of wastewater treatment
plants in Poland. Peters and Lundie (2001) used 178 dry tons/day (dt/day), which represented the
mass of biosolids that were expected to be captured at the three largest plants in Sydney, Australia.
The majority of researchers have selected one tonne of dry solids as their functional unit, as shown
in Table A.3 of Appendix A.
2.2.2.2 System boundaries
Generally, a product or system contains several unit processes, and each unit process has
one or more input and output. Therefore, the system boundary is used to define the unit processes
to include and the associated flows to track. Inputs often include raw materials, intermediate
materials or products, energy consumption, and other resources. Outputs usually include emissions
to the air, water and soil, final products, and wastes (Lim, 2012). The system boundary is affected
by the objectives of the study, the ability to get the necessary data, project budgets, and other
constraints (Crawford, 2011).
In previous LCA studies of sludge treatment methods, the system boundary usually
includes thickening, dewatering, main sludge treatment methods, storage, transportation, and final
12
disposal methods, as shown in Table A.3 of Appendix A. Previous studies have typically only
included the operational stage while the construction and decommissioning of the sludge treatment
plants are excluded due to a lack of data and high uncertainty around these costs (Tarpani &
Azapagic, 2018). Previous studies have demonstrated that these phases are insignificant
contributors to the overall environmental effect (Hong et al., 2009; Johansson et al., 2008; Yoshida
et al., 2013).
2.2.3 Life cycle inventory analysis
The life cycle inventory (LCI) analysis, the second phase of an LCA, includes the
compilation and quantification of input and output flows for a product over its life cycle (Crawford,
2011). There are generally four steps in the inventory analysis as noted by Schrijvers et al. (2018).
These steps are:
1. Data collection
2. Normalization to the functional unit
3. Allocation
4. Data evaluation
Once the system boundary is clearly defined, data collection involves the gathering of
information around the required inflows and resulting outflows for each unit process. Data
collection might include raw material inputs, energy consumption, chemical usage, greenhouse
gas releases, and credits with respect to fertilizer and energy. The quality of data used in the LCI
should be verified since it will directly affect the quality of the final results (Schrijvers et al., 2018).
The pedigree matrix approach, established by ecoinvent, is oftentimes used to quantify uncertainty
and assess data quality (Muller et al., 2016). This method is a tool for “coding” qualitative
assessment descriptions (Ciroth et al., 2016). In ecoinvent, indicator scores ranging from 1 to 5 are
13
transformed into uncertainty measures (i.e., variances) assuming that they follow a log-normal
distribution. The five data quality indicators for ecoinvent are reliability, completeness, temporal
correlation, geographical correlation, and further technological correlation (Ciroth et al., 2016).
Depending on the time and budget available, different data collection methods and data
sources can be used to collect context-specific information (Šenitková & Bednárová, 2015). It is
frequently the case that data must be used from commercial LCA software tools and databases
(e.g., ecoinvent) (Crawford, 2011). Previous studies related to sludge treatment options have
generally relied on multiple data sources, including local sewage sludge treatment facilities,
ecoinvent, or values found in the literature. For instance, Xu et al. (2014) evaluated the
environmental and economic impact of 13 sewage sludge treatment scenarios in China. To carry
out their study, three types of data were included: (1) data directly gathered from 140 wastewater
treatment facilities; (2) literature data regarding the landfilling process (Hong et al., 2010),
electricity generation, and road transport in China (Cui et al., 2012); and (3) the ecoinvent database.
After these data are collected, normalization is required to transform the information into the
relevant functional unit (Schrijvers et al., 2018). The next step is allocation, which is only needed
when there are several different products generated from a manufacturing process. This step
involves assigning resources, wastes and, emissions to different products for a given process
(Schrijvers et al., 2018). The last step is to evaluate the data by performing a quality assessment
such as a sensitivity analysis (Schrijvers et al., 2018).
2.2.4 Life cycle impact assessment
Life cycle impact assessment (LCIA), the third stage of an LCA, involves translating the
results from the inventory analysis to a more comprehensive and precise interpretation of the
environmental impacts of the product (Lim, 2012). The general steps of the LCIA include: (1)
14
selection of impact categories, indicators, and models; (2) classification of environmental loads
within the different categories of environmental impacts; and (3) characterization of environmental
loads by using different methods (ISO 14040:2006). By following these steps, life-cycle inventory
results are assigned to different impact categories. The selection of the impact categories depends
on the goal of the LCA study. Common impact categories included in LCA studies of sludge
treatment techniques include global warming potential (GWP), human toxicity potential (HTP),
eutrophication potential (EP), and acidification potential (AP) (Piao et al., 2016; Liu et al., 2011;
Li et al., 2017; Tarpani et al., 2020).
A critical step in the LCA is to choose the suitable LCIA approach. The impact assessment
method is the key towards connecting the life cycle inventory with the impacts on humans and the
environment. This connection can be made by classifying the LCI results into impact categories
based on the effects they have on human health and the environment (Alyaseri & Zhou, 2019).
The LCIA methods generally demonstrate the impact through two major stages: midpoint and
endpoint. The midpoint indicator is regarded as “a parameter in a cause-effect chain for a particular
impact category that is between the inventory data and the category endpoints” (Bare et al., 2000).
It always focuses on single environmental problems such as climate change or freshwater
ecotoxicity. The endpoint indicator is often used to determine “differences between stressors at an
endpoint in a cause-effect chain and may be of direct relevance to society’s understanding of the
final effect, such as measures of biodiversity change” (Bare et al., 2000).
In previous studies, many LCIA methods have been adapted by practitioners. Some
approaches are used to evaluate midpoint impacts such as CML developed by the Center of
Environmental Science of Leiden University in 2001 (Ray et al., 2017). Other methods may focus
on endpoint impacts such as Eco-indicator 99, which considers three types of impacts: human
15
health, ecosystem quality, and resources (Chevalier et al., 2011). Furthermore, other
methodologies consider both midpoint and endpoint impacts such as ReCipe, which is an upgraded
version from Eco-indicator 99. In the ReCipe method, 18 midpoint indicators, such as ionizing
radiation, human toxicity, and global warming, and 3 endpoint indicators, which are effects on
human health, ecosystem, and resource scarcity, are assessed (Goedkoop et al., 2013). Since most
of these impact categories are of concerns to decision-makers managing wastewater treatment
processes, it is widely used in previous LCA studies. Furthermore, this method can produce a
single weighted score for reporting the overall LCA impact (Alyaseri & Zhou, 2019). The
Environmental Development of Industrial Products (EDIP), developed by the Institue for Product
Development (IPU) at the Technical University of Denmark, is another common tool to quantify
stressors that have potential effects, such as global warming, acidification, eutrophication, and
human-health-criteria-related effects (Ray et al., 2017).
Furthermore, the level of uncertainty inherent in these methods is different. Hung and Ma
(2009) indicated that Eco-indicator 99 has the lowest uncertainty and the EDIP method has the
highest uncertainty. Some studies have also demonstrated high uncertainties from LCIA
approaches especially in the toxicity-related impact categories, which can make it difficult to get
reliable conclusions from LCA studies (Pizzol et al., 2011). Therefore, it is necessary for an LCA
practitioner to perform more data collection for those inventories and test results with more
methods in order to reduce uncertainty and increase the reliability of the final outcome of the LCA
study (Alyaseri & Zhou, 2019).
2.2.5 Probabilistic LCA and LCCA overview
LCA and LCCA are commonly used tools to evaluate the environmental and economic
impacts of a process or a product. However, an important consideration for an LCA and LCCA is
16
the explicit incorporation of uncertainty. Uncertainty is unavoidable in making life-cycle decisions
due to issues underlying the life cycle inventory and life cycle impact assessment methods
(Alyaseri & Zhou, 2019). Failure to consider these uncertainties may reduce the credibility of
outcomes (Alyaseri & Zhou, 2019). Many types of uncertainty have already been identified in
previous studies. Three major classifications of uncertainty in LCA are parameter, scenario, and
model (Lloyd & Ries, 2007). Parameter uncertainty may include uncertainty in the data related to
the process inputs and environmental releases, inherent geographical, temporal, and technological
variability in data, random errors, as well as possible missing data (Lloyd & Ries, 2007). Scenario
uncertainty occurs due to decisive choices made in developing scenarios, including the selection
of the functional unit, system boundary, valuation and weighting factors, and other methodological
choices (Bamber et al., 2020). Model uncertainty includes measurement error in physical constants
or modeled relationships, extrapolating relationships from well-studied processes to similar
processes, building models based on qualitative descriptions of relationships, and simplifications
of real-world systems (Lloyd & Ries, 2007). Three common methods are used to handle LCA
related uncertainties, which are sensitivity analysis, qualitative assessment, and quantitative
assessment (Maurice et al., 2000). The sensitivity analysis in LCA can be conducted using a one-
factor-at-a-time (OFAT) approach, meaning that only one factor is shifted and changes in the
response variable are recorded (Groen et al., 2014). The qualitative assessment includes the
classification of data and sorting them based on the factors that may cause variations in LCA
outcomes. The quantitative assessment may include probabilistic simulation and Bayesian
methods (e.g.,Lloyd & Ries, 2007; Alyaseri & Zhou, 2019).
17
2.2.6 Probabilistic LCA/LCCA for sewage sludge management
To date, not all LCA/LCCA studies have considered uncertainty in their analyses, as shown
in Table A.2 of Appendix A. Even though LCI databases, such as ecoinvent, include considerable
data for relevant life cycle processes, the lack of available LCI data beyond the European context
introduces several uncertainties (Lloyd & Ries, 2007). LCA and LCCA studies for sewage sludge
management technologies will typically have model and parameter uncertainties (Bare et al., 2000).
Previous studies have indicated that uncertainties underlying LCI data and characterization factors
can significantly affect the final results of an LCA (Lloyd & Ries, 2007; Schulze et al., 2001).
Therefore, a structured and appropriate method for handling uncertainties inherited in LCA and
LCCA is imperative. As shown in Table A.2, sensitivity analysis is a widely used method for
assessing uncertainty in LCA and LCCA studies of sewage sludge treatment approaches. A
shortcoming of simple sensitivity analysis is that it does not consider the possible correlation
between multiple uncertain factors, which has motivated the utilization of other analytical
approaches such as Monte Carlo analysis. Alyaseri and Zhou (2019) conducted a Monte Carlo
simulation to evaluate and compare LCA results for different sewage sludge treatment processes.
However, the study only considered and compared two sewage sludge treatment methods:
anaerobic digestion and incineration. Monte Carlo analysis was used to simulate various input
parameter distributions such as uniform, lognormal and normal distribution. The wide range of
probabilistic results of the studied processes generated through the Monte Carlo simulation
indicated the importance of uncertainty impact on the LCA process evaluation (Alyaseri & Zhou,
2019).
18
Knowledge gaps targeted
The study aims to address two important knowledge gaps identified in the environmental
and economic life cycle assessment for sewage sludge treatment processes:
• Previous studies have only focused on studying the environmental effects of various sludge
treatment methods using LCA. Few studies have also considered the life-cycle cost of these
different sludge treatment processes in North America.
• LCA has been widely used to assess wastewater and sewage sludge treatment techniques.
However, only a few LCA and LCCA studies have incorporated probabilistic methods such as
Monte Carlo simulation. Furthermore, uncertainty analysis is uncommon when conducting the
LCCA of sludge treatments. Many of past previous research efforts have assessed their results
by using sensitivity analysis. Due to the limitations of sensitivity analysis, this study proposes
a new way to assess the uncertainties included in the LCA and LCCA.
19
Methodologies
This study evaluates the GWP associated with alternative sewage sludge treatment
processes and end-of-life (EOL) scenarios. This thesis emphasizes GWP given that decision-
makers as well as researchers have focused on this metric in recent years (Teoh & Li, 2020). GWP
will be evaluated using the egalitarian characterization factors provided by ReCipe 2016 (M.
Huijbregts et al., 2016). This method provides characterization factors for GWP in order to
distinguish different relative contributions to global warming for different greenhouse gases, with
the unit of CO2 equivalent (CO2-e) (Lim, 2012).
The LCA is integrated with LCCA to characterize the economic impact of alternative
technologies (Hong et al., 2009; Xu et al., 2014). In this thesis, the LCCA includes capital costs,
maintenance costs, costs of waste disposal, energy costs, and possible revenue from the sales of
recovered resources. Assuming a nominal discount rate of 3.5%, the net present value over a 10-
year analysis period is computed for each scenario.
Goal and scope of the thesis
The principal goal of this study is to evaluate the life cycle environmental and economic
impacts of five sewage sludge handling scenarios with different energy consumption and energy
recovery potentials. The functional unit of the LCA study is the treatment of one tonne of sewage
sludge on a dry matter (DM) basis, a common functional unit in LCA studies of SS treatment
techniques (Tarpani et al., 2020; Yoshida et al., 2013).
The system boundary for the four selected sewage sludge treatment methods is outlined in
Figure 3-1. In this study, the closed-loop EOL allocation method is applied for assessing the
environmental and economic impacts. This approach assumes that recovered product is
recirculated back into the economy (Nordelöf et al., 2019). In this study, all the recovered products,
20
such as heat and electricity energy, are assumed to directly replace an equivalent amount of raw
material and energy inputs. The treated sludge from the anaerobic digestion and composting
process is assumed of high quality, meeting the U.S. EPA’s Class A requirements, and it is applied
on agricultural land according to local regulations. For the pyrolysis process, since high variability
existed in the quality and quantity of pyrolysis products (e.g., biochar and bio-oil), conservative
estimates related to the product yield and the unit price of each product are made in this thesis
based on previous studies (Pawar et al., 2020; Cao et al., 2013; Kim & Parker, 2008; Shahbeig &
Nosrati, 2020). Furthermore, construction and decommissioning of a wastewater treatment plant
are excluded in the study since their contributions to the outcomes of the LCA and LCCA are
assumed to be insignificant based on past studies (Johansson et al., 2008; Yoshida et al., 2013).
3.1.1 Anaerobic digestion
As shown in Figure 3-1, the thickened sludge is digested in the absence of oxygen and
under controlled conditions by the action of microorganisms to generate biogas and digested
sludge (Hospido et al., 2005). The temperature in the digester is maintained at 35 ℃. The biogas
generated in the process is normally used to produce electricity and heat, which can be reused in
the sewage sludge treatment process (Arlt et al., 2002; Tarpani & Azapagic, 2018). The surplus
can be sold on the market to generate revenue (Caposciutti et al., 2020). The digested sludge,
mixed with a polymer, is directed to a mechanical dewatering process to reduce the water content.
After that, the final product, containing about 40-50% of dry matter, can be disposed. In British
Columbia, the most common ways to dispose of biosolids are landfilling and agricultural
application (Ministry of Environment of British Columbia, 2017). For landfilling, the sewage
sludge is disposed at municipal solid waste landfill according to local regulations as shown as
Scenario 1 in Figure 3-1. For agricultural application, biosolids can be used as a substitute for
21
synthetic fertilizers in local farms, which is labelled as Scenario 2 in Figure 3-1. The amount of
fertilizer that can be produced is estimated based on the phosphorus and nitrogen content in the
treated sludge (Hospido et al., 2005; Tarpani et al., 2020).
3.1.2 Incineration
Before being delivered to the incinerator, the thickened sludge, which is mixed with a
polymer, is mechanically dewatered to approximately 70-75% to reduce transport expenditures
and augment fuel qualities (Jungbluth & Chudacoff, 2007). Afterwards, the dewatered sludge is
transferred to municipal solid waste incinerator plants (MSWI). Because the data for incineration
are retrieved from the ecoinvent database, this study primarily relies on lifecycle inventory data
for MSWI plans in Switzerland. The typical design for a MSWI plant includes two or three
incineration lines in parallel. For each line, a grate-type furnace is equipped (Jungbluth &
Chudacoff, 2007). Energy from incineration is used to generate useful heat and electricity.
According to ecoinvent 3.7, the gross heat generation efficiency is about 25.6% and the gross
electricity generation efficiency is 13% (Jungbluth & Chudacoff, 2007). The generated energy will
be reused in the incineration plant, and the surplus will be sold and used in other ways depending
on local circumstances. The solid residues of the incineration process are usually landfilled, as
shown as Scenario 3 in Figure 3-1. Generally, the bottom ash is disposed in a sanitary landfill,
and fly ash is disposed as hazardous waste (Tarpani et al., 2020).
3.1.3 Composting
For composting, the thickened sewage sludge is first dewatered by the use of centrifuge
dewatering, which can achieve about 30% of total solids (TS). Afterwards, the dewatered sludge
is mixed with a bulking agent. After mixing, the sludge is then transferred to the windrows and
composted under controlled conditions to achieve a satisfactory composition. The finished product
22
is transported to agricultural land. The system is credited for an equivalent amount of synthetic
fertilizer, which is estimated according to the phosphorus content in the compost (Tarpani &
Azapagic, 2018; Sablayrolles et al., 2010). The same range of nutrient recovery rates is used for
composting and anaerobic digestion (Tarpani et al., 2020).
3.1.4 Pyrolysis
The pyrolysis process is assumed to be a fast pyrolysis process without using any catalyst.
It is proposed that the filter press can dewater the raw municipal sewage sludge (1% total solids
and 99% moisture content) (Chen et al., 2002). After dewatering, the water content can be reduced
to 70%-85% moisture content (Zaker et al., 2019). The dewatered sewage sludge will be thermally
dried by electric dryers to make the water content in the sludge below 10%. The sludge is then
pyrolyzed to produce three products: solid char, bio-oil, and syngas. The main product of fast
pyrolysis is bio-oil. The bio-oil can be sold on the market since it has several potential commercial
applications, which include heat and power generation, production of chemicals, and upgrading to
high-quality hydrocarbon fuels (Czernik & Bridgwater, 2004). Syngas can be used to generate heat
energy that can be reused in the pyrolysis system or in the wastewater treatment plants (Crombie
& Mašek, 2014; Rastegari et al., 2019). Biochar can be used as fertilizer/soil amendment
(Palansooriya et al., 2019).
23
Treatment method 1 Anaerobic Digestion
Treatment method 2 Incineration
Treatment method 3 Composting
Treatment method 4 Pyrolysis
Figure 3-1. System boundaries for the sludge treatment methods considered in the study.
24
Inventory Analysis
Having defined the system boundary for each technology, life cycle inventory data are
collected for each unit process. Data for energy consumption, gas emissions and conventional costs
(e.g., equipment and operation) of the target scenarios are mainly sourced from ecoinvent Version
3, relevant literature papers, and municipalities in Canada. The inventory data sources for each
unit process as well as their values are summarized in Appendix B.
The unit prices of raw materials, waste disposal treatment costs, the selling price of some
recovered products (e.g., fertilizer and compost) and transportation costs of the aforementioned
scenarios are based on data sourced from Statistics Canada and local municipalities in Canada.
The GWP impact of different sewage sludge treatment methods is assessed by using the data from
ecoinvent and relevant sewage sludge treatment studies.
Estimating uncertainty of LCI data
The uncertainty analysis of the paper is conducted by using Monte Carlo simulation and
other probabilistic approaches considering the variation in data values of all related parameters.
Given that multiple data sources are used in this study, it is necessary to develop appropriate
mathematical models to describe the distribution of the value of each parameter.
Under the anaerobic digestion and incineration scenario, the exchange values between the
studied system and the environment for each unit process are primarily retrieved from the
ecoinvent database. The ecoinvent database is one of the few LCI databases that explicitly
characterizes uncertainty underlying inventory data (Muller et al., 2016). A semi-quantitative
method based on the use of ecoinvent’s pedigree matrix approach is applied to quantify uncertainty
for all flows. It involves two types of parameter uncertainty. The first type is basic uncertainty,
which is used to capture the intrinsic variability and stochastic error of the parameters (Muller et
25
al., 2016). The other type of uncertainty is due to the use of imperfect data. The quality of the data
was assessed based on five data quality indicators, which are reliability, completeness, and
temporal, geographical, and technological correlations to the target system (Yoshida et al., 2013).
The ecoinvent database assumes a lognormal probability distribution for all uncertain values. The
combination of these two types of uncertainty can be used to evaluate the overall total uncertainty
for each parameter (Ciroth et al., 2016).
To account for these uncertainties when computing LCA and LCCA impacts, Monte Carlo
simulation is utilized due to its capabilities and simplicity (M. A. J. Huijbregts et al., 2001). In
order to evaluate the variation in the values of input parameters, different distributions, such as
lognormal and uniform, were assigned for each input parameter in the model. For example, the
uncertainty of data values retrieved from the ecoinvent database follows a lognormal distribution
(Jungbluth & Chudacoff, 2007). The maximum and minimum costs of landfilling and agricultural
application are provided by local municipalities. Therefore, a uniform distribution was applied to
the costs of these two disposal methods, assuming that each value between the maximum and
minimum is equally likely. After determining the distribution for each parameter, a thousand
Monte Carlo simulation iterations were performed using random values for each input parameter
based on their probability distribution (Setchi et al., 2016). The simulation results can be used to
conduct comparative evaluation among the different processes (M. A. J. Huijbregts et al., 2001).
In order to assess the 10-year life cycle costs of the four sewage sludge treatment methods,
a price forecast model was built for this study. To forecast the future price of electricity, fuel, and
diesel energy sources, multiple time-series models have been constructed. Each model forecasts
future prices as either a difference-stationary or trend-stationary process (Swei, 2020). Given that
the data sample (e.g., electricity unit prices from Statistics Canada and natural gas unit prices from
26
the Government of Alberta) is limited, each time-series was inspected via the autocorrelation
function to test for both serial correlation and stationarity. The resulting outputs have been used to
construct a time-series forecast for each energy source. Each time-series model is estimated in log
space to account for possible heteroscedasticity, and the p-values are inspected to ensure statistical
significance. Table B.3.2 of Appendix B provides further details around each model.
Case study application
This thesis was conducted to evaluate the environmental and economic impacts of sludge
treatment processes and end uses for the wastewater treatment plant based in Vancouver, a major
city in western Canada. The plant produces 16,200 tonnes of sewage sludge annually. The research
includes a comprehensive LCI of four different sewage sludge techniques and three common end-
use options. The assessment of different sludge treatment technologies accounts for local
conditions, such as transportation distance, the unit price for each energy source, and costs of waste
disposal methods. Appendix B includes detailed descriptions of input parameters and related data
sources.
27
Results and Discussions
Life cycle assessment (LCA) -Global warming potential
Figure 4-1 presents the deterministic GWP results across the five treatment scenarios.
Included in Figure 4-1 is the relative contribution of each lifecycle phase towards the total GWP
for each scenario. Further details, including the assumption made, GHG emissions for each unit
process and resource recovery potential for each scenario, can be found in Appendix B.2. As can
be seen in Figure 4-1, pyrolysis has the lowest GWP impact at 247 Mg CO2-e (i.e., 282 kg CO2-
e/1,000 kg DM). An important reason for its relatively low total GWP is the credits received for
the production of biochar (fertilizer substitution and soil N2O emission reduction), natural gas and
crude oil substitution (-270 kg CO2-e/1,000kg DM). Anaerobic digestion with agricultural
application, which is a commonly used sewage sludge treatment technique in North America, has
the second lowest expected GWP at 296 Mg CO2-e (338 kg CO2-e /1,000kg DM). The major
contributor towards its impact is CO2 released from the biogas combustion process (576 kg CO2-
e /1,000kg DM). At the end-of-life, this technology receives a credit for energy recovery and
fertilizer substitution (-127 kg and -239 kg CO2-e/1,000 kg DM, respectively). The next best option
is composting with a total impact of 576 Mg CO2-e (658 kg CO2-e /1,000 kg DM), followed by
anaerobic digestion with landfilling with 684 Mg CO2-e (782 kg CO2-e /1,000 kg DM).
Incineration is the worst option, with 6,842 Mg CO2-e (7,822 kg CO2-e /1,000 kg DM). These
emissions are largely generated from the incineration process. The credits for energy recovery
from the incinerator can reduce the impact by -147 kg CO2-e /1,000 kg DM.
28
Figure 4-1. Global warming potential of each scenario over its full life cycle.
Table 4-1 summarizes the probabilistic results for each scenario. Negative values indicate
that the environmental credit from recovered resources exceeds the impact of resource inputs. The
uncertainty analysis highlights for decision-makers the range of possible outcomes related to the
five treatment scenarios, which can otherwise not be gleaned from the deterministic results. As
can be seen in Table 4-1, the application of sludge from anaerobic digestion to agricultural land
and pyrolysis outperform other technologies in terms of GWP. For anaerobic digestion with
agricultural application, the 95% prediction interval for GWP ranges between -129 Mg CO2-e to
487 Mg CO2-e. For pyrolysis, the 95% prediction interval is -24 Mg CO2-e to 1082 Mg CO2-e.
The results indicate that both pyrolysis process and anaerobic digestion with agricultural
application can achieve a net negative GWP impact due to the high resource recovery rate of these
two processes. According to the results from probabilistic analysis, the application of sludge from
anaerobic digestion to agricultural land is slightly better than pyrolysis process since it has lower
values of probabilistic mean, 2.5% percentile and 97.5% percentile, which is contrary to the results
29
from deterministic analysis. The deterministic mean of pyrolysis process is lower than that of
anaerobic digestion with agricultural application. The reason for that is mainly due to the
asymmetry in the underlying distribution for pyrolysis and anaerobic digestion with agricultural
application processes. The distribution of pyrolysis process is positively skewed since the right-
hand tail of the distribution is longer than the left, as can be seen from Figure 4-2. In contrast, the
distribution of anaerobic digestion with agricultural application is negatively skewed since the tail
of the distribution is longer on the left-hand side than on the right-hand side. Figure 4-2 presents
a cumulative distribution function (CDF) plot for four treatment scenarios. The incineration
scenario is removed from this figure since it has more than ten times higher GWP impact compared
with other treatment processes.
Figure 4-2. A CDF plot of four treatment scenarios (exclude incineration).
The coefficient of variation (CV) measures the relative scatter of data values around the
mean (Hayes, 2021). The CV for agricultural application of digested sludge and pyrolysis process
is higher than that of other processes. The reason for that is mostly due to higher level of variability
in the recovery of resources in these two processes compared to other scenarios. The incineration
30
process is the worst option, since it has much higher values of probabilistic mean, 2.5% percentile
and 97.5% percentile than those of other scenarios.
Table 4-1. Probabilistic analysis results of each scenario for global warming potential impact.
Treatment
process Unit
Probabilistic
mean
Standard
deviation
Coefficient
of variation
(CV)
2.5%
percentile
97.5%
percentile
AD +
Landfilling
Mg
CO2-e 689 48 0.1 604 792
AD+
Agricultural
application
Mg
CO2-e 260 156 0.6 -129 487
Incineration Mg
CO2-e 6,927 1,141 0.2 5,051 9,519
Pyrolysis Mg
CO2-e 334 288 0.9 -24 1,082
Composting Mg
CO2-e 584 95 0.2 419 798
Life cycle cost analysis (LCCA)
Figure 4-3 presents the LCCA results for each scenario as well as the relative contribution
of each lifecycle phase towards total life cycle costs (LCC). As can be noted in Figure 4-3,
composting treatment has the lowest life cycle cost ($14.0 million) primarily due to its low capital
investment costs which is about 50% lower than that of other technologies. The second-best option
is pyrolysis with expenditures estimated at $14.8 million, followed by two AD processes. The LCC
of both AD processes is around $20.5 million. By selling its by-products (biochar and bio-oil) and
recovered energy from the biogas, the pyrolysis process is able to generate revenues of $10.7
million, reducing its total LCC by about 42%. Incineration is the most expensive treatment method
with an overall LCC of $20.6 million. Although revenue can be generated from the recovered
energy from incineration, waste management costs and transportation costs of incineration process
are much higher than those of other treatment methods, contributing 22% and 13% to the total
LCC of incineration. Waste management costs are insignificant for other methods except for
31
incineration. The primary reason is the large amount of ash residues produced from the incineration
process and the long transportation distance between an incineration plant and a landfill site (~450
km). Furthermore, additional expenditures are required for managing toxic material and air
pollutant emissions from the incineration process. The main contributor to the total LCC over 10
years is capital costs for all the treatment methods, contributing around 60%-80% of the total
expenditures.
Figure 4-3. Expected Life cycle costs analysis (LCCA) of each scenario.
Monte Carlo simulations were conducted on the five sludge treatment processes. Table 4-
2 presents the results generated from the probabilistic analysis. This analysis reveals the impact of
uncertainty related to LCI data on the final LCCA results. Pyrolysis and composting have better
performance compared with other treatment methods. According to the 95% prediction interval
per Table 4-2, the total LCC of pyrolysis ranges from $9.0 million to $21.8 million, while the cost
of composting ranges from $11.8 million and $16.6 million. Although the pyrolysis process has a
higher probabilistic mean and 97.5% percentile than composting process, pyrolysis has lower
-20
-10
0
10
20
30
AD + Landfilling AD + Agricultural
application
Incineration Pyrolysis Composting
Lif
e cy
cle
cost
s (i
n m
illi
on
CA
D)
Capital Costs Maintenance Costs
Operating Costs Transportation Costs
Waste Management Costs Revenue from recovered energy and products
32
values than composting process at the 2.5 % limit. This result implies that it is fully possible that
pyrolysis will have a lower life-cycle cost than composting. Therefore, it is hard to conclude which
process is more cost-effective. Similarly, it also hard to tell which treatment process is the most
expensive one based on probabilistic results. Although the incineration process has the highest
probabilistic mean ($20.8 million) among these treatment processes, it has lower values than AD
processes (Scenario 1 and Scenario 2) at the 97.5% limit. Under some circumstances, such as AD
with low recovery rates, incineration process may achieve lower LCC than AD treatment scenarios.
In addition, the CV of pyrolysis process is found to be 0.2, which is slightly higher than other
treatment processes. This is mainly due to the higher variability in the resource recovery rate and
potential profits that can be generated from three products (bio-oil, biochar and syngas) from
pyrolysis. The economic variety of these sludge treatment methods is affected by many factors,
such as the future prices of energy, the recovery potential, and sales of their products.
Table 4-2. Probabilistic analysis results of each scenario for life cycle cost analysis.
Treatment
process Unit
Probabilistic
mean
Standard
deviation
Coefficient
of variation
(CV)
2.5%
percentile
97.5%
percentile
AD +
Landfilling
Million
CAD 20.6 2.3 0.1 16.4 25.3
AD+
Agricultural
application
Million
CAD 20.6 2.3 0.1 16.4 25.5
Incineration Million
CAD 20.8 1.9 0.1 17.3 24.7
Pyrolysis Million
CAD 15.1 3.3 0.2 9.0 21.8
Composting Million
CAD 14.0 1.3 0.1 11.8 16.6
Combine economic and environmental results
Figure 4-4 integrates the probabilistic findings presented in Table 4-1 and Table 4-2,
providing an overview of the expected GWP and LCC for each scenario and technology. The result
33
suggests that pyrolysis process is the most suitable environmental and economic method to treat
sewage sludge, with GHG emissions of 334 Mg CO2-e and total LCC of $15.1 million. The reason
is that it has the greatest future potential in both environmental and economic aspects among these
five scenarios. It has the potential to generate five to ten times more profits by recovering valuable
products (e.g., biochar and bio-oil) than other treatment methods and bring positive effects on
climate change. Although this method is still under development and limited in commercial
applications (Tarpani & Azapagic, 2018), it can be considered as a promising method and can play
a greater role in the future. It has the potential to achieve negative life cycle costs and more
favorable environmental impacts if the quality of recovered products and resource recovery rate
can be certain and greatly improved. The second-best option is the composting process since it has
the lowest life cycle costs ($14.0 million), and its GWP impact is between pyrolysis (334 Mg CO2-
e) and anaerobic digestion with landfilling (689 Mg CO2-e). Although agricultural application of
anaerobically digested sludge is the best option for the GWP impact (260 Mg CO2-e) among these
five scenarios, it has much higher life cycle costs ($20.6 million) than composting and pyrolysis
process. This is mainly due to the limited viable markets for these biosolids generated from sludge
treatment processes in Canada. This process can generate more revenue by improving the quality
of biosolids and conducting more marketing activities. For example, local governments can hire
some expertise to introduce the benefits of using biosolids as fertilizer to the local community. In
contrast, the incineration process should be avoided since it has the worst performance both in the
GWP and life cycle costs among these five scenarios. This process can be adjusted by improving
the energy recovery potential and upgrading current technologies.
34
Figure 4-4. Combined LCA and LCCA results for each scenario.
Limitations of the work
The LCA and LCCA results from the study highly depend on the data and assumptions that
are used and the local treatment conditions. Some small changes in the initial conditions and
assumptions that are made will affect the final result of the study. For example, the GWP and costs
generated from the transportation process are determined by local factors such as the mode of
transportation, the quantity of solid residue that needs to be disposed, transportation distances,
availability of land, etc. If the transportation distance between landfill sites and the incineration
plant were to become shorter, for example, the total life cycle cost of incineration process would
decrease given its considerable contribution to the life cycle cost of this process. Therefore, the
conclusion of the study may vary across different contexts and conditions.
Another important limitation of the study is that only one impact category (GWP) is
considered as part of the LCA. This study has emphasized GWP given its importance to local
decision-makers. Having said that, other impact categories should be considered to deliver a more
comprehensive and credible assessment when comparing the available sludge treatment processes
35
such as acidification potential, human toxicity potential and eutrophication potential. Different
treatment techniques will vary in their performance under various impact categories. For example,
if human toxicity potential is included in the environmental assessment, composting would likely
have a higher impact compared with other treatment methods due to emissions of manganese and
arsenic in the life cycle of electricity (Tarpani et al., 2020).
Comparison with other studies
Since the LCA and LCCA results may vary across different factors (e.g., system boundary),
it is only possible to conduct a high-level comparison of these findings relative to past studies. In
terms of global warming impact, anaerobic digestion followed by agricultural application is within
the range of values reported in other studies, which have ranged from -280 to 650 kg CO2-e/ 1,000
kg DM (Tarpani et al., 2020; Murray et al., 2008; Hong et al., 2009; Gourdet et al., 2017).
Compared to previous studies, the impact for composting (658 kg CO2-e /1,000 kg DM) is very
close to the literature value, which is estimated at about 683 kg CO2-e /1,000 kg DM (Hong et al.,
2009). The values for anaerobic digestion with landfilling and pyrolysis (782 kg CO2-e /1,000 kg
DM and 282 kg CO2-e /1,000 kg DM) are also similar to values reported in the literature, which
are 867 kg CO2-e /1,000 kg DM and 315 kg CO2-e /1,000 kg DM, respectively. (Hong et al., 2009;
Tarpani et al., 2020). For the incineration process, the value found in this study (7,822 kg CO2-e
/1,000 kg DM) is much higher than literature values, ranging from 130 to 670 kg CO2-e /1,000 kg
DM (Hong et al., 2009; Lombardi et al., 2017; Houillon & Jolliet, 2005). The large deviation is
mostly due to the assumptions (e.g., the composition of raw sewage sludge and the mode of
transportation) that are used in the studies, different sludge treatment conditions (i.e., operating
temperature and efficiency of major equipment), and the amount of energy (electricity and heat)
36
that can be recovered. The results of global warming potential obtained from this study are broadly
in agreement with those in the literature.
As mentioned in the previous section, LCCA studies of sludge treatment technologies are
scarce, and the cost for each technology varies greatly due to the differences in methodologies,
assumptions, and geographical location. Tarpani & Azapagic (2018) conducted an LCCA for
several sewage sludge treatment techniques, which included anaerobic digestion, composting,
incineration, pyrolysis, and wet air oxidation. They found that pyrolysis process can generate more
profits, and has relatively low overall life cycle costs compared to other processes if all the
recovered products are utilized. Incineration is the least preferred option, and the waste
management costs are one of the major contributors to the LCC of this process. Capital costs and
maintenance costs are significant for all scenarios. These findings are in line with the results of
this study.
37
Conclusions and Recommendations
Conclusions
This thesis has evaluated both the environmental and economic impacts of five sludge
treatment processes with resource recovery. For the environmental assessment, the results suggest
that the application of sludge from anaerobic digestion to agricultural land and pyrolysis process
have better performance in terms of GWP compared to the other scenarios. By considering the
uncertainties embedded in the parameters, these two options can achieve net negative GHG
emissions after accounting for credits associated with resource recovery. Incineration has the
highest impact in terms of GWP, primarily due to the emission of carbon dioxide and dinitrogen
monoxide from the incinerator. For the economic assessment, among these sludge treatment
scenarios, pyrolysis and composting process can result in lower total life cycle costs than other
techniques. Composting has the lowest capital costs, approximately 50% below that of the other
treatment methods. Pyrolysis can generate the highest revenue by recovering resources from its
end products (biochar, syngas, and bio-oil), and it has the potential to achieve net negative life
cycle costs depending on the assumptions for the resource recovery rate and the sales of the
products. Incineration is the most expensive option since it has higher waste management costs
and transportation costs than those of other treatment methods. Capital costs play a significant role
across all scenarios.
By considering both the economic and environmental performance of these selected sludge
handling options, pyrolysis process is an attractive treatment method since it has an outstanding
performance from both economic and climate change points of view. Although this method is not
widely used worldwide, it can be considered as a promising method since it can generate more
recovered products and resources that can be used as a source of revenue. Furthermore, the results
38
from the probabilistic analysis indicate that the pyrolysis process can achieve net negative GHG
emissions after considering the credits for recovering products. However, the incineration process
should be restricted since it is the most expensive option, and it has the highest global warming
potential impact.
Recommendations for future work
There are several opportunities to further improve and develop the work undertaken in this
thesis. First, as mentioned earlier, an important limitation of this research is that only one impact
category is evaluated when comparing the environmental impacts of these sludge handling options.
Other important environmental impact categories worth further consideration including freshwater
ecotoxicity, human toxicity, and eutrophication potential. For instance, Tarpani et al. (2020)
conducted an LCA of several sewage sludge treatment techniques (anaerobic digestion,
composting, incineration, pyrolysis, and wet air oxidation) considering all 18 impact categories
included in the ReCipe method. They found that the contribution of heavy metals in the sludge to
the freshwater ecotoxicity is significant, especially when the digested sludge is applied to the
agricultural land. Similarly, Lombardi et al. (2017) assessed and compared ten impact categories
for different sewage sludge treatment and disposal routes. They also indicated that the use of
sewage sludge in agricultural soils can show the lowest values for the abiotic depletion, fossil fuel
depletion, global warming impact categories. However, it can have the highest impacts in
categories related to toxicity for human and ecosystems. Incineration process can significantly
reduce the human and ecosystem toxicity indicators, acidification and eutrophication. Therefore,
more impact categories should be considered and evaluated in the future research to develop a
more comprehensive environmental assessment and comparison of alternative treatments for
sewage sludge.
39
Second, in order to conduct a more accurate economic assessment of these sludge treatment
options, it is necessary to determine the resource recovery rate and the revenue from the recovered
resources. In this study, the resource recovery rate is assumed to be 100%, but the actual resource
recovery rate for each scenario depends on local conditions and the technical limitations of
treatment methods and equipment. The sale of these recovered resources also highly depends on
the local market condition. Currently, in British Columbia, there is generally low interest from the
agricultural sector to use biosolids as fertilizer. In some places, municipalities need to pay their
farmers to receive and use the treated biosolids. Therefore, if biosolids would be publicly accepted
as a soil amendment and fertilizer, thus leading to a demand for biosolids, there is clearly a
potential for making more profits from these sludge treatment processes.
Finally, future studies may choose to use different end-of-life allocation methods to
determine the environmental impacts and economic impacts of different sludge treatment methods.
For example, the cut-off allocation would likely increase the global warming potential impact of
the pyrolysis process considerably given that recovered resources would not be credited. The
LCCA and LCA results will change dramatically depending on the EOL allocation method.
40
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55
Appendix A Summary of findings from previous studies
56
Table A.1. Summary of sewage sludge treatment methods and end-uses in previous studies
References Sludge Treatment Methods End-Uses/ Disposal Location
Peters & Lundie, 2001 AD, alkaline/lime stabilisation, drying, AD + drying Agricultural application, energy
recovery
Australia
Suh & Rousseaux, 2002 AD, composting/aerobic digestion, lime stabilisation,
incineration
Landfill, agricultural application,
energy recovery
France
Poulsen & Hansen, 2003 AD, composting, AD + composting, AD + co-
incineration, AD + incineration
Agricultural application, landfill,
Fuel/additive for cement kiln
firing/production, energy recovery
Denmark
Lundin et al., 2004 Co-incineration, drying/pasteurisation, fractionation
(Cambi-KREPRO), incineration
Agricultural application,
phosphorus and energy recovery
(Bio-Con, Cambi-KREPRO), fuel,
landfill
Sweden
Svanström et al., 2004 Supercritical water oxidation Landfill, energy recovery USA
Hospido et al., 2005 AD, incineration, pyrolysis/carbonisation Energy recovery, agricultural
application, landfill, Charcoal,
Crude oil
Spain
Houillon & Jolliet, 2005 Alkaline/lime stabilisation, co-incineration, incineration,
pyrolysis/carbonisation, wet oxidation
Agricultural application, landfill,
fuel for cement kiln firing, energy
recovery
France
Svanström et al., 2005 Co-incineration, drying/pasteurisation, fractionation
(Cambi-KREPRO), incineration, supercritical water
oxidation
Landfill, agricultural application,
energy recovery
Sweden
57
References Sludge Treatment Methods End-Uses/ Disposal Location
Cartmell et al., 2006 Co-combustion Fuel in power station/ fuel for
cement kiln, energy recovery
UK
Peregrina et al., 2006 Drying/pasteurisation, fry-drying Landfill France
Tarantini et al., 2007 AD + composting, incineration, AD + incineration Landfill, agricultural application,
energy recovery
Italy
Johansson et al., 2008 Composting/aerobic digestion, supercritical water
oxidation
Agricultural application, landfill Sweden
Murray et al., 2008 AD, composting/aerobic digestion, alkaline/lime
stabilisation, co-incineration, drying/pasteurization, AD +
drying, drying + composting
Cement production, agricultural
application, cement / clay brick
production, energy recovery
China
Hong et al., 2009 AD, AD + composting, composting/aerobic digestion,
drying/pasteurisation, incineration, incineration + melting,
melting, AD + drying, AD + incineration, AD +
incineration + melting, AD + melting
Landfill, agricultural application,
building material application,
energy recovery
Japan
Peters & Rowley, 2009 AD, AD + composting, alkaline/lime stabilization,
drying/pasteurisation, AD + drying
Landfill, agricultural application,
fuel for cement kiln firing, energy
recovery
Australia
Brown et al., 2010 AD, composting/aerobic digestion, alkaline/lime
stabilization, drying/pasteurisation, incineration
Landfill, energy recovery, ash
recycling, fuel for cement kiln
firing, agricultural application
Canada
Hospido et al., 2010 AD Agricultural application, energy
recovery
Italy
58
References Sludge Treatment Methods End-Uses/ Disposal Location
Lederer & Rechberger,
2010
AD + alkaline/lime stabilisation, AD + co-incineration,
AD + incineration
Agricultural application, fuel for
cement kiln firing, fuel, landfill,
energy recovery
EU
Ghazy et al., 2011 AD, aerobic digestion, composting Energy recovery, agricultural
application
Egypt
Liu et al., 2011 Co-incineration Fuel, energy recovery China
Nakakubo et al., 2012 Co-digestion, co-digestion + composting, co-digestion +
various thermo-chemical methods
Cement production, agricultural
application, fuel and landfill,
energy recovery
Japan
Cao & Pawłowski, 2012 Pyrolysis/carbonisation, AD + pyrolysis Fuel, energy recovery Generic
Liu et al., 2013 Composting/aerobic digestion, alkaline/lime stabilisation,
co-incineration, incineration
Agricultural application, fuel in
brick/cement kiln, landfill. energy
recovery
China
Wang et al., 2013 Co-incineration, incineration, pyrolysis/carbonisation Fuel, landfill, energy recovery Taiwan
Mills et al., 2014 AD, AD + drying, AD + pyrolysis Energy recovery, fuel production.
agricultural application UK
Niero et al., 2014 AD, incineration, aerobic digestion Agricultural application, energy
recovery Denmark
Xu et al., 2014 AD, AD + drying, AD + incineration Landfill, agricultural application,
energy and raw material recovery China
59
References Sludge Treatment Methods End-Uses/ Disposal Location
Bertanza et al., 2015 Wet oxidation, AD + incineration Landfill, agricultural application,
energy recovery Italy
Di Maria et al., 2016 AD, composting, incineration Landfill, energy recovery,
agricultural application Italy
Piao et al., 2016 AD, Incineration, , composting Landfill, energy recovery,
agricultural application Korea
Righi et al., 2016 AD, pyrolysis Landfill, agricultural application,
energy recovery Italy
Abuşoğlu et al., 2017 AD + co-incineration, AD + incineration Fuel for cement kiln firing, landfill,
energy recovery Turkey
Li et al., 2017 AD Landfill, energy recovery China
Lombardi et al., 2017 Composting, incineration, wet oxidation Agricultural application, landfill,
energy and material recovery Italy
Usapein & Chavalparit,
2017
Composting/aerobic digestion, co-incineration Agricultural application,
fuel/additive for cement kiln
firing/production, energy recovery
Thailand
Buonocore et al., 2018 Drying/pasteurisation, AD + drying, AD + drying +
gasification
Landfill, fuel, energy recovery Italy
Tarpani & Azapagic, 2018 AD, composting, incineration, pyrolysis, wet air oxidation Energy recovery, agricultural
application, methanol, fuel, landfill
UK
60
References Sludge Treatment Methods End-Uses/ Disposal Location
Yoshida et al., 2018 AD, alkaline/lime stabilisation, AD + incineration Agricultural application, landfill,
energy recovery
Denmark
Alyaseri & Zhou, 2019 AD, incineration Landfill, energy recovery USA
Barry et al., 2019 Pyrolysis Landfill, energy recovery,
agricultural application, coal in
cement kiln
Canada
Francini et al., 2019 Conventional co-digestion, preliminary dark-fermentation
pre-treatment of the mixture of SS-OFMSW and SS, AD
Landfill, energy recovery,
agricultural application
Spain
Arias et al., 2020 AD with cogeneration, composting, AD+ Incineration Agricultural application, energy
recovery
Spain
Tarpani et al., 2020 AD, composting, incineration, pyrolysis, wet air oxidation Energy recovery, agricultural
application, methanol, fuel, landfill
Europe
Teoh & Li, 2020 14 treatment methods Landfill, agricultural application,
fuel, material and energy recovery
Generic
Notes: AD = anaerobic digestion
61
Table A.2. Summary of LCA and LCCA- Related Papers Reviewed in this thesis
References Life Cycle Assessment
(LCA)
Life Cycle Cost
Assessment (LCCA)
Location Type of Analysis
Peters & Lundie, 2001 √
Australia Sensitivity analysis
Suh & Rousseaux, 2002 √
France Sensitivity analysis
Poulsen & Hansen, 2003 √
Denmark Deterministic analysis
Lundin et al., 2004 √ √ Sweden Deterministic analysis
Svanström et al., 2004 √
Sweden Deterministic analysis
Hospido et al., 2005 √
Spain Sensitivity analysis
Houillon & Jolliet, 2005 √
France Sensitivity analysis
Svanström et al., 2005 √
Sweden Deterministic analysis
Cartmell et al., 2006 √ √ UK Deterministic analysis
Peregrina et al., 2006 √
France Deterministic analysis
Tarantini et al., 2007 √
Italy Deterministic analysis
Johansson et al., 2008 √
Sweden Sensitivity analysis
Murray et al., 2008 √ √ China Deterministic analysis
Hong et al., 2009 √ √ Japan Deterministic analysis
Peters & Rowley, 2009 √
Australia Sensitivity analysis
Brown et al., 2010 √
Canada Sensitivity analysis
Lederer & Rechberger, 2010 √
EU Deterministic analysis
Ghazy et al., 2011 √ √ Egypt Deterministic analysis
Liu et al., 2011 √
China Deterministic analysis
Nakakubo et al., 2012 √
Japan Deterministic analysis
Cao & Pawłowski, 2012 √
Generic Sensitivity analysis
Liu et al., 2013 √
China Sensitivity analysis
Wang et al., 2013 √
Taiwan Sensitivity analysis
Mills et al., 2014 √ √ UK Deterministic analysis
62
References Life Cycle Assessment
(LCA)
Life Cycle Cost
Assessment (LCCA)
Location Type of Analysis
Niero et al., 2014 √
Denmark Uncertainty analysis
Xu et al., 2014 √ √ China Sensitivity analysis
Bertanza et al., 2015 √ √ Italy Sensitivity analysis
Di Maria et al., 2016 √
Italy Uncertainty analysis
Piao et al., 2016 √ √ Korea Sensitivity analysis &
Uncertainty analysis
Righi et al., 2016 √
Italy Deterministic analysis
Abuşoğlu et al., 2017 √
Turkey Deterministic analysis
Li et al., 2017 √ √ China Sensitivity analysis &
Uncertainty analysis
Lombardi et al., 2017 √
Italy Sensitivity analysis
Usapein & Chavalparit, 2017 √
Thailand Sensitivity analysis
Buonocore et al., 2018 √
Italy Deterministic analysis
Tarpani & Azapagic, 2018
√ UK Sensitivity analysis
Yoshida et al., 2018 √
Denmark Uncertainty analysis (Monte
Carlo analysis)
Alyaseri & Zhou, 2019 √
USA Uncertainty analysis (Monte
Carlo analysis)
Barry et al., 2019 √ √ Canada Deterministic analysis
Francini et al., 2019 √ √ Spain Sensitivity analysis & Monte
Carlo Analysis
Arias et al., 2020 √ √ Spain and
Denmark
Deterministic analysis
Tarpani et al., 2020 √
Europe Sensitivity analysis
Teoh & Li, 2020 √
Generic Deterministic analysis
63
Table A.3. Functional Units and System Boundaries Considered in LCA Studies of Sludge Treatment Methods
References Functional
Unit
Processes Included Within
System Boundaries
WWTP Main Thickening Dewatering Storage Transportation Disp./
End-Use
Peters & Lundie,
2001 178 t-DS / √ √ √ √ √ /
Suh & Rousseaux,
2002 1 t-DS / √ √ √ √ √ √
Poulsen & Hansen,
2003 1 t inc. COD / √ / √ / √ √
Lundin et al., 2004 1 t-DS / √ / √ √ √ √
Svanström et al.,
2004
1 tonne
sludge / √ √ / / √ /
Hospido et al., 2005 1 t-DS / √ / √ √ √ √
Houillon & Jolliet,
2005 1 t-DS / √ √ √ √ √ √
Svanström et al.,
2005 1 t-DS / √ / √ / √ /
Peregrina et al.,
2006 1 t-DS / √ / / / √ √
Tarantini et al., 2007 1 t-DS / √ √ √ / √ √
Johansson et al.,
2008 1 t-DS / √ / √ √ √ √
Murray et al., 2008 1 yr. sl.
prod. / √ / √ / √ √
Hong et al., 2009 1 t-DS / √ √ √ / √ √
Peters & Rowley,
2009 2 t-DS / √ / √ / √ √
Brown et al., 2010 100 t-DS / √ √ √ √ √ √
Hospido et al., 2010 10 L mixed
sludge / √ / / / / √
64
References Functional
Unit
Processes Included Within
System Boundaries
WWTP Main Thickening Dewatering Storage Transportation Disp./
End-Use
Lederer &
Rechberger, 2010
1 tonne
sludge / √ / √ / √ √
Ghazy et al., 2011
1 t-DS/
1 t digested
DS / √ / √ / √ √
Liu et al., 2011 1 TJ steam / √ / / / √ √
Nakakubo et al.,
2012
281.30 L
sludge and
food waste √ √ √ √ / √ √
Cao & Pawłowski,
2012
500 m3
sludge / √ / √ / √ √
Liu et al., 2013 1 t-DS / √ / √ / √ √
Wang et al., 2013 1 tonne
sludge / √ / / / √ √
Mills et al., 2014 1 t-DS / √ √ √ / √ √
Niero et al., 2014 1 m3 of inlet
wastewater / √ / √ / / √
Xu et al., 2014 1 t-DS / √ √ √ / √ √
Bertanza et al.,
2015
Daily inflow
to WWTP / √ √ √ / √ √
Di Maria et al.,
2016
1 tonne of
WMS/ 80 kg
on wet basis
of FVW
/ √ √ √ √ √ √
Piao et al., 2016
1 m3 of
influent
wastewater
/ √ √ √ / √ √
Abuşoğlu et al.,
2017 1 kg sludge / √ / √ / √ √
Li et al., 2017 1 t-DS / √ / √ / √ √
65
References Functional
Unit
Processes Included Within
System Boundaries
WWTP Main Thickening Dewatering Storage Transportation Disp./
End-Use
Lombardi et al.,
2017 1 t-DS / √ / √ / √ √
Usapein &
Chavalparit, 2017
1 tonne
sludge / √ / / / √ √
Buonocore et al.,
2018
1,000 m3
wastewater √ √ / / / / /
Tarpani &
Azapagic, 2018 1 t-DS / √ / √ √ √ √
Yoshida et al., 2018 1 tonne
mixed sludge / √ / √ / √ √
Tarpani et al., 2020 1 t-DS / √ / √ √ √ √
Notes:
# “Main” refers to the sludge treatment method described in the second column of Table A.1 (e.g., AD; lime stabilisation; etc.).
The tick mark (✓) refers to lifecycle stages or processes considered/included (or implied to be considered/included) within system boundaries.
The slash mark (/) refer to lifecycle stages or processes that were not considered/included, not mentioned, or not applicable in systems/within
boundaries.
Abbreviations:
Disp. = disposal of sludge treatment products or residues; t-DS = tonne(s) dry solids; WWTP = wastewater treatment plant processes (upstream of
sludge treatment); yr. sl. prod. = year of sludge production; WMS: waste-mixed sludge; FVW: fruit and vegetable waste.
66
Appendix B Summary of assumptions & input data in the
energy, LCA & LCCA models for each scenario
67
B.1 Energy models for each scenario
B.1.1 Energy model for anaerobic digestion process
Assumptions:
1. Mass of thickened sludge = 0.3 * the mass of raw sewage sludge
(References: General Aspects of Sludge Management Alexandros Stefanakis, ... Vassilios A. Tsihrintzis, in Vertical Flow
Constructed Wetlands, 2014)
2. In this scenario, the anaerobic digestion process is assumed to be mesophilic anaerobic digestion
process. The operating temperature is about 35 ℃ (Ecoinvent 3.6). It is proposed that belt filter can
be used to dewater the raw sewage sludge (5.6% TS(total solids) + 94.4% moisture content (Hospido
et al., 2005).
3. The energy consumption data for the anaerobic digestion process and biogas combustion represents
conditions of large pants in Switzerland.
4. The density of dried sludge solids is about 1400 kg/m3 (Lemmons, 2021), and the density of raw
sewage sludge is assumed to be 1015.7 kg/m3 based on the water density and dried sludge solids
density.
5. Biogas combustion process is used to produce electricity and heat from a biogas mix from sewage
sludge by burning it in a cogeneration unit with gas engine. The main product of this process is
electricity at high voltage, while heat is produced as a co-product (Ecoinvent 3.6).
6. The cogeneration unit has a capacity of 160 kWel. The degrees of efficiency are as follows:
electricity: 0.37 and heat: 0.53. A mix of biogas is treated with an average lower heating value of
22.73 MJ/Nm3 (Ecoinvent 3.6).
7. For scenario 2, the final product is applied to agricultural land and used as a substitute for synthetic
fertilizers. The amount of the displaced fertilizer is evaluated based on the phosphorus and nitrogen
content in the biosolids (~16 kg/1000kg DM) (Hospido et al., 2005).A conservative estimate of the
amount of the displaced fertilizer is about 50kg/1000kg DM. (Tarpani et al., 2020)
68
Scenario 1: Anaerobic digestion + Landfilling
Input parameter
Unit Amount References
Stage 1 Anaerobic digestion
Input
Annual production of raw
sewage sludge
tonne 16200 Canadian municipalities
Electrical consumption kWh/m3 of sludge 4.2 (Ecoinvent 3.6)
Heat, district, or industrial,
natural gas
MJ/m3 of sludge 66.8 (Ecoinvent 3.6)
Output
Mass of biogas m3/m3 of sludge 16.6 (Ecoinvent 3.6)
Mass of digested sludge 0.654*Mass of raw sewage sludge (tonne) (Jungbluth & Chudacoff,
2007)
Stage 2 Biogas combustion
Input
Mass of biogas m3/m3 of sludge 16.6 (Ecoinvent 3.6)
Output
Electricity production, high
voltage kWh/m3 2.34
(Ecoinvent 3.6)
Heat production, central or
small-scale, other than
natural gas
MJ/ m3 12
(Ecoinvent 3.6)
Stage 3 Mechanical dewatering
Input
Electricity consumption kWh/tonne 49.1 (Hospido et al., 2005)
Polymer kg/tonne 5.5 (Hospido et al., 2005)
Stage 4 Transportation + Landfilling
Input
Average fuel efficiency of
the truck (gallons per km)
Miles per gallon 6.6 US Department of
Transportation Federal
Highway administration
69
Transportation distance km 400
Truck Capacity kg/truck 2000
70
Scenario 2: Anaerobic digestion + Agricultural application
Input parameter
Unit Amount References
Stage 1 Anaerobic digestion
Input
Annual production of raw
sewage sludge
tonne 16200 Canadian municipalities
Electrical consumption kWh/m3 of sludge 4.2 (Ecoinvent 3.6)
Heat, district or industrial,
natural gas
MJ/m3 of sludge 66.8 (Ecoinvent 3.6)
Output
Mass of biogas m3/m3 of sludge 16.6 (Ecoinvent 3.6)
Mass of digested sludge 0.654*Mass of raw sewage sludge (tonne) (Jungbluth & Chudacoff,
2007)
Stage 2 Biogas combustion
Input
Mass of biogas m3/m3 of sludge 16.6 (Ecoinvent 3.6)
Output
Electricity production, high
voltage kWh/m3 2.3
(Ecoinvent 3.6)
Heat production, central or
small-scale, other than
natural gas
MJ/m3 12
(Ecoinvent 3.6)
Stage 3 Mechanical dewatering
Input
Electricity consumption kWh/tonne 49.1 (Hospido et al., 2005)
Polymer kg/tonne 5.5 (Hospido et al., 2005)
Stage 4 Transportation
Input
Average fuel efficiency of
the truck (gallons per km)
Miles per gallon 6.6 US Department of
Transportation Federal
Highway administration
71
Transportation distance km 200
Truck Capacity kg/truck 2000
Stage 5 Agricultural application
Input
Electrical consumption kWh/tonne of sludge 58.5 (Hospido et al., 2005)
Output
NPK 15-15-15 kg/tonne of DM 50 (Tarpani & Azapagic,
2018)
72
B.1.2 Energy model for incineration process
Assumptions:
1. Mass of thickened sludge = 0.3 * the mass of raw sewage sludge
(References: General Aspects of Sludge Management Alexandros Stefanakis, ... Vassilios A. Tsihrintzis, in Vertical Flow
Constructed Wetlands, 2014)
2. Compositions of raw sewage sludge: 95% of moisture content +5% of TS (total solids).
3. In this scenario, before incineration, centrifuge is used to dewater the sludge, which can reduce the
moisture content of sewage sludge from approximately 95% to 60% (Jungbluth & Chudacoff, 2007).
After that, dewatered sludge is transported to municipal solid waste incinerator plants (MSWI).
During this process, heat and electricity are generated and reused in the incineration process. The
solid residues of the incineration process are usually landfilled (Jungbluth & Chudacoff, 2007).
4. Average Swiss MSWI incinerator plants: grate incinerators with electrostatic precipitator for fly ash
(ESP), wet flue gas scrubber and 25% SNCR, 42.77% SCR-high dust, 32.68% SCR-low dust -
DeNOx facilities and 0% without Denox (weighted according to mass of burnt waste, representing
Swiss average). Efficiency of iron scrap separation from slag: 58%. Efficiency of non-ferrous scrap
separation from slag: 31%. Efficiency of non-ferrous scrap separation from slag: 31%. Gross electric
efficiency technology mix 15.84% and gross thermal efficiency technology mix 28.51%.
5. For energy generation, about 16% of energy is recovered and reused in the municipal waste.
incineration plant. 6% of reused energy is heat, and 94% of reused energy is electricity (Ecoinvent
3.6; Tarpani et al., 2020).
73
Scenario 3: Incineration + Landfilling
Input parameter
Unit Amount References
Stage 1 Incineration process (included processes: transport to incineration facility, dewatering, municipal
incineration and landfilling of solid residues)
Input
Annual production of raw
sewage sludge
tonne 16200 Canadian municipalities
Heat, district or industrial,
natural gas
MJ/kg of raw sewage sludge 0.3 (Ecoinvent 3.6)
Sewage Sludge Potential
(fixed)
MJ/kg of raw sewage sludge 5.8 (Ecoinvent 3.6)
Activation energy MJ/kg of raw sewage sludge 1.3 (Ecoinvent 3.6)
Output
Heat, for reuse in the
municipal waste
incineration only
MJ/kg of raw sewage sludge 0.7 (Ecoinvent 3.6)
Electricity, for reuse in the
municipal waste
incineration only
MJ/kg of raw sewage sludge 0.3 (Ecoinvent 3.6)
Heat, waste (Emissions to
air)
MJ/kg of raw sewage sludge 3.8 (Ecoinvent 3.6)
Heat, waste (Emissions to
water)
MJ/kg of raw sewage sludge 1.3 (Ecoinvent 3.6)
Stage 2 Transportation
Input
Average fuel efficiency of
the truck (gallons per km)
Miles per gallon 6.6 US Department of
Transportation Federal
Highway administration
Transportation distance km 450
Truck Capacity kg/truck 2000
74
B.1.3 Energy model for composting process
Assumptions:
1. Mass of thickened sludge = 0.3 * the mass of raw sewage sludge
(References: General Aspects of Sludge Management Alexandros Stefanakis, ... Vassilios A. Tsihrintzis, in Vertical Flow
Constructed Wetlands, 2014)
2. Compositions of raw sewage sludge: 95% of moisture content +5% of TS (total solids)
3. In this scenario, the thickened sludge is first dewatered by centrifuge dewatering, which can achieve
about 30% of total solids (TS) (USEPA, 2000). And then, the dewatered sludge is mixed with a
bulking agent, such as wood chips or saw dust. The mixture is then transferred to windrows and
composted under controlled conditions to achieve desired composition of compost. The finished
product can be used as soil fertilizer and the system is credited for the equivalent amount of synthetic
fertilizer (Tarpani & Azapagic, 2018; Sablayrolles et al., 2010)
.
75
Scenario 4: Composting + Agricultural application
Input parameter
Unit Amount References
Stage 1 Mechanical dewatering
Input
Annual production of raw
sewage sludge
tonne 16200 Canadian municipalities
Electrical consumption kWh/tonne of DM 52.5 (Hospido et al., 2005)
Polymer kg/tonne of DM 3.7 (Hospido et al., 2005)
Stage 2 Composting
Input
Electricity consumption kwh/tonne of DM 534 (Tarpani & Azapagic,
2018)
Diesel kg/tonne of DM 9.6 (Tarpani & Azapagic,
2018)
Output
Mass of composted sludge Mass of dewatered sludge*(1-0.194) (Breitenbeck & Schellinger,
2004)
Stage 3 Transportation
Input
Average fuel efficiency of
the truck (gallons per km)
Miles per gallon 6.6 US Department of
Transportation Federal
Highway administration
Transportation distance km 200
Truck Capacity kg/truck 2000
Stage 4 Agricultural application
Input
Electrical consumption kWh/tonne of DM 58.5 (Hospido et al., 2005)
Output
NPK 15-15-15 kg/tonne of DM 50 (Tarpani & Azapagic,
2018)
76
B.1.4 Energy model for fast pyrolysis process
Assumptions
1. Mass of thickened sludge = 0.3 * the mass of raw sewage sludge
(References: General Aspects of Sludge Management Alexandros Stefanakis, ... Vassilios A. Tsihrintzis, in Vertical Flow
Constructed Wetlands, 2014)
2. In this scenario, the pyrolysis process is selected to be fast pyrolysis without using any catalyst. It is
proposed that filter press can be used to dewater the raw municipal sewage sludge (1% TS (total
solids) + 99% moisture content (G. Chen et al., 2002)). The dewatered sewage sludge (73% moisture
content) will be dried by electric dryer and finally pyrolyzed to produce biochar, bio-oil and syngas.
The main product of fast pyrolysis is bio-oil.
1. For the fast pyrolysis, the operating temperature of this type of pyrolysis is assumed to be 500℃
(Zhang et al., 2019; Zaman et al., 2017). The thermal drying temperature is 105 ℃, and the outside
air temperature is 25 ℃ (Kim & Parker, 2008).
2. The dewatered sewage sludge consists of about 70%-85% moisture content (Zaker et al., 2019).
Thermal drying can reduce water content to less than 10% (Z. Chen et al., 2014)
3. The heating rate is selected to be 6000 ℃/min (100℃/s), and it requires about 4.8 seconds to reach
the pyrolysis operating temperature, which conforms with the characteristics of slow pyrolysis
process (Zhang et al., 2019).
4. The fluidized bed reactor and filter press are selected to be used for conducting the slow pyrolysis
process (Arazo et al., 2017; Tarpani & Azapagic, 2018).
5. Nitrogen gas was used to employ to maintain an oxygen -free environment. The flow is controlled by
a rotameter at a value of 105 L/min-kg dried sludge, and the purge time is assumed to be 5 minutes
(Alvarez et al., 2016).
6. Heat loss: Heat losses ranges from 1 to 9% of the energy required for pyrolysis process (Atienza-
Martínez et al., 2018), and 4.5% of heat losses was considered in the calculations.
7. The bio-oil produced by fast pyrolysis can be sold on the market since it has lots of potential
commercial applications, which include heat and power generation, production of chemicals and
upgrading to high-quality hydrocarbon fuels (Czernik & Bridgwater, 2004). Syngas can be used to
generate heating energy that can be used in the pyrolysis system or in the wastewater treatment plants
(Crombie & Mašek, 2014; Guruviah, Sivasankaran, & Bharathiraja, 2019). Biochar can be shipped to
local farms and used as fertilizer/soil amendment (Palansooriya et al., 2019).
77
Scenario 5: Fast pyrolysis
Input parameter
Unit Amount References
Stage 1 Mechanical dewatering
Input
Annual production of raw
sewage sludge
tonne 16200 Canadian municipalities
Electrical consumption kWh/tonne 40 (Hospido et al., 2005)
Stage 2 Thermal drying
𝑄 𝑑𝑟𝑦𝑖𝑛𝑔 = 𝑀 ∙ 𝑊 ∙ [(𝐶𝑝𝑤𝑎𝑡𝑒𝑟 ∙ ∆𝑇) + ∆𝐻𝑣𝑎𝑝] + [𝑀 ∙ (1 − 𝑊)] ∙ 𝐶𝑝𝑆𝑆 ∙ ∆𝑇
Where,
M is the mass of wet sludge after dewatering (kg).
W is the fraction of water in sludge.
∆T is the temperature difference between the outside air temperature (25℃) and thermal drying temperature (105 ℃).
Hvap (latent heat for water
vaporization) kJ/kg 2260
(Kim & Parker, 2008)
Cpwater (heat capacity of
water) kJ/kg °C 4.2
(Kim & Parker, 2008)
Cpss (heat capacity of
solids in sludge) kJ/kg °C 2.0
(Kim & Parker, 2008)
∆𝑇 °C 105-25
Stage 3 Pyrolysis
𝑄𝑝𝑦𝑟𝑜𝑙𝑦𝑠𝑖𝑠 = 𝑄𝑡𝑎𝑟𝑔𝑒𝑡 + 𝑄𝑟𝑒𝑎𝑐𝑡𝑖𝑜𝑛
𝑄𝑡𝑎𝑟𝑔𝑒𝑡 = 𝑀𝑑𝑠 ∙ 𝐶𝑝𝑠𝑠 ∙ ∆𝑇𝑡𝑎𝑟𝑔𝑒𝑡
𝑄𝑟𝑒𝑎𝑐𝑡𝑖𝑜𝑛 = 300 𝑘𝐽/𝑘𝑔
Where,
Q target: the energy consumption to heat dried sludge from the temperature after drying to the target temperature.
Mds is the mass of dried sludge after thermal drying (kg).
∆Ttarget is the temperature difference between the thermal drying temperature (105 ℃) and target pyrolysis
temperature (400℃)
Q reaction is the heat of reaction for dried sludge pyrolysis which is an endothermic process.
𝐶𝑝𝑠𝑠 kJ/kg °C 2.0 (Kim & Parker, 2008)
∆Ttarget °C 500-25
Q reaction kJ/kg 300 (Kim & Parker, 2008)
Stage 4 Energy recovery
Predicted Char Yield Around 0.2
Include uncertainty
The product yield
determined based on (Cao
et al., 2013)
78
Predicted Oil Yield Around 0.5
Include uncertainty
The product yield
determined based on (Cao
et al., 2013)
Gas yield Around 0.3
Include uncertainty
The product yield
determined based on (Cao
et al., 2013)
Bio-oil Calorific value
(HHV)
MJ/kg 36.4 (Arazo et al., 2017)
Gas Calorific value MJ/kg 9.5 (Arazo et al., 2017)
Stage 5 Transportation
Input
Average fuel efficiency
of the truck (gallons per
km)
Miles per gallon 6.6 US Department of
Transportation Federal
Highway administration
Transportation distance km 200
Truck Capacity kg/truck 2000
79
B.2 Environmental Assessment -Global Warming Potential
Scenario 1: Anaerobic digestion + landfilling
Input parameter
Unit Amount References
Stage 1 Anaerobic digestion
Carbon dioxide, non-fossil kg/m3 1.7 (Ecoinvent 3.6)
Methane, non-fossil kg/m3 5.6E-02 (Ecoinvent 3.6)
Stage 2 Biogas combustion
Carbon dioxide, non-fossil kg/m3 1.9 (Ecoinvent 3.6)
Dinitrogen monoxide kg/m3 5.7E-05 (Ecoinvent 3.6)
Methane, non-fossil kg/m3 5.2E-04 (Ecoinvent 3.6)
Stage 3 Transportation
Deterministic CO2-e Factor 3.8 (Ecoinvent 3.6)
Stage 4 Landfilling
Methane kg/tonne 3.2 (Hong et al., 2009)
80
Scenario 2: Anaerobic digestion + Agricultural application
Input parameter
Unit Amount References
Stage 1 Anaerobic digestion
Carbon dioxide, non-fossil kg/m3 1.7 (Ecoinvent 3.6)
Methane, non-fossil kg/m3 5.6E-02 (Ecoinvent 3.6)
Stage 2 Biogas Combustion
Carbon dioxide, non-fossil kg/m3 1.9 (Ecoinvent 3.6)
Dinitrogen monoxide kg/m3 5.7E-05 (Ecoinvent 3.6)
Methane, non-fossil kg/m3 5.2E-04 (Ecoinvent 3.6)
Stage 3 Transportation
Deterministic CO2-e Factor 3.8 (Ecoinvent 3.6)
Stage 4 Agricultural application
Methane kg/tonne 3.2 (Tarpani et al., 2020)
Stage 5 System credit
Fertilizer substitution kg CO2 eq/ton
DM
-127 (Tarpani et al., 2020)
Electricity substitution kg CO2 eq/ton
DM
-239 (Tarpani et al., 2020)
81
Scenario 3: Incineration + Landfilling
Input parameter
Unit Amount References
Stage 1 Incineration process (included processes are transport to incineration facility, dewatering, municipal
incineration and landfilling of solid residues)
Carbon dioxide, non-fossil kg/kg of raw
sewage sludge
0.4 (Ecoinvent 3.6)
Dinitrogen monoxide
kg/kg of raw
sewage sludge
1.8E-04
(Ecoinvent 3.6)
Methane, non-fossil
kg/kg of raw
sewage sludge
6.4E-07
(Ecoinvent 3.6)
Stage 2 Transportation
Deterministic CO2-e Factor 3.8 (Ecoinvent 3.6)
Stage 3 System credit
Energy substitution (94% electricity
and 6% heat)
kg CO2 eq/tonne
DM
-147 (Tarpani et al., 2020)
82
Scenario 4: Composting + Agricultural application
Input parameter
Unit Amount References
Stage 1 Composting
Direct emissions kg/71t of DM 40257 (Pradel & Reverdy, 2012)
Stage 2 Transportation
Deterministic CO2-e Factor 3.8 (Ecoinvent 3.6)
Stage 3 Agricultural application
Direct emissions kg/71t of DM 2274 (Pradel & Reverdy, 2012)
Stage 4 System credit
Fertilizer substitution kg CO2 eq/tonne
DM
47.3 (Tarpani et al., 2020)
83
Scenario 5: Pyrolysis
Unit Amount References
Stage 1 Pyrolysis
Sludge drying kg CO2 eq/tonne
of DM
422 (Tarpani et al., 2020)
Grid electricity
kg CO2 eq/tonne
of DM
121 (Tarpani et al., 2020)
Stage 2 Transportation
Deterministic CO2-e Factor 3.8 (Ecoinvent 3.6)
Stage 3 Agricultural Application
Methane kg/tonne 3.2 (Tarpani et al., 2020)
Stage 4 Energy recovery
Natural gas and fuel substitution kg CO2 eq/tonne
of DM
251
(Tarpani et al., 2020)
Fertilizer substitution t CO2 eq/tonne of
liquid sludge
1.0E-03
(Cao & Pawłowski, 2012)
84
B.3 Economic Assessment
Assumptions:
1. Discount rate: 3.5%
2. Analysis period: 10 years
3. Capital costs:
Table B.3.1 Capital Costs for the sludge treatment techniques
Treatment
Methods
Major Equipment Included Costs (present value) Reference
Anaerobic
digestion
Anaerobic digesters and
centrifuge
$1.6E+07 (Tarpani &
Azapagic, 2018)
Incineration Centrifuge and incinerator $1.3E+07
Composting Anaerobic digesters, aerated
composting facility and
centrifuge
$8.2E+06
Pyrolysis Centrifuge, thermal dryers and
the pyrolysis unit
$1.9E+07
1) Excluded the indirect capital costs, such as contractor design services fee, contingency fee,
contractor and subcontractor overhead, etc.
2) Excluded the cost of machinery for land application.
4. Maintenance costs: 2% of the capital costs.
5. Operating costs: include the cost of energy and material. The time-series forecast model for each
energy source and the predicted unit prices for the next 10 years are listed below.
Table B.3.2 Price forecast model for each energy source
Energy Type Deterministic Equation Residual Structure St. Dev
Electricity Δln Pt = 0.03 εt = ut – 0.39ut-1 – 0.5ut-2 0.026
Diesel ln Pt = -73.6 + 0.04t εt – 0.69εt-1 = ut 0.11
Fuel Δln Pt = 0 εt = ut 0.29
Pt: Price in time period t
ε: autoregressive error term
u: moving average error term
Table B.3.3 Predicted unit price for each energy source from 2021 to 2030
Year Elec.
($/kWh)
Natural Gas
($/MJ)
Diesel
($/Liter)
2021 1.8E-01 2.0E-03 1.4E+00
2022 1.7E-01 3.0E-03 1.8E+00
2023 1.7E-01 1.9E-03 1.8E+00
2024 1.7E-01 1.8E-03 1.7E+00
2025 1.7E-01 1.4E-03 1.7E+00
85
2026 1.8E-01 1.1E-03 1.6E+00
2027 1.8E-01 1.9E-03 1.4E+00
2028 1.8E-01 3.0E-03 1.6E+00
2029 1.8E-01 3.2E-03 2.3E+00
2030 1.8E-01 2.1E-03 2.4E+00
Reference:
1) Electricity: Statistics Canada
2) Natural Gas: Government of Alberta
3) Diesel: Statistics Canada
4) Polymer (used for dewatering process): $3.8/kg (Metro Vancouver)
6. Transportation costs: the cost is determined by diesel consumption for each process and diesel
unit costs, which are included in the previous table.
7. Waste management costs
Table B.3.4 Costs of waste disposal
Disposal methods Min Max Average
Landfilling $10 /ton of biosolids $20 /ton of biosolids $15 /ton of biosolids
Land application $60/ton of biosolids $100 /ton of biosolids $80 /ton of biosolids
Reference: Local municipalities
Incineration waste management costs: $143/tonne (Tarpani & Azapagic, 2018).
8. Savings from recovered products
1) All the recovered heat and electricity energy is reused in the sludge treatment plant.
2) Biosolids and composted product will be used as fertilizer and sold on local market.
The market prices of products replaced by the equivalent resources recovered by sludge treatment are
listed below.
Table B.3.5 Unit price for each recovered products
Item Average price Unit Reference
Electricity
0.2 $/kwhr
Statistics Canada
Natural Gas 2.0E-03 $/MJ
Government of Alberta
website
Bio-oil 2.8 $/kg
(Kim & Parker, 2008;
Shahbeig & Nosrati, 2020)
Biochar 0.1
$/kg
(Shahbeig & Nosrati, 2020)
Fertilizer 25 $/tonne Metro Vancouver
Compost product 16 $/tonne City of Vancouver
1) The bio-oil marketable value varies from 0.45 to 5.20 CAD/kg, depending on the quality
of bio-oil (Kim & Parker, 2008; Shahbeig & Nosrati, 2020).
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