© 2018 Kimmel Chamat Garcésufdcimages.uflib.ufl.edu/UF/E0/05/18/57/00001/CHAMAT_GARCES_K… ·...

OPTIMAL SCALE OF URBAN COMPOSTING SYSTEMS LIFE CYCLE SUSTAINABILITY ASSESSMENT

By

KIMMEL CHAMAT GARCÉS

A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT

OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2018

To my family

4

ACKNOWLEDGMENTS

I would like to thank all the wonderful people that contributed to this research. I

thank Dr. Joseli Macedo for her valuable support, discussions, and challenges. Dr.

Macedo was my chair for the first 4 years of my doctoral program. I thank Dr. Ferdinand

Lewis for his understanding and guidance at the early stages of my proposal. I would

like to express my gratitude to Dr. Anders Damgaard at the Technical University of

Denmark (DTU) for introducing me to his research group and allowing me to take

training courses at DTU. I thank Dr. Kathryn Frank for her collaboration in the final stage

of my doctoral program. Special thanks to Dr. Mark Brown for his inspirational courses

and writings. Finally, I thank my dissertation committee for their valuable contribution.

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TABLE OF CONTENTS page

ACKNOWLEDGMENTS .................................................................................................. 4

LIST OF TABLES ............................................................................................................ 7

LIST OF FIGURES .......................................................................................................... 8

LIST OF ABBREVIATIONS ........................................................................................... 10

ABSTRACT ................................................................................................................... 12

CHAPTER

1 INTRODUCTION .................................................................................................... 14

2 LIFE CYCLE INVENTORY OF COMPOSTING SCENARIOS ................................ 22

Background ............................................................................................................. 22 General Specifications and Assumptions................................................................ 22 Composting Scenarios ............................................................................................ 29

3 ENVIRONMENTAL ASSESSMENT........................................................................ 37

Background ............................................................................................................. 37

Methods .................................................................................................................. 39 Life Cycle Assessment (LCA) ........................................................................... 39

Goal and Scope Definition ................................................................................ 40 Life Cycle Impact Assessment ......................................................................... 43

Results and Discussion........................................................................................... 47

Concluding Remarks............................................................................................... 53

4 ECONOMIC ANALYSIS .......................................................................................... 56

Background ............................................................................................................. 56 Materials and Methods............................................................................................ 57

Cost Model: Structure ....................................................................................... 58

Cost Model: Calculations .................................................................................. 58 Results and Discussion........................................................................................... 61

General Findings .............................................................................................. 61 Infrastructure .................................................................................................... 63

Composting ...................................................................................................... 63 Collection and Transport .................................................................................. 67


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5 SOCIAL ASSESSMENT ......................................................................................... 70

Background ............................................................................................................. 70 Materials and Methods............................................................................................ 75

Goal and Scope ................................................................................................ 75 Life Cycle Inventory .......................................................................................... 77 Impact Assessment .......................................................................................... 77

Results and Discussion........................................................................................... 80 Workers ............................................................................................................ 80

Consumers ....................................................................................................... 90 Local community .............................................................................................. 91


6 CONCLUSIONS ................................................................................................... 100

APPENDIX

A LIFE CYCLE INVENTORY (LCI) CALCULATIONS BY UNIT PROCESSES ........ 109

B LIFE CYCLE COSTING CALCULATIONS BY PROCESSES ............................... 122

C SPECIFICATIONS OF COMPOSTING SCENARIOS…………………… ...... …… 147

LIST OF REFERENCES ............................................................................................. 145

BIOGRAPHICAL SKETCH .......................................................................................... 164

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LIST OF TABLES

Table page 2-1 Summary of composting scenarios ..................................................................... 28

2-2 Materials, energy, and emissions from composting scenarios (per ton of OSW composted) ............................................................................................... 34

3-1 List of datasets used from the Ecoinvent database ............................................ 44

3-2 Normalization factors for the environmental impact categories .......................... 47

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LIST OF FIGURES

Figure page 1-1 Methodological framework .................................................................................. 20

2-1 Composting scales and technologies included in the LCI. .................................. 23

2-2 Layout of windrow composting facilities .............................................................. 25

2-3 Layout of in-vessel composting facilities ............................................................. 26

2-4 Composting process for Scenario 1 .................................................................... 29








3-1 Waste management activities included in the system boundaries ...................... 41

3-2 System boundaries and life cycle stages ............................................................ 42

3-3 Environmental impacts from climate change ...................................................... 48

3-4 Environmental impacts from human toxicity-cancer effects ................................ 49

3-5 Environmental impacts from photochemical ozone formation ............................ 50

3-6 Environmental impacts from terrestrial acidification ............................................ 51

3-7 Environmental impacts from marine eutrophication ............................................ 52

3-8 Environmental impacts from freshwater ecotoxicity ............................................ 53

4-1 Life cycle cost of composting scenarios ............................................................. 62

5-1 S-LCA analytical framework ............................................................................... 71

5-2 Life cycle stages and unit processes included in the system boundary .............. 76

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5-3 Selected stakeholders, subcategories, and social indicators .............................. 78

5-4 Social performance levels .................................................................................. 80

5-5 Level of exposure of composting workers to chemical and biological agents ..... 84

5-6 Level of exposure of composting workers to diesel exhaust ............................... 86

5-7 Risk of physical injury from machinery ............................................................... 88

5-8 Physiological risks of composting workers ......................................................... 90

5-9 Level of public participation from consumers ...................................................... 91

5-10 Exposure of local community to gaseous emissions .......................................... 94

5-11 Community engagement .................................................................................... 98

5-12 Summary of social performance ......................................................................... 98

6-1 Summary of the most relevant assessment variables ...................................... 103

6-2 Linear urban metabolism .................................................................................. 108

6-3 Circular urban metabolism ................................................................................ 108

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LIST OF ABBREVIATIONS

ALM Advanced Locality Management

CO Carbon Monoxide

CO2 Carbon Dioxide

CH4 Methane

DE Diesel Exhaust

GHG Greenhouse Gas

LCA Life Cycle Assessment

LCC Life Cycle Costing

LCSA Life Cycle Sustainability Assessment

NYCCP New York City Compost Project

OSW Organic Solid Waste

NO Nitric Oxide

NO2 Nitrogen Dioxide

PE Person Equivalents

S1 Scenario 1

S2 Scenario 2

S3 Scenario 3

S4 Scenario 4

S5 Scenario 5

S6 Scenario 6

S7 Scenario 7

S8 Scenario 8

SO2 Sulphur Dioxide

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S-LCA Social Life Cycle Assessment

UCM Unit Cost Method

USEPA United States Environmental Protection Agency

VOCs Volatile Organic Compounds

12

Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy

OPTIMAL SCALE OF URBAN COMPOSTING SYSTEMS

LIFE CYCLE SUSTAINABILITY ASSESSMENT

By

Kimmel Chamat Garces

May 2018

Chair: Kathryn Frank Major: Design, Construction and Planning

Organic solid waste, composed of food waste and garden waste, is the largest

proportion of solid waste generated by cities, accounting for almost half of the municipal

solid waste stream. While organic solid waste can be recycled through composting to

produce soil amendments, most of the resource is diverted into landfills. Composting

can be performed at different scales using a variety of technologies. This dissertation

develops a life cycle sustainability assessment of urban composting, in order to

determine the optimal scale of management and the appropriate composting technology

for the Colombian context. The concept of a circular urban metabolism provides a

theoretical framework for understanding composting in relation to the flows and

storages of energy and materials in cities. Eight urban composting scenarios are

modelled covering four spatial scales (block, neighborhood, commune, and city) and

two treatment technologies (windrow and in-vessel). The life cycle sustainability

assessment comprises an environmental assessment, where the environmental impacts

associated with the energy and material consumption and emissions are evaluated; an

economic assessment, where the costs of the different management systems is

13

examined, and a social assessment, where the social performance of the different

composting scales and technologies is analyzed.

Results from the environmental and social assessments indicate that small-scale,

in-vessel composting at the block and neighborhood levels are the optimal scales and

technology for organic solid waste composting in the Colombian context. At the small

scale, the environmental impacts of fossil fuel combustion are significantly lower, as well

as social impacts on workers and local communities from the exposure to diesel

exhaust. From an economic standpoint, the city scale is optimal because of the

economies scale resulting from higher mechanization and lower labor costs. Developing

more economically efficient small-scale machinery would make small-scale composting

more competitive, aligning the environmental, economic, and social dimensions.

This dissertation develops a methodology for the sustainability assessment of

products or systems under the life cycle perspective. A circular urban metabolism

should promote sustainable production and consumption practices focused on reducing

the environmental impacts, while promoting economic development with a high level of

social performance.

14

CHAPTER 1 INTRODUCTION

Solid waste generated by world’s cities is expected to increase to 2.2 billion

metric tons per year by 2025 from the current 1.3 billion, driven primarily by urbanization

and economic development (Hoornweg & Bhada-Tata, 2012). From the global municipal

waste stream, the largest proportion is organic waste (46%), followed by paper (17%),

plastic (10%), glass (5%), and others (18%) (Hoornweg & Bhada-Tata, 2012). Organic

Solid Waste (OSW), composed primarily by food waste and garden waste (leaves, tree

cuttings, yard trimmings), is a valuable resource that can be recycled through

composting or anaerobic digestion to produce soil amendments, organic fertilizers, and

biogas (Boldrin et al., 2011). Currently, most OSW is not reinvested in soils but

disposed of in landfills, where it becomes a major source of Greenhouse Gas (GHG)

emissions (Adhikari, Trémier, Barrington, Martinez, & Daumoin, 2013). In the United

States, only 5% of food waste and 60% of garden wastes were recycled in 2013 (EPA,

2015). Europe has implemented policies to reduce the amount of OSW disposed of in

landfills to 35% of 1995 levels by 2016 (Burnley, 2001). OSW production and

management vary significantly between countries: organics make up 64% of the urban

waste stream in low-income countries and 28% in high-income countries. High-income

countries produce more waste, but recycle more and have robust waste management

systems. Waste disposal in high-income countries is largely landfilling, while open

dumping is a common practice in lower-income countries (Hoornweg & Bhada-Tata,

2012).

Composting closes the cycle of food and garden wastes by returning to the soil

the organic matter required to address the global challenges of land degradation and

15

loss of fertility (Martínez-Blanco et al., 2013). The organic matter in compost improves

soil structure, regulating the water supply to plants while delivering nutrients and

supporting beneficial microbial diversity (Jeavons, 2012). Composting improves overall

urban resource cycles by managing the largest proportion of solid waste generated by

cities, opening possibilities for sustainable local production. Compost is a key ingredient

in organic farming, also used for landscaping, wetland construction, and ecosystem

restoration (Kangas, 2004). Food parks, community gardens, urban orchards, tree

nurseries, and reforestation programs are processes that can be supported by urban

composting (Todd, 1994). Intensive urban and rural organic agriculture is the potential

outcome of a good composting infrastructure. Composting OSW from urban areas is

therefore crucial for improving resource cycles and advancing towards sustainable

cities.

Urban composting can be performed in centralized or decentralized treatment

facilities using a variety of technologies. Windrow composting takes place in open-air

piles that are manually or mechanically turned, while in-vessel composting occurs in

enclosed containers where decomposition parameters such as temperature, moisture,

and airflow are highly controlled. Greenhouse Gas (GHG) emissions from the

decomposition process (CO2, CH4, and N2O) are a negative environmental impact of

windrow technologies (Saer, Lansing, Davitt, & Graves, 2013) that can be mitigated

through biofilters in in-vessel technologies (Sánchez et al., 2015). Centralized

composting facilities, either windrow or in-vessel, involve higher mechanization and

greater costs of collection and transport than decentralized facilities (Andersen, Boldrin,

Christensen, & Scheutz, 2010; Colón et al., 2010; Lundie & Peters, 2005; Martínez-

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Blanco et al., 2010; Righi, Oliviero, Pedrini, Buscaroli, & Della Casa, 2013). In the

United States, the city of San Francisco has implemented mandatory separation of

OSW, segregated collection, and a centralized windrow composting facility at the

regional scale (Wallace, 2014). Decentralized composting facilities are more flexible in

operation and management, usually require more labor, and less collection and

transport costs (Ali & Harper, 2004). The city of Havana, Cuba, recycles 60% of OSW in

a neighborhood composting network designed to support urban agriculture development

(Körner, Saborit-Sánchez, & Aguilera-Corrales, 2008).

This dissertation presents a Life Cycle Sustainability Assessment (LCSA) of

OSW management systems through composting. The main research questions

addressed in the study are: (1) what is the optimal scale of urban composting? (2) What

is the most appropriate technology for urban composting? Optimal scale refers to the

size and treatment capacity of composting plants. Appropriate technology refers

windrow or in-vessel composting technologies. Some studies have found better

environmental performance for home composting compared to centralized industrial

composting (Barrena, Font, Gabarrell, & Sánchez, 2014; Lundie & Peters, 2005;

Martínez-Blanco et al., 2010). Environmental analysis of intermediate scales of resource

management (block, neighborhood) is a research gap in the literature of composting

and solid waste management in general. An important contribution of this dissertation is

the characterization and environmental assessment of the small and medium scale

composting facilities and its comparison to larger scales of management. When

analyzing the optimal scale of solid waste management, a trade-off between economies

of scale and transportation costs have been reported in the literature (Chen, Fujita,

17

Ohnishi, Fujii, & Geng, 2012). Larger scales of management are usually more efficient,

but imply higher transportation costs. Recommendations have been made for dispersing

smaller recycling facilities in urban areas in order to reduce the negative environmental

impacts from transportation (Iriarte, Gabarrell, & Rieradevall, 2009). The point where

transportation costs exceed the economies of scale would be a theoretical optimal scale

of management (Chen et al., 2012). One contribution of this dissertation is examining

the trade-off between economies of scale and transportation costs for the case of urban

composting.

The question of appropriate technology addresses the feasibility of windrow and

in-vessel composting technologies to be located in urban environments. Windrow

composting technologies have been characterized and analyzed in the literature

through the Life Cycle Assessment (LCA) framework (L. K. Brogaard, Petersen,

Nielsen, & Christensen, 2015; Komilis & Ham, 2004; Saer et al., 2013). The

characterization of in-vessel composting technologies is a research gap to which this

dissertation contributes. Capital goods such as buildings and machinery have been

rarely included in LCA studies of waste management systems due to lack of data (L. K.

Brogaard et al., 2015). This dissertation combines existing data on capital goods of

windrow composting systems (L. K. Brogaard et al., 2015) and collects the missing data

of in-vessel technologies and small-scale facilities. Composting technologies are

combined with collection and transport scenarios in order to determine the relevance of

capital goods (production phase) compared to the operation phase of composting

facilities.

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The urban metabolism literature serves as a conceptual framework for the

dissertation. The concept of urban metabolism uses the analogy of an ecosystem to

analyze cities in terms of inputs, outputs, and storages of energy, water, nutrients,

materials, and wastes (Kennedy, Pincetl, & Bunje, 2011). Research on the field focuses

on the urban consumption of resources, the transformation of resources into products,

and the treatment and recycling of wastes (Zhang, 2013). Urban metabolism research is

used to explore the recycling potential of urban areas (Leduc & Van Kann, 2012),

helping cities become more resilient and less dependent on external resources

(Agudelo-Vera, Leduc, Mels, & Rijnaarts, 2012). Recycling OSW is fundamental to

improve urban metabolism because it is the largest and most problematic fraction of

solid wastes generated by cities (Adhikari, Barrington, Martinez, & King, 2008).

Composting closes the resource cycle of food and garden wastes by returning to the

soil the organic matter required to address the global challenges of land degradation

and loss of fertility (Martínez-Blanco et al., 2013). This dissertation contributes to the

urban design and planning of sustainable urban metabolism (Kennedy et al., 2011).

The Life Cycle Sustainability Assessment (LCSA) is the methodological

framework used for the evaluation of the environmental, economic, and social

dimensions of composting. As described in Figure 1-1, LCSA integrates the three

dimensions of sustainability. The life cycle inventory is quantification of inputs and

outputs to the composting systems and provides a basis for the environmental,

economic, and social assessments. Environmental LCA is a widely accepted evaluation

tool in the solid waste management sector (Laurent et al., 2013). LCA takes a

comprehensive approach by considering the whole life cycle of the product system ‘from

19

cradle to grave’, encompassing the extraction of raw materials, production, distribution,

operation, maintenance, recycling, and final disposal (Clift, Doig, & Finnveden, 2000).

Environmental LCA is a well-established methodology supported by public and private

sectors for which international standards have been developed (ISO, 1997). The

international standards ISO 14040 and 14044 are the main reference in performing

LCAs. In LCA, the environmental performance is evaluated based on a detailed

compilation of the inputs (energy and materials) and outputs (emissions to air, water

and soil, including waste) produced and consumed throughout the life cycle of the

product system. Numerous studies addressing the environmental dimension of

composting through the LCA framework have been published (Colón et al., 2010;

Lundie & Peters, 2005; Martínez-Blanco et al., 2013; Saer et al., 2013). The Life Cycle

Costing (LCC) methodology has been developed to evaluate the economic dimension of

product systems under the LCA framework (Martinez-Sanchez, Kromann, & Astrup,

2015). Three types of LCC assessments have been proposed: (1) Conventional LCC,

which accounts for all marketed goods and services and represents the traditional

financial assessment carried out by particular stakeholders. (2) Environmental LCC,

which expands the system boundaries of the cost assessment in order to be consistent

with environmental LCA. (3) Societal LCC, which assigns monetary values to externality

costs related to environmental and social impacts (Martinez-Sanchez et al., 2015). This

dissertation evaluates the economic dimension of urban composting following a

Conventional LCC. The Social LCA (S-LCA) is the tool for evaluating the social

dimension under the LCA framework (Benoît et al., 2010). Although in an early stage of

development (Benoît Norris et al., 2013), S-LCA guidelines have been published by the

20

United Nations Environment Programme (UNEP, 2009). S-LCA analyses the

consequences on individual and community well-being derived from socio-economic

processes and decisions at the macro and micro levels (UNEP, 2009). Sustainability

assessments are intended to support decision makers in the urban design and planning

of composting infrastructure.

Figure 1-1. Methodological framework

The dissertation is organized in six chapters. Chapter 2 presents a life cycle

inventory of eight urban composting scenarios covering four management scales (block,

neighborhood, commune, and city) and two treatment technologies (windrow and in-

vessel). The life cycle inventory presents a detailed description of the energy and

material consumption, waste emissions, and prices for each composting scenario. The

inventory provides a quantitative basis for the environmental, economic, and social

assessments. Chapter 3 presents the environmental LCA of composting scenarios,

21

focusing on the impact categories of climate change, ozone depletion, human toxicity-

cancer effects, photochemical ozone formation, terrestrial acidification, marine

eutrophication, and freshwater ecotoxicity. Chapter 4 presents the economic analysis

using the Life Cycle Costing (LCC) methodology (Martinez-Sanchez et al., 2015). The

type of LCC presented is conventional, including all marketed goods and services, and

excluding environmental and social externalities. The analysis focuses on capital costs

(land, buildings, machinery) and operational costs (labor, fuel, energy, and

maintenance). The economic data is obtained from the national context of Colombia.

Chapter 5 presents the social LCA following the UNEP (2009) methodology. Social

impacts are analyzed for the stakeholder categories of workers, consumers, and local

community. The conclusion chapter elaborates an integrated analysis of the

environmental, economic, and social dimensions of sustainability and develops

recommendations for the future design and planning of sustainable urban metabolism.

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CHAPTER 2 LIFE CYCLE INVENTORY OF COMPOSTING SCENARIOS

Background

A Life Cycle Inventory (LCI) is the quantification of energy, materials, and

emissions associated with a product or process over its life cycle, from extraction of raw

materials, to production, use, and final disposal or recycling (end of life). This chapter

presents the LCI of 8 hypothetical urban composting scenarios covering 4 spatial scales

(block, neighborhood, commune, and city) and two treatment technologies (windrow

and in-vessel). Each composting scenario involves a set of material and energy

requirements, distributed over life cycle of the resource management system, which is

divided into 12 unit processes: 1) collection, 2) transport, 3) sorting, 4) storage, 5)

shredding, 6) transport to composting area, 7) decomposition, 8) transport to screening

area, 9) screening, 10) transport to curing area, 11) forming curing piles, and 12)

Curing. The system boundaries of the inventory comprise the life cycle stages of

production, use, and end of life of the resource management system. The use of

compost on land is not included as part of the resource management system. The

purpose of the inventory is to provide a quantitative basis for performing environmental,

economic, and social life cycle assessments.

General Specifications and Assumptions

Eight urban composting scenarios are modelled covering four treatment

capacities (100 ton/year, 1,000 ton/year, 10,000 ton/year, and 50,000 ton/year) and two

treatment technologies (windrow and in-vessel), as represented in Figure 2-1. The

geographical context of the study is Colombia. The geographical context provides the

local data necessary to perform the economic analysis. The design of composting

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facilities is based on US-EPA guidelines (Jan, 1994). Composting facilities are

composed of areas for processing, composting, administration, and buffer zones. The

processing area includes a roofed space without walls (steel hall) for unloading the

OSW, sorting, shredding, and storing of materials. The composting area includes an

unroofed composting area (windrow or in-vessel), and a steel hall for curing, screening,

bagging, and final storage. All facilities include an office building and onsite roads.

Windrow systems involve additional buffer areas for gaseous emission control. Liquid

emissions are assumed to be collected and reintroduced into the windrow pile. In-vessel

systems recycle liquid emissions and reduce gaseous emissions through bio-filters. In-

vessel biofiltration is achieved through a volume of woodchips where the gases are

translated from the gaseous phase to the liquid and solid phase. Lighting, fencing, and

drainage systems are excluded from the analysis. The total area of the composting

facility is assumed to be paved with concrete, apart from buffer zones. For windrow

facilities, a composting time of three months and a curing time of one month are

assumed. Bagging of compost is not included in the analysis. Windrows are turned

twice per week.

Figure 2-1. Composting scales and technologies included in the LCI.

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Composting generates GHG emissions associated with the biological

decomposition of organic matter. CO2 emissions are biogenic in origin and therefore not

accounted for global warming potential (Boldrin et al., 2011). Methane (CH4) and nitrous

oxide (N2O) are the most critical gaseous emissions of composting operations, and are

accounted for global warming potential (Andersen et al., 2010). CH4 is produced in

poorly managed compost piles when anaerobic pockets develop under inadequate

moisture and oxygen levels. Nitrogen is degraded during decomposition and distributed

among ammonia (NH3), nitrous oxide (N2O), and nitrogen (N2) (Boldrin et al., 2011). The

modelling approach used in this study follows the composting emission factors defined

by the Intergovernmental Panel on Climate Change (IPCC, 2006). For wet food waste, 4

kilograms of CH4 and 0.3 kilograms of N2O per ton of waste are considered. For dry

garden waste, 10 kilograms of CH4 and 0.6 kilograms of N2O per ton of waste are

assumed. In-vessel systems are equipped with bio-filters capable of reducing GHG

emissions by 96% (Hotrot, 2013).

Regarding fuel consumption of machinery such as shredders and screeners, a

value of 0.26 L per horsepower per hour is assumed. Fuel consumption for diesel

engines typically ranges from 0.18 to 0.42 L per horsepower per hour (Grisso,

Perumpral, Vaughan, Roberson, & Pitman, 2010). For front loader operations, a diesel

consumption of 0.15 L per horsepower per hour, times a load factor of 70% is assumed,

because front loaders do not always operate at maximum power (Chitkara, 1998).

Electricity consumption of composting machinery is obtained from sales literature

(HotRot, 2015; Komptech, 2015; Scheppack, 2016).

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Composting requires water to maintain the biological activity that transforms

OSW into compost. Maintaining adequate moisture levels adds considerably to the

labor and equipment costs of composting operations (Rynk, 2001). The optimal

moisture content for composting is 50% to 60%. In tropical dry areas, up to 2,500 liters

of water can be required to produce 1 ton of compost (Dalzell, 1987). While in-vessel

systems conserve water and recycle liquid emissions, windrow systems, lose a large

amount of water through evaporation and sun exposure. For windrow systems, 2,000

liters of water per ton of compost are assumed. For in-vessel systems, 1,000 liters of

water per ton of compost are assumed. Figures 2-2 and 2-3 present the basic layouts of

windrow and in-vessel composting facilities (Jan, 1994); the area of the facility

increases with the treatment capacity.

Figure 2-2. Layout of windrow composting facilities

OPEN

HALL

FOR

INPUT

MATERIAL

OPEN HALL OPEN HALL

STORAGE SCREENING

BUFFER ZONE

OPEN HALL FOR CURINGBUILDING

WINDROW COMPOSTING AREA

26

Figure 2-3. Layout of in-vessel composting facilities

The resource management system for OSW has been divided into 12 unit

processes. (1) Collection: obtaining OSW from households by means of a waste

collection truck driving from the first stop to the last stop of the collection route. (2)

Transport: driving the full truck from the last point of the collection route to the

composting facility, and driving the empty truck back the same distance (L. K.-S.

Brogaard & Christensen, 2012). (3) Sorting: removal of impurities through manual labor.

(4) Shredding: size reduction of food and garden wastes. (5) Storage: storage of

shredded materials in plastic containers (small-scale) or concrete elements (large-

scale). (6) Transport to composting area: transport of shredded materials to the

composting area using plastic containers (small-scale) or a front loader (large-scale). (7)

Forming windrows/feeding vessel: forming composting piles using a shovel (small-

scale) or a front-loader (large-scale); feeding the in-vessel composter through the

OPEN

HALL OPEN

FOR HALL

INPUT FOR

MATERIAL CURING

OPEN HALL OPEN HALL

STORAGE SCREENINGBUILDING

IN-VESSEL

COMPOSTING AREA

27

composter machine (small-scale) or using a front loader (large-scale). (8)

Decomposition: biological degradation of organic materials in windrows or vessels; the

processes of turning and watering are included in decomposition. (9) Transport to

screening area: transport of compost to screening area. (10) Screening: sieving of

compost. (11) Transport to curing area: transport of compost to curing area. (12) Curing:

biological stabilization of compost. The infrastructure component is not a process, but

accounts for the physical structures (buildings, roads, and paved areas) needed to

perform the processes. Table 2-1 summarizes the composting scenarios. Table 2-2

presents the quantities of materials, energy, and emissions from composting scenarios

(per ton of OSW composted). Appendix A presents the calculation procedures for each

unit process.

28

Table 2-1. Summary of composting scenarios S1 S2 S3 S4 S5 S6 S7 S8

Scale of Management BLOCK NEIGHBORHOOD COMMUNE CITY

Plant Capacity (ton/year) 100 1,000 10,000 50,000

Composting Technology Windrow In-vessel Windrow In-vessel Windrow In-vessel Windrow In-vessel

INFRASTRUCTURE

Building (m2) 6 6 20 20 100 100 500 500

Steel Hall (m2) 50 50 500 500 5,000 5,000 25,000 25,000

Concrete pavement (m2) 238 80 2,400 800 25,000 8,000 118,000 40,000

Land area (m2) 290 90 2,800 900 31,000 9,370 145,000 45,000

PROCESSES

Collection No No Yes Yes Yes Yes Yes Yes

Transport No No No No Yes Yes Yes Yes

Sorting Manual Manual Manual Manual Manual Manual Manual Manual

Shredding (feeding) Manual Manual Manual Manual Front-loader Front-loader Front-loader Front-loader

Storage (container) Plastic Plastic Plastic Plastic Concrete Concrete Concrete Concrete

Transport to composting Manual Manual Manual Manual Front-loader Front-loader Front-loader Front-loader

Forming windrow Manual - Manual - Front-loader - Front-loader -

Feeding vessel Bin-lifter - Bin-lifter - Front-loader - Front-loader

Decomposition Windrow In-vessel Windrow In-vessel Windrow In-vessel Windrow In-vessel

Turning Manual Manual Manual Manual Mechanical Mechanical Mechanical Mechanical

Watering Manual Manual Manual Manual Mechanical Mechanical Mechanical Mechanical

Transport to screening Manual Manual Manual Manual Front-loader Front-loader Front-loader Front-loader

Screening (feeding) Manual Manual Manual Manual Front-loader Front-loader Front-loader Front-loader

Transport to curing Manual Manual Manual Manual Front-loader Front-loader Front-loader Front-loader

Curing (container) Concrete Concrete Concrete Concrete Concrete Concrete Concrete Concrete

29

Composting scenarios

Scenario 1 (S1) is a small-scale windrow composting facility with a treatment

capacity of 100 tons per year. The composting process is presented in Figure 2-4. The

collection of OSW involves a drop-off scheme where households transport the

separated materials (food and garden wastes) to the composting facility located at a

maximum walking distance of 100 meters (block scale). The processes of shredding

and screening are performed by one operator using small-scale machinery. Transport

processes within the composting facility are performed by one operator using a plastic

mobile container. The specifications of composting scenarios are described in detail in

Appendix C.

Figure 2-4. Composting process for Scenario 1

Scenario 2 (S2) is an in-vessel composting facility with a treatment capacity of

100 tons per year. The composting process is presented in Figure 2-5. The collection of

OSW involves the same drop-off scheme of S1. Most composting processes are equal

to S1. The main difference is the use of the in-vessel composting machine. See

Appendix C for additional details.

Scenario 3 (S3) is a windrow composting facility with a treatment capacity of

1,000 tons per year. The composting process is presented in Figure 2-6. Collection is

TRANSPORT TO

SCREENING

TRANSPORT TO TRANSPORT TO

COMPOSTING CURINGFORMING WINDROW

COLLECTION DECOMPOSITION

SCREENING

CURING

TRANSPORT SORTING

SHREDDING STORAGE

30

made through a waste collection truck. Food and garden wastes are source-segregated

by households and placed in the curbside at the time of collection. Scenario 3 does not

involve transport, because the composting facility is assumed to be located right when

the truck reaches its maximum capacity of 14 tons (neighborhood scale).




1,000 tons per year. The composting process is presented in Figure 2-7. The processes

of collection, sorting, and shredding, are equal to Scenario 3.



equal to Scenario 3. A transport distance of 25 km to the composting facility is

TRANSPORT TO

SCREENING


COMPOSTING CURINGFEEDING VESSEL


SCREENING

CURING

TRANSPORT SORTING

SHREDDING STORAGE

TRANSPORT TO

SCREENING




SCREENING

CURING

TRANSPORT SORTING

SHREDDING STORAGE

31

assumed. A front-loader is introduced for the processes of shredding, screening, and

transport operations within the composting facility. See Appendix C for additional

details.




10,000 tons per year. The composting process is presented in Figure 2-9. The

processes of collection, transport, sorting, shredding, storing, and transport to

composting area are equal to Scenario 5. Decomposition occurs in two in-vessel

composting machines. The following process of transport to screening area, screening,

transport to curing area, forming curing piles, and curing are equal to Scenario 5. See


TRANSPORT TO

SCREENING




SCREENING

CURING

TRANSPORT SORTING

SHREDDING STORAGE

TRANSPORT TO

SCREENING




SCREENING

CURING

TRANSPORT SORTING

SHREDDING STORAGE

32




equal to Scenario 5. A transport distance of 50 km to the composting facility is

assumed. The following process of transport to screening area, screening, transport to

curing area, forming curing piles, and curing are equal to Scenario 5. See Appendix C

for additional details.



50,000 tons per year. The composting process is presented in Figure 2-11. The

processes of collection, transport, sorting, shredding, storing, and transport to

composting area are equal to Scenario 7. Decomposition occurs in ten in-vessel

TRANSPORT TO

SCREENING




SCREENING

CURING

TRANSPORT SORTING

SHREDDING STORAGE

TRANSPORT TO

SCREENING




SCREENING

CURING

TRANSPORT SORTING

SHREDDING STORAGE

33

composting machines. The following process of transport to screening area, screening,

transport to curing area, forming curing piles, and curing are equal to Scenario 5. See



TRANSPORT TO

SCREENING




SCREENING

CURING

TRANSPORT SORTING

SHREDDING STORAGE

34

Table 2-2. Materials, energy, and emissions from composting scenarios (per ton of OSW composted) UNIT PROCESSES LIFE ELEMENT FLOW UNITS COMPOSTING SCENARIOS

STAGES S1 S2 S3 S4 S5 S6 S7 S8

COLLECTION

Production Truck Steel kg - - 0.153 0.153 0.076 0.076 0.076 0.076

Iron kg - - 0.056 0.056 0.028 0.028 0.028 0.028

HDPE kg - - 0.009 0.009 0.004 0.004 0.004 0.004

Use Truck Diesel kg - - 2.550 2.550 2.550 2.550 2.550 2.550

End of life Truck Steel kg - - 0.153 0.153 0.076 0.076 0.076 0.076

Iron kg - - 0.056 0.056 0.028 0.028 0.028 0.028

HDPE kg - - 0.009 0.009 0.004 0.004 0.004 0.004

TRANSPORT

Production Truck Steel kg - - - - 0.076 0.076 0.076 0.076

Iron kg - - - - 0.028 0.028 0.028 0.028

HDPE kg - - - - 0.004 0.004 0.004 0.004

Use Truck Diesel kg - - - - 3.328 3.328 6.656 6.656

End of life Truck Steel kg - - - - 0.076 0.076 0.076 0.076

Iron kg - - - - 0.028 0.028 0.028 0.028

HDPE kg - - - - 0.004 0.004 0.004 0.004

SORTING Production Container HDPE kg 0.096 0.096 - - - - - -

End of life Container HDPE kg 0.096 0.096 - - - - - -

SHREDDING

Production Shredder Steel kg 0.061 0.061 0.018 0.018 0.091 0.091 0.020 0.020

Use Shredder Diesel kg 7.016 7.016 1.607 1.607 1.442 1.442 1.160 1.160

End of life Shredder Steel kg 0.096 0.096 0.018 0.018 0.091 0.091 0.020 0.020

STORAGE

Production Container HDPE kg 0.024 0.024 0.024 0.024 - - - -

L-shape Concrete kg - - - - 0.080 0.080 0.080 0.080

End of life Container HDPE kg 0.024 0.024 0.024 0.024 - - - -

L-shape Concrete kg - - - - 0.080 0.080 0.080 0.080

TRANSPORT TO COMPOST


Front-loader Steel kg - - - - 0.015 0.015 0.012 0.012

Use Front-loader Diesel kg - - - - 0.433 0.433 0.346 0.346


35

Table 2-2. Continued

UNIT PROCESSES LIFE ELEMENT FLOW UNITS COMPOSTING SCENARIOS


TRANSPORT TO COMPOST






FORMING WINDROWS

Production Shovel Steel kg 0.001 0.001 0.001 0.001 - - - -



End of life Shovel HDPE kg 0.001 0.001 0.001 0.001 - - - -

Front-loader Steel - - - - 0.015 0.015 0.012 0.012

DECOMPOSITION

Production Composter Steel kg - 2.312 - 1.951 - 1.403 - 1.403

Biofilter Fibreglass kg - 0.050 - 0.020 - - - -

Wood chip kg 0.380 - 0.608 - 0.608 - 0.608

Biofilter Concrete kg - - - - 0.032 - 0.032

Use Emissions CH4 kg 5.994 0.599 5.994 0.599 5.994 0.599 5.994 0.599

Emissions N2O kg 0.400 0.040 0.400 0.040 0.400 0.040 0.400 0.040

Composter Electricity kW/h - 93.750 - 52.834 59.200 59.200

End of life Composter Steel kg - 2.312 - 1.951 - 1.403 - 1.403

Biofilter Steel kg - 0.050 - 0.020 - - - -

Wood chip kg 0.380 - 0.608 - 0.608 - 0.608

Biofilter Concrete kg - - - - 0.032 - 0.032

TURNING

Production Shovel Steel kg 0.001 0.001 0.001 0.001 - - - -

Hard wood

kg



End of life Shovel Steel kg 0.001 0.001 0.001 0.001 - - - -

Hard wood

kg

36

Table 2-2. Continued

UNIT PROCESSES LIFE ELEMENT FLOW UNITS COMPOSTING SCENARIOS


TRANSPORT TO SCREEN






SCREENING

Production Screener Steel kg 0.024 0.024 0.100 0.100 0.033 0.033 0.023 0.023


Use Screener Electricity kW/h 1.389 1.389 0.714 0.714 - - - -

Screener Diesel kg 0.194 0.194 0.171 0.171

Front-loader Diesel kg - - - - 0.433 0.433 0.346 0.346

End of life Screener Steel kg 0.024 0.024 0.100 0.100 0.033 0.033 0.023 0.023


TRANSPORT TO CURING






CURING Production L-shape Concrete kg 0.080 0.080 0.080 0.080 0.080 0.080 0.080 0.080

End of life L-shape Concrete kg 0.080 0.080 0.080 0.080 0.080 0.080 0.080 0.080

BUILDING Production Building Building kg 0.004 0.004 0.002 0.002 0.003 0.003 0.001 0.001

End of life Building Building kg 0.004 0.004 0.002 0.002 0.003 0.003 0.001 0.001

STEEL HALL Production Steel Hall Steel Hall kg 0.033 0.033 0.020 0.020 0.038 0.038 0.036 0.036

End of life Steel Hall Steel Hall kg 0.033 0.033 0.020 0.020 0.038 0.038 0.036 0.036

CONCRETE PAVING

Production Paving Concrete kg 0.159 0.084 0.112 0.058 0.220 0.062 0.158 0.046

End of life Paving Concrete kg 0.159 0.084 0.112 0.058 0.220 0.062 0.158 0.046

TOTAL AREA m2 0.193 0.084 0.137 0.058 0.270 0.062 0.193 0.046

37

CHAPTER 3

ENVIRONMENTAL ASSESSMENT

Background

Several studies have examined the environmental performance of composting

systems through the Life Cycle Assessment (LCA) methodology, in order to identify the

most critical processes that contribute to environmental impacts and optimize OSW

management. Komilis & Ham (2004) provide a detailed life cycle inventory of typical

large-scale composting facilities in the United States. The decomposition of OSW was

responsible for more than 90% of the GHG emissions from composting; the rest was

due to fossil fuel combustion from composting machinery (Komilis & Ham, 2004). Saer

et al. (2013) evaluate a medium-scale windrow facility in the US, including the

processes of collection, processing, transportation, and land application of compost.

Similarly, the decomposition process accounted for most of the environmental impacts

due to GHG emissions, followed by fossil fuel combustion from composting machinery.

The environmental impacts on global warming, eutrophication, and acidification were

dominated by the decomposition process, while the impacts on ozone depletion,

carcinogenics, noncarcinogenics, and smog formation were determined by fossil fuel

combustion and electricity consumption from composting machinery (Saer et al., 2013).

Boldrin et al. (2011) reported similar results regarding GHG emissions from

decomposition and fuel combustion from composting machinery being the dominant

factors that contribute to the environmental impacts of centralized windrow composting.

Home composting has been found to be a viable alternative for reducing the

environmental impacts of OSW management. Home composting reduces the GHG

emissions related to collection and transportation, as well as electricity and fuel

38

consumption during the operation of centralized composting plants (Andersen et al.,

2010; Colón et al., 2010; Lleó et al., 2012). However, GHG emissions from the

decomposition process of home composting were found to be similar to centralized

composting (Andersen et al., 2010).

Few studies have considered the impact of capital goods in the environmental

performance of composting systems (Bong et al., 2017). Capital goods are processes

upstream of the resource management system, such as buildings, infrastructure, and

machinery: front-loaders, shredding and chipping machines, in-vessel composters, bio-

filters, transport devices, screening and bagging machines. Most environmental studies

of composting have focused on the operation stage of the life cycle (L. K. Brogaard et

al., 2015). The production stage accounts for capital goods, which are usually one time

payments that can be amortized. Brogaard & Christensen (2012) provide a detailed

characterization of capital goods for large-scale windrow composting facilities with a

treatment capacity of 10,000 and 50,000 tons per year. Concrete for the pavement and

steel for the composting machinery made the highest contribution to global warming.

The capital goods of composting plants may contribute 10-22% of the global warming

impact including the operation stage (L. K. Brogaard et al., 2015).

This chapter presents the Life Cycle Assessment (LCA) of 8 urban composting

scenarios covering 4 treatment capacities: 100 ton/year, 1,000 ton/year, 10,000

ton/year, and 50,000 ton/year, and 2 treatment technologies: windrow and in-vessel.

Each scale has a different set of energy and material consumption distributed over the

resource management process, which is divided into 12 unit processes: 1) collection, 2)

transport, 3) sorting, 4) shredding, 5) storage, 6) transport to composting area, 7)

39

forming windrows, 8) decomposition, 9) transport to screening area, 10) screening, 11)

transport to curing area, and 12) curing. The infrastructure component accounts for the

physical structures (buildings, roads) of the composting facility required for the

execution of the unit processes. The system boundaries of the inventory comprise the

life cycle stages of production, use, and end of life. The environmental assessment is

intended to support decision makers in the urban design and planning of composting

infrastructure.

Methods

Life Cycle Assessment (LCA)

Life Cycle Assessment (LCA) is a well-established methodology applied in the

resource management sector to evaluate the environmental performance waste

management systems in order to optimize waste treatment technologies (M. Hauschild

& Barlaz, 2009; Laurent et al., 2013). LCA takes a comprehensive approach by

accounting for environmental impacts during the different life cycle stages of the product

system under analysis: extraction of raw materials, manufacturing, distribution, use,

recycling, and final disposal. Environmental impacts occur at multiple locations around

the world where the different processes of the life cycle stages take place. An important

strength of LCA is the holistic approach which considers the entire life cycle and a full

range of environmental impacts, minimizing problem-shifting between life cycle stages,

geographical regions, or environmental problems (M. Z. Hauschild & Huijbregts, 2015).

Based on a detailed quantification of the energy and materials consumed and the waste

emissions generated through the life cycle stages, LCA estimates the environmental

impacts in three basic areas of protection: human health, natural environment, and

resource depletion (JRC, 2010).

40

The study follows an attributional modelling approach (M. Hauschild & Barlaz,

2009). The attributional LCA modelling is applied when the goal of the study is to

determine the direct environmental impacts of the product system. The consequential

LCA modelling attempts to identify the consequences of a decision on other processes

of the economy; the focus is not the direct environmental impacts of the product system,

but the environmental impacts related to the modification of markets induced by the

introduction of the product system (JRC, 2010). For the case of composting, the

attributional approach focuses on the direct environmental impacts of the OSW

management system, whereas a consequential approach would consider the

environmental impacts related to market modifications such as the reduction of

chemical fertilizer production, the reduction of peat extraction, and impacts associated

with the application of compost on land.

According to the ISO 14040-14044 guidelines (International Organization for

Standardization, 2006), there are four main steps in a LCA study: 1) Goal and scope

definition: describes the objectives and parameters of the LCA study. 2) Inventory

analysis: compiles the relevant inputs and outputs of the product system throughout its

life cycle. 3) Impact assessment: prepares the environmental impact and resource

consumption profiles based on the inventory analysis. 4) Interpretation of the results:

analyses the impact profile and resource consumption according to the goal and scope

of the study.

Goal and scope definition

The geographical context of the study is not specified. The research uses global

data on resource consumption and waste generation from the Ecoinvent database

(Frischknecht et al., 2005) for the production of basic materials such as concrete, steel,

41

or waste emissions such as diesel exhaust. The production processes modelled in the

ecoinvent database represent the current average technology operating in European

countries. The results from the LCA can be used as reference in any geographical

context, taking into account that some environmental impacts are global (i.e. climate

change), and others are regional (i.e. acidification, human toxicity).

The functional unit provides a reference for the collection of input and output data

in the inventory analysis. The functional unit for the study is the composting of one ton

of OSW. The environmental performance of composting scenarios is presented per ton

of OSW composted. The composition of the waste is assumed to be 50% residential

food waste and 50% dry green waste (tree clippings, leaves, garden waste). The

system boundaries determine what unit processes and life cycle phases are included in

the analysis; it defines the limits of the waste management system (M. Hauschild &

Barlaz, 2009). As expressed in Figure 3-1, the waste management system

encompasses the following 12 activities: 1) collection, 2) transport, 3) sorting, 4)

shredding, 5) storage, 6) transport to composting area, 7) forming windrow/feeding

vessel, 8) decomposition, 9) transport to screening, 10) screening, 11), transport to

curing, and 12) curing.

Figure 3-1. Waste management activities included in the system boundaries

TRANSPORT TO

SCREENING




SCREENING

CURING

TRANSPORT SORTING

SHREDDING STORAGE

42

The following life cycle stages are included in the analysis: (1) Production: The

production stage accounts for the energy and materials consumed and the wastes

generated during the extraction of raw materials and the production of capital goods of

the waste management system, such as collection vehicles, buildings, tools, machinery,

and other infrastructures. (2) Operation: The operation stage accounts for the energy

and materials consumed by the composting facility and the wastes (liquid, solid,

gaseous) generated during a lifetime of 15 years. This includes for instance diesel and

electricity consumption, or GHG emissions from the composting process. (3) End of life:

This stage accounts for the recycling or final disposal of the composting infrastructure

and machinery after a lifetime of 15 years. Figure 3-2 presents the life cycle stages and

the system boundaries of the study.

Figure 3-2. System boundaries and life cycle stages

43

Life Cycle Impact Assessment

The software EASETECH (Clavreul, Baumeister, Christensen, & Damgaard,

2014), developed by the Technical University of Denmark (DTU), is used for the

modelling of composting scenarios and the quantification of the environmental impacts.

EASETECH is a LCA software specialized in waste management systems and energy

technologies. The software allows the modelling of a wide range of environmental

technologies from a systems perspective (Clavreul et al., 2014). Composting scenarios

are modelled using the basic EASETECH modules, within which a set of inputs

(resource consumption) and outputs (waste generation) are defined for each of the 12

unit processes of the management system. The Ecoinvent database (Frischknecht et

al., 2005) is the source of datasets for the unit processes involved in the modelling of

composting scenarios. A dataset is a collection of inputs and outputs required to

produce a good or service. The Ecoinvent database comprises life cycle inventory data

covering the most relevant economic sectors: energy systems, building materials,

metals, packaging materials, chemicals, agriculture, transport services, and waste

treatment and disposal. Ecoinvent datasets are available for most energy and materials

used in the construction of composting facilities (steel, concrete, HDPE, iron, etc.). In

EASETECH, the datasets of energy and materials are imported into the modules and

the corresponding quantities collected in the life cycle inventory are assigned. Waste

emissions to the environment (i.e. methane, carbon dioxide) are imported as elementary

flows in EASETECH. Table 3-1 presents the list of datasets used from the Ecoinvent

database.

The production of steel dataset covers the production of machinery used at

composting facilities, including collection trucks, shredders, in-vessel composters,

44

screeners, turning machines, and front loaders. The total weight of the machinery is

assumed to be the weight of steel, following the approach used by Brogaard et al.

(2015). The collection truck includes other materials such as iron and HDPE. The

production of concrete dataset covers the pavement of composting facilities and

concrete elements used for storage in the processes of storage and curing. The

production of HDPE dataset covers the mobile plastic containers used in transport

processes, and some elements of the collection truck. The production of iron dataset

covers some elements of the collection truck. The production of fiberglass dataset

covers the biofilters of in-vessel composters. The production datasets include resource

extraction, transportation, and manufacturing of material (i.e. steel, concrete). Following

the approach used by Brogaard et al. (2015), the manufacturing of the machinery (i.e.

truck, composter) is not included in the analysis due to lack of data.

Table 3-1. List of datasets used from the Ecoinvent database

Process Dataset from Ecoinvent Production of steel steel production, converter, low-alloyed Production of concrete concrete production 20MPa, RNA only Production of HDPE polyethylene production, high density, granulate Production of iron cast iron production Production of fiberglass glass fibre production Combustion of diesel diesel, burned in building machine Construction of building building construction, hall, steel construction Recycling of steel treatment of waste reinforcement steel, recycling Recycling of concrete treatment of waste concrete, not reinforced, recycling Production of electricity electricity production, hydro, reservoir, alpineregion

The combustion of diesel dataset cover the gaseous emissions from the

operation stage of composting machinery, including the collection truck, shredders,

front-loaders, screeners, and other diesel-powered equipment, during a lifetime of 15

years. GHG emissions generated during the operation stage from the decomposition

process (methane and nitrous oxide) are modelled as elementary flows included in the

EASETCH software.

45

The environmental impact assessment was carried out for the following impact

categories: climate change, human toxicity, photochemical ozone formation, terrestrial

acidification, marine eutrophication, and freshwater ecotoxicity (JRC, 2010). These

impact categories are available in the ILCD 2013 Prosuite method, included in the

EASETECH software (Clavreul et al., 2014). Climate change covers the climate

changes induced by the accumulation of GHG in the atmosphere, which blocks heat

from radiating from the Earth towards space, resulting in global warming, rising of sea

levels, and changes of the regional and global climate (Solomon, 2007). The main GHG

generated from composting systems are carbon dioxide (CO2), methane (CH4), and

nitrous oxide (N2O). CO2 is generated during the biological decomposition of organic

material and the burning of fossil fuels. CO2 generated during composting is considered

biogenic in origin and therefore do not contribute to climate change (Solomon, 2007).

The most significant sources of CO2 of fossil origin from composting systems is

generated from the combustion of diesel fuel during the operation stage, in the

processes of collection, transport, shredding, screening, and transport operations within

the composting facility. CH4 and N2O are generated in small quantities during the

decomposition of organic materials. CH4 and N2O are 25 and 298 times more potent

than CO2 respectively (Solomon, 2007).

Human toxicity covers toxic impacts on human beings that occur through the

inhalation of air, ingestion of food and water, and physical contact with polluted surfaces

(M. Hauschild & Barlaz, 2009). The most important contributions to human toxicity from

composting systems come from the combustion of diesel fuel and the emission of

volatile organic compounds (VOCs) from the decomposition of organic material.

46

Photochemical ozone formation covers the formation of ozone at the ground level of the

troposphere by the interaction of sunlight, VOCs, carbon monoxide, and nitrogen oxides

(M. Hauschild & Barlaz, 2009). Ozone formation is recognized as an important

environmental impact at regional scale due to the human health impacts from extreme

concentration episodes in urban areas. Increased ozone levels cause damage to

vegetation, reduction of crop yields, and negative effects on human respiratory tracts

(Stranddorf, Hoffmann, & Schmidt, 2005). The most important contributions to

photochemical ozone formation from composting systems are emissions of nitrogen

oxides and VOCs from the combustion of diesel fuel.

Terrestrial acidification covers the local and regional impacts resulting from the

release of acidifying substances in terrestrial ecosystems. The most important acidifying

substances are oxides of sulfur (SOx), nitrogen oxides (NOx), and ammonia (NH3).

These substances contribute to acidification by releasing protons and causing the loss

of anions from terrestrial ecosystems, resulting in inefficient growth of vegetation, forest

decline, acid rain, and damage to buildings (Stranddorf et al., 2005). The main source of

acidifying substances from composting systems is the combustion of diesel fuel. Marine

eutrophication covers the nutrient enrichment of the oceans caused by the release of

nitrogen (N) and phosphorus (P) compounds. Releases of N and P fertilize natural

ecosystems, changing the species composition and causing algae blooms and oxygen

depletion in coastal waters (M. Hauschild & Barlaz, 2009). In composting systems, the

main contribution to marine eutrophication is derived from NOx emissions from the

combustion of diesel fuel. Freshwater ecotoxicity covers toxic impacts on freshwater

ecosystems which result in changes in the structure and function of the ecosystem,

47

ranging from death to reproductive damages and behavioral changes (M. Hauschild &

Barlaz, 2009).

The environmental impact results are normalized following the International

Reference Life Cycle Data System (ILCD), Prosuite Global NR method (Clavreul et al.,

2014). Normalization supports the interpretation of the results by converting the different

units of the impact categories into a Person Equivalent (PE) unit. The PE unit

represents the environmental impacts on each category (i.e. climate change, ozone

depletion, human toxicity, etc.) of an average global person for one year including all

aspects of life (housing, food, transport, etc.) (JRC, 2010). Table 3-2 presents the

normalization references for the environmental impact categories (Clavreul et al., 2014).

Table 3-2. Normalization factors for the environmental impact categories

Impact category Units Normalization factor Climate change kg CO2 eq./PE/year 8.10E+03 Ozone depletion kg CFC-11 eq. /PE/year 4.14E-02 Human toxicity, cancer effects CTUh/PE/year 5.42E-05 Terrestrial acidification mol H+ eq. /PE/year 4.96E+01 Marine eutrophication kg N eq. /PE/year 9.38E+00 Freshwater ecotoxicity CTUe/PE/year 6.65E+02

Results and Discussion

Results from the climate change category are presented in Figure 3-3. Significant

differences between windrow and in-vessel systems are found, due to the 90%

reduction of GHG emissions (CH4 and N2O) achieved by the biofiltration mechanism of

in-vessel systems. Climate change impacts averaged 24 PE for windrow systems and

5.6 PE for in vessel systems. GHG emissions accounted for 78% of the total impact for

windrow systems, while for in-vessel systems they account for 33%. The best

performing scenario regarding climate change was S4 (neighborhood, in-vessel) with a

value of 4.16 PE. Savings on S4 are driven by the shredding process, which was very

inefficient at the block scale (2.86 PE) compared to the neighborhood scale (0.65 PE).

48

The high horsepower requirement of the small-scale shredder translates into high diesel

consumption. Measures to reduce the horsepower of small-scale shredders are needed

for better climate change performance at the block scale. For the best performing

scenario (S4), the most impactful process was infrastructure (41%), followed by

decomposition (39%), and shredding (21%). Large-scale, in-vessel scenarios had poor

climate change performance due to the relevance of collection and transport, which

accounted for 49% of the total impact for S8, and 36% for S6. The shredding process,

on the other hand, had better performance at larger scales due to the high efficiency of

large-scale shredders in terms of diesel consumption.

Figure 3-3. Environmental impacts from climate change

Figure 3-4 presents the results for the category of human toxicity, cancer effects.

The impacts are concentrated in the infrastructure component across composting

scenarios. Within infrastructure, 90% of the impact is related to the construction of

0.00

5.00

10.00

15.00

20.00

25.00

30.00

S1 S2 S3 S4 S5 S6 S7 S8

PE

COMPOSTING SCENARIOS

Climate Change

Collection and transport

Composting process

Infrastructure

49

buildings. The impact decreases with scale due to small savings on buildings for large-

scale composting facilities.

Figure 3-4. Environmental impacts from human toxicity-cancer effects

Figure 3-5 presents the results for the photochemical ozone formation category.

Windrow systems have a higher impact on photochemical ozone formation than in-

vessel systems due to the GHG emissions from the decomposition process, which

represent 49% of the total impact for windrow systems and 9.3% for in-vessel systems.

The impact from infrastructure remains constant across scenarios, being lower for in-

vessel systems due to lower concrete paving requirement. The impact from collection

and transport is significant and increases with scale due to the significance of transport

distances. The best performing scenario was S4 (neighborhood, in-vessel) with a value

of 5.54 PE. The high diesel consumption of the shredder in S2 (block, in-vessel)

determines its higher impact of 8.24 PE. For S6 (commune, in-vessel), diesel

0.00

2.00

4.00

6.00

8.00

10.00

12.00

S1 S2 S3 S4 S5 S6 S7 S8

PE


Human Toxicity, Cancer Effects


Composting process

Infrastructure

50

consumption during collection and transport was the reason for the higher value of

10.65 PE.

Figure 3-5. Environmental impacts from photochemical ozone formation

Results for the terrestrial acidification category are presented in Figure 3-6. In-

vessel systems have a better performance regarding terrestrial acidification due to the

high impact of GHG emissions from the decomposition process, which represent 41% of

the total impact. The best performing scenario was S4 (neighborhood, in-vessel) with a

value of 5.68 PE. Savings on S4 are driven by the biofiltration mechanism of in-vessel

systems, and the high efficiency of the shredder at the neighborhood scale. The

shredding process at the block scale was inefficient compared to the neighborhood

scale, due to the high diesel consumption of the small-scale shredder. More efficient

small-scale shredders are needed for the block scale to be competitive. The impact of

collection and transport is significant for large-scale scenarios, accounting for 40% of

0.00

5.00

10.00

15.00

20.00

25.00

S1 S2 S3 S4 S5 S6 S7 S8

PE


Photochemical Ozone Formation


Composting process

Infrastructure

51

the total impact on average. The impact of collection and transport increases with scale,

from 1.83 PE at the neighborhood scale to 6.62 PE at the city scale.

Figure 3-6. Environmental impacts from terrestrial acidification

Results for the marine eutrophication category are presented in Figure 3-7. The

best performing scenario regarding marine eutrophication was S4 (neighborhood, in-

vessel) due to the biofiltration of GHG emissions from decomposition and the high

efficiency of the neighborhood shredding process. The composting process is

responsible for most of the environmental impact on marine eutrophication in windrow

systems, due to GHG emissions from decomposition and diesel consumption from the

shredding process. In average, the composting accounts for 68% of the impact for

windrow systems and 46% for in-vessel systems. Regarding scale, the composting

process accounted for 78% of the impact for small-scale scenarios and 48% for large-

scale scenarios. For windrow systems, the impact from decomposition is constant

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

16.00

18.00

20.00

S1 S2 S3 S4 S5 S6 S7 S8

PE


Terrestrial Acidification


Composting process

Infrastructure

52

across scales, but savings are obtained at the neighborhood scale from the shredding

process, which was very efficient in terms of fuel consumption. The same performance

of the shredding process can be observed for in-vessel systems. The impact from

collection and transport was significant for large-scale scenarios, given the impact of

diesel consumption, particularly from transport. The impact from transport was higher

than the impact from collection for scenarios involving transport. Collection and

transport accounted for 45% of the environmental impact for large-scale scenarios, and

12% for small-scale scenarios. The impact of infrastructure remained constant across

scales, with windrow systems having a higher impact (2.59 PE) than in-vessel systems

(1.80 PE) due to the higher concrete paving requirement of windrow systems.

Figure 3-7. Environmental impacts from marine eutrophication

Results for the freshwater ecotoxicity category are presented in Figure 3-8.

Infrastructure is main contributor to the environmental impact in freshwater ecotoxicity,

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

40.00

45.00

S1 S2 S3 S4 S5 S6 S7 S8

PE

Marine Eutrophication


Composting process

Infrastructure

53

accounting for 96% of the total impact on average for all scenarios. The impact of in-

vessel systems (19.6 PE) is lower than windrow systems (23.5 PE) due to the higher

concrete paving requirement. Within infrastructure, the steel halls represent 73% of the

impact, followed by concrete pavement (18%), and buildings (9%). For the composting

process and collection and transport, the impact in freshwater ecotoxicity originate from

fossil fuel combustion during collection, transportation, shredding, screening and

transport operations within the composting facility. The impact from collection and

transport increases with scale driven by transport distances, from 0.26 PE at the

neighborhood scale, to 0.59 PE at the commune scale, to 0.89 PE at the city scale.

Figure 3-8. Environmental impacts from freshwater ecotoxicity

Concluding Remarks

An environmental impact assessment of eight urban composting scenarios was

performed from a life cycle perspective for selected impact categories. GHG emissions

0.00

5.00

10.00

15.00

20.00

25.00

30.00

S1 S2 S3 S4 S5 S6 S7 S8

PE

Freshwater Ecotoxicity


Composting process

Infrastructure

54

(CH4 and N2O) from the decomposition of organic matter at composting facilities is the

main contributing factor to the environmental impacts in the categories of climate

change, photochemical ozone formation, terrestrial acidification, and marine

eutrophication. In-vessel systems had better environmental performance than windrow

systems due to 90% reduction in GHG emissions modelled for the biofiltration

mechanism. Biofiltration of gaseous emissions from decomposition is therefore the most

important strategy for reducing the environmental impacts from composting. Fossil fuel

combustion from collection and transport was the second most important factor

contributing to the environmental impacts in the categories of climate change,

photochemical ozone formation, terrestrial acidification, and marine eutrophication. The

third most important factor was fossil fuel combustion from the processes of shredding,

turning, screening, and transport operations within composting facilities. The shredding

process was found to be the most energy intensive, particularly at the block scale.

Switching from diesel-powered to electric-powered machinery at composting facilities

will significantly reduce the environmental impacts, provided the power source is clean

(hydropower was the selected electricity source). Reducing transport distances will also

have an important effect in reducing environmental impact for most categories. For the

categories of human toxicity-cancer effects, and freshwater ecotoxicity, the main

contribution was from infrastructure, with steel halls and concrete paving accounting

more than 95% of the environmental impact.

The best performing scenario was S4 (neighborhood scale, in-vessel) for all

impact categories. The lower environmental impact of S4 is explained primarily by the

relevance of diesel consumption from collection and transport at larger scales, and by

55

the low mechanization of transport operations within the composting facility at the

neighborhood scale. The second best performing scenario was S2 (block scale, in-

vessel). Although the block scale does not involve collection and transport, diesel

consumption of the shredding process is very high compared to other scales of

management. At larger scales, the composting process becomes more efficient in diesel

consumption, but those savings are offset by the relevance of collection and transport.

56

CHAPTER 4 ECONOMIC ANALYSIS

Background

This chapter addresses the questions of optimal scale and appropriate

technologies of urban composting from an economic perspective. Optimal scale refers

to the size and treatment capacity of composting plants. Bigger plants imply more

transportation and mechanization and can achieve economies of scale, while smaller

plants are more flexible in operation and usually employ more labor (Lardinois &

Klundert, 1993). Appropriate technologies inquiries into the level of mechanization of

composting processes and the consequences on employment creation.

Pandyaswargo & Premakumara (2014) explored the question of optimal scale of

composting facilities in the context of Asian cities. Based on financial indicators

including collection costs, compost production costs, and revenues from selling the

compost product, the results revealed that the medium-scale (51 tons per day) and

lower large-scale (200 tons per day) composting plants have a better opportunity to be

financially feasible compared with the smaller and larger capacity plants. Smaller plants

achieved better quality compost products due to manual sorting of the organic waste

resulting in higher selling prices. Subsidies from governments and external

organizations are crucial to the economic viability of composting facilities in Asian cities

(Pandyaswargo & Premakumara, 2014). Decentralized composting schemes based on

labor-intensive technologies have been better adapted to the socio-economic conditions

of Indian cities, where more than 70% of the solid waste is biodegradable (Lardinois &

Klundert, 1993). With simple hand tools and locally made equipment the production

capacity is limited to 2 or 3 tons per day and investment costs are low. Capital intensive

57

options increase the production capacity up to 100 tons per day requiring mechanized

equipment, more energy, fewer employees, and less space (Lardinois & Klundert,

1993). Neighborhood and community-based composting initiatives stimulate local

employment while creating environmental awareness in the community (Zurbrügg,

Drescher, Patel, & Sharatchandra, 2004). Improvement measures include better control

of the composting process to prevent odors and related complaints by nearby residents.

Effective source separation through public participation, finding markets for selling the

compost products, and support from municipal authorities in partnership with local

communities are crucial aspects to ensure the long-term viability of decentralized

composting (Ali & Harper, 2004).

Materials and Methods

The study follows a life cycle costing (LCC) methodology as described by

(Martinez-Sanchez et al., 2015). The cost model differentiates between three types of

costs: (1) budget costs, (2) transfers, and (3) externality costs. Budget cost and

transfers are considered as internal costs, meaning they have monetary values inside or

outside the resource management system. Externality costs are non-marketed costs

(impacts) that occur outside the economic system. Budget costs are related to the

resource management system and are incurred by stakeholders: households, waste

generators, and recycling or disposal facilities. Transfers are monetary flows that

represent income redistribution between stakeholders such as taxes, fees, or subsidies.

Externality costs represent impacts on the wellbeing of individuals or society that are not

compensated, such as environmental emissions, noise, congestion, or time spent by

household on waste sorting (Martinez-Sanchez et al., 2015). The LCC developed in this

study corresponds to a Conventional LCC, which is commonly used when

58

environmental aspects are not in focus, from a planning perspective, with the goal of

identifying the economically best- solutions for the management of OSW in urban areas.

The study focuses only on budget costs internal to the resource management system,

excluding transfers and externality costs.

Cost Model: Structure

The cost model applied a Unit Cost Method (UCM) (Gluch & Baumann, 2004;

Martinez-Sanchez et al., 2015). The resource management system for OSW is divided

into 12 processes: (1) collection, (2) transport, (3) sorting, (4) shredding, (5) storage, (6)

transport to composting area, (7) forming windrow/feeding vessel, (8) decomposition,

(9) transport to screening area, (10) screening, (11) transport to curing area, and (12)

curing. The infrastructure component is not a process, but accounts for the physical

structures needed to perform the processes. Each activity was disaggregated into

capital costs and operating costs. Capital costs are one-time expenses such as land,

buildings, machinery, or tools. Operating costs are recurring costs such as salaries, fuel,

electricity, and maintenance costs. For each cost item related to each process, two

parameters were defined: a physical parameter describing the quantity of the cost item

required to perform the process related to one ton of waste (for instance 3 hours of

labor to collect 1 ton of waste), and an economic parameter representing the unit cost of

the specific cost item ($6 USD per hour of labor). The unit cost related to 1 ton of OSW

for each process was then found by multiplying the two parameters.

Cost Model: Calculations

Capital costs

One-time capital costs were converted into capital costs per ton of waste

composted by dividing the purchase capital cost by the treatment capacity of the

59

composting plant over its lifetime. The lifetime of composting facilities and related

machinery is assumed to be 15 years.

Capital cost per ton =C

TR

Where,

C = Capital cost ($ USD)

TR = Treatment capacity of composting facility over lifetime (tons)

This calculation was made for obtaining capital costs per ton of waste of

infrastructure items (land, buildings) as well as machinery (shredders, front loader,

screeners). In the case of the collection truck, the capital cost per ton of waste was

obtained by dividing the cost of the collection truck by the tonnage collected over the

lifetime of the truck (See Appendix B for more detailed information).

Operating costs

Operating costs included four basic items: (1) labor, (2) fuel consumption, (3)

electricity consumption, and (4) maintenance. Labor costs per ton are calculated by

multiplying labor productivity (PL) in terms of hours required to manage one ton of

waste, times the labor cost (CL) per hour. Labor productivity estimates by unit processes

are described in detail in Appendix B. Two types of labor cost are defined: unskilled

labor ($2.42 USD/hr), and skilled labor ($4.84 USD/hr). Labor costs are representative

of the Colombian context in 2017, based on Decree 2209 of 2016 from the Colombian

national government.

Labor cost per ton = LP ∗ LC

Where,

LP = Labor productivity (hr/ton)

60

LC = Labor cost ($ USD)

Fuel consumption cost of composting machinery is calculated as a function of

three variables: (1) fuel consumption of the machine, (2) throughput of the machine, and

(3) cost of fuel. Fuel consumption for all diesel engines is assumed to be 0.26 liter per

horsepower (HP) per hour (Grisso et al., 2010). The cost of diesel is set at $ 0.67 USD

per liter. Fuel consumption cost per ton is equal to fuel consumption assumed for diesel

machines (lt/HP-hr), times the power of the machine (HP), divided by the throughput of

the machine (ton/hour), times the cost of fuel (USD).

Fuel consumption cost per ton = (d ∗ HP

TR) ∗ CF

Where,

d = Diesel consumption assumed for all machines (lt/HP-hour)

HP = Power of the machine (HP)

TR = Throughput of the machine (ton/hour)

CF= Cost of fuel ($ USD)

Electricity consumption cost per ton of the composter is equal to the electricity

consumption during the lifetime (kWh/lifetime) of the composter divided by the lifetime

treatment capacity of the facility (tons), times the electricity cost ($USD/kWh). For

composters, it is assumed they work 260 working days per year, 8 hours per day, during

a lifetime of 15 years. Electricity cost is set at $ 0.14 USD/kWh).

Electricity consumption cost per ton =ELFTR

∗ EC

Where,

ELF = Electricity consumption of composter during lifetime (kWh)

61

TR = Treatment capacity lifetime of composting facility (tons)

EC = Cost of electricity ($ USD/kWh)

Maintenance costs are estimated as 10% of the equipment capital cost (Ruffino,

Fiore, & Zanetti, 2014).


General Findings

Life cycle costs per ton of OSW composted are in the range of $60 to $90 for all

scenarios except for S2 (block scale), where the high value ($134) is determined by the

small-scale in-vessel composter, as discussed later. The resource management system

for OSW is divided into three main areas of analysis: infrastructure, composting

process, and collection/transport. Results are presented in Figure 4-1. Infrastructure

represents 48% of the total cost, followed by the composting process (40%), and

collection/transport (12%). The capital and operating costs of infrastructure (buildings,

steel halls, concrete paved areas, and land) represent almost half of the life cycle cost

of the resource management systems under analysis. The city scale, in-vessel system

(S8) presented the lower cost per ton of waste composted ($63), driven by the savings

on infrastructure and the economies of scale of the in-vessel composting process. The

lower infrastructure costs for S8 are explained by the lower land requirement of in-

vessel systems; windrow systems required 3 times more land for the composting facility

across scenarios. The larger composting pad and the buffer zones required to mitigate

air emissions are the reasons for the higher land requirement of windrow systems. The

cost of land represented around one third of infrastructure cost and was the most

expensive item for all scenarios. No economies of scale are found for land, being

62

around $32 for windrow systems and $10 for in-vessel systems per ton of waste

composted.

Figure 4-1. Life cycle cost of composting scenarios

Significant economies of scale were found in the composting process for in-

vessel systems. The cost decreased from $106 for S2 (block scale) to $20 for S8 (city

scale). This is explained by the high capital cost and energy consumption of small-scale

in-vessel composters. For windrow systems, economies of scale were found in the

composting process, from $27 for S1 (block scale) to $13 for S7 (city scale), but those

economies are compensated by the cost of collection and transport. Savings on

collection and transport for small-scale composting facilities averaged $10, which is

significant but not sufficient to compensate for the economies of scale of the composting

process in both windrow and in-vessel systems.

0

20

40

60

80

100

120

140

160

S1 S2 S3 S4 S5 S6 S7 S8

$ U

SD

INFRASTRUCTURE

COMPOSTING PROCESS

COLLECTION ANDTRANSPORT

63

Infrastructure

Infrastructure represents 48% of the resource management cost of OSW.

Infrastructure is dominated by land cost, accounting for 52% of infrastructure cost

across composting scenarios. Measures to decrease the cost of land should be

explored to make urban composting more feasible. Steel halls represent the second

most dominant cost of infrastructure, accounting for 32%; large covered areas are

needed for incoming OSW and the processes of shredding, screening, curing, and

storage of final product. Concrete paving area is the following cost, being 14% of

infrastructure cost; buildings within the composting facility represented only 2% of

infrastructure cost. It should be noted that only the items with the largest cost are

included in the analysis, and other infrastructure items of composting facilities, such as

water drainage and leachate collection systems, fencing, lighting, and others are no

included in the analysis and increase the cost of infrastructure. In general, no

economies of scale were found for infrastructure. The cost of land, steel halls, and

paved areas remained constant across composting scenarios. Only building costs

showed economies of scale from $1,64 (block scale) to $0.27 (city scale).

Composting

The composting process showed significant differences between windrow and in-

vessel systems. For in-vessel systems, the largest cost contribution was from the

decomposition process (71%), due to the high capital cost and energy consumption of

the composters, particularly small-scale composters at the block and neighborhood

scales. For windrow systems, decomposition accounted for only 5% of the composting

cost, because no energy consumption or capital costs are involved. However, the land

requirement for windrow systems is much larger, and land cost is accounted for as part

64

of infrastructure, therefore not represented in the decomposition process. If the

additional land cost required by windrow systems is allocated to the decomposition

process, its value will rise from 5% to 55% of the total composting cost. The tradeoff

between land cost for windrow systems, and the capital and energy consumption costs

of the composter for in-vessel systems should be carefully examined. For in-vessel

systems, the decomposition process showed significant economies of scale in the

composter cost, from $67 (block scale) to $7.4 (city scale). Similarly, the energy

consumption of the composter showed economies of scale, from $7.64 (block scale) to

$1.64 (city scale). Small-scale in-vessel composters were very expensive and its

efficiency should be improved for decentralized composting systems to be competitive.

Windrow systems, on the contrary, were found to be less expensive at the small scale,

due to the high level of mechanization of large scale windrow composting. The capital

cost of the windrow turner is the main reason why the small scale is more efficient.

Turning and watering windrows by hand was more economical given the high capital

cost and fuel consumption of large scale turning machinery. It is important to highlight

the diseconomies of scale found in windrow composting.

The following largest contribution to composting was the shredding process,

accounting for 13% of the composting cost for in-vessel systems and 38% for windrow

systems. The small-scale shredder at the block scale was very expensive due to its high

labor requirement and low throughput. Labor cost of shredding was $10.83 (block

scale), $1.36 for (neighborhood scale), $0.32 (commune scale), and $0.1 (city scale).

The small-scale shredders require an operator manually feeding the machine at the

shredder’s throughput, while large scale shredders are fed by an operator using the

65

front-loader. A significant difference in labor cost of shredding is found comparing the

block ($10.83) and neighborhood scales ($1.36). While both shredders are manually

fed, the low throughput of the shredder at block scale determines its high labor cost.

Labor requirements at larger scales are very low due to the high throughput of the

shredders and the high efficiency of the front loader feeding the shredder. The capital

cost, however, is higher for large scale shredders: $3.20 (commune scale) and $0.28 for

(neighborhood scale). Regarding fuel consumption, large scale shredders were more

efficient: $4.64 (block), $1.29 (neighborhood), $1.16 (commune), $0.93 (city). Large

scale shredders were also more efficient in fuel consumption: from $4.64 (block), $1.29

(neighborhood), $1.16 (commune), and $0.93 (city). Comparing the total shredding cost,

the neighborhood ($2.95) and the city ($2.62) were the least expensive scales of

management.

Transport processes within the composting facility accounted for 10% of the

composting cost, 5.4% for in-vessel systems, and 23% for windrow systems. The

average transport distance of composting facilities was the following: 13 m (block

windrow), 8 m (block in-vessel), 33 m (neighborhood windrow), 15 m (neighborhood in-

vessel), 150 m (commune windrow), 40 m (commune in-vessel), 300 m (city windrow),

and 100 m (city in-vessel). At the block and neighborhood scales, transport was

performed by an operator moving a plastic mobile container. At the commune and city

scales, transport was performed by an operator driving the front loader. The high land

requirements of windrow systems translated into more expensive transport operations.

The capital cost of the plastic containers doubled the cost of the front loader: $0.43

(block and neighborhood), $0.20 (commune), $0.16 (city). It should be recalled that the

66

capital cost of the front loader cost is allocated to 6 processes by equal parts, while the

cost of the plastic container is allocated to 2 processes. Labor was the most significant

cost of transport processes at the block and neighborhood scales, while fuel

consumption of the front loader determines the cost at the larger scales. Overall,

transport processes showed no economies of scale because the savings on labor at

larger scales are compensated by higher fuel consumption, levelling total transport

costs around $1.4 for windrow systems and $0.9 for in-vessel systems.

The remaining processes of sorting, storage, forming windrows/feeding vessel,

and curing represent 12.4% of the composting cost. Sorting cost was $2.18, the same

for all scenarios given the assumption of manual labor regardless of the scale of

management. Sorting is considered a crucial process for assuring the quality of the final

compost, and no information was found about sorting processes for OSW. Storage cost

was $1.82 (block and neighborhood scales), $0.57 (commune scale), and $0.53 (city

scale). The higher cost of storage at the smaller scales is determined by the labor

requirements of shoveling the materials into the plastic containers. At larger scales, the

front loader was used resulting in significant economies of scale. The plastic containers

used for storage at the block and neighborhood scales were significantly more

expensive ($0.43) than the concrete elements used at larger scales ($0.03). A similar

situation was found for the forming windrow/feeding vessel process. Forming windrows

at the small scale using manual labor translated into higher cost. The forming windrow

costs were $1.35 (block and neighborhood), $0.55 (commune scale), and $0.51 (city

scale). The feeding vessel costs were $0 (block and neighborhood), $0.55 (commune

scale), and $0.51 (city scale). At smaller scales, feeding the vessel was performed by a

67

bin-lifter attached to the composter; therefore its energy consumption is accounted for in

the decomposition process. Curing cost was $0.33 for all scenarios, the value being

determined by the fixed cost of the concrete containers used for curing.

Collection and Transport

Collection and transport represent 12% of the resource management cost of

OSW. Collection costs were $0 (block), $10.65 (neighborhood), and $8.15 (commune

and city). At the block scale there is no collection cost because households transport

their OSW to the composting facility located at maximum distance of 100 m. Collection

cost is higher at the neighborhood scale because the capital cost of the collection truck

is allocated entirely to the collection process, whereas in the commune and city scales,

this cost is allocated to collection and transport by equal parts. The collection cost is

dominated by labor cost ($3.64), followed by the collection truck cost ($2.28), and the

fuel cost ($2). Labor cost is dominant because three operators are involved in collection,

one driving the truck and two picking up the OSW. Transport cost, on the other hand, is

dominated by the cost of fuel, which was $2.67 (commune scale) and $5.33 (city scale),

followed by the cost of the truck ($2.28). Labor costs for transport were $0.42

(commune scale) and $0.67 (city scale). Looking at the combined processes of

collection and transport, the savings obtained by eliminating these processes in small

scale composting were significant: $17 (block scale) and $6.37 (neighborhood scale),

but these savings were offset in the whole resource management system by the

economies of scale resulting from the composting process.

Concluding Remarks

From an economic viewpoint, the optimal scale of composting is the city scale,

in-vessel technology. The most important factors that determine the lower cost of the

68

city scale, in-vessel technology are labor cost, land cost, and decomposition cost. Labor

cost is about 3 times higher per ton of waste for small-scale facilities compared to large-

scale facilities. However, labor cost is compensated by fuel cost, which is 3 times higher

for large scale facilities compared to the small scale. The relationship between labor

cost and fuel cost should be carefully examined for the urban planning of composting

infrastructure. For regions with high labor costs, large-scale facilities are less expensive

at the cost of higher fuel consumption. Small-scale facilities would be competitive in

regions with low labor costs. Land cost is another important factor for the urban

planning of composting infrastructure. Windrow systems require 3 times more land than

in-vessel systems for all scales of management. For high-density urban areas where

land is scarce and expensive, small-scale composting facilities would not be feasible

due to higher land cost. For low-density urban areas where land is less expensive,

small-scale facilities would be more feasible. Additionally, land cost should be examined

in conjunction with collection and transport cost. Low-density urban areas imply larger

transport distances and collection costs, which favors small scale composting. For a

high-density urban area with low collection and transport costs and low land cost, small-

scale, in-vessel technologies could be appropriate because they require less land and

can operate inside buildings (i.e. basements). Finally, decomposition cost is a decisive

factor affecting the optimal scale and composting technology. In-vessel systems

showed significant economies of scale in the decomposition process, whereas windrow

systems showed no economies of scale. The economies of scale achieved by the in-

vessel technology are the main reason why the city scale was the optimal scale. While

collection and transport costs increase significantly with the scale of management,

69

those savings are outweighed by the economies of scale of the decomposition process

of in-vessel systems. If the decomposition cost was the same at all scales, the savings

on collection and transport at the small scale could be attained and the block would be

the optimal scale. Because labor cost and fuel cost neutralize each other as the scale of

management increases, the optimal scale of composting is determined by the efficiency

of the decomposition process, followed by land cost, and collection and transport cost.

70

CHAPTER 5 SOCIAL ASSESSMENT

Background

This chapter evaluates the social impacts of hypothetical composting scenarios

in urban areas using the Social Life Cycle Assessment (S-LCA) methodology. S-LCA is

an emerging evaluation tool designed to evaluate how lifecycle activities affect people

(Dreyer, Hauschild, & Schierbeck, 2006). S-LCA complements the existing and more

developed environmental Life Cycle Assessment (LCA) and economic Life Cycle

Costing (LCC). S-LCA develops a systematic process for collecting best available data

about social impacts (positive and negative) of a product or system, encompassing

extraction of raw materials, production, distribution, use, maintenance, recycling, and

final disposal (Benoît et al., 2010). Guidelines for S-LCA have been published by the

United Nations Environment Programme (UNEP, 2009), and methodological procedures

have been developed based on the environmental LCA framework (Benoît Norris et al.,

2013). However, S-LCA is an emerging evaluation tool and the methodological

framework is in an early stage of development (van Haaster, Ciroth, Fontes, Wood, &

Ramirez, 2017).

Social impacts are defined as consequences on individual and community well-

being derived from three types of causes: (1) behaviors: impacts caused by a specific

decision, for instance allowing illegal child labor; (2) socio-economic processes: impacts

caused by socio-economic choices at the macro or micro level, for instance an

investment decision in a community; and (3) context specific attributes: impacts related

to particular attributes of individuals or communities, causing the impact to be magnified

given the specific context (UNEP, 2009). Site-specific issues related to cultural practices

71

and local ecosystems are explored through S-LCA (Norris, 2006). S-LCA can be used

to increase knowledge, analyze choices, and communicate social impacts, providing

support for policy making (Benoît et al., 2010). S-LCA can be a valuable tool for

improving the design of products and systems while promoting the enhancement of

social conditions over the life cycle (do Carmo, Margni, & Baptiste, 2017).

Figure 5-1. S-LCA analytical framework

The framework for social LCA developed by UNEP (2009) is based on the

environmental LCA. The lifecycle stages of the product or system under analysis

(extraction, production, distribution, use, maintenance, recycling, and final disposal) are

carried out in different geographical locations (mines, factories, roads, shops,

households, recycling plants, disposal sites). For each lifecycle stage and

corresponding geographical location, social impacts are described in 5 main

STAKEHOLDER SOCIAL

CATEGORIES INDICATORS

LOCAL

COMMUNITY

VALUE

CHAIN ACTORS

WORKERS

CONSUMERS

SOCIETY

SUBCATEGORIES

72

stakeholder categories: workers, local community, society (national and global),

consumers, and value chain actors. A stakeholder category is a group of stakeholders

that are expected to have shared interests due to their similar relationship to the

investigated product system. For each stakeholder category, subcategories of social

analysis are defined. Subcategories are socially significant themes or areas of impact.

The impact is described through social indicators, expressed using quantitative, semi-

quantitative, and qualitative approaches (UNEP, 2009). Figure 5-1 presents the S-LCA

analytical framework.

S-LCA is applied in four phases: definition of the goal and scope of study, life

cycle inventory analysis, life cycle impact assessment, and result interpretation (UNEP,

2009). The goal and scope specifies the objective, the functional unit, and the system

boundaries of the study. The functional unit provides a measure of the function of the

product or system under analysis; the functional unit is defined as the management of

one ton of OSW (Laurent et al., 2013). Foreground and background processes can be

included in the definition of the system boundaries, a crucial step that can significantly

affect the results and interpretation (Martínez-Blanco et al., 2014). Foreground

processes refer to processes specific to the product system under analysis (for example

processes involved in the production of compost), while background processes are

processes located upstream (i.e. production of machinery used to produce compost) or

downstream (i.e. compost application to land) of the product system under analysis.

Foreground processes require primary data of high quality, while for background

processes more average or generic data can be used (Handbook, 2010; van Haaster et

al., 2017).

73

The life cycle inventory is the phase in which data are collected on the social

performance of organizations involved in the life cycle (extraction, production,

distribution, use, maintenance, recycling, and final disposal) of the product system

(UNEP, 2009). Social impacts are derived not from processes but from the behavior of

the organization (company) involved in the process, therefore the need for site-specific

data (Jørgensen, Le Bocq, Nazarkina, & Hauschild, 2008). For this study, the social

analysis focuses not on the behavior of organizations, but on the composting processes

of different scales and technologies; therefore the data is not site-specific. The Social

Hotspots Database (SHDB) is a comprehensive database developed to identify social

impacts in product supply chains, providing information on 22 social themes and 133

indicators organized by country and economic sector (Benoit-Norris, Cavan, & Norris,

2012). In order to determine the relative importance of each unit process (and related

social impacts) of the product system throughout its life cycle, working time and added

value have been proposed as weighing measures –activity variables– for social

aggregation (Martínez-Blanco et al., 2014; UNEP, 2009). Several approaches establish

reference points of social performance based on standards and best management

practices (Garrido, Parent, Beaulieu, & Revéret, 2016). Site specific data can also be

obtained through auditing of enterprise or authority documentation, participative

methods, interviews, focus groups, questionnaires, or surveys (UNEP, 2009).

The life cycle impact assessment is the phase where the collected data is

classified, aggregated, and characterized according to performance reference points

(UNEP, 2009). Stakeholder categories, subcategories of impact, and characterization

models are defined. Benoît Norris et al. (2013) define 31 subcategories of impact,

74

classified into 5 stakeholder categories. More or less stakeholders and subcategories

may be described according to the goal of the study (UNEP, 2009). Social impact data

collected in the life cycle inventory are classified social indicators which describe the

magnitude of the social impact using quantitative, semi-quantitative, or qualitative

approaches. For this study, social impact data is derived from the composting

processes, which are variable depending on the scales and technologies. Quantitative

indicators describe the impact using numbers (for example number of accidents for

workers); semi-qualitative indicators are categorizations of qualitative indicators into a

yes/no form or a scoring system (for example presence of a stress management

program (yes-no); qualitative indicators describe the impact using words (for example

stress management strategies for workers). Social indicators are classified in the

selected subcategories and stakeholders. The characterization refers to the calculation

of the subcategory result by aggregating the social indicators; this can be done by

summarizing the qualitative information, summing up the quantitative social indicators,

or developing a scoring system based on performance reference points (UNEP, 2009).

Social indicators results can be scored according their relative importance and then

aggregated using a weighing system, following the ‘taskforce approach’ (Parent,

Cucuzzella, & Revéret, 2010). A linear scale score is usually associated with each

classification level of the subcategory indicators, although this approach does not take

into account the relative importance of each social indicator; weighing factors need to

be developed to establish a hierarchy of importance (do Carmo et al., 2017).

The interpretation of results is the phase in which the findings are evaluated,

conclusions are drawn, and recommendations are provided to support the decision-

75

making process (do Carmo et al., 2017). Significant issues are identified, highlighting

the most relevant social findings and critical methodological choices, including key

concerns, limitations, and assumptions of the study (UNEP, 2009). The actions taken to

ensure transparency and verifiability of results should be exposed, as well as the level

of completeness of the data, data gaps, and the appropriateness of the methodological

choices (UNEP, 2009). Finally, conclusions are drawn based on the goal and scope of

the study and recommendations are provided, suggesting alternative courses of action

to improve the social performance of the product system under analysis.

This study presents the S-LCA of eight urban composting scenarios, covering

four spatial scales (block, neighborhood, commune, and city) and two treatment

technologies (windrow and in-vessel). The study aims to identify and analyze the social

impacts associated with each management scale and treatment technology. The

system boundary includes the ‘use’ stage of composting facilities. Life cycle stages

located upstream (extraction, production, distribution) and downstream (recycling and

disposal) are excluded from the analysis.

Materials and Methods

Goal and Scope

The goal of the study is to compare the social life cycle performance of eight

urban composting scenarios, covering four spatial scales (block, neighborhood,

commune, and city) and two treatment technologies (windrow and in-vessel). The study

aims to identify and analyze the social impacts associated with each management scale

and treatment technology, in order to find out the alternatives with the best social

performance. The functional unit is the management of one ton of OSW. The system

boundary includes the ‘use’ stage of the resource management system of OWS, which

76

is divided into 12 unit processes: (1) collection (2) transport (3) sorting (4) shredding (5)

storage (6) transport to composting (7) forming windrow/feeding vessel (8)

decomposition (9) transport to screening (10) screening (11) transport to curing (12)

curing.

Figure 5-2. Life cycle stages and unit processes included in the system boundary

Background processes located upstream of the resource management system

such as extraction of raw materials, manufacturing of capital goods, production of

electricity and fuel, transportation of capital goods to the composting site, etc. are

excluded from the analysis because they are not related to the goal and scope of the

study. Background processes located downstream of the resource management system

such as compost distribution, compost application, recycling and disposal of machinery,

Collection

Transport

Sorting

Shredding

Storage

Transport 1

Forming/Fedding

Decomposition

Transport 2

Screening

Transport 3

Curing

PRODUCTION USE END OF LIFE

Distribution

Manufacturing

Disposal

Recycling

DeconstructionExtraction

LIFE CYCLESTAGES

SYSTEMBOUNDARY

77

decommissioning of composting facilities, are outside the system boundary. The study

focuses on comparing the influence of management scale and composting technologies

on the social performance of urban composting systems, therefore only the ‘use’ stage

of the life cycle of composting facilities is considered. Figure 5-2 presents the life cycle

stages and unit processes included in the system boundary.

Life Cycle Inventory

The life cycle inventory data are collected from a literature review on the social

impacts of waste collection operations and composting facilities. Given the hypothetical

nature of the composting scenarios under analysis, the social impacts are not derived

from organizational behavior but from the unit processes of the resource management

system. Data for the stakeholder ‘workers’ regarding ‘health and safety’ are collected

from clinical and epidemiological studies from workers of waste collection activities and

composting facilities. For the stakeholder ‘consumer’, data were collected from literature

on public participation in solid waste management. For the stakeholder ‘local

community’, data were collected from published case studies and reports describing the

social impacts of composting facilities in local communities. Regarding health and safety

of local communities, clinical and epidemiological studies assessing the impacts of

composting facilities on neighboring communities were reviewed. For the stakeholder

‘society’, a literature review was conducted to discuss the contribution of urban

composting to economic development.

Impact Assessment

The definition of stakeholder categories, subcategories, and social indicators

follows the UNEP (2009) guidelines, and Benoît Norris et al. (2013) methodological

78

sheets. Figure 5-3 presents the stakeholder categories, sub-categories, and social

indicators selected for the study.

Figure 5-3. Selected stakeholders, subcategories, and social indicators

The stakeholder category ‘workers’ refers to the employees of waste collection

and transport operations and of composting processes. ‘Health and safety’ is the only

subcategory selected for evaluating the social impacts on workers. Other subcategories

defined by UNEP (2009) such as child labor, fair salary, or working hours, do not apply

to this study because the goal is to evaluate the social performance of hypothetical

composting scenarios, focusing on the questions of management scale and composting

technologies. Occupational health should aim at the promotion of the highest degree of

physical, mental and social well-being of workers by implementing prevention and

protection measures in the occupational environment (Benoît Norris et al., 2013). The

STAKEHOLDER SOCIAL

CATEGORIES INDICATORS

Exposure to chemical risk

Exposure to biological risk

Exposure to diesel exhaust



LOCAL Exposure to diesel exhaust

COMMUNITY

Health and Safety

Health and Safety

SUBCATEGORIES

WORKERS

CONSUMERS Public Participation Level of engagement

Community Engagement Community projects

Local Employment Number of people employed

79

subcategory ‘health and safety’ of workers in composting facilities evaluates the level of

exposure to three occupational hazards: exposure to chemical agents, exposure to

biological agents, and exposure to diesel exhaust generated by composting machinery.

The stakeholder category ‘consumers’ refers to the consumers of the waste

management service. Consumers produce the OSW in their households and receive the

service of waste collection and composting. The subcategory ‘public participation’

evaluates the level of involvement of consumers in the resource management system.

The stakeholder ‘local community’ refers to the people living in the neighborhood where

the composting facility is located. ‘Health and safety’ evaluates the level of exposure of

the local community to chemical and biological agents derived from the decomposition

of OSW, as well as exposure to diesel exhaust generated by composting machinery.

‘Community engagement’ explores how composting facilities engage with individuals or

local groups on issues that affect the environment, health, and well-being of the

community; composting facilities can also support community initiatives through direct

engagement or financial support. ‘Local employment’ addresses the potential of

composting facilities to provide employment and training opportunities to the local

community, as well as relationships with locally-based suppliers that encourage local

employment and development (Benoît Norris et al., 2013).

The impact assessment is presented as qualitative discussions of the findings for

each social indicator from the literature review. A scoring method enables the

translation of qualitative, semi-quantitative, and quantitative data into numerical values,

facilitating the comparison between composting scenarios. A simple scoring system is

80

applied following Ciroth & Franze (2011). Five performance levels with a corresponding

color as described in Figure 5-4.

Figure 5-4. Social performance levels

Social indicator results of the composting scenarios are classified in the

performance levels, and then aggregated as averages in the subcategories and

stakeholder categories. The resulting score of the stakeholder categories is the average

of the social indicators and subcategories (Ciroth & Franze, 2011). No weighing system

was used to establish a hierarchy of importance between social indicators,

subcategories, and stakeholders. All social indicators are given equal importance,

although this approach does not take into account their relative significance (Chhipi-

Shrestha, Hewage, & Sadiq, 2015).


Workers

Health and safety of workers at composting facilities are related to four main

areas of concern: (1) exposure to chemical and biological agents, (2) exposure to diesel

exhaust, (3) risk of physical injury from machinery, and (4) physiological risks. Exposure

to chemical and biological agents is related to the handling of decomposing organic

material, while exposure to diesel exhaust is related to emissions of composting

machinery.

Exposure to chemical and biological agents

The exposure to chemical agents at composting facilities is related to emissions

of Volatile Organic Compounds (VOCs) (Domingo & Nadal, 2009). VOCs are trace

BEST GOOD AVRG FAIR POOR

81

gases other than carbon dioxide and monoxide emitted into the atmosphere from

anthropogenic and biogenic sources (Atkinson & Arey, 2003; Kesselmeier & Staudt,

1999). Anthropogenic VOCs are emitted from a variety of sources, the most important

being fossil fuel combustion, biomass burning, use of organic solvents, disposal of

organic wastes in landfills, and ruminant husbandry (Atkinson & Arey, 2003; Friedrich &

Obermeier, 1999). VOCs of concern at composting facilities include: (1) xenobiotics

such as trimethylbenzenes, xylenes, toluene, and carbon tetrachloride, and (2) terpenes

such as a-pinene and D-limonene (Eitzer, 1995).

Workers at composting facilities undergo an inevitable exposure to VOCs

(Domingo & Nadal, 2009). Most VOCs are concentrated in the deposit of fresh

materials, the shredding process, and the early stages of decomposition. The higher

concentrations are found in the shredding area, because shredding increases the

surface area of the organic material, allowing VOCs to volatilize (Eitzer, 1995). In

composting plants, workers are exposed to VOCs during the processes of sorting,

shredding, storage, transport to composting area, forming windrows, and turning. The

main adverse health effects related to VOCs are a consequence of the great mobility

and capacity to be inhaled by people working or living in places with high concentrations

(Domingo & Nadal, 2009). VOCs are the cause of bad odors generated in composting

facilities. Odors are produced in anaerobic pockets inside the windrows due to

insufficient aeration resulting from inadequate turning. Bad odors are associated with

diverse health effects such as nauseas, vomits, and reactions of hypersensitivity

(Domingo & Nadal, 2009). The systemic toxic effects of VOCs are relevant, including

82

renal, hematological, neurological and hepatic alterations, as well as mucosal irritations

(Domingo & Nadal, 2009).

Biological risks for workers at composting facilities are derived from the presence

of pathogenic agents present in the fresh organic material and microorganisms that

develop during the formation of compost, including viruses, bacteria, fungi, and parasitic

protozoa, which can potentially have infectious, allergenic, toxic, and carcinogenic

effects (Domingo & Nadal, 2009). Exposure to bioaerosols is the main source of

biological risk at composting facilities (Byeon, Park, Yoon, Park, & Hwang, 2008).

Bioaerosols are microorganisms (bacteria and fungi) and their metabolic by-products

(endotoxins) released into the atmosphere during the microbial decomposition of

organic material (Kummer & Thiel, 2008). Adverse health effects derived from the

exposure to bioerosols include gastrointestinal disturbances, fevers, and infections and

irritations of eyes, ear and skin (Domingo & Nadal, 2009). Threshold limits values in

terms of colony forming units by cubic meter have been proposed for occupational

atmospheres with exposure to bioaerosols (Marchand, Lavoie, & Lazure, 1995).

Measurements from composting facilities in Germany indicate that compost

workers are exposed to high concentrations of bioaerosols, averaging 107 CFU/m3 of

air, whereas waste collectors exposure was two orders of magnitude lower (J Bünger,

Antlauf-Lammers, Westphal, Müller, & Hallier, 1998). Strong evidence is available that

exposure to bioaerosols among waste collection workers exceeds recommended levels

(Kuijer, Sluiter, & Frings‐Dresen, 2010). In the composting facility, the higher exposure

to bioaerosols is concentrated in the screening area and the early stages of

decomposition (Byeon et al., 2008). Exposure to bioaerosols at composting facilities

83

was associated with adverse acute and chronic respiratory health effects, including

mucosal membrane irritation of the eyes and upper airways, and chronic bronchitis;

compost workers showed higher concentrations of specific antibodies against molds

and actinomycetes (Jürgen Bünger et al., 2000).

In-vessel composting systems are equipped with biofiltration mechanisms

capable of reducing gaseous emissions by 96% (Hotrot, 2013). Exhaust fans extract the

air from the composting vessel and pass it through a layer of filter material such as

compost, peat moss, or wood chips. During biofiltration, bacteria and fungi eat or

oxidize the odorous gas, transferring emissions from the gas phase to the liquid and

solid phase in the filter material (Hong & Park, 2005). Filter material should be replaced

and disposed of every 5 years (Hotrot, 2013). For the composting scenarios under

analysis, in-vessel systems have a better performance than windrow systems regarding

exposure to chemical and biological agents due to the existence of biofiltration

mechanisms. However, the exposure to chemical agents in composting facilities is

highest in the shredding process (Eitzer, 1995), which is the same for windrow and in-

vessel systems. Composting methods that do not require turning, such as the Chinese

composting system and the forced aeration system pose fewer health risks for workers

(Lardinois & Klundert, 1993). The exposure to biological agents is highest in the

screening process (Byeon et al., 2008), which is the same for windrow and in-vessel

systems. In general, in-vessel composting facilities have better social performance for

workers as they are capable of reducing gaseous emissions by 96% during the

decomposition process, which is responsible for part of the exposure to chemical and

biological agents. The level of exposure to these agents with regard to the scale of

84

management (block, neighborhood, commune, city) was assumed equal, because no

studies were found addressing the subject. However, it can be hypothesized that

composting workers at large scale facilities undergo higher exposure to gaseous

emissions than workers at small scale facilities. In Germany, for composting facilities

processing more than 3 tons, a minimum distance to dwellings of 300 m (enclosed) and

500 m (open windrows) is enforced (Douglas et al., 2016), which suggests that the

impacts on local communities –and therefore on workers– increase with scale; however,

no studies were found to confirm this hypothesis. Figure 5-5 presents the results from

the exposure of workers to chemical and biological agents.

Figure 5-5. Level of exposure of composting workers to chemical and biological agents


Exposure to Diesel Exhaust (DE) is another factor affecting the health of workers

at composting facilities. DE is classified as a carcinogen in humans based on evidence

of its carcinogenicity to the lung (Silverman, 2017). DE is produced by diesel-powered

equipment utilized in the composting process, including shredders, chippers, front

loaders, and screeners and turning machines. DE contains a complex mixture of gases

such as carbon monoxide (CO), nitric oxides (NO, NO2), sulphur dioxide (SO2),

hydrocarbons, formaldehyde, transition metals and carbon particles (Sydbom et al.,

2001). The adverse health effects of DE exposure is currently focused on ultrafine

particles (<0.1 mm), which have been shown to enter the bloodstream and exert toxicity

due to the content of transition metals and redox cycling chemicals (Bernstein et al.,

STAKEHOLDER SOCIAL

CATEGORY INDICATORS S1 S2 S3 S4 S5 S6 S7 S8



COMPOSTING SCENARIOSSUBCATEGORY

WORKERS Health and Safety

85

2004). Exposure to SO2, NO2, and CO has been linked to increases in cardiopulmonary

mortality, respiratory and cardiovascular hospital admissions, and emergency

admissions caused by stroke (NO2), and myocardial infarction (NO2 and CO) (Bernstein

et al., 2004). Ultrafine particles account for the systemic health effects of DE, including

increased carcinogenity, potentiation of autoimmune disorders, alteration of blood

coagulability, and increased cardiovascular disorders (Sydbom et al., 2001). The

exposure to DE of waste collection truck drivers and waste collectors has been

evaluated. Collection truck drivers showed higher exposure to DE than long distance

truck drivers, who did not differ from the control group (Guillemin, Herrera, Huynh, Droz,

& Due, 1992). Household waste collection truck drivers in urban areas have showed

higher exposure to DE than maintenance workers at waste handling facilities

(Kuusimäki et al., 2002). Waste collectors, on the other hand, were exposed to higher

levels of DE than waste collection truck drivers (Lee et al., 2015). Engine emission

standards and average driving speed were found to be the most influential factors in

determining the occupational exposure of waste collection workers to DE (Lee et al.,

2015).

Workers at composting facilities are exposed to DE during the following

processes: (1) Shredding; the shredding equipment selected for this study is diesel-

powered at all scales of management. (2) Storage; for large-scale composting scenarios

(S5, S6, S7, S8), the organic material is moved using a front-loader; for small-scale

scenarios (S1, S2, S3, S4) the material is moved by manual labor using a shovel. (3)

Transport to composting area, transport to screening area, and transport to curing area;

for large-scale scenarios (S5, S6, S7, S8), the material is moved using a front-loader;

86

for small-scale scenarios (S1, S2, S3, S4), the material is moved by manual labor using

a plastic mobile container. (4) Screening; the screening equipment selected for this

study is diesel-powered at all scales of management. The following considerations were

made for scoring the composting scenarios with regard to occupational diesel exposure:

small-scale scenarios (S1, S2) which do not involve diesel-powered waste collection

have the best social performance because they avoid the negative impacts of collection

and transport. Scenarios involving collection (S3, S4) but not transport have good social

performance. Scenarios with lower transport distances (S5, S6) have satisfactory social

performance. Scenarios with higher transport distances (S7, S8) have poor social

performance. Large-scale scenarios involving front-loaders for transport operations (S5,

S6, S7, S8) have poor social performance. Scenarios involving in-vessel composters

powered by electricity (S2, S4, S6, S8) do not generate exposure to DE, regardless of

the source of electricity. Large-scale scenarios requiring a front-loader to feed the

shredder and the screener (S5, S6, S7, S8) have poor social performance, while

scenarios where manual labor is employed to feed the shredder and screener have

satisfactory performance. Figure 5-6 presents the level of exposure of composting

workers to DE.

Figure 5-6. Level of exposure of composting workers to diesel exhaust

Risk of physical injury from machinery

The resource management system of OSW involves mechanical equipment for

the following processes: (1) collection and transport, (2) shredding, (3) storage, (4)

STAKEHOLDER SOCIAL


WORKERS Health and Safety Exposure to diesel exhaust

SUBCATEGORYCOMPOSTING SCENARIOS

87

transport operations, (5) in-vessel decomposition, (6) windrow turning, and (7)

screening. Any mechanical equipment presents safety risks for workers in proportion to

the size and power of the machine (Brown, 2016), therefore large-scale composting

facilities have poorer social performance than small-scale facilities regarding risk of

physical injury from machinery. Collection and transport operations have been reported

to imply a high risk of physical injury for workers. Municipal solid waste collection is the

fifth most dangerous civilian occupation in the United States (BLS, 2016). Waste

collection workers are most commonly exposed to musculoskeletal and dermal injury

risks such as strains, contusions, fractures, and lacerations (An, Englehardt, Fleming, &

Bean, 1999). In composting facilities, the processes of shredding and screening involve

the risk of thrown objects, so eye protection should be used against potential impacts

(Brown, 2016). The shredding process is a particularly aggressive operation involving

high-powered machinery and very fast rotational speeds, therefore deserves particular

attention in regard to safety (Brown, 2016). Large-scale composting facilities using front-

loaders for transport operations involve the risk of tractor roll-over, which causes more

fatalities than any other type of accident in farms (Brown, 2016). Tractor roll-over is a

safety risk for tractor operators as well as operators performing other tasks. Windrow

systems imply a higher risk of engulfment (Brown, 2016), and the risk is presumably

higher for large-scale facilities with bigger windrow piles. Windrow turning is a process

where operators are exposed to concentrated levels of moisture and gaseous emissions

from decomposing materials (Brown, 2016), therefore in-vessel systems where turning

is performed by the composting machine imply lower risk for workers. The screening

process involves a high risk of thrown objects and the release of dust, causing potential

88

impacts for the eyes, body, and dust inhalation (Brown, 2016). For the scoring of

composting scenarios under analysis the following considerations are made: the risk of

physical injury from machinery increases with the management scale, because safety

risks are proportional to the size and power of the machine (Brown, 2016). Scenarios

involving collection and transport have a higher risk physical injury, and larger transport

distances involve higher risk. The risk from the shredding process is considered equal

across composting scenarios. Large-scale scenarios using front-loaders for transport

operations have a higher risk than small-scale scenarios using plastic mobile

containers. Large-scale windrow scenarios imply more risk of engulfment than small-

scale windrows. Regarding turning, in-vessel systems have the best social

performance, small-scale windrow systems have good performance, and large-scale

windrow systems using turning machinery have poor performance. The risk from the

screening process is considered equal across composting scenarios. The final score for

the social category ‘risk of physical injury from machinery’ is the average of the scores

obtained from collection and transport, transport operations, and turning. Figure 5-7

presents the risks of physical injury from machinery.

Figure 5-7. Risk of physical injury from machinery

STAKEHOLDER SOCIAL



Shredding

Transport operations

Turning

Screening

WORKERS Health and Safety Risk of injury from machines


89

Physiological risks

Physiological health concerns for workers at composting facilities include noise

and physiological stress. Composting processes that require heavy equipment are noisy

and can cause hearing damage (Brown, 2016). Chronic exposure to occupational noise

has been shown to significantly increase blood pressure (Chang, Jain, Wang, & Chan,

2003). Physiological risks of solid waste collectors are caused by heavy lifting and

pulling/pushing of containers and carts; these activities entail occupational risks such as

musculoskeletal disorders of the neck, shoulders, arms, and lower back (Poulsen et al.,

1995). In addition, factors such as noise, lack of lighting, and heat can act in line with

high working speeds and muscle fatigue to increase the incidence of accidents among

waste collectors (Poulsen et al., 1995). In composting facilities, the major sources of

noise are shredders and front loaders used for transport operations (Epstein, 2011).

Shredding equipment have been reported to exceed the acceptable noise level of 90

decibels (Brown, 2016). The negative impacts of noise on workers at composting

facilities can be mitigated by wearing noise-cancelling equipment, and by rotating

workers among different tasks in order to reduce the duration of the exposure (Brown,

2016). Physiological risks at composting facilities are related to heavy lifting and

prolonged postures, aggravated by exposure to heat, cold, and direct sunlight (Brown,

2016). Composting workers at large-scale facilities can experience overexertion and

fatigue when operating heavy equipment with repetitive use and prolonged sitting

(Brown, 2016). Workers at small-scale facilities can also experience overexertion and

fatigue as a result of continuous shoveling (Watson, Shields, & Smith, 2011). For the

scoring of composting scenarios under analysis the following considerations are made:

physiological risks for workers are the average of the results obtained from the

90

processes of collection and transport, shredding, transport operations, and shoveling

activities. Scenarios involving collection have higher physiological risk for workers, but

transport does not involve higher risk. Large-scale composting scenarios in which front-

loaders are used for transport operations have lower social performance due to noise.

Small-scale composting scenarios involving shoveling have lower social performance

due to potential overexertion and fatigue. Shredding has a poor social performance, and

large-scale shredders have a very poor social performance. Figure 5-8 presents the

physiological risks of composting workers.

Figure 5-8. Physiological risks of composting workers

Consumers

Participation of households in the source-separation of food waste is crucial to

any composting program. Incorrect sorting at the household level can reduce the

efficiency of the composting process as well as the quality of the final product (A

Bernstad & la Cour Jansen, 2011). Composting requires pure waste streams, and

promoting source-separation of food waste at the household level is the best way to

achieve this (Ali & Harper, 2004). Appropriate source-separation reduces the capital,

operation, and maintenance costs of composting facilities (Epstein, 1996) The

composting scenarios under analysis all require a high level of consumer participation in

source-separation of food and garden wastes. Small-scale scenarios (S1, S2) require

STAKEHOLDER SOCIAL



Transport operations

Shoveling

Shredding

WORKERS Health and Safety Physiological risks


91

an additional level of consumer participation to transport the OSW to the composting

facility located at a maximum distance of 100 m (block scale). Consumers are required

to place their OSW in plastic containers located in the composting facility. A similar

composting scheme was initiated in 2008 in a Swedish residential area, where

households were required to separate their household waste into thirteen different

fractions; organic waste was transported by households to in-vessel composting

reactors located in neighborhood recycling buildings (Anna Bernstad, la Cour Jansen, &

Aspegren, 2012). The following considerations were made for scoring the composting

scenarios with regard to consumer participation: all scenarios require a high level of

consumer participation in the source separation of food and garden wastes; small-scale

scenarios (S1, S2) require an additional level of consumer participation to transport the

OSW to the composting facility; this additional level of consumer work is considered to

have a negative social impact, although there is no reflection on the possible economic

savings obtained by households from taking care of the processes of collection and

transport, which could theoretically lead to a positive social impact. Figure 5-9 presents

the level of public participation from consumers.

Figure 5-9. Level of public participation from consumers

Local community

Social impacts of composting facilities on local communities are organized in the

subcategories of ‘healthy living conditions’, ‘community engagement’, and ‘local

employment’.

STAKEHOLDER SOCIAL


CONSUMERS Public participation Level of work

COMPOSTING SCENARIOSSUBCATEGORY

92

Healthy living conditions

Composting facilities affect the health of surrounding communities by means of:

(1) exposure to chemical and biological agents, and (2) exposure to diesel exhaust.

Exposure to chemical and biological agents is related to the handling of decomposing

organic material, while exposure to diesel exhaust is related to emissions of collection

and composting machinery. Malodorous gaseous emissions from composting facilities

have created a lack of acceptance by surrounding communities (Domingo & Nadal,

2009). The emissions of microbial VOCs and odor perception generated by two

enclosed (equipped with biofilter), windrow composting facilities in Germany have been

evaluated. Microbial VOCs concentrations at 50 m distance from the plant was found to

be 2 to 5 times higher for a larger-scale composting facility (22,000 ton/year) compared

to a smaller-scale facility (11,000 ton/year) (Müller, Thißen, Braun, Dott, & Fischer,

2004). Similarly, compost odor was recognizable up to 800 m distance from the larger

compost plant, and 500 m distance from the smaller one (Müller et al., 2004). Odor was

found to be strong at 50 m distance and slight at 300 m from the composting plant

(Müller et al., 2004). Therefore, large-scale composting facilities have a greater negative

impact on local communities with respect to VOCs emissions and odor perception

compared to small-scale facilities. Long term exposure to low VOCs levels found in

German composting facilities has been associated with behavioral effects, chronic

fatigue and mental confusions, as well as somatic and gastric symptoms (Herr, zur

Nieden, Bödeker, Gieler, & Eikmann, 2003). However, there is a lack of long-term

investigations on the adverse health effects of composting facilities on local residents

(Müller et al., 2004). The exposure of a residential area to bioaerosols from a large-

scale, open-windrow composting plant treating 12,500 ton/year of yard trimmings and

93

organic waste was found to be as much as 100 times higher than background levels at

320 m distance downwind from the composting site (Herr et al., 2003). Odor annoyance

in the neighborhood was reported by 80% of residents, increasing to 95% in residents

living 150–200 m from the composting site (Herr et al., 2003). In the UK, bioaerosol

emissions from four composting facilities of different treatment capacity and

technologies were evaluated (Williams, Lamarre, Butterfield, Tyrrel, & Simpson, 2013).

It was clear from the observations that the processes of shredding, turning, and

screening cause significant increases in bioaerosol emissions (Williams et al., 2013).

The largest composting plant (60,000 ton/year, combination of windrow and in-vessel

systems) showed the highest bioaerosol concentrations at both >250 m and <250 m

distance from the source, exceeding acceptable regulatory levels. The smaller scale

composting plant (5,000 ton/year, open windrow system) also exceeded acceptable

levels at >250 m distance, but rapidly declined afterwards. The fully enclosed

composting plant (28,000 ton/year) was the only one in which no bioaerosols were

detected even close to the source, which suggests that full enclosure of composting

operations can be an effective control measure (Williams et al., 2013). The study

concludes that greater regulatory scrutiny should be applied to large-scale open

windrow systems due to high bioaerosol concentrations at ≥250 m downwind from the

composting plant (Williams et al., 2013).

Results from the reviewed studies from Germany and the UK confirm that small-

scale composting facilities have lower VOCs and bioaerosol concentrations in

surrounding areas. However, total gaseous emissions to the environment per ton of

waste remains the same regardless of the scale of management. Long-term exposure

94

to low levels of gaseous emissions from composting facilities has been associated with

behavioral effects and somatic and gastric symptoms (Herr et al., 2003). The negative

health effects on both workers and local communities from the gaseous emissions of

compost point toward fully enclosed buildings or vessels for most composting

processes. In-vessel facilities have the best social performance regarding both worker

and community health, as they are able to reduce noxious emissions from the

decomposition process by 96% (Hotrot, 2013). Fully enclosed buildings with biofiltration

mechanisms can significantly reduce emissions from composting processes such as

storage, shredding, screening, and curing (Williams et al., 2013). Worker exposure

within composting buildings should be avoided. The following considerations are made

for scoring the subcategory of ‘healthy living conditions’: shredding, decomposition,

turning, and screening are the processes that contribute most to gaseous emissions

(Byeon et al., 2008). Figure 5-10 presents the exposure of the local community to

gaseous emissions.

Figure 5-10. Exposure of local community to gaseous emissions

The exposure of local communities to gaseous emissions from composting

facilities increases with the scale of management (Williams et al., 2013). Large-scale

facilities handle more organic waste and therefore air emissions are more concentrated,

which produces more negative social impacts on surrounding communities related to

STAKEHOLDER SOCIAL


Shredding

Decomposition

Turning

Screening

LOCAL

COMMUNITY


Healthy living conditions Gaseous emissions

95

odors, VOCs, and bioaerosols (Byeon et al., 2008; Müller et al., 2004). For scoring

purposes, the processes of shredding, turning, and screening have a negative social

impact across composting scenarios, and the level of impact increases with the scale of

management. Decomposition in in-vessel system has a positive social impact

regardless of the scale of management, as small and large in-vessel systems can

achieve the same level of biofiltration.

Community engagement

Small-scale composting facilities at the block and neighborhood levels usually

foster community engagement through direct involvement in community initiatives.

Community-based organizations have started neighborhood composting programs

including waste collection services as a response to inadequate waste management by

municipal authorities in Indian cities (Colon & Fawcett, 2006). Since the 1990s, Indian

cities have moved toward small-scale, manually operated composting facilities at the

community level, managed by citizens’ initiatives and supported by nongovernmental

organizations (Zurbrügg et al., 2004). Decentralized composting plants usually rely on

labor intensive processes, offering employment opportunities for local residents

(Zurbrügg et al., 2004). Community engagement has been crucial to the success of

decentralized composting facilities in India. The feasibility of a small-scale composting

and collection activities is dependent on the cooperation and support of the households

in crucial issues such as source segregation of waste, willingness to pay for waste

handling, and acceptance of composting sites in the neighborhood (Zurbrügg et al.,

2004). The compost produced is purchased mainly by the local community and used as

organic fertilizer and soil amendment in private gardens and nearby parks (Zurbrügg et

al., 2004). The Advanced Locality Management (ALM) program in Mumbai, is a series

96

of community initiatives supported by the municipal government comprising on the

average 250 households from individual streets or neighborhoods; from 670 existing

ALMs, 284 compost and locally use their organic waste in community projects

(Zurbrügg et al., 2004).

Community composting in the United States is driven by the interest of local

governments in achieving recycling targets, diverting food waste from landfills, and

reducing solid waste management costs (Platt, McSweeney, & Davis, 2014). Successful

community composting initiatives are supported by local governments and a wide

network of private partners. The New York City Compost Project (NYCCP), created by

the NYC Department of Sanitation composted 892 tons of residential organic waste in

2014, through a network of 225 small-scale community composting sites located in

gardens, parks, schools, urban farms, private properties, churches, rooftops, and other

locations throughout the city (NYC-Sanitation, 2015). Most compost is used to rebuild

soil in urban green spaces like community gardens, urban farms, street trees, and parks

(NYC-Sanitation, 2015). Community composting initiatives in the United States foster

community engagement by empowering people to grow and eat healthy, organic, and

local food (Platt et al., 2014). Local communities can engage through training programs

on a wide variety of sustainability issues, including urban agriculture, soil

bioremediation, aquaculture closed-loop systems, composting, permaculture, among

others (GrowingPower, 2017). The social benefits acquired by local communities from

community composting include: neighborhood scale operation, knowledge development

about composting, social inclusion and empowerment, keeping resources and money

within the local community, building healthy soils, promotion of human-scale technology,

97

instead of large capital intensive systems, and supporting healthy food production and

consumption (Platt et al., 2014). Training and employment opportunities to

disadvantaged groups such as long-term unemployed, people with disabilities, or

minority ethnic groups is an important social benefit of community composting (Slater &

Aiken, 2015). Additional benefits reported by individuals engaged in community

composting include: improved health and well-being, feelings of safety and sense of

belonging, increased engagement in meaningful activity and learning new skills, and

increased engagement in pro-social/environmental behavior (Slater, Frederickson, &

Yoxon, 2010).

Large-scale composting facilities at urban and regional levels tend to be located

in insolated or rural areas in order to minimize the negative impacts on local

communities. The concentration of large volumes of biodegradable materials in

relatively small areas exacerbates the problems of nuisance odors and exposure to

chemical and biological risks for workers and local communities (Herr et al., 2003;

Müller et al., 2004; Williams et al., 2013). Successful composting systems based on

centralized, large-scale regional facilities have been implemented in the United States.

The city of San Francisco recycles nearly 220,000 tons of residential organic wastes per

year into compost used by farms and vineyards while reducing solid waste going into

landfills by 10-15% per year (Sullivan, 2011). However, large-scale composting facilities

in Portland (OR), Dade County (MD), and Pembroke Pines (FL) have been shut down

due to inadequate odor control systems (van Haaren, 2009). A regional scale

composting plant treating 115,000 tons per year in Wilmington (DE) was forced to shut

down due to frequent uncontrolled odors affecting surrounding cities (Biocycle, 2014).

98

While centralized composting facilities are successful in diverting organic waste from

landfills, they have a poor social performance for local communities compared to small-

scale community composting. Figure 5-11 presents the level of community engagement.

Figure 5-11. Community engagement

Figure 5-12. Summary of social performance

Concluding Remarks

The social performance of 8 urban composting scenarios was analyzed using S-

LCA. Figure 5-12 summarizes the social performance results. From a social standpoint,

the optimal scales of composting are the block and neighborhood scales. Small-scale

composting facilities have better social performance in all of the social indicators. The

health and safety of workers is negatively affected at large-scale composting facilities

due to higher exposure to gaseous emissions and related impacts. Large-scale facilities

involve more heavy machinery, resulting in higher exposure to diesel exhaust, and

higher risks of physical injury and physiological hazards. The exposure to gaseous

emissions from the decomposition of organic matter at composting facilities is the most

STAKEHOLDER SOCIAL


LOCAL

COMMUNITY


Community engagement Community engagement

STAKEHOLDER SOCIAL





Risk of injury from machines

Physiological risks

CONSUMERS Public participation Level of work

LOCAL Healthy living conditions Gaseous emissions

COMMUNITY Community engagement Community engagement

Performance Average


WORKERS Health and Safety

99

relevant factor affecting the health of both workers and local communities. Small-scale

composting facilities have better social performance as they imply lower concentrations

of gaseous emissions. Regarding technology, in-vessel systems at the small scale have

the best social performance due to the bio-filtration mechanism which reduces gaseous

emissions by 90%. Windrow systems are not appropriate to be located in urban areas,

because gaseous emissions and related chemical and biological risks would negatively

affect public health. In-vessel systems at the block and neighborhood levels are the

most appropriate technology, considering the reduction of gaseous emissions, reduction

of exposure to DE, and high level of community engagement by local communities.

Positive social impacts at the community level are relevant and should be carefully

examined in the urban design and planning of composting infrastructure. Small-scale

composting facilities can support sustainable local development strategies related to

urban farming, training and employment opportunities for local residents, and healthy

food production and consumption. The positive social benefits of small-scale

composting facilities can be extended to other circular strategies for waste

management. Recycling of other waste fractions such as plastic and paper, as well as

water reuse and recovery can be integrated in local development strategies in order to

exploit synergies while maximizing social benefits.

100

CHAPTER 6 CONCLUSIONS

A Life Cycle Sustainability Assessment (LCSA) including the environmental,

economic, and social dimensions was performed for eight urban composting scenarios

covering four spatial scales and two treatment technologies (windrow and in-vessel).

The environmental and social assessments indicate that small-scale composting at the

block and neighborhood levels are the optimal scales of OSW management. At the

small scale, the composting process is labor-intensive and therefore less dependent on

fossil fuels. The exposure to diesel exhaust resulting combustion of diesel fuel is an

important factor affecting the health of workers at composting facilities as well as the

living conditions of local communities. Similarly, diesel fuel combustion from the

composting processes has a negative effect in most environmental impact categories.

As the scale of management increases, the social and environmental performance

decreases due to higher use of fossil fuels required for heavy composting machinery.

Heavy machinery used at large-scale composting also implies a higher risk of injury and

physiological stress for composting workers. From an economic standpoint, the impact

of fossil fuel use is not proportional to its environmental and social impacts. Large-scale

composting scenarios have a better economic performance because of the economies

of scale achieved in the composting process and decreasing labor costs. However,

labor cost and fuel cost neutralize each other with increasing scale. Therefore, from an

economic viewpoint, the optimal scale is determined by the economies of scale

achieved by the in-vessel composting machinery in terms of capital cost and energy

consumption. An integrated analysis of the environmental, economic, and social results

reveals the disparity between the environmental and social impacts of fossil fuels and

101

their economic value. The price of fossil fuels favors mechanization and centralization of

composting infrastructure at the expense of lower environmental and social

performance. In order for small-scale composting facilities to be competitive from an

economic standpoint, small-scale machinery, particularly the in-vessel composter and

the shredder, would have to reach the economic efficiency of large-scale machinery in

terms of capital cost and energy consumption. Under this scenario, the economic

savings from collection and transport at the small-scale would be realized and the small

scale would be optimal. Because higher labor costs are offset by lower fuel costs at the

small scale, more economically efficient composting machinery would make small-scale

composting at the block and neighborhood scales optimal from an environmental,

economic, and social perspective.

In-vessel composting technology was found to be superior to windrow technology

from an environmental, economic, and social perspective. The emission of GHG from

the decomposition of organic materials is the most important factor responsible for the

environmental performance of composting systems. In-vessel technologies are capable

of reducing GHG by 90% from the decomposition process. From a social perspective,

gaseous emissions from decomposition are responsible for the exposure to chemical

and biological risks for composting workers and local communities. Windrow

technologies, without the capacity to control gaseous emissions, are not suitable to be

located in urban environments due to the negative impacts on public health and the

environment. From an economic perspective, windrow technologies showed no

economies of scale while in-vessel technologies showed significant economies of scale.

Windrow technologies are less expensive at the block and neighborhood scales, while

102

in-vessel technologies become competitive with increasing scale. This is explained by

the economies of scale of the in-vessel composting machine. In general, in-vessel

technologies have better environmental, economic, and social performance than

windrow technologies.

Figure 6-1 summarizes the variables that were considered for the each of the

environmental, economic, and social assessments, and highlights the most important

ones. For the environmental assessment, the most relevant materials are steel and

concrete. Steel is used extensively for the construction of buildings and composting

machinery such as trucks, front-loaders, shredder, screener, and in-vessel composters.

Concrete is mostly used for the pavement of composting facilities. The combustion of

diesel is the most relevant variable regarding energy, because GHG emissions affect

most environmental impact categories. GHG emissions from the decomposition

process, methane and nitrous oxide, are responsible for most of the environmental

impacts from composting. From the economic standpoint, land, steel halls, and the in-

vessel composter are the most important capital costs, while labor and diesel fuel are

the most relevant operating costs. Labor and diesel fuel costs are variables dependent

on national contexts. From the social perspective, the assessment was focused on the

composting processes. The processes of shredding, decomposition, and screening

were the most critical. Shredding was critical for the health of composting workers

because of the high exposure to chemical and biological agents, and the risk of injury

from the high rotational speed of the shredder. The decomposition process

concentrates most of the gaseous emissions and therefore the exposure to chemical

and biological agents for both workers and local communities. Therefore, windrow

103

systems are not suitable for urban composting and in-vessel systems need to be

equipped with biofiltration mechanisms.

Figure 6-1. Summary of the most relevant assessment variables

The optimal scale of resource management is a crucial question for urban design

and planning. Moving toward a circular urban metabolism implies managing resources

in integrated networks where the different waste outputs generated by urban areas

become resource inputs for new production. Finding the optimal scale of management

of each resource would allow the integration of networks in close spatial proximity,

MATERIALS

Steel Land Collection

Iron Buildings Transport

Concrete Steel Hall Sorting

HDPE Paving Shredding

Fiberglass Collection Truck Storage

Wood chip Shredder Transport

ENERGY Container Forming windrows

Diesel Front-Loader Decomposition

Electricity Composter Turning

EMISSIONS Turner Transport

Methane Screener Screening

Nitrous Oxide Transport

Labor Curing

Diesel Fuel

Electricity

Maintenance

OPERATING COSTS

ENVIRONMENTAL ECONOMIC SOCIAL

PROCESSESCAPITAL COSTS

104

maximizing environmental, economic, and social benefits. A main conclusion of this

dissertation is that urban composting should be performed at the small scale (block or

neighborhood); close to the point of waste generation in order to reduce environmental

impacts while maximizing social and economic benefits.

Four major limitations are identified for the study. First, the lack of data on the

environmental impacts of the manufacturing processes of composting machinery

(collection truck, front-loader, shredder, screener, and in-vessel composter). The

environmental analysis is performed based on the quantities of materials, using a

generic production dataset for each material. The impact corresponds to production of

the materials required to produce the machine, not the production of the machine itself.

A more complete environmental analysis should include the environmental impacts from

the manufacturing process. Second, the use of compost is not included in the analysis

because the use on land is outside the waste management system, which is the system

boundary. Several environmental LCA studies including the use of compost on

agricultural land have found additional environmental benefits resulting from the

substitution for peat in the production of growth media, the replacement of chemical

fertilizers, binding of carbon in the soil, the improvement of the biological properties of

the soil, among others (Lazcano et al., 2014; Martínez-Blanco et al., 2013; Saer et al.,

2013). Third, there is a variety of data sources that need to be acknowledged. The

environmental assessment is based on quantities of materials of composting

infrastructure and machinery, as well as waste emissions during the life cycle of the

facility. These quantities of materials and emissions are variable depending on the type

of machinery, the emission standards, and the materials used. The economic

105

assessment is based on capital and operational costs. While the capital costs of

composting machinery should not have significant variations from different geographical

locations, operational costs are highly dependent on national contexts, particularly labor

and fuel costs. Land cost is another volatile variable dependent on the urban economy.

Therefore, the economic assessment is site-specific and should be adapted to local

conditions. The social assessment is derived from the composting processes instead of

the geographical locations of the different life cycle phases. The focus is the social

performance of different scales and technologies of composting. A more complete

social LCA should also consider the social impacts related to the behavior of

organizations during the production of materials, the manufacturing of composting

machinery, the operation of the composting plants, and the final recycling or disposal of

materials. Finally, the sustainability performance of composting is not compared to other

waste treatment alternatives such as landfilling, incineration, or anaerobic digestion.

While several environmental LCA studies have found that composting is a better

alternative to landfilling and incineration (A Bernstad & la Cour Jansen, 2011; Grosso,

Nava, Testori, Rigamonti, & Viganò, 2012), a comprehensive sustainability evaluation

comparing the environmental, economic and social performance of composting,

landfilling, incineration, and anaerobic digestion is needed.

This dissertation fills a research gap in the waste management literature

regarding the characterization and sustainability evaluation of small-scale composting

facilities, as well as in-vessel composting technologies. Additionally, this dissertation

highlights the tradeoff between economies of scale and transportation cost for the case

of composting, which has been described in the waste management literature (Chen et

106

al., 2012). Another contribution of this study is the development of the Life Cycle

Sustainability Assessment (LCSA) methodology, in which the three sustainability

dimensions are simultaneously evaluated. An approach has been proposed to unify the

calculation procedures, the system boundaries, and the functional unit for the integrated

assessment of the environmental, economic, and social dimensions. Further integration

of the results from the three dimensions into a unified system is an area of future

research. Theoretically, this study contributes to the literature of urban metabolism by

understanding composting of OSW in relation to the multiple flows of energy and

materials that converge in cities, and identifying the synergies that can be developed

toward a circular urban metabolism.

Composting at the local level could be integrated to small-scale sanitation

systems for fertilizer production, rainwater harvesting, and stormwater management

through green infrastructure. Urban farming can take multiple forms: organic fruits and

vegetables, medicinal plants, flowers, forestry, animal husbandry, etc.; and farming

could be further articulated to small-scale manufacturing processes. The sustainability

of small-scale composting suggests alternatives for other circular strategies. What is the

optimal scale of recycling for plastics, paper, cardboard, metals, textiles, oils,

electronics, etc.? How can recycle networks be integrated at the local level to maximize

environmental, economic, and social benefits? This dissertation develops a

methodology for answering these questions in future research.

For sustainability, the linear metabolism of cities, as represented in Figure 6-2,

needs to transition toward a circular urban metabolism, as represented in Figure 6-3,

where recycling infrastructure is integrated into urban areas at optimal scales. The

107

analysis of the resource outputs associated with different land uses is critical for

improving urban metabolism. Residential areas produce a homogeneous mixture of

waste resources (organics, papers, plastics, electronics, etc.). Recycling industries at

different optimal scales would be integrated into residential areas, closing the cycles

while promoting sustainable production and consumption. Commercial and industrial

areas that produce more variable waste outputs would be connected to new industries

specifically designed to close the open cycles of the urban metabolism. Synergies

between different land uses and spatial functions need to be found in order to maximize

the exchange of materials, water, and energy in the urban environment. Urban areas

should become more complex by developing a high diversity of integrated networks of

resource flow similar to natural ecosystems. A strong process of decentralization is

required to implement sustainable local infrastructures, prioritizing flexible and

collaborative practices, operating within new institutional frameworks that embrace local

participation and experimentation. Ultimately, a circular urban metabolism should

promote sustainable production and consumption practices focused on reducing the

environmental impacts associated with resource extraction and waste generation, while

promoting economic development with a high level of social performance.

108

Figure 6-2. Linear urban metabolism

Figure 6-3. Circular urban metabolism

109

APPENDIX A LIFE CYCLE INVENTORY (LCI) CALCULATIONS BY UNIT PROCESSES

Collection

Scenarios 1 and 2.No collection activities are involved.

Scenarios 3 to 8. The whole collection truck is allocated to the collection process

when no transport is involved. When transport is involved, the collection truck is

allocated to collection and transport by equal amounts. It is assumed that the truck is

collecting OSW 5 days per week, corresponding to 261 loads of 14 tons per year. For a

lifetime of 15 years, the truck can collect 54,810 tons of OSW. Materials (Steel, iron,

HDPE) used per ton of waste collected is expressed as:

Material per ton =MwLTR

Where,

MW = Material weight of the truck (kg)

LTR = Collection capacity of the truck over its lifetime (tons)

Calculation of steel for scenarios 3 to 8:

Steelper ton =8,379 kg

54,810 tons= 0.152 kg/ton

Transport

Scenarios 5 to 8. For S5 and S6, the collected waste is transported 25 km to the

composting facility. For S7 and S8, the collected waste is transported 50 km to the

composting facility A diesel consumption of 0.16 km/L is assumed (Larsen, Vrgoc,

Lieberknecht, & Christensen, 2009). Diesel consumption is presented in kg, assuming a

diesel density of 0.832 kg/L.. Diesel consumption during transport is calculated as:

Diesel consumptionper ton = (d ∗ T) ∗ D

110

Where,

d = Diesel consumption per km (0.16 km/L)

T = Transport distance (km)

D = Density of diesel (kg/L)

Calculation for scenarios 5 and 6:

Diesel consumptionper ton = (0.16km

L∗ 25 km) x 0.832 kg = 3.32 kg/ton

Sorting

The sorting process is assumed to be manual for all composting scenarios. For

scenarios 3 to 8, no energy and materials are involved in the process. For scenarios 1

and 2, HDPE collection bins are required to store the OSW before sorting. Four plastic

containers are required to store the OSW collected for 2 days. The production and end

of life value for scenarios 1 and 2 is

HDPE per ton =MwLTR

Where,

MW = HDPE weight of the container (kg)

LTR = Lifetime treatment capacity of the composting facility (tons)

Calculation for S1 and S2:

HDPE per ton =144 kg

1,500 ton= 0.096 kg/ton

Shredding

Scenarios 1 to 8. Production and end of life values:

Steel per ton =swLTR

111

Where,

SW = Steel weight of the machine (kg)



Steel per ton =92 kg

1,500 ton= 0.061 kg/ton

Diesel consumption for all diesel engines is assumed to be 0.26 L per

horsepower per hour (Grisso et al., 2010). Diesel consumption during the use phase is:

Diesel consumption per ton = (d ∗ HP

TR) x D

Where,

d = Diesel consumption assumed for all machines (L/HP-hour)



D = Density of diesel (0.832 kg/L)


Diesel consumption per ton = (6 HP ∗ 0.26

LHP − h

0.185tonh

) ∗ 0.832kg

L= 7.01 kg/ton

Storage

Scenarios 1 to 4. Shredded materials are stored in HDPE plastic containers,

which are also used for transport. The weight of the containers is distributed by equal

parts to storage and transport. The value for the production and end of life phases is:

HDPE per ton =

MwLTR2

112

Where,



Calculations for scenarios 1 and 2:

HDPE per ton = (72 kg

1,500 ton)/2 = 0.024 kg/ton

Scenarios 5 to 8. Shredded materials are stored in concrete L-shaped elements.

The value for the production and end of life phases is

Concrete per ton =CWLTR

Where,

CW = Weight of the total concrete elements (kg)

LTR = Treatment capacity of the composting facility (tons)


Concrete per ton =10 ∗ 1,200 kg

150,000 ton= 0.08 kg/ton

Transport to composting area

Scenarios 1 to 4. The HDPE mobile containers used for storage are used for

manual transport. The weight of the containers is distributed by equal parts to storage

and transport. The value for the production and end of life phases is:

HDPE per ton =

MwLTR2

Where,



113


HDPE per ton = (72 kg

1,500 ton)/2 = 0.024 kg/ton

Scenarios 5 to 8. A front loader is used to transport materials to the composting

area. The front loader is allocated to six unit processes by equal parts: transport to

composting area, forming windrows, transport to screening area, screening, transport to

curing area, and forming curing piles. The value for the production and end of life

phases is:

Steel per ton =

MW ∗ pLTR6

Where,

MW = Weight of the front loader (kg)

p = Percentage of steel in the front loader (%)



Steel per ton = ((15,415 kg) ∗ 0.9

150,000 ton)/6 = 0.015 kg/ton

Diesel consumption during the use phase is assumed to be 0.15 L per HP per

hour, times a load factor of 70%, as front loaders do not always operate at full power

(Chitkara, 1998). The front loader is assumed to operate 4 hours per day, in six unit

processes by equal time: transport to composting area, forming windrows, transport to

screening area, screening, transport to curing area, and forming curing piles. During a

114

lifetime of 15 years, working hours would be 15,600. Density of diesel is 0.832 kg/L.

Diesel consumption calculation is:

Diesel per ton = ((HP ∗ L ∗ p ∗ h

LTR)

6) ∗ D

Where,

HP = Power of the front loader (HP)

L = Diesel consumption assumed (L/HP-hour)

p = Load factor of 70%

h = Hours of operation over the lifetime (h)

LTR = Treatment capacity of the facility over its lifetime (tons)

D = density of diesel (kg/L)


Diesel per ton = ((165 HP ∗ 0.26L ∗ 0.7 ∗ 15,600 h

150,000 ton)

6) ∗ 0.832 kg/L = 0.015 kg/ton

Forming windrows

Scenarios 1 to 4. A regular shovel is used to form windrows and feed the

vessels. The value for the production and end of life phases is:

Steel per ton =CWLTR

Where,

CW = Weight of the steel in the shovel (kg)



115

Steel per ton = (1.5 kg

1,500 ton) = 0.001 kg/ton

Scenarios 5 to 8. The front loader is used to form windrows and feed the vessels.

The production, use, and end of life values are equal to the process “transport to

composting area”. The front loader and its diesel consumption are allocated to six unit

processes by equal parts: transport to composting area, forming windrows, transport to

screening area, screening, transport to curing area, and forming curing piles.

Decomposition

For windrow systems, decomposition occurs in open-air piles that emit

greenhouse gases. For wet food waste, 4 kilograms of CH4 and 0.3 kilograms of N2O

per ton of waste are assumed. For dry garden waste, 10 kilograms of CH4 and 0.6

kilograms of N2O per ton of waste are modelled in all scenarios (Change, 2006). For in-

vessel systems, decomposition occurs in composting machines made primarily of steel.

Biofilters in in-vessel machines reduce GHG by 90%. Biofilters are made of concrete or

fiberglass. Biofilter media is wood chips. Production and end of life values are

calculated as:

Material per ton =MWLTR

Where,

MW = Weight of the material in the machine (kg)

LTR = Treatment capacity of the composting facility over its lifetime (tons)

Calculations for steel in S2:

Steel per ton = (3,467 kg

1,500 ton) = 2.312 kg/ton

116

For the use phase of in-vessel composting machines, electricity consumption is

calculated as:

Electricity per ton =P

TR

Where,

P = Power of the machine (kW)

TR = Throughput of the machine (tons/hour)

Turning

Scenarios 1 to 4. Turning is performed by one operator using a regular shovel.

The production and end of life values are calculated as:


Where,

MW = Weight of the material (kg)

LTR = Treatment capacity of the composting facility over it lifetime (tons)

Calculation for S1 to S4:

Steel per ton = (1,5 kg

1,500 ton) = 0.001 kg/ton

Scenarios 5 to 8. Turning is performed by an operator using a windrow turning

machine. Production and end of life values:

Steel per ton =swLTR

Where,

SW = Steel weight of the machine (kg)


117

Calculation for scenario 5:

Steel per ton =15,000 kg

150,000 ton= 0.1 kg/ton

Diesel consumption for all diesel engines is assumed to be 0.26 L per

horsepower per hour (Grisso et al., 2010). Diesel consumption during the use phase is:


TR) x D

Where,






Diesel consumption per ton = (205 HP ∗ 0.26

LHP − h

1,050tonh

) ∗ 0.832kg

L= 0.042 kg/ton

Transport to screening area

Scenarios 1 to 4. HDPE plastic containers are used to transport compost to

screening area. Containers are allocated by equal amounts to transport to screening

area and transport to curing area. The value for the production and end of life phases is:

HDPE per ton =

MWLTR2

Where,

MW = weight of total HDPE containers (kg)


118

Calculations for S1 and S2:

HDPE per ton =

72 kg1,500 ton

2= 0.024 kg/ton

Scenarios 5 to 8. Transport to screening area is performed by one operator using

a front loader. The production, use, and end of life values are equal to the process

“transport to composting area”. The front loader and its diesel consumption are

allocated to six unit processes by equal parts: transport to composting area, forming

windrows, transport to screening area, screening, transport to curing area, and forming

curing piles.

Screening

Scenarios 1 to 4. Screening is performed by one operator using an electric

trommel screen made primarily of steel. Production and end of life values are calculated

as:


Where,



For the use phase of screening machines, electricity consumption is calculated

as:

Electricity per ton =P

TR

Where,

P = Power of the machine (kW)

TR = Throughput of the machine (tons/hour)

119

Scenarios 5 to 8. Screening is performed by a diesel-powered trommel screen

made primarily of steel. Feeding of the machine is made by one operator using a front

loader. The production, use, and end of life values of the front loader are equal to the

process “transport to composting area”. The front loader and its diesel consumption are



curing piles. Production and end of life values of the trommel screen are calculated as:


Where,



For the use phase of screening machines, diesel consumption is calculated as:


TR) x D

Where,





Transport to curing area

Scenarios 1 to 4. HDPE plastic containers are used to transport compost to

screening area. The value for the production and end of life phases is:

HDPEper ton =MWLTR

120

Where,

MW = weight of total HDPE containers (kg)


Scenarios 5 to 8. Transport to curing area is performed by one operator using a

front loader. The production, use, and end of life values are equal to the process

“transport to composting area”. The front loader and its diesel consumption are



curing piles.

Curing

All scenarios. Concrete L-shaped elements are used to cure the compost. The

value for the production and end of life phases is:

Concrete per ton =CWLTR

Where,

CW = Weight of the total concrete elements (kg)


Building, Steel hall, Concrete paving, and Total area

The production and end of life values for building, steel hall, concrete paving, and

total area are calculated as:

Area per ton =A

LTR

Where,

A = Area of the element (m2)

121


122

APPENDIX B LIFE CYCLE COSTING CALCULATIONS BY PROCESSES

Collection

Capital

The cost of the collection truck is allocated to the processes of collection and

transport by equal amounts. In scenarios with no transport, the total cost is allocated to

collection. It is assumed that the truck is collecting OSW 5 days per week,

corresponding to 261 loads of 14 tons per year. For a lifetime of 15 years, the truck can

collect 54,810 tons of OSW. The life cycle cost of the collection truck per ton of waste is

the cost of the collection truck divided by the OSW collected over its life cycle.

Collection truck costper ton =CTWc

Where,

CT = Cost of collection truck ($USD)

WC = Waste collected over lifetime (tons)


Collection truck costper ton =$250,000

54,810 tons= $4.56

Operation

Labor cost involves driver cost and loader cost. Labor cost per ton of OSW for

collection is equal to the hourly cost of labor ($2.42 USD) times the number of workers

involved, divided by the tonnage of waste collected per hour. Three operators are

involved, one driving the truck and two picking up the waste, at a rate of 2 tons per hour.

Labor costper ton =LH ∗ N

WH

123

Where,

LH = Labor cost per hour ($USD)

N = Number of workers

WH = Waste collected per hour (tons)


Labor costper ton =$2.42 ∗ 3

2 tons= $3.63

Fuel cost is the diesel consumption per ton of waste collected times the fuel

price. Diesel consumption during collection is assumed to be 3 L per ton of waste

collected. Diesel price is assumed to be $0.67 USD per liter.

Fuel costper ton = DC ∗ PF

Where,

DC = Diesel consumption per ton collected (liters)

PF = Price of diesel ($USD)

Sample calculation for scenario 3:

Fuel costper ton = 3L ∗ $0.67 USD = $2

Maintenance cost is assumed to be 10% of equipment purchase cost for all

composting equipment, following values reported for automobile recycling equipment by

Ruffino (2014).

Transport

Capital

The cost of the collection truck is allocated to the processes of collection and

transport by equal amounts. In scenarios with no transport, the total cost is allocated to

collection. For scenarios involving transport, the life cycle cost of the collection truck per

124

ton of waste is the cost of the collection truck divided by the OSW collected over its life

cycle, divided by 2.

Collection truck costper ton = (CTLTR

)/2

Where,

CT = Cost of collection truck ($USD)

LTR = Collection capacity of the truck over its lifetime (tons)


Collection truck costper ton = ($250,000

54,810 tons)/2 = $2.28

Operation

Labor cost per ton for transport is equal to the hourly cost of labor for a driver

($4.84 USD) times the transport time, divided by the amount of waste transported.

Transport is defined as the driving of the full truck from the final stop on the collection

route to the unloading point at the composting facility, and driving of the empty truck

back the same distance (Larsen, 2009). Transport time is the distance divided by the

truck velocity (assumed to be 35 km/h), plus the unloading time at the composting

facility (assumed to be 30 min.).

Transport time = (d

V) + TU

Where,

V = Velocity (km/h)

d = Distance (km)

TU = Time of unloading (h)

125

Labor costtransport =(LDR ∗ TTR)

WT

Where,

LDR = Labor cost per hour driver ($USD)

TTR = Transport time (h)

WT = Waste transported (tons)


Transport time = (25 km

35 km/h) + 0.5 h = 1.21 h

Labor costper ton =$4.84 ∗ 1.21 h

14 tons= $ 0.42

Diesel fuel cost is the cost of diesel times the diesel consumption. Diesel

consumption during transport is assumed to be 0.16 liters per ton per km.

Diesel fuel costtransport = (DC ∗ d) ∗ CD

Where,

CD = Cost of diesel fuel per liter ($USD)

d = Distance (km)

DC = Diesel consumption during transport (lt/km/ton)

Calculation for scenarios 5:

Diesel fuel costtransport = (0.16

ltkm

ton∗ 25 km) ∗ $0.67 = $ 2.68

Sorting

Sorting of materials and removal of impurities is a manual process performed by

one operator at a rate of 900 kg per hour for all scenarios. Sorting cost per ton is equal

to the hourly cost of labor times the tonnage of waste sorted by hour.

126

Sorting cost per ton = LC ∗ WS

Where,

LC = Labor cost per hour ($USD)

WS = Waste sorted per hour (tons)

Calculation for all scenarios:

Sorting cost per ton = $2.42 ∗ 0.9 ton = $ 2.18 USD/ton

Shredding

Capital

Shredder cost per ton is equal to the shredder machine cost divided by the

treatment capacity of the composting facility over its lifetime.

Shredder cost per ton =SCTR

Where,

SC = Shredder machine cost ($ USD)



Shredder per ton =$480,000

150,000 ton= $3.2 USD/ton

Feeding the shredder using a front loader is needed for scenarios 5 to 8. The

front loader cost is allocated to 6 processes by equal parts: feeding the shredder,

transport to composting area, forming windrows/feeding vessel, transport to screening

area, screening, and transport to curing area. Feeding shredder cost per ton is equal to

the front loader cost divided by the treatment capacity of the composting facility, divided

by 7.

127

Feeding shredder cost per ton =

FLCTR6

Where,

FLC = Front loader cost ($ USD)



Feeding shredder cost per ton =

$180,000 USD150,000 ton

6= $0.20 USD/ton

Operation

Fuel consumption for all diesel engines is assumed to be 0.26 L per horsepower

per hour (Grisso et al., 2010). Fuel consumption cost of shredders is equal to fuel

consumption assumed for diesel machines (l/HP-hr), times the power of the shredder

(HP), divided by the throughput of the shredder (ton/hour), times the cost of fuel (USD).

The cost of diesel fuel is $ 0.67 USD per liter.

Fuel cost shredder per ton = (d ∗ HP

TR) ∗ CF

Where,






Fuel cost shredderper ton = (6 HP ∗ 0.26

LHP − h

0.185tonh

) ∗ $ 0.67 USD = $ 5.62 USD/ton

128

A front loader is needed to feed the shredder in scenarios 5 to 8. Front loader

productivity for feeding the shredder includes the tasks of loading, dumping, making

reversals of direction, and traveling a minimum distance of 25 meters. Such cycle time

is assumed to be 0.5 min. Bucket capacity is 2.8 m3. For a OSW density of 350 kg/m3 ,

bucket capacity is 0.98 tons. An average front loader worker efficiency is 50 minutes of

work per hour. Feeding the shredder at 1.96 tons per minute, equals 98 tons per hour.

Fuel cost per ton for feeding the shredder is equal to fuel consumption times the cost of

fuel.

Fuel cost feeding shredder per ton = (d ∗ HP

TR) ∗ CF

Where,






Fuel cost feeding shredderper ton = (165 HP ∗ 0.26

LHP − h

98 tonh

) ∗ $ 0.67 USD

= $ 5.62 USD/ton

Labor cost for shredding in scenarios 1 to 4 involves the operator feeding the

shredder manually (labor cost 1). For scenarios 5 to 8, labor cost is involves the

operator feeding the shredder with the front loader (labor cost 2). Time requirement

(hr/ton) for feeding the shredder is equal to the daily treatment capacity of the

composting facility (tons) divided by the shredders’ throughput (ton/hr), the result

129

divided by the daily treatment capacity (tons) of the facility to obtain the time

requirement per ton. Labor cost is equal to time requirement (hr/ton) times the hourly

labor cost.

Time requirement feeding shredder per ton =

TCTRTC

Where,

TC = Daily treatment capacity of composting facility (ton/day)

TR= Cost of labor ($ USD/hr)

Labor cost feeding shredder per ton = T ∗ CL

Where,

T = Time requirement (hr/day)

CL= Cost of labor ($ USD/hr)


Labor cost shreddingper ton = 0.066 hr/ton ∗ $ 4.84 USD = $ 0.32 USD/ton

Maintenance cost of shredders is assumed to be 10% of the purchase cost.

Storage

Capital

Shredded materials are stored in plastic mobile containers for scenarios 1 to 4.

Plastic mobile containers are used for storage and transport to composting area;

container cost is allocated to both processes by equal amounts. For scenarios 5 to 8,

materials are stored in concrete L-shaped elements. Storage cost per ton is equal to the

storage element (plastic container or L-shaped element) cost times the number of

required elements, divided by the lifetime treatment capacity of the composting facility.

130

Storage cost per ton =CS ∗ n

LTR

Where,

CS = Cost of storage element ($USD)

n = Number of required elements


Calculations for scenario 5:

Storage cost per ton =$450 ∗ 10

150,000 ton= $ 0.03 USD

The front loader is needed for storage in scenarios 5 to 8. The capital cost of the

front loader is equal to the front loader cost divided by the lifetime treatment capacity of

the facility, the result divided by 7. The front loader cost is allocated to 6 unit processes

by equal parts: feeding the shredder, transport to composting area, forming

windrows/feeding vessel, transport to screening area, screening, and transport to curing

area.

Operation

Labor cost of storage in scenarios 1 to 4 is performed by one operator using a

regular shovel at 1,8 tons per hour, or 0,37 hour per ton. Shoveling productivity

considers 15 scoops per minute, 4 kg of waste load per scoop, 30 minutes of shoveling

per hour. For scenarios 5 to 8, storage is performed by one operator using a front loader

at 98 tons per hour, or 0.102 hour per ton. Storage is a basic cycle for a front loader,

involving lifting the material from the shredding area, transporting and dumping in the

storage area, located in the same hall. Basic cycle time is 0.5 minutes for handling 0.98

tons of bucked capacity, worker productivity is 50 minutes per hour. Labor cost per ton

131

is equal labor productivity (shovel or front loader) in hours per ton times the cost of

labor. Labor cost 1 for shovel labor ($2,42 USD/hour), labor cost 2 for front loader labor

($4,84 USD/hour).

Labor cost storage per ton = PL ∗ CL

Where,

PL = Productivity of labor (hr/ton)

CL = Cost of labor ($USD/hr)


Labor cost storageper ton = 0.102hr

ton∗ $4,84 = $ 0.49 USD

A front loader is needed for storage in scenarios 5 to 8. Front loader productivity

for storage includes the tasks of loading, dumping, making reversals of direction, and

traveling a minimum distance of 25 meters. Such cycle time is assumed to be 0.5 min.

Bucket capacity is 2.8 m3. For a OSW density of 350 kg/m3 , bucket capacity is 0.98

tons. An average front loader worker efficiency is 50 minutes of work per hour. Feeding

the shredder at 1.96 tons per minute, equals 98 tons per hour. Fuel cost per ton for

storage is equal to fuel consumption times the cost of fuel.

Fuel cost storage per ton = (d ∗ HP

TR) ∗ CF

Where,





132


Fuel cost feeding shredderper ton = (165 HP ∗ 0.26

LHP − h

98 tonh

) ∗ $ 0.67 USD

= $ 0.29 USD/ton

Transport to composting area

Capital

The same plastic mobile containers used for storage are used for manual

transport in scenarios 1 to 4; container cost is allocated to both processes by equal

amounts. The front loader is needed for transport to composting area in scenarios 5 to

8. The capital cost of the front loader is equal to the front loader cost divided by the

lifetime treatment capacity of the facility, the result divided by 7. The front loader cost is

allocated to 6 processes by equal parts: feeding the shredder, transport to composting

area, forming windrows/feeding vessel, transport to screening area, screening, transport

to curing area, and forming curing piles.

Operation

Labor cost for scenarios 1 to 4 is defined by the time required to transport

materials to the composting area using the plastic mobile containers, which is

performed by one operator at 6 tons per hour (10 m distance). The cycle of transporting

the material in the container with a waste load of 100 kg, dropping the material, and

coming back with an empty container, is assumed to be 6 seconds for every meter

travelled (one way distance). For an average transport distance of 10 meters in the

composting facility, one operator can transport materials at a rate of 6 tons per hour. For

scenarios 5 to 8, transport to the composting area is performed by one operator using

133

the front loader at 39.2 tons per hour. The cycle of loading the material, hauling to the

composting area, dumping, and returning is assumed to be 1.5 minutes for an average

hauling distance of 150 m. Based on performance curves for wheel loaders (Komatsu,

2013), the basic cycle time of the loader is assumed 0.5 min. Hauling time is 0.6 min.

Returning time is 0.4 min. Labor cost is equal to the time requirement per ton times the

labor cost.

Labor cost transport to compostper ton = T ∗ CL

Where,

T = Time requirement (hr)



Labor cost transport to compostper ton = 0.025 hr/ton ∗ $ 4.84 USD = $ 0.12 USD/ton

Fuel cost for transport to compost area in scenarios 5 to 8 is equal to the time

requirement (hr/ton) times the diesel consumption (lt/hr) times the cost of diesel ($0.67

USD/lt). Diesel consumption of the front loader is assumed to be 43 L of diesel per hour

(Grisso et al., 2010).

Fuel cost transport to compostper ton = T ∗ DC ∗ CD

Where,

T = Time requirement (hr/ton)


DC = Diesel consumption (lt/hr)


Fuel cost transport to compostper ton = 0.025hr

ton∗ 43

lt

hr∗ $0.67 = $ 0.73 USD

134

Forming windrows/feeding vessel

Capital

Forming windrows is a manual process performed by one operator at a rate of

250 kg per hour, using a regular shovel in scenarios 1 and 3. For scenarios 2 and 4,

feeding the vessel is achieved through a bin lifter; shredded materials are placed in the

bin lifter during transport; therefore no labor is required for feeding. Energy consumption

of the bin lifter is accounted for in the composter value. For scenarios 5 and 7, forming

windrows is performed by one operator using the front loader at 117.6 tons per hour,

considering a basic cycle for the front loader, assumed to be 0.5 minutes for a bucket

capacity of 0.98 tons. For scenarios 6 and 8, feeding the vessel is performed by one

operator using the front loader at 117.6 tons per hour, considering a basic cycle for the

front loader. Forming windrows/feeding vessel cost per ton is equal to the front loader

cost divided by the treatment capacity of the composting facility, divided by 7.

Forming windrow − feeding vessel cost per ton =

FLCTR7

Where,

FLC = Front loader cost ($ USD)



Forming windrow − feeding vessel cost per ton =

$180,000 USD150,000 ton

7= $0.17 USD/ton

Operation

Labor cost of forming windrows per ton for scenarios 1 and 3 is equal time

requirement (hr/ton) using a regular shovel times the labor cost (labor cost 1). For

135

scenarios 2 and 4, feeding the vessel is achieved through a bin lifter and no labor is

required. For scenarios 5 and 7, labor cost is equal to time requirement (hr/ton) using

the front loader times the labor cost (labor cost 2). For scenarios 6 and 8, feeding vessel

cost is equal to time requirement (hr/ton) using the front loader times the labor cost

(labor cost 2).

Labor cost forming windrows − feeding vessel per ton = T ∗ CL

Where,

T = Time requirement (hr/ton)



Labor cost forming windrowper ton = 0.0085 hr/ton ∗ $ 4.84 USD = $ 0.04 USD/ton

Maintenance cost of front loader is assumed to be 10% of the equipment

purchase cost.

Decomposition

Capital

For windrow systems, no capital costs are considered as decomposition occurs

in open-air piles. For in-vessel systems, decomposition occurs in composting machines

that require ancillary equipment: biofilter, bin-lifter, and discharge auger. Decomposition

cost for in-vessel systems is equal to the sum of the composter, biofilter, bin-lifter, and

discharge auger costs, divided by the lifetime treatment capacity of the facility.

Decomposition cost per ton =CC + BFC + BLC + DAC

TR

Where,

CC = Composter cost ($ USD)

136

BFC = Biofilter cost ($ USD)

BLC = Bin-lifter cost ($ USD)

DAC = Discharge auger cost ($ USD)



Decomposition cost per ton =$312,000 + $28,120 + $110,000 + $34,000

15,000 ton

= $32.27 USD/ton

A windrow turner with integrated watering is required for windrow systems in

scenarios 5 and 7. The windrow turner cost per ton is equal to the cost of the turner

divided by the lifetime treatment capacity of the composting facility. Throughput of the

turning machine is 4,000 m3/h. Assuming a compost density of 700 kg/m3, throughput

is 2,800 ton/h.

Operation

Electricity consumption cost per ton of the composter is equal to the electricity

consumption during the lifetime (kWh/lifetime) of the composter divided by the lifetime

treatment capacity of the facility (tons), times the electricity cost ($USD/kWh). Assuming

260 working days per year during a 15 years lifetime. Electricity cost is set at $ 0.14

USD/kWh).

Electricity consumption composter cost per ton =ELFTR

∗ EC

Where,

ELF = Electricity consumption of composter during lifetime (kWh)

EC = Electricity cost ($ USD/kWh)

137



Electricity consumption composter cost per ton =507,000 kWh

15,000 ton∗ $ 0.14 = $ 4,73 USD

Fuel consumption for all diesel engines is assumed to be 0.26 L per horsepower

per hour (Grisso et al., 2010). Fuel consumption cost of the windrow turner is equal to

fuel consumption assumed for diesel machines (l/HP-hr), times the power of the

windrow turner (HP), divided by the throughput of the windrow turner (ton/hour), times

the cost of fuel (USD). The cost of diesel fuel is $ 0.67 USD per liter.

Fuel cost windrow turner per ton = (d ∗ HP

TR) ∗ CF

Where,






Fuel cost windrow turnerper ton = (390 HP ∗ 0.26

LHP − h

2.800tonh

) ∗ $ 0.67 USD

= $ 0.024 USD/ton

Labor cost for windrow systems involves the time required for turning and

watering windrows. For scenarios 1 and 3, turning is performed by an operator using a

regular shovel at 8 tons per hour; watering is performed by an operator using a water

house at 50 tons per hour. For scenarios 5 and 7, turning and watering is performed by

138

an operator using a windrow turner machine with integrated watering at 2,800 tons per

hour. Throughput of the turning machine is 4,000 m3/h (Komptech, 2016b); assuming a

compost density of 700 kg/m3, throughput is 2,800 ton/h. Labor cost is equal to time

requirement per ton for turning plus time requirement per ton for watering times the cost

of labor.

Labor cost decompositionper ton = (TT + TW) ∗ CL

Where,

TT = Time requirement turning (hr/ton)

TW = Time requirement watering (hr/ton)



Labor cost decompositionper ton = (0.125hr

ton+ 0.02

hr

ton) ∗ $ 2.42 USD = $ 0.35 USD/ton

Transport to screening area

Capital

Transport to screening area is performed by an operator using plastic mobile

containers for scenarios 1 to 4, and front loader for scenarios 5 to 8. Plastic mobile

container cost is allocated to the processes of transport to screening and transport to

curing by equal amounts. The value is equal to the processes of storage and transport

to composting area. The front loader is equal to the front loader cost divided by the



area, forming windrows/feeding vessel, transport to screening area, screening, and

transport to curing area.

139

Operation

Labor cost for transport to screening area is defined by the transport productivity

using plastic mobile container or front loader. Transport productivity (hour/ton) depends

on the capacity of the container (100 kg) or front loader (0.98 ton), and the distance

travelled in the composting facility. Transport productivity is specified in the composting

scenarios. Labor cost is equal to transport productivity (hr/ton) times the labor cost.

Labor cost 1 for container, labor cost 2 for front loader.

Labor cost transport to screen𝑖ng per ton = TP ∗ CL

Where,

TP = Transport productivity (hr/ton)



Labor cost transport to screeningper ton = 0.0306 hr/ton ∗ $ 4.84 USD = $ 0.15 USD/ton

Fuel cost for transport to screening area is equal to transport productivity, times

diesel consumption, times the cost of fuel ($0.67 USD/lt). Diesel consumption of the

front loader is assumed to be 43 L of diesel per hour (Grisso et al., 2010).

Fuel cost transport to compostper ton = TP ∗ DC ∗ CD

Where,





Fuel cost transport to screeningper ton = 0.0306hr

ton∗ 43

lt

hr∗ $0.67 = $ 0.88 USD

140

Screening

Capital

Screening cost is equal to the cost of the screener divided by the lifetime

treatment capacity of the facility.

Screening cost per ton =CSLTR

Where,

CS = Cost of screener ($USD)



Screener cost per ton = $150,000

150,000 ton= $ 1 USD

The front loader is needed for storage in scenarios 5 to 8. The capital cost of the

front loader is equal to the front loader cost divided by the lifetime treatment capacity of

the facility, the result divided by 6. The front loader cost is allocated to 6 unit processes

by equal parts: feeding the shredder, transport to composting area, forming

windrows/feeding vessel, transport to screening area, screening, and transport to curing

area.

Operation

The screeners for scenarios 1 to 4 run on electricity and for scenarios 5 to 8 run

on diesel fuel. Electricity consumption cost for scenarios 1 to 4 is equal to the power of

the screener (kW) divided by the hourly throughput (ton/hr), times the electricity cost

($0.14 USD/kWh). Fuel consumption cost of the screener is equal to fuel consumption

assumed for diesel machines (0.26 lt/HP-hr), times the power of the screener (HP),

141

divided by the throughput of the windrow turner (ton/hour), times the cost of fuel (USD).

The cost of diesel fuel is $ 0.67 USD per liter.

Fuel cost windrow turner per ton = (d ∗ HP

TR) ∗ CF

Where,






Fuel cost windrow turnerper ton = (22 HP ∗ 0.26

LHP − h

24.5tonh

) ∗ $ 0.67 USD = $ 0.16 USD/ton

Labor cost of screening for scenarios 1 to 4 is equal to the screening productivity

(ton/hr) using a regular shovel times the labor cost (labor cost 1). For scenarios 5 to 8,

labor cost is equal the screening productivity (ton/hr) using the front loader times the

labor cost (labor cost 2).

Labor cost screening per 𝑡on = T ∗ CL

Where,

T = Screening productivity (hr/ton)



Labor cost screeningper ton = 0.040 hr/ton ∗ $ 4.84 USD = $ 0.20 USD/ton

Maintenance cost of front loader is assumed to be 10% of the equipment

purchase cost.

142

Transport to curing area

Capital

Transport to curing area is performed by an operator using plastic mobile

containers for scenarios 1 to 4, and front loader for scenarios 5 to 8. Plastic mobile

container cost is allocated to the processes of transport to screening and transport to

curing by equal amounts. The value is equal to the processes of storage and transport

to composting area. The front loader is equal to the front loader cost divided by the



area, forming windrows/feeding vessel, transport to screening area, screening, and

transport to curing area.

Operation

Labor cost for transport to curing area is defined by the transport productivity

using plastic mobile container or front loader. Transport productivity (hour/ton) depends

on the capacity of the container (100 kg) or front loader (0.98 ton), and the distance

travelled in the composting facility. Transport productivity is specified in the composting

scenarios. Labor cost is equal to transport productivity (hr/ton) times the labor cost.

Labor cost 1 ($2,42 USD) for container, labor cost 2 ($4,84 USD) for front loader.

Labor cost transport to curing per ton = TP ∗ CL

Where,




143

Labor cost transport to curingpe𝑟 ton = 0.0255 hr/ton ∗ $ 4.84 USD = $ 0.12 USD/ton

Fuel cost for transport to curing area is equal to transport productivity, times

diesel consumption, times the cost of fuel ($0.67 USD/lt). Diesel consumption of the

front loader is assumed to be 43 L of diesel per hour (Grisso et al., 2010).

Fuel cost transport to curingper ton = TP ∗ DC ∗ CD

Where,

T = Transport productivity (hr/ton)




Fuel cost transport to curingper ton = 0.0255hr

ton∗ 43

lt

hr∗ $0.67 = $ 0.73 USD

Curing

Capital

Curing occurs in concrete L-shaped elements of 1.5 m height per 2.5 m width.

The storage capacity of each element is 3.8 tons of compost. A curing time of 1 month

is assumed. A 50% reduction in volume from OSW to compost is assumed. Curing cost

is equal to the monthly treatment capacity of the composting facility divided by 2,

divided by the storage capacity of the L-shaped element, times the cost of each L-

shaped element. The cost of each element is $450 USD.

Curing costper ton =

((

TRM2SL

) ∗ CL)

TRL

144

Where,

TRM = Treatment capacity of facility (ton/month)

SL = Storage capacity of L-shaped element (ton)

CL = Cost of L-shaped element ($USD)

TRL = Lifetime treatment capacity of facility (ton)


Curing costper ton =(

833

tonmonth2

3,8 ton

)

∗ $450 USD

150,000 ton= $ 0.32 USD

145

APPENDIX C SPECIFICATIONS OF COMPOSTING SCENARIOS

Scenario 1 (S1). Small-scale windrow composting facility with a treatment

capacity of 100 tons per year. Paved area: 238 m2. Buffer zone: 52 m2. Building area: 6

m2. Steel hall area: 50 m2. Collection of OSW involves a drop-off scheme where

households transport the separated materials (food and garden wastes) to the

composting facility located at a maximum walking distance of 100 meters (block scale).

Sorting of materials is a manual process performed by one operator at a rate of 900 kg

per hour. Shredding of food and garden wastes is performed by one operator using an

organic residue shredder model TR200G (92 kg, 100% steel, power 6 HP, throughput

224 kg/h, lifetime 15 years, price USD$1,246) from (Trapp, 2016). The shredder is fed

by manual labor at 224 kg/h, using a regular shovel when required for food waste and

hand feeding for trunks and small branches. Storage of shredded materials is performed

by one operator using a regular shovel at 1,800 kg/h. Shredded materials are stored in

four plastic mobile containers, model 1/3 CU-L (24 kg each, 100% HDPE, capacity 124

kg, lifetime 15 years, price USD$321 each) from (Bayhead, 2016). Transport of

shredded materials to the composting area is performed by one operator using the

plastic mobile containers at a rate of 6,000 kg per hour (10 m distance). Forming

windrows is a manual process performed by one operator at a rate of 1,8 tons per hour,

using a regular shovel (3 kg, 50% steel, 50% hard wood, lifetime 15 years, price

UDS$15). Decomposition occurs in three windrow piles of 2 meters (width) per 16

meters (length). Turning windrows is a manual process performed by one operator at 8

tons per hour, using a regular shovel. Watering windrows is a manual process

performed by one operator at 50 tons per hour, using a water hose (weight 0.3 kg per

146

meter, 100% LDPE, lifetime 5 years, price USD$150). Transport of compost to

screening area is performed by one operator at 3,000 kg per hour using a plastic mobile

container (20 m distance). Screening is performed by one operator using an electric

trommel screen model Rollsieb RS 350 (36 kg, 100% steel, power 0.25 kW, throughput

1,800 kg/h (shoveling performance), lifetime 15 years, , price USD$566) from

(Scheppack, 2016). The screener is fed by an operator using a regular shovel at

shoveling productivity of 1,800 kg/h. Transport to curing area is performed by one

operator at 6,000 kg per hour using a plastic mobile container (10 m distance). Curing

occurs in concrete L-shaped elements of 1.5 m height per 2.5 m width (1,200 kg each,

100% concrete, lifetime 15 years, capacity 3,8 tons, price USD $450 each).

Scenario 2 (S2). In-vessel composting facility with a treatment capacity of 100

tons per year. Paved area: 126 m2. Building area: 6.3 m2. Steel hall area: 50 m2. The

processes of collection, sorting, shredding, and storage are equal to S1. Transport of

shredded materials to the composting area is performed by one operator using a plastic

mobile container at 12,000 kg per hour (5 m distance). Feeding the vessel is achieved

through a bin lifter attached to the composting machine; no labor is required.

Decomposition occurs in one in-vessel composting machine model 1206 (3,000 kg,

95% steel, energy consumption 21 kW/day, throughput 380 kg/day, lifetime 15 years,

price USD$90,000) by HotRot (2015). The composting machine includes: a bin lifter

(650 kg, 100% steel, lifetime 15 years, price USD$6,000); a bio-filter of 1.5 m3 (75 kg,

100% fiberglass, lifetime 15 years, price USD$ 4,500); bio-filter media is wood chip/bark

(380 kg/m3 lifetime 5 years, price USD$ 15m3). Turning and watering processes are

controlled by the composting machine. Total composter cost USD$ 100,568. Transport

147

of compost to screening area is performed by one operator at 6,000 kg per hour using a

plastic mobile container (10 m distance). Transport of compost to curing area is

performed by one operator at 6,000 kg per hour using a plastic mobile container (10 m

distance). Curing is equal to S1.

Scenario 3 (S3). Windrow composting facility with a treatment capacity of 1,000

tons per year. Paved area: 2,400 m2. Buffer zone: 400 m2. Building area: 20 m2. Steel

hall area: 500 m2. Collection is made through a waste collection truck (14 tons capacity,

price USD $250,000), employing three operators, one driving the truck and two picking

up the waste, at a rate of 2 tons per hour (Promoambiental, 2016). Food and garden

wastes are source-segregated by households and placed in the curbside at the time of

collection, without the use of bins. The collection truck weights 13,300 tons; materials

used are steel (63%), iron (23%), and HDPE (3.6%), corresponding to 90% of the total

weight of the truck; the lifetime of the truck is assumed to be 15 years (L. K.-S.

Brogaard & Christensen, 2012). Diesel consumption during collection is assumed to be

3 L per ton of waste collected, based on measurements from urban areas in Denmark

(Larsen, Vrgoc, Lieberknecht, & Christensen, 2009). It is assumed that the truck is

collecting OSW 5 days per week, corresponding to 261 loads of 14 tons per year. For a

lifetime of 15 years, the truck can collect 54,810 tons of OSW. Scenario 3 does not

involve transport, because the composting facility is assumed to be located right when

the truck reaches its maximum capacity of 14 tons (neighborhood scale). For the LCI,

the truck is allocated to the processes of collection and transport by equal amounts;

when no transport is involved, the whole truck is allocated to collection. Sorting of

materials and removal of impurities is a manual process performed by one operator at a

148

rate of 900 kg per hour. Shredding is performed by one operator using an organic

residue shredder model TR500 (275 kg, 100% steel, power 13 HP, throughput 1,750

kg/h, lifetime 15 years, price USD$4,190) from Trapp (2016). Storage of shredded

materials is performed by one operator using a regular shovel at 1,800 kg/h. Materials

are stored in plastic mobile containers, model 1/3 CU-L (24 kg each, 100% HDPE,

capacity 100 kg, lifetime 15 years, price USD$321 each) from (Bayhead, 2016).

Transport of shredded materials to the composting area is performed by one operator

using a plastic mobile container at a rate of 2,000 kg per hour (30 m distance). Forming

windrows is a manual process performed by one operator at a rate of 1,800 kg per hour,

using a regular shovel. Decomposition occurs in eight windrow piles of 2 meters (width)

per 46 meters (length). Turning windrows is a manual process performed by one

operator at 8 tons per hour, using a regular shovel. Watering windrows is a manual

process performed by one operator at 50 tons per hour, using a 150 meter hydraulic

hose (weight 0.3 kg per meter, 100% LDPE, lifetime 5 years, price USD$150).

Transport to screening area is performed by one operator at 1,200 kg per hour using a

plastic mobile container (50 m distance). Screening is performed by one operator using

an electric trommel screen model Rollsieb RS 350 (32 kg, 100% steel, power 0.25 kW,

throughput 1,800 kg/h, price USD $566) from (Scheppack, 2016). The screener is fed

by an operator using a regular shovel at shovelling productivity of 1,800 kg/h. Transport

to curing area is performed by one operator at 3,000 kg per hour using a plastic mobile

container (20 m distance). Curing occurs in concrete L-shaped elements of 1.5 m height

per 2.5 m width (1,200 kg, 100% concrete, capacity 3,8 tons, lifetime 15 years, price

USD$450 ).

149

Scenario 4 (S4). In-vessel composting facility with a treatment capacity of 1,000

tons per year. Paved area: 900 m2. Building area: 20 m2. Steel hall area: 500 m2. The

processes of collection, sorting, and shredding, are equal to Scenario 3. Transport of

shredded materials to the composting area is performed by one operator using the

plastic mobile containers at a rate of 3,000 kg per hour (20 m distance). Feeding the

vessel is achieved through a bin lifter. Decomposition occurs in two in-vessel

composting machines HotRot model 1811 (12,500 kg, 92% steel, throughput 2.5

tons/day, energy requirement 65 kWh/day, lifetime 15 years, price USD$ 156,000) from

HotRot (2015). Two FU5 feed hoppers are needed (2,900 kg, 100% steel, throughput

2,500 kg/day, price USD $55,000 each). Two discharge augers (1,600 kg, 100% steel,

price USD $34,000) are needed. Two 4 m3 bio-filters are needed (300 kg, 100%

fiberglass, lifetime 15 years, price USD$ 14,000). Bio-filter media is wood chip/bark (380

kg/m3 lifetime 5 years, price USD$ 15m3). Mixing and watering processes are

controlled by the composting machine. Total cost $ 484,360 USD. Transport of compost

to screening area is performed by one operator at 12,000 kg per hour using a plastic

mobile container (5 m distance). Transport of compost to curing area is performed by

one operator at 3,000 kg per hour using a plastic mobile container (20 m distance).

Curing occurs in concrete L-shaped elements of 1.5 m height per 2.5 m width (1,200 kg,

100% concrete, capacity 3,8 tons, lifetime 15 years, price USD$450 ).


tons per year. Total area: 33,012 m2. Buffer zone: 7,427 m2. Building area: 500 m2.

Steel hall area: 5,752 m2. Collection is equal to Scenario 3. A transport distance of 25

km to the composting facility is assumed. Diesel consumption during transport is

150

assumed to be 0.16 L per ton per km (Larsen et al., 2009). Sorting of materials and

removal of impurities is a manual process performed by one operator at a rate of 900 kg

per hour. Shredding is performed by an electric residue shredder, model Terminator

Direct 1700 (13,600 kg, 100% steel, power 100 HP, throughput 15 ton/h, lifetime 15

years, price USD$480,000) from (Komptech, 2016a). Shredded materials are stored in

ten concrete L-shaped elements of 1.5 m height per 2.5 m width (1,200 kg each, 100%

concrete, lifetime 15 years, price USD$450 each). One front loader is needed for all

transport operations (L. K. Brogaard et al., 2015). The front loader is a model WA320-7

(15,415 kg, 90% steel, power 165 HP, lifetime 15 years, price UDS$180,000) from

(Komatsu, 2016). Diesel consumption of the front loader is assumed to be 43 L of diesel

per hour (Grisso et al., 2010). A working period of four hours daily is assumed. In the

inventory, the front loader burden is allocated to six unit processes by equal parts:

transport to composting area, forming windrows, transport to screening area, screening,

transport to curing area, and forming curing piles. Transport of shredded materials to

the composting area is performed by one operator using the front loader. Forming

windrows is performed by one operator using the front loader. Decomposition occurs in

windrows of 2.2 m height per 5 m width. Turning windrows is performed by one operator

using a turner machine model Toptorn X63 (15,000 kg, 100% steel assumed, power

205 HP, lifetime 15 years, price USD$280,000 estimate) from (Komptech, 2016b).

Watering windrows is performed by the turning machine. Transport of compost to

screening area is performed by one operator using the front loader. Screening is

performed by one operator using the front loader to feed a screening machine model

Joker (5,000 kg, 100% steel assumed, power 22 HP, throughput 24.5 ton/h, lifetime 15

151

years, price USD$270,000 estimate) from Komptech (2016). Transport of compost to

curing area is performed by one operator using the front loader. Forming curing piles is

performed by one operator using a front loader.


tons per year. Total area: 9,370 m2. Building area: 500 m2. Steel hall area: 5,752 m2.

The processes of collection, transport, sorting, shredding, storing, and transport to

composting area are equal to Scenario 5. Decomposition occurs in two in-vessel

composting machines model 3518 (206,000 kg, 92% steel, energy requirement 450

kW/day, throughput 24 tons per day, lifetime 15 years, price USD$ 950,000) from

(HotRot, 2015). The composting machines include: a feed hopper (12,500 kg, 92%

steel, lifetime 15 years, price USD$ 110,000); a discharge auger (3,000 kg, 100% steel,

lifetime 15 years, price USD$ 34,000); and a 48 m3 bio-filter (4,800 kg, 100% concrete

walls, lifetime 15 years, price USD$ 50,000). Bio-filter media is wood chip/bark (380

kg/m3 lifetime 5 years, price USD$ 15m3).Turning and watering processes are

controlled by the composting machine. The following process of transport to screening

area, screening, transport to curing area, forming curing piles, and curing are equal to

Scenario 5.


tons per year. Total area: 118,288 m2. Buffer zone: 26,667 m2. Building area: 500 m2.

Steel hall area: 27,000 m2. Collection is equal to Scenario 5. A transport distance of 50

km to the composting facility is assumed. Diesel consumption during transport is

assumed to be 0.16 L per ton per km (Larsen et al., 2009). The sorting process is equal

to all scenarios. Shredding is performed by an electric residue shredder, model

152

Terminator Direct 5000S (15,300 kg, 100% steel, power 295 HP, throughput 55 ton/h,

lifetime 15 years, price USD$750,000 estimate) from (Komptech, 2016a). Four front

loaders are needed for transport operations (L. K. Brogaard et al., 2015). Front loaders

are model WA320-7 (15,415 kg, 100% steel assumed, power 165 HP, lifetime 15 years,

price UDS$180,000) from (Komatsu, 2016). Diesel consumption of the front loaders is

assumed to be 43 L of diesel per hour (Grisso et al., 2010). A working period of four

daily hours per front loader is assumed. In the inventory, the four front loaders are

allocated by equal parts to six processes: transport to composting area, forming


curing piles. Transport of shredded materials to the composting area is performed by

one operator using a front loader. Forming windrows is performed by one operator using

the front loader. Decomposition occurs in windrows of 2.2 m height per 5 m width.

Turning windrows is performed by one operator using a turner machine model Toptorn

X63 (18,000 kg, 100% steel assumed, power 390 HP, lifetime 15 years, price

USD$350,000 estimate) from (Komptech, 2016b). Watering windrows is performed by

the turning machine at the time of turning. Transport of compost to screening area is

performed by one operator using the front loader. Screening is performed by one

operator using the front loader to feed a screening machine model Nemus 2700 (17,000

kg, 100% steel assumed, power 94 HP, throughput 119 ton/h, lifetime 15 years, price

USD$325,000 estimate). Transport of compost to curing area is performed by one

operator using the front loader. Forming curing piles is performed by one operator using

a front loader.

153


tons per year. Total area: 34,374 m2. Building area: 500 m2. Steel hall area: 27,000 m2.

The processes of collection, transport, sorting, shredding, storing, and transport to

composting area are equal to Scenario 7. Decomposition occurs in ten in-vessel

composting machines model 3518 (1,030,000 kg, 95% steel, energy requirement 2,250

kW/day, lifetime 15 years, price USD$ 4,750,000) from (HotRot, 2015). The composting

machines include: five feed hopper (62,500 kg, 92% steel, lifetime 15 years, price

USD$ 550,000); five discharge augers (15,000 kg, 100% steel, lifetime 15 years, price

USD$ 170,000); and 240 m3 of bio-filter (4,800 kg, 100% concrete walls, lifetime 15

years, price USD$ 50,000). Bio-filter media is wood chip/bark (380 kg/m3 lifetime 5

years, price USD$ 15m3). Turning and watering processes are controlled by the

composting machine. The following process of transport to screening area, screening,

transport to curing area, forming curing piles, and curing are equal to Scenario 5.

154

LIST OF REFERENCES

Adhikari, B. K., Barrington, S., Martinez, J., & King, S. (2008). Characterization of food waste and bulking agents for composting. Waste Management, 28(5), 795-804.

Adhikari, B. K., Trémier, A., Barrington, S., Martinez, J., & Daumoin, M. (2013). Gas

emissions as influenced by home composting system configuration. Journal of environmental management, 116, 163-171.

Agudelo-Vera, C. M., Leduc, W. R., Mels, A. R., & Rijnaarts, H. H. (2012). Harvesting

urban resources towards more resilient cities. Resources, Conservation and Recycling, 64, 3-12.

Ali, M., & Harper, M. (2004). Sustainable Composting: Case studies and guidelines for

developing countries: WEDC, Loughborough University. An, H., Englehardt, J., Fleming, L., & Bean, J. (1999). Occupational health and safety

amongst municipal solid waste workers in Florida. Waste Management and Research, 17(5), 369-377.

Andersen, J. K., Boldrin, A., Christensen, T. H., & Scheutz, C. (2010). Greenhouse gas

emissions from home composting of organic household waste. Waste management, 30(12), 2475-2482.

Atkinson, R., & Arey, J. (2003). Atmospheric degradation of volatile organic compounds.

Chemical reviews, 103(12), 4605-4638. Barrena, R., Font, X., Gabarrell, X., & Sánchez, A. (2014). Home composting versus

industrial composting: Influence of composting system on compost quality with focus on compost stability. Waste management, 34(7), 1109-1116.

Benoit-Norris, C., Cavan, D. A., & Norris, G. (2012). Identifying social impacts in product

supply chains: overview and application of the social hotspot database. Sustainability, 4(9), 1946-1965.

Benoît, C., Norris, G. A., Valdivia, S., Ciroth, A., Moberg, A., Bos, U., . . . Beck, T.

(2010). The guidelines for social life cycle assessment of products: just in time! The international journal of life cycle assessment, 15(2), 156-163.

Benoît Norris, C., Traverso, M., Valdivia, S., Vickery-Niederman, G., Franze, J., Azuero,

L., . . . Aulision, D. (2013). The methodological sheets for sub-categories in social life cycle assessment (S-LCA). United Nations Environment Programme (UNEP) and Society for Environmental Toxicology and Chemiastry (SETAC).

Bernstad, A., & la Cour Jansen, J. (2011). A life cycle approach to the management of

household food waste–a Swedish full-scale case study. Waste management, 31(8), 1879-1896.

155

Bernstad, A., la Cour Jansen, J., & Aspegren, H. (2012). Local strategies for efficient management of solid household waste–the full-scale Augustenborg experiment. Waste Management & Research, 30(2), 200-212.

Bernstein, J. A., Alexis, N., Barnes, C., Bernstein, I. L., Nel, A., Peden, D., . . . Williams,

P. B. (2004). Health effects of air pollution. Journal of allergy and clinical immunology, 114(5), 1116-1123.

Biocycle. (2014). State Orders Closure Of Peninsula Compost Facility. Retrieved from

https://www.biocycle.net/2014/10/23/state-orders-closure-of-peninsula-compost-facility/

BLS. (2016). NATIONAL CENSUS OF FATAL OCCUPATIONAL INJURIES IN 2015. Boldrin, A., Neidel, T. L., Damgaard, A., Bhander, G. S., Møller, J., & Christensen, T. H.

(2011). Modelling of environmental impacts from biological treatment of organic municipal waste in EASEWASTE. Waste management, 31(4), 619-630.

Bong, C. P. C., Lim, L. Y., Ho, W. S., Lim, J. S., Klemeš, J. J., Towprayoon, S., . . . Lee,

C. T. (2017). A review on the global warming potential of cleaner composting and mitigation strategies. Journal of Cleaner Production, 146, 149-157.

Brogaard, L. K., Petersen, P. H., Nielsen, P. D., & Christensen, T. H. (2015).

Quantifying capital goods for biological treatment of organic waste. Waste Management & Research, 33(2), 96-106.

Brogaard, L. K.-S., & Christensen, T. H. (2012). Quantifying capital goods (bins, trucks,

ect.) for collection and transport of waste. Waste Management & Research, 0734242X12462279.

Brown, N. J. (2016). Composting Safety and Health. Burnley, S. (2001). The impact of the European landfill directive on waste management

in the United Kingdom. Resources, Conservation and Recycling, 32(3), 349-358. Byeon, J. H., Park, C. W., Yoon, K. Y., Park, J. H., & Hwang, J. (2008). Size

distributions of total airborne particles and bioaerosols in a municipal composting facility. Bioresource technology, 99(11), 5150-5154.

Bünger, J., Antlauf-Lammers, M., Schulz, T. G., Westphal, G. A., Müller, M. M., Ruhnau,

P., & Hallier, E. (2000). Health complaints and immunological markers of exposure to bioaerosols among biowaste collectors and compost workers. Occupational and Environmental Medicine, 57(7), 458-464.

http://www.biocycle.net/2014/10/23/state-orders-closure-of-peninsula-compost-facility/

http://www.biocycle.net/2014/10/23/state-orders-closure-of-peninsula-compost-facility/

156

Bünger, J., Antlauf-Lammers, M., Westphal, G., Müller, M., & Hallier, E. (1998). [Immunological reactions and health complaints in biological refuse personnel and composting by biological aerosol exposure]. Schriftenreihe des Vereins fur Wasser-, Boden-und Lufthygiene, 104, 141-148.

Chang, T.-Y., Jain, R.-M., Wang, C.-S., & Chan, C.-C. (2003). Effects of occupational

noise exposure on blood pressure. Journal of occupational and environmental medicine, 45(12), 1289-1296.

Change, I. P. o. C. (2006). 2006 IPCC Guidelines for National Greenhouse Gas

Inventories: Intergovernmental Panel on Climate Change. Chen, X., Fujita, T., Ohnishi, S., Fujii, M., & Geng, Y. (2012). The impact of scale,

recycling boundary, and type of waste on symbiosis and recycling. Journal of Industrial Ecology, 16(1), 129-141.

Chhipi-Shrestha, G. K., Hewage, K., & Sadiq, R. (2015). ‘Socializing’sustainability: a

critical review on current development status of social life cycle impact assessment method. Clean Technologies and Environmental Policy, 17(3), 579-596.

Chitkara, K. (1998). Construction project management: Tata McGraw-Hill Education. Ciroth, A., & Franze, J. (2011). LCA of an ecolabeled notebook: consideration of social

and environmental impacts along the entire life cycle: Lulu. com. Clavreul, J., Baumeister, H., Christensen, T. H., & Damgaard, A. (2014). An

environmental assessment system for environmental technologies. Environmental Modelling & Software, 60, 18-30.

Clift, R., Doig, A., & Finnveden, G. (2000). The application of life cycle assessment to

integrated solid waste management: Part 1—methodology. Process Safety and Environmental Protection, 78(4), 279-287.

Colon, M., & Fawcett, B. (2006). Community-based household waste management:

Lessons learnt from EXNORA's ‘zero waste management’scheme in two South Indian cities. Habitat International, 30(4), 916-931.

Colón, J., Martínez-Blanco, J., Gabarrell, X., Artola, A., Sánchez, A., Rieradevall, J., &

Font, X. (2010). Environmental assessment of home composting. Resources, Conservation and Recycling, 54(11), 893-904.

Dalzell, H. (1987). Soil management: compost production and use in tropical and

subtropical environments: Food & Agriculture Org.

157

do Carmo, B. B. T., Margni, M., & Baptiste, P. (2017). Customized scoring and weighting approaches for quantifying and aggregating results in social life cycle impact assessment. The International Journal of Life Cycle Assessment, 1-11.

Domingo, J. L., & Nadal, M. (2009). Domestic waste composting facilities: a review of

human health risks. Environment international, 35(2), 382-389. Douglas, P., Bakolis, I., Fecht, D., Pearson, C., Sanchez, M. L., Kinnersley, R., . . .

Hansell, A. L. (2016). Respiratory hospital admission risk near large composting facilities. International journal of hygiene and environmental health, 219(4), 372-379.

Dreyer, L., Hauschild, M., & Schierbeck, J. (2006). A framework for social life cycle

impact assessment (10 pp). The International Journal of Life Cycle Assessment, 11(2), 88-97.

Eitzer, B. D. (1995). Emissions of volatile organic chemicals from municipal solid waste

composting facilities. Environmental science & technology, 29(4), 896-902. EPA. (2015). Advancing Sustainable Materials Management. In. Epstein, E. (1996). The science of composting: CRC press. Epstein, E. (2011). Industrial composting: Environmental engineering and facilities

management: CRC Press. Friedrich, R., & Obermeier, A. (1999). Anthropogenic emissions of volatile organic

compounds. Reactive hydrocarbons in the atmosphere, 1, 39. Frischknecht, R., Jungbluth, N., Althaus, H.-J., Doka, G., Dones, R., Heck, T., . . .

Rebitzer, G. (2005). The ecoinvent database: Overview and methodological framework (7 pp). The International Journal of Life Cycle Assessment, 10(1), 3-9.

Garrido, S. R., Parent, J., Beaulieu, L., & Revéret, J.-P. (2016). A literature review of

type I SLCA—making the logic underlying methodological choices explicit. The International Journal of Life Cycle Assessment, 1-13.

Gluch, P., & Baumann, H. (2004). The life cycle costing (LCC) approach: a conceptual

discussion of its usefulness for environmental decision-making. Building and environment, 39(5), 571-580.

Grisso, R. D., Perumpral, J. V., Vaughan, D. H., Roberson, G. T., & Pitman, R. M.

(2010). Predicting tractor diesel fuel consumption.

158

Grosso, M., Nava, C., Testori, R., Rigamonti, L., & Viganò, F. (2012). The implementation of anaerobic digestion of food waste in a highly populated urban area: an LCA evaluation. Waste Management & Research, 30(9 suppl), 78-87.

GrowingPower. (2017). Growing Power. Retrieved from http://www.growingpower.org/ Guillemin, M. P., Herrera, H., Huynh, C. K., Droz, P.-O., & Due, T. V. (1992).

Occupational exposure of truck drivers to dust and polynuclear aromatic hydrocarbons: a pilot study in Geneva, Switzerland. International archives of occupational and environmental health, 63(7), 439-447.

Handbook, I. (2010). International Reference Life Cycle Data System. General guide for

Life Cycle Assessment. Detailed guidance. European Commission, Joint Research Centre. Institute for Environment and Sustainability.

Hauschild, M., & Barlaz, M. A. (2009). LCA in waste management: Introduction to

principle and method. Solid Waste Technology & Management, Volume 1 & 2, 111-136.

Hauschild, M. Z., & Huijbregts, M. A. (2015). Life Cycle Impact Assessment: Springer. Herr, C. E., zur Nieden, A., Bödeker, R. H., Gieler, U., & Eikmann, T. F. (2003). Ranking

and frequency of somatic symptoms in residents near composting sites with odor annoyance. International journal of hygiene and environmental health, 206(1), 61-64.

Hong, J., & Park, K. (2005). Compost biofiltration of ammonia gas from bin composting.

Bioresource Technology, 96(6), 741-745. Hoornweg, D., & Bhada-Tata, P. (2012). What a waste: a global review of solid waste

management. Hotrot. (2013). Environmental discharges. Retrieved from

http://www.hotrotsolutions.com/. HotRot. (2015). In-vessel composting for food and organic waste. Retrieved from

http://www.hotrotsolutions.com/ Iriarte, A., Gabarrell, X., & Rieradevall, J. (2009). LCA of selective waste collection

systems in dense urban areas. Waste Management, 29(2), 903-914. ISO. (1997). ISO 14040: Environmental Management-Life Cycle Assessment-Principles

and Framework. Jan, B. (1994). EPA530-R-94-003. Composting Yard Trimmings and Municipal Solid

Waste. Retrieved from

http://www.growingpower.org/

http://www.hotrotsolutions.com/

http://www.hotrotsolutions.com/

159

Jeavons, J. (2012). How to Grow More Vegetables:(and fruits, nuts, berries, grains, and other crops) than you ever thought possible on less land than you can imagine: Ten Speed Press.

JRC, I. (2010). Handbook–General Guide for Life Cycle Assessment–Detailed

Guidance. Publication Office of the European Union, Luxembourg. Jørgensen, A., Le Bocq, A., Nazarkina, L., & Hauschild, M. (2008). Methodologies for

social life cycle assessment. The international journal of life cycle assessment, 13(2), 96.

Kangas, P. (2004). Ecological engineering: principles and practice: CRC Press. Kennedy, C., Pincetl, S., & Bunje, P. (2011). The study of urban metabolism and its

applications to urban planning and design. Environmental pollution, 159(8), 1965-1973.

Kesselmeier, J., & Staudt, M. (1999). Biogenic volatile organic compounds (VOC): an

overview on emission, physiology and ecology. Journal of atmospheric chemistry, 33(1), 23-88.

Komatsu. (2013). Specifications and Applications Handbook. Edition 31. In: Komatsu. Komilis, D. P., & Ham, R. K. (2004). Life-cycle inventory of municipal solid waste and

yard waste windrow composting in the United States. Journal of environmental engineering, 130(11), 1390-1400.

Komptech. (2015). Machinery for mechanical and biological treatment of organic solid

waste. Retrieved from http://www.komptech.com/about-komptech/in-brief.html

Kuijer, P., Sluiter, J. K., & Frings‐Dresen, M. H. (2010). Health and safety in waste collection: Towards evidence‐based worker health surveillance. American journal of industrial medicine, 53(10), 1040-1064.

Kummer, V., & Thiel, W. R. (2008). Bioaerosols–sources and control measures.

International journal of hygiene and environmental health, 211(3), 299-307. Kuusimäki, L., Peltonen, Y., Kyyrö, E., Mutanen, P., Peltonen, K., & Savela, K. (2002).

Exposure of garbage truck drivers and maintenance personnel at a waste handling centre to polycyclic aromatic hydrocarbons derived from diesel exhaust. Journal of Environmental Monitoring, 4(5), 722-727.

Körner, I., Saborit-Sánchez, I., & Aguilera-Corrales, Y. (2008). Proposal for the

integration of decentralised composting of the organic fraction of municipal solid waste into the waste management system of Cuba. Waste management, 28(1), 64-72.

http://www.komptech.com/about-komptech/in-brief.html

160

Lardinois, I., & Klundert, A. v. d. (1993). Organic waste: options for small-scale resource recovery (Vol. 1): Technology Transfer for Development.

Larsen, A. W., Vrgoc, M., Lieberknecht, P., & Christensen, T. H. (2009). Diesel

consumption in waste collection and transport and its environmental significance. Waste Management & Research.

Laurent, A., Bakas, I., Clavreul, J., Bernstad, A., Niero, M., Gentil, E., . . . Christensen,

T. H. (2013). Review of LCA studies of solid waste management systems–Part I: Lessons learned and perspectives. Waste Management.

Lazcano, C., Martínez-Blanco, J., Christensen, T. H., Muñoz, P., Rieradevall, J., Møller,

J., . . . Nuñez, M. (2014). Environmental benefits of compost use on land through LCA–a review of the current gaps. Paper presented at the Proceedings of the 9th International Conference on Life Cycle Assessment in the Agri-Food Sector (LCA Food 2014), San Francisco, California, USA, 8-10 October, 2014.

Leduc, W. R., & Van Kann, F. M. (2012). Spatial planning based on urban energy

harvesting towards productive urban regions. Journal of Cleaner Production. Lee, K.-H., Jung, H.-J., Park, D.-U., Ryu, S.-H., Kim, B., Ha, K.-C., . . . Yoon, C. (2015).

Occupational exposure to diesel particulate matter in municipal household waste workers. PloS one, 10(8), e0135229.

Lleó, T., Albacete, E., Barrena, R., Font, X., Artola, A., & Sánchez, A. (2012). Home and

vermicomposting as sustainable options for biowaste management. Journal of Cleaner Production.

Lundie, S., & Peters, G. M. (2005). Life cycle assessment of food waste management

options. Journal of Cleaner Production, 13(3), 275-286. Marchand, G., Lavoie, J., & Lazure, L. (1995). Evaluation of bioaerosols in a municipal

solid waste recycling and composting plant. Journal of the Air & Waste Management Association, 45(10), 778-781.

Martinez-Sanchez, V., Kromann, M. A., & Astrup, T. F. (2015). Life cycle costing of

waste management systems: Overview, calculation principles and case studies. Waste management, 36, 343-355.

Martínez-Blanco, J., Colón, J., Gabarrell, X., Font, X., Sánchez, A., Artola, A., &

Rieradevall, J. (2010). The use of life cycle assessment for the comparison of biowaste composting at home and full scale. Waste management, 30(6), 983-994.

161

Martínez-Blanco, J., Lazcano, C., Christensen, T. H., Muñoz, P., Rieradevall, J., Møller, J., . . . Boldrin, A. (2013). Compost benefits for agriculture evaluated by life cycle assessment. A review. Agronomy for sustainable development, 33(4), 721-732.

Martínez-Blanco, J., Lehmann, A., Muñoz, P., Antón, A., Traverso, M., Rieradevall, J., &

Finkbeiner, M. (2014). Application challenges for the social Life Cycle Assessment of fertilizers within life cycle sustainability assessment. Journal of Cleaner Production, 69, 34-48.

Müller, T., Thißen, R., Braun, S., Dott, W., & Fischer, G. (2004). (M) VOC and

composting facilities part 2:(M) VOC dispersal in the environment. Environmental science and pollution research, 11(3), 152.

Norris, G. A. (2006). Social impacts in product life cycles-Towards life cycle attribute

assessment. The International Journal of Life Cycle Assessment, 11, 97-104. NYC-Sanitation. (2015). New York City Community Composting Report 2014. Retrieved

from http://www1.nyc.gov/assets/dsny/downloads/pdf/studies-and-reports/2014-community-composting-report-LL77.pdf

Pandyaswargo, A. H., & Premakumara, D. G. J. (2014). Financial sustainability of

modern composting: the economically optimal scale for municipal waste composting plant in developing Asia. International Journal of Recycling of Organic Waste in Agriculture, 3(3), 4.

Parent, J., Cucuzzella, C., & Revéret, J.-P. (2010). Impact assessment in SLCA: sorting

the sLCIA methods according to their outcomes. The international journal of life cycle assessment, 15(2), 164-171.

Platt, B., McSweeney, J., & Davis, J. (2014). Growing local fertility: a guide to

community composting. Highfields Center for Composting. Institute for Local Self-Reliance, Hardwick.

Poulsen, O. M., Breum, N. O., Ebbehøj, N., Hansen, Å. M., Ivens, U. I., van Lelieveld,

D., . . . Nielsen, E. M. (1995). Collection of domestic waste. Review of occupational health problems and their possible causes. Science of the Total Environment, 170(1), 1-19.

Righi, S., Oliviero, L., Pedrini, M., Buscaroli, A., & Della Casa, C. (2013). Life cycle

assessment of management systems for sewage sludge and food waste: centralized and decentralized approaches. Journal of Cleaner Production, 44, 8-17.

Ruffino, B., Fiore, S., & Zanetti, M. C. (2014). Strategies for the enhancement of

automobile shredder residues (ASRs) recycling: results and cost assessment. Waste management, 34(1), 148-155.

http://www1.nyc.gov/assets/dsny/downloads/pdf/studies-and-reports/2014-community-composting-report-LL77.pdf

http://www1.nyc.gov/assets/dsny/downloads/pdf/studies-and-reports/2014-community-composting-report-LL77.pdf

162

Rynk, R. (2001). Getting moisture into the compost pile. Biocycle, 42(6), 51-56. Saer, A., Lansing, S., Davitt, N. H., & Graves, R. E. (2013). Life cycle assessment of a

food waste composting system: environmental impact hotspots. Journal of Cleaner Production, 52, 234-244.

Scheppack. (2016). Rollsieb rs 350 scheppach - 230V 50Hz 250W. Retrieved from

https://www.scheppach.com/product-details/Rollsieb-rs-350-scheppach----230V-50Hz-250W.aspx#technischedaten

Silverman, D. T. (2017). Diesel exhaust causes lung cancer: now what? Occup Environ

Med, 74(4), 233-234. Slater, R., & Aiken, M. (2015). Can’t you count? Public service delivery and

standardized measurement challenges–the case of community composting. Public Management Review, 17(8), 1085-1102.

Slater, R., Frederickson, J., & Yoxon, M. (2010). Unlocking the potential of community

composting: Full project report. Solomon, S. (2007). Climate change 2007-the physical science basis: Working group I

contribution to the fourth assessment report of the IPCC (Vol. 4): Cambridge University Press.

Stranddorf, H. K., Hoffmann, L., & Schmidt, A. (2005). LCA technical report: impact

categories, normalization and weighting in LCA. Update on selected EDIP97-data. In: Copenhagen: Danish Ministry of the Environment–Environmental Protection Agency.

Sullivan, D. (2011). Zero waste on San Francisco’s horizon. Biocycle, 52(7), 28. Sydbom, A., Blomberg, A., Parnia, S., Stenfors, N., Sandström, T., & Dahlen, S. (2001).

Health effects of diesel exhaust emissions. European Respiratory Journal, 17(4), 733-746.

Sánchez, A., Artola, A., Font, X., Gea, T., Barrena, R., Gabriel, D., . . . Mondini, C.

(2015). Greenhouse gas from organic waste composting: emissions and measurement. In CO2 Sequestration, Biofuels and Depollution (pp. 33-70): Springer.

Todd, J. (1994). From eco-cities to living machines: principles of ecological design:

North Atlantic Books.

http://www.scheppach.com/product-details/Rollsieb-rs-350-scheppach----230V-50Hz-250W.aspx#technischedaten

http://www.scheppach.com/product-details/Rollsieb-rs-350-scheppach----230V-50Hz-250W.aspx#technischedaten

163

UNEP. (2009). Guidelines for Social Life Cycle Assessment of Products. Retrieved from van Haaren, R. (2009). Large scale aerobic composting of source separated organic

wastes: A comparative study of environmental impacts, costs, and contextual effects. Unpublished Results, MS Thesis, Columbia University, 27.

van Haaster, B., Ciroth, A., Fontes, J., Wood, R., & Ramirez, A. (2017). Development of

a methodological framework for social life-cycle assessment of novel technologies. The International Journal of Life Cycle Assessment, 22(3), 423-440.

Wallace, P. (2014). San Francisco offers a model of recycling. Waste Management and

Environment, 25(8), 16. Watson, D. S., Shields, B. J., & Smith, G. A. (2011). Snow shovel–related injuries and

medical emergencies treated in US EDs, 1990 to 2006. The American journal of emergency medicine, 29(1), 11-17.

Williams, M., Lamarre, B., Butterfield, D., Tyrrel, S., & Simpson, A. (2013). Defra Project

WR 1121 Bioaerosols and odour emissions from composting facilities. Zhang, Y. (2013). Urban metabolism: A review of research methodologies.

Environmental Pollution. Zurbrügg, C., Drescher, S., Patel, A., & Sharatchandra, H. (2004). Decentralised

composting of urban waste–an overview of community and private initiatives in Indian cities. Waste management, 24(7), 655-662.

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BIOGRAPHICAL SKETCH

Kimmel Chamat Garcés was born in Quibdó, Colombia, in 1981. He graduated

from San Buenaventura University in Cali, Colombia, in 2006, earning a bachelor‘s

degree in architecture (laurate). The Fulbright scholarship, awarded in 2009, allowed

him to pursue a master’s program in the United States. In 2012, he received a master‘s

degree in architecture with concentration in sustainable architecture from the University

of Florida. A scholarship from Colciencias allowed him to pursue a Ph.D. program in the

United States. In 2012, he enrolled in the Urban and Regional Planning Department at

the University of Florida to pursue the degree of Doctor of Philosophy in design,

construction and planning with a concentration in urban and regional planning. He

received his Ph.D. from the University of Florida in the spring of 2018.

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