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UCD SCHOOL OF BIOSYSTEMS ENGINEERING
Life Cycle Assessment of an
BSEN 30360 – Life Cycle Assessment
Life Cycle Assessment of an Air Freshener
The product chosen for this study is an air-freshener made by an American company called
OMI Industries which I completed an internship for in 2013. OMI Industries has been odour-
abatement specialists since 1988 with a primary focus on
large industrial concerns such as malodours generated from
the asphalt industry and the paper industry. More recently
the company has expanded into commercial and consumer
markets and its flagship consumer product “Freshwave” has
gained recognition from the US EPA in its “Designed for the
Environment” (DfE) category. This recognition is given on
the basis that the gel is completely biodegradable, contains
no harmful chemicals and neutralises odours as opposed to just masking them. The
objective of this study is to apply the LCA tool in order to quantify the environmental
performance of this product and determine whether it is still worthy of its EPA recognition
after all aspects of its production characteristics are taken into consideration.
The primary goal of this LCA study is to demonstrate some form of knowledge on the topic
to the lecturer in the UCD school of Biosystems Engineering while a secondary objective is to
identify any processes in “Freshwave’s” life cycle which have a significant impact on the
environment. The results are expected to be of significant interest to the stakeholders of
OMI Industries, as they could potentially highlight any production flaws which maybe be
refined to improve the products environmental and economic performance. The LCA will be
carried out in accordance with ISO 14040:2006 standards.
Product System to be studied:
“Freshwave” is a finished product from the odour abatement industry. Figure 2 shows a
simplified product system consisting of essential oils extracted from and processed in
Australia, surfactant synthesised in Kentucky and a plastic gel obtained from Indiana. The
product is then manufactured and packaged at OMI Industry’s factory in Rising Sun, Indiana.
Figure 1: Freshwave crystal gel
The odour-neutralising gel is contained in a recyclable plastic container which is produced
nearby in Cincinnati. The product is then transported via truck to the respective wholesalers
and subsequently distributed to retail outlets throughout the USA. The product lasts
between 30-40 days depending on ambient air flow conditions and only a trace residue of
the gel remains following usage which can be disposed of along with the plastic container.
Although the product is recyclable, it is at the user’s discretion to select its disposal method.
Waste sorting is encouraged only in some US states so this should be considered when
estimating this aspect of the products life cycle.
Function of the product:
The primary function of “Freshwave” is to neutralise odours by way of “absorbing and
converting malodours resulting in no odour at all” (Ecosorb Engineering manual, 2008).
The functional unit of this study is the production of 1000kg of gel, the approximate mass of
product produced for one shipment. All other figures in this study will be expressed relative
to this figure.
+ Energy Use
+ Energy Use
+ Energy Use
+ Energy Use
Figure 2: System Boundary of OMI Air Freshener
System Boundary: (refined in the LCI stage)
The system boundary will encompass all the processes from raw material extraction in
Australia and the US; transportation of raw materials to OMI’s factory in Indiana;
manufacturing and assembly processes carried out at this factory; finished product
transportation; product usage and final disposal/recycling. Three minor aspects of the
product’s life cycle which are to be excluded are outlined in the “limitations” section.
OMI manufactures and transports several other products as well as the odour gel under
study here. Allocation procedure for product transport from factory to retail outlets and
manufacturing process can be estimated due to the greater availability of data provided by
the company from back-dated invoices. To determine the environmental burden for which
the odour gel is responsible, a ratio of odour gel to other products produced and
transported can be devised to relatively accurate detail with a brief analysis of back-dated
LCIA methodology and types of impacts:
The inventory data collected from the product cycle will be compiled into an Excel
spreadsheet where generic characterisation factors will be used to derive emissions data
from inventory data (Pennington et al. 2004). This study is focusing on three particular
impact categories; Water usage, energy use and GHG emissions the characterisation factors
of which will be sourced from the literature or alternatively from LCA tools and databases
(Pennington et al. 2004).
Interpretation to be used:
As outlined by the ISO 14044: 2006 standards, the interpretation will identify the
environmental hotspots in this air freshener’s life cycle with particular emphasis on GHG
emissions, water consumption and energy usage based on the results gained from the LCI
and LCIA phases of the project. The interpretation will also assess the consistency of the
study and highlight any errors or omissions and consider the impact this has on the study’s
robustness. A summary and recommendations for future studies along with information
specific to the stakeholders will also be provided.
The project will rely primarily on empirical data collected by liaising with the R&D
department at OMI industries. For areas such as raw material extraction and refinement or
plastic production and refinement, where OMI cannot provide data, the study will rely on
secondary data derived from ecoinvent and scientific journal articles on similar
studies(Weidema et al. 2013). Data involving transportation of raw material into the factory
and finished product out of the factory can be estimated from an administrative analysis of
invoices, using 2012 as a base year. From this analysis, amount of product transported,
transport type and the location it came from/ is going to can be determined. A rough
estimate of distance travelled can then be defined through the use of an Excel add-in
(CDXzipstream, 2014) which calculates distance between US zip-codes. Data for
manufacturing and assembly can be calculated simply from utility bills whilst allowing for an
allocation ratio for other processes which require energy use. Product use should not
require any energy use attributable to the product itself seen as it relies on ambient air
conditions. One could argue that power could be used to generate air flow but even so,
these emissions will not be added to Freshwave. Finally recycling and disposal will rely the
heaviest on secondary data largely because human discretion will determine product
With regards to transport it will be assumed that the most direct route was taken to deliver
raw materials to the factory or finished products to retail outlets. This is a necessary
assumption as the invoices only show the final destination of the products, the route taken
by the courier company is not documented by OMI.
According to (Rebitzer et al. 2004) there are a variety of other interactions with the
environment in even the simplest LCA model thus it is necessary to make a value choice
depending on the scope of the study. This study is concerned most with GHG emissions,
water usage and energy usage. For example if suggesting to improve a process to lower its
energy consumption at the expense raising of its acidic emissions, that process will be
favoured in this particular study. However it is important to mention this preference in the
interpretation the value of LCA lies in these value choices being transparent (RSC, 2010).
Secondary and tertiary packaging will be excluded from the study because these processes
are not quantified or documented. Consumer transportation from retail outlets to
residences will also be excluded due to the allocation issues arising from multiple purchases
and lack of data on transport mode and distance travelled. Design and development of the
product will also be omitted due the fact that this process itself does not make a significant
physical contribution to emissions in comparison to the other phases in the life cycle.
(Rebitzer et al. 2004)
Data quality requirements:
Every effort will be made to ensure primary data will be utilised for data calculation in this
project however when this is not possible, secondary sources will be utilised from resources
such as ecoinvent (2014) and science direct. In the worst case scenario, where parameters
are too complex to calculate, the scope of the system may be altered however the
consequences of doing such will be documented in the interpretation of the project so
stakeholders are made aware of potential drawbacks resulting from this.
Type of critical review:
The critical review will assess whether the results and interpretation of the LCA satisfied the
goal and scope outlined by the author. Whether there are any discrepancies or omissions in
the data or whether the author gave a well-rounded view of the subject. Ideally the project
should be reviewed by another LCA practitioner to ensure the absence of bias and personal
errors one might not be aware of.
Type and format of the report:
The format of the report will strictly adhere to ISO 14040 standards including a goal and
scope; inventory analysis, impact assessment and interpretation.
Life Cycle Inventory
Data were collected in accordance with ISO14044 (2006) standards focusing on material
inputs, energy inputs and water usage (grey water footprint along with water used in the
various processes expressed as on figure). The format of these data sheets are available to
view in the accompanying excel file. The sources of the data are referenced in the
worksheet. Any anomalous figures are highlighted with embedded comments in the cell
attempting to explain the associated issues with the data. Two online databases were
utilized to obtain data on the various unit processes; EcoInvent and National Renewable
Energy Laboratory (NREL). Regarding specific quantities of the respective materials and
bulk-ordered raw materials were provided by liaising with OMI’s R&D department. Table. 1
describes each of the unit processes outlined in fig.3, a flow chart roughly describing the
system of air freshener production.
Table 1: Unit Process Descriptions
Unit Process Process Description Source of Data
Plant seeds are saponified via steam hydrolysis in
order to separate oils from fats.
A biological catalyst is introduced into an ethylene-
oxygen mix to form an edible type of surfactant
Small molecules are polymerised into long-chain
hydrocarbons by introducing an initiator along with
heat or radiation.
Low density polyethylene is heated and moulded
into the desired shape.
Essential oils, surfactant and co-polymer are
combined with water, heated and mixed to be
dispensed into the LLDPE containers.
Truck Transport Bulk densities of raw materials are used to calculate
the amount of energy per kg of material
Ocean Transport Bulk densities of raw materials are used to calculate
the amount of energy per kg of material
A number of calculations were required throughout this model. Figures obtained from
online databases were all converted from their respective units into mega joules (MJ). These
figures were then expressed relative to the functional unit depending on their proportions
with the product, detailed in table. 2. Proportion of scrap for each process was calculated by
the ratio of product to overall output. Ocean and truck transport distances were estimated
using Google Earth’s path function. Electricity required for one batch of 1000kg product was
calculated from a monthly electricity bill and a breakdown of average power use from OMI
industries. All calculations are detailed in the accompanying spreadsheet.
Sensitivity analyses, in which the model was run in different scenarios, were carried out and
are displayed on the spreadsheet. The parameters analysed include altering container size,
transport distance and proportions of components per finished product.
Relating Data to the functional unit:
Table.2 shows the rough proportions of the various components of the air freshener. Exact
proportions were withheld by OMI Industries due to trade secrets however the R&D
department has stated these are close to the actual proportions.
Table 2: Proportion of component per 1000 kg of product
Air Freshener Component Proportion of weight Mass per FU (1000kg)
Essential Oils 5% 50 kg
Surfactant 5% 50 kg
Co-polymer 5% 50 kg
Water 84% 840 kg
Plastic Container 1% 10 kg
The key parameter for which allocation is necessary is transport. Raw materials are
transported to the production facility in bulk densities hence it is necessary to allocate
energy use per km travelled for on kg based on these respective densities. OMI have stated
the following bulk densities:
Table 3: Bulk densities imported to manufacturing plant
Component Imported Density (kg)
Essential Oils 13608
Plastic Container 600
Refining the System Boundary:
On account of data availability, it was necessary to alter the system boundary. In fig.3 the
red line shows the unit processes the model will encapsulate.
Crop harvesting and raw material extraction were excluded due to difficulty locating specific
data from any of the online databases. Data relating to the exact essential oils used in the
air freshener were particularly elusive. EcoInvent, NREL and LCAfood.dk had no data relating
to tea-tree or aniseed oil production. NREL had data on oil production from palm kernels
and this dataset was deemed appropriate to imitate tea-tree oil production. With respect to
downstream processes, the assumption that back-dated invoices could provide the
necessary data for product distribution was incorrect as it were too time consuming for the
administrative workers at the factory to compile. Emissions and energy use associate with
product usage were considered negligible anyway as the product requires no electricity to
+ Energy Use
+ Energy Use
+ Energy Use
+ Energy Use
Figure 3: Refined System Boundary for OMI Air freshener
function and releases no harmful emissions. Product disposal proved too difficult to
quantify; although the low density polyethylene container along with any product residues
are fully recyclable the key area it’s sold in, ie. America is not very recycling friendly. Some
states are more recycling friendly than others and trying to calculate accurate figures would
be too time-consuming for the scope of this product. Essentially, what was intended to be a
cradle-to-grave study has now become a gate-to-gate study.
Life Cycle Impact Assessment:
This phase of the LCA builds on the data from the LCI phase and deals with the evaluation of
the environmental impacts associated with the air freshener’s production process (UNEP,
Selection of impact categories, category indicators and characterization models:
As stated in the goal and scope section, the impact categories this project will focus on are
global warming potential, resource depletion and water usage. Global warming impacts
exhibit such endpoints as polar ice-cap melting, change in ocean circulation and
desertification (SIAC, 2006). Resource depletion is associated with endpoints related to
unsustainability with decreased availability of these valuable resources for future
generations. Finally unsustainable water usage can result in drought, failure of crops and
ecosystem alterations. Major contributors from this particular life cycle assessment are
summarized in table.4.
Table 4: Sources of significant impact for air freshener production
Unit Process Global warming Resource Depletion Water Usage
Category Indicator CO₂ eq emissions Kg of oil eq Kg of water used
Essential Oil Production CO₂ eq emissions from
electricity and diesel
Diesel to power machinery Water use for
Surfactant Production CO₂ eq emissions from
Water spoiled by grease/oil
and use to facilitate catalyst
Co-Polymer Production CO₂ eq emissions from oil
Direct use of hydrocarbons
for product manufacture
Water spoiled by grease/oils
CO₂ eq emissions from
electricity, diesel and oil
Diesel and Oil usage to
power machines/utilised in
Water used for cooling
CO₂ eq emissions from
High proportion of water in
Transport CO₂ eq emissions from oil
Diesel and oil usage to
The characterisation model determines the respective category indicators for each unit
process relative to the functional unit.
Assignment of LCI results to selected impact categories:
The data derived from the LCI model determines the amount of greenhouse gas for each
unit process in relation to the functional unit. The LCIA model then applies characterisation
factors to these data (kg Co₂eq/kg gas) in order to determine the global warming potential
associated with each unit process. These category indicators are expressed in CO₂
equivalents per functional unit for global warming potential. With regards to water and
resource depletion, the category indicator may be expressed as kg of resource used in
various stages of production (ISO14044, 2006).
Calculation of category indicator results:
Category indicators are calculated using characterisation factors outlined by the IPCC or
EPA. These are standardized factors for determining the potential amount of respective
GHG’s that are released per mega joule of oil or diesel. For example equation.1 shows how
the amount of CO₂ released per unit of energy can be calculated from the simplified
reaction of oil combustion.
𝑂𝑖𝑙: 𝐶8 𝐻18 + 12.5 𝑂2 ≫ 8𝐶𝑂2 + 9𝐻2 𝑂 + 50
Where molecular mass of C=12, O=16 and H=1.
Mass of kg-mole of C_8 H_18 = 114 kg
Mass of kg-mole of 8CO₂ =352 kg
) = 16.1
The characterisation factors used in the excel LCIA model carry out calculations similar to
equation. 1 to facilitate the conversion of mass amounts of data to their category indicators
Resulting data after characterization:
Table 5: Category Impacts of unit processes
Unit Process CO2eq @ 310 Water Use (m³) Oil Depletion kgoe
29.73929004 1.09401E-08 0.00021703
0.010065 6.294E-06 0
29.50332046 9.95957E-07 0.000881223
0.204545454 9.42338E-05 0
0.014744586 7.74175E-10 2.2665E-05
Total Transport 10.4093406 0 8.69573E-05
Total 69.88130614 0.000101535 0.001122337
The resulting data expressed in table. 5 and figure 6 shows the unit process “hotspots” in
the air freshener lifecycle. Essential oil and co-polymer production are the largest
contributors to the global warming impact category at 29% each, while product transport
has a surprisingly lower contribution at 15%. Co-polymer has the single largest share of
resource use per functional unit, at 73% of the total. With respect to water usage, Air
freshener dwarfs all other processes considering that the final product is made of 84%
CO2eq @ 310
Oil Depletion kgoe
Normalisation is define as the calculation of the magnitude of indicator results relative to
reference information (Guinee et al, 2004). This facilitates a clearer understanding of the
magnitude of LCI results as they are related to a specific population and time frame. GWP is
normalized by using NOx to CO₂ characterisation factors provided by the EPA. LCI energy
figures are simply multiplied by these figures to give CO₂ equivalent values which can be
used to infer the global warming potential of the various unit processes. Seen as global
warming potential affects the entire world this impact category is not confined to one
particular region. Normalized resource and water depletion figures do require regionalized
context as different areas are more susceptible to various category endpoints than others.
Hence reference data selected related to 2012 fossil and water resource use for the US,
where the product is used and distributed. Water and resource data in table.5 are that of
converted LCI data via the ratio of quantity per capita vs quantity used by each unit process
I. Data Inaccuracy: Due to a lack of specific data on a number of unit processes in this
life cycle, it was necessary to substitute in the next best or similar process instead of
omitting the unit process altogether. The essential oils component of the product
contains a mixture of different plant extracts however for the sake of simplicity these
Water Use (m³)
Figure 4: (a) Global warming potential; (b) Resource
Depletion; (c) Water depletion of the respective unit
different types were aggregated to assign just one unit process for essential oil
production. The exact surfactant used in the product, polysorbate 80 had no LCA
data available on it so a similar surfactant material was used to estimate emissions
and water use instead.
II. Data gaps: A large chunk of this product’s life-cycle has been omitted by the refining
of the system boundary to exclude raw material extraction (for which suitable data
could not be found), product use (which is considered to be negligible), final disposal
and product distribution with which no empirical data were available for. In addition
the exact proportion of ingredients could not be revealed due to trade secrets. This
narrowed scope means some of the impacts associated with the product are
III. Model Derived: The proverbial elephant in the room can now be addressed. Due to a
lack of experience on the LCA practitioner of this projects part, there is an inherent
flaw in the model hence all LCI data derived is of questionable integrity. This flaw,
involving the proportion of component per batch of product, is discussed further in
the sensitivity analysis section.
IV. Choice derived: According to ISO14044 (2006) allocation is to be avoided wherever
possible and when it is not possible to avoid, the system should be expanded to
include the additional functions. However in the case of transport, it would be
impossible to expand the system due to the amount of processes that would be
required to be included. Considering raw materials are imported in bulk, the data of
which were provided by the OMI Industries R&D department, it is necessary to
allocate the amount of emissions associated with product transport from the
required amount of product.
Sensitivity analyses were carried out for three parameters; container size, overall transport
and component proportion per batch of product. The first two analyses, shown in the
spreadsheet, show a solid linearity between parameter change and CO₂eq emissions
however the third analysis revealed a major flaw in the model. Table.6 shows that as
component per batch changes, the functional unit of the system alters slightly (highlighted
in red). This should not be the case, however the LCA practitioner of this study is uncertain
about how to rectify this error.
Table 6: Component proportion sensitivity analysis
Component SENSITIVITY Scenario 1 Scenario 2 Scenario 3
Reference flow 2000 2000 2000
Mass per container 0.5 0.5 0.5
Proportion of component per batch 0.05 0.075 0.1
Proportion of plastic per batch 0.01 0.02 0.03
Total Ocean Transport 11000 9000 7000
Total Road Transport 3400 3090 2780
Functional unit Output 1000 1085 1170
CO2 equivelents(kg) 69.881 104.85 139.83
Water Consumption(kg) 905.09 337.63 370.18
Life Cycle Interpretation
Identification of significant Issues:
The purpose of this LCA is to identify the significant environmental hotspots in the
production of an air freshener with particular emphasis on global warming potential,
resource depletion and water use.
I. Essential oils: Figure 6 (a) shows that essential oil production has the highest global
warming potential of all the unit processes with the air freshener’s life cycle. This
appears to be due to its heavy reliance on diesel and electricity during its production
phase. This diesel usage also earns this process second place in the resource
depletion category (fig.6 (b)). Despite high energy releases, this processes has a
relatively low water footprint probably due to the fact that biomass waste is minimal
and causes little harmful water pollution.
II. Co-polymer production: Ranked the second highest CO₂ emitter (fig (a)), and by far
the highest process contributing to resource depletion in this life cycle. In order to
produce 50kg of co-polymer, a whopping 232 MJ of oil is required which perhaps
explains the relatively high CO₂ emissions. The direct use of hydrocarbons in the
production process is highlighted in fig.6 (b) earning this process the majority share
in the resource depletion category. Fig.6 (c) shows co-polymer has a minor
contribution to water-use, mostly associated with the emission of oils and grease to
III. Transport: Despite a cumulative distance of almost 15000 km for one batch of
project, transport ranks only third in both global warming potential and resource
depletion. At approximately 10 kg CO₂ eq per 1000 kg (table.5) of product this is no
small emitter, however one would expect after all that travel, its contribution would
be higher. The most likely explanation to transports subdued contribution is
allocation; the relative emissions a reduced significantly when materials are
transported in bulk. Ecoinvent reports zero water use in either road or ocean
IV. Air Freshener Production: This process requires no diesel or oil power directly as
electricity is used to heat the process. With such a high water content with the
product, this process contributes most to water use (fig. 6 (c)).
V. Surfactant: The surfactant used is derived from plant extracts, requires no
hydrocarbon inputs and relies on electrical energy to facilitate the reaction hence
minimal CO₂ eq emissions and unsustainable resource depletion. Asignificant
amount of water is required however, 6% (fig6 (c)).
Uncertainty in the results stems from ignorance of the LCA process first and foremost which
leads to gaps in the data, issues with consistency and sensitivity.
I. Completeness: The LCI was compromised before the model was even created due to
the inability to locate datasets specific to the unit processes involved. This prompted
the substitution of similar unit processes which reduces the data quality and
subsequently the integrity of the model but would still yield statistically significant
results with some relevance to the actual product cycle. Secondly a number of unit
processes were eliminated altogether as even similar processes could not be located.
While this would not reduce the integrity of the model it would reduce the credibility
of the systems analysis as a whole rendering the results less valuable to the
stakeholders at OMI.
II. Sensitivity: Of the three sensitivity scenario analyses carried out, the component
sensitivity test highlighted a significant error with the model in that the functional
unit changed when the proportions of batch components were altered. This
parameter is not supposed to have any effect on this hence the final results yielded
in the LCIA section are likely to be faulty even if the LCIA characterization and
normalization processes were executed perfectly. The LCIA conversion of LCI data is
a straight-forward process and there is unlikely to be any error associated with this
aspect of the model. The normalisation procedure was followed from Guinee et al,
(2004) however the LCA practitioner’s ignorance comes to the fore again and this
calculation may have been performed incorrectly.
III. Consistency: The normalisation procedure attempted to address spatial and
temporal uncertainties by using annual reference values for a specific region. All
elements of the impact assessment are likely to have been applied consistently as
the sensitivity analyses have shown apart from the component sensitivity analysis.
Conclusions, Limitations and recommendations:
The amount of uncertainty associated with the model as a whole significantly
reduces the integrity of any results meaning what might appear as an environmental
hotspot could just be the result of an elaborate error.
Based on the results which were derived, co-polymer is the single largest contributor
to cumulative environmental impacts due to its high use of hydrocarbons. Despite
the long transport distances, transport is not the highest emitter or contributor to
resource depletion due to bulk transport of product.
The second largest contributor assuming global warming and resource depletion are
more important in this region that water use; is essential oil production. The model
only encapsulated oil production at plant and didn’t consider harvesting of the plant
components meaning this parameter has the potential to contribute even further to
resource depletion and water use.
Lack of data quality: due to a combination of ignorance, limited access to online
databases and partly due to the obscurity of the product.
Lack of model integrity: due to ignorance of the process and questionable integrity
of the input data.
Narrow system boundary: The model does not encapsulate the entire life cycle of
the product due to product complexity, lack of specific data and ignorance.
Suggestions to source essential oils from a closer locality in an effort of reducing
transport emissions would appear futile as sensitivity analysis #2 suggests.
Putting resources into sourcing a co-polymer which requires less energy and
resource use would appear to have a greater effect in reducing emissions.
Hire a decent LCA practitioner next time and the project might yield more significant
The purpose of this study is to employ the LCA technique to an allegedly environmentally
sustainable air freshener with a view to highlight any environmental “hotspots” throughout
the production cycle. The project initially set out to encompass the entire cycle, from cradle
to grave however subsequent issues with empirical data collection and incompatibility with
online databases prompted a redefinition of the system boundary. The goal and scope
clearly describes the system intended to be assessed with process flow diagrams and
defined goals reasonably well.
The life cycle inventory phase threw up a few issues regarding data collection,
forcing a number of compromises in the data sets and a redefinition of the system boundary
all of which were documented. The narrow-scoped system model’s integrity was even
further compromised by incorrect input of data. This input error reduces the integrity of the
model outputs significantly however the author appears to have a reasonable competency
in appropriately converting LCI values into LCIA category indicators.
The life cycle impact assessment phase was executed nonetheless with
compromised data to provide normalized figures based on per capita reference values
derived from the World Bank. The results were compiled into tables and charts in order to
provide food for thought in the life cycle interpretation stage.
The life cycle interpretation stage highlighted the key unit processes with the most
profound calculated impacts on mid and endpoints. This stage also acknowledged the
significant amount of experimental errors associated with the entire process.
It was difficult to draw conclusions from such a compromised dataset however
despite the uncertainties, one aspect was clear, hydrocarbons= high GWP and resource
Overall the “I” in LCIA should stand for iterative as this tedious process should rightly
have such as its middle name. This ignorant LCA practitioner feels he may have bitten off
more than he could chew with this air freshener and should have chosen a simpler product
for a first attempt. Nonetheless, the next attempt at LCA should be a lot more fruitful having
a greater understanding of the process.
Guinee, J.B. Gorree, M. Heijungs, R. Huppes, G. Koning, A. van Oers, L. Sleeswijk, A.W. Suh, S. Udo de
Haes, H.A. (2004) Handbook on Life Cycle Assesment. Kluwer Academic publishers,
Haupert, L. & Timsick, C. (2014) ‘Ecosorb odor control solutions enginerring manual’, OMI Industries.
Long Grove, Illinois.
Iso14044. (2006) ‘Environmental management- Life cycle Assessment- Requirements and guidelines’,
British Standard, UK.
Pennington, D., Potting, J., Finnveden, G., Lindeijer, E., Jolliet, O., Rydberg, T. and Rebitzer, G. (2004)
'Life cycle assessment Part 2: Current impact assessment practice', Environment
international, 30(5), 721-739.
Rebitzer, G., Ekvall, T., Frischknecht, R., Hunkeler, D., Norris, G., Rydberg, T., Schmidt, W.-P., Suh, S.,
Weidema, B. P. and Pennington, D. (2004) 'Life cycle assessment: Part 1: Framework, goal
and scope definition, inventory analysis, and applications', Environment international, 30(5),
Scientific Applications International Corporation (SIAC) (2006) 'Life cycle assessment: principles and
practice. USEPA, Cincinnati OH.
Sustainability Consortium (2013) 'Life cycle impact study of non-aerosol air fresheners', University of
Arkansas, Arkansas, US.
Weidema, B. P., Bauer, C., Hischier, R., Mutel, C., Nemecek, T., Reinhard, J., Vadenbo, C. and Wernet,
G. (2013) Overview and methodology: Data quality guideline for the ecoinvent database
version 3, Swiss Centre for Life Cycle Inventories.
World Bank, (2014) The world bank.org. Available from: