UCD SCHOOL OF BIOSYSTEMS ENGINEERING

Life Cycle Assessment of an Air Freshener

BSEN 30360 – Life Cycle Assessment

Luke Martin

12/23/2014

Life Cycle Assessment of an Air Freshener

Introduction:

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.

Goal:

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 (freshwaveworks.com, 2014)

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).

Functional Unit:

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.

Crop

Harvest

Raw Material

Extraction

Essential Oil

Production

Surfactant

Manufacture

Co-polymer

Manufacture

Plastic

Container

Manufacture

Ocean

Transport

Road Transport

Product

Manufacture

and Packaging

Product

Distribution

(Rd trans)

Product Use

Product

Disposal

GHG

Emissions/Water

+ Energy Use

GHG

Emissions/Water

+ Energy Use

GHG

Emissions/Water

+ Energy Use

GHG

Emissions/Water

+ 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.

Allocation Procedure:

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

invoices.

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.

Data Requirements:

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

disposal method.

Assumptions:

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.

Value Choices:

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).

Limitations:

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 Collection:

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

Essential Oil

Production

Plant seeds are saponified via steam hydrolysis in

order to separate oils from fats.

NREL

Surfactant

Production

A biological catalyst is introduced into an ethylene-

oxygen mix to form an edible type of surfactant

“Polysorbate 80”.

EcoInvent

Co-polymer

production

Small molecules are polymerised into long-chain

hydrocarbons by introducing an initiator along with

heat or radiation.

EcoInvent

Plastic container

production

Low density polyethylene is heated and moulded

into the desired shape.

NREL

Air freshener

manufacture

and assembly

Essential oils, surfactant and co-polymer are

combined with water, heated and mixed to be

dispensed into the LLDPE containers.

OMI Industries

Truck Transport Bulk densities of raw materials are used to calculate

the amount of energy per kg of material

OMI Industries/

EcoInvent

transported.

Ocean Transport Bulk densities of raw materials are used to calculate

the amount of energy per kg of material

transported.

OMI Industries/

EcoInvent

Data Calculation:

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.

Data Validation:

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

Allocation:

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)

Surfactant 1361

Essential Oils 13608

Co-polymer 907

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

Crop

Harvest

Raw Material

Extraction

Essential Oil

Production

Surfactant

Manufacture

Co-polymer

Manufacture

Plastic

Container

Manufacture

Ocean

Transport

Road Transport

Product

Manufacture

and Packaging

Product

Distribution

(Rd trans)

Product Use

Product

Disposal

GHG

Emissions/Water

+ Energy Use

GHG

Emissions/Water

+ Energy Use

GHG

Emissions/Water

+ Energy Use

GHG

Emissions/Water

+ 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,

2014).

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

cooling/cleaning

Surfactant Production CO₂ eq emissions from

electricity generation

Water spoiled by grease/oil

and use to facilitate catalyst

Co-Polymer Production CO₂ eq emissions from oil

usage

Direct use of hydrocarbons

for product manufacture

Water spoiled by grease/oils

Plastic container

Production

CO₂ eq emissions from

electricity, diesel and oil

Diesel and Oil usage to

power machines/utilised in

product

Water used for cooling

Air Freshener

Production

CO₂ eq emissions from

electricity generation

High proportion of water in

finished product

Transport CO₂ eq emissions from oil

and diesel

Diesel and oil usage to

transport materials

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: 𝐶8𝐻18 + 12.5 𝑂2 ≫ 8𝐶𝑂2 + 9𝐻2𝑂 + 50𝑀𝐽

𝑘𝑔

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

50𝑀𝐽

𝑘𝑔𝑥 (

114

352) = 16.1

𝑀𝐽

𝑘𝑔𝐶𝑂₂

Equation.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

with ease.

Resulting data after characterization:

Table 5: Category Impacts of unit processes

Unit Process CO2eq @ 310 Water Use (m³) Oil Depletion kgoe

Essential oil production

29.73929004 1.09401E-08 0.00021703

Surfactant production

0.010065 6.294E-06 0

Co-Polymer production

29.50332046 9.95957E-07 0.000881223

Air Freshener production

0.204545454 9.42338E-05 0

LDPE container production

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%

water.

Essential Oil

Co-Polymer

Plastic /Surfacta

nt/ Air Freshene

r <1% Transpor

t

CO2eq @ 310

Essential Oil

Co-Polymer

Plastic Container

Transport

Oil Depletion kgoe

(b) (a)

Normalization:

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

(SIAC, 2006).

Uncertainty Analysis:

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

Essential Oil/Plastic Container

Surfactant

Co-Polymer

Air Freshener

Water Use (m³)

(c)

Figure 4: (a) Global warming potential; (b) Resource Depletion; (c) Water depletion of the respective unit processes

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

unaccounted for.

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 Analysis:

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

water.

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

transport.

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)).

Evaluation:

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:

Conclusions:

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.

Limitations:

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.

Recommendations:

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

results.

Critical Review

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

depletion.

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.

Reference List

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, Dordrecht.

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