Project Report A.Setter

Evaluation of Alphasense CO2 Sensors for use in Indicative Measurements
i
Evaluation of Alphasense NDIR CO2 Sensors for use in
Indicative Measurements
Alan Setter
Department of Physical Sciences, CIT
Abstract:
The aim of this project was to determine whether or not the Alphasense NDIR CO2
sensor would be suitable for use in conducting indicative measurements by
satisfying a proposed Data Quality Objective (DQO) for uncertainty. The sensor
was evaluated through a series of tests, from which individual uncertainty
estimations could be made. The measurements taken during the project were
validated using a Teledyne 360E CO2 analyser, which provided reliable
information about the test gas concentrations used in the experiments. As the
CAFÉ Directive does not stipulate any DQOs for CO2, it was necessary to assign a
DQO for uncertainty of 25% of the Limit Value. The expanded combined
uncertainty of the Alphasense sensor was calculated to be 14.52% of the Limit
Value. This led to the conclusion that the Alphasense sensor, and similar sensors
by the manufacturer for other gases, would be suitable for indicative
measurements.

ii
Submitted in partial fulfilment of the regulations for a
Bachelor of Science (Honours)
in
B.Sc. (Hons) in Environmental Science and Sustainable Technology
February 2015
Declaration:
I hereby certify that this material, which I now submit for assessment
is entirely my own work and has not been taken from the work of
others save and to the extent that such work has been cited and
acknowledged within the text of the report.
Signed:
Date:
Acknowledgements:
I would like to express my gratitude to the Head of Department, Supervisor and
the staff in the Department of Physical Sciences for their help in preparing this
research report.

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1. Introduction..............................................................................................................1
1.1 Scope of Validation......................................................................................................4
1.1.1 Defining a Data Quality Objective...................................................................................................4
1.1.2 Defining a Limit Value.....................................................................................................................4
1.2 Experimental Overview................................................................................................7
2. Literature Review......................................................................................................8
2.1 What is CO2?................................................................................................................8
2.1.1 Background of CO2 Measurements.................................................................................................8
2.1.2 CO2: The Greenhouse Gas...............................................................................................................9
2.1.3 Anthropogenic Drivers of CO2.......................................................................................................10
2.1.4 Political Efforts to Ambient Reduce CO2 Levels ............................................................................11
2.1.5 CO2 Emissions in Ireland ...............................................................................................................13
2.1.6 Outlook .........................................................................................................................................15
2.2 Beer-Lambert Law...................................................................................................... 17
2.3 Teledyne 360E CO2 Analyser ...................................................................................... 18
2.3.1 Theory of Operation .....................................................................................................................18
2.3.2 IR Photo-Detector.........................................................................................................................20
2.3.3 The GFC Wheel .............................................................................................................................21
2.3.4 The Measurement-Reference Ratio..............................................................................................22
2.3.5 Infrared Interference Compensation............................................................................................24
2.4 Alphasense IRC-A1 NDIR CO2 Sensor........................................................................... 26
2.5 Calibration of the Sensors .......................................................................................... 29
2.6 Mass Flow Controllers................................................................................................ 29
2.7 Uncertainty Analysis .................................................................................................. 32
2.7.1 How to Calculate the Uncertainty of a Measurement Device ......................................................33
3. Experimental Design ............................................................................................... 37
3.1 Scope of Project......................................................................................................... 37

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3.2 Experimental System Design ...................................................................................... 38
3.3 Experimental Apparatus............................................................................................. 38
3.3.1 Design of the Air-tight Sampling Chamber ...................................................................................38
3.4 Gas Cylinders............................................................................................................. 40
3.5 Selecting a Reference Analyser................................................................................... 40
3.6 Generation of Test Gas Mixtures ................................................................................ 44
3.6.1 Controller Gas Correction Factors ................................................................................................44
3.7 Determination of CO2 Concentration in Mixed Gas Cylinder ........................................ 45
3.8 Experimental Configuration........................................................................................ 46
4. Laboratory Experiments .......................................................................................... 49
4.1 Response Time........................................................................................................... 50
4.2 Lack of Fit to the Linear Model ................................................................................... 50
4.3 Repeatability ............................................................................................................. 51
4.4 Lower Detectable Limit .............................................................................................. 52
4.5 Resolution ................................................................................................................. 53
4.6 Short and Long Term Drifts......................................................................................... 53
4.7 Cross Sensitivity......................................................................................................... 55
4.8 Hysteresis.................................................................................................................. 56
4.9 Reproducibility .......................................................................................................... 57
4.10 Effect of Ambient Temperature.................................................................................. 58
5. Analysis of Results .................................................................................................. 60
5.1 Response Time........................................................................................................... 60
5.2 Lack of Fit to the Linear Model ................................................................................... 61
5.3 Repeatability ............................................................................................................. 66
5.4 Lower Detectable Limit .............................................................................................. 68
5.5 Resolution ................................................................................................................. 69
5.6 Short-Term Drift ........................................................................................................ 70

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5.7 Long-Term Drift ......................................................................................................... 71
5.8 Cross Sensitivity......................................................................................................... 73
5.9 Hysteresis.................................................................................................................. 75
5.10 Reproducibility .......................................................................................................... 77
5.11 Effect of Ambient Temperature.................................................................................. 83
5.11.1 One-way ANOVA......................................................................................................................84
5.11.2 Two-Sample T-Test...................................................................................................................86
5.11.3 Regression Analysis of two level temperature Sensor Responses...........................................87
5.11.4 Uncertainty due to sensitivity of the sensor to the surrounding temperature .......................89
5.12 Summary of Results ................................................................................................... 90
6. Conclusions............................................................................................................. 91
7. References .............................................................................................................. 97
8. Bibliography ................................................................... Error! Bookmark not defined.
9. Appendix ........................................................................ Error! Bookmark not defined.
List of Tables:
Table 1: Project Limits and Thresholds for Carbon Dioxide 6
Table 2: Teledyne 360e CO2 Analyser Manufacturers Specifications 25
Table 3: Alphasense IRC-A1 NDIR CO2 Sensor Manufacturers Specification 28
Table 4: Mass Flow Controller Specifications 30
Table 5: Deviations from expected values and Standard Deviations of combined deviations for all analysers. 42
Table 6: Scorecard for Teledyne testing 43
Table 7: Response Time Test Data for the Alphasense sensor 61
Table 8: Results from Linearity test for the Teledyne and Alphasense sensors 62
Table 9: Fits and Diagnostics for All Observations 65
Table 10: Standard Deviation of repeated measurements and Repeatability values for the Teledyne and the
Alphasense 66
Table 11: Standard Deviation of repeated measurements at zero air and Lower Detectable Limit values for the
Teledyne and the Alphasense 68
Table 12: Standard Deviation of repeated measurements at zero air and Resolution values for the Teledyne and the
Alphasense 69
Table 13: Short –term Stability results for the Alphasense sensor. 70

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Table 14: Deviations of sensor response between 24 hour periods 70
Table 15: Long –term Stability results for the Alphasense sensor. 71
Table 16: Deviations of sensor response between consecutive 7 day periods 71
Table 17: Summary of Sensor Response Variables for Evaluation of Cross Sensitivity Effects 74
Table 18: Sensor Response Hysteresis Test 75
Table 19: Summary of Deviations between measurements at intervals 76
Table 20: Analysis of Variance for Sensor 2 78
Table 21: Fits and Diagnostics for All Observations for Sensor 2 79
Table 22: Analysis of Variance for Sensor 4 80
Table 23: Fits and Diagnostics for All Observations for Sensor 4 82
Table 24: Analysis of sensor response to different temperature levels 84
Table 25: Analysis of Variance Results of Temperature Effects 85
Table 26: Two-sample Test criteria 86
Table 27: Results of Sensor responses at two temperature levels 87
Table 28: Characteristics from Sensor Evaluation of the Alphasense sensor 90
Table 29: Uncertainty Values from Sensor Evaluation against the DQO 90

Page | 1 Alan Setter
1. Introduction
The aim of this project was to determine whether or not the Alphasense NDIR CO2 sensor
would be suitable for use in conducting indicative measurements by satisfying a proposed
Data Quality Objective (DQO) for uncertainty.
Non-dispersive Infrared (NDIR) CO2 sensors are characteristic of the type of emerging
measurement devices used for indicative measurements outlined by the Cleaner Air For
Europe (CAFE) Directive. The advantage of indicative measurements is that they allow for
more economical monitoring of air quality through the use of lower cost gas sensors. In
zones where the pollutant concentrations are deemed to be below the Upper Assessment
Threshold (UAT), the Directive permits the unrestricted use of indicative measurements.
Between the UAT and the Limit Value (LV), the Directive allows for up to 50 % of fixed
measurements to be replaced by indicative measurements.
Fig. 1: Hierarchy of Limits, Tolerances and Thresholds outlined by the CAFÉ Directive. Note
indicative measurements can only be used below the LimitValue.

Definitions:
‘Data Quality Objectives’ or DQO’s outlined in the CAFE directive are limit values of
measurement uncertainty, minimum data capture and minimum time coverage;
‘Fixed measurements’ measurements taken at fixed sites, to determine the levels in
accordance with the relevant Data Quality Objectives (DQO);
‘Indicative measurements’ measurements which meet DQOs that are less strict than those
required for fixed measurements;
‘Assessment’ shall mean any method used to measure, calculate, predict or estimate levels;
‘Limit value’ shall mean a level fixed on the basis of scientific knowledge, with the aim of
avoiding, preventing or reducing harmful effects on human health and/or the environment
as a whole, to be attained within a given period and not to be exceeded once attained.
The CAFE Directive does not stipulate any specific method for making indicative
measurements as it does with fixed measurements. The only requirement is that the
method can meet the Data Quality Objective (DQO) stated in the Directive (e.g. ±25% at
95% confidence level for Carbon Monoxide at the hourly limit value).
In general the DQOs for indicative measurements are less strict than those required for fixed
measurements. The uncertainty DQO is expressed as a relative expanded combined
uncertainty of the measurement. As such, it is necessary to first define an experimental
evaluation that would quantify the individual measurement uncertainties of the Alphasense
NDIR sensor. The evaluation of the Alphasense NDIR CO2 sensor for use in indicative
measurements is outlined in the flow chart (Fig. 2 below).

Fig. 2: Flow-chart depicting the procedure for the determination of sensor uncertainty
Define the Measurement Parameters
•Scope of Validation
•Data Quality Objective
•Limit Values & Thresholds
Identify the Sources of Uncertainty
Define experimental apparatus
•Exposure Chamber for Sensors
•Select a Reference Analyser
•Generation of gas mixtures
•Gas Cylinders
•Mass Flow Controllers
Conduct the Laboratory Experiments
•Response Time
•Lack of Fit to the Linear Model
•Repeatability
•Lower Detectable Limit
•Resolution
•Short and Long Term Drifts
•Cross Sensitivity
•Hysteresis
•Reproducibility
•Effect of Ambient Temperature
Calculate the Uncertainty
•Relative uncertainties
•Expanded uncertainties
•Combined expanded uncertainties

1.1 Scope of Validation
1.1.1 Defining a Data Quality Objective
The main issue, as illustrated in Fig. 3, is that Carbon Dioxide is not regulated by the CAFE
Directive and as such has no predefined DQO, Limit Values or Thresholds. It should be noted
that, in the context of this project, it is the process that is important more so than the result.
Consequently, the process of establishing a DQO, Limit Values and Thresholds is purely
academic. For the purpose of this project a DQO of 25% will be used, as this is the most
common and stringent of the uncertainties for indicative measurements outlined in the
Directive.
Fig. 3: Data Quality Objectives, CAFE Directive
1.1.2 Defining a Limit Value
The evaluation of the sensor would generally be carried out against a Limit Value defined in
the Directive. In order to establish a realistic Limit Value for CO2 it was decided to evaluate
the relationship between observed ambient levels and the Limit Values for gases that are

covered by the Directive. The most recent results published in the EPA’s ‘Air Quality in
Ireland 2013’ report, give an indication of the ambient levels of N2 and CO, which can then
be compared with the Limit Values. Figures 4 & 5 below show the most recent published
data for ambient air concentrations of N2 and CO in Ireland.
Fig. 4: Annual mean NO2 concentrations at individual stations across Ireland in 2013
Fig. 5: Max 8‐hour mean CO Concentrations at individual stations in 2013

In both examples above, the Limit Value stated by the Directive appears to be
approximately four times the mean 8-hour ambient concentration value. For the purpose of
this project the Limit Value for CO2 will be assumed to be 1600 ppm (four times ambient
Carbon Dioxide levels of 400 ppm). Typically the Upper and Lower Assessment Thresholds
are 70 % and 50 % of this Limit Values respectively. The limits and thresholds proposed for
this project are summarised in Table 1: At 25% of the Limit Value the DQO of uncertainty in
this case would be a maximum of 400 ppm.
Table 1: Project Limits and Thresholds for Carbon Dioxide
Concentration
ppm
Limit Value 1,600
Upper Assessment Thresholds 1,120
Lower Assessment Thresholds 800
Uncertainty 400
The importance of establishing these limits and thresholds is firstly, to determine if the
range of the sensor is appropriate for the given application. Secondly, it is necessary to
define the scope of validation for the experimental procedures.

1.2 Experimental Overview
The primary components that were used in the experimental phase of the sensor evaluation
were:
 The Gas Cylinders (CO2 and N2)
 The Mass Flow Controllers
 The Teledyne 360E CO2 Analyser
 The Alphasense NDIR CO2 Sensor
Fig. 6: Overview of the experimental configuration of the main components used in the
evaluation of the Alphasense senor.
CO2
Zero Air
MFC x 2
Sample Chamber
Teledyne 360E

2. Literature Review
2.1 What is CO2?
Carbon Dioxide (CO2) occurs naturally in the environment when two Oxygen atoms
covalently bond to a single Carbon atom. At standard temperature and pressure (STP),
Carbon Dioxide exists in gaseous state and is present in the atmosphere at an average
concentration of around 400 ppmv. [1]
Fig. 7: A Carbon Dioxide atom (Carbon atom is covalently bonded to two Oxygen atoms with a
bond diameter of 116.3 pm) [2]
CO2 plays an important role in photosynthesis, where plants and algae produce
carbohydrate from CO2 and water (H2O) by converting light energy into chemical energy. [3]
Primary sources of atmospheric CO2 are; respiration by aerobic organisms, decaying biomass
and the combustion of carbon based materials such as wood and hydrocarbon based fossil
fuels. [4] These days, it is widely believed by climate scientists that man-made CO2 is a key
contributor towards global warming.
2.1.1 Background of CO2 Measurements
Global warming is not a recent phenomenon in the Earth’s history by any means. In fact, ice
core records show that the Earth has been continuously heating up since the end of the
Pleistocene Ice Age 18,000 years ago [5], albeit by a rate of 6 ᵒC every 20,000 years, which
equates to merely 0.0003 ᵒC per year [6]. Accompanying this temperature rise has been an
increase in global CO₂ levels, from around 180 ppm to present day levels of 400 ppm [7]. The
last few decades however have seen this rate of increase accelerate dramatically, with
temperature record analysis showing an increase of 0.7ᵒC in average global temperature in
the last century alone (0.007 ᵒC per year). When this temperature change is viewed

alongside records of global CO₂ concentrations, such as those documented by renowned
climatologist Charles Keeling’s, they chart a near identical proportional increase. It is this
link between temperature increase and rising CO₂ levels that has led environmental
scientists to understand more about the role of greenhouse gases and human induced
climate forcing mechanisms.
Fig. 8: Correlation of the rise in atmospheric CO2 concentration (blue line) with the rise in average
temperature (red line). [8]
2.1.2 CO2: The Greenhouse Gas
Perhaps the most important in terms of anthropogenic drivers, are Greenhouse Gases
(GHG) which contribute to the Greenhouse Effect. First proposed by Joseph Fourier in 1824,
the Greenhouse Effect is “a natural mechanism that retains the heat emitted from the
earth’s surface”[9] by re-radiating infrared energy back towards the Earth. The net result this
Greenhouse Effect is an average global temperature of around 14 ᵒC, which is about 30 ᵒC
higher than the temperature would be without it. It is widely accepted that without this
natural Greenhouse Effect, life on Earth as we know it would not exist. The most important
of these GHGs in order of contribution to the Greenhouse effect are; water vapour, Carbon
Dioxide, Methane and then Ozone. From an anthropogenic point of view, however, water

vapour is not the most important. Although the most abundant GHG in the atmosphere, it is
the positive feedback loops of other GHGs that determine the amount of water vapour in
the atmosphere, as warmer air can hold more water vapour.
CO2 is the most anthropogenically influenced GHG and ambient levels have risen
exponentially since the beginning of the Industrial Revolution. The dramatic increase in the
combustion of hydrocarbons during this period, coupled with large scale deforestation, have
lead to significant increases in atmospheric concentrations of CO2. According to reports
published by the Intergovernmental Panel on Climate Change (IPCC) [10], global
concentrations have risen from pre-industrialisation levels of around 280 ppm to present
levels of 400 ppm. Analysis of ice core samples from the Antarctic ice cap show that there is
more CO₂ in the atmosphere today than at any other time in the last 650,000 years [10], an
increase of 36% since 1750, which the IPCC use as their baseline.
2.1.3 Anthropogenic Drivers of CO2
It is energy demand that is at the heart of human produced GHGs. As countries develop they
experience improved standards of living, a higher GDP per capita and with it an increase in
energy demand. The link between high personal income and energy consumption can be
seen using Hans Rosling’s Gapminder,[11] with high earning countries like Qatar, the USA and
Luxembourg registering the highest energy use per person in tonnes of oil equivalent, and
equally the highest output of CO₂ per person emitted from the burning of fossil fuels. At the
other end of the scale, the trend for poorer nations like Zimbabwe and Ethiopia is for low
energy consumption and low GHG output as a result.

Fig. 9: CO2 Emissions vs. Wealth, Gapminder visualisation of the link between personal income
and energy consumption. [12]
To further compound the situation, as countries develop they also experience population
growth due to net immigration and improvements in healthcare, e.g. reduced infant
mortality rates and increased life expectancy. This is where the problem lies for human
driven climate change drivers. A healthy, economically developed society demands a good
standard of living. This in turn requires more and more cheap, reliable energy which as a
result produces more GHGs.
2.1.4 Political Efforts to Ambient Reduce CO2 Levels
In the 1970s the global temperature rose sharply and this coincided with the onset of more
extreme weather events. The World Meteorological Organisation estimated that over a
million people died in Africa’s Sahel during the devastating droughts between 1972 and

1985 [13]. More recently, in 2010, the ten year drought in Queensland, Australia ended when
two feet of rain fell in six weeks, leading to unprecedented flooding [14]. Conversely, that
same summer Russia suffered a relentless heat wave that wiped out 9 million hectares of
crops and killed around 56,000 people, surpassing the death toll of the 2003 European heat
wave which claimed the lives of an estimated 40,000 [15].
“...what we need above all...is US leadership, for no country bears greater responsibility for
climate change, nor has greater capacity to catalyse a global response” [16]
(Claussen & Diringer, Pew Centre on Global Climate Change 2007)
Political turning points came first in 1985 with the discovery of the Antarctic Ozone hole and
then, perhaps more importantly from an American point of view, after the torrid US drought
of 1988. This event prompted a political and scientific desire to discover its causes. In its
aftermath, the International Panel on Climate Change (IPCC) was formed in 1989 to collate
research into Global Warming from all over the world. With consistent evidence of increases
in air and ocean temperatures, rising sea levels and observed retreating of ice caps, an
agreement called the UN Framework Convention on Climate Change (UNFCCC) was
introduced at the UN Earth Summit in Rio in 1992. Following the prior success of the
Montreal Protocol, which called for a phasing out of CFCs (believed to be responsible for the
Antarctic Ozone hole), the Kyoto Protocol was introduced by the UNFCCC in 1997 as a
means of tackling GHG emissions. The Protocol’s first commitment period began in 2008
and sought to reduce GHG emissions by 5.2% of 1990 levels by 2012. [17]
Attempts to introduce a post-Kyoto protocol have been met with resistance in the current
global economic downturn, with major producers of GHG’s such as the USA, China and India
indicating that they will not commit to any treaty that will legally require them to reduce
CO2 emissions in the near future.

Closer to home however the EU implemented its “20-20-20” targets in 2008, which target:
 A reduction in EU greenhouse gas emissions of at least 20% below 1990 levels
 20% of EU energy consumption to come from renewable resources
 A 20% reduction in primary energy use compared with projected levels, to be
achieved by improving energy efficiency.[18]
2.1.5 CO2 Emissions in Ireland
Fig. 10: Ambient CO2 concentrations in Ireland. Recent measurements taken at the Atmospheric
Physics Research Cluster, NUI, Galway indicate that Carbon Dioxide levels in Ireland are steadily on
the increase. [19]
In Ireland we currently import 89% of our energy in the form of fossil fuels, down from a
peak of 90% in 2006 although up from the 85% recorded in 2012 according to Sustainable
Energy Authority in Ireland (SEAI) in their 2014 Energy Statistics Report. [20]

Fig. 11: Primary Energy and CO2 emissions per capita in Ireland. This graph illustrates recent trends
in energy consumption and associated CO2 emissions per capita. High levels can be observed during
the ‘Celtic Tiger’ years, as well as the reduction associated with the economic downturn. [20]
The economic downturn lead to a reduction in energy demand and CO₂ emissions as cement
production and energy intensive construction work dwindled. This latest report also states
that Ireland’s economy contracted by 6.7% between 2007-2013, with a concurrent
reduction in energy demand of 18%, which is in line with 1999 levels. [20] CO2 emissions over
this period fell by 22% in line with levels last recorded in 1997.[20] Interestingly there has
also been a notable reduction in GDP per capita during this period , again re-enforcing the
link between living standards and anthropogenic climate change drivers, as suggested by
Rosling.

Fig. 12: CO2 Emissions by Sector. This graph displays recent key sector contributions towards CO2
emissions in Ireland [20]
2.1.6 Outlook
For the foreseeable future, fossil fuels are likely to remain the primary source of global
energy supply and, in the absence of a viable alternative, the International Energy Agency
(IEA) estimates that an additional 3,000 coal-fired plants will be built worldwide between
2005 and 2030 [21]. The emphasis therefore must be on improving efficiency, which is less
than 30% for a typical coal-fired plant, and on Carbon Capture Sequestration (CCS). The IPCC
has earmarked CCS as an important player in meeting future emission targets, although with
a lack of actual legislation, its integration will be slow to come about. Renewable energy
supplies such as wind and solar hold little promise as alternatives as they are weather
dependent and hence unreliable. More dependable are nuclear fission power plants which
currently provide around 15% of the world’s electricity demand [22]. Although carbon
intensive to build, while running their GHG contribution is far lower than their fossil fuel
counterparts. The fundamental problems of disposing of nuclear waste, together with the
risk of large scale accidents, such as Chernobyl and more recently Fukushima, have limited
their deployment on a larger scale.

In the absence of a feasible replacement for hydrocarbons in the near future, the emphasis
has shifted towards improvements in efficiency. The rising price of oil and the introduction
at government level of emissions taxes has seen huge advances across the transport sector.
Car manufacturers now advertise in terms of low CO₂ emissions and economy to bolster
sales, a clear reflection of changing public priorities. Further developments have seen the
introduction of hybrid engine cars and more recently the electric car which could technically
run GHG free if powered from renewable sources. Generally however a shift away from
individual cars towards public transport is seen as the most environmentally friendly option.
The aviation industry too has seen improvements with the introduction of the Boeing 787
Dreamliner commercial airliner. The Boeing 787 consumes 20% less fuel than its equivalent
size counterparts, which is good news considering that the IPCC estimates that the aviation
industry may be responsible for 3-5% of global man-made CO₂ emissions by 2050. [23]
Similarly, in the home, efficiency is becoming more important. Improvements in home
heating technology and higher levels of insulation can reduce fossil fuel consumption both
directly with oil and gas boilers and indirectly through energy efficient appliances. In Ireland,
the Sustainable Energy Authority of Ireland’s (SEAI) Building Energy Rating (BER)
requirement and energy saving grants have seen a shift towards renewable heating systems
such as solar and geothermal. Technologies like these are an important step towards the
zero energy homes of the future that may be both an economical and environmental
necessity.

2.2 Beer-Lambert Law
The basis of the measurement principle for both the sensors is the Beer-Lambert Law. This
Law describes the attenuation of light due to the properties of the material through which
the light is travelling. As a consequence of interactions between the photons of light and
absorbing gas particles, the intensity of the beam through a sample is attenuated from Po to
P. The transmittance T of the sample is then the fraction of incident radiation transmitted
through the sample.
Fig. 13: The intensity of beam through a sample is attenuated by interactions between the photons
of light and absorbing gas particles.
(1)
The absorbance A of a solution is defined by the equation
– (2)
Note that, in contrast to transmittance, the absorbance of a solution increases as
attenuation of the beam becomes greater.
Absorbance is directly proportional to the pathlength b through the solution and the
concentration c of the absorbing species. These relationships are given by:
(3)

where a is a proportionality constant called the absorptivity. The magnitude of a will
depend upon the units used for b and c. Often b is given in terms of centimetres and c in
grams per litre.
Absorptivity then has units of L g-1
cm-1
.
2.3 Teledyne 360E CO2 Analyser
Fig. 14: The Teledyne 360E CO2 Analyser that will be used in the experimental phase of the project
[24]
2.3.1 Theory of Operation
The Teledyne 360E uses the principle of the absorption of infrared light at certain
wavelengths proportional to the concentration of CO2 in the sample gas.

Fig. 15: Measurement Fundamentals (GFC Wheel not shown) [25]
The infrared source is created by a high energy heated element which generates a beam of
broadband infrared light of specific intensity to which the instrument is calibrated. The
beam is directed through a Gas Filter Correlation (GFC) wheel and into the multi-pass
sample chamber. This multi-pass sample chamber uses mirrors to increase the pathlength of
the beam to 2.5m. [25] The purpose of this is firstly to increase the sensitivity of the
instrument, while at the same time allowing for variations in the density of CO2 sample.
Upon leaving the sample chamber, the beam passes through a 4.3 μm (wavenumber
2325.58 cm–1
) band-pass filter to isolate the measurement band of the signal. Finally the
beam reaches the solid-state photo-detector where the signal is converted to an electrical
voltage proportional to the signal strength. [25]

Fig. 16: Absorption spectrum of carbon dioxide. The 2325.58 cm
–1
absorption band for Carbon
Dioxide is commonly used as it is both intense and narrow. [26]
2.3.2 IR Photo-Detector
The IR beam is converted into an electrical signal by a cooled solid-state photo-conductive
detector. The detector is composed of a narrow-band optical filter, a piece of lead-salt
crystal whose electrical resistance changes with temperature, and a two-stage thermo-
electric cooler. [25] When operating, a continuous electrical current is passed through the
detector. The IR beam has a heating effect when focused on the detector, which in turn
lowers the electrical resistance and creates a voltage drop across the detector proportional
to the IR beam intensity. [25]
Effectively an intense IR beam will result in a high temperature with a correspondingly low
voltage output signal. Likewise, a low intensity will result in the lower temperature across

the two-stage thermo-electric cooled detector with a correspondingly high voltage output.
[25]
2.3.3 The GFC Wheel
Fig. 17: The Gas Filter Correlation (GFC) wheel employed in the Teledyne 360E. [25]
The Gas Filter Correlation (GFC) wheel is a dual chamber wheel employed by the analyser to
overcome the interference of water vapour and other interfering gases at 4.3 μm. Each of
the two chambers, or cells, is enclosed by an IR transparent window which allows the
transmission of radiation at the target wavelength. Each of the cells is filled with a specific
gas and sealed. The Measurement Cell (CO2 MEAS) is filled with pure N2, while the
Reference Cell (CO2 REF) is filled with a mixture of N2 and a high concentration of CO2.[25]

Fig. 18: The IR beam passes through the GFC wheel generating a square wave output. [25]
As the GFC wheel rotates, the IR beam passes through the two cells alternatively. When the
IR beam passes through the reference cell, the CO2 in the cell absorbs at the 4.3 μm
wavelength and attenuates the signal strength. Conversely, as the beam passes through the
measurement cell, the N2 allows the signal to pass through unattenuated. [25] The result is a
square wave signal output from the photo-detector due the fluctuating attenuation of the IR
signal.
2.3.4 The Measurement-Reference Ratio
The concentration of CO2 in the sample chamber is determined by the calculating the ratio
(M/R) between the measurement and reference signals. In the case where there are no
gases absorbing at 4.3 μm in the sample chamber, the reference cell will attenuate the
signal by 60% which produces an M/R ratio of 2.4:1. [25]

Fig. 19: Square wave output generated in the absence of any interfering gas.
The introduction of CO2 to the sample chamber results in signal attenuation by both cells.
Since the intensity of the signal passing through the measurement cell is greater than the
reference cell, so too is the attenuation effect of the CO2 in the sample chamber. This
effectively results in a greater sensitivity to CO2 by the measurement cell. As the
concentration of CO2 in the sample chamber increases, the M/R ratio moves closer towards
1:1. [25]
Fig. 20: Effect of CO2 in the sample chamber on the square wave output. M/R ratio is reduced.

2.3.5 Infrared Interference Compensation
The incorporation of the GFC wheel in the design allows the analyser to compensate for the
interferences of IR absorbing gases such as water vapour in the sample chamber. [25] The
attenuation in the IR signal by interfering gases is identical for both the measurement and
reference cells. Therefore the reduction in the peak signal heights for both cells is the same
in both cases, which results in the M/R ratio remaining unchanged. The M/R ratio is
consequently only affected by the concentration of CO2 in the sample chamber and not by
any interfering gases. [25]
Fig. 21: Effects of Interfering gas on square wave output. Both cells are affected equally by the
presence of interfering gas and the M/R ratio remains unaffected.

Table 2: Teledyne 360e CO2 Analyser Manufacturers Specifications
Min/Max Range (Physical Analog Output) In 1ppb increments from 50ppb to 2,000ppm, dual ranges
or auto ranging
Measurement Units ppb, ppm, μg/m3, mg/m3, %(user selectable)
Zero Noise < 0.1 ppm (RMS)
Span Noise < 1% of reading (RMS)
Lower Detectable Limit * < 0.2 ppm
Zero Drift (24 hours) * <0.25 ppm
Zero Drift (7 days) * <0.5 ppm
Span Drift (7 Days) * 1% of reading above 50 PPM
Linearity * 1% of full scale
Precision 0.5% of reading
Temperature Coefficient < 0.1% of Full Scale per
o
C
Voltage Coefficient < 0.05% of Full Scale per V
Lag Time 10 sec
Rise/Fall Time * 95% in <60 sec
Sample Flow Rate 800cm3/min. ±10%
Temperature Range 5-40oC
Humidity Range 0- 95% RH, non-condensing
Dimensions H x W x D 7" x 17" x 23.5" (178 mm x 432 mm x 597 mm)
* Of interest in this evaluation

2.4 Alphasense IRC-A1 NDIR CO2 Sensor
Fig. 22: The Alphasense NDIR Sensor mounted onto a transmitter board
The Alphasense IRC-A1 uses the principles of Non-dispersive infrared absorption to
determine the concentration of CO2 in ambient air. The basic elements of the sensor are:
 The diffusion membrane
 The infrared source
 The optical cavity
 The dual channel detector
 Internal thermistor

Fig. 23: Inside the Alphasense IRC-A1. Image shows the main components of the pyroelectric
detector [28]
Gas diffuses through the membrane into the optical cavity. Light from the infrared source
interacts with the CO2 as it passes through the optical cavity, before reaching the dual
channel detector. The detector is divided into an active channel and a reference channel.
Fig. 24: Typical arrangement of an inexpensive NDIR CO2 analyser. Image shows Active and
Reference channels used to compensate for variations in the intensity in the infrared source. [27]

The active channel is filtered such that only light of a certain CO2 absorption wavelength
passes through. The reference channel is filtered to allow a different, non-absorption band
to pass through. The absorption of light is proportional to the concentration of CO2 in the
optical cavity and hence acts to reduce the intensity of light falling on the active channel of
the detector. The concentration of CO2 in the optical cavity has no effect however on the
intensity of light passing through the reference channel. The primary use of the reference
channel is to compensate for variations in the intensity in the infrared source. The
concentration of a sample is determined with the aid computer software which can be
calibrated to convert the electrical signal from the sensor into a concentration.
Table 3: Alphasense IRC-A1 NDIR CO2 Sensor Manufacturers Specification
Range 0 to 5,000 ppm
Accuracy (% FS, using universal linearization
coefficients)
1%
Zero Resolution * 1 ppm
Full Scale Resolution * 15 ppm
Zero Repeatability * ±10 ppm
Full Scale Repeatability * ±50 ppm
Temperature Signal Integral thermistor (NTC, R25 = 3450 K)
Operating Temperature Range -20°C to +50°C (linear compensation from -10 to 40°C)
Storage Temperature Range -40°C to +75°C
Humidity Range 0 to 95% rh non-condensing
Response Time (t90) * < 40s @ 20°C ambient
Warm-up Time To final zero ± 100ppm: < 30 s @ 20°C
To specification: < 30 minutes @ 20°C
* Of interest in this evaluation

2.5 Calibration of the Sensors
The calibration of both the Teledyne 360E and the Alphasense sensor will require zero air
and span gas. For the Teledyne 360E the specification for zero requires air of similar
composition to atmospheric (78% Nitrogen, 21% Oxygen, etc.) with a CO2 concentration
below 25ppb and free from any interfering gases such as Carbon Monoxide and water
vapour. [25] This may be achieved by using two Mass Flow Controllers, in a parallel
arrangement, mixing pure N2 and Oxygen in the required ratio. The span gas specification
should be equal to a concentration of 80% of the full analyser range (i.e. 1,600 ppm). Again
this could be achieved using a similar Mass Flow Controller mixing system which would
combine pure CO2 and N2.
The zero air specification for the Alphasense requires the exposure of the sensor to 100%
N2. Span gas for the Alphasense should again be equal to a concentration of 80% of the full
analyser range (i.e. 4,000 ppm).
2.6 Mass Flow Controllers
A dynamic system for the generation of known concentrations of a test gas was necessary
for the sensor evaluation. Generation of the gas mixtures for the experimental phase was
accomplished using two SEC-4400 Mass Flow Controllers (MFC) in a parallel arrangement.
An MFC is a mass flow measurement device employed for accurately measuring and rapidly
controlling the flow of gases.

Fig. 25: Mass Flow Controllers used in the experimental phase of the project
MFCs used for this project:
1. 7.5 SLM MFC (calibrated for N2)
2. 100 SCCM MFC (calibrated for Cl2)
Table 4: Mass Flow Controller Specifications
Accuracy ±1% full scale including linearity at calibration conditions
±1.5% full scale including linearity for flow ranges greater than 20 SLM
Repeatability 0.25% of rate
Response Time
Less than 3 seconds response to within 2% of full scale final value with a
0 to 100% command step.
Note: It was assumed that any errors produced by the MFCs would be prevented by using
the Teledyne as a reference to validate all measurement concentrations.

Fig. 26: Flow Sensor Operational Diagram (gas correction factors pdf)
The MFC regulates the flow of gas by measuring the temperature differential through a
bypass flow. This is accomplished by providing a precise power input to the heater element
located at the midpoint of the bypass sensor tube (Fig. 26: At zero flow conditions, the heat
reaching the temperature sensors from the element at T1 and T2 is equal. When gas flows
through the bypass, it has a cooling effect on the sensor at T1 and a heating effect on the
sensor at T2. This temperature differential is directly proportional to the gas mass flow.

Fig. 27: MFC Bypass Flow Diagram
The equation governing the temperature differential is:
ΔT = A * P * Cp * m (4)
Where:
ΔT = Temperature difference T2 - T1 (°C)
Cp = Specific heat of the gas at constant pressure (kJ/kg-°C)
P = Heater power (kJ/s)
m = Mass flow (kg/s)
A = Constant of proportionality (S2
-°C2
/kJ2
)
As each MFC is calibrated for a specific gas type it is required that a Gas Correction Factor is
applied when calculating flow rates. This is discussed further in Section 3.6.1.
2.7 Uncertainty Analysis
EPA regulations stipulate the maximum permitted uncertainties for ambient air monitoring
measurements as well as the reference technique that must be used for fixed
measurements. In most cases the equipment is tested rigorously by the manufacturers to
prove compliance with these standards, usually via MCERTS certification in the UK. The
philosophy of the EPA in Ireland is that measurements made with certified instrumentation
and operated according to a prescribed Standard Operating Procedure (SOP) are deemed

valid. Consequently ambient measurements in Ireland use almost exclusively MCERTS
approved fixed 19 inch single gas spectroscopic systems such as the Teledyne 360E.
In recent years significant progress has been made in the area of developing small
inexpensive gas sensors with surprisingly good results. These sensors are not EPA approved
but are finding increasing use in terms of indicative measurements. This project compares
the performance of MCERT approved spectroscopic analysers (Teledyne) currently available
within the department with the Alphasense NDIR CO2 analysers.
In order to determine the suitability of using Alphasense CO2 sensors for providing indicative
measurements, it was necessary to perform an uncertainty analysis based on the
performance of these sensors.
In general an uncertainty analysis procedure consists of the following:
1. Define the Measurement Process
2. Identify the Error Sources and Distributions
3. Estimate Uncertainties
4. Combine Uncertainties
5. Report the Analysis Results
2.7.1 How to Calculate the Uncertainty of a Measurement Device
To calculate the uncertainty of a sensor, it is firstly necessary to identify the individual
sources of uncertainty in the measurement and then estimate the contribution from each
source. There is detailed documentation available for the identification and estimation of
these uncertainties given in the Guidelines for Uncertainty Measurement (GUM). An overall
measurement uncertainty can be calculated by combining the individual uncertainties.
2.7.1.1 Types of Uncertainty
There are two approaches to estimating uncertainties; ‘Type A’ and ‘Type B’ evaluations. In
most uncertainty evaluations both types are required.

Type A - uncertainties estimated using statistical analysis of repeated measurements. In
Type A evaluations, the distribution of repeated measurements will usually approximate a
Normal or Gaussian distribution, and the resulting uncertainty can be estimated from the
standard deviation of the mean. In rare cases, the distribution of measurements will
approximate a rectangular distribution and the standard deviation will require an
adjustment to determine the uncertainty.
Fig. 28: Normal Probability Distribution (left) & Rectangular Probability Distribution (right)
Type B – uncertainties estimated from other information such as; previous experience,
calibration certificates, technical specifications etc. For Type B evaluations of uncertainty,
the assumption is that the true value lies within a specified interval [a, b]. In this instance
the true value could lie, with equal probability, at any point within the interval. This is
characterised by a rectangular probability distribution with the limits [a, b].
Fig. 29: Rectangular probability distribution between the limits [a, b]

Given that the expected value E(X) of a rectangular probability distribution is:
(5)
and the Variance σ2
is:
(6)
then the standard deviation of a rectangular probability distribution is exactly the
uncertainty divided by .
Similar analysis of the expected value and variance of a Normal probability distribution
shows that the standard uncertainty has a divisor of one, and as such requires no such
adjustment.
Standard Uncertainty
All sources of measurement uncertainties need to be expressed at the same confidence
level, this is done by converting them into standard uncertainties. A standard uncertainty is
defined as having a range of ‘plus or minus one standard deviation’. The standard
uncertainty tells us about the uncertainty of an average measurement and is denoted by the
symbol u (x), where x is the source of uncertainty.
Combined Uncertainty
The combined uncertainty uc (x) is determined by ‘summation in quadrature’ (also known as
‘root sum of the squares’). It can be calculated using:
(7)

Expanded Combined Uncertainty
The individual uncertainty values of all the relevant performance characteristics in the
sensor evaluation are estimated from the standard deviation. In statistics only 66.27% of the
results are said to lie within one standard deviation of the mean. Since the DQO requires the
estimation of the uncertainty of an indicative measurement be expressed within a 95%
confidence interval, the combined uncertainty must be converted to the expanded
uncertainty by multiplying by the appropriate coverage factor k. In this case the 95%
confidence interval is determined by multiplying the combined uncertainty by the coverage
factor k = 2. This is then compared against the relevant maximum uncertainty for the DQO
(in this case ±25% at 95% confidence at the hourly limit value).

3. Experimental Design
3.1 Scope of Project
The aim of this project was to determine whether or not the Alphasense NDIR CO2 sensor
would be suitable for use in conducting indicative measurements by satisfying a proposed
Data Quality Objective (DQO) for uncertainty.
To do so, the performance of the inexpensive Alphasense NDIR sensor was evaluated
alongside an MCERTS approved Teledyne 360E under a predetermined set of experimental
conditions. The assumption was, that once calibrated correctly, the measurements made by
the Teledyne 360E would represent the correct concentration of CO2 (assuming the
measurements made are within the instrument specifications) in all cases. The following
parameters were proposed for the comparison of the two analysers and the subsequent
determination of the relevant measurement uncertainties of the Alphasense sensor:
 Response Time
 Lack of Fit to the Linear Model
 Repeatability
 Lower Detectable Limit
 Resolution
 Short and Long Term Drifts
 Cross Sensitivity
 Hysteresis
 Reproducibility
 Effect of ambient temperature

3.2 Experimental System Design
Preliminary testing of the two analysers was conducted to verify that both were operational
and working correctly. As expected, a certain number of iterations were necessary prior to
and during testing, to perfect the system.
3.3 Experimental Apparatus
3.3.1 Design of the Air-tight Sampling Chamber
In order to compare the two sensors it was first necessary to construct an airtight chamber
that would seal the Alphasense sensor from ambient interference. The sample chamber was
also designed so as to accommodate the simultaneous testing of several sensors. In the
initial configuration, this sample chamber would be located between the MFCs and the
Teledyne analyser.
Fig. 30: Sampling chamber for the constructed for the Alphasense sensors

Fig. 31: Proposed system design for experimentation phase.
3.3.1.1 Design Considerations
When designing the sample chamber, the following considerations were taken into account:
 The input gas flow should be parallel to the sensor face and ideally not located
directly in line with the sensors. This would ensure uniform exposure of the test gas
to all sensors and more stability in the sensor response.
 Laminar flow through the sample chamber should be minimised, where possible, to
ensure equilibrium of the gases with the sensors. In order to achieve this, the inlet
and outlet of the chamber should be located as far away as possible and ideally
perpendicular to one another. This may also be accomplished by placing baffles
within the sample chamber.
 Consideration was given to the pneumatic connections in and out of the chamber to
ensure that the supply of zero air was void of any atmospheric CO2.
 The sample chamber should be sealed from the ambient air in the laboratory to
avoid interferences. Over pressurisation of the sample chamber was a proposed
solution for this.
CO2
Zero Air
MFC x 2
Sample Chamber
Teledyne 360E

3.4 Gas Cylinders
 The source of zero air for the project was from an uncertified cylinder of Nitrogen
(N2). Zero air was used to establish the “zero point” of a calibration curve and as a
diluent for the mixed gas cylinder to bring the test gas within the range of the
sensors.
 The source of the test gas (CO2) for the project was from an uncertified cylinder
containing approximately 4% CO2 and 96% N2.
3.5 Selecting a Reference Analyser
The principle purpose of a reference analyser was to ensure the integrity of the
experiments. The reference analyser would reliably output the CO2 concentration in the
sample chamber, which would then be compared with the expected value. In the event that
an error, such as in dilution or loss of pressure, occurred, the output from the reference
analyser would, most likely, be affected. Any deviation from the expected value would alert
the operator that something was not right and the experiment declared void.
Five Teledyne analysers were available for use as a reference analyser by the department. It
was decided that one of these five devices would be selected for the duration of the
experimental phase. Selection was based on a simple test which would evaluate
repeatability, accuracy and Lower Detectable limit (LDL) of the available analysers. The test
involved exposing the each analyser to zero air and then to approximately 1000 ppm Carbon
Dioxide, repeated three times in total. Measurements at each test interval were obtained by
recording the reading after the manufacturers specified response time had elapsed.

Fig. 32: Results of testing multiple Teledyne Analysers. Concentration of the target gas was varied
between 0 and 1000 ppm. The graph shows the analyser response at each interval (six
measurement in total).
The test results (six data points for each of the Teledyne analysers) were combined onto one
graph to illustrate their overall performance (Fig. 32). At a quick glance, Teledyne 2 could be
discounted as it fell well short of the other analysers in terms of repeatability and LDL. To
evaluate the accuracy of the analysers, a table of deviations from the expected value was
drafted using the equation:
(8)
0
100
200
300
400
500
600
700
800
900
1000
1100
1200
1300
1400
1500
1600
0 1000 0 1000 0 1000
TeledyneConcCO2ppm
Theoretical ppm
Teledyne 1
Teledyne 2
Teledyne 3
Teledyne 4
Teledyne 5

Table 5: Deviations from expected values and Standard Deviations of combined deviations
for all analysers.
Theoretical Teledyne 1 Teledyne 2 Teledyne 3 Teledyne 4 Teledyne 5
ppm ppm ppm ppm ppm ppm
0 51.92 382.54 90.07 84.40 148.97
1000 58.55 619.20 -93.25 493.71 -304.76
0 51.61 382.54 86.02 68.71 105.25
1000 57.54 -14.31 -94.26 497.76 -298.68
0 51.61 382.54 86.53 70.99 110.31
1000 56.53 -106.40 -90.21 493.71 -293.62
STD Dev 3.26 277.0 98.7 230.3 230.9
From the above table it was possible to determine the standard deviation of the residuals
which were examined using an interval plot with a 95% confidence interval. The interval plot
was used to graph the variability in the residuals and also give an indication of any
consistency in the direction (positive or negative) of the deviations. It was thought an
analyser that deviated consistently, either positively or negatively, could be corrected for, if
necessary, using a zero offset.

Fig. 33: Interval plot for Teledynes’ 1-5 using deviations from expected theoretical values. The data
indicates that Teledyne 1 performs best in terms of repeatability and shows consistent bias.
Table 6: Scorecard for Teledyne testing
Teledyne 1 Teledyne 2 Teledyne 3 Teledyne 4 Teledyne 5
Repeatability  X   
Accuracy  X  X 
L.D.L.  X   X
Std. Dev.  X X X X
Analysis of the results indicated that Teledyne 1 performed better than the other analysers
across the board in all test criteria. The analysis of residuals showed a consistent bias
indicating that the residuals were most likely due to an over estimation of the CO2 content
of the mixed gas cylinder. It was thought that this bias could be eliminated by adjusting the
theoretical concentration of the mixed gas cylinder in subsequent calculations. Teledyne 1
also performed better than the others at lower concentrations. As a result it was decided
Teledyne 5Teledyne 4Teledyne 3Teledyne 2Teledyne 1
600
400
200
0
-200
-400
Data
95% CI for the Mean
Individual standard deviations were used to calculate the intervals.
Interval Plot of Teledyne 1-5

that Teledyne 1 would be used as the reference analyser for the duration of the
experimental phase. Unless stated otherwise, all references to the Teledyne analyser will
refer to Teledyne 1.
Once again it is important to note that this test was extremely crude and, time permitting, a
more selective series of tests may have been devised. It should also be noted that any of
these analysers could have been re-calibrated and would have, in theory, been suitable for
use as a reference analyser for the experimental evaluation of the Alphasense sensor.
3.6 Generation of Test Gas Mixtures
3.6.1 Controller Gas Correction Factors
As discussed in section 2.6.1, the accurate control of gas flow from the MFCs is dependent
on the Specific heat of the gas in question. Different gases will have different Specific heat
values and the factory calibration of an MFC will be to a specific gas molecule e.g. N2. To
allow the use of the MFC with a non-calibrated gas, the manufacture will publish a list of gas
correlation factors (GCF). To calculate the corrected flow rate the following equation is
used:
(9)
No correction is required for the 7.5 SLM MFC as this will be flowing N2 as a diluent gas. For
the 100 sccm MFC, which is calibrated for Chlorine (Cl2), a correction factor will apply. As the
100 sccm MFC will flow a gas mixture of CO2 and N2 the correction factor for the mixture
was calculated using:
(10)
Where:

P1 = percentage (%) of gas 1 (by volume)
P2 = percentage (%) of gas 2 (by volume)
Substituting in to equation 10 gives:
Substituting in to equation 9 gives:
3.7 Determination of CO2 Concentration in Mixed Gas Cylinder
The source of Carbon Dioxide for use in the experimental phase was from a mixed gas
cylinder. This cylinder contained approximately 4 % CO2 and 96 % N2. By adjusting the
theoretical CO2 concentration of the mixed gas cylinder to 3.83%, it was possible to
generate known concentrations of the test gas that were consistent with the Teledyne
output. The slope of 1.0014 shows good agreement between the Teledyne response and the
theoretical concentrations of the test gas. The R2
value of 0.9994 indicates a good linear
response to the various test gas levels. The Alphasense sensors were subsequently
calibrated using this estimation (3.83%) of CO2 concentration in the mixed gas cylinder.

Fig. 34: A Linearity test of the Teledyne Analyser Response vs. The theoretical concentration shows
excellent correlation. Theoretical concentrations assume a concentration of 3.83% CO2 in the mixed
gas cylinder.
3.8 Experimental Configuration
Testing the experimental design was an important step in the project. The configuration
outlined initially (Configuration ‘A’) positioned the sample chamber, containing the
Alphasense sensors, in series before the Teledyne 360E. To test this configuration, the
sensors were exposed firstly to zero air and then to approximately 1000ppm CO2. This was
repeated a minimum of three times. It is important to note that prior to conducting this test
the Alphasense sensor was calibrated using the assumption that the mixed gas cylinder
contained exactly 4 % CO2 and without factoring in the gas correction factors of the MFCs.
As a result, the subsequent test output from the Alphasense sensor is thought to be higher
than expected.
y = 1.0014x + 11.765
R² = 0.9994
0
200
400
600
800
1000
1200
1400
1600
1800
2000
0 200 400 600 800 1000 1200 1400 1600 1800 2000
TeledyneConcentrationppm
Theoretical concentration ppm

Fig. 35: Configuration A: sample chamber containing Alphasense sensors located in series before
the Teledyne 360E.
The results of the configuration ‘A’ two level accuracy test (Fig. 36) showed good agreement
of the Alphasense with the theoretical gas concentration. The Teledyne however did not
perform as well; outputting readings too high in the case of exposure to zero air and too low
when exposed to 1000ppm. The Teledyne analyser incorporates a pump which draws in gas
through the sample port at a rate of 938 sccms. The test data in this case indicated that the
sample chamber had introduced a pressure drop between the MFCs and the Teledyne,
hence forcing the Teledyne to draw in ambient air through the pressure vent. This ambient
air, which contained approximately 400 ppm CO2, may have mixed with the test gas and
affected the results.
CO2
Zero Air
MFC x 2
Sample Chamber
Teledyne 360E
Pressure Vent

Fig. 36: Results of two-level accuracy test for configuration A
An alternative configuration (Configuration B) was examined which located the sample
chamber after the Teledyne in an effort to eliminate the possibility of the pressure drop
suspected in the previous configuration.
889.251
108.177
903.903
0
100
200
300
400
500
600
700
800
900
1000
1100
1200
1300
0 200 400 600 800 1000 1200 1400 1600 1800 2000
Concppm
Time s
Alphasense
Teledyne
Expected Value
CO2
Zero Air
MFC x 2
Sample Chamber
Teledyne 360E

Fig. 37: Configuration B: sample chamber containing Alphasense sensors located in series after the
Teledyne 360E.
The results of the test showed better agreement between the Teledyne and the theoretical
concentrations. The Alphasense however demonstrated more variability between
repetitions which may indicate a possible pressure drop.
Fig. 38: Results of two-level accuracy test for configuration B
4. Laboratory Experiments
A sensor was evaluated through a series of individual tests that will produce numerical
uncertainty values of each of the relevant performance characteristics. The following tests
were identified for the evaluation of the uncertainty of the Alphasense sensor:
 Response Time
 Lack of fit to the linear model
 Repeatability
 Lower Detectable Limit
 Resolution
1000
0
100
200
300
400
500
600
700
800
900
1000
1100
1200
400 600 800 1000 1200 1400 1600 1800 2000 2200 2400 2600 2800 3000
ConcentrationCO2ppm
Time s
Alphasense
Teledyne 360E
Expected Value

 Short and Long Term Drifts
 Cross Sensitivity
 Hysteresis
 Reproducibility
 Effect of Ambient Temperature
4.1 Response Time
The response time of the sensors was estimated as t90 (the time required for the sensor to
reach 90 % of the final value). In this instance the concentration of the target gas was varied
between 0% and the Limit Value (LV) 1600 ppm. In each case the t90 of both the rise and fall
was calculated as the time taken between the 5% and 95% of the final stable value. This was
repeated three times and the t90 result was determined by averaging the results. This
averaged result could then be compared against the manufactures specification of < 40s @
20°C ambient.
The response time of the sensor was used to establish the required duration of the
subsequent tests in the uncertainty evaluation. It was important to ensure that all
subsequent measurements recorded had allowed sufficient time to reach a stable output.
All measurements undertaken hereafter will have had a minimum duration of three times
t90, which shall be referred to as the test period. In addition, an evaluation of the response
time was also necessary to determine if the there was any significant difference between
the t90 for increasing and decreasing concentrations. A difference of less than 10% between
the average rise t90 and average fall t90 is deemed insignificant.
4.2 Lack of Fit to the Linear Model
The lack of fit uncertainty is a determination of non-linearity of the sensor response. The
test procedure for the evaluation of non-linearity requires a series of measurements to be
taken at varying concentrations between zero air and the Full Scale value (2000 ppm). The
test measurements were taken at six levels within the range and randomised according to
the following pattern; 80, 40, 0, 60, 20, 95% of the Full Scale value. Randomisation is used in
statistics testing to reduce the effects of “nuisance” variables. A nuisance variable is a

random variable that may affect the test results but is not fundamentally attributed to the
sensor, such as operator fatigue and other confounding factors that can, either directly or
indirectly, bias results.
According to the Guidelines for Uncertainty Measurement (GUM), the standard uncertainty
for the lack of fit (u (lof)) for the sensor response can be estimated using:
(11)
Where:
ρmax,LV = is the maximum residual of the model or the residual at the Limit Value.
This estimation reasonably assumes that if the maximum residual is equally likely to occur at
any point within the test range.
4.3 Repeatability
The repeatability of the sensors was estimated by calculating the standard deviation (s) of
the observed values over an extended measurement period using the equation:
(12)
Where:
x = the observed value
x = the mean of observed values
n = the number of measurements recorded
The repeatability of both the Teledyne and the Alphasense sensors was examined at zero air
and at the Limit Value (1600ppm). The repeatability is defined as the expected difference

between two measurements made under identical conditions. The repeatability of the
sensor can be calculated using:
(13)
Where:
sr = the standard deviation of repeatability at 80 % of Full Scale.
The repeatability uncertainty will not be included in this evaluation as it will be sufficiently
accounted for by the determination of uncertainty due to Short Term Drift.
4.4 Lower Detectable Limit
The repeatability data was also used to estimate other important characteristics of the
sensor, such as; the Limit of Detection (LOD) and the Limit of Quantification (LOQ) of the
sensors.
The limit of detection is defined as “is the lowest quantity of a substance that can be
distinguished from the absence of that substance (a blank value) within a stated confidence
limit (generally 1%)”. The Limit of Detection is effectively the Lower Detectable Limit (LDL)
of the sensor.
The limit of detection is estimated as:
(14)

Fig. 39: The Limit of Detection is three Standard Deviations of the sensor response at zero air. The
Limit of Quantification is ten Standard Deviations of the sensor response at zero air.
4.5 Resolution
The Limit of Quantification (LOQ) is defined as the lowest concentration level at which a
measurement is quantitatively meaningful. This is most often defined as 10 times the signal-
to-noise ratio or 10 times the standard deviation of the blank. The Limit of Quantification is
effectively the resolution of the sensor.
(15)
4.6 Short and Long Term Drifts
The short term and long term drift, sometimes referred to as stability, of the Alphasense
sensor was determined from measurements made at 0, 50 and 80% of the Full Scale of the
target gas over consecutive measurement periods.

The short term drift was determined from measurements at the three test levels over three
consecutive days. The short term stability test is used to determine the contribution of Short
Term Drift to the measurement uncertainty (u (ss)). The uncertainty due to Short Term Drift
is determined from the standard deviation and the type of distribution of the
measurements at each test level.
For a Normal distribution the uncertainty component is estimated as:
(16)
For a rectangular distribution, or where the frequency distribution is not known, GUM
recommends that the uncertainty due to short term drift is estimated using:
(17)
Where:
Rs = the sensor response (Before) and 24 hours after (After)
The long term drift in this evaluation was determined from measurements at the three test
levels over four consecutive weeks. The four week period selection is based on the
assumption that the minimum time between sensor calibrations would be one month. The
long term stability test is used to determine the contribution of Long Term Drift to the
measurement uncertainty (u (ls)). The uncertainty is determined from the standard deviation
and the type of distribution of the measurements at each test level.
For a Normal distribution the uncertainty component is estimated as:
(18)
For a rectangular distribution, or where the frequency distribution is not known, GUM
recommends that the uncertainty due to short term drift is estimated using:
(19)
Where:
Rs = the sensor response (Before) and one week after (After)

4.7 Cross Sensitivity
All gas detection sensors have the potential to respond to gases other than the target gas,
and the Alphasense NDIR sensor is no different. An NDIR sensor may respond to any
compound having similar absorption wavelengths to the target gas. This cross sensitivity
may potentially have a positive or negative effect on the sensor response. For a CO2 sensor
measuring at the 4.3 μm bandwidth, the interfering gases that would typically be present in
ambient air are Carbon Monoxide (CO) and water vapour (H2O).
The interfering effect of water vapour was not included in this study due to the difficulty in
generating and maintaining a steady concentration over the test period. In addition, it was
thought that the absence of heated gas lines would lead to the formation of condensation in
the various experimental apparatus, which could potentially cause damage to the
equipment.
The interfering effects of CO could be quantified however with the use of a certified mix gas
cylinder. The gas cylinder in question contained 3012 ppm ± 0.5% rel CO and was certified in
accordance with ISO 6141. Other gases in the cylinder in question were; Nitrogen (N),
Sulphur Dioxide (SO2) and Nitric Oxide (NO). It was believed that these other gases would
have little or no interfering effects with the Alphasense sensor. The gas correction factor for
the cylinder was calculated to be 1.003 using equation 10.
The evaluation procedure for cross sensitivity was as follows:
1. Firstly the sensor was exposed to zero air for the test period and the response was
Y0.
2. The sensor was then exposed to a mixture of zero air and the maximum
concentration of the interfering compound (CO) in ambient air (estimated as 6 ppm
referred to as “int”). The response was YZ.
3. The sensor was exposed to the Limit Value concentration of CO2 (1600 ppm referred
to as ct). The sensor response was Ct.
4. Finally the sensor was exposed to a mixture of span gas CO2 (ct) and the interfering
compound CO (int). The sensor response was Yct.

5. The influencing effect of the interfering compound at zero air was calculated using:
(20)
6. The influencing effect of the interfering compound at span gas was calculated using:
(21)
7. The overall influencing effect of the interfering compound was calculated using:
(22)
where:
LV=the Limit Value (1600 ppm)
8. The uncertainty u(int) associated with the interfering compound was calculated using:
(23)
Fig. 40: Typically the responses for the cross sensitivity test will appear as shown
4.8 Hysteresis
Ideally a sensor should be capable of following the changes of the input parameter
regardless of which direction the change is made. Hysteresis is the characteristic that a
-100
0
100
200
300
400
500
600
700
800
900
1000
1100
1200
1300
1400
1500
1600
1700
0 50 100 150 200 250 300 350 400 450 500 550 600 650
CO2concentrationppm
Time s
Yo Yz Ct Yct

sensor has in repeating measurements made in the opposite direction of operation, after
the data has been recorded in one direction.
The evaluation of the sensor response to hysteresis involved conducting a series of
measurements at incremental steps throughout the sensor range. The selected intervals of
the range were; 0, 20, 40, 60, 80, 95% of the full scale with three repetitions at each
interval. The test would begin by increasing the concentration, followed by a decreasing the
concentration and then finally increasing the concentration once again.
The uncertainty due to hysteresis can be estimated using the equation:
(24)
Where:
Rs = the sensor response increasing (Up) and decreasing (Down)
4.9 Reproducibility
The reproducibility between different sensors is a measure of the variation between two
different sensors exposed to identical conditions. There were five Alphasense sensors
available from the department for the evaluation of reproducibility tests. Each additional
sensor would be tested in parallel with the original sensor used thus far.
The test procedure for the evaluation of reproducibility requires a series of measurements
to be taken at varying concentrations between zero air and the Full Scale (2000 ppm). The
test procedure was similar to that undertaken for the evaluation of non-linearity whereby
measurements were taken at six levels within the range and randomised according to the
following pattern; 80, 40, 0, 60, 20, 95% of the Full Scale. A regression plot of the sensor
responses at each interval was used to determine a table of residuals from the line of best
fit. This table of residuals was used to estimate the standard uncertainty component due to
reproducibility.
The standard uncertainty for the reproducibility between sensors (u (rep)) for the sensor
response can be estimated using:

(25)
Where:
ρmax,LV = is the maximum residual between the sensor responses or the residual at the Limit
Value.
This estimation reasonably assumes that if the maximum residual is equally likely to occur at
any point within the test range.
4.10 Effect of Ambient Temperature
The sensor response to variations in the surrounding temperature was evaluated to quantify
the level of uncertainty due to:
 The non- linearity of the sensor responses at different surrounding temperature
levels
 The variation in sensor response per degree not corrected for by temperature
compensation
The following test procedure was implemented for the sensitivity to surrounding
temperature evaluation:
1. The sensor performance was first evaluated at a low surrounding temperature level
(18.16°C). Measurements were taken at seven levels within the range and
randomised according to the following pattern; 80, 40, 0, 60, 20, 95, 50% of the Full
Scale.
2. The sensor performance was first evaluated at a high surrounding temperature level
(41.30°C). Measurements were again taken at seven levels within the range and
randomised according to the following pattern; 80, 40, 0, 60, 20, 95, 50% of the Full
Scale.
3. The mean and standard deviation of the sensor response at each concentration level
was determined at both temperatures.

4. A One–Way Analysis of Variance (ANOVA) test and a Two-Sample T-test were
conducted to establish the significance of the surrounding temperature effect on the
sensor response.
5. The sensor responses at the two temperature levels were used to establish the linear
regression model and compile a table of residuals.
6. The uncertainty due to surrounding temperature (u(temp)) is determined by combining
two factors; the variation in sensor response per degree, and the deviation from
linearity between the two temperature levels.
(26)
Where:
t = the maximum and minimum values for average temperature encountered in ambient air.
ρ = the maximum residuals between the regression line and the sensor responses or the one
at the LV.
b = The slope of the regression line (b ) for the responses at span gas was estimated using
the least squares principle:
(27)
Where:
x = temperature (∘C)
y = sensor response to span gas at temperature x (ppm)

5. Analysis of Results
5.1 Response Time
Fig. 41: Determination of the response time of the Alphasense sensor. Both the rise and fall
response time was measured as the time taken between the 5% and 95% of the final value.
The response time was calculated as the average t90 rise/fall time repeated three times. The
data in Table 7 displays the individual response times as well as an overall average. The
averaged difference between the rise and fall responses was within 10% and as such was
deemed insignificant.
0
200
400
600
800
1000
1200
1400
1600
1800
0 50 100 150 200 250 300 350 400 450 500 550 600 650 700
ConcentrationCO2ppm
Time s
t90

Table 7: Response Time Test Data for the Alphasense sensor
Response time t90 (seconds)
Rise 1 54
Fall 1 60
Rise 2 59
Fall 2 60
Rise 3 54
Fall 3 59
Average Rise 55.6
Average Fall 59.6
Overall Average 57.6
Manufactures Specification < 40s @ 20°C ambient
The average response time was estimated to be 57.6 seconds which is significantly higher
than the manufactures specification of < 40 seconds. It is unclear if this difference may be
due to the temperature of the test gas. The overall average was used to determine a
minimum test period of ≈3 minutes which would be used for subsequent measurements
made at each required test concentration level.
5.2 Lack of Fit to the Linear Model
The response data of the both the Teledyne and the Alphasense is shown in Table 8 below.
The published sensor response at each concentration interval was obtained by averaging
the results over the second half of the test period. It can be observed that the Alphasense
recorded values consistently higher than the theoretical concentration during the test. As
expected, the Teledyne showed better agreement with the theoretical concentrations

Table 8: Results from Linearity test for the Teledyne and Alphasense sensors
Percentage of
the Full Scale
Theoretical
Concentration
Teledyne
Response
Alphasense
Response
% ppm ppm ppm
0 0 10 45
20 400 399 470
40 800 827 917
60 1200 1214 1278
80 1600 1639 1747
95 1900 1890 2072
Both sensors show a high degree of linearity when the sensor responses are plotted against
the theoretical concentrations (Fig 42). As expected, the slope of the Teledyne analyser
(1.0014) is closer to one and as is the coefficient of determination value (0.9994). The slope
value for the Alphasense sensor (1.0608) is largely irrelevant in this test, and it is the
coefficient of determination value (0.9992) that is of more interest in the estimation of
uncertainty due to lack of fit to the linear model.

Fig. 42: Linearity test results for the Teledyne analyser and Alphasense sensor.
Statistical software such as Minitab was used to analyse the significance of the linear model
between the two analysers. The p-value for the regression model tests the null hypothesis
that the slope is equal to zero (i.e. not a predictor). A low p-value (< 0.05) indicates that the
null hypothesis can be rejected. Or to put it another way, a predictor that has a low p-value
is likely to be a meaningful addition to the regression model, since changes in the predictor's
value can be related to changes in the response variable. Conversely, a larger (insignificant)
p-value suggests that changes in the predictor are not associated with changes in the
response.
y = 1.0014x + 11.765
R² = 0.9994
y = 1.0608x + 45.01
R² = 0.9992
0
200
400
600
800
1000
1200
1400
1600
1800
2000
2200
0 200 400 600 800 1000 1200 1400 1600 1800 2000
MeasuredConcentrationppm
Theoretical concentration ppm
Teledyne
Alphasense

Fig. 43: The low p-value (< 0.005) indicates that changes in the Alphasense value can be related to
changes in the Teledyne variable.
Analysis of the regression model of the Teledyne vs. the Alphasense showed that the P-
value was <0.005 (Fig. 43). In terms of Indicative vs. Fixed Measurements for the analysis of
ambient air quality, the regression analysis shows that the Alphasense sensor is statistically
a significant predictor of the response of the Teledyne.
R-squared (adjusted) 99.84% 99.84%
P-value, model <0.005* <0.005*
P-value, linear term <0.005* 0.001*
P-value, quadratic term — 0.396
Residual standard deviation 30.842 30.939
Statistics Linear
Selected Model
Quadratic
Alternative Model
2000150010005000
2000
1500
1000
500
0
Tele
Alpha
Y: Alpha
X: Tele
Fitted Line Plot for Linear Model
Y = 33.14 + 1.059 X
* Statistically significant (p < 0.05)
Regression for Alpha vs Tele
Model Selection Report

Fig. 44: The regression analysis shows that the Alphasense sensor is statistically a significant
predictor of the response of the Teledyne
In the estimation of the uncertainty for the lack of fit it was necessary to compile a table of
fits and diagnostics of the line of regression for the Alphasense responses vs. the Teledyne
responses. This was performed using the Minitab statistical analysis software. The residuals
are calculated by subtracting the Alphasense response from the line of best fit.
Table 9: Fits and Diagnostics for All Observations
Obs Alpha Fit Residual Std Residual
1 1747.0 1768.4 -21.4 -0.84
2 917.0 908.7 8.3 0.30
3 45.0 43.7 1.3 0.06
4 1278.0 1318.4 -40.4 -1.45
5 470.0 455.6 14.4 0.56
6 2072.0 2034.1 37.9 1.69

The standard uncertainty for the lack of fit (u (lof)) for the sensor response can be estimated
using:
5.3 Repeatability
The repeatability of both the Teledyne and the Alphasense sensors was examined at zero air
and at the Limit Value (1600ppm). The repeatability is defined as the expected difference
between two measurements made under identical conditions. The repeatability of the
sensor was calculated using the equation:
Table 10: Standard Deviation of repeated measurements and Repeatability values for the
Teledyne and the Alphasense
Teledyne Alphasense
ppm ppm
Standard Deviation 0.1836 11.26
Estimated Repeatability ± 0.5193 ± 31.85
Manufactures Specification - ± 50

Fig. 45: The histogram of the sensor responses of the Alphasense sensor at the Limit Value. The
spread of results closely approximates a Normal distribution.
Fig. 46: The histogram of the sensor responses of the Teledyne analyser at the Limit Value. The
Standard Deviation of the results is significantly lower than that of the Alphasense
17251710169516801665165016351620
60
50
40
30
20
10
0
Mean 1660
StDev 11.26
N 317
Alphasense Span
Frequency
Normal
Histogram of Alphasense at Span Gas
1523.891523.771523.651523.531523.411523.291523.171523.05
5
4
3
2
1
0
Mean 1523
StDev 0.1836
N 49
Teledyne Concentration ppm
Frequency
Teledyne Stability test at Span (1600 ppm)
Normal

5.4 Lower Detectable Limit
The Lower Detectable Limit of the sensor response was estimated using the Standard
Deviation of the sensor response at zero air. The LDL was calculated using the Limit of
Detection equation:
Table 11: Standard Deviation of repeated measurements at zero air and Lower Detectable
Limit values for the Teledyne and the Alphasense
Teledyne Alphasense
ppm ppm
Lower Detectable Limit 1.0518 13.596
Manufactures Specification < 0.2
-
The estimation of the LDL for the Teledyne is significantly lower as would be expected.
Although the Alphasense sensor has a more Normal distribution of measurements and may
appear to have the better response of the two, It is important to consider the range (x-axis
of Histogram) over which the deviations occur.
Fig. 47: Sensor response of the Alphasense sensor to zero air. Note the Standard Deviation is lower
that at the Limit Value
60-6-12-18-24
80
70
60
50
40
30
20
10
0
Mean -12.02
StDev 4.532
N 411
Alphasense Zero
Frequency
Normal
Histogram of Alphasense at Zero Air

Fig. 48: Sensor response of the Teledyne sensor to zero air. Note the Standard Deviation is higher
than at the Limit Value
5.5 Resolution
The Limit of Quantification was used to estimate the Resolution of the sensor using the
equation:
Table 12: Standard Deviation of repeated measurements at zero air and Resolution values
for the Teledyne and the Alphasense
Teledyne Alphasense
ppm ppm
Estimated Resolution 3.506 45.32
Manufactures Specification - 1
3.843.603.363.122.882.642.40
9
8
7
6
5
4
3
2
1
0
Mean 3.049
StDev 0.3506
N 107
Tele Zero
Frequency
Teledyne Stability test at Zero Air

The manufactures specification for the Alphasense sensor is 1 ppm at zero ppm and 15 ppm
at Full Scale (5000 ppm). The result in this case is significantly higher than expected which
suggests that the Limit of Quantification may not be an appropriate means of estimating the
resolution.
5.6 Short-Term Drift
The short-term drift was determined from measurements taken over three consecutive days
at 0, 50 and 80% of the Full Scale of the target gas.
Table 13: Short –term Stability results for the Alphasense sensor.
0 % 50 % 80 %
ppm ppm ppm
Day 1 16.6 1039.8 1726.8
Day 2 34.5 1008.4 1671.7
Day 3 -9.9 998.6 1620.4
Table 14: Deviations of sensor response between 24 hour periods
0 % 50 % 80 %
ppm ppm ppm
+ 24hours -17.98 31.4 55.05
+ 24hours 44.56 9.72 51.28
The uncertainty due to short term drift was estimated using the equation:

Fig. 49: Summary of the effect of Short term drift on the Alphasense sensor response. Sensor
exposed to three exposure levels over three consecutive days.
5.7 Long-Term Drift
The long-term drift was determined from measurements taken over four consecutive weeks
at 0, 50 and 80% of the Full Scale of the target gas.
Table 15: Long –term Stability results for the Alphasense sensor.
0 % 50 % 80 %
ppm ppm ppm
Week 1 22.42 1069.8 1818.6
Week 2 16.6 1039.8 1726.8
Week 3 -3.42 1013.00 1661.86
Week 4 -11.82 983.67 1647.44
Table 16: Deviations of sensor response between consecutive 7 day periods
0 % 50 % 80 %
ppm ppm ppm
+ 7 days 5.82 30.00 91.80
+ 7 days 20.02 26.80 64.94
+ 7 days 8.40 29.33 14.42
-100
400
900
1400
1900
1 2 3
Day
80%
50%
0%

The uncertainty due to short term drift was estimated using the equation:
Fig. 50: Summary of the effect of Long term drift on the Alphasense sensor response. Sensor
exposed to three exposure levels over four consecutive weeks.
-100
400
900
1400
1900
1 2 3 4
Week
0%
50%
80%

5.8 Cross Sensitivity
The effect of cross sensitivity was evaluated was as follows:
1. Firstly the sensor was exposed to zero air for the test period and the response was
Y0.
2. The sensor was then exposed to a mixture of zero air and the maximum
concentration of the interfering compound (CO) in ambient air (estimated as 6 ppm
referred to as “int”). The response was YZ.
3. The sensor was exposed to the Limit Value concentration of CO2 (1600 ppm referred
to as ct). The sensor response was Ct.
4. Finally the sensor was exposed to a mixture of span gas CO2 (ct) and the interfering
compound CO (int). The sensor response was Yct.
5. The influencing effect of the interfering compound at zero air was calculated using:
6. The influencing effect of the interfering compound at span gas was calculated using:
7. The overall influencing effect of the interfering compound was calculated using:
where:
LV=the Limit Value (1600 ppm)
The sensor response for each step was derived by averaging the results over the second half
of each test period.

Table 17: Summary of Sensor Response Variables for Evaluation of Cross Sensitivity Effects
Sensor Response Variable Mean Sensor Response
Y0 -3.23
Yz -12.33
Ct 1636.18
Yct 1653.25
Yint,z 9.1
Yint,ct 17.07
Yint 16.89
The uncertainty u(int) associated with the interfering compound was calculated using:
Fig. 51: Sensor response to varying input parameters
-100
0
100
200
300
400
500
600
700
800
900
1000
1100
1200
1300
1400
1500
1600
1700
0 50 100 150 200 250 300 350 400 450 500 550 600 650
CO2concentrationppm
Time s
Yo Yz Ct Yct

5.9 Hysteresis
The evaluation of the sensor response to hysteresis involved conducting a series of
measurements at incremental steps throughout the sensor range. The selected intervals of
the range were; 0, 20, 40, 60, 80, 95% of the full scale with three repetitions at each
interval. The test would begin by increasing the concentration, followed by a decreasing the
concentration and then finally increasing the concentration once again.
Table 18: Sensor Response Hysteresis Test
Percentage of Full Scale (%) Sensor Response (ppm)
0 34.58
20 398.50
40 801.55
60 1255.07
80 1671.75
95 2016.00
80 1725.16
60 1304.44
40 868.46
20 425.92
0 31.43
20 391.33
40 819.92
60 1241.20
80 1688.70
95 2034.71

Table 19: Summary of Deviations between measurements at intervals
Fig. 52: Alphasense sensor response to Hysteresis test
0
200
400
600
800
1000
1200
1400
1600
1800
2000
2200
0 10 20 30 40 50 60 70 80 90 100
SensorResponse(ppm)
Percentage of full scale (%)
Rise 1
Fall
Rise 2
Percentage of Full
Scale (%)
Deviation
from previous
Measurement
Deviation
from previous
Measurement
0 3.15 -
20 -27.42 34.58
40 -66.92 48.54
60 -49.37 63.24
80 -53.41 36.46
95 -18.71 -

The uncertainty component is calculated using the equation:
Substituting the data from Table 19 in to the equation gives:
5.10 Reproducibility
The reproducibility between different sensors is a measure of the variation between two
different sensors exposed to identical conditions. There were five Alphasense sensors
available from the department for the evaluation of reproducibility tests. Of the four
additional sensors available, only two were found to be working correctly (referred to as
sensors 2 and 4). Sensor 3 was unable to interface with the PC in the laboratory and after
numerous attempts was discarded from the evaluation. Sensor 5 was able to interface with
the PC but was producing erroneous readings, i.e. consistently reading 9999 ppm at -
240.3°C. Several attempts were made to correct the response through calibration of the
sensor however this proved unsuccessful. The evaluation was therefore conducted using
sensors 2 and 4 which were evaluated against the sensor used thus far (referred to as
sensor 1).
The test procedure for the evaluation of reproducibility requires a series of measurements
to be taken at varying concentrations between zero air and the Full Scale (2000 ppm). A
statistical regression analysis was performed comparing each additional sensor (sensors 2
and 40 to the original sensor (sensor1). ANOVA regression analysis was performed on each
sensor where sensor 1 was used as the predictor and sensors 2 and 4 were the responses.
The p-value for the regression model tested the null hypothesis that the coefficient of
determination is equal to zero (i.e. no effect). A low p-value (< 0.05) indicates that the null
hypothesis can be rejected. The coefficients in the equation of the line were largely ignored
in this evaluation, as it was believed that these could be corrected for through calibration if
necessary.

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