Application of the Nordtest method for real-time measurement uncetainty evaluation of field turbididy measurement

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Application of the Nordtest method for "real-time" uncertainty estimation of
on-line field measurement
Article in Environmental Monitoring and Assessment · September 2015
DOI: 10.1007/s10661-015-4856-0
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Environ Monit Assess (2015) 187:630
DOI 10.1007/s10661-015-4856-0
Application of the Nordtest method for Breal-time^
uncertainty estimation of on-line field measurement
Teemu Näykki & Atte Virtanen & Lari Kaukonen &
Bertil Magnusson & Tero Väisänen & Ivo Leito
Received: 26 January 2015 / Accepted: 9 September 2015
# Springer International Publishing Switzerland 2015
Abstract Field sensor measurements are becoming
more common for environmental monitoring.
Solutions for enhancing reliability, i.e. knowledge of
the measurement uncertainty of field measurements,
are urgently needed. Real-time estimations of measurement uncertainty for field measurement have not
previously been published, and in this paper, a novel
approach to the automated turbidity measuring system
with an application for Breal-time^ uncertainty estimation is outlined based on the Nordtest handbook’s
Electronic supplementary material The online version of this
article (doi:10.1007/s10661-015-4856-0) contains supplementary
material, which is available to authorized users.
T. Näykki (*) : L. Kaukonen
Environmental Measurement and Testing Laboratory, Finnish
Environment Institute, Hakuninmaantie 6, 00430 Helsinki,
Finland
e-mail: [email protected]
A. Virtanen
Data and Information Centre, Finnish Environment Institute,
Mechelininkatu 34a, 00251 Helsinki, Finland
B. Magnusson
Chemistry, Materials and Surfaces, SP Technical Research
Institute of Sweden, 50115 Borås, Sweden
measurement uncertainty estimation principles. The
term real-time is written in quotation marks, since the
calculation of the uncertainty is carried out using a set of
past measurement results. There are two main requirements for the estimation of real-time measurement uncertainty of online field measurement described in this
paper: (1) setting up an automated measuring system
that can be (preferably remotely) controlled which measures the samples (water to be investigated as well as
synthetic control samples) the way the user has programmed it and stores the results in a database, (2)
setting up automated data processing (software) where
the measurement uncertainty is calculated from the data
produced by the automated measuring system. When
control samples with a known value or concentration are
measured regularly, any instrumental drift can be detected. An additional benefit is that small drift can be taken
into account (in real-time) as a bias value in the measurement uncertainty calculation, and if the drift is high,
the measurement results of the control samples can be
used for real-time recalibration of the measuring device.
The procedure described in this paper is not restricted to
turbidity measurements, but it will enable measurement
uncertainty estimation for any kind of automated measuring system that performs sequential measurements of
routine samples and control samples/reference materials
in a similar way as described in this paper.
T. Väisänen
Environmental Measurement and Testing Laboratory, Finnish
Environment Institute, Paavo Havaksen tie 3, 90570 Oulu, Finland
I. Leito
Institute of Chemistry, University of Tartu, Ravila 14a,
50411 Tartu, Estonia
Keywords Measurement uncertainty . Field
measurement . Nordtest approach . Water quality
monitoring . Quality control . Turbidity
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Page 2 of 12
Introduction
The state of the water environment is usually
monitored by carrying out the measurements of
physical or chemical parameters of water. Information
of measurement uncertainty is essential when evaluating
the real value of the measurement result. Nowadays,
several chemical laboratories estimate uncertainty for
their measurement results according to the guides and
standards available (Eurachem/CITAC 2012; Eurolab
2007; International Organization for Standardization
2012; Joint Committee for Guides in Metrology 2008;
Magnusson et al. 2011). At the same time, there is a
global trend in which the measurements are carried out
more often in situ by field measurement devices or
sensors (Arola 2012; Azzaro 2013; Bourgeois et al.
2001; Clinch and Worsfold 1987; Glasgow et al. 2004;
Tschmelak et al. 2005; Valkama et al. 2007; Wang et al.
2004). These may function automatically or they are
operated manually.
The need to employ field measurements is apparent,
since pressures to streamline the production of environmental information have increased, mainly for economic reasons. Field measurement devices are able to produce time-resolved measurement results much more
efficiently than laboratory equipment and personnel.
Other benefits for applying field measurement devices
are the lower price compared to laboratory instruments
and the fact that sample stability during transportation
from field to the laboratory does not have to be considered. However, some deficiencies remain in applying
sensor measurements that need to be taken into account.
It may be difficult to monitor the appropriate quality of
measurement results due to uncontrolled sensor degradation e.g. by applying similar quality control procedures than in chemical laboratories (Hovind et al. 2011).
In addition, many of the field sensor operators do not
have any kind of knowledge about the measurement
uncertainty of their sensor measurement results
(Leivuori et al. 2013; Näykki et al. 2013). The uncertainty can be estimated for the sensor in the field using
quality control and validation data using the Nordtest
handbook (Magnusson et al. 2011), for example.
However, what is needed is an estimate of the uncertainty of the online results produced by the field measurement, taking into account any degradation of any
performance of the sensor.
According to the Nordtest measurement uncertainty
handbook (Magnusson et al. 2011), the combined
Environ Monit Assess (2015) 187:630
measurement uncertainty is broken down into two main
components—the within-laboratory reproducibility uRw
(also called intermediate precision) and the uncertainty
due to possible method and laboratory bias ub. Both of
these can be conveniently estimated from internal
quality control data, i.e. results of routine sample replicates and reference materials (Hovind et al. 2011).
SYKE has published a practical, user-friendly, opensource, freeware measurement uncertainty estimation
software package for laboratories (MUkit website;
Näykki et al. 2012), which is based on the Nordtest
handbook (Magnusson et al. 2011). Presently, similar
routine and daily basis quality control procedures as
applied in laboratories (Hovind et al. 2011) are not used
for online measurements, and the measurement results
are reported without any information about measurement uncertainty. In this paper, we describe a procedure
where quality assurance data is applied for real-time
measurement uncertainty estimation to an online field
measurement. This will promote the comparability of
the measurement results since the actual uncertainty at
that time is reported together with the results.
A case study was selected for a parameter where
short-time inhomogeneity is present—turbidity
measurement. It is of course easier to monitor a parameter which is homogeneously distributed in the water. In
water with high turbidity, e.g. high concentrations of
clay, also the repeatability has to be taken into account in
the uncertainty estimate. Turbidity is an essential parameter for describing the quality of water. Several
thousands of turbidity measurements are carried out annually in Finnish lake, pond, river and brackish waters.
Water turbidity is caused by particles and suspended solids
causing changes in water quality, reduced light penetration, diminished recreational values and aesthetics, as well
as direct and indirect impacts on fish, invertebrates and
aquatic plants (Kerr 1995; Appleby and Scarratt 1989;
European Inland and Fisheries Advisory Commission
1964). A large number of the testing laboratories worldwide follow the international standard method ISO 7027
in their turbidity measurements (International
Organization for Standardization 1999). Turbidity is measured nephelometrically using an instrument calibrated
with formazine standard solutions. The turbidity of the
tested water sample is expressed in formazine nephelometric units (FNU). The measurement of turbidity is based
on the scattering of light.
The procedure described in this paper is not restricted
to turbidity measurements; instead, it will enable
Environ Monit Assess (2015) 187:630
measurement uncertainty estimation for any kind of
automated measuring system that performs sequential
measurements of routine samples and control samples/
reference materials similarly as described in this paper.
Automation of measurement uncertainty estimation
in field measurements
There are two main requirements for the estimation of
real-time measurement uncertainty of an online field
measurement: (1) setting up an automated measuring
system that can be (preferably remotely) controlled,
which measures the samples (water to be investigated
as well as synthetic control samples) in the way the user
has programmed it and stores the results in a database,
and (2) setting up automated data processing (software),
where the measurement uncertainty is calculated from the
data produced by the automated measuring system.
Figure 1 describes the general procedure for measurement uncertainty estimation along with how the calculated uncertainty value is combined with the measurement
result before transferring these into water quality register.
In this study, an automated turbidity measuring system was constructed, and it functioned by taking water
samples via tubing into the instrument’s flow-through
cell. Using valves, it was possible to automatically introduce natural water samples, as well as turbidity control samples to the turbidity detector. A challenge for the
real-time uncertainty estimation is the autonomous analysis of reference samples/control solutions in the field.
This can be done with automated spectrophotometers or
by the turbidity measuring system described below, for
example. For probes, the automation is not straightforward as they are usually deployed in situ and they do not
have a flow-through cell. However, even for the probes,
constructions there can be found, where the sensor is
placed in a flow cell type of container, and the water
sample or synthetic control sample is introduced via
tubing to the cell for the measurement.
Set up for automated turbidity measurement
In order to apply the Nordtest method for Breal-time^
estimation of measurement uncertainty, the instruments
need to analyse both (1) routine samples and (2) reference materials/control sample solutions automatically in
the field. A cabin next to a river was employed for
Page 3 of 12 630
setting up the automated measuring system. The distance from the river to the cabin was ca. 8 m, and the
pumping height was ca. 5 m. The pump P1 (Biltema, 259755 water pressure pumps) controlled by the valve V1
(Danfoss, AVSI valve frames with Danfoss 018 F7397
magnetic coils DC 24 V) delivered water as scheduled
by the operator (i.e. time interval of the sampling and the
number of replicates measured) from the river to the ISO
7027 (International Organization for Standardization
1999) compliant flow-through sensor model 7998 016
(ABB, Switzerland) and the wall-mount mounted
analyser model 4690 500 (ABB). In addition to the
above-mentioned components, the measuring system
in the cabin consists of pumps (P2-P3) and valves
(V2-V5), tubings, containers for the control samples
and wastes, an ABB automation module, a Raspberry
Pi® computer with wireless communication, a Codesys®
web-virtualisation interface for controlling the ABB
automation module and a PhpMySQLAdmin® webinterface for controlling measurement data (Fig. 2).
The technical details of the automated turbidity measuring system will be published later.
The possible biofouling of the tubing used in the
pumping system for delivering samples to the measuring system is one possible source of contamination and
will depend on the sample types measured as well as the
seasonal variations such as water temperature. The representativeness of the sample water should be regularly
(e.g. once in a month) monitored with manual sampling
associated with laboratory analysis.
In addition, possible contamination or alteration of the
control sample during the storage in the cabin may be a
factor to be taken into account. Relevant stability studies
should be conducted to ensure that fresh control samples
are available. Instability of the control sample is included
as an uncertainty component when estimating the uncertainty of the reference value for the control sample.
Principle for real-time measurement uncertainty
estimation
A software application (AutoMUkit) was programmed
for carrying out the real-time measurement uncertainty
estimation. The software will be published later as freeware including the open-source program code, and the
details for downloading it can be found on the Envical
SYKE website after publication (MUkit website). In this
paper, we describe the most important features of this
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Environ Monit Assess (2015) 187:630
Fig. 1 The general procedure for
measurement uncertainty
estimation and combination of the
uncertainty value from the
measurement result
software. The application is programmed using Java®
and consists of two main parts: (1) a measurement
uncertainty solving class library and (2) a console application using the measurement uncertainty solving
Fig. 2 Automated turbidity
measurement system, which
sequentially measures river
samples and two control samples
(CTRL A and CTRL B) and
reports the data to a database
class library, which saves uncertainties to Extensible
Markup Language (XML) files or to a Java Database
Connectivity (JDBC) compatible SQL database
(Structured Query Language).
Environ Monit Assess (2015) 187:630
Ladder of errors—components contributing
to uncertainty
Similarly to that described in (Hovind et al. 2011), the
sources of errors affecting the possible deviation from a
reference value for an analytical result may be described
by the ladders presented in Fig. 3.
Step 1 The method bias—a systematic effect owing to
the method used
Step 2 The instrument bias—a systematic effect (for
an individual instrument or sensor)
Step 3 The day-to-day variation—a combination of
random and systematic effects owing to time
effects, for example
Step 4 The repeatability—a random effect occurring
between replicate determinations performed
within a short period of time; the sample inhomogeneity is part of the repeatability
Each of these steps on the ladder adds its own uncertainty. A new term Binstrument reproducibility^ is introduced in this paper for field measurements to replace
the term Bwithin-laboratory reproducibility^, which is
applied for laboratory measurements. Instrument reproducibility is largely similar to within-laboratory reproducibility, covering the variation resulting from shortterm repeatability, and also day-to-day random
Fig. 3 The ladder of errors for a measurement procedure used in
field measurements
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variation. Instrument reproducibility can be assumed to
be lower than within-laboratory reproducibility, since
the latter also includes the variation from analysts,
which may change in a laboratory between different
days.
General requirements
For the measurement uncertainty estimation, all components contributing to uncertainty need to be taken into
account. An outline according to Nordtest TR 537
(Magnusson et al. 2011), where these components are
divided into random and systematic errors, is described
also in detail in ISO 11352 (International Organization
for Standardization 2012) (Fig. 4). Regarding the turbidity case study, these are obtained through the analysis
of replicate samples of the water to be investigated in
addition to synthetic turbidity control samples.
1. Measurement results of routine samples
Replicate measurements of the routine samples
(e.g. river water) are carried out, and their measurement results reflect the random variation
(repeatability) resulting from the sample inhomogeneity, for example, due to particles in the routine
samples or the restrictions of the device’s measurement capabilities (ur,range in Fig. 4).
2. Measurement results of control samples
The control sample solution is a synthetic
solution with known concentration of the analyte. In this study, a formazine turbidity standard
(HACH, Loveland, CO, USA) with a certified
value of 4000 Formazine Nephelometric Unit
(FNU) was used for the preparation of diluted
control samples.
Typically, it is advisable to use more than one
control sample having different analyte values or
concentrations. This is mandatory since the measurement uncertainty should also be estimated
both for low concentrations near the limit of
quantification and for high concentrations at
the optimal range of detection.
The measurement results of diluted control
samples are used for calculating the uncertainty
component reflecting the instrument reproducibility (termed as within-laboratory reproducibility in uRw,stand in Fig. 4, (Hovind et al. 2011;
International Organization for Standardization
2012; Magnusson et al. 2011) and bias values.
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Environ Monit Assess (2015) 187:630
Fig. 4 Flow chart describing the estimation of measurement uncertainty using quality control and validation data. Scheme is reproduced
from Fig. 1 in ISO 11352 (International Organization for Standardization 2012) courtesy of ISO, Geneva
In this way, part of the repeatability component
will be included twice, but usually, it is small in
comparison to the between-days variation
(Magnusson et al. 2011). Uncertainty of the
certified value (ucref in Fig. 4) is obtained from
the certificate of the solution, taking into account the uncertainty of the dilution steps during
the preparation of the control samples.
The automated measurement system is required to
produce a CSV (comma-separated values) file in the
following form:
…
A,2014-05-14 15:00:00,3.523
A,2014-05-14 15:00:05,3.545
A,2014-05-14 15:00:10,3.530
D,2014-05-14 15:00:15,0.000
A,2014-05-14 16:00:00,3.427
A,2014-05-14 16:00:05,3.455
A,2014-05-14 16:00:10,3.441
D,2014-05-14 16:00:15,0.000
B,2014-05-14 16:30:00,3.987
B,2014-05-14 16:30:05,3.976
B,2014-05-14 16:30:10,3.969
…
where the comma separated columns are
1. Type of sample
A = Routine sample
B = Control sample for low concentration range
C = Control sample for high concentration range
(type of sample not shown in the example data)
D = A marker sequence showing that a replicate series of routine sample measurements has
finished
Environ Monit Assess (2015) 187:630
2. Date in the form YYYY-MM-dd HH/mm/ss (Y=
year, M=month, d=day, H=hour, m=minute, s=
second)
3. Measurement result with a dot as the decimal
separator.
A row defines a measurement result. The rows are
separated with a new line. The order of the columns and
the format of the date string can also be modified from
the settings file of the application. The types for the
control samples (B and C in the CSV presented above)
are used as identifiers for the control samples. There can
be one or more control samples.
Alternatively, an SQL database having JDBC drivers
can also be used. SQLite™ and MySQL™ databases
have been tested to work. The SQL database structure is
presented in Fig. 5.
Page 7 of 12 630
6. Timespan for the selection of control samples results (used as BCRM^ according to Nordtest
(Magnusson et al. 2011)) used for the uncertainty
calculation (bias calculation)
7. Timespan for the selection of control samples results (used as BControl sample^ according to
Nordtest (Magnusson et al. 2011)) used for the
uncertainty calculation (uRw, stand calculation)
8. Separate parameters (six different) for the minimum
number and minimum timespan of
(a) Routine
(b) Control
(c) Breference^ sample results required for the uncertainty calculation to be successful.
9. A limiting date (such as calibration date), before
which routine, control and Breference^ sample
values are not selected
Parameters
The AutoMUkit software application requires the operator to set parameters for the measurement uncertainty
estimation. The operator needs to determine the following parameters:
1. Number of ranges used for measurement uncertainty estimation (there must be exactly one control/
reference sample for each value/concentration
range)
2. Minimum and maximum values/concentrations for
the ranges
In the sample input file (presented in BGeneral
requirements^), there are two types of control sample
(BB^ and BC^). Therefore, our pilot application needs to
have two value/concentration ranges.
For each value/concentration range, the following
parameters need to be set:
1. The period of time for which one measurement
uncertainty value is valid for
2. Control sample reference value/concentration
3. Uncertainty of the reference value/concentration
4. Is absolute or relative uncertainty estimation
required
5. Timespan for the selection of routine sample replicate measurement results used for the uncertainty
calculation
The software application has two modes: Bforced^
and Bautomatic^. Using the forced mode, the application
evaluates measurement uncertainties within a specified
period of time. Using the automatic mode, the application calculates measurement uncertainties starting from
the end date of the last successful measurement uncertainty calculation for each value/concentration range.
For the automatic mode, an additional parameter needs
to be set. This defines the earliest date in the past, which
is included in the evaluation in case no successful evaluations are found.
Since the results of the past measurements are used
for calculating uncertainty, the choices that the operator
make in the settings will affect the measurement uncertainty estimation. If the time interval for collecting control sample measurement results is too long, the results
may not fully reflect the current state of the measurement bias. The replicate measurement of the routine
samples has to be carried out within a short time interval. If the time interval of the replicate measurements is
too short, the measurement result does not reflect any
possible random variation and the estimated uRw may
not be representative enough. If the time interval is too
long, the measurement results no longer represent the
same test sample when the sample is changing very fast,
for example in river water. Nordtest TR 537
(Magnusson et al. 2011) suggests having at least 50
results and a timespan of 1 year. This is true for laboratory measurements and quality control procedures
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Environ Monit Assess (2015) 187:630
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Fig. 5 The SQL database structure from which the measurement
results can be read for the measurement uncertainty calculation.
The types of column values are specified by the Java types in
brackets. ReplSeries is a database table for replicate series, with a
column for the average time (repl_series_avg_time) of the replicates in the series. MeasResult is a table for the measurement values
(column value) including columns for the time of the measurement
(meas_time) and the average time of the replicate series
(repl_series_avg_time) the measurement result belongs to.
RefResult is a table for the reference/control sample measurement
values having columns for the time of the measurement
(meas_time), the measurement value (value) and an identifier for
the concentration range the reference belongs to (conc_range_id).
The identifiers for the data presented for the CSV would be BB^ and
BC^
applied there. Field sensor measurements can be carried
out 24 h per day, and the amount of data is much higher
than in laboratory measurements. For real-time uncertainty estimation, the authors suggest that at least 30
measurement results should be collected over a time
period of 10 days or more. In the river water experiments, successive measurements carried out in 5-s intervals were regarded as replicate measurements. Three
replicates were recorded for one test sample. A new test
sample was measured every 60 min. Control samples
were measured every 12 h.
Binstrument reproducibility^ uRw. Equations 1 and 2
are used for calculating the repeatability component.
If measurement uncertainty is calculated as an absolute value, the repeatability component ur,range is
X nr c
−c
i¼1 ðiÞmax ðiÞmin
ð1Þ
ur;range ¼
nr ⋅d
Calculation of measurement uncertainty
with AutoMUkit
The queries of the application are performed, and the
measurement uncertainty is calculated according to how
the parameters were set. Figure 6 shows the raw measurement data consisting of one measurement result per
row with information about the sample type, the measurement date and time. Three parallel turbidity measurements are carried out within a 5-s interval from the
routine (river water) sample BA^. After 60 min, another
three replicates are measured. Later in the sequence,
measurement results can be seen for the synthetic control sample BB^ with a turbidity value in the low concentration range. As seen from Fig. 6, the turbidity
standard solution is used both as a control sample and
a reference material for the measurement.
The results of routine sample replicates and control
sample replicates are used for calculating the
If measurement uncertainty is calculated as a relative
value, the repeatability component ur,range% is
0
1
X nr B
cðiÞmax −cðiÞmin C
@100%⋅ c
A
i¼1
ðiÞmax þ cðiÞmin
2
ur;range% ¼
ð2Þ
nr ⋅d
wherecmax is the maximum and cmin the minimum concentration of a replicate series. nr is the number of
replicate series. d denotes a factor for converting the
mean difference to the standard deviation and depends
on the amount of replicate series (International
Organization for Standardization 2012).
Equations 3 and 4 are used for calculating the instrument reproducibility component (u Rw,stand and
uRw,stand%) from control sample replicates respectively:
uRw;stand ¼
S Rw;stand
C avg
uRw;stand% ¼ 100%⋅
ð3Þ
S Rw;stand
C avg
ð4Þ
Environ Monit Assess (2015) 187:630
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Fig. 6 The general functioning of the real-time uncertainty estimation
where sRw,stand is the standard deviation of control sample measurement results and cavg is the average of control sample measurement results.
The uRw or uRw% is calculated as described in Eqs. 5
and 6:
uRw ¼
qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
ur;range 2 þ uRw;stand2
ð5Þ
of the formazine turbidity control sample measured
every 12 h.
sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
2
sb
ð7Þ
ub ¼ b2 þ pffiffiffi þ u2cref
n
where
b
or
uRw%
qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
¼ ur;range% 2 þ uRw;stand%2
sb
ð6Þ
Equation 7 is used for calculating the uncertainty
component for bias (ub) from the measurement results
n
the difference between the average of the sensor
measurement results of the control sample (during a
certain period of time) and the actual reference
value of the control sample
the standard deviation of the sensor measurement
results of the control sample
the number of sensor measurement results of the
control sample
uCref is the standard uncertainty of the reference
value
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Saving the measurement uncertainty calculations
The AutoMUkit software application creates evaluations, which determine the measurement uncertainty
value for a concentration range and a period of time
(the length of the period is defined in the settings). These
evaluations are calculated for the whole timespan for
which measurement uncertainty is calculated (explicitly
defined if the forced mode is used and from the last
successful measurement uncertainty evaluation until
present time if the automatic mode is used). Currently,
the application supports two methods for saving the
measurement uncertainty evaluation results: XML files
or SQL database.
If measurement uncertainty evaluations are saved to
XML files, the application saves a file for each evaluation, so that the file name contains the start and end date
for the time the measurement uncertainty evaluation applies for. The file name also includes the value/
concentration range, which the evaluation belongs to
and whether the evaluation has been successful or has
failed. An example of the filename is
B2 0 1 4 _ 0 4 _ 0 2 _ 0 0 _ 0 0 _ 0 0 _ Z + 0 3 _ 0 0 2014_04_02_12_00_00 Z+03_00-B-success.xml^. The
actual XML file contains information about the evaluation, including the expanded measurement uncertainty.
If the SQL database implementation is used,
measurement uncertainty evaluation information is
saved to a database with the structure presented in
Fig. 7. In addition to Fig. 5, the database contains
the following tables:
–
–
–
–
MeasUncEval: a successful measurement uncertainty evaluation (a measurement uncertainty value
for a concentration range and a period of time).
MeasUncFail: a failed measurement uncertainty
evaluation. The requirements for the input information set in the parameters (i.e. minimum
period of time, minimum amount of results)
are not fulfilled.
MeasUncAppRun: information about every time
the AutoMUkit application’s measurement uncertainty evaluation has run. One run has evaluations
(MeasUncEval/MeasUncFail) for each value/
concentration range.
ConcRange: settings for value/concentration ranges
presented earlier (conc_range_id is the identifier for
the Bcontrol sample^ and the concentration range).
These can alternatively be loaded from a file.
Environ Monit Assess (2015) 187:630
The measurement uncertainty can then be combined
with the measurement results of the samples, by either
querying the measurement uncertainty XML files or the
SQL database.
User interface for parameter adjusting
The parameters described earlier can be adjusted
and their influence on the measurement uncertainty
can be assessed visually using the developed visualisation tool (see the Electronic Supplementary
Material (ESM)). In the visualisation tool, the user
can set the parameters for the measurement uncertainty calculation. After that, the operator can see
how the changes to the parameters affect the calculated measurement uncertainty. The visualisation
tool uses the forced mode of AutoMUkit.
The examples in the graphs are presented as
Electronic Supplementary Material. The graphs include the presentation of measurement results and
their uncertainties (k = 2) shown using error bars.
The X chart is available for visual monitoring of
the measurement results of control samples. An r%
chart is also available, where each dot is calculated
from the difference of routine sample replicate results as a percentage of their mean value (r% value
(Hovind et al. 2011). The graph for the r% chart is
very useful for monitoring the random variation as a
function of the measured value or concentration,
since the measurement uncertainty will normally
vary by concentration when the instrumental
methods are used. In the lower concentration ranges,
the absolute measurement uncertainty is usually
constant, while at higher concentrations, the relative
uncertainty is constant (Eurachem/CITAC 2012;
Magnusson et al. 2011; Magnusson and Koch
2012). Therefore, it is advisable to divide the measurable concentration range into parts. Typically, the
decision for the limit concentration between the low
and high ranges is based on the visual study of the
behaviour of relative random variation (r% values).
The operator of the sensor may set the parameters
(concentration ranges) for the measurement uncertainty calculation according to the observation,
where the random variation starts to increase or
remains constant, respectively. r% values may also
be viewed as a function of time, so the operator can
observe possible changes in the random variation
due to seasonal effects.
Environ Monit Assess (2015) 187:630
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Fig. 7 Database structure of measurement uncertainty evaluation information
Discussion and conclusions
Field sensor measurements are becoming more common
in environmental monitoring. Solutions for enhancing
reliability, i.e. knowledge of the measurement uncertainty of field measurements, are urgently needed. Real-time
estimations of measurement uncertainty for field measurement have not been published before, and in this
paper, a novel approach for the automated turbidity
measuring system with a computer software application
for real-time uncertainty estimation based on the
Nordtest handbook’s measurement uncertainty estimation principles was outlined. The software for measurement uncertainty estimation will be published later as
freeware with source code included. The software will
enable measurement uncertainty estimations for any
kind of automated measuring systems that performs
sequential measurements of routine samples and control
samples/reference materials, as described in this paper.
This automated turbidity measuring system has been
developed as a pilot system for continuous uncertainty
measurement system. Establishing such an automated
measuring system may require a cabin and also heating
during the winter time. The need for electricity is also
required. These requirements may limit the deployment
of the technology, and if the appropriate cabin is not
readily available, establishing the infrastructure may not
be cost-effective.
The benefits of advanced utilisation of field measurements are enormous. The measurement results are produced at a very high frequency, and the use of the equipment does not require manual operation, except when
maintenance is needed. The alteration of the sample during
transportation to the laboratory and storage is not relevant
in the field measurement. Sensor measurement results may
occasionally drift for many reasons e.g. due to biofouling,
but this can be monitored using the approach presented in
this study. When control samples with a known value or
concentration are measured regularly, information from
changes in calibration can also be detected. An additional
benefit is that any drift that occurs can be taken into
account (in real-time) as a bias value in the measurement
uncertainty calculation, or the measurement results of the
control samples can be used for real-time recalibration of
the measuring device. For the current version of
AutoMUkit, the operator of the instrument and the software has to define the parameters, i.e. the frequency for the
recalculation of the measurement uncertainty. As a future
630
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development area, the proportional-integral-derivative
(PID) controller widely used in adjusting industrial processes could be used for controlling when the sensor
degrading has reached a set limit and when the measurement uncertainty needs to be recalculated. The measurement results will then become much more useful when
they are associated with the uncertainty information. They
can be compared to the measurement results produced in a
laboratory, and they can be used more easily for decisionmaking processes.
Acknowledgments The Cleen Ltd MMEA programme and
Maa-ja vesitekniikan tuki ry are acknowledged for their financial
support.
This work has also been partially supported by Graduate
School BFunctional materials and technologies^ receiving funding
from the European Social Fund under project 1.2.0401.09-0079 at
the University of Tartu, Estonia.
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