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A Portable Sensor System for Measurement of Fluorescence Indices of Water Samples


Abstract and Figures

Dissolved organic matter (DOM) plays an important role in biological, physical and chemical processes in water ecosystems. Different characteristics of DOM, such as origin or formation, can be determined by different fluorescence indices. A portable instrument for field measurement of the fluorescence index (FIX) and the biological index (BIX) on water samples has been developed and characterized. The developed sensor system was tested under different scenarios and showed a sufficient performance where the typical measurement results differed less than 10% from results achieved by a stationary laboratory fluorescence spectrometer.
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Abstract—Dissolved organic matter (DOM) plays an important
role in biological, physical and chemical processes in water
ecosystems. Different characteristics of DOM, such as origin or
formation, can be determined by different fluorescence indices. A
portable instrument for field measurement of the fluorescence
index (FIX) and the biological index (BIX) on water samples has
been developed and characterized. The developed sensor system
was tested under different scenarios and showed a sufficient
performance where the typical measurement results differed less
than ±10% from results achieved by a stationary laboratory
fluorescence spectrometer.
Index T erms—Environmental monitoring, dissolved organic
matter (DOM), fluorescence index (FIX), biological index (BIX),
portable fluorescence sensor
I. I
Dissolved organic matter (DOM) has a dominant role in
biological, physical and chemical processes in water
ecosystems. For example, it provides a source and a sink for
carbon, is a mediator for photochemical processes and has an
effect on the transportation, toxicity and bioavailability of
metals [1]. DOM is a complex mixture of aromatic and
aliphatic hydrocarbon structures, which have bound different
functional groups (amide, carboxyl, hydroxyl, etc.) [2]. In
addition, heterogeneous molecular aggregates in natural
waters increase the complexity of DOM. The molecular
weight of DOM can be in the range of several hundred to 100k
Daltons (Da), which corresponds to a colloidal size range. The
major components of natural organic matter in soil and water
are humic substances (HS). More than one third to one half of
dissolved organic carbon (DOC) in surface waters is from HS
and, is therefore a key contributor in the worldwide carbon
cycle [3]. Humic substances are complex organic compounds,
This work was supported in part by the NOE Forschungs- und
Bildungsges.m.b.H. (NFB) under Grant: SC15-002.
M. Brandl is with the Department of Integrated Sensor Systems, Danube
University Krems, Krems, A-3500 Austria (e-mail: martin.brandl@donau-
T. Posnicek is with the Department of Integrated Sensor Systems, Danube
University Krems, Krems, A-3500 Austria (e-mail: thomas.posnicek@donau-
R. Preuer was with the Department of Integrated Sensor Systems, Danube
University Krems, Krems, A-3500 Austria.
G. Weigelhofer is with the Institute of Hydrobiology and Aquatic
Ecosystem Management University of Natural Resources and Life Science, A-
1180 Vienna, and with Wasser Cluster Lunz, A-3293 Lunz am See, Austria
which are components or decomposition products from soil
humus and aquatic or terrestrial plants. The chemical and
optical properties of HS depend on their source and their
composition. The color of HS typically varies from light to
dark brown and gives surface waters characteristic optical
properties. Humic substances were considered
macromolecular, but recent studies with humic substances
from soil, brown coal and water found relatively small,
primary molecular structures (100-2000 Da) with
macromolecular properties resulting from aggregates formed
by hydrogen bonding, nonpolar interactions and multivalent
cation interactions [4]-[5]. The distinction between particulate
organic material (POM) and DOM is defined by the fact that
DOM passes through a 0.45 μm filter pore while POM is
captured by the membrane [6].
A. Optical Properties of DOM
DOM is a mixture of various compounds with molecular
weights ranging from simple carbohydrates to complex
molecules of different aromaticity [7]. Due to light absorbing
chromophores and fluorophores, DOM has distinctive
spectrophotometric properties in terms of both absorption and
fluorescence [8]-[10]. UV-visible (200–800 nm) optical
properties of DOM have been used successfully to determine
DOM characteristics such as
bulk DOM aromaticity, which has
been correlated with a specific UV absorbance
) and
molecular size [11]-[12]. Recent advances in fluorescent
spectrophotometry have provided a new tool for rapidly
identifying DOM fluorophores via excitation–emission
matrices (EEM) at wavelengths from 200 nm to 500 nm [13].
An EEM reveals fluorescence centers that are attributed to
various DOM components, such as humic-, fulvic- or protein-
like fluorophores [14]. Thus, fluorescence can be used to
identify anthropogenic DOM sources in streams [15] and to
distinguish bioavailable from refractory DOM components,
which determine microbial activity and organic matter
processing [13].
Different studies employed a “peak-picking” method to
track changes in the EEM topography and relate these changes
to DOM biogeochemistry [18]. Real-time fluorescence sensors
deliver DOM data in high spatial and temporal resolution and
therefore changes in DOM composition over time in response
to diurnal patterns in bacterial and algal production,
fluctuations in DOM input, or storm events can be
continuously observed [16]. Besides, real-time monitoring will
enable scientists to continuously track DOM changes in
response to microbial degradation during incubation
A Portable Sensor System for Measurement of
Fluorescence Indices of Water Samples
M. Brandl,
Member, IEEE
, T. Posnicek, R. Preuer, G. Weigelhofer
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experiments [17]. The overall goal is to use optical methods
like fluorescence and spectroscopic measurements and indices
to characterize DOM components and their changes.
Different studies have reported significant relationships
between optical indices and molecular formulas of DOM on
freshwater and coastal systems [18]. Optical measurement
techniques are much faster and more cost effective than the
recent used methods of Fourier-transform-ion-cyclotron-
resonance-mass-spectrometry (FT-ICR-MS) for DOM
characterization [19]. A review of recent literature
demonstrates that by monitoring the fluorescence of dissolved
organic matter (DOM), the ratios of humic-like (Peak C) and
protein-like (Peak T) fluorescence peaks can be used to
identify trace sewage contamination in river waters [20]. For
online monitoring of DOM [21] and for prediction of the
degradation of DOM and trace organic contaminants during
ozonation [22] portable sensor devices were developed using
UV-light-emitting diodes at 280 nm wavelength. An example
of DOM characterization by coupling FT-ICR-MS together
with optical absorption spectra analysis in the range of 300-
600 nm was given in [23]. In [24] a field-portable fluorometer
based on UV-LEDs for the detection of phenanthrene- and
tryptophan- like compounds in natural waters is described. For
the detection of phenanthrene compounds, the fluorescence
intensity is measured at 360 nm, whereby the excitation is
performed with a LED at 255 nm. The detection of
tryptophan-like compounds is performed with excitation at
280 nm and fluorescence reception at 340 nm. A portable
system for the rapid assessment of potable water quality by
characterizing the organic and microbial matter by a LED-
based instrument for detecting the fluorescence peaks C and T
is described in [25].
Our study focuses on a portable device for the accurate and
fast measurement of the β-freshness index (BIX) and the
fluorescence index (FIX), which has not yet been reported in
the literature. Portable systems for DOM characterization
presented in the literature are based on single wavelength
fluorescence analysis [21] or broadband optical absorption
analysis together with FT-ICR-MS [23]. Other LED based
portable systems for characterizing water quality and water
parameters are shown in [24]-[25]. Our system differs from
the systems presented in literature in that we have developed a
compact and portable instrument that allows the determination
of two important fluorescence indices (FIX, BIX) under
outdoor conditions. Both indices can be used not only, but also
to characterize the DOM composition and its origin where FIX
has been used to distinguish DOM derived from terrestrial
sources vs. microbial sources and BIX is an indicator of
autotrophic production [26]. From these parameters a more
detailed characterization of DOM can be achieved by our
The fluorescence index (FIX) is calculated as the ratio of
emission at 450 nm and 500 nm for a 370 nm excitation
wavelength, while the β-freshness-index (BIX) is determined
as the ratio of fluorescence emission at 380 nm and 430 nm
with 310 nm of excitation. The BIX indicates the proportion of
recently produced DOM where the β-peak represents recently
created DOM (likely microbial) [26]. The FIX displays
whether the precursor material for DOM is of a microbial
nature (FIX ~1.8) or terrestrially derived (FIX ~1.2) [28]. The
principal design of the sensor device was pre-published in an
abstract and presented at the IEEE Eurosensors 2018
Conference [29].
For the direct measurement of both indices characterizing
the DOM composition under outdoor conditions, a portable
and lightweight sensor system was developed. The
measurement system consists of several LEDs, exciting a
fluorescence emission from a water sample, which is
dispensed into a cuvette. The excitation light from LEDs is
spectrally reshaped by different optical filters that are directly
placed in front of the LEDs. The emitted fluorescence light is
filtered by selective optical bandpass filters at the peak
emission wavelength of each index to be measured. For easy
switching between the different emission wavelengths the
optical filters are fixed on a filter wheel rotated by a servo
(Fig. 1a,b).
Fig 1.
Drawing of the optical beam path.
3D printed prototype of the
measurement chamber with cuvette (1), LEDs (2), servo (3) for rotating the
optical filter wheel (4), optical filters (5) and the photomultiplier (6).
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The spectrally narrowband filtered light (bandpass filter
310 nm, #34-972, Edmund Optics) from a 310 nm UV LED
(DUV-HL5NR, Roithner Lasertechnik GmbH) generates the
excitation light for the BIX index. For the calculation of the
BIX index, the fluorescence emission is measured at
wavelengths of 380 nm (bandpass filter 380 nm, FB380-10,
Thorlabs) and 430 nm (bandpass filter 430 nm, FB430-10,
Thorlabs) by adjusting the filter wheel on the mapped position
and by subsequent amplification with a high gain photo
multiplier (PMT, H5773-01, Hamamatsu). To avoid any
influence of surrounding stray light on the detector signal, the
light source is pulsed at 1 kHz and the photomultiplier output
signal is phase synchronously processed by a lock-in amplifier
(LIA-MV-150, FEMTO Messtechnik GmbH). The
measurement of the FIX index is based on the same principle
using a 370 nm UV LED (RLT370-10E, Roithner
Lasertechnik GmbH) with a spectral filter in the optical path
of the LED (bandpass filter 375 nm, #86-732, Edmund Optics)
and two optical filters at 450 nm (bandpass filter 450 nm,
FB450-10, Thorlabs) and 500 nm (bandpass filter 500 nm,
FB500-10, Thorlabs) for the fluorescence emission. All used
optical filters have a full width at half maximum (FWHM)
bandwidth of 10 nm.
Fig. 2. a) Hardware of the sensor prototype with measurement
chamber (1), processor board (2), wireless modules (GPS, WIFI,
Bluetooth) (3) and the lock-in amplifier (4). b) Battery powered
sensor in a robust suitcase ready to use for field experiments.
Measurement chamber with cuvette holder (1), touch display (2) and
connector to an optional external power supply (3).
Figure 1a and 1b show a drawing of the optical beam path
and the 3D printed prototype of the measurement chamber
with the housed LEDs and the wheel where the optical filters
for different fluorescence emission wavelengths are placed. In
Figure 2 the assembled sensor system is shown. For outdoor
use, the sensor is placed in a water-proof suitcase with
dimensions of 25 cm x 20 cm and a higth of 10 cm. An
integrated rechargeable battery (7.4 V; 6,000 mAh) powers the
system for outdoor operation. The overall weight is 3 kg and
the cost of the instrument is about Euro 2,000.- but a
significant reduction in costs can be achieved by replacing the
commercial lock-in amplifier by one developed in laboratory.
For stationary operation and for battery recharge an external
power supply can be connected on a plug (Fig. 2b label (3))
To perform a measurement, the cuvette with the sample has to
be placed into the measurement chamber and the “Measure”
button on the touch screen has to be pressed. For online
monitoring a flow-through cuvette in combination with an
external pumping system can be used. An integrated GPS-
module gives the opportunity to save the position of the
measurement. The measured data will be saved on a SD card
and can be read out via the on board display or wireless via the
integrated Bluetooth or WIFI interface.
The sensor measures the fluorescence using the above
mentioned wavelengths and calculates the resulting FIX or
BIX as well as the absorbance at 254 nm (A
). The A
value is used for the analysis of organic constituents in water
and also serves as a possibility for the observation of water
quality or turbidity. Furthermore, the A
value can be used as
an indicator of aromatic carbons [30].
A. Test for functionality
For testing the functionality of the proposed sensor system
(and comparison with the stationary laboratory fluorescence
spectrometer LS55 (Perkin Elmer Instruments, Waltham,
USA), different concentrations of the fluorophore DPH (1,6-
Diphenyl-1,3,5-hexatriene, product No: D208000, Sigma-
Aldrich, Inc.) dissolved in cyclohexane ranging from 10 pM to
10 nM were measured (Fig. 3). The fluorescence values
measured by the sensor device where fitted by linear
regression methods to the fluorescence values measured by the
LS55 spectrometer. No statistical differences (t-test, p>0.9)
were found between the measured standard curves performed
with the LS55 spectrometer and the developed sensor device
(Fig. 3). The limit of detection of both devices lies in the range
of 10 pM to 50 pM DPH.
B. Sample extraction
In order to investigate the portable sensor system under
realistic conditions, 19 native water samples from surface
water bodies were used for the study. The water samples were
taken near the surface from the shore of local rivers. Nine
samples were collected from the Danube river at position
48°24'15.0"N, 15°36'28.9"E and 10 samples were collected
from the Krems river at position 48°24'55.6"N, 15°36'12.9"E
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at different time points. For sample collection, the sample tube
(CellStar Tubes, Greiner bio-one; 50 ml or 15 ml) was
cautiously held with the opening in the direction of the flow to
ensure that no sediments were stirred up. Date, time,
coordinates, sample name, outside temperature and name of
the water body were noted. Furthermore, air temperature,
weather and special characteristics of the water (e.g. color,
turbidity, pollution, etc.) were documented.
C. Sample preparation
1) Samples for temperature dependency or storage
To investigate the temperature dependency of the sensor or
the influence of storage time on the samples, the samples were
measured unfiltered with the sensor and the fluorescence
spectrometer. To reduce the influence of turbidity on the
fluorescence measurements, the samples were diluted with
distilled water to an A
value lower than 0.2. The samples
were stored in the dark at room temperature (RT; 22°C) or in a
refrigerator at 4°C.
2) Sample Filtering
To investigate the influence of sample filtration on sensor
values, filters with different pore sizes were used. Therefore,
10 ml of unfiltered samples were taken up with a syringe and
filtered through Luer-Lock tip filters with either 2.7 µ m, 1.6
µm or 0.7 µm pore size (Whatman, GE Healthcare Europe
GmbH, Type GF/A, GF/D, GF/F).
D. Sample measurement with the portable sensor device
1) Standard measurement
At the beginning, the cuvette (Hellma 100-QS; Suprasil®
Quartz; Hellma GmbH; Heraeus Quarzglas GmbH & Co.) was
rinsed and then, filled with 3 ml distilled water as reference
for the A
measurement. For sample analysis, 3 ml of the
respective, unfiltered sample were filled into the cuvette and
placed into the measurement chamber of the sensor device.
Afterwards, the commands on the sensor screen were followed
and during the measurement of A
, FIX, BIX, the sensor was
closed to avoid incident light from the outside. Each sample
was tested at least three times. The A
, FIX and BIX values
were documented for all samples. After each measurement the
cuvette was cleaned with distilled water and the A
, FIX and
BIX values were documented for all samples.
2) Measurement at changed temperature conditions
In order to investigate a possible temperature dependency
on the sample measurements and to proof the temperature
stability of the sensor device, the instrument was exposed to
different ambient temperatures. The sensor was cooled or
heated to the respective temperature using a variable
temperature cabinet (Voetsch VTL 4006; Weiss
Umwelttechnik GmbH). The constructed prototype was
exposed to this temperature for 1 hour. Afterwards, the
samples were measured. For comparability, the samples were
stored at RT and not exposed to temperature changes. The
samples that were kept for all measurements at RT and were
further measured are described in chapter C1 of the materials
and methods section.
3) Reference measurements with the fluorescence
spectrometer LS55
For proof and referencing the results of the portable sensor
device, the samples were additionally analyzed with a
fluorescence spectrometer LS55 using the related FL-Winlab
software. To ensure constantly high measurement stability, the
fluorescence spectrometer was started at least 30 minutes
before the actual experiments. The LS55 spectrometer
measurement settings are shown in Table 1. An emission
spectrum from 340 to 560 nm was scanned three times, each
with an excitation of 310 or 370 nm, respectively. The slit
width in the excitation light path was 5 nm and 2.5 nm for the
emission light path. The scan speed was set to 1,000 nm/min.
Start: 340 nm End: 560 nm Excitation: 310/370 nm
EX slit: 5.0 nm EM slit: 2.5 nm Scan speed: 1,000 nm/min
E. Data collection
Microsoft Excel (Microsoft Corporation, USA) was used
for data collection. The values of the sensor (FIX, BIX) were
compared with the calculated values from the fluorescence
spectrometer. The fluorescence index (FIX) was obtained by
comparing the fluorescence intensity as follows:
 =
  
  
The Biological Index (BIX) was calculated analogously to
the FIX:
 =
  
  
For each excitation wavelength the emission spectrum of
the sample was recorded by the LS55 fluorescence
spectrometer. In order to have a closed mathematical
representation of the emission spectrum a third-degree
Fig. 3. Standard curves of the sensor system and the LS55 spectrometer with
DPH dissolved in cyclohexane in a range of 10 pM to 10,000 pM. The
fluorescence emission at 450 nm and 500 nm with 370 nm excitation (FIX
analysis) and the fluorescence emission at 380 nm and 430 nm with 310 nm
excitation (BIX analysis) were measured (N=3). The figure-inlet shows an
enlargement of the measured values in the DPH range from 10 pM to 500 pM.
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polynomial function was fit to the measured data (Fig. 4).
Fig. 4: Example of a measured fluorescence spectrum and the polynomial fit
of 3
order, which was used as basis for the calculations of the reference
values for BIX and FIX (R
Since there was a certain scatter between the fluorescence
values of the same emission wavelength, the polynomial
function was determined to eliminate this uncertainty factor.
The mean value of triplicate sample measurements was
calculated and a third-degree polynomial function was
generated using the tools available in Microsoft Excel. Then,
the function for the polynomial fit was used in equations (1)
and (2) for calculating the reference values of FIX and BIX.
F. Statistical evaluation
Data processing was done using MS Excel and the
statistical analysis was performed using SigmaSTAT (Systat
Software Inc., USA).
A. Test for functionality
The measurements of DPH showed the outstanding
functionality of our system with a sensitivity of about 10-
50 pM DPH (Fig. 3). The measured values below the limit of
detection (LOI) are disturbed by noise and can be excluded
(dots not connected to the regression line). For validation of
the mobile sensor system, the FIX and BIX values of several
water samples taken from different rivers and other water
bodies were determined and compared with lab results
achieved by the LS55 lab spectrometer. The results were
compared and statistically analyzed. In total, 19 different
water samples were used for analysis. For determination of the
measurement accuracy of the sensor device, all water samples
were analyzed 4 times each on 5 consecutive days and
compared with results from the LS55 lab spectrometer.
Mean Std Dev Median 5% 95%
Deviation BIX [%] 0 5.87 2.3 -9.2 8.2
Deviation FIX [%] 0 5.85 -0.6 -10.2 10.1
In Table II the descriptive statistics and in Fig. 5a,b a
comparison of the derived results for BIX and FIX over time
is given, respectively. The boxes represent the 25
interquartile range; the whiskers indicate the minima and the
maxima of the measured data. The horizontal black lines
inside the boxes represent the median values and any data not
included between the whiskers is plotted as outliers with small
dots. From the statistics in Table II it can be found that the
mean deviation was 0% due to the initial calibration of the
sensor, the median was 2.3% and -0.6% for BIX and FIX,
respectively. The standard deviation was very similar and
about 5.8% for both indices. The deviation of the 5
for BIX was -9.2% and for FIX -10.2%, while the deviation
for the 95
percentile for BIX was 8.2% and 10.1% for FIX,
respectively. The results from the available samples show that
the typical deviation of most values measured with the
portable sensor for BIX and FIX was within ±10% compared
to the laboratory measurements and these results are being
supported by ongoing measurements. For applications to track
the DOM composition over time, the achieved accuracy of the
instrument is quite sufficient. Preliminary to its industrial
application, further efforts on the design of structure, circuits
and algorithms are needed to improve the issues related to
accuracy, sensitivity and calibration.
B. Sample storage time
In general, the fluorescence activity of water samples
should be analyzed immediately after sampling because of the
biological activity changing the chemical composition.
Moreover, degradation of photoactive substances over time
Fig. 5: Statistical representation of the deviation between the LS55
reference measurements and the measurements by the portable sensor of
BIX and FIX. All boxes represent the 25
to 75
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takes place [13]. Therefore, the fluorescence excitation and
emission spectrum of the water sample is changing over time.
The measurement differences between lab spectrometer and
portable sensor due to this effect were investigated. The FIX
and BIX indices were not calculated from absolute values but
from ratios (eq. 1, 2), so we expected at least short time
stability for FIX and BIX by storing the samples under cool
conditions. Therefore, collected water samples were stored at
4°C until analysis, for a maximum of 5 days to minimize
biological activity changes.
Fig. 6: Comparison of the deviation between the measured BIX (
) and FIX
(b) values of the portable sensor and the lab spectrometer LS55 (reference)
over storage time.
The samples were assessed by the LS55 and the portable
sensor each day and the deviations of the measured BIX and
FIX values were calculated (Fig. 6). From statistical analysis
(Kruskal-Wallis ANOVA on Ranks) no significant change
(p=0.87) was found between the measurement deviations from
day 1 to day 5 for BIX. For FIX a significant difference of
p<0.05 from day 1 to day 5 was observed. In conclusion, a 5-
day-storage interval of the water samples did not have an
effect on the BIX measurement error, whereas significant
impacts on the FIX could be evaluated. Therefore, the FIX
measurements should be done within one day after sampling
to avoid any measurement uncertainties.
C. Sample pre-filtration
Due to the progress in biological activity induced by
microorganisms in the water sample, the fluorescence indices
related to DOM are varying over time. Additionally, the
turbidity of the sample can strongly influence the fluorescence
measurements. If the turbidity of the water sample is high due
to a large amount of suspended particles, a pre-filtration of the
sample is recommended. Therefore, it was investigated
whether pre-filtering of the water sample has an effect on the
measured BIX or FIX values. The differences of the measured
FIX and BIX values in dependency on the pore size of the
used filter paper showed significant difference in most cases
(results not shown). In general, it can be recommended that for
a valid comparison of the measurement results for BIX and
FIX the same filtration step should be applied for all samples.
D. Changes in ambient temperature to the portable sensor
The portable sensor system was designed for rapid
fluorescence measurements of water samples in outdoor
scenarios. In order to investigate whether the measurement
accuracy changes dependent on of the ambient temperature,
the sensor was placed into a variable temperature cabinet for
several hours until all components of the device had reached
the target temperature.
Fig. 7: The portable sensor was exposed to different ambient temperatures and
the measurement of BIX and FIX compared to RT.
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The target temperatures were chosen to be 5°C, 10°C, 22°C
(room temperature, RT) or 35°C to represent typical outdoor
temperatures. At each temperature cycle four different water
samples were investigated. In all cases, the tempered
instrument was challenged with water samples at RT and the
FIX and BIX measurement data were compared with the
results from the lab spectrometer. The reference temperature
for all measurements was RT. In Fig. 7 the deviations of BIX
and FIX between the portable sensor and the lab spectrometer
at different ambient temperatures are shown. The statistical
analysis showed that there was a significant deviation for BIX
only at an ambient temperature of 5°C between lab
spectrometer and portable sensor. At ambient temperatures of
10°C and 35°C no significant deviations were detected. The
deviations of FIX values were also compared to RT. Here, an
exposure of the portable sensor to ambient temperatures of
35°C and 5°C resulted in a significant deviation of the FIX
measurements. Therefore, it can be concluded that the portable
sensor should be used at ambient temperatures between 10°C
and 22°C to ensure sufficient operation.
We assume that the main reason for the differences in FIX
and BIX measurements between the portable sensor and the
LS55 lab spectrometer are caused by the designs of the
instruments. The lab spectrometer is equipped with very
narrowband optical filters (5 nm) for the excitation as well as
for the emission beam (2.5 nm; see Table I), respectively. The
portable sensor uses commercial optical filter plates with a
FWHM bandwidth of 10 nm and, therefore, the excitation
bandwidth and the integration bandwidth for the emitted light
differ from that of the lab spectrometer. This results in
different BIX and FIX values, because the measured
fluorescence intensity depends on the observation bandwidth
due to the slope of the fluorescence spectrum (Fig. 4).
Furthermore the light source intensity and wavelength
characteristics vary over time. To correct this, in lab
spectrometers a beam splitter is applied after the excitation
monochromator (filter) to direct a portion of the light to a
reference detector. This compensation method is not used in
our portable instrument and reduces the accuracy therefore.
Based on the results, there are three important findings,
which have to be considered for accurate measurements with
the portable sensor in outdoor scenarios. First, the preparation
of the water samples should be done in the same manner. Pre-
filtration is recommended, but filters having equal pore size
should be used for all samples. Secondly, the samples should
be analyzed immediately after sampling, because the
biological and chemical activity can change the composition
over time. Third: The portable sensor should be used at
ambient temperatures between 10°C and 22°C for minimal
measurement uncertainty.
However, it must also be taken into account that the costs of
the portable instrument are several times lower than those of
the laboratory spectrometers. Despite certain disadvantages in
the precision of the measurements compared to laboratory
spectrometers, field sensors also have several advantages. For
example, field sensors give immediate feed-back on certain
fluorescence properties of water samples and, thus, may help
scientists to improve the sampling design as to spatial and
temporal resolution. Portable field sensors may come into
operation in research institutes, which are not equipped with
fluorescence spectrophotometers. Lastly they can be used by
water authorities to gain insight into changes in DOM quality
and possible threats to water quality due to organic pollution
in monitoring schemes.
The authors would like to thank the NOE Forschungs- und
Bildungsges.m.b.H. (NFB) for funding the research project
(project ID: SC15-002).
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Sensors Journal
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Dr. Martin Brandl received the
Dipl.-Ing. degree in Communication
Engineering and the Dr. techn. degree
from Vienna University of Technology
(Austria) in 1997 and 2001,
respectively. From 1998–2001, he was
Research Assistant at the Department of
Industrial Electronics and Material
Science at Vienna University of Technology, where he worked
on new modulation schemes for robust wireless data
transmission systems. In 2001, he moved to the Danube
University Krems and led until 2013 a research group for
Biomedical Electronics. Since 2014 he is head of the research
group “Water- and Environmental Sensors” at the Department
for Integrated Sensor Systems. He has been engaged in the
research and development of novel devices for extracorporeal
blood purification systems, optical and biomedical sensors.
His current research interests are focused on electrochemical
and optical sensors for water and environmental sensor
Dr. Brandl is member of IEEE Communications Society,
IEEE Sensors Council and IEEE Environmental Engineering
Thomas Posnicek graduated 2001 in
electronics engineering at the Federal
Higher Technical Institute for Education
and Experimenting St. Poelten. In October
2002 he joined the Danube University
Krems, Austria as a system engineer at the
electronic R&D group at the Center for
Biomedical Technology. Since 2014 he is
working at the Department for Integrated
Sensor Systems at the Danube University Krems as an
engineer in the field of electronics. His interests are in the
areas of sensor development, microfluidics, signal processing,
wireless systems and circuit design.
Raphael Preuer BSc, was with the Department of
Integrated Sensor Systems at Danube University Krems as
project coworker.
Dr. Gabriele Weigelhofer received the
MSc. degree in zoology in in 1993 and the
PhD degree in limnology in 2002, both
from the University of Vienna, Austria.
She habilitated in limnology at the
University of Natural Resources and Life
Science Vienna (BOKU) in 2019 and is
currently employed there as senior
She worked as senior scientist at the University of Vienna
from 2004 to 2008, and at the Wasser-Cluster Lunz from 2006
to 2017. Since 2017, she is the leader of the BOKU group
“Biogeochemistry and Ecohydrology of Riverine Ecosystems”
there. Her research interests include organic carbon and
nutrient processes in headwaters and floodplains and the
management of river ecosystems.
Dr. Weigelhofer is a member of ASLO and SIL.
... Portable sensors can be used to monitor CDOM indicators rapidly and in real time to characterize their concentrations, sources, and compositions. For example, CDOM fluorescence sensors have been increasingly used in the field monitoring of freshwater and marine ecosystems to rapidly determine the absorption coefficient, absorption slope, and fluorescence indexes to identify organic pollution (Henderson et al., 2009;Ruhala and Zarnetske, 2017;Carstea et al., 2020;Brandl et al., 2020). ...
... CDOM is an important parameter closely linked to nutrients in quantic environments , and portable sensors for the rapid measurement of CDOM parameters have been widely used in the field monitoring of freshwater and marine ecosystems (Ruhala and Zarnetske, 2017;Carstea et al., 2020;Brandl et al., 2020). Therefore, it is meaningful to use CDOM parameters to predict water quality, especially TN and TP, etc., which are difficult to measure in situ at a high frequency and immediately in the environment (Zhang et al., 2018b). ...
Chromophoric dissolved organic matter (CDOM) in rivers is mainly affected by natural conditions and human activities and can reflect the watershed pollution status to a certain extent. The Yellow River is one of the largest contributors to the global riverine sediment flux from the land to ocean, and there is a paucity of information on how the optical properties of CDOM have the potential to serve as an indicator of water quality for the sediment-laden Yellow River. In this study, a three-dimensional fluorescence parallel factor (PARAFAC) analysis method was applied to investigate the seasonal and spatial variations in CDOM fluorescence components and spectral characteristics from the source region to the estuary in the mainstream of Yellow River. The relationships of CDOM with water quality indicators and trophic state were also analyzed. Six PARAFAC components (C1–C6) were identified and grouped into two categories: humic-like components (C1–C4), which accounted for 85.8 %, and protein-like components (C5 and C6), which accounted for only 14.2 %. The CDOM components, spectral parameters, and their clear correlations with the main ions (Na⁺ and Cl⁻) all indicated that the humic-like components may be primarily derived from nonpoint source erosion, and the protein-like components were mainly derived from point source discharges in the watershed. The combination of the CDOM absorption coefficient at 254 nm (a(254)), spectral slope ratio (SR), specific UV absorbance SUVA254, and fluorescence index (FI) had a good predictive ability for the key water quality indicators (total nitrogen (TN), dissolved total nitrogen (DTN), total phosphorus (TP), dissolved total phosphorus (DTP), and chlorophyll a (Chl a)) and trophic state index (TSI). Therefore, some fluorophores and UV spectral parameters of CDOM in Yellow River can be used for rapid water quality monitoring and pollution source indication, especially pollutants related to nitrogen and phosphorus nutrients in the basin.
... Brandl et al. developed a portable fluorometer for detecting DOM and three different green algae by using different excitation LEDs [94]. The fluorometer has three different wavelengths of excitation LEDs (254, 310, and 370 nm) with two bandpass filters (370 and 310 nm). ...
... Portable DOM fluorescence sensors: (a) a portable fluorescence sensor for DOM measurement using fluorescence index (FIX) and biological index (BIX)[94]; (b) a portable LED fluorescence instrumentation for the rapid measurement of TOC[95]; (c) a light-weight laser-based fluorometer for monitoring chlorophyll a and CDOM[96], and (d) a miniature fluorometer is designed to measure chlorophyll and CDOM concentration in the aquatic environment (after[97]). Licensed under a CC BY license for (a) and (b), and copyright 2015 with permission from Elsevier for (c). ...
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Portable fluorescence sensors have been developed for biochemical detection, water quality monitoring, biomedical sensing, and many other applications. With help of advancement in modern electronics, conventional fluorescence-based instrumentations are now integrated into portable sensing devices for remote and resource-limited settings. In this work, fluorescence sensing technology is introduced and different applications of portable fluorescence sensors and their characteristics are reviewed. Current issues, technological challenges, and future direction of the portable fluorescence sensor development are discussed. The goal is to provide a comprehensive survey on the recent advancements in optics, semiconductors, smartphones, and many other manufacturing technologies that increased the portability, miniaturization, and sensitivity of portable fluorescence sensor devices.
Fluorescence quantitative analysis methods are extensively used in biomedicine inspection, petrochemical industry, environmental monitoring, and many other fields in the past decades. When the analyte is composed of multiple compositions, the accuracy of the conventional method declines significantly due to the fluorescence spectral crosstalk. In this research, the interactions between the light and the multiple compositions are comprehensively analyzed. The concepts of the quenching due to mutual absorption and the fluorescence overlapping are considered and the mechanism of multi-composition fluorescence emission under single-wavelength excitation light is analyzed theoretically. The mixture experiment and the dilution experiment are designed to illustrate that the quenching due to mutual absorption has a significant nonlinear impact on fluorescence quantitative analysis and the mechanism of fluorescence spectral crosstalk gives a good explanation for these experiments. Through the in-depth theoretical analysis, the computer simulation, and the experiments, a novel principle named the Crosstalk Fluorescence Spectroscopy Analysis (CFSA) is proposed and verified, which has much higher quantitative analysis accuracy (R2>0.99 and RMSE≤0.2) than the conventional methods when analyzing the multi-composition samples. Unlike many correction approaches to fluorescence spectroscopy, the novel CFSA can serve as a complete analysis method rather than a correction method. These concepts and the principle are expected to be applied in many practical analysis fields.
On-site analysis can provide qualitative or/and quantitative information of samples at the sample location. Up to date, a variety of portable instruments have been available for on-site analysis, some of which have even been commercialized. For an on-site analysis, all the steps including sampling, sample preparation, separation/non-separation, detection and data processing must be carried out on-site. Portable GC and GC-MS have been widely used in on-site environmental sample analysis. Moreover, there are many types of optical instruments based on absorption, emission or scattering have also been used for field analysis, each with its own advantages and application capabilities. Due to the limits of detection and selectivity of instruments being used, sample preparation is used in most applications. This review summarizes the current advances of on-site sample preparation devices, on-site GC/GC-MS, and on-site optical instruments in detail. Finally, the future development direction of these field instruments is also discussed.
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We assessed the utility of online fluorescence spectroscopy for the real-time evaluation of the microbial quality of untreated drinking water. Online fluorimeters were installed on the raw water intake at four groundwater-derived UK public water supplies alongside existing turbidity sensors that are used to forewarn of the presence of microbial contamination in the water industry. The fluorimeters targeted fluorescent dissolved organic matter (DOM) peaks at excitation/emission wavelengths of 280/365 nm (tryptophan-like fluorescence, TLF) and 280/450 nm (humic-like fluorescence, HLF). Discrete samples were collected for Escherichia coli, total bacterial cell counts by flow cytometry, and laboratory-based fluorescence and absorbance. Both TLF and HLF were strongly correlated with E. coli (ρ = 0.71-0.77) and total bacterial cell concentrations (ρ = 0.73-0.76), whereas the correlations between turbidity and E. coli (ρ = 0.48) and total bacterial cell counts (ρ = 0.40) were much weaker. No clear TLF peak was observed at the sites and all apparent TLF was considered to be optical bleed-through from the neighbouring HLF peak. Therefore, a HLF fluorimeter alone would be sufficient to evaluate the microbial water quality at these sources. Fluorescent DOM was also influenced by site operations such as pump start-up and the precipitation of cations on the sensor windows. Online fluorescent DOM sensors are a better indicator of the microbial quality of untreated drinking water than turbidity and they have wide-ranging potential applications within the water industry.
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Dissolved organic matter (DOM) plays a key role in global biogeochemical cycles and experiences changes in molecular composition as it undergoes processing. In the semi-closed basins of the oligotrophic Mediterranean Sea, these gradual molecular modifications can be observed in close proximity. In order to extend the spatial resolution of information on DOM molecular composition available from ultrahigh resolution mass spectrometry in this area, we relate this data to optical (fluorescence and absorption spectroscopy) measurements. Covariance between molecular formulae signal intensities and carbon-specific fluorescence intensities was examined by means of Spearman’s rank correlations. Fifty two per cent of the assigned molecular formulae were associated with at least one optical parameter, accounting for 70% of the total mass spectrum signal intensity. Furthermore, we obtained significant multiple linear regressions between optical and intensity-weighted molecular indices. The resulting regression e
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Advances in spectroscopic techniques have led to an increase in the use of optical properties (absorbance and fluorescence) to assess dissolved organic matter (DOM) composition and infer sources and processing. However, little information is available to assess the impact of biological and photolytic processing on the optical properties of original DOM source materials. Over a 3.5 month laboratory study, we measured changes in commonly used optical properties and indices in DOM leached from peat soil, plants, and algae following biological and photochemical degradation to determine whether they provide unique signatures that can be linked to original DOM source. Changes in individual optical parameters varied by source material and process, with biodegradation and photodegradation often causing values to shift in opposite directions. Although values for different source materials frequently overlapped, multivariate statistical analyses showed that unique optical signatures could be linked to original DOM source material, with 17 optical properties determined by discriminant analysis to be significant (p<0.05) in distinguishing between DOM source and environmental processing. These results demonstrate that inferring source material from optical properties is possible when parameters are evaluated in combination even after extensive biological and photochemical alteration.
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Absorbance, 3D fluorescence and ultrahigh resolution electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry (ESI-FT-ICR-MS) were used to explain patterns in the removal of chromophoric and fluorescent dissolved organic matter (CDOM and FDOM) at the molecular level during drinking water production at four large drinking water treatment plants in Sweden. When dissolved organic carbon (DOC) removal was low, shifts in the dissolved organic matter (DOM) composition could not be detected with commonly used DOC-normalized parameters (e.g. specific UV254 absorbance - SUVA), but was clearly observed by using differential absorbance and fluorescence or ESI-FT-ICR-MS. In addition, we took a novel approach by identifying how optical parameters were correlated to the elemental composition of DOM by using rank correlation to connect optical properties to chemical formulas assigned to mass peaks from FT-ICR-MS analyses. Coagulation treatment selectively removed FDOM at longer emission wavelengths (450-600 nm), which significantly correlated with chemical formulas containing oxidized carbon (average carbon oxidation state ≥0), low hydrogen to carbon ratios (H/C: average ± SD = 0.83 ± 0.13), and abundant oxygen-containing functional groups (O/C = 0.62 ± 0.10). Slow sand filtration was less efficient in removing DOM, yet selectively targeted FDOM at shorter emission wavelengths (between 300 and 450 nm), which commonly represents algal rather than terrestrial sources. This shorter wavelength FDOM correlated with chemical formulas containing reduced carbon (average carbon oxidation state ≤0), with relatively few carbon-carbon double bonds (H/C = 1.32 ± 0.16) and less oxygen per carbon (O/C = 0.43 ± 0.10) than those removed during coagulation. By coupling optical approaches with FT-ICR-MS to characterize DOM, we were for the first time able to confirm the molecular composition of absorbing and fluorescing DOM selectively targeted during drinking water treatment. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.
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Characterising the organic and microbial matrix of water are key issues in ensuring a safe potable water supply. Current techniques only confirm water quality retrospectively via laboratory analysis of discrete samples. Whilst such analysis is required for regulatory purposes, it would be highly beneficial to monitor water quality in-situ in real time, enabling rapid water quality assessment and facilitating proactive management of water supply systems.A novel LED-based instrument, detecting fluorescence peaks C and T (surrogates for organic and microbial matter, respectively), was constructed and performance assessed. Results from over 200 samples taken from source waters through to customer tap from three UK water companies are presented. Excellent correlation was observed between the new device and a research grade spectrophotometer (r2=0.98 and 0.77 for peak C and peak T respectively), demonstrating the potential of providing a low cost, portable alternative fluorimeter. The peak C/TOC correlation was very good (r2=0.75) at low TOC levels found in drinking water. However, correlations between peak T and regulatory measures of microbial matter (2day/3day heterotrophic plate counts (HPC), E. coli, and total coliforms) were poor, due to the specific nature of these regulatory measures and the general measure of peak T. A more promising correlation was obtained between peak T and total bacteria using flow cytometry. Assessment of the fluorescence of four individual bacteria isolated from drinking water was also considered and excellent correlations found with peak T (Sphingobium sp. (r2=0.83); Methylobacterium sp. (r2=1.0); Rhodococcus sp. (r2=0.86); Xenophilus sp. (r2=0.96)). It is notable that each of the bacteria studied exhibited different levels of fluorescence as a function of their number. The scope for LED based instrumentation for in-situ, real time assessment of the organic and microbial matrix of potable water is clearly demonstrated.
This work aims to correlate signals of LED UV/fluorescence sensor with the degradation of dissolved organic matter (DOM) and trace-level organic contaminants (TOrCs) during ozonation process. Six sets of bench-scale ozonation kinetic experiments incorporated with three different water matrices and 14 TOrCs of different reactivity (group I ∼ V) were conducted. Calibrated by tryptophan and humic substances standards and verified by the lab benchtop spectroscopy, the newly developed portable/online LED sensor, which measures the UV280 absorbance, protein-like and humic-like fluorescence simultaneously, was feasible to monitor chromophores and fluorophores with good sensitivity and accuracy. The liquid chromatography with organic carbon detector combined with 2D synchronous correlation analysis further demonstrated how the DOM components of large molecular weight were transformed into small moieties as a function of the decrease of humic-like fluorescence. For TOrCs, their removal rates were well correlated with the decrease of the LED UV/fluorescence signals, and their elimination patterns were mainly determined by their reactivity with O3 and hydroxyl radicals. At approximately 50% reduction of humic-like fluorescence almost complete oxidation of TOrCs of group I and II was reached, a similar removal percentage (25–75%) of TOrCs of group III and IV, and a poor removal percentage (<25%) of group V. This study might contribute to the smart control of advanced oxidation processes for the water and wastewater treatment in the future.
Online monitoring dissolved organic matter (DOM) is urgent for water treatment management. In this study, high performance size exclusion chromatography with multi-UV absorbance and multi-emission fluorescence scans were applied to spectrally characterize samples from 16 drinking water sources across Yangzi River and Huai River Watersheds. The UV absorbance indices at 254 nm and 280 nm referred to the same DOM components and concentration, and the 280 nm UV light could excite both protein-like and humic-like fluorescence. Hence a novel UV fluorescence sensor was developed out using only one UV280 light-emitting diode (LED) as light source. For all samples, enhanced coagulation was mainly effective for large molecular weight biopolymers; while anion exchange further substantially removed humic substances. During chlorination tests, UVA280 and UVA254 showed similar correlations with yields of disinfection byproducts (DBPs); the humic-like fluorescence obtained from LED sensors correlated well with both trihalomethanes and haloacetic acids yields, while the correlation between protein-like fluorescence and trihalomethanes was relatively poor. Anion exchange exhibited more reduction of DBPs yields as well as UV absorbance and fluorescence signals than enhanced coagulation. The results suggest that the LED UV fluorescence sensors are very promising for online monitoring DOM and predicting DBPs formation potential during water treatment.
The purpose of this chapter is to acquaint the reader with the importance of biochemical processes in organic geochemistry. Unfortunately, it is not possible to explain in detail all of the biochemical processes that affect organic solutes. Therefore, this chapter introduces basic concepts of biochemical processes. First, the chapter discusses the general decomposition of organic carbon, which is a major biogeochemical pathway in natural systems. The chemical processes of life put together amino acids, carbohydrates, and fatty acids to build specific compounds, such as proteins, polysaccharides, and lipids. When the death of an organism occurs, then the biochemical processes of decay and decomposition take over, and an entirely different suite of fragmented compounds occur. The general decomposition of organic carbon is a broad view of this complicated process.