ArticlePDF Available

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.
Content may be subject to copyright.
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/JSEN.2020.2988588, IEEE
Sensors Journal
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
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/JSEN.2020.2988588, IEEE
Sensors Journal
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).
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/JSEN.2020.2988588, IEEE
Sensors Journal
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
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/JSEN.2020.2988588, IEEE
Sensors Journal
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.
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/JSEN.2020.2988588, IEEE
Sensors Journal
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
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/JSEN.2020.2988588, IEEE
Sensors Journal
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.
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/JSEN.2020.2988588, IEEE
Sensors Journal
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).
[1] J. H. Reuter, E. M. Perdue, “Importance of heavy metal-organic matter
interactions in natural waters.” Geochimica et Cosmochimica Acta, vol.
41(2), pp. 325-334, 1977.
[2] E. M. Thurman, Organic Geochemistry of Natural Waters, Kluwer
[3] J. I. Hedges, “Global biogeochemical cycles: Progress and problems,”
Mar. Chem. vol. 29, pp. 67–93, 1992.
[4] A. Piccolo, et al. “Reduced heterogeneity of a lignite humic acid by
preparative HPSEC following interaction with an organic acid.
Characterization of size-separates by Pyr-GC-MS and 1H-NMR
spectroscopy,” Environ. Sci. Technol., vol. 36(1), pp. 76-84, 2002.
[5] A. J. Leenheer, J. P. Croué, “Peer reviewed: characterizing aquatic
dissolved organic matter.” Environ. Sci. Technol., vol. 37(1), pp. 18A-
26A 2003.
[6] E. M. Thurman, Developments in biogeochemistry: organic
geochemistry of natural waters. Martin Nijhoff Junk, Dordrecht 1985.
[7] D. Graeber , J. Gelbrecht, M. T. Pusch, et al “Agriculture has changed
the amount and composition of dissolved organic matter in Central
European headwater streams,” Sci Tot Environ, no. 438, pp. 435–446,
[8] A. Baker, R. Inverarity, M. Charlton, et al “Detecting river pollution
using fluorescence spectrophotometry: case studies from the Ouseburn,”
NE England. Environ Poll, no. 124, pp. 57–70, 2003.
[9] W. Chen, P. Westerhoff, C. A. Leenheer, et al “Fluorescence Excitation
- Emission Matrix Regional Integration to Quantify Spectra for
Dissolved Organic Matter,” Environ Sci Technol, no. 37, pp. 5701–5710,
[10] N. Hudson, A. Baker, D. Reynolds, “Fluorescence analysis of dissolved
organic matter in natural, waste, and polluted waters-a review,” Riv Res
Appl, no. 23, pp. 631-649, 2007.
[11] J. L. Weishaar, M. S. Fram, R. Fujii, et al “Evaluation of Specific
Ultraviolet Absorbance as an Indicator of the Chemical Composition and
Reactivity of Dissolved Organic Carbon,” Environ Sci Technol, no. 37,
4702–4708, 2003.
[12] J. R. Helms, A. Stubbings, J. D. Ritchie, et al “Absorption spectral
slopes and slope ratios as indicators of molecular weight, source, and
photobleaching of chromophoric dissolved organic matter,” Limnol
Oceanogr, vol. 53, pp. 955–969, 2008.
[13] P. G. Cobble, J. Lead, A. Baker, et al Aquatic organic matter
fluorescence, Cambridge University Press, NY, 2014.
[14] A. Baker, R. Inverarity, M. Charlton, et al. “Detecting river pollution
using fluorescence spectrophotometry: case studies from the Ouseburn,”
NE England. Environ Poll, vol. 124, pp. 57–70, 2003.
[15] N. Hudson, A. Baker, D. Reynolds, “Fluorescence analysis of dissolved
organic matter in natural, waste, and polluted waters-a review,” Riv Res
Appl vol. 23, pp. 631-649, 2007.
[16] J.P.R. Sorensen, A. Vivanco, M.J. Ascott, D.C. Gooddy, D.J. Lapworth,
D.S. Read, C.M. Rushworth, J. Bucknall, K. Herbert, I. Karapanos, L.P.
Gumm, R.G. Taylor, “Online fluorescence spectroscopy for the real-
time evaluation of the microbial quality of drinking water,” Water
Research, vol. 137, pp. 301-309, 2018.
[17] D. N. Kothawala, E. Wachenfeldt, B. Koehler, L. J. Tranvik, “Selective
loss and preservation of lake water dissolved organic matter
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/JSEN.2020.2988588, IEEE
Sensors Journal
fluorescence during long-term dark incubations,” Science of The Total
Environment, vol. 433, pp. 238-246, 2012.
[18] P. G. Coble, S. A. Green, N. V. Blough, R. B. Gagosian,
“Characterization of dissolved organic matter in the Black Sea by
fluorescence spectroscopy,” Nature, vol. 348, pp. 432–435, 1990.
[19] A. M. Martínez-Pérez, et al. “Linking optical and molecular signatures
of dissolved organic matter in the Mediterranean Sea,Scientific reports,
vol. 7.1, pp. 3436, 2017.
[20] R.K. Henderson, A. Baker, K.R. Murphy, A. Hambly, R.M. Stuetz, S.J.
Khan, “Fluorescence as a potential monitoring tool for recycled water
systems: a review.” Water research, 43(4), pp. 863-881 2009.
[21] W.T. Li, J. Jin, Q. Li, C.F. Wu, H. Lu, Q. Zhou, A.M. Li, “Developing
LED UV fluorescence sensors for online monitoring DOM and
predicting DBPs formation potential during water treatment.” Water
research, vol. 93, pp. 1-9, 2016.
[22] W.T. Li, M. Majewsky, G. Abbt-Braun, H. Horn, J. Jin, Q. Li, A.M. Li,
“Application of portable online LED UV fluorescence sensor to predict
the degradation of dissolved organic matter and trace organic
contaminants during ozonation.” Water research, vol. 101, pp. 262-271,
[23] E.E. Lavonen, D.N. Kothawala, L.J. Tranvik, M. Gonsior, P. Schmitt-
Kopplin, S.J. Köhler, “Tracking changes in the optical properties and
molecular composition of dissolved organic matter during drinking
water production.” Water Research, 85, pp. 286-294, 2015.
[24] M. Tedetti, P. Joffre, M. Goutx, “Development of a field-portable
fluorometer based on deep ultraviolet LEDs for the detection of
phenanthrene-and tryptophan-like compounds in natural waters.”
Sensors and Actuators B: Chemical, 182, pp. 416-423, 2013.
[25] J. Bridgeman, A. Baker, D. Brown, J.B. Boxall, “Portable LED
fluorescence instrumentation for the rapid assessment of potable water
quality.” Science of the Total Environment, 524, 338-346, 2015.
[26] A.M. Hansen, T.E. Kraus, B.A. Pellerin, J.A. Fleck, B.D. Downing,
B.A. Bergamaschi, “Optical properties of d issolved organic matter
(DOM): Effects of biological and photolytic degradation,” Limnology
and Oceanography, vol. 61(3), pp. 1015-1032, 2016.
[27] E. Parlanti, K. Wörz, L. Geoffroy, M. Lamotte, “Dissolved organic
matter fluorescence spectroscopy as a tool to estimate biological activity
in a coastal zone submitted to anthropogenic inputs,” Organic
geochemistry, vol. 31(12), pp. 1765-1781, 2000.
[28] R. M. Cory, D. M. McKnight, “Fluorescence spectroscopy reveals
ubiquitous presence of oxidized and reduced quinones in dissolved
organic matter,” Environ. Sci. Technol., vol. 39(21), pp. 8142-8149,
[29] T. Posnicek, G. Weigelhofer, A. Eder, M. Brandl, “Highly Integrated
and Mobile Sensor System for Dissolved Organic Matter in Stream
Ecosystems,” in Multidisciplinary Digital Publishing Institute
Proceedings, vol. 2, no. 13, pp. 1507, 2018.
[30] J. L. Weishaar, G.R. Aiken, B. A. Bergamaschi, M. S. Fram, R. Fujii,
“Evaluation of specific ultra-violet absorbance as an indicator of the
chemical composition and reactivity of dissolved organic carbon,”
Environ. Sci. Technol., 37, pp. 4702–4708, 2003.
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.
... As is well known, many substances have endogenous fluorophores-or can be conjugated to a fluorescent reagent-and when activated by excitation light at a specific wavelength, emit fluorescence [2]. Thus, FQAM has been applied in various fields, including pharmacology [3], biology [4], physiology [5,6], and environmental sciences [7][8][9]. The measuring devices used for this application include fluorescent illuminometer (FI) [4], fluorescence spectrometer (FS) [1,7] and other special instruments [3,8,9]. ...
... Thus, FQAM has been applied in various fields, including pharmacology [3], biology [4], physiology [5,6], and environmental sciences [7][8][9]. The measuring devices used for this application include fluorescent illuminometer (FI) [4], fluorescence spectrometer (FS) [1,7] and other special instruments [3,8,9]. Although these devices have shown remarkable capabilities in analyzing small sample volumes with high sensitivity and low detection limits [10,11], their adoption has been limited outside of research laboratories, especially in in situ detection. ...
... Thus, several self-developed devices have been proposed for online or in vivo application. For instance, Martin Brandl et al. developed a portable fluorescence sensor system for the analysis of dissolved organic matter (DOM) [8]. Li Wanxiang et al. built a detection scheme using a linear CCD for simultaneous measurement of absorbance and fluorescence of chlorophyll-a [9]. ...
Full-text available
We developed an extensible LED-induced fluorescence detection module with a highly integrated and ultra-compact structure. A target-oriented design methodology was used to demonstrate the module’s optimal design. Lucigenin solution was used as a test sample in evaluation trials to demonstrate the module’s quantitative fluorescence detection capability. Results showed that the integrated module has an outstanding linear response in the range of 0–1 μmol·L−1, with sensitivity and limit of detection (LOD) of 0.1692 V/μmol·L−1 and 0.03 μmol·L−1, respectively. Statistical analyses showed that our integrated module has extremely high repeatability and accuracy, i.e., the values of Pearson’s correlation coefficient and root-mean-square error exceeded 0.9995 and 1.8‰, respectively. More importantly, the integrated module possesses favorable extensibility and can realize on-demand rapid fluorescence-signal detection of other targets using appropriate parameter combinations. This module offers new opportunities for reliable, cost-effective and easy-to-use fluorescence-signal detection, especially in resource-constrained fluorescence detection applications.
... Fluorescence sensing-based optical detection technologies have gained significant prominence as a crucial approach for quantitative and qualitative analysis of biochemicals due to their attributes of sensitivity, specificity, and accuracy [1][2][3]. Their extensive utilization is particularly notable in the field of environmental monitoring [4][5][6][7][8]. Owing to the rapid advancement in industry and agriculture, the escalation of water contamination-induced eutrophication has emerged as a pressing concern. ...
... Furthermore, the required instrumentation employed for fluorometric measurements has evolved in recent years from static designs mainly focused on laboratory assays towards more flexible, portable and handheld devices [10][11][12][13][14][15][16], which can be easily operated for in situ experiments. Thus, the increasing availability of the necessary components to build fluorometric detection devices, such as intense light sources with different excitation wavelengths (LEDs, fiber optic) [17,18] and compact micro-spectrometers for light analysis (C1280 MA) [19], and the existence of numerous computer-programmable microcontrollers (Raspberry Pi, Arduino) [20][21][22][23] allow the building of low-cost equipment [24][25][26] with unique features for in situ sample analysis with spectrofluorometric detection. ...
Full-text available
There is a growing need for portable, highly sensitive measuring equipment to analyze samples in situ and in real time. For these reasons, it is becoming increasingly important to research new experimental equipment to carry out this work with advanced, robust and low-cost devices. In this framework, a flexible, portable and low-cost fluorimeter (under EUR 500), based on a C12880 MA MEMS micro-spectrometer with an Arduino compatible breakout board, has been developed for the trace analysis of biological substances. The proposed system can employ two selectable excitation sources for flexibility, one in the visible region at 405 nm (incorporated in the board) and an external LED at 365 nm in the UV region. This additional excitation source can be easily interchanged, varying the LED type for investigating any fluorophore compound of interest. The measurement process is micro-controlled, which allows the precise control of the spectrometer sensitivity by adjusting the integration time of each experiment separately. Data acquisition is easy, reliable and interfaced with a spreadsheet for fast spectra visualization and calculations. For testing the performance of the new device in fluorescence measurements, different fluorophore molecules which can be commonly found in biological samples, such as Fluorescein, Riboflavin, Quinine, Rhodamine b and Ru (II)-bipyridyl, have been employed. A high sensitivity and low quantitation limits (in the ppb range) have been found in all cases for the investigated chemicals. The portable device is also suitable for the study of other interesting phenomena, such as fluorescence quenching induced by chemical agents (such as halide anions or even auto-quenching). In this sense, an application for the quantification of chloride anions in aqueous solutions has been performed obtaining a LOD value of 18 ppm. The obtained results for all chemicals investigated with the proposed fluorimeter are always very similar in quantification figures, or even better than the data reported in literature, when using commercial laboratory equipment.
Full-text available
In this paper a novel low-cost multi-spectral optical fluorometer is presented and evaluated. The device uses a range of LEDs in the blue and violet regions of the electromagnetic spectrum and a mini-spectrometer to detect the emitted fluorescence in the UV to IR spectrum region. Custom built electronics and software were designed to control the system and the components were housed in bespoke 3D printed parts. A number of known fluorophores were tested to determine the capabilities of the fluorometer. Application of the device is demonstrated for the detection of chlorophyll a (Chl a) from laboratory grown algae and from environmental samples while analytical performance is established using both in vivo and extracted Chl a fluorescence and by comparison with a benchtop fluorometer.
Full-text available
One of the most significant and serious issues currently affecting mankind is the degradation of natural water resources, such as rivers and lakes. Polluted water has longterm repercussions on all facets of existence. In order to maximise your water quality, it is crucial to manage your water resources. The impacts of water contents can be efficiently managed if data are analysed and water quality can be forecasted.This study’s objective is to develop a model for predicting quality of water is based on measurements of water quality using machine learning. With some data obtained through machine learning, models made of algorithms can be created. The collected data will be preprocessed, divided into training and testing portions, and exposed to machine learning classification techniques for a better assessment of parametric findings. Some of the classification type techniques used in this work are Decision Tree, LinearSVC, Random Forest, GradientBoosting, SGD, and KNeighbour. Each model’s performance indicators are computed and are different from one another. Hyper tuning is a method for raising perfor- mance metrics for models of machine learning.
When performing fluorescence measurement on a fluorescent analyte over an ultra-wide concentration range, the fluorescence intensity distribution is affected by the spatial attenuation effect of excitation light, causing the linear relationship between the received fluorescence intensity and the fluorophore concentration to no longer hold. In this work, based on the imaging analysis of fluorescence intensity spatial distribution, the spatial invariant ( K ) is defined in the form of relative fluorescence intensity, and the other spatial invariant (μ) is given in the form of spatial differential accordingly. The use of the spatial invariants instead of the absolute fluorescence intensity commonly used to reflect the fluorophore concentration can get rid of the dependence of geometrical parameters, and can also avoid systematic errors caused by unstable measurement conditions. Taking these spatial invariants as parameters has great advantages in ultra-wide range of fluorescence measurement (0.02 ~ 125.00 mg/L ) and produces more accurate concentration results ( R <sup xmlns:mml="" xmlns:xlink="">2</sup> = 0.9975, RRMSE = 0.0510).
Most organic pollutants analysis and detection in water using three-dimensional fluorescence spectroscopy usually rely on the comparison of new samples with a given set of benchmark pollutants. In our real world, however, tested samples collected from in-situ monitoring system may contain unknown organic pollutants. The conventional classification approaches were forced to choose from one of the benchmarks, which may lead to poor detection performance. In this paper, an open-set recognition approach was proposed using the wavelength-coding feature extraction network incorporated into extreme value machine (EVM) to overcome the shortages. Convolutional neural network (CNN) coupled with wavelength-coding module of spectra was designed to obtain the feature vectors from excitation-emission matrices (EEMs) data. The probability distribution for each known class can be achieved by training the EVM with the extracted feature vectors. New samples then can be identified as known or unknown according to the threshold. An online simulation pilot device was set up to simulate the in-situ monitoring conditions and verify the effectiveness of the proposed method. Twelve kinds of organic pollutants were collected and tested. Compared with conventional methods and other open-set methods, the experimental results showed that the proposed method achieved more precision regardless of whether the unknown pollutants appeared or not. The concept of open-set recognition in water quality detection and the proposed method described in this work can be applied to the online identification system with the increasing categories of pollutants, which has great potential to ensure the safety of drinking water.
In this work, we developed a portable multispectral fluorometer for determining formalin (FA). We used nitrogen-doped carbon dots (N-CDs) as the fluorescence probe, based on right-angle fluorescence spectrometry with twin excitation high-power light emitting diode sources. The multispectral spectroscopy sensor was used as a detector for fluorescence intensity. The fluorescence intensity values were displayed from 0 to 65535 a.u. The FA determination results show a linear relationship in the FA concentration range of 10-75 mg L−1 with r2 = 0.9908. The limit of detection (LOD) was 2.09 mg L−1 (calculated from 3SDblank/slope (n = 3)). In addition, the percentage of relative errors compared with the standard method and standard instrument shows less than 10 percent. The performance of a portable multispectral fluorometer in actual samples exhibited no significant difference compared to the validation instrument results. Therefore, the development of a portable multispectral fluorometer can be used as a fluorometer, and the measurement performance is comparable to a standard fluorescence spectrometer.
Full-text available
Portable applications of fluorescence detection systems have gained much attention in various fields and require system components to be small and compact. In this work, we report on a compact fluorescence detection system and demonstrate its application for fluorescence sensing and imaging. The light source and filter are integrated on a single chip for the proposed system, which not only realizes the separation between excitation and fluorescent lights but also improves the light-emitting diode (LED) light extraction efficiency. Furthermore, the detection system allows for a removable sample unit. The results indicate that the performance of the distributed Bragg reflector (DBR) filter based on an amorphous dielectric film is excellent with selection ratios larger than 4600:1. The peak emission wavelength of the LED is 528 nm. The influence of green light leakage can be neglected, and the fluorescent red light is dominant when the fluorescence detection system is used for sensing and imaging. The low-cost and monolithic DBR-integrated III-nitride LED chip makes the proposed architecture a competitive candidate for portable fluorescence detection applications.
Full-text available
The impact of agricultural land use on the composition of dissolved organic matter (DOM) and its effects on the aquatic carbon cycle are still largely unknown. A sensor for dissolved DOM in stream ecosystems based on fluorescence measurement was developed. It’s an easy to use handheld optical system for online monitoring of DOM under field-conditions. For the determination of DOM two indices are used, namely the freshness index (BIX) and the fluorescence index (FIX).
Full-text available
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.
Full-text available
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
Full-text available
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.
Full-text available
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.
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.