Introduction
Air quality is an important pre-requisite for public health. The pollution of
the air with gaseous compounds contributes relevantly to the burden of
disease in industrial and developing countries (Bernstein et al., 2008). One
of the most important pollutants is benzene (WHO Regional Office for Europe,
2010). Due to its toxicity and its carcinogenicity, very low concentrations
of benzene should be detected and monitored; threshold limits are in the ppb
range, e.g. the European Air Quality Directive set the limit value at
5 µg m-3 or 1.6 ppb as the long-term environmental limit
(European Parliament and Union, 2008). The benzene detection is also an
important topic for indoor air quality and workplace safety, where several
national regulations have been put in force, e.g. the Bundesanstalt für
Arbeitsschutz und Arbeitsmedizin (2014). For environmental monitoring,
analytic techniques, e.g. gas chromatography (GC), are used. In Europe,
monitoring of benzene in ambient air is mandatory. The European Air Quality
Directive states that the reference method for the measurement of benzene
must consist of active or online sampling followed by desorption and gas
chromatography (BSI, 2015). Due to the high price and maintenance costs of these methods, the number of points in
the measurement network is very limited, but the necessity for a higher
spatial resolution of pollution control has been reported (Batterman et al., 1994;
Heimann et al., 2015). Within the EMRP project KEY-VOCs, therefore, the use
of sensor systems as an indicative method for the measurement of benzene has
been tested in order to assess whether the demand for a low-cost measurement
device for benzene can be met. A review (Spinelle et al., 2017b) of the
existing sensor technology and its commercially available systems has
revealed that only very few manufacturers are targeting this concentration
range and that it is doubtful whether one of these systems can meet the
criteria of detection limit, selectivity and stability (Spinelle et al.,
2017a). Therefore, micro analytical systems and prototype sensor systems have
been included in the tests. The result of one prototype using metal oxide
semiconductor (MOS) gas sensors with temperature cycle operation (TCO) is
reported in this paper. This approach has previously been studied for the
selective detection of volatile organic compounds (VOCs), e.g. benzene in
indoor air (Leidinger et al., 2014; Schütze et al., 2017). MOS gas
sensors are very robust and sensitive devices (Morrison, 1981; Sasahara et
al., 2004) sensitive to a broad variety of reducing gases. The resistance of
the sensor (Eq. 1) is dominated by ionized oxygen at the surface which causes
an energy barrier EB and the height of the barrier depends in a
quadratic function on the density of the ionized oxygen NS.
G=G0⋅e-EBkBTwithEB∝NS2
The reaction of the reducing gas with the reactive surface oxygen reduces the
energy barrier and increases the conductance strongly. For constant
temperature and gas concentration the change of surface charge can be
described by a mass action law of chemisorbed species leading to a power law
for the dependence of conductance and gas concentration (Barsan and Weimar,
2001; Madou and Morrison, 1989). While in a few cases the selectivity of the
sensors can be increased by special preparation methods for example described
in Hennemann et al. (2012), Kemmler et al. (2012), and Leidinger et
al. (2016b), typically multi-signal methods like TCO are used. TCO is a
well-known method for the improvement of selectivity reported in a multitude
of papers, e.g. Eicker (1977), Gramm and Schütze (2003), and Lee and
Reedy (1999). It is dynamic operation (Nakata et al., 1998a, b) and in this
sense it enables sensor properties that cannot be found in a sensor at any
constant temperature. Following this line, some of us could prove in the last
few years that an optimized TCO can increase the sensitivity (Baur et al.,
2015) and the stability (Schultealbert et al., 2017) of the sensor signal as
well. The model-based optimization uses a set of rate equations for the
trapping and release in surface states proposed by Ding et al. (2001). For
the quantification of VOC concentrations at the ppb level, a technique based
on the relaxation of the conductance at a low temperature phase has been
demonstrated (Baur et al., 2015). The technique utilizes the fact that the
equilibrium surface coverage with ionized oxygen depends on the sensor
temperature. At high temperature (e.g. at 450 ∘C) the surface
coverage and thereby the barrier height are higher than at low temperature
(e.g. 200 ∘C) (Schultealbert et al., 2017). For a fast temperature
reduction, an excess of surface coverage can be obtained (Baur et al., 2015).
At this stage, the reaction at the sensor surface is far from equilibrium as
the ionosorption of oxygen is very unlikely. The sensor surface is then
predominantly reduced by gases, e.g. benzene, causing a strong increase in
sensor response compared to isothermal operation. This increase can be
several orders of magnitude in terms of relative conductance. The reduction
of the surface is linear to the applied gas dosage or gas concentration given
that the concentration is constant over one surface reduction (Baur,
2017).
The change in the logarithmic sensor conductance lnGinit at the
beginning of a low temperature plateau (beginning at t0=0) is linear
to the gas concentration cgas.
dlnGinit(t)dt∼const∼kgas⋅cgas+k0
Please note that Eq. (2) is only valid if the surface charge is still high
above equilibrium; otherwise, the ionosorption of new surface charge is not
negligible anymore. A detailed discussion can be found in Schultealbert et
al. (2017).
Following this line, we could show that the benzene concentration in the
range from 500 ppt to 10 ppb air can be quantified very accurately in a
purified air background, whereby compensation of the ubiquitous gas
background and interfering gas reduces the accuracy of detection (Leidinger
et al., 2017).
Experimental
Sensor system
The sensor system is equipped with three different commercial MEMS gas sensor
elements. Two sensor elements are integrated in a dual-sensor package
(MiCS 4510 from SGX, Switzerland) and the third sensor is a single-sensor
device (AS-MLV from ams Sensor Solutions, Germany). All sensors are operated
in TCO with independent control and read-out. A block diagram of the sensor
system can be found in the Supplement (Fig. S1). Rapid temperature changes
from a high temperature of 450 ∘C to lower temperatures
(200/250/300 ∘C) are used. The duration of the high temperature
plateau is 10 s each, for the 200 and 250 ∘C plateau the duration
is 35 s, and for the 300 ∘C plateau it is 20 s. The TCO control
and the read-out are done using a modified sensor system (SensorToolbox, 3S
GmbH, Germany) that can support up to four sensor modules (ToolboxModule).
The sensor signal Slog for each sensor is measured using a
logarithmic amplifier comparing the sensor current Isens with a
reference current Iref=1 mA. The sensor is operated at a
constant voltage of 0.25 V; hence, the sensor current
Isens=0.25 V ⋅ Gsens is directly linear to
the sensor conductance. The output of the logarithmic amplifier is divided by
a subsequent voltage divider to match with the voltage range of the
analogue–digital converter of the ToolboxModule (Eq. 1). Corresponding to
this, a virtual reference conductance Gref can be calculated. The
output of the logarithmic amplifier of ULogAmp=0.5 V per decade
is divided by a subsequent voltage divider to match with the voltage range of
the analogue–digital converter of the ToolboxModule, yielding a voltage
Ulog of 0.25 V per decade.
This output voltage (Eq. 3) is defined as sensor signal Slog which is
linear to the logarithm of the conductance Gsens of the gas sensing
layers.
Slog=0.25V⋅log10IrefIsens=0.25V⋅log10IrefGsens⋅0.25V
This measuring method allows us to cover a large signal range, as MOS gas
sensor resistances can vary within several orders of magnitude during rapid
temperature changes (Baur et al., 2015). Please note that this sensor signal
is different from the commonly used sensor response, which is defined as
G/G0. A change in the sensor signal ΔSlog=Slog-Slog0 can be easily transformed to a
sensor response by S=10ΔSlog/0.25V.
However, the definition of Slog allows a facile calculation of
the change surface charge as the time-derived sensor signal Slog
is proportional to the rate constant k of surface reduction (Eq. 2), which
is itself linear depending on the gas (benzene) concentration (Eq. 4).
dStdt∼-dlnGinittdt∼-k∼-c⋅kgas-k0for smallt-t0
Data processing
We used our DAV3E toolbox (Bastuck et al., 2016) for the data
processing. The data processing was performed in three steps: feature
extraction, feature selection and quantification. The feature extraction
reduces the dimensionality of the classification problem.
A set of features of each temperature cycle was extracted from the signals,
which describes the shape of the signal (mean values and slopes). The slopes
correspond in first approximation to the rate constant (derivative of the
sensor signal Eq. (4)). These features were calculated from several segments
of the cyclic sensor signal, covering all set temperatures. The ranges of the
features have been varied to find the best selection by the feature
selection. Feature selection was performed using a recursive feature
elimination support vector machine (RFESVM) (Schüler et al., 2017) to
choose the best features for classification.
Using these feature sets and the known benzene concentrations, a PLSR model
(partial least squares regression) (Bastuck et al., 2015b; Wold et
al., 2001) is calculated, which generates a linear combination of the
features to allow an estimation of the benzene concentration.
Gas tests at the Lab for Measurement Technology (LMT)
In the first laboratory (LMT – Lab for Measurement Technology,
Saarbrücken, Germany) the sensor system has been tested using a gas
mixing apparatus (GMA) operating by the principle of dynamic dilution. The
set-up of this system has been reported in detail previously (Helwig et al.,
2014). A two-stage dilution system is used to produce the benzene test gas,
starting from a gas cylinder containing 50 ppm benzene in synthetic air. The
benzene is diluted with zero air, generated from a cascade of two gas
purifiers. The first purifier includes a coarse filter to remove particles
and oil. Subsequently, humidity and CO2 are removed by two alternating
molar sieves (pressure swing) and hydrocarbons (> C3) are
removed by an active charcoal filter. The second purifier has an additional
pre-filter and pressure swing followed by a catalytic combustion of hydrogen,
carbon monoxide and short chain hydrocarbons (< C4). The
catalytic converter is furthermore equipped with a nitrogen oxide scrubber.
The pure air is split into eight gas lines, of which five have been used in
this investigation. In the first line, pure air saturated with humidity is
generated at a dew point of 20 ∘C using an isothermal blubber with
HPLC grade water (low organic carbon). The second line is used for dry air.
The third line is a two-step dilution using a dry stream of purified air and
diluted benzene test gas from a cylinder in the first dilution step. The
second dilution step is the combination with the humid and dry main gas
stream from the first two lines. In the fourth line, toluene is added to the
test gas; it uses the same set-up as the benzene line. The fifth line uses a
two-step dilution to generate a background of 500 ppb hydrogen, 150 ppb
carbon monoxide and 1820 ppb methane from a gas cylinder with a dilution of
these gases in air. These three gases are the main reducing compounds in pure
environmental air (Ehhalt and Rohrer, 2009; Gilge et al., 2010). This gas
background has a strong impact on the sensor response as well as on the
detection limit of the sensor (Leidinger et al., 2017). A mixture of pure
zero air with this background will be defined as standard air. The sensors
have been tested directly in gas flow of 200 sccm in a stainless steel
sensor housing.
Gas tests at JRC
For the second laboratory campaign, the evaluation was carried out using the
JRC (Joint Research Center) exposure chamber. This chamber allows the control
of numerous gaseous mixtures including benzene and a set of selected
interfering compounds (toluene, m,p-xylene, ethane, propane, n-butane and
n-pentane) plus temperature, relative humidity and wind velocity. The
exposure chamber is an “O”-shaped ring-tube system, covered with dark
insulation material. The full system has already been described elsewhere
(Spinelle et al., 2014). All gaseous compounds are added to pure zero air.
The micro-sensors in the stainless steel housing described above were
directly placed inside the ring tube. High concentration cylinders were used
to generate specific levels of pollutants based on the dynamic dilution
principle. A specific LabView software using multiple
proportional–integral–derivative (PID) feedback loops ensured the
stability of the concentration. The reference value for the feedback loop was
measured using a PTR-MS (proton-transfer-reaction mass spectrometer) and the
reference values were measured by a gas chromatograph with a photo ionization
detector (GC-PID 955 from Syntech). The direct input of reference
measurements of gaseous compounds, temperature, humidity and and wind speed is used to auto-correct the gas mixture, temperature controlling
cryostat and wind velocity by means of an internal fan. In particular, this
set-up allows one to set independent criteria for the stability of each
parameter and for a defined period of time.
Quantification of benzene in zero air using feature extraction and
PLSR. (a) Only training data. (b) Training and test data.
PLSR benzene quantification results for four different background
and interferent configurations (Leidinger et al., 2017): (a) benzene
in pure zero air and with 2 ppb of toluene added, at 25 %RH.
(b) Benzene in standard air and variation of CO background, 10, 25
and 40 %RH. (c) Benzene in standard air and variation of
toluene, 10, 25 and 40 %RH. (d) Benzene in standard air and
variation of toluene and CO, 25 and 40 %RH.
Measurement results and data analysis
Benzene quantification capabilities
(a) Sensor signals in temperature-cycled operation
(temperature ranges: blue arrows). The relaxation constants (slope feature)
are calculated from the grey marked domains. (b) Feature slope over
benzene concentration according to the GC955 reference measurement.
The sensor system has been tested in the LMT system in pure zero air towards
benzene at six gas concentrations from 0.5 to 10 ppb and three relative
humidities (10, 25, 40 %RH) to test the quantification and humidity
compensation. Due to the high purity of the zero air, the conductance of the
sensors at the beginning of the low temperature phases is very low. The
sensor response shows a high noise. The derivative of the sensor response is
obviously an even worse signal. Thus, a feature selection tool as described
above has been used instead of using the model-based feature directly. The
feature selection selected only signals from the less noisy parts of the
response curve. To test the quantification of benzene a PLSR has been
calculated using the measurement of 0.5, 3 and 10 ppb benzene at 10 and
40 %RH (Fig. 1a). The PLSR is in general a regression of the measured
values (e.g. sensor system output) with the “true” values (or a proper
estimate, e.g. from a reference measurement). Please note that the value of
the concentration set point (x-axis) also adds an additional uncertainty to
the regression. As the LMT gas mixing system does not provide continuous
reference measurements of the benzene concentration, an estimate of the real
value is derived from the mixing ratio of the gas flows and the certified
concentration of the gas cylinders. The gas flows are continuously measured
and recorded by the gas mixing system. The proper function of the gas mixing
system was confirmed as the error of the recorded flow rates is within the
error margin. As estimates for the true concentration, the set points of the
gas mixing system were used. Figure 1 shows that the PLSR is very accurate.
The sensor system output is obviously a linear function for benzene
concentration and the compensation of humidity cross-sensitivity is very
good. The error of the predicted response is below 0.2 ppb for all trained
concentrations. The PLSR model was applied to untrained concentrations of
benzene (1, 2 and 5 ppb) at 10 and 40 %RH and to the six concentrations
of benzene tested at 25 %RH. This test of the model prediction is shown
in Fig. 1b. The full circles denote the trained data points and open circles
denote the untrained “test” data point. The test data points do not show
any decisive deviation from the trained data points. The interpolation of the
benzene concentration and a compensation of an untrained humidity background
are demonstrated by this result. However, the quantification of benzene in
ambient air at the sub-ppb level cannot be derived from this result since
even clean air contains significant inorganic reducing gas components as
described in Sect. 2. A similar test therefore has been made under standard
air (cf. Sect. 2.2) instead of zero air. The quantification properties have
been tested in detail under standard air and other interfering gases in a
previous work (Leidinger et al., 2017) showing the strong impact of gas
background on the accuracy of the detection. Measurements were made with the
dynamic dilution set-up at LMT. In the first case (Fig. 2a), two sweeps of
the benzene concentration are included, one in pure zero air without
interferents and one with a 2 ppb toluene background, at a constant gas
humidity of 25 %RH. The benzene concentrations predicted by the PLSR
model still show a very small error of below 200 ppt with respect to the
concentration set point. The introduction of standard air has a strong impact
on the quantification of benzene. In Fig. 2a the PLSR is shown in standard
air, including a variation of the CO concentration between 150 ppb
(ubiquitous) and 500 ppb (lightly polluted air). Still, the PLSR shows a
linearity between the sensor system output and the set-point concentration,
but the error of the prediction is between 1 and 2 ppb depending on the
benzene concentration. The addition of interferents like toluene between 2
and 20 ppb (Fig. 2c) seems to reduce the accuracy of the benzene
quantification further. However, the strongest impact comes from the standard
air conditions. The quantification error can be reduced if the data from
10 %RH are removed from the data set corresponding to a reduction of
interfering complexity. Figure 2d contains only two gas humidities; the
signals recorded at the lowest value are not taken into account. For this
condition the quality of quantification of benzene was improved; compared to
the scenarios in Fig. 2b and c, the groups are more compact and error for the
benzene concentration is below 1.8 ppb over the whole concentration range.
Lab intercomparison
After the initial calibration at LMT, the system was transferred to the JRC.
During this transfer, the interface of the read-out electronic of the dual
sensor (MiCS4514) was damaged. For the lab intercomparison the remaining
sensor (AS-MLV) has been used and the signal processing has been re-trained.
Only tests with zero air background at various humidity and interferent
levels have been compared, as in the JRC set-up no addition of the inorganic
background was foreseen. The features have been calculated according to
Eq. (4) directly, without selection of the feature ranges using RFESVM.
However, a short time span at the beginning of the low temperature has been
left out manually to reduce the noise (cf. Fig. 3; the sections for feature
extraction are marked in grey). The sensor signal Slog in the low
temperature plateaus has a good linearity over the full temperature plateau
in good agreement with Eq. (4) for all temperature plateaus at all tested
benzene concentrations (Fig. 3). Obviously, the strongest response of the
sensor to benzene can be found at 300 ∘C (Fig. 3). Using these
features a PLSR model has been trained from the data of the JRC measurement
and tested with the data from the LMT measurements. Please note that only
three features can be calculated from the single sensor and that the impact
of the feature at 200 ∘C is very small, leading to an incomplete
compensation compared to the three-sensor system described in Sect. 3.1.
Therefore, only measurement results with pure benzene have been evaluated.
For the training of the PLSR, the data of the reference measurement from the
GC-PID 955 were used as estimates of the
true values. We compared the transfer of a PLSR model obtained by training
data of one test system to test data obtained by the other test system
(Fig. 4). The transfer of the model trained with JRC test data to LMT test
data is shown on the left side in Fig. 4. The black circles denote the
trained data points from the JRC lab and the red circles denote the untrained
data points from the LMT lab. The benzene concentrations predicted by the
PLSR model for the JRC data at 60 %RH still show a very small error of
below 200 ppt with respect to the concentration measured by the GC-PID 955.
We see two different trend lines of the predicted data points from the LMT
lab. Each trend line shows a specific humidity at 10 %RH and 25 %RH.
Both trend lines show a good linearity and the same slope, but also an offset
to the optimal line. The transfer from the model obtained with LMT data is
shown in Fig. 4 on the right. The training was performed with only a single
humidity (25 %RH), as obviously the humidity compensation of the single
sensor system is not sufficient. The test data from the JRC as well as the
test data from the LMT show a good linearity, but also an offset to the
training data. The offset is probably due to the humidity as the data with
60 %RH exhibit a negative offset, while the data with 10 %RH exhibit
a positive offset.
Transfer of the PLSR model from training data of one gas mixing
system to test data from another gas mixing system. (a) Training
using the JRC measurement (60 %RH black solid circles) and test data from
the LMT measurement (10 and 25 %RH open red circles).
(b) Training using LMT measurements (25 %RH black circle) and
test using the JRC data (60 %RH) and the LMT data (10 %RH). Both test
data are shown in open red circles.
Discussion and conclusion
The presented MOS gas sensor system shows very good performance for benzene
quantification, especially in pure air even with low levels of interfering
toluene, including the interpolation of unknown benzene concentrations over
the full humidity range tested. However, at standard air and a realistic
background level of interferents, especially CO, the error of quantification
is in the range of 1–2 ppb. For the environmental monitoring, especially in
rural areas, even lower detection limits are needed to monitor the benzene
concentration (Schneidemesser et al., 2010). A possible strategy for the
further reduction of the detection limit are sensor/pre-concentrator micro
systems (Leidinger et al., 2016a) and a further optimization of the sensor
system electronics to reduce the noise of the signal (Baur and Schütze,
2017). For the quantification of benzene, a combination of the DSR model for
feature extraction and a multilinear regression for the compensation of
interferents has been tested successfully. Within the measured sensor signals
all tested benzene concentrations were in good agreement with the prediction
of the DSR model. The multilinear regression yields very good compensation of
humidity and even toluene interference. The regression for all conditions
shows a good linearity without further pre-processing of the signal; this is
an advantage of the system over other TCO modes, which usually does not yield
a linear signal with concentration requiring a special pre-processing before
PLSR (Bastuck et al., 2015a). The system can be successfully calibrated at
different labs and testing conditions, indicating that the very different
methods of generating benzene yield similar levels of test gas. The transfer
of a regression model from the JRC test data to the LMT test data shows good
linearity of the measured benzene concentration but an offset of the response
curve on the order of 2 ppb. The observed offset is probably due to the
different humidity as the humidity compensation of the single-sensor system
is not as good as in the three-sensor system. Moreover, a residual
contamination of the GMA with VOC can contribute. Test of the VOC background
of the LMT system showed that it is typically in the range of a few
µg m-3 (Helwig et al., 2014), which is in the same range as
the benzene concentration tested. The result demonstrates the need for the
definition of common test standards for trace gas sensor systems and the high
potential of those systems for the quantitative detection even of small
levels of pollutants like benzene. This is an important step for the
development of monitoring grids with high resolution using indicative sensor
systems to increase the number of nodes strongly.