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Calibration of a multivariate gas sensing device for atmospheric pollution measurement
Sensors and Actuators B 118 (2006) 323–327
Calibration of a multivariate gas sensing device for atmospheric pollution measurement M. Kamionka, P. Breuil ∗ , C. Pijolat Ecole Nationale Sup´erieure des Mines, Centre SPIN, Dpt MICC, LPMG-URA CNRS 2021, 158 Cours Fauriel, 42023 Saint-Etienne, France Available online 23 May 2006
Abstract The aim of this work is to realize a device with semiconductor gas sensors for the measurement of air pollution. This device is made with three different thick film layers. Electrical conductivities are measured at different temperatures. Two kinds of calibration models are made and tested: a conventional one made with artificial gas standards and a more original using real pollution measurements. © 2006 Elsevier B.V. All rights reserved. Keywords: Atmospheric pollution; Gas sensors; Oxide semiconductors
1. Introduction Actually, the measurements for urban air pollution are performed by the networks of Pollution Control thanks to environmental monitoring stations equipped with industrial analyzers which detect and measure selectively many pollutants. A complementary solution could be the use of semiconductor microsensors which can constitute a low cost tool to densify the network [1–3]. But few studies were done on this subject [4,5] because the concentrations of the pollutants are very weak and the composition of pollution which is complex requires devices or methods with relatively good selectivity. In this study, we propose two ways to calibrate a device with three different screen-printed thick layers for the measurement of two components of the urban atmospheric pollution. In order to do this, we use a “neural network” algorithm for a multisensors (three different materials) and multivariate (for each material, several electrical measurements are made) approach. 2. The multisensors Our sensors, which we call “trisensors”, are composed with three sensitive tin dioxide (SnO2 ) thick films covered with coated thin films, electrodes and a heating device (Fig. 1). The tin dioxide thick films are prepared by screen-printing technology [2] on four gold thin films electrodes deposited on ∗
Corresponding author. Tel.: +33 4 77 42 01 51. E-mail address: [email protected] (P. Breuil).
0925-4005/$ – see front matter © 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.snb.2006.04.058
an alpha-alumina substrate by reactive sputtering prolonged by gold thick films which allow a soft solder of four wires. The SnO2 layers are finally deposited on a 2 mm × 2 mm area with a thickness of 24 m and are annealed at 700 ◦ C in air. One tin dioxide thick films is covered with a silica (SiO2 ) thin film grown by in situ CVD (chemical vapour deposition) using the platinum resistance for heating the sensing material to a temperature in the range of 550–600 ◦ C. Usually, these layers are used in order to improve selectivity to hydrogen [6], but we observed interesting results with ozone. A 5 nm platinum membrane filter is deposited on the second one by reactive sputtering. The catalytic properties of this filter can be useful to eliminate carbon monoxide and not light hydrocarbons [7]. The third thick film is pure tin dioxide [2,3]. The measurement procedure consists in decreasing an increasing of temperature between 50 and 500 ◦ C and to measure 20 conductances during each cycle which is about 15 min long. Tin dioxide sensors are very sensitive to water vapor, so absolute humidity is too measured with a polymer sensor and used as parameter, like electrical conductances. Gases are at room temperature. There are two ways to use multivariate analysis in order to measure gas concentration with semiconductor gas sensors. 3. Tests with artificial gas mixtures The first one is experiments with artificial mixtures of gases. These experiments are used on one hand to evaluate the properties of the sensors with pure gases, on the other hand to
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M. Kamionka et al. / Sensors and Actuators B 118 (2006) 323–327
Fig. 1. “Trisensor” device: the size of the three sensing elements is 2 mm × 2 mm.
build calibration models which allow to predict gas concentrations. The synthetic mixtures are composed with artificial air and ozone (0–200 ppb), unleaded petrol “S98” vapour (0–10 ppm, representative of hydrocarbons) and Nitrogen dioxide (0– 250 ppb). These gases and their range of concentrations are representative of current conditions met in urban pollution. These mixtures are generated with various relative humidity (10–90% at 25 ◦ C). The electrical measurements for pure gazes are shown on Fig. 2. In order to compute the Neural Network model, 166 injections of gas are made. They are constituted either of pure gases which are ozone (0–200 ppb), of nitrogen dioxide (0–250 ppb) and of
“S98” (0–10 ppm), or of binary or ternary mixtures of these gases with the same ranges of concentrations. In every case, the relative humidity is randomly generated. One hundred and forty two of these injections are used to build the model. The validation takes place on 24 samples of ternary mixtures and allows to stop the learning of the neural network. The results of prediction with ternary gas mixtures are shown on Fig. 3. These models, useful in order to know the possibility of modelization of the system, make very bad predictions with real air pollution (Fig. 4). Our gas mixtures cannot be a good representation of the complexity of the real pollution. 4. Calibration with real pollution 4.1. Choice of the pollutants The second way to calibrate our sensors is to make experiments with real pollution measurements. These experiments are used to build calibration models which allow to predict gas concentrations. Real concentration of the calibration samples are then measured at the same time with conventional analyzers. In our study, only two major components of urban air pollution are modelized and then predicted: the first one, which we call “automotive traffic pollution” is represented by hydrocarbons and nitrogen oxides, the second one is ozone.
Fig. 2. Relative variation of resistance or of conductance with ozone (a), gas vapor (b) or nitrogen dioxide (c).
M. Kamionka et al. / Sensors and Actuators B 118 (2006) 323–327
325
Fig. 5. Comparison of the concentration of NO2 (NOx Analyser) and of hydrocarbons (Photo-Ionisation Detector with corrections).
Fig. 3. Prediction performances with artificial gas mixtures: ozone (a) or gas vapor (b).
As the ozone pollution episodes are rare in winter and are often negatively correlated with the “automotive traffic pollution” in summer, we added an “artificial ozone pollution” by randomly powering up and down an UV lamp near the air inlet. In our tests, “automotive traffic pollution” will be represented by nitrogen dioxide, because this gas is easier to measure (with a chemiluminescence NOx Analyser) than hydrocarbons, in particular light hydrocarbons. We could check in addition that the concentrations of nitrogen dioxide and of hydrocarbons are generally well correlated (Fig. 5). However, the presence of NO2 , which is an oxidant gas, results in a decrease of the electric conductivity of our sensors. But the “automotive traffic pollution” peaks always show an increase of this conductivity (Fig. 6), it is the evidence that the effect of hydrocarbons is dominating. As shown in the last paragraph, we do not use, in our model, electrical conductance, but relative conductance (G − G0 )/G0 . The problem is then to choose this reference conductance G0 where the concentration values must be known. We chose to use artificial “zero air” before every cycle of measurements
Fig. 4. Real ozone prediction with synthetic gas calibration.
Fig. 6. Comparison of the concentration of NO2 and of the electrical conductance of a sensor.
Fig. 7. Prediction with the “trisensor” used for modelization, same period.
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M. Kamionka et al. / Sensors and Actuators B 118 (2006) 323–327
4.2. Results Fig. 7 shows tests results with the same “trisensor” for modelization and tests, during the same period (but of course with different samples). Performances for prediction are good, but not representative of a real application. Fig. 8 shows tests results with the next model and the same “trisensor”, during a second period a few days after the calibration. Performances are less good but prove the portability of the model during time. The portability of the model between different sensors has then been tested on a second “trisensor”, with the second period, showing a light decrease of the performances: (Fig. 9). Experiments made more than 1 week after the calibration show that the instability of the sensors does not allow actually to predict pollution beyond a few days without a gauging or a new modelization. Moreover, our sensors often detect events not detected by the analyzers, and thus resulting in erroneous calculations. 5. Conclusions Fig. 8. Prediction with the “trisensor” used for modelization, a few days after.
(which duration is some days). A better solution could be adjust the baseline of the signal to a local environmental monitoring station when the level of pollution is low and then uniform [5].
The problem of the measurement of pollution with semiconductor sensors is not easy because the very low concentrations of pollutants need sensitive and stable devices. But another difficulty is the complexity of the composition of atmospheric air. The first consequence is that, in order to have a chance to modelize this complexity, the used multivariate system must provide many independent informations. This is why we use a multicomponent sensor, and why, for each component, we measure electrical conductivity for different increasing and decreasing temperatures. The second consequence is that the conventional methods which consists in making a calibration model with artificial mixtures of some pollutants cannot be successful. So we must build the calibration model with real measurements of pollution, and measure only two components of pollution: ozone and “peak of automotive pollution”, typically hydrocarbons mixtures. This method presents limits, in particular when unknown components of pollution are present, and too, the model has a short longevity. Our results are not very powerful, but the aim of this study is not to have measurements as accurate as these provided by analysers: a device which can give an order of magnitude of the two principal urban pollution components can be interesting. Furthermore, this device can be improved if we can improve the sensitive materials, and too, our knowledge of the atmospheric pollution. References
Fig. 9. Prediction with a “trisensor” different than the one used for modelization, a few days after.
[1] C. Pijolat, C. Pupier, M. Sauvan, G. Tournier, R. Lalauze, Sens. Actuators B: Chem. 59 (1999) 195–202. [2] B. Rivi`ere, J.P. Viricelle, C. Pijolat, Sens. Actuators B: Chem. 93 (2003) 531–537.
M. Kamionka et al. / Sensors and Actuators B 118 (2006) 323–327 [3] P. Breuil, N. Perdreau, C. Pijolat, Analysis 28 (7) (2000) 633–636. [4] M.C. Carotta, G. Martinelli, L. Crema, M. Gallana, M. Merli, G. Ghiotti, E. Traversa, Sens. Actuators B: Chem. 68 (2000) 1–8. [5] W. Tsujita, A. Yoshino, H. Ishida, T. Moriizumi, Sens. Actuators B: Chem. 110 (2005) 304–311. [6] G. Tournier, C. Pijolat, Sens. Actuators B: Chem. 106 (2005) 553– 562. [7] P. Montmeat, C. Pijolat, G. Tournier, J.P. Viricelle, Sens. Actuators B: Chem. 84 (2002) 148–159.
Biographies Marc Kamionka worked as a PhD student in the “Microsystems, Instrumentation and Chemical Sensors” research team. The subject of his PhD was
327
“Development of multisensors and multivariate systems for the measurement of atmospheric pollution”. Philippe Breuil is engineer and teacher of Instrumentation at Ecole des Mines of Saint-Etienne (France). He developed first some instruments based on UVvisible spectrophotometry and chemometrics. Since 2000, he works on the utilization of semiconductors sensors. Christophe Pijolat is professor of chemical engineering and microsystems at Ecole des Mines of Saint-Etienne (France), he manages the MICC department (Microsystems, Instrumentation and Chemical Sensors) attached to SPIN research center (Natural and Industrial Process Sciences). Since 1980, he has been working in the field of electrical properties of solids and on the development potentiometric and semiconductor sensors. He has contributed to several technological transfers of sensors into industrial applications.
Calibration of a multivariate gas sensing device for atmospheric pollution measurement M. Kamionka, P. Breuil ∗ , C. Pijolat Ecole Nationale Sup´erieure des Mines, Centre SPIN, Dpt MICC, LPMG-URA CNRS 2021, 158 Cours Fauriel, 42023 Saint-Etienne, France Available online 23 May 2006
Abstract The aim of this work is to realize a device with semiconductor gas sensors for the measurement of air pollution. This device is made with three different thick film layers. Electrical conductivities are measured at different temperatures. Two kinds of calibration models are made and tested: a conventional one made with artificial gas standards and a more original using real pollution measurements. © 2006 Elsevier B.V. All rights reserved. Keywords: Atmospheric pollution; Gas sensors; Oxide semiconductors
1. Introduction Actually, the measurements for urban air pollution are performed by the networks of Pollution Control thanks to environmental monitoring stations equipped with industrial analyzers which detect and measure selectively many pollutants. A complementary solution could be the use of semiconductor microsensors which can constitute a low cost tool to densify the network [1–3]. But few studies were done on this subject [4,5] because the concentrations of the pollutants are very weak and the composition of pollution which is complex requires devices or methods with relatively good selectivity. In this study, we propose two ways to calibrate a device with three different screen-printed thick layers for the measurement of two components of the urban atmospheric pollution. In order to do this, we use a “neural network” algorithm for a multisensors (three different materials) and multivariate (for each material, several electrical measurements are made) approach. 2. The multisensors Our sensors, which we call “trisensors”, are composed with three sensitive tin dioxide (SnO2 ) thick films covered with coated thin films, electrodes and a heating device (Fig. 1). The tin dioxide thick films are prepared by screen-printing technology [2] on four gold thin films electrodes deposited on ∗
Corresponding author. Tel.: +33 4 77 42 01 51. E-mail address: [email protected] (P. Breuil).
0925-4005/$ – see front matter © 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.snb.2006.04.058
an alpha-alumina substrate by reactive sputtering prolonged by gold thick films which allow a soft solder of four wires. The SnO2 layers are finally deposited on a 2 mm × 2 mm area with a thickness of 24 m and are annealed at 700 ◦ C in air. One tin dioxide thick films is covered with a silica (SiO2 ) thin film grown by in situ CVD (chemical vapour deposition) using the platinum resistance for heating the sensing material to a temperature in the range of 550–600 ◦ C. Usually, these layers are used in order to improve selectivity to hydrogen [6], but we observed interesting results with ozone. A 5 nm platinum membrane filter is deposited on the second one by reactive sputtering. The catalytic properties of this filter can be useful to eliminate carbon monoxide and not light hydrocarbons [7]. The third thick film is pure tin dioxide [2,3]. The measurement procedure consists in decreasing an increasing of temperature between 50 and 500 ◦ C and to measure 20 conductances during each cycle which is about 15 min long. Tin dioxide sensors are very sensitive to water vapor, so absolute humidity is too measured with a polymer sensor and used as parameter, like electrical conductances. Gases are at room temperature. There are two ways to use multivariate analysis in order to measure gas concentration with semiconductor gas sensors. 3. Tests with artificial gas mixtures The first one is experiments with artificial mixtures of gases. These experiments are used on one hand to evaluate the properties of the sensors with pure gases, on the other hand to
324
M. Kamionka et al. / Sensors and Actuators B 118 (2006) 323–327
Fig. 1. “Trisensor” device: the size of the three sensing elements is 2 mm × 2 mm.
build calibration models which allow to predict gas concentrations. The synthetic mixtures are composed with artificial air and ozone (0–200 ppb), unleaded petrol “S98” vapour (0–10 ppm, representative of hydrocarbons) and Nitrogen dioxide (0– 250 ppb). These gases and their range of concentrations are representative of current conditions met in urban pollution. These mixtures are generated with various relative humidity (10–90% at 25 ◦ C). The electrical measurements for pure gazes are shown on Fig. 2. In order to compute the Neural Network model, 166 injections of gas are made. They are constituted either of pure gases which are ozone (0–200 ppb), of nitrogen dioxide (0–250 ppb) and of
“S98” (0–10 ppm), or of binary or ternary mixtures of these gases with the same ranges of concentrations. In every case, the relative humidity is randomly generated. One hundred and forty two of these injections are used to build the model. The validation takes place on 24 samples of ternary mixtures and allows to stop the learning of the neural network. The results of prediction with ternary gas mixtures are shown on Fig. 3. These models, useful in order to know the possibility of modelization of the system, make very bad predictions with real air pollution (Fig. 4). Our gas mixtures cannot be a good representation of the complexity of the real pollution. 4. Calibration with real pollution 4.1. Choice of the pollutants The second way to calibrate our sensors is to make experiments with real pollution measurements. These experiments are used to build calibration models which allow to predict gas concentrations. Real concentration of the calibration samples are then measured at the same time with conventional analyzers. In our study, only two major components of urban air pollution are modelized and then predicted: the first one, which we call “automotive traffic pollution” is represented by hydrocarbons and nitrogen oxides, the second one is ozone.
Fig. 2. Relative variation of resistance or of conductance with ozone (a), gas vapor (b) or nitrogen dioxide (c).
M. Kamionka et al. / Sensors and Actuators B 118 (2006) 323–327
325
Fig. 5. Comparison of the concentration of NO2 (NOx Analyser) and of hydrocarbons (Photo-Ionisation Detector with corrections).
Fig. 3. Prediction performances with artificial gas mixtures: ozone (a) or gas vapor (b).
As the ozone pollution episodes are rare in winter and are often negatively correlated with the “automotive traffic pollution” in summer, we added an “artificial ozone pollution” by randomly powering up and down an UV lamp near the air inlet. In our tests, “automotive traffic pollution” will be represented by nitrogen dioxide, because this gas is easier to measure (with a chemiluminescence NOx Analyser) than hydrocarbons, in particular light hydrocarbons. We could check in addition that the concentrations of nitrogen dioxide and of hydrocarbons are generally well correlated (Fig. 5). However, the presence of NO2 , which is an oxidant gas, results in a decrease of the electric conductivity of our sensors. But the “automotive traffic pollution” peaks always show an increase of this conductivity (Fig. 6), it is the evidence that the effect of hydrocarbons is dominating. As shown in the last paragraph, we do not use, in our model, electrical conductance, but relative conductance (G − G0 )/G0 . The problem is then to choose this reference conductance G0 where the concentration values must be known. We chose to use artificial “zero air” before every cycle of measurements
Fig. 4. Real ozone prediction with synthetic gas calibration.
Fig. 6. Comparison of the concentration of NO2 and of the electrical conductance of a sensor.
Fig. 7. Prediction with the “trisensor” used for modelization, same period.
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M. Kamionka et al. / Sensors and Actuators B 118 (2006) 323–327
4.2. Results Fig. 7 shows tests results with the same “trisensor” for modelization and tests, during the same period (but of course with different samples). Performances for prediction are good, but not representative of a real application. Fig. 8 shows tests results with the next model and the same “trisensor”, during a second period a few days after the calibration. Performances are less good but prove the portability of the model during time. The portability of the model between different sensors has then been tested on a second “trisensor”, with the second period, showing a light decrease of the performances: (Fig. 9). Experiments made more than 1 week after the calibration show that the instability of the sensors does not allow actually to predict pollution beyond a few days without a gauging or a new modelization. Moreover, our sensors often detect events not detected by the analyzers, and thus resulting in erroneous calculations. 5. Conclusions Fig. 8. Prediction with the “trisensor” used for modelization, a few days after.
(which duration is some days). A better solution could be adjust the baseline of the signal to a local environmental monitoring station when the level of pollution is low and then uniform [5].
The problem of the measurement of pollution with semiconductor sensors is not easy because the very low concentrations of pollutants need sensitive and stable devices. But another difficulty is the complexity of the composition of atmospheric air. The first consequence is that, in order to have a chance to modelize this complexity, the used multivariate system must provide many independent informations. This is why we use a multicomponent sensor, and why, for each component, we measure electrical conductivity for different increasing and decreasing temperatures. The second consequence is that the conventional methods which consists in making a calibration model with artificial mixtures of some pollutants cannot be successful. So we must build the calibration model with real measurements of pollution, and measure only two components of pollution: ozone and “peak of automotive pollution”, typically hydrocarbons mixtures. This method presents limits, in particular when unknown components of pollution are present, and too, the model has a short longevity. Our results are not very powerful, but the aim of this study is not to have measurements as accurate as these provided by analysers: a device which can give an order of magnitude of the two principal urban pollution components can be interesting. Furthermore, this device can be improved if we can improve the sensitive materials, and too, our knowledge of the atmospheric pollution. References
Fig. 9. Prediction with a “trisensor” different than the one used for modelization, a few days after.
[1] C. Pijolat, C. Pupier, M. Sauvan, G. Tournier, R. Lalauze, Sens. Actuators B: Chem. 59 (1999) 195–202. [2] B. Rivi`ere, J.P. Viricelle, C. Pijolat, Sens. Actuators B: Chem. 93 (2003) 531–537.
M. Kamionka et al. / Sensors and Actuators B 118 (2006) 323–327 [3] P. Breuil, N. Perdreau, C. Pijolat, Analysis 28 (7) (2000) 633–636. [4] M.C. Carotta, G. Martinelli, L. Crema, M. Gallana, M. Merli, G. Ghiotti, E. Traversa, Sens. Actuators B: Chem. 68 (2000) 1–8. [5] W. Tsujita, A. Yoshino, H. Ishida, T. Moriizumi, Sens. Actuators B: Chem. 110 (2005) 304–311. [6] G. Tournier, C. Pijolat, Sens. Actuators B: Chem. 106 (2005) 553– 562. [7] P. Montmeat, C. Pijolat, G. Tournier, J.P. Viricelle, Sens. Actuators B: Chem. 84 (2002) 148–159.
Biographies Marc Kamionka worked as a PhD student in the “Microsystems, Instrumentation and Chemical Sensors” research team. The subject of his PhD was
327
“Development of multisensors and multivariate systems for the measurement of atmospheric pollution”. Philippe Breuil is engineer and teacher of Instrumentation at Ecole des Mines of Saint-Etienne (France). He developed first some instruments based on UVvisible spectrophotometry and chemometrics. Since 2000, he works on the utilization of semiconductors sensors. Christophe Pijolat is professor of chemical engineering and microsystems at Ecole des Mines of Saint-Etienne (France), he manages the MICC department (Microsystems, Instrumentation and Chemical Sensors) attached to SPIN research center (Natural and Industrial Process Sciences). Since 1980, he has been working in the field of electrical properties of solids and on the development potentiometric and semiconductor sensors. He has contributed to several technological transfers of sensors into industrial applications.