WiFi Electronic Nose for Indoor Air Monitoring Chatchawal Wongchoosuk*
Mario Lutz
Department of Physics Faculty of Science, Kasetsart University Bangkok, Thailand E-mail:
[email protected]
Materials Science and Engineering Programme Faculty of Science, Mahidol University Bangkok, Thailand
Chayanin Khunarak
Department of Physics and Center of Nanoscience and Nanotechnology, Faculty of Science, Mahidol University Bangkok, Thailand NANOTEC Center of Excellence at Mahidol University, National Nanotechnology Center, Thailand E-mail:
[email protected]
Teerakiat Kerdcharoen* Materials Science and Engineering Programme Faculty of Science, Mahidol University Bangkok, Thailand
Abstract— Several indoor chemical contaminants such as CO and NO2 are highly toxic. Inhalation of CO or NO2 as low as ppm level may cause respiratory distress or failure. Therefore, detection of indoor air is very important in the industrial, medical, and environmental applications. In this paper, a new electronic nose (E-nose) architecture has been proposed for the real-time quantification and qualification of indoor air contaminations. The metal oxide TGS gas sensors were used as the sensing part. The principal component analysis (PCA) method and a set of mathematical model were employed in data analysis. By combining with the proposed mathematical model, this E-nose can estimate the amount of CO gas contaminations in air at ppm levels. Moreover, the PCA results can clearly show a classification between two different rooms. Keywords- E-nose; Wireless gas sensor; PCA method; CO detection; Environment Awareness; TGS Gas Sensor
I.
INTRODUCTION
Most people spend more than 80% (90% in industrial countries) of their time indoors [1], i.e., in offices, houses, stores, restaurants, public or private transportation vehicles, movie theatre, etc. Typically, several hundreds of indoor chemical contaminants including by-products of the combustion (CO2 , CO), cigarette smoke, particulate matter, mineral fibers etc. can be found. A list of typical indoor air pollutants can be found from Ref. [2]. In spite of the very low concentrations, some of these compounds are extremely toxic such as CO, NO2 or formaldehyde. Only as low as 667 ppm of CO may cause up to 50% of the body's hemoglobin to convert to carboxyhemoglobin [3] that is ineffective for delivering oxygen to bodily tissues. Exposure to 100 ppm of NO2 can produce pulmonary edema that may be fatal or may lead to bronchiolitis obliterans while formaldehyde was proved to be carcinogenic. Concisely, indoor air quality can greatly affect morale, emotion, productivity, and health status of people. Therefore, development of technical devices for indoor air monitoring has become an important issue of public interest. The first experiment for assessing the air quality was conducted in 1988 by Fanger [4]. He proposed a method for assessing the air quality and introduced that discomfort as caused by indoor
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air quality based on human sensory panels. However, his method is too time consuming and cannot be used for continuous measurements in long time monitoring and control. In the last decades, gas sensors have been used for monitoring air quality instead of human sensory panels. However, individual gas sensor, that provides only one output signal, is not sufficient for monitoring a wide range of gases. Currently, the new powerful tool called electronic nose (Enoses) has been developed for monitoring air quality instead of individual gas sensor [5-8]. Although many works have showed a success in fabrication of E-nose for indoor monitoring, there is still a few works that can use the E-nose to estimate the amount of indoor air contaminations at ppm levels. Therefore, design of new E-nose architectures and investigation of new analysis technique are still a current interesting topic of E-nose development for indoor air monitoring. In this paper, we have reported how to invent a new WiFi E-nose, which can analyze indoor air contaminations in both quantification and qualification. The produced E-nose can work via a wireless network with low power consumption. The methods for the estimation of the gas concentration from sensor signal will be presented. II.
EXPERIMENTAL DETAILS
A. Fabrication of Wifi Electronic Nose A WiFi electronic nose (E-nose) has been designed for the real-time quantification and qualification of indoor air contaminations. Block diagram of WiFi E-nose system is displayed in Fig. 1. The main concept of this design is to integrate all parts of E-nose (i.e. sensor array, electronic hardware, power supply etc.) on one PCB board that can be easy to be installed in a desired location. The eight metal oxide TGS gas sensors as listed in Table 1 were used to be a sensing part for indoor air monitoring. Resistance measurement on this sensor array was done by using the voltage measurement circuit with a 20-bit
high precision analog digital converter (CS5526). It should be noted that the CS5526 has a SPI interface to communicate with a microcontroller. The analog and digital parts of this analog digital converter were powered separately to reduce noise created by the digital electronic components. A constant current source was built-up from an operation amplifier. The 8channel multiplexer (MAX4617) was connected to the microcontroller used to address each of the eight sensor elements. The simple voltage divider with the load resistance of 10 kΩ was employed to be voltage measurement circuit for gas sensor array. WiFi router was connected to the measurement system in order to communicate wirelessly with a Laptop over long distances. All raw data were stored on the laptop every second for subsequent analysis.
Figure 1. Block diagram of WiFi E-nose system.
B. Data Analysis Although TGS gas sensor has high sensitivity to volatile organic compounds, its selectivity is generally low [9]. As shown in Table 1, one TGS gas sensor such as TGS 826 can response to isobutene, hydrogen, ammonia and ethanol. This may provide an ambiguous response in terms of individual components of the gas mixtures [10]. Therefore, it is very difficult to estimate quantification of indoor air contaminations. TABLE I.
LIST OF GAS SENSOR ARRAY USED IN WIFI E-NOSE AND THEIR SENSITIVITY TO DIFFERENT GASES.
principal component analysis (PCA) [12,13] was used to perform classification of data. It should be noted that PCA is a popular statistical technique usually used to visualize in two or three uncorrelated dimensions transformed from all correlated information. III.
RESULTS AND DISCUSSION
To monitor indoor air contaminations, the WiFi E-nose was tested in two different rooms, in an office and a kitchen at Mahidol University. A photograph of WiFi E-nose that was installed in a kitchen room is displayed in Fig. 2. The finished WiFi E-nose has dimension of 15 cm x 10 cm.
Figure 2. Photograph of the WiFi E-nose in a kitchen room.
The experiments were started from 9 a.m. to 4 p.m. without interruption. The sensor responses from measurements in the office and the kitchen room are shown in Fig. 3a and 3b, respectively. The sensor response is defined as follows:
S = R / R0
(1)
where R0 and R are the resistances of the sensor in reference air and in the presence of gas contamination, respectively. From Fig. 3a and 3b, it was shown that the difference of sensor responses from measurements in the office room is only 0.35 while the big difference of sensor responses (more than 1.6) occurs in measurement of the kitchen room. However, from sensor responses, it only indicates that there are more air contaminations in kitchen room comparing with the office room. To estimate the gas concentration, a set of mathematical models was applied to sensor responses signal. For metal oxide TGS gas sensors, the concentration dependence of the response to a gas exposure is nonlinear and can be described by a power law as follows: S ′ = AC α
To overcome this problem, the mathematical model based on least square regression developed by Khalaf et al. [11] was modified to apply in our system. To analyze the qualification of indoor air contaminations, a pattern analysis based on
(2)
where S ' is sensor response, A is a constant, C is the concentration of gas and α is an index. The constant A and α can be obtained from the mathematical model based on least square regression developed by Khalaf et al. [11].
n
α=
n
n
n ∑ (ln Ci ln Si' ) − ∑ (ln Ci )∑ (ln Si' ) i =1
n
i =1
n
i =1
n ∑ (ln Ci ) − ( ∑ ln Ci ) 2
i =1
(3)
Si' =
Si ,0 − Si Si ,0
2
S ′ = W1 S1' + W2 S2' + W3 S3' + .... + W8 S8'
i =1
n ⎛ n ⎞ ' ⎜ ∑ (ln Si ) − α ∑ (ln Ci ) ⎟ i =1 ⎟ A = exp ⎜ i =1 n ⎜ ⎟ ⎜ ⎟ ⎝ ⎠
(4)
where n is the number of samples. In this work, A and α were calculated based on calibration curve of sensor response available in TGS datasheets (http://www.figaro sensor.com/).
(a)
where Si ,0 and Si are the sensor response in reference air and in the presence of tested gas, respectively. The constraint weight function w1 + w2 + w3 …..+ w8= 1 is satisfied. For example, the concentration of CO can be calculated from the resistance of three TGS gas sensors (TGS2600, TGS813 and TGS822) using the equation:
⎛ ln S ′ + 2.899050 ⎞ CCO [ ppm ] = exp ⎜ ⎟ 0.507378 ⎠ ⎝ Time evolution of the CO concentration in office and kitchen rooms is shown in Fig. 4. According to activity of people, there is no hazardous source of CO emission in office room. Therefore, the CO concentration in office room is quite low. It is well-known that CO can be produced from natural gas cooking units. In this test, the natural gas cooking was used while the tested kitchen room is small and no vented exhaust hood. Therefore, the higher concentration of CO was found in the kitchen room. However, these concentrations are still safe for human. There is no effect in healthy adults when CO concentration is less than 35 ppm in air. It should be noted that this CO concentrations that occur in Fig. 4 may be a bit more quantity than the real CO concentration because the accuracy of this mathematical model depends on the number of gas sensors and calibration curve of sensor response. In this system, there are only three gas sensors that can response to CO gas, and the calibration curves of these gas sensors are not available at a tiny ppm levels (< 1 ppm). To improve the accuracy, more gas sensors may be added and the calibration curve at sub-ppm level will be studied in the future.
(b) Figure 3. Sensor responses from measurements of WiFi E-nose in (a) office room and (b) kitchen room.
To correct the ambiguous response of TGS gas sensors, the relative response with the weight function was used as follows:
Figure 4. Time evolution of the CO concentration as calculated by the proposed equation.
For qualitative analysis, the data from eight gas sensor were introduced into PCA analysis. 100 random points from sensor response during 7-hrs experiment time were used (See Fig. 5).
ACKNOWLEDGMENT This research was supported by Kasetsart University and Mahidol University. Thailand Research Fund is acknowledged for supports. REFERENCES [1]
[2]
[3]
[4]
[5] Figure 5. PCA result for classification of two diffent rooms.
From Fig. 5, it clearly shows a classification between two different rooms. This data can be used for identification of any area. Moreover, it can give a detail for estimating variety of indoor air contamination. In this case, the kitchen room has various gas contaminations over the office room.
[6]
[7]
[8]
IV. CONCLUSION The WiFi E-nose, which can communicate wirelessly with a Laptop over long distances, has been fabricated for the realtime quantification and qualification of indoor air contaminations. The typical metal oxide TGS gas sensors were used as the sensing part. PCA method and a set of mathematical models were employed in data analysis. By combining with the proposed mathematical models, this E-nose can estimate the amount of gas contaminations in air at ppm levels. Our WiFi E-nose is expected to be a solution in indoor air monitoring that is a one of serious problem since air pollution has increased dramatically in the present lifestyle of human.
[9]
[10]
[11]
[12]
[13]
B.S. Austin, S.M. Greenfield, B.R. Weir, G.E. Anderson, and J.V. Behar, “Modeling the indoor environment”, Environ. Sci. Tech., Vol.26, pp. 851–858, 1992. C. Wongchoosuk, P. Lorwongtragool, and T. Kerdcharoen, “Malodor Detection Based on Electronic Nose”, Chapter 3 in Air Quality Monitoring, Assessment and Management, ed. Nicolas Mazzeo, June, 2011, ISBN 9789533073170. P. Tikuisis. D.M. Kane, T.M. McLellan, F. Buick, S.M. Fairburn, “Rate of formation of carboxyhemoglobin in exercising humans exposed to carbon monoxide”, J. Appl. Physio.,Vol.72, pp. 1311–1319, 1992. P.O. Fanger, “Introduction of the olf and the dezipol units to quantify air pollution perceived by humans indoors and outdoors”, Energy and Buildings, Vol.12, pp. 1–6, 1988. A.C. Romain and J. Nicolas, "Long term stability of metal oxide-based gas sensors for e-nose environmental applications: An overview", Sens. Actuat. B, Vol. 146, pp. 502–506 ,2010. V.D. Lecce, R. Daria, and J. Uva, “A wireless electronic nose for emergency indoor monitoring, Proc. of the Fifth International Conference on Sensor Technologies and Applications, pp.274-279, 2011. G.F. Fine, L.M. Cavanagh, A. Afonja, and R. Binions, “Metal oxide semi-conductor gas sensors in environmental monitoring”, Sensors, Vol. 10, pp. 5469-5502, 2010. R.J. Katulski, J. Namiesnik, J. Stefanski, J. Sadowski, W. Wardencki, K. Szymanska, “Mobile monitoring system for gaseous air pollution”, Metrol. Meas. Syst., Vol. 16, pp. 677-682, 2009. P. Massok, M. Loesch, and D. Bertrand, "Comparison between two Figaro sensors (TGS 813 and TGS 842) for the detection of methane, in terms of selectivity and long-term stability", Sens. Actuat. B, Vol. 25, pp. 525-528, 1995. V. Di Lecce, M. Calabrese, and R. Dario “Computational-based volatile organic compounds discrimination: an experimental low-cost setup” Proc. of the 2010 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications Taranto, pp. 5459, 2010. W. Khalaf, C. Pace and M. Gaudioso, “Least square regression method for estimating gas concentration in an electronic nose system”, Sensors, Vol. 9, pp. 1678-1691, 2009. C. Wongchoosuk, A. Wisitsoraat, A. Tuantranont and T. Kerdcharoen, “Portable electronic nose based on carbon nanotube-SnO2 gas sensors: feature extraction techniques and its application for detection of methanol contamination in whiskeys”, Sens. Actuat. B, Vol. 147, pp. 392-399, 2010. C. Wongchoosuk, M. Lutz and T. Kerdcharoen, “Detection and classification of human body odor using an electronic nose”, Sensors, Vol. 9, pp. 7234-7249, 2009.
