An electronic nose based on solid state sensor arrays for low-cost indoor air quality monitoring applications

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Sensors and Actuators B 101 (2004) An electronic nose based on solid state sensor arrays for low-cost indoor air quality monitoring applications S. Zampolli, I. Elmi, F. Ahmed 1, M. Passini, G.C.
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Sensors and Actuators B 101 (2004) An electronic nose based on solid state sensor arrays for low-cost indoor air quality monitoring applications S. Zampolli, I. Elmi, F. Ahmed 1, M. Passini, G.C. Cardinali, S. Nicoletti, L. Dori CNR-IMM Sezione di Bologna, Via P. Gobetti 101, Bologna, Italy Received 9 October 2003; received in revised form 9 October 2003; accepted 11 February 2004 Available online 28 March 2004 Abstract The occurrence of illnesses related with poor ventilation has driven an increasing attention towards indoor air quality monitoring. In buildings equipped with climate control systems, the diseases related to the air quality can be significantly reduced if smart intervening procedures, aiming to control the concentration of pollutants in the indoor air, can be implemented in the heating, ventilation air conditioning unit. When reliable information about both the indoor and outdoor air quality is made available, the climate control system can provide the most appropriate amount of ventilation, ensuring safe and comfortable living conditions. In this paper, a dedicated, miniaturized, low-cost electronic nose based on state-of-the-art metal oxide sensors and signal processing techniques was developed. The proposed device is targeted to the quantification of carbon monoxide and nitrogen dioxide in mixtures with relative humidity and volatile organic compounds by using an optimized gas sensor array and highly effective pattern recognition techniques. The electronic nose was tested in an environment reproducing real operating conditions. Exploiting the unique response patterns of the different sensors in the array and the capability of a simple fuzzy-logic system it was possible to identify and discriminate concentrations as low as 20 ppb for NO 2 and 5 ppm for CO in the test gas environment, allowing to reach the necessary sensitivity towards the target pollutants together with the selectivity towards the typical interfering gas species Elsevier B.V. All rights reserved. Keywords: Indoor air quality; Gas sensors; Electronic nose; Fuzzy logic 1. Introduction The increasing interest in indoor air quality (IAQ) monitoring is mainly due to the growing incidence of a new class of diseases, identified as building-related illnesses (BRI) and sick building syndromes (SBS), arising from long-term occupancy of confined living spaces, like office buildings or homes/apartments. The occurrence of these diseases is related with the presence of physical, biological and/or chemical contaminants inside the building. The effects on the human health due to indoor pollution include pathologies for which the etiologic agent has been precisely identified and pathologies not directly linked with a specific physical, chemical or biological specie, since the symptoms arise from the concomitant effect of several pollutants which are all present in the ambient, e.g. from low air quality [1]. Chem- Corresponding author. Tel.: ; fax: address: (S. Zampolli). 1 On leave from: Department of Physics, Jahangirnagar University, Savar, Dhaka 1342, Bangladesh. ical contaminants, and in particular gaseous compounds, were found to be among the main responsible ones for both the BRI and the SBS diseases, since in buildings with inadequate ventilation they can accumulate with time. Typically, we can find several hundreds of indoor chemical contaminants, including by-products of the combustion (NO 2,SO 2, CO, etc.), cigarette smoke, particulate matter, mineral fibers and a number of volatile organic compounds. In spite of the very low concentrations, some of these compounds are extremely toxic, like NO 2 or CO; some other, like benzene and formaldehyde, were proved to be carcinogenic. Therefore, the monitoring of the air quality is of paramount importance to keep safe and healthy conditions. In buildings equipped with heating ventilation air conditioning (HVAC) systems, the pollutants are diluted by ventilation, which is generally operated on the basis of fixed duty cycles. This approach does not necessarily ensure an improvement of the indoor air quality, since in many cities fresh outdoor air can contain many pollutant species at concentrations higher than the threshold values. Furthermore, an uncontrolled preventive increase of the ventilation results in an increment of the overall energy consumption, /$ see front matter 2004 Elsevier B.V. All rights reserved. doi: /j.snb 40 S. Zampolli et al. / Sensors and Actuators B 101 (2004) Table 1 Typical indoor air quality contaminants Aldehydes Ozone Nitrogen dioxide Carbon dioxide Particulate matter Radon Carbon monoxide Sulfur dioxide Total VOC Formaldehyde Water vapor Lead especially when the temperature gap between indoor and outdoor air is non-negligible [2]. Several attempts to implement demand-controlled ventilation have been reported in literature. Many of them rely on the quantification of CO 2, used as a tracer of human occupancy in confined living spaces or as surrogate of inhabitant generated pollution. However, this approach is inadequate to monitor IAQ, since several toxic compounds released by building materials and furnishings or generated by human activities are also present in the indoor environment. Since each compound has a different impact on human health, it is important to monitor their concentrations individually. Table 1 reports a list of the most common indoor contaminants found in confined living spaces, which can be used as tracers for the IAQ [1,3,4]. For this reason, the availability of reliable, low-cost sensors suitable to monitor both the indoor and outdoor air quality would allow the implementation of smart HVAC intervening procedures, considering not only the typical information about temperature and relative humidity but also the concentration of a number of compounds used as air quality tracers. This way, by driving the ventilation systems on demand to keep the IAQ under control, it would be possible to maintain acceptable healthy and comfortable conditions while minimizing the overall power consumption. Being the information about the quality, the humidity and the temperature available for the inside and the outside air, the climate control system would intake fresh air, recycle the indoor air or operate active scrubbers, depending on the most favorable energetic conditions. Some low-cost tools for IAQ analyses are nowadays commercially available, but they are not suitable to provide re- liable information, since the various pollutants are not precisely identified and quantified. These devices usually give indications about the overall IAQ, without any estimation of the concentration of each pollutant. In fact, only the detailed information about the concentration of the single gas would allow an optimized control of the HVAC system, including the activation of scrubbers to catalytically convert some pollutants, like, e.g. volatile organic compounds, in CO 2 and water vapor. In this work, an electronic nose (e-nose) based on a solid state gas sensor array for the identification and the quantification of two typical indoor air quality tracers is presented. The simplicity of the proposed approach, which uses stand-alone gas sensors, simple driving electronics and fuzzy-logic pattern recognition algorithms, aims to realize a low-cost tool suitable to monitor some of the compounds of interest for IAQ. The availability of this type of devices is fundamental for a capillary analysis of the pollution level inside buildings equipped with forced ventilation and air climate control units. To validate the approach, only CO and NO 2 were taken into consideration as target compounds, while some VOCs and the humidity were considered as interfering species. 2. Experimental The aim of this work is to develop a reliable tool for IAQ monitoring using a very simple system architecture based on a stand-alone gas sensor array, driving electronics and suitable pattern recognition algorithms, without any fluidic components (valves, filters, pumps, etc.). Furthermore, the system has been designed to integrate additional sub-units able to monitor other parameters relevant for IAQ. To validate this approach, the number of compounds considered within this work was limited to two gases chosen among those reliably detectable by metal oxide solid state gas sensors. Table 2 reports these gases together with the target concentration ranges considered for effective IAQ monitoring and the relevant indoor threshold values as defined in [3].In spite of their relevance for indoor comfort, some other compounds, like carbon dioxide (CO 2 ), were not considered here because their detection is problematic with metal oxide gas sensors. However, other sensing devices capable to detect these compounds are nowadays commercially available and they could be easily integrated into a modular architecture. Table 2 Some gas species considered as IAQ tracers and their target concentration ranges Compound Concentration range Indoor threshold values (8 h exposure) CO 5 30 ppm 9 ppm NO ppb 53 ppb a VOC (benzene, toluene, m-xylene) ppb n.a. b Water vapor (RH) 5 95% No threshold a US-EPA suggested value for 24 h exposure outdoor threshold. No indications given for indoor exposure. b No standards have been set for VOCs in non-industrial indoor settings. S. Zampolli et al. / Sensors and Actuators B 101 (2004) Fig. 1. Schematic overview of the considered e-nose architecture. The system developed for this work consists of an array of thin-film metal-oxide gas sensors, driven by ad hoc electronic circuits suitable to power and measure each device independently. The gas sensors were operated under different conditions and they provided distinct response patterns. A commercial RH sensor monitored the relative humidity, which interferes in both CO and NO 2 detection. The discrimination between the different target gases and their quantification were achieved using state-of-the-art data processing and pattern recognition algorithms, which rely on the distinct response patterns provided by the sensors. In fact, the low selectivity of the solid state gas sensors has been compensated by the combination of the response patterns, allowing the quantification of the single gas species. In this work, a fuzzy-logic system was used for pattern recognition. A similar approach, based on a multi-sensor array and using an artificial neural net (ANN) for pattern recognition, has been proposed in [5]. A schematic drawing of the unit is shown in Fig. 1. This simple architecture is targeted for the detection and quantification of CO and NO 2, but it does not allow a precise quantification of volatile organic compounds (VOC), because of the lack of selectivity in metal oxide semiconductor gas sensors. We are currently addressing this problem following an approach based on a gas-chromatographic architecture realized by MEMS technologies and developed for the detection and quantification of VOC [6], which will be described in a future paper. Both the dedicated gas-chromatographic architecture and the simple e-nose presented in this work are conceived to be part of a modular system for innovative IAQ monitoring applications Metal oxide resistive gas sensors The solid state gas sensors used for the detection of the air quality tracers are based on thin films of metal oxide semi- conductor materials [7,8] deposited onto state-of-the-art micromachined hotplate arrays integrated in a single 5 mm 5 mm Si chip [9]. These hotplates consist of four suspended 200 nm thick Si 3 N 4 membranes integrating a buried platinum heater and passivated by a 800 nm thick SiO 2 layer. The very low thermal mass of the suspended structure allows to reduce the power consumption with respect to commercial devices (only 60 mw to operate the sensor at 400 C), as well as to heat up and cool down the sensing element very fast, which is necessary for fast pulsed temperature (FPT) mode sensor operation [10]. On top of these hotplates, we deposited two types of gas sensing materials by means of different deposition techniques, namely tin oxide (SnO 2 ) by rheotaxial growth and thermal oxidation (RGTO) [7] and tungsten trioxide (WO 3 ) by pulsed laser ablation (PLA) [8]. The morphology of the thin films achieved through the optimization of the deposition techniques leads to very high sensitivity towards the considered gas species, allowing the detection of the gas concentrations down to the range required for IAQ. The lack of selectivity of resistive gas sensors was partially compensated by the deposition of catalyst materials as well as very thin SiO 2 caps above the sensing layer. The response towards the single gas species was further enhanced through the use of different operating temperatures and by operating the sensors in FPT mode [10]. Table 3 shows some of the optimal combinations of materials and operating conditions and the corresponding target gas species. The optimization of both the sensing material and its operating conditions results in excellent sensor responses towards CO and NO 2. On the other hand, the low selectivity of metal oxide semiconductor gas sensors was only partially overcome by the material optimization, since interference phenomena by water vapor and VOC still affected the detection, as will be discussed later Characterization protocol The characterization was performed with a specifically designed characterization system, where up to eight pollutants could be simultaneously injected into a Pyrex measurement chamber. The large inner volume (32 l) of the chamber and the applied flow rate (500 sccm) were chosen to replicate as close as possible the operating conditions found in a room equipped with a HVAC system, where every hour a complete room volume of air is pumped-in. During the test, mixtures of all pollutants at three different concentrations were injected into the characterization chamber. The Table 3 Sensing materials and operating conditions used to detect the different gas species Sensing layer material Catalyst Operation mode Operating temperature ( C) Target gas species SnO 2 Au FPT 375 NO 2 WO 3 Au DC 250 NO 2 SnO 2 + SiO 2 Au DC 400 CO SnO 2 FPT 325 CO 42 S. Zampolli et al. / Sensors and Actuators B 101 (2004) gas distribution system delivers synthetic air, humidified air and the target pollutants from certified gas cylinders in balanced quantities to get the final concentration of each compound. The system is completely controlled by a PC running a specifically developed software package, which allows the recursive execution of automatic sequences lasting several weeks. The sensor arrays are driven and controlled by a custom-made electronic interface connected to a further PC, which allows the simultaneous and completely independent acquisition of up to 16 sensors. The sensing layer resistivity, i.e. the sensor response, was sampled by applying a 1.2 V bias to the sensing layer and measuring the flowing current. Finally, a standard GC/MS is connected to the measurement chamber, performing spot-wise quality control tests of the gas mixture inside the chamber. The set of data used for the analysis presented in this work consisted of 2000 patterns acquired within the last 45 days of a 60-day sensor characterization run. The data relative to the first 15 days were neglected, since the sensors used in this characterization were brand-new and had undergone no stabilizing burn-in procedures, thus showing a considerable drift of the conductivity during the first operation period Pattern recognition techniques The characterization data specifically collected during long-term measurement campaigns lasting more than 2 months, were then used to train and test a fuzzy system, which provided an estimation of the initial concentration of each target compound in the gas mixture. Operating the sensors under optimized conditions for the detection of a given compound does not make them fully selective, but they do provide unique response patterns, which can be used to classify and quantify the single pollutants inside complex gas mixtures, as found in real applications. To evaluate concentration of each compound in a complex gas mixture, we used a neuro-fuzzy system provided by the adaptive fuzzy modeler (AFM) developed by ST-Microelectronics [11]. AFM is a software tool for the automatic fuzzy model generation of a system starting from its input output data sampling. It implements neural training algorithms for the successive optimization of both the rules and the sets of a simple Sugeno-type zero-order fuzzy system. The automatic rules identification is carried out, thanks to an unsupervised learning on a winner-take-all fuzzy associative memory neural network, while the optimization of the fuzzy sets parameters is carried out by a supervised learning on a multilayer backward-propagation fuzzy associative memory neural network [11]. As fuzzy intersection operators (inference methods), AFM allows to select either the minimum or the product operator. The AFM package is supplied for feasibility studies of ST-Microelectronics fuzzy-logic ASICS (W.A.R.P , ST52x301), which implement all the processing capabilities of the fuzzy systems simulated in the AFM. Therefore, the processing electronics necessary for the realization of the e-noses studied in this work are already commercially available. Further details on fuzzy logic can be found in [12]. The AFM was used to simulate two fuzzy systems having three input variables and one output prediction each, being the input variables the output from the CO, NO 2 and RH sensors and the output predictions, the CO and the NO 2 concentrations, respectively. In the case of the CO sensor, five Gaussian fuzzy sets were used for the CO input and three Gaussian sets for each of the interfering NO 2 and RH inputs. In the case of the NO 2 sensor, five Gaussian fuzzy sets were used for the NO 2 input and three Gaussian sets for each of the interfering CO and RH inputs. The system was trained with the product inference operator for 50 learning epochs and converged rapidly. 3. Results and discussion 3.1. Response of the gas sensor arrays The optimization of the sensing layer materials and of the operating conditions has allowed to reach the sensitivity necessary for the reliable detection of CO down to concentrations of 5 ppm and of NO 2 down to 20 ppb. To detect CO, we used a SnO 2 + SiO 2 sensor with Au catalyst operated in DC mode at 400 C, while the sensor targeted for NO 2 was a SnO 2 sensor with Au catalyst operated in FPT mode with a pulse temperature of 375 C, a duration of 1 s, and a period of 60 s. Other sensors were used during the extensive characterization run, principally FPT mode operated SnO 2 sensors for CO detection and WO 3 -based sensors for NO 2 detection, but the validation of the sensor response patterns during the training of the fuzzy system have shown that these combinations are less effective, and therefore only the sensor responses of the first two sensor types will be considered. Fig. 2a and b shows the typical response of two sensors targeted for the detection of CO and NO 2 to three different concentrations of these pollutants. First, the sensor baseline in synthetic air with RH = 50% is shown, up to T = 0 min. Afterwards, three different concentrations of CO or NO 2 are consecutively injected and kept each one for 15 min. In this case, the considered concentrations were 5, 15 and 30 ppm for CO and 20, 100 and 200 ppb for NO 2. The plots of Fig. 2 emphasize the excellent response of the sensors. For the CO sensor, the increase of the conductivity for the first concentration of 5 ppm amounts to approximately 400%, while the NO 2 sensor shows the typical effect of an oxidizing gas specie on n-type semiconducting materials, with a variation of the resistivity of approximately 300% for the 20 ppb concentration. While the CO sensor shows a good dynamic response over the entire concentration range, the NO
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