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Electronic noses for the continuous monitoring of odours from a wastewater treatment plant at specific receptors: Focus on training methods
Available online at www.sciencedirect.com
Sensors and Actuators B 131 (2008) 53–62
Electronic noses for the continuous monitoring of odours from a wastewater treatment plant at specific receptors: Focus on training methods Laura Capelli a,∗ , Selena Sironi a , Paolo C´entola a , Renato Del Rosso a , Massimiliano Il Grande b a
Olfactometric Laboratory, Department of Chemistry, Materials and Chemical Engineering “Giulio Natta”, Politecnico di Milano, P.za Leonardo da Vinci, 32-20124 Milan, Italy b Progress S.r.l., via N.A. Porpora, 147-20131 Milan, Italy Available online 14 December 2007
Abstract The aim of our research activity is the development of a system, based on the use of electronic noses, for the continuous monitoring of environmental odours at specific receptors, i.e. directly where the presence of odours is lamented. This paper describes the experimental approach adopted in order to use three electronic noses for the continuous monitoring of odours from a wastewater treatment plant, with the aim of determining its odour impact on the neighbouring land. This work focuses on the theoretical principles and the experimental methods adopted for the identification, the definition and the optimization of the procedures for the creation of a suitable training data set, which maximizes the electronic nose capability of operating a qualitative classification and estimating the odour concentration of ambient air. The results of the application of three electronic noses to the continuous monitoring of odour emissions from a wastewater treatment plant located in the North of Italy allowed to draw some important conclusions about the odour impact of the monitored plant and, from a more general point of view, about the possibility of using electronic noses as odour impact determination tools, showing how opportunely trained electronic noses can successfully be applied for the continuous monitoring of odours of environmental interest. © 2007 Elsevier B.V. All rights reserved. Keywords: Electronic nose; Odour impact determination; Continuous monitoring; Odour concentration; Training
1. Introduction In recent years, there is an increasing care about topics regarding air quality, which brought to the development and the refinement of suitable environmental monitoring techniques. Among these techniques, there is a growing interest towards the environmental applications of electronic noses. Since the first report of the design of an electronic nose using chemical sensors and pattern recognition in 1982 by Persaud and Dodd [1], there are nowadays several scientific publications concerning for example the application of electronic noses to the identification of specific environmentally important gases [2–4], to the detection of odour abatement system failure [5] or to the correlation of electronic nose responses to other parameters for the char-
∗
Corresponding author. Tel.: +39 02 23993206; fax: +39 02 23993291. E-mail address: [email protected] (L. Capelli).
0925-4005/$ – see front matter © 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.snb.2007.12.004
acterization of air or water quality, such as for example odour concentration [6] or BOD [7,8]. The aim of our research activity in this field is the development of a system, based on the use of electronic noses, for the continuous monitoring of environmental odours at specific receptors, i.e. directly where the presence of odours is lamented. For this purpose, the instrument should be capable to continuously analyze the ambient air at specific receptors and, in real time, it first should qualitatively classify the analyzed air by attributing it to a specific olfactory class, and secondly it should quantify odour by estimating the odour concentration [9] of the analyzed air [10]. The qualitative classification and the estimation of the odour concentration are based on the comparison of the signals produced by the ambient air analyses with a database of patterns acquired by the instrument in a previous training phase. The training phase is very important and extremely delicate, especially for this kind of application, where a high sensitivity is
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required because of the increased distance of the instruments from the emission source, which entails the dilution of the odours into the atmosphere, and therefore makes their recognition more difficult. This paper describes the experimental approach adopted in order to use three electronic noses for the continuous monitoring of odours from a wastewater treatment plant. The aim of the monitoring was to determine the odour impact of the plant at issue, by installing two instruments at to different receptors, represented by two dwellings located near the plant, and a third electronic nose at the plant fence line, with the purpose of repeatedly analyzing the ambient air and determining its quality and odour concentration. This work focuses on the theoretical principles and the experimental methods adopted for the identification, the definition and the optimization of the procedures for the creation of a suitable training data set (TS), which maximizes the electronic nose capability of operating a qualitative classification and estimating the odour concentration of ambient air. The different aspects concerning the training phase that must be studied and defined are: • the identification of the principal odour sources of the plant(s) to be monitored; • the collection of representative gas samples in correspondence of these odour sources; • the preparation of a set of odorous gas samples to be analyzed by electronic nose; • the instrumental analysis of these samples; • the training data processing, i.e. the evaluation and selection of the acquired data for the creation of two optimal training sets for qualitative and quantitative recognition, respectively. As far as sampling is concerned, it must be carried out with particular care, with the aim of collecting a sufficient number of gas samples, which should be representative of all odour sources of the plant(s) to be monitored, and therefore give a complete characterization of the plant(s) odour emissions. Another extremely delicate aspect of the training phase is represented by the preparation of the samples to be analyzed by electronic nose. Our theoretical studies and experimental evidences demonstrate that, in order to maximize the electronic nose capability of recognizing diluted odours, such as those that may be typically present at receptors located at a certain distance from the emission source, it is necessary to dilute the samples collected in correspondence of the monitored odour sources at different odour concentration values. It is therefore important to determine the optimal odour concentration range of the training samples. At last, it is necessary to define a set of data processing procedures and techniques, such as exploratory data analysis or specific recognition tests [11], which allow to select the optimal data to be used for unknown pattern recognition, for example by identifying and in case eliminating outliers, or by defining the number and composition of the different olfactory classes to be distinguished.
2. Experimental methods 2.1. Electronic nose description The instruments that were used for this study have been developed in collaboration with Sacmi s.c.a.r.l. and the Sensor Laboratory of the University of Brescia [12]. The system includes a pneumatic assembly for dynamic sampling (pump, electro-valve, electronic flow meter), a thermally controlled sensor chamber with 35 cm3 of internal volume and an electronic board for controlling the sensor operational conditions. Each instrument has been equipped with an array of six different thin film metal oxide semiconductor (MOS) sensors, which makes the system sensitive to a large spectrum of volatile compounds, and a humidity sensor. In principle, the sensor selectivity and sensitivity can be changed by tuning their operating temperature [13,14]. However, we observed that, when very complex mixtures are analyzed (gaseous emissions from wastewater treatment plants contain hundreds of different compounds [15,16]), the sensor responses are strongly correlated, and their temperature dependence is not clearly definable. The instrument remote control and the data acquisition can be performed by an external personal computer through the standard communication port RS232 or a USB port. Two special software have been developed for the electronic nose data processing: the nose pattern editor (NPE), which is used for data preprocessing and for multivariate statistical analysis (e.g. PCA), and the nose pattern classifier (NPC) for pattern recognition and data classification. Each electronic nose analysis is composed by a measurement phase and by a recovery phase. The air to be analyzed is sucked during the measurement phase; during the recovery phase, reference air is pumped into the sensor chamber in order to desorb the volatile compounds from the sensor active layer and bring their response back to the base line. The measurement phase duration was set equal to 3 min, with a recovery time of 12 min, which was experimentally found to be a sufficient time in order to let the sensor response return to the base line value. One important problem connected to the use of the electronic nose on field is the base line instability, due to the variability of the atmospheric conditions (i.e. temperature, humidity, etc.) in the external ambient. This problem has been partially solved by introducing active carbon and silica gel filters for the deodourization and dehumidification of the reference air. For the analyses, the carrier flow rate was 150 cm3 min−1 and the temperature of the sensor chamber was kept constant at 50 ◦ C. 2.2. Training The training of the electronic nose is a very important and delicate phase. During this phase, it is necessary to create a complete database that the instrument uses as a reference for the subsequent pattern recognition. This database is created by analyzing a set of gas samples, which should be representative of the odours to be recognized.
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Fig. 1. Wastewater an sludge processing operations, which represent the odour sources of the monitored sewage treatment plant.
2.2.1. Sample collection The first essential step for monitoring the odours emitted by any industrial plant, is an in depth study and analysis of the process, which is necessary in order to identify all possible odour emission sources. The aim of this study was to monitor the odour emissions related to a domestic wastewater treatment plant located in the North of Italy, with a sewage treatment capacity of about 20,000 m3 /d. In general, in the case of a sewage treatment plant, odour emissions are associated both with the wastewater and sludge processing [17–19]. In this specific case the principal odour sources identified at the sewage treatment plant at issue were: • the sewage arrival/equalization tank; • the pre-aeration (for sand and oil separation) and primary sedimentation tank; • the aeration/oxidation tank; • the final sedimentation tank; • the sludge thickener; • the shed dedicated to the sludge dewatering by centrifugation; • the caisson for the storage of the dewatered sludge before removal. These sources are illustrated in Fig. 1, which represents the flow diagram of the wastewater and of the sludge treatment lines, respectively. In order to carry out a successful training, with the purpose of using electronic noses for the sewage plant odour impact determination, it is necessary to collect a set of gas samples in correspondence of each of the above mentioned odour sources. Moreover, it is necessary to create a reference olfactory class, corresponding to non-odorous, i.e. “neutral” air. For this reasons, some ambient air samples were collected near the receptors where the electronic noses had to be installed, in particular
moments in which odours attributable to the monitored wastewater plant were not perceivable. Sampling methods and equipment are extremely important, as they must allow to collect samples which should be qualitatively representative of the monitored source, and to preserve the olfactory characteristics of the samples until the moment of their analysis at the laboratory. For these reasons, the collection of air samples was carried out according with the requirements of EN 13725 (2003). The sampling bags are in NalophanTM , they have a capacity of about 8 l and are equipped with a TeflonTM inlet tube, and they are filled with the gas to be analyzed by means of a depression pump. Sampling on area sources, i.e. the surfaces of the wastewater treatment tanks or the surface of the stored sludge, was carried out using a wind tunnel system [20,21], which consists of a floating hood that is positioned over the emitting surface. A stream of neutral air is introduced into the hood, with the aim of simulating the wind action on the monitored liquid surface. The wind tunnel used during the experimentation has a circular section inlet and outlet duct, of 0.08 m diameter. The central body of the hood used was a 0.25 m wide, 0.08 m high and 0.5 m deep rectangular section chamber. Inside the inlet duct there is a perforated stainless steel grid and inside the divergent that connects this duct to the central body of the hood there are three flow deflection vanes. Both these devices have the function of making the airflow as homogeneous as possible [22]. A plant and a photograph of the wind tunnel are shown in Figs. 2 and 3, respectively. The neutral air stream introduced into the wind tunnel is obtained from an air tank, and its flow rate is regulated by means of a flow meter at 2500 l/h, which corresponds to an air velocity inside the central body of the hood of about 3.5 cm/s. The introduction of a neutral air stream at low velocity enables the obtainment of samples with a sufficiently high odour concentration (above 200 ouE /m3 ) at the wind tunnel outlet [23].
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Fig. 2. Plant of the wind tunnel.
2.2.2. Sample preparation and analysis Once the odorous gas samples are collected on the different odour sources of the monitored plant they are transported to the laboratory, with particular care towards sample storage, in order to minimize the possibility of interfering with the olfactory properties of the samples. The odour concentration of all samples collected for the electronic noses training was determined by dynamic olfactometry. The olfactometric analyses were conducted in conformity with the European Norm EN 13725 (2003) in the Olfactometric Laboratory of the Politecnico di Milano. Dynamic olfactometry is a sensorial technique that allows to determine the odour concentration of an odorous air sample relating to the sensation caused by the sample directly on a panel of opportunely selected people. The odour concentration is expressed in European odour units per cubic meter (ouE /m3 ), and it represents the number of dilutions with neutral air that are necessary to bring the concentration of the sample to its odour perception threshold concentration. The analysis is carried out by presenting the sample to the panel at increasing concentrations by means of a particular dilution device called olfactometer, until the panel members start perceiving an odour that is different from the reference neutral air. The odour concentration is then calculated as the geometric mean of the odour threshold values of each panellist, multiplied by a factor that depends on the olfactometer dilution step factor. An olfactometer ECOMA model TO8, based on the “yes/no” method, was used as a dilution device. This instrument with aluminium casing has 4 panellist places in separate open boxes. Each box is equipped with a stainless steel sniffing port and a push-button for “yes” (odour threshold). The measuring range of the TO7 olfactometer starts from a maximum dilution factor of 1:64.000, with a dilution step factor 2. All the measurements
Fig. 3. Photograph of the wind tunnel.
were conducted within 30 h after sampling, relying on a panel composed of 8 panellists (4 + 4). The odour concentration was calculated as the geometric mean √ of the odour threshold values of each panellist, multiplied by 2. In general, it is important to take account of the odour concentration of the samples before analyzing them by electronic nose. Too concentrated samples may give problems of sensor saturation or poisoning, for this reasons the samples collected in correspondence of the plant odour sources are not analyzed directly by electronic nose after sampling. Once the odour concentration of the collected samples was measured, they were diluted with neutral air at different odour concentration levels, generally comprised between 20 ouE /m3 and 1500 ouE /m3 , by means of the olfactometer, i.e. the same instrument used for the olfactometric analyses. This way it was possible to obtain more samples belonging to the same olfactory class, i.e. qualitatively representative of the same odour sources, differing for their odour concentration values. The samples prepared according to the above described principles are then analyzed by electronic nose. Important aspects relevant to this phase are the analysis duration and number. Each analysis is composed by a measurement phase, during which the electronic nose sucks the external air to be classified, and a recovery phase, during which clean reference air is flown into the sensor chamber, in order to recover the sensor active layer, and bring their response back to reference conditions (base line). The analysis duration should be set according to the following principles: the measurement duration should allow the sensors response to reach steady conditions, whereas the cleaning phase should be sufficiently long to enable complete sensor recovery. Previous studies brought us to choose a measurement duration of 3 min, and a recovery of 12 min [11]. Each analysis has therefore a duration of 15 min. Each sample should be analyzed more than once by the electronic nose, in order to have more reliable training data. The number of repetitions to be carried out should be a compromise between the necessity of creating a large training database and the necessity of conducting the whole training within a reasonable time. 2.2.3. Data processing Once all the samples were analyzed by electronic nose, the instrument has acquired a relatively large set of training data. Not all of the acquired training data is suitable for successful pattern recognition. For this reason, it is necessary to carry out a sequence of data processing steps in order to
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Fig. 4. Reciprocal position of the three electronic noses adopted for the monitoring.
select the optimal training data set (TS) to be used as a reference for the qualitative and quantitative odour recognition, respectively. The data processing phase is therefore extremely delicate. There are different techniques available in order to evaluate the training data and create a representative TS. The first technique is exploratory data analysis. It is very useful to visually explore data because, by the critical examination of specific graphical representations of data, it is possible to get a feel of their structure and to detect outliers [12]. One important aspect of exploratory data analysis is the examination of the sensor response plots, which allows the identification of possible anomalies in the response curves, e.g. measures with a high relative humidity level. Secondly, other data visualization methods based on dimensional reduction, e.g. principal component
analysis (PCA), can be successfully used for the identification of outliers. The second method adopted for training data evaluation is represented by specific recognition tests. These tests are carried out by randomly dividing the TS, which is a dataset composed of measures of known olfactory class and odour concentration, into two different test sets: a test training set (tTS) and a test match set (tMS). The recognition tests consist in the recognition of the tMS using the tTS as a reference. This method is very useful, because it allows to study and evaluate how the electronic nose odour recognition capability varies in function of the considered training data set. As far as the qualitative classification is concerned, the odour recognition capability is quantified in terms of an accuracy index (AC), which expresses the number of correctly classified
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measures with respect to the total measure: AC =
no. of correctly classified measures no. of total measures
As far as regards the odour quantification, an indication of the precision of the odour concentration estimation can be given by the calculation of the correlation index (R or R2 ) between true and predicted values: (x − x¯ )(y − y¯ ) 2 R = corr(X, Y ) = (x − x¯ )2 (y − y¯ )2 2.3. Monitoring The monitoring was conducted by using three electronic noses. Two instruments were installed at two receptors, while the third instrument was positioned at the plant fence line, in direction of the two considered receptors. More in detail, a first electronic nose (EOS 35) was installed inside a building located at about 300 m direction south-south-east from the monitored plant (receptor A), whose occupants had often lamented the presence of odours coming from the wastewater plant at issue. Contemporaneously, a second instrument (EOS 25) was installed inside a dwelling located at about 300 m in direction south-west from the plant at issue (receptor B). The third electronic nose (EOS 28) was installed inside a little building located near the southern perimeter of the plant. Fig. 4 shows the reciprocal position of the three instruments. In each case, the external air was sucked during the measurement phase through a TeflonTM inlet tube directly connected to the instrument. The reference air was represented by the ambient air inside the electronic nose installation room, opportunely filtered through active carbon and silica gel, in order to make it inodorous and humidity free. This precaution has the function of making the sensor base line relatively independent from the presence of humidity or odours in the external ambient air. The monitoring period started Monday, 9th October 2006 and ended Tuesday, 24th October 2006.
Fig. 5. Plot of the response curves of a sensor relevant to the repeated analyses of the sample collected on the primary sedimentation tank, diluted at 220 ouE /m3 .
the relative humidity sensor (Fig. 6), it is noticeable that the increase of the response amplitude is due to an increase of the sample relative humidity content. This can be explained considering that the NalophanTM that constitutes the sampling bags is permeable to humidity. For this reason the samples that are analyzed immediately after dilution with the neutral and dry air from the olfactometer have a low relative humidity content, which slowly increases with time because of the humidity transfer from the external air into the sample through the NalophanTM . Following studies showed that the achievement of an equilibrium between sample humidity and external humidity requires about 2.5–3 h. This means that this is the minimum time interval that should be waited after dilution of the samples with the neutral and dry air from the olfactometer before starting the analyses by electronic nose.
3. Results and discussion 3.1. Creation of an optimal training data set 3.1.1. Exploratory data analysis The examination of the sensor response curves and of the PCA derived from the training measurements highlighted the presence of some anomalous measures. As an example of how exploratory data analysis was used in order to become aware of some difficulties associated with the electronic nose training and to identify and eliminate outliers, Fig. 5 shows the response curves of a sensor relevant to the repeated analysis of the same sample, collected in correspondence of the primary sedimentation tank, and then diluted at an odour concentration value of 220 ouE /m3 . It is possible to observe that the response amplitude increases with time. By comparing these responses with the responses of
Fig. 6. Plot of the response curves of the humidity sensor relevant to the repeated analyses of the sample collected on the primary sedimentation tank, diluted at 220 ouE /m3 .
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Fig. 7. PCA relevant to the analyses of samples of neutral air and samples collected on the primary sedimentation tank, diluted at 220 ouE /m3 .
By plotting these response curves in a PCA, it is possible to observe how these measurements produce anomalous points, i.e. so called “outliers” (Fig. 7). Fig. 8 illustrates how the quality of the PCA is improved by eliminating the identified outliers from the training data set. 3.1.2. Qualitative recognition tests Several qualitative recognition tests were carried out by creating different tTS and tMS as subsets of the original data set acquired by the instruments during the training phase, with the aim of selecting the optimal training data set, i.e. dataset to be used as a reference for the recognition of unknown patterns which maximizes the instrument odour qualitative classification capability. The aspects that were investigated were on one hand the olfactory classes to be discriminated, and on the other hand the optimal odour concentration range of the training samples.
Fig. 8. PCA relevant to the analyses of samples of neutral air and samples collected on the primary sedimentation tank, diluted at 220 ouE /m3 after elimination of the outliers.
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It is very important to accurately define the olfactory classes to be discriminated, especially if considering that, in general, a reduction of the number of olfactory classes to be distinguished entails an increase of the accuracy index, i.e. of the number of measures classified correctly. For this reason, if it is not necessary to discriminate the single odour sources, it is possible to group some of them in one olfactory class, in order to reduce the number of different olfactory classes, and therefore improve the accuracy of the qualitative classification. In this specific case, the best results were obtained by grouping the samples into the following three macro olfactory classes: “sewage treatment”, “sludge treatment” and “neutral air”. It is also important to consider that each olfactory class should ideally be represented by the same number of experimental points. The reason is that, as the qualitative classification is based on statistical methods, such as k-Nearest-Neighbours (kNN) classification algorithm [24,25], the attribution of an unknown sample to the most numerous olfactory class is favoured. Another very important aspect to be taken into account for the creation of an optimal training set is the odour concentration level of the training samples. From a theoretical point of view, considering that the aim of the work is to use electronic noses at receptors, i.e. in presence of diluted odours, it is reasonable to assume that too concentrated samples are not suitable for the instrument training, because they differ too much from the conditions to which the electronic noses are exposed during the monitoring on field. On the other hand, it is not advisable to use too diluted samples neither, because their distinction from the neutral air could be difficult. This considerations were confirmed experimentally, through the execution of several recognition tests using different training data sets, which allowed to identify an optimal range in which the odour concentration of the training samples should be comprised in order to maximize the classification accuracy. The optimal odour concentration range was found to be between 100 ouE /m3 and 150 ouE /m3 , giving classification accuracy index values over 0.95. 3.1.3. Quantitative recognition tests The algorithm used for the quantitative odour recognition was a linear regression of the training dataset. The set of features extracted from the sensor responses, which are used as variables for the linear regression, is composed of parameters that have a different and not a priori definable dependence from the stimulus that originates the sensor signal. Given the variability of the laws that regulate the dependence of the feature set from the sample concentration, a linear interpolation was used. The choice of using a linear interpolation for the odour concentration estimation of unknown samples, even though the relation between sensor signal and sample concentration is not linear, entails the necessity of considering a narrow odour concentration range as the training dataset. The quantitative recognition tests showed that the highest correlation index values (R > 0.9) were obtained by using training data sets relevant to the samples with typical odour concentration values of an urban ambient air, i.e. comprised between 20 ouE /m3 and 80 ouE /m3 .
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Fig. 9. Results of the qualitative classification (a) and of the odour concentration estimation (b) of the air analyzed at the plant southern fence line (EOS 28).
3.2. Odour impact determination 3.2.1. Representation of the results The monitoring results are represented by large tables that report the olfactory class and the odour concentration value attributed to the analyzed air for each measurement carried out during the monitoring period. The results can be synthetized in graphs, which in abscissa report the measurement date and hour, and in ordinate report the olfactory class or the odour concentration value attributed to the analyzed air, respectively for the qualitative or quantitative odour recognition. Based on the results of the monitoring, it is possible to make some considerations about the odour impact of the monitored wastewater treatment plant. 3.2.2. Odour impact at the plant fence line The monitoring results, which are illustrated in Fig. 9, enable to make some considerations about the odour impact at the plant fence line. The number of measures that the electronic nose EOS 28 attributed to each olfactory class and their relative frequency with respect to the total number of measures are reported in Table 1. The presence of odours was detected for 37.5% of the monitoring total duration. The results show that the only olfactory class recognized except the “neutral air” is the one named “sewage treatment”, indicating that the presence of odours in the considered monitoring position must be attributed to the odorous emissions from the wastewater processing line. This relatively high percentage must be evaluated considering that the electronic nose was installed inside the plant perimeter. It is reasonable to assume that the relative frequency of odour recognition outside of the plant must be significantly lower, due to the effect of odour dispersion into the atmosphere.
3.2.3. Odour impact at receptor A The results of the monitoring at receptor A, located at about 300 m in direction south-south-east from the wastewater plant at issue (Fig. 10), allow to make some considerations about the odour impact of the monitored plant on the neighbouring land. The number of measures that the electronic nose EOS 35 attributed to each olfactory class and their relative frequency with respect to the total number of measures are reported in Table 2. The presence of odours was detected for 10.8% of the monitoring total duration. Also in this case, the only olfactory class recognized except the “neutral air” is the one relevant to the odour emissions from the sewage treatment line. The odour concentration values registered by the electronic nose EOS 35 installed at receptor A are included in a range between 5 ouE /m3 and 31 ouE /m3 . These values do not present anomalies, and are typical odour concentration values of an ambient air. 3.2.4. Odour impact at receptor B For receptor B, represented by a dwelling located at about 300 m in direction south-west from the wastewater plant at issue, based on the monitoring results, which are illustrated in Fig. 11, it is possible to make some analogous considerations in order to evaluate the plant odour impact. It is noticeable that the presence of odours from the monitored wastewater treatment plant was detected for 8.7% of the monitoring total duration (Table 3). Also in this case, the olfactory class that was recognized most frequently except the reference “neutral air” is the one relevant to the odour emissions from the sewage processing line. The odour concentration values attributed by the electronic nose EOS 25 to the analyzed air are included in a range between 24 ouE /m3 and 35 ouE /m3 . These values do not present anoma-
Table 1 Relative recognition frequency of each olfactory class at the plant fence line
Table 2 Relative recognition frequency of each olfactory class at receptor A
Olfactory class
Number of measures
Recognition frequency (%)
Olfactory class
Number of measures
Recognition frequency (%)
Neutral air Sewage treatment Sludge treatment
883 529 0
62.5 37.5 0.0
Neutral air Sewage treatment Sludge treatment
1259 153 0
89.2 10.8 0.0
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Fig. 10. Results of the qualitative classification (a) and of the odour concentration estimation (b) of the air analyzed at receptor A (EOS 35).
Fig. 11. Results of the qualitative classification (a) and of the odour concentration estimation (b) of the air analyzed at receptor B (EOS 25). Table 3 Relative recognition frequency of each olfactory class at receptor B Olfactory class
Number of measures
Recognition frequency (%)
Neutral air Sewage treatment Sludge treatment
1205 112 2
91.4 8.5 0.2
lies, and are typical odour concentration values of an ambient air. 4. Conclusions This study brought to the definition of a set of optimized procedures for the electronic nose training, which allow to maximize the instrument capability of qualitatively and quantitatively recognizing odours. The innovative aspects of the developed instruments is that they can be used at receptors, i.e. in presence of diluted odours, both for the qualitative classification of environmental odours and for the real time determination of the odour concentration of the analyzed air. Moreover, the results of the application of three electronic noses to the continuous monitoring of odour emissions from a wastewater treatment plant located in the North of Italy allow to draw some important conclusions about the odour impact of the monitored plant and, from a more general point of view, about the possibility of using electronic noses as odour impact determination tools.
As far as the odour impact of the monitored wastewater treatment plant is concerned, the high relative frequency of detections attributed by the electronic noses, installed at the plant fence line and at two different receptors respectively, to the olfactory class named “sewage treatment”, allows to affirm that the plant area dedicated to the wastewater processing represents the principal odour source of the plant at issue. The identification of the major odour source can be very useful for the plant management, as it gives important indications about which interventions to undertake for the plant odour impact reduction. In conclusion, the results show that opportunely trained electronic noses can successfully be applied for the continuous monitoring of odours of environmental interest directly where the odour nuisance is lamented.
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[5] R.M. Stuetz, W. Bourgeois, Application of non-specific gas sensors for detection of odour abatement process failure, in: Environmental Odour Management, VDI-Berichte 1850, VDI Verlag GmbH, D¨usseldorf, Germany, 2004, pp. 463–471. [6] K. Boholt, K. Andreasen, F. den Berg, T. Hansem, A new method for measuring emission of odour from a rendering plant using the Danish odour sensor system (DOSS) artificial nose, Sens. Actuator B Chem. 106 (2005) 170–176. [7] R.M. Stuetz, R.A. Fenner, G. Engin, Characterisation of wastewater using an electronic nose, Water Res. 33 (1999) 442–452. [8] G. Onkal-Engin, I. Demir, S.N. Engin, Determination of the relationship between sewage odour and BOD by neural networks, Environ. Model. Software 20 (2005) 843–850. [9] EN 13725: 2003, Air quality—determination of odour concentration by dynamic olfactometry, Comit´e Europ´een de Normalisation, Brussels, Belgium, pp. 1–70. [10] S. Sironi, L. Capelli, P. C´entola, R. Del Rosso, M. Il Grande, Continuous monitoring of odours from a composting plant, Waste Manage. 27 (2007) 389–397. [11] S. Sironi, L. Capelli, P. C´entola, R. Del Rosso, Development of a system for the continuous monitoring of odours from a composting plant: focus on training, data processing and results validation methods, Sens. Actuator B Chem. 124 (2007) 336–346. [12] M. Falasconi, M. Pardo, G. Sberveglieri, I. Ricc`o, A. Bresciani, The novel EOS835 electronic nose and data analysis for evaluating coffee ripening, Sens. Actuator B Chem. 110 (2005) 73–80. [13] A.P. Lee, B.J. Reedy, Temperature modulation in semiconductor gas sensing, Sens. Actuator B Chem. 60 (1999) 35–42. [14] N. Yamazoe, G. Sakai, K. Shimanoe, Oxide semiconductor gas sensors, Catal. Surv. Asia 7 (2003) 63–75. [15] A.P. Bianchi, M.S. Varney, Volatilisation processes in wastewater treatment plants as a source of potential exposure to VOC, Ann. Occup. Hyg. 41 (1997) 437–454. [16] A. Escalas, J.M. Guadayol, M. Cortina, J. Riviera, J. Caixack, Time and space patterns of VOC in a sewage treatment plant, Water Res. 37 (2003) 3913–3920. [17] F.-B. Frechen, Odour emissions and odour control at wastewater treatment plants in West Germany, Water Sci. Technol. 20 (1988) 261– 266. [18] A.J. Vincent, Sources of odours in wastewater treatment, in: R. Stuetz, F.-B. Frechen (Eds.), Odours in Wastewater Treatment: Measurement, Modelling and Control, IWA Publishing, London, UK, 2001, pp. 69–92 (Chapter 4). [19] P. Gostelow, S.A. Parsons, R.M. Stuetz, Odour measurements for sewage treatment works, Water Res. 35 (2001) 579–597. [20] J.K. Jiang, P.J. Bliss, T.J. Schulz, The development of a sampling system for determining odour emission rates from area surfaces. Part I. Aerodynamic Performance, J. Air Waste Manage. Assoc. 45 (1995) 917–922. [21] P.J. Bliss, J.K. Jiang, T.J. Schulz, The development of a sampling system for determining odour emission rates from area surfaces. Part II. Mathematical model, J. Air Waste Manage. Assoc. 45 (1995) 989–994.
[22] S. Sironi, L. Capelli, P. C´entola, R. Del Rosso, M. Il Grande, Design and validation of a hood system for odour sampling on liquid surfaces, in: Proceedings of the First Mediterranean Congress Chemical Engineering for Environment, San Servolo, Venice, Italy, 2006, pp. 76–84. [23] F.-B. Frechen, M. Frey, M. Wett, C. L¨oser, Aerodynamic performance of a low-speed wind tunnel, Water Sci. Technol. 50 (2004) 57–64. [24] Y. Gonz´alez Mart´ın, M.C. Cerrato Oliveros, J.L. P´erez Pav´on, C.G. Pinto, B.M. Cordero, Electronic nose based on metal semiconductor sensors and pattern recognition techniques: characterisation of vegetable oils, Anal. Chim. Acta 449 (2001) 69–80. [25] Y. Roggo, L. Duponchel, J.P. Huvenne, Comparison of supervised pattern recognition with McNemar’s statistical test: application to qualitative analysis of sugar beet by near-infrared spectroscopy, Anal. Chim. Acta 477 (2003) 187–200.
Biographies Laura Capelli is a PhD student in industrial chemistry and chemical engineering at the Politecnico di Milano. She is currently doing her research activity at the Olfactometric Laboratory of the Department of Chemistry, Materials and Chemical Engineering “Giulio Natta” of the Politecnico di Milano. Her research interest includes odour sampling and measurement techniques, and especially the development of electronic nose based systems for the continuous monitoring of environmental odours. Selena Sironi received her PhD in industrial chemistry and chemical engineering from the Politecnico of Milano in 2004. She is currently a researcher at the Department of Chemistry, Materials and Chemical Engineering “Giulio Natta” of the Politecnico di Milano. Her research interest includes waste treatment, biofiltration, odour sampling and odour measurement techniques. Paolo C´entola is a full professor of environmental chemistry at the Department of Chemistry, Materials and Chemical Engineering “Giulio Natta” at the Politecnico di Milano. In 1997, he founded the Olfactometric Laboratory of the Politecnico of Milano, the first laboratory in Italy whose activity included the execution of olfactometric analyses for the characterization of odour emissions of environmental interest. His research interest always included several important environmental topics, and especially waste treatment. Renato Del Rosso is a full professor at the Department of Chemistry, Materials and Chemical Engineering “Giulio Natta” at the Politecnico di Milano. He is the scientific responsible of the Olfactometric Laboratory of the Politecnico di Milano, and supervises the research activities of the laboratory, which now include the following main topics: odour sampling techniques, odour dispersion modelling and application of electronic noses to the environmental sector. Massimiliano Il Grande is a chemical engineer, graduated at the Politecnico di Milano in 1997. He is now a managing director of Progress S.r.l., a society founded in 1996, which offers services of environmental monitoring and consulting, and strictly cooperates with the Olfactometric Laboratory of the Politecnico di Milano.
Sensors and Actuators B 131 (2008) 53–62
Electronic noses for the continuous monitoring of odours from a wastewater treatment plant at specific receptors: Focus on training methods Laura Capelli a,∗ , Selena Sironi a , Paolo C´entola a , Renato Del Rosso a , Massimiliano Il Grande b a
Olfactometric Laboratory, Department of Chemistry, Materials and Chemical Engineering “Giulio Natta”, Politecnico di Milano, P.za Leonardo da Vinci, 32-20124 Milan, Italy b Progress S.r.l., via N.A. Porpora, 147-20131 Milan, Italy Available online 14 December 2007
Abstract The aim of our research activity is the development of a system, based on the use of electronic noses, for the continuous monitoring of environmental odours at specific receptors, i.e. directly where the presence of odours is lamented. This paper describes the experimental approach adopted in order to use three electronic noses for the continuous monitoring of odours from a wastewater treatment plant, with the aim of determining its odour impact on the neighbouring land. This work focuses on the theoretical principles and the experimental methods adopted for the identification, the definition and the optimization of the procedures for the creation of a suitable training data set, which maximizes the electronic nose capability of operating a qualitative classification and estimating the odour concentration of ambient air. The results of the application of three electronic noses to the continuous monitoring of odour emissions from a wastewater treatment plant located in the North of Italy allowed to draw some important conclusions about the odour impact of the monitored plant and, from a more general point of view, about the possibility of using electronic noses as odour impact determination tools, showing how opportunely trained electronic noses can successfully be applied for the continuous monitoring of odours of environmental interest. © 2007 Elsevier B.V. All rights reserved. Keywords: Electronic nose; Odour impact determination; Continuous monitoring; Odour concentration; Training
1. Introduction In recent years, there is an increasing care about topics regarding air quality, which brought to the development and the refinement of suitable environmental monitoring techniques. Among these techniques, there is a growing interest towards the environmental applications of electronic noses. Since the first report of the design of an electronic nose using chemical sensors and pattern recognition in 1982 by Persaud and Dodd [1], there are nowadays several scientific publications concerning for example the application of electronic noses to the identification of specific environmentally important gases [2–4], to the detection of odour abatement system failure [5] or to the correlation of electronic nose responses to other parameters for the char-
∗
Corresponding author. Tel.: +39 02 23993206; fax: +39 02 23993291. E-mail address: [email protected] (L. Capelli).
0925-4005/$ – see front matter © 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.snb.2007.12.004
acterization of air or water quality, such as for example odour concentration [6] or BOD [7,8]. The aim of our research activity in this field is the development of a system, based on the use of electronic noses, for the continuous monitoring of environmental odours at specific receptors, i.e. directly where the presence of odours is lamented. For this purpose, the instrument should be capable to continuously analyze the ambient air at specific receptors and, in real time, it first should qualitatively classify the analyzed air by attributing it to a specific olfactory class, and secondly it should quantify odour by estimating the odour concentration [9] of the analyzed air [10]. The qualitative classification and the estimation of the odour concentration are based on the comparison of the signals produced by the ambient air analyses with a database of patterns acquired by the instrument in a previous training phase. The training phase is very important and extremely delicate, especially for this kind of application, where a high sensitivity is
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required because of the increased distance of the instruments from the emission source, which entails the dilution of the odours into the atmosphere, and therefore makes their recognition more difficult. This paper describes the experimental approach adopted in order to use three electronic noses for the continuous monitoring of odours from a wastewater treatment plant. The aim of the monitoring was to determine the odour impact of the plant at issue, by installing two instruments at to different receptors, represented by two dwellings located near the plant, and a third electronic nose at the plant fence line, with the purpose of repeatedly analyzing the ambient air and determining its quality and odour concentration. This work focuses on the theoretical principles and the experimental methods adopted for the identification, the definition and the optimization of the procedures for the creation of a suitable training data set (TS), which maximizes the electronic nose capability of operating a qualitative classification and estimating the odour concentration of ambient air. The different aspects concerning the training phase that must be studied and defined are: • the identification of the principal odour sources of the plant(s) to be monitored; • the collection of representative gas samples in correspondence of these odour sources; • the preparation of a set of odorous gas samples to be analyzed by electronic nose; • the instrumental analysis of these samples; • the training data processing, i.e. the evaluation and selection of the acquired data for the creation of two optimal training sets for qualitative and quantitative recognition, respectively. As far as sampling is concerned, it must be carried out with particular care, with the aim of collecting a sufficient number of gas samples, which should be representative of all odour sources of the plant(s) to be monitored, and therefore give a complete characterization of the plant(s) odour emissions. Another extremely delicate aspect of the training phase is represented by the preparation of the samples to be analyzed by electronic nose. Our theoretical studies and experimental evidences demonstrate that, in order to maximize the electronic nose capability of recognizing diluted odours, such as those that may be typically present at receptors located at a certain distance from the emission source, it is necessary to dilute the samples collected in correspondence of the monitored odour sources at different odour concentration values. It is therefore important to determine the optimal odour concentration range of the training samples. At last, it is necessary to define a set of data processing procedures and techniques, such as exploratory data analysis or specific recognition tests [11], which allow to select the optimal data to be used for unknown pattern recognition, for example by identifying and in case eliminating outliers, or by defining the number and composition of the different olfactory classes to be distinguished.
2. Experimental methods 2.1. Electronic nose description The instruments that were used for this study have been developed in collaboration with Sacmi s.c.a.r.l. and the Sensor Laboratory of the University of Brescia [12]. The system includes a pneumatic assembly for dynamic sampling (pump, electro-valve, electronic flow meter), a thermally controlled sensor chamber with 35 cm3 of internal volume and an electronic board for controlling the sensor operational conditions. Each instrument has been equipped with an array of six different thin film metal oxide semiconductor (MOS) sensors, which makes the system sensitive to a large spectrum of volatile compounds, and a humidity sensor. In principle, the sensor selectivity and sensitivity can be changed by tuning their operating temperature [13,14]. However, we observed that, when very complex mixtures are analyzed (gaseous emissions from wastewater treatment plants contain hundreds of different compounds [15,16]), the sensor responses are strongly correlated, and their temperature dependence is not clearly definable. The instrument remote control and the data acquisition can be performed by an external personal computer through the standard communication port RS232 or a USB port. Two special software have been developed for the electronic nose data processing: the nose pattern editor (NPE), which is used for data preprocessing and for multivariate statistical analysis (e.g. PCA), and the nose pattern classifier (NPC) for pattern recognition and data classification. Each electronic nose analysis is composed by a measurement phase and by a recovery phase. The air to be analyzed is sucked during the measurement phase; during the recovery phase, reference air is pumped into the sensor chamber in order to desorb the volatile compounds from the sensor active layer and bring their response back to the base line. The measurement phase duration was set equal to 3 min, with a recovery time of 12 min, which was experimentally found to be a sufficient time in order to let the sensor response return to the base line value. One important problem connected to the use of the electronic nose on field is the base line instability, due to the variability of the atmospheric conditions (i.e. temperature, humidity, etc.) in the external ambient. This problem has been partially solved by introducing active carbon and silica gel filters for the deodourization and dehumidification of the reference air. For the analyses, the carrier flow rate was 150 cm3 min−1 and the temperature of the sensor chamber was kept constant at 50 ◦ C. 2.2. Training The training of the electronic nose is a very important and delicate phase. During this phase, it is necessary to create a complete database that the instrument uses as a reference for the subsequent pattern recognition. This database is created by analyzing a set of gas samples, which should be representative of the odours to be recognized.
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Fig. 1. Wastewater an sludge processing operations, which represent the odour sources of the monitored sewage treatment plant.
2.2.1. Sample collection The first essential step for monitoring the odours emitted by any industrial plant, is an in depth study and analysis of the process, which is necessary in order to identify all possible odour emission sources. The aim of this study was to monitor the odour emissions related to a domestic wastewater treatment plant located in the North of Italy, with a sewage treatment capacity of about 20,000 m3 /d. In general, in the case of a sewage treatment plant, odour emissions are associated both with the wastewater and sludge processing [17–19]. In this specific case the principal odour sources identified at the sewage treatment plant at issue were: • the sewage arrival/equalization tank; • the pre-aeration (for sand and oil separation) and primary sedimentation tank; • the aeration/oxidation tank; • the final sedimentation tank; • the sludge thickener; • the shed dedicated to the sludge dewatering by centrifugation; • the caisson for the storage of the dewatered sludge before removal. These sources are illustrated in Fig. 1, which represents the flow diagram of the wastewater and of the sludge treatment lines, respectively. In order to carry out a successful training, with the purpose of using electronic noses for the sewage plant odour impact determination, it is necessary to collect a set of gas samples in correspondence of each of the above mentioned odour sources. Moreover, it is necessary to create a reference olfactory class, corresponding to non-odorous, i.e. “neutral” air. For this reasons, some ambient air samples were collected near the receptors where the electronic noses had to be installed, in particular
moments in which odours attributable to the monitored wastewater plant were not perceivable. Sampling methods and equipment are extremely important, as they must allow to collect samples which should be qualitatively representative of the monitored source, and to preserve the olfactory characteristics of the samples until the moment of their analysis at the laboratory. For these reasons, the collection of air samples was carried out according with the requirements of EN 13725 (2003). The sampling bags are in NalophanTM , they have a capacity of about 8 l and are equipped with a TeflonTM inlet tube, and they are filled with the gas to be analyzed by means of a depression pump. Sampling on area sources, i.e. the surfaces of the wastewater treatment tanks or the surface of the stored sludge, was carried out using a wind tunnel system [20,21], which consists of a floating hood that is positioned over the emitting surface. A stream of neutral air is introduced into the hood, with the aim of simulating the wind action on the monitored liquid surface. The wind tunnel used during the experimentation has a circular section inlet and outlet duct, of 0.08 m diameter. The central body of the hood used was a 0.25 m wide, 0.08 m high and 0.5 m deep rectangular section chamber. Inside the inlet duct there is a perforated stainless steel grid and inside the divergent that connects this duct to the central body of the hood there are three flow deflection vanes. Both these devices have the function of making the airflow as homogeneous as possible [22]. A plant and a photograph of the wind tunnel are shown in Figs. 2 and 3, respectively. The neutral air stream introduced into the wind tunnel is obtained from an air tank, and its flow rate is regulated by means of a flow meter at 2500 l/h, which corresponds to an air velocity inside the central body of the hood of about 3.5 cm/s. The introduction of a neutral air stream at low velocity enables the obtainment of samples with a sufficiently high odour concentration (above 200 ouE /m3 ) at the wind tunnel outlet [23].
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Fig. 2. Plant of the wind tunnel.
2.2.2. Sample preparation and analysis Once the odorous gas samples are collected on the different odour sources of the monitored plant they are transported to the laboratory, with particular care towards sample storage, in order to minimize the possibility of interfering with the olfactory properties of the samples. The odour concentration of all samples collected for the electronic noses training was determined by dynamic olfactometry. The olfactometric analyses were conducted in conformity with the European Norm EN 13725 (2003) in the Olfactometric Laboratory of the Politecnico di Milano. Dynamic olfactometry is a sensorial technique that allows to determine the odour concentration of an odorous air sample relating to the sensation caused by the sample directly on a panel of opportunely selected people. The odour concentration is expressed in European odour units per cubic meter (ouE /m3 ), and it represents the number of dilutions with neutral air that are necessary to bring the concentration of the sample to its odour perception threshold concentration. The analysis is carried out by presenting the sample to the panel at increasing concentrations by means of a particular dilution device called olfactometer, until the panel members start perceiving an odour that is different from the reference neutral air. The odour concentration is then calculated as the geometric mean of the odour threshold values of each panellist, multiplied by a factor that depends on the olfactometer dilution step factor. An olfactometer ECOMA model TO8, based on the “yes/no” method, was used as a dilution device. This instrument with aluminium casing has 4 panellist places in separate open boxes. Each box is equipped with a stainless steel sniffing port and a push-button for “yes” (odour threshold). The measuring range of the TO7 olfactometer starts from a maximum dilution factor of 1:64.000, with a dilution step factor 2. All the measurements
Fig. 3. Photograph of the wind tunnel.
were conducted within 30 h after sampling, relying on a panel composed of 8 panellists (4 + 4). The odour concentration was calculated as the geometric mean √ of the odour threshold values of each panellist, multiplied by 2. In general, it is important to take account of the odour concentration of the samples before analyzing them by electronic nose. Too concentrated samples may give problems of sensor saturation or poisoning, for this reasons the samples collected in correspondence of the plant odour sources are not analyzed directly by electronic nose after sampling. Once the odour concentration of the collected samples was measured, they were diluted with neutral air at different odour concentration levels, generally comprised between 20 ouE /m3 and 1500 ouE /m3 , by means of the olfactometer, i.e. the same instrument used for the olfactometric analyses. This way it was possible to obtain more samples belonging to the same olfactory class, i.e. qualitatively representative of the same odour sources, differing for their odour concentration values. The samples prepared according to the above described principles are then analyzed by electronic nose. Important aspects relevant to this phase are the analysis duration and number. Each analysis is composed by a measurement phase, during which the electronic nose sucks the external air to be classified, and a recovery phase, during which clean reference air is flown into the sensor chamber, in order to recover the sensor active layer, and bring their response back to reference conditions (base line). The analysis duration should be set according to the following principles: the measurement duration should allow the sensors response to reach steady conditions, whereas the cleaning phase should be sufficiently long to enable complete sensor recovery. Previous studies brought us to choose a measurement duration of 3 min, and a recovery of 12 min [11]. Each analysis has therefore a duration of 15 min. Each sample should be analyzed more than once by the electronic nose, in order to have more reliable training data. The number of repetitions to be carried out should be a compromise between the necessity of creating a large training database and the necessity of conducting the whole training within a reasonable time. 2.2.3. Data processing Once all the samples were analyzed by electronic nose, the instrument has acquired a relatively large set of training data. Not all of the acquired training data is suitable for successful pattern recognition. For this reason, it is necessary to carry out a sequence of data processing steps in order to
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Fig. 4. Reciprocal position of the three electronic noses adopted for the monitoring.
select the optimal training data set (TS) to be used as a reference for the qualitative and quantitative odour recognition, respectively. The data processing phase is therefore extremely delicate. There are different techniques available in order to evaluate the training data and create a representative TS. The first technique is exploratory data analysis. It is very useful to visually explore data because, by the critical examination of specific graphical representations of data, it is possible to get a feel of their structure and to detect outliers [12]. One important aspect of exploratory data analysis is the examination of the sensor response plots, which allows the identification of possible anomalies in the response curves, e.g. measures with a high relative humidity level. Secondly, other data visualization methods based on dimensional reduction, e.g. principal component
analysis (PCA), can be successfully used for the identification of outliers. The second method adopted for training data evaluation is represented by specific recognition tests. These tests are carried out by randomly dividing the TS, which is a dataset composed of measures of known olfactory class and odour concentration, into two different test sets: a test training set (tTS) and a test match set (tMS). The recognition tests consist in the recognition of the tMS using the tTS as a reference. This method is very useful, because it allows to study and evaluate how the electronic nose odour recognition capability varies in function of the considered training data set. As far as the qualitative classification is concerned, the odour recognition capability is quantified in terms of an accuracy index (AC), which expresses the number of correctly classified
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measures with respect to the total measure: AC =
no. of correctly classified measures no. of total measures
As far as regards the odour quantification, an indication of the precision of the odour concentration estimation can be given by the calculation of the correlation index (R or R2 ) between true and predicted values: (x − x¯ )(y − y¯ ) 2 R = corr(X, Y ) = (x − x¯ )2 (y − y¯ )2 2.3. Monitoring The monitoring was conducted by using three electronic noses. Two instruments were installed at two receptors, while the third instrument was positioned at the plant fence line, in direction of the two considered receptors. More in detail, a first electronic nose (EOS 35) was installed inside a building located at about 300 m direction south-south-east from the monitored plant (receptor A), whose occupants had often lamented the presence of odours coming from the wastewater plant at issue. Contemporaneously, a second instrument (EOS 25) was installed inside a dwelling located at about 300 m in direction south-west from the plant at issue (receptor B). The third electronic nose (EOS 28) was installed inside a little building located near the southern perimeter of the plant. Fig. 4 shows the reciprocal position of the three instruments. In each case, the external air was sucked during the measurement phase through a TeflonTM inlet tube directly connected to the instrument. The reference air was represented by the ambient air inside the electronic nose installation room, opportunely filtered through active carbon and silica gel, in order to make it inodorous and humidity free. This precaution has the function of making the sensor base line relatively independent from the presence of humidity or odours in the external ambient air. The monitoring period started Monday, 9th October 2006 and ended Tuesday, 24th October 2006.
Fig. 5. Plot of the response curves of a sensor relevant to the repeated analyses of the sample collected on the primary sedimentation tank, diluted at 220 ouE /m3 .
the relative humidity sensor (Fig. 6), it is noticeable that the increase of the response amplitude is due to an increase of the sample relative humidity content. This can be explained considering that the NalophanTM that constitutes the sampling bags is permeable to humidity. For this reason the samples that are analyzed immediately after dilution with the neutral and dry air from the olfactometer have a low relative humidity content, which slowly increases with time because of the humidity transfer from the external air into the sample through the NalophanTM . Following studies showed that the achievement of an equilibrium between sample humidity and external humidity requires about 2.5–3 h. This means that this is the minimum time interval that should be waited after dilution of the samples with the neutral and dry air from the olfactometer before starting the analyses by electronic nose.
3. Results and discussion 3.1. Creation of an optimal training data set 3.1.1. Exploratory data analysis The examination of the sensor response curves and of the PCA derived from the training measurements highlighted the presence of some anomalous measures. As an example of how exploratory data analysis was used in order to become aware of some difficulties associated with the electronic nose training and to identify and eliminate outliers, Fig. 5 shows the response curves of a sensor relevant to the repeated analysis of the same sample, collected in correspondence of the primary sedimentation tank, and then diluted at an odour concentration value of 220 ouE /m3 . It is possible to observe that the response amplitude increases with time. By comparing these responses with the responses of
Fig. 6. Plot of the response curves of the humidity sensor relevant to the repeated analyses of the sample collected on the primary sedimentation tank, diluted at 220 ouE /m3 .
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Fig. 7. PCA relevant to the analyses of samples of neutral air and samples collected on the primary sedimentation tank, diluted at 220 ouE /m3 .
By plotting these response curves in a PCA, it is possible to observe how these measurements produce anomalous points, i.e. so called “outliers” (Fig. 7). Fig. 8 illustrates how the quality of the PCA is improved by eliminating the identified outliers from the training data set. 3.1.2. Qualitative recognition tests Several qualitative recognition tests were carried out by creating different tTS and tMS as subsets of the original data set acquired by the instruments during the training phase, with the aim of selecting the optimal training data set, i.e. dataset to be used as a reference for the recognition of unknown patterns which maximizes the instrument odour qualitative classification capability. The aspects that were investigated were on one hand the olfactory classes to be discriminated, and on the other hand the optimal odour concentration range of the training samples.
Fig. 8. PCA relevant to the analyses of samples of neutral air and samples collected on the primary sedimentation tank, diluted at 220 ouE /m3 after elimination of the outliers.
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It is very important to accurately define the olfactory classes to be discriminated, especially if considering that, in general, a reduction of the number of olfactory classes to be distinguished entails an increase of the accuracy index, i.e. of the number of measures classified correctly. For this reason, if it is not necessary to discriminate the single odour sources, it is possible to group some of them in one olfactory class, in order to reduce the number of different olfactory classes, and therefore improve the accuracy of the qualitative classification. In this specific case, the best results were obtained by grouping the samples into the following three macro olfactory classes: “sewage treatment”, “sludge treatment” and “neutral air”. It is also important to consider that each olfactory class should ideally be represented by the same number of experimental points. The reason is that, as the qualitative classification is based on statistical methods, such as k-Nearest-Neighbours (kNN) classification algorithm [24,25], the attribution of an unknown sample to the most numerous olfactory class is favoured. Another very important aspect to be taken into account for the creation of an optimal training set is the odour concentration level of the training samples. From a theoretical point of view, considering that the aim of the work is to use electronic noses at receptors, i.e. in presence of diluted odours, it is reasonable to assume that too concentrated samples are not suitable for the instrument training, because they differ too much from the conditions to which the electronic noses are exposed during the monitoring on field. On the other hand, it is not advisable to use too diluted samples neither, because their distinction from the neutral air could be difficult. This considerations were confirmed experimentally, through the execution of several recognition tests using different training data sets, which allowed to identify an optimal range in which the odour concentration of the training samples should be comprised in order to maximize the classification accuracy. The optimal odour concentration range was found to be between 100 ouE /m3 and 150 ouE /m3 , giving classification accuracy index values over 0.95. 3.1.3. Quantitative recognition tests The algorithm used for the quantitative odour recognition was a linear regression of the training dataset. The set of features extracted from the sensor responses, which are used as variables for the linear regression, is composed of parameters that have a different and not a priori definable dependence from the stimulus that originates the sensor signal. Given the variability of the laws that regulate the dependence of the feature set from the sample concentration, a linear interpolation was used. The choice of using a linear interpolation for the odour concentration estimation of unknown samples, even though the relation between sensor signal and sample concentration is not linear, entails the necessity of considering a narrow odour concentration range as the training dataset. The quantitative recognition tests showed that the highest correlation index values (R > 0.9) were obtained by using training data sets relevant to the samples with typical odour concentration values of an urban ambient air, i.e. comprised between 20 ouE /m3 and 80 ouE /m3 .
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Fig. 9. Results of the qualitative classification (a) and of the odour concentration estimation (b) of the air analyzed at the plant southern fence line (EOS 28).
3.2. Odour impact determination 3.2.1. Representation of the results The monitoring results are represented by large tables that report the olfactory class and the odour concentration value attributed to the analyzed air for each measurement carried out during the monitoring period. The results can be synthetized in graphs, which in abscissa report the measurement date and hour, and in ordinate report the olfactory class or the odour concentration value attributed to the analyzed air, respectively for the qualitative or quantitative odour recognition. Based on the results of the monitoring, it is possible to make some considerations about the odour impact of the monitored wastewater treatment plant. 3.2.2. Odour impact at the plant fence line The monitoring results, which are illustrated in Fig. 9, enable to make some considerations about the odour impact at the plant fence line. The number of measures that the electronic nose EOS 28 attributed to each olfactory class and their relative frequency with respect to the total number of measures are reported in Table 1. The presence of odours was detected for 37.5% of the monitoring total duration. The results show that the only olfactory class recognized except the “neutral air” is the one named “sewage treatment”, indicating that the presence of odours in the considered monitoring position must be attributed to the odorous emissions from the wastewater processing line. This relatively high percentage must be evaluated considering that the electronic nose was installed inside the plant perimeter. It is reasonable to assume that the relative frequency of odour recognition outside of the plant must be significantly lower, due to the effect of odour dispersion into the atmosphere.
3.2.3. Odour impact at receptor A The results of the monitoring at receptor A, located at about 300 m in direction south-south-east from the wastewater plant at issue (Fig. 10), allow to make some considerations about the odour impact of the monitored plant on the neighbouring land. The number of measures that the electronic nose EOS 35 attributed to each olfactory class and their relative frequency with respect to the total number of measures are reported in Table 2. The presence of odours was detected for 10.8% of the monitoring total duration. Also in this case, the only olfactory class recognized except the “neutral air” is the one relevant to the odour emissions from the sewage treatment line. The odour concentration values registered by the electronic nose EOS 35 installed at receptor A are included in a range between 5 ouE /m3 and 31 ouE /m3 . These values do not present anomalies, and are typical odour concentration values of an ambient air. 3.2.4. Odour impact at receptor B For receptor B, represented by a dwelling located at about 300 m in direction south-west from the wastewater plant at issue, based on the monitoring results, which are illustrated in Fig. 11, it is possible to make some analogous considerations in order to evaluate the plant odour impact. It is noticeable that the presence of odours from the monitored wastewater treatment plant was detected for 8.7% of the monitoring total duration (Table 3). Also in this case, the olfactory class that was recognized most frequently except the reference “neutral air” is the one relevant to the odour emissions from the sewage processing line. The odour concentration values attributed by the electronic nose EOS 25 to the analyzed air are included in a range between 24 ouE /m3 and 35 ouE /m3 . These values do not present anoma-
Table 1 Relative recognition frequency of each olfactory class at the plant fence line
Table 2 Relative recognition frequency of each olfactory class at receptor A
Olfactory class
Number of measures
Recognition frequency (%)
Olfactory class
Number of measures
Recognition frequency (%)
Neutral air Sewage treatment Sludge treatment
883 529 0
62.5 37.5 0.0
Neutral air Sewage treatment Sludge treatment
1259 153 0
89.2 10.8 0.0
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Fig. 10. Results of the qualitative classification (a) and of the odour concentration estimation (b) of the air analyzed at receptor A (EOS 35).
Fig. 11. Results of the qualitative classification (a) and of the odour concentration estimation (b) of the air analyzed at receptor B (EOS 25). Table 3 Relative recognition frequency of each olfactory class at receptor B Olfactory class
Number of measures
Recognition frequency (%)
Neutral air Sewage treatment Sludge treatment
1205 112 2
91.4 8.5 0.2
lies, and are typical odour concentration values of an ambient air. 4. Conclusions This study brought to the definition of a set of optimized procedures for the electronic nose training, which allow to maximize the instrument capability of qualitatively and quantitatively recognizing odours. The innovative aspects of the developed instruments is that they can be used at receptors, i.e. in presence of diluted odours, both for the qualitative classification of environmental odours and for the real time determination of the odour concentration of the analyzed air. Moreover, the results of the application of three electronic noses to the continuous monitoring of odour emissions from a wastewater treatment plant located in the North of Italy allow to draw some important conclusions about the odour impact of the monitored plant and, from a more general point of view, about the possibility of using electronic noses as odour impact determination tools.
As far as the odour impact of the monitored wastewater treatment plant is concerned, the high relative frequency of detections attributed by the electronic noses, installed at the plant fence line and at two different receptors respectively, to the olfactory class named “sewage treatment”, allows to affirm that the plant area dedicated to the wastewater processing represents the principal odour source of the plant at issue. The identification of the major odour source can be very useful for the plant management, as it gives important indications about which interventions to undertake for the plant odour impact reduction. In conclusion, the results show that opportunely trained electronic noses can successfully be applied for the continuous monitoring of odours of environmental interest directly where the odour nuisance is lamented.
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Biographies Laura Capelli is a PhD student in industrial chemistry and chemical engineering at the Politecnico di Milano. She is currently doing her research activity at the Olfactometric Laboratory of the Department of Chemistry, Materials and Chemical Engineering “Giulio Natta” of the Politecnico di Milano. Her research interest includes odour sampling and measurement techniques, and especially the development of electronic nose based systems for the continuous monitoring of environmental odours. Selena Sironi received her PhD in industrial chemistry and chemical engineering from the Politecnico of Milano in 2004. She is currently a researcher at the Department of Chemistry, Materials and Chemical Engineering “Giulio Natta” of the Politecnico di Milano. Her research interest includes waste treatment, biofiltration, odour sampling and odour measurement techniques. Paolo C´entola is a full professor of environmental chemistry at the Department of Chemistry, Materials and Chemical Engineering “Giulio Natta” at the Politecnico di Milano. In 1997, he founded the Olfactometric Laboratory of the Politecnico of Milano, the first laboratory in Italy whose activity included the execution of olfactometric analyses for the characterization of odour emissions of environmental interest. His research interest always included several important environmental topics, and especially waste treatment. Renato Del Rosso is a full professor at the Department of Chemistry, Materials and Chemical Engineering “Giulio Natta” at the Politecnico di Milano. He is the scientific responsible of the Olfactometric Laboratory of the Politecnico di Milano, and supervises the research activities of the laboratory, which now include the following main topics: odour sampling techniques, odour dispersion modelling and application of electronic noses to the environmental sector. Massimiliano Il Grande is a chemical engineer, graduated at the Politecnico di Milano in 1997. He is now a managing director of Progress S.r.l., a society founded in 1996, which offers services of environmental monitoring and consulting, and strictly cooperates with the Olfactometric Laboratory of the Politecnico di Milano.