A NOVEL TOOL FOR CONTROL OF ODOUR EMISSION IN WASTEWATER TREATMENT PLANT

Transcription

1 Proceedings of the 13 th International Conference on Environmental Science and Technology Athens, Greece, 5-7 September 2013 A NOVEL TOOL FOR CONTROL OF ODOUR EMISSION IN WASTEWATER TREATMENT PLANT S. GIULIANI 1, T. ZARRA 1, V. NADDEO 1 and V. BELGIORNO 1 1 Sanitary Environmental Engineering Division (SEED) at Department of Civil Engineering University of Salerno, via Ponte don Melillo, Fisciano (SA), Italy tzarra@unisa.it EXTENDED ABSTRACT Odours emitted by wastewater treatment plants are one of the major concern for local authorities in relation of the nuisance generate in the neighbourhoods. Odours may cause a variety of undesirable reactions in people, ranging from annoyance to documented health effects. Odours are difficult to measure and there is no universally accepted method for the quantification of odours. Currently, the techniques available for odour characterization and quantification are substantially of three different gropus that have different advantages and weaknesses: analytical, sensorial and mixed methods. Mixed techniques primarily use artificial noses (e.nose), which perform instrumentally the functions of human olfaction. The electronic nose has the potentiality to combine the odour perception and the field monitoring. E.nose is a multisensorial array system useful for a number of different applications form food and cosmetics industry to environmental field. While in the field of food and cosmetic the use of the e.nose is developed, in the environmental field additional studies are need to overcome a limitations associated with the properties of chemical sensors, the signal processing performances and the real operating conditions in in situ application. Aim of this study is to illustrate the potentiality of an innovative e.nose, based on a multisensorial array system, for the continuous monitoring of environmental odours at wastewater treatment plant. The study is conduct to control in the odorous emission at the plant in real time in order to minimize its potential impacts in the neighbourhoods. Experimental research was carried out at large wastewater treatment plant located in a sensitive area of Salerno city (Southern Italy, Campania Region) with two different phases. First phase where conducted at the Laboratory of Sanitary Environmental Division (SEED) at University of Salerno, where all air bags sampled at different treatment unit of plant where analysed by both: dynamic olphactometry and by e.noses. In this phase, odour emission where characterized according to European standards (EN 13725:2003) and discussed with reference at different treatment unit. In the same phase, air samples where used for the training of e.nose, developing a qualitative and quantitative model able to identify different type of odours according to their source and exsimate its concentration in OU/m 3. In the second phase e.nose where used in field to measure the real emissions of different treatment units of the plant. Obtained results highline the potential of the multisensor array system to identify and quantify the odours emitted by the wastewater treatment plant and compared to the olphactometry method has certain practical and economical advantages. Keywords: dynamic olfactometry, electronic nose (e.nose), linear discriminant analysis (LDA), multisensor array system, partial least squares (PLS), principal component analysis (PCA), wastewater.

2 1. INTRODUCTION Odours emitted by wastewater treatment plants are considered as the main source of annoyance by citizens that live near these plants (Frechen et al., 1998; Stuetz et al., 2001; Zarra et al., 2008). In communities exposed to odorous emissions, even though there may be no immediately apparent diseases or infirmities, there certainly is not an atmosphere of complete mental, social, or physical well-being (Gostelow et al., 2001; Zarra et al., 2009). The particular and complex nature of the odorous substances, their variability in time as well as in relation to the meteorological conditions and the subjectivity of the smell perception are the elements that delayed their regulation (Zarra et al., 2009a). Odours are difficult to measure and there is no universally accepted method for the quantification of odours (Zarra et al., 2009a). Currently, available techniques for odour characterization and quantification use substantially three different approaches: analytical, sensorial and senso-instrumental approach. Each of these techniques presents different advantages and disadvantages. Analytical instrumentation allows for the identification and quantification of the chemical compounds present in the malodorous emissions; these techniques have the advantage of objectivity, repeatability and accuracy in addition to the complete characterization of the composition of the air samples useful for the design of the abatement systems (Zarra et al., 2009b). While the identification and quantification of specific odorants does not directly indicate the potential odour nuisance (Iranpour et al., 2005), for these reasons sensorial techniques, such as dynamic olfactometry standardised by EN 13725:2003, use the human nose as a sensor and are able to characterise odours by referring directly to their effects on a panel of qualified examiners. The most significant disadvantages of sensorial approaches are: the difficulty to consider the variability of human olfaction between different subjects, the limits of these methods for the detection of lower concentrations and the absence of any characterisation of the odourants (Capelli et al., 2008). Senso instrumental techniques primarily proposed the use of electronic noses (e-noses). These methods have the potentiality to combine the odour perception and the field monitoring. The instrument, based on non-specific gas chemical sensor arrays combined with a chemometric processing tool (Gardner et al., 1994) are nowadays the only applicable technique to measure odours continuously at sources and/or at the receptors (Romain et al., 2008). It has probably the best potentialities to answer to the expectations of the various actors of the environmental problems in relation to odours annoyance (Romain et al., 2008). The classification of the odours is based on the comparison of the e-nose signals with a database of patterns acquired by the instrument in a previous training phase. The training of the electronic nose is one of the most important phases (Capelli et al., 2008, Giuliani et al., 2012). In addition, a number of limitations in environmental sector application are associated with the properties of chemical sensors adopted for the e-nose function (Barsan et al., 2007), the signal processing performances (Dutta et al., 2013) and the standardization of the real operating conditions (Giuliani et al., 2012). The classification of the odours is based on the comparison of the e-nose signals with a database of patterns acquired by the instrument in a previous training phase. The training of the electronic nose is a very important and delicate phase (Capelli et al., 2008, Giuliani et al., 2012). This study describes an integrated procedure and relative tools able to control odour emissions in a WWTP continuously using an innovative multisensor array system, based on combination of sixteen different specific and non specific sensors. The overall aim is to proposed a way for remove the subjective component in the measure of the odours, control in real-time all odour emissions in the WWTP and define the contribution of each source on the potential odour impact.

3 2. MATERIAL AND METHODS 2.1. Wastewater treatment plant Research studies were conducted at a Salerno WWTP located in the industrial area of the Salerno City (Italy). The plant work with a conventional biological line designed for PE (Figure 1). During the sampling programme the WWTP was operating with an average daily flow of 8000 m 3 /h. The WWTP receives domestic and industrial wastewater. The WWTP consists of pre-treatment, preliminary treatment, primary sedimentation, and secondary (biological) treatment with activated sludge system (aeration and sedimentation tanks) followed chlorination. Secondary sludge is recirculated to a primary clarifier, which improves the settling characteristics of the primary sludge and increases sludge age. A mixture of primary and secondary (activated) sludge is processed in sludge line. Sludge line consists of thickener, anaerobic digestion and mechanical drying by centrifugation of the digested sludge. Figure 1. WWTP of Salerno (Italy) with localization of sampling points Sampling According to the previous studies on odour emission from WWTPS (Zarra et al., 2008; Zarra et al., 2009) were selected the treatment units of the plant with highest tendency to emit odour. Three different treatment units of the WWTP (Figure 1) were investigated: grit (P1), primary sedimentation tank (P2) and sludge thickening (P3). Additional sampling point (P4) was identified to obtain the reference odorless air. P4 was selected in the area of the investigated WWTP far enough form odour sources, in order to have the same humidity and background air pollutants as in the odour samples, but taking care it had no smell. Air sampling were conducted using the lung technique, in accordance with EN 13725:2003, with Nalophan sampling bags with a volume of 14 litres. Sampling were collected at the investigated odour sources twice a week, during 4 consecutive months. A total of 96 odour samples (from grit, primary sedimentation and sludge thickener) and 32 odourless air samples (at P4) were collected during the whole monitoring period Odour detection by seedoa A novel multisensor array system called seedoa (Simple Environmental Electronic Device for Odour Application) was used in the research activity. seedoa is a novel prototype of e.nose designed by the Sanitary Environmental Engineering Division (SEED) of the University of Salerno (Italy). This system consists of a set 16 sensors: two series of

4 6 different metal oxides non-specific gas sensors (S), 2 specific gas sensors (SS) and 2 internal conditions sensors (humidity and temperature), placed in an innovative fluid dynamics chamber (CODE ) patented by the SEED. The sensors were selected on the basis of the potentially odorous substances emitted from the investigated type of plant according to previous studies (Zarra et al., 2009b). Figure 2. Type of odor detect sensors used in the CODE chamber of seedoa system. During the analysis the temperature in the CODE chamber is kept at 50 C by a heating resistor and natural cooling, thanks to a suitable control system. Relative humidity of the measurement chamber is also recorded during the analysis and used for the validation of the measures. Working flow rate was settled equal to 300 ml/min. All the acquired data are saved in an external computer and processed in real time by statistical and mathematical tools designed for this specific purpose. Training section of the seedoa was conducted by cycles of odour-odourless on all sampled air at the WWTP. In each cycle, the features considered for the data processing was the medium values of last 2 minutes registered by each odour detect sensor (ODS). The acquired values from the sensor in terms of electrical conductance are normalized by the square root of the sum of all the sensors conductance values squared without any reference to a base line. The normalised data were then processed using three pattern recognition techniques: Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Partial least squares (PLS). To define the quantitative odour model by PLS, the collected samples of one of the two campaigns carried out per week, were also characterized in terms of odour concentration (OU/m 3 ) by dynamic olfactometry. Once the multisensor array system seedoa had been trained, it was located at the investigated odour sources (P1, P2, P3) to validate the created odour model. Each odour source was monitored by seedoa for 7 consecutive hours. During the monitoring at every hour one sample was also collected with the vacuum pump and characterized with dynamic olfactometry. The odour concentration estimated by seedoa was compared with compared with the odour concentration measured by dynamic olfactometry at the same monitored time. A total of 7 comparisons for each odour sources was used for the validation Odour detection by dynamic olfactometry During the sampling plan at WWTP, once a week the collected samples were also characterized by dynamic olfactometry. Olfactometric analyses was conducted to

5 measure the odour concentration (OU/m 3 ) at emission points according to EN 13725:2003. A total of 48 olfactometric analysis were carried out at Laboratory of SEED (Sanitary Environmental Engineering Division) at University of Salerno for the characterization of odour emission and the trainig pahse of the seedoa. Additional 21 analyses was conduct for the validation of seedoa during the fied monitoring of odour emissions. The analyses were carried out using the olfactometer model TO8 ECOMA with the yes/no method. According to previous studies, all the measurements were analysed within 14 h after sampling (Zarra et al., 2012), relying on a panel composed of 4 trained panellists. 3. RESULTS AND DISCUSSION 3.1. Characterization of odour emissions by dynamic olfactometry Figure 3 shows the variability of the odour concentrations at investigated emission sources measured by dynamic olfactometry over the monitored period. According to the literature, the results show that odour concentrations emitted by units of the sludge line (sludge thickening) are significantly highest of the odour emitted from the unit in the wastewater line (grit and primary sedimentation). In detail the highest concentration was detected at the sludge thickening (2481 OU/m 3 ) while the lowest in the oxidation unit (72 OU/m 3 ). Figure 3. Characterization of investigated odour sources by dynamic olfactometry Characterization of odour emissions by seedoa Figure 4a shows the score plot in the plane of two first factors of the Principal Component Analysis (PCA) application at the data set of 128 observations. The results highlight a good separation between the odour classes investigated and odourless air (background). There is also some confusion between the observations of the grit group and the primary sedimentation, which is probably due to the low detected concentration of oduor ranged between 90 to 300 OU/m3. The PCA score plot explains the 83% of the total variance in the plane of two first factors. Figure 4b shows the score plot of the Linear Discriminant Analysis (LDA) application at the data set of 128 observations. To validate the classification performance of the model, it was calibrated with only 80% of the observations (chosen at random) and the remaining 20% were set aside for validation purposes. The a priori classification probabilities are set proportional to the size of the four different groups. The classification of each observation is carried out on the basis of the Mahalanobis distance between the observation and the centroid of each group (Nicolas et al., 2006). The results show a good clustering of the observations among the sources and confirms the good classification results. It indicates

6 that the sensors array is able to separate the 4 classes of observations and can be used for the odour monitoring of the odours emitted by the Salerno WWTP. Figure 4. PCA (left) and LDA (right) score plot in the respectively in the plane of the two first factors and two first roots. The scatter plot of the odour concentrations, determined by olfactometric measurement and predicted by the PLS model (scaled into the odour concentrations domain) for the 48 observations is shown in Figure 5. The results show a good regression obtained in the PLS analysis. The determined quantification model can predict the odour concentration with a high level of confidence (r2 = 0.91) for the environmental sector. Odour concentrations estimated by multisensor sensor array seedoa was comparable to the dour concentrations measured by olphactometry r 2 =0.91 Predicted odour concentration (OU/m 3 ) Measured odour concentration (OU/m 3 ) Figure 5. Scatter plot of predicted odor concentration by seedoa using the developed PLS model and odor concentrations measured by dynamic olfactometry Validation of multisensorial array system Figure 6 illustrates the field validation of the defined seedoa quali-quantitative odour model for continuous measurement and control of the investigated sources at the Salerno WWTP. The results show that there is a good fit between the predicted and measured data in P1 and P3, while the acceptable results are for the P2. Upon reading the results from the lower to higher expected concentrations, it is possible to note how the predicted concentration is much closer to the measured value

7 for the higher odour concentration and how the precision increase in the same way as odour concentration increases. The results can be explained by considering the limits of dynamic olfactometry to detect lower concentration of odours. This also explains the slightly lower confidence levels found in the point P2. Figure 7. Continuous odor concentration measured by seedoa and hourly odour concentration determined by dynamic olfactometry at different sources: grid (P1), primary sedimentation tank (P2) and sludge thickening (P3). 4. CONCLUSIONS The seedoa device was proposed for the control of odour emission in WWTPs. This study also brought to the definition of a optimized procedure for the training of a multisensor array system which allows for maximizing the instrument ability to qualitatively and quantitatively recognize odours. The results of the laboratory investigation have shown the great efficiency of the multisensor array system seedoa to discriminate the different type of odours generated from the sanitary environmental engineering plant investigated. The results of the field investigation validation confirmed that the multisensor array system is able to identify and quantify the odours emitted by the Salerno WWTP with a minimum margin of error in respect to the odour concentration values determined by dynamic olfactometric analysis, specially at lower concentration. This aspect is not critical due to the lower concentration not causing significant odour impacts. This new tool is able to give a qualitative classification of environmental odours and their determination in real time for a complete control of odour emission in WWTPs. ACKNOWLEDGMENTS Part of the research has been developed within the Vigoni Italian-German Programme funded by MIUR and the DAAD. This work has been also partially funded by the University of Salerno within FARB projects.

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