Microsoft Word - 3debree.docx CHEMICAL ENGINEERING TRANSACTIONS VOL. 54, 2016 A publication of The Italian Association of Chemical Engineering Online at www.aidic.it/cet Guest Editors: Selena Sironi, Laura Capelli Copyright © 2016, AIDIC Servizi S.r.l., ISBN 978-88-95608-45-7; ISSN 2283-9216 Odor Concentration Prediction by Gas Chromatography and Mass Spectrometry (GC-MS): Importance of VOCs Quantification and Odor Detection Threshold Accuracy Stephane Carioua*, Mathilde Chaignaudb, Pascale Montreera, Marion Fagesa, Jean-Louis Fanloa,b. a Ecole des Mines d’Alès, Laboratoire Génie de l’Environnement Industriel, 6 avenue de Clavières, F-30319 Ales Cedex, France b Olentica sas ,14 boulevard Charles Peguy, 30100 Alès stephane.cariou@mines-ales.fr Odor annoyance is the second most important nuisance in Europe. So, the knowledge of the odor concentration is very important to assess the odor impact on the environment. In Europe, EN 13725 regulates the method to measure the odor concentration. If olfactometry is the only normalized method to measure odor concentration, it is nevertheless often interesting to be able to assess it on the basis of a physico-chemical analysis. To reach this goal, the odor concentration can be obtained by the following equation: = = In this equation, is the odor activity value that corresponds to the ratio of the chemical concentration to the odor detection threshold of compound . Ci corresponds to the concentration of compound in the gaseous mixture and ODTi to the odor detection threshold of this compound. These two parameters affect odor activity value and therefore odor concentration assessment accuracy. In this context, the main goal of our experiment was to determine what conditions are suitable to obtain the most precise prediction. Our study consisted in the first step to generate a calibrated gaseous mixture of odorous compounds and to analyze it by GC-MS and olfactometry (EN 13725). The reproducibility of the gaseous mixture generation was tested. Physico-chemical results were analyzed using a global quantification (mg/m3 toluene eq.) on one side and individual quantification of each compound on the other side. In the same way, we used odor detection thresholds from the literature on one side and odor detection thresholds measured in our lab, according to EN 13725, on the other side. This study allowed us to emphasize the weight of quantification and odor thresholds accuracies on odor concentration prediction. 1. Introduction Odors became an important industrial and societal concern because "environmental stress" perceived in residential areas creates a sense of insecurity and a negative perception of the quality of life. Exposure to odor causes in some people mental disorders (depression, aggression) and somatic disorders (dry throat, immuno-depression, and nausea) identical to those observed under stress (Schlegelmilch et al., 2005, Gostelow et al., 2001, Renault et al., 2006). Although air quality has improved over the last thirty years, the odor nuisances are regularly presented in France as the second reason for complaints after those related to noise pollution. In Europe, around 20% of the population undergoes olfactory discomfort (Bokowa, 2010). DOI: 10.3303/CET1654012 Please cite this article as: Cariou S., Chaignaud M., Montreer P., Fages M., Fanlo J.L., 2016, Odour concentration prediction by gas chromatography and mass spectrometry (gc-ms): importance of vocs quantification and odour threshold accuracy, Chemical Engineering Transactions, 54, 67-72 DOI: 10.3303/CET1654012 67 Persistent odors are usually the most poorly tolerated (Day et al. 1998, Van Durme et al. 1992, Schlegelmilch et al., 2005). In this context, the importance of knowing the odor concentration is obvious. Indeed, this parameter directly represents the sensitivity of the odor to the dilution, i.e. its persistence. The odor concentration is obtained by the olfactometric measure in accordance with the standard EN13725. However, depending on the context, this method can be expensive and difficult to implement. Sometimes, access to an olfactometer may be impossible. A cost-effective strategy to circumvent this problem may be to identify the major contributing odorants compounds in the gaseous mixture and to correlate the chemical composition and its odor concentration. This approach is based on the evaluation of odorous potential of each compound in the gaseous mixture. To do that, the concept of odor activity value (OAV) defined as the ratio of the chemical concentration to the odor detection threshold has been introduced and widely used (Friedrich and Acree 1998; Kim and Park 2008; Parker et al. 2010; Parker et al. 2012; Trabue et al., 2006). = : Odor Activity Value of compound i (dimensionless) : Chemical concentration of compound i (mg.m-3) : Odor Detection Threshold of compound i (mg.m-3) The odor concentration may then be correlated to the odor activity value by adding the OAV of all individual compounds in the mixture (Gallego et al., 2012, Wu et al. 2016). = The objective of this study was to estimate the influence of the uncertainty related to the quantification of the components of the mixture and that associated with odor detection thresholds on the quality of the prediction of the odor concentration. For this, a mixture of six compounds was made and analyzed by GC-MS, and by olfactometry. The odor detection threshold of each compound was also measured. 2. Materials and methods 2.1 Selected odorous compounds A mixture of six different odorous compounds was realized for this study. These compounds are presented in table 1, along with their CAS number (Chemical Abstracts Service) and their odor detection thresholds (ODT) obtained in literature (Van Gemert, 2011). As several odor detection thresholds were frequently available for a single compound, and the order of magnitude could be considerably different, a geometric mean was used in order to obtain an average value ( ), in line with common practice (Parker et al. 2012). Table1: Selected odorous compounds – Odor detection thresholds from literature Molecule CAS number (min-max) Number of values (mg /m3) (mg/m3) n-butanol 71-36-3 0.01-42 38 0.668 Methyl butanoate 623-42-7 0.03-0.077 3 0.056 Triethylamine 121-44-8 0.022-1 3 0.206 R-Limonene 5989-27-5 0.045-55 3 0.517 Cyclopentanone 120-92-3 31 1 31 Butyl acetate 123-86-4 0.01-480 14 0.654 68 2.2 Generation of reference gaseous mixture A liquid mixture was done by injection of a well-known volume of each VOC (micropipette) and weighing of the solution after each introduction in the glass vessel. Then, 50µL of the mixture was introduced in the injection port of a chromatograph heated at 250°C to volatize the liquid. The gaseous phase was then diluted with 40 L of clean air to obtain the required concentration in a Nalophan® bag. 2.3 Analyses 2.3.1. GC-MS analyses Physico-chemical analyses were performed by a TD-GC-MS method (Turbomatrix from Perkin Elmer followed by a Thermo Scientific Trace gas chromatograph coupled with a Thermo Scientific DSQ mass detector). The analytical column was an Optima 5-ms Accent 60 m x 0.25 mm x 1 µm. Helium was used as carrier gas at 1.5 mL/min in constant flow mode. The GC oven temperature program was set as followed: 9 min at 40°C, a ramp at 15°C/min until 90°C, 4 min at 90°C then a ramp at 10°C/min until 250°C and finally 5 min at 250°C. The ionization of compounds was made by electronic impact at 70 eV. The full scan mode was used to analyze fragments from 20 to 250 amu (atomic mass unit). Compounds identification was led by comparison of our spectra with those referenced in the NIST library. The system was calibrated with toluene. 2.3.2. Olfactometric analyses The odor concentration was measured according EN 13725 standards using a dynamic dilution olfactometer ODILE (Odotech Inc., Canada). Six panellists were selected for each olfactometric session. Three different evaluations were done on the same sample to evaluate the dispersion of olfactometric measurements. 3. Results and discussion 3.1 Olfactometric analyses Three gaseous samples were constituted according to the protocol described in section 2.2 and analysed according to EN 13725 standards. The results are given in table 2. Table 2: Reference mixture – Odor concentration Odor concentration (OUE/m 3 ) Sample 1 14133 Sample 2 11506 Sample 3 10543 Mean 12061 Standard deviation 1517 The average odor concentration is 12000 UOE/m3 with a standard deviation of 1500, representing a good repeatability of gaseous mixture generation and olfactometric analysis. 3.2 Importance of VOCs quantification Table 3 shows the odor activity value ( ) of each compound and the global predicted odor concentration of the mixture obtained on the basis of measured concentrations ( ), calculated concentrations ( ) and odor detection thresholds from literature ( ) as presented in table 1. corresponds to the concentration measured by TD-GC-MS and expressed in mg/m3 toluene equivalent as usually observed in literature. corresponds to the concentration calculated on the basis of the mass of liquid mixture volatilized in the sample. 69 Table 3: Predicted odor concentrations calculated with Molecule CAS number = = (mg toluene eq./m3) (mg/m 3) (mg/m3) n-butanol 71-36-3 72.7 163.7 0.668 109 245 Methyl butanoate 623-42-7 99.9 189.4 0.056 1 777 3 367 Triethylamine 121-44-8 5.1 149.4 0.206 25 724 R-Limonene 5989-27-5 29.1 183.3 0.517 56 354 Cyclopentanone 120-92-3 57.2 198.0 31 2 6 Butyl acetate 123-86-4 59.2 186.3 0.654 91 285 = 2059 4981 The results show that none of the predicted odor concentrations is close to the value measured by olfactometry (12000 OUE/m 3). However, the use of the exact concentrations provides a significant gain in the evaluation. 3.3 Importance of odor detection thresholds The values of odor detection thresholds, if available in the literature, are often dispersed (Table 1). This is due to the wide variety of authors who have made measurements using different techniques at very different times. Making the choice of the most relevant values requires either to clean these databases with complex algorithms, or to realize its own measures. It is this second solution that was chosen in this study. The minimum and the maximum values of the odor detection thresholds measured for the six molecules of the mixture ( ) are given in Table 4. The number of determinations of each odor detection threshold measured also figures in this table. The is the geometric mean of all the values measured for each individual component. Table 4: Selected odorous compounds – Odor detection thresholds measured in our lab. Molecule CAS number Number of determinations (min-max) Standard deviation (mg /m3) (mg/m3) n-butanol 71-36-3 11 0.040-0.210 0.088 0.067 Methyl butanoate 623-42-7 4 0.024-0.033 0.029 0.004 Triethylamine 121-44-8 8 0.019-0.045 0.033 0.009 R-Limonene 5989-27-5 7 0.055-0.118 0.083 0.019 Cyclopentanone 120-92-3 5 0.731-1.389 0.933 0.255 Butyl acetate 123-86-4 9 0.046-0.100 0.061 0.024 It may be noted that except for cyclopentanone for which only one value was available, the odor detection thresholds measured in our laboratory ( ) are in the lower range of those found in the literature (Table 1). 70 Table 5 illustrates the results obtained with this new data, using measured concentrations and calculated ones. Table 5: Predicted odor concentrations calculated with Molecule CAS number = = (mg toluene eq./m3) (mg/m 3) (mg/m3) n-butanol 71-36-3 72.7 163.7 0.088 823 1854 Methyl butanoate 623-42-7 99.9 189.4 0.029 3445 6530 Triethylamine 121-44-8 5.1 149.4 0.033 154 4513 R-Limonene 5989-27-5 29.1 183.3 0.083 351 2213 Cyclopentanone 120-92-3 57.2 198.0 0.933 61 212 Butyl acetate 123-86-4 59.2 186.3 0.061 969 3046 = 5804 18368 The results obtained using the odor detection thresholds measured in our lab are globally higher and closer to the measured value (12000 OUE/m 3) than those obtained previously with the literature values. To summarize and compare all these results, table 6 gives the relative errors on odor concentration ( ) obtained using measured ( ) or calculated chemical concentrations ( ) and odor detection thresholds from literature ( ) or measured in our laboratory ( ). Relative error is defined as the quotient of the absolute error (difference between the approximate value and the actual value) and the absolute value of the actual value: = ( − )| | It is an algebraic relative error: if positive, it means that the approximate value is greater than the actual value (overestimation) and if it is negative that it is lower (underestimation). Table 6: Relative error on odor concentration prediction - 0.829 - 0.587 - 0.519 + 0.523 Examination of this table shows that the most distant results of the value measured by olfactometry are those obtained with the geometric mean of odor detection thresholds from literature and the values of the chemical concentrations evaluated on the basis of GC / MS analysis (underestimation of 83%). Using the exact concentrations and the geometric mean of odor detection thresholds from literature, or concentrations evaluated on the basis of the GC / MS analysis and odor detection thresholds measured in the laboratory lead to substantially the same underestimation of the odor concentration (respectively 52 and 59%). Only the use of the exact concentrations and odor detection thresholds measured in our laboratory value leads to an odor concentration overestimated by 52%. 71 4. Conclusion The objective of this work was to assess the influence of the uncertainty related to the components quantification of a gaseous mixture and that associated with odor detection thresholds on the quality of the odor concentration prediction as measured by olfactometry. For this, a mixture of six compounds was constituted. The first results highlight the influence of the two parameters studied (quantification of mixture components and odor perception thresholds) on the prediction of the odor concentration. Improving the precision of data, logically, results in an improvement of the prediction. Controlling the uncertainty of these data, especially that related to odor detection thresholds, appears to be crucial in view of developing a model integrating the interaction effects (synergy / inhibition) between the odorous compounds. References Bokowa A.H., Bokowa A.H., 2010, Review of odor legislation, Chem. Eng. Transac., 23, 31-36. Day M., Krzymen M., Shaw K., Zaremba L., Wilson W.R., Botden C., Thomas B., 1998, An investigation of the chemical and physical properties changes occurring during commercial composting, Compost Sci. Util., 6(2), 44-66. EN 13725, Air Quality – Determination of odor concentration by dynamic olfactometry, AFNOR, 2003. Friedrich, J.E., Acree T.E., 1998, Gas Chromatography Olfactometry (GC/O) of Dairy Products, Int. Dairy J., 8(3): 235–241. Gallego, E., Roca F.J., Perales J.F., Sanchez G., Esplugas P., 2012, Characterization and determination of the odorous charge in the indoor air of a waste treatment facility through the evaluation of volatile organic compounds (VOCs) Using TD-GC/MS, Waste manage., 32(12), 2469–2481. Gostelow P., Parsons S.A., Stuetz R.M., 2001, Odor measurements for sewage treatment works, Water Res., 35(3), 579-597. Kim, K.H., Park S.Y., 2008, A comparative analysis of malodor samples between direct (Olfactometry) and indirect (Instrumental) methods, Atmos. Environ., 42(20), 5061–5070. Parker, D.B., Koziel J.A., Cai L., Jacobson L.D., Akdeniz N., Bereznicki S.D., Lim T.T., Caraway E.A., Zhang S., Hoff S.J., Heber A.J., Heathcote K.Y., Hetchler B.P., 2012, Odor and Odorous Chemical Emissions from Animal Buildings: Part 6. Odor Activity Value, Trans. ASABE, 2357–2368. Parker D.B., Perschbacher-Buser Z.L., Cole N.A., Koziel J.A., 2010, Recovery of Agricultural Odors and Odorous Compounds from Polyvinyl Fluoride Film Bags, Sensors, 10(9): 8536–8552. Renault C., Morice E., Delery L., Deportes I., 2006, Guide méthodologique pour l’évaluation du risque sanitaire de l’étude d’impact des installations de compostage soumises à autorisation. Rapport ASTEE (Association Scientifique et Technique pour l’Eau et l’Environnement). Schlegelmilch M., Streese K., Biedermann W., Herold T., Stegmann R., 2005, Odor control at biowaste composting facilities, Waste Manag., 25, 917-927. Trabue S.L., Anhalt J.C., Zahn J.A., 2006, Bias of Tedlar Bags in the Measurement of Agricultural Odorants, J. Environ Qual, 2006, 35(5), 1668–1677. Van Durme G.P., Mc Namara B.F., Mc Ginley C.G., 1992, Bench-scale removal of odor and volatile organic compounds at a composting facility, Water Environ. Technol., 64(1), 19-27. Van Gemert L.J., 2011, Compilations of odor threshold values in air, water and other media, second edition. Wu C., Liu J., Zhao P., Piringer M., Schauberger G., 2016, Conversion of the Chemical Concentration of Odorous Mixtures into Odour Concentration and Odour Intensity: A Comparison of Methods, Atmos. Environ., 127, 283–292. 72