E-nose development to predict odour annoyance in a receptor site
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(2) 1 – Intro. Inhabitants exposition. Prev ailin g. wind. ANNOYANCE. Odour emission POLLUTION. 1-2-3-4-5-6-7-8-9-10-11-12-13-14-15-16-17-18-19-20-21-22-23-24.
(3) 1 – Intro. Pollution. Annoyance. ≠ variables→ ≠ measurement approaches Chemical evolution: nature and concentration. 1-2-3-4-5-6-7-8-9-10-11-12-13-14-15-16-17-18-19-20-21-22-23-24.
(4) 1 – Intro. Sampling Olfactory pollution metrology Sensorial techniques Dynamic olfactometry (concentration) Percentiles evaluation (concentration + frequency). Electronic nose. Hedonic character. Analytical techniques Identification/quantification. of compounds Emission factors (concentration, odour rate,distance). Sniffing team method (concentration, odour rate). population survey (neighbour panel). Intensity measurement. Measurement grid .... 1-2-3-4-5-6-7-8-9-10-11-12-13-14-15-16-17-18-19-20-21-22-23-24.
(5) 1 – Intro Sensorial Chemical. Complementary techniques. E-nose. For instance Prediction of odour taking into account the chemical composition! BE CAREFUL correlation « chemical composition – odour » is not a straight-line!. odour Mixture ≠ sum of single odorant There are Synergy and Inhibition interactions between molecules For odour perception, 1+2=4 1+2=2 and 1+2 ≠ 3. Chemical analysis alone insufficient to evaluate odour. 1-2-3-4-5-6-7-8-9-10-11-12-13-14-15-16-17-18-19-20-21-22-23-24.
(6) 2 – E-nose: environmental application “odour” Presentation. = Array of chemical sensors with overlapping sensitivities + Pattern recognition techniques → Able to detect and recognise single or complex gaseous mixture (for instance odour one) Device includes three major parts: - Sample delivery system (pump, mass flow meter, filter, “pre-concentration”, - Detection system: reactive part of the device sensor array different kind of chemical sensors (often metal oxide sensors): each are sensitive to volatile compunds but with their own selectivity Hr and T sensors - Computing part: data treatment, control system 1-2-3-4-5-6-7-8-9-10-11-12-13-14-15-16-17-18-19-20-21-22-23-24.
(7) 2 – E-nose: environmental application “odour” Principle. Odour (j) Sensors (i). ∆Kij. Pattern. Compost? Pattern Recognition techniques (PARC) 1-2-3-4-5-6-7-8-9-10-11-12-13-14-15-16-17-18-19-20-21-22-23-24.
(8) 3 – e-nose to predict annoyance Our « philosophy» Development of the “e-nose” instrument considering the application. Environment. Electronic nose. Instrumentation. Odours. Experiments. Measure. Treatment adaptation. 1-2-3-4-5-6-7-8-9-10-11-12-13-14-15-16-17-18-19-20-21-22-23-24.
(9) 2 – E-nose: environmental application “odour” To identify a mixture: LEARNING phase of the various patterns (database development) the input signals are put in relationship with the odour sources, and the membership in a specific group is known. Field sampling→lab measurement 6. =compost. 5 4. =fresh organic waste. Root 2. 3. unknown. X. 2 1 0 -1. X. X. =biogas. Landfill. -2. Waste water -3 -8. -6. -4. -2. 0. 2. 4. 6. Root 1. Or Field measurement. 1-2-3-4-5-6-7-8-9-10-11-12-13-14-15-16-17-18-19-20-21-22-23-24. Manure.
(10) 2 – E-nose: environmental application “odour” To quantify the odour (like a calibration) Field sampling-lab measurement: olfactometry + sensors array: regression models (PLS) 1801 Concentration odeur (uoE/m³). 1601. Concentration = 1+9352 *(G-G0). 1401. =compost. 500000 uo/m3. avec G0 = 0.0219. 1201 1001 801 601 401 201. r² = 0.88. 1. =fresh organic waste. 3000 uo/m3. =biogas. 0. 0.05. 0.1 0.15 0.2 Conductance du TGS 822, G(kS). Or Field measurement: sniffing team + dispersion models: correlation with field e-nose. 10000 uo/m3. 1-2-3-4-5-6-7-8-9-10-11-12-13-14-15-16-17-18-19-20-21-22-23-24. 0.25.
(11) 2 – E-nose: environmental application “odour” Some data treatment tools • Multivariate techniques (PARC) Supervised or not Linear : PCA LDA MLR PLS (rarely no linear techniques-ANN-) Simple Bayes classifiers (Mahalanobis LDA). • Univariate techniques Linear regression Simple algorithms (multiplicative or additive) Statistic (i.e. : threshold evaluation). 1-2-3-4-5-6-7-8-9-10-11-12-13-14-15-16-17-18-19-20-21-22-23-24.
(12) 2 – E-nose: environmental application “odour” Some data treatment tools Modelling 2D. Modelling 3D. Percentiles: C98(1 hour): 1 ou/m3 delimitates an ellipse outside: below1 uo during more than 98% of the time inside: below1 uo during less than 98% of the time 1-2-3-4-5-6-7-8-9-10-11-12-13-14-15-16-17-18-19-20-21-22-23-24.
(13) 2 – E-nose: environmental application “odour” E-nose in comparison to usual approaches ÷ sensorial approaches. +. ÷ chemical techniques. «Objectivity ». Global measurement. No need of human presence. Chemical composition ≠ Odour. Continuous measurement (night) :. Continuous measurement. -Variation detection -Recording and archiving -Real time, …. - response to chemical composition (odorant and non odorant compounds). -. - correlation with sensorial techniques - external variables influence -detection limit of metal oxide sensors in the field around 200 uo/m³(higher than human detection). - no specific to compounds -Weaker accuracy - no chemical analyses -. 1-2-3-4-5-6-7-8-9-10-11-12-13-14-15-16-17-18-19-20-21-22-23-24.
(14) 3 – E-nose to predict annoyance Challenge Our research team considers: • FIDOR (integrating index) - Frequency: + F is elevated, more it is painful - Intensity≈ odour concentration - Duration: continuous period of odour perception - Offensiveness≈ odour description (compost, biogas,…) - Receptor: location (wind direction, residential or industrial …) • odour perception in the neighbouring - dispersion models (weather conditions and topographic elements). - validation by sniffing team and/or local resident survey. 1-2-3-4-5-6-7-8-9-10-11-12-13-14-15-16-17-18-19-20-21-22-23-24.
(15) 3 – E-nose to predict annoyance Our last instrument: FIDOR* Algorithms developed to estimate olfactive annoyance by odour measurement. Estimation of FIDOR index: Frequency Intensity Duration Offensiveness Receptor. *A-C Romain these License to Odometric. 1-2-3-4-5-6-7-8-9-10-11-12-13-14-15-16-17-18-19-20-21-22-23-24.
(16) 3 – E-nose to predict annoyance In use, on a landfill site since spring 2009 Network of 5 e-noses on the site. 1-2-3-4-5-6-7-8-9-10-11-12-13-14-15-16-17-18-19-20-21-22-23-24.
(17) 3 – E-nose to predict annoyance. 1-2-3-4-5-6-7-8-9-10-11-12-13-14-15-16-17-18-19-20-21-22-23-24.
(18) 3 – E-nose to predict annoyance Algorithm FIDOR*:. *A-C Romain these License to Odometric. 1-2-3-4-5-6-7-8-9-10-11-12-13-14-15-16-17-18-19-20-21-22-23-24.
(19) 3 – E-nose to predict annoyance Continuous monitoring. FIDOR. Classification functions values. 10 8 6. The classification function for biogas decreases. 4 2 0 -2 -4 -6. The classification function for fresh waste remains nearly stable. -8 -10. Moving away from the landfill gas extraction well. 1-2-3-4-5-6-7-8-9-10-11-12-13-14-15-16-17-18-19-20-21-22-23-24.
(20) 3 – E-nose to predict annoyance FIDOR Odour monitoring (30/06/2009) Nose 1 Compost. odourless Compost. Air amont. D F. 1-2-3-4-5-6-7-8-9-10-11-12-13-14-15-16-17-18-19-20-21-22-23-24.
(21) 3 – E-nose to predict annoyance FIDOR. F. 1-2-3-4-5-6-7-8-9-10-11-12-13-14-15-16-17-18-19-20-21-22-23-24.
(22) 3 – E-nose to predict annoyance FIDO. R. To predict in real time odour exposition and annoyance, R is fundamental relationship between: measurement of odour on the site, by an array of e-noses exposition → distance of perception thanks to dispersion models (input data=odour rate, ou/s??) At present, search the best models between e-noses signals and odour concentration in the neighbourhood 1.. Local resident survey. 2.. A nose at the periphery of the site + a portable nose. 3.. Sniffing team. Optimisation of quantification and identification models Validation step 1-2-3-4-5-6-7-8-9-10-11-12-13-14-15-16-17-18-19-20-21-22-23-24.
(23) 3 – E-nose to predict annoyance FIDO. R. 1-2-3-4-5-6-7-8-9-10-11-12-13-14-15-16-17-18-19-20-21-22-23-24.
(24) 4 – Conclusions E-nose to predict annoyance: Under development Improvement of the FIDOR located in the receptor site (first results under interpretation-with wind direction, other pollution sources): “Odometric”. Understanding the relationships chemical composition-odour to improve e-nose models development Source 1 Source 2 These in progress to study the ability to discriminate two sources. Emitting site. Local resident survey: “Odometric”. Nonodorant source (highway) Receptor site. Improvement of learning phase (ie. odour samples collected in the surroundings and not on the sources) …. 1-2-3-4-5-6-7-8-9-10-11-12-13-14-15-16-17-18-19-20-21-22-23-24.
(25) 4 – Conclusions. Applications Controlling a processing system or sampling odour-causing process control Information: authorities, resident, industrial real time+archiving real time estimation of immission in the vicinity check of efficacy of new processes assist in correct application of reglementation:. ie « objectivity » of the complaints, monitoring of fluctuating emissions. 1-2-3-4-5-6-7-8-9-10-11-12-13-14-15-16-17-18-19-20-21-22-23-24.
(26) Anne-Claude Romain Faculté des Sciences Département Sciences & Gestion de l'Environnement. Atmosphères polluées Tel : + 32 (0) 63 23 08 59 [email protected] http://www.dsge.ulg.ac.be/arlon. Tel : + 32 (0) 63 23 08 92 185, Avenue de Longwy, B-6700 ARLON. www.odometric.be [email protected].
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