Quantification Of Odorants Research Articles

Real-Time Monitoring of Odour Emissions at the Fenceline of a Waste Treatment Plant by Instrumental Odour Monitoring Systems: Focus on Training Methods.

Waste treatment plants (WTPs) often generate odours that may cause nuisance to citizens living nearby. In general, people are becoming more sensitive to environmental issues, and particularly to odour pollution. Instrumental Odour Monitoring Systems (IOMSs) represent an emerging tool for continuous odour measurement and real-time identification of odour peaks, which can provide useful information about the process operation and indicate the occurrence of anomalous conditions likely to cause odour events in the surrounding territories. This paper describes the implementation of two IOMSs at the fenceline of a WTP, focusing on the definition of a specific experimental protocol and data processing procedure for dealing with the interferences of humidity and temperature affecting sensors' responses. Different approaches for data processing were compared and the optimal one was selected based on field performance testing. The humidity compensation model developed proved to be effective, bringing the IOMS classification accuracy above 95%. Also, the adoption of a class-specific regression model compared to a global regression model resulted in an odour quantification capability comparable with those of the reference method (i.e., dynamic olfactometry). Lastly, the validated models were used to process the monitoring data over a period of about one year.

Discrete and continuous odor quantification in gas-phase odor biosensor

Cells expressing heterologous olfactory receptors (ORs) were commonly used as the sensing elements in odor biosensors owing to their high sensitivity and selectivity. However, The odor quantification performance of these biosensors especially in speed and accuracy aspects is not so favorable yet. To overcome this problem, we proposed discrete and continuous odor quantification. In this study, two ORs, Or13a and Or56a, and their agonists, 1-octen-3-ol and geosmin were used. The cell images were handled online to accelerate the processing speed. The odor intensity that we intended to quantify was the target odor, the odor intensity that we provided was the generated odor. The target and generated odors were repeatedly applied thus mitigating the sensor drift and aging influence. The target odor quantification outcome could be one of several discrete candidates (discrete quantification) or a continuous value computed based on the standard response curve (continuous quantification). We first obtained the typical results of the discrete and continuous quantification methods using single OR. After this, we executed the experiments in multiple target odor intensities and made a comparison between two methods. Discrete quantification was simpler while continuous quantification was faster and more accurate. In addition, both methods were tested under binary odor mixture conditions and their quantification results were satisfactory. The methods proposed here will benefit the on-site precise quantification of odor biosensors.

Gas-phase odor mixture quantification based on relative comparison method using multiple olfactory receptors

The gas-phase odor biosensors exhibit outstanding sensitivity and selectivity towards target ligands. Current odor detections, however, were only operated under one or multiple separated olfactory receptors (ORs) stimulated by a single component odor. Meanwhile, the nonlinear characteristic, drift, and aging problems in biosensors were the main barriers for better quantification performance. Here, we proposed a gas-phase odor biosensor for odor mixture quantification based on relative comparison method using cell expressing ORs, the liquid thickness control and liquid exchange maintained repeatable OR responses. We repeatedly compared the known and its adjacent unknown odor response thus mitigating the biosensor drift interference and sensor nonlinearity characteristic. We first conducted the odor quantification of one odor component in a mixture of five odors. The existence of other odors did not influence the quantification of Or56a towards geosmin component in the mixture. Then, two types of OR were utilized for quantifying odor mixture with or without adding the OR label information to the individual cells. With-label quantification was more precise and efficient than without-label one. Lastly, we simultaneously expressed two ORs on one cell line for wider detection range and quantified the mixture even if OR has cross sensitivity. We believe the odor mixture quantification methods established in this study will extend the application scenarios of odor biosensors especially in rapid on-site odor detection.

The Use of Field Olfactometry in the Odor Assessment of a Selected Mechanical–Biological Municipal Waste Treatment Plant within the Boundaries of the Selected Facility—A Case Study

Odor management plans indicate the need to identify odor sources in waste management facilities. Finding the right tool for this type of task is a key element. This article covers a new approach for odor quantification and source identification at a selected waste management facility by coupling field olfactometry and the spatial interpolation method, such as inverse weighted distance. As the results show, this approach works only partially. Field olfactometry seems to be a suitable tool for odor identification that could be an instrument incorporated into odor management plans as it allowed for recognition of most odor-generating places at the selected facility, i.e., waste stabilization area, green waste storage area, and bioreactors. However, spatial distributions obtained by the selected interpolation method are characterized by high errors during cross-validation, and they tend to overestimate odor concentrations. The substantial weakness of the selected interpolation method is that it cannot handle points where the odor concentration is below the detection threshold. Therefore, the usefulness of such a method is questionable when it comes to odor management plans. Since field olfactometry is a reliable tool for odor measurements, further research into computational methods is needed, including advanced interpolation methods or dispersion modeling based on field olfactometry data.

Dynamic Olfactometry and Oil Refinery Odour Samples: Application of a New Method for Occupational Risk Assessment

Refineries are characterized by relevant odour impacts, and the control and monitoring of this pollutant have become increasingly important. Dynamic olfactometry, a sensorial analysis that involves human examiners, is currently the most common technique to obtain odour quantification. However, due to the potential presence of hazardous pollutants, the conduction of occupational risk assessment is necessary to guarantee examiners’ safety. Nevertheless, the occupational risk for olfactometric examiners, specifically correlated with oil refineries emissions, has not been investigated yet. Therefore, this paper applies a new methodology of risk assessment for workers involved in dynamic olfactometry, focusing on odorous refineries emissions. The chemical characterization of refinery emissions was obtained by TD-GC-MS, analysing odorous samples collected at different refinery odour sources. A database of chemical pollutants emitted from a refinery plant was built up, and the minimum dilution values to be adopted during the analysis of refinery odorous samples was calculated. In particular, this evaluation highlighted that, in this scenario, a non-negligible carcinogenic risk may exist for panellists exposed to refineries’ samples, and the carcinogenic risk is sometimes higher than what is acceptable. Therefore, a minimum dilution value between 1.01 and 5, according to the specific sample, must be set to guarantee the examiners’ safety.

RHINOS: A lightweight portable electronic nose for real-time odor quantification in wastewater treatment plants

SummaryQuantification of odor emissions in wastewater treatment plants (WWTPs) is key to minimize odor impact to surrounding communities. Odor measurements in WWTPs are usually performed via either expensive and discontinuous olfactometry hydrogen sulfide detectors or via fixed electronic noses. We propose a portable lightweight electronic nose specially designed for real-time odor monitoring in WWTPs using small drones. The so-called RHINOS e-nose allows odor measurements with high spatial resolution, and its accuracy is only slightly worse than that of dynamic olfactometry. The device has been calibrated using odor samples collected in a WWTP in Spain over a period of six months and validated in the same WWTP three weeks after calibration. The promising results obtained support the suitability of the proposed instrument to identify the odor sources having the highest emissions, which may give a useful indication to the plant managers as regards odor control and abatement.

A low-cost and efficient electronic nose system for quantification of multiple indoor air contaminants utilizing HC and PLSR

A novel quantification technique is presented for electronic nose (E-nose), which is based on a double-step strategy combined with hierarchical classifier (HC) and partial least squares regression (PLSR). With the tree structure of HC, the complexity of classifier training process can be reduced in the case of unbalanced samples. For each level of the class hierarchy, the extreme learning machine-based artificial neural network (ELM-ANN) is applied for classification. In order to improve the classification performance of ELM-ANN, the multiple time-domain features are selected as training inputs, and a novel optimization method of the number of hidden layer neurons is given. To validate the effectiveness of the presented quantification technique, an E-nose system is designed to quantify the gases including six toxic gases (hydrogen sulfide, carbon monoxide, ammonia, toluene, formaldehyde, acetone) and three kinds of binary gas mixtures. This presented hierarchical classifier has demonstrated outstanding performance for the identification of target gases, such as the macro-averaged precision for unlabeled data is improved from 80% to 92% compared with non-hierarchical classifiers. Furthermore, an excellent performance of concentration estimation is obtained utilizing PLSR, where average values of the coefficient of determination for training and test samples are equal to 0.957 and 0.927, respectively. Overall, our work demonstrates that the proposed approach is applicable in E-nose-based odor quantification.

Environmental Odour Quantification by IOMS: Parametric vs. Non-Parametric Prediction Techniques

Odour emissions are a global issue that needs to be controlled to prevent negative impacts. Instrumental odour monitoring systems (IOMS) are an intelligent technology that can be applied to continuously assess annoyance and thus avoid complaints. However, gaps to be improved in terms of accuracy in deciphering information, especially in the implementation of the mathematical model, are still being researched, especially in environmental odour monitoring applications. This research presents and discusses the implementation of traditional and innovative parametric and non-parametric prediction techniques for the elaboration of an effective odour quantification monitoring model (OQMM), with the aim of optimizing the accuracy of the measurements. Artificial neural network (ANN), multivariate adaptive regression splines (MARSpline), partial least square (PLS), multiple linear regression (MLR) and response surface regression (RSR) are implemented and compared for prediction of odour concentrations using an advanced IOMS. Experimental analyses are carried out by using real environmental odour samples collected from a municipal solid waste treatment plant. Results highlight the strengths and weaknesses of the analysed models and their accuracy in terms of environmental odour concentration prediction. The ANN application allows us to obtain the most accurate results among the investigated techniques. This paper provides useful information to select the appropriate computational tool to process the signals from sensors, in order to improve the reliability and stability of the measurements and create a robust prediction model.

Application of Machine Learning for Fenceline Monitoring of Odor Classes and Concentrations at a Wastewater Treatment Plant.

The development of low-cost sensors, the introduction of technical performance specifications, and increasingly effective machine learning algorithms for managing big data have led to a growing interest in the use of instrumental odor monitoring systems (IOMS) for odor measurements from industrial plants. The classification and quantification of odor concentration are the main goals of IOMS installed inside industrial plants in order to identify the most important odor sources and to assess whether the regulatory thresholds have been exceeded. This paper illustrates the use of two machine learning algorithms applied to the concurrent classification and quantification of odors. Random Forest was employed, which is a machine learning algorithm that thus far has not been used in the field of odor quantification and classification for complex industrial situations. Furthermore, the results were compared with commonly used algorithms in this field, such as artificial neural network (ANN), which was here employed in the form of a deep neural network. Both techniques were applied to the data collected from an IOMS installed for fenceline monitoring at a wastewater treatment plant. Cohen’s kappa and Normalized RMSE are used as specifical performance indicators for classification and regression: the indicators were calculated for the test dataset, and the results were compared with data in the literature obtained in contexts of similar complexity. A Cohen’s kappa of 97% was reached for the classification task, while the best Normalized RMSE, namely 4%, for the interval 20–2435 ouE/m3 was obtained with Random Forest.

From the ISOEN2019 Tutorial‘s lecture titled “Quantification of odors and the role of electronic noses” —Precautions for using electronic nose and its effective use—

From the ISOEN2019 Tutorial‘s lecture titled “Quantification of odors and the role of electronic noses” —Precautions for using electronic nose and its effective use—

Odor Discrimination Using Cell-Based Odor Biosensor System With Fluorescent Image Processing

Various types of odor sensor systems have been developed for the discrimination and quantification of odorants. Although the use of biological components may improve sensitivity, a few studies have reported on the use of an array of biological elements for odor discrimination. In this paper, odorants were discriminated using an odor biosensor system with cells expressing different types of olfactory receptors. A cell's response to an odorant was acquired as a fluorescent image followed by an image processing technique based on circle Hough transform. The principal component analysis and linear discriminant analysis demonstrated the ability to discriminate between two odorants at different concentrations. The result suggests that the combination of a biosensor and image processing technique has the potential to discriminate between different odorants even if the cells are placed at random.

Quantification of Odorants in Animal Feeds at Commercial Swine and Poultry Operations

Abstract. Odorants in animal feeds may come from various processes, such as fermentation and decay of feed ingredients, contamination by fecal materials, and sorption of volatiles in the air of animal houses. Those odorants may affect feed flavor and may serve as indicators of feed quality. The objective of this study was to examine the composition and concentration of odorants in animal feeds and to explore the variability of those odorants with animal operation type and season. Thirty-seven feed samples were collected from 14 swine and poultry operations in the U.S. Midwest and were submitted for gas chromatography-mass spectrometry (GC-MS) analysis. A total of 55 organic odorants were quantitated, including fatty acids, alcohols, aldehydes, ketones, phenols, and nitrogen-containing compounds. Those compounds together accounted for 0.46% ±0.20% of fresh feed mass, with the highest percentage (0.54% ±0.19%) found at layer hen houses and the lowest percentage (0.38% ±0.14%) at swine farrowing houses. Acetic acid and ethanol were most abundant, accounting for 0.22% ±0.13% and 0.13% ±0.07% of fresh feed mass, respectively. Fecal indicators, including indole and skatole, were <5 ppm by mass. Principal component analysis (PCA) revealed that the variability in odorant composition was largely ascribed to two loading factors that were dominated by acetic acid and ethanol, respectively. The odorant composition of feed samples showed no significant effect by animal operation type, while a gradual seasonal change was noted. This study is expected to improve the understanding of the causes of odorants in animal feeds and their implications for animal health and production performance. Keywords: Animal feed, Animal operation, GC-MS, Odorant, Principal component analysis.

Evaluation of the performance of field olfactometers by selected ion flow tube mass spectrometry

Odour quantification is generally performed by olfactometry, a technique which determines to what extent an odorous air sample should be diluted with odour-free air to be just distinguishable from odour-free air. In this study, the performance of two field olfactometers (Nasal Ranger and Scentroid 110c) was evaluated with respect to their ability to generate accurate dilutions. Therefore, an air stream with known concentrations (500 ppbv–5 ppmv) of odorous compounds (acetic acid, propanoic acid, n-butanol, dimethyl sulphide, dimethyl disulphide) was diluted by the olfactometers at their different set points after which the concentrations of the target compounds in the diluted air stream were measured by selected ion flow tube mass spectrometry (SIFT-MS). This enabled to determine the observed dilution ratios (DRobs) and relate them to the set point values (DRset). The Nasal Ranger showed good performance in the interval between DRset 3 and 31. Only at the highest set dilution ratio (DRset = 61) breakthrough of the compounds through the activated carbon filter was observed. This breakthrough resulted in lower observed dilution ratios (up to a factor of 2 for dimethyl sulphide) when compared to the DRset. For the Scentroid a good linearity between DRset and DRobs was observed but dilution ratios were much higher (up to a factor of 2) than what could be theoretically expected on the basis of air flows. This behaviour could be explained by sorption effects of the target compounds onto the metal parts and into the rubber seal of the Scentroid.

Use of Inventory Management to Mitigate Odor Emissions From Land-Applied Biosolids

Short-term (<60-d) stockpile-curing of dewatered municipal wastewater biosolids has been suggested as a practice to reduce malodors during subsequent field application. Biosolids from the City of Philadelphia were evaluated following stockpile-curing for 3-d, 10-d, and 50-d. Each material was top-dressed on small grain stubble in 37-m diameter rings. Four trained odor assessors were positioned in the center of each odor ring and olfactory observations were performed at: pre-application (background); 45-60 min, 4-hrs, 10-hrs, and 20-hrs following field application. Comparison of 10-minute Best Estimate Dilution Threshold (BET10) odor concentrations found no statistical difference between the 3-d and 10-d treatments, but 50-d BET10 levels were statistically greater (α=0.05). Odor character, intensity, and hedonic-tone observations were similar and triangular forced-choice dynamic olfactometer testing of flux chamber whole-air samples revealed no statistical differences. Sensory odor quantification techniques employed to assess the total effect of field malodors showed remarkably consistent results. Analytical analyses were unsuccessful in identifying a single odorant in whole-air field samples. This study was not able to confirm that short-term biosolids stockpile curing produces a less odorous product for field application.

Non-specific conducting polymer-based array capable of monitoring odour emissions from a biofiltration system in a piggery building

A commercial non-specific gas sensor array system was evaluated in terms of its capability to monitor the odour abatement performance of a biofiltration system developed for treating emissions from a commercial piggery building. The biofiltration system was a modular system comprising an inlet ducting system, humidifier and closed-bed biofilter. It also included a gravimetric moisture monitoring and water application system for precise control of moisture content of an organic woodchip medium. Principal component analysis (PCA) of the sensor array measurements indicated that the biofilter outlet air was significantly different to both inlet air of the system and post-humidifier air. Data pre-processing techniques including normalising and outlier handling were applied to improve the odour discrimination performance of the non-specific gas sensor array. To develop an odour quantification model using the sensor array responses of the non-specific sensor array, PCA regression, artificial neural network (ANN) and partial least squares (PLS) modelling techniques were applied. The correlation coefficient (r(2)) values of the PCA, ANN, and PLS models were 0.44, 0.62 and 0.79, respectively.

Calibration of a Sensor Array (an Electronic Tongue) for Identification and Quantification of Odorants from Livestock Buildings

This contribution serves a dual purpose. The first purpose was to investigate the possibility of using a sensor array (an electronic tongue) for on-line identification and quantification of key odorants representing a variety of chemical groups at two different acidities, pH 6 and 8. The second purpose was to simplify the electronic tongue by decreasing the number of electrodes from 14, which was the number of electrodes in the prototype. Different electrodes were used for identification and quantification of different key odorants. A total of eight electrodes were sufficient for identification and quantification in micromolar concentrations of the key odorants n-butyrate, ammonium and phenolate in test mixtures also containing iso-valerate, skatole and p-cresolate. The limited number of electrodes decreased the standard deviation and the relative standard deviation of triplicate measurements in comparison with the array comprising 14 electrodes. The electronic tongue was calibrated using 4 different test mixtures, each comprising 50 different combinations of key odorants in triplicates, a total of 600 measurements. Back propagation artificial neural network, partial least square and principal component analysis were used in the data analysis. The results indicate that the electronic tongue has a promising potential as an on- line sensor for odorants absorbed in the bioscrubber used in livestock buildings.

Odor Impact Assessments Based on Dose-Response Relationships and Spatial Analyses of Population Response

Owners of odor-emitting facilities currently lack effective strategies for assessing odorous impacts on communities. The most widely-used method for odor quantification is through estimates of odor concentration at impacted receptors in a community. This approach fails to account for the full range of dilutions over which an odor is experienced, the varied sensitivities of individuals in a population, and odor offensiveness. Therefore, dose-response relationships were developed to express the probability of response and degree of annoyance of a population as functions of odor concentration. Dispersion modeling can be used in conjunction with these relationships to calculate contours of probability of response and annoyance throughout the impacted community under many meteorological conditions. These contours serve as the basis for evaluating parameters that reflect various dimensions of odor impact on individual receptors and throughout the area of the impacted region. Spreadsheet software that was developed to aid in the calculation of these parameters is briefly presented.

Odour quantification by a sensor array: An application to landfill gas odours from two different municipal waste treatment works

The possibility of quantifying the landfill gas (LFG) odour in terms of odour-units per cubic meter (ou/m 3) using a tin oxide sensor array is investigated. The objective is to determine the most appropriate neural machines (MLP networks, RBF networks) model to perform the odour concentration approximation and evaluate the influence of multiple biogas sources modelling on the approximation quality. The structural risk minimization principle is used instead of the usual empirical risk minimization principle in the training algorithm of the neural machines. Multilayer perceptrons (MLP) networks prove to minimize best the error on the prediction of odour concentration of unknown data. The data is constituted of LFG odour samples from two municipal waste treatment works presenting different concentrations of odorous compounds. It is shown that the quality of the LFG odour approximation is in the present case influenced directly by the size of the training data set. The use of data coming from two different sources is not detrimental to the quality of the approximation.

Process studies of odour emissions from effluent ponds using machine-based odour measurement

Replicable experimental studies using a novel experimental facility and a machine-based odour quantification technique were conducted to demonstrate the relationship between odour emission rates and pond loading rates. The odour quantification technique consisted of an electronic nose, AromaScan A32S, and an artificial neural network. Odour concentrations determined by olfactometry were used along with the AromaScan responses to train the artificial neural network. The trained network was able to predict the odour emission rates for the test data with a correlation coefficient of 0.98. Time averaged odour emission rates predicted by the machine-based odour quantification technique, were strongly correlated with volatile solids loading rate, demonstrating the increased magnitude of emissions from a heavily loaded effluent pond. However, it was not possible to obtain the same relationship between volatile solids loading rates and odour emission rates from the individual data. It is concluded that taking a limited number of odour samples over a short period is unlikely to provide a representative rate of odour emissions from an effluent pond. A continuous odour monitoring instrument will be required for that more demanding task.

ODOR IMPACT ASSESSMENTS BASED ON DOSE-RESPONSE RELATIONSHIPS AND SPATIAL ANALYSES OF POPULATION RESPONSE

Owners of odor-emitting facilities currently lack effective strategies for assessing odorous impacts on communities. The most widely-used method for odor quantification is through estimates of odor concentration at impacted receptors in a community. This approach fails to account for the full range of dilutions over which an odor is experienced, the varied sensitivities of individuals in a population, and odor offensiveness. Therefore, dose-response relationships were developed to express the probability of response and degree of annoyance of a population as functions of odor concentration. Dispersion modeling can be used in conjunction with these relationships to calculate contours of probability of response and annoyance throughout the impacted community under many meteorological conditions. These contours serve as the basis for evaluating parameters that reflect various dimensions of odor impact on individual receptors and throughout the area of the impacted region. Spreadsheet software that was developed to aid in the calculation of these parameters is briefly presented.