Experimental demonstration of masking phenomena between competing odorants via an air dilution sensory test

Odour generalisation and detection dog training

Detection dogs are required to search for and alert to specific odours of interest, such as drugs, cadavers, disease markers and explosives. However, the odour released from different samples of the same target substance will vary for a number of reasons, including the production method, evaporation, degradation, or by being mixed with extraneous odours. Generalisation, the tendency to respond in the same manner to stimuli which are different - but similar to - a conditioned stimulus, is therefore a crucial requirement for working detection dogs. Odour is a complex modality which poses unique challenges in terms of reliably predicting generalisation, when compared with auditory or visual stimuli. The primary aim of this review is to explore recent advances in our understanding of generalisation and the factors that influence it, and to consider these in light of detection dog training methods currently used in the field. We identify potential risks associated with certain training practices, and highlight areas where research is lacking and which warrant further investigation.

VOC data-driven evaluation of vehicle cabin odor: from ANN to CNN-BiLSTM

Emissions of volatile organic compounds (VOCs) in vehicles represent a significant problem, causing unpleasant odors. To mitigate VOCs and odors in vehicles, it is critical to choose interior parts with low odor and VOC emissions. However, prevailing odor evaluation methods are subjective, costly, and potentially harmful to the health of evaluators. In this study, we analyzed 139 automotive interior parts and 92 vehicles, establishing a cost-effective, data-driven method for odor evaluation. The contents of benzene, toluene, ethylbenzene, xylene, styrene, formaldehyde, acetaldehyde, acrolein, and total volatile organic compounds (TVOC) were detected by thermal desorption gas chromatography-mass spectrometry (TD-GC/MS) and high-performance liquid chromatography with an ultraviolet detector (HPLC-UV). Professional odor evaluators assessed the odors, identifying intensity levels from 2.0 to 4.5 in interior parts and 2.5 to 3.5 in whole vehicles. Leveraging this data, we applied four supervised learning algorithms to develop predictive models for the odor intensity of both interior parts and entire vehicles. During model training, we implemented early stopping techniques for the artificial neural network (ANN) and convolutional neural network-bidirectional long short-term memory (CNN-BiLSTM) models, while optimizing the support vector machine (SVM) and extreme gradient boosting (XGBoost) models using the GridSearch algorithm. The evaluation results reveal that the CNN-BiLSTM model performs the best, achieving an average accuracy of 89% for unknown samples within an odor intensity level of 0.5. The root mean square error (RMSE) is 0.24, and the mean absolute error (MAE) is 0.08. The model also underwent a sevenfold cross-validation, achieving an accuracy of 83.43%. Additionally, we employed SHapley Additive exPlanations (SHAP) for the interpretative analysis of the model, which confirmed the consistency of each VOC's odor contribution with human olfactory rules. By predicting odors based on VOCs through supervised learning, this study reduces the costs and enhances the efficiency and applicability of odor assessment across various vehicle interiors.

A critical review on odor measurement and prediction

Odor pollution has become a global environmental issue of increasing concern in recent years. Odor measurements are the basis of assessing and solving odor problems. Olfactory and chemical analysis can be used for odor and odorant measurements. Olfactory analysis reflects the subjective perception of human, and chemical analysis reveals the chemical composition of odors. As an alternative to olfactory analysis, odor prediction methods have been developed based on chemical and olfactory analysis results. The combination of olfactory and chemical analysis is the best way to control odor pollution, evaluate the performances of the technologies, and predict odor. However, there are still some limitations and obstacles for each method, their combination, and the prediction. Here, we present an overview of odor measurement and prediction. Different olfactory analysis methods (namely, the dynamic olfactometry method and the triangle odor bag method) are compared in detail, the latest revisions of the standard olfactometry methods are summarized, and the uncertainties of olfactory measurement results (i.e., the odor thresholds) are analyzed. The researches, applications, and limitations of chemical analysis and odor prediction are introduced and discussed. Finally, the development and application of odor databases and algorithms for optimizing odor measurement and prediction methods are prospected, and a preliminary framework for an odor database is proposed. This review is expected to provide insights into odor measurement and prediction.

Research on odor characteristics of typical odorants of railway vehicle products

Odor annoyance was a kind of environmental air pollution. Compared to other indoor environments, vehicle interior materials were not well studied. Especially, there had been little research on odor character of the railway vehicles. This study applied the OAV method to identify the key odorants of railway vehicle materials and discussed the characteristics of these odorants through Weber Fechner law and a dual variable method. The result showed that for single odorant, Weber Fechner law can be used to estimate the perceived intensity of an odor gas sample at different concentration levels. The odorant with smaller slope had significant tolerance to human. For the mixtures of odorants, the overall intensity of the mixture is generally dominated by the strongest odor intensity of the individual substance in the mixture, and positive interaction effect can be observed in mixtures whose intensities had little difference. But there was a kind of odorants, such as methacrylate, in which a very small variation in the concentration of mixtures can affect its odor intensity largely. Meanwhile, the odor intensity modification coefficient was an effective way to identify and evaluate odor interaction effect. The interaction potential of the studied odorants from strong to weak was methacrylate, dibutyl-amine, nonanal, 2-ethyl hexanol. The odor interaction potential and odor nature should be paying much attention in the improvement of odor in railway vehicle product.

An In Vitro HL-1 Cardiomyocyte-Based Olfactory Biosensor for Olfr558-Inhibited Efficiency Detection

Some short-chain fatty acids with a pungent or unpleasant odor are important components of human body odor. These malodors severely threaten human health. The antagonists of malodors would help to improve odor perception by affecting the interaction between odors and their receptors. However, the traditional odor detection and analysis methods, such as MOS, electrochemical, conductive polymer gas sensors, or chromatography-mass spectrometry are not suitable for screening the antagonists since they are unable to detect the ligand efficacy after odor-receptor binding. In this study, RT-PCR results showed that HL-1 cardiomyocytes endogenously express the olfactory receptor 558 (Olfr558) which can be activated by several malodorous short-chain fatty acids. Therefore, an in vitro HL-1 cardiomyocyte-based olfactory biosensor (HCBO-biosensor) was developed by combining cardiomyocytes and microelectrode array (MEA) chips for screening the potential antagonists of the Olfr558. Firstly, it showed that the biosensor specifically responded to ligands of Olfr558 through odor stimulation experiments. Then, an odor response model of HL-1 cardiomyocytes was constructed by a ligand of Olfr558 (isovaleric acid). The response feature of the in vitro HCBO-biosensor to individual odors and mixtures with a potential antagonist (citral or β-damascenone) were extracted and compared. Finally, the Olfr558-inhibited efficiency was indirectly detected by comparing the half-maximal inhibitory concentration of isovaleric acid. The results showed that β-damascenone greatly inhibited Olfr558 while citral showed no significant inhibitory effect. In conclusion, we built a novel screening method for the antagonists of Olfr558 based on HL-1 cardiomyocytes and the MEA chip which will assist odor-related companies to develop novel antagonists of Olfr558.

Identification of volatile sulfur odorants emitted from ageing wastewater biosolids

Volatile sulfur compounds (VSCs) are important sources of unpleasant odours in biosolid emissions. However, the study of VSCs may be limited by complications in their gas phase measurements due to reactivity, transformations and varying reported odour detection thresholds. A range of methods were used to quantitatively analyse VSCs in wastewater biosolid emissions. VSCs were identified in aged biosolid emissions by gas chromatography (GC) with a sulfur chemiluminescence detector (SCD) and mass spectrometry coupled with olfactory detection port (MS/O). In total, 10 VSC's were identified with two volatile organic sulfur compounds (VOSCs), allyl methyl sulfide and methyl propyl sulfide being reported for the first time in biosolid emissions. The emission patterns of different VSCs varied as the biosolids aged. Initially, the median concentrations of H₂S, dimethyl sulfide (DMS), dimethyl trisulfide (DMTS), methanethiol (MeSH) and ethanethiol (EtSH) were orders of magnitude greater than their reported odour detection threshold, suggesting they would contribute to the odorous impact of the biosolids. The maximum H₂S value was equal to 59.9 × 10³ μg/m³ and was at least one magnitude higher compared to VOSCs, such as dimethyl disulfide (DMDS) (3.8×10³ μg/m³), DMS (4.53 × 10³ μg/m³), EtSH (2.83 × 10³ μg/m³) and MeSH (3.25 × 10³ μg/m³). Among the identified VSCs, H₂S was the prominent odorant in terms of the magnitude and the frequency of detection, both initially as well as throughout storage. However, DMTS should be considered as a high priority or key odorant due to its odour activity value (OAV) and frequency of detection (sensorially detected in more than 75% of samples, with an OAVs higher than 1).

An in vivo bioelectronic nose for possible quantitative evaluation of odor masking using M/T cell spatial response patterns

Odor masking is a prominent phenomenon in the biological olfactory perception system. It has been applied in industry and daily life to develop masking agents to reduce or even eliminate the adverse effects of unpleasant odors. However, it is challenging to assess the odor masking efficiency with traditional gas sensors. Here, we took advantage of the olfactory perception system of an animal to develop a system for the evaluation and quantification of odor masking based on an in vivo bioelectronic nose. The linear decomposition method was used to extract the features of the spatial response pattern of the mitral/tufted (M/T) cell population of the olfactory bulb of a rat to monomolecular odorants and their binary mixtures. Finally, the masking intensity was calculated to quantitatively measure the degree of interference of one odor to another in the biological olfactory system. Compared with the human sensory evaluation reported in a previous study, the trend of masking intensity obtained with this system positively correlated with the human olfactory system. The system could quantitatively analyze the masking efficiency of masking agents, as well as assist in the development of new masking agents or flavored food in odor or food companies.

A review of environmental odor quantification and qualification methods: The question of objectivity in sensory analysis

For several years, various issues have up surged linked to odor nuisances with impacts on health and economic concerns. As awareness grew, recent development in instrumental techniques and sensorial analysis have emerged offering efficient and complementary approaches regarding environmental odor monitoring and control. While chemical analysis faces several obstacles, the sensory approach can help overcome them. Therefore, this latter may be considered as subjective, putting the reliability of the studies at risk. This paper is a review of the most commonly sensory methodology used for quantitative and qualitative environmental assessment of odor intensity (OI), odor concentration (OC), odor nature (ON) and hedonic tone (HT). For each of these odor dimensions, the assessment techniques are presented and compared: panel characteristics are discussed; laboratory and field studies are considered and the objectivity of the results is debated. For odor quantification, the use of a reference scale for OI assessment offers less subjectivity than other techniques but at the expense of ease-of-use. For OC assessment, the use of dynamic olfactometry was shown to be the least biased. For odor qualification, the ON description was less subjective when a reference-based lexicon was used but at the expense of simplicity, cost, and lesser panel-training requirements. Only when assessing HT was subjectivity an accepted feature because it reflects the impacted communities' acceptance of odorous emissions. For all discussed dimensions, field studies were shown to be the least biased due to the absence of air sampling, except for OC, where the dispersion modeling approach also showed great potential. In conclusion, this paper offers the reader a guide for environmental odor sensory analysis with the capacity to choose among different methods depending on the study nature, expectations, and capacities.

Component Analysis of Gas Mixture Based on One-Dimensional Convolutional Neural Network

Indoor harmful gases are a considerable threat to the health of residents. In order to improve the accuracy of indoor harmful gas component identification, we propose an indoor toxic gas component analysis method that is based on the combination of bionic olfactory and convolutional neural network. This method uses the convolutional neural network's ability to extract nonlinear features and identify each component of bionic oflactory respense signal. A comparison with the results of other methods verifies the improvement of recognition rate while with the same level of time cost, which proved the effectiveness of the proposed model. The experimental results showed that the recognition rate of different types and concentrations of harmful gas components reached 90.96% and it solved the problem of mutual interference between gases.

Visual Analysis of Odor Interaction Based on Support Vector Regression Method

The complex odor interaction between odorants makes it difficult to predict the odor intensity of their mixtures. The analysis method is currently one of the factors limiting our understanding of the odor interaction laws. We used a support vector regression algorithm to establish odor intensity prediction models for binary esters, aldehydes, and aromatic hydrocarbon mixtures, respectively. The prediction accuracy to both training samples and test samples demonstrated the high prediction capacity of the support vector regression model. Then the optimized model was used to generate extra odor data by predicting the odor intensity of more simulated samples with various mixing ratios and concentration levels. Based on these olfactory measured and model predicted data, the odor interaction was analyzed in the form of contour maps. This intuitive method showed more details about the odor interaction pattern in the binary mixture. We found that that the antagonism effect was commonly observed in these binary mixtures and the interaction degree was more intense when the components’ mixing ratio was close. Meanwhile, the odor intensity level of the odor mixture barely influenced the interaction degree. The machine learning algorithms were considered promising tools in odor researches.

Emission characteristics and variation of volatile odorous compounds in the initial decomposition stage of municipal solid waste

The odour pollution occurring in the initial decomposition stage of municipal solid waste (MSW), including collection, transfer and transportation, has not been sufficiently emphasised. Thus the emission characteristics of and variation in odorant generation in this stage were investigated through simulation experiments at different temperatures, waste composition and processing durations. Out of 120 odorous compounds, 52 were detected in seven categories under all tested conditions, with significant variations. In the total concentration and emission rate, ethanol generally showed the largest proportion (larger than 80% on average), followed by unsaturated hydrocarbons which were dominated by propylene (13.1% on average of concentration proportion). The total emissions rapidly increased with processing duration when the temperatures were 15°C to 30°C. The proportion of ethanol increased significantly from 40.1% at 6h to 82.9% at 24h at 30°C. By contrast, a low temperature (5°C) resulted in low concentrations, and propylene accounted for the largest proportion instead of ethanol. With increasing temperature, biogenic compounds with large proportions increased more rapidly than xenobiotic compounds because of accelerated biological process and volatilisation. The emission rates of oxygenated compounds, saturated hydrocarbons, unsaturated hydrocarbons and halogenated compounds significantly increased (by approximately 20% to 50%) with an increase in easily biodegradable portion in the MSW. The proportions were relatively stable with the MSW composition variation, suggesting that most xenobiotic compounds were also derived from easily degradable portions. The olfactory evaluation showed that organic sulphur compounds contributed the most (approximately 75% to 95%) to odour pollution at the beginning of the stage because of their extremely low olfactory thresholds, with methanethiol as the dominant contributor (approximately 50% to 80% when detected). Results of this study can provide useful information for an improved understanding and monitoring of odorant emissions in the initial decomposition stage of MSW.

The Regular Interaction Pattern among Odorants of the Same Type and Its Application in Odor Intensity Assessment

The olfactory evaluation function (e.g., odor intensity rating) of e-nose is always one of the most challenging issues in researches about odor pollution monitoring. But odor is normally produced by a set of stimuli, and odor interactions among constituents significantly influenced their mixture’s odor intensity. This study investigated the odor interaction principle in odor mixtures of aldehydes and esters, respectively. Then, a modified vector model (MVM) was proposed and it successfully demonstrated the similarity of the odor interaction pattern among odorants of the same type. Based on the regular interaction pattern, unlike a determined empirical model only fit for a specific odor mixture in conventional approaches, the MVM distinctly simplified the odor intensity prediction of odor mixtures. Furthermore, the MVM also provided a way of directly converting constituents’ chemical concentrations to their mixture’s odor intensity. By combining the MVM with usual data-processing algorithm of e-nose, a new e-nose system was established for an odor intensity rating. Compared with instrumental analysis and human assessor, it exhibited accuracy well in both quantitative analysis (Pearson correlation coefficient was 0.999 for individual aldehydes (n = 12), 0.996 for their binary mixtures (n = 36) and 0.990 for their ternary mixtures (n = 60)) and odor intensity assessment (Pearson correlation coefficient was 0.980 for individual aldehydes (n = 15), 0.973 for their binary mixtures (n = 24), and 0.888 for their ternary mixtures (n = 25)). Thus, the observed regular interaction pattern is considered an important foundation for accelerating extensive application of olfactory evaluation in odor pollution monitoring.

Application and Uses of Electronic Noses for Clinical Diagnosis on Urine Samples: A Review

The electronic nose is able to provide useful information through the analysis of the volatile organic compounds in body fluids, such as exhaled breath, urine and blood. This paper focuses on the review of electronic nose studies and applications in the specific field of medical diagnostics based on the analysis of the gaseous headspace of human urine, in order to provide a broad overview of the state of the art and thus enhance future developments in this field. The research in this field is rather recent and still in progress, and there are several aspects that need to be investigated more into depth, not only to develop and improve specific electronic noses for different diseases, but also with the aim to discover and analyse the connections between specific diseases and the body fluids odour. Further research is needed to improve the results obtained up to now; the development of new sensors and data processing methods should lead to greater diagnostic accuracy thus making the electronic nose an effective tool for early detection of different kinds of diseases, ranging from infections to tumours or exposure to toxic agents.

Volatile trace compounds released from municipal solid waste at the transfer stage: Evaluation of environmental impacts and odour pollution

Odour pollution caused by municipal solid waste is a public concern. This study quantitatively evaluated the concentration, environmental impacts, and olfaction of volatile trace compounds released from a waste transfer station. Seventy-six compounds were detected, and ethanol presented the highest releasing rate and ratio of 14.76 kg/d and 12.30 g/t of waste, respectively. Life cycle assessment showed that trichlorofluoromethane and dichlorodifluoromethane accounted for more than 99% of impact potentials to global warming and approximately 70% to human toxicity (non-carcinogenic). The major contributor for both photochemical ozone formation and ecotoxicity was ethanol. A detection threshold method was also used to evaluate odour pollution. Five compounds including methane thiol, hydrogen sulphide, ethanol, dimethyl disulphide, and dimethyl sulphide, with dilution multiples above one, were considered the critical compounds. Methane thiol showed the highest contribution to odour pollution of more than 90%, as indicated by its low threshold. Comparison of the contributions of the compounds to different environmental aspects indicated that typical pollutants varied based on specific evaluation targets and therefore should be comprehensively considered. This study provides important information and scientific methodology to elucidate the impacts of odourant compounds to the environment and odour pollution.

Use of a modified vector model for odor intensity prediction of odorant mixtures

Odor intensity (OI) indicates the perceived intensity of an odor by the human nose, and it is usually rated by specialized assessors. In order to avoid restrictions on assessor participation in OI evaluations, the Vector Model which calculates the OI of a mixture as the vector sum of its unmixed components’ odor intensities was modified. Based on a detected linear relation between the OI and the logarithm of odor activity value (OAV—a ratio between chemical concentration and odor threshold) of individual odorants, OI of the unmixed component was replaced with its corresponding logarithm of OAV. The interaction coefficient (cosα) which represented the degree of interaction between two constituents was also measured in a simplified way. Through a series of odor intensity matching tests for binary, ternary and quaternary odor mixtures, the modified Vector Model provided an effective way of relating the OI of an odor mixture with the lnOAV values of its constituents. Thus, OI of an odor mixture could be directly predicted by employing the modified Vector Model after usual quantitative analysis. Besides, it was considered that the modified Vector Model was applicable for odor mixtures which consisted of odorants with the same chemical functional groups and similar molecular structures.

Electronic noses for environmental monitoring applications

Electronic nose applications in environmental monitoring are nowadays of great interest, because of the instruments' proven capability of recognizing and discriminating between a variety of different gases and odors using just a small number of sensors. Such applications in the environmental field include analysis of parameters relating to environmental quality, process control, and verification of efficiency of odor control systems. This article reviews the findings of recent scientific studies in this field, with particular focus on the abovementioned applications. In general, these studies prove that electronic noses are mostly suitable for the different applications reported, especially if the instruments are specifically developed and fine-tuned. As a general rule, literature studies also discuss the critical aspects connected with the different possible uses, as well as research regarding the development of effective solutions. However, currently the main limit to the diffusion of electronic noses as environmental monitoring tools is their complexity and the lack of specific regulation for their standardization, as their use entails a large number of degrees of freedom, regarding for instance the training and the data processing procedures.

The reproducibility of indoor air pollution (IAP) measurement: a test case for the measurement of key air pollutants from the pan frying of fish samples

To assess the robustness of various indoor air quality (IAQ) indices, we explored the possible role of reproducibility-induced variability in the measurements of different pollutants under similar sampling and emissions conditions. Polluted indoor conditions were generated by pan frying fish samples in a closed room. A total of 11 experiments were carried out to measure a list of key variables commonly used to represent indoor air pollution (IAP) indicators such as particulate matter (PM: PM₁, PM_2.5, PM₁₀, and TSP) and a set of individual volatile organic compounds (VOCs) with some odor markers. The cooking activity conducted as part of our experiments was successful to consistently generate significant pollution levels (mean PM₁₀: 7110 μg m⁻³ and mean total VOC (TVOC): 1400 μg m⁻³, resp.). Then, relative standard error (RSE) was computed to assess the reproducibility between different IAP paramters measured across the repeated experiments. If the results were evaluated by an arbitrary criterion of 10%, the patterns were divided into two data groups (e.g., <10% for benzene and some aldehydes and >10% for the remainders). Most noticeably, TVOC had the most repeatable results with a reproducibility (RSE) value of 3.2% (n = 11).

An odor interaction model of binary odorant mixtures by a partial differential equation method

A novel odor interaction model was proposed for binary mixtures of benzene and substituted benzenes by a partial differential equation (PDE) method. Based on the measurement method (tangent-intercept method) of partial molar volume, original parameters of corresponding formulas were reasonably displaced by perceptual measures. By these substitutions, it was possible to relate a mixture's odor intensity to the individual odorant's relative odor activity value (OAV). Several binary mixtures of benzene and substituted benzenes were respectively tested to establish the PDE models. The obtained results showed that the PDE model provided an easily interpretable method relating individual components to their joint odor intensity. Besides, both predictive performance and feasibility of the PDE model were proved well through a series of odor intensity matching tests. If combining the PDE model with portable gas detectors or on-line monitoring systems, olfactory evaluation of odor intensity will be achieved by instruments instead of odor assessors. Many disadvantages (e.g., expense on a fixed number of odor assessors) also will be successfully avoided. Thus, the PDE model is predicted to be helpful to the monitoring and management of odor pollutions.

Reduction of odours in pilot-scale landfill biocovers

Unpleasant odours generated from waste management facilities represent an environmental and societal concern. This multi-year study documented odour and total reduced sulfur (TRS) abatement in four experimental landfill biocovers installed on the final cover of the Saint-Nicéphore landfill (Canada). Performance was evaluated based on the reduction in odour and TRS concentrations between the raw biogas collected from a dedicated well and the emitted gases at the surface. Odour analyses were carried out by the sensorial technique of olfactometry, whereas TRS analyses followed the pulse fluorescence technique. The large difference of 2-5 orders of magnitude between raw biogas (average odour concentration=2,100,000OUm(-3)) and emitted gases resulted in odour removal efficiencies of close to 100% for all observations. With respect to TRS concentrations, abatement efficiencies were all greater than 95%, with values averaging 21,000ppb of eq. SO2 in the raw biogas. The influence of water infiltration on odour concentrations was documented and showed that lower odour values were obtained when the 48-h accumulated precipitation prior to sampling was higher.

The Interaction of Mixing Odorants with Similar Chemical Properties: A Case Study on Ketone Compounds

To investigate more about the interaction of mixing odorants, a series of sensory tests were conducted using five ketones [butanone (Bu), 2-Pentanone (Pe), 2-Hexanone (Hex), 2-Heptanone (Hep), 2-Octanone (Oc)] at varying concentration levels. The determination of odor threshold (OT) was initially conducted by the triangle odor bag method (GB/T 14675, China). The odor activity values (OAVs) of individual odorants at a wide range of concentrations were derived from concentration-to-odor threshold ratios. The resulting data were then evaluated to define the empirical relationship for each ketone between the OAV and odor intensity (OI) scaling. Based on the relationships defined for each individual ketone, the OI values were estimated for a synthetic mixture of five ketones. The effect of mixing was then examined by assessing those estimated OI values with the actually measured OI values. The overall results of this study confirmed that the OI values of the synthetic mixture is not governed by the common theoretical basis (e.g., rule of additivity, synergism, or a stronger component model) but is best represented by the averaged contribution of all ketone components. Thus, the odor intensity (OI) of a given mixture sample containing odorants with similar chemical properties can be accessed through the conversion from its concentration value with the application of empirical equations instead of direct measurement by the human test panel.

Odor sampling: techniques and strategies for the estimation of odor emission rates from different source types

Sampling is one of the main issues pertaining to odor characterization and measurement. The aim of sampling is to obtain representative information on the typical characteristics of an odor source by means of the collection of a suitable volume fraction of the effluent. The most important information about an emission source for odor impact assessment is the so-called Odor Emission Rate (OER), which represents the quantity of odor emitted per unit of time, and is expressed in odor units per second (ou·s⁻¹). This paper reviews the different odor sampling strategies adopted depending on source type. The review includes an overview of odor sampling regulations and a detailed discussion of the equipment to be used as well as the mathematical considerations to be applied to obtain the OER in relation to the sampled source typology.

VFA and ammonia from residential food waste as indicators of odor potential

Research was conducted to determine suitable chemical parameters as indicators of odor from decomposing food wastes. Prepared food scraps were stored in 18 l plastic buckets (2 kg wet weight each) at 20 °C and 8 °C to reproduce high and low temperature conditions. After 1, 3, 7, 10 and 14 days of storage, the odor from the buckets were marked to an intensity scale of 0 (no odor) to 5 (intense) and the corresponding leachate analyzed for volatile fatty acids, ammonia and total organic carbon. A linear relationship between odor intensity and the measured parameter indicates a suitable odor indicator. Odor intensified with longer storage period and warmer surroundings. The study found ammonia and isovaleric acid to be promising odor indicators. For this food waste mixture, offensive odors were emitted if the ammonia and isovaleric acid contents exceeded 360 mg/l and 940 mg/l, respectively.

Development of an electronic nose for environmental odour monitoring

Exhaustive odour impact assessment should involve the evaluation of the impact of odours directly on citizens. For this purpose it might be useful to have an instrument capable of continuously monitoring ambient air quality, detecting the presence of odours and also recognizing their provenance. This paper discusses the laboratory and field tests conducted in order to evaluate the performance of a new electronic nose, specifically developed for monitoring environmental odours. The laboratory tests proved the instrument was able to discriminate between the different pure substances being tested, and to estimate the odour concentrations giving correlation indexes (R²) of 0.99 and errors below 15%. Finally, the experimental monitoring tests conducted in the field, allowed us to verify the effectiveness of this electronic nose for the continuous detection of odours in ambient air, proving its stability to variable atmospheric conditions and its capability to detect odour peaks.

Perceived annoyance from environmental odors and association with atmospheric ammonia levels in non-urban residential communities: a cross-sectional study

OBJECTIVE

Odor exposure is an environmental stressor that is responsible of many citizens complains about air pollution in non-urban areas. However, information about the exposure-response relation is scarce. One of the main challenges is to identify a measurable compound that can be related with odor annoyance responses. We investigated the association between regional and temporal variation of ammonia (NH3) concentrations in five Danish non-urban regions and environmental odor annoyance as perceived by the local residents.

METHODS

A cross-sectional study where NH3 concentration was obtained from the national air quality monitoring program and from emission-dispersion modelling, and odor pollution perception from questionnaires. The exposure-response model was a sigmoid model. Linear regression analyses were used to estimate the model constants after equation transformations. The model was validated using leave-one-out cross validation (LOOCV) statistical method.

RESULTS

About 45% of the respondents were annoyed by odor pollution at their residential areas. The perceived odor was characterized by all respondents as animal waste odor. The exposure-annoyance sigmoid model showed that the prevalence of odor annoyance was significantly associated with NH3 concentrations (measured and estimated) at the local air quality monitoring stations (p < 0.01,R2 = 0.99; and p < 0.05,R2 = 0.93; respectively). Prediction errors were below 5.1% and 20% respectively. The seasonal pattern of odor perception was associated with the seasonal variation in NH3 concentrations (p < 0.001, adjusted R2 = 0.68).

CONCLUSION

The results suggest that atmospheric NH3 levels at local air quality stations could be used as indicators of prevalence of odor annoyance in non-urban residential communities.

Effect of incineration on the removal of key offensive odorants released from a landfill leachate treatment station (LLTS)

As a basic means to control odorants released from a landfill leachate treatment station (LLTS), effluents venting from this station were treated via incineration with methane rich landfill gas (at 750°C). A list of the key offensive odorants covering 22 chemicals was measured by collecting those gas samples both before and after the treatment. Upon incineration, the concentration levels of most odorants decreased drastically below threshold levels. The sum of odorant intensities (SOIs), if compared between before and after incineration, decreased from 6.94 (intolerable level) to 3.45 (distinct level). The results indicate that the thermal incineration method can be used as a highly efficient tool to remove most common odorants (e.g., reduced sulfur species), while it is not so for certain volatile species (e.g., carbonyls, fatty acids, etc.).

Effect of functional group and carbon chain length on the odor detection threshold of aliphatic compounds

Odor detection thresholds (ODTs) are used for assessing outdoor and indoor air quality. They are obtained experimentally by olfactometry and psychophysical methods, and large compilations are available in the literature. A non-linear regression equation was fitted to describe the ODT variability of 114 aliphatic compounds based on the alkyl chain length for different homologous series (carboxylic acids, aldehydes, 2-ketones, esters, 1-alcohols, amines, thiols, thioethers and hydrocarbons). The resulting equation reveals an effect of the functional group, molecular size and also an interaction between both factors. Although the mechanistic interpretation of results is uncertain, the relatively high goodness-of-fit (R² = 0.90) suggests that ODT values of aliphatic compounds can be predicted rather accurately, which is not the case for rigid molecules. This equation may serve as a basis for the development of more complex ODT models taking into account diverse structural features of odorants. The variability of power-law exponents was also investigated for the homologous series.

Swine odor analyzed by odor panels and chemical techniques

The National Research Council identified odors as a significant animal emission and highlighted the need to develop standardized protocols for sampling and analysis. The purpose of our study was to compare different odor sampling techniques for monitoring odors emitted from stored swine manure. In our study, odorous headspace air from swine manure holding tanks were analyzed by human panels and analytical techniques. Odorous air was analyzed by human panels using dynamic dilution olfactometry (DDO). Chemical analysis used acid traps for ammonia (NH₃), fluorescence for hydrogen sulfide (H₂S), and thermal desorption gas chromatography-mass spectrometry for volatile organic compounds (VOCs). Chemical analysis included the use of gas chromatography-olfactometry (GC-O) for determining key odorants. Chemical odorant concentrations were converted to odor activity values (OAVs) based on literature odor thresholds. The GC-O technique used was GC-SNIF. Dilution thresholds measured by different odor panels were significantly different by almost an order of magnitude even though the main odorous compound concentrations had not changed significantly. Only 5% of the key odorous VOCs total OAVs was recovered from the Tedlar bags used in DDO analysis. Ammonia was the only chemical odorant significantly correlated with DDO analysis in the fresh (1 wk) and aged manure. Chemical analysis showed that odor concentration stabilized after 5 to 7 wk and that HS was the most dominant odorant. In aged manure, neither volatile fatty acids (VFAs) nor HS was correlated with any other chemical odorant, but NH, phenols, and indoles were correlated, and phenols and indoles were highly correlated. Correlation of odorant concentration was closely associated with the origin of the odorant in the diet. Key odorants determined by chemical and GC-O included indoles, phenols, NH₃, and several VFAs (butanoic, 3-methylbutanoic, and pentanoic acids).

Cognitive facilitation following intentional odor exposure

This paper reviews evidence that, in addition to incidental olfactory pollutants, intentional odor delivery can impact cognitive operations both positively and negatively. Evidence for cognitive facilitation/interference is reviewed alongside four potential explanations for odor-induced effects. It is concluded that the pharmacological properties of odors can induce changes in cognition. However, these effects can be accentuated/attenuated by the shift in mood following odor exposure, expectancy of cognitive effects, and cues to behavior via the contextual association with the odor. It is proposed that greater consideration is required in the intentional utilization of odors within both industrial and private locations, since differential effects are observed for odors with positive hedonic qualities.

Odour detection methods: olfactometry and chemical sensors

The complexity of the odours issue arises from the sensory nature of smell. From the evolutionary point of view olfaction is one of the oldest senses, allowing for seeking food, recognizing danger or communication: human olfaction is a protective sense as it allows the detection of potential illnesses or infections by taking into account the odour pleasantness/unpleasantness. Odours are mixtures of light and small molecules that, coming in contact with various human sensory systems, also at very low concentrations in the inhaled air, are able to stimulate an anatomical response: the experienced perception is the odour. Odour assessment is a key point in some industrial production processes (i.e., food, beverages, etc.) and it is acquiring steady importance in unusual technological fields (i.e., indoor air quality); this issue mainly concerns the environmental impact of various industrial activities (i.e., tanneries, refineries, slaughterhouses, distilleries, civil and industrial wastewater treatment plants, landfills and composting plants) as sources of olfactory nuisances, the top air pollution complaint. Although the human olfactory system is still regarded as the most important and effective "analytical instrument" for odour evaluation, the demand for more objective analytical methods, along with the discovery of materials with chemo-electronic properties, has boosted the development of sensor-based machine olfaction potentially imitating the biological system. This review examines the state of the art of both human and instrumental sensing currently used for the detection of odours. The olfactometric techniques employing a panel of trained experts are discussed and the strong and weak points of odour assessment through human detection are highlighted. The main features and the working principles of modern electronic noses (E-Noses) are then described, focusing on their better performances for environmental analysis. Odour emission monitoring carried out through both the techniques is finally reviewed in order to show the complementary responses of human and instrumental sensing.

The averaging effect of odorant mixing as determined by air dilution sensory tests: a case study on reduced sulfur compounds

To learn more about the effects of mixing different odorants, a series of air dilution sensory (ADS) tests were conducted using four reduced sulfur compounds [RSC: hydrogen sulfide (H₂S), methanethiol (CH₃SH), dimethylsulfide (DMS), and dimethyldisulfide (DMDS)] at varying concentration levels. The tests were initially conducted by analyzing samples containing single individual RSCs at a wide range of concentrations. The resulting data were then evaluated to define the empirical relationship for each RSC between the dilution-to-threshold (D/T) ratio and odor intensity (OI) scaling. Based on the relationships defined for each individual RSC, the D/T ratios were estimated for a synthetic mixture of four RSCs. The effect of mixing was then examined by assessing the relative contribution of each RSC to those estimates with the aid of the actually measured D/T values. This stepwise test confirmed that the odor intensity of the synthetic mixture is not governed by the common theoretical basis (e.g., rule of additivity, synergism, or a stronger component model) but is best represented by the averaged contribution of all RSC components. The overall results of this study thus suggest that the mixing phenomenon between odorants with similar chemical properties (like RSC family) can be characterized by the averaging effect of all participants.

A local weighted nearest neighbor algorithm and a weighted and constrained least-squared method for mixed odor analysis by electronic nose systems

A great deal of work has been done to develop techniques for odor analysis by electronic nose systems. These analyses mostly focus on identifying a particular odor by comparing with a known odor dataset. However, in many situations, it would be more practical if each individual odorant could be determined directly. This paper proposes two methods for such odor components analysis for electronic nose systems. First, a K-nearest neighbor (KNN)-based local weighted nearest neighbor (LWNN) algorithm is proposed to determine the components of an odor. According to the component analysis, the odor training data is firstly categorized into several groups, each of which is represented by its centroid. The examined odor is then classified as the class of the nearest centroid. The distance between the examined odor and the centroid is calculated based on a weighting scheme, which captures the local structure of each predefined group. To further determine the concentration of each component, odor models are built by regressions. Then, a weighted and constrained least-squares (WCLS) method is proposed to estimate the component concentrations. Experiments were carried out to assess the effectiveness of the proposed methods. The LWNN algorithm is able to classify mixed odors with different mixing ratios, while the WCLS method can provide good estimates on component concentrations.

Development of a portable electronic nose system for the detection and classification of fruity odors

In this study, we have developed a prototype of a portable electronic nose (E-Nose) comprising a sensor array of eight commercially available sensors, a data acquisition interface PCB, and a microprocessor. Verification software was developed to verify system functions. Experimental results indicate that the proposed system prototype is able to identify the fragrance of three fruits, namely lemon, banana, and litchi.

Recovery of agricultural odors and odorous compounds from polyvinyl fluoride film bags

Accurate sampling methods are necessary when quantifying odor and volatile organic compound emissions at agricultural facilities. The commonly accepted methodology in the U.S. has been to collect odor samples in polyvinyl fluoride bags (PVF, brand name Tedlar®) and, subsequently, analyze with human panelists using dynamic triangular forced-choice olfactometry. The purpose of this research was to simultaneously quantify and compare recoveries of odor and odorous compounds from both commercial and homemade PVF sampling bags. A standard gas mixture consisting of p-cresol (40 μg m⁻³) and seven volatile fatty acids: acetic (2,311 μg m⁻³), propionic (15,800 μg m⁻³), isobutyric (1,686 μg m⁻³), butyric (1,049 μg m⁻³), isovaleric (1,236 μg m⁻³), valeric (643 μg m⁻³), and hexanoic (2,158 μg m⁻³) was placed in the PVF bags at times of 1 h, 1 d, 2 d, 3 d, and 7 d prior to compound and odor concentration analyses. Compound concentrations were quantified using sorbent tubes and gas chromatography/mass spectrometry. Odor concentration, intensity, and hedonic tone were measured using a panel of trained human subjects. Compound recoveries ranged from 2 to 40% after 1 h and 0 to 14% after 7 d. Between 1 h and 7 d, odor concentrations increased by 45% in commercial bags, and decreased by 39% in homemade bags. Minimal changes were observed in intensity and hedonic tone over the same time period. These results suggest that PVF bags can bias individual compound concentrations and odor as measured by dynamic triangular forced-choice olfactometry.