Odour Measurement Research Articles

Applying citizen science method for odor measurement in urban areas

Applying citizen science method for odor measurement in urban areas

Review on Odour Assessment Ant Odour Abatement Techniques

Abstract: Odour can be defined as a perception of smell or in scientific terms it can be defined as a sensation resulting from the reception of stimulus by the olfactory sensory system. Odours may cause pleasant or unpleasant sensation and is induced by inhaling volatile organic and inorganic odorants. The sources of odour may be air-borne or water-borne. Foul odour may cause ill health or respiratory symptoms, insomnia, asthama, nausea and discomfort and other environmental nuisance. Odours can be assessed either by sensory measurements or analytical measurements. There are various physical, chemical and biological treatments for odour abatement. The release of unpleasant odours from sewage carrier system as well as sewage treatment system has become a great public concern especially among densely populated countries. These odours cause environmental nuisance and severe damage to locals. Odour is an important physical characteristic of both water and wastewater. Thus, it requires significant attention in terms of measurement as well as abatement. This study reviews the various odour measurement and abatement techniques.

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.

Odor emission measurements: The role of n-butanol as a reference material and origins of large inter-laboratory variability

Malodors negatively impact the quality of life of people who are exposed to them. In order to enforce legal emission limit values that aim to reduce these adverse effects, reliable methods for the quantification of odor emissions are needed. The standard EN 13725 describes a method to quantify odor concentration, which is based on the use of human assessors as sensors. Previously published work that studied the performance characteristics of this method indicates that the variability of results between different laboratories is higher than expected and that the uncertainty associated with this method may be underestimated. We analyze here a unique data set, which contains the response of 368 assessors that participated in 21 odor proficiency tests performed in 2016–2022. By investigating the response of the smallest unit of an odor measurement by EN 13725, namely the individual assessor, as opposed to the panel, we gain unprecedented insight into the sources of inter-laboratory variations in odor measurements. Our results indicate that the assessor sensitivity to n-butanol is not transferable to other odorants and odor mixtures, which is contrary to the assumption made in EN 13725. By Monte-Carlo simulation coupled with our data set, we investigate how the responses of individual assessors transfer to panel responses, focusing on the influence of panel size, panel selection strategy and number of rounds. We find that for the laboratories investigated, larger panels only modestly reduce the inter-laboratory variability and we attribute this effect to non-random selection of assessors for the panels of these laboratories. The present work provides new insight into the sources of inter-laboratory variability in odor emission monitoring and is expected to be useful in the further development of reliable methods for odor concentration measurements and odor abatement technology.

Feasibility of the Field Olfactometer for Direct Measurement of Complex Odor in Fields

Feasibility of the Field Olfactometer for Direct Measurement of Complex Odor in Fields

Sustainable Sawdust/Eucalyptol Absorption Matrix for Odor Slow-Release in Plywood Floor

A sustainable matrix based on eucalyptol essential oil/sawdust was developed and applied on laminated plywood. This finish aims to serve as a eucalyptol odor slow release. Eucalyptol odor release was monitored with gas chromatography coupled with a flame ionization detector (GC-FID: Limits of Detection and Quantification of 0.70 g/m3 and 2.11 g/m3, respectively, and with linearity up to 18.6 g/m3). Measurement of the eucalyptol odor released was performed during a six-month period, and it was found that the release followed a first-order exponential decay with a decay rate constant of 0.0169 per day. The half-life was determined to be of 48 days. The granulometry and particle size porosity of sawdust were analyzed by Scanning Electron Microscopy. A sawdust size fraction of 112–200 μm showed the best eucalyptol absorption capacity, with 1:3 masses ratio (sawdust:eucalyptol). The release duration of eucalyptol is influenced by the quantity of the eucalyptol–sawdust composite and the aperture size for release. Through the determination of this relationship, it was found that applying 15.0 g of the composite through a 0.8 mm diameter aperture resulted in a 6-month eucalyptol release period. This outcome is regarded as highly favorable, considering the inherent high volatility of eucalyptol and the relatively small amount of composite required for future product applications. The new product is characterized by a carbon footprint (considering the industry frontiers) of 5.94 kg CO2eq/m2 of plywood floor.

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.

Olfactory changes after endonasal endoscopic cerebrospinal fluid fistula repair: long-term outcomes.

This study aimed to evaluate odour measurements after endoscopic repair of cerebrospinal fluid fistulas. Demographic characteristics, cerebrospinal fluid leak location, surgical procedure, aetiology and smell test results were retrospectively reviewed. A total of 27 patients were included. The mean age was 43.52 years. The aetiology was spontaneous in 13 patients (48.1 per cent) and traumatic in 14 patients (51.9 per cent). The skull base defect was at the cribriform plate in 15 patients (55.6 per cent), the frontal sinus posterior wall in 4 patients (14.8 per cent), the sphenoid sinus posterior wall in 4 patients (14.8 per cent), around the anterior ethmoid artery in 2 patients (7.4 per cent), at the ethmoid roof in 1 patient (3.7 per cent) and at the sphenoid sinus posterolateral wall in 1 patient (3.7 per cent). After the comparison of pre-operative and post-operative values in identification, discrimination and threshold categories, a statistically significant difference was seen only in the threshold category (p = 0.014). The results demonstrated that endoscopic repair of cerebrospinal fluid leak is safe in terms of olfactory functions.

Prediction of laying hen house odor concentrations using machine learning models based on small sample data

• XGBoost model served better to predict odor concentration in laying hen houses than SVR and BPNN. • XGBoost algorithm had good performance and strong learning ability to deal with the odor datasets with small-sample and non-linearity characteristics. • Gas components and environment variables directly related to gas production were the key drivers in developing odor prediction model. • The knowledge mining ability of XGBoost model on odor datasets collected from laying hen house was good. In laying hen farm, odor measurement and reduction are necessary for clean environment management and human/animal health care. However, limited quantitative data and expensive detection technologies preclude an accurate assessment of odor reduction practices. This study compared the odor prediction ability of three different machine learning models of extreme gradient boosting (XGBoost), support vector regression (SVR), and back-propagation neural networks (BPNN) based on small sample size odor datasets collected from laying hen houses to achieve timely odor monitoring and check the significant components affecting the odor concentration. The input variables were ammonia (NH 3 ) concentration, hydrogen sulfide (H 2 S) concentration, temperature, relative humidity (RH), and ventilation rate, and the output value was odor concentration. Results showed the XGBoost model had the highest prediction ability and the most accuracy with the R 2 of 0.88, followed by BPNN (R 2 = 0.75) and SVR (R 2 = 0.66). XGBoost model could be a useful tool to timely predict odor concentration with moderate accuracy. In addition, in the trained XGBoost model, NH 3 concentration was the most important factor, followed by the H 2 S concentration, temperature, RH, and ventilation rate, which indicated the gas components and environment variables related to gas production were the key drivers in training odor prediction model. This study also forecasted the influences of each gas and environmental factors on odor concentrations and verified the good knowledge mining ability of XGBoost model. The practical potentials are considerable of using XGBoost model to replace human assessors and to apply to laying hen house environmental control as the odor sensor.

Applied Research Note: Evaluation of a Bacillus-based direct-fed microbial as a strategy to reduce hydrogen sulfide emissions from poultry excreta using a practical monitoring method

Effective implementation of noxious odor mitigation strategies requires a thorough understanding of production causes, a clear definition of harmful odor limits, and practical and effective methods to measure emissions into the environment. With the increasing demand for poultry products and consequently, intensification of production systems, there is a need for versatile odor remediation approaches. Hydrogen sulfide ( H 2 S ) is a pungent noxious gas associated with poultry farms. The presence and intensity of H 2 S from animal excreta is the result of the dynamic balance of its formation and reduction, under primarily anaerobic microbial fermentation conditions. Unlike other odors such as ammonia, H 2 S production and emission is a less researched subject. In the present study, a practical monitoring method was devised to evaluate the efficacy of a Bacillus -based microbial product as a strategy to remediate H 2 S emissions from poultry excreta. The evaluated excreta were preconditioned with distilled water and then incubated for 24 h under controlled temperature before odor measurement. Plastic containers were used as incubation units, and H 2 S diffusion tubes were used as the monitoring tool. Hydrogen sulfide emitted from the excreta of broilers fed the microbial product was reduced on average by 42.9% compared to a nonmicrobial fed group. Under the current trial conditions, H 2 S emitted from growing broiler chicken excreta was easily detectable and variation was low. Moreover, the method allowed dynamic and quick H 2 S monitoring that will further offer tools to the commercial poultry industry for evaluating the effectiveness of feeding strategies in odor remediation.

Odour dispersion modelling, impact criteria, and setback distances for an oil refinery plant

Odour dispersion modelling is considered to be the most effective approach in evaluating odour impact of various air emission sources as field odour measurements are associated with several difficulties. Using odour impact criteria (OIC), odour dispersion modelling could provide a comprehensive map of impact for current and future sources, along with providing time and cost savings, yet there was very little research on odour dispersion modelling for oil refineries. The OIC are source-specific but OIC for oil refineries has not been well studied. This study investigated odour impact of a conventional oil refinery in Western Canada using the AERMOD model. Odour intensities (OIs) were measured at 62 spots downwind from 70 m to 6.7 km from the plant. Peak to Mean factors (PMF) were generated for the model. The measured OIs were converted to odour concentrations (OC) using OI-OC correlation. Comparison of modelled and measured data indicated that AERMOD predicted the OC satisfactorily (r = 0.67). The model was then used to predict odour impact for this area based on 5-year meteorological data. Comparing dispersion modelling results with the general OIC of the region and odour complaint data, a new set of OIC for the oil refinery was proposed, and the resultant appropriate setback distances for different land uses were predicted by AERMOD. The OIC proposed by this study may be applicable for other refineries yet potential differences in odour acceptance levels of other communities should be considered. The resultant setbacks from this study may not be applicable for sites with different topographic and meteorological conditions.

On data collection time by an electronic nose

<p>We use electronic nose data of odor measurements to build machine learning classification models. The presented analysis focused on determining the optimal time of measurement, leading to the best model performance. We observe that the most valuable information for classification is available in data collected at the beginning of adsorption and the beginning of the desorption phase of measurement. We demonstrated that the usage of complex features extracted from the sensors’ response gives better classification performance than use as features only raw values of sensors’ response, normalized by baseline. We use a group shuffling cross-validation approach for determining the reported models’ average accuracy and standard deviation.</p>

Current Status of Odor Measurement Technology; Addition of Official Method and Measures against COVID-19

Current Status of Odor Measurement Technology; Addition of Official Method and Measures against COVID-19

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.

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.

Measurement of odor using onsite olfactometer on school zones in Seoul

Measurement of odor using onsite olfactometer on school zones in Seoul

Optimization of Classification Prediction Performances of an Instrumental Odour Monitoring System by Using Temperature Correction Approach

Odour emissions generated by industrial and environmental protection plants are often a cause of nuisances and consequent conflicts in exposed populations. Their control is a key action to avoid complaints. Among the odour measurement techniques, the sensory-instrumental method with the application of Instrumental Odour Monitoring Systems (IOMSs) currently represents an effective solution to allow a continuous classification and quantification of odours in real time, combining the advantages of conventional analytical and sensorial techniques. However, some aspects still need to be improved. The study presents and discusses the investigation and optimization of the operational phases of an advanced IOMS, applied for monitoring of environmental odours, with the aim of increasing their performances and reliability of the measures. Accuracy rates of over 98% were reached in terms of classification performances. The implementation of automatic correction systems for the resistance values of the measurement sensors, by considering the influence of the temperature, has been proven to be a solution to further improve the reliability of IOMS. The proposed approach was based on the application of corrective coefficients experimentally determined by analyzing the correlation between resistance values and operating conditions. The paper provides useful information for the implementation of real-time management activities by using a tailor-made software, able to increase and enlarge the IOMS fields of application.

Experimenting with Odour Proficiency Tests Implementation Using Synthetic Bench Loops

Uncertainty in the quantification of odour measurements is a difficult (but needed) task. Critical aspects include panel selection (required by dynamic olfactometry), sampling, and stability of the samples. Proficiency tests (PTs) can help evaluate such contributions; however, the classical approach to PTs, in which laboratories analyse real samples taken from the field, are not as applicable in this field, and are often implemented by only using dry gas cylinders containing stable compounds. Consequently, uncertainties related to the sampling activity cannot be assessed. In particular, high odour levels and the presence of water vapour in emission sources can create significative biases due to sampling techniques used and chemical reactions that can occur before analysis. In this work, we present experimental notes, developed using the experimental facility ‘LOOP’, realised at the RSE research centre in Italy, in order to “help” the definition, in an upgraded protocol for implementing PTs for odour determinations. Using this bench loop is advantageous as it involves the possibility of implementing samples in conditions very similar to reality (i.e., high temperatures, high water content, and the presence of chemical interferents).

Artificial Olfaction in the 21st Century

The human olfactory system remains one of the most challenging biological systems to replicate. Humans use it without thinking, where it can measure offer protection from harm and bring enjoyment in equal measure. It is the system's real-time ability to detect and analyze complex odors that makes it difficult to replicate. The field of artificial olfaction has recruited and stimulated interdisciplinary research and commercial development for several applications that include malodor measurement, medical diagnostics, food and beverage quality, environment and security. Over the last century, innovative engineers and scientists have been focused on solving a range of problems associated with measurement and control of odor. The IEEE Sensors Journal has published Special Issues on olfaction in 2002 and 2012. Here we continue that coverage. In this article, we summarize early work in the 20 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> Century that served as the foundation upon which we have been building our odor-monitoring instrumental and measurement systems. We then examine the current state of the art that has been achieved over the last two decades as we have transitioned into the 21 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> Century. Much has been accomplished, but great progress is needed in sensor technology, system design, product manufacture and performance standards. In the final section, we predict levels of performance and ubiquitous applications that will be realized during in the mid to late 21 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> Century.

Adaptations of Olfactometry and Odour Measurement with COVID-19 Crisis

The year 2020 was characterised by a worldwide pandemic crisis due to dissemination of a Coronavirus, the SARS-CoV-2 also named COVID-19. Many economic and social domains were impacted by adaptions or restrictions by the objective to limit local dispersion and more global flow of the virus. Without describing all human health effects of this virus, one characteristic is temporary anosmia. In this paper, firstly the physiological impact is synthetized to explain the anosmia. Secondly, because olfactometry need human smell, adaptations of sensorial methods are described. These adapted methods were proposed by laboratories/companies in charge of olfactometry or odour measurement and mainly concern how the distance guarantees between panellists were proposed. Copyright © 2021, AIDIC Servizi S.r.l.