Odour Measurement Research Articles

Improving environmental odor measurements: comparison of lab-based standard method and portable odor measurement technology

Improving environmental odor measurements: comparison of lab-based standard method and portable odor measurement technology

Artificial Neural Network in the Measurement of Environmental Odours by E-Nose

Odour measurement plays a crucial role in environmental odour management. Continuous odour measurement systems are promoted to keep the situation always under control, such as being able to adopt the most suitable mitigation measures in real time to avoid odour complaints and impacts. Electronic Nose (eNose) represents currently the instrument of having the highest future developing potential to guarantee continuous odour measurements. To use an eNose, a training phase is however mandatory, which has the scope to create the Odour Monitoring Model (OMM) that is able to identify the presence of odour, the different odour classes and the quantification of the odorous stimuly. Statistical or biological inspired measurement techniques are applied to create the optimum OMM.The study presents and discusses the elaboration of an Artificial Neural Network (ANN) technique to recognize environmental odour with eNose. The proposed system was architected on a feed-forward neural network with Bayesian Regularization algorithm using Matlab R2017a software.The elaborated ANN was tested and validated using the seedOA eNose, realized by the Sanitary Environmental Engineering Division (SEED) of the Department of Civil Engineering of the University of Salerno (Italy). Tests were carried out analyzing odour samples collected at a large Wastewater Treatment Plant (WWTP). The comparison between the Odour Monitoring Model (OMM) elaborated through the proposed ANN system and the traditional statistical techniques, such as the Partial Least Square (PLS) and the Linear Discriminant Analysis (LDA), is also discussed.Results shown the efficiency of the elaborated ANN to identify the different odour classes and predict the odour concentration in terms of OUm-3. The artificial neural network shows smaller Root Mean Squared Errors (RMSE) and greater coefficient of determination (R2) as compared to the traditional statistical methods. The main advantages of neural networks are their adaptability in terms of learning, self-organization, training and noise-tolerance.

Odour Measurements at Different Methanisation Sites

The number of methanisation production sites increase in Europe. However, the installation of a new site sometimes creates problems with the neighborhood of the sites. The main concern is odours. To respond to these fears, the odour levels emitted by 3 biogas production plants were measured. In order to study the methanisation diversity, different waste treatment plants have been monitored. The waste came from farming (manure, slurry), WWTP sludge, or agro-food were compared. For each plant, 5 sources were investigated. A one year survey indicates that odour emissions changed according to the season. For each site, the main potential odour annoyance impact comes from the input preparation area.

A Critical Evaluation of the Influence of Different Panel Composition in the Measurement of Odour Concentration by Dynamic Olfactometry

Odour concentration expressed in terms of OUE/m3 by using the dynamic olfactometry analysis, standardized by the European Norm (EN) 13725 are by now the most commonly and worldwide accepted method to measure odours. The EN, first edition published in the year 2003, is actually under review process by the WG2 of the CEN/TC246 and the end of the revision work is expected for the year 2018. Related to the odour panel the standard fixes some parameters (e.g: panel size). Nothing is, however, performed about the composition of the panel members in terms of their gender, the age of the involved persons and their origin and nationality. Also the current literature is limited to discussing how the measurement of emission rates derived from the same odour source could be related to the panel composition.The scope of this study is to investigate the influence on the determination of odour concentration with dynamic olfactometry, according to EN 13725 standards, applying different panel conditions, with the objective to define the optimal criteria to evaluate the odour concentration and obtain the highest repeatability and accuracy of the sensorial measure.A critical evaluation was carried out in relation to the same odour source with the comparison of the different panel composition (in terms of gender, age and nationality) to measure the odour concentration. In the experimental studies the measurements were conducted within 5 h after sampling to reduce the variability of the mixture and increase the reliability. All assessors who participate in odour measurements were qualified and tested according the quality criteria assurance foreseen by the EN13725. Studies were taken considering real environmental odour emissions collected at a wastewater treatment plant (WWTP).The results obtained show a not significantly influence of the investigated characteristics of the single panel member in the repeatability assurance of the measure of the odour concentration by dynamic olfactometry. While a variation of the results in terms of OU/m3 is demonstrated related to the different nationality and biological factors (age and gender) of the assessors. There is therefore the need to consider these factors in the current odour determination practices, for example in calculating the uncertainty factor of the measurement.

Self-Organizing Map Algorithm as a Tool for Analysis, Visualization and Interpretation of Electronic Nose High Dimensional Raw Data

Electronic noses used for outdoor ambient characterization to assess odor impacts on population can produce large datasets since usually the sampling is conducted with high frequency (e.g. data per minute) for periods that can reach several months, with a number of sensors that ranges usually from four-six as a minimum, up to above thirty. The environmental analyst has thus to deal with large datasets (millions of data) that have to be properly elaborated for obtaining meaningful interpretation of the instrumental signals. A recent review questioned the capability of some classic statistical elaboration tools for application to e-noses, highlighting how very few in field application are present in scientific literature. In the present work we describe: (i) the use of Self-Organizing Map (SOM) algorithm as a tool for analysis and visualization of e-nose raw data collected at a receptor site near a bio-waste composting facility; (ii) a second level clusterization using k-means clustering algorithm to identify air types that can be detected at the receptor and (iii) the use of e-nose data related to the plant odour sources as well as odour measurements of ambient collected at the receptor site, to classify the types. Eventually we evaluate the frequency and duration of the type/s identified as malodorous.

Study on the characterization of domestic odor facilities by using the olfactory field odor measurement method

Study on the characterization of domestic odor facilities by using the olfactory field odor measurement method

Improvement of sensory odour intensity scale using 1-butanol reference solutions for environmental odour evaluation

<p>For appropriate evaluation of environmental odours, it is necessary to develop a reliable odour measurement scale. Odour intensity reflects people’s perception of odours and contributes to effective odour management. In this study, Japanese conventional six-point odour intensity scale was reconsidered to ensure equal intervals between odour intensity levels. A new series of six dilution steps of 1-butanol solutions with a concentration of 0, 10, 600, 2600, 9000 and 22500 ppm (vol/vol) was proposed and explanatory labels for odour intensity levels were determined. In addition, polyethylene (PE) bottles were proposed as containers for 1-butanol odour intensity reference solutions for convenience in on-site handling, and the effectiveness of PE bottles was suggested. These results showed that the new odour intensity scale would be applicable to on-site investigations and practically useful for all people related to environmental odour evaluation.</p>

A preconcentration method based on a new sorbent for the measurement of selected reduced sulfur compounds at ppb level in ambient air

Odour annoyance forms the main source of environmental stress in residents living nearby industrial or agricultural fields. However, despite a relative simplicity of odour measurement at the emission source, odour measurement in the field is a quite more complicated task. Because of their low odour threshold and offensive odour, this study focuses on 6 reduced sulfur compounds (RSCs): methylmercaptan (MM), ethylmercaptan (EM), dimethylsulfide (DMS), isopropylmercaptan (IPM), tertbutylmercatpan (TBM), diethylsulfide (DES). Because of their very low concentrations in ambient air (few ppb), a preconcentration step is usually required. The literature points Tenax TA® as the more suitable sorbent for sulfur compounds adsorption but shows also an important impact of humidity and storage parameters inducing an underestimation of sulfur ambient air concentrations. So only qualitative or semi-quantitative data results are obtained with Tenax TA® samplings of targeted compounds. To guarantee quantitative concentration data, a new sampling device, using a S-Sorbent Tube450 combined with an optimal drying device, was performed and optimized sampling and analysis parameters were determined. The assessment of uncertainties attests of quantitative measurements for 5 of the 6 RSCs (EM, DMS, IPM, TBM, DES) with relative expanded uncertainties less than 33% considering that samples were stored properly in freeze during 7 days. Only semi-quantitative measurements could be considered for MM with a global uncertainty estimated to 48%, storage contributing for a half.

Analysis of the influence of panel characteristics on odor measurement based on the air dilution olfactory method in Korea

This study was to objectify differences in the results of odor testing according to the characteristics of panel members that participate in air dilution olfactory method (ADOM) testing. When differences in the results of olfactory testing were reviewed in relation to characteristics of the panelists, the result of entire panels for the site boundary Proficiency Testing Materials (PTMs) showed no difference according to panelist sex, age, smoking status, and past participation in ADOM tests. As for the outlet PTMs, distinct differences appeared in relation to the smoking status and age of panelists. The frequency distribution for all the panelists (Dev(D/Ttotal)) showed a form similar to a normal distribution, so it is thought to be more appropriate for the review of effect of panelist characteristics on the result of air dilution olfactory tests. In addition, the ADOM testing method implemented as the standard method for odor compounds in Korea (Dev(D/T3)) will have to be implemented along with a concurrent assessment, for the purpose of identifying the effect of panelist characteristics on administrative dispositions against malodors. Keywords: Odor, Olfactory sense, Olfactory test, Panelists, Proficiency testing materials

Seasonal odor impact range of selected wastewater treatment plants - modeling studies using Polish reference model.

On-site odor measurements and mathematical modeling are useful for identifying the odor impact at the source and receptors located in areas adjacent to wastewater treatment plants (WWTPs). Impact of open area sources can be significant and may influence air quality both at the WWTP area and downwind in the surrounding areas. In this work, results of the odor measurements carried out in the spring and summer season for sources within three different mechanical-biological WWTPs in Poland are presented. The odor impact assessment was completed using a Polish reference model. The odor emission scenarios considering the contribution of major odor sources within WWTPs and the seasonal variation were determined. Obtained results showed significant differences between spring and summer seasons. The highest and the lowest measured odor concentrations at the WWTP property line were 75 ouE/m3 (July) and 6.4 ouE/m3 (March), respectively.

악취측정방법의 타당성과 신뢰성의 장애요인 분석

이 연구는 악취민원에 대한 근본적 해결방안을 찾기 위하여 현재의 악취측정방법에 대한 타당성과 신뢰성을 검토하였다. 복합악취 측정방법은 방법론적으로 타당성을 갖는 것으로 판단하였으나, 측정과정에서 주민들에게 신뢰성을 주지는 못하는 것으로 판단된다. 따라서 현장에서 시료채취와 동시에 분석할 수 있도록 하고, 기상상황이 반영될 수 있도록 시료채취 지점을 추가선정하며, 악취판정요원에 대한 가이드마련이 필요하다. 또한 악취성물질에 대한 ‘추가적인 지정악취물질의 지정’과 기기분석의 정확성과 정밀성 개발을 위한 ‘재정적 지원’이 필요하다.

씨간장으로부터 Bacillus velezensis SS360-1의 분리 및 특성 규명

Four bacteria harboring proteinase activity were isolated from seed soy sauce. Thereafter, their proteinase and amylase activities were assayed. Among the four isolated strains, one strain that showed the highest proteinase and amylase activities was selected tentatively as a starter culture for soy sauce and its characteristics for soy sauce starter culture were investigated. The selected strain was identified as Bacillus velezensis based on Gram-staining, spore-staining, and 16S rRNA gene sequences and finally designated as B. velezensis SS360-1. It showed high growth (A600=1.00~4.55) at 0~12% NaCl concentration. The optimum NaCl concentration for its growth was 3% NaCl. B. velezensis SS360-1 also grew well at 0% NaCl condition; however, its growth was dramatically decreased after 12 h incubation at this condition. The off-odor production ability of B. velezensis SS360-1 was investigated using portable odor measurement and GC-MS analysis. B. velezensis SS360-1 showed definitely lower production of off-odor compounds than did B. subtilis KCTC 3014, which was isolated from fermented soybean. Moreover, B. velezensis SS360-1 produced 3-methyl butanal and 2-heptanone, compounds that are considered as pleasant flavored-compounds for soy sauce. These results suggest that B. velezensis SS360-1 can be used as a suitable starter culture for soy sauce.

CATEGORIZATION OF GELAM, ACACIA AND TUALANG HONEY ODOR-PROFILE USING K-NEAREST NEIGHBORS

Honey authenticity refer to honey types is of great importance issue and interest in agriculture. In current research, several documents of specific types of honey have their own usage in medical field. However, it is quite challenging task to classify different types of honey by simply using our naked eye. This work demostrated a successful an electronic nose (E-nose) application as an instrument for identifying odor profile pattern of three common honey in Malaysia (Gelam, Acacia and Tualang honey). The applied E-nose has produced signal for odor measurement in form of numeric resistance (Ω). The data reading have been pre-processed using normalization technique for standardized scale of unique features. Mean features is extracted and boxplot used as the statistical tool to present the data pattern according to three types of honey. Mean features that have been extracted were employed into K-Nearest Neighbors classifier as an input features and evaluated using several splitting ratio. Excellent results were obtained by showing 100% rate of accuracy, sensitivity and specificity of classification from KNN using weigh (k=1), ratio 90:10 and Euclidean distance. The findings confirmed the ability of KNN classifier as intelligent classification to classify different honey types from E-nose calibration. Outperform of other classifier, KNN required less parameter optimization and achieved promising result.

Survey of restaurant odor status and measurement method

Survey of restaurant odor status and measurement method

Odor dispersion modeling with CALPUFF: Case study of a waste and residue treatment incineration and utilization plant in Kocaeli, Turkey

ABSTRACTThis study addresses the odor problem at a waste and residue treatment incineration and utilization plant located within the borders the Alikahya district of the Kocaeli province in Turkey. In the first stage of the study, odor measurements were made at designated sampling points, while in the second stage, odor concentrations were determined at the receptor points through dispersion modelling using a USEPA (United States Environmental Protection Agency) certified long-range (>50 km) CALPUFF lagrangian puff model. In the final stage, an analysis of the predicted and observed values was carried out using such statistical methods as geometric mean bias (MG), geometric variance (VG) and fractions of predictions within a factor of two observations (FAC2).During the modelling study, the highest one-hour concentration level was found to be 1868.10 OU (Odor Units), and the 24-hour concentration level was found to be 1316 OU, representing a decrease of approximately 30 percent. According to the measurement made, the maximum concentration value was 2455 OU. Odor measurements were carried out at 13 points within the area in order to assess the prediction results. When the results were assessed using the MG, VG and FAC2 statistical methods, it was observed that an acceptable model performance was not achieved for the whole sampling point. When the reason for this was investigated, it was concluded that the observed values were lower than the predicted values due to the fact on the measurement days, the odor was dispersed by wind. It was further concluded that the observed values were higher than the predicted values as a result of odors emitted by other plants in the area. When the measurements in residential areas were examined to identify the effect of the odors, it was determined that although the primary density of settlement is to the southwest of the plant, it was not this area that was affected most, but rather the area to the northeast, where there is a lower settlement density.

多床病室の臭気特性と換気量制御に関する研究 （第3報）機械換気を主体にした多床病室の臭気測定結果の報告

多床病室の臭気特性と換気量制御に関する研究 （第3報）機械換気を主体にした多床病室の臭気測定結果の報告

多床病室の臭気特性と換気量制御に関する研究 （第2報）自然換気を主体にした多床病室の臭気実測結果の報告

多床病室の臭気特性と換気量制御に関する研究 （第2報）自然換気を主体にした多床病室の臭気実測結果の報告

Distribution and sensorial relevance of volatile organic compounds emitted throughout wastewater biosolids processing

A diverse range of volatile organic compounds (VOCs) are emitted from wastewater biosolids processing. Odorous emissions are predominately made up of volatile sulfur compounds (VSCs) which are typically the only odorants measured. However, a range of VOCs are known to contribute to malodours yet previous studies often overlook the contribution of VOCs in comparison with VSCs. This study aims to evaluate how emissions are affected by different biosolids processing configurations, and if any non-sulfur VOCs should be included in odour measurement and management. Non-sulfur VOCs emitted from biosolids throughout six wastewater treatment plants in the Sydney, Australia region were measured at six locations on average twice each week over 2–3weeks at each site. Variations in types of VOCs emitted throughout and between the sites were assigned to differences in WWTP processing configurations, plant operation and variations in industrial and municipal flows to the sewer network, referred to as sewer catchments. The presence of VOCs is likely due to biotic generation as well as industrial or residential additions to the sewer network. The dewatered and stored biosolids samples had the highest levels of VOC emissions. Sensorially important odorants were p-cresol and butanoic acid, based on the frequency of detection and odour activity values. Other compounds with a high risk of nuisance impacts were trimethylamine, indole and phenol emitted from the dewatered and stored biosolids, and volatile fatty acids from the anaerobic digester inlet and outlet at one particular site. The findings show that non-sulfur VOCs should be added to odorant monitoring campaigns at WWTPs. Identification of VOCs as sensorially important odorants opens opportunities for the more efficient management of nuisance odours, through targeted odour control or process improvement.

GIS-based modelling of odour emitted from the waste processing plant: case study

The emission of odours into the atmospheric air from the municipal economy and industrial plants, especially in urbanized areas, causes a serious problem, which the mankind has been struggling with for years. The excessive exposure of people to odours may result in many negative health effects, including, for example, headaches and vomiting. There are many different methods that are used in order to evaluate the odour nuisance. The results obtained through those methods can then be used to carry out a visualization and an analysis of a distribution of the odour concentrations in a given area by using the GIS (Geographic Information System). By their application to the spatial analysis of the impact of odours, we can enable the assessment of the magnitude and likelihood of the occurrence of odour nuisance. Modelling using GIS tools and spatial interpolation like IDW method and kriging can provide an alternative to the standard modelling tools, which generally use the emission values from sources that are identified as major emitters of odours. The work presents the result, based on the odour measurements data from waste processing plant, of the attempt to connect two different tools – the reference model OPERAT FB and GIS-based dispersion modelling performed using IDW method and ordinary kriging to analyse their behaviour in terms of limited observation values.

Detailed Study of Odor from Pig Buildings to Improve Understanding of Biotrickling Filter Performance

Abstract. Recently, bioreactors, such as biotrickling filters, have been used to reduce odor and gas emissions from livestock facilities. Considerable efforts have already been made over the years to evaluate and improve the efficiencies of bioreactors used for this purpose; however, no study has yet been published that attempts to provide a thorough assessment of the performance of bioreactors in eliminating most (if not all) of the gases exhausted from swine facilities. Thus, this study was conducted to obtain a better understanding of the performance of biotrickling filters in treating swine odors by conducting a comprehensive evaluation of their overall performance in removing odor and gaseous components. Evaluating the performance of biotrickling filters using persistent gaseous components, i.e., those that remain in the treated air and contribute to the malodor, helps identify areas of the treatment system that can still be potentially improved. The gaseous components monitored were ammonia (NH3), hydrogen sulfide (H2S), and volatile organic compounds (VOCs). Samples were collected from two four-week trials using three laboratory-scale pig chamber and biotrickling filter systems. The NH3 and H2S concentrations were measured using non-dispersive infrared and ultraviolet fluorescence analyzers, respectively, while VOCs were collected using adsorption tubes and analyzed by a gas chromatograph coupled with a mass spectrometer and an olfactory detection port (GC-MS-O). Samples for odor measurement were also collected and analyzed using a dynamic olfactometer. Linear regression and odor index were used to identify the odorants that had the largest odor impacts. Odor index is an integrated parameter that expresses the combined contributions of odor intensity and hedonic tone to the perceived odor. Of the 176 VOCs identified in the samples, the odor components that had the largest odor impacts included butanoic acid, 3-methylbutanoic acid, 2-methylbutanoic acid, 2-methylpropionic acid, pentanoic acid, p-cresol, 2,3-butanedione, and dimethyl sulfide. However, most of these substances were almost completely eliminated in the biotrickling filters, except p-cresol and dimethyl sulfide. This implies that optimizing the removal of poorly water-soluble components, such as p-cresol and dimethyl sulfide, could further improve the performance of biotrickling filters in treating swine odors. Keywords: Biotrickling filters, Removal efficiency, Swine odor components.