Revealing the dual impact of VOCs on recycled rubber workers: Health risk and odor perception.

Volatile organic compounds (VOCs) pose potential hazards to human health and contribute significantly to odor pollution. This study examined VOC emissions from a representative recycled rubber industry, evaluating the occupational health risks for frontline workers in various workshops. Variables such as gender and workshop-specific concentration variations were considered using Monte Carlo simulation methods. Employees in the five production workshops and office areas face noncarcinogenic health risks with hazard indices (HIs) greater than 1, with the rubber compounding phase presenting the highest risk. Acetaldehyde is identified as the primary noncarcinogenic health risk substance, with hazard quotient (HQ) values exceeding 1 in all workshops. Carcinogenic health risks vary by area, with the highest risks found in compounding and refining workshops. Formaldehyde poses the greatest risk in rubber grinding workshops and offices, with cumulative weights exceeding unacceptable levels of M80.58 % and W77.56 % in grinding and M94.98 % and W92.24 % in the office. Male workers face 4-7 % greater noncarcinogenic VOC health risks than females and 5-14 % greater carcinogenic risks from individual VOCs, increasing their susceptibility to health risks caused by VOCs. Additionally, our analysis of odor identification and intensity classification revealed that 53 VOCs are capable of causing odor pollution, with several substances reaching odor levels of 2 or higher. The predominant perceived odors, as reflected in the odor wheel, include categories such as "solvent/aromatic" and "sweet/fruit," with aldehydes being the primary odor-causing substances. In summary, emissions of VOCs from rubber industrial processes not only pose substantial health risks to workers but also contribute significantly to odor pollution. Consequently, enterprises must prioritize optimizing workplace conditions to ensure the occupational health and well-being of their employees.

Odor lateralization test is insensitive to small degrees of intranasal trigeminal activation.

INTRODUCTION: Odors with prominent trigeminal compounds are more easily localized than purely olfactory ones. However, it is still unclear whether adding a small amount of a trigeminal compound to an olfactory odor significantly improves lateralization performance. METHODS: We included 81 healthy adults aged 25.4 ± 4.8 years to complete odor lateralization tasks using 12 odors: two "olfactory", two "trigeminal" odors, and eight odor mixtures at two low concentrations of "trigeminal" odors (4%, 8%). This task utilized a "Squeezer" delivering odor or air to either nostril, and participants indicated which nostril received the odor. Evaluations also included olfactory function, odor intensity ratings, and individual olfactory importance. RESULTS: Degrees of trigeminal compounds significantly affected lateralization performance (F = 82.32, p < 0.001), with 100% irritants showing higher performance than 0%, 4%, and 8% irritants (p's < 0.001), while no significant differences were found between odors with 0%, 4%, and 8% irritants (p's > 0.05). Chi-square tests confirmed higher percentages of above-chance lateralization with 100% irritants than with 0%, 4%, and 8% irritants (χ2 = 30.89 to 47.33, p's < 0.001). CONCLUSIONS: Adding a small amount of a trigeminal compound to a selective olfactory odor does not significantly improve lateralization performance. Trigeminal lateralization likely follows an "accumulative" pattern rather than an "all or none" rule. With only 20 trials, the task may lack sensitivity to detect low levels of trigeminal irritation in selective olfactory odors, though it does not rule out trigeminal activation. The odor lateralization task can screen for odors with prominent trigeminal compounds by comparing group-level performance with that of purely olfactory odors. Future studies should use more ideal stimuli (e.g., PEA for olfactory, CO2 for trigeminal) to test the replicability of the results.

Diet and odor hedonic ratings: comparative study between vegetarians, flexitarians, and omnivores.

OBJECTIVES: Odor hedonic perception is well known to exhibit great variability and to depend on several parameters, i.e. stimulus, context, and subject characteristics. As hedonic perception (pleasant/unpleasant character) of food odors is considered one of the most prominent dimensions in eating behavior, the question of hedonic variability in this context arises. Thus, the aim of the present study was to compare odor hedonic ratings in three populations with regard to diet (i.e. omnivore, vegetarian, and flexitarian diets). METHODS: Four categories of odors were compared: meat, vegetable, other food, and non-food odors. RESULTS: The results showed that vegetarian and flexitarian individuals rated meat odors as more unpleasant than omnivores, while no significant difference was found for other categories of odors. DISCUSSION: The question of whether the diet influences the hedonic perception or/and inversely is discussed, regarding several aspects of food consumption such as eating disorders, food education, … and could further serve to manage eating behaviors. PRACTICAL APPLICATIONS: This study evidenced that vegetarians and flexitarians specifically rated meat odors as being more unpleasant than those of omnivores. Because of the growing number of vegetarians and flexitarians in the general population, it could be suggested to take into account the odor hedonic perception (especially regarding food odors) in studies related to diets. Besides, the present results could further serve research in several aspects of food consumption such as eating disorders (anorexia, bulimia … etc.) or food education as well as the management of eating behaviors, especially in an elderly population.

Loss of olfactory sensitivity is an early and reliable marker for COVID-19.

Detection of early and reliable symptoms is important in relation to limiting the spread of an infectious disease. For COVID-19, the most specific symptom is either losing or experiencing reduced olfactory functions. Anecdotal evidence suggests that olfactory dysfunction is also one of the earlier symptoms of COVID-19, but objective measures supporting this notion are currently missing. To determine whether olfactory loss is an early sign of COVID-19, we assessed available longitudinal data from a web-based interface enabling individuals to test their sense of smell by rating the intensity of selected household odors. Individuals continuously used the interface to assess their olfactory functions and at each login, in addition to odor ratings, recorded their symptoms and results from potential COVID-19 test. A total of 205 COVID-19-positive individuals and 156 pseudo-randomly matched control individuals lacking positive test provided longitudinal data which enabled us to assess olfactory functions in relation to their test result date. We found that odor intensity ratings started to decline in the COVID-19 group as early as 6 days prior to the test result date (±1.4 days). Symptoms, such as sore throat, aches, and runny nose appear around the same point in time; however, with a lower predictability of a COVID-19 diagnosis. Our results suggest that olfactory sensitivity loss is an early symptom but does not appear before other related COVID-19 symptoms. Olfactory loss is, however, more predictive of a COVID-19 diagnosis than other early symptoms.

Genetic variation across the human olfactory receptor repertoire alters odor perception.

Humans use a family of more than 400 olfactory receptors (ORs) to detect odors, but there is currently no model that can predict olfactory perception from receptor activity patterns. Genetic variation in human ORs is abundant and alters receptor function, allowing us to examine the relationship between receptor function and perception. We sequenced the OR repertoire in 332 individuals and examined how genetic variation affected 276 olfactory phenotypes, including the perceived intensity and pleasantness of 68 odorants at two concentrations, detection thresholds of three odorants, and general olfactory acuity. Genetic variation in a single OR was frequently associated with changes in odorant perception, and we validated 10 cases in which in vitro OR function correlated with in vivo odorant perception using a functional assay. In 8 of these 10 cases, reduced receptor function was associated with reduced intensity perception. In addition, we used participant genotypes to quantify genetic ancestry and found that, in combination with single OR genotype, age, and gender, we can explain between 10% and 20% of the perceptual variation in 15 olfactory phenotypes, highlighting the importance of single OR genotype, ancestry, and demographic factors in the variation of olfactory perception.

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.

Determination of Odor Intensity of Binary Gas Mixtures Using Perceptual Models and an Electronic Nose Combined with Fuzzy Logic.

Measurement and monitoring of air quality in terms of odor nuisance is an important problem. From a practical point of view, it would be most valuable to directly link the odor intensity with the results of analytical air monitoring. Such a solution is offered by electronic noses, which thanks to the possibility of holistic analysis of the gas sample, allow estimation of the odor intensity of the gas mixture. The biggest problem is the occurrence of odor interactions between the mixture components. For this reason, methods that can take into account the interaction between components of the mixture are used to analyze data from the e-nose. In the presented study, the fuzzy logic algorithm was proposed for determination of odor intensity of binary mixtures of eight odorants: n-Hexane, cyclohexane, toluene, o-xylene, trimethylamine, triethylamine, α-pinene, and ß-pinene. The proposed algorithm was compared with four theoretical perceptual models: Euclidean additivity, vectorial additivity, U model, and UPL model.

Recognition of the Component Odors in Mixtures.

Natural olfactory stimuli are volatile-chemical mixtures in which relative perceptual saliencies determine which odor-components are identified. Odor identification also depends on rapid selective adaptation, as shown for 4 odor stimuli in an earlier experimental simulation of natural conditions. Adapt-test pairs of mixtures of water-soluble, distinct odor stimuli with chemical features in common were studied. Identification decreased for adapted components but increased for unadapted mixture-suppressed components, showing compound identities were retained, not degraded to individual molecular features. Four additional odor stimuli, 1 with 2 perceptible odor notes, and an added "water-adapted" control tested whether this finding would generalize to other 4-compound sets. Selective adaptation of mixtures of the compounds (odors): 3 mM benzaldehyde (cherry), 5 mM maltol (caramel), 1 mM guaiacol (smoke), and 4 mM methyl anthranilate (grape-smoke) again reciprocally unmasked odors of mixture-suppressed components in 2-, 3-, and 4-component mixtures with 2 exceptions. The cherry note of "benzaldehyde" (itself) and the shared note of "methyl anthranilate and guaiacol" (together) were more readily identified. The pervasive mixture-component dominance and dynamic perceptual salience may be mediated through peripheral adaptation and central mutual inhibition of neural responses. Originating in individual olfactory receptor variants, it limits odor identification and provides analytic properties for momentary recognition of a few remaining mixture-components.

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.

Beyond Distracted Eating: Cognitive Distraction Downregulates Odor Pleasantness and Interacts with Weight Status.

Considering the widespread issue of distracted eating, our study investigates how cognitive distraction influences the sensory perception of food-related odors among individuals with varying weight statuses. We conducted an exploratory, randomized, and cross-sectional experimental study, using the Tetris game to simulate real-life cognitive distraction, incorporating two distraction levels (low and high) and presenting five distinct odors. A total of 59 participants, categorized into a lean (n = 30) and overweight/obese group (n = 29) based on their body mass index (BMI), received odor stimuli while playing Tetris at low and high difficulty, corresponding to low and high distraction levels, respectively. Participants subsequently rated odor intensity and pleasantness under the two cognitive distraction conditions. Respiratory movements were monitored to ensure accurate olfactory stimulation. Our findings revealed no significant difference in odor intensity ratings across distraction levels (p = 0.903). However, there was a significant reduction in odor pleasantness under high cognitive distraction (p = 0.007), more pronounced in lean participants compared to those with an overweight status (p = 0.035). Additionally, an interaction between gender and cognitive distraction effects was observed in odor pleasantness perception. The differential effects of distraction across weight-status groups and genders are discussed in the context of hedonic motivation and compensatory mechanisms. This study sheds light onto the sensory mechanisms underlying distracted eating and could inform more personalized strategies for promoting healthier eating habits in a world dominated by distractions.

Evaluation of three granular activated carbon filters for the treatment of collections foul air entering a water resource recovery facility.

The treatment of raw foul air that could escape to the atmosphere from the head space of the incoming wastewater sewer lines into a Southern California Water Resource Recovery Facility was evaluated by using a 1/20th scale pilot unit consisting of three different granular activated carbon filter technologies, operating side by side, under similar operating conditions, each having an average 3.8-s contact time. The three activated carbon filters contained each 0.07 m3 of coconut, coal, and coconut mixed with permanganate media. The foul air entering the granular activated carbon filters contained 82% to 83% relative humidity. No moisture removal mechanism was used prior to treatment. The removal of six different odor characters from eight chemical odorants present in the foul air were assessed. These were rotten egg (hydrogen sulfide), rotten vegetables (methyl mercaptan), canned corn (dimethyl sulfide), rotten garlic (dimethyl disulfide), earthy/musty (2-methyl isoborneol and 2-isopropyl 3-methoxy pyrazine), and fecal (skatole and indole). This is the first time a study evaluates the removal of specific odors by simultaneously employing sensory analyses using the odor profile method, which defines the different odor characters and intensities, together with chemical analyses of the odorants causing these odors. The results show that the three granular activated carbon filters, before hydrogen sulfide breakthrough, provided significant improvement in odor intensity and odorant removal. Breakthrough was reached after 57 days for the coconut mixed with permanganate, 107 days for the coconut, and 129 days for the coal granular activated carbon filter. Breakthrough (the critical saturation point of the activated carbon media) was considered reached when the hydrogen sulfide percentage removal diminished to 90% and continued downward. The coconut mixed with permanganate granular activated carbon filter provided the best treatment among the media tested, achieving very good reduction of odorants, as measured by chemical analyses, and reasonable removal of odor intensities, as measured by the odor profile method. The coconut mixed with permanganate granular activated carbon is recommended for short-term odor control systems at sewer networks or emergency plant maintenance situations given its shorter time to breakthrough compared with the other granular activated carbons. The coal and coconut granular activated carbon filters are generally used as the last stage of an odor treatment system. Because of the observed poor to average performance in removing odorants other than hydrogen sulfide, the treatment stage(s) prior to the use of these granulated activated carbons should provide a good methyl mercaptan removal of at least 90% in order to avoid the formation of dimethyl disulfide, which, in the presence of moisture in the carbon filter, emit the characteristic rotten garlic odor. The differences observed between the performances based on odorant removal by chemical analysis compared with those based on sensorial analyses by the odor profile method indicate that both analyses are required to understand more fully the odor dynamics. PRACTITIONER POINTS: Three virgin granulated activated carbon media were evaluated in a field pilot unit using raw collections foul air. Coal, coconut, and coconut mixed with permanganate were tested until breakthrough. Samples were analyzed both chemically (odorants) and sensorially (odors). Coconut mixed with permanganate proved to be the media that better reduced odorants and odors.

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.

Development of a Novel HS-GC/MS Method Using the Total Ion Spectra Combined with Machine Learning for the Intelligent and Automatic Evaluation of Food-Grade Paraffin Wax Odor Level.

The intensity of the odor in food-grade paraffin waxes is a pivotal quality characteristic, with odor panel ratings currently serving as the primary criterion for its assessment. This study presents an innovative method for assessing odor intensity in food-grade paraffin waxes, employing headspace gas chromatography with mass spectrometry (HS/GC-MS) and integrating total ion spectra with advanced machine learning (ML) algorithms for enhanced detection and quantification. Optimization was conducted using Box-Behnken design and response surface methodology, ensuring precision with coefficients of variance below 9%. Analytical techniques, including hierarchical cluster analysis (HCA) and principal component analysis (PCA), efficiently categorized samples by odor intensity. The Gaussian support vector machine (SVM), random forest, partial least squares regression, and support vector regression (SVR) algorithms were evaluated for their efficacy in odor grade classification and quantification. Gaussian SVM emerged as superior in classification tasks, achieving 100% accuracy, while Gaussian SVR excelled in quantifying odor levels, with a coefficient of determination (R2) of 0.9667 and a root mean square error (RMSE) of 6.789. This approach offers a fast, reliable, robust, objective, and reproducible alternative to the current ASTM sensory panel assessments, leveraging the analytical capabilities of HS-GC/MS and the predictive power of ML for quality control in the petrochemical sector's food-grade paraffin waxes.

Predicting minty compounds binary mixtures' pleasantness by odor intensity in aqueous solutions.

The aroma of mint is well-liked by the public, and key flavor odorants in mint aroma had been found, but how these molecules interact and form a satisfying odor remains a challenge. Quality, intensity, and pleasantness are our most basic perceptions of aromas; both intensity and pleasantness can be quantified. However, compared to intensity, research on pleasantness was lacking. Pleasantness was one of the most important indicators for formulating a satisfying mint flavor, and the study of binary mixtures was fundamental to our understanding of more complex mixtures. Therefore, the purpose of this study was to explore the characteristics of pleasantness as a function of concentration and, at the same time, to investigate the relationship between intensity and pleasantness in binary mixtures. Thirty sensory evaluation volunteers participated in the evaluation of the intensity and pleasantness of six key flavor odorants of mint and five binary mixtures. The results showed that the pleasantness increased first and then decreased or stabilized with the rising of concentration; even though the interactions in binary mixtures were not the same, their pleasantness could be predicted using the intensities of the components by Response Surface Design of Experiments, and the goodness of fit was greater than 0.92, indicating that the models had the great predictive ability. PRACTICAL APPLICATION: Whether blending flavors or evaluating them, a great deal of experience is required, yet the acquisition of this experience is a long process. Performing these tasks is difficult for the novice, and it helps to quantify the feeling for the flavor and build some mathematical models.

Exploring the effects of mixture composition factors and perceptual interactions on the perception of icewine odor: An olfactometer-based study.

The perception of food odor, derived from complex mixtures of odorants, remains poorly understood. This study investigated how key odorants of icewine influence odor mixture perception and mixture-induced perceptual interactions. A multichannel olfactometer was used to deliver 90 mixtures to 36 trained participants who used a Rate-All-That-Apply method to rate the odor samples. Results showed that adding odorants to a mixture affected both the characteristic odor of the individual component and other odor characteristics, revealing specific perceptual interactions. Combining up to six odorants with icewine odor influenced a maximum of two odor characteristics in the mixture, regardless of the specific combination. Interestingly, adding odorants had a stronger impact on the overall mixture odor profile than omitting them, particularly when manipulating fewer than three odorants. These findings emphasize the complexity of odor mixture perception and provide new insights into the influence of key odorants on the aroma of wine.

Joint majorization of waterworks and secondary chlorination points considering the chloric odor and economic investment in the DWDS using machine learning and optimization algorithms.

The traditional methods of increasing the chlorine disinfectant dosage in the drinking water distribution system (DWDS) to control microorganisms and improve the safety of drinking water quality are subjected to several challenges. One noticeable problem is the unpleasant odor generated by chlorine and chloramines. However, the generally proposed chlorine dosage optimization model ignores the chloric odor distribution in the DWDS. This study proposes a comprehensive multi-parameter water quality model and aims to balance the trade-offs between: (i) minimize the flavor profile analysis (FPA) degree of the chloric odor produced by chlorine and chloramines in the DWDS, and (ii) minimize the economic investment (chlorine dosage and operation cost). EPANET and back propagation (BP) network integrated with the Borg algorithm were employed as innovative approaches to simulate the chlorine, chloramines, and chloric odor intensity in the DWDS. Moreover, the application of the multi-parameter model was demonstrated in a real-world DWDS case study. 0.5 mg-Cl2/L (mg/L) chlorine at 8 secondary chlorination points was added to the DWDS as an optimized chlorine dosing scheme considering the olfactory and financial objective functions simultaneously. When switching to a superior water source, the FPA of the chloric odor in DWDS increased by a maximum of 1.4 at most if the initial chlorine dosage remained as before. To avoid the occurrence of chloric odor and also control the residual free chlorine (residual chlorine) at a suitable value, the initial and secondary chlorine dosages were optimized to 0.4 mg/L and 0.3 mg/L, respectively. Under this condition, the initial chlorine dosage was reduced by 50% compared to the original operation scheme in City J, China, the qualification rate of the residual chlorine reached 97.2%, basically consistent with that before water source switching, and the chloric odor intensity of the DWDS was controlled below FPA 3.4.

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.

Health impact of odor from on-situ sewage sludge aerobic composting throughout different seasons and during anaerobic digestion with hydrolysis pretreatment.

Aerobic composting and anaerobic digestion with hydrolysis pretreatment are two mainstream methods used to recycle and reclaim sewage sludge. However, during these sludge treatment processes, many odors are emitted that may cause severe emotional disturbance and health risks to those exposed. This study identified odor pollution (i.e. sensory influence, odor contribution, and human risks) from samples collected during sludge aerobic composting throughout different seasons as well as during anaerobic digestion with hydrolysis pretreatment. Odor intensity, odor active values, and permissible concentration-time weighted averages for ammonia and five volatile sulfur compounds were assessed. The results revealed serious odor pollution from all sampling sites during aerobic composting, especially in winter. Excessively strong odors were identified in the composting workshop, with total odor active values between 997 and 8980 which accounted for 78.45%-96.18% of the total sludge aerobic composting plant. Levels of ammonia and dimethyl disulfide in the ambient air were high enough to harm employees' health. During anaerobic digestion, excessively strong odors were identified in dehydration workshop 2, and the total odor active values of six odors reached 32,268, with ammonia and hydrogen sulfide levels significant enough to harm human health.

Odor impact zones around landfills: Delineation based on atmospheric conditions and land use characteristics.

Odors emitted from landfills can result in complaints by the residents living near the landfills. The aim of this study was to develop an assessment and delineation tool to identify the areas which can be impacted by the odors released from landfills based on land use characteristics and atmospheric conditions; and estimate the number of people who may be impacted. Odor emissions and dispersion analyses were conducted for three case study landfills under different atmospheric conditions in view of the land use characteristics around each landfill. Odor emissions and odor intensity levels were estimated based on the total gas production and the level of odorous compounds present in the landfill gas using the Landfill Gas Emissions Model (LandGEM) software. To delineate the odor impact zones, air dispersion characteristics of the odorous gases were analyzed using the dispersion modeling software, Areal Locations of Hazardous Atmospheres (ALOHA), and mapped using ArcGIS. Impact zone analyses were conducted based on the odor perception thresholds. The methodology developed involved coupling landfill gas emissions model (LandGEM), dispersions model (ALOHA) and mapping software for land use and population density (ArcGIS) allows visualization of the potential impact zones for preliminary delineation of the buffer zones around landfills, developing appropriate mitigation measures in view of the changing land use characteristics and population density around the existing and planned landfills. The odor impact zone delineation methodology was named Land-OZ (short for Landfill Odor Impact Zone). Results of using the odor impact zone tool showed that atmospheric stability could increase the odor impact radius around the three landfills evaluated between 340 and 1100 percent depending on the land use characteristics of the surrounding areas.

Odor assessment of NH3 and volatile sulfide compounds in a full-scale municipal sludge aerobic composting plant.

Methods for assessing odors in municipal sewage sludge aerobic composting plants (MSSACPs) have been ineffective. This study identified the emission amount of typical odor-producing compounds, including NH3 and volatile sulfide compounds from a full-scale MSSACP, and evaluated risks of odor emissions based on odor intensity and odor active value. Results revealed all sampling sites (i.e. sludge stacking yard, composting workshop, and screening workshop) produced serious odors, especially in the composting workshop. In the composting workshop, the amounts of DMDS (174.59 µg·dry kg-1) and DMS (71.64 µg·dry kg-1) emitted were far lower than that of NH3 (6062.56 µg·dry kg-1). However, DMDS and DMS showed a similar intensity as NH3 according to odor intensity assessment. Furthermore, both of their odor active values were higher than that of NH3. Using results from both odor intensity and odor active value were more reliable for the assessment of odors from MSSACPs.