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

Odor nuisance, environmental impact and health risk of priority-controlled VOCs generated from three decentralized aerobic biological modes in treating rural perishable waste

Utilization of perishable waste has emerged as the pivotal factor in enhancing the quality and efficiency of garbage classification in rural regions of China. Nevertheless, the operation of small-scale decentralized aerobic biological treatment facilities in rural areas will inevitably result in the emission of malodorous volatile organic compounds (VOCs). In this study, VOCs emission characteristics of three typical decentralized facilities for the treatment of perishable waste in rural areas were investigated using cold trap enrichment combined with gas chromatography and mass spectrometry to elucidate the characteristics and potential effects on environment and human health. The concentration range of different points in the mechanical composting (MC) treatment mode is from 43.555 to 4154.281 (mean value, 947.292) µg/m3, in the solar-assisted composting (SAC) it is from 99.050 to 2064.308 (636.170) µg/m3, and in the bioconversion by black soldier fly larvae (BBSF) it is 93.712 to 718.644 (283.444) µg/m3. Odor nuisance analysis showed that oxygenated compounds and aromatic compounds were the main odoriferous VOCs. Among all detected VOCs, o-xylene, toluene, and acrolein have the highest ozone formation potential (OFP). Toluene, ethyl benzene, and xylene are the VOCs with secondary organic aerosol generation potential (SOAP). Health risk analysis revealed that six VOCs collectively represent a potential carcinogenic risk, while acrolein exhibits a non-carcinogenic risk. In light of the odor nuisance, environmental impact, and potential health risk, the priority-controlled VOCs identified in decentralized aerobic treatment modes of rural perishable waste were acrolein, benzyl chloride, ethyl acetate, etc. The findings of this research can serve as a valuable reference for the selection of proper strategies in the precise control of VOCs.

SERS-AI Based Detection and Bioanalysis of Malodorous Components in Kitchen Waste

The prevention and control of odor gas generated from kitchen waste are significant missions in research on environmental pollution. Because of the high complexity and variability of kitchen waste, the development of a suitable technique with high sensitivity for the accurate detection of odor gas is an urgent and core task in this frontier field. Here, a technique combining surface-enhanced Raman spectroscopy (SERS) and artificial intelligence (AI) is explored for detecting malodorous components in the leachate of kitchen waste. Initially, 1706 SERS spectra were collected from synthetic kitchen waste under various fermentation parameters. Several AI algorithms were used to classify three levels of odor intensity based on SERS spectra, among which the Random Forest Classifier algorithm model showed a high prediction accuracy of 86.5%. Then, by integrating Raman data, the AI algorithm model identified hydrogen sulfide (H2S) and ammonia (NH3) as the dominant malodorous components in the odor gas. Finally, the structural characteristics of the microbial communities are investigated. With the help of Raman's intensities of malodorous components, many more insights into microorganisms in the fermentation process are revealed, which has important research value in the prevention and controlling of odor gas generated from kitchen waste. Furthermore, the microbial metabolic pathways of sulfur and nitrogen are discussed here. This SERS-AI-based novel technique not only has a broad potential for odor pollution but also could be applied to another complicated biochemical system with functional bacteria.

Uncovering the features of industrial odors-derived environmental complaints and proactive countermeasures by using machine-learning

Industrial odor-derived environmental complaints pose an emerging and far-reaching challenge in cities worldwide with intensive industries. Developing effective odor complaint management strategies is essential for mitigating the public impact of industrial odors. Based on a typical case of persistent tire manufacturing odors affecting local communities, we proposed an environmental complaint risks (ECR) prediction model using machine-learning (ML) approaches, which combined complaints with temporal-resolution manufacturing-meteorology-environment data. Through intensive match-making between ML algorithms and multi-source parameters, Random Forest models can achieve a reliable ECR-prediction model performance with an average ROC-AUC of 0.79 at a monthly timescale, indicating the effectiveness of ML-based ECR prediction models. The interpretable ML model quantitively depicted the underlying mechanisms of ECR prediction, driven by process emission behaviors, local wind direction, and historical high-risk period. Furthermore, to mitigate predictable ECR within a future period, we designed a model framework that integrated ECR prediction models with an adaptive optimization genetic algorithm. This enabled the proactive management by precisely and dynamically allocating limited resources of emission regulatory to high-ECR periods in advance. The strategy was proven effective, achieving a significant 24.7% average reduction in the overall ECR forecast during a period with intensive complaints. Overall, this study proposed a data-driven model framework that efficiently helps the multi-stakeholders shift from passive response to proactive ECR management, thereby enhancing the environmental and social sustainability.

Lignite reduces odour from broiler farms without reducing production performance

An unavoidable consequence of chicken meat production is the generation of substantial quantities of spent litter. This poses several environmental and social challenges, as broiler farms become hotspots for odour emissions. The main source of odour from broiler litter is the microbial decomposition of organic material. Thus, lignite's previously shown capacity to reduce microbial activity in broiler litter was expected to reduce odour emissions from broiler housing. The effect of lignite on odour emission rate (OER) (ou/s) was investigated using dynamic olfactometry over two consecutive summer broiler grow-out cycles in Victoria, Australia, with lignite applied at 3.5 kg/m2. The air quality dispersion model, AERMOD, was used to investigate how lignite's effect on OER influenced the predicted odour dispersion distances and concentrations in the context of various Australian regulatory requirements. Additionally, the effect of lignite on bird production performance was also determined. This study showed that lignite reduced the OER from commercial broiler housing by an average of 56% over both grow-outs. This effect was observed for the duration of the trial. The observed reduction in OER reduced the required separation distances by 44-53% according to the state odour criteria for Victoria, New South Wales, Queensland and South Australia. There were no observed differences in feed conversion ratio, bird live weight or mortality throughout either grow-out. This study demonstrated the capacity of lignite to reduce odour emissions from commercial broiler housing without reducing bird production performance.

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.

Comprehensively assessing priority odorants emitted from swine slurry combining nontarget screening with olfactory threshold prediction

The lack of one-to-one olfactory thresholds (OTs) poses an obstacle to the comprehensive assessment of priority odorants emitted from swine slurry using mass spectrometric nontarget screening. This study screened out highly performing quantitative structure-activity relationship (QSAR) models of OT prediction to complement nontarget screening in olfactory perception evaluation. A total of 27 compounds emitted at different slurry removal frequencies were identified and quantified using gas chromatography-mass spectrometry (GC-MS), including thiirane, dimethyl trisulfide (DMTS), and dimethyl tetrasulfide (DMQS) without OT records. Ridge regression (RR, R2 = 0.77, RMSE = 0.93, MAE = 0.73) and random forest regression (RFR, R2 = 0.76, RMSE = 0.97, MAE = 0.69) rather than the commonly used principal component regression (PCR) and partial least squares regression (PLSR) were used to assign OTs and assess the contributions of emerging volatile sulfur compounds (VSCs) to the sum of odor activity value (SOAV). Priority odorants were p-cresol (25.0-58.9 %) > valeric acid (8.3-31.7 %) > isovaleric acid (6.7-19.0 %) > dimethyl disulfide (4.7-15.7 %) > methanethiol (0-13.6 %) > isobutyric acid (0-8.6 %), whereas the contributions of three emerging VSCs were below 10 %. Vital olfactory active structures were identified by QSAR models as having high molecular polarity, high hydrophilicity, high charge quantity, flexible structure, high reactivity, and a high number of sulfur atoms. This protocol can be further extended to evaluate odor pollution levels for distinct odor sources and guide the development of pertinent deodorization technologies.

Release characteristics of volatile organic compounds at residential garbage collection points: a case study of Hangzhou, China

With the implementation of garbage classification, perishable waste has become increasingly concentrated. This has led to a significant change in the VOC release characteristics at residential garbage collection points, posing a potential risk with unknown characteristics. This study investigated the release characteristics, odor pollution, and health risks of VOCs at garbage collection points under different classification effectiveness, seasons, garbage drop-off periods, and garbage collection point types. The results showed that the average concentration of VOCs released from the garbage sorting collection points (SPs) was 341.43 ± 261.16 μg/m3, and oxygenated compounds (e.g., ethyl acetate and acetone) were the main VOC components. The VOC concentration increased as the community classification effectiveness improved, and it was higher in summer (followed by spring, autumn, and winter). Moreover, the VOC concentrations were higher in the evenings than in the mornings and at centralized garbage collection points (CPs) than at SPs. Further, odor activity value (OAV) assessments indicated that acrolein, styrene, and ethyl acetate were the critical odorous components, with an average OAV of 0.87 ± 0.85, implying marginal odor pollution in some communities. Health risk assessments further revealed that trichloroethylene, benzene, and chlorotoluene were the critical health risk substances, with an average carcinogenic risk (CR) value of 10-6-10-4, and a non-carcinogenic risk (HI) value < 1. These results indicated that HIs were acceptable, but potential CRs existed in the communities. Therefore, VOC pollution prevention and control measures should be urgently strengthened at the garbage collection points in high pollution risk scenarios.

Slaughterhouse by-products composting: can microorganisms inoculum addition mitigate final compost odor emission?

Small slaughterhouses generate biowaste, which for economic reasons, is generally destined for composting. Inoculating appropriate microorganisms can improve biodegradation efficiency and mitigate odor generation during the composting process and can give rise to composts with neutral or pleasant odors. Therefore, the aim of this study was to compare the odor intensity reduction of compost generated with and without a formulated inoculum (Saccharomyces cerevisiae, Bacillus subtilis, and Rhodopseudomonas palustris). A set of experimental data was collected and analyzed according to the German "Verein Deutscher Ingenieure" odor protocol. The results showed that adding microorganisms was effective in reducing unpleasant odors in all three composts generated from swine, cattle, and poultry slaughterhouse by-products during both summer and winter seasons. Additionally, soil odor was predominant in composts that were inoculated in the two tested seasons (i.e., summer and winter). On the other hand, composts without inoculation had odors similar to peat for swine compost, ammonia for cattle compost, and manure for poultry compost, regardless of the season tested. Overall, composting process with appropriate inoculum can help in the correct disposal of slaughterhouse wastes by transforming organic matter into composts, which can have economic and environmental value as a soil conditioner and/or fertilizer.

Determining the soil odor control area: A case study of an abandoned organophosphorus pesticide factory in China

Currently, soil odor-active substance screening and evaluation methods for contaminated sites are underdeveloped, with unclear treatment objectives and areas. Consequently, some sites suffer from odor issues during and even after remediation. This study focused on an organophosphorus pesticide factory site in Guangdong Province, China. It established a method of determining the odorant control area using a comprehensive approach combining instrumental and olfactory soil sample analyses. The main odor-active substances identified were ethylbenzene, phenol, m, p-xylene, styrene, toluene, and o-xylene, with odorant control values (the limit of odor-active substance contents) of 35.2, 28.1, 8.0, 11.3, 40.2 and 89.3 mg/kg respectively. Instrumental analysis of soil samples revealed 11 sampling points where the main odor-causing substances exceeded standard levels. Among the substances, ethylbenzene (1.48E+04 mg/kg) had the highest content, exceeding the limit up to 421-fold. Olfactory analysis indicated 14 sampling points with odor intensity surpassing the standard (OI > 2). Based on the instrumental analysis results and the odorant control value, the initial estimated odor control area (area with the risk of odor nuisance) was 5.64E+03 m2. Incorporating the olfactory analysis findings, the control area was adjusted by 1.25E+03 m2, leading to a final calculated soil odor control area of 6.89E+03 m2 for the study site. The comprehensive approach to analyzing soil samples for odor control can help evaluate the extent of soil odor pollution in contaminated sites and provide a scientific basis for effectively removing and managing odor-causing substances in soil.

Combined assessment of health hazard and odour impact of soils at a contaminated site: a case study on a defunct pharmaceuticals factory in China

Surveys and assessments of contaminated sites primarily focus on hazardous pollutants in the soil with less attention paid to odorants. This makes the management of contaminated sites difficult. In this study, hazardous and odorous pollutants in the soil were assessed for a large site that was previously used for production of pharmaceuticals to determine the degree and characteristics of soil contamination at pharmaceutical production sites, for undertaking rational remediation measures. The main hazardous pollutants at the study site were triethylamine, n-butyric acid, benzo(a)pyrene (BaP), N-nitrosodimethylamine (NDMA), dibenzo(a,h)anthracene (DBA), total petroleum hydrocarbons (C10-C40) (TPH), and 1,2-dichloroethane; TEA, BA, and isovaleric acid (IC) were the main odorants. As the type and distribution of hazardous and odorous pollutants differ, it is necessary to separately assess the impact of these pollutants at a contaminated site. Soils in the surface layer pose significant non-carcinogenic (HI = 68.30) and carcinogenic risks (RT = 3.56E-5), whereas those in the lower layer only pose non-carcinogenic risks (HI > 7.43). Odorants were found at considerable concentrations both in the surface and lower layers, with the maximum concentrations being 29,309.91 and 41.27, respectively. The findings of this study should improve our understanding of soil contamination at former pharmaceutical production sites and should inform the assessment of the risks posed by contaminated sites, with problems associated with odour, and possible remediation strategies.

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.

Artificial Olfactory Biohybrid System: An Evolving Sense of Smell

The olfactory system can detect and recognize tens of thousands of volatile organic compounds (VOCs) at low concentrations in complex environments. Bioelectronic nose (B-EN), which mimics olfactory systems, is becoming an emerging sensing technology for identifying VOCs with sensitivity and specificity. B-ENs integrate electronic sensors with bioreceptors and pattern recognition technologies to enable medical diagnosis, public security, environmental monitoring, and food safety. However, there is currently no commercially available B-EN on the market. Apart from the high selectivity and sensitivity necessary for volatile organic compound analysis, commercial B-ENs must overcome issues impacting sensor operation and other problems associated with odor localization. The emergence of nanotechnology has provided a novel research concept for addressing these problems. In this work, the structure and operational mechanisms of biomimetic olfactory systems are discussed, with an emphasis on the development and immobilization of materials. Various biosensor applications and current developments are reviewed. Challenges and opportunities for fulfilling the potential of artificial olfactory biohybrid systems in fundamental and practical research are investigated in greater depth.

Odor emission assessment of different WWTPs with Extended Aeration Activated Sludge and Rotating Biological Contactor technologies in the province of Cordoba (Spain)

In this study, five urban WWTPs (Wastewater Treatment Plant) with different biological treatment (Extended Aeration Activated Sludge - EAAS; Rotating Biological Contactor - RBC), wastewater type (Urban; Industrial) and size, were jointly evaluated. The aim was twofold: (1) to analyze and compare their odor emissions, and (2) to identify the main causes of its generation from the relationships between physico-chemical, respirometric and olfactometric variables. The results showed that facilities with EAAS technology were more efficient than RBC, with elimination yields of organic matter higher than 90%. In olfactometric terms, sludge managements facilities (SMFs) were found to be the critical odor source in all WWTPs compared to the Inlet point (I) or Post primary treatment (PP), and for seasonal periods with ambient temperature higher than 25 °C. Moreover, the global odor emissions quantified in all SMFs revealed that facilities with EAAS (C-WWTP, V-WWTP and Z-WWTP) had a lower odor contribution (19,345, 14,800 and 11,029 ou_E/s·m², respectively) than for those with RBC technology (P-WWTP and NC-WWTP) which accounted for 19,747 ou_E/s·m² and 80,061 ou_E/s·m², respectively. In addition, chemometric analysis helped to find groupings and differences between the WWTPs considering the wastewater (71.27% of total variance explained) and sludge management (64.52% of total variance explained) lines independently. Finally, odor emissions were adequately predicted from the physico-chemical and respirometric variables in the wastewater (r² = 0.8738) and sludge (r² = 0.9373) lines, being pH, volatile acidity and temperature (wastewater line), and pH, moisture, temperature, SOUR (Specific Oxygen Uptake Rate) and OD₂₀ (Cumulative Oxygen Demand at 20 h) (sludge line) the most influential variables.

Odour concentrations prediction based on odorants concentrations from biosolid emissions

Biosolids storage areas are a significant contributor to wastewater treatment plant (WWTPs) odour emissions which can cause sensorial impact to surrounding communities. Most odour impact regulations are based on odour concentration (COD) measurements determined by dynamic olfactometry. Understanding the relationship between odorants concentrations and COD in the biosolids emission is important to identify how the measurement and monitoring can be conducted using analytical rather than sensorial techniques. Some of the odorants are unknown in biosolid emissions, increasing the uncertainty in predicting COD. In this study, emissions from 56 biosolid samples collected from two WWTPs located in Sydney, Australia, were analysed by analytical and sensorial methods, including olfactory detection port (ODP) and dynamic olfactometry. Concentrations of 25 odorants and two ordinal variables represented odour events detected by ODP assessors were linked to COD values. Bayesian Model Averaging and Variable Selection with Bayesian Adaptive Sampling were applied to model the relation between COD and odorants concentrations. Results indicate the usability of the probabilistic methods and nonlinear transformations in modelling the odour concentrations based on odorants concentrations from biosolids emission and the accuracy of a small dataset.

Odors Emitted from Biological Waste and Wastewater Treatment Plants: A Mini-Review

In recent decades, a new generation of waste treatment plants based on biological treatments (mainly anaerobic digestion and/or composting) has arisen all over the world. These plants have been progressively substituted for incineration facilities and landfills. Although these plants have evident benefits in terms of their environmental impact and higher recovery of material and energy, the release into atmosphere of malodorous compounds and its mitigation is one of the main challenges that these plants face. In this review, the methodology to determine odors, the main causes of having undesirable gaseous emissions, and the characterization of odors are reviewed. Finally, another important topic of odor abatement technologies is treated, especially those related to biological low-impact processes. In conclusion, odor control is the main challenge for a sustainable implementation of modern waste treatment plants.

Electronic Noses and Their Applications for Sensory and Analytical Measurements in the Waste Management Plants-A Review

Waste management plants are one of the most important sources of odorants that may cause odor nuisance. The monitoring of processes involved in the waste treatment and disposal as well as the assessment of odor impact in the vicinity of this type of facilities require two different but complementary approaches: analytical and sensory. The purpose of this work is to present these two approaches. Among sensory techniques dynamic and field olfactometry are considered, whereas analytical methodologies are represented by gas chromatography–mass spectrometry (GC-MS), single gas sensors and electronic noses (EN). The latter are the core of this paper and are discussed in details. Since the design of multi-sensor arrays and the development of machine learning algorithms are the most challenging parts of the EN construction a special attention is given to the recent advancements in the sensitive layers development and current challenges in data processing. The review takes also into account relatively new EN systems based on mass spectrometry and flash gas chromatography technologies. Numerous examples of applications of the EN devices to the sensory and analytical measurements in the waste management plants are given in order to summarize efforts of scientists on development of these instruments for constant monitoring of chosen waste treatment processes (composting, anaerobic digestion, biofiltration) and assessment of odor nuisance associated with these facilities.

Recruiting, training and managing a sensory panel in odor nuisance testing

A growing level of environmental awareness in societies has led to an increased interest in the odor nuisance. Residents’ complaints have ultimately revealed the need to develop adequate research methods that would ensure objective measurements, thus shedding more light on this complex problem. One of the possible ways to conduct an objective odor assessment is to use a sensory panel in the tests. This paper presents the procedure for selecting and training the sensory analysis team especially for the purposes of odor nuisance testing. Several useful suggestions for conducting studies that involve a sensory panel are also provided. This in turn can prove a useful tool, supplementing the EN 13725 standard within the scope of building and convening a sensory panel. In this paper a performance comparison of two sensory panels is also discussed: one that received a basic training of 20h, solely based on the guidelines of the EN 13725 standard and another, whose members completed an extended training of 60h, based on the standards and guidelines used in the field of sensory analysis. It has been shown that acquiring more knowledge and developing certain competences in the field of sensory analysis may contribute to an increase in the overall and individual precision of determinations as adequate sensory training improves the panel’s performance.