A critical review on odor measurement and prediction

Multi-scenario adaptive electronic nose for the detection of environmental odor pollutants

With the rapid development of sensing technologies, electronic noses have become an important tool for real-time environmental monitoring, but ensuring their applicability and accuracy across various scenarios remains a key challenge. In this study, an electronic nose system with multi-scenario applicability and enhanced accuracy was developed to measure four common key pollutant concentrations in three typical pollution scenarios: landfills, wastewater treatment plants and livestock farms. A scenario-adaptive strategy was proposed to minimize the impact of interferences on the measurement accuracy by constructing a hierarchically structured qualitative-scenario-specific qualitative sub-network to process the sensor response data. Random Forest and Support Vector Machine algorithms were used and evaluated in scenario classification, with the Random Forest model performing best, achieving 100 % classification accuracy for 176 samples across all scenarios. Subsequently, scenario-specific qualitative models and unified model were developed with Random Forest Regression (RFR) and Artificial Neuron Networks (ANNs) after eliminating sensor features affected highly by interferences with feature importance analysis. The scenario-adaptive strategy achieved R² values exceeding 0.88 in target pollutant concentration prediction across all scenarios, with a mean absolute percentage error (MAPE) reduction of at least 15 % compared with the unified model for the test set. Furthermore, by flexibly integrating the most applicable algorithms, the scenario-adaptive strategy allows the benefits of different algorithms to be fully utilized in various scenarios. This study highlights the effectiveness of the adaptive strategy in improving electronic nose performance across various scenarios, laying a foundation for robust, adaptive electronic nose systems.

Odor impact patterns and health risks of various enterprises in the rubber product manufacturing industry

The rubber product manufacturing (RPM) industry generates a large number of odor complaints because persistent and distinctive volatile compounds are released during the associated processes. Such compounds represent a nuisance and may pose health risks to nearby residents. Extensive monitoring and sampling identified 146 volatile organic compounds (VOCs) from 20 enterprises across 6 subcategories of the RPM industry. Their odor impact patterns and health risks were assessed via dispersion modeling from both horizontal and vertical perspectives. Nine out of the 20 enterprises were found to cause odor impacts to the surrounding residents, with separation distances ranging from 0.3 to 3.5 km depending on the prevailing wind directions. The separation distances were more subject to odor concentrations than exhaust velocities. The odor impacts peaked at 40 to 60 m due to the horizontal and vertical dispersion as well as the height of exhaust ports. From a health risk perspective, none of the enterprises posed carcinogenic or noncarcinogenic risks to the surrounding areas based on the cumulative hazard quotient and carcinogenic risk values; however, acetaldehyde and benzene required further attention. This study provides important evidence for the management and control of VOCs in the RPM industry from both odor and health perspectives.

Efficacy of composite bacterial deodorant constructed with Camellia sinensis and its in-situ deodorization mechanism on pig manure

Here, we constructed a novel bacterial deodorant (BD) composed of Delftia tsuruhatensis, Paracoccus denitrificans, Pediococcus acidilactici, and Bacillus velezensis. The BD alone removed 64.84 % of NH3, 100 % of H2S, and 63.68 % of comprehensive odor (OU) during a five-day fermentation of pig manure. The effect was enhanced by introducing Camellia sinensis in the composite bacterial deodorant (CBD) treatment, with the removal efficiency (RE) of NH3 and OU being 88.68 % and 88.14 %, respectively. In prolonged trials, maximum RE of NH3, H2S and OU RE reached 90.16 %, 92.32 % and 100 % in CBD group. Bacterial composition of manure revealed that the abundance of odor-producing microbes (Kurthia, Solibacillus, Proteiniphilum and Acholeplasma) and potential pathogens decreased after CBD application. Functional prediction and correlation analyses indicated that the process of nitrification, denitrification and S/N assimilation were facilitated, while S/N mineralization and methanogenesis processes might be inhibited. This deodorant promoted the conversion of malodorous substances into non-odorous forms, establishing an efficient odor removal system in hoggery. Therefore, the bacterial deodorant compounded with C. sinensis has been shown to be an effective method for deodorizing pig farms. This approach will advance the livestock industry toward greener practices and environmental protection, contributing positively to the development of a sustainable agro-ecosystem.

Geosmin Events Associated with Dolichospermum circinale Abundance Promoted by Nitrogen Supply in a Chinese Large Tropical Eutrophic Reservoir

Taste and odor (T/O) compounds are a global threat in drinking water, mainly produced by cyanobacteria in freshwater environments. Temperature plays a crucial role in regulating geosmin dynamics in temperate and subtropical lakes, while its influence may be lower in tropical waters. To better understand the factors affecting geosmin occurrence in tropical waters, a dataset from a field investigation conducted in a large tropical reservoir was analyzed. The water temperature varied between 16 °C and 32 °C, with geosmin concentration ranging from below the detection limit (3 ng/L) to as high as 856 ng/L. Elevated geosmin levels exceeding > 10 ng/L were observed over the whole year except for in September, suggesting that the annual temperature was suitable for geosmin production. Among the diverse cyanobacteria, Dolichospermum circinale was identified as the main producer of geosmin in the reservoir, both by correlation analysis and cells' geosmin measurements. Geosmin concentration was also significantly related to the abundance of D. circinale. None of the environmental variables (temperature, pH, transparency and nutrients) were significantly directly correlated with geosmin concentration. But the high total nitrogen significantly explained the increase in D. circinale abundance associated with geosmin elevation. Our results suggest that nutrients, particularly nitrogen, directly affected the competitive advantage and abundance of key geosmin producers and thus modified geosmin levels in this tropical reservoir. Our study thus hints at the possible management of the geosmin problem through nutrient reduction in tropical reservoirs.

The production of methyl mercaptan is the main odor source of chicken manure treated with a vertical aerobic fermenter

The process of harmless treatment of livestock manure produces a large amount of odor, which poses a potential threat to human and livestock health. A vertical fermentation tank system is commonly used for the environmentally sound treatment of chicken manure in China, but the composition and concentration of the odor produced and the factors affecting odor emissions remain unclear. In this study, we investigated the types and concentrations of odors produced in the mixing room (MR), vertical fermenter (VF), and aging room (AR) of the system, and analyzed the effects of bacterial communities and metabolic genes on odor production. The results revealed that 34, 26 and 26 odors were detected in the VF, MR and AR, respectively. The total odor concentration in the VF was 66613 ± 10097, which was significantly greater than that in the MR (1157 ± 675) and AR (1143 ± 1005) (P < 0.001), suggesting that the VF was the main source of odor in the vertical fermentation tank system. Methyl mercaptan had the greatest contribution to the odor produced by VF, reaching 47.82%, and the concentration was 0.6145 ± 0.2164 mg/m3. The abundance of metabolic genes did not correlate significantly with odor production, but PICRUSt analysis showed that cysteine and methionine metabolism involved in methyl mercaptan production was significantly more enriched in MR and VF than in AR. Bacillus was the most abundant genus in the VF, with a relative abundance significantly greater than that in the MR (P < 0.05). The RDA results revealed that Bacillus was significantly and positively correlated with methyl mercaptan. The use of large-scale aerobic fermentation systems to treat chicken manure needs to focused on the production of methyl mercaptan.

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.

Odor emission pattern of the waste storage workshop of kitchen waste treatment plant and control strategy study with CFD simulation

Odor emission has become a great issue for kitchen waste management plants. Among all, unorganized emission source such as waste storage tank is the key cause. It is necessary to understand the odor emission characteristics and provide a proper control solution. In this study, a typical kitchen waste treatment plant located in Guangdong Province of China was selected to investigate the odor emission characteristics. According to the survey, the main complaint due to odor emission is on waste storage workshop. Hence, its odor emission has been investigated in this study. The gas samples were collected from the workshop in different season. According to the results, the odor emission during summer is the worst. In total, 105 odorous gases were detected from the waste storage workshop. The main odorous gases can be categorized into sulfur compounds, oxygen-containing organic compounds and terpenes. In specific, ethanol, acetic acid, methylmercaptan, α-pinene, methioether and limonene were the major odorous pollutants. Based on grey correlation, principal component analysis (PCA) and step-up regression analysis, methylmercaptan contributes the most to the odor concentration. It suggests that the odor emission control should pay more attention on methylmercaptan. The Computational Fluid Dynamics (CFD) stimulation was employed to investigate the odor distribution with applying air blowing as a curtain to separate the inside and outside atmosphere or suction to vacuum the inside air to prevent the odor emission. It was found that it could efficiently prevent odor emission by setting a 45° inclined air suction port at the top of the entrance gate. The study provides a theoretical basis on odor control for the waste storage workshop of kitchen waste management plants.

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.

Predicting Odor Sensory Attributes of Unidentified Chemicals in Water Using Fragmentation Mass Spectra with Machine Learning Models

Knowing odor sensory attributes of odorants lies at the core of odor tracking when addressing waterborne odor issues. However, experimental determination covering tens of thousands of odorants in authentic water is not pragmatic due to the complexity of odorant identification and odor evaluation. In this study, we propose the first machine learning (ML) model to predict odor perception/threshold aiming at odorants in water, which can use either molecular structure or MS2 spectra as input features. We demonstrate that model performance using MS2 spectra is nearly as good as that using unequivocal structures, both with outstanding accuracy. We particularly show the model's robustness in predicting odor sensory attributes of unidentified chemicals by using the experimentally obtained MS2 spectra from nontarget analysis on authentic water samples. Interpreting the developed models, we identify the intricate interaction of functional groups as the predominant influence factor on odor sensory attributes. We also highlight the important roles of carbon chain length, molecular weight, etc., in the inherent olfactory mechanisms. These findings streamline the odor sensory attribute prediction and are crucial advancements toward credible tracking and efficient control of off-odors in water.

Integration of nontarget analysis with machine learning modeling for prioritization of odorous volatile organic compounds in surface water

Assessing the odor risk caused by volatile organic compounds (VOCs) in water has been a big challenge for water quality evaluation due to the abundance of odorants in water and the inherent difficulty in obtaining the corresponding odor sensory attributes. Here, a novel odor risk assessment approach has been established, incorporating nontarget screening for odorous VOC identification and machine learning (ML) modeling for odor threshold prediction. Twenty-nine odorous VOCs were identified using two-dimensional gas chromatography-time of flight mass spectrometry from four surface water sampling sites. These identified odorants primarily fell into the categories of ketones and ethers, and originated mainly from biological production. To obtain the odor threshold of these odorants, we trained an ML model for odor threshold prediction, which displayed good performance with accuracy of 79%. Further, an odor threshold-based prioritization approach was developed to rank the identified odorants. 2-Methylisoborneol and nonanal were identified as the main odorants contributing to water odor issues at the four sampling sites. This study provides an accessible method for accurate and quick determination of key odorants in source water, aiding in odor control and improved water quality management. ENVIRONMENTAL IMPLICATION: Water odor episodes have been persistent and significant issues worldwide, posing severe challenges to water treatment plants. Unpleasant odors in aquatic environments are predominantly caused by the occurrence of a wide range of volatile organic chemicals (VOCs). Given the vast number of newly-detected VOCs, experimental identification of the key odorants becomes difficult, making water odor issues complex to control. Herein, we propose a novel approach integrating nontarget analysis with machine learning models to accurate and quick determine the key odorants in waterbodies. We use the approach to analyze four samples with odor issues in Changsha, and prioritized the potential odorants.

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.

Optimizing Winter Air Quality in Pig-Fattening Houses: A Plasma Deodorization Approach

This study aimed to evaluate the effect of two circulation modes of a plasma deodorization unit on the air environment of pig-fattening houses in winter. Two pig-fattening houses were selected, one of which was installed with a plasma deodorizing device with two modes of operation, alternating internal and external circulation on a day-by-day basis. The other house did not have any form of treatment and was used as the control house. Upon installing the system, this study revealed that in the internal circulation mode, indoor temperature and humidity were sustained at elevated levels, with the NH3 and H2S concentrations decreasing by 63.87% and 100%, respectively, in comparison to the control house. Conversely, in the external circulation mode, the indoor temperature and humidity remained subdued, accompanied by a 16.43% reduction in CO2 concentration. The adept interchange between these two operational modes facilitates the regulation of indoor air quality within a secure environment. This not only effectively diminishes deleterious gases in the pig-fattening house but also achieves the remote automation of environmental monitoring and hazardous gas management; thereby, it mitigates the likelihood of diseases and minimizes breeding risks.

Food waste anaerobic digestion plants: Underestimated air pollutants and control strategy

Food waste management is an important global issue, and anaerobic digestion (AD) is a sustainable technology for treating food waste and developing a circular economy. Odor and health problems in AD plants have drawn increasing public attention. Therefore, this study investigated the odor characteristics and health risks in different workshops of food waste AD plants. At each site, the treatment capacities for kitchen and restaurant waste were 200 and 200-250 tons per day, respectively. Among the detected odorants, ethanol was the dominant component in terms of concentrations, while methanethiol, propanethiol, H2S, and acetaldehyde were the major odor contributors in different workshops. The odor contribution of propanethiol had been previously overlooked in several workshops. The unloading, pretreatment, and bio-hydrolysis workshops were identified as major areas requiring odor control. Besides odor, carcinogenic and non-carcinogenic risks commonly existed in food waste AD plants. The carcinogenic risk of acetaldehyde had been underestimated previously, and it was identified as the dominant carcinogen. Furthermore, benzene was a potential carcinogen. Non-carcinogenic risks were mainly caused by acetaldehyde, H2S, and ethyl acetate. The health risks were not always consistent with odor nuisance. Based on the odor and health risk assessments, several air pollution control strategies for food waste AD plants were proposed, including food waste source control, in-situ pollution control, and ex-situ pollution control.