A critical review on odor measurement and prediction

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.

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.