The hedonic tone is a suitable evaluation index which can truly reflect the psychological impact of an odor. To find out the relationship between the odor concentration (OC) and hedonic tone (H), dimethyl disulfide, limonene and butyl acetate were presented as typical odorants with different characters. A panel of 16 persons was engaged to rate the hedonic tone of a series sample with various concentrations according to the nine-point scale. The relationship between the hedonic tone and OC was established based on a multivariate logistic regression analysis. The research results demonstrated that the smell of dimethyl disulfide is unpleasant at various concentration levels, and its perceived unpleasantness is increased with OC, and at the critical point (H = −0.5), the odor index of dimethyl disulfide is 0.5 (OC = 3 OUE·m−3). For limonene, its smell is pleasant when the odor index is between 1.4 and 3.3 (OC = 25~1995 OUE·m−3). For butyl acetate, the average results showed an unpleasant character with the corresponding odor index of 1.87 (OC = 74 OUE·m−3). Each odorant has a unique hedonic behavior curve from which the annoyance potential of different odorants can be clearly discriminated, with the order of dimethyl disulfide > butyl acetate > limonene. The regression equations showed a quadratic nonlinear function between the hedonic tone and OC.

Municipal wastewater treatment plants (WWTPs) inside cities have been the major complained sources of odor pollution in China, whereas there is little knowledge about the dose–response relationship to describe the resident complaints caused by odor exposure. This study explored a dose–response relationship between the modelled exposure and the annoyance surveyed by questionnaires. Firstly, the time series of odor concentrations were preliminarily simulated by a dispersion model. Secondly, the perception-related odor exposures were further calculated by combining with the peak to mean factors (constant value 4 (Germany) and 2.3 (Italy)), different time periods of “a whole year”, “summer”, and “nighttime of summer”, and two approaches of odor impact criterion (OIC) (“odor-hour” and “odor concentration”). Thirdly, binomial logistic regression models were used to compare kinds of perception-related odor exposures and odor annoyance by odds ratio, goodness of fit and predictive ability. All perception-related odor exposures were positively associated with odor annoyance. The best goodness of fit was found when using “nighttime of summer” in predicting odor-annoyance responses, which highlights the importance of the time of the day and the time of the year weighting. The best predictive performance for odor perception was determined when the OIC was 4 ou/m3 at the 99th percentile for the odor exposure over time periods of nighttime of summer. The study of dose–response relationship could be useful for the odor management and control of WWTP to maximize the satisfaction of air quality for the residents inside city.

One of the causes of public discomfort and complaint about odour in China is the nuisance odour, generated from the municipal sewage treatment plants. With the ability to be dispersed over a long distance, the odours can affect a large number of people. With the aim of identifying the compounds contributing the most to the overall odour emanating from municipal sewage treatment plant, and developing a prediction model for sensory odour concentration based on the compound odour activity value (OAV), odour samples from 2 days were collected at a municipal sewage treatment plant in Tianjin in the months of October and November 2013. Odour concentrations (OCs) were measured by the triangular odour bag method. Chemical components were quantified by gas chromatography-mass spectrometry. According to the analysis of odour emission characteristics, it was found that hydrogen sulfide and methyl mercaptan were the key odorants responsible for the overall odour. To understand the interrelationship of these two odorants, 10 groups of a binary mixture of hydrogen sulfide and methyl mercaptan, representing different levels of odour concentration and intensity, were prepared in the laboratory. OCs were regressed against OAV using multivariate linear regression. A statistically significant positive correlation was found between single-compound OAV and odour concentration (by both SPSS and Minitab software). Furthermore, the models were validated by field monitoring data, which showed the odour prediction concentration had a good fit to the measured concentration by using Minitab software. Lastly, the Austal 2000 model system was used for the simulation of the odour emission dispersion into the surrounding area. This study provides an effective way to predict the odour emission condition in municipal sewage treatment plant.

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