Weihua Yang1,2, Weifang Li2, Yan Zhang This email address is being protected from spambots. You need JavaScript enabled to view it.1,2, Meng Han2, Zengxiu Zhai1,2, Huanwen Cui1,2 

1 Tianjin Sinodour Environmental Technology Co., Ltd., Tianjin 300191, China
2 State Environmental Protection Key Laboratory of Odor Pollution Control, Tianjin Academy of Eco-environmental Sciences, Tianjin 300191, China

Received: December 24, 2021
Revised: April 2, 2022
Accepted: April 19, 2022

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.4209/aaqr.210314  

  • Download: PDF

Cite this article:

Yang, W., Li, W., Zhang, Y., Han, M., Zhai, Z., Cui, H. (2022). Exposure-response Relationship and Chemical Characteristics of Atmospheric Odor Pollution from a Cigarette Factory. Aerosol Air Qual. Res. 22, 210314. https://doi.org/10.4209/aaqr.210314


  • We conducted a dose effect study on industrial odor for the cigarette production.
  • C0, 1-hour = 4 ou m3 was identified as the acceptable level.
  • Acids, esters contributed the most to the odor in the cigarette factory.
  • The odor wheel of cigarette factory was established for the first time.


Odor pollution is one of the most complained environmental problems in China. At present, the odor control is mainly aimed at municipal facilities. There is a lack of reports on the atmospheric odor pollution produced by some special industrial emissions. This paper studied the odor impact of a cigarette factory on surrounding residents in order to obtain the exposure-response relationship, which can provide a scientific basis for odor control of cigarette production. Exhaust emission measurement, sample collection and laboratory analysis, as well as community survey were carried out. The air dispersion model Aermod was used to simulate the olfactory odor concentrations in surrounding residential areas. Face-to-face questionnaire was used to collect the information of residential responses to the cigarette odor. The exposure-response relationship between the percentage of highly annoyed people (%) and the 98th percentile of odor concentrations was analyzed using the univariate binomial logistic model. In addition, the typical odor pollutants were found by odor activity value. The results showed that the proportion of highly annoyed people in the residential area was ranged from 5% to 64%, and the acceptable level of exposure odor concentration was 4 OU m–3 at the 98th percentile. The main odor substances of the cigarette factory were oxygenated organic substances including esters, fatty acids and ketones. Based on the chemical composition and the odor character of each substance, the odor wheel of cigarette factory was established for the first time.

Keywords: Odor exposure, Annoyance, Typical odor pollutant, Odor wheel


Odor is one of the environmental stress issues with the biggest cause of complaints for a variety of industries (Marlon et al., 2019; Chaignaud et al., 2014; Rappert and Müller, 2005; Gostelow et al., 2001; Hayes et al., 2014; Henshaw et al., 2006). In order to avoid the negative impact caused by odor pollution, some countries have formulated special laws, regulations and standards. Although ambient air quality has improved significantly over the past few decades, residents' tolerance of odor impacts seems to be reduced (Li et al., 2018; Zhang et al., 2021). Continuous or intermittent odor emitted form industrial sources will lead to nuisance and negative evaluations among the residents living in this environment, which will increase the possibility of community complaints (Perng et al., 2011; Hayes et al., 2017).

Regulations and guidelines on odor control are based on quantification of impacts and criteria for acceptable exposure to odors (Brancher et al., 2017; Blanes-Vidalet al., 2014). The first sector to be regulated on a national level specifically for odor impacts was the intensive livestock sector. In 1971, Netherlands imposed a practical guideline on new and existing livestock operations (Harreveld and Klarenbeek, 1995). In 1984, a quantitative air quality guideline for odors from industrial sources was introduced. Taking into account the differences in offensiveness of various odors (e.g., the smell of bread and sewage), a more flexible National Emission Guideline was imposed in 2000, which set different criteria for different odor sources (Infomil, 2000). The approach in the Netherlands was typical for a trend in other Northern European countries, such as Germany and Denmark. In the UK H4 guidance, an indication of possible criteria was provided, which range from C98.0, 1-hour = 1.5 OUE m–3 for more offensive odors to C98.0, 1-hour = 6 OUE m–3 for odors with a low annoyance potential (Environment Agency, 2011).

Learned from the research in these countries, the formulation of environmental odor standards requires impact assessment research to master the exposure-response relationship between environmental exposure and residential annoyance, so as to determine a reasonable acceptable level of odor concentration. Dispersion models are widely used to quantify ambient odor concentrations exposed near odor sources (Pandey and Sharan, 2018; Schauberger et al., 2012; Sironi et al., 2010; Dresser and Huizer, 2011). In this work, the odor release was calculated by steady-state plume model—Aermod model, which can predict and evaluate the concentration distribution, risk range, and duration of pollutants. Aermod model has been widely used to quantify the environmental odor concentration near odor sources such as industrial enterprises, livestock, wastewater treatment plants and other sources (Jeong, 2011; Latos et al., 2011; Huang and Guo, 2019; Perry et al., 2005). Meanwhile, the assessment of odor annoyance was done by means of direct interviews before the odor measurements. An area was determined around source where odor exposure was expected to vary from maximal to hardly detectable.

In addition, to accurately represent and mitigate potential odor impacts, key odorants emitted throughout processing need to be identified (Kamarulzaman et al., 2018). There is a wide range of odor sources, odor substances and their characteristics emitted from different sources vary greatly. The compositions of odor are complex with distinct odorous species, common types of odor substances include sulfur-containing compounds, nitrogen-containing compounds, aromatic hydrocarbons, aldehydes, ketones and volatile fatty acids, etc. (Ubeda et al., 2014). Studying odor characteristics is an important method to identify the typical odor pollutants.

The tobacco industry occupies an important position in the national economy in China. With the development of the tobacco industry, the off-flavor gases generated during the cigarette processing will have a certain impact on the living atmospheric environment of the surrounding residents. At present, few studies have been conducted on the compositional characteristics of odous from cigarette factories and its effects on surrounding sensitive localities. In this work, a representative cigarette factory was selected, through measurement of odor emissions, followed by dispersion modeling with the Aermod model, the time series of exposure odor concentration in the surrounding residential areas were obtained. Community questionnaire was surveyed to obtain odor annoyance in eleven sub-areas around the cigarette factory. A logic model was used to study the expose-response relationship to derive a certain acceptable concentration level. The chemical composition of cigarette odors were further to identify the key odor-causing substances.


2.1 Introduction of the Cigarette Factory

The cigarette factory investigated in this work was located in northern China, it covered an area of about 0.19 km2, including tobacco drying workshop (TDW) and tobacco blending workshop (TBW). The odor generated from the workshop was discharged through the emission stack (ES) after treatment. The sensitive areas like school, hospital, residential area around cigarette factory often occurred odor complaints.

2.2 Sample Collection

Odor sampling was conducted in July, 2017. The selection period was based on the fact that the number of odor complaints in summer was much higher than that in other seasons (according to the complaint data from the Local Ecology and Environment Bureau). Sampling sites were set at the discharge outlet of the production facility in two workshops, while set at the center of the emission stack pipe in the two emission stacks.

The samples were collected according to ‘‘Emission from stationary sources-Sampling of volatile organic compounds-Bags method (MEE, 2014)’’ and ‘‘Technical specification for environmental monitoring of odor (MEE, 2017)’’. A 3 L Tedlar bag was used for constant flow sampling of the exhaust gas, and each sampling time was 3 min. The sampling pipeline was made of low-adsorption materials such as Teflon. A filter head was installed between the pipeline and the Tedlar bag to filter impurities such as particulate matter and water. All samples were collected while the production facility was operating normally. Three parallel samples were collected at each point. In total, 12 air samples were gathered for chemical and sensory analyses. All samplings were light-free stored and analyzed within 24 h.

2.3 Experimental Method

2.3.1 Detection of odor compounds

Compounds analyses were performed by using GC-MS 7890A-5975C (Agilent Technology Co., Ltd.). 200 mL air sample was first injected into the pre-concentration system (Entench 7100, USA) and get concentrated by cryogenic liquid nitrogen, then the gas passed through a chromatographic column DB-5MS (60 m × 0.32 mm × 1.0 mm) with the high-purity helium gas (purity greater than 99.999%) as the carrier gas. The mass spectrometer was operated in full scan for qualitative analysis and selected scan for quantitative analysis, in the m z1 range of 15–300. The temperature program had three ranges as following: 35°C–150°C with a step rate of 5°C min1, 150°C–220°C with a step rate of 15°C min1 and then hold for 7 min at 220°C; the temperature of injection port was 100°C; the flow rate of carrier gas was 1.5 mL min1. Compound concentrations were quantified by the internal standard method.

Identifying the main odor-causing substances is the key step to control odor pollution. The main odor-causing substance is not the substance with the highest concentration but with the highest OAV (Wu et al., 2015). The OAV is the mass concentration of the substance divided by the odor threshold of the substance, see Eq. (1), that is, the lower the odor threshold of the substance, the more likely it is contribute to odor. The OAV is a dimensionless factor.


Ci is the concentration of the compound (mg m–3), and OTi refers to odor threshold concentrations, OT used in this study was derived from Nagata (2003) which was determined based on panel’ average threshold through standard procedures. Compounds with an OAV > 0.1 in emissions were identified as potential odorants. This method can be used to initially assess the sensory and olfactory effects of the odorants.

2.3.2 Determination of odour concentration

Odor concentration was measured according to the Air Quality-Determination of Odor-Triangle Odor Bag Method (MEE, 1993a, GB/T 14675-93). The odor concentration of air sample was determined by a sniffer group which was consist of at least 6 well-trained sniffing panelists. During the determination, two of the three 3L odorless bags were filled with neutral gas, the third bag was filled with neutral gas and sample gas at a certain dilution ratio. The panel needed to sniff the further diluted sample gas after correctly identifying the odorous bag. When the odor concentration of the diluted sample was lower than the sniffer’ olfactory threshold, the test for this panel was considered finished. Odor concentration was calculated based on the olfactory threshold. Each sample was measured twice in parallel. For parallel samples, select the maximum odor concentration as the final result.

2.4 Odor Exposure

The Aermod dispersion model was used to estimate the odor concentration at the respondents' home address. Aermod is the preferred air quality model by the U.S. Environmental Protection Agency (U.S. EPA) to demonstrate regulatory compliance in the near field (< 50 km). The model is essentially a steady-state Gaussian plume model and combines the Monin-Obukhov similarity theory to describe atmospheric stability in a continuous manner. The basic equation for predicting downwind odour concentrations is expressed in Eq. (2):


where C is the downwind odor concentration, OU m–3; Q is the odour emission rate of point source, OU s1; σy, σz is the Pasquill-Gifford plume spread parameters based on stability class; u is the average wind speed at pollutant release height, m s1; H is the effective height above ground of emission source, m; V is the vertical term used to describe vertical distribution of the plume; x is the upwind direction, m; and y is the cross wind direction, m.

The product of the odor concentration and exhaust volume per minute of emission stack was termed in the odor emission rate (OER). OER is one of the essential input parameters in air dispersion model. The value of OER directly determines the atmospheric impact of odor source on the surrounding environment. The OER calculation method for point source pollution is shown in Eq. (3).


where Q is the odor emission rate of point source, OU s1; OC is the odor concentration, OU m–3; V is the flow rate measured by the gas flow meter, m3 s1.

The meteorological data used for the study area was consisted of two parts, surface meteorological data and upper-air meteorological data. Surface meteorological data was collected from the study area using ground-based weather stations for 2017. The datasets included observations of atmospheric pressure, air temperature, wind direction, wind speed, relative humidity, total cloud cover and low cloud cover. The weather station was about 10 kilometers from the cigarette factory, which could better reflect the atmospheric meteorological parameters of pollution source. In 2017, the dominant wind direction was southwest, the annual average wind speed was 1.55 m s1, and the static wind frequency (wind speed less than 0.5 m s1) was 1.44%, about 126 hours. The frequency between 0.5 m s1 and 2 m s1 was the highest (71.1%). Upper-air meteorological data was generated by WRF (Weather Research and Fore-casting) model with 1 km resolution relevant to the studied area.

2.5 Questionnaire Data Collection

Questionnaire survey was conducted in eleven residential areas (Fig. 1), according to the annual dominant wind direction of the pollution source and the distribution of odor complaints in surrounding sensitive areas. About 50–80 households were investigated in each residential area based on the population distribution of the area. Face-to-face survey was mainly carried out with fully trained interviewers. The interviewees were residents over the age of 18 who had lived at least 3 months.

Fig. 1. The eleven regions of study around the cigarette factory.
Fig. 1. The eleven regions of study around the cigarette factory.

Questionnaire survey was mainly composed of two parts, based on Likert-scales. The first part mainly investigated the general social and demographic data, such as the age, gender, address and residence years of the respondents, while the second part mainly involved environmental stress factors, including satisfaction of living environment and pollution sources (i.e., noise, cooking fume, waste, sewage or others). There were several alternative answers at the bottom of each question for respondents to choose. Regarding the impact of the cigarette smell on the surrounding residents, the five most important questions were shown in Table 1. Data processing was mainly completed using SPSS 21.0, Visio 2007 and Excel 2016. Arithmetic means and 95%-confidence intervals were calculated to describe respondents’ odor annoyance.

Table 1. The most important questions from the questionnaire.

 2.6 Exposure-response Relationship Analysis

Binomial logistic regression analyses had been used to estimate the association between the odor exposure and the odor annoyance from questionnaires. For binomial models, the outcome variables of odor annoyance degrees were dichotomized into two levels (the outcome “score = 0” represents the following response categories “not annoyed”, “slightly annoyed”; and the outcome “score = 1” represents the following response categories “moderately annoyed”, “very annoyed” and “extremely annoyed”). The odor exposure data had been transformed into natural logarithms values, basing on the logarithmic fit was found to be closer than a linear fit in previous studies (Sucker et al., 2008).


3.1 Odor Impact Assessment

Odor problems are generally, caused by many kinds of malodorous substances at low concentration, so it is almost impossible to analyse the whole components of the odor completely by any instrumental method, not only qualitatively but quantitatively. In addition, odor pollution also has the characteristics of subjectivity and complexthere. Therefore, the odor concentration value obtained by sensory test method is used to reflect the impact of odor pollution generated by enterprise on human senses. The maximum odor concentration in TDW was 1318, whlie the maximum odor concentration in both ES and TBW was 5495 (dimensionless) which exceeds the current odor emission standard in China (MEE, 1993b, GB 14554-93). The odor concentration cannot be reduced to an acceptable range through atmospheric diffusion, and it still had a sensory impact on the surrounding residents after landing.

The result of odor diffusion simulation was shown in Fig. 2. The odor concentrations (C98) was ranged from 1 to 45 OU m–3, based on 98th percentile hourly averages of the odor concentration. Odor concentration was greater than 10 OU m–3 within approximately 1300 m from the east, northeast, north, and northwest sides of the cigarette factory. There was no residential area on the east side of 1 km from the cigarette factory, the impact on the east side can be temporarily ignored.

Fig. 2. The result of the odor dispersion simulation using the Aermod model system.
Fig. 2. The result of the odor dispersion simulation using the Aermod model system.

3.2 Socio-demographic Characteristics of Respondents

In this survey, there were 199 valid questionnaires. The age group and gender characteristics of the respondents are shown in Table 2. The number of men and women respondents was basically the same. The age of respondents were mainly more than 45 years old, about 61.8%, which was mainly because most of them were retiring at home during the daytime of the survey.

Table 2. Age and gender distribution of respondents.

Odor annoyance levels were reported by 199 respondents from surrounding residential areas. To the question “How do you rate that odor with respect to annoyance?”, the answers should be selected from the descriptive rating scale (no annoyance, very little annoyance, moderately annoyance, very annoyance, extremely annoyance). The percentage of highly annoyed people (HA%) in this study was defined as the proportion of people who chose the answer “moderately annoyance”, “very annoyance” and “extremely annoyance” to the total number of people.

Comprehensively analyzed the subjective annoyance of respondents on cigarette odor, the HA% in 11 sub-areas are shown in Fig. 3.

Fig. 3. The HA% of respondents disturbed in 11 sub-areas.
Fig. 3. The HA% of respondents disturbed in 11 sub-areas.

It can be seen that in 11 sub-areas, HA% varied from 5% to 64%. The 7# residential area was relatively close to the cigarette factory, with HA% reaching 64%. The respondents said that they often smelled the odor from the cigarette factory, sometimes odor intensity was very strong. The distance from 6# and 4# residential area to the factory was less than 500 m, and the residents were more troubled by the cigarette smell affected by the local predominant wind direction. On the whole, as the distance increases, the level of HA% residents gradually decrease. The odor emitted from the cigarette factory had a significant impact on the surrounding residents. Long-term household odor exposure had a positive and significant association with increased frequency of reported odor annoyance in residential communities, which was consistent with other researches (Boers et al., 2015; Lowman et al., 2013; Brancher et al., 2019).

3.3 Exposure-response Relationship

The univariate binomial logistic model was used to characterize the exposure-response relationship between HA% and the 98th percentile of the odor concentrations, and the probability density function was shown in Eq. (4). The exposure-response association can be graphically represented by an S-shaped curve as illustrated in Fig. 4.


where p is the probability of odor annoyance, 0%‒100%; C98 is the 98th percentile of the odor concentrations, calculated by Aermod model throughout the whole year.

Fig. 4. Exposure-response univariate binomial logistic model between the 98th percentile of the odor concentrations and probability of odor annoyance.Fig. 4. Exposure-response univariate binomial logistic model between the 98th percentile of the odor concentrations and probability of odor annoyance.

The curve showed that an individual at the residence exposed to the probability of being moderately annoyed, very annoyed or extremely annoyed from the cigarette factory. As the exposure concentration increases, the proportion of the probability annoyed residents significantly increased. In order to limit the percentage of people who experienced some form of odor-induced annoyance to 10% or less (Invernizzi, 2020), the acceptable level of odor concentration was 4 OU m–3 at the 98th percentile of the odor exposure from cigarette factory calculated by exposure-response relationship curve. This value can be used as the acceptable level of odor concentration for this cigarette factory. It has important reference significance for related departments to set a reasonable odor protection distance and determine the degree of annoyance of residents by odor.

However, there were also uncertainties in this research. First, the questionnaire data we used to investigate the odor annoyance of surrounding residents would have deviation. Some residents thought that the surrounding company would have a bad effect on their health, they hoped these surrounding company to be relocated or shut down. When answering questionnaire, some people exaggerated the degree of annoyance from factory odor, which would result in a higher proportion of the annoyed population.

The second problem was that the odor exposure model had uncertainties: (1) The Gaussian equations simulated atmospheric physics had inherently uncertainties. (2) The assumption by AERMOD that meteorological data from the weather station which was about 10 kilometers from the cigarette factory were representative of the wind fields throughout the entire modeling grid was a source of uncertainty in the representative of the model input data. (3) The input data of OER were derived from the sampling data in the July, which had a certain degree of uncertainty.

3.4 Chemical Characterization of the Cigarette Factory

Chemical composition analysis was performed on each sample collected. According to the analysis results of the instrument, a total of 39 odor compounds were detected with concentrations ranging from 1.9 to 78147.2 µg m–3, mainly including aromatic compounds such as benzene, toluene, xylene and ethylbenzene, halogen compounds such as dichloromethane and tetrachloroethylene, hydrocarbons such as propane and butane, oxygenated compounds such as ethanol, acetone and butanone, organic sulfides such as carbon disulphide and terpenes such as limonene. The average concentrations of compounds detected in sampling units were shown in Table 3.

Table 3. Concentration of the odor compounds in each unit (µg m–3).

Oxygenated compounds had the greatest concentration proportion of the total VOCs in each unit (more than 95%). Ethanol accounted for 33.4% of the total concentration of oxygenated compounds in ES, followed by valeric acid (23.6%) and isovaleric acid (18.3%). Oxygenated compounds were the dominant oxygenated components in TDW and TBW where the ethanol reached 78147.2 µg m–3 and 50710.0 ± 178.3 µg m–3, respectively.

By calculating the OAV of the detected compounds, as shown in Table 4, the typical odor pollutants were ethyl acetate, styrene, pyridine and ethanol in TBW, propyl acetate, 2-butanone. valeric acid, isovaleric acid and ethanol in TDW, and valeric acid, isovaleric acid, butyric acid, isobutyric acid, propionic acid and ethanol in ES. Butyric acid, isobutyric acid and propionic acid can cause a strong sour odor even at low concentration, due to their low odor threshold. Alcohols have a certain influence on the aroma and taste of cigarettes. The detected concentration of ethanol was relatively higher due to it is often used as a solvent for cigarette flavors (Perestrelo et al., 2012). Ester mainly affect the aroma and taste of cigarette. Among the detected compounds, Ethyl acetate and propyl acetate were the main sources of fruity flavour (Forney et al., 2016).

Table 4. the potential odorants with an OAV > 0.1 in each unit.

Pyridine can produce Maillard reaction with other substances (Chansataporn et al., 2018), which will affect the aroma of cigarette smell.

In combination with the characteristics of the typical potential odor pollutants and odor types from the cigarette factory, the odor wheel chart of the cigarette factory was obtained, as shown in Fig. 5. The determination of the hedonic tone refers to the research of Li et al. (2019).

Fig. 5. The odor wheel of cigarette factory.
Fig. 5. The odor wheel of cigarette factory.

The inner circle of the wheel represents the hedonic tone of odor, divided into pleasant, unpleasant and neutral. The outer circle represents the variation of each of the major odor categories, for example, the primary odor in the cigarette factory was sour, wine, fragrant, fruity, medicinal, spicy. The outermost layer of specific chemicals associated with each sub-group represents these sub-categories. The odor wheel is dynamic and evolves according to the specific odor (Suffet and Rosenfeld, 2007). The odor wheel diagram is an effective way to describe odor problems. It links sensory information with possible odor substances, which will improve the accuracy of odor identification, and help the factory to select suitable treatment technologies.


In addition to enriching the existing literature on the odor compound emissions from cigarette factory, this research should be the first study aimed at determining an acceptable level of exposure odor concentration for industrial sources. The results of this study indicate a strong relation between modeled odor exposure and odor annoyance from cigarette factory. The exposure-response relationship between the percentage of highly annoyed people (HA%) and the 98 percentile of odour concentrations (C98.0, 1-hour) was established, and C98.0, 1-hour = 4 OU m–3 was identified as the acceptable level for the cigarette production. The typical odor pollutants for the cigarette factory were ethyl acetate, styrene, pyridine, valeric acid, isovaleric acid, butyric acid, isobutyric acid, propionic acid, ethanol, propyl acetate, 2-butanone. Combined the chemical composition and odour character of each substance, it’s the first time to establish the odour wheel of cigarette factory. The odor wheel of cigarette factory can be used to characterize and manage onsite malodorous.


This work was supported by the National Natural Science Foundation of China (NSFC) (No. 21577096) and Science and Technology Program Project of Tianjin, China (No. 20JCZDJC00530).


  1. Blanes-Vidal, V., Bælum, J., Nadimi, E.S., Løfstrøm, P., Christensen, L.P. (2014). Chronic exposure to odorous chemicals in residential areas and effects on human psychosocial health: Dose–response relationships. Sci. Total Environ. 490, 545–554. https://doi.org/10.1016/j.scitotenv.​2014.05.041

  2. Boers, D., Geelen, L., Erbrink, H., Smit, L.A.M., Heederik, D., Hooiveld, M., Yzermans, C.J., Huijbregts M., Wouters, I.M. (2015). The relation between modeled odor exposure from livestock farming and odor annoyance among neighboring residents. Int. Arch. Occup. Environ. Health 89, 521–530. https://doi.org/10.1007/s00420-015-1092-4

  3. Brancher, M., Griffiths, K.D., Franco, D., de Melo Lisboa, H. (2017). A review of odour impact criteria in selected countries around the world. Chemosphere 168, 1531–1570. https://doi.org/​510.1016/j.chemosphere.2016.11.160

  4. Brancher, M., Piringer, M., Franco, D., Filho, P.B., Lisboa, H., Schauberger, G. (2019). Assessing the inter-annual variability of separation distances around odour sources to protect the residents from odour annoyance. J. Environ. Sci. 79, 11–24. https://doi.org/10.1016/j.jes.2018.09.018

  5. Chaignaud, M., Cariou, S., Poette, J., Fages, M., Despres, J.F., Fanlo, J.L. (2014). A new method to evaluate odour annoyance potential. Chem. Eng. Trans. 40, 13–18. https://doi.org/10.3303/​CET1440003

  6. Chansataporn, W., Prathumars, P., Nopharatana, M., Siriwattanayotin, S., Tangduangdee, C. (2018). Kinetics of Maillard reaction in a chicken meat model system using a multiresponses modeling approach. Int. J. Chem. Kinet. 51, 14–27. https://doi.org/10.1002/kin.21224

  7. Dresser, A.L., Huizer, R.D. (2011). CALPUFF and AERMOD model validation study in the near field: Martins creek revisited. J. Air Waste Manage. Assoc. 61, 647–659. https://doi.org/10.3155/​1047-3289.61.6.647

  8. Environment Agency (2011). Additional Guidance for H4 Odor Management: How to Comply with Your Environmental Permit. Environment Agency, Bristol, UK.

  9. Forney, C.F., Jordan, M.A., Cue, K.R. (2016). Identification of aroma-active compounds of whole and macerated “Honeycrisp” and “Ambrosia” apples. Acta Hortic. 1120, 137–142. https://doi.org/10.17660/actahortic.2016.1120.20

  10. Gostelow, P., Parsons, S.A., Stuetz, R.M. (2001). Odour measurements for sewage treatment works. Water Res. 35, 579–597. https://doi.org/10.1016/S0043-1354(00)00313-4

  11. Harreveld, A.Ph.V., Klarenbeek, J.V. (1995). On the regulations, measurement and abatement of odours emanating from livestock housing in the Netherlands, in Proceedings: New knowledge in Livestock Odor, Proceedings of the International Livestock Odor Conference 1995, 16–21.

  12. Hayes, J., Stevenson, R., Stuetz, R. (2017). Survey of the effect of odour impact on communities. J. Environ. Manage. 204, 349–354. https://doi.org/10.1016/j.jenvman.2017.09.016

  13. Hayes, J.E., Stevenson, R.J., Stuetz, R.M. (2014). The impact of malodour on communities: A review of assessment techniques. Sci. Total Environ. 500–501, 395–407. https://doi.org/10.1016/j.​scitotenv.2014.09.003

  14. Henshaw, P., Nicell, J., Sikdar, A. (2006). Parameters for the assessment of odour impacts on communities. Atmos. Environ. 40, 1016–1029. https://doi.org/10.1016/j.atmosenv.2005.11.014

  15. Huang, D., Guo, H. (2019). Dispersion modeling of odour, gases, and respirable dust using AERMOD for poultry and dairy barns in the Canadian Prairies. Sci. Total Environ. 690, 620–628. https://doi.org/10.1016/j.scitotenv.2019.07.010

  16. Infomil (2000). NeR Nederlandse Emissie Richtlijn. (Netherlands Emissions Guideline), ISBN 90 76323 01 1, 2000 (English version available via www.infomil.nl).

  17. Invernizzi, M., Brancher, M., Sironi, S., Capelli, L., Piringer, M., Schauberger, G. (2020). Odour impact assessment by considering short-term ambient concentrations: A multi-model and two-site comparison. Environ. Int. 144, 105990. https://doi.org/10.1016/j.envint.2020.105990

  18. Jeong, S.J. (2011). CALPUFF and AERMOD dispersion models for estimating odor emissions from industrial complex area sources. Asian J. Atmos. Environ. 5, 1–7. https://doi.org/10.5572/ajae.​2011.5.1.001

  19. Kamarulzaman, N.H., Le-Minh, N., Fisher, R.M., Stuetz, R.M. (2018). Quantification of VOCs and the development of odour wheels for rubber processing. Sci. Total Environ. 657, 154–168. https://doi.org/10.1016/j.scitotenv.2018.11.451

  20. Latos, M., Karageorgos, P., Kalogerakis, N., Lazaridis, M. (2011). Dispersion of odorous gaseous compounds emitted from wastewater treatment plants. Water Air Soil Poll. 215, 667–677. https://doi.org/10.1007/s11270-010-0508-8

  21. Li, J.Y, Zou, K.H., Li W.F., Wang, G., Yang, W.H. (2019). Olfactory characterization of typical odorous pollutants Part I: Relationship between the hedonic tone and odor concentration. Atmosphere 10, 524. https://doi.org/524.10.3390/atmos10090524

  22. Li, W.F., Yang, W.H., Li, J.Y. (2018). Characterization and prediction of odours from municipal sewage treatment plant. Water Sci. Technol. 77, 762–769. https://doi.org/10.2166/wst.2018.233

  23. Lowman, A., McDonald, M.A., Wing, S., Muhammad, N. (2013). Land application of treated sewage sludge: Community health and environmental justice. Environ. Health Perspect. 121, 537–542. https://doi.org/10.1289/ehp.1205470

  24. Marlon, B., Martin, P., Davide, F., Paulo, B.F., Henrique, D.M.L., Günther, S. (2019). Assessing the inter-annual variability of separation distances around odour sources to protect the residents from odour annoyance. J. Environ. Sci. 79, 11–24. https://doi.org/10.5572/ajae.2011.5.1.001

  25. Ministry of Ecology and Environment of China (MEE) (1993a). Air Quality-Determination of Odor-Triangle Odor Bag Method (GB 14675-93); Ministry of Ecology and Environment of China: Beijing, China.

  26. Ministry of Ecology and Environment of China (MEE) (1993b). Emission standards for odor pollutants (GB 14554-93). Ministry of Ecology and Environment of China, Beijing, China.

  27. Ministry of Ecology and Environment of China (MEE) (2014). Emission from stationary sources-Sampling of volatile organic compounds-Bags method (HJ 732-2014). Ministry of Ecology and Environment of China, Beijing, China.

  28. Ministry of Ecology and Environment of China (MEE) (2017). Technical specification for environmental monitoring of odor (HJ 905-2017). Ministry of Ecology and Environment of China, Beijing, China.

  29. Nagata, Y. (2003). Measurement of odour threshold by triangle odour bag method. Tokyo: Japan ministry of the environment [Conference presentation] Meeting of the Japan Society of Air Pollution. pp. 118–127.

  30. Pandey, G., Sharan, M. (2016). Performance evaluation of dispersion parameterization schemes in the plume simulation of FFT-07 diffusion experiment. Atmos. Environ. 172, 32–46. https://doi.org/10.1016/j.atmosenv.2017.10.043

  31. Perestrelo, R., Caldeira, M., Câmara, J.S. (2012). Solid phase microextraction as a reliable alternative to conventional extraction techniques to evaluate the pattern of hydrolytically released components in Vitis vinifera L. grapes. Talanta 95, 1–11. https://doi.org/10.1016/j.​talanta.2012.03.005

  32. Perng, C.H., Cheng, I.L., Wang, I.C., Chou, M.S. (2011). Ozonation of odorous compounds in gases emitted from rubber processing industries. Aerosol Air Qual. Res. 11, 51–58. https://doi.org/​10.4209/aaqr.2010.06.0047

  33. Perry, S.G., Cimorelli, A.J., Paine, R.J., Brode, R.W., Weil, J.C., Venkatram, A., Wilson, R.B., Lee, R.F., Peters, W.D. (2005). AERMOD: A dispersion model for industrial source applications. Part II: Model performance against 17 field study databases. J. Appl. Meteorol. 44, 694–708. https://doi.org/10.1175/JAM2228.1

  34. Rappert, S, Müller, R. (2005). Odor compounds in waste gas emissions from agricultural operations and food industries. Waste Manage. 25, 887–907. https://doi.org/10.1016/j.​wasman.2005.07.008

  35. Schauberger, G., Schmitzer, R., Kamp, M., Sowa, A., Koch, R., Eckhof, W., Eichler, F., Grimm, E., Kypke, J., Hartung, E. (2012). Empirical model derived from dispersion calculations to determine separation distances between livestock buildings and residential areas to avoid odour nuisance. Atmos. Environ. 46, 508–515. https://doi.org/10.1016/j.atmosenv.2011.08.025

  36. Sironi, S., Capelli, L., Céntola, P., Del Rosso, R., Pierucci, S. (2010). Odour impact assessment by means of dynamic olfactometry, dispersion modelling and social participation. Atmos. Environ. 44, 354–360. https://doi.org/10.1016/j.atmosenv.2009.10.029

  37. Sucker, K., Both, R., Bischoff, M., Guski, R., Krämer U., Winneke, G. (2008). Odor frequency and odor annoyance Part II: Dose-response associations and their modification by hedonic tone. Int. Arch. Occp. Environ. Health 81, 683. https://doi.org/10.1007/s00420-007-0262-4

  38. Suffet, I.H., Rosenfeld, P. (2007). The anatomy of odour wheels for odours of drinking water, wastewater, compost and the urban environment. Water Sci. Technol. 55, 335. https://doi.org/​10.2166/wst.2007.196

  39. Ubeda, Y., Lopez-Jimenez, P.A., Nicolas, J., Calvet, S. (2014). Strategies to control odours in livestock facilities: A critical review. Span. J. Agric Res. 11, 1004–1015. https://doi.org/10.5424/sjar/​2013114-4180

  40. Wu, C., Liu, J., Yan, L., Chen, H., Shao, H., Meng, T. (2015). Assessment of odor activity value coefficient and odor contribution based on binary interaction effects in waste disposal plant. Atmos. Environ. 103, 231–237. https://doi.org/10.1016/j.atmosenv.2014.12.045

  41. Zhang, Y., Yang, W.H., Schauberger, G., Wang, J.Z., Meng, J. (2021). Determination of dose–response relationship to derive odor impact criteria for a wastewater treatment plant. Atmosphere 12, 371. https://doi.org/10.3390/atmos12030371

Share this article with your colleagues 


Subscribe to our Newsletter 

Aerosol and Air Quality Research has published over 2,000 peer-reviewed articles. Enter your email address to receive latest updates and research articles to your inbox every second week.

79st percentile
Powered by
   SCImago Journal & Country Rank

2023 Impact Factor: 2.5
5-Year Impact Factor: 2.8

Aerosol and Air Quality Research partners with Publons

CLOCKSS system has permission to ingest, preserve, and serve this Archival Unit
CLOCKSS system has permission to ingest, preserve, and serve this Archival Unit

Aerosol and Air Quality Research (AAQR) is an independently-run non-profit journal that promotes submissions of high-quality research and strives to be one of the leading aerosol and air quality open-access journals in the world. We use cookies on this website to personalize content to improve your user experience and analyze our traffic. By using this site you agree to its use of cookies.