Freeman and Cudmore [1
] suggested that five factors determine annoyance due to odors: frequency (F), intensity (I), duration (D), offensiveness (O), and location (L), such as when considering differences between living, rural or industrial areas. Their concept, coined as FIDOL, was later endorsed and embraced by others [2
]. But usually odors are assessed in the living area. As such, the fifth aspect, location, can be considered as a constant. Different characteristics of the living area may play a role, but are hard to quantify. We have shown that administrative zoning rules in the Austrian context are not sensitive enough to serve as predictors for complaint rates and thus of annoyance [5
The first three factors are all quantitative measures of exposure and therefore likely to be highly correlated with each other: If the average odor concentration at a given place is high, then the odor threshold is also likely to be exceeded more frequently (F), the peak concentrations will be higher (I), and the exceedances will last longer (D). Therefore, both in the regulatory setting and in field research, it is usually neither possible nor necessary to include all three factors in one model.
In this paper, the focus is on offensiveness. We will compare findings and rules regarding the offensiveness of three different odors (animal husbandry of pigs, cattle, and poultry) that are widespread and therefore have been studied repeatedly and are covered by rules and regulations. We will compare our own findings regarding complaint rates [5
] with other studies from Germany [6
] and the Netherlands [9
] regarding percentage of highly annoyed persons. We will further compare the results with weighting factors applied in German and Austrian regulations for environmental odors [11
Guidelines in Austria and Germany differ from other European guidelines since they use percentages of odor hours (F) instead of other measures such as percentiles of concentration (I). For Austria, no comprehensive common guideline exists yet, although a new guideline was recently issued for the regions of Styria and Salzburg [12
]. Hence, Austrians often refer to the German Guideline on Odour in Ambient Air (GOAA) [11
], where—depending on the location (residential, industrial)—between 10% and 15% of odor hours are defined as thresholds of acceptability. Accounting for different annoyance potency, GOAA uses weighting factors for the three types of animals considered in this study.
Here we will examine more closely the offensiveness of these three types of odors as models for other types of odor as well: Knowing from field studies that the offensiveness differs by animal type, we will try to quantify that difference and examine if this difference can also be captured by panel ratings. If this is the case, then it might aid in improving the assessment of offensiveness of other odors that are either new, little known, or not studied yet. More specifically, we will compare our findings on relative offensiveness of the three odors to the ratings of qualified panelists following the polarity-profile approach [14
Rating impressions on a polarity profile roots back to the method of the semantic differential developed in the 1950s [16
]. The application in the context of odors has been tried for numerous purposes, e.g., for general scientific research [17
] or for consumer research [18
]. But the polarity profile method has also been used successfully in the context of annoyance [15
]. As opposed to a rating scheme based simply on the pleasantness vs. unpleasantness of odors, three basic dimensions prevail in the semantic room: Evaluation (pleasantness), potency (strength), and activity (arousal). The polar adjectives used in the semantic differential can be either pure or more oblique in representing one or several of the basic dimensions [17
], but either way a polarity profile provides a more detailed definition than “just” the hedonic quality. The use of polarity profiles for odor assessment in ambient air is regulated in a German VDI (Verein Deutscher Ingenieure) guideline [19
]: Trained panel members assess the hedonic quality of odors based on 29 polar adjectives (e.g., “harmonious-disharmonious”) using a seven-point rating scale. The method, which focuses on the subjective rating of assessors, can be seen overall as valid [14
], even though the reliability is higher if the assessment does not happen on a seven-point rating scale—as it is used in the VDI guideline—but a three-point scale instead. In the case of a three-point scale, assessors determine whether an odor is harmonious or disharmonious (or none of it), but there is no further quantification. That takes different rating tendencies of individuals into account, with some tending more to extremes than others. With this restricted scale, it is only possible to classify odors as either stench or smell but a more detailed sub-division of annoying odors into different categories is not possible. Regarding the sample size, it has been shown that eight assessors are sufficient to gain statistically stable profiles [14
Of course, not all ratings regarding annoyance potency are based on polarity profiles: A new method suggests evaluating acceptability on a single 11-categorical scale (from very unpleasant to very pleasant) for several dilution levels and extrapolating the acceptability for dilution levels from −∞ to +∞. The actual annoyance potential could then be calculated by multiplying acceptability with odor concentration [20
]. The idea of considering concentration seems plausible, but not much research exists yet evaluating the latter approach. Including duration and/or frequency of odor concentration levels would be quite challenging in this approach, making it hardly applicable in a current regulatory setting.
Hence, the present research aims to compare annoyance with complaint rates and to evaluate whether panelist ratings using a polarity profile can predict differences in offensiveness as observed in field studies.
Overall, our model including frequency and type of odors shows a better model fit than models just considering odor frequency or models based on the 98th percentile alone. Complaint rates in our data are comparable in magnitude to percentage of annoyed residents from the German study [6
] and the Dutch study [10
]. Our results are not completely comparable with the other studies, as those also considered personal health data as possible confounders. Personal health data of complainants were not available from our data set and would not be available in the administrative planning process of a new industrial plant or of a new stable. The Dutch study reported effect estimates for different models with different confounders. Effect estimates of odor intensity were not strongly influenced by the choice of confounders. Also, the German study demonstrated that most of the confounders considered did only have a small impact on annoyance and thus likely did not act as strong confounders. The Dutch and the German study did differ in their definition of annoyance and are therefore not fully comparable either. Generally, explained variance (R2
values) are modest in our models with exception for the extremely annoying odor of bowel cleanse (Table 3
), but the underlying sample for the latter is very small. In particularly regarding swine farms, the explanatory power is low if the site is not considered (Table 4
). However in general, the model fit for complaints is similar to that for annoyance in German and Dutch studies.
Generally, complaint rates for a given odor frequency were lower than in our previous study [5
]. This is mostly due to the fact that in the new study we applied more realistic default emission rates. But it also reflects the fact that in our first study, only sites were included that triggered complaints, and complaints were probably overrepresented. In many parts of Austria, dispersion modeling is not mandatory in the planning process and it will only be performed when complaints are raised. Using administrative data to assess the association between odor frequency and complaint rates could in that case lead to somewhat biased results. In Styria for some time now dispersion modeling is mandatory in the planning phase and it is also a necessary requirement under the new Styrian guideline [12
Needless to say, in authorization processes of farms and industrial plants, not only annoyance but even more strongly adverse health effects to neighbors must be prevented. Thus, if an odorous substance also has toxic, then properties emission control should be based on a toxicological risk assessment, not primarily based on an odor assessment. Toxic and odorous emissions do not necessarily go hand-in-hand: For example, an Italian study examining anaerobic digestions of cattle slurry showed that while digested slurry reduced odor emissions, ammonia emissions increased [32
]. Hence, an assessment of both toxic and odorous emissions is necessary.
In our data, the individual site contributed substantially to the explanatory power of the regression model. A reason for this could be that calculated default emission values will not always be equally correct for every individual farm and when a farm’s emissions are over- or underestimated, all surrounding neighbors are affected by that. We strongly recommend setting conservatively high default values and inviting the applicant to introduce odor reduction measures like specific feeding practices or waste removal schemes or filter techniques. New developments such as chemical scrubbers [33
] for low-cost removals for NOX
and formaldehyde enlarge the pool of odor reduction techniques, and methods like the combination of non-thermal plasma adsorbers with mineral adsorbers show good removal rates for volatile organic compounds [34
], but are currently not broadly used in Austrian stables. After providing evidence of the effectiveness of each measure (e.g., guarantee by the filter producer or third party gas analysis), emission factors could be adapted. That would act as an incentive to the introduction of such measures. But differences in complaint rates between stables are certainly also due to the social competence of the farmer, since personal relationships with farm owners could contribute to complaint rates from neighbors. But this factor cannot easily be assessed in a scientific study and also cannot be considered in the authorization process of a stable or any other source of odors.
Nevertheless, it can be shown that—in case complaint data are available—this data can be well-used for odor assessments in a regulatory setting. However, since types of odors differ greatly in causing complaints, this approach is applicable for odor sources where data already exists, but cannot be applied for new odor sources alone. The German guideline [10
] does generally not differentiate between types of odors. Limit values for odor frequency are principally based on zoning plan categories with a limit value of 10% for housing areas. The German guideline only introduced certain weighting factors in the case of animal odors (1.5 for poultry, 0.75 for swine and 0.5 for cattle). When applying these weighting factors, the German guideline would permit about 17% for highly annoyed, 46% for annoyed (estimates based on Sucker et al. [6
]), and for 24% complaining (own data) about poultry, as well as about 12%, 37%, and 17% for swine and about 5%, 20%, and (estimated) 0% for cattle.
In comparison, the Austrian guideline [12
] defines four categories of odors based on their offensiveness and permits an odor frequency of 2%, 10%, 15%, and 40% depending on the category. For each category only examples are provided. For the 40% category, cattle and horses are mentioned, for the 15% category swine and for the 10% category poultry. This would translate into about 23% highly annoyed and 33% complaints for poultry, 13% and 19% for swine, and about 6% and (estimated) 0% for cattle.
To put these figures into perspective, it should be noted that for noise as another relevant environmental stressor, WHO Europe [35
] suggests a permissible threshold of 10% highly annoyed people.
Regarding new odor sources, the method of polarity profiles seemed promising at first, but data do not support a very promising outcome. In the given form, the usability of the polarity profiles for rating new odors seems doubtful: First, regarding their connection to annoyance potency, polarity profiles for swine and cattle look nearly identical in our data (Figure 2
), which differs from a German study [6
] where cattle odors have also been reported to be less unpleasant than swine [6
]. Also in our data—contrary to swine odors—no complaints about cattle odors were filed. A second flaw lies within the reliability of the method: The agreement in rating a representative stench or smell is sufficient and comparable with a German study [14
], so the method seems to be valid overall when it comes to representative odors. Nevertheless, the agreement is insufficient when it comes to specific odors, with agreement for cattle (κ = 0.08) and swine (κ = 0.05) being basically non-existent on a 7-point-scale. Hence, on a seven-point rating scale, assessor agreements for specific odors do not seem sufficient to serve as improvement for existing guidelines. According with German results, a three-point-scale seems rather advisable (with κ = 0.42 for cattle and κ = 0.24 for swine), but would also need some improvement and generally contains the disadvantage that further sub-characterization disappears.
As an improvement for the polarity profiles, we suggest to also consider odor concentration. A French study [21
] clearly showed the importance of odor concentration for annoyance potency, but so far the concentration has not been considered in the VDI 3940-4 guideline [19
]. Also German studies [36
] showed that intensity has an influence on the hedonic tone. Therefore, it seems possible that different concentrations could be a cause for the questionable validity and reliability of assessing specific odors. In our present data, the single polar of pleasantness did not discriminate between cattle and swine at all, so it is assumed that considering several polars might rather be helpful. But it should still be tested whether removing some non-discriminating polars might improve the model. Generally, the method of the polarity profile seems to need several adaptions before it can be applied for assessing the annoyance potency of new odor sources.
We have shown for some typical odors from animal husbandry that the percentage of complaining households and of (highly) annoyed persons are fairly comparable in magnitude and in statistical model performance. We have also shown that odor intensity which is used as an exposure metric in many countries and the odor units at the 98th percentile as used in the Netherlands in particular are comparable in predictive power to odor frequency as it is used in Austria and Germany. We have further demonstrated that polarity profiles as proposed by the VDI guideline [19
] can distinguish between pleasant and unpleasant odors, but in their current form of application cannot discern between different grades of unpleasantness. The latter would be important to assess acceptable exposure for less well investigated odors. Since complaint rates are available from administrative registers, it should be easier to use these data than to generate annoyance data through new surveys. We propose utilizing this wealth of data, especially for less studied types of odor.
For regulatory purposes during planning processes, annoyance due to odors can be sufficiently well predicted based on odor frequency or intensity and type of odor. Complaint rates are often available in the administrative context and should be used to inform future policy. Complaint rates are a valid surrogate of annoyance and can therefore help defining relative offensiveness of still understudied odors.
An additional consideration of hedonic tone, assessed via polarity profiles, does not seem to be able to contribute in the present form due to reliability issues. Hence, it is suggested to investigate more thoroughly the influence of odor concentration also on the hedonic tone and on the performance of the polarity profile approach.