In this study, we analyzed the changes in concentrations of volatile fatty acids (VFA), phenols, and indoles, as well as odor contribution in pig slurry. The pig slurry was stored for approximately two months after the manure excretion of pigs which had been fed 3% level of peat moss additive. The investigation was carried out through lab-scale experiments simulating slurry pit conditions within pig house. Throughout the storage period, the concentration of VFA exhibited a tendency to be 11%-32% higher in the pig manure treated with peat moss as compared to the control group. From a concentration perspective, phenol and acetic acid accounted for the majority of the total odor compounds produced during the pig slurry storage period. However, their significance diminished when the concentration of odoros compounds are converted into odor activity value and odor contribution. Despite the odor reduction effect of the ammonia (NH3) adsorption by peat moss, if it cannot effectively reduce the high odor-contributing compounds such as indoles and p-cresol, the sole use of peat moss may not be considered an effective means of mitigating odors produced by pig slurry. According to this study, indoles, p-cresol, skatole, and valeric acid were consistently revealed as major odor-contributing substances during the two-month storage of pig slurry. Therefore, a comprehensive odor mitigation methodology should be proposed, taking into consideration the odor generation characteristics (including temporal concentration and odor contribution) of pig slurry-derived odors during storage.

Odor is a type of sensory pollution that can stimulate the human sense of smell when it occurs, causing discomfort and making it difficult to create a pleasant environment. For this reason, there is a high possibility of complaints regarding odors if odors occur in pigsties near residential properties, and the number of such complaints is also increasing. In addition, odors emanating from pigsties around military installations can cause physical and psychological harm, not only to the soldiers living in these type of facilities but also to the families belonging to military personnel living there as well. Because the concentration of odors varies due to diverse factors such as temperature, humidity, wind direction, wind speed, and interaction between causative materials, predicting odors based on only one factor is not proper or appropriate. Therefore, in this work, we sought to construct models that are based on several regression techniques of machine learning using data collected in field. And we selected and utilized the model that has the highest-accuracy in order to notify and warn residents of odors in advance. In this work, 3672 data items were used to train and test the model. The several machine learning algorithms to build the models are polynomial regression, ridge regression, K-nearest neighbor regression (KNN Regression), and random forest. Comparing the performance of models based on each algorithm, the study found that KNN Regression was the most suitable model, and the result obtained from KNN regression was significant.

The effect of the change in air inflow velocity has been investigated at the opening of the malodor emission source to determine its influence on the Complex odor concentration. Both the Complex odor collection efficiency and concentrations were measured according to the change in airflow velocity. When the air inflow velocity was 0.1 m/s, it was observed that some of the generated gas streams were diffused to the outside due to low collection efficiency. In contrast, only the increased gas collection volume up to 0.5 m/s showed no substantial reduction of the Complex odor concentration, which indicates an increase in the size of the local exhaust system as well as the operation cost for the Complex odor control device. When the air inflow velocity reached 0.3 m/s, the Complex odor concentrations not only were the lowest, but the odorous gas could also be collected efficiently. The air inflow velocity at the opening of the malodor emission source was considered the key factor in determining the gas collection volume. Therefore, based on the results of this study, an optimal air inflow velocity might be suggestive to be 0.3 m/s.

This study was conducted to investigate the influence of moisture removal using a moisture condensation tube on the odor concentration, when sampling a malodorous substance from an odor discharge facility’s emission sites. For high-temperature and humid gas streams, the odor concentration was decreased through the use of a moisture condensation tube. The multiple odor concentration of the high-temperature and humid gas streams emitted from boiler-burning equipment decreased from a 3,000 to a 1,221 dilution factor when using one or two moisture condensation tube. This multiple odor concentration was further decreased to a 1,000 dilution factor by using two moisture condensation tubes and glass beads, and also was decreased to a 374 dilution factor by using two moisture condensation tubes and silicagel. Among the designated offensive odorous substances, ammonia, trimethylamine and acetaldehyde that have high solubility in water showed high reduction rate of their concentration. Compared to the result using a sampling tube only, the concentration was decreased by 94.8% ~ 97.7% for ammonia, by 87.5% ~ 95.9% for trimethylamine and by 100% for acetaldehyde. The findings of this study indicate that sampling using a moisture condensation tube affects the concentration of multiple odors. Therefore, it is considered that using a sampling tube only for emissions sampling enhances analytical accuracy and precision rather than using moisture condensation tube with sampling tube, even for the emissions containing moisture.

This study aims to evaluate the relationship between concentration and odor intensity using the odor sensory method for 4 types of fatty acid compounds and i-butyl alcohol. For the measurement, 18 panelists were selected based on several criteria through a panel test. Panelists chosen for their closely similar sensitivities provided more reproducible values. The estimation showed that the correlation of the concentration with odor intensity for the 5 compounds, including the fatty acid compounds and i-butyl alcohol can be reasonably expressed by the Weber-Fechner equation. Notably the standards regulation fatty acid concentrations are very strict, and the butyl acetate standards are very loose. It is suggested than the results of this study can be used as basic data for research on measures to improve the regulation standards on complex odor concentrations on site boundaries in operation, as well as the correlation between concentration and odor intensity for the designated foul odor substances, and their characteristics.

This study was carried out to estimate odors emitted from toilets in Mugunghwa trains. Two parameters, odor intensity and odor concentration, were used for odor estimation. A direct olfactory method to observe odor intensity for 67 toilets and an air dilution olfactory method to estimate odor concentration for 6 samples were performed. Results showed that odor concentrations in toilets in trains ranged from 6 OU/m3 to 30 OU/m3, and odor intensity in 67 toilets varied from 0 degree to 3.3 degree. The results suggest that odors emitted from existing toilets should be controlled because the odor intensity in 16 toilets reached a level that resulted in complaints about the odor.

우리나라에서 악취 규제는 배출원의 부지경계선과 배출구에서 농도 규제치를 사용한다. 그러므로 수용체에서 정확한 체감악취의 유무를 쉽게 판단하기는 곤란하다. 비록 개개인의 악취에 대한 응답을 결정하는 변수는 다양하고 응답 종류도 광범위하게 나타나지만, 일반적으로 악취에 대한 규제는 다양한 변수를 고려하여 구성되며, 이들 변수에는 악취 발생빈도, 강도, 기간, 불쾌도, 지역 변수 등이 있다. 본 연구에서는 매사추세츠(미국), 뉴질랜드, 덴마크, 네덜란드, 호주 서부, 타이완 등 6개 지역의 악취 규제를 사용하여 소각장 주변에서 악취 영향 거리를 비교하였다. 악취 영향 거리를 평가하기 위하여 이들 6개 지역 규제는 악취 농도와 허용 빈도를 고려하고 있다. 연구 결과에 의하면 악취에 의한 영향 거리는 0.5~1.4 km의 범위로 나타났다. 악취 농도를 고정하고 허용 빈도를 변경하는 경우 허용 빈도가 높을수록 영향 거리는 크게 나타났다. 허용 빈도를 고정하고 농도를 변화시키는 경우 농도가 높으면 영향거리가 줄어들었다. 결론적으로 악취 영향 범위는 악취 농도뿐 아니라 허용 빈도에 따라 변화하였다.

There are many pollutants emitted into the air. Some of these pollutants have a malodor. Unlike other pollutants, people are able to detect and feel discomfort when this type of pollutant becomes high peak concentration instantaneously. In this sense, the peak concentration has an important meaning in the odor management and modeling. In previous odor modeling, the peak concentration was calculated by correcting the one-hour average concentration using the correlation equation. This study was carried out to find appropriate method to predict the peak concentration using meteorological input data of high time resolution in the odor modeling. It show that the peak concentration could be directly calculated from the dispersion modeling without using the correction equation when fine time scales such as 1 min or less time intervals are used as the meteorological input.

This study aims to evaluate the relationship with the concentration and odor intensity using the odor sensory method for 4 types of sulfur compounds, ammonia, and trimethylamine. For the measurement, 13 panelists were selected by several criteria through a panel test. Panelists chosen for their closely similar sensitivities provide more reproducible values. The estimation showed that the correlation of the concentration with odor intensity for the 6 compounds can be reasonably expressed by the equation I=Aㆍlog C+B (I: Odor Intensity, C: material concentration, A: material constant, B: constant). The result of this study is suggested to be used as a base data for research on measures to improve the regulation standards for complex odor concentration on site boundary in operation, as well as a correlation between the concentration and odor intensity for the designated foul odor substances, and their characteristics.

Odor from sewage treatment plants have the potential to cause significant annoyance and to impact the amenity. In this study, odor emission characteristics at unit process of 48 sewage treatment facilities in 39 plants were evaluated using composite odor concentration and hydrogen sulfide (H2S) concentration. The values of composite odor concentration (geometry mean) and H2S concentration (median) for sludge treatment processes are higher than those for the other treatment processes. The composite odor concentration and H2S concentration are distributed over a wide area in each process. Composite odor concentration (dilution ratio) was found to have the significant correlation with H2S concentration (p=0.000<0.05). The H2S concentration accounted for 67.1% of composite odor concentration.