n-Butanol is used to assess how odor intensity correction affects judges’ evaluation of the odor intensity based on the concentration. The odor intensity correction effect is verified by using three types of test solutions which are used for the selection of judges based on their concentration levels. The correction effect is statistically analyzed according to gender, odorant type, and concentration on the group and individual level. The result shows that n- Butanol correction affects the odor intensity evaluation for three odorants in different ways. In most cases, n- Butanol correction increases the panelists' sensitivity to the odor intensity change, and results to be close to the theoretical value. The female panelists can more accurately evaluate the sourness intensity of acetic acid after n- Butanol correction. All panelists regardless of gender can more accurately evaluate the fishiness intensity of trimethylamine after n-Butanol correction. For evaluating the caramel smell intensity of methylcyclopentenolone, a full panel without n-Butanol correction is recommended. Therefore, n-Butanol correction should be included in the process of judge selection and the odor intensity assessment.

Recently, a number of researchers have produced research and reports in order to forecast more exactly air quality such as particulate matter and odor. However, such research mainly focuses on the atmospheric diffusion models that have been used for the air quality prediction in environmental engineering area. Even though it has various merits, it has some limitation in that it uses very limited spatial attributes such as geographical attributes. Thus, we propose the new approach to forecast an air quality using a deep learning based ensemble model combining temporal and spatial predictor. The temporal predictor employs the RNN LSTM and the spatial predictor is based on the geographically weighted regression model. The ensemble model also uses the RNN LSTM that combines two models with stacking structure. The ensemble model is capable of inferring the air quality of the areas without air quality monitoring station, and even forecasting future air quality. We installed the IoT sensors measuring PM2.5, PM10, H2S, NH3, VOC at the 8 stations in Jeonju in order to gather air quality data. The numerical results showed that our new model has very exact prediction capability with comparison to the real measured data. It implies that the spatial attributes should be considered to more exact air quality prediction.

The stench of various sources has become a complex issue that all governments must face and solve. n-Butanol is often used as an odor intensity reference for daily air quality monitoring and evaluation. However, its odor space, including odor sensation and odor effect, is still not fully understood, especially in wide concentration ranges. This study described n-butanol odor character profiles with objective descriptors. They are mostly presented as “odorless” or “offensive” at low concentrations, and frequently characterized as “chemical” or “medicinal” at high concentrations. The semantic differential shows that n-butanol odor is a negative emotional odor rather than a positive one. The principal component analysis shows that the representative factors of the n-butanol sensibility structure according to the sensibility evaluation are expressed with diverse sensibility vocabulary, and ‘esthetics’ represent its characterless nature. The good linearity between intensity and concentration, the near absence of gender difference, diverse odor types rather than a specific type, and ease with which to make a wide range of concentrations, makes n-butanol a candidate to be considered as a suitable standard odorant.

The labeled magnitude scale (LMS) was proposed as the magnitude estimation of perceived odor intensity while the direct olfactory method is a basis of odor evaluation. Six chemicals (pyridine, ethanol, ethyl acetate, acetone, trimethylamine, and β-phenylethyl alcohol) were tested to demonstrate the limitation of the current odor intensity scale and the possibility of the alternative method. The 6-point odor intensity reference scale, which is wildly used in the field, has the inevitable limitation of the perceived magnitude of odor intensity. It has failed to express the magnitude objectively when odor intensity increased and the magnitude scale was limited. It was experimentally proven that LMS presents the function of the existing method and effectively evaluates the wide range of odor intensity.

This study aims to understand the correlation between odor intensity and dilution factor using the Air Dilution Olfactory Method, which is suggested in the Standard Method of Odor Compounds, by measuring odor intensity and dilution factor for fatty acids and i-butyl alcohol. For the measurement, 18 panel members were selected through a panel test, and odor intensity and dilution factor by substance produced from the selected panel were estimated. The estimation showed that the correlation of odor intensity with dilution factor for a fatty acids and i-butyl alcohol can be reasonably expressed by the equation I = A·log D + 0.5 (I : odor Intensity, D : dilution factor, A : material constant). The material constant was in order of propionic acid 2.0709, n-butyric acid 1.6006, n-valeric acid 1.3369, i-valeric acid 1.182, i-butyl alcohol 1.4326. The geometric average of increased dilution factor for the 5 compounds is about 4.8 time, 3.0 time for propionic acid and 7.0 tme for i-valeric acid due to odor intensity 1 increasing. It is suggested that the result of this study could be used as a base data for research on measures to improve the regulation standards for complex odor concentrations at a boundary sites in operation.

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 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.

  The aim of this study was to investigate effects of odor intensity on the olfactory sensibility and sensibility structure. Three odor samples(B, C, and D) of T&T olfactometer were selected by the preference rank：the lowest preference(C)； the moderate on