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.

Indoor air environments for people are recently being observed because the time we spend inside the house or a building throughout the day has been extended during the present circumstances. This is why formaldehyde and volatile organic compounds (VOCs) are regulated, which can cause Sick Building Syndrome (SBS). There might be other VOCs not regulated by law in newly built collective housing, however, in order to compensate for the reduced concentration of regulated VOCs such as benzene, toluene, ethylbenzene, xylene, and styrene. In this study, the concentration of unregulated VOCs in newly built collective housing structures located in the Seoul Special City was researched to find potential indoor hazards for citizens and to prepare basic data for further research.

The concentrations of volatile organic compounds (VOCs) and odor-inducing substances were measured using selected ion flow tube mass spectrometers (SIFT-MS) and a drone equipped with an air quality monitoring system. SIFT-MS can continuously measure the concentration of VOCs and odor-inducing substances in realtime without any pre-treating steps for the sample. The vehicle with SIFT-MS was used for real-time measurement of VOC concentration at the site boundaries of pollution sources. It is possible to directly analyze VOCs concentration generated at the outlets by capturing air from the pollution sources with a drone. VOCs concentrations of nine spots from Banwol National Industrial Complex were measured by a vehicle equipped with SIFT-MS and were compared with the background concentration measured inside the Metropolitan Air Quality Management Office. In three out of the nine spots, the concentration of toluene, xylene, hydrogen sulfide, and methyl ethyl ketone was shown to be much higher than the background concentration. The VOCs concentrations obtained using drones for high-concentration suspected areas showed similar tendencies as those measured using the vehicle with SIFTMS at the site boundary. We showed that if both the drone and real-time air quality monitoring equipment are used to measure VOCs concentration, it is possible to identify the pollutant sources at the industrial complex quickly and efficiently check sites with high concentrations of VOCs.

The characteristics of ammonia during the growing period of pigs in a facility with a mechanical ventilation system were analyzed, and the emission factor was calculated. Real-time ammonia concentration was measured using photoacoustic spectroscopy equipment, and a ventilation measuring device was fabricated to measure the amount of air vented from an exhaust fan according to the operation rate. All data were collected as one-hour averages. The mean ammonia concentration, indoor temperature, and ventilation rate was 1.44~2.08 ppm, 25.5~26.4oC, and 24~32 m3/h per pig, respectively. Both concentration and ventilation rate are important factors in terms of emission management, but correlation analysis shows that the impact of concentration is higher than that of ventilation. Using ammonia concentration and ventilation data, the ammonia emissions per pig were calculated by considering the number of pigs (0.25~1.74 g/day·pig). The final ammonia emission factor yielded a value of 0.81 g/day·pig.

Most of the odorous substances generated in public environment facilities are treated using a local exhaust ventilation system. This study assessed the leakage of odorous substances by measuring design criteria, collected air volume, and complex odor in the unit process of sewage treatment facilities. The closer the guideline/design operation airflow ratio of each odor source is to 1.0, the more identical the guideline:design:operation airflow. The average air volume ratio of the facilities under the study was 0.95~0.99 for the MBR process and 0.29~0.68 for the BNR process. As a result of comparing the types of wastewater treatment processes, the leakage of odorous substances was about 9.7 times higher in the BNR process (192,732 m3/min) than in the MBR process (19,838 m3/ min). In this study, it was found that the following two means are important for the proper collection and prevention of odorous substances. The first is the estimation of collection air volume with consideration to the characteristics of the odor source (temperature, odor generation condition, etc.). The second is the design and operation of the local exhaust ventilation system.

The purpose of this study is to provision the standard method for ensuring the reliability of measuring indoor air quality in public transportation. The objective is to determine the difference in the measured concentration values according to various conditions. These variables include measurement conditions, measurement equipment, measurement points, and measurement time. The value differences are determined by measuring the PM10 and CO2 concentration of subways, and express buses and trains, which are targets of indoor air quality management. The concentration of CO2 was measured by the NDIR method and that of PM10 was measured by the gravimetric method and light-scattering method. Statistically, the results of the concentration comparison according to the measurement points of the public transportation modes were not significantly different (p > 0.05), and it is deemed that the concentration is not affected by the measurement points. In terms of the concentration analysis results according to the measurement method, there was a difference of the concentration between the gravimetric and light scattering method. In the case of the light scattering method, the concentration differed depending on whether it was corrected with standard particles in the laboratory environment.

Recently, the importance of air filters used in air purifiers and ventilation systems is emphasized in Korea. As a result, air filter test reports are required by users to ensure the removal efficiency of particulate matter. However, the tests are conducted for the filter material alone, which lead to a possible discrepancy between the test report and actual efficiency when applied to actual devices. Therefore, in this study, the removal efficiency data of the filter test reports were compared with actual filter efficiency data after application to the ventilation systems for some ventilation systems in the market. For ventilation system A, the field test results using filter leakage test method were slightly lower than those in the test report but nearly the same. For ventilation system B, the field test result was much higher than reported in the test report. This was due to the broad range of particle sizes measured using the filter leakage test method. The field tests using the particle counter method showed that the removal efficiency of ventilation system A for 0.3 μm was under 50% which translates to less than half of those of the filter test reports. For ventilation system B, the removal efficiency was 15%~21%. much lower than reported in the filter test reports. The lower removal efficiencys are mainly assumed to be caused by leakage of the filter installation among other factors. Therefore, the field test methods for the particulate matter removal efficiency of ventilation systems should be established to verify actual efficiency and improve the efficiency in the future.

Many people have often reused their disposable respirator multiple times due to reasons of economy and purchasing inconvenience. However, multiple use of a disposable respirator may lead to an adverse effect of its protection performance. In this study, we aimed to determine changes in the protection performance of disposable respirators related to multiple use and to suggest the appropriate number of times of use. The masks of the brands T and Y were selected in this study, respectively. A mask of each brand was used for testing at the same time and place for two hours per day. The protection performance of each disposable mask was tested after consecutive use on days two, three, and four. The protection performance tests for each used mask following wear on a mannequin were performed in the laboratory using GRIMM’s fine dust monitoring device and TSI’s nebulizer. The concentration of PM10, PM2.5, and PM1.0 inside and outside the mask was measured. The filtration rate was calculated and the results were analyzed using the SAS statistical program. The protection rates of the brandnew masks of T and Y brands (blank test) were 80% and 60%, respectively. After one-day usage, the protection rates of the brand-new T and Y branded masks significantly decreased from 80% to 60% and from 60% to 50%, respectively. Rather, the slight drops of protection rate were observed after two or more days of respirator use. Our findings indicate that the change of protection performance could occur after one-time use. It would not be recommended to use disposable respirators several times in terms of in terms of protecting human health from environmental hazards.

In this study, the main odorous substances were selected for each swine facility by investigating the concentration and occurrence characteristics of odorous substances according to farm facilities. The objective was to find a solution to manage odor effectively in swine farms. Samples collected from the boundary site, manure storage, fan, and indoor the swine building were analyzed for concentration, odor activity value (OAV), and odor contribution. As a result, there was a difference in the concentration of odorous substances as well as the tendency of OAV in each swine facility. Also, the main substances of odor in the farms were similar, but odor contribution differed from facility to facility. Therefore, it is considered that the odor management efficiency will be improved only if the proper odor reduction method is applied according to the types of main odorous substances in swine facilities.

This study investigated the convergence of the QEEG and mood state changes in healthy females from late teens to twenties in relation to the inhalation of peppermint oil (peppermint) in different concentrations (0.1~100%). Brain activity upon stimulation by different concentrations was analyzed based on the RPVs and CVB (%), representing a rate of change compared with the background QEEG. Peppermint showed the effect of relaxation at low concentrations (0.1 and 1%) with increased θ and α waves and decreased β and γ waves. As the concentration of peppermint increases, the α wave decreases, and the θ, β and w waves increase, and there is an arousal effect. Peppermint was rated as having a refreshing, exhilarating, and intense smell. At a low concentration of 0.1%, peppermint chiefly produced a feeling of pleasantness, freshness and refreshment. However, the increase in peppermint concentration resulted in a masculine, exiting, and hard emotional response. As the concentration of peppermint increased, the odor intensity increased in a linearly. The amount of change (ΔSEF50 and ΔSEF90) in edge frequencies closely related to the activation and stress of the brain showed a positive correlation. Odor intensity has a stronger association with ΔSEF90, a stress-related brain wave indicator than with ΔSEF50, a brain activation-related indicator. In addition, both ΔSEF50 and ΔSEF90 showed a positive correlation with APIGF (feelrefreshing average preference index) and a negative correlation with APIAC (Active-Calm average preference) and APIAll (total average preference index). These findings indicate that (1) the correlation between odor intensity and stress and (2) the relationship between emotional and QEEG can be interpreted in terms of brain wave study.

This study was conducted as a part of the research for the “Development of Big Data Analysis Techniques and AI-based Integrated Support System for Energy-Environment Management.” We collected research results on characterization of distribution of fine dust and re-analyzed using meta-analysis techniques to build “big data” with high potential for school environments. The results of prior studies on the characteristics of fine dust concentration distribution in a school environment conducted in Korea were collected and re-analyzed the results using the metaanalysis technique. In this manner, the variables that could be used to derive the independent variables needed to produce the e-coding book prior to the big data collection, were first derived. The possibility of using the data as independent variables was then evaluated. In this study, three variables: “elementary school vs. middle school vs. high school,” “general classroom vs. special classroom,” and “new classroom vs. old classroom” were evaluated for their application as major classification variables with priority. The necessity of being derived as a major classification variable was examined by testing the difference in fine dust concentration distribution in the school environment by each variable case. Results showed that “elementary school vs. middle school vs high school” and “general classroom vs. special classroom” could be used as independent variables, while “new classroom vs. old classroom” was less likely to be used as an independent variable.