• The Journal of Korean Society for Radiation Therapy
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• v.21 no.2
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• pp.75-82
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• 2009

Purpose: Measure the ozone level in the treatment room while treating a patient so want to know the degree of contamination caused by ozone occurrence. Materials and Methods: Use the linear accelerator (Clinac 21EX, Varian, USA) with the ozone meter (series-200, aeroQual, New Zealand) and water phantom (Wellhofer, IBA, Germany) is irradiated the radiation so that measured the ozone generation level according to MU, dose-rate, SSD, field size, energy, delay time and put the ozone meter in the treatment room actually while treating a patient so measured the daily ozone level variation. Results: While irradiating the radiation, degree of ozone contamination wasn't affected by the energy but mostly in case of electron beam, ozone level was higher than photon beam. The higher dose-rate (0.016~0.025 ppm/hr), the farther SSD (0.018~0.030 ppm/hr), the wider field sizes (0.016~0.025 ppm/hr), the more MU (0.018~0.046 ppm/hr), it occurred high ozone level. Ozone decrement according to delay time changed the background level (0.016 ppm/hr) after elapsed time of 10 minutes from irradiating radiation. And daily ozone occurrence level in the treatment room was below ozone standard level 0.1 ppm/hr (average:0.06 ppm/8 hr) but it could confirm that ozone generation level was included the level (max:0.038 ppm/hr) above 0.02 ppm/hr which patient could perceive. Conclusion: Through ozone level according to variation of certain conditions, actually in the treatment room ozone generation level didn't damaged to patients or workers. Commonly peoples think that ozone was harmful gas but it thought that small amount of ozone generation level while treating a patient was beneficial in the treatment room through air purge action of pathogenic germ or virus sterilization.

• Journal of Korean Society of Forest Science
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• v.107 no.4
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• pp.351-360
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• 2018

This study was conducted to analyze the responses of chlorophyll contents and growth of Pinus densiflora and Quercus variabilis seedlings on distance from the well and $CO_2$ flux after the artificial $CO_2$ release. From June 1 to 30, 2016, $CO_2$ gas was injected at the rate of $6L\;min^{-1}$ at the study site in Eumseong. Chlorophyll content was analyzed in the middle of July, 2016, and root collar diameter (RCD), height (H), and biomass were measured in May and December, 2016 after planting 2-year-old P. densiflora and 1-year-old Q. variabilis seedlings in May, 2015. The chlorophyll content of P. densiflora seedlings did not show a significant correlation with $CO_2$ flux, whereas the chlorophyll content of Q. variabilis seedlings showed a significant negative correlation with increasing $CO_2$ flux (P<0.05). The RCD and H growth rates of both species showed the significant difference in the distance from the well of the $CO_2$ anthropogenic release treatment. In particular, the RCD and H growth rate of P. densiflora seedlings and the RCD growth rate of Q. variabilis seedlings increased significantly as the seedlings were closer to the well, but the H growth rate of Q. variabilis seedlings decreased significantly. In addition, as the $CO_2$ concentration in the ground increases, ${\Delta}R/S$ ratio increases in both species, suggesting that the high $CO_2$ concentration in the soil promotes carbon distribution relative to the root part. The results of this study can be used as data necessary to monitor the $CO_2$ leakage and physiological and growth responses of both species to leakage of stored $CO_2$ in the future.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.3
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• pp.149-155
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• 2021

Early warning systems for weather risk management in the agricultural sector have been developed to predict potential wind damage to crops. These systems take into account the daily maximum wind speed to determine the critical wind speed that causes fruit drops and provide the weather risk information to farmers. In an effort to increase the accuracy of wind risk predictions, an artificial neural network for binary classification was implemented. In the present study, the daily wind speed and other weather data, which were measured at weather stations at sites of interest in Jeollabuk-do and Jeollanam-do as well as Gyeongsangbuk- do and part of Gyeongsangnam- do provinces in 2019, were used for training the neural network. These weather stations include 210 synoptic and automated weather stations operated by the Korean Meteorological Administration (KMA). The wind speed data collected at the same locations between January 1 and December 12, 2020 were used to validate the neural network model. The data collected from December 13, 2020 to February 18, 2021 were used to evaluate the wind risk prediction performance before and after the use of the artificial neural network. The critical wind speed of damage risk was determined to be 11 m/s, which is the wind speed reported to cause fruit drops and damages. Furthermore, the maximum wind speeds were expressed using Weibull distribution probability density function for warning of wind damage. It was found that the accuracy of wind damage risk prediction was improved from 65.36% to 93.62% after re-classification using the artificial neural network. Nevertheless, the error rate also increased from 13.46% to 37.64%, as well. It is likely that the machine learning approach used in the present study would benefit case studies where no prediction by risk warning systems becomes a relatively serious issue.