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피톤치드(모노테르펜) 농도 예측을 위한 회귀분석 기반 모델식 : 춘천 수리봉을 중심으로
이석종(Seog-Jong Lee),김병욱(Byoung-Ug Kim),홍영균(Young-Kyun Hong),이영섭(Yeong-Seob Lee),고영훈(Young-Hun Go),양승표(Seung-Pyo Yang),현근우(Geun-Woo Hyun),이건호(Geon-Ho Yi),김재철(Jea-Chul Kim),김대열(Dae-Yeoal Kim) 한국환경보건학회 2021 한국환경보건학회지 Vol.47 No.6
Background: Due to the emergence of new diseases such as COVID-19, an increasing number of people are struggling with stress and depression. Interest is growing in forest-based recreation for physical and mental relief. Objectives: A prediction model equation using meteorological factors and data was developed to predict the quantities of medicinal substances generated in forests (monoterpenes) in real-time. Methods: The concentration of phytoncide and meteorological factors in the forests near Chuncheon in South Korea were measured for nearly two years. Meteorological factors affecting the observation data were acquired through a multiple regression analysis. A model equation was developed by applying a linear regression equation with the main factors. Results: The linear regression analysis revealed a high explanatory power for the coefficients of determination of temperature and humidity in the coniferous forest (R2=0.7028 and R2=0.5859). With a temperature increase of 1°C, the phytoncide concentration increased by 31.7 ng/Sm³. A humidity increase of 1% led to an increase in the coniferous forest by 21.9 ng/Sm³. In the deciduous forest, the coefficients of determination of temperature and humidity had approximately 60% explanatory power (R2=0.6611 and R2=0.5893). A temperature increase of 1°C led to an increase of approximately 9.6 ng/Sm³, and 1% humidity resulted in a change of approximately 6.9 ng/Sm³. A prediction model equation was suggested based on such meteorological factors and related equations that showed a 30% error with statistical verification. Conclusions: Follow-up research is required to reduce the prediction error. In addition, phytoncide data for each region can be acquired by applying actual regional phytoncide data and the prediction technique proposed in this study.
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