• Journal of Environmental Health Sciences
• /
• v.47 no.6
• /
• pp.548-557
• /
• 2021

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℃, 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℃ 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.

• Proceedings of the KSRS Conference
• /
• 2002.10a
• /
• pp.739-744
• /
• 2002

Recently we have discovered that sediments should be separated from lithosphere, and soil should be separated from biosphere, both sediment and soil will be mixed sediments-soil-sphere (Seso-sphere), which is using particulate mechanics to be solved. Erosion and sediment both are moving by particulate matter with water or wind. But ancient sediments will be erosion same to soil. Nowadays, real soil has already reduced much more. Many places have only remained sediments that have ploughed artificial farming layer. Thus it means sediments-soil-sphere. This paper discusses sediments-soil-sphere erosion modeling. In fact sediments-soil-sphere erosion is including water erosion, wind erosion, melt-water erosion, gravitational water erosion, and mixed erosion. We have established geographical remote sensing information modeling (RSIM) for different erosion that was using remote sensing digital images with geographical ground truth water stations and meteorological observatories data by remote sensing digital images processing and geographical information system (GIS). All of those RSIM will be a geographical multidimensional gray non-linear equation using mathematics equation (non-dimension analysis) and mathematics statistics. The mixed erosion equation is more complex that is a geographical polynomial gray non-linear equation that must use time-space fuzzy condition equations to be solved. RSIM is digital image modeling that has separated physical factors and geographical parameters. There are a lot of geographical analogous criterions that are non-dimensional factor groups. The geographical RSIM could be automatic to change them analogous criterions to be fixed difference scale maps. For example, if smaller scale maps (1:1000 000) that then will be one or two analogous criterions and if larger scale map (1:10 000) that then will be four or five analogous criterions. And the geographical parameters that are including coefficient and indexes will change too with images. The geographical RSIM has higher precision more than mathematics modeling even mathematical equation or mathematical statistics modeling.

• Journal of Ecology and Environment
• /
• v.47 no.2
• /
• pp.49-62
• /
• 2023

Background: Over the past three decades, gradual eustatic sea-level rise has been considered a primary exogenous factor in the increased frequency of flooding and biological changes in several salt marshes. Under this paradigm, the potential importance of short-term events, such as ocean storminess, in coastal hydrology and ecology is underrepresented in the literature. In this study, a simulation was developed to evaluate the influence of wind waves driven by atmospheric oscillations on sedimentary and vegetation dynamics at the Skallingen salt marsh in southwestern Denmark. The model was built based on long-term data of mean sea level, sediment accretion, and plant species composition collected at the Skallingen salt marsh from 1933-2006. In the model, the submergence frequency (number yr^-1) was estimated as a combined function of wind-driven high water level (HWL) events (> 80 cm Danish Ordnance Datum) affected by the North Atlantic Oscillation (NAO) and changes in surface elevation (cm yr^-1). Vegetation dynamics were represented as transitions between successional stages controlled by flooding effects. Two types of simulations were performed: (1) baseline modeling, which assumed no effect of wind-driven sea-level change, and (2) experimental modeling, which considered both normal tidal activity and wind-driven sea-level change. Results: Experimental modeling successfully represented the patterns of vegetation change observed in the field. It realistically simulated a retarded or retrogressive successional state dominated by early- to mid-successional species, despite a continuous increase in surface elevation at Skallingen. This situation is believed to be caused by an increase in extreme HWL events that cannot occur without meteorological ocean storms. In contrast, baseline modeling showed progressive succession towards the predominance of late-successional species, which was not the then-current state in the marsh. Conclusions: These findings support the hypothesis that variations in the NAO index toward its positive phase have increased storminess and wind tides on the North Sea surface (especially since the 1980s). This led to an increased frequency and duration of submergence and delayed ecological succession. Researchers should therefore employ a multitemporal perspective, recognizing the importance of short-term sea-level changes nested within long-term gradual trends.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.60 no.6
• /
• pp.43-54
• /
• 2018

The accurate estimation of reference crop evapotranspiration ($ET_o$) is essential in irrigation water management to assess the time-dependent status of crop water use and irrigation scheduling. The importance of $ET_o$ has resulted in many direct and indirect methods to approximate its value and include pan evaporation, meteorological-based estimations, lysimetry, soil moisture depletion, and soil water balance equations. Artificial neural networks (ANNs) have been intensively implemented for process-based hydrologic modeling due to their superior performance using nonlinear modeling, pattern recognition, and classification. This study adapted two well-known ANN algorithms, Backpropagation neural network (BPNN) and Generalized regression neural network (GRNN), to evaluate their capability to accurately predict $ET_o$ using daily meteorological data. All data were obtained from two automated weather stations (Chupungryeong and Jangsu) located in the Yeongdong-gun (2002-2017) and Jangsu-gun (1988-2017), respectively. Daily $ET_o$ was calculated using the Penman-Monteith equation as the benchmark method. These calculated values of $ET_o$ and corresponding meteorological data were separated into training, validation and test datasets. The performance of each ANN algorithm was evaluated against $ET_o$ calculated from the benchmark method and multiple linear regression (MLR) model. The overall results showed that the BPNN algorithm performed best followed by the MLR and GRNN in a statistical sense and this could contribute to provide valuable information to farmers, water managers and policy makers for effective agricultural water governance.

• Journal of Korea Water Resources Association
• /
• v.47 no.3
• /
• pp.257-267
• /
• 2014

The impact of the combination of changes in temperature and rainfall due to climate change on surface water resources is important in hydro-meteorological research. In this study, 4 hydro-meteorological (HM) models from the Rainfall Runoff Library in the Catchment Modeling Toolkit were used to model the impact of climate change on runoff in streams for 5 river basins in the Republic of Korea. Future projections from 2021 to 2040 (2030s), 2051 to 2070 (2060s) and 2081 to 2099 (2090s), were derived from 12 General Circulation Models (GCMs) and 3 representative concentration pathways (RCPs). GCM outputs were statistically adjusted and downscaled using Long-Ashton Research Station Weather Generator (LARS-WG) and the HM models were well calibrated and verified for the period from 1999 to 2009. The study showed that there is substantial spatial, temporal and HM uncertainty in the future runoff shown by the interquartile range, range and coefficient of variation. In summary, the aggregated runoff will increase in the future by 10~24%, 7~30% and 11~30% of the respective baseline runoff for the RCP2.6, RCP4.5 and RCP8.5, respectively. This study presents a method to model future stream-flow taking into account the HM model and climate based uncertainty.

• Applied Chemistry for Engineering
• /
• v.10 no.8
• /
• pp.1155-1160
• /
• 1999

The screening methodology modeling, dispersion modeling procedures for continuous and instantaneous releases of the gas phase flow from the storage tank and pressure relief valve were considered. This study was performed to develop the screening methodology for prediction and control of hazardous/toxic gas releases by estimating the 1-hr average maximum ground-level concentration of $Cl_2$ gas vs. downwind distance by incorporating source term model including the general/physical properties of released material and release mode of the $Cl_2$ storage tank of the chemical plant facilities, dispersion model, and meteorological/topographical data into the TSCREEN model. As the results of the study, it was found that dispersion modes of the dense gas were affected by the state of the released material, the released conditions, physical-chemical properties of released material, and the released modes (continuous and instantaneous releases), and especially largely affected by initial (depressurized) density of the released material and release emission rate as well as the wind velocity. Especially, this study was considered to release hazardous material as meteorological data. It was thought that this screening methodology can be useful as a preliminary guideline for application of the refined analysis model by developing the generic sliding scale methodology for various senarios selected.

• Atmosphere
• /
• v.16 no.1
• /
• pp.33-48
• /
• 2006

A classification of snowfall type based on development mechanism is proposed using previous snowfall studies, operational experiences, etc. Five types are proposed: snowfall caused by 1) airmass transformation (AT type), 2) terrain effects in a situation of expanding Siberian High (TE type), 3) precipitation systems associated with extratropical cyclones (EC type), 4) indirect effects of extratropical cyclones passing over the sea to the south of the Korean peninsula (ECS type), and 5) combined effects of TE and ECS types (COM type). Snowfall events during 1981-2001 are classified according to the 5 types mentioned above. For this, 118 events, with at least one station with daily snowfall depth greater than 20 cm, are selected. For the classification, synoptic weather charts, satellite images, and precipitation data are used. For TE and COM types, local sea-level pressure chart is also used to confirm the presence of condition for TE type (this is done for events in 1990 and thereafter). The classification shows that 109 out of 118 events can be classified as one of the 5 types. In the remaining 8 events, heavy snowfall occurred only in Ullung Island. Its occurrence may be due to one or more of the following mechanism: airmass transformation, mesoscale cyclones and/or mesoscale convergence over the East Sea, etc. Each type shows different characteristics in location of snowfall and composition of precipitation (i.e., dry snow, rain, and mixed precipitation). The AT-type snowfall occurs mostly in the west coast, Jeju and Ullung Islands whereas the TE-type snowfall occurs in the East coast especially over the Young Dong area. The ECS-type snowfall occurs mostly over the southern part of the peninsula and some east cost area (sometimes, whole south Korea depending on the location of cyclones). The EC- and COM-type snowfalls occur in wider area, often whole south Korea. Precipitation composition also varies with the type. The AT-type has a snow ratio (SR) higher than the mean value. The TE- and EC-type have SR similar to the mean. The ECS- and COM-type have SR values smaller than the mean. Generally the SR values at high latitude and mountainous areas are higher than those at the other areas. The SR value informs the characteristics of the precipitation composition. An SR value larger than 10 means that all precipitation is composed of snow whereas a zero SR value means that all precipitation is composed of rain.

• Journal of Korea Water Resources Association
• /
• v.54 no.3
• /
• pp.145-156
• /
• 2021

Forest fires have frequently occurred around the world, and the damages are increasing. In Korea, most forest fires are initiated by human activities, but climate factors such as temperature, humidity, and wind speed have a great impact on combustion environment of forest fires. In this study, therefore, based on statistics of forest fires in Gyeonggi-do over the past five years, meteorological and hydrological factors (i.e., temperature, humidity, wind speed, precipitation, and drought) were selected in order to quantitatively investigate causal relationships with forest fire. We applied a partial least squares structural equation model (PLS-SEM), which is suitable for analyzing causality and predicting latent variables. The overall results indicated that the measurement and structural models of the PLS-SEM were statistically significant for all evaluation criteria, and meteorological factors such as humidity, temperature, and wind speed affected by amount of -0.42, 0.23 and 0.15 of standardized path coefficient, respectively, on forest fires, whereas hydrological factor such as drought had an effect of 0.23 on forest fires. Therefore, as a practical method, the suggested model can be used for analyzing and evaluating influencing factors of forest fire and also for planning response and preparation of forest fire disasters.

• Journal of Environmental Science International
• /
• v.16 no.8
• /
• pp.921-928
• /
• 2007

The objective of this work is the air quality modeling according to the scenarios of emission on complex terrain. The prognostic meteorological fields and air quality field over complex areas of Seoul, Korea are generated by the PSU/NCAR mesoscale model (MM5) and the Third Generation Community Multi-scale Air Quality Modeling System (Models - 3/CMAQ), respectively. The emission source was driven from the Clean Air Policy Support System of the Korea National institute of Environmental Research (CAPSS), which is a 1 km x 1 km grid in South Korea during 2003. In comparison of air quality fields, the simulated averaged $PM_{10},\;NO_2,\;and\;O_3$ concentration on complex terrain in control case were decreased as compared with base case. Particularly $PM_{10}$ revealed most substantial localized differences by $(18{\sim}24{\mu}g/m^3)$. The reduction rate of $PM_{10},\;NO_2,\;and\;O_3$ is respectively 18.88, 13.34 and 4.17%.

• Journal of the Korean Institute of Gas
• /
• v.2 no.4
• /
• pp.7-17
• /
• 1998

This study is to develop the consequence modeling methodology for quantitative prediction of the hazard distance(or toxic buffer distance) for two-phase flow continuous releases from the pressurized chlorine saturated liquid storage tank of the chemical plant facilities. The source term modeling was peformed by the refined analysis method based on USEPA's guideline and SuperChems model self-calculation, respectively. The hazard distance was predicted for STEL, IDLH and ERPGs(ERPG-2 and ERPG-3) concentrations being used as the toxic regultaion concentration in hazard estimation. To use as hazard estimation guideline for the establishment of the emergency response planning, the effects of source characteristics and meteorological vaiations on the hazard distance was especially considered for ERPG-2 concentration.