• Korean Journal of Agricultural and Forest Meteorology
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• v.17 no.4
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• pp.348-357
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• 2015

The main purpose of this study is to understand the potential geographic distribution of P. koraiensis, which is known to be one of major economic tree species, based on the RCP (Representative Concentration Pathway) 8.5 scenarios and current geographic distribution from National Forest Inventory(NFI) data using ecological niche modeling. P. koraiensis abundance data extracted from NFI were utilized to estimate current geographic distribution. Also, GARP (Genetic Algorithm for Rule-set Production) model, one of the ecological niche models, was applied to estimate potential geographic distribution and to project future changes. Environmental explanatory variables showing Area Under Curve (AUC) value bigger than 0.6 were selected and constructed into the final model by running the model for each of the 27 variables. The results of the model validation which was performed based on confusion matrix statistics, showed quite high suitability. Currently P. koraiensis is distributed widely from 300m to 1,200m in altitude and from south to north as a result of national greening project in 1970s although major populations are found in elevated and northern area. The results of this study were successful in showing the current distribution of P. koraiensis and projecting their future changes. Future model for P. koraiensis suggest large areas predicted under current climate conditions may be contracted by 2090s showing dramatic habitat loss. Considering the increasing status of atmospheric $CO_2$ and air temperature in Korea, P. koraiensis seems to experience the significant decrease of potential distribution range in the future. The final model in this study may be used to identify climate change impacts on distribution of P. koraiensis in Korea, and a deeper understanding of its correlation may be helpful when planning afforestation strategies.

• Journal of Korean Society for Atmospheric Environment
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• v.33 no.5
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• pp.445-457
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• 2017

In this study, we developed an approach to better account for uncertainties in estimated contributions from fine particulate matter ($PM_{2.5}$) modeling. Our approach computes a Concentration Correction Factor (CCF) which is a ratio of observed concentrations to baseline model concentrations. We multiply modeled direct contribution estimates with CCF to obtain revised contributions. Overall, the modeling system showed reasonably good performance, correlation coefficient R of 0.82 and normalized mean bias of 2%, although the model underestimated some PM species concentrations. We also noticed that model biases vary seasonally. We compared contribution estimates of major source sectors before and after applying CCFs. We observed that different source sectors showed variable magnitudes of sensitivities to the CCF application. For example, the total primary $PM_{2.5}$ contribution was increased $2.4{\mu}g/m^3$ or 63% after the CCF application. Out of a $2.4{\mu}g/m^3$ increment, line sources and area source made up $1.3{\mu}g/m^3$ and $0.9{\mu}g/m^3$ which is 92% of the total contribution changes. We postulated two major reasons for variations in estimated contributions after the CCF application: (1) monthly variability of unadjusted contributions due to emission source characteristics and (2) physico-chemical differences in environmental conditions that emitted precursors undergo. Since emissions-to-$PM_{2.5}$ concentration conversion rate is an important piece of information to prioritize control strategy, we examined the effects of CCF application on the estimated conversion rates. We found that the application of CCFs can alter the rank of conversion efficiencies of source sectors. Finally, we discussed caveats of our current approach such as no consideration of ion neutralization which warrants further studies.

• Korean Journal of Psychosomatic Medicine
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• v.24 no.1
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• pp.109-116
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• 2016

Objectives : This study aimed to investigate the effects of resilience on posttraumatic stress symptoms and dissociation and whether depression mediates the relationships between resilience and posttraumatic stress symptoms and dissociation. Methods : A total of 115 firefighters participated in the study. Data were collected via the Life Events Checklist, Impact of Event Scale-Revised, Dissociative Experience Scale, Beck Depression Inventory, and Connor-Davidson Resilience Scale. Structural equation modeling and path analysis were applied to estimate the relationships between resilience, depression, posttraumatic stress symptoms, and dissociation. Results : Greater resilience was associated with lower posttraumatic stress symptoms and dissociation, and the relationship between them was fully mediated by depression. Conclusions : Specific aspects of depression may help explain the relationships between resilience and posttraumatic stress symptoms and dissociation. Tailored prevention programs and treatments based on resilience and depression may prevent posttraumatic stress symptoms and dissociation in firefighters and improve treatments outcomes among firefighters with posttraumatic stress symptoms and/or dissociation.

• Journal of the Korean Association of Geographic Information Studies
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• v.14 no.3
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• pp.236-256
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• 2011

Forest carbon stocks change due to land use change is an important data required by UNFCCC(United Nations framework convention on climate change). Spatially explicit estimation of forest carbon stocks based on IPCC GPG(intergovernmental panel on climate change good practice guidance) tier 3 gives high reliability. But a current estimation which was aggregated from NFI data doesn't have detail forest carbon stocks by polygon or cell. In order to improve an estimation remote sensing and GIS have been used especially in Europe and North America. We divided research trends in main countries into 4 categories such as remote sensing, GIS, geostatistics and environmental modeling considering spatial heterogeneity. The easiest way to apply is combination NFI data with forest type map based on GIS. Considering especially complicated forest structure of Korea, geostatistics is useful to estimate local variation of forest carbon. In addition, fine scale image is good for verification of forest carbon stocks and determination of CDM site. Related domestic researches are still on initial status and forest carbon stocks are mainly estimated using k-nearest neighbor(k-NN). In order to select suitable method for forest in Korea, an applicability of diverse spatial data and algorithm must be considered. Also the comparison between methods is required.

• Journal of Korean Public Health Nursing
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• v.15 no.2
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• pp.351-362
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• 2001

This study was conducted to investigate and compare the degree of professional self -concept (PSC) of the psychiatric mental health nurse practitioners (PMHNP) in hospitals and public health centers and to identify the factors predicting PSC of them, in order to provide basic data for developing PSC increasing program PSC. The 355 PMHNP were sampled from the university or general hospitals. mental hospitals, community mental health centers and public health centers across the country. The scales used in this study were PSCNI by Arthur (1990), PSI by Heppner & Petersen (1982) and the Index of work satisfaction by Slavitt et al. (1978). The results of the study were as follows : 1. The average item score of PSCNI of PMHNP in hospitals was $2.83\pm0.27$, and that of PMHNP in public health centers was $2.76\pm0.28$. There was significantly different between two groups (p=0.0202) 2. A comparison of the scores for the dimensions of the PSCNI were as follows ; the mean item score of professional practice of nurses in hospital was $2.90\pm0.30$, and that in public health centers was $2.83\pm0.35$. There were significant differences between two groups in the scores of professional practices (p=0.0315), leadership (p=0.0071) and skills (p=0.0231). 3. There were significant differences between two groups according to education (p=0.0057) with no significant interaction effect of group and education. 4. Job satisfaction (JS) was the highest factor predicting PSC of PMHNP in hospitals $(34.5\%)$, and problem solving inventory score (PS) was the highest factor predicting PSC of PMHNP in public health centers $(33.6\%)$. JS and PS accounted for $42.6\%$ in PSC of PMHNP in hospitals. and PS, JS, age, marital status, religion, and career accounted for $57.6\%$ in PSC of PMHNP in public health centers. In the light of these results. to gam the professional self-concept. nurses should be educated continuously through role modeling in clinical nursing and research. Also, nurse educators and administrators need to develop programs and policies to increase professional self-concept of nurses, particularly of community PMHNP.

• Journal of muscle and joint health
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• v.8 no.1
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• pp.27-50
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• 2001

Rheumatoid arthritis is a chronic systemic autoimmune disease. Although the joints are the major loci of the disease activity, fatigue is a common extraarticular symptom that exists in all gradations of rheumatoid arthritis. Fatigue is defined as a subjective sense of generalized tiredness or exhaustion and has multiple dimensions. Therefore fatigue is a common and frequent problem for those with rheumatoid arthritis. In fact, 88-100% of individuals with rheumatoid arthritis experience fatigue. Especially the degree of fatigue is higher in women than men with rheumatoid arthritis. Despite the importance of fatigue among the patients with rheumatoid arthritis, the mechanism that leads to fatigue in rheumatoid arthritis is not completely understood. This study was intended to test and validate a model to predict fatigue in women with rheumatoid arthritis. Especially it was intended to identify the direct and indirect effects of the variables of pain, disability, depression, sleep disturbance, morning stiffness, and symptom duration to fatigue. Data were collected by questionnaires including Multidimensional Assesment of Fatigue(Tack, 1991), numeric scale of pain, graphic scale of joints, Ritchie Articular Index, Korean Health Assessment Questionnaire(Bae, et al., 1998), Inventory of Function Status(Tulman, et al., 1991), Center for Epidemiologic Studies-Depression, and Korean Sleep Scale(Oh, et al 1998). The sample consisted of 345 women with a mean duration of rheumatoid arthritis for 10.06 years and a mean age of 49.64 years. SPSS win and Win LISREL were used for the data analysis. Structural equation modeling revealed the overall fit of the model. Pain predicted fatigue directly and indirectly through disability, depression, and sleep disturbance. Disability, sleep disturbance predicted fatigue only directly, while depression only indirectly through disability and sleep disturbance. Also morning stiffness and symptom duration predicted fatigue through disability and depression. All predictors accounted for 65% of the variance of fatigue. Depression, pain, and disability predicted sleep disturbance. Depression had reciprocal relationship with disability and they both were predicted by pain directly and indirectly. In summary, pain, depression, disability, sleep disturbance, morning stiffness, and symptom duration contributed to the fatigue of patients with rheumatoid arthritis. The best predictor of fatigue was pain. This finding indicates that the modification of pain, depression, disability, sleep disturbance, morning stiffness could be nursing intervention for relief or prevention of fatigue.

• Korean Journal of Remote Sensing
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• v.36 no.2_2
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• pp.325-335
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• 2020

Forest canopy height can be used for estimate of above-ground forest biomass (AGB) by means of the allometric equation. The remote locations and harsh conditions of mangrove forests limit the number of field inventory data stations needed for large-scale modeling of carbon and biomass dynamics. Although active and passive spaceborne sensors have proven successful in mapping mangroves globally, the sensors generally have coarse spatial resolution and overlook small-scale features. Here we generate a 12 m spatial resolution mangrove canopy height map from TanDEM-X data acquired over the world largest intact mangrove forest located in the Sundarbans. With single-pol. TanDEM-X data from 2011 to 2013, the proposed technique makes use of the fact that the double-bounce scattering that occurs between the water and mangrove trees yields water surface level elevation over mangrove forest areas, thus allowing us to estimate forest height with the assumption of an underlying flat topography. Our observations have led to a large-scale mangrove canopy height map over the entire Sundarbans region at a 12 m spatial resolution. Our canopy height estimates were validated with ground measurements acquired in 2015, a correlation coefficient of 0.83 and a RMSE of 0.84 m. With globally available TanDEM-X data, the technique described here will potentially provide accurate global maps of mangrove canopy height at 12 m spatial resolution and provide crucial information for understanding biomass and carbon dynamics in the mangrove ecosystems.

• Journal of Korean Society for Atmospheric Environment
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• v.33 no.2
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• pp.159-173
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• 2017

The Weather Research and Forecast (WRF) - Community Multiscale Air Quality (CMAQ) system was applied to investigate the influence of major point sources located in Chungcheongnam-do (CN) on surface $PM_{2.5}$ (Particulate Matter of which diameter is $2.5{\mu}m$ or less) concentrations in its surrounding areas. Uncertainties associated with contribution estimations were examined through cross-comparison of modeling results using various combinations of model inputs and setups; two meteorological datasets developed with WRF for 2010 and 2014, and two domestic emission inventories for 2010 and 2013 were used to estimate contributions of major point sources in CN. The results show that contributions of major point sources in CN to annual $PM_{2.5}$ concentrations over Seoul, Incheon, Gyeonggi, and CN ranged $0.51{\sim}1.63{\mu}g/m^3$, $0.71{\sim}1.62{\mu}g/m^3$, $0.63{\sim}1.66{\mu}g/m^3$, and $1.04{\sim}1.86{\mu}g/m^3$, respectively, depending on meteorology and emission inventory choice. It indicates that the contributions over the surrounding areas can be affected by model inputs significantly. Nitrate was the most dominant $PM_{2.5}$ component that was increased by major point sources in CN followed by sulfate, ammonium, and others. Based on the model simulations, it was estimated that primary $PM_{2.5}$ $(PPM)-to-PM_{2.5}$ conversion rates were 41.3~50.7 ($10^{-6}{\mu}g/m^3/TPY$) for CN, and 12.4~18.3 ($10^{-6}{\mu}g/m^3/TPY$) for Seoul, Incheon, and Gyeonggi, respectively. In addition, spatial gradients of PPM contributions show very steep trends. $NO_X$-to-nitrate conversion rates were 7.61~12.3 ($10^{-6}{\mu}g/m^3/TPY$) for CN, and 3.94~11.3 ($10^{-6}{\mu}g/m^3/TPY$) for the sub-regions in the SMA. $SO_2$-to-sulfate conversion rates were 4.04~5.28 ($10^{-6}{\mu}g/m^3/TPY$) for CN, and 3.73~4.43 ($10^{-6}{\mu}g/m^3/TPY$) for the SMA, respectively.

• Journal of Korean Society for Atmospheric Environment
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• v.34 no.1
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• pp.101-119
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• 2018

In this study, we analyzed long-term measurements and air quality simulation results of four criteria air pollutants ($PM_{10}$, $O_3$, $NO_2$, and $SO_2$) for 10 years, from 2006 to 2015, with emphasis on trends of annual variabilities. With the observation data, we conducted spatial interpolation using the Kriging method to estimate spatial distribution of pollutant concentrations. We also performed air quality simulations using the CMAQ model to consider the nonlinearity of the secondary air pollutants such as $O_3$ and the influence of long-range transport. In addition, these simulations are used to deduce the effect of long-term meteorological variations on trends of air quality changes because we fixed the emissions inventory while changing meteorological inputs. The nation-wide inter-annual variability of modeled $PM_{10}$ concentrations was $-0.11{\mu}g/m^3/yr$, while that of observed concentrations was $-0.84{\mu}g/m^3/yr$. For the Seoul Metropolitan Area, the inter-annual variability of observed $PM_{10}$ concentrations was $-1.64{\mu}g/m^3/yr$ that is two times rapid improvement compared to other regions. On the other hand, the inter-annual variability of observed $O_3$ concentrations is 0.62 ppb/yr which is larger than the simulated result of 0.13 ppb/yr. Magnitudes of differences between the modeled and observed inter-annual variabilities indicated that decreasing trend of $PM_{10}$ and increasing trend of $O_3$ are more influenced by emissions and oxidation states than meteorological conditions. We also found similar patterns in $NO_2$. However, $NO_2$ trends showed greater regional and seasonal differences than other pollutants. The analytic approach used in this study can be applicable to estimate changes in factors determining air quality such as emissions, weather, and surrounding conditions over a long term. Then analysis results can be used as important data for air quality management planning and evaluation of the chronic impact of air quality.

• Spatial Information Research
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• v.16 no.1
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• pp.19-32
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• 2008

Usually in South Korea, land cover type and topographic undulation are frequently changed even in a narrow area. However, most of researches using aerial LIDAR(LIght Detection And Ranging) data in abroad had been acquired in the study areas to be changed infrequently. This research was performed to explore reconstruction methodologies of 3D surface models considering the distribution of land cover type and topographic undulation. Composed of variously undulatory forests, rocky river beds and man-made land cover such as streets, trees, buildings, parking lots and so on, an area was selected for the research. First of all, the area was divided into three zones based on land cover type and topographic undulation using its aerial ortho-photo. Then, aerial LIDAR data was clipped by each zone and different 3D modeling processes were applied to each clipped data before integration of each models and reconstruction of overall model. These kinds of processes might be effectively applied to landscape management, forest inventory and digital map composition. Besides, they would be useful to resolve less- or over-extracted problems caused by simple rectangle zoning when an usual data processing of aerial LIDAR.