In case of studied area located around the sea, the data measured from the regional meteorological office is highly different from the local weather data because the diffusivity of fugitive dust varies considerably with meteorological conditions. Especially, it is very difficult to predict the amount of fugitive dust accurately as wind speed remains high frequently. In this study, the fluxes of suspended particulates as a function of the friction velocity were applied to consider the effect of wind speed on the amount of fugitive dust generated from the reclamation site. The amount of fugitive dust estimated as mentioned above was simulated by using ISCST3 model. As a result, in case of using only the Fugitive Dust Formula which is usually used in Environment Impact Assessment, the predicted $PM_{10}$ concentrations with points were $43.4{\sim}67.8{\mu}g/m^3$. However, in case of applying to the flux of suspended particulates, the predicted values of $PM_{10}$ with points were $43.3{\sim}69.1{\mu}g/m^3$, $49.5{\sim}90.4{\mu}g/m^3$ and $76.0{\sim}182.6{\mu}g/m^3$ with the wind speeds of 4.4, 5.8 and 7.7m/s, respectively. It could be possible to predict the amount of fugitive dust accurately because these predicted values were similar to the measured values. Consequently, we can establish alternatives for reduction of fugitive dust in this area damaged by fugitive dust which is caused by wind.

Impacts of global warming have been identified in many areas including natural ecosystem. A good number of studies based on climate models forecasting future climate have been conducted in many countries worldwide. Due to its global coverage, GCM, which is a most frequently used climate model, has limits to apply to Korea with such a narrower and complicated terrain. Therefore, it is necessary to perform a study impact assessment of climate changes with a climate model fully reflecting characteristics of Korean climate. In this respect, this study was designed to compare and analyze the GCM and RCM in order to determine a suitable climate model for Korea. In this study, spatial scope was Korea for 10 years from 1981 to 1990. As a research method, current climate was estimated on the basis of the data obtained from observation at the GHCN. Future climate was forecast using 4 GCMs furnished by the IPCC among SRES A2 Scenario as well as the RCM received from the NIES of Japan. Pearson correlation analysis was conducted for the purpose of comparing data obtained from observation with GCM and RCM. As a result of this study, average annual temperature of Korea between 1981 and 1990 was found to be around $12.03^{\circ}C$, with average daily rainfall being 2.72mm. Under the GCM, average annual temperature was between 10.22 and $16.86^{\circ}C$, with average daily rainfall between 2.13 and 3.35mm. Average annual temperature in the RCM was identified $12.56^{\circ}C$, with average daily rainfall of 5.01mm. In the comparison of the data obtained from observation with GCM and RCM, RCMs of both temperature and rainfall were found to well reflect characteristics of Korea's climate. This study is important mainly in that as a preliminary study to examine impact of climate changes such as global warming it chose appropriate climate model for our country. These results of the study showed that future climate produced under similar conditions with actual ones may be applied for various areas in many ways.

The purpose of this study is to understand the relation between the land use status in watersheds and stream turbidity. Major water quality components (flow rate, turbidity, SS, BOD, TN, TP, etc.) of two streams (Jaun and Naerin) and the land use status for each correspondent watershed have been analyzed through the field sampling and the geographical overlaying of land use and watershed map. The detailed results of this study showed that; turbidity has been increased rapidly from 1.9 to 13.0 NTU for Jaun Stream, 0.4 to 0.7 NTU for Naerin Stream, due to the increased flow rate during the period of June. The agricultural area of the Jaun watershed was $13.5km^2$ (10.1% of the overall watershed), comparing to $2.0km^2$(1.4%) of upper watershed of Naerin stream. The forest was widely distributed along the 30m buffering zone from the center of Naerin stream, which comprised 64.14% of the whole watershed area. But in case of the Jaun, the ratio of forest was 17.84%, while the ratio of farming field was 30.33%.

In this study, we investigated the applicable plan of GIS on the environmental impact assessment of 60 Hz Powerline. So we assessed distance data based on calculations by use of 2D and 3D Geographical information systems(GIS) and distance data based on measurements on 1: 5000 maps accord with on site distance measurements to use input data for calculating magnetic field. One hundred eight of the on site measured addresses were selected from residences. The data were achieved by measuring the distance between residence and power line on maps with scales of 1: 5000. The digital map was obtained from National Geographic Information Institute with scales of 1: 5000, and we made 2D and 3D map. Correlation analyses were performed for statistical analyses. For the 3D GIS versus on site comparison of different exposure categories, 70 of 108 measurements were assigned to the correct category. Similarly for 2D GIS versus on site comparison, 71 of 108 were correctly categorized. When comparing map measurement with on site measurement, 62 of 108 were correctly categorized. When the correlation analysis was performed, best correlation was found between 3D GIS and on site measurements with r = 0.84947 (p<0.0001). The correlation between map and on site measurement yielded an r of 0.76517 (p<0.0001). Since the GIS measurements and map measurement were made from the center point in the building and the on site measurements had to be made from the closest wall on the building, this might introduce and additional error in urban areas. The difference between 2D and 3D calculations were resulted from the height of buildings.

Saemangeum reclamation project was started in 1991 with the purpose of eco-friendly reclamation, but without clear definition of eco-friendliness. This study was carried out to find out the environmental indicators to define and evaluate the eco-friendliness of Saemangeum project. The methodology to identify the indicators included the analysis of similar reclamation projects, professional discussions, and Delphi survey. The eco-friendly indicators are theoretically based on the five elements of eco-friendliness; 1) resource consumption, 2) energy consumption, 3) pollution emission, 4) bio-diversity, 5) social amenity. The 69 indicators were explained in detail for the convenient use of the indicators by the potential users. The 69 indicators were classified into three categories such as pre-evaluation, process evaluation, and post-evaluation. In terms of spatial consideration, the 69 indicators were classified into two categories such as direct influence area and indirect influence area. Also the 69 indicators were classified into 7 categories: 1) sea, 2) lake, 3) agricultural land use, 4) industrial land use, 5) residential land use, 6) tourism land use, and 7) environmental land use. Twenty core indicators were selected from the 69 indicators by surveying the importance of each indicator. The core indicators are recommended in case of time and budget strain. When there are few quantitative data accumulated for the reclamation project, 5 macro indicators are recommended to be used. Macro indicators are qualitative in nature, and may be used in order to evaluate the overall eco-friendliness of a reclamation project.