본 연구는 대기오염으로 인한 SO2와 NO2 그리고 산성비 등의 건성 및 습성강하물의 영향을 받고 있는 공단지역 주변 산림과 대조지역의 해송림에서 해송림의 쇠퇴도 사이에 상관분석과 단계적 회귀분석을 통하여 해송 잎의 쇠퇴와 엽록소 함량에 가장 영향을 미치는 인자를 구명하였다. 수관통과우의 pH는 도시공단지역이 봄철 4.65, 가을철 4.72이었고, 대조지역이 봄철 5.32과 가을철 5.34였다. 음이온 성분 중에서 NO3-의 농도는 도시공단지역이 52.13 ㎎/ℓ, 대조지역은 37.85 ㎎/ℓ이었다. SO42- 함량은 도시공단지역이 57.89 ㎎/ℓ, 대조지역이 36.21 ㎎/ℓ이었다. Ca2+의 농도는 도시공단지역이 27.27 ㎎/ℓ, 대조지역이 9.48 ㎎/ℓ이었다. 해송 잎의 엽록소 a의 함량은 도시공단지역이 0.2378이었고, 대조지역은 0.4378이었다. 해송의 쇠퇴도는 도시공단지역 2.97, 대조지역 1.20이었다. 해송 임분의 쇠퇴도와 강우의 이온성분, SO2, NO2 농도와 상관분석 결과에서 강우산도 상호간에는 부의 상관(r=-0.8672)이었고, 대기 중 SO2 농도는 정의 상관(r=0.8924)이었으며, NO2 농도도 정의 상관(r=0.8428)이었다. 이러한 산성강하물 상호간의 상관관계는 pH(r=-0.8672), NO3-(r=0.6996), SO42-(r=0.8497), SO2 (r=0.8924), NO2(r=0.8428)는 1% 수준에서 상관이 있었다.

To evaluate environment of farm lands using indicator insects and evaluation indices, the insect abundance of which is one of the major criteria for the evaluation of agricultural environment of farm land in urban areas and industrial complex, three sites (Ansan, Daesan, Suncheon) were designated and monitored from 2004 to 2006. The flora of agricultural land was more than urban areas and industrial complex of that in three sites. Soil, water and air pollution of urban areas and industrial complex were more serious than those of agricultural land in three sites. Overall population of insects were high from June to August in the surveyed three sites. Collected insects in agricultural land were 12 order, 106 family and 166 species, those in urban areas were 11 order, 102 family and 148 species, and in industrial complex were 11 order, 100 family and 152 species. Species and population belonging to Coleoptera was dominant in the surveyed sites. The insect diversity indices of farm land were 2.36 in agricultural land, 1.92 urban areas and industrial complex. And agricultural environment of agricultural land was good, urban areas was common and industrial complex was poor. Based on the major criteria of evaluation items, the criteria were selected as diversity index over 2.1, insect indicator Pheropsophus javanus in agricultural land, diversity index 1.5-2.0, insect indicator Nephotettix cincticeps in urban areas, diversity index below 1.5, insect indicator Pagria signata in industrial complex.

High-resolution meteorological simulations were conducted using a Weather Research and Forecasting (WRF) model with an Urban Canopy Model (UCM) in the Ulsan Metropolitan Region (UMR) where large-scale industrial facilities are located on the coast. We improved the land cover input data for the WRF-UCM by reclassifying the default urban category into four detailed areas (low and high-density residential areas, commercial areas, and industrial areas) using subdivided data (class 3) of the Environmental and Geographical Information System (EGIS). The urban area accounted for about 12% of the total UMR and the largest proportion (47.4%) was in the industrial area. Results from the WRF-UCM simulation in a summer episode with high temperatures showed that the modeled temperatures agreed greatly with the observations. Comparison with a standard WRF simulation (WRF-BASE) indicated that the temporal and spatial variations in surface air temperature in the UMR were properly captured. Specifically, the WRF-UCM reproduced daily maximum and nighttime variations in air temperature very well, indicating that our model can improve the accuracy of temperature simulation for a summer heatwave. However, the WRF-UCM somewhat overestimated wind speed in the UMR largely due to an increased air temperature gradient between land and sea.

Development of an artificial neural network model was presented to predict the daily maximum SO2 concentration in the urban-industrial area of Ulsan. The network model was trained during April through September for 2000-2005 using SO2 potential parameters estimated from meteorological and air quality data which are closely related to daily maximum SO2 concentrations. Meteorological data were obtained from regional modeling results, upper air soundings and surface field measurements and were then used to create the SO2 potential parameters such as synoptic conditions, mixing heights, atmospheric stabilities, and surface conditions. In particular, two-stage clustering techniques were used to identify potential index representing major synoptic conditions associated with high SO2 concentration. Two neural network models were developed and tested in different conditions for prediction: the first model was set up to predict daily maximum SO2 at 5 PM on the previous day, and the second was 10 AM for a given forecast day using an additional potential factors related with urban emissions in the early morning. The results showed that the developed models can predict the daily maximum SO2 concentrations with good simulation accuracy of 87% and 96% for the first and second model. respectively, but the limitation of predictive capability was found at a higher or lower concentrations. The increased accuracy for the second model demonstrates that improvements can be made by utilizing more recent air quality data for initialization of the model.