PURPOSES : In this study, a model was developed to estimate the concentrations of particulate matter (PM2.5 and PM10) in expressway tunnel sections. METHODS : A statistical model was constructed by collecting data on particulate matter (PM2.5 and PM10), weather, environment, and traffic volume in the tunnel section. The model was developed after accurately analyzing the factors influencing the PM concentration. RESULTS : A machine learning-based PM concentration estimation model was developed. Three models, namely linear regression, convolutional neural network, and random forest models, were compared, and the random forest model was proposed as the best model. CONCLUSIONS : The evaluation revealed that the random forest model displayed the least error in the concentration estimation model for (PM2.5 and PM10) in all tunnel section cases. In addition, a practical application plan for the model developed in this study is proposed.

PURPOSES : The purpose of this fundamental study is to estimate the concentration of resuspended road dust in urban areas. This involves examining and measuring the factors that affect the dust concentration and measuring these factors and the concentration directly and indirectly by analyzing the factor-effect relationship of the dust in actual operation. METHODS : From the literature review, the factors that influence resuspended road dust, including traffic, environment, and weather data of roads and their relationship analysis were obtained to determine the effects of each element on resuspended road dust. The data characteristics and the quantitative changes in the factors when a high concentration of resuspended road dust is generated are analyzed for each condition. The concentrations of the resuspended dust are presented from the perspective of each factor. RESULTS : When the vehicle speed increased from 60 to 80 km/h, the measured resuspended dust concentration increased by 8㎍/m3 on the average. When the traffic was grouped, the resuspended concentration at 1200-1400 veh/h was 15.84㎍/m3 higher than that of 500-800 veh/h. A high concentration of 60㎍/m3 or more was generated in the SCL high and middle sections, and a high concentration of 10㎍/m3 or more was generated in the SCL low section. Eight cases were observed in the SCL high and middle section at an intense atmospheric wind speed of 3 m/s or more than the SCL level of zero cases. A high concentration of 89.8㎍/m3 resuspended dust was observed after 31 h of rainfall, and the dust concentration gradually decreased by over 50 h. Hence, the passing time after the rainfall, SCL and wind speed, traffic and vehicle speed, and air background (observation) concentration, all have a direct effect on the resuspended dust concentration. Atmospheric temperature and relative humidity have a significant effect on atmospheric dust concentration. CONCLUSIONS : The quantitative indicators of the factors using an estimation model of resuspended road dust in urban areas can be obtained if the conditions for high concentrations of resuspended dust are established using the quantitative relationship of the resuspended road dust factors presented in this study.

In the study, public facilities in Korea covered by the law, including PC-rooms, child care facilities, bus terminalwaiting rooms, elderly nursing facilities, movie theaters, underground subway stations, super super markets andindoor parking lots (8 types of facility, for a total of 32 locations) were investigated for indoor gas phase PAHsand particulate phase PAHs. PAHs source profiles were investigated as well. Finally, public facilities PAHs wereestimated the main influencing factors and sources of indoor by factor analysis. Underground subway stations andPC-rooms tended to be higher the concentration than other facilities. It judged each the effects of car exhaust,smoking, and elderly nursing facilities, child care facilities, movie theaters, where the influence of the outdoor airis less relatively direct effect that car exhaust and incoming of ambient air, were showed low concentration. Supersuper markets displayed a large amount of different products and bus terminal waiting rooms influenced car exhaustis higher than those that. Sources of indoor PAHs in public facilities make out profiling(cooking process: broilingmeat and fish, incense, shampoo, decorative candles, tobacco) and on the effects of ambient on reported existingliterature(of diesel and gasoline engines, heating fuel, coke oven, a wood combustion) was referred for factor analysisto estimate emission sources. As a result of particulate PAHs phase, three major factors were showed that factor1: cooking, use of gas fuel and combustion devices, factor 2: smoking. Factor 3: car exhaust. Factor analysis resultsof gas PAHs phase are similar to particulate PAHs phase. Additionally, factors such as air fresheners was estimated.

이 연구의 목적은 한반도에서 CH4 농도의 수치모의 검증을 통하여 CH4 배출원의 기여 농도를 추정하는 것이고, 이 수치모의에 사용된 CH4 배출량을 상자모델로부터 추정된 CH4 배출량과 비교하는 것이다. 한반도에서 2010년 4월 1일부터 8월 22일까지 CH4의 평균 농도를 추정하기 위해 WRF-CMAQ 모델이 사용되었다. 모델에서 CH4 배출량은 전지구 배출량인 EDGAR와 한국에서의 온실기체 배출량인 GHG-CAPSS로부터 인위적 배출 인벤토리와 전지구 자연적 인벤토리인 MEGAN이 적용되었다. 이들 CH4 배출량은 안면도 및 울릉도에서 측정된 CH4 농도와 모델링 농도 자료를 비교함으로써 검증되었다. 울릉도에서 국내 배출원으로부터 추정된 CH4의 기여 농도는 약 20%로 나타났고, 이것은 한반도 내 농장(8%), 에너지 기여 및 산업공정(6%), 일반폐기물(5%), 생체 및 토지이용(1%) 등 CH4 배출원으로부터 기원하였다. 그리고 중국으로부터 수송된 CH4의 기여 농도는 약 9%였고, 나머지 배경농도는 약 70%로 나타났다. 박스모델로 추정된 CH4 배출량은 WRF-CMAQ 모델에서 사용한 CH4 배출량과 유의미한 결과를 얻었다.

본 연구에서는 한반도 황사 사례 동안 WRF 기상모델과 SMOKE 배출량모델, CMAQ 및 CMAQ-MADRID 대기질 모델을 이용하여 다양한 황사 발생량 경험식에 대한 PM10의 농도를 추정하였다. 특별히 Wang et al.(2000), US EPA 모델, Park and In(2003), GOCART 모델, DEAD 모델의 5가지 황사 발생 경험식이 중국과 몽골 등의 황사 발생량을 추정하기 위해 WRF-SMOKE-CMAQ(MADRID) 모델에 적용되었다. 일기도, 후방궤적 및 위성이미지 분석에 따르면 한반도로의 황사 수송은 절리저기압(위성에서 콤마형 구름)과 관련된 지상 전선의 뒤쪽에서, 그리고 상층 제트류의 발달에 기인한 파의 정체현상과 함께 상층 골에서의 풍속이 하층으로 전이되는 풍하 바람에 의해 생성되었다. 그리고 WRF-SMOKE-CMAQ 모델링 결과, 황사의 시 공간적 분포에 있어서는 Wang et al.(2000)의 경험식이, 평균 편의 및 평균 제곱근 오차에서의 정확도 부분에서는 GOCART 모델의 경험식이 관측값을 보다 잘 모사하는 것으로 나타났다. 또한 Wang et al.의 경험식을 이용한 황사의 연직분포 분석 결과에서 강한 황사 사례(2007년 3월 31에서 4월 1일 800 μg/m3 이상)의 경우는 황사 수송이 한반도 상공 대기 경계층 내를 통과하였기 때문으로, 약한 황사 사례(2009년 3월 16일과 17에 400 μg/m3 이하)의 경우는 황사 수송이 경계층 위를 통과하였기 때문으로 나타났다. 또한 CMAQ 모델과 CAMQ-MADRID 모델에서의 미세먼지(PM10) 민감도 분석 결과에서는 CMAQ-MADRID 모델이 CMAQ 모델에 비해 한반도를 포함한 동아시아 지역에서 최대 25 μg/m3 정도가 높게 모사되었고, 모델 내 구름 액상과정에 의해서는 최대 15 μg/m3 정도가 제거되는 것으로 나타났다.

Exposure to nitrogen dioxide (NO₂) can produce adverse health effects. Various indoor and outdoor combustion sources make NO₂ the most ubiquitous pollutant in the indoor environment. Indoor air quality can be affected by indoor sources, ventilation, decay and outdoor levels. Although technologies exist to measure these factors, direct measurements are often difficult. In the present paper, we used a mass balance model and regression analysis, penetration factor (ventilation rate divided by the sum of ventilation rate and deposition constant) and source strength factor (source strength divided by the sum of ventilation rate and deposition constant) were calculated using multiple indoor and outdoor measurements with 10 houses. Subsequently, mean contributions of indoor and outdoor sources were 28.86% and 81.09%, respectively, suggesting that both indoor and outdoor sources had contributions to indoor concentrations of NO₂.

In this study, it is an object to develop a regression model for the estimation of TOC (total organic carbon) concentration using investigated data for three years from 2010 to 2012 in the Gam Stream unit watershed, and applied in 2009 to verify the applicability of the regression model. TOC and CODMn (chemical oxygen demand) were appeared to be derived the highest correlation. TOC was significantly correlated with 5 variables including BOD (biological oxygen demand), discharge, SS (suspended solids), Chl-a (chlorophyll a) and TP (total phosphorus) of p<0.01. As a result of PCA (principal component analysis) and FA (factor analysis), COD, TOC, SS, discharge, BOD and TP have been classified as a first factor. TOCe concentration was estimated using the model developed as an independent variable BOD5 and CODMn. R squared value between TOC and measurement TOC is 0.745 and 0.822, respectively. The independent variable were added step by step while removing lower importance variable. Based on the developed optimal model, R squared value between measurement value and estimation value for TOC was 0.852. It was found that multiple independent variables might be a better the estimation of TOC concentration using the regression model equation(in a given sites).

2013년 3월에 발사된 Landsat 8 인공위성의 이미지데이터를 이용하여 금강유역을 대상으로 수질인자에 대한 평가를 수행하였다. 본 연구의 목적은 다양한 수질인자 중 녹조에 직접적인 영향을 미치는 총질소와 총인의 농도를 추정함으로써 궁극적으로 수생태계에 악영향을 미치는 녹조의 발생을 모니터링 하는 것이다. 현장실측데이터와 인공위성 데이터간의 상관관계를 규명하기 위하여 Pearson' 상관계수를 이용하여 그 관계를 파악하였다. Landsat 8이 촬영되는 시기를 포함하는 총 20개의 현장실측 데이터가 수집되었으며 Landsat 8의 11개의 밴드 중, 밴드2, 3, 4의 반사도 값이 총인과 총질소를 탐지하는데 있어서 가장 상관성 높은 것으로 나타났다. 총질소는 유의수준 0.05에서 밴드2(0.48), 3(0.62), 4(0.57)과 높은 양의 상관관계를 보였으며, 총인의 경우, 유의수준 0.01에서 밴드2(0.59), 3(0.59), 4(0.58)로 높은 양의 상관관계를 나타냈다. 5번 밴드는 유의수준을 벗어남으로써 두 수질인자를 탐지하는데 상관성이 떨어지는 것으로 나타났다. 상관성이 높았던 밴드간의 조합을 통해서 총질소와 총인에 대한 각각의 최적 회귀식이 다중 회귀식을 근거로 구축되었다. 유도된 회귀식으 로 계산된 총질소와 총인의 농도값은 통계기법인 Bias와 RMSE를 이용하여 현장실측데이터들과 비교·검증되었다. 최종적으로, 2014년 4월 21과 2013년 11월 12일에 대한 맵핑을 수행함으로써 총질소와 총인의 공간적인 분포를 시각적으로 확인할 수 있었다.

Photochemical-Trajectory model was used to understand the production of ozone in the atmospheric boundary layer. This model was composed of the trajectory and the photochemical models. To calculate trajectories of air parcels, winds were obtained from the three-dimensional nonhydrostatic mesoscale model (PSU/NCAR MM5V2), and the results were interpolated into constant height surfaces. Numerical integration in the trajectory model was performed by the Runge-Kutta method. The photochemical model consisted of chemical reactions and photodissociation processes. Chemical equations were integrated by the semi-implicit Bulirsch-Stoer method. We performed our experiments from 21 July to 23 July 1994 during the summer time for Seoul area. During the time of maximum ozone concentration in Seoul, four trajectories of air parcels which traveled from Inchon to Seoul were selected. Ozone concentrations estimated by two models are compared with observed one in Seoul area and the photochemical-trajectory model is better fitted than pure photochemical model. During the selected period, high ozone concentrations in Seoul area were more influenced by transferred pollutants from Inchon than emitted pollutants in Seoul.