This study sought to improve the accuracy of estimating national emissions of volatile organic compounds (VOCs) from consumer solvent products (CSPs) by updating emission factors and category-specific activity data. The classification of the CSPs, which was originally proposed by the U.S. Environmental Protection Agency, was reorganized to reflect domestic consumption patterns in Korea. VOC contents, product sales, and atmospheric evaporation rates of the CSPs were analyzed for subcategories including personal care products, household products, and automotive aftermarket products to update their emission factors. Additionally, the category-specific activity data, previously based on only population statistics, were newly applied to count the characteristics of each classification, such as the number of households and the number of registered automobiles. The updated emission factors were calculated to be 1.90 kg/capita·yr for personal care products, 4.37 kg/household·yr for household products, and 2.36 kg/car·yr for automotive products. An evaluation of uncertainties revealed the limitation in the product classification, the shortage of sales data, and the lack of information on VOC contents depending on the product forms (liquid, solid, and aerosol). This study highlighted the necessity of developing detailed classification systems and standardized VOC content measurement methods, ultimately contributing to more accurate and practical assessments of VOC emissions from the CSPs.

In this study, uncertainty ranges for bias-corrected temperature and precipitation in seven metro-cities were estimated using nine GCM-RCM Matrix, and climate changes were predicted based on the corrected temperature and precipitation. During the present climate (1981-2005), both uncertainties for annual temperature and precipitation and differences in regional uncertainties were reduced by bias correction methods. Model’s systematic errors such as cold bias of surface air temperature and underestimated precipitation during the second-Changma period were improved by a bias correction method. Uncertainties of annual variations for bias corrected temperature and precipitation were also decrease. Furthermore, not only mean values but also extreme values were improved by bias correction methods. During the future climate (2021-2050), differences in temperature and precipitation between two RCP scenarios (RCP4.5/8.5) were not quite large. Temperature had an obvious increasing tendency, while future precipitation did not change significantly compared to present one in terms of mean values. Uncertainties for future biascorrected temperature and precipitation were also reduced. In mid-21st centuries, models prospected that mean temperature increased thus lower extremes associated with cold wave decreased and upper extremes associated with heat wave increased. Models also predicted that variations of future precipitation increased thus the frequency and intensity of extreme precipitation increased.

최근 도시개발에 따른 인구증가와 기후변화의 영향으로 강우의 경년변화 등과 같이 수문기상학적 변동성 등으로 우리나라의 수자원 부족문제가 대두되고 있는 실정이다. 따라서 본 연구는 강우모의기법과 강우-유출 관계의 불확실성을 동시에 고려할 수 있는 통합 저수지 유량 산정 모형을 개발하였다. 본 연구의 목적은 제시된 방법론을 저수지 둑높임 사업에 적용하여 저수지 유입량을 산정하고 최종적으로 저수지 하류에 위치한 하류하천의 하천유지유량 산정 방안에 대한 편의성을 제공하고자 하였다. 대상유역은 한강권역 추평저수지, 금강권역 한계저수지, 영산강권역 광주호로 3곳을 선정하였으나 일반적으로 농촌용수개발에 따른 저수지는 유역면적이 협소한 미계측 유역으로 주변에 인접한 계측유역의 충분한 관측기간 동안 양질의 자료를 보유한 관측소의 수문자료를 이용하였다. 강우 모의기법으로 불연속 Kernel-Pareto 분포형 기반 Markov Chain 모형에 적용하여 기존 Markov Chain 모형의 문제점인 극치강수량을 효과적으로 재현하였으며 동시에 국내외에서 주로 이용되고 있는 NWS-PC 강우-유출 모형을 대상으로 보다 진보된 매개변수 추정 및 검정과정을 통하여 불확실성 분석이 가능한 Bayesian Markov Chain Monte Carlo 기법을 적용하였다. 이를 통해서 총 13개 강우-유출모형 매개변수에 대한 사후분포를 추정하고 유출수문곡선의 불확실성 구간을 추정하였으며 물수지 분석을 병행하여 매개변수와 강우모의에 대한 불확실성을 포함한 각 저수지의 시기별 가능공급량을 산정하였다.