장기간 대기오염 및 기상자료를 이용한 유효강수세정 기여율 회귀모델의 개발 및 유효성 검사

Development and Validation Test of Effective Wet Scavenging Contribution Regression Models Using Long-term Air Monitoring and Weather Database

This study used long-term air and weather data from 2000 to 2009 as raw data sets to develop regression models in order to estimate precipitation scavenging contributions of ambient $PM_{10}$ and $NO_2$ in Korea. The data were initially analyzed to calculate scavenging ratio (SR), defined as the removal efficiency for $PM_{10}$ and $NO_2$ by actual precipitation. Next, the effective scavenging contributions (ESC) with considering precipitation probability density were calculated for each sector of precipitation range. Finally, the empirical regression equations for the two air pollutants were separately developed, and then the equations were applied to test the model validity with the raw data sets of 2010 and 2011, which were not involved in the modeling process. The results showed that the predicted $PM_{10}$ ESC by the model was 23.8% and the observed $PM_{10}$ ESCs were 23.6% in 2010 and 24.0% in 2011, respectively. As for $NO_2$, the predicted ESC by the model was 16.3% and the observed ESCs were 16.4% in 2010 and 16.6% in 2011, respectively. Thus the developed regression models fitted quite well the actual scavenging contribution for both ambient $PM_{10}$ and $NO_2$. The models can then be used as a good tool to quantitatively apportion the natural and anthropogenic sink contribution in Korea. However, to apply the models for far future, the precipitation probability density function (PPDF) as a weather variable in the model equations must be renewed periodically to increase prediction accuracy and reliability. Further, in order to apply the models in a specific local area, it is recommended that the long-term oriented local PPDF should be inserted in the models.