Comprehensive Air Quality Index Research Articles

Long-Term Trends in Visibility and Its Relationship with Mortality, Air-Quality Index, and Meteorological Factors in Selected Areas of Korea

We investigated temporal trends in atmospheric visibility as well as their relationship to non-accident-related daily mortality, the Comprehensive Air-quality Index (CAI) in Korea, and selected meteorological factors in four cities (Seoul, Busan, Daegu, and Ulsan) and one remote island (Ullungdo) for the period 2001–2009. According to the mean values, Seoul exhibited the lowest visibility (12.2 km), followed by Ullungdo (12.6 km), Ulsan (14.2 km), Daegu (14.7 km), and Busan (17.3 km). Conversely, Ullungdo had the lowest CAI value (82.4) and no respiratory mortality (RM) occurrences at all, and Seoul had both the highest mean CAI value (122) and the highest occurrence of daily RM (5.3). There were negative correlations between visibility and two meteorological parameters (relative humidity and ambient temperature) in all five areas, whereas there were generally positive correlations between visibility and the other two meteorological parameters (precipitation and wind speed). For some lag periods, the mortality in the cities was significantly correlated with visibility and the CAI, whereas mortality in Ullungdo was not. Busan had the highest excess risk for non-accidentrelated daily mortality, followed by Daegu, Seoul, and Ulsan.

Analysis on Relationship between Urban Development Characteristics And Air Pollution level - A Case of Seoul Metropolitan Region -

This study aimed to analyze the relationships between urban development patterns and air pollution level for the five pollutants (PM10, CO, NO2, SO2, O3) in the Seoul metropolitan area (SMA) using multiple regression models. We used Kriging methods to interpolate air pollution levels for the areas which are not covered by the 120 air pollution check stations. The analysis results can be summarized as follows. First, population and employment densities are positively related to CAI(Comprehensive air-quality index) values for CO and NO2 with a high statistical significance. Second, manufacturing industry has contributed to increasing CAI levels of O3 and SO2, while areas surrounding thermal power plants have relatively higher levels of PM10, CO, and NO2. Third, number of intersections is positively related to CAI levels of O3 and SO2, while the higher proportion of road area in a zone has contributed to rising CAI levels of CO and NO2. Fourth, it is interesting to find that the CAI levels of PM10, O3, and SO2 are more likely to increase in the suburban areas than in the central city, but those of CO and NO2 tend to rise in the central city. Finally, the CAI levels of PM10, CO, SO2, and NO2 are likely to be high along the west coast of the SMA(Seoul metropolitan area), possibly due to the high concentration of manufacturing industry and proximity to China.

대구지역 대기오염자동측정망 위치의 타당성 분석

Air quality monitoring networks are very important facilities to manage urban air pollution control and to set up an environmental policy. Since air quality monitoring network of Daegu was allocated from 1980s to mid-90s, there is need to reevaluate it and relocated its site. This study was evaluated the position of Daegu air quality monitoring station by unit environmental sensitivity index, grid emission rate, CAI (Comprehensive Air-quality Index) point. The investigation domain covered an area of 16 × 24 km centered at the metropolitan area of Daegu with grid spacing of 2 km. The location of alternative air quality monitoring networks was selected through optimization and quintiles analysis of total score. The result showed that all things considered, new air quality monitoring network need to install grid numbers 10, 28, 36, 37, 46. We also recommand three scenarios of alternative air quality monitoring network when considering unit environmental sensitivity index, emission rate and CAI point.

미세먼지 예보시스템 개발

The forecasting system for Today's and Tomorrow's PM10 was developed based on the statistical model and the forecasting was performed at 9 AM to predict Today's 24 hour average PM10 concentration and at 5 PM to predict Tomorrow's 24 hour average PM10. The Today's forecasting model was operated based on measured air quality and meteorological data while Tomorrow's model was run by monitored data as well as the meteorological data calculated from the weather forecasting model such as MM5 (Mesoscale Meteorological Model version 5). The observed air quality data at ambient air quality monitoring stations as well as measured and forecasted meteorological data were reviewed to find the relationship with target PM10 concentrations by the regression analysis. The PM concentration, wind speed, precipitation rate, mixing height and dew-point deficit temperature were major variables to determine the level of PM10 and the wind direction at 500 hpa height was also a good indicator to identify the influence of long-range transport from other countries. The neural network, regression model, and decision tree method were used as the forecasting models to predict the class of a comprehensive air quality index and the final forecasting index was determined by the most frequent index among the three model's predicted indexes. The accuracy, false alarm rate, and probability of detection in Tomorrow's model were 72.4%, 0.0%, and 42.9% while those in Today's model were 80.8%, 12.5%, and 77.8%, respectively. The statistical model had the limitation to predict the rapid changing PM10 concentration by long-range transport from the outside of Korea and in this case the chemical transport model would be an alternative method.