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A two-dimensional visibility estimation model was developed, based on random forest (RF), a machine learning-based technique. A geostatistical method was introduced into the visibility estimation model for the first time to interpolate point measurement data to gridded data spatially with a pixel size of 10 km. The RF-based model was trained using gridded visibility data, as well as meteorological and air pollution input variable data, for each location in South Korea, which were characterized by complex geographical features and high air pollution levels. Generally, relative humidity was the most important input variable for the visibility estimation (average mean decrease accuracy: 35%). However, PM2.5 tended to be the most crucial variable in polluted regions. The spatial interpolation was found to result in an additional visibility estimation error of 500 m in locations where no adjacent visibility observations within 0.2° were available. The performance of the proposed model was preliminarily assessed. Generally, the best detection performance was achieved in good visibility conditions (visibility range: 10 to 20 km). This study is the first to demonstrate a visibility estimation model based on a geostatistical method and machine learning, which can provide visibility information in locations for which no observations exist.

This present study suggests a method to calculate the SO2 flow rate from a source area to receptor areas on a regional scale using Ozone Monitoring Instrument (OMI) SO2 products, surface in situ SO2 data, and the hybrid single particle Lagrangian integrated trajectory (HYSPLIT) model. The method was implemented to calculate the SO2 flow rate from continental Asia to northeast Asia and the Northwest Pacific Ocean. For the high SO2 events when SO2 was transported from continental Asia to Japan via the Korean Peninsula on 22–24 December 2006, the long-range transported SO2 flow rates were 14.0 (21.0) Mg·h−1 OMI·gird−1 at Gangneung (Seoul) in Korea and 4.2 (5.3) Mg·h−1 OMI·gird−1 at Hiroshima (Kumamoto) in Japan. For the long-range transport of SO2 from continental Asia to the Northwest Pacific Ocean on 6–7 October 2008 (9–11 October 2006), the flow rates were 16.1 (16.2) Mg·h−1 OMI·gird−1 at Hokkaido, Japan (Vladivostok, Russia) and 5.6 (16.7) Mg·h−1 OMI·gird−1 at the Aleutian Islands, Northwest Pacific Ocean (Bering Sea). The mean rates of decrease in the SO2 flow rate per 1000 km were also calculated between continental Asia and the receptor areas. Uncertainties in the flow rate estimates were also assessed and discussed.

This present study suggests a method to calculate the SO2 flow rate from a source area to receptor areas on a regional scale using Ozone Monitoring Instrument (OMI) SO2 products, surface in situ SO2 data, and the hybrid single particle Lagrangian integrated trajectory (HYSPLIT) model. The method was implemented to calculate the SO2 flow rate from continental Asia to northeast Asia and the Northwest Pacific Ocean. For the high SO2 events when SO2 was transported from continental Asia to Japan via the Korean Peninsula on 22-24 December 2006, the long-range transported SO2 flow rates were 14.0 (21.0) Mg·h-1 OMI·gird-1 at Gangneung (Seoul) in Korea and 4.2 (5.3) Mg·h-1 OMI·gird-1 at Hiroshima (Kumamoto) in Japan. For the long-range transport of SO2 from continental Asia to the Northwest Pacific Ocean on 6-7 October 2008 (9-11 October 2006), the flow rates were 16.1 (16.2) Mg·h-1 OMI·gird-1 at Hokkaido, Japan (Vladivostok, Russia) and 5.6 (16.7) Mg·h-1 OMI·gird-1 at the Aleutian Islands, Northwest Pacific Ocean (Bering Sea). The mean rates of decrease in the SO2 flow rate per 1000 km were also calculated between continental Asia and the receptor areas. Uncertainties in the flow rate estimates were also assessed and discussed. [ABSTRACT FROM AUTHOR]