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For evaluating the performance of the Korean operational air quality model (Weather Research and Forecasting-Community Multiscale Air Quality), we compared the simulated concentrations of particulate matter with diameters ≤ 10 μm (PM10) against observations in Seoul, Korea for the cold seasons (October through March) of 2015–2018. The simulations yield about 70% hit rate for PM10 concentrations within 10 μg m−3 from the observations. For the 50–90 μg m−3 range, the simulations systematically overestimate the PM10 frequency mainly due to overestimation/underestimation of PM10 concentrations in the moderate-PM10 (30

The National Institute of Environmental Research, the Ministry of Environment, has been forecasting the concentrations of particulate matter (PM) with a diameter ≤ 2.5 μm (PM2.5) over Seoul, Republic of Korea, in terms of four PM2.5 concentration categories (low, moderate, high, and very high) since August 31, 2013. The current model, the Community Multiscale Air Quality (CMAQ) model, is run four times a day to forecast air quality for up to two days in 6-h intervals. In 2018, the hit ratio (i.e., accuracy) of the model was 60%, with an additional increase of 10% with the involvement of a forecaster. The CMAQ was improved in this study by incorporating a recurrent neural network (RNN) algorithm for the Seoul Metropolitan Area. Input datasets to the RNN model—PM values, meteorological parameters, and back-trajectory tracks obtained from both observations and model forecasts—were sorted according to time as the RNN algorithm learns time sequence series information, unlike typical neural network algorithms. To reflect the seasonality of the meteorological parameters that influence the PM2.5 concentrations in the region, one year was divided into 36 sets of three-month periods (i.e., there are three sets for July: May–June–July, June–July–August, and July–August–September). Several indices representing the accuracy of the forecast were calculated based on the RNN model results for 2018 after training the model for the previous three years (2015–2017). The accuracy of the RNN model is 74–81% for forecast lead times up to two days, about 20% higher than the CMAQ-only forecasts and ~10% higher than the combined CMAQ-forecaster forecast. The RNN model probabilities of detection for both high and very high PM2.5 events are comparable to those of the CMAQ model; however, the RNN model notably reduces the false alarm rate. Overall, the RNN model yields higher performance than the current forecast methods. Hence, this model can be adopted as an operational forecast model in Korea.

A record-breaking episode of highly concentrated particulate matter (PM) with diameters ≤ 2.5 μm (PM2.5) and ≤10 μm (PM10) occurred in the Republic of Korea during the period January 11–15, 2019: the hourly PM2.5 (PM10) in Seoul was 188 μg m−3 (262 μg m−3) on January 14. At the Baengnyeong and Socheongcho stations, located in the Yellow Sea between China and Korea, the hourly PM2.5 concentration reached up to 155 μg m−3 and 119.8 μg m−3, respectively, 16−19-h prior to this episode. This and the fact that the maximum PM10 concentration at Baengnyeong was 199 μg m−3 indicated a westerly transport of air pollutants. Satellite observations and 72-h back trajectory analysis clearly indicate that air pollutants from China flowed into Korea via the westerlies. According to contribution analysis using particulate matter source apportionment technology in the Comprehensive Air-quality Model with extension, air pollutants originating from northeastern China including Hebei and Shandong provinces were largely linked to the present record-breaking high concentration event in Seoul. This study will elucidate the mechanism of transboundary transport of air pollutants and help East Asian countries cooperate on air quality management.

A 58-year record (1950–2007) of cloudiness measured at 11 ground stations in South Korea revealed that summertime cloudiness for Wednesday through Friday versus that for Saturday through Monday exhibited a bell-shaped curve; the midweek/weekend difference increased up to 6% in the 1960s and early 1970s, and decreased in the mid and late 1970s. Since 1980, the anomaly has become as small as before 1960 (within ±2%). This bell-shaped interdecadal variation in midweek/weekend difference in cloudiness had a strong negative correlation with those in insolation and diurnal temperature range and a positive correlation with those in relative humidity and number of days with light rain (