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Cold-season air quality in Seoul, South Korea, has been improved noticeably between 2001 and 2015 with a near-50% decrease in the mean concentration of particulate matter with aerodynamic diameters ≤10 μm (PM10). Like the change in mean concentration, the occurrence frequency and intensity of the extreme-high-PM10 episodes exceeding 100 μg m−3 has significantly decreased as well. In addition to the multilateral efforts of the South Korean government to reduce air pollutant emissions, this study proposes that large-scale circulation changes also could have contributed to the air quality improvements. Specifically, the recent weakening of the Aleutian low may have intensified the tropospheric westerlies around the Korean Peninsula, resulting in a shorter residence time of particulate matter over South Korea. Thus, despite constant governmental effort to reduce pollutant emissions, the improvement in air quality over South Korea may be delayed if the Aleutian low recovers its past strength in the future. This study emphasizes the importance of the meteorological field in determining the air quality over South Korea.

As of November 2014, the Korean Ministry of Environment (KME) has been forecasting the concentration of particulate matter with diameters ≤ 10 μm (PM10) classified into four grades: low (PM10 ≤ 30 μg m−3), moderate (30 150 μg m−3). The KME operational center generates PM10 forecasts using statistical and chemistry-transport models, but the overall performance and the hit rate for the four PM10 grades has not previously been evaluated. To provide a statistical reference for the current air quality forecasting system, we have developed a neural network model based on the synoptic patterns of several meteorological fields such as geopotential height, air temperature, relative humidity, and wind. Hindcast of the four PM10 grades in Seoul, Korea was performed for the cold seasons (October–March) of 2001–2014 when the high and very high PM10 grades are frequently observed. Because synoptic patterns of the meteorological fields are distinctive for each PM10 grade, these fields were adopted and quantified as predictors in the form of cosine similarities to train the neural network model. Using these predictors in conjunction with the PM10 concentration in Seoul from the day before prediction as an additional predictor, an overall hit rate of 69% was achieved; the hit rates for the low, moderate, high, and very high PM10 grades were 33%, 83%, 45%, and 33%, respectively. Our findings also suggest that the synoptic patterns of meteorological variables are reliable predictors for the identification of the favorable conditions for each PM10 grade, as well as for the transboundary transport of PM10 from China. This evaluation of PM10 predictability can be reliably used as a statistical reference and further, complement to the current air quality forecasting system.

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.

As of November 2014, the Korean Ministry of Environment (KME) has been forecasting the concentration of particulate matter with diameters ≤ 10 μm (PM

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 (

This study demonstrates that absorbed solar radiation (ASR) at the top of the atmosphere in early summer (May–July) plays a precursory role in determining the Arctic sea ice concentration (SIC) in late summer (August–October). The monthly ASR anomalies are obtained over the Arctic Ocean (65°N–90°N) from the Clouds and the Earth's Radiant Energy System during 2000–2013. The ASR changes primarily with cloud variation. We found that the ASR anomaly in early summer is significantly correlated with the SIC anomaly in late summer (correlation coefficient, r ≈ −0.8 with a lag of 1 to 4 months). The region exhibiting high (low) ASR anomalies and low (high) SIC anomalies varies yearly. The possible reason is that the solar heat input to ice is most effectively affected by the cloud shielding effect under the maximum TOA solar radiation in June and amplified by the ice-albedo feedback. This intimate delayed ASR-SIC relationship is not represented in most of current climate models. Rather, the models tend to over-emphasize internal sea ice processes in summer.

[1] The 7-day cycle of human activities may lead to the “weekend effect” in climate variables and air pollutants. The weekend effect is defined as the average value (e.g., the diurnal temperature range) for Saturday through Monday minus the average value for Wednesday through Friday. A composite of the ground observations over northeastern China presents that, during 26-year (1980–2005) summers, the weekend effect in the diurnal temperature range increased by 1.2°C. Conversely, the weekend effects in the relative humidity, cloud amount, and light rain (≤5 mm day−1) events decreased. These changes are due to a shifted phase of the weekly cycle of the meteorological variables. The long-term change in weekend effects have a high correlation coefficient (∣r∣ ≈ 0.8) with the decrease in relative humidity over the region, which is likely induced by regional warming. The results suggest that regional warming is a possible factor in a transition of dominant aerosol effects in the weekend effect.