The Geostationary Environment Monitoring Spectrometer (GEMS) is scheduled for launch in February 2020 to monitor air quality (AQ) at an unprecedented spatial and temporal resolution from a geostationary Earth orbit (GEO) for the first time. With the development of UV–visible spectrometers at sub-nm spectral resolution and sophisticated retrieval algorithms, estimates of the column amounts of atmospheric pollutants (O₃, NO₂, SO₂, HCHO, CHOCHO, and aerosols) can be obtained. To date, all the UV–visible satellite missions monitoring air quality have been in low Earth orbit (LEO), allowing one to two observations per day. With UV–visible instruments on GEO platforms, the diurnal variations of these pollutants can now be determined. Details of the GEMS mission are presented, including instrumentation, scientific algorithms, predicted performance, and applications for air quality forecasts through data assimilation. GEMS will be on board the Geostationary Korea Multi-Purpose Satellite 2 (GEO-KOMPSAT-2) satellite series, which also hosts the Advanced Meteorological Imager (AMI) and Geostationary Ocean Color Imager 2 (GOCI-2). These three instruments will provide synergistic science products to better understand air quality, meteorology, the long-range transport of air pollutants, emission source distributions, and chemical processes. Faster sampling rates at higher spatial resolution will increase the probability of finding cloud-free pixels, leading to more observations of aerosols and trace gases than is possible from LEO. GEMS will be joined by NASA’s Tropospheric Emissions: Monitoring of Pollution (TEMPO) and ESA’s Sentinel-4 to form a GEO AQ satellite constellation in early 2020s, coordinated by the Committee on Earth Observation Satellites (CEOS).

Nearly three million premature deaths are attributed to air pollution each year;¹ $\sim 90\%$ of the world’s population is in constant danger of exposure to high levels of particulate matter (PM) with an aerodynamic $\text{diameter}\le 2.5\mu \mathrm{m}$ (PM2.5).² Due to various factors including ongoing industrialization and urbanization with significant changes in climate and geography, especially over East Asia, South Korea had the worst air quality among the Organization for Economic Co-operation and Development (OECD) nations in 2013. Averaged annual exposure to PM2.5 over 3 years (2010 to 2012) was $29.09\mu \mathrm{g}/{\mathrm{m}}^3$, significantly greater than the OECD mean of $14.05\mu \mathrm{g}/{\mathrm{m}}^3$.³ In addition, PM10 levels (PM with a $\text{diameter}\le 10\mu \mathrm{m}$), which had been consistently improving since the 1990s, have deteriorated since 2013, while high concentrations of fine particles are becoming more common.

The international journal of Aerosol and Air Quality Research (AAQR) covers all aspects of aerosol science and technology, atmospheric science and air quality related issues.

Variations in concentrations of PM_2.5, particulate matters of diameters below 2.5 μm, vary following both meteorological conditions and emissions controls. Meteorological conditions particularly affect short‐term high PM_2.5 episodes through accumulations, transports, and secondary formations. This study quantifies the meteorological impacts on high PM_2.5 episodes in the Seoul Metropolitan Area (SMA), Korea, for the period 2016–2018 using empirical and statistical methods. Synoptic weather maps of 77 high PM_2.5 episodes in 2016 are grouped into two synoptic types: onshore winds associated with migratory pressure systems over the SMA and offshore winds from continental high pressure extending toward the SMA. We applied principal component analysis and regression to extract the dominant synoptic types controlling PM_2.5 variability. It identifies two major principal components (PCs) from 12 surface and upper‐air meteorological variables for 2017–2018. Weather patterns in 49 examples of the high‐positive PCs show that the two PCs are capable of reproducing the synoptic weather patterns relevant for high PM_2.5 episodes. To quantify the relationship between the synoptic weather patterns and PM_2.5 levels, the two PCs are further classified into four groups according to their signs. Positive‐ and negative‐PC groups are associated with about 82% and 73% of high‐ and low‐PM_2.5 episodes, respectively, suggesting that most of the high/low PM_2.5 episodes in the SMA can occur under the two PCs‐dominant weather conditions. The results can be utilized as a reference for daily predictions of high PM_2.5 episodes, as well as for quantitative analysis of the climatic influence on the long‐term PM_2.5 variability.

This study investigates the impact of industrial complexes on the air quality in the Ulsan Metropolitan City, Korea, by analyzing the concentration of trace substances. Importantly, this study performs segmentation and analysis of the components of particulate matter for tracking emission sources. Concentrations of particulate matter with aerodynamic diameters ≤10 and ≤2.5 µm (PM10 and PM2.5, respectively) and 19 substances comprising PM2.5 (such as ions, carbon, and nine elements) were measured hourly during the year 2017 in the southeastern intensive air quality monitoring station of the National Institute of Environmental Research, Korea. This study identified and investigated the time periods during which the vanadium content in PM2.5 was higher than the annual mean (1.026 ng/µg) through selection cases (SCs). The annual mean concentrations of PM2.5 and PM10 were 18.50 and 32.35 µg/m³, respectively, and were higher (i.e., 26.54 and 45.84 µg/m³, respectively) in SCs. Notably, the concentrations were high even when the main wind direction of SCs was southeasterly, which was mainly the case in summer. Furthermore, the emission sources contributing to PM2.5 were estimated using the correlations of organic carbon, elemental carbon, zinc, iron, manganese, and titanium concentrations in the SCs. This study demonstrated that a detailed tracking of the emission sources at a local scale is possible by analyzing the composition of the components of PM2.5.