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).

A deep recurrent neural network system based on a long short-term memory (LSTM) model was developed for daily PM₁₀ and PM_2.5 predictions in South Korea. The structural and learnable parameters of the newly developed system were optimized from iterative model training. Independent variables were obtained from ground-based observations over 2.3 years. The performance of the particulate matter (PM) prediction LSTM was then evaluated by comparisons with ground PM observations and with the PM concentrations predicted from two sets of 3-D chemistry-transport model (CTM) simulations (with and without data assimilation for initial conditions). The comparisons showed, in general, better performance with the LSTM than with the 3-D CTM simulations. For example, in terms of IOAs (index of agreements), the PM prediction IOAs were enhanced from 0.36–0.78 with the 3-D CTM simulations to 0.62–0.79 with the LSTM-based model. The deep LSTM-based PM prediction system developed at observation sites is expected to be further integrated with 3-D CTM-based prediction systems in the future. In addition to this, further possible applications of the deep LSTM-based system are discussed, together with some limitations of the current system.

To more accurately estimate direct radiative forcing (DRF) by aerosols, and better investigate particulate pollution over East Asia, precise calculations of the optical properties of aerosols, such as aerosol optical depth (AOD), single scattering albedo (SSA) and aerosol extinction coefficient (σ_ext), are of primary importance. The aerosol optical properties over East Asia were investigated in this study, based on US EPA Models-3/CMAQ v4.5.1 model simulations. The CMAQ model simulations in this study were improved in several ways compared to those in a previous study (Song et al., 2008). Although the details of the improvements were described in the manuscript, the following points should be emphasized: (1) two data assimilation techniques were employed for producing more accurate AOD products and meteorological fields over East Asia; (2) updated/upgraded emission inventories were used in the CMAQ model simulations with a fine grid resolution of 30 × 30 km²; and (3) the 4-D particulate composition calculated from the CMAQ model simulations was converted into 3-D or 4-D aerosol optical products, using the Malm and Hand (2007) algorithm with significant further modifications. The results from the CMAQ model simulations (without assimilation) showed great improvements compared to those from a previous study. For example, in terms of the regression coefficients (R), R values were increased from 0.48–0.68 (previous study) to 0.62–0.79 (this study). The monthly-averaged CMAQ-simulated single scattering albedo (SSA) also agreed well with the AERONET SSA, with the exceptions of the Hong Kong and Taipei sites, where the air qualities were strongly influenced by active biomass burning events from January to April. There were also excellent matches between the vertical profiles of the CMAQ-simulated σ_ext and LIDAR-retrieved σ_ext. It was also found that the contributions of (NH₄)₂SO₄ during summer, NH₄NO₃ during winter, sea-salt particles during winter and dust particles during spring to the total AOD were large over East Asia. In particular, the largest contribution of NH₄NO₃ to the total AOD was found over East Asia during winter. Therefore, it was suggested that this contribution of NH₄NO₃ should not be neglected. In order to produce more accurate AOD products, the CMAQ-simulated AODs were further assimilated with the MODIS-retrieved AODs. Both of the assimilated and AERONET AODs were better correlated with each other than the CMAQ-simulated and AERONET AODs. The obvious benefits from this study would be that with these improved aerosol optical properties, the particulate pollution (e.g. AOD can be served as a proxy to PM_2.5 or PM₁₀) and DRF by aerosols over East Asia can be more satisfactorily investigated in future.

In this study, the spatio-temporal and seasonal distributions of EOS/Terra Moderate Resolution Imaging Spectroradiometer (MODIS)-derived aerosol optical depth (AOD) over East Asia were analyzed in conjunction with US EPA Models-3/CMAQ v4.3 modeling. In this study, two MODIS AOD products (τ_MODIS: τ_M-BAER and τ_NASA) retrieved through a modified Bremen Aerosol Retrieval (M-BAER) algorithm and NASA collection 5 (C005) algorithm were compared with the AOD (τ_CMAQ) that was calculated from the US EPA Models-3/CMAQ model simulations. In general, the CMAQ-predicted AOD values captured the spatial and temporal variations of the two MODIS AOD products over East Asia reasonably well. Since τ_MODIS cannot provide information on the aerosol chemical composition in the atmosphere, different aerosol formation characteristics in different regions and different seasons in East Asia cannot be described or identified by τ_MODIS itself. Therefore, the seasonally and regionally varying aerosol formation and distribution characteristics were investigated by the US EPA Models-3/CMAQ v4.3 model simulations. The contribution of each particulate chemical species to τ_MODIS and τ_CMAQ showed strong spatial, temporal and seasonal variations. For example, during the summer episode, τ_MODIS and τ_CMAQ were mainly raised due to high concentrations of (NH₄)₂SO₄ over Chinese urban and industrial centers and secondary organic aerosols (SOAs) over the southern parts of China, whereas during the late fall and winter episodes, τ_MODIS and τ_CMAQ were higher due largely to high levels of NH₄NO₃ formed over the urban and industrial centers, as well as in areas with high NH₃ emissions. τ_CMAQ was in general larger than τ_MODIS during the year, except for spring. The high biases (τ_CMAQ>τ_MODIS) may be due to the excessive formation of both (NH₄)₂SO₄ (summer episode) and NH₄NO₃ (fall and winter episodes) over China, possibly from the use of overestimated values for NH₃ emissions in the CMAQ modeling. According to CMAQ modeling, particulate NH₄NO₃ made a 14% (summer) to 54% (winter) contribution to σ_ext and τ_CMAQ. Therefore, the importance of NH₄NO₃ in estimating τ should not be ignored, particularly in studies of the East Asian air quality. In addition, the accuracy of τ_M-BAER and τ_NASA was evaluated by a comparison with the AOD (τ_AERONET) from the AERONET sites in East Asia. Both τ_M-BAER and τ_NASA showed a strong correlation with τ_AERONET around the 1:1 line (R=0.79), indicating promising potential for the application of both the M-BAER and NASA aerosol retrieval algorithms to satellite-based air quality monitoring studies in East Asia.

In this study, NO₂ columns from the US EPA Models-3/CMAQ model simulations carried out using the 2001 ACE-ASIA (Asia Pacific Regional Aerosol Characterization Experiment) emission inventory over East Asia were compared with the GOME-derived NO₂ columns. There were large discrepancies between the CMAQ-predicted and GOME-derived NO₂ columns in the fall and winter seasons. In particular, while the CMAQ-predicted NO₂ columns produced larger values than the GOME-derived NO₂ columns over South Korea for all four seasons, the CMAQ-predicted NO₂ columns produced smaller values than the GOME-derived NO₂ columns over North China for all seasons with the exception of summer (summer anomaly). It is believed that there might be some error in the NO_x emission estimates as well as uncertainty in the NO_x chemical loss rates over North China and South Korea. Regarding the latter, this study further focused on the biogenic VOC (BVOC) emissions that were strongly coupled with NO_x chemistry during summer in East Asia. This study also investigated whether the CMAQ-modeled NO₂/NO_x ratios with the possibly overestimated isoprene emissions were higher than those with reduced isoprene emissions. Although changes in both the NO_x chemical loss rates and NO₂/NO_x ratios from CMAQ-modeling with the different isoprene emissions affected the CMAQ-modeled NO₂ levels, the effects were found to be limited, mainly due to the low absolute levels of NO₂ in summer. Seasonal variations of the NO_x emission fluxes over East Asia were further investigated by a set of sensitivity runs of the CMAQ model. Although the results still exhibited the summer anomaly possibly due to the uncertainties in both NO_x-related chemistry in the CMAQ model and the GOME measurements, it is believed that consideration of both the seasonal variations in NO_x emissions and the correct BVOC emissions in East Asia are critical. Overall, it is estimated that the NO_x emissions are underestimated by ~57.3% in North China and overestimated by ~46.1% in South Korea over an entire year. In order to confirm the uncertainty in NO_x emissions, the NO_x emissions over South Korea and China were further investigated using the ACE-ASIA, REAS (Regional Emission inventory in ASia), and CAPSS (Clean Air Policy Support System) emission inventories. The comparison between the CMAQ-calculated and GOME-derived NO₂ columns indicated that both the ACE-ASIA and REAS inventories have some uncertainty in NO_x emissions over North China and South Korea, which can also lead to some errors in modeling the formation of ozone and secondary aerosols in South Korea and North China.

Comparison between the CMAQ (Community Multi-scale Air Quality Model)-calculated and OMI (Ozone Monitoring Instrument)-retrieved tropospheric NO₂ columns was carried out for 2006 over East Asia (100–150° E; 20–50° N) to evaluate the bottom-up NO_x emission fluxes of INTEX-B, CAPSS, and REAS v1.11 inventories. The three emission inventories were applied to the CMAQ model simulations for the countries of China, South Korea, and Japan, respectively. For the direct comparison between the two NO₂ columns, the averaging kernels (AKs) obtained from the Royal Netherlands Meteorological Institute (KNMI)/DOMINO v2.0 daily product were applied to the CMAQ-simulated data. The analysis showed that the two tropospheric NO₂ columns from the CMAQ model simulations and OMI observations (Ω_CMAQ,AK and Ω_OMI) had good spatial and seasonal correlation, with correlation coefficients ranging from 0.71 to 0.96. In addition, the normalized mean errors (NMEs) between the Ω_CMAQ,AK and Ω_OMI were found to range from ~ 40 to ~ 63%. The Ω_CMAQ,AK were, on annual average, ~ 28% smaller (in terms of the NMEs) than the Ω_OMI, indicating that the NO_x emissions used were possibly underestimated in East Asia. Large absolute differences between the Ω_CMAQ,AK and Ω_OMI were found, particularly over central eastern China (CEC) during winter (annual averaged mean error of ~ 4.51 × 10¹⁵ molecules cm⁻²). Although such differences between the Ω_CMAQ,AK and Ω_OMI are likely caused by the errors and biases in the NO_x emissions used in the CMAQ model simulations, it can be rather difficult to quantitatively relate the differences to the accuracy of the NO_x emissions, because there are also several uncertain factors in the CMAQ model, satellite-retrieved NO₂ columns and AK products, and NO_x and other trace gas emissions. In this context, three uncertain factors were selected and analyzed with sensitivity runs (monthly variations in NO_x emissions; influences of different NO_x emission fluxes; and reaction probability of N₂O₅ radicals). Other uncertain or possible influential factors were also discussed to suggest future direction of the study.