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This paper is an overview of the progress in sky radiometer technology and the development of the network called SKYNET. It is found that the technology has produced useful on-site calibration methods, retrieval algorithms, and data analyses from sky radiometer observations of aerosol, cloud, water vapor, and ozone. A formula was proposed for estimating the accuracy of the sky radiometer calibration constant F0 using the improved Langley (IL) method, which was found to be a good approximation to observed monthly mean uncertainty in F0, around 0.5 % to 2.4 % at the Tokyo and Rome sites and smaller values of around 0.3 % to 0.5 % at the mountain sites at Mt. Saraswati and Davos. A new cross IL (XIL) method was also developed to correct an underestimation by the IL method in cases with large aerosol retrieval errors. The root-mean-square difference (RMSD) in aerosol optical thickness (AOT) comparisons with other networks took values of less than 0.02 for λ≥500 nm and a larger value of about 0.03 for shorter wavelengths in city areas and smaller values of less than 0.01 in mountain comparisons. Accuracies of single-scattering albedo (SSA) and size distribution retrievals are affected by the propagation of errors in measurement, calibrations for direct solar and diffuse sky radiation, ground albedo, cloud screening, and the version of the analysis software called the Skyrad pack. SSA values from SKYNET were up to 0.07 larger than those from AERONET, and the major error sources were identified as an underestimation of solid viewing angle (SVA) and cloud contamination. Correction of these known error factors reduced the SSA difference to less than 0.03. Retrievals of other atmospheric constituents by the sky radiometer were also reviewed. Retrieval accuracies were found to be about 0.2 cm for precipitable water vapor amount and 13 DU (Dobson Unit) for column ozone amount. Retrieved cloud optical properties still showed large deviations from validation data, suggesting a need to study the causes of the differences. It is important that these recent studies on improvements presented in the present paper are introduced into the existing operational systems and future systems of the International SKYNET Data Center.

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).

We use Aerosol Robotic Network (AERONET) observation data to empirically determine how natural and anthropogenic aerosol categories (i.e. mineral dust, biomass burning, and urban-industrial aerosols) affect light extinction, showing that their radiative forcing varies strongly with the surface albedo. Generally, the radiative forcing depends on the aerosol loading, but the efficiency varies with the aerosol type and aerosol-radiation-surface interactions. Desert dust, biomass burning and urban-industrial aerosols can exhibit dramatic shifts in radiative forcing at the top of the atmosphere, from cooling to warming, at surface albedos from below 0.5 to above 0.75. Based on the linear relationship between the radiative forcing efficiency and surface albedo for aeolian aerosols, using Moderate Resolution Imaging Spectroradiometer (MODIS) AOT (Aerosol Optical Thickness) and surface albedo data, we characterized a large Asian dust event during the spring of 2001, and demonstrate its immense spatially varying radiative forcing, ranging from about −84.0 to +69.3 W/m2. For extensive Russian wildfires during the summer of 2010, strong radiative cooling forcing variability of biomass combustion aerosols is found, ranging from about −86.3 to +3.1 W/m2. For a thick urban-industrial aerosol haze over northern India during the winter of 2017, a large range of about −85.0 to −0.3 W/m2 is found. These wide ranges underscore the need to accurately define aerosol-radiation-surface interactions.

Retrievals of total column NO2 (TCNO2) are compared for 14 sites from the Ozone Measuring Instrument (OMI using OMNO2-NASA v3.1) on the AURA satellite and from multiple ground-based PANDORA spectrometer instruments making direct-sun measurements. While OMI accurately provides the daily global distribution of retrieved TCNO2, OMI almost always underestimates the local amount of TCNO2 by 50 % to 100 % in polluted areas, while occasionally the daily OMI value exceeds that measured by PANDORA at very clean sites. Compared to local ground-based or aircraft measurements, OMI cannot resolve spatially variable TCNO2 pollution within a city or urban areas, which makes it less suitable for air quality assessments related to human health. In addition to systematic underestimates in polluted areas, OMI's selected 13:30 Equator crossing time polar orbit causes it to miss the frequently much higher values of TCNO2 that occur before or after the OMI overpass time. Six discussed Northern Hemisphere PANDORA sites have multi-year data records (Busan, Seoul, Washington DC, Waterflow, New Mexico, Boulder, Colorado, and Mauna Loa), and one site in the Southern Hemisphere (Buenos Aires, Argentina). The first four of these sites and Buenos Aires frequently have high TCNO2 (TCNO2 > 0.5 DU). Eight additional sites have shorter-term data records in the US and South Korea. One of these is a 1-year data record from a highly polluted site at City College in New York City with pollution levels comparable to Seoul, South Korea. OMI-estimated air mass factor, surface reflectivity, and the OMI 24 km × 13 km FOV (field of view) are three factors that can cause OMI to underestimate TCNO2. Because of the local inhomogeneity of NOx emissions, the large OMI FOV is the most likely factor for consistent underestimates when comparing OMI TCNO2 to retrievals from the small PANDORA effective FOV (measured in m2) calculated from the solar diameter of 0.5∘.

The Geostationary Environment Monitoring Spectrometer (GEMS) is scheduled to be launched in 2019–2020 on board the GEO-KOMPSAT (GEOstationary KOrea Multi-Purpose SATellite)-2B, contributing as the Asian partner of the global geostationary constellation of air quality monitoring. To support this air quality satellite mission, we perform a cross-evaluation of simulated GEMS ozone profile retrievals from OMI (Ozone Monitoring Instrument) data based on the optimal estimation and ozonesonde measurements within the GEMS domain, covering from 5∘ S (Indonesia) to 45∘ N (south of the Russian border) and from 75 to 145∘ E. The comparison between ozonesonde and GEMS shows a significant dependence on ozonesonde types. Ozonesonde data measured by modified Brewer–Mast (MBM) at Trivandrum and New Delhi show inconsistent seasonal variabilities in tropospheric ozone compared to carbon–iodine (CI) and electrochemical condensation cell (ECC) ozonesondes at other stations in a similar latitude regime. CI ozonesonde measurements are negatively biased relative to ECC measurements by 2–4 DU; better agreement is achieved when simulated GEMS ozone retrievals are compared to ECC measurements. ECC ozone data at Hanoi, Kuala Lumpur, and Singapore show abnormally worse agreements with simulated GEMS retrievals than other ECC measurements. Therefore, ECC ozonesonde measurements at Hong Kong, Pohang, Naha, Sapporo, and Tsukuba are finally identified as an optimal reference dataset. The accuracy of simulated GEMS retrievals is estimated to be ∼5.0 % for both tropospheric and stratospheric column ozone with the precision of 15 % and 5 %, which meets the GEMS ozone requirements.

In this present study, an aerosol effective height (AEH) retrieval algorithm has been developed using the O4 air mass factor (AMF) at 477 nm from the hyperspectral Ozone Monitoring Instrument (OMI). We investigated the magnitude of change in topographical and seasonal O4 vertical column density (VCD) in Northeast Asia and evaluated its effect on AEH retrieval accuracy using our AEH retrieval algorithm. In addition, the effect of a temperature-dependent cross-section for O4 (TDCS) on Look Up Table (LUT)-based AEH retrieval accuracy was quantified. A comparison between the retrieved AEH and those from the NIES lidar network for the period from January 2005 to June 2009, applying both the TDCS and the seasonal and topographical O4 VCDs, resulted in a root mean square error (RMSE) of 0.44 km for both smoke and dust aerosols. However, when both a TDCS (an O4 absorption cross-section at a single temperature of 293 K; SCS) and a single O4 VCD value were applied to the LUT, the RMSE for both aerosol types was calculated to be 0.52 km (0.51 km), which implies that TDCS contributes most to AEH retrieval accuracy when accurate O4 VCDs are applied to the LUT. For smoke aerosols only, both TDCS and multiple O4 VCD (SCS and single O4 VCD) applications had RMSE values of 0.46 km (0.66 km). The retrieved AEHs were additionally compared with satellite-based lidar measurements. We also investigated the effects of uncertainties in our algorithm input data (e.g., O4 VCD, TDCS, AOD, and surface reflectance) on AEH retrieval error using synthetic radiances. Large errors can be caused by uncertainties in O4 VCD and AOD. In particular (0.4 ≤ AOD

To quantify the impact of the direct aerosol effect accurately, this study incorporated the Geostationary Ocean Color Imager (GOCI) aerosol optical depth (AOD) into a coupled meteorology-chemistry model. We designed three model simulations to observe the impact of AOD assimilation and aerosol feedback during the KORUS-AQ campaign (May – June 2016). By assimilating the GOCI AOD with high temporal and spatial resolutions, we improve the statistics from the comparison AOD and AERONET data (RMSE: 0.12, R: 0.77, IOA: 0.69, MAE: 0.08). The inclusion of the direct effect of aerosols produces the best model performance (RMSE: 0.10, R: 0.86, IOA: 0.72, MAE: 0.07). AOD values were increased as much as 0.15, which is associated with an average reduction in solar radiation of −31.39 W/m(2), a planetary boundary layer height (−104.70 m), an air temperature (−0.58 °C), and a surface wind speed (−0.07 m/s) over land. In addition, concentrations of major gaseous and particulate pollutants at the surface (SO(2), NO(2), NH(3), [Formula: see text] , [Formula: see text] , [Formula: see text] , PM(2.5)) increase by 7.87 – 34% while OH concentration decreases by −4.58 %. Changes in meteorology and air quality appear to be more significant in high-aerosol loading areas. The integrated process rate analysis shows decelerated vertical transport, resulting in an accumulation of air pollutants near the surface and the amount of nitrate, which is higher than that of sulfate because of its response to reduced temperature. We conclude that constraining aerosol concentrations using geostationary satellite data is a prerequisite for quantifying the impact of aerosols on meteorology and air quality.

We investigated the influence of long-range transboundary transport (LRTT) on the aerosol concentrations in the Korean peninsula using the ground- and satellite-based remote sensing with back-trajectory calculations during the Korea-United States Air Quality (KORUS-AQ) campaign. Specifically, aerosol optical depth (AOD) observations from a geostationary satellite can directly provide the progression and evolution of aerosol plume transport. During high pollution cases in western Korea, we found the AOD enhancement over the Yellow Sea and east-central China, at maximum >200% over the pathway of LRTT compared to the mean condition. Particularly, high AOD in the Shandong peninsula appears coincidentally with the high AOD over South Korea in a day, revealing the strong influence of the east-central Chinese emission. Back-trajectory patterns remarkably capture the movement of high AOD bands detected by the geostationary satellite monitoring. LRTT cases through the inside of boundary layer at east-central China usually contribute to the high AOD in Korea, while air-masses above the boundary layer in north China and Mongolia do not much relate to the Korean pollution, showing the importance for both the direction and height of the air-mass movement. Travel speed is another significant factor to describe the LRTT effect. Despite the large effect of LRTT to both urban and rural sites in Korea, sometimes urban sites are more affected by the domestic emission when the air-mass travels shorter than ∼250 km per day, notifying that the effect of Korean domestic emission cannot be negligible as well. Our findings reveal that usage of geostationary satellite observations enables us to better evaluate the influence of LRTT on the local air pollution.

This study newly applies measurements from two geostationary satellites, the Advanced Himawari Imager (AHI) onboard the geostationary satellite Himawari-8 and the Geostationary Ocean Color imager (GOCI) onboard the geostationary satellite COMS, to evaluate a unique regional aerosol-transport model coupled to a non-hydrostatic icosahedral atmospheric model (NICAM) at a high resolution without any nesting technique and boundary conditions of the aerosols. Taking advantage of the unique capability of these geostationary satellites to measure aerosols with unprecedentedly high temporal resolution, we focus on a target area (115°E-155°E, 20°N-50°N) in East Asia in May 2016, which featured the periodic transport of industrial aerosols and a very heavy aerosol plume from Siberian wildfires. The aerosol optical thickness (AOT) fields are compared among the AHI, GOCI, MODIS, AERONET and NICAM data. The results show that both AHI- and GOCI-retrieved AOTs were generally comparable to the AERONET-retrieved ones, with high correlation coefficients of approximately 0.7 in May 2016. They also show that NICAM successfully captured the detailed horizontal distribution of AOT transported from Siberia to Japan on the most polluted day (18 May 2016). The monthly statistical metrics, including correlation between the model and either AHI or GOCI, are estimated to be >0.4 in 42–49% of the target area. With the aid of sensitivity model experiments with and without Siberian wildfires, it was found that a long-range transport of aerosols from Siberian wildfires (from as far as 3000 km) to Japan influenced the monthly mean aerosol levels, accounting for 7–35% of the AOT, 26–49% of the surface PM2.5 concentrations, and 25–66% of the aerosol extinction above 3 km in height over Japan. Therefore, the air pollutants from Siberian wildfire cannot be ignored for the spring over Japan.

Long-term exposure to particulate matter (PM) with aerodynamic diameters 10) and 2.5 µm (PM2.5) has negative effects on human health. Although station-based PM monitoring has been conducted around the world, it is still challenging to provide spatially continuous PM information for vast areas at high spatial resolution. Satellite-derived aerosol information such as aerosol optical depth (AOD) has been frequently used to investigate ground-level PM concentrations. In this study, we combined multiple satellite-derived products including AOD with model-based meteorological parameters (i.e., dew-point temperature, wind speed, surface pressure, planetary boundary layer height, and relative humidity) and emission parameters (i.e., NO, NH3, SO2, primary organic aerosol (POA), and HCHO) to estimate surface PM concentrations over South Korea. Random forest (RF) machine learning was used to estimate both PM10 and PM2.5 concentrations with a total of 32 parameters for 2015–2016. The results show that the RF-based models produced good performance resulting in R2 values of 0.78 and 0.73 and root mean square errors (RMSEs) of 17.08 and 8.25 µg m−3 for PM10 and PM2.5, respectively. In particular, the proposed models successfully estimated high PM concentrations. AOD was identified as the most significant for estimating ground-level PM concentrations, followed by wind speed, solar radiation, and dew-point temperature. The use of aerosol information derived from a geostationary satellite sensor (i.e., Geostationary Ocean Color Imager, GOCI) resulted in slightly higher accuracy for estimating PM concentrations than that from a polar-orbiting sensor system (i.e., the Moderate Resolution Imaging Spectroradiometer, MODIS). The proposed RF models yielded better performance than the process-based approaches, particularly in improving on the underestimation of the process-based models (i.e., GEOS-Chem and the Community Multiscale Air Quality Modeling System, CMAQ).

The Korea–United States Air Quality (KORUS-AQ) field study was conducted during May–June 2016. The effort was jointly sponsored by the National Institute of Environmental Research of South Korea and the National Aeronautics and Space Administration of the United States. KORUS-AQ offered an unprecedented, multi-perspective view of air quality conditions in South Korea by employing observations from three aircraft, an extensive ground-based network, and three ships along with an array of air quality forecast models. Information gathered during the study is contributing to an improved understanding of the factors controlling air quality in South Korea. The study also provided a valuable test bed for future air quality–observing strategies involving geostationary satellite instruments being launched by both countries to examine air quality throughout the day over Asia and North America. This article presents details on the KORUS-AQ observational assets, study execution, data products, and air quality conditions observed during the study. High-level findings from companion papers in this special issue are also summarized and discussed in relation to the factors controlling fine particle and ozone pollution, current emissions and source apportionment, and expectations for the role of satellite observations in the future. Resulting policy recommendations and advice regarding plans going forward are summarized. These results provide an important update to early feedback previously provided in a Rapid Science Synthesis Report produced for South Korean policy makers in 2017 and form the basis for the Final Science Synthesis Report delivered in 2020.

By using multiple satellite measurements, the changes of the aerosol optical depth (AOD) and nitrogen dioxide (NO2) over South Korea were investigated from January to March 2020 to evaluate the COVID-19 effect on the regional air quality. The NO2 decrease in South Korea was found but not significant, which indicates the effects of spontaneous social distancing under the maintenance of ordinary life. The AODs in 2020 were normally high in January, but they became lower starting from February. Since the atmosphere over Eastern Asia was unusually stagnant in January and February 2020, the AOD decrease in February 2020 clearly reveals the positive effect of the COVID-19. Considering the insignificant NO2 decrease in South Korea and the relatively long lifetime of aerosols, the AOD decrease in South Korea may be more attributed to the improvement of the air quality in neighboring countries. In March, regional atmosphere became well mixed and ventilated over South Korea, contributing to large enhancement of air quality. While the social activity was reduced after the COVID-19 outbreak, the regional meteorology should be also examined significantly to avoid the biased evaluation of the social impact on the change of the regional air quality.

The retrieval of optimal aerosol datasets by the synergistic use of hyperspectral ultraviolet (UV)–visible and broadband meteorological imager (MI) techniques was investigated. The Aura Ozone Monitoring Instrument (OMI) Level 1B (L1B) was used as a proxy for hyperspectral UV–visible instrument data to which the Geostationary Environment Monitoring Spectrometer (GEMS) aerosol algorithm was applied. Moderate-Resolution Imaging Spectroradiometer (MODIS) L1B and dark target aerosol Level 2 (L2) data were used with a broadband MI to take advantage of the consistent time gap between the MODIS and the OMI. First, the use of cloud mask information from the MI infrared (IR) channel was tested for synergy. High-spatial-resolution and IR channels of the MI helped mask cirrus and sub-pixel cloud contamination of GEMS aerosol, as clearly seen in aerosol optical depth (AOD) validation with Aerosol Robotic Network (AERONET) data. Second, dust aerosols were distinguished in the GEMS aerosol-type classification algorithm by calculating the total dust confidence index (TDCI) from MODIS L1B IR channels. Statistical analysis indicates that the Probability of Correct Detection (POCD) between the forward and inversion aerosol dust models (DS) was increased from 72% to 94% by use of the TDCI for GEMS aerosol-type classification, and updated aerosol types were then applied to the GEMS algorithm. Use of the TDCI for DS type classification in the GEMS retrieval procedure gave improved single-scattering albedo (SSA) values for absorbing fine pollution particles (BC) and DS aerosols. Aerosol layer height (ALH) retrieved from GEMS was compared with Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data, which provides high-resolution vertical aerosol profile information. The CALIOP ALH was calculated from total attenuated backscatter data at 1064 nm, which is identical to the definition of GEMS ALH. Application of the TDCI value reduced the median bias of GEMS ALH data slightly. The GEMS ALH bias approximates zero, especially for GEMS AOD values of >~0.4 and GEMS SSA values of

TEMPO was selected in 2012 by NASA as the first Earth Venture Instrument, for launch between 2018 and 2021. It will measure atmospheric pollution for greater North America from space using ultraviolet and visible spectroscopy. TEMPO observes from Mexico City, Cuba, and the Bahamas to the Canadian oil sands, and from the Atlantic to the Pacific, hourly and at high spatial resolution (~2.1 km N/S×4.4 km E/W at 36.5°N, 100°W). TEMPO provides a tropospheric measurement suite that includes the key elements of tropospheric air pollution chemistry, as well as contributing to carbon cycle knowledge. Measurements are made hourly from geostationary (GEO) orbit, to capture the high variability present in the diurnal cycle of emissions and chemistry that are unobservable from current low-Earth orbit (LEO) satellites that measure once per day. The small product spatial footprint resolves pollution sources at sub-urban scale. Together, this temporal and spatial resolution improves emission inventories, monitors population exposure, and enables effective emission-control strategies. TEMPO takes advantage of a commercial GEO host spacecraft to provide a modest cost mission that measures the spectra required to retrieve ozone (O), nitrogen dioxide (NO), sulfur dioxide (SO), formaldehyde (HCO), glyoxal (CHO), bromine monoxide (BrO), IO (iodine monoxide), water vapor, aerosols, cloud parameters, ultraviolet radiation, and foliage properties. TEMPO thus measures the major elements, directly or by proxy, in the tropospheric O chemistry cycle. Multi-spectral observations provide sensitivity to O in the lowermost troposphere, substantially reducing uncertainty in air quality predictions. TEMPO quantifies and tracks the evolution of aerosol loading. It provides these near-real-time air quality products that will be made publicly available. TEMPO will launch at a prime time to be the North American component of the global geostationary constellation of pollution monitoring together with the European Sentinel-4 (S4) and Korean Geostationary Environment Monitoring Spectrometer (GEMS) instruments.

We investigated the characteristics of the total ozone column (TOC) around West Antarctica (near the Weddell Sea) compared with ambient meteorological factors. For this analysis, we used ground-based and satellite TOC measurements as well as meteorology (air temperature, potential vorticity and wind field) from reanalysis data. Long-term patterns of TOC show the large year-to-year variation (e.g., maximumly ∼200 DU at King Sejong) but a steady recovering trend recently. Despite a generally consistent pattern, the TOC around West Antarctica did not correlate well between high- and low-latitude regions during austral spring; this result implies that the ozone hole area had a spatial variation over West Antarctica. The TOC pattern around West Antarctica correlated well with air temperature but showed a vertical difference; high positive correlations appeared in the lower stratosphere (maximumly R > 0.9 at ∼50–100 hPa height) showing enhanced ozone depletion in colder conditions, but negative correlations appeared in the upper stratosphere (minimum R 0.9 at ∼500–600 K height) during the austral spring but a moderately negative correlation in the lower stratosphere (minimum R

This study examines the semi-empirical models to evaluate the atmospheric broadband transmission of erythemally weighted ultraviolet (EUV), total ultraviolet (TUV), and global solar radiation (GS) by clouds, aerosols, and ozone in Seoul, Korea (37.57°N, 128.98°E). Climatological values of surface solar irradiance (SSI) for Seoul are briefly summarized, and atmospheric transmission is defined as the ratio of measured SSI to clear-sky irradiance calculated using harmonic analysis. Three multiple linear regression models are developed for the EUV, TUV, and GS spectral bands for all-sky (clear and cloudy conditions) transmission using three independent variables: cloud-cover amount, aerosol optical depth, and total ozone. The modeled total transmissions are 75%, 71%, and 67% for the EUV, TUV, and GS spectral bands, respectively, which are slightly overestimated than the measured values. The modeled annual mean clearness index, KT, is 1%, 45%, and 48% for the three spectral bands, respectively. Four semi-empirical models were developed to calculate transmission and were evaluated. Among the various empirical models, estimates from the exponential models were found to be the closest to measured values, having the lowest mean bias error (−0.3% to −2.4%) and highest explained variance (R2 = 0.17 to 0.50; p = 0.000), as was expected from their theoretical bases. The annual average transmissions of cloud, aerosol, ozone, and all three combined show decrease from the EUV to the TUV and GS bands.

Spatial distribution of diurnal variations of aerosol properties in South Korea, both long term and short term, is studied by using 9 AERONET (AErosol RObotic NETwork) sites from 1999 to 2017 and an additional 10 sites during the KORUS-AQ (Korea–United States Air Quality) field campaign in May and June of 2016. The extent to which the WRF-Chem (Weather Research and Forecasting coupled with Chemistry) model and the GOCI (Geostationary Ocean Color Imager) satellite retrieval can describe these variations is also analyzed. On a daily average, aerosol optical depth (AOD) at 550 nm is 0.386 and shows a diurnal variation of 20 to −30 % in inland sites, which is larger than the AOD of 0.308 and diurnal variation of ±20 % seen in coastal sites. For all the inland and coastal sites, AERONET, GOCI, and WRF-Chem, and observed PM2.5 (particulate matter with aerodynamic diameter less than 2.5 µm) data generally show dual peaks for both AOD and PM2.5, one in the morning (often at ∼08:00–10:00 KST, Korea Standard Time, especially for PM2.5) and another in the early afternoon (∼14:00 KST, albeit for PM2.5 this peak is smaller and sometimes insignificant). In contrast, Ångström exponent values in all sites are between 1.2 and 1.4 with the exception of the inland rural sites having smaller values near 1.0 during the early morning hours. All inland sites experience a pronounced increase in the Ångström exponent from morning to evening, reflecting an overall decrease in particle size in daytime. To statistically obtain the climatology of diurnal variation of AOD, a minimum requirement of ∼2 years of observation is needed in coastal rural sites, twice as long as that required for the urban sites, which suggests that the diurnal variation of AOD in an urban setting is more distinct and persistent. While Korean GOCI satellite retrievals are able to consistently capture the diurnal variation of AOD (although it has a systematically low bias of 0.04 on average and up to 0.09 in later afternoon hours), WRF-Chem clearly has a deficiency in describing the relative change of peaks and variations between the morning and afternoon, suggesting further studies for the diurnal profile of emissions. Furthermore, the ratio between PM2.5 and AOD in WRF-Chem is persistently larger than the observed counterparts by 30 %–50 % in different sites, but spatially no consistent diurnal variation pattern of this ratio can be found. Overall, the relatively small diurnal variation of PM2.5 is in high contrast with large AOD diurnal variation, which suggests the large diurnal variation of AOD–PM2.5 relationships (with the PM2.5 ∕ AOD ratio being largest in the early morning, decreasing around noon, and increasing in late afternoon) and, therefore, the need to use AOD from geostationary satellites to constrain either modeling or estimate of surface PM2.5 for air quality application.

Formaldehyde (HCHO) is either directly emitted from sources or produced during the oxidation of volatile organic compounds (VOCs) in the troposphere. It is possible to infer atmospheric HCHO concentrations using space-based observations, which may be useful for studying emissions and tropospheric chemistry at urban to global scales depending on the quality of the retrievals. In the near future, an unprecedented volume of satellite-based HCHO measurement data will be available from both geostationary and polar-orbiting platforms. Therefore, it is essential to develop retrieval methods appropriate for the next-generation satellites that measure at higher spatial and temporal resolution than the current ones. In this study, we examine the importance of fine spatial and temporal resolution a priori profile information on the retrieval by conducting approximately 45 000 radiative transfer (RT) model calculations in the Los Angeles Basin (LA Basin) megacity. Our analyses suggest that an air mass factor (AMF, a factor converting observed slant columns to vertical columns) based on fine spatial and temporal resolution a priori profiles can better capture the spatial distributions of the enhanced HCHO plumes in an urban area than the nearly constant AMFs used for current operational products by increasing the columns by ∼ 50 % in the domain average and up to 100 % at a finer scale. For this urban area, the AMF values are inversely proportional to the magnitude of the HCHO mixing ratios in the boundary layer. Using our optimized model HCHO results in the Los Angeles Basin that mimic the HCHO retrievals from future geostationary satellites, we illustrate the effectiveness of HCHO data from geostationary measurements for understanding and predicting tropospheric ozone and its precursors.

In this study, Geostationary Ocean Color Imager (GOCI) AOD and Visible Infrared Imaging Radiometer Suite (VIIRS) AOD data were assimilated to forecast surface PM2.5 concentrations over Eastern China, by using the three–dimensional variational (3DAVR) data assimilation (DA) system, to compare DA impacts by assimilating AOD retrievals from these two types of satellites. Three experiments were conducted, including a CONTROL without the AOD assimilation, and GOCIDA and VIIRSDA with the assimilation of AOD retrievals from GOCI and VIIRS, respectively. By utilizing the Weather Research and Forecasting with Chemistry (WRF/Chem) model, 48-h forecasts were initialized at each 06 UTC from 19 November to 06 December 2013. These forecasts were evaluated with 248 ground-based measurements from the air quality monitoring network across 67 China cities. The results show that overall the CONTROL underestimated surface PM2.5 concentrations, especially over Jing–Jin–Ji (JJJ) region and Yangtze River Delta (YRD) region. Both the GOCIDA and VIIRSDA produced higher surface PM2.5 concentrations mainly over Eastern China, which fits well with the PM2.5 measurements at these eastern sites, with more than 8% error reductions (ER). Moreover, compared to CONTROL, GOCIDA reduced 14.0% and 6.4% error on JJJ region and YRD region, respectively, while VIIRSDA reduced respectively 2.0% and 13.4% error over the corresponding areas. During the heavy polluted period, VIIRSDA improved all sites within YRD region, and GOCIDA enhanced 84% sites. Meanwhile, GOCIDA improved 84% sites on JJJ region, while VIIRSDA did not affect that region. These geographic distinctions might result from spatial dissimilarity between GOCI AOD and VIIRS AOD at time intervals. Moreover, the larger increment produced by AOD DA under stable meteorological conditions could lead to a longer duration (e.g., 1–2 days, > 2 days) of AOD DA impacts. Even though with AOD DA, surface PM2.5 concentrations were still underestimated clearly over heavy polluted periods. And 3% sites performed worse, where low PM2.5 values were observed and CONTROL performed well. With this study, the results indicate that AOD DA can partially improve the accuracy of PM2.5 forecasts. And the obvious geographic differences on forecasts emphasize the potential and importance of combining AOD retrievals from GOCI and VIIRS into data assimilation.

Clouds are an important component of the atmosphere that affects both climate and weather, however, their contributions can be very difficult to determine. Ceilometer measurements can provide high resolution information on atmospheric conditions such as cloud base height (CBH) and vertical frequency of cloud occurrence (CVF). This study presents the first comprehensive analysis of CBH and CVF derived using Vaisala CL51 ceilometers at two urban stations in Seoul, Korea, during a three-year period from January 2014 to December 2016. The average frequency of cloud occurrence detected by the ceilometers is 54.3%. It is found that the CL51 is better able to capture CBH as compared to another ceilometer CL31 at a nearby meteorological station because it could detect high clouds more accurately. Frequency distributions for CBH up to 13,000 m providing detailed vertical features with 500-m interval show 55% of CBHs below 2 km for aggregated CBHs. A bimodal frequency distribution was observed for three-layers CBHs. A monthly variation of CVF reveals that frequency concentration of lower clouds is found in summer and winter, and higher clouds more often detected in spring and autumn. Monthly distribution features of cloud occurrence and precipitation are depending on seasons and it might be easy to define their relationship due to higher degree of variability of precipitation than cloud occurrence. However, a fluctuation of cloud occurrence frequency in summer is similar to precipitation in trend, whereas clouds in winter are relatively frequent but precipitation is not accompanied. In addition, recent decrease of summer precipitation could be mostly explained by a decrease of cloud occurrence. Anomalous precipitation recorded sometimes is considerably related to corresponding cloud occurrence. The diurnal and daily variations of CBH and CVF from ceilometer observations and the analysis of microwave radiometer measurements for two typical cloudiness cases are also reviewed in parallel. This analysis in finer temporal scale exhibits that utilization of ground-based observations together could help to analyze the cloud behaviors.

During the Megacity Air Pollution Studies-Seoul (MAPS-Seoul) campaign from May to June 2015, aerosol optical properties in Korea were obtained based on the AERONET sunphotometer measurement at five sites (Anmyon, Gangneung_WNU, Gosan_SNU, Hankuk_UFS, and Yonsei_University). Using this dataset, we examine regional aerosol types by applying a number of known aerosol classification methods. We thoroughly utilize five different methods to categorize the regional aerosol types and evaluate the results from each method by inter-comparison. The differences and similarities among the results are also discussed, contingent upon the usage of AERONET inversion products, such as the single scattering albedo. Despite several small differences, all five methods suggest the same general features in terms of the regionally dominant aerosol type: Fine-mode aerosols with highly absorbing radiative properties dominate at Hankuk_UFS and Yonsei_University; non-absorbing fine-mode particles form a large portion of the aerosol at Gosan_SNU; and coarse-mode particles cause some effects at Anmyon. The analysis of 3-day back-trajectories is also performed to determine the relationship between classified types at each site and the regional transport pattern. In particular, the spatiotemporally short-scale transport appears to have a large influence on the local aerosol properties. As a result, we find that the domestic emission in Korea significantly contributes to the high dominance of radiation-absorbing aerosols in the Seoul metropolitan area and the air-mass transport from China largely affects the western coastal sites, such as Anmyon and Gosan_SNU.

Vertical column density (VCD) of nitrogen dioxide was measured using Pandora spectrometers at six sites on the Korean Peninsula during the Megacity Air Pollution Studies-Seoul (MAPS-Seoul) campaign from May to June 2015. To estimate the tropospheric nitrogen dioxide VCD, the stratospheric nitrogen dioxide VCD from the Ozone Monitoring Instrument (OMI) was subtracted from the total nitrogen dioxide VCD from Pandora. European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis wind data was used to analyze variations in tropospheric nitrogen dioxide VCD caused by wind patterns at each site. The Yonsei/SEO site was found to have the largest tropospheric nitrogen dioxide VCD (1.49 DU on average) from a statistical analysis of hourly tropospheric nitrogen dioxide VCD measurements. At rural sites, remarkably low nitrogen dioxide VCDs were observed. However, a wind field analysis showed that trans-boundary transport and emissions from domestic sources lead to an increase in tropospheric nitrogen dioxide VCD at NIER/BYI and KMA/AMY, respectively. At urban sites, high nitrogen dioxide VCD values were observed under conditions of low wind speed, which were influenced by local urban emissions. Tropospheric nitrogen dioxide VCD at HUFS/Yongin increases under conditions of significant transport from urban area of Seoul according to a correlation analysis that considers the transport time lag. Significant diurnal variations were found at urban sites during the MAPS Seoul campaign, but not at rural sites, indicating that it is associated with diurnal patterns of nitrogen dioxide emissions from dense traffic.

Using NASA’s Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) reanalysis for aerosol optical depth (AOD) and satellite-observed carbon monoxide (CO) data, we examined the basic pattern of AOD variations over the three polar stations of Korea: the Jangbogo and King Sejong stations in Antarctica and the Dasan station in the Arctic. AOD values at the King Sejong and Dasan stations show maximum peaks in spring and appear to be associated with large amounts of atmospheric CO emitted from natural burning and biomass burning. The Jangbogo station shows a much lower AOD than the other two stations and does not appear to be strongly affected by the transport of airborne particles generated from mid-latitude regions. All three polar stations show an increasing trend in AOD in general, indicating that the polar background air quality is becoming polluted.

In this study, we present and compare regional correlations between CO total column density (TCD CO ) from the data set of Measurement of Pollution in the Troposphere (MOPITT), and high-absorbing BC dominant aerosol optical depth (AOD BC ) from the retrieval algorithm using Moderate Resolution Imaging Spectroradiometer (MODIS) and Ozone Monitoring Instrument (OMI) (MODIS-OMI algorithm, MOA). TCD CO shows positive relationship to both fine-mode AOD (AOD FM ) and AOD BC in general, but TCD CO better correlates with AOD BC than AOD FM . This enhanced correlation between TCD CO and AOD BC appears more clearly during spring and summer. Correlation between TCD CO and AOD BC is exceptionally poor in Northern Africa where the BC-dominated aerosols are frequently mixed with mineral dust particles from the Sahara. Another issue is also found in Southern Africa; the correlation between AOD BC and TCD CO in this region is not much higher than that between the AOD FM and TCD CO in spite of large occurrence of biomass burning and wildfire. This can be explained by the cloud perturbation near the source regions and dispersion effect due to the typical wind pattern. Correlations between AOD BC and TCD CO increase further when fire detected areas are only considered, but does not change much over the urban area. This difference clarifies the large contribution of burning events to the positive relationship between BC and CO. All findings in this study demonstrate a possible use of satellite CO product in evaluating the BC-dominated aerosol product from satellite remote sensing over the globe.

Aerosols affect the insolation at ground and thus the Aerosol Optical Depth (AOD, a measure of aerosol pollution) plays an important role on the variation of the Physiological Equivalent Temperature (PET) at locations with different aerosol climatology. The aerosol effects upon PET were studied for the first time at four East Asian cities by coupling a radiative transfer model and a human thermal comfort model which were previously well evaluated. Evident with the MODIS and AERONET AOD observations, the aerosol pollution at Beijing and Seoul was higher than at Chiayi (Taiwan) and Hong Kong. Based on the AERONET data, with background AOD levels the selected temperate cities had similar clear-sky PET values especially during summertime, due to their locations at similar latitudes. This also applied to the sub-tropical cities. Increase in the AOD level to the seasonal average one led to an increase in diffuse solar radiation and in turn an increase in PET for people living in all the cities. However, the heavy aerosol loading environment in Beijing and Seoul in summertime (AODs > 3.0 in episodic situations) reduced the total radiative flux and thus PET values in the cities. On the contrary, relatively lower episodic AOD levels in Chiayi and Hong Kong led to strong diffuse and still strong direct radiative fluxes and resulted in higher PET values, relative to those with seasonal averaged AOD levels. People tended to feel from “hot” to “very hot” during summertime when the AOD reached their average levels from the background level. This implies that in future aerosol effects add further burden to the thermal environment apart from the effects of greenhouse gas-induced global warming. Understanding the interaction between ambient aerosols and outdoor thermal environment is an important first step for effective mitigation measures such as urban greening to reduce the risk of human heat stress. It is also critical to make cities more attractive and enhancing to human well-being to achieve enhancing sustainable urbanization as one of the principal goals for the Nature-based Solutions.

Korea will launch the Geostationary Environment Monitoring Spectrometer (GEMS) instrument in 2018 onboard the Geostationary Korean Multi-Purpose Satellite to monitor tropospheric gas concentrations with high temporal and spatial resolutions. The purpose of this study is to examine the performance of total column ozone (TCO) measurements from ground-based Pandora and Brewer instruments that will be used for validation of the GEMS ozone product. Satellite measurements can be used to detect erroneous outliers at a particular ground station, which deviate significantly from co-located satellite measurements relative to other stations. This is possible because a single satellite retrieval algorithm is used to process the entire satellite dataset, and instrument characteristics typically change slowly over the life of the satellite. Thus, the short-term stability (months) of satellite measurements can be used to estimate the performance of the ground-based measurement network as well as to identify potential problems at individual stations. As a reference for satellite ozone measurements, we have selected TCO data derived from OMI-TOMS V8.5 algorithm, because it is a robust algorithm that has been well studied to identify its various error sources. We validated ground-based Brewer and Pandora TCO measurements using OMI-TOMS TCO data collected over South Korea from March 2012 to December 2014. The Brewer TCO measurements at Pohang showed significant deviation from overall seasonal variation during the study period. In addition, in the presence of clouds, Pandora TCO measurements are unusually ∼7% higher than OMI-TOMS TCO data. To filter out these cloud-contaminated data, we applied a Kalman filter to the Pandora measurements. The diurnal variation in the Kalman-filtered Pandora data agrees well with the Brewer data, and the correlation of Kalman-filtered Pandora data with OMI-TOMS TCO is significantly improved from 0.89 to 0.99 at Seoul and from 0.93 to 0.99 at Busan.

The sensitivities of oxygen dimer (O4) slant column densities (SCDs) were examined by applying temperature-dependent O4 cross sections using the radiative transfer model (RTM) calculation with the linearized pseudo-spherical vector discrete ordinate radiative transfer model. For the sensitivity study, we used a newly developed cross section database in place of the database used in the operational algorithm. Newly investigated O4 cross section databases for 203 K and 293 K were used for the radiance simulation by interpolating temperature for each atmospheric layer based on the vertical profile of standard atmosphere in the RTM. The effect of the temperature-dependent cross sections was a significant O4 SCD increase of 8.3% with dependence on satellite and solar viewing geometries. Furthermore, the O4 SCD generally increased by an estimated 3.9% based on the observation geometries of the Ozone Monitoring Instrument. For the long-term comparison, the O4 SCD estimated from the temperature-dependent cross sections corrects 20% of the total underestimation of O4 SCD between the observation and simulation. Although the surface pressure variation and background aerosol effect also correct the O4 SCD discrepancy, the effect of temperature-dependent cross sections was more important than the effects of surface pressure variation and background aerosols. Therefore, temperature dependence of the cross section in the RTM calculation is essential for the accurate simulation of O4 SCD.

It is important to estimate the effects of the long-range transport of atmospheric pollutants for efficient and effective strategies to control air quality. In this study, the contributions of trans-boundary transport to the mean concentrations of SO 2 , NO 2 , CO, and PM 10 in Seoul, Korea from 2001 to 2014 were estimated based on the conditional potential source contribution function (CPSCF) method. Eastern China was found to be the major source of trans-boundary pollution in Seoul, but moderate sources were also located in northeastern China. The contribution of long-range transport from Japan was negligible. The spatial distributions of the potential source contribution function (PSCF) values of each pollutant showed reasonable consistency with their emission inventory and satellite products. The PSCF values of SO 2 and PM 10 from eastern China were higher than those of NO 2 and CO. The mean concentrations of SO 2 , NO 2 , CO, and PM 10 in Seoul for the period from 2001 to 2014 were 5.34, 37.0, and 619.1 ppb, and 57.4 4 μg/m 3 , respectively. The contributions of long-range transport to the mean concentrations of SO 2 , NO 2 , CO, and PM 10 in Seoul were 0.74, 3.4, and 39.0 ppb, and 12.1 μg/m 3 , respectively, which are 14%, 9%, 6%, and 21% of the mean concentrations, respectively. The annual mean concentrations of SO 2 and NO 2 followed statistically significant increasing linear trends (0.5 and 1.6 ppb per decade, respectively), whereas the trends in the annual mean concentrations of CO and PM 10 were statistically insignificant. The trends in the ratio of the increased concentrations associated with long-range transport to the annual mean concentrations of the pollutants were statistically insignificant. However, the results indicate that the trans-boundary transport of SO 2 , NO 2 , CO, and PM 10 from eastern China consistently affected air quality in Seoul over the study period (2001–2014). Regionally, the effects of the long-range transport of pollutants from Beijing and Harbin-Changchun on air quality in Seoul have become more significant over this period.

Satellite-derived aerosol optical depth (AOD) products are one of main predictors to estimate ground-level particulate matter (PM10 and PM2.5) concentrations. Since AOD products, however, are only provided under high-quality conditions, missing values usually exist in areas such as clouds, cloud shadows, and bright surfaces. In this study, spatially continuous AOD and subsequent PM10 and PM2.5 concentrations were estimated over East Asia using satellite- and model-based data and auxiliary data in a Random Forest (RF) approach. Data collected from the Geostationary Ocean Color Imager (GOCI; 8 times per day) in 2016 were used to develop AOD and PM models. Three schemes (i.e. G1, A1, and A2) were proposed for AOD modeling according to target AOD data (GOCI AOD and AERONET AOD) and the existence of satellite-derived AOD. The A2 scheme showed the best performance (validation R2 of 0.74 and prediction R2 of 0.73 when GOCI AOD did not exist) and the resultant AOD was used to estimate spatially continuous PM concentrations. The PM models with location information produced successful estimation results with R2 of 0.88 and 0.90, and rRMSE of 26.9 and 27.2% for PM10 and PM2.5, respectively. The spatial distribution maps of PM well captured the seasonal and spatial characteristics of PM reported in the literature, which implies the proposed approaches can be adopted for an operational estimation of spatially continuous AOD and PMs under all sky conditions.

For the purpose of providing reliable and robust air quality predictions, an operational air quality prediction system was developed for the main air quality criteria species in South Korea (PM10, PM2.5, CO, O3, and SO2). The main caveat of the system is to prepare the initial conditions (ICs) of the Community Multi-scale Air Quality (CMAQ) model simulations using observations from the Geostationary Ocean Color Imager (GOCI) and ground-based monitoring networks in northeast Asia. The performance of the air quality prediction system was evaluated during the Korea-United States Air Quality Study (KORUS-AQ) campaign period (1 May–12 June 2016). Data assimilation (DA) of optimal interpolation (OI) with Kalman filter was used in this study. One major advantage of the system is that it can predict not only particulate matter (PM) concentrations but also PM chemical composition including five main constituents: sulfate, nitrate, ammonium, organic aerosols (OAs), and elemental carbon (EC). In addition, it is also capable of predicting the concentrations of gaseous pollutants (CO, O3 and SO2). In this sense, this new operational air quality prediction system is comprehensive. The results with the ICs (DA RUN) were compared with those of the CMAQ simulations without ICs (BASE RUN). For almost all of the species, the application of ICs led to improved performance in terms of correlation, errors, and biases over the entire campaign period. The DA RUN agreed reasonably well with the observations for PM10 (IOA = 0.60; MB = −13.54) and PM2.5 (IOA = 0.71; MB = −2.43) as compared to the BASE RUN for PM10 (IOA = 0.51; MB = −27.18) and PM2.5 (IOA = 0.67; MB = −9.9). A significant improvement was also found with the DA RUN in terms of bias. For example, for CO, the MB of −0.27 (BASE RUN) was greatly enhanced to −0.036 (DA RUN). In the cases of O3 and SO2, the DA RUN also showed better performance than the BASE RUN. Further, several more practical issues frequently encountered in the operational air quality prediction system were also discussed. In order to attain more accurate ozone predictions, the DA of NO2 mixing ratios should be implemented with careful consideration of the measurement artifacts (i.e., inclusion of alkyl nitrates, HNO3, and PANs in the ground-observed NO2 mixing ratios). It was also discussed that, in order to ensure accurate nocturnal predictions of the concentrations of the ambient species, accurate predictions of the mixing layer heights (MLH) should be achieved from the meteorological modeling. Several advantages of the current air quality prediction system, such as its non-static free parameter scheme, dust episode prediction, and possible multiple implementations of DA prior to actual predictions, were also discussed. These configurations are all possible because the current DA system is not computationally expensive. In the ongoing and future works, more advanced DA techniques such as the three-dimensional variational (3DVAR) method and ensemble Kalman filter (EnK) are being tested and will be introduced to the Korean operational air quality forecasting system.

We describe a formaldehyde (HCHO) retrieval algorithm for the Geostationary Environment Monitoring Spectrometer (GEMS) that will be launched by the Korean Ministry of Environment in 2019. The algorithm comprises three steps: preprocesses, radiance fitting, and postprocesses. The preprocesses include a wavelength calibration, as well as interpolation and convolution of absorption cross sections; radiance fitting is conducted using a nonlinear fitting method referred to as basic optical absorption spectroscopy (BOAS); and postprocesses include air mass factor calculations and bias corrections. In this study, several sensitivity tests are conducted to examine the retrieval uncertainties using the GEMS HCHO algorithm. We evaluate the algorithm with the Ozone Monitoring Instrument (OMI) Level 1B irradiance/radiance data by comparing our retrieved HCHO column densities with OMI HCHO products of the Smithsonian Astrophysical Observatory (OMHCHO) and of the Quality Assurance for Essential Climate Variables project (OMI QA4ECV). Results show that OMI HCHO slant columns retrieved using the GEMS algorithm are in good agreement with OMHCHO, with correlation coefficients of 0.77–0.91 and regression slopes of 0.94–1.04 for March, June, September, and December 2005. Spatial distributions of HCHO slant columns from the GEMS algorithm are consistent with the OMI QA4ECV products, but relatively poorer correlation coefficients of 0.52–0.76 are found compared to those against the OMHCHO products. Also, we compare the satellite results with ground-based multi-axis differential optical absorption spectroscopy (MAX-DOAS) observations. OMI GEMS HCHO vertical columns are 9 %–25 % lower than those of MAX-DOAS at Haute-Provence Observatory (OHP) in France, Bremen in Germany, and Xianghe in China. We find that the OMI GEMS retrievals have less bias than the OMHCHO and OMI QA4ECV products at OHP and Bremen in comparison with MAX-DOAS.

The Geostationary Ocean Color Imager (GOCI) Yonsei aerosol retrieval (YAER) version 1 algorithm was developed to retrieve hourly aerosol optical depth at 550 nm (AOD) and other subsidiary aerosol optical properties over East Asia. The GOCI YAER AOD had accuracy comparable to ground-based and other satellite-based observations but still had errors because of uncertainties in surface reflectance and simple cloud masking. In addition, near-real-time (NRT) processing was not possible because a monthly database for each year encompassing the day of retrieval was required for the determination of surface reflectance. This study describes the improved GOCI YAER algorithm version 2 (V2) for NRT processing with improved accuracy based on updates to the cloud-masking and surface-reflectance calculations using a multi-year Rayleigh-corrected reflectance and wind speed database, and inversion channels for surface conditions. The improved GOCI AOD τG is closer to that of the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) AOD than was the case for AOD from the YAER V1 algorithm. The V2 τG has a lower median bias and higher ratio within the MODIS expected error range (0.60 for land and 0.71 for ocean) compared with V1 (0.49 for land and 0.62 for ocean) in a validation test against Aerosol Robotic Network (AERONET) AOD τA from 2011 to 2016. A validation using the Sun-Sky Radiometer Observation Network (SONET) over China shows similar results. The bias of error (τG−τA) is within −0.1 and 0.1, and it is a function of AERONET AOD and Ångström exponent (AE), scattering angle, normalized difference vegetation index (NDVI), cloud fraction and homogeneity of retrieved AOD, and observation time, month, and year. In addition, the diagnostic and prognostic expected error (PEE) of τG are estimated. The estimated PEE of GOCI V2 AOD is well correlated with the actual error over East Asia, and the GOCI V2 AOD over South Korea has a higher ratio within PEE than that over China and Japan.

To assess air pollution emitted in Southern Hemisphere mid-latitudes and transported to Antarctica, we investigate the climatological mean and temporal trends in aerosol optical depth (AOD), carbon monoxide (CO), nitrogen dioxide (NO2), and formaldehyde (HCHO) columns using satellite observations. Generally, all these measurements exhibit sharp peaks over and near the three nearby inhabited continents: South America, Africa, and Australia. This pattern indicates the large emission effect of anthropogenic activities and biomass burning processes. High AOD is also found over the Southern Atlantic Ocean, probably because of the sea salt production driven by strong winds. Since the pristine Antarctic atmosphere can be polluted by transport of air pollutants from the mid-latitudes, we analyze the 10-day back trajectories that arrive at Antarctic ground stations in consideration of the spatial distribution of mid-latitudinal AOD, CO, NO2, and HCHO. We find that the influence of mid-latitudinal emission differs across Antarctic regions: western Antarctic regions show relatively more back trajectories from the mid-latitudes, while the eastern Antarctic regions do not show large intrusions of mid-latitudinal air masses. Finally, we estimate the long-term trends in AOD, CO, NO2, and HCHO during the past decade (2005–2016). While CO shows a significant negative trend, the others show overall positive trends. Seasonal and regional differences in trends are also discussed.

Formaldehyde (HCHO) in the ambient air not only causes cancer but also is an ideal indicator for volatile organic compounds (VOCs) which are major precursors of ozone (O3) and secondary organic aerosol (SOA) near the surface. It is meaningful to differentiate between direct emission and secondary formation for HCHO pollution control and sensitivity study of O3 production. However, the understanding of the sources of HCHO is still poor in China, due to limited measurements of HCHO in the field, both spatially and temporally. In this study, tropospheric HCHO vertical column densities (VCDs) in the Yangtze River Delta (YRD), East China where HCHO pollution is serious were retrieved from Ozone Mapping and Profiler Suite (OMPS) onboard the Suomi National Polar-orbiting Partnership (Suomi-NPP) satellite from 2014 to 2017, and kept good agreement with the tropospheric HCHO columns measured by ground-based high resolution Fourier transform infrared spectrometry (FTS) with the correlation coefficient (R) of 0.78. Based on this, the cancer risk was estimated nationwide and in the YRD region. At least, 7840 people in the YRD region would develop cancer in their lives due to outdoor HCHO exposure, which occupied 23.4 % of total national cancer risk. Besides, the contributions of primary and secondary sources were apportioned, combining with primary and secondary tracers from surface observation. Overall, HCHO from secondary formation contributed most to ambient HCHO and can be regarded as the indication of the VOCs reactivity in Hangzhou and urban areas of Nanjing and Shanghai from 2015 to 2017, due to strong correlation between total HCHO and secondary HCHO. At industrial sites in Nanjing, primary emission influenced most to ambient HCHO in 2015 and showed an obvious decreasing trend. Seasonally, HCHO from secondary formation reached the maximum in summer and minimum in winter. In the spring, summer, and autumn, secondary formation played a curial effect on variation of ambient HCHO in urban regions of Nanjing, Hangzhou, and Shanghai; while in the winter the contribution from secondary formation became less significant. The understanding of the variation of the primary and secondary contributions to ambient HCHO is in favor for a better understanding the role of HCHO in atmospheric chemistry and formulating effective control measures to decrease HCHO pollution and cancer risk.

PM2.5 has been considered as a crucial issue in densely populated cities because of its adverse effects on human health. As a way to mitigate this problem on city scale, urban forest is recently suggested as a solution due to its potential to absorb and adhere PM2.5. However, limited observations in the forest prevent a full understanding in the role of forest on urban PM2.5 concentration. In this paper, we present an application of deep learning using satellite data to clarify the role of forest with respect to the PM2.5 concentration mitigation in the city. Based on PM2.5 data collected from the ground-based observation stations, two PM2.5 prediction models are constructed through the deep learning approach. Satellite-derived aerosol optical depth from the GOCI and MODIS are used as the main predictors of two models. Both models predict spatial distribution throughout Seoul, Korea (R2 of 0.61 and 0.78). Particularly, the western parts of Seoul tend to have higher PM2.5 concentrations than the other parts, whereas mid-outskirts have noticeably low concentrations. A comparison of spatial distributions of PM2.5 between observations and predictions show that the existing observation network only reflects 15%–60% of entire characteristics of Seoul. Utilizing city-wide PM2.5 estimations, a comparison of estimated PM2.5 between urban and forest regions has been performed. The results show that the estimated PM2.5 concentrations in the forest regions are less than those in urban regions by up to 16.4 μg m−3. In contrast to urban regions, the average of PM2.5 concentrations in the forest regions is likely below the daily mean WHO PM2.5 outdoor standard. Our study suggests that urban forest could be a potential way to improve urban air quality with a specific focus on PM2.5. In addition, the deep learning approach used in this study can be applied to other cities where obtaining observation measurements is difficult.

In this study, we examined the change rates of PM2.5 concentrations, aerosol optical depth (AOD), and the concentrations of PM2.5 precursors, such as SO2 and NO2, in China and South Korea using surface and satellite observations from 2015 to 2018. To quantify the impacts of the emissions and meteorology on the concentration changes, we performed a series of air quality simulations with year-specific meteorology and a fixed anthropogenic emissions inventory. The surface PM2.5 observations in China and South Korea decreased at rates of 9.1 and 4.3%/yr during the study period, respectively. The AODs from Moderate Resolution Imaging Spectroradiometer (MODIS) and Geostationary Ocean Color Imager (GOCI) also decreased faster over China than the AODs over South Korea. For the PM2.5 decrease in China, the emission impact was more significant (73%) than the meteorology impact (27%). On the contrary, in South Korea, the emissions and meteorology impacts on PM2.5 reductions were similar (51% vs 49%). The SO2 concentration over China in 2018 significantly reduced to approximately half of the level in 2015. In turn, the sulfate concentration in Baengnyeong (BN), located in a downwind pathway from China to South Korea, decreased at a rate of 0.79%/month. However, the nitrate concentration in BN showed an increasing trend due to the non-linear chemical reactions among sulfate-nitrate-ammonium. The increased nitrate compensated for the reduced PM2.5 concentration from the sulfate decrease at BN. Additionally, the number of high (>50 μg/m3) PM2.5 concentration days continuously decreased in China, but the number in South Korea increased. It is noted that emission reductions in an upwind area do not guarantee corresponding air quality improvement in the downwind area when complex secondary aerosol formation processes, as well as spatiotemporal changes in meteorology, are involved in the transboundary transport of air pollutants.

Clouds act as a major reflector that changes the amount of sunlight reflected to space. Change in radiance intensity due to the presence of clouds interrupts the retrieval of trace gas or aerosol properties from satellite data. In this paper, we developed a fast and robust algorithm, named the fast cloud retrieval algorithm, using a triplet of wavelengths (469, 477, and 485 nm) of the O2–O2 absorption band around 477 nm (CLDTO4) to derive the cloud information such as cloud top pressure (CTP) and cloud fraction (CF) for the Geostationary Environment Monitoring Spectrometer (GEMS). The novel algorithm is based on the fact that the difference in the optical path through which light passes with regard to the altitude of clouds causes a change in radiance due to the absorption of O2–O2 at the three selected wavelengths. To reduce the time required for algorithm calculations, the look-up table (LUT) method was applied. The LUT was pre-constructed for various conditions of geometry using Vectorized Linearized Discrete Ordinate Radiative Transfer (VLIDORT) to consider the polarization of the scattered light. The GEMS was launched in February 2020, but the observed data of GEMS have not yet been widely released. To evaluate the performance of the algorithm, the retrieved CTP and CF using observational data from the Global Ozone Monitoring Experiment-2 (GOME-2), which cover the spectral range of GEMS, were compared with the results of the Fast Retrieval Scheme for Clouds from the Oxygen A band (FRESCO) algorithm, which is based on the O2 A-band. There was good agreement between the results, despite small discrepancies for low clouds.

To better understand air quality issues in South Korea, it is essential to identify the main contributors of air pollution and to quantify the effects of transboundary transport. In this study, geostationary satellite measurements were used to assess the effects of aerosol transport on air quality in South Korea. This study proposes a method to define the long-range transport (LRT) of aerosols into the Korean Peninsula using remote sensing obervations and back-trajectories and estimates the LRT effects on air quality in Seoul using in-situ particulate matter (PM) measurements. Aerosol optical depths (AODs) are obtained from the Geostationary Ocean Color Imager (GOCI), and the back-trajectories are from the National Ocean and Atmospheric Administration (NOAA) HYbrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model. For LRT events, satellite observations showed high AOD plumes over the Yellow Sea, a pathway between Eastern China and South Korea, and the movements of aerosol plumes transported to South Korea were also detected. PM2.5 concentrations, PM10 concentrations, and AOD during LRT increased by 52%, 49%, and 81%, respectively, relative to their average values for 2015–2018. To quantitatively characterize the LRT of aerosols, the effects of LRT on PM2.5 concentrations were estimated for each PM concentration category. The contribution of LRT to PM2.5 concentrations was estimated to be 33% during 2015–2018. When high concentrations of PM2.5 were observed in Seoul, they were likely to be associated with LRT events.

Springtime trans-Pacific transport of Asian air pollutants has been investigated in many ways to figure out its mechanism. Based on the Western Pacific (WP) pattern, one of climate variabilities in the Northern Hemisphere known to be associated with the pattern of atmospheric circulation over the North Pacific Ocean, in this study, we characterize the pattern of springtime trans-Pacific transport using long-term satellite measurements and reanalysis datasets. A positive WP pattern is characterized by intensification of the dipole structure between the northern Aleutian Low and the southern Pacific High over the North Pacific. The TOMS/OMI Aerosol Index (AI) and MOPITT CO show the enhancement of Asian pollutant transport across the Pacific during periods of positive WP pattern, particularly between 40 and 50°N. This enhancement is confirmed by high correlations of WP index with AI and CO between 40 and 50°N. To evaluate the influence of the WP pattern, we examine several cases of trans-Pacific transport reported in previous research. Interestingly, most trans-Pacific transport cases are associated with the positive WP pattern. During the period of negative WP pattern, reinforced cyclonic wave breaking is consistently found over the western North Pacific, which obstructs zonal advection across the North Pacific. However, some cases show the trans-Pacific transport of CO in the period of negative WP pattern, implying that the WP pattern is more influential on the transport of particles mostly emitted near ∼40°N. This study reveals that the WP pattern can be utilized to diagnose the strength of air pollutant transport from East Asia to North America.

Absorption and scattering characteristics of various aerosol events are investigated using 2-years of measurements from a skyradiometer at Yonsei University in Seoul, Korea. Both transported dust and anthropogenic aerosols are observed at distinct geo-location of Seoul, a megacity located a few thousand kilometers away from dust source regions in China. We focus on the wavelength dependence of Angstrom exponent (AE) and single scattering albedo (SSA), showing the characteristics of regional aerosols. The correlation between spectral SSAs and AEs calculated using different wavelength pairs generally indicates relatively weak absorption of fine-mode aerosols (urban pollution and/or biomass burning) and strong absorption of coarse-mode aerosols (desert dust) at this location. AE ratio (AER), a ratio of AEs calculated using wavelength pair between shorter (340–675 nm) and longer wavelength pair (675–1020 nm) correlates differently with SSA according to the dominant size of local aerosols. Correlations between SSA and AER show strong absorption of aerosols for AER 2.0. Based on the seasonal pattern of wavelength dependence of AER and SSA, this correlation difference looks to reveal the separated characteristics of transported dust and anthropogenic particles from urban pollution respectively. The seasonal characteristics of AER and SSAs also show that the skyradiometer measurement with multiple wavelengths may be able to detect the water soluble brown carbon, one of the important secondary organic aerosols in the summertime atmospheric composition.

The sensitivities of oxygen-dimer (O4) slant column densities (SCDs) to changes in aerosol layer height are investigated using the simulated radiances by a radiative transfer model, the linearized pseudo-spherical vector discrete ordinate radiative transfer (VLIDORT), and the differential optical absorption spectroscopy (DOAS) technique. The sensitivities of the O4 index (O4I), which is defined as dividing O4 SCD by 1040 molecules2 cm−5, to aerosol types and optical properties are also evaluated and compared. Among the O4 absorption bands at 340, 360, 380, and 477 nm, the O4 absorption band at 477 nm is found to be the most suitable to retrieve the aerosol effective height. However, the O4I at 477 nm is significantly influenced not only by the aerosol layer effective height but also by aerosol vertical profiles, optical properties including single scattering albedo (SSA), aerosol optical depth (AOD), particle size, and surface albedo. Overall, the error of the retrieved aerosol effective height is estimated to be 1276, 846, and 739 m for dust, non-absorbing, and absorbing aerosol, respectively, assuming knowledge on the aerosol vertical distribution shape. Using radiance data from the Ozone Monitoring Instrument (OMI), a new algorithm is developed to derive the aerosol effective height over East Asia after the determination of the aerosol type and AOD from the MODerate resolution Imaging Spectroradiometer (MODIS). About 80 % of retrieved aerosol effective heights are within the error range of 1 km compared to those obtained from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) measurements on thick aerosol layer cases.

An optimal-estimation(OE)-based aerosol retrieval algorithm using the OMI (Ozone Monitoring Instrument) near-ultraviolet observation was developed in this study. The OE-based algorithm has the merit of providing useful estimates of errors simultaneously with the inversion products. Furthermore, instead of using the traditional look-up tables for inversion, it performs online radiative transfer calculations with the VLIDORT (linearized pseudo-spherical vector discrete ordinate radiative transfer code) to eliminate interpolation errors and improve stability. The measurements and inversion products of the Distributed Regional Aerosol Gridded Observation Network campaign in northeast Asia (DRAGON NE-Asia 2012) were used to validate the retrieved aerosol optical thickness (AOT) and single scattering albedo (SSA). The retrieved AOT and SSA at 388 nm have a correlation with the Aerosol Robotic Network (AERONET) products that is comparable to or better than the correlation with the operational product during the campaign. The OE-based estimated error represented the variance of actual biases of AOT at 388 nm between the retrieval and AERONET measurements better than the operational error estimates. The forward model parameter errors were analyzed separately for both AOT and SSA retrievals. The surface reflectance at 388 nm, the imaginary part of the refractive index at 354 nm, and the number fine-mode fraction (FMF) were found to be the most important parameters affecting the retrieval accuracy of AOT, while FMF was the most important parameter for the SSA retrieval. The additional information provided with the retrievals, including the estimated error and degrees of freedom, is expected to be valuable for relevant studies. Detailed advantages of using the OE method were described and discussed in this paper.

Surface-level PM10 distribution was estimated from the satellite aerosol optical depth (AOD) products, taking the account of vertical profiles and hygroscopicity of aerosols over Jeju, Korea during March 2008 and October 2009. In this study, MODIS AOD data from the Terra and Aqua satellites were corrected with aerosol extinction profiles and relative humidity data. PBLH (Planetary Boundary Layer Height) was determined from MPLNET lidar-derived aerosol extinction coefficient profiles. Through statistical analysis, better agreement in correlation (R = 0.82) between the hourly PM10 concentration and hourly average Sunphotometer AOD was the obtained when vertical fraction method (VFM) considering Haze Layer Height (HLH) and hygroscopic growth factor f(RH) was used. The validity of the derived relationship between satellite AOD and surface PM10 concentration clearly demonstrates that satellite AOD data can be utilized for remote sensing of spatial distribution of regional PM10 concentration.

The Geostationary Environment Monitoring Spectrometer (GEMS) onboard the Geostationary Korean Multi-Purpose Satellite 2B was successfully launched in February 2020. GEMS is a hyperspectral spectrometer measuring solar irradiance and Earth radiance in the wavelength range of 300 to 500 nm. This paper introduces the spectral calibration algorithm for GEMS, which uses a nonlinear least-squares approach. Sensitivity tests for a series of unknown algorithm parameters such as spectral range for fitting, spectral response function (SRF), and reference spectrum were conducted using the synthetic GEMS spectrum prepared with the ground-measured GEMS SRF. The test results show that the required accuracy of 0.002 nm is achievable provided the SRF and the high-resolution reference spectrum are properly prepared. Such a satisfactory performance is possible mainly due to the inclusion of additional fitting parameters of spectral scales (shift, squeeze, and high order shifts) and SRF (width, shape and asymmetry). For the application to the actual GEMS data, in-orbit SRF is to be monitored using an analytic SRF function and the measured GEMS solar irradiance, while a reference spectrum is going to be selected during the instrument in-orbit test. The calibrated GEMS data is expected to be released by the end of 2020.

Quantifying the spectral variation of column aerosol absorption in the ultraviolet (UV) and visible (Vis) wavelengths is required for accurate satellite-based aerosol and trace-gas retrievals. Retrievals of the column-averaged imaginary part of refractive index and single scattering albedo (SSA) in the UV–Vis range have been performed at Yonsei University, Seoul, Korea, since 2016 by combining co-located measurements from the NASA Aerosol Robotic Network (AERONET) Cimel sun-sky photometer, the Ultraviolet Multifilter Rotating Shadowband Radiometer (UV-MFRSR), the SKYNET Prede sky radiometer, and the NASA Pandora sun spectrometer. We investigated the spectral variation of column-averaged imaginary part of refractive index for UV–Vis wavelengths to refine models used in our aerosol retrieval algorithm to process measurements from the upcoming Geostationary Environment Monitoring Satellite (GEMS). The retrieved imaginary part of refractive index for highly absorbing fine pollution particles (BC), dust (DS), and non-absorbing (NA) particles in the selected UV–Vis range (380–440 nm) showed 0–20%, 30%, and 0–40% of spectral dependence, respectively. Retrievals of Ozone Monitoring Instrument (OMI) measurement data using the improved aerosol model showed improved correlation with AERONET data compared to the old algorithm that did not properly account for aerosol absorption effects. These results corroborate the advantage of using local climatology derived from ground-based UV–Vis spectral aerosol absorption measurements for satellite GEMS aerosol retrievals over East Asia. Moreover, this study reveals that spectral variations in the UV column aerosol absorption in East Asia differ from those in other regions.

This investigation examines aerosol dynamics during major fine mode aerosol transboundary pollution events in South Korea primarily during the KORUS-AQ campaign from May 1 – June 10, 2016, particularly when cloud fraction was high and/or fog was present to quantify the change in aerosol characteristics due to near-cloud or fog interaction. We analyze the new AERONET Version 3 data that have significant changes to cloud screening algorithms, allowing many more fine-mode observations in the near vicinity of clouds or fog. Case studies for detailed investigation include May 25–26, 2016 when cloud fraction was high over much of the peninsula, associated with a weak frontal passage and advection of pollution from China. These cloud-influenced Chinese transport dates also had the highest aerosol optical depth (AOD), surface PM2.5 concentrations and fine mode particle sizes of the entire campaign. Another likewise cloud/high relative humidity (RH) case is June 9 and 10, 2016 when fog was present over the Yellow Sea that appears to have affected aerosol properties well downwind over the Korean peninsula. In comparison we also investigated aerosol properties on air stagnation days with very low cloud cover and relatively low RH (May 17 & 18, 2016), when local Korean emissions dominated. Aerosol volume size distributions show marked differences between the transport days (with high RH and cloud influences) and the local pollution stagnation days, with total column-integrated particle fine mode volume being an order of magnitude greater on the pollution transport dates. The PM2.5 over central Seoul were significantly greater than for coastal sites on the transboundary transport days yet not on stagnation days, suggesting additional particle formation from gaseous urban emissions in cloud/fog droplets and/or in the high RH humidified aerosol environment. Many days had KORUS-AQ research aircraft flights that provided observations of aerosol absorption, particle chemistry and vertical profiles of extinction. AERONET retrievals and aircraft in situ measurements both showed high single scattering albedo (weak absorption) on the cloudy or cloud influenced days, plus aircraft profile in situ measurements showed large AOD enhancements (versus dried aerosol) at ambient relative humidity (RH) on the pollution transport days, consistent with the significantly larger fine mode particle radii and weak absorption.