This study presents an comprehensive evaluation of Geostationary Environment Monitoring Spectrometer (GEMS) ozone products using daily ozonesonde data measured during the Asian Summer Monsoon Chemical and Climate Impact Project (ACCLIP). The analysis uses a total of 38 ozonesonde measurements along with atmospheric reanalysis to better understand ozone variability and circulation impacts during the Asian summer monsoon. It shows significant variability of tropospheric and lower stratospheric ozone related to convective activities associated with the Asian monsoon rainband and strong anticyclone in the upper troposphere and lower stratosphere (a.k.a. Tibet high). The comparison of the ozonesonde data and GEMS ozone products reveals GEMS’s capability to capture these variabilities, and also highlights its potential utility in the studies of chemical transport and regional-scale air quality in Asia.

The hydroxyl radical (OH) plays a significant role in the atmosphere, driving the oxidation and removal of most trace gases. Therefore, quantifying the sources of OH is of great importance to the scientific community. Researchers have been particularly interested in the role of nitrous acid (HONO) in tropospheric photochemistry, as HONO serves as a source of OH. While ground-based measurements have been conducted in certain regions, there is a need for more extensive observations of HONO to enhance our understanding of its chemistry. In this study, the HONO retrieval algorithm from the Geostationary Environment Monitoring Spectrometer (GEMS) are presented, utilizing the ultraviolet spectra. The retrieval process consists of three steps: spectral fitting, air mass factor calculation, and post-processing. The retrieval window of 343.0 – 371.0nm is employed to obtain HONO slant columns and air mass factor calculation is performed using a monochromatic wavelength of 357 nm. Reference sector correction is then applied to compensate for the HONO slant columns from radiance reference spectrum. Focusing on biomass burning events, the increase in HONO from fire plumes was presented as the retrieved results. By refining the retrieval algorithm, more information on HONO chemistry as well as diurnal patterns is expected to be obtained.

An algorithm for aerosol effective height (AEH) was developed for operational use with observations from the Geostationary Environment Monitoring Spectrometer (GEMS). The retrieval technique uses the slant column density of the oxygen dimer (O2–O2) at 477 nm, which is converted into AEH after retrieval of aerosol and surface optical properties from GEMS operational algorithms. The retrieved AEHs provide continuous vertical information on severe dust plumes over East Asia with reasonably good validation results and the collection of plume height information for anthropogenic aerosol pollutants over India. Compared to the AEH retrieved from Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP), the retrieval results show a bias of −0.03 km with a standard deviation of 1.4 km for the AEH difference over the GEMS observation domain from January to June 2021. The AEH difference depends on aerosol optical properties and surface reflectance. Compared to the aerosol layer height obtained from the Tropospheric Monitoring Instrument (TROPOMI), differences of 1.50±1.08 km, 1.59±1.22 km, and 1.71±1.24 km were obtained for pixels with single-scattering albedo (SSA) <0.90, 0.90 < SSA < 0.95, and SSA > 0.95, respectively.

Nitrogen oxides are key gas components of emissions from fossil-fuel combustion, are known to degrade air quality and have adverse health effects. Diurnal NO2 observations are crucial for enhancing our understanding of NOx emissions, lifetime, and chemistry. Geostationary Environment Monitoring Spectrometer (GEMS) has been providing hourly observations NO2 columns over Asia since November 2020. The latest NO2 version 3 products have significantly improved with updated air mass factors (AMFs) and the separation of stratospheric and tropospheric columns. To identify the dependency of the distribution on the time of the day, we investigated hourly tropospheric NO2 cycles of cities over Asia using GEMS measurements for the first time. The cities show similar diurnal concentration patterns with peaks in the morning and troughs in the afternoon, although the amplitude and specific times vary by city. The reduction rate of NO2 was influenced by the temporal dependence of the spatial distribution within and around cities. We also observed distinct NO2 diurnal patterns in certain industrial areas and cities where NOx emissions are thought to be controlled. To explain the location-dependent variations of the tropospheric NO2 columns, we compared the diurnal NO2 cycles obtained from the GEMS measurement with WRF-Chem models for some cities. In addition, estimated top-down NOx emissions from GEMS measurements are presented in comparison with bottom-up emission inventory, showing a smaller difference compared to the top-down emission from TROPOMI measurements. It is expected that hourly top-down NOx emissions using GEMS measurements can provide a useful information in improving the future performance of air quality modeling.

In South Korea, Asian dust frequently occurs during the spring, causing various health issues, including respiratory diseases. Consequently, public awareness and concern about air pollutants have increased, leading to demands for improved air quality and accurate forecasting. To meet these demands, the Ministry of Environment has deployed the Geostationary Environment Monitoring Spectrometer (GEMS) on the GK2B satellite to monitor atmospheric pollutants and climate change-inducing substances in real-time. The current GEMS dust product, generated using thresholds of the UV-aerosol index and visible-aerosol index, has shown limitations in accurately detecting suspended particulate matter. This study aims to develop a comprehensive AI dataset for improving GEMS Asian dust detection. Data were collected from January to May 2021, focusing on dates with significant dust events. Label data were meticulously generated through annotations based on outputs from various satellites and groundbased observations. Subsequent data preprocessing and augmentation techniques, including normalization and cut-mix, were applied to enhance the dataset’s robustness and generalizability. To evaluate the dataset, model training was conducted. The results predicted by the model showed improvements over the detection results of existing algorithms. Future datasets will be developed with improved labeling methods and accuracy verification techniques. These dataset improvements are expected to contribute to the development of deep learning models with superior predictive performance compared to current dust detection algorithms.

Nitrogen oxide radicals (NOx≡NO+NO2) emitted by fuel combustion are important precursors of ozone and particulate matter pollution, and NO2 itself is harmful to public health. The Geostationary Environment Monitoring Spectrometer (GEMS), launched in space in 2020, now provides hourly daytime observations of NO2 columns over East Asia. This diurnal variation offers unique information on the emission and chemistry of NOx, but it needs to be carefully interpreted. Here we investigate the drivers of the diurnal variation in NO2 observed by GEMS during winter and summer over Beijing and Seoul. We place the GEMS observations in the context of ground-based column observations (Pandora instruments) and GEOS-Chem chemical transport model simulations. We find good agreement between the diurnal variations in NO2 columns in GEMS, Pandora, and GEOS-Chem, and we use GEOS-Chem to interpret these variations. NOx emissions are 4 times higher in the daytime than at night, driving an accumulation of NO2 over the course of the day, offset by losses from chemistry and transport (horizontal flux divergence). For the urban core, where the Pandora instruments are located, we find that NO2 in winter increases throughout the day due to high daytime emissions and increasing NO2/NOx ratio from entrainment of ozone, partly balanced by loss from transport and with a negligible role of chemistry. In summer, by contrast, chemical loss combined with transport drives a minimum in the NO2 column at 13:00–14:00 local time (LT). Segregation of the GEMS data by wind speed further demonstrates the effect of transport, with NO2 in winter accumulating throughout the day at low winds but flat at high winds. The effect of transport can be minimized in summer by spatially averaging observations over the broader metropolitan scale, under which conditions the diurnal variation in NO2 reflects a dynamic balance between emission and chemical loss.

This study presents the validation of total column ozone (TCO) data retrieved from the Geostationary Environment Monitoring Spectrometer (GEMS) against ground-based Pandora spectrometer observations during the GEMS Map of Air Pollution (GMAP) campaign from November 2020 to January 2021 in Seosan, South Korea. To evaluate the accuracy of the Pandora TCO measurements obtained during the campaign period, all Pandora instruments were installed at the Seosan supersite for intercomparison analysis. Subsequently, the instruments were relocated to four sites for direct sunlight measurements. The Pandora instruments exhibited a high degree of consistency with an average difference of 0.5 +/- 1.0 DU. This study demonstrated that accurate comparison of ground-based ozone measurements with satellite ozone measurements depended on the threshold values set for the spatial and temporal alignment of the two datasets and the size of the satellite footprint and viewing angle. The comparison of the GEMS, TROPOspheric Monitoring Instrument (TROPOMI), and Ozone Mapping and Profiler Suite Nadir Mapper (OMPS) satellite TCO with the Pandora TCO showed high agreement across measurements. However, a distinct downward trend was observed in the Mean Bias (MB) for GEMS from December-January, indicating an issue with the GEMS Level 1C irradiance. The validation using hourly Pandora data demonstrated GEMS capability to monitor daily ozone variations but with a bias of approximately -1% to -2.6% compared with that of Pandora.

This study evaluated the reliability and applicability of the Geostationary Environment Monitoring Spectrometer (GEMS) data for monitoring atmospheric conditions in South Korea. This study analyzed correlations between GEMS satellite products (Aerosol Optical Depth [AOD], NO2, HCHO, Ozone Profile [O3P]), Airkorea ground-based data, Local Data Assimilation and Prediction System (LDAPS) meteorological data, and land cover map across 161 administrative districts from 2021 to 2023. Despite the satellite's low spatial resolution, analysis revealed significant correlations between GEMS observations and ground-based measurements. GEMS AOD showed strong negative correlations (-0.6) with atmospheric instability indices and positive correlations with ground PM2.5 (0.57) and carbon monoxide (CO) (0.6) measurements in spring, while summer ozone measurements demonstrated high correlations with ground observations (0.6) and temperature (0.59). Particularly strong correlations were observed in spring and fall, with GEMS AOD showing a distinct positive correlation with ground Particulate Matter (PM) concentrations and a negative correlation with atmospheric instability. The study found varying correlation patterns across different land cover types: urban areas demonstrated high positive correlations between GEMS AOD and PM substances, while forest regions showed generally lower pollutant concentrations, confirming their air purification function. Seasonal analysis revealed complex patterns, with spring and fall showing more interpretable correlations between variables compared to summer. Interpreting correlation patterns in summer was difficult due to the unique atmospheric meteorological factors of the Korean Peninsula. Regional analysis showed effective capture of pollutant transport phenomena, particularly along the west coast where spring westerly winds influence pollution patterns. The study also found that meteorological conditions, especially Boundary Layer Height (BLH) and atmospheric instability, significantly influenced pollutant concentrations and their spatial distribution. These findings suggest that GEMS satellite data can effectively complement ground-based monitoring networks for comprehensive air quality assessment in South Korea, particularly in monitoring broad-scale pollution phenomena and tracking pollutant transport patterns. However, there are limitations to local spatial analysis and special meteorological phenomena, so satellite and ground observation data must be integrated to build an optimal air quality monitoring system.

The Geostationary Environment Monitoring Spectrometer (GEMS), launched in February 2020, performs hourly measurements of earthshine radiances to retrieve column amounts of air pollutants over Asia. However, the charge-coupled device detector of GEMS has bad pixels that exhibit abnormal radiometric responses, which translates to a decrease in the quality of radiance measurements. Permanent bad pixels result in an information gap in the aerosol product at similar to 14.4 degrees N-16.1 degrees N latitudes (e.g., in Manila, the Philippines, and Mainland Southeast Asia), which cannot be filled even with long-term observations owing to the structure of the east-west scanning mechanism of GEMS. Here, we propose a robust method to reconstruct radiances measured inaccurately by the bad pixels, based on spectral correlation induced mainly by the Fraunhofer line structures. The reconstruction aims at the bad pixels in the wavelength range of similar to 485-491 nm, which affects aerosol retrieval. We estimate that uncertainties in the reconstructed optical depths are similar to 2 orders of magnitude smaller than typical aerosol optical depths. Our results demonstrate that the reconstructed radiances effectively restore the physical distributions of visible aerosol indices, improving the determination of aerosol types. Furthermore, the reconstructed radiances enhance retrievals of aerosol layer height (ALH), holding particular significance for the long-term accumulation of ALH data over Southeast Asia using GEMS.

Abstract. This study presents advancements in the processing of satellite remote sensing data, focusing mainly on Aerosol Optical Depth (AOD) retrievals from the Geostationary Environment Monitoring Spectrometer (GEMS). The transformation of Level 2 (L2) data, which includes atmospheric state retrievals, into higher-quality Level 3 (L3) data is crucial in remote sensing. Our contributions lie in two novel improvements to the processing algorithm. First, we improve the inverse distance weighting algorithm by incorporating quality flag information into the weight calculation. By assigning weights inversely proportional to the number of unreliable grids, the method can provide more accurate L3 products. We validate this approach through simulation studies and apply it to GEMS AOD data across various regions and wavelengths. The use of the quality flags in the algorithm can provide a more accurate analysis in remote sensing. Second, we employ a spatio-temporal merging method to address both spatial and temporal variability in AOD data, a departure from previous approaches that solely focused on spatial variability. Our method considers temporal variations spanning previous time intervals. Furthermore, the computed mean fields show similar spatio-temporal patterns to the previous studies, confirming that they can capture real-world phenomena. Lastly, utilizing this procedure, we compute the mean field estimates for GEMS AOD data, which can provide a deeper understanding of the impact of aerosols on climate change and public health.

This study presents advancements in the processing of satellite remote sensing data, focusing mainly on aerosol optical depth (AOD) retrievals from the Geostationary Environment Monitoring Spectrometer (GEMS). The transformation of Level-2 (L2) data, which includes atmospheric-state retrievals, into higher-quality Level-3 (L3) data is crucial in remote sensing. Our contributions lie in two novel improvements to the processing algorithm. First, we improve the inverse-distance-weighting algorithm by incorporating quality flag information into the weight calculation. By assigning weights that are inversely proportional to the number of unreliable grids, the method can provide more accurate L3 products. We validate this approach through simulation studies and apply it to GEMS AOD data across various regions and wavelengths. The use of quality flags in the algorithm can provide a more accurate analysis of remote sensing. Second, we employ a spatiotemporal merging method to address both spatial and temporal variability in AOD data, a departure from previous approaches that solely focused on spatial variability. Our method considers temporal variations spanning previous time intervals. Furthermore, the computed mean fields show similar spatiotemporal patterns to previous studies, confirming their ability to capture real-world phenomena. Lastly, utilizing this procedure, we compute the mean field estimates for GEMS AOD data, which can provide a deeper understanding of the impact of aerosols on climate change and public health.

South Korea is a country that emits a large amount of pollutants as a result of population growth and industrial development and is also severely affected by transboundary air pollution due to its geographical location. As pollutants from both domestic and foreign sources contribute to air pollution in Korea, the location of air pollutant emission sources is crucial for understanding the movement and distribution of pollutants in the atmosphere and establishing national-level air pollution management and response strategies. Based on this background, this study aims to effectively acquire spatial information on domestic and international air pollutant emission sources, which is essential for analyzing air pollution status, by utilizing high-resolution optical satellite images and deep learning-based image segmentation models. In particular, industrial parks and quarries, which have been evaluated as contributing significantly to transboundary air pollution, were selected as the main research subjects, and images of these areas from multi-purpose satellites 3 and 3A were collected, preprocessed, and converted into input and label data for model training. As a result of training the U-Net model using this data, the overall accuracy of 0.8484 and mean Intersection over Union (mIoU) of 0.6490 were achieved, and the predicted maps showed significant results in extracting object boundaries more accurately than the label data created by course annotations.

Callicarpa dichotoma (Lour.) K. Koch has a number of ingredients that are recognized to have physiological activity in hematuria, rheumatism, inflammation, and other conditions. Although study on the contents of C. dichotoma has not yet been done, it is known that the principal constituents are chemicals from the flavonoid and phenylethanoid glycoside class. Consequently, in this work, the amounts of echinacoside, poliumoside, isoacteoside and acacetin-diglucuronide found in the C. dichotoma leaf extract were measured utilizing a diode array detector in high-performance liquid chromatography (HPLC). HPLC was used to carry out the content analysis of each component, and tests for accuracy, specificity, and precision were used to validate the analysis technique. The result showed that the calibration curves of the four compounds, echinacoside, poliumoside, isoacteoside and acacetin-diglucuronide, a large linearity with a correlation coefficient (R²) of 0.9935, 0.9909, 0.9906 and 0.9934. Intra and inter day measurement accuracy of the four compounds was 94.56 to 117.77% and showed precision was less than 3%. Therefore, content analysis showed that echinacoside (1.75±0.28%), poliumoside (4.82±0.66%), isoacteoside (1.40±0.18%) and acacetin-diglucuronide (6.38±0.11%).

Nitrogen dioxide (NO2) and nitrogen oxide (NO), usually referred to as nitrogen oxides (NOx), are emitted into the atmosphere by anthropogenic and natural sources. The detection and monitoring of NO2 plays a key role in air quality managements because of its effects on health and of its contribution to the increase of tropospheric ozone and nitrate aerosols. Unfortunately, up to now, observations were possible only once a day based on satellites in low-earth orbit (GOME, GOME-2, OMI and TROPOMI). However, from now on, it is possible to observe the diurnal variations over the Asia based on Geostationary Environment Monitoring Spectrometer (GEMS) in geostationary earth orbit. Here, we present results of tropospheric nitrogen dioxide column observations with high temporal (hourly) and spatial resolutions over major cities in Asia. In addition, we evaluate the GEMS NO2 operational algorithm by comparing GEMS total and tropospheric NO2 columns with independent observations from ground-based Pandora (total column) and MAX-DOAS (tropospheric column). Additionally, we retrieved the GEMS tropospheric NO2 columns by subtracting the stratospheric NO2 columns, which are assumed based on SLIMCAT model data and then scalded with the real GEMS observations, from the total NO2 columns. Then we also compared the tropospheric NO2 columns that are retrieved based on GEMS NO2 operational algorithm and SLIMCAT model, respectively. Lastly, we compared the GEMS NO2 with other low-earth orbit satellite instruments that include OMI and TROPOMI.

Machine learning is widely used to infer ground-level concentrations of air pollutants from satellite observations. However, a single pollutant is commonly targeted in previous explorations, which would lead to duplication of efforts and ignoration of interactions considering the interactive nature of air pollutants and their common influencing factors. We aim to build a unified model to offer a synchronized estimation of ground-level air pollution levels. We constructed a multi-output random forest (MORF) model and achieved simultaneous estimation of hourly concentrations of PM2.5, PM10, O-3, NO2, CO, and SO2 in China, benefiting from the world's first geostationary air-quality monitoring instrument Geostationary Environment Monitoring Spectrometer. MORF yielded a high accuracy with cross-validated R-2 reaching 0.94. Meanwhile, model efficiency was significantly improved compared to single-output models. Based on retrieved results, the spatial distributions, seasonality, and diurnal variations of six air pollutants were analyzed and two typical pollution events were tracked.

본 연구는 간호대학생의 임상추론역량, 비판적 사고 성향과 핵심기본간호술 수행자신감의 관계를 확인하고자 수행 하였다. 연구 대상은 대전광역시 D 대학교 간호대학생 3, 4학년 157명으로 2022년 11월 2일부터 11월 15일까지 자가설문 지를 통해 조사하였다. 수집된 자료는 SPSS/WIN26.0 Program을 이용하였다. 연구결과 간호대학생의 임상추론역량과 비판 적 사고 성향(r=.417, p <.001), 임상추론역량과 핵심기본간호술 수행자신감(r=.659, p <.001), 비판적 사고 성향과 핵심기본 간호술 수행자신감(r=.303, p <.001)이 유의한 양의 상관관계를 보였다. 따라서 본 연구결과는 간호대학생의 핵심기본간호술 수행자신감을 높이기 위한 다양한 교육과정 개발의 근거로 활용될 수 있을 것으로 보인다.

Air pollution is a serious problem in the world, and it is necessary to monitor air pollution emission sources in other neighboring countries to respond to the problem of air pollution spreading across borders. In this study, we utilized domestic and international optical images from KOMPSAT-3/3A satellites to build an AI training dataset for classifying industrial parks and quarries, which are representative sources of air pollution emissions. The data can be used to identify the distribution of air pollution emission sources located at home and abroad along with various state-of-the-art models in the image segmentation field, and is expected to contribute to the preservation of Korea’s air environment as a basis for establishing air-related policies.

The Geostationary Environmental Monitoring Spectrometer (GEMS) was developed with the objective of identifying the sources of fine dust and analyzing the effects of climate change in Asia and the Korean Peninsula through continuous monitoring of atmospheric pollutants, including long-range transported air pollution. The instrument provides quantitative and qualitative measurements of both long- and short-lived climate forcers (LLCFs and SLCFs). Real-time observation data from GEMS are made available to national and international institutions, enhancing the accuracy of air quality forecasts and contributing to the protection of public health. Moreover, GEMS-derived atmospheric data over Asia can be utilized for analyzing air quality situations and formulating response strategies of Asian countries within the GEMS domain. To ensure the reliability of GEMS data for public release, the Environmental Satellite Center (ESC) at the National Institute of Environmental Research has conducted a series of international joint campaigns on Asian air quality, engaging researchers and scientists worldwide.

&lt;p&gt;Geostationary Environment Monitoring Spectrometer (GEMS), the world&#039;s first geostationary environmental senor onboard Geo-Kompsat 2B, was launched in February, 2020 to monitor atmospheric pollutants (such as -&gt; e.g. Aerosol properties, Nitrogen dioxide, Sulphur dioxide, Formaldehyde and Ozone) with high temporal and spatial resolution over ASIA. Environmental Satellite Center (ESC) of National Institute of Environmental Research has distributed these data since March, 2021 after in-orbit test was completed.&amp;#160;&lt;br /&gt;We performed the accuracy validation of GEMS atmospheric pollutants retrieval algorithm using other environmental satellite (such as TROPOMI, OMPS, etc.) and ground-based measurements (such as Pandora, Max-DOAS, etc.) data through GEMS Map of Air Pollution (GMAP) and Satellite Integrated Joint monitoring of Air Quality (SIJAQ) campaign.&amp;#160;&lt;br /&gt;We validated the accuracy of GEMS atmospheric-pollutants-retrieval algorithm using the data from other environmental satellites (e.g. TROPOMI, OMPS, etc.) and ground-based measurements data (e.g. Pandora, Max-DOAS, etc.).&lt;br /&gt;After that, we improved the accuracy of retrieval algorithm and released GEMS version two data in November last year. In this version two data, we found improvements needed in a priori data, cloud data, and surface reflectance data. In this present study, we introduce the difference and improvements GEMS version two and one data.&lt;/p&gt;

The importance of ozone monitoring has been growing due to the polar ozone depletion and increasing tropospheric ozone concentration over many Asian countries, includ-ing South Korea. In-situ measurement of the vertical ozone structure has advantages for ozone research, but observations are not sufficient. In this study, ozonesonde measurements were per-formed from October to November in Yongin during the GMAP (The GEMS Map of Air Pol-lution) 2021 campaign. The procedure for ozonesonde preparation and initial analysis of the observed ozone profile are documented. The observed ozone concentrations are in good agreement with previous studies in the troposphere, and they capture the stratospheric ozone distribution as well, including stratosphere-troposphere exchange event. These balloon-borne in situ measurements can contribute to the evaluation of remote sensing measurements such as Geostationary Environment Monitoring Spectrometer (GEMS). This document focuses on providing essential information of ozonesonde preparation and measurement for domestic researchers.