Geostationary satellite remote sensing systems are a useful tool for forest fire detection and monitoring because of their high temporal resolution over large areas. In this study, we propose a combined 3-step forest fire detection algorithm (i.e., thresholding, machine learning-based modeling, and post processing) using Himawari-8 geostationary satellite data over South Korea. This threshold-based algorithm filtered the forest fire candidate pixels using adaptive threshold values considering the diurnal cycle and seasonality of forest fires while allowing a high rate of false alarms. The random forest (RF) machine learning model then effectively removed the false alarms from the results of the threshold-based algorithm (overall accuracy ~99.16%, probability of detection (POD) ~93.08%, probability of false detection (POFD) ~0.07%, and 96% reduction of the false alarmed pixels for validation), and the remaining false alarms were removed through post-processing using the forest map. The proposed algorithm was compared to the two existing methods. The proposed algorithm (POD ~ 93%) successfully detected most forest fires, while the others missed many small-scale forest fires (POD ~ 50–60%). More than half of the detected forest fires were detected within 10 min, which is a promising result when the operational real-time monitoring of forest fires using more advanced geostationary satellite sensor data (i.e., with higher spatial and temporal resolutions) is used for rapid response and management of forest fires.

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 <~0.95. Finally, the AOD products from the GEMS algorithm and MI were used in aerosol merging with the maximum-likelihood estimation method, based on a weighting factor derived from the standard deviation of the original AOD products. With the advantage of the UV–visible channel in retrieving aerosol properties over bright surfaces, the combined AOD products demonstrated better spatial data availability than the original AOD products, with comparable accuracy. Furthermore, pixel-level error analysis of GEMS AOD data indicates improvement through MI synergy.

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

This study investigates the uncertainties associated with estimates of the long-range transport SO₂ (LRT-SO₂) flow rate calculated hourly using Geostationary Environment Monitoring Spectrometer (GEMS) synthetic radiances. These radiances were simulated over the Korean Peninsula and the surrounding regions using inputs from the GEOS-Chem model for January, April, July, and October 2016. The LRT-SO₂ calculation method, which requires SO₂ vertical column densities, wind data, and planetary boundary layer information, was used to quantify the effects of the SO₂ slant column density (SCD) retrieval error and uncertainties in wind data on the accuracy of the LRT-SO₂ estimates. The effects were estimated for simulations of three anthropogenic and three volcanic SO₂ transport events. When there were no errors in the GEMS SO₂ SCD and wind data, the average true LRT-SO₂ flow rates (standard deviation) and those calculated for these events were 1.17 ($\pm$ 0.44) and 1.21 ($\pm$0.44) Mg/h, respectively. However, the averages of the true LRT-SO₂ flow rates and those calculated for the three anthropogenic (volcanic) SO₂ events were 0.61 (1.17) and 0.64 (1.20) Mg/h, respectively, in the presence of GEMS SO₂ SCD errors. In the presence of both errors in the GEMS SO₂ SCD and wind data, the averages of the true LRT-SO₂ flow rates and those calculated for the three anthropogenic (volcanic) SO₂ events were 0.61 (1.17) and 0.61 (1.04) Mg/h, respectively. This corresponds to differences of 2.1% to 23.1% between the simulated and true mean LRT-SO₂ flow rates. The mean correlation coefficient (R), intercept, and slope between the true and simulated LRT-SO₂ flow rates were 0.51, 0.43, and 0.45 for the six simulated events, respectively. This study demonstrates that SO₂ SCD accuracy is an important factor in improving estimates of LRT-SO₂ flow rates.

Air pollutants adversely affect human health, and thus a global improvement in air quality is urgent. A Geostationary Environment Monitoring Spectrometer (GEMS) was mounted on the geostationary Chollian 2B satellite in 2020 to observe the spatial distribution of air pollution, and sequential observations have been released since July 2022. The reliability of GEMS must be analyzed because it is the first payload on the geostationary Earth orbit satellite to observe trace gases. This study analyzed the initial results of GEMS observations such as the aerosol optical depth and vertical column densities (VCD) of ozone (O₃), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), and formaldehyde (HCHO), and compared them with previous studies. The correlation coefficient of O₃ ranged from 0.90 (Ozone Monitoring Instrument, OMI) to 0.97 (TROPOspheric Monitoring Instrument, TROPOMI), whereas that of NO₂ ranged from 0.47 (winter, OMI and OMPS) to 0.83 (summer, TROPOMI). GEMS yielded a higher VCD of NO₂ than that of OMI and TROPOMI. Based on the sources of O₃ and NO₂, GEMS observed the maximum VCD at a different time (3–4 h) to that of the ground observations. Overall, GEMS can make observations several times a day and is a potential tool for atmospheric environmental analysis.