Search

A two-dimensional visibility estimation model was developed, based on random forest (RF), a machine learning-based technique. A geostatistical method was introduced into the visibility estimation model for the first time to interpolate point measurement data to gridded data spatially with a pixel size of 10 km. The RF-based model was trained using gridded visibility data, as well as meteorological and air pollution input variable data, for each location in South Korea, which were characterized by complex geographical features and high air pollution levels. Generally, relative humidity was the most important input variable for the visibility estimation (average mean decrease accuracy: 35%). However, PM2.5 tended to be the most crucial variable in polluted regions. The spatial interpolation was found to result in an additional visibility estimation error of 500 m in locations where no adjacent visibility observations within 0.2° were available. The performance of the proposed model was preliminarily assessed. Generally, the best detection performance was achieved in good visibility conditions (visibility range: 10 to 20 km). This study is the first to demonstrate a visibility estimation model based on a geostatistical method and machine learning, which can provide visibility information in locations for which no observations exist.

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 (± 0.44) and 1.21 (±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.

Aerosol types in Asian capital cities were classified using a random forest (RF) satellite-based aerosol classification model during 2018–2020 in an investigation of the contributions of aerosol types, with or without Aerosol Robotic Network (AERONET) observations. In this study, we used the recently developed RF aerosol classification model to detect and classify aerosols into four types: pure dust, dust-dominated aerosols, strongly absorbing aerosols, and non-absorbing aerosols. Aerosol optical and microphysical properties for each aerosol type detected by the RF model were found to be reasonably consistent with those for typical aerosol types. In Asian capital cities, pollution-sourced aerosols, especially non-absorbing aerosols, were found to predominate, although Asian cities also tend to be seasonally affected by natural dust aerosols, particularly in East Asia (March–May) and South Asia (March–August). No specific seasonal effects on aerosol type were detected in Southeast Asia, where there was a predominance of non-absorbing aerosols. The aerosol types detected by the RF model were compared with those identified by other aerosol classification models. This study indicates that the satellite-based RF model may be used as an alternative in the absence of AERONET sites or observations.

The spatial coverage of satellite aerosol classification was improved using a random forest (RF) model trained with observational data including target (aerosol type) and input (satellite measurement) variables. The AErosol RObotic NETwork (AERONET) aerosol-type dataset was used for the target variables. Satellite input variables with many missing data or low mean-decrease accuracy were excluded from the final input variable set, and good performance in aerosol-type classification was achieved. The performance of the RF-based model was evaluated on the basis of the wavelength dependence of single-scattering albedo (SSA) and fine-mode-fraction values from AERONET. Typical SSA wavelength dependence for individual aerosol types was consistent with that obtained for aerosol types by the RF-based model. The spatial coverage of the RF-based model was also compared with that of previously developed models in a global-scale case study. The study demonstrates that the RF-based model allows satellite aerosol classification with improved spatial coverage, with a performance similar to that of previously developed models.

A new method was developed for classifying aerosol types involving a machine-learning approach to the use of satellite data. An Aerosol Robotic NETwork (AERONET)-based aerosol-type dataset was used as a target variable in a random forest (RF) model. The contributions of satellite input variables to the RF-based model were quantified to determine an optimal set of input variables. The new method, based on inputs of satellite variables, allows the classification of seven aerosol types: pure dust, dust-dominant mixed, pollution-dominant mixed aerosols, and pollution aerosols (strongly, moderately, weakly, and non-absorbing). The performance of the model was statistically evaluated using AERONET data excluded from the model training dataset. Model accuracy for classifying the seven aerosol types was 59%, improving to 72% for four types (pure dust, dust-dominant mixed, strongly absorbing, and non-absorbing). The performance of the model was evaluated against an earlier aerosol classification method based on the wavelength dependence of single-scattering albedo (SSA) and fine-mode-fraction values from AERONET. Typical wavelength dependences of SSA for individual aerosol types are consistent with those obtained for aerosol types by the new method. This study demonstrates that an RF-based model is capable of satellite aerosol classification with sensitivity to the contribution of non-spherical particles.

An investigation into the diurnal characteristics of vertical formaldehyde (HCHO) profiles was conducted based on multi-axis differential optical absorption spectroscopy (MAX-DOAS) measurements in Beijing during the CAREBEIJING campaign, covering a month-long period through August and September 2006. Vertical HCHO profiles were retrieved based on a combined differential optical absorption spectroscopy (DOAS) technique and an optimal estimation method (OEM). The HCHO volume-mixing ratio (VMR) was found to be highest in the layer from the surface up to an altitude of 1 km and to decrease with altitude above this layer. In all retrieved profiles, HCHO was not detected in the layer from 3–4 km. Over the diurnal cycle, the HCHO VMR values were generally highest at 15:00 local time (LT) and were lower in the morning and late afternoon. The mean HCHO VMRs were 6.17, 1.82, and 0.80 ppbv for the 0–1, 1–2, and 2–3-km layers, respectively, at 15:00 LT, whereas they were 3.54 (4.79), 1.06 (1.43), and 0.46 (0.63) ppbv for the 0–1, 1–2, and 2–3-km layers, respectively, at 09:00 (17:00) LT. The HCHO VMRs reached their highest values at 15:00 LT on August 19, which were 17.71, 5.20, and 2.31 ppbv for the 0–1, 1–2, and 2–3-km layers, respectively. This diurnal pattern implies that the photo-oxidation of volatile organic compounds (VOCs) was most active at 15:00 LT for several days during the campaign period. In a comparison of the derived HCHO VCDs with those obtained from the Ozone Monitoring Instrument (OMI) measurements, the HCHO vertical column density (VCD) values obtained from the OMI measurements tend to be smaller than those from the MAX-DOAS.

We investigate the effects of aerosol peak height (APH) and various parameters on the air mass factor (AMF) for SO2 retrieval. Increasing aerosol optical depth (AOD) leads to multiple scattering within the planetary boundary layer (PBL) and an increase in PBL SO2 AMF. However, under high AOD conditions, aerosol shielding effects dominate, which causes the PBL SO2 AMF to decrease with increasing AOD. The height of the SO2 layer and the APH are found to significantly influence the PBL SO2 AMF under high AOD conditions. When the SO2 and aerosol layers are of the same height, aerosol multiple scattering occurs dominantly within the PBL, which leads to an increase in the PBL SO2 AMF. When the APH is greater than the SO2 layer height, aerosol shielding effects dominate, which decreases the PBL SO2 AMF. When the SO2 and aerosol layers are of the same height under low AOD and solar zenith angle (SZA) conditions, increased surface reflectance is found to significantly increase the PBL SO2 AMF. However, high AOD dominates the surface reflectance contribution to PBL SO2 AMF. Under high SZA conditions, Rayleigh scattering contributes to a reduction in the light path length and PBL SO2 AMF. For volcanic SO2 AMF, high SZA enhances the light path length within the volcanic SO2 layer, as well as the volcanic SO2 AMF, because of the negligible photon loss by Rayleigh scattering at high altitudes. High aerosol loading and an APH that is greater than the SO2 peak height lead to aerosol shielding effects, which reduce the volcanic SO2 AMF. The SO2 AMF errors are also quantified as a function of uncertainty in the input data of AOD, APH, and surface reflectance. The SO2 AMF sensitivities and error analysis provided here can be used to develop effective error reduction strategies for satellite-based SO2 retrievals.

The attenuating effects of clouds and aerosols on global horizontal irradiance (GHI) and ultraviolet erythemal irradiance (UVER) were evaluated and compared using data from four sites in South Korea (Gangneung, Pohang, Mokpo, and Gosan) for the period 2005−2016. It was found that GHI and UVER are affected differently by various attenuating factors, resulting in an increase in the ratio of UVER to GHI with a decrease in the clearness index of GHI. A comparative analysis of the clearness indices of GHI and UVER identified an almost linear relationship between two transmittances by applying UVER with fixed slant ozone ( UVER 300 ) and there was a latitudinal difference in the relationship. Some nonlinearity remained in this relationship, which suggests a contribution by other factors such as clouds and aerosols. Variations of the UVER 300 ratio to GHI with cloud cover and aerosol optical depth were analyzed. The ratio increased with cloud cover and decreased with aerosol optical depth, indicating that clouds attenuate GHI more efficiently than UVER and that the attenuation by aerosols is greater for UVER than for GHI. A multiple linear regression analysis of the clearness indices of GHI and UVER 300 quantitively demonstrates differences in the radiation-reducing effects of clouds and aerosols, with some regional differences by site that can be attributed to local climatic characteristics in South Korea.

We developed a Raman lidar system that can remotely detect CO2 leakage and its volume mixing ratio (VMR). The system consists of a laser, a telescope, an optical receiver, and detectors. Indoor CO2 cell measurements show that the accuracy of the Raman lidar is 99.89%. Field measurements were carried out over a four-day period in November 2017 at the Eumsong Environmental Impact Evaluation Test Facility (EIT), Korea, where a CO2 leak was located 0.2 km from the Raman lidar. The results show good agreement between CO2 VMR measured by the Raman lidar system (CO2 VMRRaman LIDAR) and that measured by in situ instruments (CO2 VMRIn-situ). The correlation coefficient (R), mean absolute error (MAE), root mean square error (RMSE), and percentage difference between CO2 VMRIn-situ and CO2 VMRRaman LIDAR are 0.81, 0.27%, 0.37%, and 4.92%, respectively. The results indicate that Raman lidar is an effective tool in detecting CO2 leakage and in measuring CO2 VMR remotely.

In the present study, we investigate the effects of signal to noise (SNR), slit function (FWHM), and aerosol optical depth (AOD) on the accuracy of formaldehyde (HCHO) vertical column density (HCHOVCD) using the ground-based direct-sun synthetic radiance based on differential optical absorption spectroscopy (DOAS). We found that the effect of SNR on HCHO retrieval accuracy is larger than those of FWHM and AOD. When SNR = 650 (1300), FWHM = 0.6, and AOD = 0.2, the absolute percentage difference (APD) between the true HCHOVCD values and those retrieved ranges from 54 (30%) to 5% (1%) for the HCHOVCD of 5.0 × 1015 and 1.1 × 1017 molecules cm−2, respectively. Interestingly, the maximum AOD effect on the HCHO accuracy was found for the HCHOVCD of 3.0 × 1016 molecules cm−2. In addition, we carried out the first ground-based direct-sun measurements in the ultraviolet (UV) wavelength range to retrieve the HCHOVCD using Pandora in Seoul. The HCHOVCD was low at 12:00 p.m. local time (LT) in all seasons, whereas it was high in the morning (10:00 a.m. LT) and late afternoon (4:00 p.m. LT), except in winter. The maximum HCHOVCD values were 2.68 × 1016, 3.19 × 1016, 2.00 × 1016, and 1.63 × 1016 molecules cm−2 at 10:00 a.m. LT in spring, 10:00 a.m. LT in summer, 1:00 p.m. LT in autumn, and 9:00 a.m. LT in winter, respectively. The minimum values of Pandora HCHOVCD were 1.63 × 1016, 2.23 × 1016, 1.26 × 1016, and 0.82 × 1016 molecules cm−2 at around 1:45 p.m. LT in spring, summer, autumn, and winter, respectively. This seasonal pattern of high values in summer and low values in winter implies that photo-oxidation plays an important role in HCHO production. The correlation coefficient (R) between the monthly HCHOVCD values from Pandora and those from the Ozone Monitoring Instrument (OMI) is 0.61, and the slope is 1.25.

We investigate the effects of aerosol optical depth (AOD), single scattering albedo (SSA), aerosol peak height (APH), measurement geometry (solar zenith angle (SZA) and viewing zenith angle (VZA)), relative azimuth angle, and surface reflectance on the accuracy of NO2 slant column density using synthetic radiance. High AOD and APH are found to decrease NO2 SCD retrieval accuracy. In moderately polluted (5 × 1015 molecules cm−2 < NO2 vertical column density (VCD) < 2 × 1016 molecules cm−2) and clean regions (NO2 VCD < 5 × 1015 molecules cm−2), the correlation coefficient (R) between true NO2 SCDs and those retrieved is 0.88 and 0.79, respectively, and AOD and APH are about 0.1 and is 0 km, respectively. However, when AOD and APH are about 1.0 and 4 km, respectively, the R decreases to 0.84 and 0.53 in moderately polluted and clean regions, respectively. On the other hand, in heavily polluted regions (NO2 VCD > 2 × 1016 molecules cm−2), even high AOD and APH values are found to have a negligible effect on NO2 SCD precision. In high AOD and APH conditions in clean NO2 regions, the R between true NO2 SCDs and those retrieved increases from 0.53 to 0.58 via co-adding four pixels spatially, showing the improvement in accuracy of NO2 SCD retrieval. In addition, the high SZA and VZA are also found to decrease the accuracy of the NO2 SCD retrieval.

Surface NO2 volume mixing ratio (VMR) at a specific time (13:45 Local time) (NO2 VMRST) and monthly mean surface NO2 VMR (NO2 VMRM) are estimated for the first time using three regression models with Ozone Monitoring Instrument (OMI) data in four metropolitan cities in South Korea: Seoul, Gyeonggi, Daejeon, and Gwangju. Relationships between the surface NO2 VMR obtained from in situ measurements (NO2 VMRIn-situ) and tropospheric NO2 vertical column density obtained from OMI from 2007 to 2013 were developed using regression models that also include boundary layer height (BLH) from Atmospheric Infrared Sounder (AIRS) and surface pressure, temperature, dew point, and wind speed and direction. The performance of the regression models is evaluated via comparison with the NO2 VMRIn-situ for two validation years (2006 and 2014). Of the three regression models, a multiple regression model shows the best performance in estimating NO2 VMRST and NO2 VMRM. In the validation period, the average correlation coefficient (R), slope, mean bias (MB), mean absolute error (MAE), root mean square error (RMSE), and percent difference between NO2 VMRIn-situ and NO2 VMRST estimated by the multiple regression model are 0.66, 0.41, −1.36 ppbv, 6.89 ppbv, 8.98 ppbv, and 31.50%, respectively, while the average corresponding values for the other two models are 0.75, 0.41, −1.40 ppbv, 3.59 ppbv, 4.72 ppbv, and 16.59%, respectively. All three models have similar performance for NO2 VMRM, with average R, slope, MB, MAE, RMSE, and percent difference between NO2 VMRIn-situ and NO2 VMRM of 0.74, 0.49, −1.90 ppbv, 3.93 ppbv, 5.05 ppbv, and 18.76%, respectively.

Cloud and aerosol modification factors are investigated in the spectral range of ultraviolet (UV) to correct for cloud and aerosol extinction effects from clear sky irradiance. The cloud modification factor (CMF) and aerosol modification factor (AMF) are estimated using radiative transfer model (RTM) simulations and ground-based observations in Seoul, Korea. The modification factors show a spectral dependence within the spectral range of 300 to 360 nm, which is the range used to estimate erythemal UV. The CMF and AMF values are estimated with high spectral resolution with considerations of solar zenith angle (SZA), cloud/aerosol amount, and total ozone variation. From the simulation studies, the variation in the CMFs within the spectral range of 300–360 nm is estimated to be 0.031–0.055, which is twice as large as the decrease in CMFs resulting from a SZA increase of 10°. In addition, the CMFs estimated from observational data show significant spectral dependence, varying from 2.5% to 10.0%. Because of the small aerosol optical depth (AOD) value, however, the variation in the AMF calculated from simulations is estimated to be between 0.007 and 0.045, indicating lower spectral dependence than the CMF. Furthermore, the spectral difference in the AMF calculated from observational data is insignificant relative to the daily-averaged total ozone error and uncertainties related to the reference irradiance spectrum under aerosol-free conditions.

We investigate the simultaneous effects of aerosol peak height (APH), aerosol properties, measurement geometry, and other factors on the air mass factor for NO2 retrieval at sites with high NO2 concentration. A comparison of the effects of high and low surface reflectance reveals that NO2 air mass factor (AMF) values over a snowy surface (surface reflectance 0.8) are generally higher than those over a deciduous forest surface (surface reflectance 0.05). Under high aerosol optical depth (AOD) conditions, the aerosol shielding effect over a high-albedo surface is revealed to reduce the path-length of light at the surface, whereas high single scattering albedo (SSA) conditions (e.g., SSA = 0.95) lead to an increase in the aerosol albedo effect, which results in an increased AMF over areas with low surface reflectance. We also conducted an in-depth study of the APH effect on AMF. For an AOD of 0.1 and half width (HW) of 5 km, NO2 AMF decreases by 29% from 1.36 to 0.96 as APH changes from 0 to 2 km. In the case of high-AOD conditions (0.9) and HW of 5 km, the NO2 AMF decreases by 240% from 1.85 to 0.54 as APH changes from 0 to 2 km. The AMF variation due to error in the model input parameters (e.g., AOD, SSA, aerosol shape, and APH) is also examined. When APH is 0 km with an AOD of 0.4, SSA of 0.88, and surface reflectance of 0.05, a 30% error in AOD induces an AMF error of between 4.85% and −3.67%, an SSA error of 0.04 leads to NO2 VCD errors of between 4.46% and −4.77%, and a 30% error in AOD induces an AMF error of between −9.53% and 8.35% with an APH of 3 km. In addition to AOD and SSA, APH is an important factor in calculating AMF, due to the 2 km error in APH under high-SZA conditions, which leads to an NO2 VCD error of over 60%. Aerosol shape is also found to have a measureable effect on AMF under high-AOD and small relative azimuth angle (RAA) conditions. The diurnal effect of the NO2 profile is also examined and discussed.

In East Asia, where aerosol concentrations are persistently high throughout the year, most satellite CO2 retrieval algorithms screen out many measurements during quality control in order to reduce retrieval errors. To reduce the retrieval errors associated with aerosols, we have modified YCAR (Yonsei Carbon Retrieval) algorithm to YCAR-CAI to retrieve XCO2 from GOSAT FTS measurements using aerosol retrievals from simultaneous Cloud and Aerosol Imager (CAI) measurements. The CAI aerosol algorithm provides aerosol type and optical depth information simultaneously for the same geometry and optical path as FTS. The YCAR-CAI XCO2 retrieval algorithm has been developed based on the optimal estimation method. The algorithm uses the VLIDORT V2.6 radiative transfer model to calculate radiances and Jacobian functions. The XCO2 results retrieved using the YCAR-CAI algorithm were evaluated by comparing them with ground-based TCCON measurements and current operational GOSAT XCO2 retrievals. The retrievals show a clear annual cycle, with an increasing trend of 2.02 to 2.39 ppm per year, which is higher than that measured at Mauna Loa, Hawaii. The YCAR-CAI results were validated against the Tsukuba and Saga TCCON sites and show an root mean square error of 2.25, a bias of −0.81 ppm, and a regression line closer to the linear identity function compared with other current algorithms. Even after post-screening, the YCAR-CAI algorithm provides a larger dataset of XCO2 compared with other retrieval algorithms by 21% to 67%, which could be substantially advantageous in validation and data analysis for the area of East Asia. Retrieval uncertainty indicates a 1.39 to 1.48 ppm at the TCCON sites. Using Carbon Tracker-Asia (CT-A) data, the sampling error was analyzed and was found to be between 0.32 and 0.36 ppm for each individual sounding.

Based on an optimal estimation method, an algorithm was developed to retrieve the column-averaged dry-air mole fraction of carbon dioxide (XCO2) using Shortwave Infrared (SWIR) channels, referred to as the Yonsei CArbon Retrieval (YCAR) algorithm. The performance of the YCAR algorithm is here examined using simulated radiance spectra, with simulations conducted using different Aerosol Optical Depths (AODs), Solar Zenith Angles (SZAs) and aerosol types over various surface types. To characterize the XCO2 retrieval algorithm, reference tests using simulated spectra were analysed through a posteriori XCO2 retrieval errors and averaging kernels. The a posteriori XCO2 retrieval errors generally increase with increasing SZA. However, errors were found to be small (

This present study suggests a method to calculate the SO2 flow rate from a source area to receptor areas on a regional scale using Ozone Monitoring Instrument (OMI) SO2 products, surface in situ SO2 data, and the hybrid single particle Lagrangian integrated trajectory (HYSPLIT) model. The method was implemented to calculate the SO2 flow rate from continental Asia to northeast Asia and the Northwest Pacific Ocean. For the high SO2 events when SO2 was transported from continental Asia to Japan via the Korean Peninsula on 22–24 December 2006, the long-range transported SO2 flow rates were 14.0 (21.0) Mg·h−1 OMI·gird−1 at Gangneung (Seoul) in Korea and 4.2 (5.3) Mg·h−1 OMI·gird−1 at Hiroshima (Kumamoto) in Japan. For the long-range transport of SO2 from continental Asia to the Northwest Pacific Ocean on 6–7 October 2008 (9–11 October 2006), the flow rates were 16.1 (16.2) Mg·h−1 OMI·gird−1 at Hokkaido, Japan (Vladivostok, Russia) and 5.6 (16.7) Mg·h−1 OMI·gird−1 at the Aleutian Islands, Northwest Pacific Ocean (Bering Sea). The mean rates of decrease in the SO2 flow rate per 1000 km were also calculated between continental Asia and the receptor areas. Uncertainties in the flow rate estimates were also assessed and discussed.

The Geostationary Environment Monitoring Spectrometer (GEMS) over Asia is the first geostationary Earth orbit instrument in the virtual constellation of sensors for atmospheric chemistry and composition air quality research and applications. For the first time, the hourly observations enable studies of diurnal variation of several important trace gas and aerosol pollutants including nitrogen dioxide (NO2) which is the focus of this work. NO2 is a regulated pollutant and an indicator of anthropogenic emissions in addition to being involved in tropospheric ozone chemistry and particulate matter formation. We present new quantitative measures of NO2 tropospheric column diurnal variation which can be greater than 50% of the column amount especially in polluted environments. The NO2 distribution is seen to change hourly and can be quite different from what would be seen by a once-a-day low Earth orbit satellite observation. We use GEMS data in combination with TROPOMI satellite and Pandora ground-based remote sensing measurements and MUSICAv0 3D chemical transport model analysis to examine the NO2 diurnal variation in January and June 2023 over Northeast Asia and Seoul, South Korea, study regions to distinguish the different emissions, chemistry, and meteorological processes that drive the variation. Understanding the relative importance of these processes will be important for including pollutant diurnal variation in models aimed at determining true pollutant exposure levels for air quality studies. The work presented here also provides a path for investigating similar NO2 diurnal cycles in the new TEMPO data over North America, and later over Europe with S-4. [ABSTRACT FROM AUTHOR]

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 of 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 of NO2 columns in GEMS, Pandora, and GEOS-Chem, and we use GEOS-Chem to interpret these variations. NOx emissions are four 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 negligible role of chemistry. In summer, by contrast, chemical loss combined with transport drives a minimum in the NO2 column at 13-14 local time. 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 of NO2 reflects a dynamic balance between emission and chemical loss. [ABSTRACT FROM AUTHOR]

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 AEH retrieval results show significant AEH values and continuously monitor aerosol vertical height information in severe dust plumes over East Asia, 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 insignificant bias with a standard deviation of 1.4 km for the AEH difference over the GEMS observation domain from January to June 2021 due to uncertainty in input parameters for aerosol and surface. The AEH difference depends on aerosol optical properties and surface albedo. Compared to the aerosol layer height obtained from the tropospheric monitoring instrument (TROPOMI), differences of 0.78 ± 0.81 and 1.16 ± 0.92 km were obtained for pixels with single scattering albedo (SSA) < 0.90 and 0.90 < SSA < 0.95, respectively, with significant dependence on aerosol type. [ABSTRACT FROM AUTHOR]

The Geostationary Environmental Monitoring Spectrometer (GEMS) is a UV-visible spectrometer onboard the GEO-KOMPSAT-2B satellite launched into geostationary orbit in February 2020. To evaluate GEMS NO2 column data, comparison was carried out using NO2 vertical column density (VCD) measured using direct-sunlight observations by the Pandora spectrometer system at four sites in Seosan, South Korea, during November 2020 to January 2021. Correlation coefficients between GEMS and Pandora NO2 data at four sites ranged from 0.35 to 0.48, with root mean square errors (RMSEs) from 4.7 × 1015 molec. cm-2 to 5.5 × 1015 molec. cm-2 for cloud fraction (CF) < 0.7. Higher correlation coefficients of 0.62-0.78 with lower RMSEs from 3.3 × 1015 molec. cm-2 to 4.3 × 1015 molec. cm-2 were found with CF < 0.3, indicating the higher sensitivity of GEMS to atmospheric NO2 in less-cloudy conditions. Overall, GEMS NO2 column data tend to be lower than those of Pandora due to differences in representative spatial coverage, with a large negative bias under high-CF conditions. With correction for horizontal representativeness in Pandora measurement coverage, the correlation coefficients range from 0.69 to 0.81 with RMSEs from 3.2 × 1015 molec. cm-2 to 4.9 × 1015 molec. cm-2 were achieved for CF < 0.3, showing the better correlation with the correction than that without the correction. [ABSTRACT FROM AUTHOR]

Nitrogen dioxide (NO2) is a major air pollutant. Tropospheric NO2 vertical column densities (VCDs) retrieved from sun-synchronous satellite instruments have provided abundant NO2 data for environmental studies, but such data are limited by insufficient temporal sampling (e.g., once a day). The Geostationary Environment Monitoring Spectrometer (GEMS) launched in February 2020 monitors NO2 at an unprecedented high temporal resolution. Here we present a research product for tropospheric NO2 VCDs, referred to as POMINO-GEMS. We develop a hybrid retrieval method combining GEMS and TROPOMI observations as well as GEOS-Chem simulations to generate hourly tropospheric NO2 slant column densities (SCDs). We then derive tropospheric NO2 air mass factors (AMFs) with explicit corrections for the anisotropy of surface reflectance and aerosol optical effects, through pixel-by-pixel radiative transfer calculations. Prerequisite cloud parameters are retrieved with the O2-O2 algorithm by using ancillary parameters consistent with those used in NO2 AMF calculations. Initial retrieval of POMINO-GEMS tropospheric NO2 VCDs for June-August 2021 reveals strong hotspot signals over megacities and distinctive diurnal variations over polluted and clean areas. POMINO-GEMS NO2 VCDs agree well with our POMINO-TROPOMI v1.2.2 product (R = 0.97, and NMB = 3.6%) over Asia. Comparison with ground-based MAX-DOAS VCD data at nine sites shows a small bias of POMINO-GEMS (NMB = -15.7%); however, the correlation for diurnal variation varies from -0.66 to 0.90, suggesting location-dependent performance. Surface NO2 concentrations estimated from POMINO-GEMS VCDs are consistent with measurements from the Ministry of Ecology and Environment of China at 855 sites (NMB = -24.1%, and R = 0.95 for diurnal correlation averaged over all sites). POMINO-GEMS data will be made freely available for users to study the spatiotemporal variations, sources and impacts of NO2. [ABSTRACT FROM AUTHOR]