Carbon dioxide (CO2) emissions from industrial point sources are one of the main driving factors contributing to uncertainties in CO2 emissions from fossil fuels. This study demonstrates, for the first time, the feasibility of a portable Raman Lidar system that is cost-effective and employs simple optics for the monitoring of anthropogenic CO2 emissions from stack sources. Two outdoor field campaigns were conducted to investigate emissions from waste combustor stacks in South Korea. In Haman, we examined the CO2 enhancement caused by emissions and estimated the CO2 instant emission flux over a waste combustor stack, which was located 440 m away from the Lidar. The CO2 volume mixing ratio peaked at 856.05 ppmv on the night of August 22, 2022, demonstrating effective CO2 source capture. Higher standard deviations on the night of August 22 were correlated to calm and light wind conditions. Instant emission fluxes were calculated at 265.6 kg/h (night of August 15) and 622.6 kg/h (night of August 22). Interestingly, the CO2 emission flux over the stack varied significantly with time, which implied the importance of continuous CO2 emission tracking. In Seoul, CO2 emission measurements were conducted on April 27, 2022, and a broader CO2 plume was detected compared to the observations in Haman. Instant emission fluxes were quantified at 771.3 kg/h (22:38) and 623.4 kg/h (22:49) over the stack, which was located 820 m away from the Lidar. Furthermore, the performance of the Raman Lidar was evaluated based on the comparison between Lidar measurements and data collected from in situ CO2 sensors. The outcomes validated the high accuracy of the Lidar under outdoor conditions (averaged percent difference = 0.06 %). Our study highlights the efficacy of Raman Lidar in tracking high CO2 emissions, thereby contributing to the quantification of greenhouse gas emissions and supporting global climate action initiatives.

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.

Satellite measurements of nitrogen dioxide have been used to infer nitrogen oxide emissions, a critical component in tropospheric chemistry and pollution. New observations from the Geostationary Environmental Monitoring Spectrometer offer a breakthrough by providing a daytime record of nitrogen dioxide over Asia. Here we present the summertime diurnal patterns of nitrogen dioxide at major cities, power plant regions, and the Strait of Malacca. The Geostationary Environmental Monitoring Spectrometer data across various regions show high nitrogen dioxide in the morning which decrease in the afternoon, with varying hourly peaks, troughs, and amplitudes reflecting diurnal characteristics of local emissions and chemistry. Nitrogen oxide emissions inferred from Geostationary Environmental Monitoring Spectrometer and the Weather Research and Forecasting model coupled with Chemistry also show distinct patterns among regions: early morning peaks occur over Hanoi, Guangzhou, and Bangkok; mid-to-late morning peaks appear over Seoul and Beijing; and late afternoon peaks are noted in the Yangtze River Delta region. Top-down emissions incorporating temporal changes in the Geostationary Environmental Monitoring Spectrometer nitrogen dioxide yield the most accurate nitrogen dioxide simulations.

The Geostationary Environment Monitoring Spectrometer (GEMS) onboard the Geostationary Korea Multi-Purpose Satellite-2B (GEO-KOMPSAT-2B) satellite observes the hourly volcanic SO2 over Asia. In this study, the various physical characteristics of volcanic plumes have been investigated based on hourly volcanic SO2 measurements. The transport direction, path and speed, and altitude of volcanic SO2 plume emitted from Nishinoshima in Japan, Etna in Italy, and Dukono located in Halmahera, Indonesia were calculated. The SO2 plume from Nishinoshima, Japan, moved westward at a maximum speed of 57 km/h on August 4, 2020. The SO2 plume generated from Etna was observed to move over China using both GEMS and TROPOMI, and moved at an altitude of 11–14 km and a speed of 162–190 km/h. In the case of the SO2 plume from the Dukono volcano flowed into an average of 3.6 Mg of SO2 per hour to the cities of nearby islands. GEMS can be utilized for an improvement in the prediction accuracy of SO2 plume transport using a chemical transport model due to the availability of hourly volcanic SO2 height information. In addition, hourly observations of SO2 concentrations are expected to protect SO2 exposure through rapid forecasting for people in cities around the volcano.

Carbon dioxide (CO2) emissions from industrial point sources are one of the main driving factors contributing to uncertainties in CO2 emissions from fossil fuels. This study demonstrates, for the first time, the feasibility of a portable Raman Lidar system that is cost-effective and employs simple optics for the monitoring of anthropogenic CO2 emissions from point sources. Two outdoor field campaigns were conducted to investigate emissions from waste combustor stacks in South Korea. In Haman, we examined the CO2 enhancement caused by emissions and estimated the CO2 emission flux over a waste combustor stack, which was located 440 m away from the Lidar. The CO2 volume mixing ratio peaked at 856.05 ppmv on the night of August 22, 2022, demonstrating effective CO2 source capture. Higher standard deviations on the night of August 22 were correlated to calm and light wind conditions. Emission fluxes were calculated at 265.6 kg/h (night of August 15) and 622.6 kg/h (night of August 22). Interestingly, the CO2 emission flux over the stack varied significantly with time, which implied the importance of continuous CO2 emission tracking. In Seoul, CO2 emission measurements were conducted on April 27, 2022, and a broader CO2 plume was detected compared to the observations in Haman. Emission fluxes were quantified at 771.3 kg/h (22:38) and 623.4 kg/h (22:49) over the stack, which was located 820 m away from the Lidar. Furthermore, the performance of the Raman Lidar was evaluated based on the comparison between Lidar measurements and data collected from in situ CO2 sensors. The outcomes validated the high accuracy of the Lidar under outdoor conditions (averaged percent difference = 0.06%). Our study highlights the efficacy of Raman Lidar in tracking high CO2 emissions, thereby contributing to the quantification of greenhouse gas emissions and supporting global climate action initiatives.

The Geostationary Environmental Monitoring Spectrometer (GEMS) is a UV-visible (UV-Vis) spectrometer on board the GEO-KOMPSAT-2B (Geostationary Korea Multi-Purpose Satellite 2B) satellite launched into a geostationary orbit in February 2020. To evaluate the GEMS NO2 total column data, a comparison was carried out using the NO2 vertical column density (VCD) that measured direct sunlight using the Pandora spectrometer system at four sites in Seosan, South Korea, from 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 x 10(15) to 5 :5 x 10(15) molec. cm(-2) for a cloud fraction (CF) < 0 :7. Higher correlation coefficients of 0.62-0.78 with lower RMSEs from 3 :3 x 10(15) to 5 :0 x 10(15) molec. cm(-2) were found with CF < 0 :3, indicating the higher sensitivity of GEMS to atmospheric NO2 in less cloudy conditions. Overall, the GEMS NO2 total column data tended to be lower than the Pandora data, owing to differences in the representative spatial coverage, with a large negative bias under high CF conditions. With a correction for horizontal representativeness in the Pandora measurement coverage, correlation coefficients ranging from 0.69 to 0.81, with RMSEs from 3 :2 x 10(15) to 4 :9 x 10(15) molec. cm(-2), were achieved for CF < 0 :3, showing a better correlation with the correction than without the correction.

We, for the first time, developed a Raman lidar system which can remotely detect surface CO2 volume mixing ratio (VMR). Indoor CO2 cell measurements show that the accuracy of the Raman lidar is calculated to be 99.89%. We carried out the field measurement using our Raman lidar at an artificial CO2 leakage site where a CO2 leakage spot is located 0.2 km away from the Raman lidar. The results show good agreement between CO2 VMRs measured by the Raman lidar system (CO2 VMRRaman LIDAR) and those 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. Surface CO2 VMRs were retrieved at several distances from the Raman Lidar. The average values of R, MAE, and RMSE between CO2 VMRIn-situ and CO2 VMRRaman LIDAR are 0.92, 2.78 ppm, and 3.26 ppm, respectively. We detected CO2 emitted from a stack of a resource recovery facility 450 m away from Raman lidar. CO2 VMRRaman LIDAR was in the range of 600 to 1400 ppmv.

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.

To validate the Geostationary Environment Monitoring Spectrometer (GEMS), the GEMS Map of Air Pollution (GMAP) campaign was conducted during 2020–2021 by integrating Pandora Asia Network, aircraft, and in situ measurements. In the present study, GMAP-2020 measurements were applied to evaluate urban air quality and explore the synergy of Pandora column (PC) NO2 measurements and surface in situ (SI) NO2 measurements for Seosan, South Korea, where large point source (LPS) emissions are densely clustered. Due to the difficulty of interpreting the effects of LPS emissions on air quality downwind of Seosan using SI monitoring networks alone, we explored the combined analysis of both PC-NO2 and SI-NO2 measurements. Agglomerative hierarchical clustering using vertical meteorological variables combined with PC-NO2 and SI-NO2 yielded three distinct conditions: synoptic wind-dominant (SD), mixed (MD), and local wind-dominant (LD). These results suggest meteorology-dependent correlations between PC-NO2 and SI-NO2. Overall, yearly daytime mean (11:00–17:00 KST) PC-NO2 and SI-NO2 statistical data showed good linear correlations (R=∼0.73); however, the differences in correlations were largely attributed to meteorological conditions. SD conditions characterized by higher wind speeds and advected marine boundary layer heights suppressed fluctuations in both PC-NO2 and SI-NO2, driving a uniform vertical NO2 structure with higher correlations, whereas under LD conditions, LPS plumes were decoupled from the surface or were transported from nearby cities, weakening correlations through anomalous vertical NO2 gradients. The discrepancies suggest that using either PC-NO2 or SI-NO2 observations alone involves a higher possibility of uncertainty under LD conditions or prevailing transport processes. However, under MD conditions, both pollution ventilation due to high surface wind speeds and daytime photochemical NO2 loss contributed to stronger correlations through a decline in both PC-NO2 and SI-NO2 towards noon. Thus, Pandora Asia Network observations collected over 13 Asian countries since 2021 can be utilized for detailed investigation of the vertical complexity of air quality, and the conclusions can be also applied when performing GEMS observation interpretation in combination with SI measurements.

In this study, the effect of wavelength range and absorption cross-section used to retrieve nitrogen dioxide (NO2) vertical column density (VCD) from Pandora was analyzed using Differential Optical Absorption Spectroscopy (DOAS). During the GEMS Map of the Air Pollution (GMAP) 2020 campaign, data from direct sunlight observation with Pandora instrument in Seosan was used, and NO2 VCD was retrieved under four conditions. The average NO2 VCD under the four conditions ranged from 1.22 Chi 10(16)similar to 1.38 Chi 10(16) molec. cm(-2), with a maximum difference of 0.16 Chi 10(16) molec. cm(-2) between each condition. The fitting error averaged 3.19 similar to 9.59%, showing an error within 10% in all cases, and the RMS was 5.11 Chi 10(-3)similar to 7.16 Chi 10(-3) molec. cm(-2). The retrieved NO2 VCD using 4 conditions shows a slope in the range of 0.98 to 1.09 and correlation of 0.96 to 0.98 in comparison with Pandonia Global Network (PGN).

Abstract. To validate the Geostationary Environment Monitoring Spectrometer (GEMS), the GEMS Map of Air Pollution (GMAP) campaign was conducted during 2020–2021 by integrating Pandora Asia Network, aircraft, and in situ measurements. In the present study, GMAP-2020 measurements were applied to evaluate urban air quality and explore the synergy of Pandora column (PC) NO2 measurements and surface in situ (SI) NO2 measurements for Seosan, South Korea, where large point source (LPS) emissions are densely clustered. Due to the difficulty of interpreting the effects of LPS emissions on air quality downwind of Seosan using SI monitoring networks alone, we used a combination of PC and SI measurements, and explored the synergy of this approach through correlation analysis of PC-NO2 and SI-NO2. Agglomerative hierarchical clustering using vertical meteorological variables combined with PC-NO2 and SI-NO2 yielded three distinct conditions: synoptic wind-dominant (SD), mixed (MD), and local wind-dominant (LD). These results suggested meteorology-dependent correlations between PC-NO2 and SI-NO2. Overall, yearly daytime mean (11:00–17:00 KST) PC-NO2 and SI-NO2 statistical data showed good linear correlations (R = ~0.73); however, these correlations were dependent on meteorological conditions. SD conditions characterized by higher wind speeds and planetary boundary layer heights suppressed fluctuations in both PC-NO2 and SI-NO2, driving a uniform vertical NO2 structure with higher correlations, whereas under LD conditions, stack plumes decoupled from LPS or were transported from nearby cities, weakening correlations through anomalous vertical NO2 gradients. However, under MD conditions, both pollution ventilation due to high surface wind speeds and daytime photochemical NO2 loss contributed to stronger correlations through a decline in both PC-NO2 and SI-NO2 toward noon. Thus, Pandora Asia Network observations collected over 13 Asian countries since 2021 can be utilized for investigation of the vertical complexity of air quality in combination with SI measurements. The results of this study also indicate that caution is required when performing GEMS validation using either PC or SI observations alone, particularly under prevailing local wind meteorological conditions or transport processes.

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.

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.

We, for the first time, retrieved sulfur dioxide (SO2) vertical column density (VCD) in industrial and volcanic areas from TROPOspheric Monitoring Instrument (TROPOMI) using the Principle component analysis (PCA) algorithm. Furthermore, SO2 VCDs retrieved by the PCA algorithm from TROPOMI raw data were compared with those retrieved by the Differential Optical Absorption Spectroscopy (DOAS) algorithm (TROPOMI Level 2 SO2 product). In East Asia, where large amounts of SO2 are released to the surface due to anthropogenic source such as fossil fuels, the mean value of SO2 VCD retrieved by the PCA (DOAS) algorithm was shown to be 0.05 DU (-0.02 DU). The correlation between SO2 VCD retrieved by the PCA algorithm and those retrieved by the DOAS algorithm were shown to be low (slope = 0.64; correlation coefficient (R) = 0.51) for cloudy condition. However, with cloud fraction of less than 0.5, the slope and correlation coefficient between the two outputs were increased to 0.68 and 0.61, respectively. It means that the SO2 retrieval sensitivity to surface is reduced when the cloud fraction is high in both algorithms. Furthermore, the correlation between volcanic SO2 VCD retrieved by the PCA algorithm and those retrieved by the DOAS algorithm is shown to be high (R = 0.90) for cloudy condition. This good agreement between both data sets for volcanic SO2 is thought to be due to the higher accuracy of the satellite-based SO2 VCD retrieval for SO2 which is mainly distributed in the upper troposphere or lower stratosphere in volcanic region.

We, for the first time, developed a Raman lidar system which can remotely detect surface CO2 volume mixing ratio (VMR). The Raman lidar system consists of the Nd: YAG laser of wavelength 355 nm with 80 mJ, an optical receiver, and detectors. Indoor CO2 cell measurements show that the accuracy of the Raman lidar system is calculated to be 99.89%. We carried out the field measurement using our Raman lidar at Pukyong National University over a seven-day period in October 2019. The results show good agreement between CO2 VMRs measured by the Raman lidar (CO2 Raman Lidar) and those measured by in situ instruments (CO2 In situ) which located 300 m and 350 m away from the Raman lidar system. The correlation coefficient (R), mean absolute error (MAE), and root mean square error (RMSE) between CO2 In situ and CO2 Raman Lidar are 0.67, 2.78 ppm, and 3.26 ppm, respectively.

We have estimated the vertical column density (VCD) of formaldehyde (HCHO) on a global scale using a multiple linear regression method (MRM) with Ozone Monitoring Instrument (OMI) and Moderate-Resolution Imaging Spectroradiometer (MODIS) data. HCHO VCDs were estimated in regions of biogenic, pyrogenic, and anthropogenic emissions using independent variables, including NO2 VCD, land surface temperature (LST), an enhanced vegetation index (EVI), and the mean fire radiative power (MFRP), which are strongly correlated with HCHO. To evaluate the HCHO estimates obtained using the MRM, we compared estimates of HCHO VCD data measured by OMI (HCHOOMI) with those estimated by multiple linear regression equations (MRE) (HCHOMRE). Good MRM performances were found, having the average statistical values (R = 0.91, slope = 1.03, mean bias = -0.12 x 10(15) molecules cm(-2), percent difference = 11.27%) between HCHOMRE and HCHOOMI in our study regions where high HCHO levels are present. Our results demonstrate that the MRM can be a useful tool for estimating atmospheric HCHO levels.

본 연구에서는 처음으로 주성분분석(Principle component analysis; PCA) 방법을 이용하여 Sentinel-5p의 TROPOspheric Monitoring Instrument (TROPOMI) 위성센서 원시자료로부터 산업활동 및 화산활동에 의해 발생한 이산화황 연직칼럼농도(Vertical column density; VCD)를 산출하였다. 본 연구에서 TROPOMI로부터 주성분분석방법을 이용하여 산출된 이산화황 연직칼럼농도는 차등흡수분광법(Differential Optical Absorption Spectroscopy; DOAS)을 이용하여 산출된 TROPOMI Level 2 이산화황 연직칼럼농도 산출물과 비교되었다. 산업활동과 같은 인위적 요인에 의하여 다량의 이산화황을 지표부근에 배출하는 동아시아 지역에서 TROPOMI로부터 주성분분석방법으로 산출된 이산화황 연직칼럼농도와 차등흡수분광법을 이용하여 산출된 TROPOMI 이산화황 연직칼럼농도의 평균값은 각각 0.05 Dobson Unit (DU)와 -0.02 DU로 비슷한 값으로 나타났다. 두 산출물 사이의 기울기(Slope)는 모든 구름조건에 대하여 0.64, 상관계수(Correlation coefficient, R)는 0.51로 다소 낮은 상관관계를 보였으나, 구름비율이 0.5 이하인 픽셀에 대한 기울기는 0.68, 상관계수는 0.61로 증가하였다. 이러한 결과는 두 알고리즘에서 공통적으로 구름비율이 높을 때 지표부근에 대한 이산화황의 산출 민감도가 감소한다는 것을 의미한다. 화산활동에 의한 고농도 이산화황이 발생하는 지역인 인도네시아와 일본 남부 지역에서 두 알고리즘으로 산출된 이산화황 연직칼럼농도 사이의 상관계수는 모든 구름 조건에 대하여 0.90으로 높은 상관관계를 보였다. 이는 화산지역에서의 가스 분출로 인하여 고농도로 대류권 상층 혹은 성층권 하부에 주로 분포하는 이산화황에 대한 위성 기반 이산화황 산출 정확도가 높게 나타나기 때문인 것으로 사료된다.

In this present study, the effects of Signal to Noise Ratio (SNR), Full Width Half Maximum (FWHM), Aerosol Optical Depth (AOD), O-3 Vertical Column Density (O-3 VCD), and Solar Zenith Angle (SZA) on the accuracy of sulfur dioxide Vertical Column Density (SO2 VCD) retrieval have been quantified using the Differential Optical Absorption Spectroscopy (DOAS) method with the ground-based direct-sun synthetic radiances. The synthetic radiances produced based on the Beer-Lambert-Bouguer law without consideration of the diffuse effect. In the SNR condition of 650 (1300) with FWHM = 0.6 nm, AOD = 0.2, O-3 VCD = 300 DU, and SZA = 30 degrees, the Absolute Percentage Difference (APD) between the true SO2 VCD values and those retrieved ranges from 80% (28%) to 16% (5%) for the SO2 VCD of 8.1 x 10(15) and 2.7 x 10(16) molecules cm(-2), respectively. For an FWHM of 0.2 nm (1.0 nm) with the SO2 VCD values equal to or greater than 2.7 x 10(16) molecules cm(-2), the APD ranges from 6.4% (29%) to 6.2% (10%). Additionally, when FWHM, SZA, AOD, and O-3 VCD values increase, APDs tend to be large. On the other hand, SNR values increase, APDs are found to decrease. Eventually, it is revealed that the effects of FWHM and SZA on SO2 VCD retrieval accuracy are larger than those of O-3 VCD and AOD. The SZA effects on the reduction of SO2 VCD retrieval accuracy is found to be dominant over the that of FWHM for the condition of SO2 VCD larger than 2.7 x 10(16) molecules cm(-2).

In this present study, we investigated long term changes in trace gases (SO2, NO2, and O-3) from shipping emissions over major ports in each continent using Ozone Monitoring Instrument (OMI) and Microwave Limb Sounder (MLS) measurements from 2006 to 2015. Additionally, surface nitrogen dioxide volume mixing ratio (NO2 VMR), which can be used to air quality regulation, is retrieved using tropospheric nitrogen dioxide column density and atmospheric measurement data from Atmospheric Infrared Sounder (AIRS) sensor. The long term variation of retrieved NO2 VMRs are investigated. During ten-years, column densities of sulfur dioxide in planetary boundary layer (PBL SO2) and tropospheric nitrogen dioxide (Trop. NO2) decreased by -0.3 DU decade(-1) and -1.8 x 10(15) molecules cm(-2) decade(-1), respectively. However, tropospheric ozone (Trop. 0 3 ) tends to increase (2.9 DU decade(-1)). The decreasing trends of PBL SO2 and Trop. NO2 are thought be due to regulation of NOx and SOx from shipping emission of International Maritime Organization. The NO2 VMRs averaged over Busan, Jebel Ali, Rotterdam, LA, and Melbourne are tends to decrease with 0.64 ppbv decade(-1). Especially, the NO2 VMR in Los Angles, which showed high decreasing trend of Trop. NO2, are decreased 1.5 ppbv per decade. The amount of nitrogen dioxide, one of the ozone precursors, decreased due to the emission reduction policy, while the actual Trop. O-3 tends to increase. Additional research is needed, however, the increase in Trop. O-3 column density is thought to be due to changes in volatile organic compound emissions, one of the precursors of ozone.

In this present study, we, for the first time, retrieved total column of ozone (O-3) and tropospheric ozone vertical profile using the Optimal Estimation (OE) method based on the MAX-DOAS measurement at the Yonsei University in Seoul, Korea. The optical density fitting is carried out using the OE method to calculate ozone columns. The optical density between the MAX-DOAS data obtained by dividing the measured intensities for each viewing elevated angle by those at the zenith angle. The retrieved total columns of the ozone are 375.4 and 412.6 DU in the morning (08:13) and afternoon (17:55) on 23 May, 2017, respectively. In addition, under 10 km altitude, the O-3 vertical profile was retrieved with about 5% of retrieval uncertainty. However, above 10 km altitude, the O-3 vertical profile retrieval uncertainty was increased (>10%). The spectral fitting errors are 16.8% and 19.1% in the morning and afternoon, respectively. The method suggested in this present study can be useful to measure the total ozone column using the ground-based hyper-spectral UV sensors.