Deep Learning with Pretrained Framework Unleashes the Power of Satellite-Based Global Fine-Mode Aerosol Retrieval.

Fine-mode aerosol optical depth (fAOD) is a vital proxy for the concentration of anthropogenic aerosols in the atmosphere. Currently, the limited data length and high uncertainty of the satellite-based data diminish the applicability of fAOD for climate research. Here, we propose a novel pretrained deep learning framework that can extract information underlying each satellite pixel and use it to create new latent features that can be employed for improving retrieval accuracy in regions without in situ data. With the proposed model, we developed a new global fAOD (at 0.5 µm) data from 2001 to 2020, resulting in a 10% improvement in the overall correlation coefficient (R) during site-based independent validation and a 15% enhancement in non-AERONET site areas validation. Over the past two decades, there has been a noticeable downward trend in global fAOD (-1.39 × 10-3/year). Compared to the general deep-learning model, our method reduces the global trend's previously overestimated magnitude by 7% per year. China has experienced the most significant decline (-5.07 × 10-3/year), which is 3 times greater than the global trend. Conversely, India has shown a significant increase (7.86 × 10-4/year). This study bridges the gap between sparse in situ observations and abundant satellite measurements, thereby improving predictive models for global patterns of fAOD and other climate factors.

Validations of satellite ozone profiles in austral spring using ozonesonde measurements in the Jang Bogo station, Antarctica.

Using ozonesonde measurements from 2015 to 2018 at the Jang Bogo station located in the southeastern Antarctic region, we evaluate ozone profiles retrieved from the three satellite measurements that are widely used: Ozone Monitoring Instrument (OMI), Microwave Limb Sounder (MLS), and Ozone Mapping Profiler Suite (OMPS) data. For the fair validation, ozonesonde profiles are smoothed using the weighting function of each satellite retrieval algorithm (i.e., convolution process). Compared with limb-viewing MLS and OMPS ozone profiles, the OMI ozone profiles are relatively less qualified: coarser vertical resolution and larger inter-annual variation. Nevertheless, our validation reveals that the quality of all three satellite ozone profiles looks comparable; In general, difference from ozonesonde profile is ∼1 ppm absolutely, and -20 to 30% relatively at maximum. This quantitative range well corresponds to previous work, meaning that our new validation confirms the reliability of satellite ozone profiles in the southeastern Antarctic region where the measurement data for the validation were not enough. Another interesting feature is the role of a priori ozone profile; Nadir-viewing OMI satellite can have qualified ozone profiles by a proper assumption of a priori ozone profile. Since the performance of limb-viewing ozone profiles is better, however, the careful usage of nadir-viewing ozone profile is still required. We think that the simultaneous usage of multiple satellite ozone profiles can contribute to better understanding of Antarctic ozone characteristics.

Aerosol pollution and its potential impacts on outdoor human thermal sensation: East Asian perspectives.

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

Effects of meteorological conditions on the mixing height of Nitrogen dioxide in China using new-generation geostationary satellite measurements and machine learning.

Nitrogen dioxide (NO2) plays a critical role in terms of air quality, human health, ecosystems, and its impact on climate change. While the crucial roles of the vertical structure of NO2 have been acknowledged for some time, there is currently limited knowledge about this aspect in China. The Geostationary Environment Monitoring Spectrometer (GEMS) is the world's first geostationary satellite instrument capable of measuring the hourly columnar amount of NO2. The study presented here introduces the use of mixing height for NO2 in the atmosphere. A thorough examination of spatiotemporal variations in the mixing height of NO2 was conducted using data from both the GEMS and ground-based air quality monitoring networks. A random forest model based on machine learning techniques was utilized to examine how meteorological parameters affect the mixing height of NO2. The results of our study reveal a notable seasonal fluctuation in the mixing height of NO2, with the highest values observed during the summer and the lowest values during the winter. Additionally, there was an increasing diurnal trend from early morning to mid-afternoon. Moreover, the study discovered elevated NO2 mixing heights in the dry regions of northern China. The results also indicated a positive correlation between the mixing height of NO2 and temperature and wind speed, while negative associations were found with relative humidity and air pressure. The machine learning model's predicted NO2 mixing heights were in good agreement with the measurement-based outcomes, as evidenced by a coefficient of determination (R2) value of 0.96 (0.84 for the 10-fold cross-validation). These findings emphasize the noteworthy influence of meteorological variables on the vertical distribution of NO2 in the atmosphere and enhance our comprehension of the three-dimensional variations in NO2.

North Korean CO emissions reconstruction using DMZ ground observations, TROPOMI space-borne data, and the CMAQ air quality model.

Emission uncertainty in North Korea can act as an obstacle when developing air pollution management plans in the country and neighboring countries when the transboundary transport of air pollutants is considered. This study introduces a novel approach for adjusting and reallocating North Korean CO emissions, aiming to complement the limited observational and emissions data on the country's air pollutants. We utilized ground observations from demilitarized zone (DMZ) and vertical column density (VCD) data from a TROPOspheric Monitoring Instrument (TROPOMI), which were combined with the Community Multi-Scale Air Quality (CMAQ) chemistry transport model simulations. The Clean Air Support System (CAPSS) and Satellite Integrated Joint Monitoring of Air Quality (SIJAQ) emissions inventories served as the basis for our initial simulations. A two-step procedure was proposed to adjust both the emission intensity and the spatial distribution of emissions. First, air quality simulations were conducted to explore model sensitivity to changes in North Korean CO emissions with respect to ground concentrations. DMZ observations then constrained these simulations to estimate corresponding emission intensity. Second, the spatial structure of North Korean CO emission sources was reconstructed with the help of TROPOMI CO VCD distributions. Our two-step hybrid method outperformed individual emissions adjustment and spatial reallocation based solely on surface or satellite observations. Validation using ground observations from the Chinese Dandong site near the China-North Korea border revealed significantly improved model simulations when applying the updated CO emissions. The adjusted CO emissions were 10.9 times higher than those derived from the bottom-up emissions used in this study, highlighting the lack of information on North Korean pollutants and emission sources. This approach offers an efficient and practical solution for identifying potential missing emission sources when there is limited on-site information about air quality on emissions.

Elemental Characterization of Ambient Particulate Matter for a Globally Distributed Monitoring Network: Methodology and Implications.

Global ground-level measurements of elements in ambient particulate matter (PM) can provide valuable information to understand the distribution of dust and trace elements, assess health impacts, and investigate emission sources. We use X-ray fluorescence spectroscopy to characterize the elemental composition of PM samples collected from 27 globally distributed sites in the Surface PARTiculate mAtter Network (SPARTAN) over 2019-2023. Consistent protocols are applied to collect all samples and analyze them at one central laboratory, which facilitates comparison across different sites. Multiple quality assurance measures are performed, including applying reference materials that resemble typical PM samples, acceptance testing, and routine quality control. Method detection limits and uncertainties are estimated. Concentrations of dust and trace element oxides (TEO) are determined from the elemental dataset. In addition to sites in arid regions, a moderately high mean dust concentration (6 µg/m3) in PM2.5 is also found in Dhaka (Bangladesh) along with a high average TEO level (6 µg/m3). High carcinogenic risk (>1 cancer case per 100000 adults) from airborne arsenic is observed in Dhaka (Bangladesh), Kanpur (India), and Hanoi (Vietnam). Industries of informal lead-acid battery and e-waste recycling as well as coal-fired brick kilns likely contribute to the elevated trace element concentrations found in Dhaka.

Assessment of air quality in North Korea from satellite observations.

North Korea's air quality is poorly understood due to a lack of reliable data. Here, we analyzed urban- to national-scale air quality changes in North Korea using multi-year satellite observations. Pyongyang, Nampo, Pukchang, and Munchon were identified as pollution hotspots. On a national scale, we found that North Korea experienced 6.7, 17.8, and 20.6 times greater amounts of nitrogen dioxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO) per unit primary energy supply (PES) than South Korea from 2005 to 2018. Besides, North Korea had a 24.3 times larger aerosol optical depth (AOD) per PES than South Korea from 2011 to 2018. Severe CO and aerosol pollution is aligned with extensive biofuel combustion. High SO2 pollution corresponds with the strong coal dependence of the industry. The change rates of the national average columns for NO2, SO2, and CO were + 3.6, -4.4, and -0.4 % yr-1, respectively. The AOD change rate was -4.8 % yr-1. Overall decreasing trends, except for NO2, are likely due to a decline in coal-fired PES. Positive NO2 trends are consistent with increasing industrial activities. Each pollutant showed consistent patterns of linear trends, even after correcting the influence of transboundary pollution. Flue gas control and biofuel consumption reduction seem necessary to improve North Korea's air quality.

Retrieval of aerosol optical properties from GOCI-II observations: Continuation of long-term geostationary aerosol monitoring over East Asia.

Since the Geostationary Ocean Color Imager (GOCI) was successfully launched in 2010, the GOCI Yonsei aerosol retrieval (YAER) algorithm has been continuously updated to retrieve hourly aerosol optical properties. GOCI-II has 4 more channels including UV, finer spatial resolution (250 m), and daily full disk coverage as compared to GOCI, and was launched in February 2020, onboard the GEO-KOMPSAT-2B (GK-2B) satellite. In this study, we extended the YAER algorithm to GOCI-II data based on its improved performance in many aspects and present the first results of aerosol optical properties retrieved from GOCI-II data. Utilizing the overlapping period between the GOCI-II and GOCI in geostationary Earth orbit, we present GOCI-II aerosol retrievals for high aerosol-loading cases over East Asia and show that these have a consistent spatial distribution with those from GOCI. Furthermore, GOCI-II provides AOD at an even higher spatial resolution, revealing finer changes in aerosol concentrations. Validation results for one year data show that the GOCI-II AOD has a correlation coefficient of 0.83 and a ratio within the expected error (EE) of 59.4 % when compared with the aerosol robotic network (AERONET) data. We compared statistical metrics for the GOCI and GOCI-II AODs to assess the consistency between the two datasets. In addition, it was found that there is a strong correlation between the two datasets from the comparison of gridded GOCI and GOCI-II AOD products. It is expected that data from GOCI-II will continue long-term aerosol records with high accuracy that can be used to address air-quality issues over East Asia.

Geostationary satellite-derived ground-level particulate matter concentrations using real-time machine learning in Northeast Asia.

Rapid economic growth, industrialization, and urbanization have caused frequent air pollution events in East Asia over the last few decades. Recently, aerosol data from geostationary satellite sensors have been used to monitor ground-level particulate matter (PM) concentrations hourly. However, many studies have focused on using historical datasets to develop PM estimation models, often decreasing their predictability for unseen data in new days. To mitigate this problem, this study proposes a novel real-time learning (RTL) approach to estimate PM with aerodynamic diameters of <10 µm (PM10) and <2.5 µm (PM2.5) using hourly aerosol data from the Geostationary Ocean Color Imager (GOCI) and numerical model outputs for daytime conditions over Northeast Asia. Three schemes with different weighting strategies were evaluated using 10-fold cross-validation (CV). The RTL models, which considered both concentration and time as weighting factors (i.e., Scheme 3) yielded consistent improvement for 10-fold CV performance on both hourly and monthly scales. The real-time calibration results for PM10 and PM2.5 were R2 = 0.97 and 0.96, and relative root mean square error (rRMSE) = 12.1% and 12.0%, respectively, and the 10-fold CV results for PM10 and PM2.5 were R2 = 0.73 and 0.69 and rRMSE = 41.8% and 39.6%, respectively. These results were superior to results from the offline models in previous studies, which were based on historical data on an hourly scale. Moreover, we estimated PM concentrations in the ocean without using land-based variables, and clearly demonstrated the PM transport over time. Because the proposed models are based on the RTL approach, the density of in-situ monitoring sites could be a major uncertainty factor. This study identified that a high error occurred in low-density areas, whereas a low error occurred in high-density areas. The proposed approach can be operated to monitor ground-level PM concentrations in real-time with uncertainty analysis to ensure optimal results.

Estimation of the contributions of long range transported aerosol in East Asia to carbonaceous aerosol and PM concentrations in Seoul, Korea using highly time resolved measurements: a PSCF model approach.

The contributions of long range transported aerosol in East Asia to carbonaceous aerosol and particulate matter (PM) concentrations in Seoul, Korea were estimated with potential source contribution function (PSCF) calculations. Carbonaceous aerosol (organic carbon (OC) and elemental carbon (EC)), PM(2.5), and PM(10) concentrations were measured from April 2007 to March 2008 in Seoul, Korea. The PSCF and concentration weighted trajectory (CWT) receptor models were used to identify the spatial source distributions of OC, EC, PM(2.5), and coarse particles. Heavily industrialized areas in Northeast China such as Harbin and Changchun and East China including the Pearl River Delta region, the Yangtze River Delta region, and the Beijing-Tianjin region were identified as high OC, EC and PM(2.5) source areas. The conditional PSCF analysis was introduced so as to distinguish the influence of aerosol transported from heavily polluted source areas on a receptor site from that transported from relatively clean areas. The source contributions estimated using the conditional PSCF analysis account for not only the aerosol concentrations of long range transported aerosols but also the number of transport days effective on the measurement site. Based on the proposed algorithm, the condition of airmass pathways was classified into two types: one condition where airmass passed over the source region (PS) and another condition where airmass did not pass over the source region (NPS). For most of the seasons during the measurement period, 249.5-366.2% higher OC, EC, PM(2.5), and coarse particle concentrations were observed at the measurement site under PS conditions than under NPS conditions. Seasonal variations in the concentrations of OC, EC, PM(2.5), and coarse particles under PS, NPS, and background aerosol conditions were quantified. The contributions of long range transported aerosols on the OC, EC, PM(2.5), and coarse particle concentrations during several Asian dust events were also estimated. We also investigated the performance of the PSCF results obtained from combining highly time resolved measurement data and backward trajectory calculations via comparison with those from data in low resolutions. Reduced tailing effects and the larger coverage over the area of interest were observed in the PSCF results obtained from using the highly time resolved data and trajectories.

Role of emissions and meteorology in the recent PM2.5 changes in China and South Korea from 2015 to 2018.

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

Assessment of long-range transboundary aerosols in Seoul, South Korea from Geostationary Ocean Color Imager (GOCI) and ground-based observations.

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

The Korea-United States Air Quality (KORUS-AQ) field study.

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

A global observational analysis to understand changes in air quality during exceptionally low anthropogenic emission conditions.

This global study, which has been coordinated by the World Meteorological Organization Global Atmospheric Watch (WMO/GAW) programme, aims to understand the behaviour of key air pollutant species during the COVID-19 pandemic period of exceptionally low emissions across the globe. We investigated the effects of the differences in both emissions and regional and local meteorology in 2020 compared with the period 2015-2019. By adopting a globally consistent approach, this comprehensive observational analysis focuses on changes in air quality in and around cities across the globe for the following air pollutants PM2.5, PM10, PMC (coarse fraction of PM), NO2, SO2, NOx, CO, O3 and the total gaseous oxidant (OX = NO2 + O3) during the pre-lockdown, partial lockdown, full lockdown and two relaxation periods spanning from January to September 2020. The analysis is based on in situ ground-based air quality observations at over 540 traffic, background and rural stations, from 63 cities and covering 25 countries over seven geographical regions of the world. Anomalies in the air pollutant concentrations (increases or decreases during 2020 periods compared to equivalent 2015-2019 periods) were calculated and the possible effects of meteorological conditions were analysed by computing anomalies from ERA5 reanalyses and local observations for these periods. We observed a positive correlation between the reductions in NO2 and NOx concentrations and peoples' mobility for most cities. A correlation between PMC and mobility changes was also seen for some Asian and South American cities. A clear signal was not observed for other pollutants, suggesting that sources besides vehicular emissions also substantially contributed to the change in air quality. As a global and regional overview of the changes in ambient concentrations of key air quality species, we observed decreases of up to about 70% in mean NO2 and between 30% and 40% in mean PM2.5 concentrations over 2020 full lockdown compared to the same period in 2015-2019. However, PM2.5 exhibited complex signals, even within the same region, with increases in some Spanish cities, attributed mainly to the long-range transport of African dust and/or biomass burning (corroborated with the analysis of NO2/CO ratio). Some Chinese cities showed similar increases in PM2.5 during the lockdown periods, but in this case, it was likely due to secondary PM formation. Changes in O3 concentrations were highly heterogeneous, with no overall change or small increases (as in the case of Europe), and positive anomalies of 25% and 30% in East Asia and South America, respectively, with Colombia showing the largest positive anomaly of ~70%. The SO2 anomalies were negative for 2020 compared to 2015-2019 (between ~25 to 60%) for all regions. For CO, negative anomalies were observed for all regions with the largest decrease for South America of up to ~40%. The NO2/CO ratio indicated that specific sites (such as those in Spanish cities) were affected by biomass burning plumes, which outweighed the NO2 decrease due to the general reduction in mobility (ratio of ~60%). Analysis of the total oxidant (OX = NO2 + O3) showed that primary NO2 emissions at urban locations were greater than the O3 production, whereas at background sites, OX was mostly driven by the regional contributions rather than local NO2 and O3 concentrations. The present study clearly highlights the importance of meteorology and episodic contributions (e.g., from dust, domestic, agricultural biomass burning and crop fertilizing) when analysing air quality in and around cities even during large emissions reductions. There is still the need to better understand how the chemical responses of secondary pollutants to emission change under complex meteorological conditions, along with climate change and socio-economic drivers may affect future air quality. The implications for regional and global policies are also significant, as our study clearly indicates that PM2.5 concentrations would not likely meet the World Health Organization guidelines in many parts of the world, despite the drastic reductions in mobility. Consequently, revisions of air quality regulation (e.g., the Gothenburg Protocol) with more ambitious targets that are specific to the different regions of the world may well be required.

Satellite-based estimation of hourly PM2.5 levels during heavy winter pollution episodes in the Yangtze River Delta, China.

In the developing countries such as China, most well-developed areas have suffered severe haze pollution, which was associated with increased premature morbidity and mortality and attracted widespread public concerns. Since ground-based PM2.5 monitoring has limited temporal and spatial coverage, satellite aerosol remote sensing data has been increasingly applied to map large-scale PM2.5 characteristics through advanced spatial statistical models. Although most existing research has taken advantage of the polar orbiting satellite instruments, a major limitation of the polar orbiting platform is its limited sampling frequency (e.g., 1-2 times/day), which is insufficient for capturing the PM2.5 variability during short but intense heavy haze episodes. As the first attempt, we quantitatively investigated the feasibility of using the aerosol optical depth (AOD) data retrieved by the Geostationary Ocean Color Imager (GOCI) to estimate hourly PM2.5 concentrations during winter haze episodes in the Yangtze River Delta (YRD). We developed a three-stage spatial statistical model, using GOCI AOD and fine mode fraction, as well as corresponding monitoring PM2.5 concentrations, meteorological and land use data on a 6-km modeling grid with complete coverage in time and space. The 10-fold cross-validation R2 was 0.72 with a regression slope of 1.01 between observed and predicted hourly PM2.5 concentrations. After gap filling, the R2 value for the three-stage model was 0.68. We further analyzed two representative large regional episodes, i.e., a "multi-process diffusion episode" during December 21-26, 2015 and a "Chinese New Year episode" during February 7-8, 2016. We concluded that AOD retrieved by geostationary satellites could serve as a new valuable data source for analyzing the heavy air pollution episodes.

Estimation of spatially continuous daytime particulate matter concentrations under all sky conditions through the synergistic use of satellite-based AOD and numerical models.

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

The implication of the air quality pattern in South Korea after the COVID-19 outbreak.

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

The impact of the direct effect of aerosols on meteorology and air quality using aerosol optical depth assimilation during the KORUS-AQ campaign.

To quantify the impact of the direct aerosol effect accurately, this study incorporated the Geostationary Ocean Color Imager (GOCI) aerosol optical depth (AOD) into a coupled meteorology-chemistry model. We designed three model simulations to observe the impact of AOD assimilation and aerosol feedback during the KORUS-AQ campaign (May - June 2016). By assimilating the GOCI AOD with high temporal and spatial resolutions, we improve the statistics from the comparison AOD and AERONET data (RMSE: 0.12, R: 0.77, IOA: 0.69, MAE: 0.08). The inclusion of the direct effect of aerosols produces the best model performance (RMSE: 0.10, R: 0.86, IOA: 0.72, MAE: 0.07). AOD values were increased as much as 0.15, which is associated with an average reduction in solar radiation of -31.39 W/m2, a planetary boundary layer height (-104.70 m), an air temperature (-0.58 °C), and a surface wind speed (-0.07 m/s) over land. In addition, concentrations of major gaseous and particulate pollutants at the surface (SO2, NO2, NH3, SO 4 2 - , NO 3 - , NH 4 + , PM2.5) increase by 7.87 - 34% while OH concentration decreases by -4.58 %. Changes in meteorology and air quality appear to be more significant in high-aerosol loading areas. The integrated process rate analysis shows decelerated vertical transport, resulting in an accumulation of air pollutants near the surface and the amount of nitrate, which is higher than that of sulfate because of its response to reduced temperature. We conclude that constraining aerosol concentrations using geostationary satellite data is a prerequisite for quantifying the impact of aerosols on meteorology and air quality.

Regional characteristics of NO2 column densities from Pandora observations during the MAPS-Seoul campaign.

Vertical column density (VCD) of nitrogen dioxide (NO2) was measured using Pandora spectrometers at six sites on the Korean Peninsula during the Megacity Air Pollution Studies-Seoul (MAPS-Seoul) campaign from May to June 2015. To estimate the tropospheric NO2 VCD, the stratospheric NO2 VCD from the Ozone Monitoring Instrument (OMI) was subtracted from the total NO2 VCD from Pandora. European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis wind data was used to analyze variations in tropospheric NO2 VCD caused by wind patterns at each site. The Yonsei/SEO site was found to have the largest tropospheric NO2 VCD (1.49 DU on average) from a statistical analysis of hourly tropospheric NO2 VCD measurements. At rural sites, remarkably low NO2 VCDs were observed. However, a wind field analysis showed that trans-boundary transport and emissions from domestic sources lead to an increase in tropospheric NO2 VCD at NIER/BYI and KMA/AMY, respectively. At urban sites, high NO2 VCD values were observed under conditions of low wind speed, which were influenced by local urban emissions. Tropospheric NO2 VCD at HUFS/Yongin increases under conditions of significant transport from urban area of Seoul according to a correlation analysis that considers the transport time lag. Significant diurnal variations were found at urban sites during the MAPS-Seoul campaign, but not at rural sites, indicating that it is associated with diurnal patterns of NO2 emissions from dense traffic.

Utilization of O4 slant column density to derive aerosol layer height from a spaceborne UV-Visible hyperspectral sensor: Sensitivity and case study.

The sensitivities of oxygen-dimer (O4) slant column densities (SCDs) to changes in aerosol layer height are investigated using the simulated radiances by a radiative transfer model, the Linearlized pseudo-spherical vector discrete ordinate radiative transfer (VLIDORT), and the Differential Optical Absorption Spectroscopy (DOAS) technique. The sensitivities of the O4 index (O4I), which is defined as dividing O4 SCD by 1040 molecules2cm-5, to aerosol types and optical properties are also evaluated and compared. Among the O4 absorption bands at 340, 360, 380, and 477 nm, the O4 absorption band at 477 nm is found to be the most suitable to retrieve the aerosol effective height. However, the O4I at 477 nm is significantly influenced not only by the aerosol layer effective height but also by aerosol vertical profiles, optical properties including single scattering albedo (SSA), aerosol optical depth (AOD), particle size, and surface albedo. Overall, the error of the retrieved aerosol effective height is estimated to be 1276, 846, and 739 m for dust, non-absorbing, and absorbing aerosol, respectively, assuming knowledge on the aerosol vertical distribution shape. Using radiance data from the Ozone Monitoring Instrument (OMI), a new algorithm is developed to derive the aerosol effective height over East Asia after the determination of the aerosol type and AOD from the MODerate resolution Imaging Spectroradiometer (MODIS). About 80% of retrieved aerosol effective heights are within the error range of 1 km compared to those obtained from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) measurements on thick aerosol layer cases.