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The Seoul Metropolitan Area (SMA) has a population of 24 million and frequently experiences unhealthy levels of ozone (O3). In this work, measurements taken during the Korea-United States Air Quality Study (KORUS-AQ, 2016) are used to explore regional gradients in O3 and its chemical precursors, and an observationally-constrained 0-D photochemical box model is used to quantify key aspects of O3 production including its sensitivity to precursor gases. Box model performance was evaluated by comparing modeled concentrations of select secondary species to airborne measurements. These comparisons indicate that the steady state assumption used in 0-D box models cannot describe select intermediate species, highlighting the importance of having a broad suite of trace gases as model constraints. When fully constrained, aggregated statistics of modeled O production rates agreed with observed changes in O3, indicating that the box model was able to represent the majority of O3 chemistry. Comparison of airborne observations between urban Seoul and a downwind receptor site reveal a positive gradient in O3 coinciding with a negative gradient in NOx, no gradient in CH2O, and a slight positive gradient in modeled rates of O3 production. Together, these observations indicate a radical-limited (VOC-limited) O3 production environment in the SMA. Zero-out simulations identified C7+ aromatics as the dominant VOC contributors to O3 production, with isoprene and anthropogenic alkenes making smaller but appreciable contributions. Simulations of model sensitivity to decreases in NOx produced results that were not spatially uniform, with large increases in O3 production predicted for urban Seoul and decreases in O3 production predicted for far-outlying areas. The policy implications of this work are clear: Effective O3 mitigation strategies in the SMA must focus on reducing local emissions of C7+ aromatics, while reductions in NOx emissions may increase O3 in some areas but generally decrease the regional extent of O3 exposure.

Dust and pollution are frequently mixed together in East Asia, causing large uncertainties in assessing climate change and environmental influence and in relevant policymaking. To discern the dust effect on particle mass, we carried out long-term measurements of the mass and key chemical compositions of PM10, PM2.5, and PM1 from August 2007 to February 2012 and collected hourly data of PM10 and PM2.5 concentrations from January 2012 to October 2020 at Gosan, South Korea. The principal component analysis of measured species reveals two dominant factors, pollution and dust, accounting for 46% and 16% of the total variance, respectively. The mode distribution of PM10, PM2.5, and PM1 mass in addition to the dust events helps to provide a robust criterion of the dust impact. Dust can be identified by the mean + standard deviation (σ) of PM10, while the threshold is down to the mean concentration when dust particles experience precipitation. High PM2.5 concentration also presents dust impact; however, the criterion decreases from mean + σ in 2007–2012 to mean in 2012–2020. It indicates that dust is no longer a high-concentration event of PM2.5, but its influence gradually appears in low-concentration particles. Therefore, the dust criterion obtained from long-term PM10 concentration data is robust; however, the standard is based on PM2.5 changes over time and still needs to be determined by follow-up long-term observations.

This paper presents the development of the global to mesoscale air quality forecast and analysis system (GMAF) and its application to particulate matter under 2.5 μm (PM2.5) forecast in Korea. The GMAF combined a mesoscale model with a global data assimilation system by the grid nudging based four-dimensional data assimilation (FDDA). The grid nudging based FDDA developed for weather forecast and analysis was extended to air quality forecast and analysis for the first time as an alternative to data assimilation of surface monitoring data. The below cloud scavenging module and the secondary organic formation module of the community multiscale air quality model (CMAQ) were modified and subsequently verified by comparing with the PM speciation observation from the PM supersite. The observation data collected from the criteria air pollutant monitoring networks in Korea were used to evaluate forecast performance of GMAF for the year of 2016. The GMAF showed good performance in forecasting the daily mean PM2.5 concentrations at Seoul; the correlation coefficient between the observed and forecasted PM2.5 concentrations was 0.78; the normalized mean error was 25%; the probability of detection for the events exceeding the national PM2.5 standard was 0.81 whereas the false alarm rate was only 0.38. Both the hybrid bias correction technique and the Kalman filter bias adjustment technique were implemented into the GMAF as postprocessors. For the continuous and the categorical performance metrics examined, the Kalman filter bias adjustment technique performed better than the hybrid bias correction technique.

In support of the first Tropospheric Ozone Assessment Report (TOAR) a relational database of global surface ozone observations has been developed and populated with hourly measurement data and enhanced metadata. A comprehensive suite of ozone data products including standard statistics, health and vegetation impact metrics, and trend information, are made available through a common data portal and a web interface. These data form the basis of the TOAR analyses focusing on human health, vegetation, and climate relevant ozone issues, which are part of this special feature. Cooperation among many data centers and individual researchers worldwide made it possible to build the world's largest collection of 'in-situ' hourly surface ozone data covering the period from 1970 to 2015. By combining the data from almost 10,000 measurement sites around the world with global metadata information, new analyses of surface ozone have become possible, such as the first globally consistent characterisations of measurement sites as either urban or rural/remote. Exploitation of these global metadata allows for new insights into the global distribution, and seasonal and long-term changes of tropospheric ozone and they enable TOAR to perform the first, globally consistent analysis of present-day ozone concentrations and recent ozone changes with relevance to health, agriculture, and climate. Considerable effort was made to harmonize and synthesize data formats and metadata information from various networks and individual data submissions. Extensive quality control was applied to identify questionable and erroneous data, including changes in apparent instrument offsets or calibrations. Such data were excluded from TOAR data products. Limitations of 'a posteriori' data quality assurance are discussed. As a result of the work presented here, global coverage of surface ozone data for scientific analysis has been significantly extended. Yet, large gaps remain in the surface observation network both in terms of regions without monitoring, and in terms of regions that have monitoring programs but no public access to the data archive. Therefore future improvements to the database will require not only improved data harmonization, but also expanded data sharing and increased monitoring in data-sparse regions.

The Korean Geostationary Ocean Color Imager (GOCI) satellite has monitored the East Asian region in high temporal and spatial resolution every day, providing unprecedented information on air pollutants over the upstream region of the Korean peninsula for the last decade. In this study, the GOCI Aerosol optical depth (AOD), retrieved at 550 nm wavelength, is assimilated to ameliorate the analysis quality, thereby making systematic improvements on air quality forecasting in South Korea. For successful data assimilation, GOCI retrievals are carefully investigated and processed based on data characteristics. The preprocessed data are then assimilated in the three-dimensional variational data assimilation (3DVAR) technique for the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). During the Korea-United States Air Quality (KORUS-AQ) period (May 2016), the impact of GOCI AOD on the accuracy of air quality forecasting is examined by comparing with other observations including Moderate Resolution Imaging Spectroradiometer (MODIS) sensors and fine particulate matter (PM2.5) observations at the surface. Consistent with previous studies, the assimilation of surface PM2.5 concentrations alone systematically underestimates surface PM2.5 and its positive impact lasts mainly for about 6 h. When GOCI AOD retrievals are assimilated with surface PM2.5 observations, however, the negative bias is diminished and forecasts are improved up to 24 h, with the most significant contributions to the prediction of heavy pollution events over South Korea. [ABSTRACT FROM AUTHOR]

In East Asia, soil dust is a major component of aerosols and is mixed with various pollutants during transport, resulting in large uncertainty in climate and environmental impact assessment and relevant policymaking. To diagnose the influence of soil dust and anthropogenic pollution on bulk aerosol, we conducted long-term measurements of mass, water-soluble ions, and carbonaceous compounds of PM10, PM2.5, and PM1 at Gosan Climate Observatory, South Korea, from August 2007 to February 2012. The principle component analyses of all measured species reveal that the impact of anthropogenic pollution, soil dust, and agricultural fertilizer accounts for 46 %, 16 %, and 9 % of the total variance, respectively. Particularly, the loadings of agricultural component were high in the warmer months with the least occurrence of high concentration events and have increased over time. In mode analysis of PM10, PM2.5, and PM1 mass concentrations, the mean + σ was comparable to the 90th percentile and thus, suggested as a robust criterion that determines the substantial impact of soil dust and haze pollution on particulate matter. The results of this study imply that non-combustion sources such as soil dust will impose constraints to the reduction of PM2.5 as well as PM10 concentrations. In addition, questions are raised as to whether the yearly average concentration is suitable for environmental standard in northeast Asian region. [ABSTRACT FROM AUTHOR]