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Concentrations of ambient particulate matter (such as PM2.5 and PM10) have come to represent a serious environmental problem worldwide, causing many deaths and economic losses. Because of the detrimental effects of PM2.5 on human health, many countries and international organizations have developed and operated regional and global short-term PM2.5 prediction systems. The short-term predictability of PM2.5 (and PM10) is determined by two main factors: the performance of the air quality model and the accuracy of the initial states. While specifically focusing on the latter factor, this study attempts to demonstrate how information from ‘classical’ ground observation networks, a ‘state-of-the-art’ geostationary (GEO) satellite sensor, and an advanced air quality modeling system can be synergistically combined to improve short-term PM2.5 predictability over South Korea. Such a synergistic combination of information can effectively overcome the major obstacle of scarcity of information, which frequently occurs in PM2.5 prediction systems using low Earth orbit (LEO) satellite-borne observations. This study first presents that the scarcity of information is mainly associated with cloud masking, sun-glint effect, and ill-location of satellite-borne data, and it then demonstrates that an advanced air quality modeling system equipped with synergistically-combined information can achieve substantially improved performances, producing enhancements of approximately 10%, 17%, 49%, and 19% in the predictability of PM2.5 over South Korea in terms of IOA (index of agreement), R (correlation coefficient), MB (mean biases), and HR (hit rate), respectively, compared to PM2.5 prediction systems using only LEO satellite-derived observations.

The GEMS MAP of Air Pollution (GMAP) 2021 field campaign for South Korea was conducted in October-November 2021 to understand the changes in air quality after the KORUS-AQ field study and to support efficient pollution management for ozone and aerosol. Extensive aircraft and ground network observations from the campaign offer an opportunity to reduce model-observation disagreements. This study examines these issues using model evaluation against the GMAP 2021 observations and intercomparisons between models. Four regional and one global chemistry transport model using identical anthropogenic emissions participated in the model intercomparison study. Based on the KORUSv5.0 emission inventory that supported the KORUS-AQ campaign, GMAP/SIJAQv2.0 emission inventory was developed to reflect the latest emission trends of major countries affecting South Korea’s air quality.From the results of the model using Global Forecast System (GFS) and final (FNL) Operational Global Analysis data during and after the campaign, the accuracy of the modeling results using FNL was higher than that of GFS, which is a result of informing that the accurate meteorological input data is important for the prediction of aerosol and ozone. In comparisons of simulated versus observed (AirKorea network) CO, O3, NO2, SO2, and PM2.5 concentrations in surface air averaged for the campaign period, the models successfully reproduced observed pollutants in surface air but similar to the results in KORUS-AQ showed low biases for carbon monoxide (CO), implying that there were possible missing CO sources in the inventory in East Asia. Observations show the highest values in the Seoul Metropolitan Area (SMA) and industrial regions except for O3, which is strongly titrated by high NOx levels from traffic emissions. Relative contributions to air quality in South Korea by local and long-range transport pollution influences were classified using the Brute Force Method (BFM) for the campaign period. Observed aerosol chemical composition at the Olympic Park ground site showed that inorganic components (nitrate, sulfate, ammonium) contributed to PM2.5 by 83% during the transboundary dominant case. On the other hand, in the case of local dominant, the contribution of organic carbonaceous aerosol was 42% to PM2.5, indicating a clear difference between the two cases. In model simulations, there is a difference in the ratio between organic and inorganic aerosol, but the difference between the two cases was well simulated. And models showed a tendency to simulate Elemental Carbon (EC) at a concentration more than twice as high as observed due to the effect of emissions. From the model evaluation, we find that ensemble results of multiple models show the most consistent results with observations during the campaign period. In addition to improving the accuracy of individual models and emission inventory, evaluation of model accuracy according to ensemble techniques is necessary to improve forecast results. This research was supported by the FRIEND(Fine Particle Research Initiative in East Asia Considering National Differences) Project through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (Grant No.: 2020M3G1A1114615)

The Comprehensive Automobile Research System (CARS) is an open-source Python-based automobile emissions inventory model designed to efficiently estimate high-quality emissions from motor vehicle emission sources. It can estimate air pollutant, greenhouse gas, and air toxin criteria at any spatial resolution based on the spatiotemporal resolutions of input datasets. The CARS is designed to utilize local vehicle activity data, such as vehicle travel distance, road-link-level network geographic information system (GIS) information, and vehicle-specific average speed by road type, to generate an automobile emissions inventory for policymakers, stakeholders, and the air quality modeling community. The CARS model adopted the European Environment Agency's on-road automobile emissions calculation methodologies to estimate the hot exhaust, cold start, and evaporative emissions from on-road automobile sources. It can optionally utilize average speed distribution (ASD) of all road types to reflect more realistic vehicle speed variations. In addition, through utilizing high-resolution road GIS data, the CARS can estimate the road-link-level emissions to improve the inventory's spatial resolution. When we compared the official 2015 national mobile emissions from Korea's Clean Air Policy Support System (CAPSS) against the ones estimated by the CARS, there is a significant increase in volatile organic compounds (VOCs) (33 %) and carbon monoxide (CO) (52 %) measured, with a slight increase in fine particulate matter (PM2.5) (15 %) emissions. Nitrogen oxide (NOx) and sulfur oxide (SOx) measurements are reduced by 24 % and 17 %, respectively, in the CARS estimates. The main differences are driven by different vehicle activities and the incorporation of road-specific ASD, which plays a critical role in hot exhaust emission estimates but was not implemented in Korea's CAPSS mobile emissions inventory. While 52 % of vehicles use gasoline fuel and 35 % use diesel, gasoline vehicles only contribute 7.7 % of total NOx emissions, whereas diesel vehicles contribute 85.3 %. However, for VOC emissions, gasoline vehicles contribute 52.1 %, whereas diesel vehicles are limited to 23 %. Diesel buses comprise only 0.3 % of vehicles and have the largest contribution to NOx emissions (8.51 % of NOx total) per vehicle due to having longest daily vehicle kilometer travel (VKT). For VOC emissions, compressed natural gas (CNG) buses are the largest contributor at 19.5 % of total VOC emissions. For primary PM2.5, more than 98.5 % is from diesel vehicles. The CARS model's in-depth analysis feature can assist government policymakers and stakeholders in developing the best emission abatement strategies.

Environmental context The production and consumption of chlorofluorocarbons (CFCs) is regulated under the Montreal Protocol and its amendments, due to their role in stratospheric ozone depletion. Global atmospheric levels of CFC-11 did not decline as rapidly as expected during 2012–2018, in large part due to emissions from eastern China. In order to further clarify global CFC-11 emissions, this work provides a rare set CFC-11 measurements from understudied countries and sources throughout Asia (1998–2018). Abstract Trichlorofluoromethane (CFC-11) is an ozone-depleting substance whose production and consumption are regulated under the Montreal Protocol. Global atmospheric CFC-11 levels declined less quickly than expected during 2012–2018, largely because of ongoing emissions from eastern Asia. Satellite measurements suggest additional CFC-11 hotspots in the Arabian Peninsula and north India/Nepal. Here we present CFC-11 levels measured in dozens of Asian cities during 1998–2018, including China and Pakistan before the 2010 phaseout of CFC-11, and China, Nepal, Pakistan, Saudi Arabia and South Korea after the phaseout. Surface measurements of CFCs in Nepal, Pakistan and Saudi Arabia are very rare, and these surveys provide important observational constraints from understudied regions. During pre-phaseout campaigns, higher CFC-11 levels were measured in Beijing than Karachi, despite much higher overall volatile organic compound (VOC) levels in Karachi. During post-phaseout campaigns, average CFC-11 levels were higher in inland Shandong Province and Seoul (1.11–1.23× background) than in western Saudi Arabia, Lahore and Kathmandu (1.02–1.11× background), despite higher levels of other VOCs in the latter regions. While China is known to emit excess CFC-11, elevated CFC-11 levels in Seoul, especially during stagnant meteorological conditions, suggest local emissions in 2015–2016. Rough emission estimates suggest that South Korea is likely a relatively minor global source of excess CFC-11. Hotspot CFC-11 levels were measured from a landfill fire in Mecca (average of 1.8× background) and from garbage burning in Nepal (1.5× background). Because garbage burning and open burning in dumps are common practices, further investigation of CFC-11 emissions at dumps and landfills worldwide is encouraged to determine their global impact.

The main focus of this study is to develop a dynamic-coupling “inline” air quality modeling system for the meteorology-induced emissions with simulated meteorological data. To improve the spatiotemporal representations and accuracy of onroad vehicle emissions, which are largely senstivie to local meteorology, we developed the “inline” coupler module called “MetEmis” for Meteorology-Induced Emission sources within the Community Multiscale Air Quality (CMAQ) version 5.3.2 modeling system. It can dynamically estimate meteorology-induced hourly gridded emissions within the CMAQ modeling system using modeled meteorology. The CMAQ air quality modeling system is applied over the continental U.S. for two months (January and July 2019) for two emissions scenarios: a) current “offline” based onroad vehicle emissions, and b) “inline” CMAQ-MetEmis onroad vehicle emissions. Overall, the “MetEmis” coupler allows us to dynamically simulate onroad vehicle emissions from the MOVES onroad emission model for CMAQ with a better spatio-temporal representation compared to the “offline” scenario based on static temporal profiles. With an instance interpolation calculation approach, the new “inline” approach significantly enhances the computational efficiency and accuracy of estimating mobile source emissions, compared to the existing “offline” approach that yields almost identical hourly emission estimation. The domain total of daily VOC emissions from the “inline” scenario shows the largest impacts from the local meteorology, which is approximately 10 % lower than the ones from the “offline” scenario. Especially, the major difference of VOC estimates was shown over the California region. These local meteorology impacts on onroad vehicle emissions via CMAQ-MetEmis revealed an improvement in hourly NO2, daily maximum ozone, and daily average PM2.5 patterns with a higher agreement and correlation with daily ground observations.

This study, performed under the umbrella of the Task Force on Hemispheric Transport of Air Pollution (TF-HTAP), responds to the need of the global and regional atmospheric modelling community of having a mosaic emission inventory of air pollutants that conforms to specific requirements: global coverage, long time series, spatially distributed emissions with high time resolution, and a high sectoral resolution. The mosaic approach of integrating official regional emission inventories based on locally reported data, with a global inventory based on a globally consistent methodology, allows modellers to perform simulations of a high scientific quality while also ensuring that the results remain relevant to policymakers. HTAP_v3, an ad-hoc global mosaic of anthropogenic inventories, has been developed by integrating official inventories over specific areas (North America, Europe, Asia including Japan and Korea) with the independent Emissions Database for Global Atmospheric Research (EDGAR) inventory for the remaining world regions. The results are spatially and temporally distributed emissions of SO2, NOx, CO, NMVOC, NH3, PM10, PM2.5, Black Carbon (BC), and Organic Carbon (OC), with a spatial resolution of 0.1 x 0.1 degree and time intervals of months and years covering the period 2000–2018 (DOI 10.5281/zenodo.7516361, https://edgar.jrc.ec.europa.eu/dataset_htap_v3). The emissions are further disaggregated to 16 anthropogenic emitting sectors. This paper describes the methodology applied to develop such an emission mosaic, reports on source allocation, differences among existing inventories, and best practices for the mosaic compilation. One of the key strengths of the HTAP_v3 emission mosaic is its temporal coverage, enabling the analysis of emission trends over the past two decades. The development of a global emission mosaic over such long time series represents a unique product for global air quality modelling and for better-informed policy making, reflecting the community effort expended by the TF-HTAP to disentangle the complexity of transboundary transport of air pollution.

The role of anthropogenic NOx emissions in secondary organic aerosol (SOA) production is not fully understood but is important for understanding the contribution of emissions to air quality. Here, we examine the role of organic nitrates (RONO2) in SOA formation over the Korean Peninsula during the Korea-United States Air Quality field study in Spring 2016 as a model for RONO2 aerosol in cities worldwide. We use aircraft-based measurements of the particle phase and total (gas + particle) RONO2 to explore RONO2 phase partitioning. These measurements show that, on average, one-fourth of RONO2 are in the condensed phase, and we estimate that ≈15% of the organic aerosol (OA) mass can be attributed to RONO2. Furthermore, we observe that the fraction of RONO2 in the condensed phase increases with OA concentration, evidencing that equilibrium absorptive partitioning controls the RONO2 phase distribution. Lastly, we model RONO2 chemistry and phase partitioning in the Community Multiscale Air Quality modeling system. We find that known chemistry can account for one-third of the observed RONO2, but there is a large missing source of semivolatile, anthropogenically derived RONO2. We propose that this missing source may result from the oxidation of semi- and intermediate-volatility organic compounds and/or from anthropogenic molecules that undergo autoxidation or multiple generations of OH-initiated oxidation.

Low-volume bowel preparation has been developed to increase patient compliance. We compared 1 L of polyethylene glycol/ascorbic acid (PEG/Asc) and oral sodium sulfate (OSS) with respect to bowel preparation efficacy, compliance, and safety.A multicenter, prospective, randomized, single-blinded, non-inferiority trial was conducted in 3 hospitals. Patients were randomized to receive a bowel-cleansing agent. Bowel-cleansing efficacy was evaluated using the Boston Bowel Preparation Scale (BBPS). Satisfaction, feeling, taste of the bowel cleanser, and adverse events after taking the bowel cleanser were investigated through a questionnaire. Additionally, blood samples were analyzed before and after bowel cleansing.In total, 172 patients were analyzed (85 with 1 L PEG/Asc and 87 with OSS), and the mean BBPS scores were comparable between agents. The 1L PEG/Asc group tended to have a higher BBPS score in the right colon (2.22 vs 2.02; P = .08). The compliance of 1 L of PEG/Asc was comparable to that of OSS. Patients taking 1 L PEG/Asc reported greater thirst and dizziness (P = .04 and P = .047, respectively) than the OSS cohort. On the other hand, gastrointestinal symptoms such as vomiting and abdominal distension were more common in the OSS group, without statistical significance. In terms of laboratory adverse events, elevation of serum creatinine was found in both groups after taking the bowel cleansing agent (P .001 for the 1L PEG/Asc group; P = .04 for the OSS group). However, most of the increased values were within the normal ranges.The 1L PEG/Asc treatment was comparable to OSS in terms of bowel preparation efficacy, compliance, and safety.

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.

For the first time, mobile campaign and building monitoring experiments were conducted in downtown Seoul, Korea during the 2018/2019 winter period to assess the possibility of the co-benefit of CO2 emission reduction and air quality improvement. Given that CO2 and air pollutants in cities both originate from fossil fuel combustion, monitoring urban CO2 concentrations using a mobile vehicle and from the top of tall buildings can be a potentially effective approach for diagnosing the air quality. The local enhancement values (△species) related to urban emission sources were estimated by eliminating baseline concentrations. The results obtained showed high correlation coefficients between △CO2 and △air pollutants (i.e., 0.753, 0.505, and 0.525 for △CO, △NOX, and △NO2), confirming the applicability of CO2 as an indicator of air pollutant sources in downtown Seoul. The evaluation of the air quality in the study area using air pollutant ratios, including the ratios of CO and NOX/NO2, relative to CO2 showed that the ratios of △CO, △NOX, and △NO2 relative to △CO2 were 5.76, 1.89, and 0.21 ppb ppm−1. The estimated △NO2/△NOX ratio (11%) and a comparison of the △NOX/△CO2 ratios obtained with those reported for other cities in different countries suggested the possibility of a relationship between the current air quality in downtown Seoul and the use of diesel vehicles. Results suggest that the development of municipal policies aimed at mitigating climate change should consider the co-benefit of air quality improvement so as to ensure the effective reduction of CO2 and air pollutant emissions.

High levels of fine particulate matter (PM2.5) pollution in East Asia often exceed local air quality standards. Observations from the Korea United States-Air Quality (KORUS-AQ) field campaign in May and June 2016 showed that development of extreme pollution (haze) occurred through a combination of long-range transport and favorable meteorological conditions that enhanced local production of PM2.5. Atmospheric models often have difficulty simulating PM2.5 chemical composition during haze, which is of concern for the development of successful control measures. We use observations from KORUS-AQ to examine the ability of the GEOS-Chem chemical transport model to simulate PM2.5 composition throughout the campaign and identify the mechanisms driving the pollution event. In the surface level, the model underestimates campaign average sulfate aerosol by −64 % but overestimates nitrate aerosol by 36 %. The largest underestimate in sulfate occurs during the pollution event in conditions of high relative humidity, where models typically struggle to generate the high concentrations due to missing heterogeneous chemistry in aerosol liquid water in the polluted boundary layer. Hourly surface observations show that the model nitrate bias is driven by an overestimation of the nighttime peak. In the model, nitrate formation is limited by the supply of nitric acid, which is biased by +100 % against aircraft observations. We hypothesize that this is due to a missing sink, which we implement here as a factor of five increase in dry deposition. We show that the resulting increased deposition velocity is consistent with observations of total nitrate as a function of photochemical age. The model does not account for factors such as the urban heat island effect or the heterogeneity of the built-up urban landscape resulting in insufficient model turbulence and surface area over the study area that likely results in insufficient dry deposition. Other species such as NH3 could be similarly affected but were not measured during the campaign. Nighttime production of nitrate is driven by NO2 hydrolysis in the model, while observations show that unexpectedly elevated nighttime ozone (not present in the model) should result in N2O5 hydrolysis as the primary pathway. The model is unable to represent nighttime ozone due to an overly rapid collapse of the afternoon mixed layer and excessive titration by NO. We attribute this to missing nighttime heating driving deeper nocturnal mixing that would be expected to occur in a city like Seoul. This urban heating is not considered in air quality models run at large enough scales to treat both local chemistry and long-range transport. Key model failures in simulating nitrate, mainly overestimated daytime nitric acid, incorrect representation of nighttime chemistry, and an overly shallow and insufficiently turbulent nighttime mixed layer, exacerbate the model’s inability to simulate the buildup of PM2.5 during haze pollution. To address the underestimate in sulfate most evident during the haze event, heterogeneous aerosol uptake of SO2 is added to the model which previously only considered aqueous production of sulfate from SO2 in cloud water. Implementing a simple parameterization of this chemistry improves the model abundance of sulfate but degrades the SO2 simulation implying that emissions are underestimated. We find that improving model simulations of sulfate has direct relevance to determining local vs. transboundary contributions to PM2.5. During the haze pollution event, the inclusion of heterogeneous aerosol uptake of SO2 decreases the fraction of PM2.5 attributable to long-range transport from 66 % to 54 %. Locally-produced sulfate increased from 1 % to 46 % of locally-produced PM2.5, implying that local emissions controls would have a larger effect than previously thought. However, this additional uptake of SO2 is coupled to the model nitrate prediction which affects the aerosol liquid water abundance and chemistry driving sulfate-nitrate-ammonium partitioning. An additional simulation of the haze pollution with heterogeneous uptake of SO2 to aerosol and simple improvements to the model nitrate simulation results in 30 % less sulfate due to 40 % less nitrate and aerosol water, and results in an underestimate of sulfate during the haze event. Future studies need to better consider the impact of model physical processes such as dry deposition and boundary layer mixing on the simulation of nitrate and the effect of improved nitrate simulations on the overall simulation of secondary inorganic aerosol (sulfate+nitrate+ammonium) in East Asia. Foreign emissions are rapidly changing, increasing the need to understand the impact of local emissions on PM2.5 in South Korea to ensure continued air quality improvements.

We propose a method to adjust contributions from upwind emissions to downwind PM

The absence of up-to-date emissions has been a major impediment to accurately simulating aspects of atmospheric chemistry and to precisely quantifying the impact of changes in emissions on air pollution. Hence, a nonlinear joint analytical inversion (Gauss–Newton method) of both volatile organic compounds (VOCs) and nitrogen oxide ( NOx ) emissions is made by exploiting the Smithsonian Astrophysical Observatory (SAO) Ozone Mapping and Profiler Suite Nadir Mapper (OMPS-NM) formaldehyde (HCHO) and the National Aeronautics and Space Administration (NASA) Ozone Monitoring Instrument (OMI) tropospheric nitrogen dioxide ( NO2 ) columns during the Korea–United States Air Quality (KORUS-AQ) campaign over East Asia in May–June 2016. Effects of the chemical feedback of NOx and VOCs on both NO2 and HCHO are implicitly included by iteratively optimizing the inversion. Emission uncertainties are greatly narrowed (averaging kernels > 0.8 , which is the mathematical presentation of the partition of information gained from the satellite observations with respect to the prior knowledge) over medium- to high-emitting areas such as cities and dense vegetation. The prior amount of total NOx emissions is mainly dictated by values reported in the MIX-Asia 2010 inventory. After the inversion we conclude that there is a decline in emissions (before, after, change) for China ( 87.94±44.09 Gg d −1 , 68.00±15.94 Gg d −1 , −23 %), North China Plain (NCP) ( 27.96±13.49 Gg d −1 , 19.05±2.50 Gg d −1 , −32 %), Pearl River Delta (PRD) ( 4.23±1.78 Gg d −1 , 2.70±0.32 Gg d −1 , −36 %), Yangtze River Delta (YRD) ( 9.84±4.68 Gg d −1 , 5.77±0.51 Gg d −1 , −41 %), Taiwan ( 1.26±0.57 Gg d −1 , 0.97±0.33 Gg d −1 , −23 %), and Malaysia ( 2.89±2.77 Gg d −1 , 2.25±1.34 Gg d −1 , −22 %), all of which have effectively implemented various stringent regulations. In contrast, South Korea ( 2.71±1.34 Gg d −1 , 2.95±0.58 Gg d −1 , + 9 %) and Japan ( 3.53±1.71 Gg d −1 , 3.96±1.04 Gg d −1 , +12 %) are experiencing an increase in NOx emissions, potentially due to an increased number of diesel vehicles and new thermal power plants. We revisit the well-documented positive bias (by a factor of 2 to 3) of MEGAN v2.1 (Model of Emissions of Gases and Aerosols from Nature) in terms of biogenic VOC emissions in the tropics. The inversion, however, suggests a larger growth of VOCs (mainly anthropogenic) over NCP (25 %) than previously reported (6 %) relative to 2010. The spatial variation in both the magnitude and sign of NOx and VOC emissions results in nonlinear responses of ozone production and loss. Due to a simultaneous decrease and increase in NOx∕VOC over NCP and YRD, we observe a ∼53 % reduction in the ratio of the chemical loss of NOx ( LNOx ) to the chemical loss of ROx ( RO2+HO2 ) over the surface transitioning toward NOx -sensitive regimes, which in turn reduces and increases the afternoon chemical loss and production of ozone through NO2+OH ( −0.42 ppbv h −1 ) ∕HO2 (and RO2 ) +NO ( +0.31 ppbv h −1 ). Conversely, a combined decrease in NOx and VOC emissions in Taiwan, Malaysia, and southern China suppresses the formation of ozone. Simulations using the updated emissions indicate increases in maximum daily 8 h average (MDA8) surface ozone over China (0.62 ppbv), NCP (4.56 ppbv), and YRD (5.25 ppbv), suggesting that emission control strategies on VOCs should be prioritized to curb ozone production rates in these regions. Taiwan, Malaysia, and PRD stand out as regions undergoing lower MDA8 ozone levels resulting from the NOx reductions occurring predominantly in NOx -sensitive regimes.

With the increasing importance of monitoring and verifying urban CO2 concentrations to mitigate carbon emissions from cities, a high-resolution ground networks of low-cost CO2 sensors are emerging that have a price advantage over existing high-cost sensors. This study develops a low-cost CO2 instrument called the Seoul National University CO2 Measurement (SNUCO2M) to monitor and analyze intra-city CO2 concentrations over Seoul, Korea. The first step of establishing this low-cost CO2 sensor network involved installing the SNUCO2M at the Yongsan Building (YSB; 113 m MASL) and Namsan Seoul Tower-High (NSTH; 385 m MASL) on October 18, 2018 and July 8, 2019, respectively. For comparison with the SNUCO2M, we utilized CO2 concentrations measured by the Picarro instrument (high-cost CO2 sensor) at Gwanak Mountain (GWA) background measurement site and the lower part of the NST (NSTL). The initial CO2 measurements from the SNUCO2M, show that 1) the SNUCO2M has a 0.71% maximum error range and is sufficient to measure urban atmospheric CO2 concentrations; and 2) the SNUCO2M captures 26.92 and 20.30 ppm of Seoul’s summer and winter time CO2 enhancement compared to the GWA background site. Our results suggest that the low-cost sensors such as SNUCO2M successfully detect urban CO2 characteristics over the megacity and has great potential to replace high-cost sensors with cost and management limitations.

The Geostationary Environment Monitoring Spectrometer (GEMS) is scheduled for launch in February 2020 to monitor air quality (AQ) at an unprecedented spatial and temporal resolution from a geostationary Earth orbit (GEO) for the first time. With the development of UV–visible spectrometers at sub-nm spectral resolution and sophisticated retrieval algorithms, estimates of the column amounts of atmospheric pollutants (O3, NO2, SO2, HCHO, CHOCHO, and aerosols) can be obtained. To date, all the UV–visible satellite missions monitoring air quality have been in low Earth orbit (LEO), allowing one to two observations per day. With UV–visible instruments on GEO platforms, the diurnal variations of these pollutants can now be determined. Details of the GEMS mission are presented, including instrumentation, scientific algorithms, predicted performance, and applications for air quality forecasts through data assimilation. GEMS will be on board the Geostationary Korea Multi-Purpose Satellite 2 (GEO-KOMPSAT-2) satellite series, which also hosts the Advanced Meteorological Imager (AMI) and Geostationary Ocean Color Imager 2 (GOCI-2). These three instruments will provide synergistic science products to better understand air quality, meteorology, the long-range transport of air pollutants, emission source distributions, and chemical processes. Faster sampling rates at higher spatial resolution will increase the probability of finding cloud-free pixels, leading to more observations of aerosols and trace gases than is possible from LEO. GEMS will be joined by NASA’s Tropospheric Emissions: Monitoring of Pollution (TEMPO) and ESA’s Sentinel-4 to form a GEO AQ satellite constellation in early 2020s, coordinated by the Committee on Earth Observation Satellites (CEOS).

Efforts to reduce greenhouse gas emissions are ongoing in many East Asian countries; however, their influences on future drought remain unknown. We analyzed future changes in drought intensity over East Asia using the Representative Concentration Pathway (RCP) 8.5 and the new two limiting local greenhouse gas emission scenarios in East Asia: National Institute of Environmental Research (NIER)-H scenario for high emission and NIER-L scenario for low emission. The two scenarios were made by a fully coupled global climate model with prescribed gas boundaries of high and low greenhouse gas emissions from an atmospheric chemistry model reflecting the up-to-date environmental policies of South Korea, China, and Japan. The application of a clustering analysis to the calculated drought index time series identified four zonally distributed drought regions in the present-day period (1951 - 2016). Among these regions, higher latitude regions showed a more rapidly increasing trend in drought intensity than other regions for the present-day period. All future scenarios projected the continuation of this meridional intensification trend of drought until 2100; however, the intensification rates in the NIER-L were much smaller than those in the RCP8.5 and NIER-H scenarios. Our results suggest that reducing greenhouse gas emissions is critical for East Asian countries to alleviate the potential damages of future droughts.

We use the CAMx (Comprehensive Air Quality Model with Extensions) chemical transport model (CTM) with 4-km horizontal resolution over the Korean Peninsula to investigate source contributions to PM2.5 in Korea from domestic and upwind sources. We modeled 2015 and 2016 to account for meteorological variation with Korean emissions from the Clean Air Policy Supporting System (CAPSS), meteorology from WRF (Weather, Research, and Forecasting) model, and regional boundary concentrations from the GEOS-Chem global CTM. The CAMx particulate source apportionment technology (PSAT) provided PM2.5 source contributions from 5 source sectors and 6 geographic regions within Korea, international sources, and boundary concentrations. PM2.5 contributions from outside Korea are important with boundary concentrations plus the “other” emissions sector (includes marine shipping, agricultural ammonia, and international emissions from North Korea and Japan within the CAMx domain) contributing 67% of annual average PM2.5 in Seoul in 2016 and 71% in 2015. The boundary concentrations contributed between 30% and 50% of PM2.5 at different Korean cities with contributions generally lower in 2016 than in 2015. For Korean sources, PM2.5 contributions from Electric Generating Unit (EGU) emissions were smaller than contributions from mobile and industrial emissions sources although there is considerable day-to-day variation in contributions. On an annual basis in 2016, the “other” category contributed 25% followed by mobile sources at 23%, industrial sources at 6%, and EGU sources at 3%. For 2015, the contributions were similar. Focusing on March when PM2.5 concentrations were higher than other months, the contributions from other, mobile, industrial, and EGUs were 21%, 18%, 4%, and 4%, respectively in 2016. For 2015, contributions from these four categories were 18%, 15%, 3%, and 3%, respectively.

The Comprehensive Automobile Research System (CARS) is an open-source python-based automobile emissions inventory model designed to efficiently estimate high quality emissions from motor-vehicle emission sources. It can estimate the criteria air pollutants, greenhouse gases, and air toxics in various temporal resolutions at the national, state, county, and any spatial resolution based on the spatiotemporal resolutions of input datasets. The CARS is designed to utilize the local vehicle activity database, such as vehicle travel distance, road link-level network Geographic Information System (GIS) information, and vehicle-specific average speed by road type, to generate a temporally and spatially enhanced automobile emissions inventory for policymakers, stakeholders, and the air quality modeling community. The CARS model adopted the European Environment Agency's (EEA) onroad automobile emissions calculation methodologies to estimate the hot exhaust, cold start, and evaporative emissions from onroad automobile sources. It can optionally utilize road link-specific average speed distribution (ASD) inputs to reflect more realistic vehicle speed variations by road type than a road-specific single averaged speed approach. Also, utilizing high-resolution road GIS data allows the CARS to estimate the road link-level emissions to improve the inventory's spatial resolution. When we compared the official 2015 national mobile emissions from Korea's Clean Air Policy Support System (CAPSS) against the ones estimated by the CARS, there is a moderate increase of VOC (33 %), CO (52 %), and fine particulate matter (PM2.5) (15 %) emissions while NOx and SOx are reduced by 24 % and 17 % in the CARS estimates. The main differences are driven by the usage of different vehicle activities and the incorporation of road-specific ASD, which plays a critical role in hot exhaust emission estimates but wasn’t implemented in Korea’s CAPSS mobile emissions inventory. While 52% of vehicles use gasoline fuel and 35 % use diesel, gasoline vehicles only contribute 7.7 % of total NOx emissions while diesel vehicles contribute 85.3 %. But for VOC emissions, gasoline vehicles contribute 52.1 % while diesel vehicles are limited to 23 %. While diesel buses are only 0.3 % of vehicles, each vehicle has the largest contribution to NOx emissions (8.51 % of NOx total) due to its longest daily VKT. For VOC, CNG buses are the largest contributor with 19.5 % of total VOC emissions. It indicates that the CNG bus is better for the rural area while the diesel bus is better applicable for the urban area for a better ozone control strategy because the rural area is usually NOx limited for ozone formation and urban area is VOC limited region. For primary PM2.5, more than 98.5 % is from diesel vehicles. The CARS model's in-depth analysis feature can assist government policymakers and stakeholders develop the best emission abatement strategies.

Geostationary satellite sensors over East Asia (GOCI and AHI) are now providing continuous mapping of aerosol optical depth (AOD) at 550 nm to improve monitoring of fine particulate matter (PM2.5) air quality. Here we evaluate our understanding of the physical relationships between AOD and PM2.5 over East Asia by using the GEOS-Chem atmospheric chemistry model to simulate observations from multiple sources: 1) the joint NASA-NIER Korea – United States Air Quality aircraft campaign over South Korea (KORUS-AQ; May–June 2016); 2) AODs from the AERONET ground-based network; 3) AOD from a new GOCI/AHI fused product; and 4) surface PM2.5 networks in South Korea and China. The KORUS-AQ data show that 550 nm AOD is mainly contributed by sulfate-nitrate-ammonium (SNA) and organic aerosols in the planetary boundary layer (PBL), despite large dust concentrations in the free troposphere, reflecting the optically effective size and the high hygroscopicity of the PBL aerosols. Although GEOS-Chem is successful in reproducing the KORUS-AQ vertical profiles of aerosol mass, its ability to link AOD to PM2.5 is limited by under-accounting of coarse PM and by a large overestimate of nighttime PM2.5 nitrate. A broader analysis of the GOCI/AHI AOD data over East Asia in different seasons shows agreement with AERONET AODs and a spatial distribution consistent with surface PM2.5 network data. The AOD observations over North China show a summer maximum and winter minimum, opposite in phase to surface PM2.5. This is due to low PBL depths compounded by high residential coal emissions in winter, and high relative humidity (RH) in summer. Seasonality of AOD and PM2.5 over South Korea is much weaker, reflecting weaker variation of PBL depth and lack of residential coal emissions. Physical interpretation of the satellite AOD data in terms of surface PM2.5 is sensitive to accurate information on aerosol size distributions, PBL depths, RH, the role of coarse particles, and diurnal variation of PM2.5.

The emission of pollution from Chinese point sources, and its migration to Korea, downwind of China, is an important issue in terms of Korean air quality management. Therefore, this study intended to improve the Asian Emission Inventory, NIER/KU-CREATE, by improving information about the point source emissions, their location, and discharge conditions, in China. By using the Global dataset, we can establish point source location information for Coal Power Plants and Non-Coal Power Plants in the Chinese power sector, and calculate and allocate emissions in the inventory. In addition, the emission inventory of NH3, as a by-product of NOx abatement processing, was regarded as being insufficiently characterized in the previous emission inventory, and has been re-estimated. As a result, 3798 LPSs in China were identified, and NH3 emissions from those power plants have increased significantly. In addition, the vertical distribution of emissions through the allocation stack height parameters showed atmospheric injection heights of between 250 m and 1000 m. Using this information, HY-SPLIT trajectory modeling found that during periods of active westerly from China to Korea, emissions from the higher injection height were more prone to move towards Korea, than those from the lower height.

High concentrations of PM2.5 have become a serious environmental issue in South Korea, which ranked 1st or 2nd among OECD countries in terms of population exposure to PM2.5. Quantitative understanding of PM2.5 source attribution is thus crucial for developing efficient air quality mitigation strategies. Here we use a suite of extensive observations of PM2.5 and its precursors concentrations during the international KORea-US cooperative Air Quality field study in Korea (KORUS-AQ) in May–June 2016 to investigate source contributions to PM2.5 in South Korea under various meteorological conditions. For the quantitative analysis, we updated a 3-D chemical transport model, GEOS-Chem, and its adjoint with the latest regional emission inventory and other recent findings. The updated model is evaluated by comparing against observed daily PM2.5 and its component concentrations from six ground sites (Bangnyung, Bulkwang, Olympic park, Gwangju, Ulsan, and Jeju). Overall, simulated concentrations of daily PM2.5 and its components are in a good agreement with observations over the peninsula. We conduct an adjoint sensitivity analysis for simulated surface level PM2.5 concentrations at five ground sites (except for Bangnyung because of its small population) under four different meteorological conditions: dynamic weather, stagnant, extreme pollution, and blocking periods. Source contributions by regions vary greatly depending on synoptic meteorological conditions. Chinese contribution accounts for almost 68% of PM2.5 in surface air in South Korea during the extreme pollution period of the campaign, whereas an enhanced contribution from domestic sources (57%) occurs for the blocking period. Results from our sensitivity analysis suggest that the reduction of domestic anthropogenic NH3 emissions could be most effective in reducing population exposure to PM2.5 in South Korea (effectiveness = 14%) followed by anthropogenic SO2 emissions from Shandong region (effectiveness = 11%), domestic anthropogenic NOx emissions (effectiveness = 10%), anthropogenic NH3 emissions from Shandong region (effectiveness = 8%), anthropogenic NOx emissions from Shandong region (effectiveness = 7%), domestic anthropogenic OC emissions (effectiveness = 7%), and domestic anthropogenic BC emissions (effectiveness = 5%).

In this paper we forecast energy demand at the local level using a new two-dimensional downscaling methodology that respects regional and subnational variation and, as well as consistency at the aggregate level. This methodology combines the one-dimensional statistical downscaling and the Iterative Proportional Fitting (IPF) procedure. Previous projection studies based on downscaling methods have focused on allocating one-dimensional quantity indicators such as GDP, population, and emissions, into geographically or administratively lower levels. In case of energy demand projections, however, downscaling involves not only regional allocation of energy demand quantities but also energy mixes of the demands in the regions. Our novel two-dimensional downscaling methodology allows us to downscale national energy projections in both demand quantities and their energy mix to regional levels by combining the statistical downscaling and the IPF procedure. We illustrate the methodology by deriving energy demand projections for provinces and metropolitan cities in Korea from national scenarios. In addition, we provide two more simple applications of the IPF to show utilization potentials of the IPF in energy demand forecasting and statistics.

In this work, we investigate the NOx emissions inventory in Seoul, South Korea, using a regional ozone monitoring instrument (OMI) NO2 product derived from the standard NASA product. We first develop a regional OMI NO2 product by recalculating the air mass factors using a high-resolution (4 km × 4 km) WRF-Chem model simulation, which better captures the NO2 profile shapes in urban regions. We then apply a model-derived spatial averaging kernel to further downscale the retrieval and account for the subpixel variability. These two modifications yield OMI NO2 values in the regional product that are 1.37 times larger in the Seoul metropolitan region and >2 times larger near substantial point sources. These two modifications also yield an OMI NO2 product that is in better agreement with the Pandora NO2 spectrometer measurements acquired during the South Korea–United States Air Quality (KORUS-AQ) field campaign. NOx emissions are then derived for the Seoul metropolitan area during the KORUS-AQ field campaign using a top-down approach with the standard and regional NASA OMI NO2 products. We first apply the top-down approach to a model simulation to ensure that the method is appropriate: the WRF-Chem simulation utilizing the bottom-up emissions inventory yields a NOx emissions rate of 227±94 kt yr −1 , while the bottom-up inventory itself within a 40 km radius of Seoul yields a NOx emissions rate of 198 kt yr −1 . Using the top-down approach on the regional OMI NO2 product, we derive the NOx emissions rate from Seoul to be 484±201 kt yr −1 , and a 353±146 kt yr −1 NOx emissions rate using the standard NASA OMI NO2 product. This suggests an underestimate of 53 % and 36 % in the bottom-up inventory using the regional and standard NASA OMI NO2 products respectively. To supplement this finding, we compare the NO2 and NOy simulated by WRF-Chem to observations of the same quantity acquired by aircraft and find a model underestimate. When NOx emissions in the WRF-Chem model are increased by a factor of 2.13 in the Seoul metropolitan area, there is better agreement with KORUS-AQ aircraft observations and the recalculated OMI NO2 tropospheric columns. Finally, we show that by using a WRF-Chem simulation with an updated emissions inventory to recalculate the air mass factor (AMF), there are small differences ( ∼8 %) in OMI NO2 compared to using the original WRF-Chem simulation to derive the AMF. This suggests that changes in model resolution have a larger effect on the AMF calculation than modifications to the South Korean emissions inventory. Although the current work is focused on South Korea using OMI, the methodology developed in this work can be applied to other world regions using TROPOMI and future satellite datasets (e.g., GEMS and TEMPO) to produce high-quality region-specific top-down NOx emissions estimates.

A combination of mesoporous silica with an LaCuS2-graphene composite has been prepared by a self-assembly method in support of the CTAB surfactant as the structure creator and basic medium. The characterization was investigated using XRD, SEM, TEM, HR-TEM, EDS, XPS, Raman, and DRS analysis. The pore size of the as-fabricated composite was calculated to be 5.83 nm by the nitrogen adsorption/desorption isotherms. In addition, the photocatalytic properties revealed that the mesoporous SiO2/LaCuS2-graphene catalyst presented a good result for the photocatalytic activity of organic dyes and gallic acid under visible light irradiation. Moreover, the as-prepared mesoporous SiO2/LaCuS2-graphene nanocomposite exhibited a good photodegradation rate for a cationic organic dye group under a pH medium of 11. The photocatalytic performance of the phenol compound representation presented a good result, and the optimum amount of the catalytic for the gallic acid photocatalytic activity is 0.05 g. In addition, the mesoporous SiO2/LaCuS2-graphene catalyst can be exploited for the application of solar energy conversion with high hydrogen production capacity.

The Korea-United States Air Quality (KORUS-AQ) field study was conducted during May–June 2016 to understand the factors controlling air quality in South Korea. Extensive aircraft and ground network observations from the campaign offer an opportunity to address issues in current air quality models and reduce model-observation disagreements. This study examines these issues using model evaluation against the KORUS-AQ observations and intercomparisons between models. Six regional and two global chemistry transport models using identical anthropogenic emissions participated in the model intercomparison study and were used to conduct air quality simulations focusing on ozone (O3), aerosols, and their precursors for the campaign. Using the KORUSv5 emissions inventory, which has been updated from KORUSv1, the models successfully reproduced observed nitrogen oxides (NOx) and volatile organic compounds mixing ratios in surface air, especially in the Seoul Metropolitan Area, but showed systematic low biases for carbon monoxide (CO), implying possible missing CO sources in the inventory in East Asia. Although the DC-8 aircraft-observed O3 precursor mixing ratios were well captured by the models, simulated O3 levels were lower than the observations in the free troposphere in part due to too low stratospheric O3 influxes, especially in regional models. During the campaign, the synoptic meteorology played an important role in determining the observed variability of PM2.5 (PM diameter ≤ 2.5 μm) concentrations in South Korea. The models successfully simulated the observed PM2.5 variability with significant inorganic sulfate-nitrate-ammonium aerosols contribution, but failed to reproduce that of organic aerosols, causing a large inter-model variability. From the model evaluation, we find that an ensemble of model results, incorporating individual models with differing strengths and weaknesses, performs better than most individual models at representing observed atmospheric compositions for the campaign. Ongoing model development and evaluation, in close collaboration with emissions inventory development, are needed to improve air quality forecasting.

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.

Nonmethane volatile organic compounds (NMVOCs) result in ozone and aerosol production that adversely affects the environment and human health. For modeling purposes, anthropogenic NMVOC emissions have been typically compiled using the “bottom-up” approach. To minimize uncertainties of the bottom-up emission inventory, “top-down” NMVOC emissions can be estimated using formaldehyde (HCHO) observations. In this study, HCHO vertical column densities (VCDs) obtained from the Geostationary Trace gas and Aerosol Sensor Optimization spectrometer during the Korea–United States Air Quality campaign were used to constrain anthropogenic volatile organic compound (AVOC) emissions in South Korea. Estimated top-down AVOC emissions differed from those of the up-to-date bottom-up inventory over major anthropogenic source regions by factors of 1.0 ± 0.4 to 6.9 ± 3.9. Our evaluation using a 3D chemical transport model indicates that simulated HCHO mixing ratios using the top-down estimates were in better agreement with observations onboard the DC-8 aircraft during the campaign relative to those with the bottom-up emission, showing a decrease in model bias from –25% to –13%. The top-down analysis used in this study, however, has some limitations related to the use of HCHO yields, background HCHO columns, and AVOC speciation in the bottom-up inventory, resulting in uncertainties in the AVOC emission estimates. Our attempt to constrain diurnal variations of the AVOC emissions using the aircraft HCHO VCDs was compromised by infrequent aircraft observations over the same source regions. These limitations can be overcome with geostationary satellite observations by providing hourly HCHO VCDs.

We present trace gas vertical profiles observed by instruments on the NASA DC-8 and at a ground site during the Korea-US air quality study (KORUS) field campaign in May to June 2016. We focus on the region near the Seoul metropolitan area and its surroundings where both anthropogenic and natural emission sources play an important role in local photochemistry. Integrating ground and airborne observations is the major research goal of many atmospheric chemistry field campaigns. Although airborne platforms typically aim to sample from near surface to the free troposphere, it is difficult to fly very close to the surface especially in environments with complex terrain or a populated area. A detailed analysis integrating ground and airborne observations associated with specific concentration footprints indicates that reactive trace gases are quickly oxidized below an altitude of 700 m. The total OH reactivity profile has a rapid decay in the lower part of troposphere from surface to the lowest altitude (700 m) sampled by the NASA DC-8. The decay rate is close to that of very reactive biogenic volatile organic compounds such as monoterpenes. Therefore, we argue that photochemical processes in the bottom of the boundary layer, below the typical altitude of aircraft sampling, should be thoroughly investigated to properly assess ozone and secondary aerosol formation.

For the purpose of improving PM prediction skills in East Asia, we estimated a new background error covariance matrix (BEC) for aerosol data assimilation using surface PM2.5 observations that accounts for the uncertainties in anthropogenic emissions. In contrast to the conventional method to estimate the BEC that uses perturbations in meteorological data, this method additionally considered the perturbations using two different emission inventories. The impacts of the new BEC were then tested for the prediction of surface PM2.5 over East Asia using Community Multi-scale Air Quality (CMAQ) initialized by three-dimensional variational method (3D-VAR). The surface PM2.5 data measured at 154 sites in South Korea and 1,535 sites in China were assimilated every six hours during the Korea-United States Air Quality Study (KORUS-AQ) campaign period (1 May–14 June 2016). Data assimilation with our new BEC showed better agreement with the surface PM2.5 observations than that with the conventional method. Our method also showed closer agreement with the observations in 24-hour PM2.5 predictions with ~ 44 % fewer negative biases than the conventional method. We conclude that increased standard deviations, together with horizontal and vertical length scales in the new BEC, tend to improve the data assimilation and short-term predictions for the surface PM2.5. This paper also suggests further research efforts devoted to estimating the BEC to improve PM2.5 predictions.

The absence of up-to-date emissions has been a major impediment to accurately simulate aspects of atmospheric chemistry, and to precisely quantify the impact of changes of emissions on air pollution. Hence, a non-linear joint analytical inversion (Gauss–Newton method) of both volatile organic compounds (VOC) and nitrogen oxides (NOx) emissions is made by exploiting the Smithsonian Astrophysical Observatory (SAO) Ozone Mapping and Profile Suite Nadir Mapper (OMPS-NM) formaldehyde (HCHO) and the National Aeronautics and Space Administration (NASA) Ozone Monitoring Instrument (OMI) tropospheric nitrogen dioxide (NO2) retrievals during the Korea-United States Air Quality (KORUS-AQ) campaign over East Asia in May–June 2016. Effects of the chemical feedback of NOx and VOCs on both NO2 and HCHO are implicitly included through iteratively optimizing the inversion. Emissions estimates are greatly improved (averaging kernels > 0.8) over medium- to high-emitting areas such as cities and dense vegetation. The amount of total NOx emissions is mainly dictated by values reported in the MIX-Asia 2010 inventory. After the inversion we conclude a decline in the emissions (before, after, change) for China (87.94 ± 44.09 Gg/day, 68.00 ± 15.94 Gg/day, −23 %), North China Plain (NCP) (27.96 ± 13.49 Gg/day, 19.05 ± 2.50 Gg/day, −32 %), Pearl River Delta (PRD) (4.23 ± 1.78 Gg/day, 2.70 ± 0.32 Gg/day, −36 %), Yangtze River Delta (YRD) (9.84 ± 4.68 Gg/day, 5.77 ± 0.51 Gg/day, −41 %), Taiwan (1.26 ± 0.57 Gg/day, 0.97 ± 0.33 Gg/day, −23 %), and Malaysia (2.89 ± 2.77 Gg/day, 2.25 ± 1.34 Gg/day, −22 %), all of which have effectively implemented various stringent regulations. In contrast, South Korea (2.71 ± 1.34 Gg/day, 2.95 ± 0.58 Gg/day, +9 %) and Japan (3.53 ± 1.71 Gg/day, 3.96 ± 1.04 Gg/day, +12 %) experience an increase in NOx emissions potentially due to risen number of diesel vehicles and new thermal power plants. We revisit the well-documented positive bias of the model in terms of biogenic VOC emissions in the tropics. The inversion, however, suggests a larger growth of VOC (mainly anthropogenic) over NCP (25 %) than previously reported (6 %) relative to 2010. The spatial variation in both magnitude and sign of NOx and VOC emissions results in non-linear responses of ozone production/loss. Due to simultaneous decrease/increase of NOx/VOC over NCP and YRD, we observe an ~ 53 % reduction in the ratio of the chemical loss of NOx (LNOx) to the chemical loss of ROx (RO2 + HO2) transitioning toward NOx-sensitive regimes, which in turn, reduces/increases the afternoon chemical loss/production of ozone through NO2 + OH (−0.42 ppbv hr−1)/HO2 (and RO2) + NO (+0.31 ppbv hr−1). Conversely, a combined decrease in NOx and VOC emissions in Taiwan, Malaysia, and the southern China suppresses the formation of ozone. Ultimately, model simulations indicate enhancements of maximum daily 8-hour average (MDA8) surface ozone over China (0.62 ppbv), NCP (4.56 ppbv), and YRD (5.25 ppbv) due to the non-linear ozone chemistry, suggesting that emissions standards should be extended to regulate VOCs to be able to curb ozone production rates. Taiwan, Malaysia, and PRD stand out as the regions undergoing lower MDA8 ozone levels resulting from the NOx reductions occurring predominantly in NOx-sensitive regimes.

Concentration of air pollutants such as tropospheric ozone and aerosols are mainly affected by meteorological variables and emissions. East Asia has large amount of anthropogenic and natural air pollutant emissions and has been putting lots of efforts to improve air quality. In order to seek effective ways to mitigate future air pollution, it is essential to understand the current emissions and their impacts on air quality. Emission inventory is one of the key datasets required to understand air quality and find ways to improve it. Amounts and spatial-temporal distributions of emissions are, however, not easy to estimate due to their complicate nature, therefore introduce significant uncertainties.In this study, we had developed an updated version of our Asian emissions inventory, named NIER/KU-CREATE (Comprehensive Regional Emissions inventory for Atmospheric Transport Experiment) in support of climate-air quality study. We first inter-compare multiple bottom-up inventories to understand discrepancies among the dataset(sectoral, spatial). We then inter-compare those bottom-up emissions to the satellite-based top-down emission estimates to understand uncertainties of the databases. The bottom-up emission inventories used for this study are: CREATE, MEIC(Multiresolution Emission Inventory for China), REAS (Regional Emission inventory in ASia), and ECLIPSE(Evaluating the Climate and Air Quality Impacts of Short-Lived Pollutants). The satellite-derived top-down emission inventory had been acquired from the DECSO (Daily Emission derived Constrained by Satellite Observations) algorithm data from the GlobEmissions website.The analysis showed that some discrepancies, in terms of emission amounts, sectoral shares and spatial distribution patterns, exist among the datasets. We analyzed further to find out which parameters could affect more on those discrepancies. Co-analysis of top-down and bottom-up emissions inventory help us to evaluate emissions amount and spatial distribution. These analysis are helpful for the development of more consistent and reliable inventories with the aim of reducing the uncertainties in air quality study. More results of evaluation of emissions will be presented on site. Acknowledgements : This work was supported by National Institute of Environment Research (NIER-2019-03-02-005), Korea Environment Industry & Technology Institute(KEITI) through Public Technology Program based on Environmental Policy Program, funded by Korea Ministry of Environment(MOE)(2019000160007). This research was supported by the National Strategic Project-Fine particle of the National Research Foundation of Korea(NRF) funded by the Ministry of Science and ICT(MSIT), the Ministry of Environment(ME), and the Ministry of Health and Welfare(MOHW) (NRF-2017M3D8A1092022).

Atmospheric nitrogen deposition in China has attracted huge public attention in recently years due to the increasing anthropogenic emission of reactive nitrogen (Nr) and its impacts on the terrestrial and aquatic ecosystems. However, limited long-term and multi-site measurements have restrained the understanding on the mechanism of the Nr deposition as well as the chemical transport model (CTM) improvement. In this study, the performance of the simulated wet and dry deposition for different Nr species, i.e., particulate NO3− and NH4+, gaseous NOx, HNO3 and NH3, have been conducted using the framework of Model Inter-Comparison Study for Asia (MICS-Asia) phase III. Nine Models, including 5 WRF-CAMQ models, 2 self-developed regional models, a global model and a RAMS-CMAQ model, have been selected for the comparison. For wet depositions, observation data from 83 measurement sites of EANET, CREN, CAUDN, NADMN and DEE of China have been collected and normalized to compare with model results. In general, most models showed the consistent spatial and temporal variation of both oxidized N (Nox) and reduced N (Nrd) wet depositions in China with the NME around at 50 %, which is lower than the value of 70 % based on EANET observation over Asia. Both the ratio of wet or dry deposition to the total inorganic N deposition (TIN) and the ratios of TIN to their emissions have shown the consistent results with the NNDMN estimations. The performance of ensemble results (ENM) was further assessed with the measurement from satellite. In different regions of China, the results showed that the simulated Nox wet deposition was overestimated in North East China (NE) but underestimated in south of China (SE+SW), while the Nrd wet deposition was underpredicted in all regions by all models. The deposition of Nox have large uncertainties than the Nrd especially in North China (NC), indicating chemical reaction process is one of the most importance factors that affecting the model performance. Compared to Critical load (CL) value, the Nr deposition in NC, SE and SW reached or exceeded the reported CL value and exerted serious ecological impacts. The control of Nrd in NC and SW and Nox in SE would be effective to mitigate the TIN deposition in these regions. More interestingly, the Nr deposition in Tibet plateau with the high ratio of TIN/emission (~3.0), indicating a significant import from outside should be focused in the future due to its climatical influence to the sensitive ecosystem in whole China.

Accurate and consistent monitoring of anthropogenic combustion is imperative because of its significant health and environmental impacts, especially at city-to-regional scale. Here, we assess the performance of the Copernicus Atmosphere Monitoring Service (CAMS) global prediction system using measurements from aircraft, ground sites, and ships during the Korea-United States Air Quality (KORUS-AQ) field study in May to June 2016. Our evaluation focuses on CAMS CO and CO2 analyses as well as two higher-resolution forecasts (16 and 9 km horizontal resolution) to assess their capability in predicting combustion signatures over east Asia. Our results show a slight overestimation of CAMS CO2 with a mean bias against airborne CO2 measurements of 2.2, 0.7, and 0.3 ppmv for 16 and 9 km CO2 forecasts, and analyses, respectively. The positive CO2 mean bias in the 16 km forecast appears to be consistent across the vertical profile of the measurements. In contrast, we find a moderate underestimation of CAMS CO with an overall bias against airborne CO measurements of − 19.2 (16 km), − 16.7 (9 km), and − 20.7 ppbv (analysis). This negative CO mean bias is mostly seen below 750 hPa for all three forecast/analysis configurations. Despite these biases, CAMS shows a remarkable agreement with observed enhancement ratios of CO with CO2 over the Seoul metropolitan area and over the West (Yellow) Sea, where east Asian outflows were sampled during the study period. More efficient combustion is observed over Seoul ( d CO / d CO 2 = 9 ppbv ppmv −1 ) compared to the West Sea ( d CO / d CO 2 = 28 ppbv ppmv −1 ). This “combustion signature contrast” is consistent with previous studies in these two regions. CAMS captured this difference in enhancement ratios (Seoul: 8–12 ppbv ppmv −1 , the West Sea: ∼30 ppbv ppmv −1 ) regardless of forecast/analysis configurations. The correlation of CAMS CO bias with CO2 bias is relatively high over these two regions (Seoul: 0.64–0.90, the West Sea: ∼0.80 ) suggesting that the contrast captured by CAMS may be dominated by anthropogenic emission ratios used in CAMS. However, CAMS shows poorer performance in terms of capturing local-to-urban CO and CO2 variability. Along with measurements at ground sites over the Korean Peninsula, CAMS produces too high CO and CO2 concentrations at the surface with steeper vertical gradients ( ∼0.4 ppmv hPa −1 for CO2 and 3.5 ppbv hPa −1 for CO) in the morning samples than observed ( ∼0.25 ppmv hPa −1 for CO2 and 1.7 ppbv hPa −1 for CO), suggesting weaker boundary layer mixing in the model. Lastly, we find that the combination of CO analyses (i.e., improved initial condition) and use of finer resolution (9 km vs. 16 km) generally produces better forecasts.

Understanding how ozone (O3) and fine particulate matter (PM) formation respond to the precursor concentrations in the presence of biogenic emissions (BEs) and thereby changes in health effects can be a key step to design effective air quality management plans. This is particularly true in the Seoul Metropolitan Area (SMA), where future significant controls of anthropogenic sources of O3 and PM2.5 precursors are expected. In this paper, we investigate the effects of BEs on O3 and fine PM (PM2.5) concentrations during a strong photochemical air pollution season in the SMA in Korea. O3 and PM2.5 levels are modeled with and without BEs in June 2008. Further, we perform the health impact assessments (HIA) of O3 and PM2.5 concentration changes due to BEs to seek useful implications for air quality management by utilizing the adjusted exposure concentration fields for O3 and PM2.5 with an observation fusing (OBF) method. With BEs, daily maximum 8-h average O3 (maximum 8-h O3) and secondary organic aerosol (SOA) concentrations in the SMA increase by 17 and 474%, respectively. These increments are associated with significant consumption of photochemical oxidants (Ox), such as a ~ 60% reduction in OH∙ radicals. The reduction in Ox, conversely, lowers the production of secondary inorganic aerosols (SIOAs) by 2.7%. Adjusted O3 and PM2.5 exposure metrics and the subsequent HIA reveal that large mean increments of O3, about 8.43 ppb, due to BEs are responsible for approximately 62 all-cause premature mortalities in the SMA in June. However, mean increment of PM2.5 due to BEs is approximately 0.3 μg m−3 and results in negligible impacts on the all-cause mortality. Significant correlations of O3 and mortality rates (MR) with the VOC/NOx ratios across the SMA suggest that controlling volatile organic compounds (VOCs) from anthropogenic sources can be a priority to reduce O3 levels and population health risks in the SMA. Specifically, linear relationships of log [O3] and log [MR] to log [VOC/NOx] ensure that a 10% decrease in the VOC/NOx ratios through the VOC abatements would lead to a 1.5% decrease in the O3 levels and a 4.3% decrease in the MR on average across the SMA.

Fast economic growth and urbanization of China have been causing air pollution not only over its domestic but transboundary atmosphere. Recent high fine particle pollution episodes in China made the government move toward more stringent air pollution control policies - which are mostly fuel switching and emissions control. In this research, we tried to understand characteristics of Chinese emissions and their change by analyzing its emissions inventory by year, sector, and region. From the inter-comparison of existing bottom-up emission inventories, we found relatively good agreements (