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.

A new aerosol microphysical module MATRIX, the Multiconfiguration Aerosol TRacker of mIXing state, and its application in the Goddard Institute for Space Studies (GISS) climate model (ModelE) are described. This module, which is based on the quadrature method of moments (QMOM), represents nucleation, condensation, coagulation, internal and external mixing, and cloud-drop activation and provides aerosol particle mass and number concentration and particle size information for up to 16 mixed-mode aerosol populations. Internal and external mixing among aerosol components sulfate, nitrate, ammonium, carbonaceous aerosols, dust and sea-salt particles are represented. The solubility of each aerosol population, which is explicitly calculated based on its soluble and insoluble components, enables calculation of the dependence of cloud drop activation on the microphysical characterization of multiple soluble aerosol populations. A detailed model description and results of box-model simulations of various aerosol population configurations are presented. The box model experiments demonstrate the dependence of cloud activating aerosol number concentration on the aerosol population configuration; comparisons to sectional models are quite favorable. MATRIX is incorporated into the GISS climate model and simulations are carried out primarily to assess its performance/efficiency for global-scale atmospheric model application. Simulation results were compared with aircraft and station measurements of aerosol mass and number concentration and particle size to assess the ability of the new method to yield data suitable for such comparison. The model accurately captures the observed size distributions in the Aitken and accumulation modes up to particle diameter 1 μm, in which sulfate, nitrate, black and organic carbon are predominantly located; however the model underestimates coarse-mode number concentration and size, especially in the marine environment. This is more likely due to oversimplifications of the representation of sea salt emissions – sea salt emissions are only calculated for two size classes – than to inherent limitations of MATRIX.

High levels of fine particulate matter (PM_2.5) 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 PM_2.5. Atmospheric models often have difficulty simulating PM_2.5 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 PM_2.5 composition throughout the campaign and identify the mechanisms driving the pollution event. At the surface, the model underestimates sulfate by −64 % but overestimates nitrate by +36 %. The largest underestimate in sulfate occurs during the pollution event, for which models typically struggle to generate elevated sulfate 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 large missing sink, which we implement here as a factor of 5 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 PM_2.5 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 PM_2.5. During the haze pollution event, the inclusion of heterogeneous aerosol uptake of SO₂ decreases the fraction of PM_2.5 attributable to long-range transport from 66 % to 54 %. Locally produced sulfate increased from 1 % to 25 % of locally produced PM_2.5, implying that local emissions controls could 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 this 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 nighttime 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 PM_2.5 in South Korea to ensure continued air quality improvements.

Space-borne ozone (O₃) measurements have indicated consistent positive trends across the entire Asia–Pacific region despite the considerable reduction of NO_x since 2000s. The rate of increase in O₃ derived from lower free tropospheric column measurements was observed to be 0.21 ± 0.05 DU/decade during 2005–2018. Our space-borne-based diagnosis of the nonlinear photochemical formation regimes, NO_x-limited and NO_x-saturated, show that O₃ chemistry is undergoing a transitional process to the NO_x-limited regime throughout most of the Asian region. Nevertheless, NO_x-saturated conditions persist at present in and over eight major megacities. These NO_x-saturated conditions in megacities contribute to the increased O₃ due to NO_x reduction, which could also affect the enhanced O₃ concentrations throughout the Asia–Pacific region via long-range transport. This indicates that VOC limits along with NO_x reductions are needed in megacities in the short term to reduce O₃ levels. Moreover, NO_x saturation in major megacities will continue until 2025, according to the forecast emission scenarios from the Intergovernmental Panel on Climate Change (IPCC). These scenarios gradually shift nearly all cities to the NO_x-limited regime by 2050 with the exception of few cities under IPCC RCP8.5. Thus, continued reductions in NO_x will be a key factor in reducing O₃ in the long term.

The Geostationary Environmental Monitoring Spectrometer (GEMS) is a UV-visible (UV-Vis) spectrometer on board the GEO-KOMPSAT-2B (Geostationary Korea Multi-Purpose Satellite 2B) satellite launched into a geostationary orbit in February 2020. To evaluate the GEMS NO₂ total column data, a comparison was carried out using the NO₂ vertical column density (VCD) that measured direct sunlight using the Pandora spectrometer system at four sites in Seosan, South Korea, from November 2020 to January 2021. Correlation coefficients between GEMS and Pandora NO₂ data at four sites ranged from 0.35 to 0.48, with root mean square errors (RMSEs) from 4.7×10¹⁵ to 5.5×10¹⁵ molec. cm⁻² for a cloud fraction (CF) <0.7. Higher correlation coefficients of 0.62–0.78 with lower RMSEs from 3.3×10¹⁵ to 5.0×10¹⁵ molec. cm⁻² were found with CF <0.3, indicating the higher sensitivity of GEMS to atmospheric NO₂ in less cloudy conditions. Overall, the GEMS NO₂ total column data tended to be lower than the Pandora data, owing to differences in the representative spatial coverage, with a large negative bias under high CF conditions. With a correction for horizontal representativeness in the Pandora measurement coverage, correlation coefficients ranging from 0.69 to 0.81, with RMSEs from 3.2×10¹⁵ to 4.9×10¹⁵ molec. cm⁻², were achieved for CF <0.3, showing a better correlation with the correction than without the correction.

Comparison between the CMAQ (Community Multi-scale Air Quality Model)-calculated and OMI (Ozone Monitoring Instrument)-retrieved tropospheric NO₂ columns was carried out for 2006 over East Asia (100–150° E; 20–50° N) to evaluate the bottom-up NO_x emission fluxes of INTEX-B, CAPSS, and REAS v1.11 inventories. The three emission inventories were applied to the CMAQ model simulations for the countries of China, South Korea, and Japan, respectively. For the direct comparison between the two NO₂ columns, the averaging kernels (AKs) obtained from the Royal Netherlands Meteorological Institute (KNMI)/DOMINO v2.0 daily product were applied to the CMAQ-simulated data. The analysis showed that the two tropospheric NO₂ columns from the CMAQ model simulations and OMI observations (Ω_CMAQ,AK and Ω_OMI) had good spatial and seasonal correlation, with correlation coefficients ranging from 0.71 to 0.96. In addition, the normalized mean errors (NMEs) between the Ω_CMAQ,AK and Ω_OMI were found to range from ~ 40 to ~ 63%. The Ω_CMAQ,AK were, on annual average, ~ 28% smaller (in terms of the NMEs) than the Ω_OMI, indicating that the NO_x emissions used were possibly underestimated in East Asia. Large absolute differences between the Ω_CMAQ,AK and Ω_OMI were found, particularly over central eastern China (CEC) during winter (annual averaged mean error of ~ 4.51 × 10¹⁵ molecules cm⁻²). Although such differences between the Ω_CMAQ,AK and Ω_OMI are likely caused by the errors and biases in the NO_x emissions used in the CMAQ model simulations, it can be rather difficult to quantitatively relate the differences to the accuracy of the NO_x emissions, because there are also several uncertain factors in the CMAQ model, satellite-retrieved NO₂ columns and AK products, and NO_x and other trace gas emissions. In this context, three uncertain factors were selected and analyzed with sensitivity runs (monthly variations in NO_x emissions; influences of different NO_x emission fluxes; and reaction probability of N₂O₅ radicals). Other uncertain or possible influential factors were also discussed to suggest future direction of the study.