A bottom-up emissions inventory is one of the most important data sets needed to understand air quality (AQ) and climate change (CC). Several emission inventories have been developed for Asia, including Transport and Chemical Evolution over the Pacific (TRACE-P), Regional Emission Inventory in Asia (REAS), and Inter-Continental Chemical Transport Experiment (INTEX) and, while these have been used successfully for many international studies, they have limitations including restricted amounts of information on pollutant types and low levels of transparency with respect to the polluting sectors or fuel types involved. To address these shortcomings, we developed: (1) a base-year, bottom-up anthropogenic emissions inventory for Asia, using the most current parameters and international frameworks (i.e., the Greenhouse gas—Air pollution INteractions and Synergies (GAINS) model); and (2) a base-year, natural emissions inventory for biogenic and biomass burning. For (1), we focused mainly on China, South Korea, and Japan; however, we also covered emission inventories for other regions in Asia using data covering recent energy/industry statistics, emission factors, and control technology penetration. The emissions inventory (Comprehensive Regional Emissions inventory for Atmospheric Transport Experiment (CREATE)) covers 54 fuel classes, 201 subsectors, and 13 pollutants, namely SO₂, NO_x, CO, non-methane volatile organic compounds (NMVOC), NH₃, OC, BC, PM₁₀, PM_2.5, CO₂, CH₄, N₂O, and Hg. For the base-year natural emissions inventory, the Model of Emissions of Gases and Aerosols from Nature (MEGAN) and BlueSky-Asia frameworks were used to estimate biogenic and biomass burning emissions, respectively. Since the CREATE emission inventory was designed/developed using international climate change/air quality (CC/AQ) assessment frameworks, such as GAINS, and has been fully connected with the most comprehensive emissions modeling systems—such as the US Environmental Protection Agency (EPA) Chemical Manufacturing Area Source (CMAS) system—it can be used to support various climate and AQ integrated modeling studies, both now and in the future.

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.

Urban photochemical ozone (O₃) formation regimes (NO_x- and VOC-limited regimes) at nine megacities in East Asia were diagnosed based on near-surface O₃ columns from 900 to 700 hPa, nitrogen dioxide (NO₂), and formaldehyde (HCHO), which were inferred from measurements by ozone-monitoring instruments (OMI) for 2014–2018. The nine megacities included Beijing, Tianjin, Hebei, Shandong, Shanghai, Seoul, Busan, Tokyo, and Osaka. The space-borne HCHO–to–NO₂ ratio (FNR) inferred from the OMI was applied to nine megacities and verified by a series of sensitivity tests of Weather Research and Forecasting model with Chemistry (WRF-Chem) simulations by halving the NO_x and VOC emissions. The results showed that the satellite-based FNRs ranged from 1.20 to 2.62 and the regimes over the nine megacities were identified as almost NO_x-saturated conditions, while the domain-averaged FNR in East Asia was >2. The results of WRF–Chem sensitivity modeling show that O₃ increased when the NO_x emissions reduced, whereas VOC emission reduction showed a significant decrease in O₃, confirming the characteristics of VOC-limited conditions in all of the nine megacities. When both NOx and VOC emissions were reduced, O₃ decreased in most cities, but increased in the three lowest-FNRs megacities, such as Shanghai, Seoul, and Tokyo, where weakened O₃ titration caused by NOx reduction had a larger enough effect to offset O₃ suppression induced by the decrease in VOCs. Our model results, therefore, indicated that the immediate VOC emission reduction is a key controlling factor to decrease megacity O₃ in East Asia, and also suggested that both VOC and NO_x reductions may not be of broad utility in O₃ abatement in megacities and should be considered judiciously in highly NO_x-saturated cities in East Asia.

Nitrogen oxide (NO_x) emissions from the South Korean cement industry are investigated with remote-sensing measurements, surface observations, and in situ aircraft measurements. In the Yeongwol, Danyang, and Jecheon regions of central South Korea, six closely located cement factories produce 31 million tons of cement annually. Their impact on the regional environment has been a public-policy issue, but their pollutants have not been continuously monitored nor have emissions inventories been fully verified. Using a newly developed downscaling technique, remote-sensing analyses show that Ozone Monitoring Instrument (OMI) NO₂ column densities over the cement kilns have more than twice the modeled concentrations, indicating that the kilns are one of the most dominant NO_x emission point sources in South Korea. Observed NO_x emissions are stronger in the spring, suggesting that these sources play an important role in the formation of surface ozone and secondary particulate matter. These emissions also slightly increased in recent years, even while most major South Korean cities posted a declining trend in NO_x emissions. Photochemical models (during May to July 2015) demonstrate that emissions from the South Korean cement industry have significant environmental impacts, both on surface ozone (up to approximately 4 ppb) and PM_2.5 (up to approximately 2 µg/m³).

Despite recent progress of numerical air quality models, accurate prediction of fine particulate matter (PM_2.5) is still challenging because of uncertainties in physical and chemical parameterizations, meteorological data, and emission inventory databases. Recent advances in artificial neural networks can be used to overcome limitations in numerical air quality models. In this study, a deep neural network (DNN) model was developed for a 3 d forecasting of 6 h average PM_2.5 concentrations: the day of prediction (D+0), 1 d after prediction (D+1), and 2 d after prediction (D+2). The DNN model was evaluated against the currently operational Community Multiscale Air Quality (CMAQ) modeling system in South Korea. Our study demonstrated that the DNN model outperformed the CMAQ modeling results. The DNN model provided better forecasting skills by reducing the root-mean-squared error (RMSE) by 4.1, 2.2, and 3.0 µg m⁻³ for the 3 consecutive days, respectively, compared with the CMAQ. Also, the false-alarm rate (FAR) decreased by 16.9 %p (D+0), 7.5 %p (D+1), and 7.6 %p (D+2), indicating that the DNN model substantially mitigated the overprediction of the CMAQ in high PM_2.5 concentrations. These results showed that the DNN model outperformed the CMAQ model when it was simultaneously trained by using the observation and forecasting data from the numerical air quality models. Notably, the forecasting data provided more benefits to the DNN modeling results as the forecasting days increased. Our results suggest that our data-driven machine learning approach can be a useful tool for air quality forecasting when it is implemented with air quality models together by reducing model-oriented systematic biases.

Biogenic volatile organic compound (BVOCs) emissions are the largest VOC emission source globally, and are precursors to ozone and secondary organic aerosols, both of which are strong, short-lived climate pollutants. BVOC emissions are usually estimated using the Model of Emissions of Gases and Aerosols from Nature (MEGAN), which requires Plant Functional Types (PFTs) and Leaf Area Indexes (LAIs) as inputs. Herein, the effects of refined input data on regional BVOC emission estimates are analyzed. For LAIs, lower resolution MODerate-resolution Imaging Spectroradiometer (MODIS), and higher spatio-temporal resolution Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) LAI were generated. For PFTs, local land cover maps were developed, in addition to MODIS PFT. In South Korea, annual emissions of isoprene and monoterpenes in 2015 were estimated as 384 and 160 Gg/year, respectively, using STARFM LAI and Local PFT (Case 4). For North Korea, 340 Gg/year isoprene and 72 Gg/year monoterpenes emissions were estimated using STARFM LAI and MODIS PFT. These estimates were 14–110% higher than when using MODIS LAI and MODIS PFT (Case 1). Inter-comparison with satellite-based inverse isoprene emission estimates from GlobEmission shows 32% (North Korea) to 34% (South Korea) overestimation in bottom-up data. Our new vegetation inputs improve MEGAN performance and resulting BVOC emission estimations. Performance of Weather Research and Forecasting (WRF) meteorological modeling requires improvement, especially for solar radiation, to avoid overestimation of isoprene emissions.