The MIX inventory is developed for the years 2008 and 2010 to support the Model Inter-Comparison Study for Asia (MICS-Asia) and the Task Force on Hemispheric Transport of Air Pollution (TF HTAP) by a mosaic of up-to-date regional emission inventories. Emissions are estimated for all major anthropogenic sources in 29 countries and regions in Asia. We conducted detailed comparisons of different regional emission inventories and incorporated the best available ones for each region into the mosaic inventory at a uniform spatial and temporal resolution. Emissions are aggregated to five anthropogenic sectors: power, industry, residential, transportation, and agriculture. We estimate the total Asian emissions of 10 species in 2010 as follows: 51.3 Tg SO₂, 52.1 Tg NO_x, 336.6 Tg CO, 67.0 Tg NMVOC (non-methane volatile organic compounds), 28.8 Tg NH₃, 31.7 Tg PM₁₀, 22.7 Tg PM_2.5, 3.5 Tg BC, 8.3 Tg OC, and 17.3 Pg CO₂. Emissions from China and India dominate the emissions of Asia for most of the species. We also estimated Asian emissions in 2006 using the same methodology of MIX. The relative change rates of Asian emissions for the period of 2006–2010 are estimated as follows: −8.1 % for SO₂, +19.2 % for NO_x, +3.9 % for CO, +15.5 % for NMVOC, +1.7 % for NH₃, −3.4 % for PM₁₀, −1.6 % for PM_2.5, +5.5 % for BC, +1.8 % for OC, and +19.9 % for CO₂. Model-ready speciated NMVOC emissions for SAPRC-99 and CB05 mechanisms were developed following a profile-assignment approach. Monthly gridded emissions at a spatial resolution of 0.25° × 0.25° are developed and can be accessed from http://www.meicmodel.org/dataset-mix.

Emissions of air pollutants in East Asia play an important role in the regional and global atmospheric environment. In this study we evaluated the recent emission trends of sulfur dioxide (SO₂), nitrogen oxides (NO_x), particulate matter (PM), and non-methane volatile organic compounds (NMVOC) in East Asia, and projected their future emissions up until 2030 with six emission scenarios. The results will provide future emission projections for the modeling community of the model inter-comparison program for Asia (MICS-Asia). During 2005–2010, the emissions of SO₂ and PM_2.5 in East Asia decreased by 15 and 12%, respectively, mainly attributable to the large-scale deployment of flue gas desulfurization (FGD) at China's power plants, and the promotion of highly efficient PM removal technologies in China's power plants and cement industry. During this period, the emissions of NO_x and NMVOC increased by 25 and 15%, driven by rapid increase in the emissions from China due to inadequate control strategies. In contrast, the NO_x and NMVOC emissions in East Asia except China decreased by 13–17%, mainly due to the implementation of stringent vehicle emission standards in Japan and South Korea. Under current regulations and current levels of implementation, NO_x, SO₂, and NMVOC emissions in East Asia are projected to increase by about one-quarter over 2010 levels by 2030, while PM_2.5 emissions are expected to decrease by 7%. Assuming enforcement of new energy-saving policies, emissions of NO_x, SO₂, PM_2.5 and NMVOC in East Asia are expected to decrease by 28, 36, 28, and 15%, respectively, compared with the baseline case. The implementation of "progressive" end-of-pipe control measures would lead to another one-third reduction of the baseline emissions of NO_x, and about one-quarter reduction of SO₂, PM_2.5, and NMVOC. Assuming the full application of technically feasible energy-saving policies and end-of-pipe control technologies, the emissions of NO_x, SO₂, and PM_2.5 in East Asia would account for only about one-quarter, and NMVOC for one-third, of the levels of the baseline projection. Compared with previous projections, this study projects larger reductions in NO_x and SO₂ emissions by considering aggressive governmental plans and standards scheduled to be implemented in the next decade, and quantifies the significant effects of detailed progressive control measures on NMVOC emissions up until 2030.

This study, performed under the umbrella of the Task Force on Hemispheric Transport of Air Pollution (TF-HTAP), responds to the global and regional atmospheric modelling community's need of 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 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 South 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, NO_x, CO, non-methane volatile organic compounds (NMVOCs), NH₃, PM₁₀, PM_2.5, black carbon (BC), and organic carbon (OC), with a spatial resolution of 0.1^∘ × 0.1^∘ and time intervals of months and years, covering the period 2000–2018 (https://doi.org/10.5281/zenodo.7516361, Crippa, 2023, https://edgar.jrc.ec.europa.eu/dataset_htap_v3, last access: June 2023). The emissions are further disaggregated into 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 2 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 policymaking, reflecting the community effort expended by the TF-HTAP to disentangle the complexity of transboundary transport of air pollution.

Organic aerosol (OA) is an important fraction of submicron aerosols. However, it is challenging to predict and attribute the specific organic compounds and sources that lead to observed OA loadings, largely due to contributions from secondary production. This is especially true for megacities surrounded by numerous regional sources that create an OA background. Here, we utilize in situ gas and aerosol observations collected on board the NASA DC-8 during the NASA–NIER KORUS-AQ (Korea–United States Air Quality) campaign to investigate the sources and hydrocarbon precursors that led to the secondary OA (SOA) production observed over Seoul. First, we investigate the contribution of transported OA to total loadings observed over Seoul by using observations over the Yellow Sea coupled to FLEXPART Lagrangian simulations. During KORUS-AQ, the average OA loading advected into Seoul was ∼1–3 µg sm⁻³. Second, taking this background into account, the dilution-corrected SOA concentration observed over Seoul was ∼140 $\mathrm{\mu }\mathrm{g}{\mathrm{sm}}^{-\mathrm{3}}{\mathrm{ppmv}}^{-\mathrm{1}}$ at 0.5 equivalent photochemical days. This value is at the high end of what has been observed in other megacities around the world (20–70 $\mathrm{\mu }\mathrm{g}{\mathrm{sm}}^{-\mathrm{3}}{\mathrm{ppmv}}^{-\mathrm{1}}$ at 0.5 equivalent days). For the average OA concentration observed over Seoul (13 µg sm⁻³), it is clear that production of SOA from locally emitted precursors is the major source in the region. The importance of local SOA production was supported by the following observations. (1) FLEXPART source contribution calculations indicate any hydrocarbons with a lifetime of less than 1 day, which are shown to dominate the observed SOA production, mainly originate from South Korea. (2) SOA correlated strongly with other secondary photochemical species, including short-lived species (formaldehyde, peroxy acetyl nitrate, sum of acyl peroxy nitrates, dihydroxytoluene, and nitrate aerosol). (3) Results from an airborne oxidation flow reactor (OFR), flown for the first time, show a factor of 4.5 increase in potential SOA concentrations over Seoul versus over the Yellow Sea, a region where background air masses that are advected into Seoul can be measured. (4) Box model simulations reproduce SOA observed over Seoul within 11 % on average and suggest that short-lived hydrocarbons (i.e., xylenes, trimethylbenzenes, and semi-volatile and intermediate-volatility compounds) were the main SOA precursors over Seoul. Toluene alone contributes 9 % of the modeled SOA over Seoul. Finally, along with these results, we use the metric ΔOA/ΔCO₂ to examine the amount of OA produced per fuel consumed in a megacity, which shows less variability across the world than ΔOA∕ΔCO.

Global coupled chemistry–climate models underestimate carbon monoxide (CO) in the Northern Hemisphere, exhibiting a pervasive negative bias against measurements peaking in late winter and early spring. While this bias has been commonly attributed to underestimation of direct anthropogenic and biomass burning emissions, chemical production and loss via OH reaction from emissions of anthropogenic and biogenic volatile organic compounds (VOCs) play an important role. Here we investigate the reasons for this underestimation using aircraft measurements taken in May and June 2016 from the Korea–United States Air Quality (KORUS-AQ) experiment in South Korea and the Air Chemistry Research in Asia (ARIAs) in the North China Plain (NCP). For reference, multispectral CO retrievals (V8J) from the Measurements of Pollution in the Troposphere (MOPITT) are jointly assimilated with meteorological observations using an ensemble adjustment Kalman filter (EAKF) within the global Community Atmosphere Model with Chemistry (CAM-Chem) and the Data Assimilation Research Testbed (DART). With regard to KORUS-AQ data, CO is underestimated by 42 % in the control run and by 12 % with the MOPITT assimilation run. The inversion suggests an underestimation of anthropogenic CO sources in many regions, by up to 80 % for northern China, with large increments over the Liaoning Province and the North China Plain (NCP). Yet, an often-overlooked aspect of these inversions is that correcting the underestimation in anthropogenic CO emissions also improves the comparison with observational O₃ datasets and observationally constrained box model simulations of OH and HO₂. Running a CAM-Chem simulation with the updated emissions of anthropogenic CO reduces the bias by 29 % for CO, 18 % for ozone, 11 % for HO₂, and 27 % for OH. Longer-lived anthropogenic VOCs whose model errors are correlated with CO are also improved, while short-lived VOCs, including formaldehyde, are difficult to constrain solely by assimilating satellite retrievals of CO. During an anticyclonic episode, better simulation of O₃, with an average underestimation of 5.5 ppbv, and a reduction in the bias of surface formaldehyde and oxygenated VOCs can be achieved by separately increasing by a factor of 2 the modeled biogenic emissions for the plant functional types found in Korea. Results also suggest that controlling VOC and CO emissions, in addition to widespread NO_x controls, can improve ozone pollution over East Asia.

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.