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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.

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.

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.

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.

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.

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.

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.

Topic 3 of the Model Inter-Comparison Study for Asia (MICS-Asia) Phase III examines how online coupled air quality models perform in simulating high aerosol pollution in the North China Plain region during wintertime haze events and evaluates the importance of aerosol radiative and microphysical feedbacks. This paper discusses the estimates of aerosol radiative forcing, aerosol feedbacks, and possible causes for the differences among the models. Over the Beijing-Tianjin-Hebei (BTH) region, the ensemble mean of aerosol direct radiative forcing (ADRF) at the top of atmosphere, inside the atmosphere and at the surface are −1.9, 8.4 and −10.3 W/m2, respectively. Subdivisions of direct and indirect aerosol radiative forcing confirm the dominant roles of direct forcing. During severe haze days (January 17–19, 2010), the averaged reduction in near surface temperature for the BTH region can reach 0.3–3.0 ºC. The responses of wind speeds at 10 m (WS10) inferred from different models show consistent declines in eastern China. For the BTH region, aerosol-radiation feedback induced changes in PM2.5 range from 6.0 to 8.8 µg/m3 (2.5.

Long-term ozone (O3) and nitric oxide (NOx) from fourteen state-of-the-art chemical transport models are evaluated and intercompared to ozone observations in East Asia, within the framework of the Model Inter-Comparison Study for ASIA phase III (MICS-ASIA III), designed to evaluate the capabilities and uncertainties of current chemical transport models (CTMs) simulations in Asia and provide multi-model estimates of pollution distributions. These models were run by fourteen independent groups in China, Japan, Korea, United States of America and other countries/regions. Compared with MICS-ASIAII, the evaluation against observations were extended to be one-full year in China and the western Pacific Rim from four months and the western Pacific Rim. Potential causes of the discrepancies between model results and observation have also been investigated by assessing the PBL heights, emission fluxes, dry deposition, O3-NOx relationships and vertical profiles among models in this study. In general, the model skills for O3 varied largely with region and seasons. Most models captured the key pattern of monthly and diurnal surface O3 and its precursors in North China Plain, Yangtze River Delta and western Pacific rim, but failed in Peral River Delta of China. A significant overestimation of surface ozone was found in May–September/October and January–May over North China Plain, western Pacific rim and Peral River Delta of China, respectively. A large intermodel variability of O3 existed in all subregions over East Asia in this study, which was caused by the internal parameterizations of chemistry, dry deposition and vertical mixing of models, even though the native schemes in models are similar. Ensemble average of 13 models on O3 did not always exhibit a superior performance compared to certain individual model, in contrast to its superiority in Europe. This suggested that the spread of ensemble-model values hadn't represented all uncertainties of ozone or most models in MICS-ASIAIII missed key processes. Compared with the previous phase of MICS-ASIA（MICS-ASIAII, this study improved the performance of ozone in March at Japan sites. But it predicted too enhanced surface O3 concentrations at western Japan in July, which has not been found in MICS-ASIAII. Major challenges still remain with regards to recognizing the sources of bias in surface O3 over East Asia in CTMs.

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.

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 plus two higher resolution forecasts (16-km 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-km and 9-km CO2 forecasts, and analyses, respectively. The positive CO2 mean bias in the16-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 show a remarkable agreement with observed enhancement ratios of CO with CO2 over Seoul metropolitan and over the West Sea, where East Asian outflows were sampled during the study period. More efficient combustion is observed over Seoul (delta(CO)/delta(CO2) = 9 ppbv/ppmv) compared to the West Sea (delta(CO)/delta(CO2) = 28 ppbv/ppmv). 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, West Sea: ~30 ppbv/ppmv) 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, 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 for CO2 and 3.5 ppbv/hPa for CO) in the morning samples than observed (~0.25 ppmv/hPa for CO2 and 1.7 ppbv/hPa 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 produce better forecasts.

Topic 3 of the Model Inter-Comparison Study for Asia (MICS-Asia) Phase III examines how online coupled air quality models perform in simulating high aerosol pollution in the North China Plain region during wintertime haze events and evaluates the importance of aerosol radiative and microphysical feedbacks. A comprehensive overview of the MICS-ASIA III Topic 3 study design, including descriptions of participating models and model inputs, the experimental designs, and results of model evaluation, are presented. Two winter months (January 2010 and January 2013) were selected as study periods, when severe haze occurred in North China. Simulations were designed to evaluate radiative and microphysical feedbacks, together and separately, relative to simulations without feedbacks. Six modeling groups from China, Korea and the United States submitted results from seven applications of online coupled chemistry-meteorology models. Results are compared to meteorology and air quality measurements, including the Campaign on Atmospheric Aerosol Research Network of China (CARE-China) network, and the Acid Deposition Monitoring Network in East Asia (EANET). The analysis focuses on model evaluations and aerosol effects on meteorology and air quality, and potentially other interesting topics, such as the impacts of model resolutions on aerosol-radiation-weather interactions. The model evaluations for January 2010 show that current online-coupled meteorology-chemistry model can generally well reproduced meteorological features and variations of major air pollutants, including aerosol concentrations. The correlation coefficients between multi-model ensemble mean and observed near-surface temperature, water vapor mixing ratio and wind speeds can reach as high as 0.99, 0.99 and 0.98. The correlation coefficients between multi-model ensemble mean and the CARE-China observed near-surface air pollutants range from 0.51 to 0.94 (0.51 for ozone and 0.94 for PM2.5). However, large discrepancies exist between simulated aerosol chemical compositions from different models, which is due to different parameterizations of chemical reactions. The coefficient of variation (standard deviation divided by average) can reach above 1.3 for sulfate in Beijing, and above 1.6 for nitrate and organic aerosol in coastal regions, indicating these compositions are less consistent from different models. During clean periods, simulated Aerosol Optical Depths (AOD) from different models are consistent, but peak values differ during severe haze event, which can be explained by the differences in simulated inorganic aerosol concentrations and the hygroscopic growth efficiency (affected by varied RH). These results provide some brief senses of how current online-coupled meteorology-chemistry models reproduce severe haze events, and some directions for future model improvements.

We use a 3-D regional atmospheric chemistry transport model (WRF-Chem) to examine ozone dry deposition in East Asia, which is an important but uncertain research area because of insufficient observation and numerical studies focusing on East Asia. Here we compare two widely used dry deposition parameterization schemes, the Wesely and M3DRY schemes, which are used in the WRF-Chem and Community Multiscale Air Quality (CMAQ) models, respectively. Simulated ozone dry deposition velocities with the two schemes under identical meteorological conditions show considerable differences (a factor of 2) owing to surface resistance parameterization discrepancies. Resulting ozone concentrations differ by up to 10 ppbv for a monthly mean in May when the peak ozone typically occurs in East Asia. An evaluation of the simulated dry deposition velocities shows that the Wesely scheme calculates values with more pronounced diurnal variation than the M3DRY and results in a good agreement with the observations. However, we find significant changes in simulated ozone concentrations using the Wesely scheme but with different surface type data sets, indicating the high sensitivity of ozone deposition calculations to the input data. The need is high for observations to constrain the dry deposition parameterization and its input data to improve the use of air quality models for East Asia.

Plant functional type (PFT) distributions affect the results of biogenic emission modeling as well as O3 and particulate matter (PM) simulations using chemistry-transport models (CTMs). This paper analyzes the variations of both surface biogenic volatile organic compound (BVOC) emissions and O3 concentrations due to changes in the PFT distributions in the Seoul Metropolitan Areas, Korea. The Fifth-Generation NCAR/Pennsylvania State Meso-scale Model (MM5)/the Model of Emissions of Gases and Aerosols from Nature (MEGAN)/the Sparse Matrix Operator Kernel Emissions (SMOKE)/the Community Multiscale Air Quality (CMAQ) model simulations were implemented over the Seoul Metropolitan Areas in Korea to predict surface O3 concentrations for the period of 1 May to 31 June 2008. Starting from a performance check of CTM predictions, we consecutively assessed the effects of PFT area deviations on the MEGAN BVOC and CTM O3 predictions, and we further considered the basis of geospatial and statistical analyses. The three PFT data sets considered were (1) the Korean PFT, developed with Korea-specific vegetation database; (2) the CDP PFT, adopted from the community data portal (CDP) of US National Center for Atmospheric Research in the United States (NCAR); (3) MODIS PFT, reclassified from the NASA Terra and Aqua combined land cover products. Although the CMAQ performance check reveals that all of the three different PFT data sets are applicable choices for regulatory modeling practice, noticeable primary data (i.e., PFT and Leaf Area Index (LAI)) was observed to be missing in many geographic locations. Based on the assessed effect of such missing data on CMAQ O3 predictions, we found that this missing data can cause spatially increased bias in CMAQ O3. Thus, it must be resolved in the near future to obtain more accurate biogenic emission and chemistry transport modeling results. Comparisons of MEGAN biogenic emission results with the three different PFT data showed that broadleaf trees (BTs) are the most significant contributor, followed by needleleaf trees (NTs), shrub (SB), and herbaceous plants (HBs) to the total BVOCs. In addition, isoprene from BTs and terpene from NTs were recognized as significant primary and secondary BVOC species in terms of BVOC emissions distributions and O3-forming potentials in the study domain. A geographically weighted regression analysis with locally compensated ridge (LCR-GWR) with the different PFT data (δO3 vs. δPFTs) suggests that addition of BT, SB, and NT areas can contribute to O3 increase, whereas addition of an HB area contributes to O3 decrease in the domain. Assessment results of the simulated spatial and temporal changes of O3 distributions with the different PFT scenarios reveal that hourly and local impacts from the different PFT distributions on occasional inter-deviations of O3 are quite noticeable, reaching up to 13 ppb. The simulated maximum 1 h O3 inter-deviations between different PFT scenarios have an asymmetric diurnal distribution pattern (low in the early morning, rising during the day, peaking at 05:00 p.m., and decreasing during the night) in the study domain. Exponentially diverging hourly BVOC emissions and O3 concentrations with increasing ambient temperature suggest that the use of different PFT distribution data requires much caution when modeling (or forecasting) O3 air quality in complicated urban atmospheric conditions in terms of whether uncertainties in O3 prediction results are expected to be mild or severe.

An anthropogenic emission inventory for Asia 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) projects by a mosaic of up-to-date regional emission inventories. Emissions are estimated for all major anthropogenic sources in 30 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. We estimate the total Asian emissions of ten species in 2010 as follows: 51.3 Tg SO2, 52.1 Tg NOx, 336.6 Tg CO, 67.0 Tg NMVOC (non-methane volatile organic compounds), 28.8 Tg NH3, 31.7 Tg PM10, 22.7 Tg PM2.5, 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: −8.0 % for SO2, +19 % for NOx, +4 % for CO, +15 % for NMVOC, +2 % for NH3, −3 % for PM10, −2 % for PM2.5, +6 % for BC, +2 % for OC and +20 % 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.

In this study, NO2 columns from the US EPA Models-3/CMAQ model simulations carried out using the 2001 ACE-ASIA (Asia Pacific Regional Aerosol Characterization Experiment) emission inventory over East Asia were compared with the GOME-derived NO2 columns. There were large discrepancies between the CMAQ-predicted and GOME-derived NO2 columns in the fall and winter seasons. In particular, while the CMAQ-predicted NO2 columns produced larger values than the GOME-derived NO2 columns over South Korea for all four seasons, the CMAQ-predicted NO2 columns produced smaller values than the GOME-derived NO2 columns over North China for all seasons with the exception of summer (summer anomaly). It is believed that there might be some error in the NOx emission estimates as well as uncertainty in the NOx chemical loss rates over North China and South Korea. Regarding the latter, this study further focused on the biogenic VOC (BVOC) emissions that were strongly coupled with NOx chemistry during summer in East Asia. This study also investigated whether the CMAQ-modeled NO2/NOx ratios with the possibly overestimated isoprene emissions were higher than those with reduced isoprene emissions. Although changes in both the NOx chemical loss rates and NO2/NOx ratios from CMAQ-modeling with the different isoprene emissions affected the CMAQ-modeled NO2 levels, the effects were found to be limited, mainly due to the low absolute levels of NO2 in summer. Seasonal variations of the NOx emission fluxes over East Asia were further investigated by a set of sensitivity runs of the CMAQ model. Although the results still exhibited the summer anomaly possibly due to the uncertainties in both NOx-related chemistry in the CMAQ model and the GOME measurements, it is believed that consideration of both the seasonal variations in NOx emissions and the correct BVOC emissions in East Asia are critical. Overall, it is estimated that the NOx emissions are underestimated by ~57.3% in North China and overestimated by ~46.1% in South Korea over an entire year. In order to confirm the uncertainty in NOx emissions, the NOx emissions over South Korea and China were further investigated using the ACE-ASIA, REAS (Regional Emission inventory in ASia), and CAPSS (Clean Air Policy Support System) emission inventories. The comparison between the CMAQ-calculated and GOME-derived NO2 columns indicated that both the ACE-ASIA and REAS inventories have some uncertainty in NOx emissions over North China and South Korea, which can also lead to some errors in modeling the formation of ozone and secondary aerosols in South Korea and North China.

Potential impacts of global climate and emissions changes on regional air quality over southern (western and eastern) Canada and northern Mexico are examined by comparing future summers' (i.e., 2049–2051) average regional O3 and PM2.5 concentrations with historic concentrations (i.e., 2000–2002 summers). Air quality modeling was conducted using CMAQ and meteorology downscaled from the GISS-GCM using MM5. Emissions for North America are found using US EPA, Mexican and Canadian inventories and projected emissions following CAIR and IPCC A1B emissions scenario. Higher temperatures for all sub-regions and regional changes in mixing height, insolation and precipitation are forecast in the 2049-2051 period. Future emissions are calculated to be lower over both Canadian sub-regions, but higher over northern Mexico. Global climate change, alone, is predicted to affect PM2.5 concentrations more than O3 for the projections used in this study: average daily maximum eight (8) hour O3 (M8hO3) concentrations are estimated to be slightly different in all examined sub-regions while average PM2.5 concentrations are estimated to be higher over both Canadian sub-regions (8% over western and 3% over eastern) but 11% lower over northern Mexico. More days are forecast where M8hO3 concentrations are over 75 ppb in all examined sub-regions but the number of days where PM2.5 concentration will be over 15 μg/m3 is projected higher only over western Canada. Climate change combined with the projected emissions lead to greater change in pollutant concentrations: average M8hO3 concentrations are simulated to be 6% lower over western Canada and 8% lower over eastern Canada while average PM2.5 concentrations are simulated to be 5% lower over western Canada and 11% lower over eastern Canada. Although future emissions over northern Mexico are projected higher, pollutant concentrations are simulated to be lower due to US emissions reductions. Global climate change combined with the projected emissions will decrease average M8hO3 4% and PM2.5 17% over northern Mexico. Significant reductions in the number of days where M8hO3 concentrations are over 75 ppb and PM2.5 concentration over 15 μg/m3 are also projected with a significant reduction in peak values.