scholarly journals Application of WRF/Chem-MADRID and WRF/Polyphemus in Europe – Part 2: Evaluation of chemical concentrations and sensitivity simulations

2013 ◽  
Vol 13 (14) ◽  
pp. 6845-6875 ◽  
Author(s):  
Y. Zhang ◽  
K. Sartelet ◽  
S. Zhu ◽  
W. Wang ◽  
S.-Y. Wu ◽  
...  

Abstract. An offline-coupled model (WRF/Polyphemus) and an online-coupled model (WRF/Chem-MADRID) are applied to simulate air quality in July 2001 at horizontal grid resolutions of 0.5° and 0.125° over Western Europe. The model performance is evaluated against available surface and satellite observations. The two models simulate different concentrations in terms of domainwide performance statistics, spatial distribution, temporal variations, and column abundance. WRF/Chem-MADRID at 0.5° gives higher values than WRF/Polyphemus for the domainwide mean and over polluted regions in Central and southern Europe for all surface concentrations and column variables except for the tropospheric ozone residual (TOR). Compared with observations, WRF/Polyphemus gives better statistical performance for daily HNO3, SO2, and NO2 at the European Monitoring and Evaluation Programme (EMEP) sites, maximum 1 h O3 at the AirBase sites, PM2.5 at the AirBase sites, maximum 8 h O3 and PM10 composition at all sites, column abundance of CO, NO2, TOR, and aerosol optical depth (AOD), whereas WRF/Chem-MADRID gives better statistical performance for NH3, hourly SO2, NO2, and O3 at the AirBase and BDQA (Base de données de la qualité de l'air) sites, maximum 1 h O3 at the BDQA and EMEP sites, and PM10 at all sites. WRF/Chem-MADRID generally reproduces well the observed high hourly concentrations of SO2 and NO2 at most sites except for extremely high episodes at a few sites, and WRF/Polyphemus performs well for hourly SO2 concentrations at most rural or background sites where pollutant levels are relatively low, but it underpredicts the observed hourly NO2 concentrations at most sites. Both models generally capture well the daytime maximum 8 h O3 concentrations and diurnal variations of O3 with more accurate peak daytime and minimal nighttime values by WRF/Chem-MADRID, but neither model reproduces extremely low nighttime O3 concentrations at several urban and suburban sites due to underpredictions of NOx and thus insufficient titration of O3 at night. WRF/Polyphemus gives more accurate concentrations of PM2.5, and WRF/Chem-MADRID reproduces better the observations of PM10 concentrations at all sites. The differences between model predictions and observations are mostly caused by inaccurate representations of emissions of gaseous precursors and primary PM species, as well as biases in the meteorological predictions. The differences in model predictions are caused by differences in the heights of the first model layers and thickness of each layer that affect vertical distributions of emissions, model treatments such as dry/wet deposition, heterogeneous chemistry, and aerosol and cloud, as well as model inputs such as emissions of soil dust and sea salt and chemical boundary conditions of CO and O3 used in both models. WRF/Chem-MADRID shows a higher sensitivity to grid resolution than WRF/Polyphemus at all sites. For both models, the use of a finer grid resolution generally leads to an overall better statistical performance for most variables, with greater spatial details and an overall better agreement in temporal variations and magnitudes at most sites. The use of online biogenic volatile organic compound (BVOC) emissions gives better statistical performance for hourly and maximum 8 h O3 and PM2.5 and generally better agreement with their observed temporal variations at most sites. Because it is an online model, WRF/Chem-MADRID offers the advantage of accounting for various feedbacks between meteorology and chemical species. However, this model comparison suggests that atmospheric pollutant concentrations are most sensitive in state-of-the-science air quality models to vertical structure, inputs, and parameterizations for dry/wet removal of gases and particles in the model.

2013 ◽  
Vol 13 (2) ◽  
pp. 4059-4125 ◽  
Author(s):  
Y. Zhang ◽  
K. Sartelet ◽  
S. Zhu ◽  
W. Wang ◽  
S.-Y. Wu ◽  
...  

Abstract. An offline-coupled model (WRF/Polyphemus) and an online-coupled model (WRF/Chem-MADRID) are applied to simulate air quality in July 2001 at horizontal grid resolutions of 0.5° and 0.125° over western Europe. The model performance is evaluated against available surface and satellite observations. The two models simulate different concentrations in terms of domainwide performance statistics, spatial distribution, temporal variations, and column abundance. WRF/Chem-MADRID at 0.5° gives higher values than WRF/Polyphemus for the domainwide mean and over polluted regions in central and southern Europe for all surface concentrations and column variables except for TOR. Compared with observations, WRF/Polyphemus gives better statistical performance for daily HNO3, SO2, and NO2 at the EMEP sites, max 1-h O3 at the AirBase sites, PM2.5 at the AirBase sites, max 8-h O3 and PM10 composition at all sites, column abundance of CO, NO2, TOR, and AOD, whereas WRF/Chem-MADRID gives better statistical performance for NH3, hourly SO2, NO2, and O3 at the AirBase and BDQA sites, max 1-h O3 at the BDQA and EMEP sites, and PM10 at all sites. WRF/Chem-MADRID generally reproduces well the observed high hourly concentrations of SO2 and NO2 at most sites except for extremely high episodes at a few sites, and WRF/Polyphemus performs well for hourly SO2 concentrations at most rural or background sites where pollutant levels are relatively low, but it underpredicts the observed hourly NO2 concentrations at most sites. Both models generally capture well the daytime max 8-h O3 concentrations and diurnal variations of O3 with more accurate peak daytime and minimal nighttime values by WRF/Chem-MADRID, but neither models reproduce extremely low nighttime O3 concentrations at several urban and suburban sites due to underpredictions of NOx and thus insufficient titration of O3 at night. WRF/Polyphemus gives more accurate concentrations of PM2.5, and WRF/Chem-MADRID reproduces better the observations of PM10 concentrations at all sites. The differences between model predictions and observations are mostly caused by inaccurate representations of emissions of gaseous precursors and primary PM species, as well as biases in the meteorological predictions. The differences in model predictions are caused by differences in the heights of the first model layers and thickness of each layer that affect vertical distributions of emissions, model treatments such as dry/wet deposition, heterogeneous chemistry, and aerosol and cloud, as well as model inputs such as emissions of soil dust and sea-salt and chemical boundary conditions of CO and O3 used in both models. WRF/Chem-MADRID shows a higher sensitivity to grid resolution than WRF/Polyphemus at all sites. For both models, the use of a finer grid resolution generally leads to an overall better statistical performance for most variables, with greater spatial details and an overall better agreement in temporal variations and magnitudes at most sites. The use of online BVOC emissions gives better statistical performance for hourly and max 8-h O3 and PM2.5 and generally better agreement with their observed temporal variations at most sites. Because it is an online model, WRF/Chem-MADRID offers the advantage to account for various feedbacks between meteorology and chemical species. The simulations show that aerosol leads to reduced net shortwave radiation fluxes, 2-m temperature, 10-m wind speed, PBL height, and precipitation and increases aerosol optical depth, cloud condensation nuclei, cloud optical depth, and cloud droplet number concentrations over most of the domain. However, this model comparison suggests that atmospheric pollutant concentrations are most sensitive in state-of-the-science air quality models to vertical structure, inputs, and parameterizations for dry/wet removal of gases and particles in the model.


2019 ◽  
Vol 19 (18) ◽  
pp. 11911-11937 ◽  
Author(s):  
Lei Chen ◽  
Yi Gao ◽  
Meigen Zhang ◽  
Joshua S. Fu ◽  
Jia Zhu ◽  
...  

Abstract. A total of 14 chemical transport models (CTMs) participated in the first topic of the Model Inter-Comparison Study for Asia (MICS-Asia) phase III. These model results are compared with each other and an extensive set of measurements, aiming to evaluate the current CTMs' ability in simulating aerosol concentrations, to document the similarities and differences among model performance, and to reveal the characteristics of aerosol components in large cities over East Asia. In general, these CTMs can well reproduce the spatial–temporal distributions of aerosols in East Asia during the year 2010. The multi-model ensemble mean (MMEM) shows better performance than most single-model predictions, with correlation coefficients (between MMEM and measurements) ranging from 0.65 (nitrate, NO3-) to 0.83 (PM2.5). The concentrations of black carbon (BC), sulfate (SO42-), and PM10 are underestimated by MMEM, with normalized mean biases (NMBs) of −17.0 %, −19.1 %, and −32.6 %, respectively. Positive biases are simulated for NO3- (NMB = 4.9 %), ammonium (NH4+) (NMB = 14.0 %), and PM2.5 (NMB = 4.4 %). In comparison with the statistics calculated from MICS-Asia phase II, frequent updates of chemical mechanisms in CTMs during recent years make the intermodel variability of simulated aerosol concentrations smaller, and better performance can be found in reproducing the temporal variations of observations. However, a large variation (about a factor of 2) in the ratios of SNA (sulfate, nitrate, and ammonium) to PM2.5 is calculated among participant models. A more intense secondary formation of SO42- is simulated by Community Multi-scale Air Quality (CMAQ) models, because of the higher SOR (sulfur oxidation ratio) than other models (0.51 versus 0.39). The NOR (nitric oxidation ratio) calculated by all CTMs has larger values (∼0.20) than the observations, indicating that overmuch NO3- is simulated by current models. NH3-limited condition (the mole ratio of ammonium to sulfate and nitrate is smaller than 1) can be successfully reproduced by all participant models, which indicates that a small reduction in ammonia may improve the air quality. A large coefficient of variation (CV > 1.0) is calculated for simulated coarse particles, especially over arid and semi-arid regions, which means that current CTMs have difficulty producing similar dust emissions by using different dust schemes. According to the simulation results of MMEM in six large Asian cities, different air-pollution control plans should be taken due to their different major air pollutants in different seasons. The MICS-Asia project gives an opportunity to discuss the similarities and differences of simulation results among CTMs in East Asian applications. In order to acquire a better understanding of aerosol properties and their impacts, more experiments should be designed to reduce the diversities among air quality models.


2015 ◽  
Vol 15 (6) ◽  
pp. 3445-3461 ◽  
Author(s):  
J. Hu ◽  
H. Zhang ◽  
Q. Ying ◽  
S.-H. Chen ◽  
F. Vandenberghe ◽  
...  

Abstract. For the first time, a ~ decadal (9 years from 2000 to 2008) air quality model simulation with 4 km horizontal resolution over populated regions and daily time resolution has been conducted for California to provide air quality data for health effect studies. Model predictions are compared to measurements to evaluate the accuracy of the simulation with an emphasis on spatial and temporal variations that could be used in epidemiology studies. Better model performance is found at longer averaging times, suggesting that model results with averaging times ≥ 1 month should be the first to be considered in epidemiological studies. The UCD/CIT model predicts spatial and temporal variations in the concentrations of O3, PM2.5, elemental carbon (EC), organic carbon (OC), nitrate, and ammonium that meet standard modeling performance criteria when compared to monthly-averaged measurements. Predicted sulfate concentrations do not meet target performance metrics due to missing sulfur sources in the emissions. Predicted seasonal and annual variations of PM2.5, EC, OC, nitrate, and ammonium have mean fractional biases that meet the model performance criteria in 95, 100, 71, 73, and 92% of the simulated months, respectively. The base data set provides an improvement for predicted population exposure to PM concentrations in California compared to exposures estimated by central site monitors operated 1 day out of every 3 days at a few urban locations. Uncertainties in the model predictions arise from several issues. Incomplete understanding of secondary organic aerosol formation mechanisms leads to OC bias in the model results in summertime but does not affect OC predictions in winter when concentrations are typically highest. The CO and NO (species dominated by mobile emissions) results reveal temporal and spatial uncertainties associated with the mobile emissions generated by the EMFAC 2007 model. The WRF model tends to overpredict wind speed during stagnation events, leading to underpredictions of high PM concentrations, usually in winter months. The WRF model also generally underpredicts relative humidity, resulting in less particulate nitrate formation, especially during winter months. These limitations must be recognized when using data in health studies. All model results included in the current manuscript can be downloaded free of charge at http://faculty.engineering.ucdavis.edu/kleeman/ .


2014 ◽  
Vol 14 (14) ◽  
pp. 20997-21036
Author(s):  
J. Hu ◽  
H. Zhang ◽  
Q. Ying ◽  
S.-H. Chen ◽  
F. Vandenberghe ◽  
...  

Abstract. For the first time, a decadal (9 years from 2000 to 2008) air quality model simulation with 4 km horizontal resolution and daily time resolution has been conducted in California to provide air quality data for health effects studies. Model predictions are compared to measurements to evaluate the accuracy of the simulation with an emphasis on spatial and temporal variations that could be used in epidemiology studies. Better model performance is found at longer averaging times, suggesting that model results with averaging times ≥ 1 month should be the first to be considered in epidemiological studies. The UCD/CIT model predicts spatial and temporal variations in the concentrations of O3, PM2.5, EC, OC, nitrate, and ammonium that meet standard modeling performance criteria when compared to monthly-averaged measurements. Predicted sulfate concentrations do not meet target performance metrics due to missing sulfur sources in the emissions. Predicted seasonal and annual variations of PM2.5, EC, OC, nitrate, and ammonium have mean fractional biases that meet the model performance criteria in 95%, 100%, 71%, 73%, and 92% of the simulated months, respectively. The base dataset provides an improvement for predicted population exposure to PM concentrations in California compared to exposures estimated by central site monitors operated one day out of every 3 days at a few urban locations. Uncertainties in the model predictions arise from several issues. Incomplete understanding of secondary organic aerosol formation mechanisms leads to OC bias in the model results in summertime but does not affect OC predictions in winter when concentrations are typically highest. The CO and NO (species dominated by mobile emissions) results reveal temporal and spatial uncertainties associated with the mobile emissions generated by the EMFAC 2007 model. The WRF model tends to over-predict wind speed during stagnation events, leading to under-predictions of high PM concentrations, usually in winter months. The WRF model also generally under-predicts relative humidity, resulting in less particulate nitrate formation especially during winter months. These issues will be improved in future studies. All model results included in the current manuscript can be downloaded free of charge at http://faculty.engineering.ucdavis.edu/kleeman/.


2016 ◽  
Author(s):  
Efisio Solazzo ◽  
Roberto Bianconi ◽  
Christian Hogrefe ◽  
Gabriele Curci ◽  
Ummugulsum Alyuz ◽  
...  

Abstract. Through the comparison of several regional-scale chemistry transport modelling systems that simulate meteorology and air quality over the European and American continents, this study aims at i) apportioning the error to the responsible processes using time-scale analysis, ii) helping to detect causes of models error, and iii) identifying the processes and scales most urgently requiring dedicated investigations. The analysis is conducted within the framework of the third phase of the Air Quality Model Evaluation International Initiative (AQMEII) and tackles model performance gauging through measurement-to-model comparison, error decomposition and time series analysis of the models biases for several fields (ozone, CO, SO2, NO, NO2, PM10, PM2.5, wind speed, and temperature). The operational metrics (magnitude of the error, sign of the bias, associativity) provide an overall sense of model strengths and deficiencies, while apportioning the error to its constituent parts (bias, variance and covariance) can help to assess the nature and quality of the error. Each of the error components is analysed independently and apportioned to specific processes based on the corresponding timescale (long scale, synoptic, diurnal, and intra-day) using the error apportionment technique devised in the former phases of AQMEII. The application of the error apportionment method to the AQMEII Phase 3 simulations provides several key insights. In addition to reaffirming the strong impact of model inputs (emissions and boundary conditions) and poor representation of the stable boundary layer on model bias, results also highlighted the high inter-dependencies among meteorological and chemical variables, as well as among their errors. This indicates that the evaluation of air quality model performance for individual pollutants needs to be supported by complementary analysis of meteorological fields and chemical precursors to provide results that are more insightful from a model development perspective. The error embedded in the emissions is dominant for primary species (CO, PM, NO) and largely outweighs the error from any other source. The uncertainty in meteorological fields is most relevant to ozone. Some further aspects emerged whose interpretation requires additional consideration, such as, among others, the uniformity of the synoptic error being region and model-independent, observed for several pollutants; the source of unexplained variance for the diurnal component; and the type of error caused by deposition and at which scale.


2008 ◽  
Vol 8 (22) ◽  
pp. 6627-6654 ◽  
Author(s):  
C. H. Song ◽  
M. E. Park ◽  
K. H. Lee ◽  
H. J. Ahn ◽  
Y. Lee ◽  
...  

Abstract. In this study, the spatio-temporal and seasonal distributions of EOS/Terra Moderate Resolution Imaging Spectroradiometer (MODIS)-derived aerosol optical depth (AOD) over East Asia were analyzed in conjunction with US EPA Models-3/CMAQ v4.3 modeling. In this study, two MODIS AOD products (τMODIS: τM-BAER and τNASA) retrieved through a modified Bremen Aerosol Retrieval (M-BAER) algorithm and NASA collection 5 (C005) algorithm were compared with the AOD (τCMAQ) that was calculated from the US EPA Models-3/CMAQ model simulations. In general, the CMAQ-predicted AOD values captured the spatial and temporal variations of the two MODIS AOD products over East Asia reasonably well. Since τMODIS cannot provide information on the aerosol chemical composition in the atmosphere, different aerosol formation characteristics in different regions and different seasons in East Asia cannot be described or identified by τMODIS itself. Therefore, the seasonally and regionally varying aerosol formation and distribution characteristics were investigated by the US EPA Models-3/CMAQ v4.3 model simulations. The contribution of each particulate chemical species to τMODIS and τCMAQ showed strong spatial, temporal and seasonal variations. For example, during the summer episode, τMODIS and τCMAQ were mainly raised due to high concentrations of (NH4)2SO4 over Chinese urban and industrial centers and secondary organic aerosols (SOAs) over the southern parts of China, whereas during the late fall and winter episodes, τMODIS and τCMAQ were higher due largely to high levels of NH4NO3 formed over the urban and industrial centers, as well as in areas with high NH3 emissions. τCMAQ was in general larger than τMODIS during the year, except for spring. The high biases (τCMAQ>τMODIS) may be due to the excessive formation of both (NH4)2SO4 (summer episode) and NH4NO3 (fall and winter episodes) over China, possibly from the use of overestimated values for NH3 emissions in the CMAQ modeling. According to CMAQ modeling, particulate NH4NO3 made a 14% (summer) to 54% (winter) contribution to σext and τCMAQ. Therefore, the importance of NH4NO3 in estimating τ should not be ignored, particularly in studies of the East Asian air quality. In addition, the accuracy of τM-BAER and τNASA was evaluated by a comparison with the AOD (τAERONET) from the AERONET sites in East Asia. Both τM-BAER and τNASA showed a strong correlation with τAERONET around the 1:1 line (R=0.79), indicating promising potential for the application of both the M-BAER and NASA aerosol retrieval algorithms to satellite-based air quality monitoring studies in East Asia.


Atmosphere ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1344
Author(s):  
Peng Wang ◽  
Lyudmila Mihaylova ◽  
Rohit Chakraborty ◽  
Said Munir ◽  
Martin Mayfield ◽  
...  

The monitoring and forecasting of particulate matter (e.g., PM2.5) and gaseous pollutants (e.g., NO, NO2, and SO2) is of significant importance, as they have adverse impacts on human health. However, model performance can easily degrade due to data noises, environmental and other factors. This paper proposes a general solution to analyse how the noise level of measurements and hyperparameters of a Gaussian process model affect the prediction accuracy and uncertainty, with a comparative case study of atmospheric pollutant concentrations prediction in Sheffield, UK, and Peshawar, Pakistan. The Neumann series is exploited to approximate the matrix inverse involved in the Gaussian process approach. This enables us to derive a theoretical relationship between any independent variable (e.g., measurement noise level, hyperparameters of Gaussian process methods), and the uncertainty and accuracy prediction. In addition, it helps us to discover insights on how these independent variables affect the algorithm evidence lower bound. The theoretical results are verified by applying a Gaussian processes approach and its sparse variants to air quality data forecasting.


2015 ◽  
Vol 15 (5) ◽  
pp. 2387-2404 ◽  
Author(s):  
B. Zhang ◽  
Y. Wang ◽  
J. Hao

Abstract. The aerosol-radiation-cloud feedbacks on meteorology and air quality over eastern China under severe winter haze conditions in January 2013 are simulated using the fully coupled online Weather Research and Forecasting/Chemistry (WRF-Chem) model. Three simulation scenarios including different aerosol configurations are undertaken to distinguish the aerosol's radiative (direct and semi-direct) and indirect effects. Simulated spatial and temporal variations of PM2.5 are generally consistent with surface observations, with a mean bias of −18.9 μg m−3 (−15.0%) averaged over 71 big cities in China. Comparisons between different scenarios reveal that aerosol radiative effects (direct effect and semi-direct effects) result in reductions of downward shortwave flux at the surface, 2 m temperature, 10 m wind speed and planetary boundary layer (PBL) height by up to 84.0 W m−2, 3.2°C, 0.8 m s−1, and 268 m, respectively. The simulated impact of the aerosol indirect effects is comparatively smaller. Through reducing the PBL height and stabilizing lower atmosphere, the aerosol effects lead to increases in surface concentrations of primary pollutants (CO and SO2). Surface O3 mixing ratio is reduced by up to 6.9 ppb (parts per billion) due to reduced incoming solar radiation and lower temperature, while the aerosol feedbacks on PM2.5 mass concentrations show some spatial variations. Comparisons of model results with observations show that inclusion of aerosol feedbacks in the model significantly improves model performance in simulating meteorological variables and improves simulations of PM2.5 temporal distributions over the North China Plain, the Yangtze River delta, the Pearl River delta, and central China. Although the aerosol–radiation–cloud feedbacks on aerosol mass concentrations are subject to uncertainties, this work demonstrates the significance of aerosol–radiation–cloud feedbacks for real-time air quality forecasting under haze conditions.


2021 ◽  
Vol 21 (4) ◽  
pp. 2527-2550
Author(s):  
Youhua Tang ◽  
Huisheng Bian ◽  
Zhining Tao ◽  
Luke D. Oman ◽  
Daniel Tong ◽  
...  

Abstract. The National Air Quality Forecast Capability (NAQFC) operated in the US National Oceanic and Atmospheric Administration (NOAA) provides the operational forecast guidance for ozone and fine particulate matter with aerodynamic diameters less than 2.5 µm (PM2.5) over the contiguous 48 US states (CONUS) using the Community Multi-scale Air Quality (CMAQ) model. The existing NAQFC uses climatological chemical lateral boundary conditions (CLBCs), which cannot capture pollutant intrusion events originating outside of the model domain. In this study, we developed a model framework to use dynamic CLBCs from the Goddard Earth Observing System Model, version 5 (GEOS) to drive NAQFC. A mapping of the GEOS chemical species to CMAQ's CB05–AERO6 (Carbon Bond 5; version 6 of the aerosol module) species was developed. The utilization of the GEOS dynamic CLBCs in NAQFC showed the best overall performance in simulating the surface observations during the Saharan dust intrusion and Canadian wildfire events in summer 2015. The simulated PM2.5 was improved from 0.18 to 0.37, and the mean bias was reduced from −6.74 to −2.96 µg m−3 over CONUS. Although the effect of CLBCs on the PM2.5 correlation was mainly near the inflow boundary, its impact on the background concentrations reached further inside the domain. The CLBCs could affect background ozone concentrations through the inflows of ozone itself and its precursors, such as CO. It was further found that the aerosol optical thickness (AOT) from satellite retrievals correlated well with the column CO and elemental carbon from GEOS. The satellite-derived AOT CLBCs generally improved the model performance for the wildfire intrusion events during a summer 2018 case study and demonstrated how satellite observations of atmospheric composition could be used as an alternative method to capture the air quality effects of intrusions when the CLBCs of global models, such as GEOS CLBCs, are not available.


2020 ◽  
Vol 20 (4) ◽  
pp. 2319-2339 ◽  
Author(s):  
Zhining Tao ◽  
Mian Chin ◽  
Meng Gao ◽  
Tom Kucsera ◽  
Dongchul Kim ◽  
...  

Abstract. Horizontal grid resolution has a profound effect on model performances on meteorology and air quality simulations. In contribution to MICS-Asia Phase III, one of whose goals was to identify and reduce model uncertainty in air quality prediction, this study examined the impact of grid resolution on meteorology and air quality simulation over East Asia, focusing on the North China Plain (NCP) region. The NASA Unified Weather Research and Forecasting (NU-WRF) model has been applied with the horizontal resolutions at 45, 15, and 5 km. The results revealed that, in comparison with ground observations, no single resolution can yield the best model performance for all variables across all stations. From a regional average perspective (i.e., across all monitoring sites), air temperature modeling was not sensitive to the grid resolution but wind and precipitation simulation showed the opposite. NU-WRF with the 5 km grid simulated the wind speed best, while the 45 km grid yielded the most realistic precipitation as compared to the site observations. For air quality simulations, finer resolution generally led to better comparisons with observations for O3, CO, NOx, and PM2.5. However, the improvement of model performance on air quality was not linear with the resolution increase. The accuracy of modeled surface O3 of the 15 km grid was greatly improved over the one from the 45 km grid. A further increase in grid resolution to 5 km, however, showed diminished impact on model performance improvement on O3 prediction. In addition, a 5 km resolution grid showed large advantage in better capturing the frequency of high-pollution occurrences. This was important for the assessment of noncompliance with ambient air quality standards, which was key to air quality planning and management. Balancing the modeling accuracy and resource limitation, a 15 km grid resolution was suggested for future MICS-Asia air quality modeling activity if the research region remained unchanged. This investigation also found a large overestimate of ground-level O3 and an underestimate of surface NOx and CO, likely due to missing emissions of NOx and CO.


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