• Journal of Korean Society for Atmospheric Environment
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• v.34 no.5
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• pp.687-696
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• 2018

In order to develop effective emission abatement strategies for coal-fired power plants, we analyzed the shutdown effects of coal-fired power plants on $PM_{2.5}$ concentration in June by employing air quality model for the period from 2013 to 2016. WRF (Weather Research and Forecast) and CMAQ(Community Multiscale Air Quality) models were used to quantify the impact of emission reductions on the averaged $PM_{2.5}$ concentrations in June over Chungcheongnam-do area in Korea. The resultant shutdown effects showed that the averaged $PM_{2.5}$ concentration in June decreased by 1.2% in Chungcheongnam-do area and decreased by 2.3% in the area where the surface air pollution measuring stations were located. As a result of this study, it was confirmed that it is possible to analyze policy effects considering the change of meteorology and emission and it is possible to quantitatively estimate the influence at the maximum impact region by utilizing the air quality model. The results of this study are expected to be useful as a basic data for analyzing the effect of $PM_{2.5}$ concentration change according to future emission changes.

• Journal of Environmental Science International
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• v.28 no.3
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• pp.341-356
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• 2019

In this study, the impacts of local meteorology caused by tidal changes in the West Sea on ozone distributions in the Seoul Metropolitan Area (SMA) were analyzed using a meteorological model (WRF) and an air quality (CMAQ) model. This study was carried out during the day (1200-1800 LST) between August 3 and 9, 2016. The total area of tidal flats along with the tidal changes was calculated to be approximately $912km^2$, based on data provided by the Environmental Geographic Information Service (EGIS) and the Ministry of Oceans and Fisheries (MOF). Modeling was carried out based on three experiments, and the land cover of the tidal flats for each experiment was designed using the coastal wetlands, water bodies (i.e., high tide), and the barren or sparsely vegetated areas (i.e., low tide). The land cover parameters of the coastal wetlands used in this study were improved in the herbaceous wetland of the WRF using updated albedo, roughness length, and soil heat capacity. The results showed that the land cover variation during high tide caused a decrease in temperature (maximum $4.5^{\circ}C$) and planetary boundary layer (PBL) height (maximum 1200 m), and an increase in humidity (maximum 25%) and wind speed (maximum $1.5ms^{-1}$). These meteorological changes increased the ozone concentration (about 5.0 ppb) in the coastal areas including the tidal flats. The increase in the ozone concentration during high tide may be caused by a weak diffusion to the upper layer due to a decrease in the PBL height. The changes in the meteorological variables and ozone concentration during low tide were lesser than those occurring during high tide. This study suggests that the meteorological variations caused by tidal changes have a meaningful effect on the ozone concentration in the SMA.

• Journal of Korean Society for Atmospheric Environment
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• v.31 no.5
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• pp.430-436
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• 2015

Numerical air quality forecasting suffers from the large uncertainties of input data including emissions, boundary conditions, earth surface properties. Data assimilation has been widely used in the field of weather forecasting as a way to reduce the forecasting errors stemming from the uncertainties of input data. The present study aims at evaluating the effect of input data on the air quality forecasting results in Korea when data assimilation was invoked to generate the initial concentrations. The forecasting time was set to 36 hour and the emissions and initial conditions were chosen as tested input parameters. The air quality forecast model for Korea consisting of WRF and CMAQ was implemented for the test and the chosen test period ranged from November $2^{nd}$ to December $1^{st}$ of 2014. Halving the emission in China reduces the forecasted peak value of $PM_{10}$ and $SO_2$ in Seoul as much as 30% and 35% respectively due to the transport from China for the no-data assimilation case. As data assimilation was applied, halving the emissions in China has a negligible effect on air pollutant concentrations including $PM_{10}$ and $SO_2$ in Seoul. The emissions in Korea still maintain an effect on the forecasted air pollutant concentrations even after the data assimilation is applied. These emission sensitivity tests along with the initial condition sensitivity tests demonstrated that initial concentrations generated by data assimilation using field observation may minimize propagation of errors due to emission uncertainties in China. And the initial concentrations in China is more important than those in Korea for long-range transported air pollutants such as $PM_{10}$ and $SO_2$. And accurate estimation of the emissions in Korea are still necessary for further improvement of air quality forecasting in Korea even after the data assimilation is applied.

• Asian Journal of Atmospheric Environment
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• v.11 no.1
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• pp.33-36
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• 2017

China is suffering from severe air pollution especially fine $PM_{2.5}$ pollution. In 2015, the annual average $PM_{2.5}$ concentration of the 338 municipal cities was $50{\mu}g/m^3$, 78% cities at or above the prefectural level failed to comply with the $PM_{2.5}$ concentration standards. The $13^{th}$ Five-Year Plan for National Economic and Social Development set the goal that the annual average concentration of $PM_{2.5}$ in the municipal cities which failed to attain the ambient air quality standards shall be decreased by 18% by 2020 (CCCPC, 2016). In this study, an air pollution control scenario during the $13^{th}$ Five-Year Plan period was proposed and the $SO_2$, $NO_x$ and PM emission reductions in response to different measures in 31 provincial-level regions mainland China by 2020 were estimated. The air quality in the target year (2020) was simulated using the WRF-CMAQ model. The results showed that by 2020, the emissions of $SO_2$, $NO_x$ and primary PM in mainland China will be reduced by 4.19 million tons, 3.94 million tons and 4.41 million tons, a drop of 23%, 21% and 25% respectively compared with that in 2015, and the annual average concentration of $PM_{2.5}$ will decrease by 19%. Coal-fired power plant contributes the most pollutant emission reduction.

• Journal of Korean Society for Atmospheric Environment
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• v.33 no.5
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• pp.497-514
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• 2017

This study quantitatively analyzes the effects of emission inventory choices on the simulated particulate matter (PM) concentrations and the domestic/foreign contributions in the Seoul Metropolitan Area (SMA) with an air quality forecasting system. The forecasting system is composed of Weather Research and Forecasting (WRF)-Sparse Matrix Operator Kernel Emissions (SMOKE)-Community Multi-Scale Air Quality (CMAQ). Different domestic and foreign emission inventories were selectively adopted to set up four sets of emissions inputs for air quality simulations in this study. All modeling cases showed that model performance statistics satisfied the criteria levels (correlation coefficient >0.7, fractional error <50%) suggested by previous studies. Notwithstanding the apparently good model performance of total PM concentrations by all emission cases, annual average concentrations of simulated total PM concentrations varied up to $20{\mu}g/m^3$ (160%) depending on the combination of emission inventories. In detail, the difference in simulated annual average concentrations of the primary PM coarse (PMC) was up to $25.2{\mu}g/m^3$ (6.5 times) compared with other cases. Furthermore, model performance analyses on PM species showed that the difference in the simulated primary PMC led to gross model overestimation in general, which indicates that the primary PMC emissions need to be improved. The contribution analysis using model direct outputs indicated that the domestic contributions to the annual average PM concentrations in the SMA vary from 44% to 67%. To account for the uncertainty of the simulated concentration, the contribution correction factor method proposed by Bae et al. (2017) was applied, which resulted in converged contributions(from 48% to 57%). We believe this study shows that it is necessary to improve the simulated concentrations of PM components in order to enhance the accuracy of the forecasting model. It is deemed that these improvements will provide more accurate contribution results.