• 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.

• Journal of Environmental Health Sciences
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• v.41 no.2
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• pp.71-81
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• 2015

Objectives: Photochemical ozone pollution is associated with increased mortality risk. This study aims to assess the population exposure to ozone according to population characteristics for high ozone days in the Busan metropolitan region (BMR). Methods: The ozone exposure assessment in this study was performed using the WRF-CMAQ simulated ozone concentrations and the population data in the BMR. The settled and daytime population and their activity were considered to conduct the static and dynamic ozone exposure assessment. Results: Applying a static exposure assessment, in case that high ozone occurred throughout Busan area, the highest exposure levels were evaluated in urban neighborhoods. In case of ozone pollution in outer Busan, because sensitive groups have been relatively higher exposure, this case was also evaluated as part of that should not be overlooked. The dynamic exposure was higher than static exposure because the number of population exposed to ozone of high concentration is increased. This approach is important in a regard consider that daytime population distribution when high ozone occur. Conclusion: This study shows the different population exposure according to various ozone distributions for each episode day. Considering demographic characteristic such as population density and activity should be important to understanding the population exposure assessment when ozone pollution occurs.

• Journal of Environmental Science International
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• v.24 no.1
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• pp.31-46
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• 2015

In this study, the regional climate (WRF) and air quality (CMAQ) models were used to simulate the effects of future urban growth on surface ozone concentrations in the Seoul metropolitan region (SMR). These analyses were performed based on changes in ozone concentrations during ozone seasons (May-June) for the year 2050 (future) relative to 2012 (present) by urban growth. The results were compared with the impacts of RCP scenarios on ozone concentrations in the SMR. The fractions of urban in the SMR (25.8 %) for the 2050 were much higher than those (13.9 %) for the 2012 and the future emissions (e.g., CO, NO, $NO_2$, $SO_2$, VOC) were increased from 121 % (NO) to 161.3 % ($NO_2$) depending on emission material. The mean and daily maximum 1-h ozone in the SMR increased about 3 - 7 ppb by the effect the RCP scenarios. However, the effect of urban growth reduced the mean ozone by 3 ppb in the SMR and increased the daily maximum 1-h ozone by 2 - 5 ppb over the northeastern SMR and around the coastline. In particular, the ozone pollution days exceeding the 1-h regulatory standard (100 ppb) were far more affected by urban growth than mean values. As a result, the average number of days exceeding the 1-h regulatory standard increased up to 10 times.

• Asian Journal of Atmospheric Environment
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• v.8 no.1
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• pp.25-34
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• 2014

In winter 2013, extreme air pollution by fine particulate matter ($PM_{2.5}$) in China attracted much public attention. In order to simulate the $PM_{2.5}$ pollution, the Community Multiscale Air Quality model driven by the Weather Research and Forecasting model was applied to East Asia in a period from 1 January 2013 to 5 February 2013. The model generally reproduced $PM_{2.5}$ concentration in China with emission data in the year 2006. Therefore, the extreme $PM_{2.5}$ pollution seems to be mainly attributed to meteorological (weak wind and stable) conditions rather than emission increases in the past several years. The model well simulated temporal and spatial variations in $PM_{2.5}$ concentrations in Japan as well as China, indicating that the model well captured characteristics of the $PM_{2.5}$ pollutions in both areas on the windward and leeward sides in East Asia in the study period. In addition, contribution rates of four anthropogenic emission sectors (power generation, industrial, residential and transportation) in China to $PM_{2.5}$ concentration were estimated by conducting zero-out emission sensitivity runs. Among the four sectors, the residential sector had the highest contribution to $PM_{2.5}$ concentration. Therefore, the extreme $PM_{2.5}$ pollution may be also attributed to large emissions from combustion for heating in cold regions in China.

• 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.

• Asian Journal of Atmospheric Environment
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• v.12 no.1
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• pp.59-66
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• 2018

This study aims to evaluate the performance of the Air Quality Model (AQM) for the seasonal and spatial distribution of the $NH_3$ concentration in the atmosphere. To obtain observational data for the model validation, observations based on biweekly sampling have been conducted using passive samplers since April 2015 at multiple monitoring sites in the Tokyo metropolitan area. AQM, built based on WRF/CMAQ, was applied to predict the $NH_3$ concentration observed from April 2015 to March 2016. The simulation domain includes the Kanto region, which is the most densely populated area in Japan. Because the area also contains large amount of livestock, especially in its northern part, the density of the $NH_3$ emissions derived from human activities and agriculture there are estimated to be the highest in Japan. In the model validation, the model overestimated the observed $NH_3$ concentration in the summer season and underestimated it in the winter season. In particular, the overestimation in the summer was remarkable at a rural site (Komae) in Tokyo. It was found that the overestimation at Komae was caused by the transportation of $NH_3$ emitted in the northern part of the Kanto region during the night. It is suggested that the emission input used in this study overestimated the $NH_3$ emission from human sources around the Tokyo suburbs and agricultural sources in the northern part of the Kanto region in the summer season. In addition, the current emission inventories might overestimate the difference of the agricultural $NH_3$ emissions among seasons. Because the overestimation of $NH_3$ in the summer causes an overestimation of $NO_3{^-}$ in $PM_{2.5}$ in the AQM simulation, further investigation is necessary for the seasonal variation in the $NH_3$ emissions.

• 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.

• Korean Journal of Remote Sensing
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• v.36 no.5_4
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• pp.1267-1276
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• 2020

To quantitatively predict the impacts of large-scale volcanic eruptions of Mt. Baekdu on air quality and damage around the Korean Peninsula, a three-dimensional chemistry-transport modeling system (Weather Research & Forecasting - Sparse Matrix Operation Kernel Emission - Comunity Multi-scale Air Quality) was adopted. A worst-case meteorology scenario was selected to estimate the direct impact on Korea. This study applied the typical worst-case scenarios that are likely to cause significant damage to Korea among worst-case volcanic eruptions of Mt. Baekdu in the past decade (2005~2014) and assumed a massive VEI 4 volcanic eruption on May 16, 2012, to analyze the concentration of PM_2.5 caused by the volcanic eruption. The effects of air quality in each region-cities, counties, boroughs-were estimated, and vulnerable areas were derived by conducting an exposure assessment reflecting vulnerable groups. Moreover, the effects of cities, counties, and boroughs were analyzed with a high-resolution scale (9 km × 9 km) to derive vulnerable areas within the regions. As a result of analyzing the typical worst-case volcanic eruptions of Mt. Baekdu, a discrepancy was shown in areas between high PM_2.5 concentration, high population density, and where vulnerable groups are concentrated. From the result, PM_2.5 peak concentration was about 24,547 ㎍/㎥, which is estimated to be a more serious situation than the eruption of Mt. St. Helensin 1980, which is known for 540 million tons of volcanic ash. Paju, Gimpo, Goyang, Ganghwa, Sancheong, Hadong showed to have a high PM_2.5 concentration. Paju appeared to be the most vulnerable area from the exposure assessment. While areas estimated with a high concentration of air pollutants are important, it is also necessary to develop plans and measures considering densely populated areas or areas with high concentrations of susceptible population or vulnerable groups. Also, establishing measures for each vulnerable area by selecting high concentration areas within cities, counties, and boroughs rather than establishing uniform measures for all regions is needed. This study will provide the foundation for developing the standards for disaster declaration and preemptive response systems for volcanic eruptions.