본 연구에서는 광화학 대기질 모델인 CMAQ을 활용해 화력발전소 배출량 제거에 따른 O3 농도의 변화 특성을 분석하였다. 하동 화력발전소를 대상으로 주변 지역의 O3 농도 변화에 대한 발전소 배출량의 영향을 조사하기 위해 하 동 화력발전소의 배출량 제거 전과 후의 CMAQ 수치 모의를 수행하였다. 수치 모의 결과 O3의 주요 전구 물질인 NOx (-18.87%)와 VOCs (-11.27%)의 농도가 감소한 반면에 O3 (25.24%)의 농도는 증가한 것으로 나타났다. 화력발전소 배출량 제거로 인한 NO와 O3 농도의 상대적인 변화를 비교해 본 결과 높은 음의 상관관계(R= -0.72)를 나타내는 것이 확인되었다. 이러한 결과는 O3의 농도 증가가 NO 농도 감소로 인한 O3의 적정 효과 완화로 설명 될 수 있음을 의미한 다. 해당 지역의 O3의 농도 증가가 NO의 농도 감소에 주로 영향을 받은 이유는 해당 지역이 VOC-limited (i.e., NOxsaturated) 지역이기 때문으로 분석되었다. 이러한 결과는 특정 지역의 O3의 농도가 단순히 배출량의 증감에 따라 비례하게 나타나지 않을 수 있다는 것을 암시한다. 따라서 화력발전소 배출량 저감 조치로 인한 대기 중 O3 농도 개선 효과를 정확히 예측 및 평가하기 위해서는 지역 별 O3의 생성 및 소멸 기작에 대한 심도 있는 이해가 필요하다.

In this study, numerical simulations using community multiscale air quality (CMAQ) were conducted to analyze the change in ozone (O3) concentration due to the reduction in nitrogen oxides (NOx) and volatile organic compounds (VOCs) emissions in Busan. When the NOx and, VOCs emissions were reduced by 40% and, 31%, respectively, the average O3 concentration increased by 4.24 ppb, with the highest O3 change observed in the central region (4.59 ppb). This was attributed to the decrease in O3 titration by nitric oxide (NO) due to the reduction of NOx emissions in Busan, which is classified as a VOCs-limited area. The distribution of O3 concentration changes was closely related to NOx emissions per area, and inland emissions were highly correlated with daily maximum concentrations and 8-h average O3 concentrations. Contrastingly, the effect of emission reduction depended on the wind direction. This suggests that the emission reduction effects may vary depending on the environmental conditions. Further research is needed to comprehensively analyze the emission reduction effects in Busan.

In this study, we quantitatively analyze the effect of ocean emission sources on the simulated O3 concentrations in South Korea using the community multi-scale air quality (CMAQ) model. To analyze changes in O3 concentrations by ocean emissions, two different CMAQ simulations considering ocean emissions (OE case) and without considering ocean emissions (NE case) were conducted during the Korea-United States air quality (KORUS-AQ) campaign period (May–June 2016). The changes in the simulated O3 concentrations due to the effect of ocean emissions (OE case-NE case) appeared mostly in the ocean areas (+1.201 ppbv). The effect of ocean emissions was positive during the daytime (+1.813 ppbv), but negative during the nighttime (–0.612 ppbv). Analysis using the integrated process rate (IPR) confirmed that the increase or decrease in O3 concentration by ocean emissions was mainly due to chemical processes. Further analysis using the integrated reaction rate (IRR) showed that the daytime increase in O3 concentration was mainly attributable to the increased O3 production via O + O2 + M → O3 + M reaction as photolysis of NO2 increased due to the added ocean emissions. The nighttime decrease in O3 concentration was mainly due to the increased O3 titration by NO (NO + O3 → O2 + NO2) due to the increased NO emission. These results indicate that the changes in the concentration O3 in the sea area by the effect of ocean emissions are mainly due to increased NOx emissions. However, there could be a number of uncertainties in ocean emissions data used in this study, thus continuous comparative research using the most updated data will need to be carried out in the future.

Recently, a variety of modeling studies have been conducted to examine the air quality over South Korea during the Korea United States Air Quality (KORUS-AQ) campaign period (May 1 to June 10, 2016). This study investigates the impact of different meteorological initializations on atmospheric modeling results. We conduct several simulations during the KORUS-AQ period using the Weather Research and Forecasting (WRF) model with two different initial datasets, which is FNL of NCEP and ERA5 of ECMWF. Comparing the raw initial data, ERA5 showed better accuracy in the temperature, wind speed, and mixing ratio fields than those of NCEP-FNL. On the other hand, the results of WRF simulations with ERA5 showed better accuracy in the simulated temperature and mixing ratio than those with FNL, except for wind speed. Comparing the nudging efficiency of temperature and wind speed fields, the grid nudging effect on the FNL simulation was larger than that on the ERA5 simulation, but the results of mixing ratio field was the opposite. Overall, WRF simulation with ERA5 data showed a better performance for temperature and mixing ratio simulations than that with FNL data. For wind speed simulation, however, WRF simulation with FNL data indicated more accurate results compared to that with ERA5 data.

In this study, we investigated the impact of different initial data on atmospheric modeling results using the Weather Research and Forecast (WRF) model. Four WRF simulations were conducted with different initialization in March 2015, which showed the highest monthly mean PM10 concentration in the recent ten years (2006-2015). The results of WRF simulations using NCEP-FNL and ERA-Interim were compared with observed surface temperature and wind speed data, and the difference of grid nudging effect on WRF simulation between the two data were also analyzed. The FNL simulation showed better accuracy in the simulated temperature and wind speed than the Interim simulation, and the difference was clear in the coastal area. The grid nudging effect on the Interim simulation was larger than that of the FNL simulation. Despite of the higher spatial resolution of ERA-Interim data compared to NCEP-FNL data, the Interim simulation showed slightly worse accuracy than those of the FNL simulation. It was due to uncertainties associated with the Sea Surface Temperature (SST) field in the ERA-Interim data. The results from the Interim simulation with different SST data showed significantly improved accuracy than the standard Interim simulation. It means that the SST field in the ERA-Interim data need to be optimized for the better WRF simulation. In conclusion, although the WRF simulation with ERA-Interim data does not show reasonable accuracy compared to those with NCEP-FNL data, it would be able to be Improved by optimizing the SST variable.