• Journal of Korean Society for Atmospheric Environment
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• v.29 no.2
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• pp.186-198
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• 2013

The present work is an attempt to improve the performance of a regional air quality model by means of liking it with a global chemistry transport model. The global chemical transport model of GEOS-Chem is used to provide BC (Boundary Condition)s which reflect temporal and spatial variations at boundaries of regional chemical transport model of CMAQ over East Asia. First, GEOS-Chem outputs are evaluated by comparing predicted concentrations with observed monthly data of gas phase species and secondary inorganic aerosols from EANET (Acid Deposition Monitoring Network in East Asia) sites. The results show that predicted PM10 concentrations are in good agreement with the observations. This implies that GEOS-Chem outputs could be used to provide BCs to CMAQ. Simulated daily and monthly mean PM10 concentrations of CMAQ with the linkage of GEOS-Chem's BCs and constant BCs are then evaluated by comparing predicted concentrations with observations at API (Air Pollution Index) sites in China as well as EANET sites in Korea. CMAQ with the GEOS-Chem outputs improves model simulation in depicting observed PM10 concentrations comparing with those with constant BCs. It is also found that influence of aerosol species are largely dependent on the BCs over East Asia and Korea. Mean biases between simulated versus observed daily and monthly mean concentrations of PM10 with the GEOS-chem were improved by 1~8 ${\mu}g/m^3$ in China region, 3.26 ${\mu}g/m^3$ in Korea.

• Asian Journal of Atmospheric Environment
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• v.5 no.4
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• pp.263-277
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• 2011

Global atmospheric $CO_2$ distributions were simulated with a chemical transport model (GEOS-Chem) and compared with space-borne observations of $CO_2$ column density by GOSAT from April 2009 to January 2010. The GEOS-Chem model simulated 3-D global atmospheric $CO_2$ at $2^{\circ}{\times}2.5^{\circ}$ horizontal resolution using global $CO_2$ surface sources/sinks as well as 3-D emissions from aviation and the atmospheric oxidation of other carbon species. The seasonal cycle and spatial distribution of GEOS-Chem $CO_2$ columns were generally comparable with GOSAT columns over each continent with a systematic positive bias of ~1.0%. Data from the World Data Center for Greenhouse Gases (WDCGG) from twelve ground stations spanning $90^{\circ}S-82^{\circ}N$ were also compared with the modeled data for the period of 2004-2009 inclusive. The ground-based data show high correlations with the GEOS-Chem simulation ($0.66{\leq}R^2{\leq}0.99$) but the model data have a negative bias of ~1.0%, which is primarily due to the model initial conditions. Together these two comparisons can be used to infer that GOSAT $CO_2$ retrievals underestimate $CO_2$ column concentration by ~2.0%, as demonstrated in recent validation work using other methods. We further estimated individual source/sink contributions to the global atmospheric $CO_2$ budget and trends through 7 tagged $CO_2$ tracers (fossil fuels, ocean exchanges, biomass burning, biofuel burning, net terrestrial exchange, shipping, aviation, and CO oxidation) over 2004-2009. The global $CO_2$ trend over this period (2.1 ppmv/year) has been mainly driven by fossil fuel combustion and cement production (3.2 ppmv/year), reinforcing the fact that rigorous $CO_2$ reductions from human activities are necessary in order to stabilize atmospheric $CO_2$ levels.

• Journal of Korean Society for Atmospheric Environment
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• v.28 no.5
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• pp.581-594
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• 2012

The goal of this study is to investigate the effects of the chemical lateral boundary conditions (CLBCs) on Community Multi-scale Air Quality (CMAQ) simulations of tropospheric ozone for East Asia. We developed linking tool to produce CLBCs of CMAQ from Goddard Earth Observing System-Chemistry (GEOS-Chem) as a global chemistry model. We examined two CLBCs: the fixed CLBC in CMAQ (CLBC-CMAQ) and the CLBC from GEOS-Chem (CLBC-GEOS). The ozone fields by CMAQ simulation with these two CLBCs were compared to Tropospheric Emission Spectrometer (TES) satellite data, ozonesonde and surface measurements for May and August in 2008. The results with CLBC-GOES showed a better tropospheric ozone prediction than that with CLBC-CMAQ. The CLBC-GEOS simulation led to the increase in tropospheric ozone concentrations throughout the model domain, due to be influenced high ozone concentrations of upper troposphere and near inflow western and northern boundaries. Statistical evaluations also showed that the CLBC-GEOS case had better results of both the index of Agreement (IOA) and mean normalized bias. In the case of IOA, the CLBC-GEOS simulation was improved about 0.3 compared to CLBC-CMAQ due to the better predictions for high ozone concentrations in upper troposphere.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1069-1080
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• 2022

The tropospheric ozone is a pollutant that causes a great deal of damage to humans and ecosystems worldwide. In the event that ozone moves downwind from its source, a localized problem becomes a regional and global problem. To enhance ozone monitoring efficiency, geostationary satellites with continuous diurnal observations have been developed. The objective of this study is to derive the Tropospheric Ozone Movement Vector (TOMV) by employing continuous observations of tropospheric ozone from geostationary satellites for the first time in the world. In the absence of Geostationary Environmental Monitoring Satellite (GEMS) tropospheric ozone observation data, the GEOS-Chem model calculated values were used as synthetic data. Comparing TOMV with GEOS-Chem, the TOMV algorithm overestimated wind speed, but it correctly calculated wind direction represented by pollution movement. The ozone influx can also be calculated using the calculated ozone movement speed and direction multiplied by the observed ozone concentration. As an alternative to a backward trajectory method, this approach will provide better forecasting and analysis by monitoring tropospheric ozone inflow characteristics on a continuous basis. However, if the boundary of the ozone distribution is unclear, motion detection may not be accurate. In spite of this, the TOMV method may prove useful for monitoring and forecasting pollution based on geostationary environmental satellites in the future.

• Proceedings of the Korean Information Science Society Conference
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• 2010.06a
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• pp.347-348
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• 2010

• Journal of the Korean earth science society
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• v.27 no.6
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• pp.643-652
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• 2006

Data of the carbon monoxide concentration observed in Mt. Waliguan in China (WLG), Ulaan Uul in Mongolia (UUM), Tae-ahn Peninsula in Korea (TAP), and Ryori in Japan (RYO) were analyzed for a long period between 1991 and 2004. The annual average concentration of carbon monoxide was the highest at TAP $(233{\pm}41ppb)$ followed by $RYO(171{\pm}36ppb),\;UUM(155{\pm}26ppb),\;and\;WLG(135{\pm}22ppb)$. The seasonal variations being high in spring and low in summer were observed in other areas of Eastern Asia except WLG. TAP was high in carbon monoxide concentration in all seasons compared to WLG, UUM and RYO and shows wide distribution of concentration in the histogram, which is caused by the influence of large-scale air pollution due to its downwind location close to the East Asian continent, China in particular. Also, our data was compared with data measured at Mauna Loa (MLO) in Hawaii. According to the origin of the isentropic backward trajectory and its transport passage, carbon monoxide concentration observed in TAP was analyzed as follows: continental background airflows (CBG) were $216{\pm}47ppb$; regionally polluted continental airflows (RPC) were $316{\pm}56ppb$; Oceanic background airflows (OBG) were $108{\pm}41ppb$; and Partly perturbed oceanic airflows (PPO) were $161{\pm}6ppb$. The high concentration of carbon monoxide in TAP is due to the airflow from East Asian continent origin rather than that from the North Pacific origin. Especially, RPC which passes through the eastern China appeared to be the highest in concentration in spring, fall, and winter. However, OBG was affected by the North Pacific air mass with a low carbon monoxide concentration in summer. The NOAA satellite images and GEOS-CHEM model simulation confirmed a large-scale air pollution event that was in the course of expansion from southeastern China bound to the Korean Peninsula and the Korea East Sea by way of the Yellow Sea.

• Atmosphere
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• v.19 no.3
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• pp.227-235
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• 2009

Global climate warming induced by long-lived greenhouse gases is expected to cause increases in wildfire frequencies and intensity in boreal forest regions of mid- and high-latitudes in the future. Siberian forest fires are one of important sources for air pollutants such as ozone and aerosols over East Asia. Thus an accurate quantification of forest fire influences on air quality is crucial, in particular considering its higher occurrences expected under the future warming climate conditions. We here use the 3-D global chemical transport model (GEOS-Chem) with the satellite constrained fire emissions to quantify Siberian fire effects on ozone concentrations in East Asia. Our focus is mainly on spring 2003 when the largest fires occurred over Siberia in the past decade. We first evaluated the model by comparing to the EANET observations. The model reproduced observed ozone concentrations in spring 2003 with the high $R^2$ of 0.77 but slightly underestimated by 20%. Enhancements in seasonal mean ozone concentrations were estimated from the difference in simulations with and without Siberian fires and amounted up to 24 ppbv over Siberia. Effects of Siberian fires also resulted in 3-10 ppbv incresases in Korea and Japan. These increases account for about 5-15% of the ozone air quality standard of 60 ppbv in Korea, indicating a significant effect of Siberian fires on ozone concentrations. We found however that possible changes in regional meteorology due to Siberian fires may also affect air quality. Further study on the interaction between regional air quality and meteorology is necessary in the future.

• Asian Journal of Atmospheric Environment
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• v.11 no.4
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• pp.330-343
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• 2017

Climate change is an important issue, with many researches examining not only future climatic conditions, but also the interaction of climate and air quality. In this study, a new version of the emissions processing software tool - Python-based PRocessing Operator for Climate and Emission Scenarios (PROCES) - was developed to support climate and atmospheric chemistry modeling studies. PROCES was designed to cover global and regional scale modeling domains, which correspond to GEOS-Chem and CMAQ/CAMx models, respectively. This tool comprises of one main system and two units of external software. One of the external software units for this processing system was developed using the GIS commercial program, which was used to create spatial allocation profiles as an auxiliary database. The SMOKE-Asia emissions modeling system was linked to the main system as an external software, to create model-ready emissions for regional scale air quality modeling. The main system was coded in Python version 2.7, which includes several functions allowing general emissions processing steps, such as emissions interpolation, spatial allocation and chemical speciation, to create model-ready emissions and auxiliary inputs of SMOKE-Asia, as well as user-friendly functions related to emissions analysis, such as verification and visualization. Due to its flexible software architecture, PROCES can be applied to any pregridded emission data, as well as regional inventories. The application results of our new tool for global and regional (East Asia) scale modeling domain under RCP scenario for the years 1995-2006, 2015-2025, and 2040-2055 was quantitatively in good agreement with the reference data of RCPs.