• Journal of Korean Society for Atmospheric Environment
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• v.27 no.3
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• pp.337-346
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• 2011

24-hr integrated measurements of water-soluble organic carbon (WSOC) in PM2.5 were made between May 5 and September 25, 2010, on a six-day interval basis, at the Metropolitan Area Air Pollution Monitoring Supersite. A macro-porous XAD7HP resin was used to separate hydrophilic and hydrophobic WSOC. Compounds that penetrate the XAD7HP column are referred to hydrophilic WSOC, while those retained by the column are defined as hydrophobic WSOC. Laboratory calibrations using organic standards suggest that hydrophilic WSOC includes lowmolecular aliphatic dicarboxylic acids and carbonyls with less than 4 or 5 carbons, amines, and saccharides. While the hydrophobic WSOC is composed of compounds of aliphatic dicarboxylic acids with carbon numbers larger than 4~5, phenols, aromatic acids, cyclic acid, and humic-like Suwannee River fulvic acid. Over the entire study period, total WSOC accounted for on average 48% of OC, ranging from 32 to 65%, and hydrophilic WSOC accounted for on average 30.5% (9.3~66.7%) of the total WSOC. Based on the previous results, our measurement result suggests that significant amounts of hydrophobic WSOC during the study period were probably from primary combustion sources. However, on June 9 when 1-hr highest ozone concentration of 130 ppb was observed, WSOC to OC was 0.61, driven by increases in the hydrophilic WSOC. This result also suggests that processes, such as secondary organic aerosol formation, produce significant levels of hydrophilic WSOC compounds that add substantially to the fine particle fraction of the organic aerosol.

• Journal of Korean Society for Atmospheric Environment
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• v.32 no.1
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• pp.9-20
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• 2016

Ozone has been a problem in big cities. That is secondary air pollutant produced by nitrogen oxide and VOCs in the atmosphere. In order to solve this, the first to be the analysis of the $NO_x$ and VOCs. The main source of nitrogen oxide is the road mobile. Industrial sources in Seoul are particularly low, and mobile traffics on roads are large, so 45% of total $NO_x$ are estimated that road mobile emissions in Seoul. Thus, it is necessary to clarify the relation with the activity of road mobile source and $NO_x$ concentration. In this study, we analyzed the 4 locations with roadside automatic monitoring systems in their center. The V.K.T. calculating areas are set in circles with 50 meter spacing, 50 meter to 500 meter from their center. We assumed the total V.K.T. in the set radius affect the $NO_x$ concentration in the center. We used the hourly $NO_x$ concentrations data for the 4 observation points in July for the interference of the other sources are minimized. We used the intersection traffic survey data of all direction for construction of the V.K.T. data, the mobile activities on the roads. ArcGIS application was used for calculating the length of roads in the set radius. The V.K.T. data are multiplied by segment traffic volume and length of roads. As a result, the $NO_x$ concentration can be expressed as linear function formula for V.K.T. with high predictive power. Moreover we separated background concentration and concentrations due to road mobile source. These results can be used for forecasting the effect of traffic demand management plan.

• Journal of Korean Society for Atmospheric Environment
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• v.23 no.E1
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• pp.16-28
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• 2007

An improved method was developed to determine gas-phase hydrogen peroxide($H_2O_2$) and organic hydro-peroxides (ROOH) in real-time, The analytical system for $H_2O_2$ is based on formation of hydroxybenzoic acid (OHBA), a strong fluorescent compound. OHBA is formed by a sequence of reactions, photoreduction of Fe(III)-EDTA to Fe(II)-EDTA, the Fenton reaction of Fe(II)-EDTA with $H_2O_2$, and hydroxylation of benzoic acid. By use of this analytical method rather than a previous similar method, Fenton reaction time was reduced from 2 min. to 30s. Air samples were collected by a surfaceless inlet to prevent inlet line losses. With a special arrangement of the sampling apparatus, sample delivery time was drastically reduced from ${\sim}5\;min\;to\;{\sim}20\;s$. The automated system was found to be sensitive, capable of continuous monitoring, and affordable to operate. A comparison of this method with a well-established one showed an excellent linear correlation, validating applicability of this technique to $H_2O_2$ determination. The system was applied to field measurements conducted during summertime of 2004 in Gwangju, South Korea. $H_2O_2$ was found to be a predominant species of peroxides. The diurnal variation of $H_2O_2$ displayed the maximum in early afternoon and the broad minimum throughout night. $H_2O_2$ was correlated positively with ozone, photochemical age, and temperature, however, negatively with $NO_x$ and relative humidity.

• Journal of Korean Society for Atmospheric Environment
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• v.21 no.3
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• pp.357-365
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• 2005

The typical patterns of surface $O_3$ pollution and their dependence on meteorology were studied in the Greater Seoul Area (GSA) during warm season (April-September) from 1998 to 2002. In order to classify the $O_3$ pollution patterns, two-stage (average linkage then k-means) clustering technique was employed based on daily maximum $O_3$ concentrations obtained from 53 monitoring sites during high $O_3$ events (118 days). The clustering technique identified four statistically distinct $O_3$ pollution patterns representing the different horizontal distributions and levels of $O_3$ in GSA. The prevailed pattern (93 days, $49.5\%$) distinctly showed the gradient of $49.5\%$ concentrations going from west to east in GSA. Very high $49.5\%$ concentrations throughout GSA (24 days, $12.8\%$) were also found as a significant pattern of severe $O_3$ pollution. In order to understand the characteristics of $O_3$ pollution patterns, the relationship between $O_3$ pollution patterns and meteorological conditions were analyzed using both synoptic charts and surface/upper air data. Each pattern was closely associated with surface wind interacted with synoptic background flow allowing to transport and accumulate $O_3$ and its precursor. In particular, the timing and inland penetration of sea-breeze were apparently found to play very important role in determining $O_3$ distributions.

• Journal of the Korean Data and Information Science Society
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• v.25 no.6
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• pp.1293-1300
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• 2014

The temperature data influences on various policies of the country. In this article, the autoregressive error (ARE) model has been considered for analyzing the monthly and seasonal temperature data at the northern part of the Chungcheong Namdo, Seosan monitoring site in Korea. In the ARE model, five meteorological variables, four greenhouse gas variables and five pollution variables are used as the explanatory variables for the temperature data set. The five meteorological variables are wind speed, rainfall, radiation, amount of cloud, and relative humidity. The four greenhouse gas variables are carbon dioxide ($CO_2$), methane ($CH_4$), nitrous oxide ($N_2O$), and chlorofluorocarbon ($CFC_{11}$). And the five air pollution explanatory variables are particulate matter ($PM_{10}$), sulfur dioxide ($SO_2$), nitrogen dioxide ($NO_2$), ozone ($O_3$), and carbon monoxide (CO). The result showed that the monthly ARE model explained about 39-63% for describing the temperature. However, the ARE model will be expected better when we add the more explanatory variables in the model.

• Journal of odor and indoor environment
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• v.16 no.3
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• pp.277-286
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• 2017

The objectives of this study were to investigate the effects of $PM_{10}$ and $O_3$ concentration on the symptoms of allergic diseases. The questionnaire was used to determine whether or not symptoms of allergic diseases were present from September to October 2012. The air pollution concentration data used was the corresponding point CEM (continuous emission monitoring) data. The average concentration of $PM_{10}$ was $56.09{\mu}g/m^3$ in the control area, and the concentration in the exposed area was $40.44{\mu}g/m^3$. In the two areas, concentration of $O_3$ was 28.73 ppb and 28.74 ppb, respectively. The total average concentrations of $PM_{10}$ and $O_3$ were $45.66{\mu}g/m^3$ and 28.73 ppb in the Gwangyang area. The rate of asthma diagnosis was higher in the control area (9.6%) than in the exposed area (4.1%), but the rate of allergy eye disease was higher in the exposed area (23.9%) than in the control area (16.5%). There was a significant difference in the symptoms of some allergic diseases when the relative concentration of $PM_{10}$ and $O_3$ were high and low.

• Journal of Environmental Health Sciences
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• v.44 no.6
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• pp.563-571
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• 2018

Objectives: The purpose of this study was to investigate the environmental pollution caused by exposure to air pollution in Korea. Therefore, it is necessary to investigate environmental and health factors through big data. Methods: Among the environmental diseases, the data centered on "percentage per day in 2015 to 2018". Data of environmental diseases and concentrations of air pollution monitoring network were analyzed. Results: Lung cancer and bronchiolitis obliterans were correlated with 0.027 and 0.0158, respectively, in the contamination concentration of fine dust ($PM_{10}$). Ozone, COPD, allergic rhinitis, and bronchiolitis obliterans were correlated with 0.0022, 0.0028 and 0.0093, respectively. At the concentration of $SO_2$ and the diseases of asthma, atopic dermatitis, lung cancer and bronchiolitis obliterans were 0.0008, 0.0523, 0.0016 and 0.0126, respectively. Conclusions: We surveyed the trends of air pollution according to the characteristics of Seoul area in Korea and evaluated the perception of Korea and the world. As a result, respiratory lung disease is thought to be a major factor in exposure to environmental pollution.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.2
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• pp.294-305
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• 2022

Recently, drones have been widely used to solve environmental problems such as environmental protection and natural disaster monitoring. This study focuses on the problem of the search altitude of drones when using drones to search for air pollution in order to maintain the urban air environment. In particular, when exploring air pollution in cities using drones, various experiments are conducted to determine the appropriate search altitude for each air pollution source and each communication module. Through the experiment, the maximum measurable altitude for the most common air pollutants, such as CO (carbon monoxide), NO₂ (nitrogen dioxide), O₃ (ozone), and P10, P2.5 (fine dust), was identified, and the effective search altitude for each air pollution source was determined. As a result of the experiment, three types of drone search altitudes including legally measurable altitudes were suggested. The communication module measurable altitude was 60m to 120m depending on the communication module, and the effective measurable altitude was analyzed from 10m to 100m.

• Korean Journal of Remote Sensing
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• v.33 no.6_1
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• pp.981-992
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• 2017

We, for the first time, retrieved tropospheric nitrogen dioxide ($Trop.NO_2$) vertical column density (VCD) from ground-based instrument, Pandora, using the optical density fitting based on Differential Optical Absorption Spectroscopy (DOAS)in Seoul for the period from May 2014 to December 2014. The $Trop.NO_2$ VCDs retrieved from Pandora were compared with those obtained from Ozone Monitoring Instrument (OMI). A correlation coefficient (R) between those retrieved from Pandora and those obtained from OMI is 0.55. To compare with surface $NO_2$ VMRs obtained from in-situ, Trop. $NO_2$ VCDs retrieved from Pandora and those obtained from OMI are converted into $NO_2$ VMRs in boundary layer (BLH $NO_2$ VMRs) using data measured from Atmospheric Infrared Sounder (AIRS). Surface $NO_2$ VMRs obtained from in-situ range from 5.5 ppbv to 61.5 ppbv. BLH $NO_2$ VMRs retrieved from Pandora and OMI range from 2.1 ppbv to 44.2 ppbv and from 0.9 ppbv to 11.6 ppbv, respectively. The range of BLH $NO_2$ VMRs retrieved from OMI is narrower than that of BLH $NO_2$ VMRs retrieved from Pandora and surface $NO_2$ VMRs obtained from in-situ. There is a batter correlation between surface $NO_2$ VMRs obtained from in-situ and BLH $NO_2$ VMRs retrieved from Pandora (R= 0.50)than the correlation between surface $NO_2$ VMRs obtained from in-situ and BLH $NO_2$ VMRs retrieved from OMI (R = 0.36). This poor correlation is thought to be due to the lower near-surface sensitivity of the satellite-based instrument (OMI) than Pandora, the ground-based instrument.

• Korean Journal of Remote Sensing
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• v.33 no.2
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• pp.135-147
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• 2017

We, for the first time, estimated daily and monthly surface nitrogen dioxide ($NO_2$) volume mixing ratio (VMR) using three regression models with $NO_2$ tropospheric vertical column density (OMIT-rop $NO_2$ VCD) data obtained from Ozone Monitoring Instrument (OMI) in Seoul in South Korea at OMI overpass time (13:45 local time). First linear regression model (M1) is a linear regression equation between OMI-Trop $NO_2$ VCD and in situ $NO_2$ VMR, whereas second linear regression model (M2) incorporates boundary layer height (BLH), temperature, and pressure obtained from Atmospheric Infrared Sounder (AIRS) and OMI-Trop $NO_2$ VCD. Last models (M3M & M3D) are a multiple linear regression equations which include OMI-Trop $NO_2$ VCD, BLH and various meteorological data. In this study, we determined three types of regression models for the training period between 2009 and 2011, and the performance of those regression models was evaluated via comparison with the surface $NO_2$ VMR data obtained from in situ measurements (in situ $NO_2$ VMR) in 2012. The monthly mean surface $NO_2$ VMRs estimated by M3M showed good agreements with those of in situ measurements(avg. R = 0.77). In terms of the daily (13:45LT) $NO_2$ estimation, the highest correlations were found between the daily surface $NO_2$ VMRs estimated by M3D and in-situ $NO_2$ VMRs (avg. R = 0.55). The estimated surface $NO_2$ VMRs by three modelstend to be underestimated. We also discussed the performance of these empirical modelsfor surface $NO_2$ VMR estimation with respect to otherstatistical data such asroot mean square error (RMSE), mean bias, mean absolute error (MAE), and percent difference. This present study shows a possibility of estimating surface $NO_2$ VMR using the satellite measurement.