The objective of this study was to estimate air quality trends in the study area by surveying monthly and seasonal concentration trends. To do this, the mass concentration of PM10 samples and the metals, ions, and total carbon in the PM10 were analyzed. The mean concentration of PM10 was 33.9 ㎍/㎥. The composition of PM10 was 39.2% ionic species, 5.1% metallic species, and 26.6% carbonic species (EC and OC). Ionic species, especially sulfate, ammonium, and nitrate, were the most abundant in the PM10 and had a high correlation coefficient with PM10. Seasonal variation of PM10 showed a similar pattern to those of ionic and metallic species. with high concentration during the winter and spring seasons. PM10 showed high correlation with the ionic species NO3 - and NH4 +. In addition, NH4 + was highly correlated with SO4 2- and NO3 -. We obtained four factors through factor analysis and determined the pollution sources using the United States Environmental Protection Agency(U.S. EPA) pollution profile. The first factor accounted for 51.1% of PM10 from complex sources, that is, soil, motor vehicles, and secondary particles: the second factor indicated marine sources; the third factor, industry-related sources; and the last factor, heating-related sources. However, the pollution profile used in this study may be somewhat different from the actual situation in Korea because it was from US EPA. Therefore, to more accurately estimate the pollutants present, it is necessary to create a pollution profile for Korea.

In order to investigate the PM10 concentration trend and its characteristics over five different sub area in Busan from 2013 to 2015, data analysis with considering air flow distribution according to its topography was carried out using statistical methodology. The annual mean concentrations of PM10 in Busan tend to decrease from 49.6㎍/m³ in 2013 to 46.9㎍/m³ in 2015. The monthly mean concentrations value of PM10 were high during spring season, from March to May, and low during summer and fall due to frequent rain events. The concentration of PM10 was the highest in five different sub-area in Busan. High concentration episodes over 90 percentile of daily PM10 concentration were strongly associated with mean daily wind speed, and often occurred when the westerly wind or southwesterly wind were dominant. Regardless of wind direction, the highest correlation of PM10 concentrations was observed between eastern and southern regions, which were geographically close to each other, and the lowest in the western and eastern regions blocked by mountains. Wind flow along the complex terrain in Busan is also one of the predominant factors to understand the temporal variation of PM10 concentrations.

This study identified physical characteristics and aerosol particle sources of PM10 and PM2.5 in the industrial complex of Busan Metropolitan City, Korea. Samples of PM10, PM2.5 and also soil, were collected in several areas during the year of 2012 to investigate elemental composition. A URG cyclone sampler was used for collection. The samples were collected according to each experimental condition, and the analysis method of SEM-EDX was used to determine the concentration of each metallic element. The comparative analysis indicated that their mass concentration ranged from 1% to 3%. The elements in the industrial region that were above 10% were Si, Al, Fe, and Ca. Those below 5% were Na, Mg, and S. The remaining elements (1% of total mass) consisted of elements such as Ni, Co, Br and Pb. Finally, a statistical tool was applied to the elemental results to identify each source for the industrial region. From a principal components analysis (SPSS, Ver 20.0) performed to analyze the possible sources of PM10 in the industrial region, five main factors were determined. Factor 1 (Si, Al), which accounted for 15.8% of the total variance, was mostly affected by soil and dust from manufacturing facilities nearby, Factors 2 (Cu, Ni), 3 (Zn, Pb), and 4 (Mn, Fe), which also accounted for some of variance, were mainly related to iron, non-ferrous metals, and other industrial manufacturing sources. Also, five factors determined to access possible sources of PM2.5, Factor 1 (Na, S), accounted for 13.5% of the total variance and was affected by sea-salt particles and fuel incineration sources, and Factors 2 (Ti, Mn), 3 (Pb, Cl), 4 (K, Al) also explained significant proportions of the variance. Theses factors mean that the PM2.5 emission sources may be considered as sources of incineration, and metals, and non-ferrous manufacturing industries.

This study was conducted to determine correlations and similarity between the ozone and PM10 data of 19 air quality monitoring stations in Busan from 2013 to 2016, using correlation and cluster analyses. Ozone concentrations ranged from 0.0278±0.0148 ppm at Gwangbok to 0.0378±0.017 ppm at Taejongdae and were high in suburban areas, such as Yongsuri and Gijang, as well as in coastal areas, such as Jaw, Gwangan, Taejongdae and Noksan. PM10 concentrations ranged from 37.2±25.0 ug/m3 at Gijang to 58.3±32.2 ug/m3 at and Jangrim. PM10 concentrations were high in the west, exceeding the annual ambient air quality standard of 50 ug/m3. Positive correlations were observed for ozone at most stations, ranging from 0.61 between Taejongdae and Sujeong to 0.92 between Bugok and Myeongjang. The correlation coefficients of PM10 between stations ranged from 0.62 between Jangrim and Jaw to 0.9 between Gwangbok and Sujeong. Yeonsan, Daeyeon, and Myeongjang were highly correlated with other stations, so they needed to be reviewed for redundancy. Ozone monitoring stations were initially divided into two sections, north-western areas and suburban-coastal areas. The suburban-coastal areas were subsequently divided into three sections. PM10 monitoring stations were initially divided into western and remaining areas, and then the remaining areas were subsequently divided into three sections.

This study was conducted to investigate how PM10 concentration and Relative Humidity (RH) affected visibility in Jinju, Korea. A 9-yr dataset of 1 h averages for visibility, PM10, and RH data was analyzed to examine the correlation between these variables. On average, visibility decreased by 1.4 km for every 10 μg/㎥ increase in PM10 and by 2.1 km for every 10% increase in RH. In general, a negative correlation was observed between visibility and and PM10 concentration. However, under conditions of low PM10 concentration(< 15 μg/㎥) and visibility(< 2 km), there was a positive correlation between these two variables. In this case, RH levels were high (> 75%). A high correlation analysis between two variables need to be under control conditions with RH < 75%, PM10 15~100 μg/㎥, and visibility > 2 km.

To determine the effect of air pollution reduction policies, the long-term trend of air pollutants should be analyzed. Kolmogorov-Zurbenko (KZ) filter is a low-pass filter, produced through repeated iterations of a moving average to separate each variable into its temporal components. The moving average for a KZ(m, p) filter is calculated by a filter with window length m and p iterations. The output of the first pass subsequently becomes the input for the next pass. Adjusting the window length and the number of iterations makes it possible to control the filtering of different scales of motion. To break down the daily mean PM10 into individual time components, we assume that the original time series comprises of a long-term trend, seasonal variation, and a short-term component. The short-term component is attributable to weather and short-term fluctuations in precursor emissions, while the seasonal component is a result of changes in the solar angle. The long-term trend results from changes in overall emissions, pollutant transport, climate, policy and/or economics. The long-term trend of the daily mean PM10 decreased sharply from 59.6 ug/m3 in 2002 to 44.6 ug/m3 in 2015. This suggests that there was a long-term downward trend since 2005. The difference between the unadjusted and meteorologically adjusted long-term PM10 is small. Therefore, we can conclude that PM10 is unaffected by the meteorological variables (total insolation, daily mean temperature, daily mean relative humidity, daily mean wind speed, and daily mean local atmospheric pressure) in Busan.

The classification of airflow patterns during high ozone (O3) and PM10 episodes on Jeju Island in recent years (2009-2015), as well as their correlation with meteorological conditions according to classified airflow patterns were investigated in this study. The airflow patterns for O3 and PM10 were classified into four types (Types A-D) and three types (Types E-G), respectively, using the HYbrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model and synoptic weather charts. Type A was the most dominant airflow pattern for O3 episodes, being characterized by the transport of airflows from urban and industrial areas in China with the highest frequency (about 69%, with a mean of 67 ppb). With regard to the PM10 episodes, Type E was the most dominant airflow pattern, and was mostly associated with long distance transport from Asian dust source regions along northwesterly winds, having the highest frequency (about 92%, with a mean of 136 μg/m3). The variations in the concentration of O3 and PM10 during the study period were clarified in correlation with two pollutant and meteorological variables; for example, the high (low) O3 and PM10 concentrations with high (low) air temperature and/or wind speed and vice versa for precipitation. The contribution of long-range transport to the observed PM10 levels in urban sites for different airflow patterns (Types E-F), if estimated in comparison to the data from the Gosan background site, was found to account for approximately 87-93% (on average) of its input. The overall results of the present study suggest that the variations in O3 and PM10 concentrations on Jeju Island are mainly influenced by the transport effect, as well as the contribution of local emissions.

Asthma deaths in Seoul peaked on the third, fifth, and second days after the PM concentration exceeded the daily average concentration standard. We classified the synoptic meteorological conditions, based on the days involving such cases, into three categories. Type 1 included the meteorological condition likely to cause high air pollution concentrations in the leeward region, the dominant wind direction of which is the northwest. Type 2 included the meteorological condition likely to cause high air pollution concentrations due to the weak wind velocity under stable atmospheric conditions. Type 3 was when the passage low atmospheric pressure and the expansion of high atmospheric pressure occurred at the rear, indicating a meteorological condition likely to cause high air pollution, in certain regions. Type 1 occurred 11 times, with high concentrations of over 100㎍/m³ being observed in the southeastern part of Seoul. Type 2 occurred 24 times, often accompanied by a PM concentration of 100~400 ㎍/m³. Type 3 occurred 11 times, and was accompanied by several days of yellow dust that accounted for the highest concentrations.

This study analyzes the PM10 characteristics (particulate matter with aerodynamic diameter less than 10 ㎛), concentration, and emissions in eight large South Korean cities (Seoul, Incheon, Daejeon, Daegu, Gwangju, Ulsan, Busan, Jeju). The annual median of PM10 concentration showed a decline of 0.02~1.97 ㎍/㎥ in the regions, except for Incheon, which recorded an annual 0.02 ㎍/㎥ increase. The monthly distribution levels were high in spring, winter, fall, and the summer, but were lower in summer for all regions except for Ulsan. These differences are thought to be due to the dust in spring and the cleaning effect of precipitation in summer. The variation in concentrations during the day (diurnal variation) showed that PM10 levels were very high during the rush hour and that this was most extreme in the cities (10.00 and 18.00-21.00). The total annual PM10 emissions analysis suggested that there had been a general decrease, except for Jeju. On-road mobile (OM) sources, which contributed a large proportion of the particulates in most regions, decreased, but fugitive dust (FD) sources increased in the remaining regions, except for Daegu. The correlation analysis between PM10 concentrations and emissions showed that FD could be used as a valid, positive predictor of PM10 emissions in Seoul (74.5% (p<0.05)), Dajeon (47.2% (p<0.05)), and Busan (59.1% (p<0.01)). Furthermore, industrial combustion (IC) was also a significant predictor in Incheon (61.7% (p<0.01)), and on-road mobile (OC) sources were a valid predictor in Daegu (24.8% (p<0.05)).

This study investigates the characteristics of metallic and ionic elements concentration, concentration according to transport path, and factor analysis in PM10 at Guducsan in Busan in the springtime of 2015. PM10 concentration in Guducsan and Gwaebeopdong were 59.5± 9.04 ㎍/㎥ and 87.5±20.2 ㎍/㎥, respectively. Contribution rate of water-soluble ions and secondary ion in PM10 concentration in Guducsan were 37.0% and 27.8% respectively. [NO3 -/SO4 2-] ratio and contribution rate of sea salt of PM10 in Guducsan and Gwaebeopdong were 0.91 and 1.12, 7.0% and 5.3%, respectively. The results of the backward trajectory analysis indicates that PM10 concentration, total inorganic water-soluble ions and total secondary ions were high when the air parcels moved from Sandong region in China than non-Sandong and northen China to Busan area. The results of the factor analysis at Guducsan indicates that factor 1 was anthropogenic source effects such as automobile emissions and industrial combustion processes, factor 2 was marine sources such as sea salts from sea, and factor 3 was soil component sources.

Long-term variations of PM10 and the characteristics of local meteorology related to its concentration changes were analyzed at 4 air quality sites (Ido-dong, Yeon-dong, Donghong-dong, and Gosan) in Jeju during two different periods, such as PI (2001-2006) and PII (2007-2013), over a 13-year period. Overall, the long-term trend of PM10 was very slightly downward during the whole study period, while the high PM10 concentrations in PII were observed more frequently than those in PI. The concentration variations of PM10 during the study period was clarified in correlation between PM10 and meteorological variables, e.g. the low (high) PM10 concentration with large (small) precipitation or high (low) radiation and in part high PM10 concentrations (especially, Donghong-dong and Gosan) with strong wind speed and the westerly/northwesterly winds. This was likely to be caused by the transport effect (from the polluted regions of China) rather than the contribution of local emission sources. The PM10 concentrations in “Asian dust” and “Haze” weather types were higher, whereas those in “Precipitation”, “Fog”, and “Thunder and Lighting” weather types were lower. The contribution of long-range transport to the observed PM10 levels in the urban center (Ido-dong, Yeon-dong, and Donghong-dong), if estimated by comparison to the data of the background site (Gosan), was found to explain about 80% (on average) of its input.

The production of highly concentrated PM10 is in the spotlight as a social issue, and it increases the attack rate of Asthma. This study aimed to analyze the characteristics of concentration and distribution for PM10 from 2000 to 2011, and investigate its correlation with the death from Asthma. Furthermore, this study was designed to analyze it by dividing into two cases like including Asian dust and excluding Asian dust because it presented the high concentration when Asian dust was occurred in the spring. This study has found that the annual average concentration distribution of PM10 in Seoul was higher in the central area than the peripheral area. The annual average concentration of PM10 and death from asthma displayed the tendency to gradually decrease. The correlation coefficient for all period was 0.92(p=0.000), and the correlation was 0.84(p=0.001) in case of remove Asian dust. The monthly average concentration of PM10 has increased in the winter and decreased in the summer. The death from Asthma and correlation coefficient for all period was 0.588(p=0.044) and 0.640(p=0.025) in case of removing Asian dust. Although the causes of Asthma had a great diversity, the similar tendency by a factor of PM10 meant that the correlation was high.

The study investigates the characteristics of PM10 concentration in Guducsan air quality observatory and in particular, analyzes the relationship between sudden increase of PM10 concentration in the morning of spring 2014 and meteorological parameters. PM10 concentration in April was 46.9 ㎍/㎥, the highest, followed by 45.5 ㎍/㎥ and 44.6 ㎍/㎥ in March and May, and 21.9 ㎍/㎥ in August. The low concentration in the early morning appeared on 0800 LST in spring, summer, and fall, whereas it emerged on 0900 LST in winter. High concentration in daytime lasted from 1200 LST to 1500 LST in spring and fall, whereas it continued from 1300 LST to 1600 LST in winter. The findings of PM10 concentration and change of meteorological parameters in Guducsan from April 20th to 27th in 2014 are as follows. The low concentration at dawn and in the morning decreased due to strong land breeze. Also, the sudden increase of PM10 concentration in the morning was attributable to low wind speed. Lastly, the sudden decrease of PM10 concentration in the afternoon was attributed to diffusion by strong sea breeze.

The purpose of this study is to find out the air flow patterns affecting the PM10 concentration in Busan and the potential sources within each trajectory pattern. The synoptic air flow trajectories are classified into four clusters by HYSPLIT model and the potential sources of PM10 are estimated by PSCF model for each cluster from 2008 to 2012. The potential source locations of PM10 are compared with the distribution of PM10 anthropogenic emissions in east Asia developed in 2006 for the NASA INTEX-B mission. The annual mean concentrations of PM10 in Busan decreased from 51 ug/m3 in 2008 to 43 ug/m3 in 2012. The monthly mean concentrations of PM10 were high during a spring season, March to May and low during a summer season, August and September. The cluster2 composed of the air trajectories from the eastern China to Busan through the west sea showed the highest frequency, 44 %. The cluster1 composed of the air trajectories from the inner Mongolia region to Busan through the northeast area of China showed the second high frequency, 26 %. The cluster3 and 4 were composed of the trajectories originated in the southeast sea and the east sea of Busan respectively and showed low frequencies. The concentrations of in each cluster were 47 ug/m3 in cluster1, 56 ug/m3 in cluster2, 42 ug/m3 in cluster3 and 37 ug/m3 in cluster4. From these results, it was proved that the cluster1 and 2 composed of the trajectories originated in the east and northeast area of China were the causes of high PM10 concentrations in Busan. The results of PSCF and CWT model showed that the potential sources of the high PM10 concentrations were the areas of the around Mongolia and the eastern China having high emissions of PM10 from Beijing, Hebei to Shanghai through Shandong, Jiangsu.

This study investigates weekday/weekend characteristics of PM10 concentration and chemical composition of water-soluble ions in Busan in the spring of 2013. Contribution rate of water-soluble ions to PM10 concentration in weekday/weekend were 41.5% and 38.5%, respectively. Contribution rate of SO4 2- to total ion mass in weekday/weekend were 30.4% and 33.8%, respectively. Contribution rate of total inorganic water-soluble ions in PM10 in weekday/weekend were 42.2% and 39.1% (mean 41.4%), respectively. [NO3 -/SO4 2-] ratio in weekday/weekend were 1.01 and 0.97(mean 0.99), respectively, which indicated that weekday ratio was higher. Contribution rate of sea salts and Cl-/Na+ ratio in PM10 in weekday/weekend were 8.1% and 7.6%, 0.37% and 0.41%, respectively. This research will help understand chemical composition of water-soluble ions during the weekday/weekend and will be able to measure the contribution level of artificial anthropogenic source on urban air.

This study investigates weekday/weekend characteristics of PM10 and PM2.5 concentration and metallic elements in Busan in the springtime of 2013. PM10 concentration on weekday/weekend were 77.54 and 67.28 ㎍/㎥, respectively. And PM2.5 concentration on weekday/weekend were 57.81 and 43.83 ㎍/㎥, respectively. Also, PM2.5/PM10 concentration ratio on weekdays/weekend was 0.75 and 0.65, respectively. The contribution rates of Na to total metallic elements in PM10 on weekday/weekend were 38.3% and 38.9%, respectively. It would be useful in control effectively with management of urban fine particle to understand characteristics of fine particle concentration on weekday/weekend.

Ambient particulate matters(PM10 and PM2.5) were investigated at GNTECH university in Jinju city. Samples were collected using a dichotomous sampler(series 240, Andersen Corp.) and a TEOM(Tapered Element Oscillating Microbalance) monitor period from November 2012 to October 2013. For the dichotomous sampler measurements, daily 24-h integrated PM2.5 and PM10–2.5 ambient air samples were collected at a total flow rate of 16.7 L /min. For the TEOM monitor measurements, daily 1-h integrated PM10 ambient air samples were collected at a flow rate of 16.7 L /min. The annual average concentrations of PM10-2.5 and PM2.5 by a dichotomous sampler were 10.0±6.1 μg/m3 and 22.6±9.3 μg/m3, respectively. And PM10 concentration by dichotomous sampler were similar to TEOM monitor by 32.7±12.9 μg/m3 and 31.7±11.3 μg/m3, respectively. And good correlation (R2=0.964) between the two methods was observed. The annual average of PM2.5/PM10 ratio was 0.70±0.12.

The study investigates weekday/weekend characteristics of PM10 and PM2.5 concentrations and meteorological elements in Busan. The PM10 concentration is highest on Wednesday and Thursday, and lowest on Sunday. On the other hand, the PM2.5 concentration is highest on Wednesday and lowest on Sunday. The location where concentrations of weekdays and weekend differ the most is Hwakjang-dong, the industrial area, and where they differ the least is Gijang-up and Joa-dong, the residential area. Fine particle concentration in the industrial area was consistent at dawn and in the morning, but varied in the afternoon and at night. The visibility of Sunday was 0.49 km higher than that of weekdays, and the solar radiation of Sunday was 0.11 MJ/㎡ higher than that of weekdays. These results indicate that the concentration of fine particles had influence on the change of visibility and solar radiation.

This study analyzes the chemical composition of metallic elements and water-soluble ions in PM10 and PM2.5. PM10 and PM2.5 concentrations in Busan during 2010-2012 were 97.2±67.5 and 67.5±32.8 ㎍/㎥, respectively, and the mean PM2.5/PM10 concentration ratio was 0.73. The contribution rate of water-soluble ions to PM10 ranged from 29.0% to 58.6%(a mean of 38.6%) and that to PM2.5 ranged from 33.9% to 58.4%(a mean of 43.1%). The contribution rate of sea salt to PM10 was 13.9% for 2011 and 9.7% for 2012, while that to PM2.5 was 17.4% for 2011 and 10.1% for 2012. PM10 concentration during Asian dust events was 334.3 ㎍/㎥ and 113.3 ㎍/㎥ during non-Asian dust events, and the PM10 concentration ratio of Asian Dust/Non Asian dust was 2.95. On the other hand, the PM2.5 concentration in Asian dust was 157.4 ㎍/㎥ and 83.2㎍/㎥ in Non Asian dust, and the PM2.5 concentration ratio of Asian Dust/Non Asian dust was 1.89, which was lower than that of PM10.

The number concentrations, the mass concentrations and the elemental concentrations of PM10 have measured at Gosan site in Jeju, Korea, from March 2010 to December 2010. And the correlation and the factor analysis for the number, the mass and the elemental concentrations of PM10 are performed to identify their relationships and sources. The average PM10 number concentration is observed 246 particles/㎝3(35.7∼1,017 particles/㎝3) and the average PM10 mass concentration is shown 50.1 ㎍/㎥(16.7∼441.4 ㎍/㎥) during this experimental period. The number concentrations are significantly decreased with increasing particle size, hence the concentrations for the smaller particles less than 2.5 ㎛(PM2.5) are contributed 99.6% to the total PM10 number concentrations. The highest concentration of the 20 elements in PM10 determined in this study is shown by S with a mean value of 1,497 ng/㎥ and the lowest concentration of them is found by Cd with a mean value of 0.57 ng/㎥. The elements in PM10 are evidently classified into two group based on their concentrations: In group 1, including S>Na> Al>Fe>Ca>Mg>K, the elemental mean concentrations are higher than several hundred ng/㎥, on the other hand, the concentrations are lower than several ten ng/㎥ in group 2, including Zn>Mn>Ni>Ti>Cr>Co>Cu>Mo>Sr>Ba>V >Cd. The size-separated number concentrations are shown positively correlated with the mass concentrations in overall size ranges, although their correlation coefficients, which are monotonously increased or decreased with size range, are not high. The concentrations of the elements in group 1 are shown highly correlated with the mass concentrations, but the concentrations in group 2 are shown hardly correlated with the mass concentrations. The elements originated from natural sources have been predominantly related to the mass concentrations while the elements from anthropogenic sources have mainly affected on the number concentrations of PM10.