To analyze the effects of PM10 and PM2.5 on daily mortality cases, the relations of death counts from natural causes, respiratory diseases, and cardiovascular diseases with PM10 and PM2.5 concentrations were applied to the generalized additive model (GAM) in this study. From the coefficients of the GAM model, the excessive mortality risks due to an increase of 10 μg/m3 in daily mean PM10 and PM2.5 for each cause were calculated. The excessive risks of deaths from natural causes, respiratory diseases, and cardiovascular diseases were 0.64%, 1.69%, and 1.16%, respectively, owing to PM10 increase and 0.42%, 2.80%, and 0.91%, respectively, owing to PM2.5 increase. Our result showed that particulate matter posed a greater risk of death from respiratory diseases and is consistent with the cases in Europe and China. The regional distribution of excessive risk of death is 0.24%–0.81%, 0.34%–2.6%, and 0.62%–1.94% from natural causes, respiratory diseases, and cardiovascular diseases, respectively, owing to PM10 increase, and 0.14%–1.02%, 1.07%–3.92%, and 0.22%–1.73% from natural causes, respiratory diseases, and cardiovascular diseases, respectively, owing to PM2.5 increase. Our results represented a different aspect from the regional concentration distributions. Thus, we saw that the concentration distributions of air pollutants differ from the affected areas and identified the need for a policy to reduce damage rather than reduce concentrations.

The purpose of this study is to quantitatively analyze the effects of a restoration project on the decrease in the temperature in the surrounding areas. The thermal environment characteristics of the investigation area were analyzed using the meteorological data from the Busanjin Automatic Weather System which is closest to the target area. The terrain data of the modeling domain was constructed using a digital map and the urban spatial information data, and the numerical simulation of the meteorological changes before and after the restoration of the stream was performed using the Envi-met model. The average temperature of the target area in 2016 was 15.2℃ and was higher than that of the suburbs. The monthly mean temperature difference was the highest at 1.1℃ in November and the lowest in June, indicating that the temperatures in the urban areas were high in spring and winter. From the Envi-met modeling results, reductions in temperature due to stream restoration were up to 1.7℃ in winter, and decreased to 3.5℃ in summer. The effect of temperature reduction was seen in the entire region where streams are being restored.

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.

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.

Comparing to the other air pollutants like SO2, CO, the number of exceedance of the ozone national ambient air quality standard(NAAQS) and the ozone warning increased recently in Busan. The purpose of this study is to find out the preliminary symptoms for high ozone days in Busan area. In order to find out the preliminary symptoms, the hourly ozone data at air quality monitoring stations and the hourly meterological parameters at Busan regional meteorological 2007 to 2013 were used for the analysis. Averaged daily max ozone concentration was the highest(0.055 ppm) at Noksan and Youngsuri in the ozone season from 2007 to 2013. The horizontal distributions of daily max. ozone including all stations in Busan at high ozone days(the day exceeding 0.1 ppm of ozone concentration at least one station) were classified from two to five clusters by hierarchial cluster analysis. The meteorological variables showing strong correlation with daily max. ozone were the daily mean dew point temperature, averaged total insolation, the daily mean relative humidity and the daily mean cloud amount. And the most frequent levels were 19-23℃ in dew point temperature, 21-24 MJ/m2 in total insolation on the day before, 2.6-3.0 MJ/m2 on the very day, 67-80% in relative humidity and 0-3 in cloud amount.

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.

PM10 concentration is related to the meteorological variables including to local and synoptic meteorology. In this study the PM10 concentrations of Busan in 2007~2011 were analyzed and the days of yellow sand or rainfall which is more than 5 mm were excluded. The sections of PM10 concentration were divided according to 10-quantiles, quartiles and 90-quantiles. The 90-quantiles of daily PM10 concentration were selected as high concentration dates. In the high concentration dates the daily mean averaged cloudness, mean daily surface wind speed, daily mean surface pressure and PBL height were low and diurnal variation of surface pressure and daily maximum surface temperature were high. When the high PM10 dates occurred, the west and south wind blew on the ground and the west wind blew strongly on the 850 hPa. So it seemed that long range transboundary air pollutants made effects on the high concentration dates. The cluster analysis using Hysplit model which is the backward trajectory was made on the high concentration dates. As a result, 3 clusters were extracted and on the short range transboundary cluster the daily mean relative humidity and cloudness were high and PBL height was low.

Mobile sources produce a significant fraction of total anthropogenic emissions in Korea and have harmful effects on air quality. Mobile emissions are intrinsically difficult to estimate due to complicated road networks and variations of traffic volume with location and time. To measure traffic pollutants with high temporal and spatial resolution under real conditions a mobile laboratory was designed. The mobile laboratory provide concentrations of SO2, CO, NO, NO2 and location coordinate value. This approach allowed for pollutant level measurements on many roads within short periods of time. In this study, on-road concentrations of SO2, CO, NO and NO2 were measured using mobile platform measurement along the 25 main roads in Busan to estimate the average air pollution level in short time difference. The measurements were conducted on favorable meteorological days from 2010 to 2012 and the overall concentrations of SO2, CO, NO and NO2 were 0.006, 0.8, 0.182 and 0.055 ppm respectively. The result showed that the concentration of CO, NO and NO2 on road were twice, 18 times and 2.5 times higher than regional air quality monitoring sites mean in same period.

In this study, eight episode days of high-concentration PM10 occurrences in the Gimhae region between 2006 and 2011 were analyzed. Most of them appeared in winter and the highest concentration was observed around 12 LST. Furthermore, the wind direction, wind velocity, and temperature elements were compared with observed values to verify the WRF numerical simulation results used in this study, and they simulated well in accordance with the trend of the observed values. The wind was generally weak in the high-concentration episode days that were chosen through surface weather chart and the numerical simulation results for wind field, and the air pollutants were congested due to the effects of the resulting local winds, thereby causing a high concentration of air pollutants. Furthermore, the HYSPLIT model was performed with the WRF numerical simulation results as input data. As a result, they originated from China and flowed into Gimhae in all eight days, and the lowest concentration appeared on the days when recirculation occurred.

Ozone is the secondary photochemical pollutant formed from ozone precursor such as nitrogen dioxide and non-methane volatile organic compounds(VOCs). The ambient concentration of ozone depends on several factors: sunshine intensity, atmospheric convection, the height of the thermal inversion layer, concentrations of nitrogen oxides and VOCs. Busan is located in the southeast coastal area of Korea so the ozone concentration of Busan is mainly affected from the meteorological variables related to the sea such as sea breeze. In this study the ozone concentrations of Busan in 2008~2010 were used to analyse the cause of the regional ozone difference in eastern area of Busan. The average ozone concentration of Youngsuri was highest in Busan however the average ozone concentration of Gijang was equal to the average ozone concentration of Busan in 2008~2010. The two sites are located in eastern area of Busan but the distance of two sites is only 9km. To find the reason for the difference of ozone concentration between Youngsuri and Gijang, the meteorological variables in two sites were analyzed. For the analysis of meteorological variables the atmospheric numerical model WRF(Weather Research and Forecasting) was used at the day of the maximum and minimum difference in the ozone concentration at the two sites. As a result of analysis, when the boundary layer height was lower and the sea breeze was weaker in Youngsuri, the ozone concentration of Youngsuri was high. Furthermore when the sea breeze blew from the south in the eastern area of Busan, the sea breeze at Youngsuri turned into the southeast and the intensity of sea breeze was weaker because of the mountain in the southern region of Youngsuri. In that case, the difference of ozone concentration between Youngsuri and Gijang was considerable.

The annual variations of the urban heat island in Busan is investigated using surface temperature data measured at 3 automatic weather stations(AWSs) for the 5 years period, 2006 to 2010. Similar to previous studies, the intensity of the urban heat island is calculated using the temperature difference between downtown(Busanjin, Dongnae) and suburb(Gijang). The maximum hourly mean urban heat island are 1.4℃ at Busanjin site, 2300LST and 1.6 ℃ at Dongnae site, 2100LST. It occurs more often at Dongnae than Busanjin. Also the maximum hourly mean urban heat island appears in November at both sites. The urban heat island in Busan is stronger in the nighttime than in the daytime and decreases with increasing wind speed, but it is least developed in summer. Also it partly causes the increasement of nighttime PM10 concentration.

This study is conducted to estimate the air temperature decreasing effects by restoring urban streams using WRF/CALMET coupled system. The types of land use on covered streams are constructed with the land cover map from Korea ministry of environment. Restoring covered streams changes the types of land use on covered areas to water. Two different types of land use(CASE 1 and CASE 2) are inputted to the WRF/CALMET coupled system in order to calculate the temperature difference. The results of the WRF/CALMET coupled system are similar to the observed values at automatic weather stations(AWS) in Busan area. Restoring covered streams causes temperature to be decreased by about 0.34～2℃ according to the locations of streams and the regions that temperature is reduced are widely distributed over the restored area. Reduction of temperature is increased rapidly from morning and maximus at 13LST. Natural restoration of streams will reduce the built-up area within urban. With this, temperature reductions which are the cause to weaken the urban heat island appear. Relief of urban heat island will help to improve the air quality such as accumulation of air pollutants in within urban area.

The urban microscale wind field around the air quality monitoring station was investigated in order to check how a building complex influences it. For this study as the high density areas Jwa-dong and Yeonsan-dong monitoring sites in Busan were chosen. As the direction of inflow which is perpendicular to the building of the monitoring station was expected to cause the considerable variation of the wind field, that direction was selected. The model Envi-met was used as the diagnostic numerical model for this study. It is suitable for this investigation because Envi-met has the microscale resolution. After simulating it, on the leeward side around a building complex the decrease of flow velocity and some of vortexes or circulation area were discovered. In addition, on the edge of the top at the building and at the back of the building the upward flow was developed. If the sampling hole of monitoring site were located in this upward flow, it would be under the influence of upward flow from the near street.

To investigate the effect of NOx and VOCs(volatile organic compounds) on the generation of high ozone episode, examined the hourly variations of ozone, NOx and VOCs concentrations, and calculated the ozone isopleth about maximum ozone concentrations using OZIPR which was presented by U. S. EPA at three sites in Busan. There was some difference by the sites, but decreasing VOCs concentration was effective for reduction of ozone at 22 July, the episode day of 2005. In the year 2006, the episode day was 8 August and the variations of NOx and VOCs concentration was little than variation of ozone. So it was estimated that the photochemical production of ozone was low than transportation of ozone. And the result of the OZIPR modeling was that decreasing VOCs concentration was effective for reduction of ozone.

In this study, climate analysis and wind sector division were conducted for a propriety assessment to determine the location of air quality monitoring sites in the Busan metropolitan area. The results based on the meteorological data(2000～2004) indicated hat air temperature is strongly correlated between 9 atmospheric monitoring sites, while wind speed and direction are not. This is because wind is strongly affected by the surrounding terrain and the obstacles such as building and tree. In the next stage, we performed cluster analysis to divide wind sector over the Busan metropolitan area. The cluster analysis showed that the Busan metropolitan area is divided into 6 wind sectors. However 1 downtown and 2 suburbs an area covering significantly broad region in Busan are not divided into independent sectors, because of the absence of atmospheric monitoring site. As such, the Busan metropolitan area is finally divided into 9 sectors.

The urban pollution if affected by local environmental, so it is necessary to consider area characteristics such as emission source and meteorological phenomena, in studying urban air pollution. Ulsan is laocated on south-east coast and has many industrial facilities, so many people have concerned about air pollution. This study contain conducting numerical simulation of air pollutant concentration considered land and sea breeze in Ulsan area with the numerical model.