• Asian Journal of Atmospheric Environment
• /
• v.5 no.2
• /
• pp.105-112
• /
• 2011

In order to investigate the effects of local and synoptic meteorological conditions on urban scale particulate air pollutants observed over the Busan coastal area, power spectrum analysis was applied to observed particulate matter with an aerodynamic diameter $\leq10\;{\mu}m$ ($PM_{10}$) for the period from 1 October, 1993 to 31 December, 2004. Fast Fourier Transform (FFT) analysis was used to obtain the hourly mean observed $PM_{10}$ concentrations to identify different periodicity scales of $PM_{10}$ concentrations. The results showed that, aside from the typical and well-known periodicities such as diurnal and annual variations caused by anthropogenic influences, three other significant power spectral density peaks were identified: 7-day, 21-day and 2.25-year periodicities. Cospectrum analysis indicated that the seven-day variations were closely related to the synoptic meteorological conditions such as weak wind speed, which are relevant to the stagnant high pressure system slowly passing through the Korean Peninsula. The intra-seasonal 21-day variation was negatively correlated with wind speed but was consistently positively correlated with relative humidity, which is related to aerosol formation that can be achieved as a result of the hygroscopic characteristics of aerosols. However, the quasibiennial 2.25-year variation was correlated with the frequency of Asian dust occurrence, the periodicities of which have been recorded inter-annually over the Korean Peninsula.

• Journal of Korean Society for Atmospheric Environment
• /
• v.19 no.5
• /
• pp.561-568
• /
• 2003

Surface ozone concentrations are highly sensitive to meteorological variability. Therefore, in order to reveal the long-term changes in ozone due to the changes in precursor emissions, we need to remove the effects of meteorological fluctuations on the annual distribution of surface ozone. In this paper, the meteorologically adjusted trends of daily maximum surface ozone concentrations in two major Korean cities (Seoul and Busan) are investigated based on ozone data from 11 (Seoul) and 6 (Busan) sites over the period 1992 ∼ 2000. The original time series consisting of the logarithm of daily maximum ozone concentrations are splitted into long-term, seasonal and short-term component using Kolmogorov-Zurbenko (KZ) filter. Meteorological effects are removed from filtered ozone series using multiple linear regression based on meteorologcial variables. The long-term evolution of ozone forming capability due to changes in precursor emission can be obtained applying the KZ filter to the residuals of the regression. The results indicated that meteorologically adjusted long-term daily maximum ozone concentrations had a significant upward trend (Seoul: ＋ 3.02% yr$^{-1}$ , Busan: ＋ 3.45% yr$^{-1}$ ). These changes of meteorologically adjusted ozone concentrations represent the effects of changing background ozone concentrations as well as the more localized changes in emissions.

• Asian Journal of Atmospheric Environment
• /
• v.4 no.2
• /
• pp.89-96
• /
• 2010

To identify the domains that have the greatest impacts on air quality at the surface, both the upwind and downwind areas of Seoul were determined by season using refined wind fields. Four consecutive days were selected as the study period typical of each season. The mesoscale meteorology of the study period was reproduced by using the MM5 prognostic meteorological model (PSU/NCAR Mesoscale Model) with horizontally nested grids. The gridded meteorological field, which was used on the study area of $242\;km{\times}226\;km$ with grid spacing of 2 km, was generated by using the CALMET diagnostic meteorological model. Upwind and downwind areas of Seoul were determined by calculating 24-hour backward and forward air parcel trajectories, respectively, with u, v, and w velocity vectors. The results showed that the upwind and downwind areas were extended far to the northwest and the southeast as a result of high wind speeds in the spring and winter, while they were restricted on the fringe of Seoul in the summer and fall.

• Journal of Korean Society for Atmospheric Environment
• /
• v.8 no.4
• /
• pp.213-220
• /
• 1992

Simple correlation analysis, factor analysis, and multi-variate analysis have been performed to analyze the relationship between air pollution and meteorological factors for air pollution and meteorological data measured at Kwanghwamun in Seoul during the period of one year(January 1990 $\sim$ December 1990). As a result of simple correlation and factor analysis, $SO_2$, TSP and CO concentrations have shown high negative correlation with temperature and among these indicating that these are related with pollutant emission trend based upon heating fuel usage. Ozone has a good corrleation with solar radiation and relative humidity to have a closed relation with $O_3$ generation reaction mechanism. The result of multi-variate correlation analysis shows that the concentration of $SO_2$ and CO are adequate for correlation model with ambient temperature and wind speed and $O_3$ concentrations are adequate for that with solar radiation and wind speed. $SO_2$ and CO levels are considered to be affected first of all by heating fuel usage as a emssion source and wind speed as a dispersion effect. The $SO_2$ concentration in the condition that the temperature fall below zero is explained by multilicative model with wind speed, only one variable.

• Journal of Environmental Science International
• /
• v.23 no.6
• /
• pp.1085-1093
• /
• 2014

We investigate the amount of potential electricity energy generated by wind power in Busan metropolitan area, using the mesoscale meteorological model WRF (Weather Research & Forecasting), combined with small wind power generators. The WRF modeling has successfully simulated meteorological characteristics over the urban areas, and showed statistical significant to predict the amount of wind energy generation. The highest amount of wind power energy has been predicted at the coastal area, followed by at riverbank and upland, depending on predicted spatial distributions of wind speed. The electricity energy prediction method in this study is expected to be used for plans of wind farm constructions or the power supplies.

• Journal of Korean Society for Atmospheric Environment
• /
• v.10 no.4
• /
• pp.260-280
• /
• 1994

The STEM II(Sulfur Transport Eulerian Model II) was adapted to simulate transport/ chemistry/deposition of air Pollutants in the Eastern China and Korea. A 32 hour model simulation starting from 9 A.M. of 1989 November 25 during which no preciptation was observed. The Prevailing wind direction is from west to east. The MM4(Meteorological Model Version 4) was used to generate meteorological data such as temperatures, horizontal wind velocities and directions, humidities, air densities. Eddy diffusivities, dry deposition velocities and vertical wind velocities were calculated from the meteorological data. The initial condition and the emission data base were constructed from the measurements and governmental reports respectively. The model predictions of NO, NO$_2$, SO$_2$, $O_3$ at Seoul, Inchon and Pusan agree reasonably well with measurements. The model's predictability for the primary air pollutants is improved considerably as the time passes. Thus, it is concluded that the model's predictability can be significantly enhanced by reducing the uncertainties of initial conditions.

• Journal of Korean Society for Atmospheric Environment
• /
• v.10 no.E
• /
• pp.343-356
• /
• 1994

In this study, a statistical investigation was carried out for the evaluation of any relationship between polycyclic aromatic hydrocarbons (PAHss) associated with ambient aerosols and other air quality parameters under varying meteorological conditions. Daily measurements for PAHs and air quality/meteorological parameters were selected from a data-base constructed by a comprehensive air monitoring in London during 1985-1987. Correlation coefficients were calculated to examine any significant relationship between the PAHs and other individual variables. Statistical analysis was further Performed for the air quality/meteorological data set using a principal component analysis to derive important factors inherent in the interactions among the variables. A total of six components were identified, representing vehicle emission, photochemical activity/volatilization, space heating, atmospheric humidity, atmospheric stability, and wet deposition. It was found from a stepwise multiple regression analysis that the vehicle emission component is overall the most important factor contributing to the variability of PAHs concentrations at the monitoring site. The photochemical activity/volatilzation component appeared to be also an important factor particularly for the lower molecular weight PAHs. In general, the space heating component was found to be next important factor, while the contributions of other three components to the variance of each PAHs did not appear to be as much important as the first three components in most cases. However, a consistency for these components in their negative correlations with PAHs data was found, indicating their roles in the depletion of PAHs concentrations in the urban atmosphere.

• Journal of Korean Society for Atmospheric Environment
• /
• v.25 no.4
• /
• pp.264-274
• /
• 2009

Concentration of Air Quality Models (CMAQ) has a deep connection with emissions and wind fields. In particular the wind field is highly affected by local topography and plays an important role in transport and dispersion of contaminants from the pollution sources. The purpose of this study is to examine the impact of interpolation on Air quality model. This study was designed to evaluate enhancement of MM5 and CMAQ predictions by using Four Dimensional Data Assimilation (FDDA), the SONDE data and the national meteorological station and the MODerate resolution Imaging Spectroradiometer (MODIS). The alternative meteorological fields predicted with and without MODIS data were used to simulate spatial and temporal variations of ozone in combined with CMAQ on June 2006. The result of this study indicated that data assimilation using MODIS data provided an attractive method for generating realistic meteorological fields and dispersion fields of ozone in the Korea peninsular, because MODIS data in 10 km domain are grid horizontally and vertically. In order to ensure the success of Air quality model, it is necessary to FDDA using MODIS data.

• Journal of Korean Society for Atmospheric Environment
• /
• v.24 no.5
• /
• pp.604-612
• /
• 2008

The ozone concentration is one of the important environmental issue for measurement of the atmospheric condition of the country. This study focuses on applying the Autoregressive Error (ARE) model for analyzing the ozone data at middle part of the Gyeonggi-Do, Anyang monitoring site in Korea. In the ARE model, eight meteorological variables and four pollution variables are used as the explanatory variables. The eight meteorological variables are daily maximum temperature, wind speed, amount of cloud, global radiation, relative humidity, rainfall, dew point temperature, and water vapor pressure. The four air pollution variables are sulfur dioxide $(SO_2)$, nitrogen dioxide $(NO_2)$, carbon monoxide (CO), and particulate matter 10 (PM10). The result shows that ARE models both overall and monthly data are suited for describing the oBone concentration. In the ARE model for overall ozone data, ozone concentration can be explained about 71% to by the PM10, global radiation and wind speed. Also the four types of ARE models for high level of ozone data (over 80 ppb) have been analyzed. In the best ARE model for high level of ozone data, ozone can be explained about 96% by the PM10, daliy maximum temperature, and cloud amount.

• Journal of Korean Society for Atmospheric Environment
• /
• v.21 no.3
• /
• pp.377-397
• /
• 2005

A new box model is proposed to describe the dynamic trend of the spatially averaged concentrations of pollutants over a large urban area such as metropolitan Seoul. Being averaged temporally and spatially over a thresh-hold scales, the dynamics of the pollutant concentration becomes simple enough that the governing equation can be expressed in an explicit algebraic form as a function of several meteorological factors and the pollutant emission rate. The single most important meteorological factor is the wind speed dominating the daily variations of the pollutant concentrations. Given the meteorological data from the surface station in the metropolitan Seoul, the model concentration shows excellent agreement with observations from January 1, 1990 to December 31, 2000: the modeling uncertainty, for example, of $NO_2$ concentrations, defined as mean differences between the model concentrations and observations is $16\%$ of the model concentrations. Even for $PM_{10}$ of which secondary sources are considered to be very important and simple box model is irrelevant to, the model performance turns out good, modeling uncertainty being about $32\%$.