• 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.

• Journal of Environmental Science International
• /
• v.26 no.2
• /
• pp.221-230
• /
• 2017

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 $PM_{10}$ 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 $PM_{10}$ decreased sharply from $59.6ug/m^3$ in 2002 to $44.6ug/m^3$ in 2015. This suggests that there was a long-term downward trend since 2005. The difference between the unadjusted and meteorologically adjusted long-term $PM_{10}$ is small. Therefore, we can conclude that $PM_{10}$ 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.

• Journal of the Korean earth science society
• /
• v.40 no.6
• /
• pp.549-560
• /
• 2019

The effects of the coal-fired power plant emissions, as the biggest point source of air pollutants, on spatiotemporal surface air pollution over the remote area are investigated in this study, based on a set of date selection and statistical technique to consider meteorological and geographical effects in the emission-concentration (source-receptor) relationship. We here proposed the sophisticated technique of data processing to separate and quantify the effects. The data technique comprises a set of data selection and statistical analysis procedure that include data selection criteria depending on meteorological conditions and statistical methods such as Kolmogorov-Zurbenko filter (K-Z filter) and empirical orthogonal function (EOF) analysis. The data selection procedure is important for filtering measurement data to consider the meteorological and geographical effects on the emission-concentration relationship. Together with meteorological data from the new high resolution ECMWF reanalysis 5 (ERA5) and the Korea Meteorological Administration automated surface observing system, air pollutant emission data from the telemonitoring system (TMS) of Dangjin and Taean power plants as well as spatio-temporal air pollutant concentrations from the air quality monitoring system are used for 4 years period of 2014-2017. Since all the data used in this study have the temporal resolution of 1 hour, the first EOF mode of spatio-temporal changes in air pollutant concentrations over the Seoul metropolitan area (SMA) due to power plant emission have been analyzed to explain over 97% of total variability under favorable meteorological conditions. It is concluded that SO₂, NO₂, and PM₁₀ concentrations over the SMA would be decreased by 0.468, 1.050 ppb, and 2.045 ㎍ m^-3 respectively if SO₂, NO₂, and TSP emissions from Dangjin power plant were reduced by 10%. In the same way, the 10% emission reduction in Taean power plant emissions would cause SO₂, NO₂, and PM₁₀ decreased by 0.284, 0.842 ppb, and 1.230 ㎍ m^-3 over the SMA respectively. Emissions from Dangjin power plant affect air pollution over the SMA in higher amount, but with lower R value, than those of Taean under the same meteorological condition.