• Journal of Korean Society for Atmospheric Environment
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• v.24 no.1
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• pp.63-71
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• 2008

Local meteorological conditions exert a strong influence over day-to-day variations in air pollutant concentrations. Therefore, the meteorological fluctuations have to be removed in order for air quality planners and managers to examine underlying emissions-related trends and make better air quality management decisions for future. In this study, the meteorologically adjusted $PM_{10}$ trends in Seoul are investigated over the period $1999{\sim}2006$ using Kolmogorov-Zurbenco (KZ) filter. The result indicated that meteorologicaJ variability accounts for about 25% of $PM_{10}$ variability. Both the meteorologically adjusted and unadjusted Jong-term daily $PM_{10}$ concentrations had a significant downward trends and the difference between the meteorologically adjusted and unadjusted was small. So it was assumed that in long-term daily $PM_{10}$ changes, localized changes in emissions is more important than the changes caused by meteorological conditions.

• Journal of Environmental Science International
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• v.26 no.2
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• pp.221-230
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• 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 Environmental Impact Assessment
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• v.24 no.2
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• pp.111-118
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• 2015

This study was conducted for the establishment of statistical method to determine the representative air quality monitoring station representing long-term ozone trends of Seoul. In this study, hourly ozone concentrations from 2002 to 2011 were used for further analysis. KZ-filter, correlation matrix, cluster analysis, and Kriging method were applied to select the representative station. The analysis based on correlation matrix found that long-term trend of ozone concentrations measured at Sinjung, Sadang, and Bun-dong showed a high correlation. The cluster analysis found that the former three stations belonged to the same cluster. The analysis based on Kriging method also showed that the former three stations were highly correlated with other stations in spatial distribution. Considering these results and the highest correlation coefficient of Sinjung station, the Sinjung station was the most suitable as the representative station used to understand the long-term ozone trend of Seoul. This result could be applied to understand long-term trend of other pollutants. Furthermore, this result can also be used to assess the appropriacy of spatial distribution of national air quality monitoring stations.

• The Journal of the Acoustical Society of Korea
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• v.10 no.3
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• pp.5-12
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• 1991

In this paper, 10 city names of Korean were recognized. The name are articulated each 5 times by 10 male speakers. Filter bank output on total 500 words were extracted and they were used as feature parameters. Filter bank was constructed of 15 channels with 1/3 octave spacing from 200[Hz], using RC active circuit. Reference templates were created by clustering algorithm. DTW algorithm was used to compare similarity between reference templates and input words. Euclidean distance equation and Chebyshev distance equation were used to know the distinction between the recognition results obtained by the method of distance caculation, error rates are 16.4[%], 15.0[%], respectively.