• Title/Summary/Keyword: PM monitoring station networks

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Evaluation of Air Pollution Monitoring Networks in Seoul Metropolitan Area using Multivariate Analysis (다변량분석법을 활용한 수도권지역의 대기오염측정망 평가)

  • Choi, Im-Jo;Jo, Wan-Keun;Sin, Seung-Ho
    • Journal of Environmental Science International
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    • v.25 no.5
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    • pp.673-681
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    • 2016
  • The adequacy of urban air quality monitoring networks in the largest metropolitan city, Seoul was evaluated using multivariate analysis for $SO_2$, $NO_2$, CO, PM10, and $O_3$. Through cluster analysis for 5 air pollutants concentrations, existing monitoring stations are seen to be clustered mostly by geographical locations of the eight zones in Seoul. And the stations included in the same cluster are redundantly monitoring air pollutants exhibiting similar atmospheric behavior, thus it can be seen that they are being operated inefficiently. Because monitoring stations groups representing redudancy were different depending on measurement items and several pollutants are being measured at the same time in each air monitoring station, it is seemed to be not easy to integrate or transmigrate stations. But it may be proposed as follows : the redundant stations can be integrated or transmigrated based on ozone of which measures are increasing in recent years and alternatively the remaining pollutants other than the pollutant exhibiting similar atmospheric behavior with nearby station's can be measured. So it is considered to be able to operate air quality monitoring networks effectively and economically in order to improve air quality.

The Variation Analysis on Spatial Distribution of PM10 and PM2.5 in Seoul (서울시 PM10과 PM2.5의 공간적 분포 변이분석)

  • Jeong, Jongchul
    • Journal of Environmental Impact Assessment
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    • v.27 no.6
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    • pp.717-726
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    • 2018
  • PM(Particulate Matter) cause serious diseases of air pollution. Most of the studies have analyzed local distribution trends using satellite images or modeling techniques. However,the method using the spatial interpolation method based on the meteorological value is insufficient in Korea. In this study, monthly spatial distribution of $PM_{10}$ and $PM_{2.5}$ in January, February, March, and April of 2018 Seoul Metropolitan City were analyzed based on 39 PM monitoring networks. In addition, a distribution map showing the difference between $PM_{10}$ and $PM_{2.5}$ was based on the distribution obtained through this study. The regions of high $PM_{10}$ and $PM_{2.5}$ emissions were selected. In addition, the correlation between $PM_{10}$ and $PM_{2.5}$ was confirmed through the distribution map. This study analyzed the spatial distribution variation results of analyzing $PM_{10}$ and $PM_{2.5}$ in Seoulthrough spatial analysis technique. As a result of this study, it was confirmed that $PM_{10}$ shows high measured value on the roadside measurement station.

A study on the monitoring of high-density fine particulate matters using W-station: Case of Jeju island (W-Station을 활용한 고밀도 초미세먼지 모니터링 연구: 제주도 사례)

  • Lee, Jong-Won;Park, Moon-Soo;Won, Wan-Sik;Son, Seok-Woo
    • Particle and aerosol research
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    • v.16 no.2
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    • pp.31-47
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    • 2020
  • Although interest in air quality has increased due to the frequent occurrence of high-concentration fine particulate matter recently, the official fine particulate matter measuring network has failed to provide spatial detailed air quality information. This is because current measurement equipment has a high cost of installation and maintenance, which limits the composition of the measuring network at high resolution. To compensate for the limitations of the current official measuring network, this study constructed a spatial high density measuring network using the fine particulate matter simple measuring device developed by Observer, W-Station. W-Station installed 48 units on Jeju Island and measured PM2.5 for six months. The data collected in W-Station were corrected by applying the first regression equation for each section, and these measurements were compared and analyzed based on the official measurements installed in Jeju Island. As a result, the time series of PM2.5 concentrations measured in W-Station showed concentration characteristics similar to those of the environmental pollution measuring network. In particular, the results of comparing the measurements of W-Station within a 2 km radius of the reference station and the reference station showed that the coefficient of determination (R2) was 0.79, 0.81, 0.67, respectively. In addition, for W-Station within a 1 km radius, the coefficient of determination was 0.85, 0.82, 0.68, respectively, showing slightly higher correlation. In addition, the local concentration deviation of some regions could be confirmed through 48 high density measuring networks. These results show that if a network of measurements is constructed with adequate spatial distribution using a number of simple meters with a certain degree of proven performance, the measurements are effective in monitoring local air quality and can be fully utilized to supplement or replace formal measurements.

Comparative Analysis on the Outlier Data of Each Parameter in Automatic Water Quality Monitoring Networks (수질자동측정망 자료의 항목별 이상치 비교 분석)

  • Lim, Byungjin;Hong, Eunyoung;Yeon, Insung
    • Journal of Korean Society on Water Environment
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    • v.26 no.4
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    • pp.700-706
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    • 2010
  • Along the 4 major rivers in korea, there are automatic water quality monitoring (AWQM) stations to immediately respond to any pollution incident. Real-time data (temperature, DO, pH, EC and TOC) collected at each station were statistically treated to exclude outliers and keep valid data using Dixon's test and Discordance test. These applied methods were compared in terms of the number of the outliers sorted out. There was no significant difference between these methods. On the other hand, more outliers were sorted out from EC and TOC data, comparing with other water quality items. EC data did not show partly any variation for a long time at H station. If measured signal does not exceed ${\pm}0.001mS/cm$ from the sectional mean, the signal should be treated as normal data. Therefore, another routine was added to the data screening system, some data which were removed as outlier were restored.