부유분진측정기(PM10) 관측 자료 실시간 품질관리 알고리즘 개발 및 평가
Development and Assessment of Real-Time Quality Control Algorithm for PM10 Data Observed by Continuous Ambient Particulate Monitor
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초록
A real-time quality control algorithm for PM10 concentration measured by Continuous Ambient Particulate Monitor (FH62C14, Thermo Fisher Scientific Inc.) has been developed. The quality control algorithm for PM10 data consists of five main procedures. The first step is valid value check. The values should be within the acceptable range limit. Upper (5,000 μg m−3) and lower (0 μg m−3) values of instrument detectable limit have to be eliminated as being unrealistic. The second step is valid error check. Whenever unusual condition occurs, the instrument will save error code. Value having an error code is eliminated. The third step is persistence check. This step checks on a minimum required variability of data during a certain period. If the PM10 data do not vary over the past 60 minutes by more than the specific limit (0 μg m−3) then the current 5-minute value fails the check. The fourth step is time continuity check, which is checked to eliminate gross outlier. The last step is spike check. The spikes in the time series are checked. The outlier detection is based on the double-difference time series, using the median. Flags indicating normal and abnormal are added to the raw data after quality control procedure. The quality control algorithm is applied to PM10 data for Asian dust and non-Asian dust case at Seoul site and dataset for the period 2013~2014 at 26 sites in Korea.
Abstract
A real-time quality control algorithm for PM10 concentration measured by Continuous Ambient Particulate Monitor (FH62C14, Thermo Fisher Scientific Inc.) has been developed. The quality control algorithm for PM10 data consists of five main procedures. The first step is valid value check. The values should be within the acceptable range limit. Upper (5,000 μg m−3) and lower (0 μg m−3) values of instrument detectable limit have to be eliminated as being unrealistic. The second step is valid error check. Whenever unusual condition occurs, the instrument will save error code. Value having an error code is eliminated. The third step is persistence check. This step checks on a minimum required variability of data during a certain period. If the PM10 data do not vary over the past 60 minutes by more than the specific limit (0 μg m−3) then the current 5-minute value fails the check. The fourth step is time continuity check, which is checked to eliminate gross outlier. The last step is spike check. The spikes in the time series are checked. The outlier detection is based on the double-difference time series, using the median. Flags indicating normal and abnormal are added to the raw data after quality control procedure. The quality control algorithm is applied to PM10 data for Asian dust and non-Asian dust case at Seoul site and dataset for the period 2013~2014 at 26 sites in Korea.
참고문헌 (19)
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