期刊
ATMOSPHERIC POLLUTION RESEARCH
卷 8, 期 4, 页码 700-708出版社
TURKISH NATL COMMITTEE AIR POLLUTION RES & CONTROL-TUNCAP
DOI: 10.1016/j.apr.2017.01.004
关键词
Outlier detection; Particulate matter; Kernel smoothing; Six sigma; Control charts
资金
- Ministry of Defence [PASVRII - DZRO K110]
Outliers, which can have significant effects on further analysis and modelling, occur between continuously measured environmental data. Most methods for outlier detection depends on model or distribution of observed variable. However the distribution of environmental variables cannot be estimated quite often. This paper presents two procedures, which do not impose restrictions on the distribution of analysed variable, and which permit the intervals of the environmental observations, where the outliers occur, to be detected. The proposed procedures are based on smoothing original data and subsequent analysis of the residuals. The output of both methods is an interval of observations, where the residual process behaves substandard, and whose quality must be further manually assessed. Thus the value of the proposed methodology is that the number of observations for manual data control is reduced. Both methods are applied to problem of detection outliers in hourly PM10 measurements. However, the methodology is general and can be applied to different type of data whose quality control is required. (c) 2017 Turkish National Committee for Air Pollution Research and Control. Production and hosting by Elsevier B.V. All rights reserved.
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