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Probabilistic Automatic Outlier Detection for Surface Air Quality Measurements from the China National Environmental Monitoring Network
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Probabilistic Automatic Outlier Detection for Surface Air Quality Measurements from the China National Environmental Monitoring Network
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Journal Article
Probabilistic Automatic Outlier Detection for Surface Air Quality Measurements from the China National Environmental Monitoring Network
2018
Overview
Although quality assurance and quality control procedures are routinely applied in most air quality networks, outliers can still occur due to instrument malfunctions, the influence of harsh environments and the limitation of measuring methods. Such outliers pose challenges for data-powered applications such as data assimilation, statistical analysis of pollution characteristics and ensemble forecasting. Here, a fully automatic outlier detection method was developed based on the probability of residuals, which are the discrepancies between the observed and the estimated concentration values. The estimation can be conducted using filtering—or regressions when appropriate—to discriminate four types of outliers characterized by temporal and spatial inconsistency, instrument-induced low variances, periodic calibration exceptions, and less PM10 than PM
2.5
in concentration observations, respectively. This probabilistic method was applied to detect all four types of outliers in hourly surface measurements of six pollutants (PM
2.5
, PM
10
, SO
2
, NO
2
, CO and O
3
) from 1436 stations of the China National Environmental Monitoring Network during 2014–16. Among the measurements, 0.65%–5.68% are marked as outliers, with PM
10
and CO more prone to outliers. Our method successfully identifies a trend of decreasing outliers from 2014 to 2016, which corresponds to known improvements in the quality assurance and quality control procedures of the China National Environmental Monitoring Network. The outliers can have a significant impact on the annual mean concentrations of PM
2.5
, with differences exceeding 10 μg m
−3
at 66 sites.
Publisher
Science Press,Springer Nature B.V,State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry,Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China,University of Chinese Academy of Science, Beijing 100049, China%State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry,Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China%International Center for Climate and Environment Sciences, Institute of Atmospheric Physics,Chinese Academy of Sciences, Beijing 100029, China
