Filling the gaps of in situ hourly PM<sub>2.5</sub> concentration data with the aid of empirical orthogonal function analysis constrained by diurnal cycles
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Published:2020-03-11
Issue:3
Volume:13
Page:1213-1226
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ISSN:1867-8548
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Container-title:Atmospheric Measurement Techniques
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language:en
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Short-container-title:Atmos. Meas. Tech.
Author:
Bai Kaixu, Li Ke, Guo JianpingORCID, Yang Yuanjian, Chang Ni-Bin
Abstract
Abstract. Data gaps in surface air quality measurements significantly impair
the data quality and the exploration of these valuable data sources. In this
study, a novel yet practical method called diurnal-cycle-constrained
empirical orthogonal function (DCCEOF) was developed to fill in data gaps
present in data records with evident temporal variability. The hourly
PM2.5 concentration data retrieved from the national ambient air
quality monitoring network in China were used as a demonstration. The DCCEOF
method aims to reconstruct the diurnal cycle of PM2.5 concentration
from its discrete neighborhood field in space and time firstly and then
predict the missing values by calibrating the reconstructed diurnal cycle
to the level of valid PM2.5 concentrations observed at adjacent times.
The statistical results indicate a high frequency of data gaps in our
retrieved hourly PM2.5 concentration record, with PM2.5
concentration measured on about 40 % of the days suffering from data gaps.
Further sensitivity analysis results reveal that data gaps in the hourly
PM2.5 concentration record may introduce significant bias to its
daily averages, especially during clean episodes at which PM2.5 daily
averages are observed to be subject to larger uncertainties compared to the
polluted days (even in the presence of the same amount of missingness). The
cross-validation results indicate that our suggested DCCEOF method has a
good prediction accuracy, particularly in predicting daily peaks and/or
minima that cannot be restored by conventional interpolation approaches,
thus confirming the effectiveness of the consideration of the local diurnal
variation pattern in gap filling. By applying the DCCEOF method to the
hourly PM2.5 concentration record measured in China from 2014 to
2019, the data completeness ratio was substantially improved while the
frequency of days with gapped PM2.5 records reduced from 42.6 % to
5.7 %. In general, our DCCEOF method provides a practical yet effective
approach to handle data gaps in time series of geophysical parameters with
significant diurnal variability, and this method is also transferable to
other data sets with similar barriers because of its self-consistent
capability.
Funder
National Natural Science Foundation of China
Publisher
Copernicus GmbH
Subject
Atmospheric Science
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