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AMT | Articles | Volume 13, issue 3
Atmos. Meas. Tech., 13, 1213–1226, 2020
https://doi.org/10.5194/amt-13-1213-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Atmos. Meas. Tech., 13, 1213–1226, 2020
https://doi.org/10.5194/amt-13-1213-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 11 Mar 2020

Research article | 11 Mar 2020

Filling the gaps of in situ hourly PM2.5 concentration data with the aid of empirical orthogonal function analysis constrained by diurnal cycles

Kaixu Bai et al.

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Cited articles

Aydilek, I. B. and Arslan, A.: A hybrid method for imputation of missing values using optimized fuzzy c-means with support vector regression and a genetic algorithm, Inf. Sci., 233, 25–35, https://doi.org/10.1016/j.ins.2013.01.021, 2013. 
Bai, K., Chang, N.-B., Zhou, J., Gao, W., and Guo, J.: Diagnosing atmospheric stability effects on the modeling accuracy of PM2.5/AOD relationship in eastern China using radiosonde data. Environ. Pollut., 251, 380–389, https://doi.org/10.1016/j.envpol.2019.04.104, 2019a. 
Bai, K., Ma, M., Chang, N.-B., and Gao, W.: Spatiotemporal trend analysis for fine particulate matter concentrations in China using high-resolution satellite-derived and ground-measured PM2.5 data, J. Environ. Manage., 233, 530–542, https://doi.org/10.1016/j.jenvman.2018.12.071, 2019b. 
Bai, K., Li, K., Chang, N.-B., and Gao, W.: Advancing the prediction accuracy of satellite-based PM2.5 concentration mapping: A perspective of data mining through in situ PM2.5 measurements, Environ. Pollut., 254, 113047, https://doi.org/10.1016/j.envpol.2019.113047, 2019c. 
Beckers, J. M. and Rixen, M.: EOF Calculations and Data Filling from Incomplete Oceanographic Datasets, J. Atmos. Ocean. Tech., 20, 1839–1856, https://doi.org/10.1175/1520-0426(2003)020<1839:ECADFF>2.0.CO;2, 2003. 
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A novel gap-filling method called the diurnal-cycle-constrained empirical orthogonal function (DCCEOF) is proposed. Cross validation indicates that this method gives high accuracy in predicting missing values in daily PM2.5 time series by accounting for the local diurnal phases, especially by reconstructing daily extrema that cannot be accurately restored by other approaches. The DCCEOF method can be easily applied to other data sets because of its self-consistent capability.
A novel gap-filling method called the diurnal-cycle-constrained empirical orthogonal function...
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