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Open AccessArticle

Application of the Orthogonal Polynomial Fitting Method in Estimating PM2.5 Concentrations in Central and Southern Regions of China

1
College of Ocean Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
2
Physical Oceanography Laboratory/CIMST, Ocean University of China and Qingdao National Laboratory for Marine Science and Technology, Qingdao 266200, China
3
First Institute of Oceanography, Ministry of Natural Resources and Laboratory for Regional Oceanography and Numerical Modeling, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266061, China
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2019, 16(8), 1418; https://doi.org/10.3390/ijerph16081418
Received: 5 March 2019 / Revised: 13 April 2019 / Accepted: 14 April 2019 / Published: 19 April 2019
(This article belongs to the Special Issue Air Quality Monitoring and Assessment)
Sufficient and accurate air pollutant data are essential to analyze and control air contamination problems. An orthogonal polynomial fitting (OPF) method using Chebyshev basis functions is introduced to produce spatial distributions of fine particle (PM2.5) concentrations in central and southern regions of China. Idealized twin experiments (IE1 and IE2) are designed to validate the feasibility of the OPF method. IE1 is designed in accordance with the most common distribution of PM2.5 concentrations in China, whereas IE2 represents a common distribution in spring and autumn. In both idealized experiments, prescribed distributions are successfully estimated by the OPF method with smaller errors than kriging or Cressman interpolations. In practical experiments, cross-validation is employed to assess the interpolation results. Distributions of PM2.5 concentrations are well improved when OPF is applied. This suggests that errors decrease when the fitting order increases and arrives at the minimum when both orders reach 6. Results calculated by the OPF method are more accurate than kriging and Cressman interpolations if appropriate fitting orders are selected in practical experiments. View Full-Text
Keywords: fine particles (PM2.5); orthogonal polynomial fitting (OPF); interpolation fine particles (PM2.5); orthogonal polynomial fitting (OPF); interpolation
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Li, B.; Liu, Y.; Wang, X.; Fu, Q.; Lv, X. Application of the Orthogonal Polynomial Fitting Method in Estimating PM2.5 Concentrations in Central and Southern Regions of China. Int. J. Environ. Res. Public Health 2019, 16, 1418.

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