MULTIFRACTAL TEMPORALLY WEIGHTED DETRENDED CROSS-CORRELATION ANALYSIS OF PM10, NOX AND METEOROLOGICAL FACTORS IN URBAN AND RURAL AREAS OF HONG KONG

Shan Jiang, Zu-Guo Yu, Vo V. Anh and Yu Zhou
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Shan Jiang: Key Laboratory of Intelligent Computing, Information Processing of Ministry of Education, Hunan Key Laboratory for Computation, Simulation in Science and Engineering, Xiangtan University, Xiangtan, Hunan 411105, P. R. China
Zu-Guo Yu: Key Laboratory of Intelligent Computing, Information Processing of Ministry of Education, Hunan Key Laboratory for Computation, Simulation in Science and Engineering, Xiangtan University, Xiangtan, Hunan 411105, P. R. China
Vo V. Anh: Faculty of Science, Engineering and Technology, Swinburne University of Technology, PO Box 218, Hawthorn, Victoria 3122, Australia
Yu Zhou: Institute of Future Cities and Department of Geography, Resource Management, The Chinese University of Hong Kong, Shatin, Hong Kong, P. R. China

FRACTALS (fractals), 2021, vol. 29, issue 06, 1-17

Abstract: Understanding of its correlation to some relevant factors is of paramount importance for modeling and predication of the air pollution process. Compared with the traditional cross-correlation analysis, multifractal detrended cross-correlation analysis (MFDCCA) was argued to be a more suitable method to analyze air pollutant time series due to their non-stationarity nature. Multifractal temporally weighted detrended cross-correlation analysis (MF-TWXDFA) was proposed to improve the shortcomings of MFDCCA. In this study, we apply MF-TWXDFA to investigate the cross-correlation between pollutants (PM10 and NOX) and meteorological factors (temperature, pressure, wind speed (WS) and relative humidity (RH)). The results on the dataset from 1 January 2005 to 31 December 2014 in urban and rural areas of Hong Kong show the existence of multifractal cross-correlation between all pairs of pollutants and meteorological factors in both urban and rural areas. Different from the previous MFDCCA results, we found that the multifractal degree of cross-correlation between PM10 and (temperature, pressure) is more obvious in urban area. The multifractal strength of cross-correlation between NOX and WS is very weak in either urban or rural area. Furthermore, the MF-TWXDFA cross-correlation coefficient Ï MF-TWXDFA can capture negative correlation between pollutants and meteorological factors. For PM10, Ï MF-TWXDFA in urban area is less than or close to that in rural area with respect to these four meteorological factors. The Ï MF-TWXDFA of NOX in urban and rural areas shows more complex patterns for varied meteorological factors. Compared with MFDCCA, MF-TWXDFA can provide much richer information about the relationships between pollutants and meteorological factors, which is beneficial to further modeling and prediction of the air pollution process.

Keywords: Air Pollutant; Cross-Correlation; Meteorological Factor; Multifractality; Scaling Behavior (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1142/S0218348X21501668

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