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

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 ([Formula: see text] and [Formula: see text]) 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 [Formula: see text] and (temperature, pressure) is more obvious in urban area. The multifractal strength of cross-correlation between [Formula: see text] and WS is very weak in either urban or rural area. Furthermore, the MF-TWXDFA cross-correlation coefficient [Formula: see text] can capture negative correlation between pollutants and meteorological factors. For [Formula: see text], [Formula: see text] in urban area is less than or close to that in rural area with respect to these four meteorological factors. The [Formula: see text] of [Formula: see text] 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.