Fractal Description of the Temporal Fluctuation of PM2.5 and PM10 Concentrations and their Cross-correlation at Cotonou Autonomous Port and the “Boulevard de la Marina” area (Benin Republic, West Africa)

Document Type : Original Research Paper

Authors

1 Laboratoire de Physique du Rayonnement (LPR), Université d’Abomey-Calavi, Abomey-Calavi BP : 526 UAC, Bénin

2 International Chair in Mathematical Physics and Applications (UNESCO-Chaire UAC-CIPMA)

3 International Chair in Mathematical Physics and Applications (ICMPA-UNESCO Chair), Université d’Abomey-Calavi, Abomey-Calavi BP: 526 UAC, Bénin

Abstract

The present study aims to provide baseline information on the temporal characteristics of PM2.5 and PM10 concentration time series variations, mainly on the cross-correlation between PM2.5 and PM10, using the improved mathematical and nonlinear methods. Firstly, the fractal theory such as fractal dimension is used to detect the pollution level in PM2.5 and PM10 time series. Secondly, the Multifractal Detrending Moving-Average Analysis (MFDMA) is used to analyze the multifractal characteristics of PM2.5 and PM10 concentrations. Thirdly, Multifractal Detrending Moving-Average cross-correlation Analysis (MFXDMA) is used to study the cross-correlation between PM2.5 and PM10 concentrations measured from January 1 to December 31, 2020, along the Boulevard de la Marina, one of the major roads in Cotonou. The results have indicated that: (1) PM10 and PM2.5 concentration time series are characterized by a fractal dimension, which can permit to detect the pollution levels and to analyze the differences in emissions sources; (2) there is a significant multifractal structure in the PM2.5 and PM10 concentration data and their fluctuations are long-range correlated, however, the multifractal properties and self-memory characteristics change with the months; (3) generally, the multifractal degree and the complexity of PM10 are much stronger than those of PM2.5. However, they present a similar multifractality degree in some months of the year; (4) except, in February, the cross-correlation between PM2.5 and PM10 time series in the months of the year presents multifractal characteristics with positive persistence; (5) the cross-correlation multifractal features show monthly variation. This paper provides the inter-relationship between air PM2.5 and PM10 time series which may help taking steps in controlling the air quality and management of the Cotonou port area environment.

Keywords

References

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Pollution
Volume 9, Issue 2
April 2023
Pages 628-645

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