Scaling Correlation Analysis of Particulate Matter Concentrations of Three South Indian Cities DOI Creative Commons

S. Adarsh,

Susan Mariam Rajesh,

Muraleekrishnan Bahuleyan

et al.

Pollutants, Journal Year: 2024, Volume and Issue: 4(4), P. 498 - 514

Published: Nov. 13, 2024

Analyzing the fluctuations of particulate matter (PM) concentrations and their scaling correlation structures are useful for air quality management. Multifractal characterization PM2.5 PM10 three cities in India wase considered using detrended fluctuation procedure from 2018 to 2021. The cross-correlation PM concentration a multifractal viewpoint analysis (MFCCA) framework is proposed this study. It was observed that more complex than at all locations. PM–gaseous pollutant (GP) PM–meteorological variable (MV) correlations across scales were found be weak moderate different cities. There no definite pattern with meteorological gaseous pollutants variables. nature pairwise associations diverse mixed time All series exhibited multifractality when analyzed analysis. However, there reduction individual cases during PM–GP PM–MV paired analyses. insights gained into behavior structure study valuable developing prediction models PMs by integrating them machine learning techniques.

Language: Английский

Scaling Correlation Analysis of Particulate Matter Concentrations of Three South Indian Cities DOI Creative Commons

S. Adarsh,

Susan Mariam Rajesh,

Muraleekrishnan Bahuleyan

et al.

Pollutants, Journal Year: 2024, Volume and Issue: 4(4), P. 498 - 514

Published: Nov. 13, 2024

Analyzing the fluctuations of particulate matter (PM) concentrations and their scaling correlation structures are useful for air quality management. Multifractal characterization PM2.5 PM10 three cities in India wase considered using detrended fluctuation procedure from 2018 to 2021. The cross-correlation PM concentration a multifractal viewpoint analysis (MFCCA) framework is proposed this study. It was observed that more complex than at all locations. PM–gaseous pollutant (GP) PM–meteorological variable (MV) correlations across scales were found be weak moderate different cities. There no definite pattern with meteorological gaseous pollutants variables. nature pairwise associations diverse mixed time All series exhibited multifractality when analyzed analysis. However, there reduction individual cases during PM–GP PM–MV paired analyses. insights gained into behavior structure study valuable developing prediction models PMs by integrating them machine learning techniques.

Language: Английский

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