Multivariate Analysis of Air Pollutants

Multivariate linear correlation analysis is a useful method to statistically determine the relationships between pollutants or other atmospheric factors influencing the air quality and to reveal the most significant parameters on the concentrations of atmospheric pollutants [12 (link),43 (link),77 (link)]. In this work, IBM^® SPSS^® statistics software (Ver. 24) was used to perform descriptive statistics, sample comparison, and multivariate linear correlation analyses for the hourly mean values of the measured eBC concentrations and meteorological parameters. Bivariate polar plots of concentrations have recently been used to reveal the collective impact of wind velocity and wind direction on air pollutants’ observed concentrations [78 (link),79 (link),80 (link)]. In this research, bivariate polar plots were drawn to illustrate the impact of wind components on hourly mean values of eBC concentrations through Pre-LD, LD, and Post-LD phases.

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Anil I, & Alagha O. (2020). Source Apportionment of Ambient Black Carbon during the COVID-19 Lockdown. International Journal of Environmental Research and Public Health, 17(23), 9021.

Publication 2020

SPSS® statistics software (Ver. 24)

Air pollutants Pollutants Wind

Corresponding Organization : Imam Abdulrahman Bin Faisal University

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Variable analysis

independent variables

Wind velocity
Wind direction

dependent variables

Hourly mean values of eBC concentrations

control variables

Hourly mean values of meteorological parameters

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