Multivariate Analysis of Air Quality in Selected Oil Operating Areas in the Niger Delta Region of Nigeria

Abstract

Abstract This study assessed air quality in selected oil operating areas in Rivers State of Nigeria with consideration to land use type such as oil and gas facilities, major bus stops, schools, Markets, hospitals, residential areas, vegetation and commercial centers. Air quality and meteorological parameters across 25 locations were monitored during the wet season (April to October) 2015 to ascertain major air pollutant sources. The parameters monitored were, Nitrogen dioxide (NO2), Sulphur dioxide (SO2), Carbon ii oxide (CO), Volatile organic compounds (VOCs), particulate matter (PM) 1, PM2.5, PM4, PM7, PM10, TSP, wind speed, humidity and temperature. Descriptive statistics, agglomerative hierarchical cluster analysis (AHCA), Principal component analysis (PCA), Multiple linear regression (MLR) and Principal component regression (PCR) were used in the data analysis and modeling of Air quality index. Results showed that SO2 values ranged from 0.04 ppm to 0.15 ppm, NO2 values ranged from 0.025 ppm to 0.162 ppm, CO concentration ranged from 0.24 ppm to 6.36 ppm, VOC values ranged from 0.18 ppm to 2.19 ppm across all study locations. Particulate matter also varied across the study locations. Three clusters were obtained from AHCA. The dendogram plot from AHCA showed that the observed variation can be attributed to the land use pattern in the study area. Three principal components were extracted from the PCA based on eigenvalue of >1 and from the biplot after varimax rotation revealed that the major sources of air pollution in the area based on land use type are markets, major bus stops, residential and commercial centers. Vehicular emissions, biomass burning and dusty roads are the major sources of air pollution in the study area. MLR performed better than PCR with MLR and PCR having an R2 of 0.967 and 0.930 respectively. PCA, AHCA, MLR and PCR proved to be useful tools in air quality analysis for source apportionment and prediction as this can aid in air pollution abatement planning. There is need to improve existing policies in other to proactively mitigate the impacts of air pollution from oil and non-oil operation sources.