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DOI: https://doi.org/10.4491/eer.2018.414
Spatiotemporal variations and source apportionment of NOx, SO2, and O3 emissions around heavily industrial locality
Meshari Al-Harbi1, Abdulrahman Al-majed1, and Asma Abahussain2
1Department of Environmental Technology Management, College of Life Sciences, Kuwait University, P.O. Box 5969, 13060 Safat, Kuwait
2Department of Natural Resources and Environment, College of Graduate Studies, Arabian Gulf University, Manama 26671, Bahrain
Corresponding Author: Meshari Al-Harbi ,Tel: +965-24633000, Fax: +965-22551157, Email: dr.meshari@ku.edu.kw
Received: November 26, 2018;  Accepted: February 22, 2019.
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The main objective of this study is to estimate the levels of pollution to which the community is presently exposed and to model the regimes of local air quality. Diurnal, daily, and monthly variations of NO, NO2, SO2, and O3 were thoroughly investigated in three areas; namely, residential, industrial, and terminal in Ras Al-Khafji. There is obvious diurnal variation in the concentration of these pollutants that clearly follows the diurnal variation of atmospheric temperature and main anthropogenic and industrial activities. Correlation analysis showed that meteorological conditions play a vital role in shaping the pattern and transportation of air pollutants and photochemical processes affecting O3 formation and destruction. Bivariate polar plots, an effective graphical tool that utilizes air pollutant concentrations’ dependence on wind speed and wind direction, were used to identify prevailing emission sources. Non-buoyant ground-level sources like domestic heating and street transport emissions, various industrial stacks, and airport-related activities were considered dominant emission sources in observatory sites. This study offers valuable and detailed information on the status of air quality, which has considerable, quantifiable, and important public health benefits.
Keywords: Ambient air pollution | Bivariate polar plots | Source apportionment | Spatiotemporal variations Ambient air pollution | Bivariate polar plots | Source apportionment | Spatiotemporal variations
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