Integrated System for Air Pollution around Refineries

Samir Al-Khafaji, Muna; H. Al-Suhaili, Rafa

	Integrated System for Air Pollution around Refineries
Journal of Engineering
Article 1, Volume 15, Issue 4, 2009, Pages 4204-4218
Authors
Muna Samir Al-Khafaji; Rafa H. Al-Suhaili
Abstract
A mathematical model for integrated air pollution modelling around refineries is built and named as Computerized System for Integrated Air Pollution Modelling Around Oil Refineries (CSIAPMAOR).The model based on Gaussian equation to estimate concentration of pollutants (SO2, NO2, CO, Particulates) that are emitted from a continuous air pollution elevated source. The model is designed by using Visual Basic as a main core of the system and linked with auxiliary models such as ArcMap (GIS), Surfer software, Ms-Excel and Ms-Access. The model has flexibility to select either rural, urban or stability (Smith equation) wind speed profile. It also has the option of using three types of dispersion coefficients equations for rural condition (PGT-Briggs-Martin equation) and one equation for urban condition (Briggs equation).The model has many options to display results as concentrations versus center line-downwind distance or as Three-Dimensional (3D) map. The model can compute maximum concentration with the contribution of each stack to the overall maximum concentration. Moreover the model has the ability to perform a sensitivity analysis for the effect of the most important parameters according to the Gaussian equation. AL-Doura Oil Refinery was taken as a case study using the available observed data of two sites1 and 2 for periods 15th -21st and 23rd -29th August 1997 in order to check the performance potential of the model. Results showed that Briggs equation for dispersion coefficients with rural wind speed profile has the best degree of agreement with the observed values of 0.86, 0.90 for SO2; 0.69, 0.80 for NO2; 0.73, 0.79 for CO; 0.63, 0.60 for particulates at site 1 and 2 respectively. It is found, that for AL-Doura Oil Refinery stacks number 6, 2, 7, and 3 have a large contribution on the overall maximum concentration. The model demonstrates the influence of atmospheric stability, wind speed, emission rate, exit velocity, physical height, exit temperature and rural-urban area in reducing the concentrations of pollutants. Sensitivity analysis shows that the concentrations are sensitive to stability class in comparison with other input parameters.
Keywords
Air pollution; Gaussian Model; GIS and Environmental Modeling; Designed Model

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