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The goal of this study was to investigate the ambient concentrations and dispersion patterns of SO₂ originating from a gas refinery located in Asaluyeh, Iran, to determine the refinery’s contribution in emitting SO₂ in the region and also to assess SO₂-associated health risks in the study area. First, SO₂ emissions from the stacks and ambient SO₂ concentrations at 10 receptors in and around the refinery were measured from summer 2014 to spring 2015 using a Testo 350XL analyzer and a portable device (LSI-Lastem Babuc A). The amounts of SO₂ concentrations due to flaring were also calculated using the emission factors. Then ambient concentrations and dispersion patterns of SO₂ in the study area at 1-hr, 24-hr, and annual mean values were simulated on a scale of 10×10 km², using an AERMOD model. Moreover, a non-carcinogenic risk assessment was performed using a U.S. Environmental Protection Agency procedure. The results indicated that about 64% of ambient SO₂ concentrations were due to this refinery and the remaining concentrations were due to contributions from neighboring sources. The values of maximum simulated ambient SO₂ concentrations at average periods of 1-hr, 24-hr, and annual for the scale of 10×10 km² were 24,588, 1,366.1, and 498 μg/m³, respectively, which were higher than the U.S. EPA standard limits. There was also a potential health risk for short-term exposure (HQ = 1.4), but in long-term exposure an acceptable level of concentration (HQ = 0.28) was created.
Management of air pollution in Tehran, Iran, has been a significant challenge for urban authorities in recent years owing to the number and complexity of the factors affecting the formation and spread of the pollutions. The present study used an integrated modeling approach involving Spatio, Temporal, Uncertainty Decision Support Systems (STUDSS) using Multi Criteria Decision Analysis (MCDA) and an Artificial Neural Network (ANN) for the virtual simulation and strategy assessment of air pollution. Since sources of air pollution and associated pollution control strategies are dependent on location, time and uncertain variables, Multi-Dimensional Decision Support System (MDDSS) can be efficient tool for urban air quality decision-making process. In order to model and evaluate air pollution, time-series data over a period of four years, screened and classified management strategies as well as other structural and environmental data such as land uses, terrain topography, heights of buildings, climatic conditions, population density and pollution sources were modeled using advanced software packages of MCDA and ANN. They were ultimately simulated and evaluated using the MDDSS. The results obtained from the implementation of the STUDSS showed that this tool could be used to provide sustainable solutions to air quality in metropolises and could respond to social satisfaction and economic development.
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