Abstract: Source apportionment using Dispersion model depends primarily on the quality of Emission Inventory. In the present study, a CMB receptor model has been used to identify the sources of PM2.5, while the AERMOD dispersion model has been used to account for missing sources of PM2.5 in the Emission Inventory. A statistical approach has been developed to quantify the missing sources not considered in the Emission Inventory. The inventory of each grid was improved by adjusting emissions based on road lengths and deficit in measured and modelled concentrations. The results showed that in CMB analyses, fugitive sources - soil and road dust - contribute significantly to ambient PM2.5 pollution. As a result, AERMOD significantly underestimated the ambient air concentration at most locations. The revised Emission Inventory showed a significant improvement in AERMOD performance which is evident through statistical tests.
Abstract: The main objectives of this paper are to measure
pollutants concentrations in the oil refinery area in Kuwait over three
periods during one year, obtain recent emission inventory for the
three refineries of Kuwait, use AERMOD and the emission inventory
to predict pollutants concentrations and distribution, compare model
predictions against measured data, and perform numerical
experiments to determine conditions at which emission rates and the
resulting pollutant dispersion is below maximum allowable limits.
Abstract: This paper focuses on assessment of air pollution in Umm-Alhyman, Kuwait, which is located south to oil refineries, power station, oil field, and highways. The measurements were made over a period of four days in March and July in 2001, 2004, and 2008. The measured pollutants included methanated and nonmethanated hydrocarbons (MHC, NMHC), CO, CO2, SO2, NOX, O3, and PM10. Also, meteorological parameters were measured, which includes temperature, wind speed and direction, and solar radiation. Over the study period, data analysis showed increase in measured SO2, NOX and CO by factors of 1.2, 5.5 and 2, respectively. This is explained in terms of increase in industrial activities, motor vehicle density, and power generation. Predictions of the measured data were made by the ISC-AERMOD software package and by using the ISCST3 model option. Finally, comparison was made between measured data against international standards.