The impact of power plant emission variability and fuel switching on the air quality of Kuwait

摘要

Power plant emissions have a significant impact on air quality, and a frequent assumption made in estimating impacts is to assume annual or monthly average emission rates. This study investigates the impact, on predicted ambient concentrations, of assuming annual average emissions, compared to resolving emissions on an hourly basis (base case). A case study of emissions from power plants in Kuwait, for the year 2014, is presented. In Kuwait, power plants operate on a mix of natural gas, gas oil, crude oil, and heavy fuel oil, and the type of fuel used varies on an hourly basis. Because of this fuel variability, a fuel switching strategy was also simulated in this work, replacing high sulfur fuels with natural gas during hours with high predicted SO2 concentrations. Emissions estimates were combined with an air quality dispersion model to simulate the temporal variability and spatial dispersion of sulfur dioxide (SO2) and nitrogen dioxide (NO2) in Kuwait, for a one-year episode. The results indicate that emission averaging and fuel switching operations result in lower area-wide annual maximum SO2 concentrations compared to the base case (1747 mu g/m(3), 1063 mu g/m(3), 616 mu g/m(3) for base case, annual average emissions and fuel switching scenarios, respectively). The number of receptor sites recording daily exceedances of the SO2 standard for annual average emissions were one seventh of those predicted for hourly averaged emissions and 92% lower for the fuel switching scenario. For NO2, while the overall number of exceedances of air quality criteria was much lower than for SO2, the numbers of exceedances were also predicted to be lower using annual averaged emissions compared to the base case. These results document the importance of using emission estimates that capture hourly variability over annually averaged emissions, particularly in locations such as Kuwait where multiple fuels are used in power production. (C) 2019 Elsevier B.V. All rights reserved.