Assessment of High-resolution Local Emissions and Land-use in Air Quality Forecasting at an Urban, Coastal, Desert Environment

Abstract

Fine particulate matter, ozone and nitrogen oxides are forecasted using a three-dimensional atmospheric meteorology-chemistry model (WRF-Chem) and a triple-nesting configuration over the Middle East and the Arabian Peninsula focusing on the hot desert climate of Qatar. We analyze the impact of a local anthropogenic emission inventory (EI) on model predictions, compared to the most commonly used EDGAR global emissions. The model’s forecast accuracy was assessed against measurement data from five ground air quality monitoring stations in the greater metropolitan area of Doha over a one-month period. The footprint of the Doha metropolitan area on the geographical distribution of the anthropogenic emissions is much more realistically represented in the new version of emissions, which includes major differences in the magnitude of emission rates, locally, compared to the base case. The use of the local emissions allowed for a significant improvement in the representation of air quality levels in the city. The overall forecast error decreased from –51% to 8% for PM2.5 and from –88% to 20% for NOx while a significant improvement was observed in the diurnal profile of predicted ozone. The ability of the model to forecast the air quality health index in this urban, coastal, hot desert climate is encouraging for future applications of this modeling platform as an early warning system (EWS).