Back

Biography

Ammar M G Gaber is a Senior Meteorologist at Sudan Meteorological Authority, with more than 10 years of experience in weather forecast, climate perdition and climate services. He completed his MBA in 2011 and now he is doing his MSc in Environmental Science in Chiang Mai University. His area of interest is air pollution modeling using both statistical and numerical methods.

Abstract

Daily PM10 concentrations were simulated over Upper Northern Thailand during the dry season (January-April, 2015) with high resolution (4 km) using CMAQ model. Meteorological and emission data were prepared using WRF and SMOKE models, respectively. Emission Inventory (EI), especially developed for this study, includes four criteria pollutants (PM10, CO, SO2, NO2) from three types of biomass (Rice Straw, Maize Residue and Leaf Litter). Temporal variations of PM10 concentrations showed that the peaks occurred in April with concentrations exceed AQI, because of increased biomass open burning activities and the effect of prevailing meteorological conditions that support pollutants’ suspension for several days. Daily fluctuations of PM10 concentrations were captured by the model and the daily maximum concentrations were identified. The spatial variations of PM10 concentrations were found to be mainly due to the topographical influences although the other parameters have their own effects. CMAQ model performance evaluation showed some discrepancies with observations. Mean bias, mean errors, normalized mean bias and correlation coefficient showed good agreement between the model and the observations in some stations. While the model tended to underestimate the PM10 concentration levels in some parts of the simulating domain, this can be attributed to the topography influence, EI quality, uncertainty in meteorological data, and trans-boundaries pollution effects. Improving the model performance can be achieved by including more pollutants in EI and expanding the simulating domain. Forecasting air quality in this region using this model is one of the potential applications of this study besides providing reliable and near-time information to aid decision-making process for better air quality management.