<![CDATA[The dispersion of pollutants originating from traffic activities has become a major environmental issue in many developing countries. Emissions such as SO2 and CO present significant challenges for air quality management due to their serious health impacts. Air Quality Monitoring Systems (AQMS) are commonly used to measure pollutant concentrations; however, limited availability and spatial coverage necessitate alternative approaches such as dispersion modeling using AERMOD. This study aims to evaluate the performance of AERMOD as an alternative method for estimating SO2 and CO concentrations, particularly those associated with traffic-related emissions. The simulation results indicate a strong alignment between dominant wind direction and pollutant dispersion patterns over the seven-day modeling period. Concentration accuracy assessed through regression analysis and Root Mean Square Error (RMSE) revealed positive correlations between AERMOD simulations and observational data for both SO2 and CO, with RMSE values of 21.86 µg/m3 for SO2 and 485.25 µg/m3 for CO. Overall, statistical evaluations demonstrate a high level of agreement for SO2 and a moderate level of agreement for CO. These findings underscore the significant potential of AERMOD as an alternative monitoring tool for estimating pollutant dispersion in areas lacking AQMS infrastructure, thereby supporting more effective air quality management and pollution control strategies. However, the model’s performance remains influenced by several limitations, including dependency on the quality of meteorological and emission input data, the assumption of steady-state atmospheric conditions, and greater prediction uncertainty for CO compared to SO2. These factors should be carefully considered when applying AERMOD in regions without ground-based monitoring stations.]]>
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