AI Summary & Key Takeaways
AI InsightModel results indicatedthat the measures represented by these scenarios are capable of reducing exposure levels between 2.4 and 99.5percent.
📌 3 Key Takeaways
Özet
Traffic-related emissions are the main sources of certain air pollutants that affect large number of people in manycity centers. Because of this fact, quantification of traffic-related emissions and their dispersion modeling arerequired to determine human exposure to these pollutants. In this study, the most populated Ortahisar district ofTrabzon, a city located in the Eastern Black Sea region of Turkey, was selected as the study area since high trafficdensity is known to be one of the major sources of air pollution in this district. Moreover, air quality mea-surement stations located close to major roads are available to provide experimental data for model validation.Traffic emission inventory for some pollutants (NOx, SO2, CO, PM and VOCs) was prepared. Considering thatNOxis the most representative air pollutant of road traffic emissions, its inventory together with topographicalproperties and meteorological conditions were used in AERMOD dispersion model to calculate NOxcon-centrations at selected receptor points. For validation, model data were compared to measurement data bycertain statistical tools. According to the model results, 10.1% of the population living in Ortahisar district wasexposed to traffic-related NOxconcentrations higher than the regulatory limit value. In the study, five differentemission reduction scenarios were tested to observe the effects on the exposure levels. Model results indicatedthat the measures represented by these scenarios are capable of reducing exposure levels between 2.4 and 99.5percent.