Modeling of PM₁₀ emissions from motor vehicles at signalized intersections and cumulative model validation

Motor vehicle emissions especially occur at signalized intersections during idling, acceleration, and deceleration phases. The reduction of exhaust emissions from motor vehicles is on the focus of environmental studies. The main targets of this paper are the modeling of motor vehicle particulate matter (PM₁₀) emissions by American Meteorological Society/Environmental Protection Agency Regulatory Model (AERMOD) and California Line Source for Queuing and Hot Spot Calculations (CAL3QHCR) models and investigating the effectiveness of a hypothetical green wave scenario as a pollution reduction strategy. The portion of D010 State Road in Zonguldak (Turkey) is selected. Vehicle counting is applied for determining the traffic volume. Then, the PM₁₀ emission inventory is prepared. After that, PM₁₀ pollution distribution maps at signalized intersections are created by running air quality models. Next, the CAL3QHCR model is run again for the green wave scenario which assumes free flow at signalized intersections. The maximum PM₁₀ concentrations predicted by AERMOD and CAL3QHCR models are 16.8 µg/m³ and 14.9 µg/m³, respectively. Although these values are below the threshold value, it can be said that air quality may pose a threat to public health in the existence of other sources. With the implementation of signal optimization, the PM₁₀ pollution is reduced by 10-50% at intersections. Cumulative model validation is employed including other PM₁₀ sources in the study area. PM₁₀ contribution of other sources at Zonguldak air quality monitoring station is determined by the AERMOD model. Finally, the sum of model outputs is validated against measured concentrations. According to the validation, both models are found as satisfactory and AERMOD performed better than CAL3QHCR.