AI Summary & Key Takeaways
AI InsightA Generalized estimating model (GEE) modelwas developed and the model suggested that vehicle age, mileage, and speed are the factors positively associatedwith the AERs estimated for WO and WC scenarios under realistic driving conditions.
📌 3 Key Takeaways
Özet
School students are exposed to the increased particulate (PM) number concentration inside the school bus ondaily basis while commuting on urban road networks. However, the potential effect of different ventilation overin-cabin pollutants is still unclear in many aspects. The present paper investigates the influence of varyingventilation scenarios over in-cabin particle number concentration (PNC) and air exchange rates (AERs) insideidle and moving (unoccupied (SBu) and occupied (SBo)) school bus. The in-cabin total PNC sharply increased bya factor of 1.53, 1.57 and 1.14 for SBp, SBu, and SBo respectively under windows open (WO) from windowsclosed (WC) scenarios. In-vehicle student activity and ventilation scenarios were positively correlated with thechange in in-cabin PNC. For SBp, the average estimated AERs are 9.19 h−1 and 3.30 h−1 under WO and WCscenarios respectively whereas, total PNC deposition rate under natural convection condition varied between2.52 to 6.89 h−1. In the present study, inside-outside measurements were recorded for 10 km long road segmentunder realistic driving conditions. The average AERs value of SBu and SBo are highest for WO (42.40 h−1 and14.46 h−1) compared to WC (10.91 h−1 and 10.94 h−1) scenarios. A Generalized estimating model (GEE) modelwas developed and the model suggested that vehicle age, mileage, and speed are the factors positively associatedwith the AERs estimated for WO and WC scenarios under realistic driving conditions.