Validating MOVES PM2.5 Emission Factor Empirically by Considering Accumulative Emissions Effect

Abstract

Estimating the short-term PM2.5 emission factor is a critical subject in analyzing the PM2.5 emission impact of traffic activities and population exposure level to the vehicle emission in the ambience of roadways that bear heavy-duty traffic. In applications of the Motor Vehicle Emission Simulator (MOVES) model, finding out an effective way to validate the MOVES model with respect to short-term emission estimation remains a challenge to practitioners. In order to address this issue, the PM2.5 emission factor in 1-minute intervals is estimated by the MOVES model based on the observed traffic data. On the other hand, the realistic emission factor (or termed as the ground-truth factor) is determined from the monitored minute-by-minute PM2.5 concentration data and its concurrent meteorological data. The validation procedure is undertaken by comparing the estimated emission factor with the ground-truth factor data. The testing result indicates a major inconsistency in the existence of the two datasets. The ground-truth emission factor is found to be about 30 to 50 times larger than the MOVES result. The inconsistency is possibly caused due to neglect of the accumulative effect of the traffic emissions over time. To overcome such an underestimation issue in application of the MOVES model, a modeling methodology is developed in the presented study to take into account the accumulative emission effect. The result from the case study shows a significant increase in the accuracy of estimating the short-term PM2.5 emission factor. The presented modeling methodology lays out a great foundation to develop a computing tool to improve the MOVES estimations.