SENSITIVITY ANALYSIS OF MOBILE6 MOTOR VEHICLE EMISSION FACTOR MODEL

On January 29, 2002, the US Environmental Protection Agency (EPA) officially released the latest motor vehicle emission factor model - MOBILE6. This release represents significant achievements in understanding both the motor vehicle performance and driver behavior when estimating motor vehicle emissions. The model also establishes routines to compute and analyze fuel and vehicle certification standards, state programs and different highway facilities as related to vehicle emission factors. Significant efforts by the US EPA were also carried out to establish a national default database used by the computer model. Along with the MOBILE6 release, more than 48 technical papers related to MOBILE6 development were also released. Overall, the MOBILE6 model predicts higher emission rates in the near future years and lower emission rates in the out years when compared to the MOBILE5 series models. MOBILE6 is the approved US EPA motor vehicle emission factor model for estimating volatile organic compounds (VOC), nitrogen oxides (NOx), and carbon monoxide (CO) from different vehicles. State and local air quality and transportation agencies outside of California are required to use MOBILE6 in State Implementation Plan (SIP) development, and transportation conformity determination. The official release of MOBILE6 on January 29, 2002 started the 2-year grace period before MOBILE6 is required for new conformity determinations in most areas. As the end of the grace period approaches, transportation as well as air quality agencies are gaining an understanding of the behavior of the model, especially in impacts of using localized data as compared to EPA's national default data. Understanding the behavior of the model under various conditions becomes more critical as the tasks of collecting local data is time and resources intensive. The purpose of this paper is to provide a basic evaluation of the MOBILE6 model behavior under various conditions. Through the understanding of the model's behavior, state and local agencies can prioritize costly data collecting efforts and ultimately initiate emission control strategies according to parameter sensitivity.