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DEVELOPMENT OF A MICROSCALE EMISSION FACTOR MODEL FOR CO FOR PREDICTING REAL-TIME MOTOR VEHICLE EMISSIONS
Citation:
Singh, R. B. AND A H. Huber. DEVELOPMENT OF A MICROSCALE EMISSION FACTOR MODEL FOR CO FOR PREDICTING REAL-TIME MOTOR VEHICLE EMISSIONS. JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION 50(11):1980-1991, (2000).
Description:
The United States Environmental Protection Agency's (EPA) National Exposure Research Laboratory (NERL) has initiated a project to improve the methodology for modeling human exposure to motor vehicle emission. The overall project goal is to develop improved methods for modeling the source through the air pathway to human exposure in significant microenvironments of exposure. This paper presents the technical description of a newly developed model for CO emission. The sensitivity analysis and evaluation of this emission model is presented in a companion paper. The MOBILE models (used in the United States, except California) and EMFAC models (used in California only) used to estimate emission roadways. A number of independent studies have found that current mobile source emission factor models are not very reliable at estimating microscale emission and are therefore inappropriate for use with microscale modeling that is necessary to estimate human exposures near roadways.
A microscale emissions factor model for predicting real-world motor vehicle carbon monoxide (MicroFacCO) emission has been developed. It uses available information on the vehicle fleet composition. The algorithm used to calculate emission factors in MicroFacCO is disaggregated based on the on-road vehicle fleet. The emission factors are calculated from a real-time fleet, rather than from a fleet-wide average estimated by a vehicle miles traveled (VMT) weighting of the emission factors for different vehicle classes. MicroFacCO uses the same database, which is used to develop the MOBILE6 model. As compared to MOBILE emission models, MicroFacCO requires only a few input variables which are necessary to characterize the real-time fleet being modeled. The main input variables required are on-road vehicle fleet, time and day of the year, ambient temperature and relative humidity.