MOVES2014 Project-Level Sensitivity Analysis: Impacts of Onroad Fleet Composition and Operation Aggregation on Emission Results

摘要

Motor Vehicle Emission Simulator (MOVES) analysis in project level requires refined input data, including meteorology, calendar year, fuel type, inspection and maintenance program elements, fleet activity data and fleet composition data. The key to obtaining accurate model output is to enhance the accuracy of input data. The US EPA transportation conformity guidance for quantitative hot-spot analyses recommends generating emission rates for each roadway segment with the similar operation modes for use in dispersion modeling, and the operations are aggregated within the segment. However, sometimes there are significant differences in speed and acceleration conditions across different vehicle types, and between inner and outer lanes within the roadway segment. Furthermore, most MOVES modelers are doing analysis based on MOVES default driving schedule, instead of onroad operations. The goal of this paper is to evaluate the impact of aggregating input data in roadway scales and use of MOVES default schedule to emission results. Using MOVES project-level analysis, the paper will utilize vehicle activity data collected from a 500-foot segment at Ⅰ-85, Atlanta in 2012. By collecting second-by-second speed in each lane and in each source type, the study is able to estimate emissions using disaggregated input that employs onroad speed fluctuations, operations differences across lanes, and interactions between fleet composition and variations in modal activity. The case study shows as large as 69% overall CO emissions differences, and 39% overall PM_(2.5) emissions difference from inappropriate input aggregation. The analysis can help modelers better understand the potential biases that are in emissions estimation as a function of data aggregation.