MOVES-Matrix and distributed computing for microscale line source dispersion analysis.
MOVES and AERMOD are the U.S. Environmental Protection Agency's recommended models for use in project-level transportation conformity and hot-spot analysis. However, the structure and algorithms involved in running MOVES make analyses cumbersome and time-consuming. Likewise, the modeling setup process, including extensive data requirements and required input formats, in AERMOD lead to a high potential for analysis error in dispersion modeling. This study presents a distributed computing method for line source dispersion modeling that integrates MOVES-Matrix, a high-performance emission modeling tool, with the microscale dispersion models CALINE4 and AERMOD. MOVES-Matrix was prepared by iteratively running MOVES across all possible iterations of vehicle source-type, fuel, operating conditions, and environmental parameters to create a huge multi-dimensional emission rate lookup matrix. AERMOD and CALINE4 are connected with MOVES-Matrix in a distributed computing cluster using a series of Python scripts. This streamlined system built on MOVES-Matrix generates exactly the same emission rates and concentration results as using MOVES with AERMOD and CALINE4, but the approach is more than 200 times faster than using the MOVES graphical user interface. Because AERMOD requires detailed meteorological input, which is difficult to obtain, this study also recommends using CALINE4 as a screening tool for identifying the potential area that may exceed air quality standards before using AERMOD (and identifying areas that are exceedingly unlikely to exceed air quality standards). CALINE4 worst case method yields consistently higher concentration results than AERMOD for all comparisons in this paper, as expected given the nature of the meteorological data employed.



The paper demonstrates a distributed computing method for line source dispersion modeling that integrates MOVES-Matrix with the CALINE4 and AERMOD. This streamlined system generates exactly the same emission rates and concentration results as traditional way to use MOVES with AERMOD and CALINE4, which are regulatory models approved by the U.S. EPA for conformity analysis, but the approach is more than 200 times faster than implementing the MOVES model. We highlighted the potentially significant benefit of using CALINE4 as screening tool for identifying potential area that may exceeds air quality standards before using AERMOD, which requires much more meteorology input than CALINE4.
a School of Civil and Environmental Engineering, Georgia Institute of Technology , Atlanta , GA , USA.
Haobing
H
Liu
a School of Civil and Environmental Engineering, Georgia Institute of Technology , Atlanta , GA , USA.
Xiaodan
X
Xu
a School of Civil and Environmental Engineering, Georgia Institute of Technology , Atlanta , GA , USA.
Michael O
MO
Rodgers
a School of Civil and Environmental Engineering, Georgia Institute of Technology , Atlanta , GA , USA.
Yanzhi Ann
YA
Xu
a School of Civil and Environmental Engineering, Georgia Institute of Technology , Atlanta , GA , USA.
Randall L
RL
Guensler
D000393:Air Pollutants / Q000032:analysis* / Q000737:chemistry; D000465:Algorithms; D004784:Environmental Monitoring / Q000379:methods*; D005845:Georgia; D008962:Models, Theoretical; D014186:Transportation; D014481:United States; D014484:United States Environmental Protection Agency; D001335:Vehicle Emissions / Q000032:analysis*
D000393:Air Pollutants; D001335:Vehicle Emissions