Evaluating AERMOD with measurements from a major U.S. airport located on a shoreline. (1st February 2023) - Pandey, Gavendra; Venkatram, Akula; Arunachalam, Saravanan
Abstract: The impact of airport operations on air quality is a key public health concern for the population surrounding an airport. Air pollution regulations require the assessment of this impact using dispersion models. Modeling dispersion of aircraft-related sources poses challenges because of the large number and variety of airport sources, which include aircraft, ground operation vehicles, and traffic in and out of the airport, most of which are mobile. Emissions from aircraft sources are transient, buoyant, and occur at different heights from the ground. Quantifying these emissions as well as modeling the governing processes is challenging. An added complexity occurs when the airport is situated near a shoreline where meteorological conditions are far from being spatially uniform. These features that characterize the dispersion of airport emissions are being incorporated into the AERMOD model in this paper. This paper examines the impact of shoreline meteorology and urban effects on dispersion by comparing model estimates of SO2 with corresponding measurements made during a field study conducted at the Los Angeles International Airport (LAX) during winter and summer of 2012 at all the four core sites (AQ, CN, CE, and CS) as a part of the LAX Air Quality Source Apportionment Study (AQSAS). We modified outputs from AERMOD's meteorological preprocessor AERMET to account for 1) the formation of the internal boundary layer that is formed when stable air from the ocean flows onto the warmer land surface of the airport, and 2) urban roughness effects on winds flowing from Los Angeles, east of the airport. Simulations with unmodified AERMET yielded concentrations that were substantially higher than the concentrations at AQ and CS and much lower than those at CN and CE. Model performance improved when AERMOD used the modified meteorology. The fraction of model estimates within a factor of two of the observations improved from 34 to 84% at the CS site and CE site, by up to 79% in winter season whereas in summer, FAC2 values are almost comparable at all the sites. The ratio of robust highest modeled values to measured values improved from 7.72 to 2.53 and 4.92 to 1.94 in winter and summer seasons respectively.