Open AccessAssessment of the AERMOD dispersion model in complex terrain with different types of digital elevation data
Author(s) -
Mateusz Rzeszutek,
Adriana Szulecka
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/642/1/012014
Subject(s) - aermod , shuttle radar topography mission , digital elevation model , terrain , elevation (ballistics) , advanced spaceborne thermal emission and reflection radiometer , environmental science , atmospheric dispersion modeling , scale (ratio) , remote sensing , meteorology , cartography , geography , engineering , air pollution , chemistry , organic chemistry , structural engineering
The AERMET/AERMOD (American Meteorological Society (AMS)/EPA Regulatory Model) dispersion modeling system constitutes a tool recommended by the United States Environmental Protection Agency (U.S. EPA) both for flat and complex terrain in a local scale with a distance of 50 km. This model requires several input data for pollutant prediction. As part of the research, the effectiveness evaluation of the AERMOD model was conducted based on two of the model evaluation databases (Martin’s Creek and Lovett) depending on different available DEM sources. The analysis involved comparison of different modeling results obtained with the application of different DEM datasets, i.e. NED (National Elevation Dataset), ASTER (Aster Global Digital Elevation Model), SRTM (Shuttle Radar Topography Mission) and USDEM (US GeoData Digital Elevation Models). Achieved outcomes indicated, that the use of different elevation datasets did not influence the evaluation results of the AERMOD model in a local scale and complex terrain significantly. Regardless of the field experiment and DEM dataset, for each case the values of FB and FB RHC fell within the range of ± 0.33. The highest values of the model performance measures reached 0.89 – 0.91 for IOA and 0.78 – 0.81 for COE in the case using the NED dataset. Slightly worse model performance was observed for the SRTM data with IOA equal to 0.82 – 0.91 and COE reaching 0.64 – 0.83.