Investigation of Primary Factors Affecting the Variation of Modeled Oak Pollen Concentrations: A Case Study for Southeast Texas in 2010 |
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| Authors: | Wonbae Jeon Yunsoo Choi Anirban Roy Shuai Pan Daniel Price Mi-Kyoung Hwang Kyu Rang Kim Inbo Oh |
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| Institution: | 1.Department of Earth and Atmospheric Sciences,University of Houston,Houston,USA;2.Honors College,University of Houston,Houston,USA;3.Division of Earth Environmental System,Pusan National University,Busan,Korea;4.Applied Meteorology Research Division,National Institute of Meteorological Sciences,Jeju,Korea;5.Environmental Health Center,University of Ulsan College of Medicine,Ulsan,Korea |
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| Abstract: | Oak pollen concentrations over the Houston-Galveston-Brazoria (HGB) area in southeastern Texas were modeled and evaluated against in-situ data. We modified the Community Multi-scale Air Quality (CMAQ) model to include oak pollen emission, dispersion, and deposition. The Oak Pollen Emission Model (OPEM) calculated gridded oak pollen emissions, which are based on a parameterized equation considering a plant-specific factor (C e ), surface characteristics, and meteorology. The simulation period was chosen to be February 21 to April 30 in the spring of 2010, when the observed monthly mean oak pollen concentrations were the highest in six years (2009-2014). The results indicated C e and meteorology played an important role in the calculation of oak pollen emissions. While C e was critical in determining the magnitude of oak pollen emissions, meteorology determined their variability. In particular, the contribution of the meteorology to the variation in oak pollen emissions increased with the oak pollen emission rate. The evaluation results using in-situ surface data revealed that the model underestimated pollen concentrations and was unable to accurately reproduce the peak pollen episodes. The model error was likely due to uncertainty in climatology-based C e used for the estimation of oak pollen emissions and inaccuracy in the wind fields from the Weather Research and Forecast (WRF) model. |
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