287 Finescale Air Quality Modeling over the Denver Area: Model Evaluation and Sensitivity Simulations

Monday, 13 January 2020
Hall B (Boston Convention and Exhibition Center)
Kai Wang, North Carolina State Univ., Raleigh, NC; and Y. Zhang, P. Doraiswamy, and S. H. Cho

High personal exposure to air pollution and associated health impacts have been one of key issues introduced by the environmental injustice. Fine-scale monitoring or air quality model data are required for an accurate assessment of personal exposure using exposure models. The main objective of this work is to conduct fine-scale air quality simulations using the state-of-science online-coupled Weather Research and Forecast Model with chemistry (WRF/Chem) over an environmental justice community in the Denver area during the summer of 2017, where the sensor-based ambient monitoring data have been collected over multiple sites in the same area by RTI international. The predicted O3 and PM2.5 concentrations will be used to estimate personal exposure to ambient air pollution. A one-month triple-nested simulation using WRF/Chemv3.9 has been previously performed for August, 2017, with the 36-km domain covering the continental U.S., 6-km covering the state of Colorado, and 1-km covering the Denver Metropolitan area. The evaluation of this initial application identified some model biases such as relatively large underprediction of 8-hr O3 against the AQS data and large overprediction of PM2.5 against the RTI sensor-based monitoring data at the 1-km grid resolution, due in part to inaccurate model inputs. This study is aiming to improve the model performance for O3 and PM2.5, especially over the 1-km domain by adjusting boundary conditions for O3, improving wildfire emissions, and reducing anthropogenic emissions for gas and aerosol precursors based on comparisons between National Emissions Inventory 2011 and 2014. Multiple one-month sensitivity simulations have been conducted to assess the impacts of the improved model inputs. The preliminary evaluation shows noticeable model improvements for 8-hr O3 and PM2.5 in terms of domain-mean biases. The optimal model configurations will be determined based on additional sensitivity simulations and more comprehensive evaluation for improving O3 and PM2.5 predictions at 1-km resolution for personal exposure assessment.
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