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Evaluating the Effect of Domain Size of the Community Multiscale Air Quality (CMAQ) Model on Regional PM2.5 Simulations

  • Xiangyu Jiang
  • Eun-Hye YooEmail author
Chapter
Part of the Global Perspectives on Health Geography book series (GPHG)

Abstract

A growing number of urban health impact studies use Community Multiscale Air Quality (CMAQ) models for air pollution exposure estimation, although the performance of CMAQ models is likely to be affected by multiple parameters, including the configuration setting of the study domain. We presented an approach for CMAQ model uncertainty assessment with respect to domain size and reported spatial and temporal variations of CMAQ model performance over two study domains, a relatively small domain (DS) and a large domain (DL). Specifically, we simulated daily PM2.5 concentrations over two domains during 2011 and quantified the difference between the model predictions. The model performance was assessed by comparing modeled PM2.5 against measured PM2.5 values at monitoring sites located in the region of overlap for each domain. The results suggest that the CMAQ simulations over two domains were in good agreement across the study area except in southwestern areas. We also found that the overall model performance was better for CMAQ simulations with a large domain relative to the smaller domain. Based on our findings, we recommend applying a large domain for PM2.5 simulations, particularly for urban health risk assessments conducted over summer months, which generally contain more emissions.

Keywords

CMAQ model Domain size Sensitivity analysis PM2.5 Model performance evaluation 

Abbreviations

AQS

Air Quality System

BCs

Boundary conditions

CMAQ

Community Multiscale Air Quality

EPA

Environmental Protection Agency

FB

Fractional bias

FE

Fractional error

NEI

National Emission Inventory

NYC

New York City

PM2.5

Fine particulate matter with aerodynamic diameter less than or equal to 2.5 μm

SMOKE

Sparse Matrix Operator Kernel Emission

WRF

Weather Research and Forecasting

Notes

Acknowledgments

The authors thank for the support provided by the Center for Computational Research (CCR) as well as the seed grant from University at Buffalo’s Research and Education in Energy, Environment & Water (RENEW) Institute.

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of GeographyState University of New York at BuffaloBuffaloUSA

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