Spatial study of particulate matter distribution, based on climatic indicators during major dust storms in the State of Arizona

Abstract

Arizona residents have been dealing with the suspended particulate matter caused health issues for a long time due to Arizona’s arid climate. The state of Arizona is vulnerable to dust storms, especially in the monsoon season because of the anomalies in wind direction and magnitude. In this study, a high-resolution Weather Research and Forecasting (WRF) model coupled with a chemistry module (WRF-Chem) was simulated to compute the particulate matter spatiotemporal distribution as well as the climatic parameters for the state of Arizona. Subsequently, Ordinary Least Square (OLS), spatial lag, spatial error, and Geographically Weighted Regression (GWR) techniques were utilized to develop predictive models based on the climatic indicators that impacted the formation and dispersion of the particulate matter during dust storms. Census tracts were adopted to create local spatial averages for the chosen variables. Terrain height, temperature, wind speed, and vegetation fraction were designated as the most significant variables, whereas base state and perturbation pressures, planetary boundary layer height and soil moisture were adopted as supplementary variables. The determination coefficient for OLS, spatial lag, spatial error, and GWR models peaked at 0.92, 0.93, 0.96, and 0.97, respectively. These models provide a better understanding of the current distribution of the particulate matter and can be used to forecast future trends.

Keywords

particulate matter / dust storm / Weather Research and Forecasting / census tracts / Ordinary Least Square / Geographically Weighted Regression

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Acknowledgments

The grant for this study was awarded by the University Transportation Centers of the US Department of Transportation to Northern Arizona University as a sub awardee. The author would like to thank Dr. Fernando Sánchez-Trigueros of the University of Arizona for his constructive feedback that significantly contributed to improving the final version of the manuscript. Also, the work of Mr. Gabriel Green, the first author’s former graduate student in developing the initial pilot study model is greatly appreciated.

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