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

Amin Mohebbi, Fan Yu, Shiqing Cai, Simin Akbariyeh, Edward J. Smaglik

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
JournalFrontiers of Earth Science
DOIs
StateAccepted/In press - Jan 1 2020

Keywords

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

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)

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