Development of Dust Storm Modeling for Use in Freeway Safety and Operations Management: An Arizona Case Study

Abstract

Extreme weather conditions such as strong winds, hail, heavy rainfall, heavy snowfall, and high air temperature impact roads, traffic, and operational decisions. Strong winds in arid regions may pick up fine dust particles and create massive blowing plumes dramatically reducing the visibility. This reduced visibility severely impairs driving ability causing catastrophic crashes. The purpose of this research was to investigate the impacts of dust storms on freeway safety and operations. Interstates 8, 10, 15, 17, 19, and 40 running through Arizona were studied in relation to dust loading and crash risks. To achieve this, nine severe Arizona dust storms (2009–2016) were modeled using Weather Research and Forecasting (WRF) model coupled with a chemistry module (WRF-Chem). WRF is a mesoscale numerical weather prediction system with a software architecture allowing for parallel computation. When coupled with a chemistry module (WRF-Chem), it could be used to model the fate and transport of the particulate matter. Dust hot spots were calculated based on Getis-Ord Gi* statistical method and were correlated to dust storm caused crashes. It was shown that a positive Gi* accompanied by dust loading of at least 50 kgm–2 will result in a crash with a 90% confidence level. The outcome of this research could be used by local and federal transportation agencies to communicate warnings to drivers for improved safety.

When a Dust Storm Is Not a Dust Storm: Reliability of Dust Records From the Storm Events Database and Implications for Geohealth Applications

“…For example, in the atmospheric science literature, it was used to examine mortality rates due to heat and cold (Dixon et al., 2005 ), injuries and mortality due to lightning strikes (Ashley & Gilson, 2009 ; López et al., 1993 ), the impact of winter precipitation on fatalities (Black & Mote, 2015 ), as well as the associations of convective and non‐convective wind events on fatalities (Ashley & Black, 2008 ; Black & Ashley, 2010 ). The SED has been widely used by researchers in fields outside the geosciences to investigate correlations between dust storms in the USA and factors including mortality (Crooks et al., 2016 ), freeway safety (Mohebbi et al., 2019 ), intensive care unit admissions (Rublee et al., 2020 ), birth weight and premature birth rates (Jones, 2020 ), the incidence of Valley fever (Comrie, 2021 ), neighborhood‐scale public health impacts of air pollution (Lothrop et al., 2022 ), and violent crime (Jones, 2022 ). The SED has been proposed as the data source for events including dust storms for creating a hazard assessment for locating healthcare facilities (Skinner, 2010 ), and for creating a “natural disasters index” (Mahanama et al., 2022 ).…”

Application of a satellite-retrieved sheltering parameterization (v1.0) for dust event simulation with WRF-Chem v4.1

“…The AFWA dust emission equations assume that winddriven dust entrainment primarily occurs through a process called saltation bombardment, in which wind-lofted sandsized particles (∼ 50-2000 µm diameter) too heavy to remain suspended in the air collide with the land surface and eject smaller dust-sized particles (generally < 20 µm diameter) upon impact (e.g., Gillette, 1977;Kok et al, 2012). Several studies have investigated the use of the AFWA dust emission module for a variety of dust modeling applications (e.g., Aragnou et al, 2021;Francis et al, 2022;Hamzeh et al, 2021;Karumuri et al, 2022;Kuchera et al, 2021;Mesbahzadeh et al, 2020;Miller et al, 2021;Mohebbi et al, 2019;Péré et al, 2018;Saidou Chaibou et al, 2020;Solomos et al, 2018;Teixeira et al, 2016;Tsarpalis et al, 2018Tsarpalis et al, , 2020Uzan et al, 2016;Xu et al, 2017;Zhang et al, 2022;Zhou et al, 2019). In general, these studies highlight useful applications of the model.…”

The Influence of Seasonal Climate Variability on Vehicle Exhaust PM2.5 in the State of California: A Hybrid Approach based on Artificial Neural Network and Spatial Analysis

“…5 (PM2.5). Both classes of particulate matter are highly dependent on climatic factors such as precipitation, relative humidity, wind speed, and air temperature, and it is positively correlated with the aerosol optical depth (AOD) [2,3]. In particular, AOD, which can be retrieved from remote sensing products, is considered a good proxy for PM2.5 [4].…”