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Article

Applying Wind Erosion and Air Dispersion Models to Characterize Dust Hazard to Highway Safety at Lordsburg Playa, New Mexico, USA

by
Iyasu G. Eibedingil
1,
Thomas E. Gill
1,2,*,
R. Scott Van Pelt
3,
John Tatarko
4,†,
Junran Li
5 and
Wen-Whai Li
6
1
Environmental Science and Engineering Program, University of Texas at El Paso, El Paso, TX 79968, USA
2
Department of Earth, Environmental and Resource Sciences, University of Texas at El Paso, El Paso, TX 79968, USA
3
USDA—Agricultural Research Service, 302 W. I-20, Big Spring, TX 79720, USA
4
USDA—Agricultural Research Service, 2150 Centre Avenue, Bldg. D. Suite 200, Fort Collins, CO 80526, USA
5
Department of Geography, The University of Hong Kong, Hong Kong SAR, China
6
Department of Civil Engineering, University of Texas at El Paso, El Paso, TX 79968, USA
*
Author to whom correspondence should be addressed.
†
Retired.
Atmosphere 2022, 13(10), 1646; https://doi.org/10.3390/atmos13101646
Submission received: 5 August 2022 / Revised: 1 October 2022 / Accepted: 5 October 2022 / Published: 9 October 2022
(This article belongs to the Special Issue Sources, Characterization and Control of Particulate Matter)
Browse Figures
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Abstract

:
Lordsburg Playa, a dry lakebed in the Chihuahuan Desert of southwestern New Mexico (USA), is crossed by Interstate Highway 10 (I-10). Dust from the playa threatens highway safety and has caused dozens of fatal accidents. Two numerical models—the U.S. Department of Agriculture’s Single-Event Wind Erosion Evaluation Program (SWEEP) and the American Meteorological Society and U.S. Environmental Protection Agency Regulatory Model (AERMOD)—were used to simulate and predict the generation and dispersion of windblown soil, dust, and PM10 from playa hotspots and estimate PM10 concentrations downwind. SWEEP simulates soil loss and particulate matter emissions from the playa surface, and AERMOD predicts the concentration of transported dust. The modeling was informed by field and laboratory data on Lordsburg Playa’s properties, soil and land use/land cover databases, and weather data from meteorological stations. The integrated models predicted that dust plumes originating on the playa—including a large, highly emissive area away from the highway and a smaller, less emissive site directly upwind of the interstate—can lead to hourly average PM10 concentrations of tens, to hundreds of thousands, of micrograms per cubic meter. Modeling results were consistent with observations from webcam photos and visibility records from the meteorological sites. Lordsburg Playa sediment contains metals, as will its dust, but human exposures will be short-term and infrequent. This study was the first to successfully combine the SWEEP wind erosion model and the AERMOD air dispersion model to evaluate PM10 dispersion by wind erosion in a playa environment. With this information, land managers will be able to understand the potential levels of dust and PM10 exposure along the highway, and better manage human health and safety during conditions of blowing dust and sand at Lordsburg Playa.

1. Introduction

According to the World Health Organization [1], traffic crashes are the ninth leading cause of death worldwide. Hazards causing reduced visibility, such as dust, smoke, and fog, cause more average annual fatalities on USA roads than the annual toll of fatalities caused by other weather-related hazards, including floods, tornadoes, lightning, and tropical cyclones [2,3]. Windblown (aeolian) dust and sand crossing highways is an immediate threat to health and safety [4,5,6], not only due to loss of visibility, but also due to reduced traction on the road surface [7]. Blowing dust was reported by Lader et al. [8] to be the third leading weather-related cause of casualties on highways in the USA state of Arizona. In addition to health and safety concerns, dust and sand crossing highways also pose great economic impacts, due to expenses for emergency medical care, public safety personnel, and road maintenance, as well as the cost of delays in the delivery of goods and services due to road closures (reviewed in Van Pelt et al. [5]).
Playas (ephemeral lake beds), being flat, windswept, and unvegetated [9], are the predominant source of blowing dust and sand globally [10,11], including in the North American Chihuahuan Desert [12,13]. Crusted surfaces, typically forming the inner part of the playa [14], and fluctuations in sediment loading and supply at the edge of the playa [15] are critical controlling factors modulating dust emission on playas. If the crust is weakened or broken, for example by disturbance by motor vehicles or livestock [16], and if the playa surface is supplied by wind with sediments from its edges [15,17] or from surrounding basins through runoff onto the playa [18,19], saltating particles can rapidly degrade the crust and lead to intense dust emissions [17].
A case in point is the Lordsburg Playa, in the northwestern Chihuahuan Desert of southwest New Mexico, USA. This playa is crossed by Interstate Highway 10 (I-10) and the Union Pacific Railroad (Figure 1A). Approximately 15,000 vehicles per day, ~30% of them being trucks, crossed the playa on I-10 in 2016 [20]. Dust clouds emitted from the playa and crossing the highway (Figure 1A) represent an immediate hazard to motorists, due to a potential near-instantaneous loss of visibility and sudden exposure to extreme concentrations of particulate matter [2,4,5]. As a result, I-10 across Lordsburg Playa was judged to have the greatest dust hazard of any stretch of highway in the United States [21]. I-10 across Lordsburg Playa was closed for dust safety reasons at least 39 times between 2012 and 2019 [22]. When the playa crossing is closed, vehicles must either wait, potentially for hours, until weather improves, or detour 172 km and an additional >2 h travel time onto two-lane secondary roads not designed for heavy truck traffic [23].
Despite extensive fixed highway warning signage on and approaching the playa in both directions (Figure 1B), at least 117 dust/wind-related traffic crashes were recorded on I-10 crossing Lordsburg Playa by public safety authorities between 1980 and 2017 [5,24]. At least 41 dust-related traffic fatalities have occurred on Lordsburg Playa since 1965, including 21 deaths since 2012; seven persons were killed in one dust event in May 2014, and 10 travelers were killed in four dust events during 2017 [5,22,25]. The state highway departments of New Mexico and Arizona, the New Mexico Environment Department, and the USA federal departments of Interior and Transportation have spent tens of millions of dollars to assess the dust threat, create improved alternate routes, and mitigate the dust hazard with engineering-based and biological-based approaches [5].
Numerical models have long been used to attempt to effectively, efficiently, and realistically predict pollutant concentrations (including aerosols) at receptor sites [26]. The development of dispersion models for simulation of the airborne transport of substances in the planetary boundary layer was summarized by Cimorelli et al. [27]. The American Meteorological Society (AMS) and U.S. Environmental Protection Agency (EPA) Regulatory Model (AERMOD) [27,28] is a steady-state plume model that represented a significant improvement and superior performance compared to the previous commonly applied regulatory models, as shown by comparisons of the modeled and observed pollutant concentrations [28].
Numerical modeling of the wind detachment and transport of soil is also used to simulate and evaluate the effects of wind erosion at various spatial and temporal scales [29]. Decades of research by the U.S. Department of Agriculture (USDA) led to the development of the process-based Wind Erosion Prediction System (WEPS) model and the Single-Event Wind Erosion Evaluation Program (SWEEP) submodel, used for predicting loss of soil from single storms in various settings, including non-agricultural environments [29]. SWEEP and AERMOD were first integrated to model particulate matter emissions for health risk assessment in an industrial setting [30]. However, these two leading models had not previously been used together for the assessment of wind erosion of soil and dust dispersion, either in an agricultural setting or in a natural desert environment, such as at Lordsburg Playa.
Computational fluid dynamics (CFD) is a modeling methodology that uses the finite volume method, in conjunction with various parameterizations/techniques for turbulence closure, to solve simultaneously the equations for conservation of mass and conservation of momentum, as well as the equations of motion. CFD has grown in importance for understanding aeolian processes [31] and pollutant dispersion [32]. However, CFD modeling for pollutant transport in the atmospheric environment becomes computationally intensive and difficult beyond short ranges, such as very close to roadways where road-induced and vehicle-induced turbulence need to be considered. It has been demonstrated [33] that CFD predictions of downwind pollutant concentrations are consistent with AERMOD results in open fields, and it was suggested [33] that CFD simulations do not need to be used where applications of Gaussian straight-line plume models work well in the environment, without complicated building influences. Furthermore, CFD predictions for atmospheric dispersion are not yet accepted by any regulatory agencies in the USA. Thus, while CFD holds promise in many applications of windblown dust transport [34], the plume dispersion model was chosen for use in this case.
The objectives of this study were to integrate the SWEEP and AERMOD models for the first time in a non-industrial setting, to characterize particulate matter emissions and dispersion in the form of windblown dust from Lordsburg Playa. The integrated models were used to estimate the levels of soil loss during dust events and to understand the levels of dust and PM10 exposure of vehicular traffic and motorists on the I-10 crossing the playa. Combined with our previous field study of dust emissivity characteristics of different land surfaces around the playa [5], land managers and transportation and public safety authorities will be able to understand the potential levels of dust and PM10 exposure along the highway and their causes, for a more informed management of health and safety during conditions of blowing dust and sand at Lordsburg Playa. Demonstrating the feasibility of using these models together can lay the groundwork for their future joint use in other locations where wind-eroded dust from natural or disturbed land surfaces threatens infrastructure such as transportation corridors, or exposes vulnerable communities to potentially excessive levels of particulate matter.

2. Methods

Field and laboratory investigations [5,35] were initially performed to assess the physical and chemical properties and dust emissivities of potential dust source soils at Lordsburg Playa. Those data were used as inputs for the prediction of potential particulate matter exposures along the I-10 using the wind erosion model SWEEP, with its outputs and local meteorological data serving as inputs for the dispersion model AERMOD. The integrated models were used to simulate the soil loss, saltation/creep (large particle) loss, suspension (dust) transport, and PM10 emissions from selected portions of the playa, under windy, dusty conditions. The spatiotemporal dispersion of the PM10 emissions from the playa was characterized at the receptor site (I-10), to represent the potential dust hazard and exposure at the highway.

2.1. Site Description

Lordsburg Playa, representing a modern remnant of the Pleistocene Lake Animas [36], is a complex of several lake beds in the northern Animas Basin, of the Basin and Range geological province [5]. The Peloncillo Mountains to the west, Burro Peak to the northeast, Pyramid Mountains to the east, and Animas Valley to the south drain into the playa. Although it can be intermittently inundated with shallow water during the summer rainy season [37], the arid climate (average annual precipitation 25 cm, average annual evaporation 184 cm) [36] renders the playa surface (elevation 1263 m) dry and barren for the vast majority of the year.
The playa develops crusts on its surfaces once the water evaporates [37], but these crusts are vulnerable to disturbances (livestock and vehicular traffic, inflow of sediments from degraded control structures above the playa), which alter the surface hydrology, degrade vegetation, and expose loose erodible material on the playa. These disturbances promote the development of plumes of windblown dust on the playa [5,38,39]. Lordsburg Playa was closed by the United States Bureau of Land Management (BLM) to off-highway vehicle use in 1998 “to reduce impacts to the soil on the Lordsburg Playa. Once the soil surface is disturbed, it is highly susceptible to wind erosion” [40]. However, observations by the authors and New Mexico state employees suggest that unauthorized, crust-destroying activities still take place on the playa surface. As a result, Lordsburg Playa is a site of intense dust storms [5,41].
The mountains facing the playa were extensively mined in the first part of the 20th century, with over 1.5 million tons of metal ores extracted from them [42]. If a playa basin includes metal deposits unearthed by mining, those metals can accumulate on the playa surface [43] and cause the particulate matter emitted as dust to be enriched in metals [44]. Ingestion or inhalation of metal-enriched particles increases the human health hazard from exposure to aerosols [45].

2.2. SWEEP Model

The USDA’s WEPS model [46] and its SWEEP sub-model [47] are used to characterize soil loss and particulate matter emission from aeolian transport hot spots [48], such as agricultural fields, industrial sites, or, as considered here, a playa. SWEEP is appropriate for modeling dust emission from non-agricultural disturbed lands if estimates are available of surface soil/biomass conditions and meteorological parameters [47,49]. The model calculations in SWEEP are identical to the WEPS erosion model, but are independent of the five other submodels used to build the WEPS model [47]. The erosion submodel initiates the process, by determining the static threshold friction velocity at which erosion begins for each defined cell, based on the surface conditions [50]. Following that, the soil loss is determined any time the friction velocity exceeds the static threshold friction velocity.
SWEEP predicts the potential soil loss from single day windstorm events using site-specific surface soil/biomass conditions and meteorological parameters, in terms of total soil loss (particle size <2.0 mm), creep/saltation (0.1 to 2.0 mm), suspension (<0.1 mm), and PM10 (<0.01 mm) flux entering the atmosphere [47,49]. The estimated PM10 emissions from SWEEP can be used as primary input to an air dispersion model, used to examine the temporal and spatial exposure of the public to particulate matter pollution. Although SWEEP has primarily been used for understanding soil loss and dust emission from agricultural lands [50], it has also been utilized in other environments [30,51,52]. For example, Maurer and Gerke [53] and Jia et al. [54] employed the SWEEP model to simulate aeolian sediment fluxes from an artificial hydrological watershed and a tailings dam, respectively, where both were conceptually similar settings to that of a desert playa.
The SWEEP model simulation was run for two areas of the playa (fields) (Figure 2), selected as potentially emitting dust that could impact I-10, for two dust event days. One field was along the southern edge of the North Playa located to the north of I-10, a region identified as a frequent source of dust plumes blowing southeast across I-10 (seen in Figure 1A). The other field was Road Forks Playa (RFP), an ephemerally-flooded small playa south of I-10 where it enters the Lordsburg Basin from the west; this area has been the site of frequent dust plumes propagating northward across the highway, and a focus for BLM and New Mexico Department of Transportation (NMDOT) mitigation efforts [55,56]. The dust event days selected were 3 February 2020 and 5 June 2020, representing cool-season synoptic-scale and warm-season convective (thunderstorm outflow) driven events, respectively, and typical of the vast majority of dust events in the Chihuahuan Desert [57]. These days were selected through analysis of visibility data from the NMDOT weather stations NM003 and NM004 and webcam photos from NMDOT traffic cameras, all located on the playa (Figure 2).
The SWEEP model was primarily designed to characterize soil loss from rectangular fields. To address this limitation, the irregular shaped fields at Lordsburg Playa were divided into strips of rectangular subfields. For 3 February 2020, the field from the North Playa was divided into 12 rectangular subfields, and the field from Road Forks into 9 rectangular subfields, covering 3.171 km2 and 0.279 km2, respectively (Figure 3 and Table 1). In all cases, the rectangular subfields were aligned with the wind direction obtained from the NMDOT meteorological stations. For the North Playa field, rectangles were aligned at 232.69° and 124.44° from true north on 3 February and 5 June 2020, respectively. For the Road Forks field, the rectangles were aligned at 251.19° and 161.58° from true north during 3 February and 5 June 2020, respectively. For both dates combined, 39 SWEEP model simulations were performed for the 39 subfields. These runs represented the worst-case scenario of the maximum possible wind erodibility of the surface.
As required by the SWEEP model, 38 parameters were defined through the model’s series of tabs: field, biomass, soil layers, soil surface, and weather (Table A1 in Appendix A). These groups define vegetation, residue, soil, and weather characteristics. Soil layer and soil property data were acquired from the USDA Soil Survey Geographic (SSURGO) database [59], by creating areas of interest from imported GIS shapefiles of the North Playa and Road Forks fields. The biomass-associated data were extracted from the BLM Assessment, Inventory, and Monitoring (AIM) strategy data [60]. Meteorological parameters (wind speed, wind direction, and air temperature) were retrieved from the NM003 and NM004 weather stations on the playa, and accessed through the New Mexico Climate Center [61]. Daily time series of wind speed and air temperature for the four cases (two fields on two dates) used in the study are given in Figure 4. The average daily air temperature and elevation of the fields were used to estimate the air density for the day. Hourly wind speeds were used to run the erosion simulation, fulfilling the requirement of AERMOD for hourly pollutant emissions. The hourly surface water content required by the soil surface tab was assumed to be zero, to consider the worst-case scenario. The soil particle size distribution data recommended by the SWEEP model user guide [48] was based on laboratory analysis of samples gathered in the field [35]. The textural composition of soil samples representing the North Playa field (Figure 5) was silt loam, while the Road Forks site (Figure 6) had a loam texture, consistent with the SSURGO database.

2.3. AERMOD

The American Meteorological Society (AMS) and EPA Regulatory Model (AERMOD), one of the U.S. Environmental Protection Agency (EPA)’s preferred and recommended air quality dispersion models, is a steady-state plume model that incorporates airborne pollutant dispersion, based on planetary boundary layer turbulence structure and scaling concepts, and providing estimates of ambient concentrations [62]. AERMOD has the capability to simulate dispersion from rural and urban areas, flat and complex terrain, surface and elevated releases, and multiple sources. As an output, it provides estimates of ambient pollutant concentrations, primarily for regulatory purposes [63,64].
In the stable boundary layer (SBL), AERMOD assumes the concentration distribution to be Gaussian, in both the vertical and horizonal directions [65]. In the convective boundary layer (CBL), the model assumes the horizontal concentration distribution is Gaussian; however, the vertical distribution is characterized with a bi-Gaussian probability density function [66]. In the case of a CBL, the AERMOD model incorporates the concept of “plume lofting”, whereby a portion of plume mass, released from the buoyant source, rises to, and remains near the top of the boundary layer before being mixed into the CBL [65]. Based on the measurements and extrapolations of those measurements, AERMOD estimates the vertical profiles of wind speed, wind direction, turbulence, temperature, and temperature gradient. In the final stage, the model utilizes different algorithms, depending on the combinations of the input data to calculate the pollutant concentrations at downwind receptors.
AERMOD aims to simulate the near-field (<50 km) dispersion from rural and urban settings, surface and elevated sources, fixed and mobile sources, and simple and complex topographical settings [62,67,68]. Among other dispersion models, AERMOD has been extensively applied to simulate the PM10 pollutant dispersion from transportation emissions [69], industrial sites [70,71,72], agricultural fields [73], mining areas [74,75], and landfill sites [76,77]. Focusing on the dust-emissive playa of Mono Lake, California, Ono et al. [78] assimilated sand flux monitoring, ambient PM10 monitoring, and the AERMOD model, to estimate dust emissions and their downwind impact on a receptor site, confirming the effectiveness of the AERMOD model in this type of topographical setting, as compared to other air dispersion models.
The AERMOD modeling system (Figure 7) accompanies two data preprocessors that are regulatory components, AERMET and AERMAP. Other non-regulatory components include AERSURFACE, a surface characteristics preprocessor model; AERMINUTE, a 1-min Automated Surface Observing Stations (ASOS) wind data processor; pollutant source parameters (location and geometry); and pollutant emission rate (provided by the SWEEP outputs). AERMINUTE processes the 1-min ASOS wind data to generate hourly average winds for input to the AERMET model in stage two [79]. AERSURFACE processes land cover data from the National Land Cover Data (NLCD), to generate surface characteristics including surface aerodynamic roughness length, noontime albedo, and daytime Bowen ratio, for use in stage three of the AERMET model [80].
The AERMET model, a meteorological data preprocessor, processes meteorological observations from hourly surface observations that are typically collected at airports by the National Weather Service (NWS) and/or Federal Aviation Administration (FAA), twice-daily upper air soundings collected by NWS, and data collected from an on-site measurement program [62]. The AERMET model processes these three data inputs through three stages, and each stage requires a separate run. In the first stage, the surface, upper air, and on-site data are written in specific file formats and their quality is examined. The second stage merges the data processed in stage one and the hourly average ASOS data from AERMINUTE into distinct 24-h periods and saves the merged data to an intermediate file. In the final stage, the AERMET assimilates the merged data from stage two and the surface characteristics from the AERSURFACE model, to compute the boundary layer parameters (e.g., surface friction velocity, mixing height, and Monin–Obukhov length), and produces the two input files required by the AERMOD model. The AERMAP model, a regulatory preprocessor for terrain data, processes terrain data (Digital Elevation Model), to generate data containing the elevation and hill-height scaling factors of each receptor and source, for use within the AERMOD model [81].
The 1-min ASOS wind data, as an input to AERMINUTE for aggregation to hourly average data, was downloaded from the National Centers for Environmental Information (NCEI) for Deming Municipal Airport, Deming, New Mexico, the closest ASOS site with similar environmental and topographical features to the Lordsburg Playa. These data are primarily used to reduce the number of calm-wind observations and missing wind records in the surface data, which are the main limitations in the NWS surface meteorological data. National Land Cover Data (NLCD) for 2016, including tree canopy, land cover, and impervious data, at a spatial resolution of 30 m, representing the input data processed by the AERSURFACE model to create surface characteristic parameters, were acquired from the Multi-Resolution Land Characteristics (MRLC) [82,83]. This was done by outlining the area corresponding to the NMDOT meteorological stations on Lordsburg Playa and the hourly surface meteorological data source in Deming. Two types of meteorological data, in addition to the hourly Deming Municipal Airport data, were acquired and processed for use in AERMET. Upper air soundings from Tucson International Airport, which is the nearest site and which is generally upwind of and most closely representative of conditions at the Lordsburg Playa, and collected by NWS, were used as parameters for the Upper Air Meteorological Data. On-playa meteorological data (wind speed, wind direction, relative humidity, air temperature, and dew point temperature data) from the NM003 and NM004 weather stations (Figure 2) were acquired from the New Mexico Climate Center. In stage three, the AERMET model prioritized the on-site data, to generate an input to the AERMOD model, and utilized the data from NM003 and NM004. For the AERMAP preprocessor model, the digital elevation model (DEM) with spatial resolution of 1/3 arc-second was adopted from the United States Geological Survey. In addition, Cartesian grid networks of receptors were used at an elevation of 1.7 m above the land surface covering the Lordsburg Playa and the transportation corridor. The 1.7 m receptor height was determined as the average value of the current standards for driver’s eye height within passenger cars and trucks [84], for determining visibility, and represents a reasonable standard for the height of a standing human, such as a public safety officer or highway worker, inhaling ambient air containing dust from the playa. The horizontal spacing between the receptors was 150 m. The receptors were defined by the discretely placed receptor locations referenced to a Cartesian system. For AERMOD, this study used a rotated area source, rural environment, flat terrain, ground-level release, and on-site meteorological observations. All the model runs, including AERMOD, AERMET, AERMAP, AERSURFACE, and AERMINUTE models, were performed using executable files from the US EPA.

3. Results and Discussion

3.1. SWEEP

The SWEEP model simulated the likely maximum soil loss from wind erosion, in terms of the total mass, saltation/creep and suspension components, and PM10 from the North Playa and RFP fields for the dusty days of 3 February and 5 June 2020, as presented in Figure 8, Figure 9, Figure 10 and Figure 11. SWEEP estimated that the North Playa fields emitted 1245.92 and 1664.73 metric tons of PM10 during the 3 February and 5 June 2020 dust event days, respectively, and that the Road Forks fields discharged 30.16 kg and 50.75 kg of PM10 on the 3 February and 5 June 2020 dust event days, respectively. The hourly PM10 emission rate for use in the AERMOD model was also generated by SWEEP. In all four scenarios, the soil loss started at 11:00 local time, primarily influenced by the magnitude of the wind speed, indicating that during this time, the friction velocity started to exceed the static friction velocity threshold.
During 3 February 2020, the simulated soil loss from the North Playa showed a distribution consistent with the known meteorology of Chihuahuan Desert dust events [57], peaking at around 17:00, with around a 0.41 g m−2 sec−1 soil loss (Figure 8). During this time, in the largest fields, the saltation/creep, suspension, and PM10 soil losses were around 0.05, 0.35, and 0.015 g m−2 sec−1, respectively; and in the smallest field (Field #1), the losses were 0.19, 0.20, and 0.08 g m−2 sec−1, respectively. The total loss (per area unit time) was the same across all subfields, irrespective of geometry, since the biomass, soil properties, and meteorology inputs to the SWEEP model were the same for each subfield. However, the other three soil loss modes (saltation/creep, suspension, and PM10) varied with the length of the fields. The suspension and PM10 soil losses increased with the increase in field lengths, because the suspension component continued to increase with downwind distance and the sources for suspension-size material were usually active over the entire field [48]. In addition, this component has a greater transport capacity than that of the saltation/creep components. On the other hand, the saltation/creep modes showed a greater soil loss in the shorter fields, due to the ability of the saltation/creep components to reach transport capacity at shorter distances, by absorbing momentum from the wind. Thus, in the shorter fields, the saltation/creep components may reach capacity within this allowable short distance and discharge more soil than the suspension mode. For example, the Field #1 saltation/creep loss reached a maximum soil emission at around 200 m downwind from the southwest edge of the field. However, the suspension and PM10 emission reached their maxima at the other end of the field (at 401 m).
On 5 June 2020 (Figure 9), the North Playa field emitted much greater amounts of soil dust than during 3 February 2020, due to a greater wind speed, peaking at 15:00 in all soil loss modes. During that time, in the longer fields (Fields #2 to #9), the saltation/creep, suspension, and PM10 losses were 0.22, 1.5, and 0.063 g m−2 sec−1, respectively; and in the shortest field (Field #1), the losses were 0.58, 1.13, and 0.04 g m−2 sec−1, respectively. For Field #1, the saltation/creep soil discharge reached a maximum at around 400 m, from the southeast edge of the field. However, the suspension and PM10 losses reached a maximum at the other end of the field (at 619 m). Similarly to the dust event of 3 February 2020, the dusty day of 5 June 2020 also showed a greater soil loss through saltation/creep modes in the shortest fields.
The Road Forks Playa field is much smaller in size than the North Playa field, and thus the modeled soil loss during the two dusty days was less. However, due to its position along the dominant southwesterly wind direction during the windy spring season and immediately upwind of I-10, the Road Forks Playa poses a serious threat to highway safety. During the dust event on 3 February 2020, the Road Forks subfields showed a multimodal distribution of soil loss across all subfields peaking at 16:00 (Figure 10). Saltation/creep components dominated the soil loss across five subfields (Fields #1, #2, #3, #8, and #9), due to their shorter length. In the longest field, the SWEEP-modeled saltation/ creep, suspension, and PM10 components of soil emission were 0.13, 0.22, and 0.09 g m−2 sec−1, respectively; and in the shortest field: 0.23, 0.009, and 0.003 g m−2 sec−1, respectively. During the convective dust event on 5 June 2020 (Figure 11), similarly to 3 February 2020, the soil loss showed a multimodal distribution, with creep/saltation dominating the shorter fields. However, the rates of soil loss were greater during the 5 June 2020 event. In the longest field the wind-discharged soil through saltation/creep, suspension, and PM10 reached 0.25, 0.41, and 0.18 g m−2 sec−1, respectively.

3.2. AERMOD

The hourly dispersion of PM10 across Lordsburg Playa, simulated by the AERMOD modeling system, driven by SWEEP model outputs for the dust event days of 3 February 2020 and 5 June 2020, are presented in Figure 12 and Figure 13, respectively. These dispersions represent the PM10 concentration at the receptor site (I-10), with a height of 1.7 m above the playa surface. The hourly concentrations of PM10 reaching the highway during these dust events were determined in terms of time and space. Exposure to this particulate pollution will affect public safety and health, by reducing visibility and air quality, and will affect motorists, highway workers, and public safety personnel, including persons stopped on the right-of-way during or after a dust storm or traffic halt. As expected, the concentrations of dust and PM10 were higher at, and near, the source areas, and gradually decreased with increasing distances downwind. This finding also demonstrated that when the playa is exposed to high winds, I-10 and its right- of-way can be impacted by particulate matter pollution from all wind directions. The size of the respective fields (area affected by wind erosion) is also an important factor that determines the amount of particulate matter channeled to the highway.
The highest hourly concentration of PM10 estimated during the dust event day of 3 February 2020 was modeled for the 19:00 h from the North Playa field, at 130,266 µg/m3 (Figure 12), 866-fold the US EPA daily NAAQS concentration limit for PM10 of 150 µg/m3, at the downwind edge of the North Playa field, with the hourly concentration exceeding 40,000 µg/m3 alongside I-10. Although this may initially appear to be an astonishingly high concentration of particulate matter, other AERMOD studies predicted and confirmed extreme concentrations of PM10 from playa dust sources. For example, Ono et al. [78] simulated an hourly PM10 concentration of 60,000 µg/m3 at Mono Lake playa in California using AERMOD during the dust storm of 20 November 2009, which was validated by a ground-based instrument reaching 65,112 µg/m3 of maximum hourly PM10 concentration during that day. Although the prevailing wind direction was from the southwest during the 3 February 2020 event at Lordsburg Playa, the greatest concentrations of PM10 on the highway were directed from the northwest during the evening hours. This observation was supported by the visibility data from the NM003 meteorological site (Figure 14) and webcam photos from the NMDOT traffic cameras at Mile Post 11 in Lordsburg Playa (Figure 15). Due to blowing dust, the minimum hourly visibility measured at NM003 on 3 February 2020 dropped abruptly from 20.0 km to 1.66, 3.78, and 8.96 km at 15:00, 16:00, and 17:00, respectively. The webcam photos (Figure 15) also display blowing dust that moved from the North Playa towards the camera between 15:31:23 and 15:39:23, confirming that AERMOD properly simulated the PM10 emission around 15:00 and 16:00.
On 5 June 2020, the maximum modeled hourly PM10 concentration reached 217,565 µg/m3 from the North Playa field at 15:00, and the dominant wind direction was southwesterly. At this hour, the simulated emission advected to the north (Figure 13), away from the transportation route, and the impact on I-10 was minimal. However, the PM10 emission from the Road Forks field was channeled directly towards the highway and may have impaired visibility. Reinforcing this observation, the NM003 meteorological site exhibited a visibility of 2.59 km (Figure 14), a sudden drop from 20.0 km at 14:00. The webcam photos from the NMDOT traffic camera (Figure 16) support these findings. At 17:00, AERMOD simulated the PM10 emitted from both fields blowing towards the northeast, with greater emissions from the North Playa. Webcam photos between 17:22:23 and 17:29:23 also showed dust blowing from both fields, and thicker on the north side of I-10 (Figure 16).

3.3. Potential Metal Exposures

Laboratory experiments [35] measured up to 20 parts per million of bioavailable lead (Pb) in the silt (<50 µm) fraction of Lordsburg Playa sediments, posing a potential hazard from airborne lead exposure to persons who breathe the dust. The U.S. Clean Air Act Federal standard for airborne lead is 0.15 µg/m3 Pb in total suspended particles (TSP) (wind-transported particles of all sizes) as a 3-month average. Assuming 20 ppm of bioavailable lead in PM10 (the actual lead concentration in the PM10 dust could be higher than that in the overall silt fraction, due to the general inverse relationship of metal concentration and soil grain size [85]), and the values of PM10 obtained by SWEEP and AERMOD, this could lead to exposures on the order of 1 µg/m3 in bioavailable Pb in the PM10 fraction alone at I-10. Given that the concentrations of TSP in the dust will be much higher than PM10, the exposure to airborne lead in the regulated category of TSP along I-10 during Lordsburg Playa dust events would be higher than the estimated microgram per cubic meter of bioavailable Pb estimated in PM10. However, the NAAQS for lead represents a 3-month average: actual exposures to dust for travelers at Lordsburg Playa would be extremely transient in time, not exceeding several hours in a worst-case scenario for a traveler or public safety worker forced to remain in a dusty area of the playa during a traffic delay or crash investigation. Therefore, total average quarterly exposure to airborne lead to persons exposed to multiple dust events at Lordsburg playa would increase by at most a few hundredths of a microgram per cubic meter in an extreme scenario.
Based on measurements of copper and zinc in Lordsburg Playa sediments [35], dust exposures to those elements have the potential to be much higher than lead, although no air quality regulatory standard exists for those elements, and their human health effects from aerosol exposure are less well studied than those of lead. Evidence exists for bioavailable copper as a specific promoter of the generalized respiratory inflammation caused by metal mixtures in airborne particulate matter [86]; thus, copper should be considered a component of potential human health concern in inhaled Lordsburg Playa dust. Inhalation of zinc oxide aerosols has been shown to cause respiratory inflammation [87], so a concern over zinc exposure from Lordsburg Playa dust could also be explored (zinc in near-roadway dust is likely derived in large part from tire wear [88], which would occur anywhere alongside highways; thus, it is not strictly a playa-specific hazard). As with lead, however, travelers and highway workers exposure to other metals at Lordsburg Playa, although acute, would be short-term and infrequent.

3.4. Limitations

Unlike in previous studies, in this case, the SWEEP model was made to consider irregular-shaped fields, by dividing the playa sites into rectangular subfields. By doing so, the model adequately simulated the soil loss from the subfields. There is a need to upgrade SWEEP, to take account of spatially variable or heterogeneous data inputs in grid or polygon formats for a single run and the spatially variable wind direction for each time step in which erosion is calculated. This will address the limitation of SWEEP in considering wind direction for limited periods, with the fastest wind speeds and single input from each of the 38 parameters required by the model (Table A1 in Appendix A). A prototype, spatially capable version of the SWEEP model is currently under development by the USDA.
PM10 was treated as a passive pollutant in this study, where dry deposition during pollutant dispersion was not enhanced in the AERMOD modeling. However, the deposited fraction of PM10 in this study was estimated to be less than 10%, considering Tartakovsky et al. [89] reported approximately 12% of PM smaller than 14 µm would be deposited within 1 km downwind of an area source under a high wind, neutral atmospheric stability (Pasquill-Gifford D Stability) condition.
With the lowest averaging period of 1 h for calculating pollutant concentrations in a particular run, the opportunities for the AERMOD model to fully capture the PM10 emission potential of short-lived, high-intensity dust events due to gusty winds are limited. Many of the hazardous dust events on the Lordsburg Playa are linked to small-temporal-and-spatial scale “dust channels”, which intensify and diminish quickly (Figure 15 and Figure 16). Therefore, there is a need to develop AERMOD to consider finer (<1 h) temporal runs and to capture the impact of short-lived pollution plumes.

4. Conclusions, Implications, and Recommendations

Clouds of dust blowing from Lordsburg Playa, a dry lakebed in the Chihuahuan Desert of southwestern New Mexico, represent an acute health and safety hazard to highway traffic on I-10, which crosses the playa. Using data from field investigations, published databases, and on-playa and regional meteorological stations, the aeolian emission of dust and sand from the playa and its dispersion to the highway was modeled for the example dust event days of 3 February and 5 June 2020. Soil losses from wind erosion (dust and sand emission), in terms of total, saltation/creep, suspension, and PM10 components were simulated using the SWEEP model. The model estimated that the North Playa fields emitted approximately 1250 and 1660 metric tons of PM10 during the 3 February and 5 June 2020 dust event days, respectively, and that the Road Forks fields discharged approximately 30 and 51 kg of PM10 on the 3 February and 5 June 2020 dust event days, respectively. The size of the eroding areas affected the amount of airborne soil loss, by controlling the activity of the soil loss components with downwind distance. In the very long fields, the suspension and PM10 modes showed a greater soil loss; and in the very small fields, saltation/creep was the dominant component. The North Playa field emitted much greater amounts of wind-eroded soil on 5 June 2020 than during the event of 3 February 2020, due to the higher wind speed on that date. The Road Forks Playa is much smaller than the North Playa and generated a much lower amount of soil loss and PM10 emissions during the dust events. However, due to its position relative to the prevailing wind and I-10, this field poses a more serious threat to the public health and safety. Using the hourly PM10 soil loss output from SWEEP model, NLCD land cover data, emission locations and their geometry, and multiple meteorological data products, hourly concentrations of PM10 over the playa emanating from the selected fields were simulated using the AERMOD modeling system for the two dust event days. The highest hourly concentration of PM10 during the dust event of 3 February 2020 was around 130,000 µg/m3; more than 860-times the NAAQS daily concentration limit for PM10. In the convective dust event with stronger winds on 5 June 2020, the maximum hourly PM10 concentration was estimated to exceed 217,000 µg/m3. The results from the AERMOD-simulated PM10 dispersions were consistent with blowing dust observations from NMDOT webcam photos and visibility data from meteorological sites on the playa.
The dust emitted from hotspot fields on the playa, both immediately adjacent to the highway and at further distances can lead to PM10 concentrations on the order of tens of thousands of micrograms per cubic meter at I-10, representing associated concentrations on the order of micrograms per cubic meter of bioavailable metals in airborne particles. However, these exposures should be temporally brief and are not likely to significantly impact the long-term aerosol inhalation burden to anyone caught in a playa dust event. If judged to be a health hazard as well as to a traffic safety concern, in addition to the existing fixed roadside signage reminding motorists to beware of and drive properly during dust storms (Figure 1), transportation authorities may wish to consider adding signage reminding travelers to set vehicular ventilation systems to block intake of outside air during dusty conditions.
This study was the first to integrate the USDA’s SWEEP process-based wind erosion model and EPA’s AERMOD air dispersion model to quantify dust emission in a non-industrial environment. Although these models have previously been widely-used independently [28,29,47,50,51,52,53,54,64,66,67,68,69,70,71,72,73,74,75,76,77,78], demonstrating their use together represents a novel methodology, in simulating and quantifying the impact of wind-eroded dust on air quality and public health and safety. Throughout the globe, other playas are becoming increasingly wind-erodible, as a result of drought and degradation [90], and communities downwind of them are increasingly vulnerable to exposure to high particulate matter concentrations in the form of dust. SWEEP integrated with AERMOD now joins the toolbox of methods to assess potential windblown dust particulate matter concentrations and to prioritize the mitigation of wind erosion hotspots.

Author Contributions

Conceptualization, T.E.G., J.L., R.S.V.P. and W.-W.L.; Methodology, I.G.E., J.L. and J.T.; Software, J.L., J.T. and W.-W.L.; Formal analysis, I.G.E., J.L. and J.T.; Validation, I.G.E., T.E.G. and R.S.V.P.; Investigation, I.G.E., R.S.V.P. and T.E.G.; Resources, T.E.G. and R.S.V.P.; Data curation, I.G.E., R.S.V.P. and T.E.G.; Writing—original draft preparation, I.G.E.; Writing—review and editing, T.E.G., J.L., J.T. and W.-W.L.; Visualization, I.G.E.; Supervision, T.E.G., R.S.V.P. and W.-W.L.; Project administration, T.E.G.; Funding acquisition, T.E.G., J.L., R.S.V.P. and W.-W.L. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by Project UTEP-01-13 from the Center for Advancing Research in Transportation Emissions, Energy, and Health (CARTEEH), a U.S. Department of Transportation University Transportation Center, and by grant 80NSSC19K0195 from NASA.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data sets for wind erosion and air dispersion modeling to characterize surface properties, dust emission, and PM10 dispersion at Lordsburg Playa are archived in Appendix C of Eibedingil [91] and at http://dx.doi.org/10.17632/4pn5vsxxm7.1 (accessed on 30 June 2022).

Acknowledgments

The authors acknowledge Mayra Chavez (University of Texas at El Paso) and multiple staff members of the New Mexico Department of Transportation, New Mexico Department of Environment and National Weather Service- Santa Teresa, New Mexico for useful discussions; Marcos Mendez and German Rodriguez for technical assistance: and the New Mexico Climate Center for data access.

Conflicts of Interest

The authors declare no conflict of interest.

Disclaimer

The contents are solely the responsibility of the authors and do not necessarily represent the official views of NASA, the U.S. Department of Agriculture, or the U.S. Department of Transportation. Use of brands or trade names is for information only and does not imply endorsement by or exclusion of similar products by USDA or the funding agencies. USDA and the funding agencies are equal opportunity employers and providers.

Appendix A

Table
Table A1. Parameters required by the SWEEP model to simulate wind erosion for the two selected plots.
Table A1. Parameters required by the SWEEP model to simulate wind erosion for the two selected plots.
ParametersNorth Playa Road ForksSource of Data
03 Feb 202006 Jun 202003 Feb 2020 06 Jun 2020
1. Field
x and y dimensions of fields Refer to Table 1
Angle, degrees 52.69304.4471.19341.58Wind direction via New Mexico Climate Center
Number of fields 12999-
Wind barriers 0000-
2. Biomass
Residue average height (m) 0000Bureau of Land
Management
(BLM)
Residue stem area index (m2/m2)0000
Residue leaf area index (m2/m2)0000
Residue flat cover (m2/m2)0000
Growing crop average height (m)0000
Growing crop stem area index 0000
Growing crop leaf area index 0000
Row spacing (m) 0000
Seed placement FurrowFurrowFurrowFurrow
3. Soil Layers
Number of layers 23NRCS, USDA
Thickness 150, 1370200, 330, 990
Sand Fraction (Mg/Mg) 0.18, 0.0310.18, 0.311, 0.551Soil sampling and
particle size
distribution
from field measurements and Gill et al. [35]
Very fine sand fraction (Mg/Mg) 0.13, 0.0240.13, 0.086, 0.111
Silt fraction (Mg/Mg) 0.59, 0.4440.59, 0.309, 0.174
Clay fraction (Mg/Mg) 0.1, 0.5250.1, 0.38, 0.275
Rock volume fraction (m3/m3) 0, 00, 0, 0Natural Resources
Conservation
Service (NRCS)
USDA through
Soil Survey
Geographic
(SSURGO)
database
Dry bulk density (Mg/m3) 1.491, 1.4911.307, 1.677, 1.426
Avg. aggregate density (Mg/m3) 1.8, 1.81.8, 1.8, 1.8
Avg. dry aggregate stability (ln(J/kg)) 2.73, 2.733.018, 3.359, 3.348
GMD of aggregate sizes (mm) 4.914, 26.1744.409, 11.118, 17.7
GSD of aggregate sizes (mm/mm) 14.989, 10.50614.745, 14.735, 12.778
Minimum aggregate size (mm) 0.01, 0.010.01, 0.01, 0.01
Maximum aggregate size (mm) 36.8, 59.74836.042, 44.489, 51.383
Soil wilting point w. content 0.267, 0.2670.103, 0.217, 0.145
4. Soil Surface
Surface crust fraction (m2/m2) 0.50.5Klose et al. [38]
Surface crust thickness (mm) 10′10Field measurements
Loose material on crust (m2/m2) 0.80.8Klose et al. [38]
Loose mass on crust (kg/m2) 1.51.5Assumed
Crust density (Mg/m3) 1.81.8Natural
Resources
Conservation
Service (NRCS)
USDA through
Soil Survey
Geographic
(SSURGO) database
Crust stability (ln(J/kg)) 2.733.02
Allmaras random roughness (mm) 44
Ridge height (mm) 00
Ridge spacing (mm) 1010
Ridge width (mm) 1010
Ridge orientation (deg) 00
Dike spacing (mm) 00
Snow depth (mm) 00
Hourly surface water content 00Assumed
5. Weather
Air density (kg/m3) 1.06920.99931.06980.9999Estimated
Wind direction (deg. from north) 232124.44251.19161.58New Mexico
Climate Center
Anemometer height (m) 10101010
Aerodynamic roughness (mm) 25252525NRCS, USDA
Z0 location flag StationStationFieldFieldNew Mexico
Climate Center
Number of interval/day to run 24242424
Wind speed (m/s) Refer to Figure 4

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Figure 1. (A) I-10 crossing Lordsburg Playa, looking northeastward from west edge of playa, with dust crossing the center of the playa from northwest to southeast. Photo by T.E. Gill. (B) Highway signs warning of dust hazard and prescribing safe actions for drivers in reduced visibility as vehicles approach Lordsburg Playa on I-10. Photo by R.S. Van Pelt.
Figure 1. (A) I-10 crossing Lordsburg Playa, looking northeastward from west edge of playa, with dust crossing the center of the playa from northwest to southeast. Photo by T.E. Gill. (B) Highway signs warning of dust hazard and prescribing safe actions for drivers in reduced visibility as vehicles approach Lordsburg Playa on I-10. Photo by R.S. Van Pelt.
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Figure 2. (a) True RGB (Band 4—Red, Band 3—Green, and Band 2—Blue) image of the study area covering Lordsburg Playa from Sentinel-2 MSI (MultiSpectral Instrument) Level 2A at a 10-m spatial resolution [58]. The North Playa Field is bounded by the green rectangle, and the Road Forks Playa Field is bounded by the blue-green rectangle. (b) North Playa field, used to simulate soil loss using the SWEEP model. (c) Road Forks field, used to simulate soil loss using the SWEEP model. (d) Example of hard crusted surface from the inner part of the playa on 7 March 2018. (e) Inundated area on the playa, adjacent to the highway and railroad, on 20 September 2018.
Figure 2. (a) True RGB (Band 4—Red, Band 3—Green, and Band 2—Blue) image of the study area covering Lordsburg Playa from Sentinel-2 MSI (MultiSpectral Instrument) Level 2A at a 10-m spatial resolution [58]. The North Playa Field is bounded by the green rectangle, and the Road Forks Playa Field is bounded by the blue-green rectangle. (b) North Playa field, used to simulate soil loss using the SWEEP model. (c) Road Forks field, used to simulate soil loss using the SWEEP model. (d) Example of hard crusted surface from the inner part of the playa on 7 March 2018. (e) Inundated area on the playa, adjacent to the highway and railroad, on 20 September 2018.
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Figure 3. Geometry of field locations at North Playa (top) and Road Forks (bottom) used by the SWEEP and AERMOD models to simulate soil loss, and dust and PM10 dispersion, respectively, during dust events on 3 February 2020 and 5 June 2020.
Figure 3. Geometry of field locations at North Playa (top) and Road Forks (bottom) used by the SWEEP and AERMOD models to simulate soil loss, and dust and PM10 dispersion, respectively, during dust events on 3 February 2020 and 5 June 2020.
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Figure 4. Meteorological data for wind speed and air temperature, required by SWEEP for the two sites and cases.
Figure 4. Meteorological data for wind speed and air temperature, required by SWEEP for the two sites and cases.
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Figure 5. Grain size distribution of the soil at the North Playa field.
Figure 5. Grain size distribution of the soil at the North Playa field.
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Figure 6. Grain size distribution of the soil at the Road Forks field.
Figure 6. Grain size distribution of the soil at the Road Forks field.
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Figure 7. AERMOD modeling system. AERMOD accompanies two data preprocessors that are regulatory components, the AERMET and AERMAP models. Other non-regulatory components include AERSURFACE, a surface characteristics preprocessor model; AERMINUTE, a 1-min Automated Surface Observing Stations (ASOS) wind data processor; pollutant source parameters (location and geometry); and pollutant emission rate (derived from the SWEEP outputs).
Figure 7. AERMOD modeling system. AERMOD accompanies two data preprocessors that are regulatory components, the AERMET and AERMAP models. Other non-regulatory components include AERSURFACE, a surface characteristics preprocessor model; AERMINUTE, a 1-min Automated Surface Observing Stations (ASOS) wind data processor; pollutant source parameters (location and geometry); and pollutant emission rate (derived from the SWEEP outputs).
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Figure 8. SWEEP simulated soil loss in terms of the total, saltation/creep, suspension, and PM10 from the North Playa field during the dust event of 3 February 2020. The left y-axis represents the total, saltation/creep, and suspension loss, and the right y-axis (twin axis) represents PM10.
Figure 8. SWEEP simulated soil loss in terms of the total, saltation/creep, suspension, and PM10 from the North Playa field during the dust event of 3 February 2020. The left y-axis represents the total, saltation/creep, and suspension loss, and the right y-axis (twin axis) represents PM10.
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Figure 9. SWEEP simulated soil loss, in terms of the total, saltation/creep, suspension, and PM10 from the North Playa field during the dust event of 5 June 2020. The left y-axis represents the total, saltation/creep, and suspension loss, and the right y-axis (twin y-axis) represents PM10.
Figure 9. SWEEP simulated soil loss, in terms of the total, saltation/creep, suspension, and PM10 from the North Playa field during the dust event of 5 June 2020. The left y-axis represents the total, saltation/creep, and suspension loss, and the right y-axis (twin y-axis) represents PM10.
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Figure 10. SWEEP simulated soil loss, in terms of the total, saltation/creep, suspension, and PM10 from the Road Forks Playa field during the dust event of 3 February 2020. The left y-axis represents the total, saltation/creep, and suspension loss, and the right y-axis (twin y-axis) represents PM10.
Figure 10. SWEEP simulated soil loss, in terms of the total, saltation/creep, suspension, and PM10 from the Road Forks Playa field during the dust event of 3 February 2020. The left y-axis represents the total, saltation/creep, and suspension loss, and the right y-axis (twin y-axis) represents PM10.
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Figure 11. SWEEP simulated soil loss, in terms of the total, saltation/creep, suspension, and PM10 from the Road Forks field during the dusty day of 5 June 2020. The left y-axis represents the total, saltation/creep, and suspension loss, and the right y-axis (twin y-axis) represents PM10.
Figure 11. SWEEP simulated soil loss, in terms of the total, saltation/creep, suspension, and PM10 from the Road Forks field during the dusty day of 5 June 2020. The left y-axis represents the total, saltation/creep, and suspension loss, and the right y-axis (twin y-axis) represents PM10.
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Figure 12. Hourly dispersion of PM10 over Lordsburg Playa simulated by AERMOD for the dust event day of 3 February 2020. The two closed polygons represent the PM10 sources. The red and blue lines represent I-10 and the Union Pacific Railroad, respectively.
Figure 12. Hourly dispersion of PM10 over Lordsburg Playa simulated by AERMOD for the dust event day of 3 February 2020. The two closed polygons represent the PM10 sources. The red and blue lines represent I-10 and the Union Pacific Railroad, respectively.
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Figure 13. Hourly dispersion of PM10 over Lordsburg Playa simulated by AERMOD for the dust event day of 5 June 2020. The two closed polygons represent the PM10 sources. The red and blue lines represent I-10 and the Union Pacific Railroad, respectively.
Figure 13. Hourly dispersion of PM10 over Lordsburg Playa simulated by AERMOD for the dust event day of 5 June 2020. The two closed polygons represent the PM10 sources. The red and blue lines represent I-10 and the Union Pacific Railroad, respectively.
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Figure 14. Minimum hourly visibility for the dust event days of 3 February 2020 and 5 June 2020 from the NM003 meteorological station.
Figure 14. Minimum hourly visibility for the dust event days of 3 February 2020 and 5 June 2020 from the NM003 meteorological station.
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Figure 15. Blowing dust recorded by webcam photos at Lordsburg Playa from NMDOT traffic cameras at Mile Post 11 looking west on 3 February 2020.
Figure 15. Blowing dust recorded by webcam photos at Lordsburg Playa from NMDOT traffic cameras at Mile Post 11 looking west on 3 February 2020.
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Figure 16. Blowing dust recorded in webcam photos at Lordsburg Playa from NMDOT traffic cameras at Mile Post 11 looking west on 5 June 2020.
Figure 16. Blowing dust recorded in webcam photos at Lordsburg Playa from NMDOT traffic cameras at Mile Post 11 looking west on 5 June 2020.
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Table
Table 1. Dimensions of each axis of the rectangular subfields shown in Figure 3. Axis lengths x and y in meters, and area in m2.
Table 1. Dimensions of each axis of the rectangular subfields shown in Figure 3. Axis lengths x and y in meters, and area in m2.
Field3 February 20205 June 2020
North PlayaRoad ForksNorth PlayaRoad Forks
xyAreaxYAreaXYAreaxyArea
1213.13401.7085,60429.62186.025509143.25619.8888,78554.30125.046789
2213.14714.72152,18829.62309.809182214.901416.86304,40054.30400.1121,724
3213.151159.38247,30829.62454.7413,467214.901549.33332,93854.30550.1529,871
4213.161472.94313,893118.48579.2268,561214.901549.69332,93554.31625.1733,944
5213.171517.59323,41088.86558.0849,585214.901549.68332,932108.61625.1767,889
6213.171517.61323,41588.86454.6740,403214.901549.67332,929108.61600.1765,174
7213.171517.62323,41988.86434.0738,567214.901549.65332,92554.31575.1631229
8213.171517.63323,42188.86372.0733,057501.391549.66776,80854.30225.0612220
9213.171517.63323,42359.24351.5720,814143.271239.69177,55354.30100.035431
10213.171517.64323,424---------
11213.171517.64323,424---------
12142.10757.98107,808---------
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Eibedingil, I.G.; Gill, T.E.; Van Pelt, R.S.; Tatarko, J.; Li, J.; Li, W.-W. Applying Wind Erosion and Air Dispersion Models to Characterize Dust Hazard to Highway Safety at Lordsburg Playa, New Mexico, USA. Atmosphere 2022, 13, 1646. https://doi.org/10.3390/atmos13101646

AMA Style

Eibedingil IG, Gill TE, Van Pelt RS, Tatarko J, Li J, Li W-W. Applying Wind Erosion and Air Dispersion Models to Characterize Dust Hazard to Highway Safety at Lordsburg Playa, New Mexico, USA. Atmosphere. 2022; 13(10):1646. https://doi.org/10.3390/atmos13101646

Chicago/Turabian Style

Eibedingil, Iyasu G., Thomas E. Gill, R. Scott Van Pelt, John Tatarko, Junran Li, and Wen-Whai Li. 2022. "Applying Wind Erosion and Air Dispersion Models to Characterize Dust Hazard to Highway Safety at Lordsburg Playa, New Mexico, USA" Atmosphere 13, no. 10: 1646. https://doi.org/10.3390/atmos13101646

APA Style

Eibedingil, I. G., Gill, T. E., Van Pelt, R. S., Tatarko, J., Li, J., & Li, W. -W. (2022). Applying Wind Erosion and Air Dispersion Models to Characterize Dust Hazard to Highway Safety at Lordsburg Playa, New Mexico, USA. Atmosphere, 13(10), 1646. https://doi.org/10.3390/atmos13101646

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