DAQ-2024-011371

UTAH DIVISION OF AIR QUALITY REQUEST FOR PROPOSAL
Summary Information Page

Projecting the impacts of a shrinking Great Salt Lake on dust
exposure along the Wasatch Front

University of Utah Principal Investigator:
Derek V. Mallia: Research Assistant Professor, Department of Atmospheric Sciences,
University of Utah, Salt Lake City, UT. Phone: [phone]. Email: [e-mail]

University of Utah Co-Principal Investigator:
Kevin Perry, Professor, Department of Atmospheric Sciences,
University of Utah, Salt Lake City, UT. Phone: [phone]. Email: [e-mail]

University of Utah Institutional Representative
Erica Trejo, Sponsored Projects Officer, Office of Sponsored Projects, University of Utah, Salt
Lake City, UT. Phone: [phone]. Email: [e-mail]
Address: 155 S. 1452 E., INSCC Building, Room 350, Salt Lake City, UT 84112-8906.

Funding Opportunity: Science for Solutions Research Grant - FY 2025

Project Period: July 1, 2024 – December 31st, 2025

Submission Date: February 2nd, 2024

Funding Request:
University of Utah
Year 1 $42,463
Year 2 $43,181
Total $85,643
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SCOPE OF WORK

ABSTRACT: Aeolian dust from the shrinking Great Salt Lake (GSL) is a major concern for those
living in Northern Utah. Over the past 2 decades, GSL water levels have steadily declined exposing
large areas of erodible land surfaces to the atmosphere. The Wasatch Front (population ~2.5
million), which includes Salt Lake City, is downwind of the GSL and is directly exposed to dust
emitted from dry portions of the GSL lakebed. GSL water levels will continue to decline without
strategic reductions in human and agricultural consumptive water use. For this project, we will use
a physics-based dust modeling framework to quantitatively assess the impact of a shrinking GSL
on dust exposure along the Wasatch Front. This framework will use the FENGSHA model to
estimate dust emissions across the Intermountain West, while HYSPLIT-STILT will be used to
simulate the transport of dust from different emission sources to the Wasatch Front (Figure 1).
Field measurements collected across the GSL and other major dust emission sources in Utah will
be used to improve the dust emission
model. In addition, a new urban dust
parameterization will be implemented
within the FENGSHA dust emission
model. This urban dust emission
parameterization will leverage high-
resolution land use and mineral extraction
data to estimate dust emissions from
tailings from mining operations and
quarries. The work carried out here will
provide the Utah Division of Air Quality
(UDAQ) with a dust modeling
framework that can determine
contributions of PM2.5 and PM10 from
different source regions throughout
Utah, including but not limited to the GSL
shoreline, the Lake Sevier and Bonneville
dry lakes, and quarry and mining
operations. Our dust modeling framework will also be used to run a series of sensitivity tests where
GSL water levels will be set to different levels. This analysis will provide UDAQ with estimates
on how different GSL water levels could impact dust exposure along the Wasatch Front.
Modeled dust concentrations will be mapped with population data sets for Utah to determine the
communities most at risk to dust from the GSL.
BASIS AND RATIONALE: Aeolian dust is a major concern for population centers located
throughout the western U.S (Sweeney et al. 2022). Dust can degrade air quality, reduce visibility
(Steenbugh et al. 2012), pose a substantial risk to human health, alter the microphysical properties
of clouds (DeMott et al. 2003; Karydis et al. 2011), and increase the stability of the atmosphere
(Dunion et al. 2004). The emission of dust typically occurs in arid environments with strong
winds. The Great Basin is a large endorheic watershed with many terminal and desiccated lakes,
deserts, mining operations, and agricultural activities. The Great Basin is home to more than 4.9
million residents, more than half of them (~2.5 million) residing along the Wasatch Front,
including Salt Lake City (SLC), which is the largest city in the Great Basin. The Wasatch Front is
on the eastern periphery of the Great Basin and is downwind of many dust emission hot spots such
Figure 1. Average PM2.5 concentrations for all dust
events identified for the spring of 2022 for the Wasatch
Front. PM2.5 concentrations estimated from HYSPLIT-
STILT dust simulations.
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as the West Desert, Tule Dry Lake, Lake Sevier, and exposed portions of the GSL lake bed
(Hahnenberger and Nicoll 2012). Dust events along the Wasatch Front are most common during
the spring and fall, when the weather pattern is more active and favorable for strong frontal
systems, which bring strong winds (Steenburgh et al. 2012). The West Desert, Lake Sevier + Tule
Dry Lake, Carson Sink, and the exposed portions of the GSL shoreline were responsible for 45,
17, 6 and 23% of the dust arriving to the Wasatch Front during the spring of 2022 (Lang et al.
2022).
The potential for dust events along the Wasatch Front will likely increase in the coming decades
due to several factors related to changing climate and increased consumptive use of water. The
Wasatch Front, and many areas across the western U.S. has been under the influence of a 20-year
megadrought, which has increased aridity across the region (Williams et al. 2020). There is
mounting evidence that drier conditions have increased the number of fine particulates with a
diameter less than 2.5 μm across the Western U.S. due to increased fire activity and soil erosion
(Haller et al. 2014). Going forward, it projected that increasing aridity would cause dust across the
Western U.S. to increase by 40% by the end of the 21st century (Achakulwisut et al. 2019). In
addition, dust emission sources such as the GSL have observed declining water levels relative to
the 1903-2022 average. In 2022, the GSL hit a record low lake elevation (1277 mASL), which
exposed nearly 2,070 km2 of playa immediately adjacent to SLC. Human and natural consumptive
water use is thought to be responsible for ~70% of deviation from the average GSL water level
(Great Salt Lake Strike Team, 2022; Mohammed and Tarboton 2012). Playa can be a potent emitter
of dust, especially for playa surfaces with limited or broken crusts (Gillette et al. 2001; Mallia et
al. 2017).
A preliminary analysis carried out by PI’s Mallia and Perry found that changes in GSL water levels
resulted in large differences in dust exposure along the Wasatch Front. This analysis included a
series of model sensitivity tests based on several dust events observed during the spring of 2022.
The sensitivity analyses suggested that further decreases in GSL water levels could increase PM2.5
exposure by ~50% for parts of the Salt Lake Valley (Figure 2). Many of the communities that
experienced large increases in dust from the GSL consist of populations that primarily comprised
of disadvantaged groups (Grineski et al. In Review). On the other hand, increasing GSL water
Figure 2. Changes in dust (PM2.5) for the (a) no lake and (b) 1281-mASL GSL scenarios as predicted by
HYSPLIT-STILT. Changes in dust estimated here are based off a baseline lake level of 1277-mASL
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levels to average lake levels could reduce dust exposure along the Wasatch Front by up to 55%,
especially in areas adjacent to Farmington Bay. There is also evidence that dust originating from
the GSL could be depositing dust in the Wasatch Mountains and accelerating springtime snow
melt (Lang et al. 2022).
While the analyses described provide a preliminary assessment of GSL impacts on dust
exposure along the Wasatch, these analyses are based on a limited subset of dust events
(spring of 2022) that may not necessarily represent dust events from other years. Therefore, a more
rigorous modeling analysis is needed to accurately predict how the shrinking GSL could impact
air quality along the Wasatch Front. In addition, an analysis is needed that can separate dust
impacts from the GSL from other major sources of dust such as Lake Sevier and the West Desert.
Finally, the SLC metropolitan region is home to several quarries and mining operations, which can
produce very localized but elevated concentrations of PM2.5 and PM10. Being able to separate local,
but controllable sources of dust from regional dust hot spots will help UDAQ develop effective
dust mitigation strategies.
For this project, we propose to extend the 2022 dust analysis to include dust events from other
years (2017 - 2024). The analysis will leverage the HYSPLIT-STILT modeling framework’s
unique ability to separate dust by source region. Improvements will also be made to the HYSPLIT-
STILT dust modeling framework by (1) including anthropogenic dust emission sources, i.e., mines
and quarries, and (2) using field measurements from the GSL to improve various parameters within
the FENGSHA dust emission model. The dust analysis from 2017-2024 will also include a
sensitivity analysis where the GSL water levels are adjusted within the dust emission model. This
sensitivity analysis will provide UDAQ with a rigorous analysis that can estimate how changes in
GSL water levels could impact air quality along the Wasatch Front. This project will address the
Great Salt Lake topic listed in UDAQ’s Science for Solutions Request for Proposals (RFP) and
will provide UDAQ with a model that can simulate dust events (Topic 4.a), projections for future
GSL water levels (Topic 4.c) and provide an assessment on how GSL and other sources of dust
will impact communities along the Wasatch Front (Topic 4.d). The scientific questions addressed
by this project are summarized below:

TECHNICAL APPROACH: This project will leverage a dust modeling framework, combined
with field measurements of dust, to address the scientific questions described above. First, the Dust
Modeling Framework section will introduce the tool that we will use to (1) simulate all dust events
from 2017-2024, (2) quantify the contribution of PM2.5 & PM10 from different dust sources located
throughout the Intermountain West and (3) run a sensitivity analysis that can project how changes
in GSL water levels could impact dust exposure along the Wasatch Front. This project will consist
of four different tasks to help answer the scientific questions listed above and to address the topics
listed in UDAQ’s Science for Solutions RFP. Task 1 will focus on gathering data that could
improve parameters used to simulate dust emissions from various sources. The primary objective
of Task 2 will be to incorporate new data sets into the HYSPLIT-STILT dust modeling framework
1. What are the characteristics of local dust emission sources?
2. Which dust emission hot spots have the largest contributions of PM2.5 and PM10
during wind-blown dust events?
3. How does dust from the shrinking GSL impact communities along the Wasatch Front?
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to improve the model’s ability to simulate dust along the Wasatch Front. Task 3’s objective will
be to run our dust modeling framework for all dust events identified between 2017 - 2024. Dust
model simulations will then be used to identify the largest emitters of dust arriving to the Wasatch
Front. The last task of this project (Task 4) will focus on running a series of sensitivity tests where
the size of the GSL will be adjusted within our model to examine how different lake levels could
impact dust exposure along the Wasatch Front. This sensitivity analysis will be carried out for all
dust events identified between 2017 – 2024.
Dust Modeling Framework: Our team has developed a dust modeling tool that is capable of
tracking dust from emission sources to receptor sites (HYSPLIT-STILT). The first iteration of
this dust modeling framework was developed as part of a previous UDAQ project title:
“Identifying and Quantifying the Impact of Wildfires and Dust Events on Utah’s Air Quality” (Lin
and Mallia, 2016). An overview of the dust modeling framework is shown in Figure 3. A major
component of this modeling framework is the Stochastic Time Inverted Lagrangian Transport
model (STILT; Lin et al. 2003). STILT is an open source Lagrangian particle dispersion model
that can be used to simulate the transport of pollution through the atmosphere. STILT builds upon
the National Oceanic and Atmospheric Administration (NOAA) Air Resources Laboratory’s
(ARL) popular HYSPLIT model by providing a wrapper that simplifies atmosphere modeling
workflows and improves HYSPLIT’s treatment of vertical dispersion of atmospheric pollutants
(Loughner et al. 2021). This extension of HYSPLIT will henceforth be referred to as HYSPLIT-
STILT. Once a receptor has been specified and the starting time and date has been selected,
HYSPLIT-STILT will release an ensemble of backward particles and follow them backward in
time. The average pathway for the ensemble of backward particles is calculated using
meteorological wind fields from the High-Resolution Rapid Refresh (HRRR; Dowell et al. 2023)
model. Turbulence within HYSPLIT-STILT is parameterized as a stochastic process, which is
used to perturb each backward trajectory. Backward trajectories from HYSPLIT-STILT are then
used to derive the atmospheric footprint, which quantifies the sensitivity of changes in atmospheric
pollutants at the receptor location to upwind pollutant sources, e.g., dust (Figure 3). Footprints
will be convolved with dust emissions to simulate dust concentrations at the receptor location
(Mallia et al. 2017). This framework allows the HYSPLIT-STILT modeling framework to
determine the contributions of dust from upwind source regions to the concentrations measured at
Figure 3. HYPSLIT-STILT framework used to simulate atmospheric dust. The middle panel shows an
ensemble of backward trajectories (colored circles) released from a receptor site and transported
backward in time (open circle). The rightmost panel shows the atmospheric footprint, which is derived
from the HYSPLIT-STILT backward trajectories.
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the receptor location (Mallia et al. 2017; Skiles et al. 2018; Lang et al. 2023). This framework has
also been used to estimate dust concentrations for a grid of receptors as seen in Figure 1.
HYSPLIT-STILT simulations of dust will also account for the effects of wet and dry deposition
(Zang et al. 2001). Here, dry deposition of dust particles will be parameterized using an algorithm
that computes the removal of dust due to gravitational settling and surface roughness. The removal
of dust by precipitation processes has been accounted for by integrating a wet deposition scheme
from GEOS-Chem into HYSPLIT-STILT.
Dust emissions (F) will be calculated using the FENGSHA dust emission model (Fu et al. 2014;
Huang et al. 2015):
F = K × A × ρ
g × SEP × u* × "u*2 - u*ti,j2# x 𝑆! for u* > u*t
where K represents the ratio of the vertical flux to horizontal sediment (Marticorena and
Bargametti 1995), A is the supply limitation factor, ρ is the air density, and g denotes the gravity
constant. The soil erodibility potential (SEP) reflects the inherent emissivity of a particular soil
type. The threshold friction velocity (u*t) is a
function of the land use (i) and soil type (j) and is
used to determine the friction velocity (u*) needed to
loft dust into the atmosphere. Fluxes of dust are
multiplied by the grid cell soil erodibility fraction
(S), which is dependent on the land use type
(barren= 0.75, shrubland = 0.5, shrubgrass = 0.25).
Soil type data will be obtained from the U.S.
Geological Survey (USGS). Land use data for the
dust model is provided the National Land Cover
Database (NLCD). Additional updates were made to
the FENGSHA dust emission model to include a soil
type category for playa (Mallia et al. 2017). Many
dry lakes across the State of Utah are associated with
playa and are major dust emission hot spots
(Hahnenberger and Nicoll 2012; Mallia et al. 2017).
Playa soil types were assigned a threshold friction
velocity of 0.34 m s-1 (Gillette et al. 2001). Adding
a playa soil category within FENGSHA
substantially improved dust simulations for two major dust storms in Utah during the spring of
2010 (Mallia et al. 2017). Bathymetry data for the GSL from the USGS has also been incorporated
into our model and is used to simulate dust emissions for the GSL with varying sizes. If the lake
is forced to shrink in our model, newly exposed lake bed is reclassified from water to playa. In this
scenario, a grid cell that was originally classified as ‘water’ now has the potential to emit dust.
HYSPLIT-STILT dust model simulations for the spring of 2022 were evaluated with PM2.5
measured at UDAQ air quality monitoring stations (Figure 4). Model predicted PM2.5
concentrations from dust sources compared well with observed PM2.5 concentrations (r = 0.68,
bias = 6.2 µg m-3).
The following sections will outline how the HYSPLIT-STILT dust modeling framework described
above will be used to carry out Tasks 1 – 4, which will help address UDAQ priorities related to
GSL dust.
Figure 4. Scatter plot showing comparison
between air PM2.5 observations collected by
sites maintained by the UDAQ and modeled
contributions of PM2.5 from dust.
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Task 1: The FENGSHA dust emission model requires detailed information about dust source
regions that can only be obtained through in situ measurements. These measurements include
information about the particle size distribution (e.g., PM2.5/PM10 ratio), threshold friction velocity
as a function of soil moisture, and the surface characteristics including the type and extent of
surface crusts and vegetation. While Co-PI Perry has completed a survey of the surface crust and
vegetation for the entire GSL playa (Perry et al. 2019), measurements of the other inputs for the
FENGSHA model have only been made in Farmington Bay. The goal of Task #1 is to expand
these model input variable measurements to other portions of the GSL and some of the more
emissive dust source regions in Utah. Task #2 will then use these in situ measurements to improve
the dust model.
The threshold friction velocity and PM2.5/PM10 ratio will be measured using a Portable In-Situ
Wind Erosion Laboratory (PI-SWERL). The PI-SWERL is a device that can produce comparable
data to a traditional straight-line field wind
tunnel, but it is a fraction of the size
(Etyemezian et al. 2007; Sweeney et al.
2008). The device is mounted on a dolly and
is towed around the playa by a fat-tired
bicycle (Figure 5). The device has an
annular blade oriented parallel to the soil
surface that rotates and produces a shear
stress on the area in which it is placed. The
rotations per minute (RPM) of the blade can
be used to estimate an associated frictional
velocity. The annular blade is mounted
inside an open-bottomed cylindrical
chamber, which is placed on a soil surface
for sampling. The PI-SWERL is equipped
with two DustTrak II 8530 model
instruments used for recording the mass
concentration of particulate matter within
the open-bottomed cylindrical chamber.
One DustTrak measures PM10, while the
other measures PM2.5. The ratio of the two
DustTraks provides a direct measure of the
PM2.5/PM10 ratio. All PI-SWERL
measurements will be accompanied by soil
moisture, surface crust, and vegetation observations. Collection of this ancillary data is necessary
to put the PI-SWERL measurements into the necessary context and allow the data to be merged
with previously sampled locations. This analysis could also be used to examine how previously
sampled locations on the GSL playa have evolved over time. This task will help UDAQ better
understand the characteristics of local dust source regions.
Task 2: Field measurements from Task #1 will be used to improve several model parameters
within the FENGSHA dust emission model. A relationship between soil moisture and the threshold
friction velocity will be established and used to modify dust emissions from the GSL. Preliminary
results from Co-PI Perry suggests that there may be a soil moisture threshold that effectively shuts
Figure 5. Image showing the deployment of the PI-
SWERL on Great Salt Lake playa. Shown here are the
PI-SWERL, the transportation dolly (with yellow
battery), and the two DustTraks (PM10 and PM2.5). The
black line shows the outline of the dust hotspot
(foreground).
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off dust emissions for playa. Spatially explicit playa erodibility data collected by PI Perry for the
GSL will also be used to modify the soil erodibility fraction for the GSL dry lake bed. At this point
in time, our model assumes a homogenous erodibility fraction of 20% for the GSL dry lake bed
based on measurements from Farmington Bay. Dust emissions within FENGSHA are calculated
on a ~5-km mesh that covers the entire Western U.S. A sub-gridscale mesh will be added into the
FENSGA dust emission model for the GSL. This sub-gridscale mesh would allow our model to
estimate dust emissions for GSL at a much higher spatial resolution that can take advantage of
high-resolution GSL bathymetry data (~100-m).
Mining operations and quarries in and around the Salt Lake Valley are also potentially large
emitters of local dust (Lin et al. 2023). Shape files (Figure 6) obtained from the Utah Geological
Survey (https://geology.utah.gov/energy-minerals/industrial-minerals/) for mining operations and
quarries will be integrated within the FENGSHA dust emission and used to adjust the erodibility
fraction of the corresponding grid cells. Quarries and tailings are typically extremely erodible since
the soil crust is often disturbed, which drastically reduces the threshold friction velocity (<0.1 m
s-1; Wagenbrenner et al. 2017). A dust parameterization will be integrated within the FENGSGA
dust emission model to account for urban sources of dust. Improvements to our dust emission
model will be benchmarked against model simulations of dust for the spring of 2022. PM2.5 data
already collected by the Google Street View car (Lin et al. 2023) will be used to evaluate the
performance of our urban dust emission parameterization. The improved dust emission model
developed as part of this Task will be used to generate the long-term dust analysis (Task #3) and
GSL dust projections (Task #4).
Task 3: The HYSPLIT-STILT dust modeling framework will be used to expand the spring 2022
dust analysis carried out by PI Mallia (Grineski et al. In Prep). Here, the HYSPLIT-STILT dust
model will simulate all dust events along the Wasatch Front from 2017 to 2024. Here, NOAA’s
HRRR will provide HYSPLIT-STILT with meteorology, which is needed to drive backward
trajectories. HRRR model data is only available
consistently starting in 2017, which is why the
dust analysis will start in 2017. Surface
meteorology from HRRR will provide the
FENGSGA dust emission model with friction
velocities, air density, and soil moisture. We
will identify dust events between 2017 to 2024
by using a combination of PM2.5 and PM10 data
from UDAQ air quality monitoring sites and
wind data from nearby meteorological sites.
Our backward trajectories will be released from
a grid of receptors (grid resolution = 2 x 2-km)
that will cover major population centers along
the Wasatch Front, including but not limited to,
Salt Lake City, Provo, Ogden, Tooele, and
Brigham City. Backward trajectories will run
24 hours, backwards in time, which will allow
our transport model to capture most local and
regional sources of dust. HYSPLIT-STILT dust
model simulations of dust events from 2017 to 2024 will be aggregated to determine the relative
Figure 6. Google Earth image of quarry polygon
layer from the Utah Geological Survey energy and
minerals database. Quarry polygons are outlined in
red.
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contribution of dust hotspots to dust arriving at the Wasatch Front. A short-term dust source
attribution analysis was carried out for the Salt Lake Valley for the spring of 2022 (Figure 7). The
subplot on the left shows a map of gridded percent contribution of dust arriving to the Wasatch
Front (colored contours), while the pie chart on the right aggregates dust contributions by source.
The gray region on the left subplot shows the average transport path, i.e., the atmospheric footprint,
for all dust events. A similar analysis will be generated for each population center. This analysis
will incorporate the model updates in Task #2, and will include anthropogenic sources of dust, i.e.,
mines and quarries. Model simulated dust exposure from the GSL will be mapped with Utah
population databases (2020 Census Tract) to quantify the communities at the highest risk for
dust exposure.
Task 4: A preliminary modeling analysis using HYSPLIT-STILT was carried out for the spring
of 2022, which estimated how changes in GSL water levels would impact dust along the Wasatch
Front. A series of sensitivity tests were carried out for lake levels of ‘no lake’, 1276-, 1277-, and
1280-mASL. This analysis, which is based on a relatively small number of dust events (N = 5)
from a single season, indicated that further decreases in GSL water levels could increase dust
exposure by 20-50% in SLC, while increasing GSL water levels would decrease dust by ~ 50% if
the lake were to rise to ~1280 mASL (Figure 8). While this preliminary work provided some
insights on how different GSL water levels could impact dust, this analysis was based on a small
subset of dust events from a single dust season. In addition, many of the dust events examined in
2022 were predominately associated with ‘postfrontal’ winds, when typically, many of the dust
events in Utah are initiated by ‘prefrontal’ winds (Steenburgh et al. 2012). Task #4 will expand
the 2022 analysis to all dust events for 2017-2024 to increase the likelihood that our model is
sampling enough dust events to accurately project the impacts of a shrinking GSL. All dust events
from 2017-2024 will be recreated by our model with different GSL sizes to determine the
sensitivity of dust to varying lake levels. This sensitivity test would provide UDAQ with an
Figure 7. (Left) STILT footprints averaged (gray-filled contour) across all dust events for a receptor site
in the Wasatch Mountains (red dot) and contributions from potential erodible surfaces upwind of the
receptor site (rainbow colored-filled contours). (Right) Potential dust contribution by source region for
all dust events during the spring of 2022 (Note: GSLD = Great Salt Lake Desert, GSL = Great Salt Lake
Playa)
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analysis that could estimate dust exposure for future GSL scenarios. Furthermore, these
simulations would provide the State of Utah with an analysis that identifies the optimal lake
level that significantly reduces dust exposure along the Wasatch Front.
EXPECTED OUTCOMES AND DELIVERABLES: This project will provide UDAQ with a
model-based analysis that quantifies dust exposure along the Wasatch Front from 2017 to 2024.
Spatially explicit maps of dust exposure (dust contributed PM2.5 and PM10) will fill in gaps where
air quality information is not available, while also sourcing the origin of dust that is arriving at the
Wasatch Front. Maps of dust would also provide UDAQ with an analysis that could identify the
communities that are most impacted by wind-blown dust. This analysis would help UDAQ
determine which dust hotspots are contributing the most dust to the Wasatch Front and could
provide insight on optimal dust mitigation strategies. This analysis would also provide UDAQ and
the State of Utah with a robust estimate on how different GSL water levels could impact dust
exposure along the Wasatch Front. This project directly addresses UDAQ’s GSL priorities to
(1) better understand local dust source regions, (2) examine how sources change over time,
and (3) identify which population centers are most impacted by dust.
The dust modeling framework code will be made available on GitHub and the University of Utah’s
Center for High Performance Computing (CHPC), along with output files, which will be stored as
Figure 8. Estimated projections of dust for different GSL scenarios: (a) no GSL, (b) 1277-, (c) 1278-,
and (d) 1280-mASL. These projections are based dust events from the spring of 2022. The image on the
lower left of each panel visualizes the GSL for each scenario.
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NetCDF files formatted using Climate and Forecast (CF) conventions. Quarterly reports will be
submitted to UDAQ until the conclusion of this project. A final report will be provided to UDAQ
within 90 days after the completion of the project (December 31st, 2025). A manuscript will be
written up on the analyses generated as part of this project and then submitted to a peer-review
journal. Results from this project will be disseminated at the Science for Solutions annual
conference.

SCHEDULE: The timeline of this project can be found in the Gantt chat below. Each colored
section of the Gantt chart is divided by the Tasks described in the Technical Approach section of
this proposal. ‘Other’ lists tasks that are not directly associated with Tasks 1-4.

BUDGET AND BUDGET NARRATIVE:
Senior Personnel ($49,200): PI Mallia is a Research Assistant Professor at the University of Utah’s
Department of Atmospheric Sciences and is requesting 4 calendar months of support. Mallia’s
annual salary in 2024 will be $79,000. The cost for Mallia’s time is $26,992 per year. Co-PI Perry
is a full professor the University of Utah’s Department of Atmospheric Sciences and requesting 2
calendar months of support. Perry’s annual salary in 2024 will be $130,000. The cost of Perry’s
time is $22,208 per year.
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Fringe benefits ($19,680): Benefits are calculated at the standard University of Utah rate of ~40%
for faculty.

Computing resources ($3,000): PI Mallia has access to 5 dedicated compute nodes on the
University of Utah’s Center for High Performance Computing (CHPC) purchased through other
projects. Each node has 512 Gb of memory. These nodes will be used to generate dust model
simulations for the Wasatch Front. Model inputs and outputs will need to be stored on disk space;
therefore, we will need to purchase 20 TB of storage to store model input and outputs.

Page-charges ($3,000): A paper on the analyses carried out as part of this project will be submitted
to a peer-reviewed journal at the conclusion of this project. We anticipate that page charges will
be $3,000.

Milage ($3,250): Milage includes driving to and from the GSL to collect field measurements of
dust and soil. A total of 5,000 miles will be driven throughout the duration of this project

Indirect ($7,513): We request indirect costs on personnel salaries, fringe benefits, and travel. The
indirect cost rate set by the Utah Division of Air Quality is 10%.

PERSONAL ROLES AND RESPONSIBILITIES:
PI Mallia will be responsible for managing the project, running model simulations of dust from
2017 to 2024, updating dust model with data from Co-PI Perry and the Utah Geological Survey,
writing the project final report, presenting findings at the annual Science for Solutions workshop,
and writing up results in a manuscript that will be submitted to a peer-reviewed journal.

Co-PI Perry will continue field data collection across the GSL. Perry will provide observational
data to PI Mallia, which will be used to further improve the dust emission model. Perry will also
provide guidance on how the data could be incorporated in the dust modeling framework. Perry
will assist Mallia with writing up quarterly and final reports.

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References:
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Dowell, D. C., and Coauthors, 2022: The High-Resolution Rapid Refresh (HRRR): An Hourly
Updating Convection-Allowing Forecast Model. Part I: Motivation and System Description.
Weather and Forecasting, 37, 1371–1395

Etyemezian, and Coauthors, 2007: The Portable In Situ Wind Erosion Laboratory (PI-SWERL):
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