Prototype Model Development and Application
for the Utah Division of Air Quality ‘Natural Events Action Plan’
by
David Strohm and Patrick Barickman
I. Introduction
The goal of this analysis is to determine the relative impact of various wind blown dust
sources on PM10 monitors that exceed the NAAQS during high wind events. Based on a
general understanding of topography, meteorology, land cover and the episodic nature of
high-wind dust events, the Utah Division of Air Quality (UDAQ) has developed a hybrid
modeling analysis for its Natural Events Action Plan. The approach uses a GIS
framework to integrate elements of three different models as well as the UDAQ annual
emissions inventory in the analysis. The models used are the Industrial Source Complex
Model – Short Term (ISCST3), procedures and emission factors from the WRAP wind
blown dust model, and the HYSPLIT back trajectory model.
As mentioned above, a conceptual understanding of wind blown dust events suggests that
when high winds come from the south, southwest, west, or northwest of the Wasatch
Front and are sustained for long periods of time, long-range transport of dust can be
significant. The analysis accounts for this by separately calculating near and far-field
dust sources. The far-field and near-field construction dust sources are calculated using
the WRAP dust model. Other near-field sources; point, mobile, and fugitive dust from
unpaved construction roads, use emission estimates from the UDAQ annual inventory.
This document presents a detailed description of a prototype analysis using a dust episode
that occurred on April 15, 2002. The study focuses on the UDAQ monitor at Lindon, in
Utah County, which exceeded the standard for six hours. As part of future analyses in the
UDAQ NEAP, when more than one monitor exceeds the standard during a given event,
each monitor will be assessed separately using the process described below.
II. Determination of Study Area
In order to determine the upwind extent of the area with a potential to deliver wind-blown
dust to the monitor, back-trajectories are utilized to ensure that a realistic area is
developed. The analysis begins with TEOM values for the exceeding monitor or the
closest real-time monitor being graphed against the exceedence standard. These graphs
are used to determine which hours exceeded the NAAQS standard. For each exceeding
hour a HYSPLIT back-trajectory is calculated for a period that is representative of the
overall time-period of the wind event. This process provides a series of back-trajectories
that all culminate at the exceedence monitor location and typically produce an expanding
cone as they move away from the monitor in the direction of the wind.
The back-trajectories that are produced by the HYSPLIT model are then reduced to a
series of latitude/longitude points and imported into a GIS program (Figure 1). This
import allows a study area to be defined. The study area is the shape formed by the
combination of the back-trajectories (Figure 2). This area is likely to represent the region
from which wind blown dust is produced and entrained during each wind event.
One caveat to this procedure occurs at or near cold-frontal boundaries. During situations
where cold fronts move over the exceedence monitor during the dust event, the
trajectories have a tendency to shift quickly to a westerly or northwesterly track. This
produces a study area that includes western regions that likely have little impact on dust
production. The conclusion that these areas have little dust production has to do with the
steep decrease in PM10 concentrations that occurs in the post-frontal environment. In
these situations post-frontal trajectories are not included in the development of the study
area so as not to incorrectly include areas with little dust production.
Finally, when more than one monitor records an exceedence value during a single wind
event, the analysis outlined in this document will be preformed for each monitor.
Figure 1. Trajectories
Figure 2. Study Area
III. Far-Field Dust Sources
The WRAP Dust Model
The dust model used by UDAQ for this analysis was developed by ENVIRON and the
WRAP Regional Modeling Center (RMC) as a part of the phase one modeling contract
for development of the regional haze SIP’s. Mansell, et. al. summarize the development
and theoretical basis of the model in a final draft report to the Western Governor’s
Association1. Briefly, the model uses the characterization of land cover and soil type to
classify areas and link them to specific emission factors. The emission factor is actually a
function of how much dust will be generated from one of five soil types. These emission
factors, or dust fluxes, are derived from limited wind tunnel study results and are a
function of wind speed and soil texture. Land use characterization is based on two land
cover data sets for Utah: the State Department of Natural Resources Water Related Land
Use database and the USGS GAP Analysis database. Soil texture was derived from the
Department of Agriculture State Soil Geographic Database (STATSGO) (Figure 3 and 4).
Figure 3. Land cover.
Figure 4. Soil type.
Very simply put, the dust model uses a combination of wind velocity and soil type to
derive an emission factor for a given land area. The emission factors are developed for
barren soil of varying types and are tabulated by wind speed. The factors are then
reduced using an equation that accounts for the percentage of ground that is covered by
vegetation. This vegetation reduction is based on the average vegetated cover that is
typical for each land use category.
For each episode an average wind speed is determined using a combination of wind data
at the exceedence monitor as well as average back trajectory length. The average wind
speed is then used to determine which emission factor table should be selected for the
event. Once the emission factors have been determined they are multiplied by the
reduction factors for each vegetation percentage. This allows an emission factor to be
calculated for each land use category and soil type. Once the emission factors are
calculated the GIS is used to calculate varying dust emissions throughout the study area
by intersecting the land cover and the soil types in the study area (Figure 5). After the
emission factors are calculated they are linked to the associated polygons of the land
cover GIS layer. These are then combined with the soils layer so that the soil type
directly covered by the land cover layer can be linked to the correct emission factor.
1
Final Report - Determining Fugitive Dust Emissions from Wind Erosion. 2004.
The final intersection done with the GIS divides the area polygons into groupings
according to their distance from the exceedence monitor. This grouping of polygons
links the associated ISCST3 deposition and dispersion factors (discussed below) with the
rates of dust production and is used to calculate the final apportionment of dust on the
filter.
Figure 5.
Once these GIS intersections are done, the associated data tables are exported and the
dust calculations using the dust emission factors and ISCST3 deposition rates are
completed.
ISCST3 Model Deposition and Dispersion
The Industrial Source Complex - Short Term regulatory air dispersion model (ISCST3) is
a Gaussian plume model and is widely used to assess pollution concentration and/or
deposition flux on receptors from a wide variety of sources. In the case of wind blown
dust the ability of the ISCST3 model to calculate particle deposition and dispersion is
helpful to the calculations in this analysis.
The WRAP dust model is used to estimate the up-wind particulate emissions, but a
second mechanism is needed to account for the dilution and fallout of the particulate as it
travels to the receptor. In order to account for these factors the ISCST3 model is utilized
to develop a general numeric that can account for both dispersion and deposition. The
resultant reduction factor is related to the distance of the source from the monitoring
location and essentially transforms the emissions rate into an equivalent concentration at
the receptor.
The reduction factor is developed by modeling an area source that is 10 meters by 10
meters and has an emission rate of 1 gram/sec/meter2. The source is modeled over flat
terrain and has meteorological conditions consistent with those of the event period.
Receptor concentrations are recorded along a line upwind from the source at varying
distances that represent the domain of the event study area. This general modeling
provides a unit factor that can be applied to the modeled emissions developed in the GIS
environment. The result is a concentration (mg/m3) that is representative of a value the
monitor would record.
The factors developed have a significant effect on the emissions produced by the WRAP
dust model and other emission sources. The impact of emitted dust is highly dependent
on its spatial relationship to the monitor, with sources near the monitor having an effect
that is orders of magnitude larger than those farther a field. This relationship helps to
reduce the large magnitude of far-field emissions and increase the impact of smaller nearfield emissions.
In order to apply the distance relative factors produced with ISCST3, GIS is utilized to
bin near and far-field sources into discreet rings. The center of these area rings are
defined by a distance for which a specific ISCST3 factor was calculated. The sources in
each area ring are then multiplied by the reduction factor from the ISCST3 model and the
impact of each emission source on the monitor is calculated. See Pg. 6 Appendix A.
This procedure of using ISCST3 allows the reduction due to particle deposition and
dispersion to be quantified. Although the method uses an idealized ISCST3 analysis,
applying it uniformly it enables the impact of various emission sources on the exceedence
monitor to be compared.
IV. Near-Field Sources
The geographic distinction between near and far-field sources is determined by several
attributes. For this analysis the southern edge of the county administrative boundary was
used to make this distinction. Had this episode been centered on a different monitor, or,
had the study area been defined by a different set of back trajectories, the basic outline of
the urbanized area may have been used. In general, the urbanized area will follow the
contours of the mountain front and eastern lakeshores in the entire Wasatch Front area.
Within the boundary of the near-field area the dust emissions are estimated differently
than the far-field emissions. This is done to account for anthropogenic sources that are
near to the monitor and that would not be well captured by the dust model.
The near-field emissions fall into three categories and are based on the UDAQ annual
emissions inventory. They are based on point, mobile and area sources. A method to
estimate the near-field contribution from vacant house lots that are under construction has
also been included.
The annual inventory of PM10 point sources is used and the values converted from
ton/year into grams/second. To account for deposition and dispersion the point source
locations are grouped by distance from the monitor in the same way as the soil parcels
were. After the point sources are grouped by distance, the appropriate
deposition/dispersion factor derived from ISCST3 is applied in just the same way as is
done with the output of the dust model.
Mobile source emissions are placed in the near-field study area using population as a
surrogate. Since the annual inventory is county-wide, a simple apportionment method is
used:
Study area emissions = (area population / county population) * county mobile emissions.
Three categories from the area source inventory are used and distributed to the study area
by using the proportion of population in the study area to the population in the entire
country. Those three categories are building and construction related dust, agricultural
unpaved road dust and non-agricultural unpaved road dust. Mobile and area sources are
processed with the ISCST3 deposition/dispersion factor in the same way that the point
source inventory is calculated.
The final category to be accounted for is building lots under construction. That category
is processed in the following way.
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Source: Bureau of Economic and Business Research – Quarterly Utah
Construction Report – Listing of authorized building permits by county and
municipality
Sum total number of building permits for each county for the 3 months prior to
the episode
Assume lot size of 0.2 acre
0.2 acre = 809.4 sq. meters
Multiply number of permits times 809.4
Define urbanized boundary area for each county
Calculate the area of the wind trajectory within the county urbanized area
County vacant lot area = wind trajectory area divided by county urbanized area
multiplied by total square meters of vacant lots in county
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Vacant lots are assumed to be barren soil and are processed using the WRAP
emission factors for barren soil as in the far-field calculations
The total area of vacant lots is distributed evenly throughout the study within the
county
After each of these categories is processed, the data tables and dust calculation are
completed in the same fashion as those completed for the far-field emissions.
V. Final Source Apportionment
After calculations are performed for each source category and source distance, a series of
concentrations representative of the maximum concentration in a six-hour period due to
each of the sources remains. These numbers are collated in a series of Excel
spreadsheets. Once concentration figures are collated, each of the sources is normalized
and the relative impact of each source is calculated. This normalization is performed in
Excel and involves dividing the relative contribution of each source by the sum
contribution of all the sources.
These calculations provide a final apportionment for each source and allow the State to
develop appropriate mitigation strategies when completing the NEAP processes. Since
the calculations allow the relative impact of each source to be analyzed, the sources can
be divided or grouped according to the needs of future analyses. Using an
anthropogenic/non-anthropogenic division for example, sources that are outside the
influence of human interaction could be separated from those sources that have human
interaction.
In the April 15, 2002 Lindon monitor episode, the break down of source apportionment at
the monitor is found below in figure 6.
Percent Filter Apportionment
Forest
1%
Barren
12%
Mobile Vacant Lots
1%
0%
Ag Total
11%
PJ
3%
Sage
5%
Shrub
67%
Vacant Lots
Area
PJ
Mobile
Figure 6.
Ag Total
Shrub
Forest
Point
Sage
Barren
Point
0%
Area
0%